Housing in Crisis - Economy.com · Sebastian-Vero Beach, FL -60.2 2005Q4 2010Q3 West Palm...

Senior Director, Housing EconomicsCelia Chen, Ph.D.

EconomistAndres Carbacho-Burgos, Ph.D.

Director, Credit Analytics

Cristian deRitis, Ph.D.

Chief EconomistMark Zandi, Ph.D.

When WillMetro Markets

Recover?

February 2009

Housing inCrisis

Housing in Crisis: When Will Metro Markets Recover? February 2009

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Table of ContentsExecutive Summary ...........................................................................5Chapter 1: Housing Bust Worsens .....................................................9 Dismal conditions .........................................................................9 Housing rebalances .....................................................................12Chapter 2: How Did the Market Unravel? ........................................15 Foreclosure waves .......................................................................16 Financial system losses ...............................................................17 Financial panic ...........................................................................18 Economic fallout .........................................................................18Chapter 3: Policymakers Respond ....................................................21 Financial market stabilization .....................................................21 Foreclosure relief ........................................................................23 Fiscal support .............................................................................24 Lowering borrowing costs ...........................................................24 Meaningful support ....................................................................24Chapter 4: When Will It End? .........................................................27 Risks ...........................................................................................28Chapter 5: House Price Primer ........................................................31 NAR ...........................................................................................31 FHFA ..........................................................................................31 LoanPerformance ........................................................................31 Fiserv Case-Shiller and S&P/Case-Shiller ....................................32 Zillow .........................................................................................32 Radar Logic ................................................................................34 Altos ...........................................................................................34 Advantages of CSI .......................................................................34Chapter 6: Measuring House Price Risk ...........................................35 Income-to-price ..........................................................................35 Affordability ................................................................................35 Price-to-rent ................................................................................36 User cost-to-rent .........................................................................39Chapter 7: Structural Econometric Model ........................................41 Theory ........................................................................................41 Historical data ............................................................................42 Equilibrium equation ..................................................................42 Adjustment equation ..................................................................47 Validation ...................................................................................51 Alternative specifications ............................................................52

4


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Calibration..................................................................................52 Valuation ....................................................................................52Chapter 8: Forecast Accuracy ...........................................................55 National accuracy .......................................................................55 Regional accuracy .......................................................................56 U.S. regional forecast consistency ...............................................59 Conclusion .................................................................................59Chapter 9: House Price Outlook ......................................................61 House price decline ....................................................................61 House price recovery ..................................................................62Chapter 10: Metropolitan Area Outlook ..........................................65 Central Valley, CA .......................................................................65 Southwest FL ..............................................................................67 Southeast FL ...............................................................................69 Riverside-San Bernardino, CA .....................................................70 Phoenix, AZ ................................................................................71 Las Vegas, NV .............................................................................72 Central FL ..................................................................................73 Los Angeles, CA ..........................................................................74 San Diego, CA.............................................................................76 Tampa, FL ...................................................................................76 Greater Washington ...................................................................77 Detroit, MI..................................................................................79 San Francisco Bay Area, CA ........................................................80 New York, NY .............................................................................82 Chicago, IL .................................................................................84 San Antonio, TX ..........................................................................85 Indianapolis, IN ..........................................................................86 Little Rock, AR ............................................................................87 Pittsburgh, PA .............................................................................87 Houston, TX ...............................................................................88Chapter 11: Conclusions .................................................................91Appendixes ......................................................................................93About Moody’s Economy.com .......................................................186

Table of Contents



The U.S. housing market down-turn has gone from bad to worse, dragging the broader economy into what threatens to be the worst economic setback since the Great Depression. Policymakers have not yet been able to break the downward spiral that has developed among the sinking housing market, job losses, frozen credit markets, and rising foreclosures.

Almost three years into the housing downturn, most indicators of the market’s performance continue to worsen. By the end of 2008, construction had fallen to its slowest pace since the Census Bureau began collecting data in 1959, invento-ries of homes were at a record high, and house prices had plummeted. Home sales seemed to be stabilizing, but only because of a surge in sales of foreclosed properties. Rising job losses and mounting negative home equity, combined with lax mortgage underwriting standards earlier in the de-cade, have rapidly eroded mortgage credit quality, further depressing the market.

The housing bubble was inflated by numerous forces. The most important were the flawed process of mortgage secu-ritization, a lack of regulatory oversight, and old-fashioned hubris. The bubble is now deflating with a vengeance. The boom began to recede in earnest in the spring 2006 selling season. The surge in prices during the first half of the decade, along with monetary tightening by the Federal Reserve, had made housing unaf-fordable, even with anything-goes mort-gage lending. House prices have since fall-en by 25%, bringing prices back to where they were at the beginning of 2004.

The erosion of credit quality fueled the financial market crisis, which further depressed the housing market. Financial institutions are taking hundreds of billions of dollars in losses from the bad mortgage-related investments they made during the boom. These losses fed the financial panic that hit a fever pitch at the end of last sum-mer after the government took over Fannie Mae and Freddie Mac. Credit markets froze, hurting not only mortgage borrowers

but also disrupting nearly all businesses. These forces sent a mildly contracting econ-omy into a full-blown recession.

Not only is this housing downturn unprecedented in depth, but it reaches across the nation. House prices have fallen in about 70% of all metro areas over the past several years. Although prices in most metro areas declined modestly dur-ing this period, price depreciation from peak exceeded 5% in 116 metro areas and exceeded 20% in about 50 metro areas. Those metro areas with the most exposure to nonprime and investor lending, and that consequently experienced the greatest runup in prices during the boom, are suf-fering the greatest declines on the down-side of the housing cycle. California and Florida prices, for example, have descend-ed by 50% or more from peak, but no area of the country has been spared.

Despite the darkening national economic outlook and the weak con-ditions in the housing market, some positive signs give hope that a bottom in the housing market is coming into view. First, the market’s correction to date has been substantial, wringing out many of the excesses that precipitated the crash. More than three years since the market began correcting, inventories are flatten-ing, prices are coming back down to earth, and sales are approaching stability.

Another cause for optimism is that policymakers, though largely unsuccess-ful at stemming the correction, have been spurred to respond more aggressively be-cause the malaise has spread into a painful recession whose reach extends far beyond housing. Stronger policy measures are coming that will help place a floor under the housing downturn. These measures will help housing directly, by lowering mortgage interest rates and forestalling foreclosures, and indirectly, through job-generating fiscal stimulus spending that will bolster demand.

The objective of this study is to assess how much further the housing market has to fall and to consider the regional varia-tions in the timing of the housing cycle. It

considers how much longer activity will weaken, the degree of that downturn, and where in the country the downturn will be most severe. This assessment is done through the prism of prices. Home sales and construction will closely follow house price trends.

To this end, Moody’s Economy.com has augmented its structural econometric model of housing supply and demand with information on metro area mortgage de-faults. Normally, defaults and foreclosures have little impact on prices. However, the volume of foreclosures is so large that they are an important driver of falling house prices. The model is based on statistically estimating the historical relationships among economic, demographic, financial and housing-related variables. House price forecasts are produced by extrapo-lating these relationships into the future. In addition to the default data, a wide range of variables is accounted for, includ-ing mortgage rates and mortgage lending conditions to demographic trends and job market conditions and supply constraints.

According to the structural econo-metric forecast model, nearly 62% of the nation’s 381 metro areas will expe-rience double-digit, peak-to-trough de-clines in house prices. Moreover, these declines will exceed 20% in about 100 metro areas (see Table 1). Although the declines will be broad-based, the sharpest drops are expected in Southeast Florida and its Paradise Coast, California’s Cen-tral Valley, and the Riverside CA metro area. Prices in these areas are expected to decline by upward of 50% before the cor-rection is complete. Moreover, it will be the second half of next year before many of these markets hit bottom. Only a handful of regional markets are expected to escape a downturn: In about 42 markets, house prices will fall by less than 1%. These ar-eas are concentrated in the South and tend to be small.

Notwithstanding the intensifying economic gloom, the bottom of the housing downturn is within sight for the nation. This outlook assumes stron-

Executive Summary


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ger action by policymakers. Even with further government intervention, the reces-sion will keep the housing market from fully recovering until the end of this year. With this help, sales are likely at bottom, stabilized by foreclosure sales. Construc-tion will hit bottom in the first half of this year, although the pace of housing starts will remain very depressed until 2011. From peak to trough, Moody’s Economy.com expects that total single-family home sales will have declined by 40%, housing

starts by 70%, and the Case-Shiller house price by about 36%.

Although the end of the housing downturn may be in sight, this has been a debilitating correction. The impact on the broader economy has been severe. The ensuing recession is deep, and the neg-ative effects will linger as the hobbled finan-cial system slowly puts itself back together. Even if the recession ends late this year, as expected, the subsequent recovery looks to be lackluster. Real GDP is not expected

to return to its prerecession peak until late 2010, and the nation will not approach a full-employment jobless rate of 5% before President Obama’s term nears its conclu-sion in 2012. A number of uncertainties in both the housing and economic outlooks remain, and the risks tilt to the downside. Moody’s Economy.com will continue to update the tools and analysis described in detail in the study that follows to assess the health of the housing market and the broader economy.

Table 1: Where House Prices Will Suffer the Most

Peak-to-Trough% change Peak quarter Trough quarter

United States -36.2 2006Q1 2009Q4

Naples-Marco Island, FL -70.1 2006Q1 2010Q4Merced, CA -69.6 2006Q1 2009Q4Salinas, CA -67.9 2006Q1 2009Q4Modesto, CA -67.4 2006Q1 2010Q2Stockton, CA -67.1 2006Q1 2009Q4Miami-Miami Beach-Kendall, FL -66.4 2007Q1 2011Q2Cape Coral-Fort Myers, FL -66.4 2006Q1 2010Q2Punta Gorda, FL -65.4 2006Q1 2010Q2Fort Lauderdale-Pompano Beach-Deerfield Beach, FL -64.4 2006Q2 2010Q4Vallejo-Fairfield, CA -63.9 2006Q1 2010Q2Riverside-San Bernardino-Ontario, CA -62.7 2006Q2 2010Q2Bradenton-Sarasota-Venice, FL -62.1 2006Q1 2010Q3Orlando-Kissimmee, FL -62.1 2006Q2 2011Q1Port St. Lucie, FL -60.8 2006Q1 2010Q2Sebastian-Vero Beach, FL -60.2 2005Q4 2010Q3West Palm Beach-Boca Raton-Boynton Beach, FL -59.2 2006Q1 2010Q3Phoenix-Mesa-Scottsdale, AZ -57.9 2006Q2 2010Q2Santa Barbara-Santa Maria-Goleta, CA -57.7 2005Q3 2009Q4Santa Rosa-Petaluma, CA -57.1 2005Q4 2009Q4El Centro, CA -56.6 2006Q2 2009Q4Las Vegas-Paradise, NV -55.7 2006Q1 2010Q2Bakersfield, CA -55.5 2006Q2 2009Q4Deltona-Daytona Beach-Ormond Beach, FL -55.3 2006Q2 2010Q3Fort Walton Beach-Crestview-Destin, FL -55.0 2005Q4 2010Q3Sacramento-Arden-Arcade-Roseville, CA -54.2 2005Q4 2009Q4Oakland-Fremont-Hayward, CA -54.0 2006Q1 2009Q4Lakeland-Winter Haven, FL -54.0 2006Q2 2010Q3Fresno, CA -53.6 2006Q1 2009Q4Los Angeles-Long Beach-Glendale, CA -53.0 2006Q2 2010Q3Visalia-Porterville, CA -51.9 2006Q1 2009Q4Oxnard-Thousand Oaks-Ventura, CA -50.7 2006Q2 2009Q4San Diego-Carlsbad-San Marcos, CA -50.5 2006Q1 2009Q4Tampa-St. Petersburg-Clearwater, FL -49.9 2006Q2 2010Q4Madera-Chowchilla, CA -49.9 2006Q3 2010Q1Yuba City, CA -49.6 2006Q1 2010Q2Napa, CA -48.2 2006Q1 2009Q4Pensacola-Ferry Pass-Brent, FL -48.2 2006Q2 2010Q4



Table 1: Where House Prices Will Suffer the Most (cont.)


Reno-Sparks, NV -47.9 2006Q1 2010Q2Palm Bay-Melbourne-Titusville, FL -47.4 2006Q2 2010Q1Prescott, AZ -46.3 2006Q2 2010Q4Jacksonville, FL -45.9 2006Q2 2011Q1Redding, CA -44.4 2006Q1 2009Q4Santa Cruz-Watsonville, CA -44.3 2005Q4 2009Q3Tucson, AZ -44.0 2006Q2 2010Q3Hanford-Corcoran, CA -43.8 2006Q2 2009Q4Palm Coast, FL -42.5 2007Q1 2010Q1Ocean City, NJ -42.1 2005Q4 2010Q4Santa Ana-Anaheim-Irvine, CA -40.3 2006Q1 2009Q4Gainesville, FL -39.4 2007Q1 2011Q2Washington-Arlington-Alexandria, DC-VA-MD-WV -38.3 2006Q1 2010Q1Atlantic City, NJ -37.0 2006Q2 2010Q4Detroit-Livonia-Dearborn, MI -36.6 2006Q1 2010Q1Chico, CA -36.2 2005Q4 2009Q4Ocala, FL -35.0 2007Q1 2010Q3Warren-Troy-Farmington Hills, MI -34.1 2005Q3 2009Q4San Jose-Sunnyvale-Santa Clara, CA -34.0 2007Q1 2009Q3Lake Havasu City-Kingman, AZ -33.6 2007Q1 2010Q2San Luis Obispo-Paso Robles, CA -33.4 2006Q1 2009Q1New York-White Plains-Wayne, NY-NJ -33.3 2006Q3 2010Q3Panama City-Lynn Haven-Panama City Beach, FL -32.3 2007Q1 2010Q2Baltimore-Towson, MD -31.6 2007Q1 2010Q3Providence-New Bedford-Fall River, RI-MA -31.5 2006Q1 2010Q2Nassau-Suffolk, NY -31.4 2006Q2 2010Q3Salisbury, MD -31.3 2007Q3 2010Q2Winchester, VA-WV -30.9 2006Q2 2010Q2Peabody, MA -30.8 2005Q3 2010Q2Carson City, NV -30.6 2006Q1 2009Q4Manchester-Nashua, NH -30.2 2005Q4 2010Q2Bethesda-Frederick-Rockville, MD -30.0 2006Q2 2010Q1Worcester, MA -29.5 2005Q4 2009Q4Flagstaff, AZ -28.3 2006Q2 2010Q2Trenton-Ewing, NJ -28.1 2006Q1 2010Q1Edison-New Brunswick, NJ -28.0 2006Q2 2010Q3San Francisco-San Mateo-Redwood City, CA -27.0 2007Q3 2009Q4Wilmington, DE-MD-NJ -26.9 2007Q1 2010Q2Camden, NJ -26.7 2006Q3 2010Q2Rockingham County-Strafford County, NH -26.4 2006Q2 2010Q1Boston-Quincy, MA -26.1 2005Q3 2010Q2Newark-Union, NJ-PA -26.1 2006Q2 2010Q2Yuma, AZ -25.5 2007Q1 2010Q3Virginia Beach-Norfolk-Newport News, VA-NC -25.3 2007Q4 2011Q2Boise City-Nampa, ID -25.2 2007Q2 2010Q2Barnstable Town, MA -25.1 2005Q4 2010Q1Ann Arbor, MI -24.7 2005Q4 2009Q4Honolulu, HI -24.2 2008Q1 2010Q2Bend, OR -23.7 2007Q1 2009Q4Flint, MI -23.7 2005Q3 2010Q1St. George, UT -23.7 2007Q1 2010Q3Minneapolis-St. Paul-Bloomington, MN-WI -23.6 2006Q1 2010Q1Medford, OR -23.4 2007Q1 2010Q3



Portland-Vancouver-Beaverton, OR-WA -23.0 2007Q2 2010Q3New Haven-Milford, CT -22.8 2006Q2 2010Q2Provo-Orem, UT -22.7 2008Q1 2010Q4Bridgeport-Stamford-Norwalk, CT -21.6 2006Q1 2010Q2Lansing-East Lansing, MI -21.6 2006Q1 2009Q4Springfield, MA -20.9 2007Q1 2010Q1Salt Lake City, UT -20.4 2008Q1 2010Q2Monroe, MI -20.4 2005Q3 2009Q4Cambridge-Newton-Framingham, MA -20.3 2005Q2 2009Q4Jackson, MI -20.3 2005Q4 2010Q1Hagerstown-Martinsburg, MD-WV -20.3 2007Q1 2010Q3Coeur d’Alene, ID -20.2 2007Q3 2010Q2Sources: Fiserv Lending Solutions, Moody’s Economy.com

Table 1: Where House Prices Will Suffer the Most (cont.)




In the last year, the housing market has gone from bad to worse, pulling the U.S. economy down into the worst re-cession in decades and triggering global financial upheaval of epic proportions. A self-reinforcing downward spiral has devel-oped among the sinking housing market, job losses, frozen credit markets, and ris-ing foreclosures that policymakers have not been able to break. The crumbling housing market helped drag the economy into recession as falling house prices ate into homeowner equity, an important sup-port for consumer spending. Contraction in homebuilding also cut into job and income creation. By the fall of 2008, the economy was in a full-blown recession, job losses had deepened, and notwithstand-ing unprecedented government interven-tion in financial markets, mortgage credit remained tight. Moreover, bad news from nearly all facets of the economy pummeled the confidence of those consumers who still had the ability to purchase homes. Concurrent with the slide in demand for homes, the supply remained elevated be-cause of the surge in foreclosures. Rapid house price depreciation and mounting job losses combined to throw more ho-meowners into foreclosure. The surge in foreclosures not only further depressed house prices but also sparked a crisis in the mortgage market that spread rapidly to the global financial system. Policymakers

have tried to break this cycle to no avail. The housing market, which is close to entering its fourth year of contrac-tion, has sunk to a mul-tidecade low.

Dismal condi-tions. Not only have house prices fallen by an unprecedented 25% from their early 2006 peak, according to the S&P/Case-Shiller 20 metro area price index, but residential construc-tion has fallen off sharp-ly to proceed at its slow-est pace since the Census Bureau began collecting data in 1959. Home sales have leveled off in the last year but are running at 43% below peak. This decline is worse than the drop in sales that occurred during the housing downturn of the 1980s. In-deed, by these three top-line measures of housing, the current downturn is the worst in at least 50 years (see Chart 1-1). Even after adjusting for inflation, house prices have posted their worst decline. Home sales have dropped substantially and resi-dential construction has fallen off as badly as during the 1980s downturn.

Further, unlike previous housing busts that were concentrated geographically—

Texas in the 1980s and Cali-fornia and the East Coast in the 1990s—the weakness this time is broad across the na-tion, with the malaise spread-ing ever wider and deeper in the last year. About 70% of all metro areas registered a price decline

over the past several years. While house prices in most metro areas declined mod-estly during this period, price deprecia-tion from peak exceeded 5% in 116 metro areas and exceeded 20% in about 50 metro areas. House price depreciation is the greatest and the most concentrated in California and Florida, where prices have descended by 50% or more from peak, but no area of the country has been spared (see Chart 1-2).

Job losses and tight credit have been constraining the demand for housing: The U.S. economy fell into recession at the end of 2007, and the recession deepened and spread across the nation throughout last year. The labor market has sunk to its worst condition in decades. Without jobs, people will not purchase homes and mortgage holders are more likely to go into foreclosure. Employment fell sharply by year’s end, declining by 1.9 million jobs since the beginning of 2008. As a result, the job base has contracted by 1.4% since the beginning of 2008.

The recession has spread across the country, encompassing nearly three-fourths of the U.S. All of the Midwest and Southeast are in decline. The downturn has edged across the Northeast and now includes New York. Most of the Pacific Coast is in recession. Only the Western Plains states and parts of the Southwest are still expanding, but they too are at risk.

Chart 1-1: Unprecedented Housing Downturn% change from peak

-70

-60

-50

-40

-30

-20

-10

0

10

20

30

CSI Real CSI Sales Starts

Current Early 1990sEarly 1980s

Sources: Fiserv Lending Solutions, Census, NAR

Chart 1-2: House Price Declines Are Widespread% change decline, peak to 2008Q3

Source: Fiserv Lending Solutions0% to 15%

No decline

15% to 30%

> 30%

Chapter 1: Housing Bust Worsens



Export trade and industrial production have fallen along with employment across the country (see Chart 1-3).

Not only have job losses hurt consum-ers, but they are watching the largest asset on their balance sheets rapidly lose value. With house prices falling, total household net worth is down 6% from its mid-2007 peak. Indeed, many households are suffer-ing from negative equity, as the outstand-ing amount on their mortgages is greater than the value of their homes. Nearly 14 million households were underwater in the fourth quarter of last year (that is, they owed more than their properties were worth), and more are likely to fall into this status before the housing downturn completely plays out. Under these circum-stances, and with uncertainty regarding the length of the housing downturn, it is no wonder that consumer confidence as

measured by the Conference Board has tumbled to a historic low and that house-holds remain very reluctant to purchase homes. Weak demand for homes further undermines the housing market, with neg-ative feedback to the broader economy.

Weak economic conditions do damp-en demand for homes, but an additional force constraining home sales is the lack of mortgage credit, a condition that poli-cymakers—who have already intervened in financial markets in unprecedented ways—will have to work even harder to reverse. The demand that does exist for housing is difficult to realize, as tighter lending standards prevent many potential buyers from completing purchases. Lend-ers remained hesitant to extend credit to households, with the overwhelming major-ity of respondents to the Federal Reserve Board’s senior loan officer opinion survey

indicating that they tightened standards across all types of mortgage loan prod-ucts, including prime mortgages, in the fourth quarter of last year. That marked the ninth consecu-tive quarter of higher qualifying standards. Moreover, the tight-ening became more widespread among respondents. All subprime lenders responding had tightened standards.

Only the prime category stepped back a bit in terms of the percent of respondents tightening; however, a still-high 70% of lenders are tightening conditions for bor-rowers in this category (see Chart 1-4).

Further, although the Treasury Depart-ment has injected billions of dollars into the financial system, the amount of loan-able funds remains low as falling house prices keep investors away from housing: The market for private-label residential mortgage-backed securities remains mori-bund. Issuance of RMBS fell from a peak of more than $1 trillion in 2005 to a mere $4 billion in the third quarter of last year (see Chart 1-5). Further, the agency loans, while encompassing a larger share of mortgage originations, have been hampered by Fan-nie Mae and Freddie Mac’s own balance sheet problems. Consequently, they have not been able to extend as much credit as hoped. The FHA is the only provider of loans that is expanding rapidly, but it can-not make up for all the lost funds.

On the supply side of housing, inven-tories remain bloated. Homes vacant and available for sale are near a record high, both in terms of the level and as a share of the nation’s housing stock. Homebuild-ers have been doing their part by cutting back sharply on construction. Indeed, residential construction is seeing its dark-est days since the Census Bureau started recording monthly data in January 1959. In the fourth quarter of last year, housing starts fell to 656,000 units, an annualized decline of 67% and the worst quarterly de-cline on record. This pace of construction was about than half its long-run trend and

Chart 1-4: No Letup in Mortgage Credit TighteningNet % of banks tightening standards for mortgage loans

-20

0

20

40

60

80

100

99 00 01 02 03 04 05 06 07 08

All loansPrimeNontraditionalSubprime

Source: Federal Reserve Board

Chart 1-5: Moribund Mortgage MarketRMBS issuance, $ bil, annualized

0

200

400

600

800

1,000

1,200

00 01 02 03 04 05 06 07 08H1 08Q3

Source: Thompson

Chart 1-3: Recession Spreads Across the NationRecession status, based on industrial production and employmentDec. 2008

Expansion

Recovery

At risk

In recession



Chart 1-6: Inventories Must Fall Before Construction Picks Up

-60

-40

-20

0

20

40

60

80

100

120

68 73 78 83 88 93 98 03 08

3

4

5

6

7

8

9

10

11

12

13SF housing starts (L)

New homes months of supply (R)

Chart 1-7: Builder Expectations Stuck at a Low PointNAHB single-family housing index

0

20

40

60

80

100

85 87 89 91 93 95 97 99 01 03 05 07

Current conditions Expectations Current buyer traffic

some 30% below what demand could sup-port even during a recession.

Builders are still absorbing the ex-cess number of homes constructed dur-ing the housing boom. The exceptionally weak demand for new homes has driven the months of inventory of available new homes for sale up to more than double the norm at 12.5 months. Historically, months of inventory have to decline down to about six months before builders are willing to step up the pace of single-family construc-tion (see Chart 1-6). With supply high relative to demand and with job losses ac-cumulating, homebuilders naturally are ob-serving dismal housing market conditions. The National Association of Homebuilders housing market index has slid to an all-time low of 11 in the fourth quarter (see Chart 1-7). At 18, the expectations component of the index looks better than the current conditions index, but any reading below 50

indicates that more builders view the hous-ing market as poor rather than good.

Builders are working hard to cut back production, but inventories are falling slowly because of the surge in foreclosures. Indeed, foreclosures and the broader prob-lems they create for housing demand and supply are the crux of the housing market’s problems. Mounting foreclosures are up-ending the mortgage industry and keeping borrowing conditions tight. Foreclosures are also adding to the already-bloated inventory of homes: Just over 2.7 million first mort-gages went into default last year, according to data from Equifax, following 1.4 million defaults in 2007. The regions where fore-closures are rising the quickest are those where housing markets were particularly overinflated during the boom: California, Nevada, Arizona and Florida. The indus-trial Midwest is also suffering because of its early and sharp recession.

Despite measures by the Bush ad-ministration to encourage the mortgage indus-try to prevent foreclosures, they continue to mount. A num-ber of efforts have been put in place to enable stressed home-owners to avoid foreclosure, and more are in the works. Already

in place are FHA Secure, Hope Now, and most recently Hope for Homeowners, a plan that allows mortgage owners to con-vert some of their troubled loans into FHA-insured mortgages in exchange for taking write-downs on the loans.

The fruits of these efforts are still small compared with the problem, however. Since the beginning of last year, lenders have stepped up these efforts, with more than 300,000 mortgages modified in the fourth quarter of 2008, according to the Hope Now Alliance. However, the number of foreclosures started also increased steadi-ly over this period to nearly double the number of modified loans (see Chart 1-8). More loans are in the modification pipeline: In the fourth quarter, lenders initiated about 350,000 formal repayment plans.

Further, the recidivism rate is high. The workouts have not sufficiently reduced monthly mortgage payments to allow enough distressed homeowners to remain current with their payments for long. The OCC recently reported that more than half the loans modified in early 2008 were back in delinquency within six months. The problem appears to be that most modifica-tions involve only interest-rate reductions and term extensions. The lower monthly mortgage payments are insufficient to fore-stall credit problems for homeowners who are significantly underwater. These hom-eowners have few incentives to make good on their loans, as they believe there is little chance of building equity or ultimately owning the home free and clear.

While painful, foreclosures are help-ing to stabilize existing-home sales and drive down bloated house prices. The

Chapter 1-8: Loans Worked Out, but Foreclosures Rising FasterThs

0

100

200

300

400

500

600

07Q1 07Q2 07Q3 07Q4 08Q1 08Q2 08Q3 08Q4

Modifications completed

Foreclosures started

Source: Hope Now Alliance



National Association of Realtors estimated that at the end of last year, 45% of all existing-home sales were foreclosure sales. Foreclosure sales have helped to keep existing-home sales stable at around 5 million for well over one year now. Indeed, sales of existing homes in Nevada, California and Arizona surged in the third quarter of last year because of foreclosures (see Chart 1-9).

Concurrently, fire-sale prices are evi-dent in the 25% year-over-year decline in the house price in these states, the third consecutive double-digit decline. Eager to move these units off their balance sheets, lenders with foreclosed homes are selling them at hefty discounts. In fact, declines in all three commonly cited house price indices deepened in their most recent re-ports. The CSI index likely declined by an annualized 15% in the fourth quarter com-pared with 14% in the third quarter. The fall in the Realtors’ median existing house price accelerated in the fourth quarter of 2008, declining by 12% on a year-ago ba-sis and an annualized 28% quarter to quar-ter. In the third quarter, the median price declined by an annualized 14%. Similarly, the Federal Housing Finance Agency (for-merly OFHEO) purchase-only house price index fell by 12% quarter to quarter, its fastest decline ever. Falling prices further depress homeowner equity and raise the probability of default, resulting in a down-ward spiral that has already helped take down the broader economy.

Housing rebalances. Despite the darkening national economic outlook and the weak conditions in the housing

market, some positive signs give hope that housing is about to hit bottom. First, the hous-ing market’s correction to date has been substantial and has helped to wring out many of the excesses that precipi-tated the crash. More than three years since the housing market

began correcting, measures of imbalance are at least stabilizing. Inventories are flattening, house prices are coming back down to earth, and sales are approaching stability. Second, policymakers, though largely unsuccessful at stemming the housing correction, have been spurred to respond more aggressively because the malaise has spread into a painful recession whose reach extends far beyond housing. Stronger policy measures are coming that will help place a floor under the housing downturn. The policy actions will help housing both directly, through measures that will lower mortgage interest rates and programs to forestall foreclosures, and in-directly, through job-generating fiscal stim-ulus spending that will bolster consumer demand for housing.

Recent trends in two key indicators, house prices and inventories, suggest that the housing market’s excesses are dimin-ishing. Plummeting house prices, while painful, have helped bring a major imbal-ance in the housing market back in line. The 21% decline in the CSI house price index since the first quarter of 2006 has brought measures of price imbalance back in line. Homes are much more affordable than they were in 2006; the NAR’s affordability in-

dex has improved by 25% since the peak in the median price. Similarly, the house price-to-rent ratio, a measure of imbalance that compares house prices with apart-ment rents, is narrowing (see Chart 1-10). The average price-to-rent ratio for 54 met-ro area markets is 28% down from peak. The price-to-rent ratio, however, is still not back to its long-term average, which sug-gests that house prices will need to correct a bit more. Furthermore, the affordability index may be high, but this measure does not reflect the difficulty many potential buyers are having in obtaining a mortgage in this environment of tight credit. High affordability does, however, set the stage for a sturdy rebound in housing demand once the economy and mortgage markets get back on their feet.

Another imbalance is righting itself. The inventories of unsold, vacant homes are no longer climbing, according to data from the Census Bureau’s Housing Vacan-cy Survey (see Chart 1-11). The number of homes vacant and available for sale has been knocking around 2.2 million units, or 1.7% of total housing stock, for the last year. This level of inventories is still exceptionally high. Historically, the share has averaged 0.9%. The current reading is consistent with an excess of 1 million units of homes. Still, inventories are no longer rising as they had in the last couple of years, thanks in large part to the rapid contraction of residential construction. Stabilizing inventory is a necessary step toward health, although it will take sub-stantial declines in this figure to right the housing market.

Chart 1-9: Foreclosures Prop Up Home Sales in CA, AZ and NVExisting-home sales, % change year ago, 2008Q3

Source: National Association of Realtors

-16% to 0%

> 0%

-23% to -16%

< -23%

Chart 1-10: Housing Affordability Is Returning

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86 88 90 92 94 96 98 00 02 04 06 08

1.8

2.0

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Price-rent ratio (L)

Price-income ratio (R)



Several other factors will help stabilize housing in the coming year. The impact of stimulus measures at the end of last year will bear some fruit this year. For example, equity investments in financial institutions will help shore up the financial system, and the effectively zero fed funds rate will bolster demand for housing. Indeed, lower mortgage interest rates seemed to boost demand for homes. At the end of last year, the Federal Reserve’s announce-ment that it will purchase securities, most of which are backed by mortgages from government-related agencies, pushed mortgage rates down quickly. These lower rates led to a spike in mortgage applica-tions, according to the Mortgage Bankers

Association’s mort-gage applications index, especially in refinance applications where applicants are trying to lock in lower rates and monthly payments (see Chart 1-12). The lower monthly mortgage payments that usu-ally accompany a refinanced mortgage will help strengthen consumer balance sheets, a positive for both housing and

the broader economy. Though purchase ap-plications did increase, the index did not ex-perience the same up-ward thrust and now stands at about two-thirds of year-ago lev-els. Rising joblessness and exceptionally weak consumer confidence, along with still-tight mortgage qualifying conditions are con-straining purchases.

Also, policymak-ers will continue to

put into place measures that directly shore up the housing and mortgage markets and the broader economy. The Treasury is con-sidering a plan that would pull mortgage rates down to 4.5%, and policymakers continue to toss about mortgage forbear-ance plans.

Indeed, the Moody’s Economy.com housing outlook assumes that policymak-ers will act further and will help place a floor under the housing market. Fi-nally, falling energy prices will help cash-strapped consumers. Even with more intervention, however, the housing correc-tion is a train too fast to stop, and house prices will not stabilize until at least the second half of next year.

Chart 1-12: Mortgage Applications SurgeIndex, March 1990=100

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Purchase originations (L)

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Chart 1-11: Inventories Are PeakingNumber of vacant homes for sale, ths

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Housing supply 700,000Single 450,000Multi 200,000Manufactured 50,000

Housing demand 1,400,000Households 800,000Obsolescence 400,000Second homes 100,000





Not since the Great Depression has the nation suffered a housing downturn as severe and broad as this one. The funda-mental fuel for the crisis is the hundreds of billions of dollars in losses that financial institutions are taking on from their bad mortgage-related investments made during the housing boom and that are now unrav-eling in the housing bust. National house prices had nearly doubled during the first half of the decade (see Chart 2-1). 1 In California and Florida, they rose threefold (see Chart 2-2 and Appendix 1-1). It was a bubble: House prices had become un-hinged from household income and rents.

The bubble was inflated by numerous forces. The most important of these were the flawed process of mortgage securitiza-tion, a lack of regulatory oversight, and old-fashioned hubris. In securitizing mort-gage loans, no one involved had enough at stake to make sure that the mortgage loans being made were good. Lenders originated the loans but quickly sold them to investment banks. Investment banks

1 This is measured by the national Fiserv Case-Shiller index. There are numerous measures of house prices, but the CSI is the closest representation of reality in the housing market. It is a repeat-sales index for all homes sold in arm’s length transactions. It does include homes sold at auction in foreclosure proceedings, but it does not include nonprice discounts offered by home sellers that have become ubiquitous in the housing bust.

Chapter 2: How Did the Market Unravel?

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Chart 2-1: Housing Boom and BustFiserv Case-Shiller® National House Price Index, 2000Q1=100

-25%

Chart 2-2: House Prices Soared in Many Metro AreasCSI, % change 2000Q1-2006Q1

Sources: Fiserv Lending Solutions, FHFA, Moody’s Economy.com

41% to 85%

>85%

30% to 41%

<30%

packaged the loans and made them into securities that they quickly sold to global investors. Rating agencies grading the riski-ness of the securi-ties based their assessments on loan information provided by the investment banks; they did not check the accuracy of that information. Global investors took the rating agencies’ grades as a given and did little of their own due diligence, not checking whether the loans backing their securities were good.

No bubble is possible without wild optimism. House prices rose strongly one year and then the next, and then everyone began forecasting with a ruler, expecting house prices to rise strongly ad infinitum. Given such a forecast, homebuyers began flipping homes. Homeowners added pools and decks. And people who previously could not afford homes were able to pur-chase them. Lenders made more aggressive loans, thinking they could refinance the

mortgages before the buyers went delinquent. Investors were willing to buy increasingly arcane securities with yields that provided little cush-ion if the securities did not perform as planned.

The appetite for housing as an invest-ment engulfed nearly the entire nation. Ac-cording to mortgage originations data from the Home Mortgage Disclosure Act, the in-vestor share of national purchase originations for housing rose from

7.6% in 2001 to a peak of 15.6% in 2005.2 Investment in some previously more active housing markets surged even more, dou-bling to one-fourth of originations in Arizo-na and Nevada and 30% in Florida, fueling the speculative bubble in these markets. The HMDA mortgage originations data show that the investor demand for hous-ing was concentrated in those markets that overinflated during the boom (see Chart 2-3 and Appendix 2-1 and 2-2). Similarly, easy credit allowed many borrowers to purchase homes that were priced much higher than their incomes could bear, as evidenced by the high share of mortgage originations that were nonprime in 2006, the height of the lending frenzy (see Chart 2-43 and Appendix 2-3 and 2-4).

Banking and securities regulators were silent during all of this. Deregulatory sentiment that had begun nearly a quarter-

2 HMDA data are based on reports by nearly all mortgage lenders who are required to submit this information for purposes of monitoring mortgage lending discrimination. The HMDA data may understate the level of and increase in investor demand, as homebuyers have a financial incentive to claim they will live in the residence, as lending terms are easier on an owner-occupied loan.

3 Moody’s Economy.com estimates the number and amount of nonprime mortgages by using the rate spread available in the HMDA data. The Federal Financial Institutions Examination Council requires lenders to identify loans on which the APR on the loan exceeds a comparable Treasury APR. Moody’s Economy.com assumes that all originated loans with a rate spread greater than or equal to 3% are nonprime.



century earlier was at its apex, the view being that market forces would be self-dis-ciplining. That is, self-interested investors would make sure the loans backing the securities they were buying would be good loans. This sentiment was strongest at the Federal Reserve under Chairman Alan Greenspan. The mortgage lending industry is regulated by an alphabet soup of federal and state institutions with acronyms such as the OCC, OTS and FDIC, but the Fed is the nation’s most important regulator. It had been empowered by Congress in the 1990s (under the HOEPA laws) to set guidelines for mortgage lending that would apply across all regulators. Yet only early last year did the Fed take Congress up on that authority. Some regulators had publicly worried about egregious mortgage lending driving the housing boom, but it was all but impossible for them to respond effectively. Given the fractured regulatory framework, lenders were able to take on corporate structures allowing them to skirt oversight, and regulators could not agree among themselves on how to respond quickly enough to make a difference.

The housing boom began to unravel in earnest in the spring 2006 selling sea-son. The surge in house prices during the first half of the decade, along with Fed monetary tightening, had made housing unaffordable, even with anything-goes mortgage lending. Subprime loans with no down payment, no mortgage insurance, and no proof of income and piggyback sec-ond mortgages had become more common than not. House prices have since fallen by 25%, bringing prices back to where they

were at the beginning of 2004. The metro areas that had the highest exposure to nonprime and investor lending and conse-quently experienced the greatest runup in house prices during the boom are suffering the greatest declines on the downside of the housing cycle.

Foreclosure waves. When house prices began to flag in the spring of 2006, investors realized the market had turned on them and started defaulting on their loans. House flippers, looking to make a quick buck, had juiced their returns by us-ing low, or no, down payment loans. Many had lied about living in the homes to obtain better terms. Lenders did little to thwart this behavior. Now the result was a wave of so-called early payment defaults as flippers turned their keys back to their lenders, in some cases without making even a single mortgage payment (see Chart 2-5).

A second wave of foreclosures hit in 2007, when subprime borrowers who had gotten loans at the peak of the housing market in 2005 faced their first payment reset. Nearly all the subprime loans made in 2005 and 2006 were “2-28” loans: The payments were fixed for two years, then reverted to adjustable rate mortgages with adjust-ments tied to the six-month Libor. With rates still very high in 2007—this was before the Fed began cut-ting rates in earnest—the

resets were too large for many subprime borrowers to handle; the average monthly mortgage payment jumped by $350 from $1,200 to $1,550 per month.

Resetting was not a serious problem in 2008 and will not be a problem this year. The Federal Reserve has worked hard to reduce mortgage interest rates by both aggressively cutting the fed funds rate and announcing that it will purchase some mortgage-backed securities. However, a third wave of foreclosures has hit, driven by a combination of negative equity and a weakening job market. Fourteen million of the 51 million homeowners with first mortgages in the fourth quarter of last year were underwater—that is, the market val-ue of their homes was less than they owed. This was up from 3.5 million homeowners at the beginning of 2007 and 2.5 million at the beginning of 2006.

Chart 2-4: Nonprime Lending Concentrated in Frothiest MarketsNonprime share of purchase originations by $ volume, 2006, %

Source: Federal Financial Institutions Examination Council: Home Mortgage Disclosure Act, 2006

25% to 30%Near average

Less than 15%

30% to 45%

U.S. average = 23%MSA average = 19%

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Chart 2-5: Foreclosure Waves First mortgage loan defaults, ths, SAAR

Sources: Equifax, Moody's Economy.com

Chart 2-3: Investor Share of Purchase Origination VolumeBy $ volume, 2006, %

Source: Federal Financial Institutions Examination Council:Home Mortgage Disclosure Act, 2006

15% to 25%10% to 15%<10%

>25%

U.S.=15.3%



Negative equity by itself is generally not a reason for a mortgage default. Most homeowners do not have a precise grip on the value of their homes. Even if they could determine that they were underwa-ter, they would not walk away, given the hefty transaction and psychological costs. However, when negative equity is mixed with disruptions to income—from unem-

ployment or lost overtime hours—or with unexpected expenses—from a divorce or even a leaky roof—default can ensue. With the jobless rate rising everywhere, defaults are mounting. There is growing anecdotal evidence of more so-called walk-aways in the most distressed areas of the country, where prices are already off more than 30% from their peak, such as in the

Central Valley of California and Southwest Florida. The NAR, for example, reported that 45% of existing-home sales at the end of 2008 were sales of foreclosed homes.

Financial system losses. About 20 million sketchy mortgage loans were made during the teeth of the housing boom from mid-2004 to mid-2007. Of these, 7.25 million were subprime loans, 5.25 million

Table 2-1: Financial System Losses on Unsecuritized Loans and SecuritiesOn Assets Originated Through Year-End 2007$ bil

Expected LossesPension GSEs and Hedge funds and

Outstanding Expected losses Loss rate Banks Insurance funds government mutual funds

Residential mortgages 10,070 1,105 11.0 477 176 150 202 100 Consumer credit 2,025 170 8.4 98 31 9 - 32 Commercial real estate 3,425 392 11.4 195 62 44 30 61 Corporate 7,835 631 8.1 337 109 88 - 97

Total Loans 23,355 2,298 9.8 1,107 378 291 232 290

Financial System Losses on Unsecuritized Loans $ bil



Subprime 320 117 36.6 65 8 5 26 13 Alt-A 605 107 17.7 37 6 3 52 9 Prime 3,850 91 2.4 43 5 4 36 3 Commercial real estate 2,475 149 6.0 88 15 13 5 28 Consumer loans 1,400 115 8.2 74 16 6 - 19 Corporate loans 3,700 172 4.6 112 16 12 - 32 Leveraged loans 175 45 25.7 21 6 5 - 13

Total loans 12,525 796 6.4 440 72 48 119 117

Financial System Mark-to-Market Losses on Securities Holdings$ bil



ABS 1,095 344 31.4 151 56 61 38 38 ABS CDOs 400 366 91.5 180 83 48 22 33 Prime MBS 3,800 80 2.1 1 18 29 28 4 CMBS 950 243 25.6 107 47 31 25 33 Consumer ABS 625 55 8.8 24 15 3 - 13 High-grade corporate debt 3,010 167 5.5 83 39 34 - 11 High-yield corporate debt 605 154 25.5 74 35 27 - 18 CLOs 345 93 27.0 47 13 10 - 23

Total securities 10,830 1,502 13.9 667 306 243 113 173



were so-called alt-A loans, and 2.5 million were jumbo option ARMs or other types of risky mortgages. An additional 5 million were prime loans made to households with somewhat lower FICO scores and higher LTVs than normal.

It is reasonable to expect that about 10 million of these loans will ultimately default.4 Not all of these will go through the entire foreclosure process, but of those that do, the loss on the mortgages will be close to 50%. If this arithmetic holds, total losses on residential mortgage loans and securities to the financial system will ex-ceed $1 trillion.5

The losses the financial system will have to bear do not stop there. Total losses suffered on all assets originated through 2007 are expected to amount to some $2.3 trillion, equal to a stunning 10% of all as-sets (see Table 2-1).6 Construction and land development loans and commercial mort-gage loans will suffer significantly, with loss-es expected to total $392 billion.7 Another $631 billion will be lost on corporate loans, and losses on consumer loans including vehicle and credit card debt will amount to $170 billion. Abstracting from the govern-ment’s nationalization of big parts of the financial system, depository institutions are expected to suffer $1.1 billion in losses, while broker-dealers, insurance compa-nies, pension funds, hedge funds, and the government-sponsored enterprises will each lose between $200 and $400 billion.

Financial panic. Although eye-popping, the magnitude of these loss esti-mates has been known for some time. And although they fed the panic in the financial system that hit a fever pitch at the end of last summer, the catalyst seems to have been the government’s takeover of Fannie Mae and

4 Default is the first step in the foreclosure process. A default usually occurs after a borrower is more than five months delinquent on his or her loan.

5 This loss forecast assumes that house prices will fall by almost another 11%, making the peak-to-trough decline in prices approximately 36%. The bottom in national house prices is expected in the fourth quarter of 2009. It also assumes that the nation’s unemployment rate will peak at 9% by the beginning of 2010, up from 6.6% in the third quarter of last year. Other key assumptions are that oil prices will be about $50 per barrel and that the major financial failures will subside by spring.

6 This includes cash flow losses by domestic and foreign-based institutions on their U.S.-based assets.

7 This assumes a 15% peak-to-trough decline in commercial real estate prices as measured by the Moody’s/REAL repeat-sales index.

Freddie Mac. Global investors reasoned that if these two government-backed institutions could run aground, anybody could.

Investors immediately turned their attention to Lehman Brothers, a highly leveraged investment bank that had been struggling since the failure of Bear Stearns. Yet Bear’s failure in March was driven by a lack of liquidity; that was not the issue for Lehman. The Federal Reserve had estab-lished two new credit facilities to provide emergency cash for investment banks after the Bear collapse. Lehman had access to these.8 Yet, even with enough liquidity to operate, Lehman’s business evaporated as its counterparties lost faith that it would remain a going concern. Lehman’s plung-ing stock price, driven in part by rampant short-selling of its stock and the ballooning cost of insurance to protect against a default on its debt, heightened those fears.9

The Treasury Department, Federal Reserve, and a host of major financial insti-tutions tried to find a graceful alternative to the Lehman collapse; they failed, and so did Lehman. Unlike Bear’s collapse, which had been a complete surprise, Lehman’s unraveling was widely predicted. Policy-makers felt that Lehman’s counterparties had had plenty of time to plan for it and thus decided not to help Lehman as they had Bear. It was a calculated risk. Lehman made markets for all kinds of fixed-income assets, including credit default swaps and foreign exchange. Policymakers believed, but were not certain, that these markets would be able to smoothly adjust to a Leh-man bankruptcy.

The gamble seemed to pay off initially. Lehman filed for bankruptcy, but markets continued to trade with little disruption. One casualty of the Lehman failure, how-ever, was the Reserve Fund, one of the na-tion’s oldest money market mutual funds for institutional investors. The Reserve Fund’s investments included commercial paper issued by Lehman. As this paper plunged in value, it forced the fund’s net asset value below $1—an extremely rare phenomenon known as “breaking the buck.” This panicked investors, who had

8 These include the Primary Dealer Credit Facility and Term Security Lending Facility.

9 This is reflected in the ballooning spread on Lehman’s credit default swaps. CDS are insurance contracts on bonds in which one party for a premium agrees to pay another if the issuer of the bond defaults.

come to think of money market funds as so safe and liquid as to be one step away from the mattress. Redemptions from the fund began to accelerate.

This took the financial crisis to anoth-er level. Money funds hold some $3.5 tril-lion and are large investors in short-term securities such as commercial paper, which funds operations for factories, stores, and other nonfinancial businesses. If money fund redemptions continued, the market for commercial paper would dry up, en-dangering the operations of firms through-out the real economy.

Adding to the turmoil was the collapse of American International Group, one of the world’s largest insurers. In this case, policymakers deemed that an AIG bank-ruptcy would be too much for the global financial system to bear and stepped in with an $85 billion loan in exchange for a nearly 80% equity stake in the firm. AIG is a big player in the credit default swap mar-ket, through which it insures as much as $450 billion in bonds. An AIG bankruptcy could cause all that insurance to fail as well, since any financial institutions hold-ing AIG-insured bonds would be forced to further mark down the value of their holdings. The resulting hit to those institu-tions’ capital could very well force more failures, spreading the damage throughout the financial system. Policymakers judged AIG as too big to fail.

By the morning of September 18, the global financial system was near collapse. The panic was evident in plunging stock prices, soaring gold prices, ballooning credit spreads, and three-month Treasury bill yields that were effectively offering a 0% interest rate. The spread between three-month Libor and Treasuries, a good proxy for the angst in the financial system, surged to an all-time high (see Chart 2-6).

Given the widespread financial panic and the clear and imminent threat this posed to the economy, policymakers had no choice but to take unprecedented and massive ac-tion. And they did, with the passage of Trea-sury Secretary Henry Paulson’s $700 billion rescue plan in early October that included the Troubled Asset Relief Program to stabilize the quickly foundering mortgage market.

Economic fallout. Since the crisis hit a fevered pitch in October 2008, the Treasury has used the TARP funds to inject capital into the financial system, while the



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Chart 2-6: On the Precipice of Financial CollapseDifference between 3-mo. Libor and Treasury bill yields

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Chart 2-7: The Economic Fallout Is SignificantReal GDP growth, annualized % change

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Pre-panicPost-panic

Federal Reserve and Congress have enacted additional measures, including the Fed’s program to purchase mortgage-backed securities and the auto industry bailout. Despite these policy actions, substantial damage has already been done to the frag-ile economy and the actions have not pre-vented the recession from deepening.

Indeed, the economy is suffering the worst recession since the Great Depres-sion. According to the National Bureau of Economic Research, the arbiter of the na-tional business cycle, the current recession began in December 2007. Not only is the

downturn already one year old—the aver-age recession lasts 10 months—but it is on track to be longer, more severe, and more broadly based than any recession since the 1930s. Nearly every significant economic statistic has plunged: Payroll employment fell by more than a half-million jobs in November alone, and is down by almost 2 million since the beginning of the year. Sales of light vehicles have plunged to lev-els last seen in the early 1980s. Consumer confidence has never been as dark, and housing starts are as low as they have been in over 60 years.

The recession that began in late 2007 will continue until the third quarter of this year. Real GDP is expected to fall by almost 3% from its peak in the second quarter to its expected trough in the third quarter. Payroll employment will fall by nearly 5 million through the end of 2009, and the unemployment rate is expected to rise well into 2010, peaking at 9%. The outlook has darkened rapidly since the financial panic broke out in full force (see Chart 2-7). As recently as this past summer, the unem-ployment rate was still projected to top out at only 6.5% in mid-2009.





As the housing downturn worsened and spread into the broader economy, policymak-ers acted—slowly at first—but by the end of 2008 had stepped up their intervention in an unprecedented manner. Even before last fall’s freeze-up of financial markets, the Bush administration and Congress had instituted various measures to shore up the economy and housing, starting with the Economic Stimulus Act that was signed last winter and culminat-ing at the very end of last year with the bailout of the auto industry (see Table 3-1). Although

policymakers have been actively engaged, they have not acted fast enough to prevent the hous-ing market or the economy from deteriorating further. Consequently, more action is certain to come under the new administration: Before entering office, President Obama was formulat-ing a huge stimulus plan and paving the way for its speedy passage by Congress.

Through this crisis, policymakers have been attacking it from four directions, each with varying degrees of success. Measures have been taken to:

Stabilize capital markets, including direct capital infusions into banks and other financial institutions.

Reduce foreclosures to shore up the housing market.

Stimulate or stabilize the economy through tax refunds and extended un-employment benefits.

Reduce the cost of borrowing for busi-nesses and more recently for homebuyers.Financial market stabilization.

The public policy response to the financial

Chapter 3: Policymakers Respond

Table 3-1: Policymakers Work to Quell the Panic

Pledged

February 13 President Bush signs Economic Stimulus Act of 2008 $170 billionMarch 6 Congress temporarily raises FHA loan limitsMarch 11 Fed promises loans to financial institutions $200 billionMarch 15 Fed provides line of credit to Bear Stearns $27 billionMarch 16 JPMorgan Chase receives loan $29 billionMarch 17 Fed starts lending via Primary Dealer Credit FacilityJuly 30 Housing bill signed $300 billion

Congress extends unemployment benefits for an additional 13 weeksSeptember 7 Treasury takes over Fannie Mae and Freddie Mac, promising asset insurance $200 billion

September 15 Fed begins Term Securities Lending Facility, pledging $200 billion $200 billionSeptember 16 Fed provides funds to AIG and additional funds to the financial system $85 billion/$70 billionSeptember 19 Money market funds receive temporary guaranteed from the Treasury $50 billion

Fed finances money market mutual funds through asset-backed commercial paperSeptember 29 Fed expands lending programs: More in loans made available to banks; credit made available for other

central banks $150 billion/$330 billionOctober 3 Congress passes bailout package creating TARP fund $700 billionOctober 7 Fed begins buying debt from private companies as part of the Commercial Paper Funding Facility $1.3 trillionOctober 8 AIG receives another loan $38 billionOctober 14 FDIC guarantees interbank loans $$1.4 trillion

Treasury uses TARP funds, begins Capital Purchase Program $250 billionOctober 22 Fed provides liquidity to money market investors, pledging funds through the money market investor

funding facility $600 billionOctober 29 Fed conducts currency swaps with central banks in four emerging economiesNovember 10 Aid package replaces loans to AIG $150 billionNovember 15 Leaders of 20 nations gather in Washington to discuss economic crisisNovember 24 Fed guarantees Citigroup debt $300 billionNovember 25 Fed agrees to buy mortgage-backed securities; lends to holders of consumer loan-backed securities $600 billion/$200 billion

New York Fed purchases CDOs from AIG $20 billionDecember 1 Fed creates Term Asset-Backed Securities Loan Facility to ease household credit $200 billionDecember 12 New York Fed purchases residential mortgage-backed securities from AIG $20 billionDecember 19 President Bush uses bailout money to support automakers $17 billion

Sources: FDIC, Federal Reserve, FHA, Department of Treasury, Moody’s Economy.com



crisis is without precedent. Multiple agen-cies have stepped in to inject cash and confidence into financial markets. Perhaps the most significant policy measures taken to date—both in dollars promised and in creativity—are those that have targeted the credit crisis. These measures include the Troubled Asset Relief Program, new credit facilities at the Federal Reserve, the rescue of Bear Stearns and AIG, the purchase of GSE debt, and the FDIC’s guarantee of U.S. banks’ debt. As a consequence, the full faith and credit of the United States government now effectively backstop the financial system, significant parts of which have been nationalized. With the takeover of Fannie Mae and Freddie Mac, the government makes nearly all the na-tion’s residential mortgage loans. And as TARP funds are deployed, the government is gaining sizable ownership stakes in the nation’s largest financial institutions.

Through the TARP alone, the Treasury Department has promised $700 billion in funds. Under the financial rescue legisla-tion passed on October 3, the Treasury was to establish a program funded by taxpayers to purchase up to $700 billion in bad residential and commercial mort-gage assets from financial institutions. The legislation was intended to allow financial institutions—defined quite loosely—to sell their bad assets at a discount that could be determined through reverse auction or other model-based approaches. The pro-gram was as important for its symbolism as for its substance. Given the potential size of the program and its comprehen-sive approach, it clearly signaled the fed-

eral government’s commitment to protect the global financial system. By so doing, poli-cymakers hoped to quell the finan-cial panic.

When first proposed by the Treasury, the $700 billion TARP fund was envis-aged as a way to entice new private capital into the financial system. TARP money was

intended to purchase distressed mortgage loans and securities through a reverse auc-tion process.1 The auctions would estab-lish market prices, which were not avail-able because trading in these assets had collapsed. The Treasury would be the buy-er, taking bids from the institutions that owned the assets. The lowest bids would set market prices so that institutions, and just as importantly potential private inves-tors, could determine how much capital the institutions needed.

The Treasury’s initial vision of the TARP, however, was overwhelmed by the financial panic. With markets unraveling, officials had no time and thus no choice but to use TARP funds to make direct equity investments in financial institutions like AIG, Wells Fargo and Citigroup. Further, the subsequent spillover of financial woes to the auto industry has prompted the Trea-sury to set aside TARP funds to invest in two of the Big Three auto companies. The federal government has committed a total of more than $23 billion to GM, its financ-ing company GMAC, and Chrysler. Indeed, by the end of December, the Treasury had committed or disbursed slightly more than the first $350 billion of the TARP funds that Congress had authorized.

In another effort to restart money and credit markets, the Federal Reserve has vastly expanded its role. The central bank

1 A description of how the TARP reverse auctions could work is found in “A Troubled Asset Reverse Auction,” Lawrence Ausbel and Peter Cramton, October 5, 2008. http://www.cramton.umd.edu/papers2005-2009/ausubel-cramton-troubled-asset-reverse-auction.pdf

can now lend to whomever and buy what-ever it deems necessary, essentially with-out limit. The Federal Reserve has extend-ed nonrecourse loans at the primary credit rate to U.S. depository institutions and bank holding companies to finance their purchases of high-quality asset-backed commercial paper from money-market mu-tual funds. It has also extended this action to include unsecured commercial paper.

The Federal Reserve has also broad-ened the types of collateral eligible for loans from the Primary Dealer Credit Facil-ity and Term Securities Lending Facility, increased the size and frequency of TSLF auctions, and relaxed restrictions on bank lending to nonbank affiliates. Below invest-ment-grade securities and even equities can now be used as collateral. The purpose of these actions was to reduce liquidity pressures on broker-dealers, and it seems to have helped.

These measures have proved partially effective. Although TARP has not proceed-ed as envisioned, the federal government’s bold moves in October, combined with further action since such as reducing the federal funds rate effectively to zero, seem to have stemmed the financial panic and restored at least a fragile stability to the global financial system. The government has sent a clear message to investors that it will do whatever is needed to ensure that the system does not unravel.

Although some measure of stability has been restored, with junk bonds, stocks and commodities rebounding slightly in recent weeks, these programs are only slowly unclogging credit markets. Even as the Treasury and the Federal Reserve have injected cash into the finance indus-try, many institutions are still in survival mode or have had their confidence shaken deeply enough to remain reluctant lenders. Indeed, they continue to batten down their underwriting standards. According to the Federal Reserve’s quarterly senior loan of-ficer survey, the net percent of lenders that say they are tightening their underwriting has jumped to record highs (see Chart 3-1). The dramatic tightening has occurred across all assets, ranging from commercial and industrial loans to credit cards and vehicle loans.

The credit spigot is even more tightly shut in the credit markets. Not only has resi-dential mortgage-backed securities issuance

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Chart 3-1: Battening Down the Credit HatchesNet % tightening standards

Source: Federal Reserve Board



completely evaporated, so too has commer-cial mortgage-backed issuance. Junk corpo-rate bond issuance has stalled, and asset-backed issuance for credit cards and vehicles is occurring only because the large money center banks making these loans are willing to hold the riskiest pieces of the securities. As a consequence, total nonfinancial credit market debt is contracting at its fastest pace since the 1970s (see Chart 3-2). Until the flow of credit is restored, the housing mar-ket’s recovery, as well as that of the broader economy, will remain hampered.

Foreclosure relief. Policymakers have also been working to stem foreclo-sures. Surging losses on defaulting mort-gage assets are eroding the financial sys-tem’s capital base, the genesis of the credit crunch that is undermining the economy. Stemming these defaults is thus an impor-tant policy objective.

As mortgage defaults have mounted, the policy response has intensified. The response began with FHA Secure and Hope Now in late 2007. FHA Secure was established to help distressed homeowners refinance into FHA-insured loans. Hope Now, a consortium of mortgage servicers and lenders, was created soon thereafter to streamline foreclosure mitigation ef-forts. The FDIC has also been aggressively modifying the mortgage loans of institu-tions it has placed into receivership, and Fannie Mae and Freddie Mac announced plans last fall to modify loans they own and insure in which the borrowers are seriously delinquent. The Hope for Homeowners program, in which mortgage owners can re-finance borrowers into FHA-insured loans if they reduce the mortgage amount owed by the borrower, began operating in late 2008.

These are laudable efforts, but they are being over-whelmed by the size of the foreclosure problem. Accord-ing to data from the credit bureau Equi-fax, first-mortgage loan defaults—the first step in the fore-closure process—are running at an annualized pace of 3 million. In a good year, annual de-faults are well below 1 million. Many more are coming; an estimated 14

million homeowners now have mortgage debt exceeding the value of their homes (see Chart 3-3).2 Although negative equity alone need not result in default, negative equity combined with surging unemploy-ment often does. With house prices ex-pected to fall at least an additional 10% and with unemployment expected to rise to 8% during the coming year, defaults and foreclosures in 2009 threaten to dwarf anything experienced so far.

Moreover, there is mounting evidence that foreclosure modification efforts to date have not been very successful. The Office of the Comptroller of the Currency recently reported that more than half the loans modified in early 2008 were back in delinquency within six weeks. The prob-lem appears to be that most modifications involve interest-rate reductions and term extensions. The new monthly mortgage payments are not low enough to forestall credit problems among homeowners who are significantly underwater. These hom-eowners have few incentives to make good on their loans as they believe there is little chance of building equity or ultimately owning the home free and clear.

Finally, even under the best of circum-stances, mortgage defaults are expected to remain elevated well into the next decade, as many option ARM loans will hit their first payment resets in 2010 and 2011 (see Chart 3-4). These loans are 5-25 loans—

2 Estimates of the number of underwater homeowners are based on credit file data from Equifax and house price estimates using data from the Census Bureau, Case-Shiller and Zillow.

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Chart 3-2: Debt Levels ShrinkingAnnualized % change

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Average growth

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Chart 3-3: Negative Equity MountingNumber of underwater homeowners, ths

Sources: Equifax, Moody’s Economy.com

Chart 3-4: Foreclosures Will Remain a Long-Term ProblemMortgage debt outstanding facing first payment reset, $ bil

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with fixed rates for five years and adjust-able thereafter. Most option ARMs that were originated in 2005 and 2006 when house prices had peaked will likely be un-derwater when they reset and thus will be good candidates for default.

Fiscal support. The federal govern-ment has also tried to support the econo-my, and thus housing, through income tax rebates and aid to businesses. In February 2008, President Bush signed the Economic Stimulus Act in response to the ailing housing market and the rapidly weakening economy. The $170 billion plan included $100 billion in tax rebates to boost con-sumer spending, as well as business tax incentives designed to encourage invest-ment. Also, to help consumers, the Bush administration extended unemployment benefits. Eligible unemployed workers are allowed to claim 13 additional weeks of insurance beyond the basic 26 weeks that most states allow. The benefit applies to those who lose their jobs by March 31, 2009. The extension is projected to cost $8.2 billion over the next 11 years. Al-though the programs provided substantial support and did bolster consumer spend-ing, neither the stimulus nor the exten-sion of unemployment benefits has been aggressive enough to reverse the recession, nor did either measure keep housing mar-kets from deteriorating further.

Lowering borrowing costs. Policy-makers have also been working to bring down the costs of borrowing. The Fed engineered an unprecedented coordinated interest rate cut with other central banks in the fall of 2008 to provide liquidity.

Even though the fed funds rate has since been cut to effectively zero, mortgage rates have remained stubbornly high relative to their benchmark Trea-suries because of the still-crippled market for mort-gage-backed securities (see Chart 3-5).

To heal credit markets, the Federal Reserve

and Treasury in late November announced plans to finance consumer loans and mort-gage debt. It was the first time that the Fed had intervened to finance consumer debt and pump money into specific markets. Promising $600 billion, the Fed began pur-chasing investment-grade mortgage-backed securities with the intent of forcing down interest rates. All the debt being acquired is coming from Fannie Mae and Freddie Mac and other government-controlled fi-nancing companies.

These actions have been effective in reducing mortgage rates. By the day after the Fed’s announcement, 30-year fixed contract rates were 50 basis points lower, ending the week below 5.5% for the first time in three years. Since then, rates have been slowly trending downward (see Chart 3-6). The impact of lower mortgage rates on the housing market has been minimal thus far. Refinancing has soared; the MBA’s mortgage applica-tions index for refis is at nearly a five-year high. Refinancing to lower monthly pay-ments is helpful for consumer balance sheets and thus to the broader economy. However, the index reflects applications only. Considering the dramatic erosion in home values equity since 2003, a smaller share of applicants

are likely to be approved compared with five years ago.

Further, the lift from lower mortgage rates on home sales has been much more modest. Applications for purchase are still low, falling about 25% below its year-ago reading. Indeed, there is little to suggest that the interest rate is the primary fac-tor keeping borrowers out of the market. Current prevailing rates are already near historical lows; moreover, the cost sav-ing between a 5.5% rate and a 4.5% rate amounts to $122 a month on a $200,000 mortgage. Uncertainty about employment and wages is a much more substantial force keeping borrowers out of the market.

Meaningful support. More action is needed and more is coming. So far, the government has assumed nearly $9 trillion in direct and indirect financial obligations through Wall Street bailouts, emergency lending, and federal guarantees on loans, deposits and mortgages. Despite these ac-tions, the national housing crisis is inten-sifying, further exacerbating the financial panic and increasingly severe recession. More help will be needed, and, fortunately, policymakers appear ready to provide whatever is necessary.

A number of additional measures can be taken to quell the financial panic and mitigate what threatens to become the worst economic setback since the Great Depression. A much larger and more comprehensive foreclosure mitigation plan is certainly needed, and a consensus is building around this idea. Although issues of fairness and moral hazard will arise with a more aggressive mitigation effort, such

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Chart 3-6: Mortgage Rates Falling%

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problems grow less important as the dam-age grows from so many foreclosures. Mil-lions of homeowners owe more than their home is worth and unemployment is rising quickly; foreclosures, already at record high levels, are sure to mount. The Hope for Homeowners program faces severe impediments and even under the best of circumstances will likely be overwhelmed by the next wave of foreclosures. No plan will keep house prices from falling further, but quick action could avoid the darker scenarios in which crashing house prices force millions more households from their homes, completely undermining the finan-cial system and the economy.

Additional fiscal stimulus measures are also necessary. At this writing, a large economic stimulus was expected soon af-ter the new administration took office. The package was to total some $750 billion over 2009-2010 and include aid to state and local governments, more infrastructure spending, tax cuts for lower- and middle-income households, and some business tax cuts as well. The expected mix of tax cuts and spending was designed to provide both quick relief and a substantial boost

to the struggling economy. The tax cuts will not pack a big punch, as some of the money will be saved and some used to repay debt, but they can be implemented quickly. Aid to state and local governments will not help lift the economy, but it will keep states from having to cut programs and payrolls to balance their budgets. Infrastructure spending will not help the economy quickly—it will take time to get even “shovel-ready” projects going—but it will provide a significant boost. Given that the economy’s problems are not expected to abate soon, this spending will be espe-cially helpful this time next year.

With the government making so many monumental decisions in such a short time, there will surely be unintended con-sequences. Some may already be evident: Nationalizing Fannie Mae and Freddie Mac while not rescuing Lehman Brothers from bankruptcy may very well have set off the current financial panic. And policymak-ers need to be wary of the costs of their actions, as global investors will eventually demand higher interest rates on the soar-ing volume of U.S. Treasury debt. Any measurable increase in long-term interest

rates would be counterproductive; the ef-fect on the housing market and the rest of the economy would offset the economic benefits of the fiscal stimulus.

But policymakers’ most serious mis-steps so far have come from acting too slowly, too timidly, and in a seemingly scattershot way. Early on in the crisis there were reasonable worries about moral hazard and fairness. Bailing out those who took on, originated or invested in untenable mortgage loans would only en-courage such bad behavior in the future. And it would certainly be unfair to those homeowners still managing to make their mortgage payments. But as the crisis deep-ened and persisted, those worries hindered policymakers far too long, triggering panic. With so many people suffering so much financial loss, moral hazard is no longer an issue; debate over whether it is fair to help distressed homeowners keep their homes appears quaint. Their problems are clearly everyone’s problems. Only quick, overwhelming and consistent government action will instill the confidence necessary to restore financial stability and restart economic growth.





The outlook for the housing market is dark. Housing indicators remain very weak amid a rapidly deepening and spreading national recession, continued deteriora-tion in credit quality, and still-fragile capital markets. However, notwithstanding the economic gloom that has intensified in the last year, the bottom of the housing down-turn is within sight. This outlook rests on a key assumption that stronger action on the part of policymakers will break the down-ward spiral among the housing correction, financial market instability, and job losses. Indeed, the substantial correction thus far in the housing market—plummeting house prices and residential construction—has brought it into better balance, setting the stage for a recovery. Even with the gov-ernment intervening further, however, the forces that have been unleashed will keep the housing market from hitting bottom un-til the second half of this year. Even then, economic growth and housing activity will remain tepid.

The expected greater than $800 bil-lion stimulus package that will include tax cuts and spending will help end the reces-sion late this year. However, the subse-quent recovery looks to be lackluster. Real GDP will not return to its prerecession peak until late 2010, and the nation will not approach a full-employment jobless rate of 5% before President Obama’s term ends in 2012 (see Table 4-1). Nonethe-less, fewer job losses, combined with mortgage interest rates heading to postwar lows, will help stabilize the demand for housing. Crucially for housing, the stimu-lus package will also include some sort of help for distressed homeowners. A more aggressive foreclosure mitigation plan will help stabilize house prices and conse-quently stanch the accumulation of nega-tive equity. Stable house prices will also help the mortgage market, which has been restricted by fears of further loan losses.

Indeed, the principal impediment to a stronger housing market recovery is the hobbled financial system and what will likely be prolonged aversion to risk-taking. The credit crunch will eventually

abate, but it will take at least several years for credit to flow freely again. This aversion to extending credit will constrain the housing market directly and indirectly. The direct impact will be through continued tightness in mortgage credit. Although lenders will loosen credit some-what, they will come nowhere near the lax mortgage underwrit-ing standards of earlier this decade. Indirectly, tight credit will continue to restrict eco-nomic growth by hampering business expansion. With the economy expected to function below potential, housing activity will also remain weak.

Credit will remain tight, since the banking system has more losses to digest—from deteriorating consumer, commercial real estate, and corporate loans—and it will be difficult for banks to attract new private capital. Lending will not normalize until capital positions are fully restored. Credit markets will also be difficult to resurrect. Since securitization collapsed, it has be-come clear that this process was fundamen-tally flawed: No market participants, from lenders to investment banks to rating agen-cies to investors, thought they had enough at stake to make sure the underlying loans were prudently made. Even when lending resumes, lenders will demand substantial risk premiums in the form of higher inter-est rates and fees, raising the cost of capital for household and corporate borrowers. Tighter credit and higher costs mean less investment and risk-taking. Start-up com-panies will have a harder go of it, stifling innovation and ultimately constraining pro-ductivity gains. The economy’s longer-term prospects appear dimmer.

Thus, even with help from policymak-ers, the housing downturn has a bit further to go, not hitting rock bottom until mid-

year, and it will remain there until 2010. Leading the recovery are home sales, which are likely already at bottom. From peak to trough, sales have declined by about 40% (see Chart 4-1), not as bad as during the 1980s downturn. Foreclosure sales, however, are largely behind the stabi-lization. Home sales will be flat at around 5.6 million units this year and pick up to 6.4 million next year, a pace that about matches that of 2001.

Construction will bottom out in the first half of this year at an abysmal 640,000 units, and the pace of housing starts will remain depressed until 2011. Although homebuilders have worked hard to reduce their inventories and are now putting up homes at well below the long-run pace of construction of about 1.7 million units a year, a large inventory of foreclosed homes will continue to pressure homebuilders this year. Construction will drop by 70% before the housing correction is through, the worst decline since data began being collected in 1959. Even when demand for homes picks up, it will pick up only weakly. Further, the homebuilding industry will be much slimmer, as many builders will go under. Surviving builders will face difficulties raising capital or bor-rowing to fund construction.

Although policymakers are working hard to forestall at least some foreclosures,

Chapter 4: When Will It End?

Chart 4-1: Housing Correction Is Near Bottom% change from peak

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CSI Real CSI Sales Starts

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mortgage credit quality is expected to remain impaired for at least the remainder of this year. The record pace of mortgage foreclo-sures will keep inventories of homes on the market bloated in the near term, particularly as it will take time to put into place any foreclosure plan that is passed. Concur-rently, demand will remain soft due to the weak economy and continued tightness in lending. These forces will keep house prices descending until the second half of this year. From peak to trough, the Case-Shiller index is expected to decline by about 36%. House prices will not significantly appreciate on a year-ago basis until the end of 2010.

Risks. The most significant upside risk to the housing outlook is that policymak-ers appear fully engaged in stanching the financial turmoil and fighting the economic recession. However, many uncertainties to this outlook remain. Although it seems certain that policymakers will throw bil-lions of dollars at the economy’s problems, these measures may fall short of the mark. Mounting job losses and rising negative eq-uity may overwhelm foreclosure mitigation programs. Details have yet to be worked out on any foreclosure mitigation plan. In

particular, the ultimate owners of securi-tized mortgage loans may be reluctant to allow them to be modified. Further stimu-lus measures may not be enough to loosen lenders’ purse strings, leaving mortgage availability more constricted than expected. The unprecedented housing downturn may cool investor interest in the RMBS market as well as homebuyers’ desire to step back into the market for a long time: Irrational exuberance helped to fuel the housing boom, and irrational fear may keep the downturn simmering. Considering the very weak consumer and investor confidence, there is a real possibility that the house price correction will overshoot, resulting in a deeper than 36% decline.

Swing factors regarding the broader economy’s recovery—in particular the weak global economy and the direction of energy prices—also add to the uncertainty for housing’s recovery. The deepening global economic recession—arguably the first synchronized global downturn since the early 1980s—substantially threatens U.S. exports. Real exports are expected to remain flat in 2009, but will decline outright unless overseas markets find their footing later this

year. It is thus encouraging that policymak-ers across the globe are acting vigorously to shore up their economies. Central banks are slashing interest rates almost everywhere. The average nominal target interest rate set by global central banks—weighted based on relative GDP—has fallen from 4.25% to nearly 1% over the past two years, and the real target rate has turned firmly negative. Large fiscal stimulus plans will soon be implemented in Germany, France, Japan, and the United Kingdom.

Continued low prices for oil and other commodities are also essential to the economy’s prospects. With crude oil falling below $50 per barrel, the cost of a gallon of regular unleaded gasoline is approaching $1.50. This compares with almost $150 for a barrel of oil and $4 for a gallon of gasoline this past summer. Oil prices are expected to stay near $50 this year, saving American households a whop-ping $200 billion on their energy bills in 2009 compared with 2008. Any significant increase in oil prices—because of more significant OPEC production cutbacks, for instance—would be a very heavy weight for the economy to bear.



Table 4-1: U.S. Macro Summary—Forecast

Units 08Q1 08Q2 08Q3 08Q4 09Q1 09Q2 09Q3 09Q4 10Q1 2006 2007 2008 2009 2010 2011NIPAGross Domestic Product %AR 0.9 2.8 -0.5 -3.8 -5.3 -2.1 -0.5 1.5 1.9 2.8 2.0 1.3 -2.3 1.7 4.6 Consumption %AR 0.9 1.2 -3.8 -3.5 -4.1 -1.7 -0.8 0.3 1.3 3.0 2.8 0.3 -2.5 1.0 3.8 Durables %AR -4.3 -2.8 -14.8 -22.4 -16.4 -3.7 0.6 3.1 6.3 4.5 4.8 -4.4 -11.2 3.6 5.7 Motor Vehicles %AR -10.7 -19.7 -26.6 -38.3 -39.9 -4.2 8.8 12.8 18.9 -2.6 2.0 -13.3 -23.2 10.5 11.6 Nondurables %AR -0.4 3.9 -7.1 -7.1 -3.5 -2.0 -1.1 -0.2 0.5 3.7 2.5 -0.4 -3.4 0.5 3.0 Services %AR 2.4 0.7 -0.1 1.7 -1.3 -1.1 -0.9 -0.1 0.6 2.5 2.6 1.5 -0.3 0.7 3.7 Fixed Investment %AR -5.6 -1.7 -5.3 -20.1 -12.7 -9.4 -4.0 0.6 3.1 2.0 -3.1 -4.8 -10.2 1.9 9.1 Nonresidential %AR 2.4 2.5 -1.7 -19.1 -9.2 -9.0 -6.0 -2.2 -0.1 7.5 4.9 1.8 -8.7 -0.7 5.7 Structures %AR 8.6 18.5 9.7 -1.8 -13.2 -21.7 -17.8 -9.2 -5.1 8.2 12.7 11.8 -8.8 -6.9 6.0 Equipment %AR -0.6 -5.0 -7.5 -27.9 -8.0 -4.5 -2.0 0.1 1.6 7.2 1.7 -2.9 -10.1 1.3 5.8 Residential %AR -25.1 -13.4 -16.0 -23.6 -24.4 -9.6 5.2 12.7 15.9 -7.1 -17.9 -20.8 -14.6 12.6 20.4 Single-Family %AR -46.1 -30.8 -30.4 -44.2 -31.3 -11.0 11.8 27.6 35.7 -9.5 -27.4 -36.6 -23.7 26.9 38.2 Multifamily %AR 0.0 24.3 7.7 -15.0 -8.3 2.6 6.6 3.9 -4.5 0.7 -12.8 -4.5 -1.4 1.1 20.6 Other %AR -3.5 -2.6 -8.2 -8.0 -6.0 -10.1 1.2 5.6 7.8 -4.8 -5.2 -6.5 -5.7 5.7 7.0 Exports %AR 5.1 12.3 3.0 -19.8 1.3 2.1 2.6 3.3 4.7 9.1 8.4 6.5 -1.8 4.8 9.3 Merchandise %AR 4.5 16.3 3.7 -27.7 -2.2 -1.6 -1.2 -0.1 2.4 9.9 7.5 6.4 -5.6 2.6 11.3 Services %AR 6.4 3.8 1.4 0.7 9.6 10.4 10.9 10.6 9.5 7.2 10.5 6.8 6.8 9.4 5.6 Imports %AR -0.8 -7.3 -3.5 -15.7 0.1 1.4 0.6 2.2 3.9 6.0 2.2 -3.3 -3.6 3.9 7.6 Merchandise %AR -2.0 -7.1 -4.7 -18.8 -0.4 1.0 0.1 1.9 3.8 6.0 1.7 -4.1 -4.7 3.8 8.0 Services %AR 5.5 -8.0 3.3 0.9 2.6 3.2 3.5 3.9 4.5 6.0 4.4 0.7 2.0 4.3 5.4 Government %AR 1.9 3.9 5.8 1.9 3.7 2.6 2.0 2.0 1.5 1.7 2.1 2.9 3.1 2.0 2.6 Defense %AR 7.2 7.3 18.0 2.1 2.8 2.2 1.1 1.7 0.6 1.6 2.5 7.1 4.3 1.1 1.3 Nondefense %AR 2.9 5.0 5.0 14.6 5.5 3.9 3.1 2.9 2.9 3.6 -0.2 3.6 6.3 3.5 4.1 State and Local %AR -0.3 2.5 1.4 -0.5 3.7 2.6 2.1 2.0 1.6 1.3 2.3 1.2 2.0 2.1 2.9 Final Sales %AR 0.9 4.4 -1.3 -5.1 -3.8 -2.0 -0.5 0.7 1.6 2.8 2.4 1.4 -2.2 1.4 4.4 Final Domestic Sales %AR 0.1 1.3 -2.2 -4.9 -3.4 -2.0 -0.7 0.6 1.6 2.6 1.8 0.0 -2.4 1.3 4.3

ConsumersPersonal Saving Rate % 0.2 2.5 1.2 2.9 4.2 6.2 5.3 5.5 5.7 0.8 0.6 1.7 5.3 5.8 5.5 Retail Sales and Food Services tri$ 4.55 4.59 4.53 4.22 4.12 4.12 4.16 4.23 4.29 4.30 4.50 4.50 4.16 4.37 4.64 Change %AR -2.2 3.7 -5.1 -24.6 -9.3 -0.2 4.1 6.3 6.6 5.8 4.2 -0.4 -7.1 5.2 6.1 Total Vehicle Sales m 15.2 14.1 12.9 10.3 10.1 10.5 11.0 11.7 12.5 16.5 16.1 13.1 10.8 13.2 15.0

Housing Starts m 1.05 1.02 0.88 0.66 0.65 0.64 0.67 0.71 0.74 1.80 1.30 0.90 0.67 0.84 1.35

ProducersIndustrial Production 1997=100 112.3 111.3 108.8 105.5 104.7 104.2 104.2 104.8 105.3 109.6 111.4 109.5 104.5 106.2 109.5 Change %AR 0.4 -3.4 -8.9 -11.5 -3.1 -1.6 -0.1 2.3 1.9 2.2 1.7 -1.7 -4.5 1.7 3.1 Capacity Utilization % 78.7 77.6 75.5 71.9 70.8 70.3 70.1 70.4 70.6 79.2 79.3 75.9 70.4 70.9 72.2

Labor MarketsTotal Employment m 137.9 137.7 137.3 136.0 134.8 133.9 133.3 133.2 133.3 136.1 137.6 137.2 133.8 134.2 137.5 Change %AR -0.3 -0.6 -1.1 -3.7 -3.5 -2.8 -1.6 -0.5 0.4 1.8 1.1 -0.3 -2.5 0.3 2.5 Unemployment Rate % 4.9 5.4 6.1 6.9 7.6 8.3 8.7 8.9 9.1 4.6 4.6 5.8 8.4 8.9 7.6

PricesConsumer Price Index 1982=100 212.8 215.4 219.0 213.8 212.8 213.3 214.5 215.9 217.1 201.6 207.3 215.2 214.1 218.9 223.2 Change %AR 4.3 5.0 6.7 -9.2 -1.7 0.8 2.2 2.6 2.4 3.2 2.9 3.8 -0.5 2.2 2.0 Producer Price Index 1982=100 183.9 196.0 200.6 178.4 174.7 174.7 176.5 180.0 182.5 164.8 172.7 189.7 176.5 184.2 187.7 Change %AR 15.3 29.1 9.7 -37.4 -8.1 -0.1 4.4 8.1 5.6 4.7 4.8 9.9 -7.0 4.4 1.9 West Texas Intermediate $/Bbl 97.95 123.71 122.31 59.42 48.12 50.08 54.12 61.34 68.00 66.10 72.40 100.80 53.42 77.55 85.37

Financial MarketsFederal Funds % 3.18 2.09 1.94 0.51 0.08 0.14 0.16 0.24 0.31 5.00 5.00 1.90 0.15 1.22 3.22 Prime Rate % 6.21 5.08 5.00 4.06 3.08 3.14 3.16 3.24 3.31 8.00 8.10 5.10 3.15 4.22 6.22 30-Year Treasury % 4.41 4.58 4.45 3.68 2.73 3.06 3.11 3.49 4.26 5.00 4.80 4.30 3.10 4.92 5.39

FRB 10-Country Index Jan97=100 97.3 95.8 97.9 108.4 107.2 106.1 104.3 102.0 101.4 108.5 103.4 99.8 104.9 100.7 101.4 Change %AR -7.2 -6.1 9.0 50.2 -4.1 -4.1 -6.6 -8.4 -2.3 -2.0 -4.7 -3.4 5.1 -4.1 0.8

Government Balance NIPA Basis b$ -330.7 -649.6 -547.6 -483.4 -517.6 -723.4 -592.2 -635.7 -679.7 -201.1 -229.3 -502.8 -617.2 -754.8 -914.2 Unified Budget b$ -205.9 26.9 -168.9 -485.2 -687.6 -301.4 -515.9 -562.3 -673.9 -209.2 -187.9 -833.2 -2,067.2 -1,924.3 -1,672.2





To assess the severity and length of the housing market correction, the remain-der of this study will focus primarily on the prospects for house prices and the de-tails of the Moody’s Economy.com regional house price models. Prices reflect changing housing demand and supply and also in-fluence a wide range of economic activity, from consumer spending to mortgage de-linquencies and defaults.

Eight sources of house price data are available for the nation and many metro-politan areas. The five most often men-tioned are the National Association of Re-altors’ median price of an existing house, the repeat-sales house price index from the FHFA, the LoanPerformance repeat-sales house price index from First American Corelogic (formerly FARES/MRAC), and Fiserv Lending Solution’s Case-Shiller re-peat-sales house price index—the primary data source for this study. Several newer providers of house price data are receiving more attention: Standard and Poor’s/Case-Shiller, Altos Research, Zillow Real Estate, and Radar Logic. Each price measure has its advantages and limitations.

NAR. A strong suit of the Realtors’ data is that they are based on transactions recorded by regional real estate associa-tions, which is a broad-based and timely source of information. However, although their estimate of the median price captures actual home purchases across the house price spectrum, it may be swayed by dif-ferences in the mix of homes sold from period to period. Moreover, NAR data are available for only about 150 metro areas. Moody’s Economy.com does construct estimates of house prices for the nation’s remaining metro areas based on other housing indicators.

FHFA. The most favorable attributes of the FHFA series are its ability to mea-sure house price changes based on repeat sales of the same homes over time and its geographic breadth. The FHFA controls, at least in part, for the quality of homes sold by reporting only on matched pairs of home sales that are adjusted for the time between sales. The time adjustment takes

into account the fact that the longer this time period, the more likely alterations or improvements were made to the home. During any quarter, the house price index includes in its sample homes from 352 MSAs and 29 divisions that are sold in the current quarter if data are available on at least one other sale of this house in previ-ous quarters of the index history. This is not exactly a constant-quality index in the spirit of hedonic price indices, since im-provements or additions made to a home between sales are not strictly controlled for, but it is much closer to a constant-quality index than the Realtors’ measure.

A weakness of the FHFA data is that their coverage is limited to houses that were purchased with Freddie Mac or Fannie Mae mortgages. It leaves out most houses in lower price tiers that are transacted with government loans such as FHA and Community Reinvestment Act loans. It also leaves out higher-priced homes bought with jumbo loans or even cash. The current limit on a loan from a government-sponsored enterprise is $417,000 in most markets, although the limit is substantially higher in the most expensive housing markets such as those in California and on the East Coast. How-ever, because these limits were raised only last year, the transactions data still omit many sales of higher-end homes. In addi-tion, the FHFA index undersamples homes purchased using subprime mortgages, as the GSEs own or securitize a small share of subprime mortgages. The omission of subprime data biases the FHFA price index upward, because homes purchased with subprime loans are subject to greater price declines. Along a similar vein, the FHFA indices are subject to biases related to changes in GSE underwriting standards. Tightening or loosening of underwriting standards impacts the type of transactions that underlie the FHFA index.

Another weakness of the FHFA metro data is the inclusion of home values based on refinance transactions, which may bias the indices. There are several sources of refinance bias. First, valuations from refi-

nance transactions are based on house price appraisals, rather than on actual purchase prices. Valuations based on appraisals are constructed under different circumstances than those surrounding purchase prices, as appraisers operate under specific pres-sures and may compare different properties to estimate value than those used, at least implicitly, to establish a purchase price. Second, refinance appraisals may lag during periods of rapidly changing prices to the extent that they utilize historical price data that may quickly become out of date. Final-ly, houses that are refinanced may be ones that have appreciated the most. Indeed, houses with weak or negative house price appreciation may have insufficient equity, preventing their owners from refinancing at the most favorable interest rates. Note that the FHFA has constructed a purchase-only index for national and state house prices but not for metro areas.

The FHFA data are also lagged a bit, given the 30- to 45-day lag from loan origination to Fannie and Freddie funding. The FHFA receives data on new funding for one additional month following the last month of the quarter. This funding contains many loans originating in that most recent quarter, and especially the last month of the quarter. Although this is not a particularly significant problem in a more stable housing market, it is a problem in a fast-changing one.

LoanPerformance. The LoanPer-formance house price indices are transac-tions-weighted repeat sales indices. They exclude refinancings and are not restricted to conforming loans and also include cash transactions. The LoanPerformance indices are constructed from public re-cords and start in 1976, its frequency is monthly, and the indices are available just five weeks after the month’s end for over 7,600+ zip codes; for 958 core-based statistical areas with populations of more than 10,000; and for counties, states and the U.S. Measures of median prices are also available for homes in 10 categories: those whose prices were established less than or greater than five years from the

Chapter 5: House Price Primer



previous valuation; those bought with con-forming and nonconforming loans; single-family detached or attached units; and those priced at 0% to 75% of the median house price, 75% to 100% of the median, 100% to 125% of the median, and greater than 125% of the median.

Calculated in a similar manner to the FHFA data, LoanPerformance indices ad-just for the time between transactions to assess price dispersion. Also, transactions that are not arm’s length or occur less than six months apart are filtered. Drawbacks are that the indices are not seasonally ad-justed and the small sample size for many

of the estimations implies that the power of the house price statistic is low.1

Fiserv Case-Shiller and S&P/Case-Shiller. The Case-Shiller house price indi-ces are similar in construction to those from LoanPerformance but are available on a sea-sonally adjusted basis and filter for renova-tions occurring close to the date of transac-tions, thereby more accurately representing price movements of homes in 110 of the

1 In this case, low power means that for a given level of confidence concerning the estimate of the house price, the probability of a Type II statistical error is relatively high.

largest metro areas.2 Since the price data upon which the index is based are home sales, the CSI improves upon the FHFA in that the CSI does not have a refi bias, nor is it limited to prices based on home sales involving conforming and conventional mortgages. The CSI is also available for about 3,000 zip codes. Fiserv also provides a national index that is constructed from the weighted average of the CSI indices for the nine Census divisions. The weights come from the Census Bureau’s decennial census of the value of housing stock. Con-dominium pricing is available for 34 metro areas, and CSI offers a high-medium-low price tier breakout for 40 large metro areas (see Tables 5-1 and 5-2).

Though calculated by Fiserv Lend-ing Solutions using the same algorithms, the Fiserv Case-Shiller Index does differ from the S&P/Case-Shiller Index. First, the S&P/Case-Shiller Index is published monthly with a two-month lag, shorter than its counterpart. Furthermore, the S&P data are reported as a three-month moving average. Also, the S&P covers only 20 metro areas. The S&P CSI reports the same components as the Fiserv CSI, including the tier indices. However, the S&P CSI does not report a condo index. While the CSI data go back to 1975, the S&P data only go back to 1987. The S&P Case-Shiller data include 10- and 20-city composite indices every month. Once a quarter, they also include a quarterly na-tional index, which is identical to the CSI.

The two indices track each other closely but not perfectly. First, there may be a slight difference in the way geog-raphies are defined. Second, because the two indices are updated on different schedules, transactions for specific dates will make their way into each type of index at different times. The relative weighting of transactions may also be slightly differ-ent for individual transactions within each type of index calculation. Consequently, the two indices may evolve over slightly different paths as new transaction data ar-rive and the indices are updated.

Zillow. Zillow, like many other estima-tors of house prices, relies on same-house transactions for its estimates. Using data

2 The 110 metros account for approximately 50% of the nation’s housing stock. Fiserv fills the indices for the remaining metros with FHFA estimates of price growth.

Table 5-1: CSI Condo Price Index, 2008Q3

Q/Q Y/Y ann. % change % change

Boston, MA 1.0 -0.7Cambridge, MA 1.5 -2.9Chicago, IL -4.5 -4.7Peabody, MA 2.6 -10.0Fort Lauderdale, FL -39.8 -39.3Los Angeles, CA -19.2 -18.1New York, NY -0.9 2.5Oakland, CA -25.3 -24.6Philadelphia, PA 2.9 0.3Rockingham County, NH -12.3 -11.4San Francisco, CA -9.1 -7.9Santa Ana, CA -14.6 -22.4Akron, OH -3.5 -9.0Bridgeport, CT -10.4 -6.2Cincinnati, OH -1.1 -4.7Cleveland, OH -1.9 -7.1Columbus, OH -4.9 -4.6Dayton, OH -8.6 -5.7Denver, CO 1.1 -7.4Flagstaff, AZ 19.7 -1.4Hartford, CT -3.1 -2.6Las Vegas, NV -33.1 -37.2Manchester, NH -17.3 -8.7New Haven, CT -4.0 -5.2Oxnard, CA -17.9 -25.9Phoenix, AZ -25.5 -23.6Riverside, CA -39.0 -34.4San Diego, CA -18.2 -21.2Santa Barbara, CA -22.4 -33.5Toledo, OH -4.3 -4.8Washington, DC -13.6 -15.3Worcester, MA -8.7 -13.0Miami, FL -39.1 -32.1West Palm Beach, FL -39.1 -32.1Source: Fiserv Lending Solutions



reported by counties, the most recent selling price is compared with the values of similar homes in the area. The price is then adjusted, representing the underlying value of the home. Because Zillow uses a proprietary formula that has not been made public, few details are known about what exogenous variables are included in

the model. Thus, it is difficult to assess the true value of its model. Homeowners are able to influence the valuation of their property by reporting their own estimates. Zillow acknowledges this shortcoming and down-weights self-reported estimates. Its accuracy depends on the number of trans-actions in a geographic area, limiting the

value of the estimates for less populated areas. Zillow states that in larger metro areas, most of its estimates are within 10% of the selling price of the home. This accu-racy declines in smaller metros.

The major advantage of Zillow is its timeliness. Once reported, any changes to the house, including new construction

Table 5-2: CSI House Price Tier Index, 2008Q3

High Tier Middle Tier Low TierQ/Q Y/Y Q/Q Y/Y Q/Q Y/Y

ann. % change % change ann. % change % change ann. % change % change

Atlanta, GA -6.5 -8.6 -8.0 -10.1 -13.7 -13.6Boston, MA -8.8 -3.7 -4.7 -6.6 5.5 -7.0Bridgeport, CT -5.6 -5.3 3.7 -4.2 -8.4 -8.8Cambridge, MA 7.3 -0.5 -3.9 -5.0 -3.8 -10.2Chicago, IL -9.7 -9.6 -7.5 -11.0 -16.7 -14.7Cincinnati, OH -8.9 -4.9 3.3 -2.3 -5.4 -2.8Cleveland, OH -2.0 -5.5 1.2 -7.8 -0.7 -16.6Columbus, OH 1.5 -4.0 -10.9 -3.8 -7.8 -1.8Denver, CO -9.0 -6.2 -1.7 -4.8 -1.0 -7.2Detroit, MI -14.5 -16.3 -14.5 -19.6 -14.5 -34.5Edison, NJ -3.8 -4.9 -5.4 -7.1 -2.8 -7.6Fort Lauderdale, FL -17.4 -24.1 -25.5 -28.4 -56.5 -40.1Los Angeles, CA -19.6 -22.5 -27.0 -30.3 -37.4 -42.2Manchester, NH -3.7 -7.1 2.7 -8.5 -19.6 -10.4Miami, FL -27.9 -29.8 -27.2 -33.2 -59.5 -40.9Minneapolis, MN -5.0 -13.3 -6.6 -14.3 -8.6 -19.6New York, NY -7.8 -5.9 -5.5 -7.8 -9.2 -11.3Newark, NJ -2.4 -5.5 -5.6 -7.9 -13.1 -13.9Oakland, CA -16.0 -19.0 -24.0 -31.9 -40.5 -46.4Orlando, FL -24.9 -23.9 -28.6 -26.4 -32.4 -26.3Oxnard, CA -12.0 -18.9 -21.6 -27.7 -32.2 -33.9Peabody, MA -12.4 -6.3 -7.1 -6.2 -18.0 -13.2Philadelphia, PA 8.1 -1.1 4.4 0.0 3.0 -0.9Phoenix, AZ -29.4 -29.6 -31.3 -30.9 -46.0 -38.6Portland, OR -15.5 -8.9 -13.9 -6.7 -7.3 -5.9Providence, RI -2.8 -7.0 -11.3 -11.5 -15.8 -16.5Riverside, CA -23.1 -33.9 -33.7 -38.7 -45.8 -48.4Sacramento, CA -13.9 -25.7 -12.4 -25.5 -29.8 -38.8San Diego, CA -18.3 -18.2 -23.6 -25.7 -29.4 -33.6San Francisco, CA 5.3 -5.7 -0.5 -15.2 -21.3 -24.7San Jose, CA -11.7 -11.9 -24.1 -23.0 -37.1 -36.6Santa Ana, CA -7.9 -15.4 -13.8 -22.9 -24.4 -31.6Santa Rosa, CA -42.0 -24.4 -20.1 -29.2 -36.4 -39.6Seattle, WA -5.7 -8.2 -7.3 -8.4 -7.9 -8.8Tampa, FL -7.8 -17.3 -17.5 -19.7 -25.9 -26.3Toledo, OH -7.4 -5.2 -0.8 -8.0 -14.0 -13.3Tucson, AZ -19.1 -13.6 -19.7 -15.9 -18.6 -20.0Warren, MI -18.4 -17.7 -18.3 -17.7 -18.4 -28.3Washington, DC -9.5 -12.7 -18.7 -22.3 -41.0 -33.5Worcester, MA -4.2 -6.4 -6.6 -9.8 -4.9 -13.6Source: Fiserv Lending Solutions



or renovations, are immediately included in the estimate of the home. Estimates for houses are continuously updated. But since Zillow is new, most datasets are lim-ited to this decade. Zillow estimates price data for 163 MSAs, more than are reported by NAR.

Radar Logic. Radar Logic calculates a house price index based on a proprietary econometric model. Reported daily, the Radar Logic index includes data from all the different properties for which transac-tions are recorded, including new homes and condos. However, the data are report-ed daily, resulting in significant volatility because of small sample sets. Radar Logic reduces the volatility by using a traditional four-week moving average. Similar to mea-sures of values in commercial real estate, Radar Logic uses the price of a house per square foot and is therefore able to com-pare prices across all types of housing. Fur-thermore, by calculating price per square foot, the data incorporate any structural changes, including renovations.

Despite being reported daily, the data are lagged. Radar Logic reports data only once they are recorded at the county level; there is sometimes a delay between the closing of the contract and the recording of the transaction. With each metro area recording data at its own pace, there are time discrepancies across the data. Also, the data are extremely limited. Radar Logic now has data for only 25 metros but plans to expand. Historical data go back only as far as 2002, limiting the usefulness.

Altos. Like Radar Logic, Altos uses real-time data to give a more current pic-

ture of the housing market. However, Altos updates its data weekly, which reduces volatility. Altos uses Realtor data, includ-ing the National Association of Realtors, to estimate current market trends. Unlike many other estimates, Altos does not use the transaction price. Instead, the listing price is used, which allows more timely re-porting. Altos is further able to report not only at the metro level, but also by neigh-borhood and zip code. However, using the listing price has limitations. Normally, the difference between the listing price and sale price might not be great, but under current market conditions, the asking price may be discounted significantly before the house actually sells.

In addition to calculating prices, Altos estimates supply and demand by calculat-ing the average number of days a home is on the market. Altos uses this report whether the metro area is a buyer’s or a seller’s market and is then able to fore-cast price movements. Like the CSI, Altos breaks its data into tiers based on price; Altos, however, uses quartiles. Therefore, Altos is able to calculate price movements for each cohort, even when they do not move in line with the overall market.

Advantages of CSI. The work pre-sented in the remainder of this study is based on the CSI price data. Most impor-tantly, the CSI dataset consists of repeat sales that are then seasonally adjusted, that account for renovations, and that are not restricted to conforming loan lim-its. Many of the regions weakening most dramatically at this juncture of the hous-ing cycle are those in which the median

house price easily exceeds the $417,000 benchmark. Therefore, the conforming loan bias in the FHFA data is likely causing that measure to miss the rapid decelera-tion or outright declines in house prices now occurring in many places. The biases in the NAR data render it difficult to tease out true price trends. The median price understated the decline in house prices earlier in the downturn, as the subprime mortgage meltdown shifted the mix of homes sold away from lower-end homes toward a greater share of expensive homes. With subprime borrowers tending to be lower-income borrowers as well, the dry-ing up of credit hit sales of less expensive homes hard, thus skewing upward changes in the median price. This bias is fading as the credit crisis, plummeting stock wealth, and the recession hit households all along the income distribution.

The main disadvantage to using the CSI is that it lags considerably in reporting—as long as four months after the quarter ends. It is important to note that none of the house price measures are able to measure changes in the use of vari-ous incentives and discounts that are not reflected in actual transaction prices. In today’s weak housing market, for example, sellers are reportedly offering a myriad of incentives, from fixing the deck to helping with financing. If so, then effective house prices would be even weaker than mea-sured prices. The advantages of the CSI significantly outweigh its disadvantages, however, and it is thus used as a basis for the work presented in the remainder of this study.



There are several traditional approaches to assessing the prospects for house prices by gauging whether prices are measurably over- or undervalued. These approaches typically involve comparing prices with household income, rents and user costs.

Income-to-price. A popular ap-proach for identifying whether housing is appropriately valued is to compare house prices with household income.1 Over long periods, house price gains have closely mirrored household income gains nationally and across metropolitan areas, although the years of the housing price bubble are an important exception (see Chart 6-1).

That home values and household income should be closely related is based on the special importance most people seemingly place on owning a home. This importance is rooted in both household psychology and the significant tax advan-tages of homeownership. Households have historically purchased as much housing as their income allows. The strong relation-ship between house prices and income can also be established through land and construction costs. The value of land is ultimately determined by its opportunity cost, which equals the value of goods and services produced in the geographic area. Given a constant labor share of output, the growth in land values and income will thus be equivalent. The growth in construction costs also closely tracks income, since these costs are predominantly labor costs.

When house prices and household in-come diverge substantially, this is only sug-gestive that a housing market is overvalued or speculative. House prices and income can diverge over extended periods when mortgage rates or other transaction costs are steadily rising or falling, as they have done for much of the last 25 years, for example, or when non-labor construction costs like the cost of materials are growing at a persistently strong or weak rate.

1 See Case, Karl and Shiller, Robert, “Is There a Bubble in the Housing Market? An Analysis,” in Brookings Papers, Fall 2003.

By this measure, the housing market correction has brought the national house price index back in line with its long-run historical level relative to household in-come. When compared with the long-run average ratio that prevailed before the house price bubble began in earnest in 2004, the price-to-income ratio is just slightly elevated for the U.S.—about 2% above the historical average. This is a far cry from the 30% overvaluation that oc-curred during the height of the boom in 2005. Some individual metro areas, how-ever, are still substantially overpriced by a much larger factor (see Appendix 6-1). The metro areas with the highest price-income ratios in the third quarter of 2008 include some areas that were infected by a high degree of speculation and substantial house price inflation—San Jose CA and Bradenton FL, for example. But also in this group are metro areas where house price growth was moderate but substan-tially faster than income growth such as Hot Springs AR, Cincinnati, and Santa Fe. More affordable areas, where there was considerably less froth during the housing boom—the Midwest, the Southeast (ex-cept Florida) and upstate New York—had relatively flat or slightly falling price-to-in-come ratios during the decade. Although some areas remain overpriced, a number of metro areas such as Modesto and Merced CA that have sustained sharp house price corrections are now underpriced. The house prices in these markets are overcor-recting for the excesses that occurred during the boom.

Affordability. A more comprehensive method of compar-ing home prices with income is to look at income in the context of mortgage servicing. The idea is to compare the median household income in a given re-gion with the income

it would take to qualify for a standard mortgage on a median-priced home in that region at the prevailing mortgage interest rate. Moody’s Economy.com calculates this ratio at the state, metro and county level in a manner similar to the National Association of Realtors’ U.S. aggregate af-fordability index. For example, median income for each metro area is divided by the yearly mortgage payment for a median-priced home under certain assumptions—a down payment of 20% of the home price; an effective mortgage interest rate from the Federal Housing Finance Board for each state; and a qualifying income-mortgage payment ratio of 25%. The ratio is then converted to a percentage, so that an affordability index of 90 indicates that a family earning the median income can qualify for a mortgage on a house costing 90% of the median price.

The years of the house price bubble led to a steep decline in affordability for the whole U.S. and especially for those metro areas that had the most speculative lending. Some metro areas with the great-est speculative lending and house price increases up to 2006 still have affordability ratios substantially below their pre-bubble averages, despite substantial price correc-tion. These include West Palm Beach and Ocala FL and Los Angeles and Riverside CA (see Appendix 6-2). There are also metro areas that have below average af-fordability ratios because their price cor-

Chapter 6: Measuring House Price Risk

0

1

2

3

4

5

6

7

8

75 80 85 90 95 00 05

Chart 6-1: House Prices and Household IncomeIndex: 1975Q1=1.0

Sources: Fiserv Lending Solutions, BEA, Census

CSI

After-taxhousehold income



120

130

140

150

160

170

180

190

93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08

Chart 6-2: Housing Affordability Index% of median house price household earning median income can afford

Metro average

U.S. aggregate

Source: Moody’s Economy.com0.81.01.21.41.61.82.02.22.42.62.83.03.2

86 88 90 92 94 96 98 00 02 04 06 08

Chart 6-3: House Prices and Apartment RentsIndex: 1986Q3=1.0

CSISources: Property & Portfolio Research, Fiserv Lending Solutions

Effective apartment rents, U.S.

Effective apartment rents, MSA average

rection has been too small so far—these include the largest Pacific Northwest metro areas such as Seattle and Spokane as well as some Northeast metro areas like Balti-more MD and Newark and Atlantic City NJ.2 By contrast, many metro areas in the industrial Midwest such as Cleveland, Sag-inaw and Youngstown had been declining economically before the current recession and have affordability ratios substantially above average because of long-term house price declines. Finally, as with the simple price-to-income ratio, the house price cor-rection has brought affordability up over their prebubble average for some previ-ously overvalued regions. Such regions include Santa Ana CA and Fort Lauderdale FL. Falling house prices and falling mort-gage interest rates have helped to push up overall U.S. affordability above its long-term average (see Chart 6-2). However, the affordability index fails to account for an impediment to borrowing that is signifi-cant at this juncture in the housing cycle: the availability of credit. Homes may be affordable relative to income, but lending standards remain tight. The affordability index thus overstates the true degree of the affordability of housing.

Price-to-rent. Another traditional approach to gauging whether housing is

2 One important methodological difference between the price-income and price-rent ratios is that the affordability index uses NAR median house prices, whereas the other two ratios described in the appendices use dollarized Case-Shiller prices. For most metro areas, this difference does not alter any conclusions, but it might do so for smaller metro areas where the Fiserv’s Case-Shiller data set relies on FHFA data. The FHFA data diverge in several ways from NAR median house price data.

over- or undervalued is to compare house prices with apartment rents3—that is, to value houses by the amount of net income (or net rent) they generate. This is similar to the stock market’s earnings yield, or taking the inverse, the price-to-earnings ratio. Over long periods, house price gains and the growth in apartment rents have tracked closely across the nation and met-ropolitan areas, the housing price bubble again being a short-term exception (see Chart 6-3).

That fundamental housing values and rents are closely related is simply because multifamily housing and single-family housing are close substitutes. If house prices deviate substantially from rents, then this suggests that the cost of housing services provided via owning is substantial-ly different from the cost of those services via renting. Households will eventually adjust, as will house prices and rents.

The strong relationship between house prices and rents is also due to the tautology that house prices equal the present value of the future services provided by housing. Those services are equal to what it would cost homeowners to rent their homes back to themselves, which in turn is equal to the rent on a comparable apartment.

House price and rent growth can diverge over extended periods, however, because of forces unrelated to specula-tion. Steadily rising or falling mortgage rates or other transaction costs, changes in

3 See Gallin, J., 2004, “The Long-Run Relationship Between House Prices and Rents,” Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System, No. 2004-50.

the availability of mortgage credit, and tax law changes that affect the cost of hom-eownership via renting are all factors that can cause house price and rent growth to diverge for extended periods. Eventually, these forces abate, and house price and rent gains converge. If the gap between price and rent gains is large and persists, however, then speculation is likely influ-encing housing markets.

The gap can be measured by an equiva-lent housing PE ratio that values house prices relative to the net income or rent they can generate. The PE ratio is calculated as the ratio of the dollarized Case-Shiller price index to yearly apartment rent. Apart-ment rent is reported by Property Portfolio Research and is measured as average dollars per year. Trends in the PE ratio over time can be approximated by looking at the ratio of the median house price to the average apartment rent. The average PE for metro areas with apartment rate data soared from 15 at the start of the decade to nearly 25 in 2006 (see Chart 6-4).

The PE ratio has declined somewhat since, as the housing correction has low-ered the median house price. Still, the ratio remains near a historic high. Consis-tent with the patterns found when evalu-ating the price-to-income ratio, PEs have expanded substantially more in regions where the housing boom was particularly heated such as the Oakland, San Jose, San Francisco and Orange County metro areas in California, and Seattle (see Appendix 6-3). Notably, the PE ratio in many of these metro areas came down substan-tially in 2007-2008 compared with 2005 (see Chart 6-5). In concert with national



trends, the cooling housing markets have resulted in falling median house prices in these metro areas as well. There are many regional exceptions, however. Raleigh NC, Virginia Beach VA, and Nashville have price-rent ratios in the top 10 and have yet to experience a decline in this ratio. House price trends in these markets are much healthier than average. At the other end of the spectrum, the plunge in mort-gage lending has pushed down the price-rent ratio even in lower-priced markets such as Detroit, Cincinnati and Cleveland. Indeed, conditions in Detroit are weak enough that the price-rent ratio is below its long-term average.

Despite nearly two years of house price correction, the average metro area price-rent ratio is still above its historical average, which has significant implica-tions for the house-price outlook. The overvaluation reflected in the high PE ratio suggests that house prices must fall further. Indeed, based on PPR’s forecasts for apartment rents, house prices must fall sharply in a number of metro area mar-kets in order for the cost of owning to be consistent with the cost of renting a home (see Table 6-1). These are the metro areas where the house price correction has been moderate so far and include Philadelphia, Portland OR, Seattle and Virginia Beach. By contrast, metro areas where prices have declined greatly—such as Riverside CA and Detroit—have overshot and are now considered underpriced according to their long-run averages.

Chart 6-6 maps the 381 metro-politan statistical areas by the degree of over- or undervaluation according to how

far the current price-income and price-rent ratios deviate from equi-librium.4 The main determinant is the price-income ratio, which is available for all metro areas, whereas existing apartment rent data limit price-rent ratios to just 54 of the larger metro areas. A metro area is determined to be correctly valued if the third quarter 2008 value for price-income and price-rent (if available) is no more that 5% above or below the equilibrium ratio.5 If one or both equilibrium ratios are below their third quarter 2008 values by 5% to 20%, the market is said to be overvalued. If one or both equilibrium ratios are below that quarter’s value by more than 20%, the housing market is significantly overvalued. By contrast, if one or both equilibrium

4 The equilibrium ratios are calculated as the average ratio between 1984-2004 for income and 1986-2004 for rent. This equilibrium period covers two full “normal” housing cycles.

5 The boundary percentages corresponded roughly to the sample standard deviations for both the price-income and price-rent ratios. A difference from the equilibrium of plus or minus 5% from the ratios for the third quarter of 2008 is about equal to half a standard deviation for the 1984-2004 sample of all metro area price-income ratios, while a deviation of 20% is about equal to two standard deviations for the whole sample. A similar analysis was used to determine the boundary percentages for the price-rent ratios.

ratios are greater by 5% or more compared with the third quarter 2008 values, the market is undervalued.6

The house price correction has clearly reduced the price overhang in many of the housing markets that were notoriously overvalued during the house price bubble years. Indeed, there is even evidence that the correction is overshooting in some of the markets that were most overheated. Riverside CA, Southeast Florida, and the upper California Central Valley are now actually underpriced according to the logic of both price-income and price-rent ratios,

6 For several metro areas, the classification was not as simple as the above description. Eight metro areas had price- income ratios that indicated substantial undervaluation, while the price-rent ratios indicated substantial overvaluation. In these areas, the two percentage deviations were added and then the same numerical cutoffs were used—5% or higher for undervaluation, 5% to -5% for correct valuation, 5% to 20% for overvaluation, and 20% or higher for significant overvaluation.

0 10 20 30 40 50 60

Detroit

Pittsburgh

U.S.

Miami

Las Vegas

Portland, OR

San Diego

Orange County

San Francisco

Seattle

San Jose

Oakland

2000Q32005Q32008Q3

Chart 6-5: …And by Much More in Some Metro AreasHousing P/E ratio

Sources: Fiserv Lending Solutions,Property & Portfolio Research, Moody's Economy.com

Chart 6-6: Many Markets Still Overvalued

UndervaluedCorrectly valuedOvervaluedSignificantly overvalued

Sources: Fiserv Lending Solutions,PPR, Moody’s Economy.com



Tabl

e 6-

1: W

here

Hou

se P

rice

s A

re H

igh

and

Low

Com

pare

d W

ith

Effe

ctiv

e R

ents

Hou

sing

P/E

Apt

. ren

t gr

owth

% D

iffe

renc

e in

P/E

Ren

t gr

owth

Hou

se p

rice

gro

wth

Top

10:

Sep-

90Se

p-95

Sep-

00Se

p-05

Sep-

08 1

8-yr

avg

. †

%, 1

5-yr

avg

. ‡

18-y

r av

g. v

s. C

urre

nt

5-ye

ar o

utlo

ok, %

5-ye

ar o

utlo

ok, %

Hon

olul

u, H

I24

.423

.921

.735

.333

.923

.62.

1-3

0.5

24.0

-6.5

San

Fran

cisc

o, C

A26

.821

.726

.043

.231

.325

.04.

4-2

0.0

30.2

10.2

Brid

gepo

rt, C

T17

.417

.221

.533

.530

.419

.91.

9-3

4.5

24.2

-10.

3

East

Bay

, CA

33.4

25.9

29.4

53.3

29.6

29.9

4.3

1.2

18.3

19.5

San

Jose

, CA

24.5

20.8

26.6

46.2

29.3

24.3

4.1

-16.

821

.95.

1

Port

land

, OR

11.4

16.7

19.7

29.6

28.5

17.9

2.0

-37.

311

.3-2

6.0

Seat

tle, W

A17

.016

.920

.330

.528

.317

.72.

4-3

7.4

14.2

-23.

2

Ora

nge

Cou

nty,

CA

22.4

17.5

20.5

42.8

27.4

21.4

3.0

-21.

924

.93.

0

Ral

eigh

, NC

ND

16.0

17.4

22.3

24.7

17.8

0.6

-28.

18.

6-1

9.5

Virg

inia

Bea

ch, V

A17

.016

.616

.123

.524

.416

.73.

9-3

1.4

10.8

-20.

5

Bot

tom

10:

Phila

delp

hia,

PA

9.4

9.4

9.2

16.5

16.1

9.8

2.1

-39.

416

.4-2

3.0

Cin

cinn

ati,

OH

13.4

14.2

15.4

17.5

15.6

14.7

1.9

-5.5

9.9

4.4

Los

Ang

eles

, CA

14.0

10.2

11.7

24.4

15.4

12.4

3.2

-19.

421

.72.

3

Phoe

nix,

PA

12.7

10.8

12.6

25.1

15.3

12.2

2.5

-20.

312

.6-7

.6

New

Yor

k, N

Y9.

18.

29.

917

.914

.79.

82.

8-3

2.8

41.8

9.0

Rive

rsid

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A20

.116

.314

.427

.614

.516

.84.

915

.817

.233

.0

Cle

vela

nd, O

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.116

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7-2

.29.

47.

2

Okl

ahom

a C

ity, O

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9.5

-6.6

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sbur

gh, P

A9.

09.

610

.212

.212

.310

.01.

6-1

8.4

12.1

-6.3

Det

roit,

MI

6.8

8.8

11.8

13.9

9.6

10.1

1.3

5.2

9.2

14.4

Aver

age

of M

etro

Are

as14

.614

.215

.524

.219

.815

.22.

6-2

3.2

19.2

-4.0

† 19

86-2

004

aver

age

‡ 19

93-2

008

aver

age

Sour

ces:

Pro

pert

y Po

rtfo

lio R

esea

rch,

Nat

iona

l Ass

ocia

tion

of R

ealto

rs, M

oody

’s E

cono

my.c

om



while Bakersfield and Oxnard-Ventura CA, and Las Vegas are now correctly valued after going through serious house price declines. Nevertheless, many prominent housing bust markets, including Miami, Orlando and Phoenix, continue to be over-valued, as well as larger metro areas with better mortgage quality but substantial demand-supply imbalances such as Seattle and the greater Washington, DC and New York metro areas. Despite the pronounced house price correction over the past two years, price-rent and price-income ratios still indicate that overvaluation still lingers in housing market.

User cost-to-rent. A third common approach is a type of accounting exercise in which the user cost of housing is compared with rents, or the net present value of own-ing a home is calculated and compared

with prevailing house prices.7 If the user cost is measurably higher than rents or the net present value of owning a home is lower than house prices, then the housing market is deemed to be overvalued or speculative.

This approach is very sensitive to the measurement of housing costs, however, including property taxes and maintenance costs. These costs are very difficult to measure accurately, particularly at a metro level. Risk premiums and discount rates, things that cannot be directly observed, must also be assumed to perform the cal-

7 See Himmelberg, C.; Mayer, C.; and Sinai, T., 2005, “Assessing High House Prices: Bubbles, Fundamentals, and Misperceptions,” Federal Reserve Bank of New York Staff Reports, no. 218, and Smith, G., and Smith, M., 2006, “Bubble, Bubble, Where’s the Bubble?” forthcoming in Brookings Paper on Economic Activity.

culations of the user cost and net present value. It is also worth noting that the re-sults in some cases are hard to explain. In one study, for example, Los Angeles house prices were found to have always been un-dervalued to varying degrees.

Each of the previous efforts at deter-mining the appropriate level of house prices thus has consequential limitations. Simply comparing household incomes and apart-ment rents with house prices ignores the possibility that they may diverge for extend-ed periods. Accounting exercises are useful, but the results are severely impaired by the quality of the data used and the assump-tions made. The methods developed and employed in this study provide an alterna-tive approach to identifying housing mar-kets that will experience price declines that address, at least in part, these limitations.





Moody’s Economy.com has developed an econometric model of house prices. The approach is based on a structural model of housing demand and supply that allows for serial correlation and mean re-version in metropolitan area housing mar-kets. The model that Moody’s Economy.com has developed is a tool for identifying the forces driving house prices and assess-ing to what degree house prices can be ex-plained by fundamental, persistent forces and to what degree they are explained by more temporal forces.

The structural econometric model used in this study can determine whether metro area housing markets are overvalued or undervalued, the degree to which overvalu-ation or undervaluation exists, and how these markets will ultimately adjust toward a long-run equilibrium. The model—in conjunction with forecasts of the economic, demographic and financial drivers that the Moody’s Economy.com regional and macro-economic forecast models generate within each housing market—also produces ex-plicit metro-area house price forecasts.

Several classes of models may be con-sidered to study the dynamics of and pro-duce forecasts for house prices. Time series models can provide some insights but are highly dependent on history. For this rea-son, they tend to be less accurate in times of significant shifts in behavior than a structural model that considers market fundamentals.

Another approach is the leading in-dicator approach, which econometrically identifies variables that have historically led changes in housing values. The infor-mation provided by a structural model is richer than that provided by a leading indi-cator, including the magnitude and timing of a change in house price in addition to the direction of that change, but it also has its clear disadvantages. Most importantly, a structural model cannot predict events that have never occurred historically and may not fully reflect the myriad factors that affect housing demand, supply and prices. Moreover, the forecasts produced by such a model are only as accurate as the forecasts of the drivers. Fundamentally, however, the leading indicator and structural model

approaches are complements rather than substitutes, as they provide different types of information about the future of house prices. The general approach of Moody’s Economy.com is to rely primarily on the results of a fully specified structural model while incorporating adjustments based on information from leading indicators and other models as well as forward-looking changes in housing policy, mortgage mar-kets and consumer preferences.

The theoretical basis for the structural model, its estimation and validation, and the outlook for house prices derived from the model follow.

Theory. The structural econometric model of housing demand, supply and price allows for both serial correlation and mean reversion in the housing market.1 Mean reversion implies that in the long run, housing markets move toward equilibrium values based on fundamental supply and demand factors. In each metro area k and each period t, it is assumed that there is a long-run equilibrium value for the unit price of housing space that is determined by:

P*tk = ƒ(xtk) (1)

Where P* is the real equilibrium house value per quality adjusted square foot in the metro area, and xtk is a vector of explanatory variables affecting either supply or demand. Equation (1) can be thought of as the reduced form of a long-run housing supply and demand relationship.2

The explanatory variables in the equilibrium equation can include factors such as real household income, real household nonhousing wealth, population growth, the age and ethnic composition of the population, regulatory conditions and permitting requirements, structural

1 Capozza, Dennis R.; Hendershott, Patric H.; Mack, Charlotte, March 2004, “An Anatomy of Price Dynamics in Illiquid Markets: Analysis and Evidence from Local Housing Markets,” Real Estate Economics.

2 It can also be derived from urban theory. See Capozza, Dennis; Helsley, R., 1989, “The Fundamentals of Land Prices and Urban Growth,” Journal of Urban Economics, 26, 295-306.

changes in lenders’ underwriting standards, consumer preferences, and the long-run, risk-adjusted return to housing and other household assets.

The change in real house prices is determined by:

∆Ptk = ak ∆Pt,k-1 + bk(P*

t-1,k - Pt-1,k) +

ck∆P*

tk + Dtk (2)

The first set of terms in Equation (2) captures serial correlation, where ak is the serial correlation coefficient, bk is the rate of mean reversion, and ck captures the immediate adjustment to changing fundamentals. The vector Dtk includes various business cycle factors—such as unemployment, volume of foreclosed properties within a market, and user cost—that affect changes in house prices around its long-run equilibrium. These factors are also interacted with the adjustment terms a, b and c. The degree of serial correlation and the rate of mean reversion are affected by where the economy is in its business cycle.

It is important to note that Equation (2) can be written in difference equation form and its dynamic properties examined. The parameters ak and bk determine whether house prices exhibit oscillatory or damped behavior, and convergent or divergent behavior. 3

The user cost of housing, which measures the after-tax cost of homeownership, is a key explanatory variable in the model and is computed as:

Utk = (1-Taxtk)(rtk +Ptaxtk)+Mtk – Petk (3)

Where Utk is the user cost, Taxtk is the effective marginal income tax rate, rtk is the effective mortgage rate, Ptaxtk is the effective property tax rate, Mtk captures maintenance costs and obsolescence, and Pe

tk represents the homeowners’ expected

3 Cappozza et al, 2004, calculate the dynamic properties of equation (2) under the simplifying assumption that P*

tk = P*k,

a constant.

Chapter 7: Structural Econometric Model



house price growth over the horizon of their homeownership and is estimated using long-run household income growth.

Historical data. The structural model estimated in this study is based on the Case-Shiller repeat-purchase house price data from Fiserv Lending Solutions/FHFA.4 The model combines these house price data with a plethora of other historical housing market, economic and demographic data measured at the national, state and metro area levels that have been constructed by Moody’s Economy.com. Historical data ranging from housing starts to household income are derived from various government sources and trade organizations, but are cleaned and adjusted to be consistent across metro areas and over time.

Data on mortgage credit quality and the predicted stock of foreclosed properties within each metro area are taken from CreditForecast.com, a partnership between Equifax and Moody’s Economy.com. This unique database provides history and forecasts for a wide range of household credit variables at a detailed level of geography. It is a random sample of all retail credit borrowers that allows users to examine, segment and stratify credit risk and economic data across states, metropolitan areas, and rest of states. The data provide industry-level information on the number and dollar amount of account balances outstanding as well as the performance of the various segments of the credit market over time.

A comprehensive list of all of the variables tested in the estimation is shown in Table 7-1.

Equilibrium equation. The model is estimated in two stages. In stage 1, the equilibrium house price specification in Equation (1) is estimated. In stage 2, the adjustment house price equation in Equation (2) is estimated based on the fitted values for the equilibrium house price from stage 1. Both equations are estimated

4 The three commonly cited measures of house price appreciation—the NAR’s median price, the FHFA price index, and the Case-Shiller price index—are, broadly speaking, similar over the long term. Near-term movements can vary considerably, however. Not surprisingly, the two repeat-purchase indices are similar in terms of movements over time, while price growth according to the NAR is far more volatile. The correlation between growth according to the national FHFA and CSI data is about 90%, while correlations with NAR growth are much weaker, at about 40%.

using pooled cross-sectional estimation with metro-specific fixed effects.5

In order to capture broad regional differences in the response of markets to explanatory variables, the 381 metro areas included in the estimation were grouped into seven distinct regions. (See Appendix 7-1 for a list of metros.) The pools are based on geography, with pool 1 including East Coast metro areas, pool 2 including Mountain West metro areas, pool 3 including Florida metro areas, pool 4 including metro areas in the Midwest, pool 5 including California metro areas, pool 6 including Southern states, and pool 7 including the West Coast (except for California). The classification of the regions is based on clustering of long-run trends in demographics and economic composition. The industrial and demographic makeup of the metro areas in each pool is similar, as is the supply side of their housing markets, including the degree of building constraints and the prevalence of restrictive regulatory requirements. Alternative groupings of the metro areas were tested, but the seven defined regions provided the most accurate results while maintaining consistency with known regional differences.

The data covered the vast majority of total dollar balances of arm’s length transactions in the nation. The CSI data are provided quarterly and cover 110 metro areas over more than 30 years, resulting in over 11,000 data points. The most important explanatory variable in the equilibrium house price equation, Equation (1), is real per capita income, particularly in the East Coast and California regions (see Table 7-2). On average, a 1% increase in real per capita income in a metro area in these regions leads to an approximately seven-tenths of a percentage point increase in real house prices. This means that households are buying 7% more housing when incomes rise 10%. Income is less important in

5 A criticism of this approach is that it is assumed that there is a cointegrating relationship between the variables included in the equilibrium equation, when in fact there may not be. Standard unit root tests for cointegration based upon Dicky-Fuller or augmented Dicky-Fuller are not appropriate in a panel setting as used in this study. If the urban theory, which is used as the basis for the derivation of the equilibrium equation, is correct, however, then there is a cointegrating relationship among the variables. Nevertheless, the criticism applies.

the Florida and South pools. This is likely because of the large number of migrants and wealthier second-home and vacation-home buyers from outside the area who purchase homes in the region. Local income may be less of a factor in determining prices in these areas.

Equilibrium house prices have also been affected by a significant shift in mortgage lending underwriting standards in recent years. During the boom years for housing, subprime and alternative-A mortgages, interest-only and option ARMs grew rapidly along with second liens and home equity lines of credit, expanding the availability of mortgage credit to households that did not previously have access to any type of credit. In 2007, the lending landscape shifted abruptly again when the credit spigot was abruptly shut, shifting downward demand for housing. Moody’s Economy.com proxies this phenomenon in the equilibrium equation by the ratio of total commercial bank assets in home equity lines of credit. The explosive growth of HELOCs through the middle of the decade and the subsequent decline is symptomatic of this expansion of mortgage credit. One example of this is the popularity of piggyback loans, which had been used aggressively by lenders and borrowers to avoid the cost of homeowner’s insurance. In a piggyback loan structure, the borrower takes out a first mortgage with a 20% down payment that is paid for with a borrower’s savings along with a second lien. The impact of the change in underwriting standards was most important in the more heated and expensive markets, where the use of piggyback loans and other products was the only way that many borrowers could afford their homes. On average, a 1 percentage point increase in the HELOC share of bank assets generates a 2 to 4.5 percentage point increase in equilibrium house prices.

The collapse in stock prices and the plunge in short-term interest rates earlier this decade drew attention to housing as an attractive alternative investment for households. Households were incentivized to engage in seemingly rational portfolio shifting by the higher risk-adjusted returns to housing compared with the risk-adjusted returns on stocks and cash. Falling house prices had the reverse effect in the last two years. Even



Table 7-1: Variable Definitions and Sources

Variable Sources

Case-Shiller® House Price Index Fiserv Lending SolutionsFHFA Repeat-Sales Price Index FHFAConsumer Price Index BLS, Moody’s Economy.comAverage Household Income BEA, BOC, BLS, Moody’s Economy.comMedian Household Income BOC, Moody’s Economy.comHousehold Non-Housing Wealth FRB, BOC, BLS, Equifax, Moody’s Economy.comHome Equity Lines Outstanding at Commercial Banks FRBTotal Commercial Bank Assets FRBConstruction Costs BLS, R.S. MeansEffective Apartment Rent Property Portfolio ResearchHousing Stock BOC, Moody’s Economy.comHouseholds BOC, Moody’s Economy.comPopulation by Age Cohort BOC, Moody’s Economy.comForeign Immigration BOC, Moody’s Economy.comUnemployment Rate BLSS&P 500 Stock Index S&PTreasury Interest Rates FRBEffective Mortgage Rate FHFB, Moody’s Economy.comEffective Personal Income Tax Rate BEAProperty Tax Rate BEA, BOC, Moody’s Economy.comNet Migration BOC, Moody’s Economy.comAffordability Index National Association of Realtors, Moody’s Economy.comHousing Starts BEA, Moody’s Economy.comHousing Permits BEA, Moody’s Economy.comComposite Effective Mortgage Interest Rate FHFBConsumer Confidence Conference BoardConsumer Debt Obligations Ratio FRBDebt Service Burden FRBHousehold Net Worth FRBRental Vacancy Rate CensusHousehold Financial Obligations Ratio FRBDebt to Income Ratio FRBMSA Percentage of Loans Past Due Equifax, Moody’s Economy.comMSA Percentage of Loans in Default Equifax, Moody’s Economy.comState Percentage of Loans in Foreclosure Mortgage Bankers AssociationHousing Starts/Housing Stock BEA, BOC, Moody’s Economy.comVacant Housing Stock/Housing Stock BEA, BOC, Moody’s Economy.com

Note: Most of these variables are available at a metropolitan area level from the source or are constructed by Moody’s Economy.com.BEA - Bureau of Economic AnalysisBLS - Bureau of Labor StatisticsBOC - Bureau of CensusFRB - Federal Reserve BoardFHFB - Federal Housing Finance BoardFHFA - Federal Housing Finance Agency

though stock performance has weakened substantially, an investor is better off holding Treasuries than buying a house. The returns to housing are measured in the equilibrium house price equation by the difference between the risk-adjusted

returns on stocks and cash, weighted according to their share of assets in the average household balance sheet, and the risk-adjusted return on housing. The risk-adjusted return is in turn measured by a Sharpe ratio, proxied by the ratio of

a five-year moving average of returns to the standard deviation of those returns.6

6 Alternative moving averages were tested. A five-year moving average provides the best statistical results.



Table 7-2: Equilibrium House Price Equation (Equation 1)CSWDependent variable: Log of Real House PriceMethod: Pooled EGLS (Cross-section weights)Sample: 1980Q1 to 2008Q3Included observations: 115Cross-sections included: 110Total pool (unbalanced) observations: 12,649R-squared 0.9512Adjusted R-squared 0.9506S.E. of regression 0.1244F-statistic 1765.54

Variable Coefficient Std. Error t-Statistic Prob.

Constant 2.0012 0.0426 47.0134 0.0000Market Portfolio Versus Housing Returns, East Coast -0.0049 0.0004 -13.8922 0.0000Market Portfolio Versus Housing Returns, Mountain Region -0.0069 0.0006 -10.8607 0.0000Market Portfolio Versus Housing Returns, Florida -0.0062 0.0005 -12.8715 0.0000Market Portfolio Versus Housing Returns, Inland -0.0045 0.0002 -21.5453 0.0000Market Portfolio Versus Housing Returns, California -0.0098 0.0003 -30.4081 0.0000Market Portfolio Versus Housing Returns, South excl. Florida -0.0021 0.0003 -7.4417 0.0000HELOC Share of Bank Assets, East Coast 0.0239 0.0023 10.3534 0.0000HELOC Share of Bank Assets, Florida 0.0351 0.0030 11.5784 0.0000HELOC Share of Bank Assets, California 0.0468 0.0016 28.9961 0.0000HELOC Share of Bank Assets, Pacific Northwest 0.0236 0.0030 7.8454 0.0000Vacation Home Share of Stock Interacted With Pop. Share 50-64, East Coast 0.0014 0.0002 9.3953 0.0000Vacation Home Share of Stock Interacted With Pop. Share 50-64, Mountain Region 0.0031 0.0003 10.4477 0.0000Vacation Home Share of Stock Interacted With Pop. Share 50-64, Florida 0.0086 0.0003 25.8948 0.0000Vacation Home Share of Stock Interacted With Pop. Share 50-64, Inland 0.0193 0.0015 13.1180 0.0000Vacation Home Share of Stock Interacted With Pop. Share 50-64, California 0.0052 0.0005 10.5429 0.0000Vacation Home Share of Stock Interacted With Pop. Share 50-64, South excl. Florida 0.0308 0.0012 25.9125 0.0000Vacation Home Share of Stock Interacted With Pop. Share 50-64, Pacific Northwest 0.1136 0.0050 22.9180 0.0000Log 5-year Pop. Growth, Florida 1.6323 0.0799 20.4249 0.0000Log 5-year Pop. Growth, Inland 1.5514 0.0914 16.9692 0.0000Log 5-year Pop. Growth, California 1.6706 0.0653 25.5984 0.0000Log 5-year Pop. Growth, South excl. Florida 1.1349 0.1061 10.6987 0.0000Log 5-year Pop. Growth, Pacific Northwest 1.4784 0.1436 10.2921 0.0000Log Real Per Capita Income, East Coast 1.0275 0.0340 30.2442 0.0000Log Real Per Capita Income, Mountain Region 0.9327 0.0400 23.3114 0.0000Log Real Per Capita Income, Florida 0.3273 0.0519 6.3091 0.0000Log Real Per Capita Income, Inland 0.6404 0.0141 45.2900 0.0000Log Real Per Capita Income, California 1.2801 0.0307 41.7207 0.0000Log Real Per Capita Income, South excl. Florida 0.1624 0.0177 9.1840 0.0000Log Real Per Capita Income, Pacific Northwest 0.4563 0.0619 7.3653 0.0000

Fixed Effects (Cross)Bethesda, MD -0.2318Boston, MA -0.1119Cambridge, MA -0.1045Camden, NJ -0.5194Chicago, IL 0.8828Detroit, MI 0.4904Edison, NJ -0.3656Fort Lauderdale, FL 1.2283Los Angeles, CA -0.9230



Miami, FL 1.4502Nassau, NY -0.3074Newark, NJ -0.1401New York, NY -0.0222Oakland, CA -0.7917Peabody, MA -0.1840Philadelphia, PA -0.6978Rockingham County, NH -0.4964San Francisco, CA -0.9283Seattle, WA 0.5989Santa Ana, CA -0.8296Tacoma, WA 0.2434Washington, D.C. -0.2896West Palm Beach, FL 0.8011Wilmington, DE -0.3859Warren, MI 0.5539Akron, OH 0.4327Albuquerque, NM 0.0378Ann Arbor, MI 0.5953Atlantic City, NJ -0.7062Atlanta, GA 1.9516Bakersfield, CA -1.0687Baltimore, MD -0.5217Barnstable Town, MA -0.7923Battle Creek, MI 0.1700Bradenton, FL 0.5250Bridgeport, CT -0.2408Burlington, NC 1.9834Burlington, VT -0.6161Canton, OH 0.3492Cape Coral, FL -0.1877Chico, CA -0.7128Charlotte, NC 2.1168Charleston, SC 1.3496Cincinnati, OH 0.5920Clarksville, TN 1.4580Cleveland, OH 0.6071Columbus, OH 0.6259Dayton, OH 0.5272Deltona, FL 0.6725Denver, CO -0.0092El Centro, CA -1.6403Flagstaff, AZ -0.2818Fresno, CA -0.9450Fort Walton Beach, FL 1.1312Gainesville, FL 1.5062Hanford, CA -1.2363Hartford, CT -0.3894Jacksonville, FL 1.1596Johnson City, TN 1.7193Knoxville, TN 1.8010Lancaster, PA 0.5931Lakeland, FL 0.5320

Table 7-2: Equilibrium House Price Equation (Equation 1) (cont.)

Fixed Effects (Cross) Coefficient



Table 7-2: Equilibrium House Price Equation (Equation 1) (cont.)

Lansing, MI 0.5601Las Vegas, NV 0.0504Merced, CA -0.9266Memphis, TN 2.1046Minneapolis, MN 0.5568Manchester, NH -0.6948Modesto, CA -1.2843Monroe, MI 0.5274Napa, CA -1.3458Nashville, TN 2.0416Naples, FL -0.5508New Haven, CT -0.3467Norwich, CT -0.4137Ocean City, NJ -1.4555Orlando, FL 1.2240Oxnard, CA -0.9881Pensacola, FL 1.2796Phoenix, AZ -0.1186Pittsfield, MA -0.5907Pittsburgh, PA 0.3319Portland, OR 0.4670Prescott, AZ -0.1239Providence, RI -0.2658Port St. Lucie, FL 0.3447Punta Gorda, FL -0.1194Redding, CA -0.9481Riverside, CA -1.3722Santa Rosa, CA -0.9198Sacramento, CA -1.4239San Jose, CA -0.8268Salinas, CA -0.8289San Diego, CA -0.8844Salisbury, MD -0.3727Santa Barbara, CA -0.9148Santa Cruz, CA -0.8273San Luis Obispo, CA -1.2319Springfield, OH 0.3580Springfield, MA -0.3786Stockton, CA -0.8773Tampa, FL 1.0621Toledo, OH -0.0667Trenton, NJ -0.6429Tucson, AZ 0.0381Vallejo, CA -0.8291Sebastian, FL 0.4754Visalia, CA -0.9080Worcester, MA -0.2228Youngstown, OH 0.2758

Note: A similar model is available for the FHFA house price indices and this model is used where metro area CSI data are unavailable.

Fixed Effects (Cross) Coefficient



A 100-basis point increase in the risk-adjusted returns to stock and cash results in a 21- to 98-basis point decline in equilibrium house prices, depending on the region. This impact is uniformly evident across all metro areas within a region.

The age composition of the population also affects equilibrium house prices, as people age 50 to 64 tend to have stronger demand for second and vacation homes. As the large baby boom generation has moved into this cohort, demand for second and vacation homes has significantly increased, lifting housing demand and prices. This is most prevalent in parts of the country where the housing stock is dominated by such homes. This effect is captured in the equilibrium house price equation by the share of stock in second and vacation homes interacted with the share of the population age 50 to 64. As would be expected, the elasticity of equilibrium house prices to this variable is high in the Florida and Southern pools, where retiree in-migration is strong, and measurably lower in the Mountain West and East Coast markets. In Florida, for example, a 100-basis point increase in the share of the population 50 to 64 lifts equilibrium house prices by 86 basis points. The rest of the South also has a high sensitivity to growth in this population cohort. Surprisingly high sensitivity is found of house prices to the share of the population 50 to 64 in the Midwest and Pacific Northwest regions.

A population growth variable is included in the equilibrium equation and is strongest for the Florida and California pools, as expected. It is designed to capture the uniquely strong migration flows, both domestic and international, into the various regions. Builders in Florida and California in particular were spurred to build because of significant acceleration in population growth in recent years, which contributed to the higher prices before the housing bubble popped. Migration and population are likely to continue to increase in coming years with continued foreign immigration and, more importantly, increased retiree migration by the aging baby boom generation.

The equilibrium equation is estimated with metro area fixed effects in order to capture any systematic differences in the average quality of housing across areas.

The fixed effects also capture the impact of those land supply constraints that do not vary over time.7

Variables that change substantially over the course of the business cycle were not included in the equilibrium equation, most notably construction costs, fluctuations in the housing stock due to foreclosures, housing permits and starts, and the user cost of housing. These variables were tested in the adjustment equation, which is described in the discussion that follows. The residuals from the equilibrium equation thus provide an estimate of the overvaluation or undervaluation of metro area house prices relative to their long-run equilibrium. Overvaluation and undervaluation can be due to temporary business cycle forces, speculation, or both.

Adjustment equation. The adjustment house price equation determines how house prices that deviate from their long-run equilibrium ultimately return to that equilibrium.

The fitted values from the long-run equilibrium house price equation in Equation (1) are thus an important explanatory variable in the adjustment house price equation in Equation (2) (see Table 7-3). The contemporaneous change in house prices to changes in the long-run equilibrium price ranges from 4% to 22%. This response is measurably smaller than that found in other studies and may reflect the unique housing market conditions of recent years. The response is strongest for Florida and the West Coast. The East Coast metro areas respond more weakly, suggesting that factors other than long-term drivers have been at play in driving house price changes.

Serial correlation terms, and house prices that are lagged one, two and three quarters are also included in the adjustment equation, reflecting the persistence of house price changes. House price persistence is strongest in the California, East Coast, Mountain West and Florida metro areas and weakest in the inland markets. This suggests that speculative pressures are least likely to develop in the Midwest markets. These

7 F-tests of the metro area effects reject that these effects are zero at the 0.001 confidence level. Similar tests for time effects were not found to be significant.

results are consistent with those found in other studies.

Mean reversion captures the tendency of markets to revert to their long-run fundamental values and is calculated as the equilibrium price less the market price. Thus, for example, if this term is positive—that is, prices are below equilibrium—then price growth will be faster. Formally, mean reversion is defined as8:

Mean Reversion = 0.02* log( P*t-1 – Pt-1)+0.04* log( P*t-2 – Pt-2)+0.06* log( P*t-3 – Pt-3)+0.08* log( P*t-4 – Pt-4)+0.10* log( P*t-5 – Pt-5)+0.15* log( P*t-6 – Pt-6)+0.25* log( P*t-7 – Pt-7)+0.30* log( P*t-8 – Pt-8)

Reversion of house prices to their equilibrium price is most pronounced in the West Coast and California markets and weakest in the Mountain West and Midwest markets. West Coast and California metro areas have historically experienced the most volatile house prices, with large price gains eventually followed by sharp price declines. House prices in the inland markets, in contrast, tend not to deviate far from their equilibrium, which in turn dampens any reversion to equilibrium.

There are several business cycle variables in the adjustment equation, including the unemployment rate, user cost, and one-year population and income growth rates. Of particular interest at this juncture in the housing cycle, foreclosure rates were also tested. These variables come in with the correct signs and are significant; that is, the higher the unemployment rate and user cost, the slower real price growth, while the faster the population or income growth or the lower the foreclosure rate, the faster real price growth. The direct impact of these factors on the adjustment to equilibrium, however, is small relative to the impact of serial correlation and mean reversion, given that changes in these variables are typically quite small.

A wide range of interaction terms were also tested in the adjustment equation in an effort to capture the impact of

8 This eight-quarter moving average was found to fit the best empirically.



Table 7-3: Adjustment House Price Equation (Equation 2)CSWDependent variable: Change in the Log of Real House Price (DLOG)Method: Pooled EGLS (Cross-section weights)Sample (adjusted): 1982Q1 to 2008Q3Included observations: 107 after adjustmentsCross-sections included: 110Total pool (unbalanced) observations: 11,769The mean reversion variable represents the difference between equilibrium and actual house prices.

R-squared 0.6411Adjusted R-squared 0.6364S.E. of regression 0.0178F-statistic 137.4147Durbin-Watson stat 1.9592


Constant -0.0010 0.0003 -3.317 0.0009DLOG Equilibrium House Price, East Coast 0.0363 0.0164 2.215 0.0268DLOG Equilibrium House Price, Mountain Region 0.1198 0.0354 3.388 0.0007DLOG Equilibrium House Price, Florida 0.1987 0.0273 7.277 0.0000DLOG Equilibrium House Price, Inland 0.1328 0.0323 4.116 0.0000DLOG Equilibrium House Price, California 0.0893 0.0131 6.810 0.0000DLOG Equilibrium House Price, Pacific Northwest 0.1870 0.0681 2.747 0.0060DLOG Equilibrium House Price Lagged 1 Quarter, East Coast 0.3818 0.0163 23.356 0.0000DLOG Equilibrium House Price Lagged 1 Quarter, Mountain Region 0.4592 0.0365 12.572 0.0000DLOG Equilibrium House Price Lagged 1 Quarter, Florida 0.3779 0.0229 16.469 0.0000DLOG Equilibrium House Price Lagged 1 Quarter, Inland 0.1151 0.0216 5.335 0.0000DLOG Equilibrium House Price Lagged 1 Quarter, California 0.5968 0.0191 31.298 0.0000DLOG Equilibrium House Price Lagged 1 Quarter, Pacific Northwest 0.3178 0.0497 6.389 0.0000DLOG Equilibrium House Price Lagged 2 Quarters, East Coast 0.2142 0.0161 13.282 0.0000DLOG Equilibrium House Price Lagged 2 Quarters, Mountain Region 0.1417 0.0357 3.970 0.0001DLOG Equilibrium House Price Lagged 2 Quarters, Florida 0.2111 0.0226 9.329 0.0000DLOG Equilibrium House Price Lagged 2 Quarters, Inland 0.1510 0.0215 7.013 0.0000DLOG Equilibrium House Price Lagged 2 Quarters, California 0.0511 0.0195 2.626 0.0087DLOG Equilibrium House Price Lagged 2 Quarters, South excl. Florida 0.1890 0.0309 6.115 0.0000DLOG Equilibrium House Price Lagged 3 Quarters 0.0679 0.0089 7.612 0.0000Mean Reversion, East Coast 0.0272 0.0021 12.970 0.0000Mean Reversion, Mountain Region 0.0197 0.0050 3.928 0.0001Mean Reversion, Florida 0.0291 0.0035 8.332 0.0000Mean Reversion, Inland 0.0256 0.0050 5.122 0.0000Mean Reversion, California 0.0332 0.0030 10.979 0.0000Mean Reversion, South excl. Florida 0.0276 0.0054 5.120 0.0000Mean Reversion, Pacific Northwest 0.1072 0.0170 6.291 0.0000LOG 1 Year Change in User Cost, East Coast -0.0164 0.0018 -9.319 0.0000LOG 1 Year Change in User Cost, Mountain Region -0.0059 0.0032 -1.827 0.0677LOG 1 Year Change in User Cost, Florida -0.0127 0.0025 -5.189 0.0000LOG 1 Year Change in User Cost, Inland -0.0109 0.0022 -4.928 0.0000LOG 1 Year Change in User Cost, South excl. Florida -0.0058 0.0025 -2.303 0.0213LOG 1 Year Change in Unemployment Rate -0.0025 0.0008 -3.072 0.0021Mean Reversion Interaction With LOG 1 Year Change in User Cost -0.0321 0.0078 -4.127 0.0000LOG 1 Year Change in Population 0.1338 0.0181 7.409 0.0000LOG 1 Year Change in Income 0.0219 0.0061 3.585 0.0003



Table 7-3: Adjustment House Price Equation (Equation 2) (cont.)

LOG 1 Year Change in Foreclosure Rate, East Coast -0.0050 0.0007 -7.039 0.0000LOG 1 Year Change in Foreclosure Rate, Mountain Region -0.0073 0.0019 -3.935 0.0001LOG 1 Year Change in Foreclosure Rate, Florida -0.0093 0.0010 -9.243 0.0000LOG 1 Year Change in Foreclosure Rate, Inland -0.0032 0.0012 -2.601 0.0093LOG 1 Year Change in Foreclosure Rate, California -0.0084 0.0007 -11.792 0.0000LOG 1 Year Change in Foreclosure Rate, South excl. Florida -0.0048 0.0013 -3.635 0.0003

Fixed Effects (Cross)Bethesda, MD -0.0005Boston, MA 0.0033Cambridge, MA 0.0036Camden, NJ 0.0015Chicago, IL 0.0031Detroit, MI 0.0012Edison, NJ 0.0013Fort Lauderdale, FL -0.0013Los Angeles, CA 0.0004Miami, FL -0.0006Nassau, NY 0.0045Newark, NJ 0.0031New York, NY 0.0033Oakland, CA -0.0005Peabody, MA 0.0030Philadelphia, PA 0.0027Rockingham County, NH 0.0006San Francisco, CA 0.0020Seattle, WA 0.0010Santa Ana, CA -0.0004Tacoma, WA 0.0009Washington, D.C. -0.0003West Palm Beach, FL -0.0026Wilmington, DE 0.0008Warren, MI -0.0006Akron, OH 0.0009Albuquerque, NM -0.0010Ann Arbor, MI -0.0005Atlantic City, NJ 0.0005Atlanta, GA -0.0021Bakersfield, CA -0.0025Baltimore, MD 0.0010Barnstable Town, MA 0.0016Battle Creek, MI 0.0011Bradenton, FL -0.0019Bridgeport, CT 0.0027Burlington, NC 0.0012Burlington, VT 0.0020Canton, OH 0.0004Cape Coral, FL -0.0041Chico, CA -0.0004Charlotte, NC -0.0010Charleston, SC 0.0028Cincinnati, OH 0.0006





Clarksville, TN -0.0006Cleveland, OH 0.0013Columbus, OH -0.0004Dayton, OH 0.0016Deltona, FL -0.0022Denver, CO -0.0014El Centro, CA 0.0001Flagstaff, AZ -0.0004Fresno, CA -0.0024Fort Walton Beach, FL -0.0019Gainesville, FL -0.0012Hanford, CA -0.0013Hartford, CT 0.0018Jacksonville, FL -0.0015Johnson City, TN 0.0007Knoxville, TN 0.0018Lancaster, PA 0.0013Lakeland, FL -0.0027Lansing, MI -0.0005Las Vegas, NV -0.0063Merced, CA -0.0021Memphis, TN -0.0020Minneapolis, MN -0.0001Manchester, NH 0.0003Modesto, CA -0.0027Monroe, MI 0.0010Napa, CA 0.0009Nashville, TN -0.0002Naples, FL -0.0046New Haven, CT 0.0023Norwich, CT 0.0025Ocean City, NJ 0.0042Orlando, FL -0.0040Oxnard, CA -0.0005Pensacola, FL -0.0012Phoenix, AZ -0.0043Pittsfield, MA 0.0037Pittsburgh, PA 0.0029Portland, OR 0.0001Prescott, AZ -0.0035Providence, RI 0.0028Port St. Lucie, FL -0.0036Punta Gorda, FL -0.0029Redding, CA 0.0001Riverside, CA -0.0041Santa Rosa, CA -0.0011Sacramento, CA -0.0019San Jose, CA 0.0008Salinas, CA 0.0000San Diego, CA -0.0005Salisbury, MD -0.0007Santa Barbara, CA 0.0006

Variable Coefficient



information costs and business cycle effects on serial correlation and mean reversion. The interaction of mean reversion and user cost was found to be negative and significant. This implies that the adjustment down to equilibrium in an overpriced market will be quicker the higher the user cost. However, similar to the business cycle effects, the impact of this interaction term is relatively small when considering that user costs usually do not vary much from year to year.

Validation. The model was validated by determining the degree to which metro area house prices were overvalued or undervalued in the late 1980s and comparing this with actual house price performance through the early 1990s. This historical period was chosen to validate the model as it was the last time house prices rose sharply in large parts of the country and was subsequently followed by sharp price declines.

Overvaluation or undervaluation is determined by the difference between actual metro area house prices and the prices expected based on long-run fundamental economic and demographic factors as determined by the equilibrium house price equation, Equation (1). This calculation was done for both the fourth quarter of 1987 and the fourth quarter of 1989 and the first quarter of 2006 (see Appendix 7-2).

As of the fourth quarter of 1987, about 40 metro areas, mostly along the East Coast and the Southern border states

were deemed to be overvalued by 20% or more, meaning that actual prices were over 20% greater than prices determined by the equilibrium equation. Although house prices in most of these areas continued to rise in 1988, about 70% of them reported a lower price three years later, of which close to half of those markets experienced double-digit, peak-to-trough price declines. Another 35 metro areas were determined to be undervalued by more than 10% as of the fourth quarter of 1987, including San Francisco and Bakersfield. Each of these metro areas experienced sturdy and consistent price growth throughout the early 1990s. The correlation coefficient between the degree of overvaluation or undervaluation as of the fourth quarter of 1987 and subsequent house price growth from 1989 to 1991 was -0.69.

A similar exercise was performed for the fourth quarter of 1989. By this time, many California metro areas, from San Francisco to Los Angeles, were determined to be overvalued. The Santa Rosa metro area just north of the Bay Area, for example, was 22% overvalued, while just south of the Bay Area, the Santa Cruz metro was 33% overvalued. Farther south, Los Angeles and the nearby community Oxnard were both estimated to be overvalued by more than 20%. House prices in all of these markets were peaking by late 1989 and all experienced peak-to-trough price declines ranging from 10% to 25%. The price declines continued for some of the markets

into 1995. The correlation coefficient between the degree of overvaluation or undervaluation as of the fourth quarter of 1989 and subsequent house price growth from 1990-1993 was -0.70.

The model was also evaluated for the current housing cycle. More than 70 markets were considered 20% or more overvalued at the peak of the housing boom in the first quarter of 2006. Markets in the East Coast, California and Florida were all considered highly overvalued. Nearly 90 other markets were overvalued by 10% to 20%. Not only is the regional breadth of overvaluation greater than the other two cycles, but the degree of overvaluation is much higher. In Miami FL, for example, prices were 77% above equilibrium. The consequences of this overpricing are still playing out, but thus far in the downturn there is a -0.64 correlation between overvaluation at the beginning of 2006 and the decline in house prices through the third quarter of 2008.

In both the fourth quarter of 1987 and the fourth quarter of 1989 validations, there were virtually no major errors; that is, no metro area that was determined to be undervalued by 10% or more in 1987 subsequently experienced substantial house price losses. Conversely, the handful of large metro areas that were determined to be significantly overvalued in 1987 and subsequently experienced substantial house price increases are typically characterized as supply-constrained.

Santa Cruz, CA 0.0014San Luis Obispo, CA 0.0002Springfield, OH 0.0020Springfield, MA 0.0021Stockton, CA -0.0018Tampa, FL -0.0013Toledo, OH 0.0004Trenton, NJ 0.0024Tucson, AZ -0.0020Vallejo, CA -0.0020Sebastian, FL -0.0028Visalia, CA -0.0019Worcester, MA 0.0018Youngstown, OH 0.0024

Note: A similar model is available for the FHFA house price indices and this model is used where metro area CSI data are unavailable.


Variable Coefficient



Alternative specifications. Many alternative specifications were tested. The model was also estimated using the FHFA repeat-purchase data and NAR median existing prices. An important similarity between the NAR and FHFA series is that the metro area pools found to provide the best model were the same using either series. A notable difference between the model results using the repeat-purchase price indices and Realtors’ data is that serial correlation is lower and mean reversion slightly higher using the Realtors’ data. However, estimates of the median house price from the NAR are not likely to capture the net effect of important dynamics occurring at both the higher and lower ends of the housing markets.

A number of different variables were tested in the equilibrium house price equation, but ultimately were not used. Most notable is a variable measuring the percentage of land within a metro area that is available for development. Greater land constraints in a growing list of metro areas are an oft-cited reason for rapidly rising house prices. The inability to find a relationship is likely because of the quality of the data. Another notable variable ultimately not included in the equilibrium equation is foreign immigration and foreign direct investment. Increasing globalization has likely also played a role in lifting house prices in recent years. That it was not found to be significant likely reflects measurement problems, particularly at a metro area level.

Another variable checked for significance in this model is the construction cost index. Because of the limited regional data available for construction costs, Moody’s Economy.com constructed several measures based on national data and R.S. Means annual regional indices. None of these measures was statistically significant when included in the adjustment equation. This likely reflects the inadequacies of the data rather than the unimportance of construction costs’ influence on prices.

Other variables tested included net migration, affordability, housing starts, housing permits, state-level average effective mortgage rate, consumer confidence, consumer debt obligations ratio, debt service burden, household balance sheets, metro rental vacancy rate, household financial obligations

ratio, debt-to-income ratio, percentage of loans past due, percentage of loans in foreclosure, housing starts relative to housing stock, and vacancy rates. Except for the measure of loans in foreclosure, none of these variables contributed significant additional explanatory power to the model. This is largely because the variables are already in the model in some related form. For example, migration flows are already captured by the population growth variable in the model. Mortgage rates are incorporated in the user cost variable. Other variables may be subject to measurement error at the metro level, which limits their ability to explain house prices. Composite differences between the metro areas are captured by the fixed effects incorporated into the model.

Calibration. The structural model is firmly grounded in theory, with a core fundamental equation governing the dynamics within a particular region and an adjustment equation that captures market dynamics due to persistence, mean reversion, and the speed of change of fundamental factors. That said, the housing market is a complex interaction of supply and demand forces with transactions costs, regulations and policies that can also influence both the equilibrium values and the speed at which a market reaches equilibrium. The models are based on the historical relationships between factors and therefore will not fully capture broader macroeconomic trends within the economy nor will they be able to incorporate information on future policy changes. Moody’s Economy.com produces a broader macroeconomic model on a monthly basis with multiple equations that can account for the interactions and feedback effects among key factors within the economy such as energy prices, labor markets and migration flows that can have an impact on the national housing market. Moody’s Economy.com leverages the additional information contained in this broader model by calibrating the weighted average forecasts across metros from the structural housing model to the U.S. forecasts generated by the macroeconomic models. This approach is particularly valid during times of extreme shifts in the market such as those experienced in the last decade. A purely statistical approach would place undue weight on the unprecedented growth rates experienced

and extrapolate them without accounting for changes in policy and market structure that will limit the speed of house price growth for the foreseeable future.

In addition to ensuring consistency with national forecasts, the models are calibrated to obtain consistency among the Case-Shiller, FHFA and NAR median home price indices. These are three different data series based on different data sources and segments of the housing market and therefore will not produce the same growth rates by definition. However, projections are examined to ensure they are directionally consistent with one another and that variations in speed and level fall within a reasonable range.

Finally, forecasts for each metro area are examined by the team of economists and regional specialists at Moody’s Economy.com to ensure that the projections of house price growth or declines incorporate information regarding the local economic outlooks. Forward-looking information regarding employment or policy changes may be used to refine the predictions coming out of the housing model.

Valuation. The degree to which metro area housing markets are over- or undervalued is determined by calculating the difference between current actual house prices and the prices expected based on long-run fundamental economic and demographic factors as determined by the equilibrium equation, Equation (1).

Because house prices have fallen substantially in the past two years in those metro areas that were most overvalued at the peak of the housing boom, house prices are close to their equilibrium in many parts of the nation. Thus for example, Miami, which was the most overvalued market in 2006, is now considered a balanced market (see Appendix 7-3). Indeed, prices have declined enough in some markets that the correction seems to have overshot. House prices in Bakersfield CA, for example, are almost 30% below their long-run equilibrium price. Other metro areas in California such as Visalia, Stockton and Santa Ana are also considered undervalued.

The most overvalued metro areas are now those in North Carolina, Texas, and other parts of the South, where house prices have not depreciated by much. House prices are overvalued by about 40% in Charlotte NC. A number of East Coast



metro areas such as Bridgeport CT and Poughkeepsie NY also have house prices well above equilibrium. These areas have not sustained the same degree of price depreciation as the California markets have. Of the metro areas that became highly overpriced during the boom, a number are still overvalued, although to a lesser degree. House prices in Riverside and Merced CA and Jacksonville FL are still 20% or more above equilibrium. Las Vegas also remains overvalued, with

house prices 16% above their long-term equilibrium. Some metro areas in the Pacific Northwest, Utah and Idaho, areas where house prices continued to rise into early 2007, are also overvalued. Their housing cycles have lagged the nation’s and will likely sustain larger house price declines this year.

The impact of the housing correction is also evident in the house price balance metric for the nation. The national housing market, as measured by a weighted average

of all the 381 metro areas where the weights are equal to the value of their single-family housing stock, is about balanced in the third quarter of 2008 Recent house price declines have brought this overvaluation down from a peak of nearly 22% in mid-2006. The mid-2006 reading is easily the largest degree of overvaluation over the period for which repeat purchase data are available back to 1980. Even at the height of the early 1980s boom, the national average overvaluation topped out at 12%.





This chapter evaluates the accuracy of Moody’s Economy.com’s house price pro-jections based on historical data through 2006 for the year 2007, the last full year of data that is available. The regional fore-casts evaluated in this chapter were pro-duced by a previous version of the house price model than that discussed in Chap-ter 7. The major addition in the newer forecast model is the inclusion of mortgage default rates.

Forecast accuracy, as opposed to vali-dation of the forecast model, is a more rel-evant way to evaluate Moody’s Economy.com’s house price projections during this unprecedented housing cycle. Economet-ric models are based on historic trends and relationships. Moody’s Economy.com ana-lysts add value to the econometric models by adjusting for factors that the model can not easily pick up such as investor specu-lation and sentiment, and, in the case of the older model, the rapid deterioration of mortgage credit quality.

The housing market’s performance, as measured by changes in house prices, was broadly in keeping with our expectations for a sizable correction in 2007, although the magnitude of the price decline was difficult to forecast in some areas. Likely, direct inclusion of default data in the new model will improve the regional house price forecasts.

National accuracy. Conditions in the housing market deteriorated markedly in 2007. The combination of rapid house price appreciation in prior years, loose lending standards, and weakening eco-nomic growth fed into the abrupt contrac-tion that played out in 2007. The resulting loss of many potential homebuyers from

the market aggressively cut into home prices in a way that has not been seen in the postwar period. Falling demand caused the inventory of homes to soar. The number of months of supply for both new and existing homes for sale continued to rise throughout 2007 and finally reached 10 months, compared with around 4.5 months of supply of homes for sale at the beginning of 2005. Home sales plum-meted from nearly one million new homes sold at the end of 2006 to about 660,000 by the end of 2007. The number of exist-ing homes sold fell from 5.5 million late in 2006 to 4.4 million by the end of 2007. As a result of the rising surplus of homes for sale, house prices fell by most measures throughout the year.

While house prices fell by most measures in 2007 and generated some improvement in housing affordability, the rise in affordability was not enough to off-set tightened lending standards, the loss of many speculative buyers in the market, and the effects of the lax lending standards that prevailed earlier in the decade. Re-setting mortgage interest rates on many subprime loans in 2007 created payment difficulties for many of these homeown-ers who were already highly leveraged. Further, outright fraud helped to drive up delinquency rates and foreclosures as a higher than normal portion of new mort-gage loans began to go delinquent. The resulting rise in foreclosures further added to downward pressures on house prices, particularly in the second half of last year, as these units added to an already-bloated inventory of homes for sale.

Moody’s Economy.com accurately predicted that house prices would begin to

decline in 2007 based on the three house price indices, including the NAR’s median existing house price, the FHFA’s repeat-purchase price index, and Fiserv Case-Shiller’s repeat-purchase price index. The NAR’s median price was expected to slow from 2.1% growth in 2006 to a decline of 3.5% in 2007. The forecast correctly predicted a decline in the median price, and the magnitude of the decline was not far off, with the median price actually fall-ing by 2.9% during the year (see Table 8-1). The FHFA repeat-sales index also weakened, but not as much as expected. Instead of slipping from 8.2% growth in 2006 to 0.5% in 2007 as expected, it actu-ally only slowed to 2.8%. CSI-measured house prices declined by more than the expected amount. The CSI price index went from appreciation of 5.4% in 2006 to a decline of 4.6% in 2007, more than the 1.8% decline that was originally expected.

The comparative performance among the three national price indices is fairly easy to explain. While the forecast models are all based on similar driver variables, the FHFA index has generally underes-timated the magnitude of the housing market downturn. This is because the index underrepresents nonconforming and subprime loans, the very segments of the market that weakened the earliest and the hardest. Because FHFA collects the data from Freddie Mac and Fannie Mae, the repeat-purchase index only cov-ers conforming loans. With a conforming limit of $417,000 in 2007, the FHFA index missed many transactions in high priced markets such as in California and Florida, where the median existing house price is very high. Additionally, these markets have

Chapter 8: Forecast Accuracy

Table 8-1: U.S. Near-Term House Price Forecast Accuracy% change year ago, 2007

History for 2006 Forecast of 2007 Actual 2007

Median Price Existing Single-Family Home 2.1 -3.5 -2.9FHFA Conventional and Conforming Home Price Index 8.2 0.5 2.8Case-Shiller® Home Price Index: Single-Family Aggregate Index 5.4 -1.8 -4.6Source: Moody’s Economy.com



a high subprime share of mortgages origi-nated in recent years. Also, the FHFA in-dex includes nonmarket transactions, such as refis, where appraisals may not reflect the true value of homes. During housing downturns, the inclusion of refis will bias upward the FHFA index since refi valua-tions will lag the true market price.

The NAR price measure also has some shortcomings that can make forecast-ing more difficult. Primarily, a change in the mix of homes sold can result in large swings in the median sales price and great-er volatility than in the other two measures (see Chart 8-1). At the beginning of 2007, when the housing market downturn was in its early stages, the upper end of the mar-ket was still holding up quite well while the lower end of the market was being hit harder by the subprime crisis. This result-ed in an upward bias in the median price as a larger share of home sales were at the high end in 2007 as compared with 2006.

The drivers for national house prices include the user cost of housing, which in-corporates mortgage rates, households and disposable income. Contributing to the forecast error for house prices was the dif-ficulty in anticipating the subprime mort-

gage crisis. Although Moody’s Economy.com was well aware of the mounting risks associated with the large share of subprime loans that had been issued in recent years, as evidenced by our alternative forecast scenarios at the time that anticipated sharper price declines, it was nonetheless impossible to predict the timing and mag-nitude of an exogenous shock such as this on the housing market.

Moody Economy.com’s long-term house price forecast accuracy also reflects the difficulties in predicting the turning point in the housing cycle (see Table 8-2). The accuracy over the last five years is pre-sented for the NAR and FHFA prices only, as the model for CSI was only fully devel-oped in 2005. Based on the growing gap between the fundamentals driving house prices, expectations were for the housing market to weaken sooner. The growing dis-equilibrium in house prices can be most easily illustrated by comparing long-term growth trends in per household income and house prices. Although there were some structural and business cycle factors driving a wedge between income growth and price growth since the beginning of the decade, speculative forces had also

played a substantial role. However, the size of this gap shrank noticeably through 2007 as house prices fell (see Chart 8-2). With income continuing to grow at a relatively stable pace and home prices de-clining dramatically throughout the year, the balance between the two narrowed substantially, although it is still not back to historical norms.

Regional accuracy. The accuracy of house price forecasts for states and the 100 largest metro areas is also judged for each of the three house price series. The forecasts for 2007 are, for the most part, based on historical data through the end of 2006. Accuracy of the projected price appreciation is considered, as well as the accuracy of relative rankings and the stabil-ity of the regional ranking over time. The stability analysis, however, is limited by the short period these models have been in operation as currently specified. The FHFA and CSI house price models were finalized two years ago and the NAR model less than two years ago.

Similar to the U.S. price forecasts, the regional forecasts are driven most fundamentally by household income and mortgage rates. The regional models also

Chart 8-2: Prices and Fundamentals Are Slowly Balancing OutIndex, 1976Q1=100

0

100

200

300

400

500

600

700

76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 06

Sources: NAR, BOC, Moody's Economy.com

Median existing-house price

Per household income

Chart 8-1: Realtors' Price Is Most VolatileAnnualized % change

-20

-15

-10

-5

0

5

10

15

20

25

76 78 80 82 84 86 88 90 92 94 96 98 00 02 04 06

FHFA Realtor Case-Shiller®

Sources: NAR, FHFA, Fiserv Lending Solutions, Moody's Economy.com

Standard deviation FHFA=4.0Realtor=6.0Case-Shiller®=5.6

Table 8-2: U.S. Long-Term House Price Forecast Accuracy

3/03 Forecast of 2003-2007 Actual

Median Price Existing Single-Family Home 2.6 4.9FHFA Conventional and Conforming Home Price Index 2.6 7.9Source: Moody’s Economy.comNote: Growth rates are per annum



go one step further, attempting to explic-itly account for the structural shifts in the underlying demand for housing by includ-ing proxies for the secular changes in the mortgage industry and the impact of 9/11 on housing demand—an exogenous shock early on in the housing cycle. The impact of 9/11 is further broken down to account for shifts in investment preferences as a result of the terrorist attack and the stock market rout in 2001. The model allows the direct impact of 9/11 to taper off with time. Metro area variations in household wealth and vacation home demand are considered as well as broader regional vari-ances in house price determination.

The metro area model also explicitly accounts for business cycle dynamics such as the jobless rate and mortgage interest rates that drive house prices around the value that would be consistent with the above-mentioned underlying demand driv-ers. The tendency for house price trends to persist—that is strong (weak) price appreciation is followed by strong (weak) price appreciation—and also for prices to eventually revert back to the equilib-rium is accounted for as well in the metro area models. Finally, the metro area price forecasts are reconciled to the U.S. price forecast in order to ensure forecast con-sistency among the different geographies.1 While the metro area models offer richer dynamics, there is still an important aspect of house price dynamics that is difficult

1 For a detailed discussion of the Moody’s Economy.com metro area price models, see “Is the Price Right?” Regional Financial Review, March 2006.

to model. Specifically, the model does not fully account for the speculative mo-tive for purchasing homes, although the persistence term in part reflects specula-tion. Further, it is difficult to forecast the collapse of the subprime mortgage market, which helped to fuel the speculation. Thus, the subprime mortgage meltdown, as well as the sudden loss of speculative forces, which had played such a significant role in the market in prior years, had a par-ticularly large impact on forecast accuracy over the past year.

Given these difficulties, the mean absolute percent error for state forecasts is fairly good (see Appendices 8-1A, 8-1B and 8-1C). The MAPEs for all three price measures are close, although that of the NAR is the smallest. For states, the MAPE is highest for the CSI forecasts. As the CSI index includes sales price information from jumbo loans and nonconventional loans such as FHA and VA loans, the highs and lows are more exaggerated and thus harder to pin down in magnitude than the FHFA index. For the CSI, the forecasts tended to be more conservative, resulting in a higher MAPE. Conversely, the NAR and FHFA forecasts tended to expect a more dramatic price decline than often actually occurred.

The forecast accuracy of the relative ranking of the states varies with each of the three price measures. The rank correlation coefficient for the FHFA index was the best at 87% while the correlation coefficient on the rank order for the NAR median sales price was lowest at 52%. The CSI’s rank correlation coefficient was in between the

two others, as can be seen at the bottom of Appendices 8-1A, 8-1B and 8-1C. Half of the top 10 fastest-appreciating states in the forecast are among the actual top 10 for all three price measures. For the FHFA price index, eight of the actual bottom 10 states were correctly forecasted to be at the bot-tom, while the CSI forecasted five of the actual bottom 10 correctly. The NAR price forecast only placed four of the actual bot-tom 10 correctly, as the changing mix of homes sold—which added to the volatility of the median price—made accurate pre-diction that much more difficult.

Analysis of forecast accuracy for the metro areas yields similar comparisons among the price measures and across geographies. Overall, however, the NAR measure of accuracy for metro areas is not as strong as the state forecasts, which is not surprising given the heterogeneity among the MSAs and the greater volatility in the price series for the smaller geograph-ic unit (see Appendices 8-2A, 8-2B and 8-2C). The NAR forecast model has the highest MAPE, given the difficulties associ-ated with forecasting the changing mix of homes sold in particular metro areas. The NAR and FHFA measures had a greater tendency to overpredict the forecast—that is, the forecasted growth rates are higher than the actual growth rates—while the CSI errors are more balanced (see Charts 8-3, 8-4 and 8-5). The CSI rank results were better for the metro areas than the states, and were the best of all three mea-sures. For all three measures of price, the overpredicting is most prevalent in the coastal markets.

Chart 8-3: Metro Area NAR Forecast Accuracy, 2007% error

< -1.0

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> 1.0

Chart 8-4: Metro Area FHFA Forecast Accuracy, 2007% error

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These forecast errors would suggest that the models had some difficulty in forecasting the magnitude of the price cor-rection in many areas, particularly with the CSI price measure. In the case of the FHFA measure, many places actually performed better than expected. It was difficult to forecast the magnitude of the correction in many cases because conditions were changing more quickly than at any time in the past. For example, Charts 8-6, 8-7 and 8-8 show actual price growth versus forecasted price growth for the Oxnard-Thousand Oaks-Ventura, CA MSA. For each of the price measures, the error of the forecast is greater than the average error for the top 100 metro areas. For the me-dian price, the error is among the largest, missing the actual growth rate for 2007 by 7 percentage points. Yet, in the context of the huge gains in price between 2003 and 2005, the prediction error is small.

Moreover, the direction of the forecast is accurate; that is, all three price forecasts accurately predict a decline in prices, or in the case of the FHFA index, a marked slowdown in price appreciation.

The accuracy of the forecasts based on the rankings varied for the three price measures (bottom of Appendices 8-2A, 8-2B and 8-2C). The forecast accuracy of the CSI was the best with a rank cor-relation coefficient of 92%. For the FHFA index, the rank correlation coef-ficient was 82% but that of the NAR was only 58%. This can be explained by the substantial volatility of the NAR measure as well as the difficulty in estimating the changing mix of homes sold, which had an impact on the median price.

Another important feature of house price forecasts is the ability of the model to detect turning points in house price trends. While the actual price forecast may not be

pinned down with great accuracy, this model is fairly good at picking up turning points and changes in trend. A turning point is defined as the first quarter with a decline in year-over-year growth. The model will be evaluated for how well it picks up price declines between 2007 and 2006. This ques-tion is relevant, as the price correction gained momentum in 2007 after beginning to take hold in 2006. Evidence will be provided to illustrate the model’s ability to pick the pre-cise quarter in which the price declined.

Appendices 8-3A, 8-3B and 8-3C show metro areas in which prices declined in 2007 vis-à-vis 2006. All three price indices performed reasonably well by this measure, with the model picking a price decline at least 73% of the time for all three measures. The NAR price model had a 73% accuracy rate, although it performed the least well of the three models. The errors in prediction tend to be relatively evenly distributed for

Chart 8-6: Oxnard, CA NAR Forecast Versus Actual% change year ago

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Sources: NAR, Moody's Economy.com

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Chart 8-7: Oxnard, CA FHFA Forecast Versus Actual% change year ago

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Sources: FHFA, Freddie Mac, Fannie Mae, Moody's Economy.com

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Chart 8-8: Oxnard, CA CSI Forecast Versus Actual% change year ago

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Sources: Fiserv Lending Solutions, Moody's Economy.com

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Chart 8-5: Metro Area CSI Forecast Accuracy, 2007% error

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the NAR, but slightly skewed toward the conservative side. In contrast, the FHFA errors are more to the aggressive side; the model falsely predicted a price decline more often than it missed one. Finally, the CSI was the most accurate in terms of cor-rectly forecasting a price decline. The rate at which the model misses a price decline (a Type I error) is smallest for the FHFA and CSI, at 9%. The NAR had a 15% failure rate in predicting a downturn. The tendency for the model to falsely predict a slowing in price appreciation (a Type II error) was 12% for the NAR, 13% for FHFA, and only 1% for the CSI. The actual number of metro areas that fall into the Type II error category is small (see Charts 8-9, 8-10 and 8-11).

U.S. regional forecast consistency. Regional forecast consistency over time is similar for both the state and metro area forecasts. Appendices 8-4A, 8-4B and 8-4C show the state price forecasts over time as well as the relative ranking among states compared with the fourth quarter of 2007. Appendices 8-5A, 8-5B and 8-5C display the metro areas consistency comparisons. The relative rank correlation coefficient is relatively high for the three price indices. Note also that throughout the year in 2007, the price forecasts were generally brought down as other indicators of housing persistently deteriorated. The one exception to this was the FHFA index at the metro area level between the third

and fourth quarters when the forecast ac-tually improved. This shift in the Moody’s Economy.com outlook was due to the increasing evidence that the FHFA index does a poor job in capturing price declines in large MSAs where many loans are out-side of the conforming limits and to the index’s failure to capture the impact of the subprime crisis in markets. Because of the biases, the FHFA index has consistently come in stronger than projected.

Conclusion. Forecasting house prices has become an increasingly difficult endeavor. Confounding the difficulties in forecasting any type of turning point is the impact of the subprime mortgage crisis—an un-precedented and difficult to anticipate event—that has sent hous-ing markets into a down-ward spiral. While Moody’s Economy.com recognized the mounting risks associated with the easy mortgage credit extended dur-ing the housing

boom, the lack of information regarding the exposure of the various players in the mortgage market made it difficult to quan-tify the risks. Under these circumstances, forecast accuracy in 2007 was respectable. The degree of the correction in prices that occurred last year was underestimated, but the forecast was directionally accurate for the nation and most states and metro areas. The forecast was also reasonably ac-curate in gauging the relative performance of house prices across the nation’s regions. Improvements to the regional house price models, in particular inclusion of mortgage default data, will likely strengthen the fore-casts this year.

Chart 8-9: Metro Area NAR Trend Forecast Accuracy, 2007

Forecast accurately predicts a price decline

Forecast misses a price decline

Forecast falsely predicts a price decline

Chart 8-10: Metro Area FHFA Trend Forecast Accuracy, 2007




Chart 8-11: Metro Area CSI Trend Forecast Accuracy, 2007








The housing market has not finished bringing down the excessively high prices generated during the boom, and prices are likely to end up excessively low in those metro areas that are typically subject to pronounced housing cycles: Weak con-sumer and investor confidence will keep markets from recovering even when their fundamentals are well-aligned with prices. Yet, after more than two years of a declin-ing national house price, stabilization is within sight.

The national house price index is ex-pected to continue declining through the first half of this year but hit bottom by the end of the year. The subsequent recovery in house prices will be weak, hampered by more stringent lending conditions. Although prices will depreciate in nearly all regions of the nation before the cycle is complete, those markets that had been the most exuberant during the boom will be hit the hardest.

House price decline. To date, the national house price has fallen approxi-mately 25% from its peak, according to the Case-Shiller index. It is expected to fall by about another 11% for a total decline of 36% by its nadir (see Chart 9-1). The downturn is already more severe than any in the post-World War II period. In the early 1990s downturn, which was largely concentrated in New England and Cali-fornia, national house prices declined by only 3%. During the early 1980s housing downturn that hit the Southwest, particu-

larly Texas, the nomi-nal house price never fell (see Chart 9-2). In real terms, condi-tions look even worse. Real house prices fell during both the early 1980s and early 1990s downturns, with a peak-to-trough decline of about 13% each time. By the time this housing downturn plays out, real house prices are expected to have declined by a whopping 42%.

The current housing downturn is notable for its breadth. Of the nation’s 381 metropolitan areas, 144 are expected to ultimately suffer double-digit price de-clines. Price declines will exceed 20% in about 100 MSAs. An additional 75 areas will experience price declines of 5% to 10%, and 124 more will see prices fall 1% to 5%. Only 38 MSAs will escape with overall price depreciation no worse than 1%. Nearly all metro areas will suffer a bout of house-price depreciation at some point during this downturn (see Chart 9-3). Indeed, by the third quarter of last year, many more metro areas had suffered declines than in the early 1980s housing downturn, and the declines then were not nearly as persistent or severe.

The most serious price declines are expected in the Central Valley of Califor-nia, including Stockton, Modesto and Merced, and along the west coast of Flor-ida, including the Punta Gorda, Naples, Sarasota and Cape Coral metro areas (see Chart 9-4). Miami-Fort Lauderdale-West Palm Beach, Phoenix, Las Vegas, and Los Angeles and Riverside CA will also end the housing cycle with house prices less than half of what they were at the top of the cycle. Prices are expected to decline 30% to 40% in Washington, D.C., Detroit, New York City, and in metro areas in Southern California (see Appendix 9-1).

The only other times when house prices had fallen as sharply include the early 1990s in California, beginning in the

Chapter 9: House Price Outlook

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Chart 9-1: Collapsing National House PricesCase-Shiller® single-family repeat-sales house price

Sources: Fiserv Lending Solutions, Moody’s Economy.com

% change year ago

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Nominal

Inflationadjusted

Early 1980s

Current, forecast

Chart 9-2: The Current Downturn Will Be the Most SevereCSI house price index, peak-to-trough % change

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Chart 9-3: An Extraordinarily Broad-Based Housing DownturnNumber of markets suffering year-over-year price declines



late 1980s in New England, and in the mid-1980s in Texas and other parts of the Southwest. Peak-to-trough price declines during these episodes were 20% to 30%. Most other cases of regional house-price declines have been modest. Most recently, house prices declined briefly in the San Francisco Bay Area amid the so-called Y2K tech scare and in various midwestern metro areas wracked by the manufacturing downturn earlier this decade.1

House price recovery. The recovery in house prices will be slow. Although the national price index will hit bottom by year-end, it will remain at this low level until mid-2010. Even then, inflation-adjusted price growth will remain below the 2% annual average that prevailed in the 20 years before the onset of the hous-ing boom in 2004. On a year-ago basis, price growth will strengthen by mid-2011. The rebound will remain modest, however, with real growth topping out at 4.5%, slightly under the price gains fol-lowing previous downturns (see Chart 9-5). Further, the housing crisis will have a long-lasting negative impact on house-price trends: Prices will not return to their previous peak until 2018.

Like the nation, the vast majority of metro markets will return to more normal conditions only slowly. The price outlook for most metro areas resembles a U, with a fairly flat bottom. Only a handful of metro areas will either sustain little correction or will rebound more quickly. Although the

1 There have been 6,276 instances during the past 30 years when house prices as measured by the CSI have fallen on a year-ago basis in one of the nation’s 381 metro areas, equal to just over 13%.

shape of the recoveries will be similar for most metro areas, the depth and length of the downturns will vary substantially. The number of quarters it will take for metro house prices to regain their previous peaks ranges from one to 61 (see Appendix 9-2). California metro areas have the longest climb back up, with South Florida metro areas close behind. Most of the areas that are expected to rebound quickly are small metro areas in the Midwest, upstate New York, Western Pennsylvania and Texas.

A number of factors will determine how long it takes markets to recover: the degree of house-price overvaluation dur-ing the boom, the level of investor activity and nontraditional mortgage lending, the amount of inventory, and the pace of job growth. Broadly, those areas suffering the deepest price declines and weakest recov-eries were generally more overvalued and oversupplied during the boom, are more exposed to subprime mortgage lending and investor demand, and have weaker economies and job markets.

Overvaluation during the boom—due to investor speculation and aggressive nontraditional lending—and the weak economic outlook are the most significant factors shaping the recovery. Although overvaluation has improved substantially in the last two years as house prices have plummeted, the degree of price imbalance during the boom and the recession are ex-pected to cause the correction to overshoot fundamentals in those previously over-valued markets. Confidence in housing markets on the part of both investors and homebuyers has been shaken severely by this correction. With a glut of foreclosed homes on the market in these metro ar-

eas, and more in the pipeline, buyers are holding back or are purchasing only those homes being offered at fire-sale prices. Job losses only compound the fears surround-ing the housing market’s outlook, further fueling the downturn. Finally, although lenders are expected to loosen standards in the next year, they will remain quite tight relative to the frenzy of lending that occurred earlier in the decade.

Speculation comes in many forms. Investor speculation fueled a number of housing markets, but particularly those in California, Florida, Arizona and Nevada. In 2005 and 2006, short-term investors ag-gressively purchased properties and bid up prices in a quest for quick profits. Markets where nontraditional lending was particu-larly prevalent were infected by another type of speculation—in which buyers hoped that house prices would appreci-ate fast enough to refinance mortgages before they reset—and will also struggle with slow price recoveries (see Appendixes 2-1 and 2-2). Subprime, alt-A, and vari-ous types of jumbo loans are experiencing particularly serious erosion in mortgage quality, and the larger number of foreclo-sures is exacerbating the price correction. California’s Central Valley and Long Island NY are good examples of this phenomenon (see Appendixes 2-3 and 2-4).

The job outlook will also help define the depth of the downturn. The economic recession will depress housing demand and prices. Although the recession is broad-based regionally, in the near term, sharp cutbacks in the auto assembly and finan-cial services industries will determine the regional pattern of economic activity. The Midwest and Northeast will be hit hardest

Chart 9-4: House Prices Correct Most on the CoastsCase-Shiller® house price index, expected peak-to-trough decline

Sources: Fiserv Lending Solutions,FHFA, Moody's Economy.com

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Chart 9-5: Worst Correction in Recent MemoryCSI house price index, % change year ago

Inflation adjusted

Nominal




in this year’s first half by accelerated de-clines in employment and personal income. In the Northeast, at least, this weak growth will offset its generally average or below average exposure to nonprime lending. In the South and West, the primary, although not only, weaknesses are housing and credit conditions. While economic conditions have soured in all regions, the oil-producing and agricultural states in the nation’s mid-section will fare the best.

The industrial Midwest, particularly Michigan and Ohio, will feel the brunt of cutbacks in auto assembly and auto parts production as domestic automakers strive to reduce inventories. Some temporary plant closures will become permanent, and any consolidation of the auto industry will inten-sify such effects. Elsewhere in the Midwest, and in the Southeast, there will be short-term plant closures but few permanent ones. The impact in the Southeast will be longer term, as new investment in the region, already delayed, may be put off further if there is no near-term improvement in demand.

The full brunt of cutbacks in employ-ment and bonuses in financial services will be felt in the Northeast this quarter and through midyear at least. Wage income from financial services accounts for 16% of the Northeast’s total, twice the 8% average elsewhere across the U.S. The figure is as high as 24% in New York state and 18% in Connecticut, but even Massachusetts and

New Jersey are above average. The indus-try’s payroll employment has been falling across the region for nearly the past year, with cutbacks expected all this year.

Even a significant federal stimulus in the near term will be hard-pressed to offset this pattern of accelerating weakness in the Northeast and Midwest. The impact of a payroll tax cuts in these regions will cer-tainly be positive, but it will be difficult to offset the expected decline in employment in manufacturing and finance.

Housing inventory levels also influ-ence the severity of price declines and the strength of the subsequent recovery. Considering the lack of timely and consis-tent inventory data across a large number of metro areas, inventories are proxied in this analysis by the Moody’s Economy.com months of excess supply. This mo-mentum indicator measures the buildup of new homes against the growth in demand. The main caveat to using this measure as a proxy for inventories is that it does not capture foreclosures, which are adding significantly to inventories in California, Florida, Arizona and Nevada.

Aside from markets on the Gulf Coast, which are still being skewed by the de-struction of housing stock and the exodus from New Orleans after Hurricane Katrina, the metro areas where supply is still build-ing up faster than demand are in Florida and the industrial Midwest (see Appendix

9-3). The roots of the supply imbalances in these two regions differ. In Florida, overbuilding during the boom, com-bined with a decline in population due to weaker economic conditions is keeping the months of supply high in these markets. In Michigan and Ohio, very weak popula-tion growth, and not rapid construction, is driving the excess supply. A rapidly fall-ing pace of construction has helped bring the months of supply back into balance in California, where the population continues to expand. Indeed, in San Francisco, de-mand is building up more quickly than the supply of homes. Similarly, the Boston area is also undersupplied by this measure.

In a small number of metro areas, prices will correct only mildly, or even just stay flat, then resume rising. The largest Texas metro areas will suffer little in the way of price declines. Similarly, some smaller metro areas in the South-east, the Farm Belt, and inland markets in the Northeast will fare better. House prices in Little Rock AR and Pittsburgh, for example, will remain fairly stable. Those markets were stable when the national market was booming and now are well-balanced. Homebuilders throughout these areas have also been largely successful in matching new supply with underlying de-mand. However, the longer-term prospects for many of these markets are lackluster because of weak population trends.





This chapter focuses on some of the regional housing markets that are expected to experience the most severe house price de-clines. These areas include much of Califor-nia (Central Valley, Riverside, Los Angeles, San Diego and the Bay Area), Phoenix, Las Vegas, most of Florida, Detroit, Washington, D.C., New York City and Chicago. Several housing markets with brighter house price prospects are also considered, including San Antonio, Indianapolis, Little Rock, Pittsburgh and Houston. These metro areas are discussed in order from worst to best in terms of peak-to-trough price declines.

The housing market of California’s Cen-tral Valley remains one of the weakest in the nation. Price declines have been severe and have shown no sign of decelerating, while home construction is hitting multidecade lows nearly every month. The deterioration in the labor market this year is a major reason why there has been no significant rebound in demand: At 12.3%, the unemployment rate is nearly twice the national average of 6.5%. However, the unprecedented decline in prices has led to a steady increase in affordability across the region this year, causing a modest rebound in sales. The region is still burdened by a large inventory overhang, but the supply/demand imbalance has shown signs of im-provement in recent months.

The region’s problems were caused by a wave of speculative building in the middle of the decade. This speculation pushed home values far above those justified by fundamen-tals. Yet, with lending standards increasingly lax and interest rates low, many people still felt they could afford to live in the area. When rising delinquency rates caused banks and

other financial institutions to tighten their lending standards, demand fell sharply, leav-ing the area saddled with a massive supply of unsold homes that has only been exacerbated by the surge in foreclosures. The deterioration in the labor market over the past year and a half accelerated the decline in demand, mak-ing the inventory problem even worse.

Although there has been a general in-crease in affordability across the region, the improvement varies widely among metro areas. Visalia and Fresno are still less af-fordable than they were five years ago, even though prices have fallen by 32% and 38%, respectively, since the first quarter of 2006. Moreover, a substantial percentage of the increase in sales this year can be attributed to foreclosure sales. According to data from Equifax, the default rate—which predicts foreclosures—was 5.5% in the third quar-ter, substantially higher than the national average of 1.9%. Foreclosure sales, which take place at substantial discounts, are put-ting tremendous downward pressure on prices. Although this is a painful process as residents watch the equity in their homes dwindle, it is necessary to bring the region’s housing markets back into balance. Demand is now outpacing supply in Visalia, Fresno and Stockton, and there have been signs of improvement in Merced and Modesto.

Prices will continue to fall until the end of 2009 for Fremont, Merced, Stockton and Visalia and until mid-2010 for Modesto, with declines ranging from 21% in Visalia to 31% in Modesto. While home prices will begin recovering in 2010 for Visalia, the other four metro areas will not see substantial price increases until 2011. The sharp downturn in

the region’s labor market will prevent demand from recovering sooner. Moreover, a growing number of homeowners, particularly those who purchased their homes in 2005 or 2006, are underwater; that is, they owe more on their mortgages than their homes are worth. This has created a powerful incentive for those residents to walk away from their mortgage obligations. Although this is a national phe-nomenon, the problem is more acute in the Central Valley. This negative feedback loop of falling prices and rising foreclosures is a major downside risk to the forecast.

The pace at which housing demand recovers will largely be determined by the health of the labor market. On the bright side, the region has little exposure to the financial services industry and practically none to domestic auto manufacturers, two parts of the economy that have been losing jobs the fastest. Moreover, the vast majority of layoffs in construction have already taken place, limiting the downside risk from that industry. Although the region’s unemployment rate will rise in 2009, most job losses will have taken place by the middle of the year.

Longer term, the outlook for the Central Valley’s housing market is upbeat. This is due to above average population growth in the re-gion, which will fuel demand for new homes. Population growth was equal to or exceeded the national average in every year but one from 1990 to 2007, and there is no reason to believe that the current downturn will change this. Indeed, the increase in affordability may accelerate population growth over the next few years as potential in-migrants who had been priced out of the market decide they can afford to live in the area.

Chapter 10: Metropolitan Area Outlook

Fresno, CA2002 2003 2004 2005 2006 2007 2008 Indicators 2009 2010 2011 2012 2013

26.3 26.6 27.6 28.8 29.5 30.0 30.0 Gross Metro Product, C$B 29.4 29.7 30.8 32.1 32.95.4 1.4 3.8 4.1 2.4 1.6 0.3 % Change -2.2 1.2 3.6 4.2 2.5

282.0 282.7 287.0 294.4 302.6 306.0 305.4 Total Employment (000) 295.3 294.9 302.7 313.9 322.52.2 0.3 1.5 2.6 2.8 1.1 -0.2 % Change -3.3 -0.1 2.6 3.7 2.7

11.5 11.7 10.5 9.0 8.0 8.6 10.6 Unemployment Rate 14.1 14.7 13.4 10.5 8.65.9 5.1 5.1 4.1 6.5 5.7 3.3 Personal Income Growth -0.1 2.7 4.8 5.8 5.3

829.8 846.3 859.8 870.8 882.4 895.0 909.6 Population (000) 923.4 938.6 953.1 967.9 982.33,655 4,470 5,402 5,865 3,904 3,645 2,318 Single-Family Permits 724 1,197 2,918 3,450 3,450

182 1,283 1,755 1,535 1,374 1,210 637 Multifamily Permits 612 675 1,021 939 1,11317.9 23.7 27.2 27.3 6.8 -12.8 -27.8 Case-Shiller House Price Growth -23.1 -1.1 4.6 3.3 3.2

5,773 9,983 9,099 11,808 10,524 7,245 5,968 Mortgage Originations ($Mil) 5,995 5,802 6,060 6,453 6,7668.1 7.8 4.3 1.5 1.4 1.2 3.9 Net Migration (000) 2.7 3.7 2.5 2.4 1.7

3,528 3,497 3,208 4,092 926 1,630 2,313 Personal Bankruptcies 2,775 4,050 3,887 3,083 2,759



Merced, CA

Stockton, CA

Visalia, CA

Modesto, CA

2002 2003 2004 2005 2006 2007 2008 Indicators 2009 2010 2011 2012 20135.0 5.1 5.3 5.5 5.7 5.9 5.8 Gross Metro Product, C$B 5.8 5.8 6.0 6.2 6.48.1 1.9 4.3 4.5 2.9 2.9 -0.9 % Change -1.2 1.3 3.2 3.8 2.3

55.5 56.0 57.1 58.3 58.2 59.0 59.5 Total Employment (000) 58.2 58.4 59.5 60.8 61.64.5 0.8 1.9 2.1 -0.2 1.5 0.9 % Change -2.2 0.3 1.8 2.3 1.3

11.0 11.5 11.0 10.1 9.5 10.1 12.7 Unemployment Rate 18.5 21.2 18.4 14.5 12.64.8 7.4 7.4 1.8 3.6 5.1 3.9 Personal Income Growth 0.4 2.9 4.6 5.4 4.9

223.3 228.8 233.5 238.1 241.4 244.3 248.4 Population (000) 252.6 256.8 260.9 265.3 269.31,674 2,465 2,544 3,479 2,136 780 158 Single-Family Permits 82 89 657 1,202 1,306

79 277 54 186 80 14 90 Multifamily Permits 3 3 5 6 613.5 13.5 23.7 36.5 7.6 -23.7 -43.5 Case-Shiller House Price Growth -25.5 0.9 12.8 8.7 2.8

1,645 2,985 3,188 4,959 4,194 2,095 1,847 Mortgage Originations ($Mil) 1,789 1,781 1,873 2,016 2,1403.4 2.9 2.1 1.8 0.2 -0.3 1.0 Net Migration (000) 1.0 0.9 0.7 0.8 0.4

719 752 670 787 153 317 435 Personal Bankruptcies 540 842 800 612 538


150.6 152.3 154.6 159.1 159.8 160.0 158.2 Total Employment (000) 151.4 150.2 153.0 157.9 161.30.6 1.1 1.5 2.9 0.5 0.1 -1.1 % Change -4.3 -0.8 1.9 3.2 2.29.7 9.9 9.3 8.5 8.0 8.8 11.4 Unemployment Rate 17.0 18.7 15.5 11.7 9.95.0 4.3 7.6 4.6 5.2 5.2 2.5 Personal Income Growth -0.2 2.6 4.5 5.5 5.0

477.4 486.9 492.2 499.6 505.1 508.8 513.2 Population (000) 516.5 520.8 528.6 538.2 547.82,991 3,868 4,027 4,085 2,271 1,352 566 Single-Family Permits 501 965 2,278 3,028 3,015

182 251 494 440 373 513 287 Multifamily Permits 704 997 1,171 1,363 1,36112.6 16.2 21.8 30.3 5.6 -19.4 -37.3 Case-Shiller House Price Growth -30.4 -5.1 6.0 8.5 10.1

5,601 9,066 8,711 12,259 9,711 5,141 4,601 Mortgage Originations ($Mil) 4,582 4,564 4,890 5,329 5,6549.6 5.5 1.2 2.9 0.8 -1.5 -0.6 Net Migration (000) -1.8 -1.0 2.3 3.9 3.7


2002 2003 2004 2005 2006 2007 2008 Indicators 2009 2010 2011 2012 201316.4 17.0 17.9 18.4 18.6 18.4 18.0 Gross Metro Product, C$B 17.7 18.0 18.7 19.6 20.15.2 4.0 4.7 2.9 1.3 -1.4 -2.1 % Change -1.5 1.6 3.8 4.8 2.9

194.0 197.3 200.7 205.8 209.1 211.8 212.2 Total Employment (000) 206.3 206.9 211.9 218.6 222.81.6 1.7 1.7 2.6 1.6 1.3 0.2 % Change -2.8 0.3 2.4 3.2 2.08.9 9.2 8.8 7.9 7.4 8.2 10.4 Unemployment Rate 13.6 15.5 13.6 10.5 8.93.6 5.3 6.3 3.8 6.0 5.7 3.8 Personal Income Growth 0.3 2.8 4.7 5.6 5.0


489 106 495 185 191 225 68 Multifamily Permits 46 42 72 77 808.4 11.5 22.9 30.0 4.5 -18.0 -39.7 Case-Shiller House Price Growth -29.5 -1.3 9.4 7.8 4.0

8,717 13,812 13,819 18,633 14,345 8,043 8,228 Mortgage Originations ($Mil) 8,053 7,962 8,273 8,782 9,27312.2 11.7 10.4 7.7 -0.2 -1.9 6.2 Net Migration (000) 6.5 6.7 6.0 6.6 5.9


2002 2003 2004 2005 2006 2007 2008 Indicators 2009 2010 2011 2012 20139.0 9.3 9.7 10.5 10.9 11.0 11.1 Gross Metro Product, C$B 11.1 11.4 11.9 12.5 13.06.2 3.1 3.9 8.2 4.3 1.2 0.5 % Change -0.0 2.5 4.3 5.2 3.7

102.5 102.7 104.5 108.3 112.6 113.4 111.9 Total Employment (000) 108.7 109.6 112.4 116.1 119.02.2 0.3 1.7 3.7 3.9 0.8 -1.3 % Change -2.8 0.8 2.5 3.3 2.5

12.0 12.3 11.6 9.5 8.5 9.3 11.4 Unemployment Rate 16.9 19.7 17.2 12.9 10.83.8 6.1 8.1 3.8 5.4 5.4 1.7 Personal Income Growth -0.1 3.0 4.8 5.9 5.4

378.9 387.3 396.2 405.3 412.3 419.5 429.3 Population (000) 439.8 450.7 461.2 472.1 482.61,832 1,962 2,186 2,944 2,815 2,254 1,183 Single-Family Permits 174 369 1,012 1,864 2,104

161 308 306 373 760 435 94 Multifamily Permits 158 57 157 199 2218.5 14.3 29.0 35.8 9.0 -13.7 -25.9 Case-Shiller House Price Growth -20.0 1.1 10.1 6.9 4.0


1,909 1,881 1,872 2,104 378 678 996 Personal Bankruptcies 1,213 1,894 1,828 1,382 1,204



As in many metro areas in California and Nevada, the Southwest Florida hous-ing market correction began before the nation’s. Since the late 2005 peak, house prices in Naples, Cape Coral and Bra-denton have declined by between 39% and 49%, according to the Fiserv Case-Shiller house price index. The latest year-over-year change is in excess of 20% for all three metro areas and more than 30% for Cape Coral. One-third of houses sold over the past 12 months have sold for a loss. The rate of price decline has accelerated in recent quarters, with the most severe correction occurring in the Cape Coral market. Naples, however, will sustain the greatest peak-to-trough house price de-cline among all metro areas. Condo prices have been falling slightly faster than single-family house prices.

The upside of falling house prices is that it speeds the recovery in residential markets. Affordability is rising in all three metro areas, with the greatest increase oc-curring in the Cape Coral market, where the price decline has been the steepest. Despite the increase, affordability is still low in Naples, where housing prices and per capita income are well above the na-tion’s. A lack of inventory in 2005 among the metro areas has now turned into a six- to seven-month overhang. Sales have stabilized and are rising modestly. Sales equilibrium will be reached by mid-2010, settling at 50% to 60% off the sales peak of 2005. Like the bulk of the owner-occupied

housing stock, the majority of sales will occur in Cape Coral and Bradenton.

The uptick in prices of existing houses will lag the upturn in sales and is not expected until early 2011. This is due in part to the depressing effect of a continued rise in mortgage delinquencies and fore-closures. Since 2005, the delinquency rate on first mortgages as a percent of dollar volume has soared sixfold and now ranges from 8% to 10% in Southwest Florida. Foreclosures are now rising even faster, especially in Cape Coral, where the mort-gage write-off rate is among the highest in the nation. Foreclosures make up a rising proportion of the houses for sale through-out the region.

Weaker housing demand is also dampening house prices. The coastal economy is in recession. Employment is contracting, with losses occurring in every industry except healthcare and gov-ernment. Real personal income is falling, dragging down retail sales and spending on personal services. The stronger dollar is constraining international tourism and international demand for residential prop-erties. Most importantly, net in-migration is down to one-fifth to one-third of the 2005 level. The prolonged recession has impeded the relocation of older adults from New York, New Jersey, Ohio, Penn-sylvania and Illinois to Florida’s Gulf Coast. Not only have persons 55 and older lost home equity; the value of their portfolios has also declined greatly.

The retiree market is a huge driver of Florida’s west coast economy. Nearly 47 out of every 100 adult residents are 55 or older, compared with 31 out of 100 adult residents throughout the nation. Nonwage income provides a disproportionate share of personal income. The in-migration of retirees to Southwest Florida is expected to rebound relatively quickly for numerous reasons. First, the baby boomers are surg-ing into the retirement age cohorts. Sec-ond, six out of 10 households with owners over the age of 55 have nonmortgaged primary residences. Third, the median price of existing houses in sending metros areas such as New York, Chicago and Philadelphia is down an average of just 6% from peak. In other words, prices of houses in Southwest Florida have fallen five times faster than prices in the metros of origination for the in-migrating retirees. Fourth, although the value of individual portfolios has fallen over the past 18 months, the rate of return over the past decade or more is still in the 4% to 6% range when annualized.

As in-migration of seniors to South-west Florida speeds up again in late 2009 and 2010, the region’s economy will recov-er. In response to the increased demand for housing, residential permits will begin to rise by mid-2010, and that will reverse the course of the construction industry. The additional income of seniors will reig-nite demand for retail wares, restaurants, and legal, financial, accounting, and other professional services.

2002 2003 2004 2005 2006 2007 2008 Indicators 2009 2010 2011 2012 20137.4 7.6 7.8 7.7 7.8 7.9 7.8 Gross Metro Product, C$B 7.7 7.8 8.0 8.3 8.50.7 3.1 2.0 -0.6 0.3 1.1 -0.0 % Change -1.9 1.0 3.4 3.8 2.1

93.4 95.1 95.9 95.3 95.1 95.0 94.0 Total Employment (000) 91.3 91.5 93.2 95.1 96.01.2 1.8 0.8 -0.6 -0.2 -0.1 -1.1 % Change -2.8 0.2 1.8 2.0 1.04.7 5.3 5.1 4.9 5.0 4.8 5.5 Unemployment Rate 7.9 8.9 7.5 5.8 5.10.8 2.3 7.3 0.5 7.1 6.6 4.1 Personal Income Growth -0.8 2.5 5.2 5.6 4.7

227.2 228.3 227.1 225.2 223.8 222.8 223.9 Population (000) 224.7 227.8 230.6 232.4 234.31,325 1,095 1,265 1,152 788 679 406 Single-Family Permits 229 484 1,055 1,419 1,332

68 133 210 236 98 139 132 Multifamily Permits 18 17 31 38 3219.0 15.6 12.7 8.2 -1.0 -3.9 -6.2 Case-Shiller House Price Growth -11.9 -1.5 5.8 7.8 6.7

5,578 8,529 5,345 5,284 4,547 4,071 2,730 Mortgage Originations ($Mil) 3,064 2,878 2,807 2,811 2,8133.0 2.1 -0.1 -0.9 -0.6 -0.7 1.9 Net Migration (000) 1.6 3.8 3.5 2.5 2.5

638 629 621 841 354 522 788 Personal Bankruptcies 969 1,385 1,373 1,194 1,175

Bradenton, FL



Naples, FL

Vero Beach, FL

Palm Bay, FL

2002 2003 2004 2005 2006 2007 2008 Indicators 2009 2010 2011 2012 20135.3 5.3 5.4 5.6 5.7 5.6 5.5 Gross Metro Product, C$B 5.4 5.5 5.6 5.9 6.01.1 -0.1 1.0 3.9 1.7 -1.1 -1.3 % Change -2.3 0.9 3.2 4.0 2.2

60.6 60.4 60.9 61.6 62.7 63.5 63.0 Total Employment (000) 61.1 60.8 61.6 62.9 63.6-0.7 -0.3 0.8 1.2 1.8 1.2 -0.8 % Change -3.1 -0.5 1.4 2.1 1.14.6 4.9 4.8 4.4 3.9 4.1 5.3 Unemployment Rate 7.7 8.4 7.1 5.5 4.81.9 3.2 8.2 7.3 7.3 5.8 3.0 Personal Income Growth 0.3 2.4 4.1 5.0 4.5

128.7 130.0 130.2 130.1 131.0 131.9 132.8 Population (000) 133.0 133.7 134.2 134.6 135.0610 568 640 628 267 272 195 Single-Family Permits 89 274 482 603 595393 39 277 59 240 58 15 Multifamily Permits 89 202 229 262 260

14.0 15.4 18.3 17.8 3.5 -7.1 -27.5 Case-Shiller House Price Growth -21.1 2.3 7.8 4.9 3.83,280 5,024 3,444 3,762 3,221 2,571 2,026 Mortgage Originations ($Mil) 2,319 2,263 2,372 2,552 2,696

1.7 1.1 -0.0 -0.3 0.3 0.4 0.5 Net Migration (000) -0.3 0.2 -0.1 -0.2 -0.2255 232 238 380 83 132 196 Personal Bankruptcies 241 367 349 270 239


192.5 196.6 205.3 212.5 216.1 212.4 210.0 Total Employment (000) 201.7 199.6 206.5 216.7 224.6-0.4 2.1 4.4 3.5 1.7 -1.7 -1.1 % Change -4.0 -1.0 3.5 4.9 3.75.7 5.2 4.4 3.7 3.4 4.4 6.5 Unemployment Rate 9.6 10.7 9.3 7.7 7.13.9 5.3 8.2 7.5 7.5 4.2 2.8 Personal Income Growth -2.5 1.3 6.3 7.9 6.9

494.1 502.8 515.6 525.8 530.8 534.6 539.7 Population (000) 550.1 562.2 575.9 589.4 603.74,956 5,607 6,487 7,321 3,967 2,039 1,115 Single-Family Permits 532 1,384 2,826 3,568 3,5871,666 562 2,438 1,466 1,077 888 389 Multifamily Permits 749 1,411 1,601 1,653 1,75510.0 11.8 23.6 30.8 11.3 -7.5 -18.5 Case-Shiller House Price Growth -24.4 -5.3 11.2 11.0 6.4




42.5 44.2 44.7 47.2 48.7 48.2 47.6 Total Employment (000) 45.4 44.7 45.9 47.9 49.31.0 4.1 1.0 5.8 3.1 -1.1 -1.3 % Change -4.5 -1.6 2.7 4.2 3.17.2 6.8 6.7 4.9 4.3 5.8 8.3 Unemployment Rate 12.4 14.0 12.2 10.1 9.22.8 4.4 20.1 7.7 12.8 7.1 3.4 Personal Income Growth -3.4 -0.7 3.6 5.3 4.7



1,193 1,783 1,536 2,148 1,947 1,492 954 Mortgage Originations ($Mil) 992 1,025 1,073 1,112 1,1403.0 2.4 5.0 3.3 3.0 2.3 1.3 Net Migration (000) 1.5 2.5 3.2 3.4 3.7

400 461 322 519 72 176 328 Personal Bankruptcies 426 634 629 533 507

Cape Coral, FL


182.4 190.0 204.8 221.7 232.9 229.8 219.1 Total Employment (000) 211.3 211.3 219.2 230.3 240.43.2 4.1 7.8 8.3 5.0 -1.3 -4.6 % Change -3.6 0.0 3.7 5.1 4.44.8 4.6 4.0 3.2 3.0 4.7 8.0 Unemployment Rate 10.8 11.7 10.4 9.0 8.43.5 6.4 17.1 12.7 11.2 5.0 1.6 Personal Income Growth -1.0 3.3 7.6 8.5 7.9

474.2 490.2 512.2 541.5 568.9 588.9 595.0 Population (000) 606.9 622.7 640.5 658.8 678.67,149 9,668 14,958 22,211 14,700 4,356 1,020 Single-Family Permits 604 1,879 5,446 6,806 6,8673,997 6,007 5,437 7,119 4,046 1,549 379 Multifamily Permits 289 1,212 3,663 3,940 4,10814.3 13.1 17.3 38.4 13.3 -18.4 -33.1 Case-Shiller House Price Growth -29.3 -9.0 9.2 15.7 16.6





The Southeast Florida housing market is perhaps the worst housing market in the nation. House prices have fallen by more than 36% from peak values on average, and un-like housing markets in California that have experienced similar price declines in excess of 30%, Southeast Florida housing markets are still overpriced. While excess housing invento-ry has declined appreciably from stratospheric levels at the beginning of last year, the area re-mains the most oversupplied housing market in the nation. An improvement in home sales has aided in reducing excess inventory; how-ever, the improvement in existing-home sales is being driven by a surge in foreclosures and short sales. Residential construction remains at a very slow pace.

Southeast Florida was formerly one of the hottest housing markets in the nation. Robust demand for mortgage-related assets encour-aged lenders to lend unscrupulously to un-qualified borrowers. In no place was this more the case than in Southeast Florida. In 2006, nearly half the mortgages originated in Mi-ami-Dade County were subprime. Broward, Martin and Palm Beach counties were not far behind. This irresponsible lending was the precursor to the surge in foreclosures that is plaguing the area’s housing market. Fur-thermore, speculation was rife among both homebuilders and investors looking to turn a quick profit. Such speculation was instrumen-tal in driving house prices higher by artificially boosting housing demand. This artificial demand exacerbated the housing correction in Southeast Florida as realization of planned construction delivered even more housing supply to an already-saturated market.

The excesses of the housing boom, coupled with a deteriorating economy, have produced the most severe housing recession in recent memory. Despite price declines in excess of 36%, Southeast Florida housing

markets remain overpriced on both price-to-income and price-rent metrics. Price declines are uneven across tiers. In Miami, lower-priced homes have declined by 42% from their peak as compared with middle-tier homes, which have declined by 36%, and upper-tier homes, which have declined by 35%. This is because more lower-priced home mortgages were sub-prime, raising the risk that borrowers default. The resulting foreclosure would subsequently reduce the value of the home.

The Southeast Florida housing market suffers from some of the highest foreclosure rates in the nation. The first-mortgage fore-closure rates in Miami-Dade, Broward, Palm Beach and Martin counties are 8.5%, 6.8%, 6% and 5.2%, respectively, compared with the national average of 1.9%. Foreclosures are ex-acerbating the housing cycle in two key ways. First, foreclosures create a self-reinforcing cycle whereby house price declines prompt homeowners to default, which further de-presses house prices. Second, foreclosures add to excess inventory, forestalling any housing market recovery. Unlike the first two waves of foreclosures that were driven by early payment defaults and adjustable rate mortgage resets, this third wave of foreclosures is due to the combination of negative equity positions and income disruptions. Homeowners losing their jobs who are in negative equity positions are increasingly defaulting.

The near-term future of the Southeast Florida housing market depends on the resil-ience of the local economy and the speed at which it can purge excess inventory. A deep-ening consumer-driven recession does not bode well for the speed of any recovery. Labor market weakness is broad-based across not only geographies but also industries. Indeed, employment is declining in all four counties and in mostly all of those counties’ industries. Unemployment rates have soared and will not

peak until 2010. A deteriorating economy not only weighs on housing demand by reducing consumer purchasing power, it also boosts housing supply via foreclosures. Moreover, the Southeast Florida economy relies heavily on immigration, which declines markedly in times of recession. House prices will decline through 2010, not bottom-ing out until early 2011 in Miami’s case, at which point affordability will have been restored, payrolls will have stopped contract-ing, and excess inventory will have been worked off. House prices will first bottom in Palm Beach and Martin counties, as their corrections began sooner than did those of Broward and Miami-Dade. House prices will not begin to rise meaningfully in any county until 2011. The long-term outlook for the Southeast Florida housing market is mark-edly better. Ultimately, the area’s strong ties to Latin America and stalwart economic and demographic fundamentals will stabilize its economy and allow its housing markets to return to form.

Risks to this dour outlook are weighted firmly to the downside. Predominantly, South-east Florida is a procyclical economy that depends heavily on tourism, trade, finance and housing-related industries. The area will especially suffer if the recession morphs into something worse as adverse feedback loops intensify. Skittish consumers could further reduce spending, prompting employers to slash payrolls, further reducing spending. Fur-thermore, house prices could overshoot the level consistent with affordability should the foreclosure-house price spiral intensify. Finally, the financial panic could undermine the hous-ing recovery to the degree that it damages consumers’ propensity and wherewithal to purchase a home, produces an increase in long-term interest rates, or causes more fore-closures via negative equity or a loss of wealth.

Fort Lauderdale, FL


695.5 705.0 732.7 769.7 786.0 789.8 778.5 Total Employment (000) 744.8 739.3 764.2 796.9 821.80.8 1.4 3.9 5.1 2.1 0.5 -1.4 % Change -4.3 -0.7 3.4 4.3 3.15.8 5.4 4.6 3.7 3.2 3.6 5.4 Unemployment Rate 8.2 9.4 8.1 6.5 5.94.3 2.6 7.2 9.4 7.6 3.4 1.0 Personal Income Growth -4.1 0.5 5.8 7.7 7.0

1,700.2 1,720.7 1,742.8 1,767.4 1,769.0 1,754.6 1,738.9 Population (000) 1,733.1 1,738.3 1,761.8 1,790.9 1,822.25,705 3,880 4,784 3,609 3,550 1,754 956 Single-Family Permits 396 1,138 2,361 3,714 3,6226,323 4,338 3,925 3,342 3,166 2,179 1,217 Multifamily Permits 279 707 1,963 2,101 2,14714.1 15.4 19.4 30.5 13.2 -9.4 -26.8 Case-Shiller House Price Growth -32.9 -16.9 -0.6 7.4 9.7

20,072 29,553 25,197 34,061 31,909 22,457 21,386 Mortgage Originations ($Mil) 20,371 19,721 19,838 19,851 19,43125.1 14.7 14.2 16.6 -6.1 -22.6 -22.9 Net Migration (000) -13.1 -2.1 16.1 21.7 23.7

9,873 9,390 7,865 10,417 2,196 3,613 6,620 Personal Bankruptcies 8,615 13,061 12,909 10,827 10,199



West Palm Beach, FL

Miami, FL

The housing markets of Riverside and San Bernardino counties are among the hardest hit in the nation. Foreclosure sales dominate these Inland Empire markets, a re-sult of the fallout from the subprime lending crisis. Delinquencies and foreclosures contin-ued to increase sharply in 2008, both in vol-ume and as a percentage of all home loans. However, the pace of foreclosures slowed at the end of the year as lenders increased efforts to modify delinquent loans. As the number of foreclosure sales increased to over half of total homes for sale, house prices dropped. Since its 2006 peak, the median sale price in the two counties declined by over 40% through the second half of 2008.

Dropping prices and increasing afford-ability fueled an increase in home sales in the second half of 2008. Although total sales of existing and new homes were on track to finish the year slightly below 2007 levels, the third quarter of 2008 witnessed a 40% increase from four quarters earlier. The supply of new homes—both those under construction and those for sale—dropped in the third quarter by 50% from a year earlier. The distribution of new homes for sale was uneven, however, with 40% of new supply in the real estate bust-prone areas of the High Desert.

The Inland Empire is once again regaining its affordability relative to neigh-

boring Orange and San Diego counties. Affordability almost doubled from its low point in 2006. During the housing boom, the area’s affordability attracted many first-time and prospective buyers who had been priced out of the neighboring counties and were willing to travel farther to their jobs. Many of these buyers used no interest, alternative rate, and other subprime mort-gage instruments to purchase their homes.

Improvements in the Inland Empire’s sales and price fundamentals must be weighed against weaknesses in the regional economy. The unemployment rates in Riverside and San Bernardino counties at the end of 2008 were higher than in all

Port St. Lucie, FL


991.3 984.3 1,005.8 1,029.9 1,048.7 1,060.2 1,050.9 Total Employment (000) 991.9 972.5 996.4 1,033.1 1,057.8-1.7 -0.7 2.2 2.4 1.8 1.1 -0.9 % Change -5.6 -2.0 2.5 3.7 2.46.5 5.9 5.5 4.5 3.8 3.8 5.4 Unemployment Rate 9.1 10.6 9.0 7.0 6.03.7 3.4 7.7 7.9 7.1 4.4 3.0 Personal Income Growth -4.4 -0.4 4.8 6.9 5.7

2,306.2 2,319.1 2,334.5 2,351.9 2,371.3 2,380.4 2,387.0 Population (000) 2,400.8 2,417.2 2,443.8 2,469.8 2,497.56,374 8,740 9,603 9,922 6,548 3,246 1,120 Single-Family Permits 405 2,577 4,923 5,842 5,8968,232 6,793 13,253 16,198 13,469 4,836 2,255 Multifamily Permits 738 1,737 5,004 5,297 5,27815.6 17.5 19.7 29.5 20.9 -0.1 -29.8 Case-Shiller House Price Growth -32.9 -20.7 -5.2 4.7 7.9

19,043 29,617 29,113 38,208 42,587 30,567 29,776 Mortgage Originations ($Mil) 31,438 31,680 31,617 31,305 30,69810.4 -0.8 3.6 5.2 5.7 -6.2 -8.9 Net Migration (000) -1.7 0.7 10.7 9.9 11.6



107.8 112.1 119.3 127.6 133.2 133.8 133.1 Total Employment (000) 130.1 131.0 136.7 144.0 150.43.0 4.0 6.4 7.0 4.4 0.5 -0.5 % Change -2.3 0.7 4.4 5.3 4.56.6 6.1 5.8 4.6 4.1 5.3 7.9 Unemployment Rate 11.5 12.8 11.1 9.1 8.21.7 4.6 15.1 8.0 10.7 5.8 3.5 Personal Income Growth -0.5 3.5 8.2 9.8 9.1

335.8 346.9 361.9 375.6 387.8 399.0 408.5 Population (000) 418.1 426.5 436.8 447.8 460.04,744 8,195 8,856 9,112 5,572 2,008 796 Single-Family Permits 69 937 2,509 3,155 3,1651,288 1,495 1,700 1,670 756 401 384 Multifamily Permits 91 316 877 980 1,03913.5 20.0 25.0 29.7 5.2 -15.2 -28.8 Case-Shiller House Price Growth -26.5 -8.6 7.3 12.0 12.0


1,519 1,436 1,186 1,608 317 713 1,285 Personal Bankruptcies 1,652 2,445 2,405 2,001 1,874


523.5 528.8 546.8 572.5 585.2 583.9 577.2 Total Employment (000) 555.1 551.8 570.2 593.5 612.91.7 1.0 3.4 4.7 2.2 -0.2 -1.2 % Change -3.8 -0.6 3.3 4.1 3.36.1 5.7 5.1 4.2 3.7 4.3 6.3 Unemployment Rate 9.4 10.7 9.2 7.4 6.52.8 1.3 12.8 7.6 10.5 5.3 2.6 Personal Income Growth -2.8 1.3 6.0 7.7 7.1

1,183.9 1,205.9 1,235.7 1,256.9 1,263.7 1,262.9 1,270.2 Population (000) 1,288.8 1,316.7 1,350.8 1,386.2 1,424.49,437 10,920 10,266 8,689 4,652 2,101 1,283 Single-Family Permits 583 4,016 6,224 7,477 7,6523,523 4,924 4,253 3,802 3,725 1,029 1,056 Multifamily Permits 639 1,325 3,499 3,785 4,02814.1 15.0 21.0 30.4 8.6 -12.6 -24.5 Case-Shiller House Price Growth -27.5 -11.6 4.4 9.4 10.3





Riverside-San Bernardino, CA

The Phoenix housing market contin-ues to plummet and is one of the worst in the nation, rivaling that of most markets in California and Florida. House prices have been in free fall, a necessary correc-tion to restore affordability to the area. The house price-to-income ratio reached twice its historical long-run average at the peak in 2005. In a little over two years, prices fell by 39%, according to the Fiserv Case-Shiller house price index. The decline has helped to bring buyers back to the market, but there is still a large supply-demand imbalance, with more than six months of inventory at the current pace of sales.

Prices rose beyond sustainable levels and encouraged an overproduction of new homes as profit margins expanded. Prices were pushed higher by second-home buy-ers and aggressive lending. When home prices peaked in 2005, 17% of mortgage originations were by buyers not intending to occupy the homes they were buying. This compared with 12% nationally. These buyers were not sensitive to underly-ing wages in the area. Instead, they were driven by the robust returns on investment over the short term. The demand pushed prices to unaffordable levels.

Overwhelmingly poor lending stan-dards were the second driver. Households had consistently used more debt to finance home purchases. The debt-to-value ratio for the U.S. was 40% in 2004, compared with 60% in Phoenix. This means that interest payments ate up a larger share of disposable income. When house prices took off, households began to extract the growing equity and supported robust spending. Now the wealth effect has the opposite impact and has dragged the econ-omy into a deep recession.

The greatest problem for the housing market this year will be the rise of fore-closures. New construction has slowed to a point that at current prices, under normal conditions, demand would begin to draw down inventories. However, the economy will stumble throughout next year and lead to an accelerated pace of foreclosures, which will provide a new wave of supply. Delinquency and foreclo-sure rates soared in 2008, while the un-employment rate jumped 200 basis points from 3.5% to 5.5%. This year will bring more of the same, with the unemployment rate expected to top out near 8%. Barring a moratorium, expect an increase in the

rate of foreclosures compared with last year as the unemployed are forced out of their homes. Foreclosures will measurably impede progress being made in drawing down inventories.

Job losses will be pervasive through-out the metro area and sap demand for housing. Phoenix was more dependent on housing-related employment than any oth-er metro area with a population over 1 mil-lion. At its peak, 17% of jobs in the metro area had some tie to housing. Much of the excess in this segment of the economy has unwound, but the downturn will enter its second phase this year. Consumer-related industries will suffer from the substantial decline in household spending. From its peak, home equity has declined by about $80,000 per household. This will diminish consumer demand, as much of the gain over the last few years was a result of eq-uity extraction. Rising unemployment will discourage in-migration, a key driver of growth in the metro area.

House prices are expected to fall an-other 31% to the forecasted trough in mid-2010, and the risks are to the downside. Prices are already expected to overshoot, but macroeconomic conditions have de-

other Southern California counties except for agricultural-based Imperial County. The Inland Empire continued to eliminate construction jobs in 2008. However, it has largely been spared the loss of a large num-ber of white-collar jobs, unlike its coastal neighbors. The area had attracted sizable investment in logistics and warehousing operations before the recession. These operations have slowed hiring but are avoiding the large-scale layoffs occurring in real estate-related industries.

Assuming that additional job losses in Riverside and San Bernardino are limited

and that a sustainable recovery begins in the first half of 2009, additional defaults will be limited. This will keep the inventory of foreclosed homes for sale from growing and will slow and eventually reverse the price declines of the last two years, with house prices bottoming out in the first half of 2010. Under this scenario, the median price of a home for sale will fall to around $160,000. Home sales will continue to in-crease in 2009 and beyond, even if a price bottom is reached fairly soon as a result of the counties’ relative affordability. However, sales will not return to boom levels.

Risks to this outlook are plentiful and weigh on the downside. An extended reces-sion and more layoffs than expected would trigger additional foreclosures, driving the median house price below $150,000 as the supply of foreclosed homes grows even larg-er. Personal income in the Inland Empire is already among the lowest in Southern California. A deeper recession and restricted credit will sideline more potential home-buyers. Despite increasing affordability, would-be homeowners in the area will find it difficult to qualify for mortgages if home lending standards remain tight.

2002 2003 2004 2005 2006 2007 2008 Indicators 2009 2010 2011 2012 201383.9 89.4 97.5 104.3 109.7 113.5 113.6 Gross Metro Product, C$B 113.9 117.1 122.1 128.0 132.16.0 6.6 9.0 7.0 5.2 3.4 0.1 % Change 0.2 2.8 4.3 4.8 3.3

1,064.6 1,099.2 1,160.0 1,222.0 1,267.7 1,269.4 1,244.6 Total Employment (000) 1,216.1 1,226.9 1,262.6 1,306.3 1,347.23.4 3.2 5.5 5.3 3.7 0.1 -2.0 % Change -2.3 0.9 2.9 3.5 3.16.3 6.4 5.9 5.3 4.9 5.9 8.3 Unemployment Rate 10.9 11.8 10.3 8.4 7.34.7 6.3 8.1 7.2 7.9 5.0 1.9 Personal Income Growth 0.3 2.9 4.7 5.6 5.3

3,486.0 3,615.5 3,750.8 3,869.1 3,980.5 4,061.8 4,149.8 Population (000) 4,239.2 4,332.2 4,427.3 4,523.7 4,621.330,038 35,965 43,142 45,485 33,498 16,019 5,579 Single-Family Permits 1,767 7,242 18,825 24,339 23,5592,436 6,287 8,321 5,523 4,591 4,067 3,153 Multifamily Permits 2,040 2,258 3,697 3,727 3,53114.2 20.3 32.8 23.4 10.8 -13.1 -36.4 Case-Shiller House Price Growth -29.1 -3.8 4.5 4.2 4.6





Phoenix, AZ

teriorated to the point that the current forecast may underestimate the severity. Credit availability has been tightened as banks and investors become extremely risk-averse. Phoenix may suffer significant job losses through the end of the year. If the metro area rapidly loses jobs and stricter underwriting standards prevent households from purchasing homes, the

market could continue on a downward spiral beyond 2010.

The long-term outlook is much more optimistic. Phoenix has a relatively diverse economy and business cost advantages relative to California. Technology, defense and finance are three industries that have positive long-term prospects with roots in Phoenix. The metro area should continue

to attract businesses from its neighbor and provide the job growth necessary to drive strong in-migration, which has supported the housing market in the past. The popu-lation is relatively young and has access to a solid university system in the state. The Phoenix housing market will emerge from the current crisis primed for solid long-term growth.

Las Vegas’ sharp housing market cor-rection is decelerating but will continue into the second half of next year. The temporary halt of economic and job growth will be accompanied by substantially lower net in-migration, contributing to sluggish demand for housing, especially given the elimination of relatively easy credit and investor interest in the market.

According to Fiserv, house prices peaked in the first quarter of 2006, and by the third quarter of this year they had fallen by 39%. In response to slack demand and reduced prices, local residential permitting has slowed markedly and is down nearly 80% from peak levels reached in 2005. Al-though permitting is recently showing signs of bottoming out, it is still 30% below levels of one year ago. Housing starts are exhibit-ing a similar pattern.

Housing in Las Vegas has never been highly affordable compared with other parts of the country, and affordability has steadily eroded since the middle of 2003, coincident with a sharp acceleration in house price appreciation. Whereas a fam-ily earning the median income could pay 30% more than the median price for a house at the beginning of 2003, that fam-ily can now afford only 20% more than the median price, and this is with a significant improvement in affordability since the end of 2006. This compares poorly with the national average affordability of 50% above

the median price. Even though affordability is recovering with the decline in prices from peak levels, area homes are still much less affordable than homes nationally. Erosion in affordability resulted in existing-home sales falling substantially below peak levels in the first quarter of 2004. Even with some recent recovery, they were still down 25% in the third quarter of 2008 from their peak.

That existing-home sales are rising is somewhat misleading. Looking at other metrics such as permits, housing starts and pricing, it becomes evident that housing is still headed downward, albeit at a slowing pace. Prices are likely to bottom in the sec-ond quarter of 2010, but at levels over 27% below already-reduced prices, and recovery is still a long way off.

Credit quality is being hit hard in Las Vegas, and delinquency rates that were lower than the national average at the end of 2005 are now much higher. The worsen-ing debt service performance runs across all types of loans but is particularly pro-nounced in first mortgages. The explosion of homebuying was financed to a large de-gree with exotic loans, and the area is highly exposed to more defaults and foreclosures.

Certainly the plunge of housing prices is behind the improvement in affordability. At more than 5%, the much higher than average and still-rising rate of foreclosures is putting more homes on the market at bargain-basement prices. A mortgage delin-

quency rate surpassing 9%, nearly double the national average, suggests this increased burden will persist in the near term. Be-cause available credit through exotic loans has dried up, putting homeownership for previously marginal buyers out of reach, demand will languish even at lower prices.

Persistently weak demand will keep Las Vegas house prices dropping through the second quarter of 2010, with the trough 56% below the recent peak. The housing market will not turn around quickly, as the local economy will suffer through several more quarters of weak performance.

The near-term outlook for Las Vegas is for recession to be sustained by broader na-tional and global economic weakness. Gam-ing revenue will grow substantially slower in the coming quarters as the national reces-sion spreads abroad, putting a damper on vacation spending. The drags from tighter lending standards and the ongoing deterio-ration in credit quality will further weigh on home sales, construction and consumption. The dependence on consumer-driven gam-ing and housing means that recovery can-not get under way until conditions improve across the broad national economy.

Because affordability is low and job growth that will resume next year is unlikely to provide adequate incomes to qualify in the tougher credit environment, expect fur-ther downward adjustment in house prices before the market recovers significantly.

2002 2003 2004 2005 2006 2007 2008 Indicators 2009 2010 2011 2012 2013121.0 127.1 131.2 143.3 154.0 157.4 156.6 Gross Metro Product, C$B 155.4 159.4 165.2 169.8 175.2

2.9 5.0 3.3 9.2 7.4 2.2 -0.5 % Change -0.8 2.6 3.6 2.8 3.21,596.1 1,619.8 1,683.7 1,787.7 1,884.2 1,908.6 1,880.6 Total Employment (000) 1,800.9 1,786.3 1,823.5 1,884.1 1,936.5

-0.1 1.5 3.9 6.2 5.4 1.3 -1.5 % Change -4.2 -0.8 2.1 3.3 2.85.6 5.2 4.4 4.1 3.5 3.3 4.5 Unemployment Rate 6.5 7.7 6.7 5.1 4.33.8 4.7 9.4 10.8 9.5 4.3 2.0 Personal Income Growth 0.3 3.0 5.5 6.7 6.5

3,500.4 3,604.4 3,728.0 3,889.9 4,055.2 4,189.1 4,271.2 Population (000) 4,359.4 4,465.0 4,581.0 4,697.5 4,816.240,002 47,285 57,360 60,926 38,753 28,939 12,024 Single-Family Permits 6,087 10,396 27,585 47,986 46,2007,897 7,575 7,899 8,304 7,998 10,868 6,673 Multifamily Permits 4,651 4,949 7,047 7,318 7,337

4.9 6.4 13.7 41.5 14.2 -8.5 -28.4 Case-Shiller House Price Growth -27.3 -9.8 8.0 10.1 8.948,681 69,601 53,622 93,195 85,197 60,832 32,409 Mortgage Originations ($Mil) 23,169 20,465 19,630 18,778 19,007

74.4 65.5 86.1 120.5 124.2 85.0 37.4 Net Migration (000) 42.4 58.6 67.8 67.0 68.220,282 22,084 22,301 28,023 5,254 7,180 15,352 Personal Bankruptcies 19,811 27,049 25,091 21,409 19,948



Las Vegas, NV

The Central Florida housing market is one of the weakest housing markets in the nation, although it is in line with the aver-age housing market in Florida. The Central Florida housing market is dominated heavily by the Orlando metro area, which contains Lake, Seminole, Orange and Osceola coun-ties. House prices have fallen by 31% in Or-lando and Deltona, and by just 14% in Oc-ala as compared with the statewide average of 33%, according to the Fiserv Case-Shiller house price index. Despite these appreciable price declines, homes in each of these metro areas remain significantly overpriced. While home sales hit bottom at the end of 2007, they remain at very depressed levels, which is a reflection of tenuous housing demand. Homebuilders are in full retreat; both single-family and multifamily construction contin-ues to tumble to new lows.

Investor demand for housing played a key role in the appreciation of Central Florida house prices during the housing boom. Over 33% of all mortgages origi-nated in Ocala and Deltona during 2006 were non-owner occupied, as compared with 28% in Orlando and 15% nationally. Investors boosted housing demand to ar-tificially high levels, adding another layer of cyclicality to the area’s housing mar-ket. Artificially high demand encouraged homebuilders to overbuild many suburban markets. The subsequent erosion in inves-tor activity has weighed on housing de-mand and resulted in excess housing sup-ply. Unscrupulous lending to unqualified borrowers also fueled the housing boom. While lending standards were not as lax in Central Florida as they were in Southeast Florida, the area’s percentage of subprime mortgages originated during the boom is greater than the national average.

The excesses of the housing boom, coupled with a deteriorating economy, have produced the most severe housing

recession in recent memory. Notwith-standing the substantial price declines that the Central Florida housing market has suffered to date, housing markets are still overpriced by approximately 20% on a price-to-income basis and by 40% ac-cording to house price-to-rent ratios. The Moody’s Economy.com housing afford-ability index is 17% below its historical average dating back to 1990. House price declines are uneven across geography. In Orlando, house prices are falling by more in suburban areas. Generally, the closer a house is to Orlando’s urban core, the more resilient it is when value is declining.

Foreclosures are playing a key role in the housing downturn. Foreclosures create a downward spiral in house prices, de-pressing the prices of surrounding homes, rendering those homeowners more suscep-tible to default, which further depresses house prices. Furthermore, foreclosures add to already-bloated housing inventory, forestalling any housing market recovery. The first-mortgage foreclosure rates in Orlando, Deltona and Ocala are 4.6%, 3.7% and 3.7%, respectively. While these foreclosure rates are well-below those of housing markets in Southeast Florida, they are about double the national average. The proximate cause of the most recent wave of foreclosures is the combination of nega-tive equity positions and income disrup-tions. Homeowners losing their jobs who are also in negative equity positions are increasingly defaulting.

The housing market will not recover until excess inventory is absorbed. Accord-ing to the Orlando Association of Realtors, there is currently close to 23 months of supply at the current pace of sales in Or-lando, and months’ supply is still rising. The pace at which the recovery will occur depends on housing supply, which is a function of new construction and foreclo-

sures, and housing demand, which is a function of housing affordability, popula-tion, and the strength of the Central Florida economy. Homebuilders are certainly doing their part to aid a housing recovery, as resi-dential construction is running well below the pace consistent with household formation.

However, the pace of housing de-mand is exceptionally weak as the Central Florida economy is mired in recession. All of the area’s labor markets will contract throughout the year, and unemployment rates will not peak until 2010. Not only is the local economy more reliant on hous-ing-related industries that are contracting, but like Southeast Florida, it also it relies on immigration, which slows during reces-sions. House prices will decline through at least the first quarter of 2010, at which point affordability will have been restored, payrolls will stop contracting, and excess inventory will be worked off. House prices will first bottom in Ocala, as its house price bubble and oversupply were smaller than that of Orlando and Deltona. House prices will not bottom in Deltona and Orlando until the second half of 2010, possibly not bottoming out until the start of 2011 in the case of Orlando. The total declines to the trough in each market will be around 24% for Ocala, 36% for Delto-na, and 44% for Orlando. House prices will not begin to rise meaningfully in any metro area until 2011. The long-term outlook for the Central Florida housing market is much more positive. The area’s strong demographic trends, steady supply of educated workers, and moderate liv-ing costs will stabilize the local economy and allow its housing markets to return to form.

Risks to the Central Florida housing market are weighted to the downside. Pre-dominantly, the recession could be steeper and last longer if adverse feedback loops

2002 2003 2004 2005 2006 2007 2008 Indicators 2009 2010 2011 2012 201353.1 56.4 62.7 68.7 73.0 73.8 73.1 Gross Metro Product, C$B 73.3 76.1 80.2 84.5 88.43.0 6.2 11.2 9.6 6.3 1.1 -1.0 % Change 0.3 3.8 5.4 5.5 4.6

730.9 760.2 812.6 871.6 917.3 926.8 925.0 Total Employment (000) 922.2 943.1 974.0 1,005.2 1,037.30.6 4.0 6.9 7.3 5.2 1.0 -0.2 % Change -0.3 2.3 3.3 3.2 3.25.9 5.4 4.6 4.2 4.2 4.8 6.6 Unemployment Rate 9.1 10.3 9.0 7.2 6.34.7 7.5 13.1 14.5 7.4 5.4 3.2 Personal Income Growth 2.1 6.0 7.2 7.2 7.3






Orlando, FL

The housing markets of Los Angeles and Orange counties remain among the weakest in the nation, although they are average for California. Substantial house price depreciation is clearing these markets. Since their 2006 peak, house prices in the Los Angeles metro division have declined by more than 34%, according to the Fiserv Case-Shiller house price index, while prices in Orange County have fallen by 30%. As a measure of improvement, months of in-ventory have declined substantially since the beginning of 2008, and existing-home

sales in the metro area are rising. However, distress sales are driving the improvement in existing-home sales, and residential con-struction is nearly at a standstill.

Foreclosure sales dominate these markets, a consequence of the easy credit offered during the earlier part of the de-cade. The default rate for first mortgages in both counties has increased sharply since 2006. This trend is evident nationally, but the increase is particularly severe in the Los Angeles metro area, where the default rate—which predicts foreclosures—has

risen by nearly 200 basis points, compared with 100 basis points nationally, accord-ing to data from Equifax and Moody’s Economy.com. Likely half of all existing-home sales are foreclosure sales, transac-tions that occur at a substantial discount. This discounting, while painful, is playing an important role in bringing the region’s housing markets back into balance. Hous-ing is becoming more affordable, attracting investors and first-time homebuyers to the market. Indeed, in Los Angeles, home sales have jumped 22% from the end of

Ocala, FL

intensify. Skittish consumers could further reduce spending, prompting employers to slash payrolls, further reducing spending.

Furthermore, house prices could overshoot the level consistent with affordability should the foreclosure-house price spiral

intensify. Finally, the local economy relies heavily on discretionary tourism that could easily disappoint.


86.1 89.0 95.0 100.6 105.2 105.9 104.0 Total Employment (000) 100.6 101.2 104.8 109.3 113.10.4 3.4 6.7 5.9 4.6 0.6 -1.8 % Change -3.3 0.6 3.6 4.2 3.55.9 5.4 4.6 3.9 3.6 4.6 7.5 Unemployment Rate 10.8 12.0 10.5 8.9 8.22.2 6.9 10.9 11.6 10.2 6.0 3.3 Personal Income Growth -1.4 2.9 6.7 8.0 7.5

271.1 278.9 289.4 301.2 313.6 323.9 330.4 Population (000) 339.6 350.5 362.9 375.6 389.05,384 5,841 5,268 6,542 6,753 2,536 1,130 Single-Family Permits 172 286 1,425 2,092 2,077

777 634 158 911 310 499 19 Multifamily Permits 31 79 198 231 2575.5 7.3 12.0 25.3 22.4 2.5 -11.9 Case-Shiller House Price Growth -19.6 -7.1 6.1 8.2 5.0




908.2 929.2 978.8 1,035.9 1,076.0 1,098.8 1,098.9 Total Employment (000) 1,061.3 1,061.7 1,097.9 1,142.0 1,179.6-0.8 2.3 5.3 5.8 3.9 2.1 0.0 % Change -3.4 0.0 3.4 4.0 3.35.6 5.2 4.4 3.6 3.2 3.8 5.7 Unemployment Rate 8.3 9.4 8.1 6.6 5.94.2 5.8 9.6 10.0 7.9 4.6 2.1 Personal Income Growth -3.1 1.2 5.7 7.0 6.3


7.0 8.4 14.2 33.5 17.6 -6.4 -24.0 Case-Shiller House Price Growth -33.0 -17.1 -1.7 6.1 8.416,401 24,211 19,718 34,401 35,634 25,545 22,277 Mortgage Originations ($Mil) 21,960 21,490 21,393 21,214 20,679


Deltona, FL



458.1 465.6 476.2 485.4 494.8 499.0 506.2 Population (000) 514.3 524.7 536.8 548.7 561.14,131 5,033 4,820 5,068 2,906 1,520 925 Single-Family Permits 552 1,007 1,801 2,115 2,109

809 1,383 1,616 1,816 909 665 353 Multifamily Permits 107 269 675 747 7849.3 14.4 17.6 30.0 13.4 -9.9 -20.2 Case-Shiller House Price Growth -28.4 -11.4 2.3 8.0 9.2





2007, and the inventory of unsold units is down to close to its long-term average of eight months from a high of 18 months. In Orange County, supply conditions have improved even more. Months of inventory have fallen from a high of 31 months to eight. Weak homebuilding is also helping to right the markets. Single-family con-struction in both metro divisions is run-ning at its slowest pace in 28 years.

Although the correction has been substantial thus far, improving affordability and reducing the overhang of inventory, house prices will continue to decline until mid-2010 in Los Angeles and until the fourth quarter of 2009 in Orange County. Affordability is still very low in both coun-ties. Also, the region is in recession, and this will constrain the demand for homes. Employment has been declining at an ac-celerating rate in Los Angeles. The unem-ployment rate is above, and pulling away from, the national average, as jobs have been lost in all major industries except healthcare, education and tourism. Income growth is slowing, and together with the

continued deterioration of household credit quality, is pulling down retail sales. Further, import and export trade—key drivers of the local economy—are decelerating as the global economy heads toward reces-sion. Similarly, Orange County’s downturn has broadened considerably. Layoffs in the area’s key subprime mortgage lending in-dustry triggered its recession. Other indus-tries had buoyed the economy until early 2007, but since then, the soft labor market infected the housing market, and a loss of construction jobs added to the area’s eco-nomic woes. There has been a net loss of almost 40,000 jobs thus far. The local reces-sion will extend through late 2009, when employment losses will begin to moderate.

Weaknesses in the region’s economy will reduce demand for homes, and com-bined with the increase in negative home equity, will increase the incidence of de-fault. Consequently, house prices will fall by some 28% more in Los Angeles and 14% more in Orange County before the bottom is reached. In turn, the housing market correction is draining jobs from

construction and financial services. This downward cycle will break once the inven-tory of homes and affordability reach more normal levels, likely in the second half of this year. The pace of residential construc-tion is stabilizing but will remain largely flat through this year. Longer term, the outlook is solid. Global links, economic diversity, and a skilled workforce will re-store economic growth at a modest pace, and with it, healthy housing markets.

Risks to this outlook are plentiful and weigh to the downside. The region’s economy is more highly exposed to do-mestic consumer spending than most large U.S. metropolitan areas through its tour-ism, entertainment and apparel industries, so there is a deeper risk to its economy should the U.S. recession persist beyond this year. The exposure to more financial services layoffs than expected also poses a downside risk. Finally, given the very weak consumer confidence and the steady news about severe discounting because of distress sales, it is possible that house pric-es will overshoot the already-weak outlook.

Los Angeles, CA

Santa Ana, CA



-1.2 -1.1 0.3 0.7 1.7 0.6 -0.5 % Change -3.1 -0.8 2.4 3.5 1.96.8 7.0 6.5 5.3 4.8 5.0 7.2 Unemployment Rate 10.3 11.7 10.2 8.1 7.12.2 2.9 5.4 5.7 7.6 4.8 3.4 Personal Income Growth 0.6 2.4 4.6 5.7 5.0


148,095 225,249 180,917 188,430 174,537 141,977 99,482 Mortgage Originations ($Mil) 108,570 96,218 90,216 91,116 94,2958.0 -15.2 -39.6 -78.9 -103.0 -106.3 -36.9 Net Migration (000) -24.2 -9.1 6.7 6.2 -0.4



1.9 5.8 7.0 5.6 4.4 -0.2 -1.9 % Change -1.3 2.2 4.3 5.1 3.21,403.6 1,428.9 1,456.7 1,490.9 1,518.9 1,513.3 1,482.6 Total Employment (000) 1,428.4 1,429.0 1,467.7 1,518.9 1,550.3

-0.7 1.8 1.9 2.4 1.9 -0.4 -2.0 % Change -3.7 0.0 2.7 3.5 2.15.0 4.8 4.3 3.8 3.4 3.9 5.3 Unemployment Rate 7.2 8.0 7.1 5.8 5.22.5 5.3 6.9 7.4 7.3 3.6 1.1 Personal Income Growth -0.2 2.6 4.6 5.5 4.9

2,923.9 2,950.0 2,969.1 2,973.6 2,975.3 2,982.6 2,999.4 Population (000) 3,029.6 3,074.0 3,115.8 3,156.9 3,197.16,794 6,108 4,828 4,103 3,744 2,279 1,333 Single-Family Permits 832 843 2,714 3,916 3,5495,002 3,140 4,428 3,040 4,559 5,093 2,084 Multifamily Permits 2,347 3,603 5,249 5,074 4,69014.5 19.3 30.3 15.4 7.1 -7.7 -22.4 Case-Shiller House Price Growth -14.1 2.1 6.9 3.9 4.8

75,401 112,399 76,999 74,485 65,173 50,287 40,355 Mortgage Originations ($Mil) 42,680 37,588 35,556 36,240 37,5942.5 0.1 -6.1 -21.5 -24.2 -20.1 -12.9 Net Migration (000) -0.5 12.5 8.8 7.0 5.1




The correction in San Diego’s housing market is severe by many measures. Construction permits are down by nearly half since 2005 and are nearly at their same low level that followed the late-1980s housing cycle. While house prices in San Diego were among the first to fall—beginning over one year ago—they continue to fall. Prices are down by 7% over the year as of the second quarter according to Fiserv, about equal to Tampa and Phoenix, which are among the weakest housing markets nationwide. This depreciation will only worsen in the coming six to nine months as sales of foreclosed homes further dampen prices. The default rate for mortgages according to Equifax has shifted from well below average to slightly above average over the past year. Inventories are already high: the California Association of Realtors estimate a more than nine months excess supply of detached single-family units in the area.

There is some upside potential, however; as San Diego’s housing market was one of the first to correct, it could also be one of the first to emerge once again, particularly if population growth truly is close to 1% as reported by the state finance department rather than the weaker Census estimates. Because price growth slowed earlier in the cycle and because per household income growth was stronger, San Diego’s house prices did not become as overvalued as other regions in California. While the market

will see a house-price crash, it will be less severe than the rest of southern California and will likely rebound more quickly.

San Diego’s economy has slowed considerably as the housing market has weakened over the past year. There has been some improvement in recent months, but the jobless rate continues to rise and industrial production growth is lackluster, so it is too soon to say that a corner has been turned. The housing market remains weak and household credit quality is slipping quickly. Basic economic drivers of the economy continue to expand, but the housing and credit markets are keeping the economy in check. Related to the slowdown in mortgage lending, the financial services industry has stumbled over the past year, shedding more than 3,000 jobs. Conditions will worsen before they get better as the credit crunch limits subprime lending in coming months.

The performance of San Diego’s basic industries supports the growth that remains in the economy. Defense spending has led to expansion of aerospace manufacturing, in which close to 500 jobs have been created over the past year. Shipbuilding is stable.

Travel and tourism also support the economy. Job growth has been strong over the past year at a rate above its average of the past decade. Hotel occupancy rates are holding steady and room rates continue to rise. Considerable new space under construction will ease some of this

upward pricing pressure, but demand appears to remain strong.

Biotech also maintains ample momentum. Venture capital placements among San Diego biotech firms reached a record high in the first quarter, about double the rate of such local investments over the past three years. While VC placements are volatile, they currently are at about the same level as the last peak in 2001.

Further, international trade through San Diego ports of entry is rising by double-digit rates. This is led by imports, due to a weakening of the Mexican peso since the beginning of this year. Increasingly, however, long delays at border crossings, up to three hours or more, work to limit cross-border trade.

There is some upside potential to the housing market outlook. The volume of new supply corrected early in San Diego, with permits issuance peaking in mid-2005; thus, there is some potential for the supply/demand balance to improve very soon. Further, much of the imbalance is in the multifamily market, so the single-family market could rebalance sooner. The condominium market will take longer to stabilize, however, adding some downside pressure to the rents at the upper end of the market as condos are put on the rental market. Still, the near-term outlook for the San Diego economy is muted due to the severity of the housing market correction and to risks in national demand for travel and tourism as corporate profit growth eases and household balance sheets deteriorate.

San Diego, CA


3.5 4.7 5.7 4.9 3.4 -0.6 -1.5 % Change -1.9 1.7 3.7 4.7 2.91,230.6 1,240.1 1,260.3 1,282.1 1,301.5 1,308.5 1,301.8 Total Employment (000) 1,263.5 1,263.7 1,293.1 1,333.2 1,359.6

1.0 0.8 1.6 1.7 1.5 0.5 -0.5 % Change -2.9 0.0 2.3 3.1 2.05.2 5.2 4.7 4.3 4.0 4.6 6.0 Unemployment Rate 8.1 9.0 8.0 6.5 5.83.8 3.9 8.0 4.7 7.3 5.3 3.6 Personal Income Growth 0.4 2.5 4.3 5.2 4.7

2,904.2 2,927.0 2,931.2 2,933.1 2,937.9 2,960.6 2,984.9 Population (000) 3,006.2 3,037.3 3,070.9 3,114.4 3,166.98,880 9,758 9,122 7,576 4,743 3,422 2,018 Single-Family Permits 876 2,103 5,762 8,275 8,7854,804 8,273 6,465 6,730 4,448 4,013 3,418 Multifamily Permits 3,525 4,322 5,914 5,793 5,40915.3 18.3 28.9 11.6 0.0 -9.4 -24.9 Case-Shiller House Price Growth -23.9 0.6 7.8 6.5 6.7

68,226 100,901 79,658 78,024 62,291 48,866 36,133 Mortgage Originations ($Mil) 38,687 34,560 33,671 34,883 36,31113.7 1.0 -18.1 -23.1 -20.7 -4.8 -2.4 Net Migration (000) -6.4 2.5 3.8 12.8 20.6


Tampa is experiencing a signifi-cant housing correction, on par with the average housing market in Florida. Even though house prices have fallen by 29% from their peak, the Tampa market has proved more resilient than those in

Southeast, Southwest and Central Florida. Nonetheless, the combination of still-overpriced housing and a glut of homes for sale suggests that a recovery is unlikely to materialize in 2009-2010 and may have to wait until the second half of 2011. Reflect-

ing dour homebuilder sentiment, residen-tial permits remain at a record low dating back to 1980. Although home sales have risen since the beginning of 2008, a surge in foreclosure and short sales is driving much of the increase.



Tampa, FL2002 2003 2004 2005 2006 2007 2008 Indicators 2009 2010 2011 2012 2013

82.8 85.5 89.2 94.9 98.3 98.0 96.5 Gross Metro Product, C$B 94.5 96.4 101.1 106.4 110.42.0 3.2 4.4 6.4 3.6 -0.2 -1.5 % Change -2.2 2.1 4.9 5.2 3.8

1,176.2 1,183.3 1,236.4 1,281.5 1,304.7 1,300.8 1,281.3 Total Employment (000) 1,218.9 1,202.0 1,240.4 1,288.7 1,325.2-0.5 0.6 4.5 3.7 1.8 -0.3 -1.5 % Change -4.9 -1.4 3.2 3.9 2.85.6 5.3 4.5 3.8 3.4 4.2 6.2 Unemployment Rate 9.0 10.2 8.8 7.1 6.33.4 3.1 7.9 7.5 8.7 4.2 2.1 Personal Income Growth -3.7 0.4 6.0 7.6 6.7




The seeds of a severe housing reces-sion were sown during the boom years of 2005-2007, when the confluence of speculation, cheap credit, and lax lending standards produced an epic surge in house prices. To be sure, Tampa area lenders were not as unscrupulous as lenders had been in Southeast Florida or investors as prominent as in Southwest Florida. None-theless, subprime lending and speculation in Tampa well exceeded national averages during the housing boom. Both of these factors artificially boosted housing de-mand, exacerbating the local market’s rise and fall.

The excesses of the housing boom, coupled with a deteriorating economy, have produced the most severe housing recession in recent memory. Tampa has the seventh most oversupplied housing market in the nation. Further, despite price declines in excess of 30%, the local market is still overpriced by approximately 10% on a price-to-income basis and by 24% ac-cording to house price-to-rent ratios. The Moody’s Economy.com housing affordabil-ity index is 9% below its historical average dating back to 1990.

Price declines are uneven. Lower-priced homes have declined by 33% from their peak, mid-priced homes by 30%, and prices of upper-tier homes have declined by 26%. This is because more mortgages on lower-priced homes were subprime,

raising the risk of default. The resulting foreclosure reduces the value of the home.

Foreclosures are playing a significant role in the housing downturn. The fore-closure rate in Tampa has surged to 4.52% from 0.24% in the second quarter of 2006. Both the magnitude and the speed of the deterioration in credit conditions are un-precedented. Although the metro area’s foreclosure rate is lower than elsewhere in South Florida, it is still among the highest in the nation. Foreclosures create a down-ward spiral in house prices, depressing the prices of surrounding homes, rendering those homeowners more susceptible to de-fault, which further depresses house prices. Foreclosures also add to already-bloated housing inventory, forestalling recovery.

The Tampa housing market will not stabilize until excess inventory is absorbed. This is a function of both the supply of homes and demand. The pace of resi-dential construction is well below that of trend household formation, ensuring that excess inventory will eventually be worked off. Nonetheless, this will take some time. Moreover, a deepening recession does not bode well for housing demand. The unem-ployment rate has risen to 6.9%, a 15-year high, even as the labor force has ceased to expand. Procyclical industries are hem-orrhaging. Tampa will especially suffer because of its heavy reliance on housing-related industries, which are firmly con-

tracting. The metro area also depends greatly on immigration, which typically declines in times of recession. Heretofore, weakness in the economy has created a self-reinforcing adverse feedback loop in this consumer-driven recession: Tapped-out consumers are reducing spending, which is forcing employers to lay off work-ers, further depressing spending.

House prices will decline through at least the third quarter of 2010, at which point affordability will have been restored, payrolls will have stopped contracting, and excess inventory will have been worked off. In the long term, the metro area’s robust demographic trends and growing promi-nence as a center for international trade will create heady demand for housing and services, allowing the housing market to return to form.

The housing recovery could be under-mined by further declines in demand. The intensification of the adverse feedback loops between spending and hiring and foreclo-sures and house prices could prolong the recession and forestall a recovery. In this scenario, house prices would overshoot af-fordable levels. Moreover, the financial pan-ic could undermine the housing recovery to the extent that it damages consumers’ propensity and wherewithal to purchase homes, triggers an increase in long-term interest rates, or causes more foreclosures via negative equity or a loss of wealth.

Greater Washington’s housing mar-ket, consisting of the Bethesda, MD and Washington, DC metro divisions—the last incorporating Northern Virginia and Prince George’s and Charles counties in Maryland—is in the midst of a correction, and signs point to further deterioration in the near term. House prices in Greater Washington have been trending downward since peaking in the second quarter of 2006. To date, single-

family homes in the metro area have lost 27% of their peak value in Washington compared with the 18% decline in house prices nation-ally, according to Fiserv Case-Shiller’s home price index. Although prices are receding throughout the metro area, they have fallen more in the Washington metro division than in Bethesda. House prices in Bethesda were down only 17% as of the third quarter. Ex-isting-home sales in the region are beginning

to turn upward, but they are still 50% below their peak reached in 2004. An increase in short and foreclosure sales is driving existing-home sales higher. Permitting of new housing units has fallen substantially since late 2005 as builders aggressively reduce construction in tandem with toppling sales. However, an oversupply of housing on the market still ex-ists, and rising foreclosures are adding to the stockpile.



The roots of Greater Washington’s housing boom extend back a decade. De-mand for homes in Greater Washington increased measurably in the late 1990s, fueled by the expansion of federal payrolls and increased spending that boosted em-ployment in nearly every sector of the econ-omy. Demand for housing quickly exceeded the supply, causing house prices to surge in the late 1990s. Price appreciation steadily accelerated until the bubble burst in 2006.

Adding steam to Washington’s housing bubble were abundant credit and speculative construction. Rapid price appreciation eroded affordability, forcing buyers to turn to exotic, nonconforming mortgages—in ample supply before the subprime meltdown—to finance their purchases in 2004 and 2005. Specula-tive purchasing increased along with prices as well; in fact, mortgage originations for non-owner-occupied homes more than doubled from 2001 to 2005. In addition, homebuyers with less than desirable credit took advantage of banks’ relatively loose lending standards in the counties surrounding the District of Columbia, increasing the nonprime share of mortgages outstanding in these areas.

As a consequence of these factors, those counties with the greatest exposure to sub-prime and exotic mortgages are also those chalking up the largest price declines to date.

For instance, while the price of a single-family home in the District of Columbia has fallen by a mere 3%, prices in Loudon, Prince George’s, Frederick and Charles counties have fallen by more than 15% from their respective peaks.

Despite the 21% decline in prices thus far, the market remains overvalued. Al-though the market’s price-to-rent ratio has fallen back in line with its historical norm, house prices have yet to align with in-come; house price growth has outstripped income growth for the better part of this decade. Thus, additional declines are necessary to restore the affordability that existed before the housing boom.

House prices in Greater Washington will fall another 15% to 16% compared with prices in the third quarter of 2008 before reaching bottom at the start of 2010, making for a peak-to-trough decline of about 30% for Bethesda and 38% for Washington. Excess supply is depressing prices. Furthermore, adjustable-rate mort-gage resets are stretching already-strapped homeowners, leading to rising mortgage delinquency in the metro area. Accord-ing to data from Equifax and Moody’s Economy.com, the delinquency rate on first mortgages began to rise rapidly in 2006 and is now above the national aver-age. This rise is directly increasing foreclo-

sure sales, which exacerbate the inventory overhang and depress prices. Moreover, as prices tumble, more investors are walking away from their speculative purchases, fu-eling even further declines in prices.

Greater Washington’s long-term hous-ing outlook remains upbeat because of strong economic fundamentals. The area will emerge from the national recession with only a mild reduction in employment. Federal discretionary spending and hiring are expected to remain intact in the near term, mitigating the effects of reduced con-sumer confidence and mounting job losses in financial services and housing-related employment. Overall, employment will con-tract by only 0.5% later this year, compared with the expected 3% decline nationally. When hiring resumes early next year, hous-ing affordability will be restored, excess inventory will be absorbed, and new-home construction will begin to recover.

Risks to housing’s outlook are mixed. On the downside, a steeper decline in em-ployment, especially in Northern Virginia, would further deteriorate mortgage credit conditions, leading to a greater than expect-ed decline in prices. Upside risks stem from the incoming administration’s economic stimulus plan, which may fuel hiring in ar-eas surrounding the nation’s capital.


553.2 554.0 558.8 569.2 576.2 576.7 579.2 Total Employment (000) 578.4 583.6 598.7 612.5 621.30.6 0.1 0.9 1.9 1.2 0.1 0.4 % Change -0.1 0.9 2.6 2.3 1.43.5 3.4 3.2 3.1 2.9 2.8 3.2 Unemployment Rate 4.2 4.5 3.7 3.1 3.03.3 3.4 7.0 7.2 6.6 6.3 3.4 Personal Income Growth 2.2 3.3 6.0 5.9 5.1

1,114.6 1,125.0 1,132.7 1,140.9 1,147.3 1,155.6 1,171.0 Population (000) 1,183.8 1,198.4 1,212.9 1,225.6 1,237.54,261 3,944 4,094 3,114 2,335 2,411 1,601 Single-Family Permits 629 993 2,538 3,091 2,9022,330 2,321 1,500 2,349 1,996 2,336 705 Multifamily Permits 840 1,007 1,425 1,640 1,56515.3 15.5 20.0 20.0 5.6 -4.7 -11.1 Case-Shiller House Price Growth -12.3 -4.3 3.3 5.7 6.3

26,891 40,179 24,058 27,728 22,169 18,283 15,136 Mortgage Originations ($Mil) 14,076 13,281 15,293 16,655 17,3469.7 2.1 -1.4 -0.9 -3.5 -2.1 5.6 Net Migration (000) 2.7 4.4 4.0 2.1 1.1


Bethesda, MD

Hagerstown, MD2002 2003 2004 2005 2006 2007 2008 Indicators 2009 2010 2011 2012 2013

6.7 6.6 6.8 7.1 7.3 7.4 7.4 Gross Metro Product, C$B 7.5 7.8 8.1 8.3 8.46.4 -0.0 2.6 4.0 3.2 0.9 0.4 % Change 1.6 3.2 3.8 2.6 2.0

96.8 97.0 97.9 100.7 102.6 102.2 102.6 Total Employment (000) 102.4 103.0 105.4 107.7 109.10.6 0.2 1.0 2.9 1.9 -0.4 0.4 % Change -0.1 0.5 2.4 2.2 1.34.8 4.8 4.4 4.2 4.2 4.4 5.2 Unemployment Rate 6.8 7.2 6.0 4.9 4.74.8 3.8 6.7 7.0 6.8 6.3 5.1 Personal Income Growth 1.3 2.1 4.7 4.3 3.5


347 196 352 567 360 201 233 Multifamily Permits 86 81 146 166 1517.5 8.8 17.7 22.2 14.0 0.6 -5.5 Case-Shiller House Price Growth -9.6 -4.4 1.1 2.2 1.0


1,462 1,599 1,522 1,774 389 688 963 Personal Bankruptcies 1,162 1,716 1,677 1,377 1,281



Washington, D.C.


1.9 4.9 5.5 4.4 2.3 3.2 1.9 % Change 0.8 2.2 3.8 4.3 3.22,175.3 2,230.6 2,296.1 2,348.3 2,390.8 2,413.2 2,441.6 Total Employment (000) 2,425.6 2,431.7 2,488.0 2,561.8 2,615.1

0.3 2.5 2.9 2.3 1.8 0.9 1.2 % Change -0.7 0.3 2.3 3.0 2.14.2 4.0 3.8 3.6 3.1 3.1 3.9 Unemployment Rate 5.6 6.5 5.4 4.1 3.52.5 4.1 7.6 8.2 6.5 5.1 4.0 Personal Income Growth 1.4 2.8 5.3 6.3 5.4


78,617 123,120 91,806 112,745 94,644 71,532 59,773 Mortgage Originations ($Mil) 46,054 32,076 30,932 34,221 35,15427.2 15.6 20.6 15.0 -8.7 -2.5 15.0 Net Migration (000) 15.8 17.4 14.6 11.2 9.3


The Detroit’s metro area’s eight-year-long recession and a high concentration of subprime loans have combined to produce one of the weakest housing markets in the nation. Unlike other troubled hous-ing markets in the nation, Detroit did not experience a house price bubble earlier in the decade, but has nonetheless expe-rienced the bust. Since their 2005 peak, house prices in the metro area have de-clined by an average of 30%, according to the Fiserv Case-Shiller house price index. Price declines have been equally sharp in both divisions of the metro area, Detroit and Warren-Farmington Hills.

Price declines are explained in part by a large share of foreclosure sales, which helped boost home sales in the area. Price declines have also boosted affordability; a family earning the median income can afford to purchase a home costing more than twice the median-priced home. Single-family permitting stabilized in 2008 but is at its lowest level since the early 1980s and is down about 90% from the 2004 peak. Demand for homes remains very weak nonetheless, and an inventory overhang persists.

Sales increased by 17% through Octo-ber 2008 compared with the same period in 2007, according to the Michigan Asso-ciation of Realtors, while the average sales price fell by 28% to $113,000. In the City of Detroit, sales increased by 47%, but the average sales price was down 54% to only $18,500. Foreclosure sales explain this sig-nificant increase. Elsewhere in the metro area, sales were also higher. Only in Lapeer County were sales much lower in 2008.

Poor household credit quality, which had been damaged further by the persis-tently poor economy, had made Detroit households especially attracted to sub-prime lending early in the decade. Nearly one-half of all home sales in the Detroit metro division (Wayne County) were fi-nanced through subprime lending at the peak of the subprime lending craze. The share was much lower than average in the suburban and wealthier Warren-Farming-ton Hills division, however.

Both parts of the metro area have seen large and steady employment losses since 2000, as the Detroit area has borne the brunt of motor vehicle industry restructur-ing and the deep contraction in construc-tion- related industries since the middle of the decade. Total employment is down nearly 20% in the Detroit metro division and by 13% in Warren-Farmington Hills. About 330,000 jobs have been lost in the whole metro area, and losses have been broad-based across industries. This has contributed to a loss of 7% in real income per capita. The protracted downturn has also led to a net loss of 173,000 residents.

As a result, the default rate on first mortgages in both metro divisions has been increasing since 2005, much sooner than it did nationwide; the default rate in the Detroit metro division is much higher than in the Warren-Farmington Hills divi-sion. In the third quarter of 2008, 2.2% of first mortgages in the Detroit division were in default, compared with 1.9% nationally. This is far lower than in some other trou-bled housing markets, but since the weak-ness began much sooner, the cumulative

share of mortgages that defaulted is high. The default rate has been rising by an aver-age of 40% annually since 2005; nationally foreclosures escalated only during the past two years.

Plunging vehicle sales over the past year have accelerated the restructuring in the motor vehicle industry, and with sales expected to remain depressed in 2009, the metro area’s economy is expected to shed another 100,000 jobs this year. The housing market will continue to struggle as a result. House prices are expected to bottom out at the start of 2010, while per-mitting will hit bottom slightly sooner. The expected decline in house prices will be modest, however, with a loss of 8% in the Detroit division and a smaller 6% loss in the Warren division.

Risks to the outlook are stacked to the downside, given the precarious position of domestic automakers and the possibil-ity that the government bailout will not prevent bankruptcy. If one or more of De-troit’s automakers were to fail, the losses for the area would be considerably higher than the baseline expectation, especially because of the large multiplier in the auto industry. Losses could be twice as high as the baseline expectation.

Since the metro area has had little success diversifying its economy away from dependence on the motor vehicle industry, the long-term outlook is bleak. The metro area will never regain all the jobs or resi-dents lost in recent years. Therefore, it will take several decades before new construc-tion and house prices return to the levels of the early part of this decade.



The Bay Area housing market is one of marked contrasts. Those markets that entered into a housing crisis early such as Vallejo and Oakland were hurt by increas-ing foreclosures and dropping prices in 2008. More affluent markets that were late to enter the downturn—such as San Francisco and San Jose—were less affected last year. House prices throughout the area continued to fall from their peaks reached from 2005 to 2007. The largest drop has been in Vallejo, where the CSI house price dropped by 47% from 2005 to 2008. Further, price depreciation ac-celerated in Vallejo in 2009. At the other end of the regional spectrum, the San Francisco metro division—whose home values had been growing as recently as the third quarter of 2007—saw its house price index drop year over year by 16% from its peak.

Mortgage credit conditions continued to weaken in the hardest-hit markets in the Bay Area. Delinquencies and foreclosures increased in Vallejo in 2008, pushing the number of fore-closure sales to more than 50% of total sales. The Bay Area’s more affluent sections experi-enced large increases last year in the value and percentage of distressed mortgages relative to all mortgages—more than a 400% increase in San Francisco and San Jose. However, these spikes started from a very small base. Foreclo-sure sales in San Francisco County were less than 8% of the third quarter sales.

Except in San Jose, Bay Area hous-ing affordability began increasing in 2006 before or right after prices peaked. To date, the biggest increase has occurred in Vallejo, where affordability doubled from its low point in 2006 to the third quarter of 2008. The increases in San Francisco and San Jose were relatively moderate from their recent troughs, approaching 40% at the end of 2008. Historical data reflect that all Bay Area markets have overshot their lower limit price bounds—by as much as 30% in Vallejo. This suggests that house prices throughout the Bay Area will begin to appreciate once the regional and national economies begin to recover.

Although the performance of the Bay Area housing market has varied widely in recent years, the region’s economic fun-damentals are largely interconnected. Job losses in the region picked up in 2008 as even the strong economies of San Francisco and San Jose began faltering. Smaller areas, including Sonoma and Vallejo, continued to eliminate construction jobs. The larger job centers began losing white-collar positions in law and banking as business regionally and nationally slowed. At the end of the year, several high-technology manufacturers and research and development centers announced layoffs and suspended activity as national and global demand for computer equipment

slowed. Throughout the Bay Area, retail companies eliminated positions before the holiday shopping season as consumers reined in their spending. Layoffs in the region’s job centers are felt throughout the entire Bay Area because of commuting linkages, despite vast distances and geographic obstacles such as the San Francisco Bay. Unemployment in all Bay Area sections rose in 2008, with Vallejo passing the 8% rate at the high end and San Francisco approaching 6% by the end of the year on the low end.

An apparent overshoot in historical house prices puts the Bay Area real estate market in a position to recover relatively quickly if the recession begins to fade by the second half of 2009. The momentum of job cuts will increase delinquencies and foreclosures. This will depress prices in all Bay Area markets through 2009—and into the second half of 2010 for Vallejo—before prices can recover appreciably. Dropping prices and increasing affordability will drive sales throughout the area, especially in areas such as Vallejo with large inventories of fore-closed homes.

As in the rest of California, the risks to the forecast are weighted toward a severe and deep recession at the end of 2008. Business and consumer confidence could have continued to worsen, and the Bay Ar-ea’s overseas markets could have slowed or

Detroit, MI

Warren, MI

2002 2003 2004 2005 2006 2007 2008 Indicators 2009 2010 2011 2012 201363.6 64.4 63.8 63.6 61.9 61.3 59.8 Gross Metro Product, C$B 56.1 55.1 56.8 59.5 60.80.8 1.3 -1.0 -0.3 -2.6 -1.1 -2.4 % Change -6.1 -1.9 3.1 4.8 2.2

856.5 839.9 829.1 821.8 805.3 784.1 755.4 Total Employment (000) 705.6 685.4 695.2 714.7 722.2-3.7 -1.9 -1.3 -0.9 -2.0 -2.6 -3.7 % Change -6.6 -2.9 1.4 2.8 1.07.3 8.5 8.6 8.6 8.4 8.8 9.9 Unemployment Rate 13.0 14.7 12.9 10.5 9.2

-0.6 3.0 -0.1 1.1 1.0 3.4 1.1 Personal Income Growth -3.1 -0.6 2.7 4.0 3.42,052.5 2,045.0 2,035.9 2,024.3 2,008.7 1,980.8 1,958.0 Population (000) 1,945.9 1,934.3 1,922.0 1,912.0 1,903.2

3,388 3,729 4,211 3,138 2,056 919 588 Single-Family Permits 494 897 1,437 1,891 1,7251,454 1,909 2,106 1,436 378 176 216 Multifamily Permits 179 360 697 744 708

3.8 4.6 2.6 2.8 -0.5 -10.0 -19.7 Case-Shiller House Price Growth -9.9 -0.5 4.5 6.0 6.117,204 23,288 13,812 12,638 9,843 7,151 3,731 Mortgage Originations ($Mil) 3,817 3,088 2,952 3,192 3,413

-23.1 -24.8 -25.7 -27.7 -31.2 -36.2 -31.7 Net Migration (000) -21.1 -20.4 -21.1 -18.7 -17.416,367 18,325 18,852 25,007 10,532 12,808 18,977 Personal Bankruptcies 23,086 31,235 31,396 28,054 27,227


3.6 1.9 -1.8 0.8 -2.4 -1.2 -1.1 % Change -3.1 0.5 3.8 4.5 2.41,233.3 1,227.3 1,218.8 1,224.5 1,197.9 1,179.9 1,156.9 Total Employment (000) 1,100.3 1,094.8 1,120.4 1,150.3 1,166.0

-1.3 -0.5 -0.7 0.5 -2.2 -1.5 -2.0 % Change -4.9 -0.5 2.3 2.7 1.45.7 6.2 6.2 6.3 6.4 6.9 7.8 Unemployment Rate 10.0 11.3 10.1 8.5 7.71.7 3.1 1.0 3.1 1.5 3.9 1.8 Personal Income Growth -2.0 1.3 3.5 4.3 3.7

2,433.0 2,448.2 2,464.1 2,473.1 2,478.0 2,477.1 2,476.4 Population (000) 2,482.6 2,492.1 2,504.1 2,513.4 2,524.611,820 12,605 13,188 10,493 5,412 2,914 1,413 Single-Family Permits 1,790 4,177 7,512 8,998 8,6002,372 1,657 2,303 1,325 1,074 316 329 Multifamily Permits 528 1,010 2,175 2,383 2,290

3.7 3.3 3.4 2.7 -3.4 -9.6 -16.6 Case-Shiller House Price Growth -8.4 0.4 5.3 6.4 6.346,014 62,833 31,757 27,282 20,296 16,923 7,499 Mortgage Originations ($Mil) 5,727 3,177 2,706 3,080 3,454

2.8 6.0 5.6 0.2 -4.5 -11.8 -11.1 Net Migration (000) -4.2 -1.1 1.5 -1.3 0.711,640 13,839 14,114 20,310 8,023 12,083 16,986 Personal Bankruptcies 19,791 26,979 27,549 25,131 24,987



San Jose, CA

Oakland, CA

San Francisco, CA

Napa, CA

entered into recession. Under this scenario, job cuts and reduced business activity would have hurt the region’s real estate market more severely. Despite historical signals of a

lower price limit, prices would drop further into the first half of 2010 as unemployment increases and credit quality deteriorates be-yond current levels. The loss of high-paying

white-collar jobs would hurt housing mar-kets in the affluent areas of the region that have been spared the worst of the housing downturn of the past two years.

2002 2003 2004 2005 2006 2007 2008 Indicators 2009 2010 2011 2012 20135.3 5.3 5.4 5.6 5.7 5.6 5.5 Gross Metro Product, C$B 5.4 5.5 5.6 5.9 6.01.1 -0.1 1.0 3.9 1.7 -1.1 -1.3 % Change -2.3 0.9 3.2 4.0 2.2

60.6 60.4 60.9 61.6 62.7 63.5 63.0 Total Employment (000) 61.1 60.8 61.6 62.9 63.6-0.7 -0.3 0.8 1.2 1.8 1.2 -0.8 % Change -3.1 -0.5 1.4 2.1 1.14.6 4.9 4.8 4.4 3.9 4.1 5.3 Unemployment Rate 7.7 8.4 7.1 5.5 4.81.9 3.2 8.2 7.3 7.3 5.8 3.0 Personal Income Growth 0.3 2.4 4.1 5.0 4.5

128.7 130.0 130.2 130.1 131.0 131.9 132.8 Population (000) 133.0 133.7 134.2 134.6 135.0610 568 640 628 267 272 195 Single-Family Permits 89 274 482 603 595393 39 277 59 240 58 15 Multifamily Permits 89 202 229 262 260


1.7 1.1 -0.0 -0.3 0.3 0.4 0.5 Net Migration (000) -0.3 0.2 -0.1 -0.2 -0.2255 232 238 380 83 132 196 Personal Bankruptcies 241 367 349 270 239


1,039.8 1,025.5 1,023.6 1,032.1 1,045.4 1,047.6 1,032.6 Total Employment (000) 996.7 990.4 1,014.1 1,048.1 1,067.8-1.4 -1.4 -0.2 0.8 1.3 0.2 -1.4 % Change -3.5 -0.6 2.4 3.4 1.96.3 6.6 5.7 5.0 4.4 4.8 6.3 Unemployment Rate 8.2 8.8 7.7 6.2 5.5

-1.0 2.0 6.3 5.0 7.1 5.0 2.4 Personal Income Growth 0.0 2.4 4.5 5.4 4.82,444.6 2,443.9 2,441.4 2,444.6 2,452.3 2,471.9 2,497.4 Population (000) 2,523.3 2,551.6 2,578.7 2,606.1 2,632.8

7,572 7,171 6,654 7,074 5,003 4,068 1,694 Single-Family Permits 1,840 4,352 7,297 8,715 8,3721,747 4,181 4,312 3,766 5,495 2,770 2,763 Multifamily Permits 2,264 2,149 3,082 3,082 2,926


-15.2 -18.3 -18.4 -12.9 -9.5 1.2 5.4 Net Migration (000) 5.0 6.4 4.3 3.8 2.36,770 7,318 6,794 9,196 2,485 4,382 5,784 Personal Bankruptcies 6,913 9,950 9,549 7,609 6,817


-4.1 -0.9 3.9 2.6 3.8 3.6 0.0 % Change -1.6 2.4 5.5 6.9 4.4987.2 950.8 939.4 945.9 964.4 987.0 996.7 Total Employment (000) 958.9 950.1 975.3 1,013.5 1,036.0

-6.3 -3.7 -1.2 0.7 2.0 2.3 1.0 % Change -3.8 -0.9 2.7 3.9 2.26.2 6.1 5.2 4.6 3.9 4.0 5.0 Unemployment Rate 6.7 7.5 6.5 5.2 4.5

-5.4 -0.7 7.9 10.4 9.5 6.0 4.5 Personal Income Growth 0.6 2.6 4.6 5.5 4.71,714.3 1,699.6 1,689.5 1,689.9 1,697.4 1,711.8 1,724.2 Population (000) 1,738.0 1,752.3 1,765.9 1,779.7 1,793.1

1,082 1,512 1,332 1,028 897 987 573 Single-Family Permits 393 666 1,224 1,507 1,4552,140 1,964 2,941 3,015 3,088 2,735 3,226 Multifamily Permits 2,609 2,123 3,004 3,048 2,934

4.6 5.1 15.8 15.4 4.5 0.4 -13.1 Case-Shiller House Price Growth -12.9 3.1 6.9 4.5 3.556,570 75,708 49,161 45,812 38,653 38,059 27,770 Mortgage Originations ($Mil) 32,004 26,911 24,965 25,111 26,070

-27.2 -23.8 -17.1 -7.6 -0.3 6.2 3.6 Net Migration (000) 4.3 4.4 3.2 2.9 2.23,906 4,129 3,968 5,976 1,346 1,841 2,667 Personal Bankruptcies 3,322 5,095 4,840 3,721 3,276

2002 2003 2004 2005 2006 2007 2008 Indicators 2009 2010 2011 2012 201392.9 92.5 95.7 105.1 112.7 116.0 115.9 Gross Metro Product, C$B 116.6 120.4 126.2 133.3 138.4-8.0 -0.5 3.5 9.9 7.2 2.9 -0.1 % Change 0.6 3.3 4.8 5.7 3.8

917.2 870.2 862.0 869.9 891.1 908.1 909.9 Total Employment (000) 883.3 881.6 903.3 932.7 949.5-9.9 -5.1 -1.0 0.9 2.4 1.9 0.2 % Change -2.9 -0.2 2.5 3.3 1.88.4 8.4 6.5 5.4 4.6 4.8 6.2 Unemployment Rate 8.4 9.2 8.1 6.6 5.9

-7.4 0.8 4.8 7.2 9.8 6.4 4.4 Personal Income Growth 0.4 2.4 4.3 5.2 4.51,733.2 1,730.3 1,733.3 1,748.6 1,769.2 1,795.0 1,817.3 Population (000) 1,833.9 1,853.1 1,871.0 1,888.7 1,906.0

2,239 2,565 2,787 2,416 2,151 1,955 942 Single-Family Permits 702 825 1,872 2,476 2,2992,382 4,538 2,705 3,320 3,999 2,239 2,148 Multifamily Permits 2,321 2,851 4,053 3,937 3,646


-33.7 -22.5 -14.0 -1.8 2.6 7.3 2.0 Net Migration (000) -4.6 -2.6 -4.6 -5.4 -6.44,536 5,245 5,145 6,286 1,578 2,374 3,090 Personal Bankruptcies 3,771 5,620 5,368 4,198 3,732



House price declines in the New York City metropolitan area have so far been tame compared with those in the rest of the nation. The New York area is expected to experience smaller peak-to-trough de-clines through the end of the year. In the Edison and Newark NJ division and the Nassau-Suffolk NY and New York City division, single-family house prices have declined 10% to 11% from their peaks in early 2006. This is about half of the national decline to date, although the cor-rections are less than halfway through. The condominium market, which makes up a significant share of New York City’s hous-ing stock, has seen no declines in prices yet, although the ongoing financial market turmoil and deepening recession all but ensure that this stability will not last.

Single-family home sales continue to decline and are down nearly 60% from their early 2005 peak. Inventory levels remain high, and there is no evidence that the supply of housing has peaked yet. In the condo market, which is concentrated in Manhattan, sales have been falling since the third quarter of 2007, when the credit and financial crises began, but prices are holding up, according to data from Miller Samuel. Listing inventory of condos and co-ops is up by nearly one-third from a

year ago. Builders are attempting to bring supply back in line with demand; mul-tifamily permitting in the New York City metro division is at its lowest level since the start of the decade. In Edison and Nassau-Suffolk, building intentions are at all-time lows.

As New York City’s economy has dete-riorated, so too has credit quality, spurring defaults and foreclosures. The record low levels of housing affordability and soar-ing price appreciation at the peak of the market spurred overzealous lending. The whole metro area had an average share of subprime mortgage originations in 2006, though the prevalence of subprime lend-ing varied greatly throughout the area. For instance, in the Edison metropolitan division, where one in five mortgages origi-nated in 2006 was subprime, credit quality has deteriorated the least as measured by mortgage delinquency and default rates. In Nassau-Suffolk where nearly 30% of loans were subprime, mortgage credit quality is among the worst in the nation.

Although the financial and credit market crisis had its origins on Wall Street, New York City was one of the last large metropolitan areas in the nation to enter recession. The contraction will be severe and will last through the first half

of 2010 as thousands of high-paying jobs in the financial services industry are lost. More importantly, the loss of wages as a result of the mounting layoffs and reduced bonus payments, which in good times play a large role in supporting real estate prices, will be devastating to the economy and real estate market. Because house prices have not fallen nearly as far in the New York metro area compared with other highly overpriced areas, affordability is a long way from being restored. After falling to a record low in mid-2006, affordability has increased 12%—a far cry from the 32% increase needed to return the area its long-run equilibrium.

As well-paying jobs are lost at a faster pace throughout the metro area, house prices will decline more severely. Single-family prices will fall another 16% to 18% for the New Jersey divisions and about 24% for the New York divisions through the end of the year for an average peak-to-trough decline of about 30%. The dif-ference in price movements is no surprise considering that both New York City and Nassau-Suffolk are highly overpriced in price-income terms whereas the New Jersey divisions are only moderately over-priced. Throughout the metro area, house prices are expected to overshoot their long-run equilibrium levels.

Vallejo, CA

Santa Rosa, CA2002 2003 2004 2005 2006 2007 2008 Indicators 2009 2010 2011 2012 2013

16.4 16.5 17.1 17.5 17.9 18.3 18.1 Gross Metro Product, C$B 17.9 18.4 19.2 20.2 20.80.4 0.6 3.5 2.6 2.4 2.0 -0.8 % Change -1.2 2.6 4.5 5.0 3.1

186.6 183.2 184.9 186.4 189.1 190.4 190.7 Total Employment (000) 186.8 188.8 194.6 201.5 206.1-1.7 -1.8 0.9 0.8 1.4 0.7 0.1 % Change -2.0 1.1 3.1 3.6 2.35.1 5.5 5.0 4.5 4.0 4.4 5.7 Unemployment Rate 7.4 8.0 7.0 5.7 5.0

-0.0 1.7 4.6 4.4 7.3 5.8 3.6 Personal Income Growth 0.7 3.1 4.8 5.7 5.1463.9 464.2 463.8 462.3 460.9 462.2 465.7 Population (000) 469.3 477.2 485.3 494.3 503.31,350 1,503 1,342 1,598 1,357 910 546 Single-Family Permits 230 695 1,550 1,959 1,869

578 749 587 1,221 555 521 181 Multifamily Permits 313 407 572 567 5366.8 11.9 18.4 17.5 0.3 -8.3 -28.9 Case-Shiller House Price Growth -29.8 -0.5 11.8 8.2 3.4

11,857 17,077 11,619 12,695 10,041 7,541 4,570 Mortgage Originations ($Mil) 4,702 3,689 3,396 3,596 3,884-2.3 -1.3 -2.0 -3.3 -3.2 -1.0 1.4 Net Migration (000) 1.5 5.5 5.6 6.4 6.2




407.9 408.5 408.6 407.1 406.9 406.6 407.2 Population (000) 407.9 408.9 410.1 411.4 412.71,806 2,050 2,386 1,962 1,002 866 253 Single-Family Permits 36 214 733 1,236 1,377


8,538 12,913 10,671 13,276 10,298 6,418 7,503 Mortgage Originations ($Mil) 7,474 6,964 6,829 6,991 7,2361.1 -2.2 -2.7 -4.2 -3.1 -3.4 -2.8 Net Migration (000) -2.8 -2.6 -2.6 -2.6 -2.6




Nassau, NY

Newark, NJ

New York, NY

Edison, NJ

Risks to the forecasts for the New York metro area’s housing markets are weighted heavily to the downside in light of the ongoing crisis in financial markets and the restructur-ing of the finance industry headquartered in

New York City. The forecast calls for job losses in financial services through the first half of 2010—two quarters later than the national recovery. If the loss of jobs is more severe and prolonged than expected, house prices could

fall further in New York City and Nassau-Suffolk. Newark’s high rate of foreclosures will continue to add supply to an already-oversup-plied market and could cause house prices to overshoot more than expected.


997.1 1,002.2 1,008.8 1,013.8 1,031.4 1,041.8 1,037.4 Total Employment (000) 1,019.7 1,024.3 1,044.6 1,071.8 1,092.50.5 0.5 0.7 0.5 1.7 1.0 -0.4 % Change -1.7 0.5 2.0 2.6 1.95.4 5.4 4.5 4.2 4.3 3.8 5.0 Unemployment Rate 7.3 8.2 7.0 5.5 4.71.3 2.0 5.5 4.2 7.7 5.8 2.6 Personal Income Growth -0.8 3.8 6.1 6.0 5.2

2,240.2 2,262.7 2,280.4 2,294.4 2,299.4 2,310.9 2,328.9 Population (000) 2,347.2 2,371.4 2,393.1 2,409.5 2,425.18,081 8,071 8,175 7,267 5,396 4,145 2,795 Single-Family Permits 2,157 2,989 4,011 6,193 6,3511,354 2,260 2,255 2,647 3,163 2,590 2,302 Multifamily Permits 1,535 1,333 1,149 943 92414.1 14.0 14.7 15.1 5.0 -4.3 -6.5 Case-Shiller House Price Growth -11.4 -7.8 2.2 5.7 5.8

57,675 50,077 30,805 35,591 31,472 27,670 18,007 Mortgage Originations ($Mil) 17,759 16,953 17,807 18,225 18,28619.5 10.9 5.8 3.0 -4.4 0.6 6.1 Net Migration (000) 6.0 11.5 8.5 2.9 1.6




-0.2 0.6 0.9 0.5 0.7 1.2 0.4 % Change -2.3 -0.1 1.3 2.7 2.04.7 4.8 4.6 4.2 3.9 3.7 4.9 Unemployment Rate 7.3 8.1 7.0 5.5 4.9

-0.2 1.9 6.5 7.1 6.9 6.2 3.3 Personal Income Growth -3.3 1.2 3.4 5.2 4.82,789.4 2,796.2 2,800.2 2,793.3 2,781.8 2,778.6 2,782.4 Population (000) 2,787.4 2,795.3 2,802.9 2,810.4 2,817.6

4,221 3,284 3,675 5,438 3,701 2,767 1,993 Single-Family Permits 906 1,405 2,825 4,329 4,1651,148 911 899 1,180 324 181 1,271 Multifamily Permits 435 415 573 609 59515.0 16.9 13.3 13.7 6.4 -3.1 -6.9 Case-Shiller House Price Growth -12.5 -11.1 0.9 3.8 3.7

35,703 54,502 37,314 39,720 37,259 30,490 14,702 Mortgage Originations ($Mil) 15,171 11,912 11,029 11,936 12,873-0.9 -4.0 -11.9 -22.9 -25.2 -22.0 -9.7 Net Migration (000) -9.0 -6.6 -7.4 -8.1 -8.8


2002 2003 2004 2005 2006 2007 2008 Indicators 2009 2010 2011 2012 201396.1 98.8 100.5 99.7 102.1 103.6 105.2 Gross Metro Product, C$B 105.7 108.3 113.0 118.5 122.3-0.2 2.8 1.7 -0.7 2.4 1.4 1.5 % Change 0.5 2.4 4.4 4.9 3.2

1,025.3 1,033.8 1,031.4 1,026.3 1,032.6 1,036.3 1,033.0 Total Employment (000) 1,009.6 1,006.8 1,026.3 1,055.3 1,075.4-0.0 0.8 -0.2 -0.5 0.6 0.4 -0.3 % Change -2.3 -0.3 1.9 2.8 1.96.0 6.0 5.0 4.5 4.7 4.3 5.5 Unemployment Rate 8.0 9.0 7.6 5.9 5.20.4 2.1 6.2 3.9 7.5 5.5 2.5 Personal Income Growth -1.2 3.0 5.7 5.9 5.2


30,190 40,934 25,444 29,694 28,391 24,634 16,095 Mortgage Originations ($Mil) 16,714 16,225 17,136 18,269 18,838-0.6 -6.3 -10.5 -13.1 -12.6 -13.2 -5.4 Net Migration (000) -5.3 -4.8 -5.7 -8.1 -8.7



-2.2 2.2 2.9 4.7 6.7 4.8 1.2 % Change -3.1 -0.0 2.8 5.1 5.05,035.9 4,992.0 5,016.0 5,070.0 5,139.9 5,232.1 5,258.8 Total Employment (000) 5,098.1 5,062.5 5,136.8 5,265.0 5,365.4

-2.5 -0.9 0.5 1.1 1.4 1.8 0.5 % Change -3.1 -0.7 1.5 2.5 1.97.3 7.4 6.4 5.3 4.8 4.7 5.4 Unemployment Rate 8.8 10.1 8.3 6.2 5.1

-0.8 1.6 7.0 7.2 8.2 6.9 4.0 Personal Income Growth -3.0 1.4 5.1 6.6 6.311,435.3 11,485.0 11,536.3 11,576.3 11,615.2 11,666.0 11,710.5 Population (000) 11,739.4 11,770.7 11,802.6 11,825.4 11,836.3

4,599 4,508 4,053 4,340 3,532 2,905 2,123 Single-Family Permits 818 2,071 3,045 4,124 3,93820,926 23,534 30,335 37,219 36,366 38,402 33,043 Multifamily Permits 8,357 12,043 20,523 22,521 22,736





Chicago’s housing market is in the midst of a correction, but houses remain overpriced. The Chicago metro division’s housing market held up longer than many others, with prices peaking at the begin-ning of 2007, a year after the national peak. Since then, home prices have fallen by 13%, according to the Fiserv Case-Shiller house price index. This is about half of the national decline. The correction has been even smaller in the Lake County metro division, only 3%.

Downward pressure on prices has persisted as house sales have declined pre-cipitously. Through November, sales in the Chicago and Lake County metro divisions had declined by 27%, and the pace of de-cline has accelerated in recent months in part because of the credit crunch.

Homebuilding has yet to stabilize. Through November, single-family permit-ting has fallen by more than half compared with the same period in 2007 and is down more than 80% from the mid-decade peak. The last time that homebuilding was as low as it was in 2008 was during the early 1980s, when Chicago’s economy experi-enced a severe downturn. The decline in new construction has resulted in construc-tion industry job losses that exceed the na-

tional average. However, the sharp reduc-tion in new homebuilding has also allowed the market to reach a balance, suggesting that the correction was justified by the un-derlying demographics of the area.

Sales of foreclosed properties at sub-stantial discounts provide some support to the Chicago housing market. According to data from Equifax, foreclosure rates are typ-ically higher in the Chicago metro division than they are nationally. Since 2006 they have risen in step with the nation and were about equal to the national rate, at about 1.8% of mortgages outstanding during the third quarter of 2008. The default rate has risen precipitously in the Lake County met-ro division since early 2007, but at 1.3% remains well below the rate in Chicago.

Once credit begins to flow more freely and the broader economy improves, the balanced market should facilitate a recov-ery in construction, while rising afford-ability will revive existing-home sales. A family earning the median income in the Chicago metro division can now afford to purchase a home costing 15% more than the median priced home; in 2006, that hy-pothetical family could only afford a house costing 5% less than the median price. Af-fordability has improved even more in the

wealthier Lake County division. Further, although the market remains overpriced, the median price-to-income ratio is now equal to its long-term average.

Although the Chicago housing market is well-positioned for recovery, economic challenges will determine the speed of the recovery. The two divisions have shed about 50,000 jobs since early 2008, with employment declines accelerating in re-cent months. The unemployment rate has surged by 2 percentage points to about 7%—higher than the national rate. The area is set to lose an additional 127,000 jobs by the end of 2009. Chicago’s economy tracks the national economy very closely, but its downturns tend to be more severe than national downturns. Most of the losses have been concentrat-ed in housing and consumer-dependent industries, including financial services. However, this year the losses will spread to Chicago’s important business/profes-sional and convention/tourism industries. Further, the widening global economic downturn will increasingly dampen Chi-cago’s transportation/distribution and manufacturing industries.

Weaknesses in the metro area’s econ-omy will continue to depress demand for

Chicago, IL


-0.6 2.6 1.4 1.2 2.5 1.5 0.1 % Change -2.2 2.2 5.2 6.0 3.53,799.0 3,756.9 3,754.1 3,790.9 3,843.8 3,874.8 3,870.5 Total Employment (000) 3,775.6 3,803.9 3,921.1 4,065.6 4,152.1

-2.3 -1.1 -0.1 1.0 1.4 0.8 -0.1 % Change -2.5 0.8 3.1 3.7 2.16.9 6.9 6.3 6.0 4.4 4.8 6.6 Unemployment Rate 9.4 10.6 9.3 7.4 6.51.6 2.0 5.0 5.0 6.0 6.8 3.3 Personal Income Growth 0.6 4.0 5.9 6.4 5.3




Lake County, IL


381.0 383.1 386.8 393.2 396.7 399.4 401.1 Total Employment (000) 394.0 397.0 405.7 417.8 426.20.4 0.5 1.0 1.7 0.9 0.7 0.4 % Change -1.8 0.8 2.2 3.0 2.05.6 5.9 5.5 5.0 4.6 5.1 6.4 Unemployment Rate 8.8 9.7 8.6 7.1 6.31.7 2.3 5.1 5.3 7.6 6.5 3.1 Personal Income Growth -0.3 3.1 4.9 6.4 5.6

826.2 833.7 845.0 855.4 864.4 871.6 883.1 Population (000) 894.3 907.4 919.1 930.2 941.84,508 4,620 4,905 4,389 2,933 2,356 1,028 Single-Family Permits 925 1,504 2,910 3,604 3,505

825 1,232 958 917 480 386 142 Multifamily Permits 734 1,106 1,441 1,370 1,2866.5 5.2 8.0 8.0 5.3 1.8 -2.0 Case-Shiller House Price Growth -3.3 -0.1 1.3 1.4 1.6





The San Antonio housing market is performing somewhat better than those of many other major metropolitan areas. Me-dian prices of existing single-family homes held their value up to mid-2008, while the Case-Shiller house price index is expected to fall by less than 2% up to 2010. The inventory of homes for sale has risen re-cently, from five to nine months, which is of some concern. However, this increase is far less than the big increases occurring in many other areas. Although local delin-quency rates on home mortgages have ris-en 1.5 percentage points since the begin-ning of 2007, this increase has been only one-half the erosion nationally. Likewise, the foreclosure rate is little changed since 2006, and is now well below the average for the top 100 metro areas in the country.

However, because of the credit crunch, sales of existing homes have con-tinued to fall and are now 25% below their peak in 2006. Likewise, construction of new homes has not yet improved, and per-mits of single-family units have declined to levels just one-third of their peaks in 2005.

In terms of its overall economy, San Antonio is beating the odds. Despite the national recession, local employment has continued to grow moderately, both recently and on a year-ago basis. Gains

would have been larger but for the decline in national car sales, which caused Toyota to shut the Tundra plant from August through October, and the departure over the summer of the AT&T headquarters to Dallas, which hit professional services. These have caused the unemployment rate to rise more than a percentage point to 5.1%. In contrast, em-ployment in construction, retail and govern-ment is growing as the BRAC-related projects to redevelop Fort Sam Houston and Brooke Army Medical Center are in full swing.

As a result, although the single-family housing market has not yet recovered from the modest downturn, conditions are set to im-prove soon, provided the national recession is not worse than expected. On the demand side, BRAC-driven population gains as soldiers and their families arrive augment the above average population growth that San Antonio already enjoys. Concurrently, on the supply side, the total of both single- and multi-unit construc-tion has fallen to below the 10,000 range annually. Moreover, the inventory of finished vacant homes declined to just 2,000 at the end of September, from nearly 3,000 at the begin-ning of the year. Additionally, over the summer, about 1,500 new home lots were delivered to the market and yet more than 2,200 were absorbed, the first time in six years that absorp-tion has been positive for a full quarter.

Longer term, San Antonio’s prospects are above average, because of several key drivers. The most important, of course, is BRAC-related construction, which en-compasses 78 new and renovated primary facilities totaling 6 million square feet, the bulk at Fort Sam Houston. The largest of the projects, which must be completed by 2011, include the renovation of Brooks Army Medical Center into an in-patient and trauma care center, conversion of Wil-ford Hall Medical to an ambulatory care center, and the consolidation of medical training for the military. Despite federal budget concerns, the downside risks to the completion of this activity are low as many contracts have already been awarded.

Another long-term driver is the Toyota Tundra plant, which was completed in late 2006. Although Toyota closed the San Antonio plant over much of the second half of 2008 because of the deep decline in sales, the company will now consolidate U.S. Tundra production there. The risks are mixed. Overall demand for less fuel-efficient vehicles could decline further, weakening prospects. However, if U.S. automakers are forced to consolidate more than expected, Toyota could gain share. Currently, San Antonio Tundra production is well below capacity.

homes. As a result, house prices in Lake County will decline through the end of 2009 while house prices in Chicago will not bottom until the first quarter of 2010. House prices will depreciate by about 3% for Lake County and by 4% for Chicago. The rebound will proceed slowly; prices will not return to peak until 2015. Con-struction will begin to revive a bit sooner, but, at its peak, permitting will reach only about two-thirds of the mid-decade pace.

The Chicago economy will rebound in step with the national recovery. However,

growth will lag the national average because of a middling population profile and a ma-ture industrial base. Growth will be driven by its core industries, including professional and tech services, tourism/conventions, and transportation/distribution/warehousing—industries that benefit from the increasingly globalized economy and that require a spe-cialized and educated workforce.

The risks to the outlook align with the risks to the national outlook, because of Chicago’s dependence on business spend-ing for business and financial services,

capital goods, and travel. A more pro-tracted U.S. recession would also extend Chicago’s downturn.

However, thus far, Chicago’s reces-sion has been less severe than the national downturn. Although the baseline forecast calls for a more severe downturn in 2009, the transition of the Chicago economy away from dependence on goods produc-tion may have made the metro area more resilient than expected. As a result, the housing market could rebound with more alacrity than expected.

San Antonio, TX


754.0 751.4 760.0 782.0 811.5 832.4 849.7 Total Employment (000) 855.4 871.9 902.2 939.4 970.10.1 -0.3 1.1 2.9 3.8 2.6 2.1 % Change 0.7 1.9 3.5 4.1 3.35.7 6.1 5.6 5.0 4.6 4.1 4.7 Unemployment Rate 6.5 7.2 6.7 5.3 4.61.9 4.5 6.7 9.2 8.1 7.5 5.5 Personal Income Growth 2.0 4.4 6.5 7.5 6.5


4.0 4.4 4.4 7.6 8.2 9.0 2.3 Case-Shiller House Price Growth -1.0 -0.3 0.3 0.4 0.57,703 11,847 8,517 9,207 10,300 10,509 7,119 Mortgage Originations ($Mil) 8,096 8,009 8,092 8,516 8,91718.9 15.7 20.4 19.1 35.9 33.1 27.7 Net Migration (000) 24.3 24.5 24.5 22.3 22.2




2002 2003 2004 2005 2006 2007 2008 Indicators 2009 2010 2011 2012 201360.7 62.9 65.8 65.6 65.3 66.2 67.1 Gross Metro Product, C$B 67.5 69.0 71.6 74.8 77.23.0 3.6 4.6 -0.3 -0.5 1.5 1.4 % Change 0.5 2.2 3.8 4.6 3.1

857.5 865.5 877.6 887.9 902.4 916.6 920.9 Total Employment (000) 914.4 926.9 950.7 980.3 1,002.9-0.4 0.9 1.4 1.2 1.6 1.6 0.5 % Change -0.7 1.4 2.6 3.1 2.34.6 4.8 4.7 4.8 4.3 4.0 5.1 Unemployment Rate 7.3 8.0 7.0 5.9 5.33.0 3.2 5.4 3.7 6.3 4.6 3.3 Personal Income Growth 0.8 3.1 4.3 5.3 4.9


3.3 2.5 2.2 3.6 0.9 1.9 1.0 Case-Shiller House Price Growth -1.0 1.2 2.9 2.9 2.716,530 23,004 12,872 12,738 11,770 11,296 5,734 Mortgage Originations ($Mil) 6,413 5,626 5,529 5,906 6,251


Indianapolis, IN

The housing market in Indianapolis is now one of the sturdiest in the nation, since the area has felt relatively little effect from the national housing boom and bust cycle. The metro area’s house price appre-ciation did not accelerate significantly from its historical average pace, as year-to-year price growth remained consistently below 5% throughout 2003-2005, according to the FHFA house price index. By compari-son, house price appreciation throughout the rest of the nation reached well into double digits during the same period. However, now that the national housing bubble has burst and prices have fallen by about 20% from their peak in early 2006, Indianapolis’s housing market still faces little downward pressure and prices have yet to fall on a year-ago basis.

The lack of a local housing boom is the main factor behind the sturdy housing market. In general, house price appreciation was much tamer in the Midwest during the boom than it was in the nation as a whole. Price growth was even slower in Indianapo-lis, as the metro area had already built up a sizeable backlog of unsold houses in 2003. Therefore, single-family building permits were essentially flat during the housing boom. Construction began to fall rapidly in 2006 and, with the metro area’s robust rate of household formation, the supply of houses is now very limited. Furthermore, without the runup in prices, Indianapo-lis’s already-high level of affordability has increased, making it one of the most afford-able among the nation’s largest metro areas.

In addition to the lack of a bubble, the comparatively sturdy economy is also sup-

porting home values. Indianapolis was the only major metro area in the Midwest where total employment held steady throughout 2008. The metro area is much better insulated from most of the drags on the region’s labor market. Financial services have been hit par-ticularly hard during this recession, but India-napolis has a smaller exposure to the industry than other Midwest financial centers such as Chicago, Minneapolis and Des Moines. Most of Indianapolis’s banks only have a regional focus and thus face a smaller impact from the state’s mild housing correction. Indianapolis also has a smaller share of manufacturing jobs compared with the rest of the region. Of the manufacturing that the metro area does have, much of it is focused on pharmaceuticals and medical equipment, which are better insu-lated from the effects of the recession.

Indianapolis’s relatively strong credit conditions are another critical support for the housing market. Delinquency and de-fault rates were above the national average throughout most of the decade, but they have increased at a much more gradual pace in recent quarters. Indianapolis had a smaller exposure to the subprime mortgage market, and the area’s households were not as de-pendent on mortgage equity withdrawal to supplement their income. According to Equi-fax and Moody’s Economy.com, Indianapo-lis’s first mortgage foreclosure rate by dollar volume is only about 80% of the national average. This limits the downward pressure that foreclosures have on house prices. Good credit is also propping up the metro area’s consumer-related industries. The continued growth of these industries is another impor-tant pillar of the local labor market.

Despite its many strengths, the India-napolis housing market is still expected to feel a minor correction over the months ahead, although the area’s peak-to-trough decline is expected to total about 1.5%, which will be far less than the national average. The metro area is not expected to completely avoid a correction because the national recession will weigh on its sturdy economy, reducing the demand for housing. In particular, the nationwide problems of rising unemploy-ment, falling household wealth, and plunging consumer confidence will spark a contrac-tion among consumer-related industries. The national recession is the primary threat to the Indianapolis housing market, and the pos-sibility of a more severe national downturn tilts the metro area’s risks on the downside. Even though the metro area’s recession will be comparatively mild, other national fac-tors such as the reduced availability of credit, tighter lending standards, and a sharp decline in equity markets will all weigh on the local housing market. Therefore, the housing cor-rection could be somewhat deeper than ex-pected, but it will still be mild in comparison with the national average.

Indianapolis’s house prices are expected to start recovering by the end of 2009, which is around the same time as the rest of the nation. However, the metro area’s price ap-preciation will level off around 2011 at its longer-term pace of more than 2.5% each year, which is below the national average. The high level of housing affordability is a key factor behind the demand for housing. Therefore, the area’s below average rate of per capita income growth will contain house price appreciation over the long run.

Tourism is another driver. The deepening national recession will have a relatively smaller impact on San Antonio than on some other tourist destinations. As a premier travel des-tination in Texas because of the Alamo, River

Walk, and various other sights, San Antonio established itself as a “drive market,” meaning that much of the tourism arrived by car during the first half of 2008. Even though gasoline prices were up at the time, the relative cost of

auto transportation was down compared with the cost and problems associated with air trav-el. Consequently, the subsequent reduction in gas prices has offset the short-term down-side risks associated with the recession.



The hallmark of the Little Rock housing market is stability. It has not deteriorated sig-nificantly thus far, nor will it. The local hous-ing market contraction has lagged the national housing downturn in both timing and sever-ity. Its housing recovery will take hold before that of the nation but will be less robust than elsewhere. The local housing market did not overheat in recent years, as many markets around the country had, leading to its current relative stability. House prices, as measured by the Federal Housing Finance Agency, are about flat from their 2007 third quarter peak. Prices will descend slightly next year before returning to sustained growth.

One factor bolstering demand for local housing is affordability. Although afford-ability has fallen from its elevated level in recent years—house prices grew faster than income—it remains high. The metro area’s house price-to-income ratio reflects this af-fordability. The ratio is near its long-term average, another indication that prices do not need to correct to stimulate demand.

Price declines will have accelerated in late 2008, but the peak-to-trough decline will be contained below 2%. Although prices in Little Rock are fairly balanced, the market is slightly oversupplied. Sales of existing single-family homes have dropped off since the beginning of 2008, pushing the months of supply higher. According to the National Association of Realtors, the months of supply of existing single-family homes on the market have been steadily rising since 2005. Although

prices have not noticeably deteriorated in Little Rock, excess supply can reinforce price declines, as buyers then have more room to negotiate lower prices.

The local supply swell creates downside risk for Little Rock house prices, but slowing new construction is providing a small counter-balance for the rise in inventory on the market. Permit applications have fallen slightly, which will hold construction of new homes in check. Permit applications now stand below their long-run average, but sputtering demand will, none-theless, lead to a near-term supply buildup.

The metro area has been supported by a stronger labor market than in most other locales. Although Little Rock’s payroll em-ployment growth decelerated significantly last year, general economic conditions have held up well, supporting the housing market. The metro area benefits from a diverse indus-trial base as well as the dominance of several stable industries, namely state government, healthcare and the military. As fewer resi-dents lose their jobs, fewer are defaulting on mortgages. The absence, to date, of either significant payroll job losses or house price declines is supporting household finances and preventing more widespread defaults. Zillow.com indicates that fewer than one in five Little Rock homes had sold for a loss by the third quarter of 2008, compared with more than half of homes sold in some of the worst markets in California.

Although Little Rock’s mortgage de-linquency rate has historically exceeded

the national average, conditions have dete-riorated far less than nationally, according to Equifax and Moody’s Economy.com. Indeed, the local mortgage delinquency rate had exceeded that of the nation for a decade, but that relationship reversed course in early 2007. Data from RealtyTrac echo this observation; in 2007, its measure-ment of the foreclosure rate in Little Rock was just less than 1%, compared with 1.4% nationally. Stronger mortgage credit quality positions the local housing market to glide through the broader downturn.

The near-term risks to the outlook are to the downside, but the long-term risks are to the upside. The decline in home prices is almost negligible—less than 1% from its peak in late 2007 to the expected mid-2009 trough. Although the lack of house price declines and credit deterioration has prevented severe deterioration of the local housing market, economic conditions in Little Rock have begun to show signs of stress and will deteriorate over the com-ing year. Higher joblessness and eroding consumer confidence could deepen house price declines. The local housing market avoided some of the boom’s pitfalls, such as exposure to subprime mortgages and specu-lative investments that trapped some of the now-suffering markets, which has insulated Little Rock from steeper declines. In the long term, strong employment growth will attract a steady flow of new residents to the area, increasing demand for local housing.

Little Rock, AR2002 2003 2004 2005 2006 2007 2008 Indicators 2009 2010 2011 2012 2013

19.8 20.2 20.9 21.6 22.5 22.9 23.1 Gross Metro Product, C$B 23.0 23.4 24.3 25.5 26.41.5 2.5 3.3 3.1 4.1 1.9 0.7 % Change -0.3 1.8 3.8 4.9 3.5

321.0 323.5 328.2 332.9 341.0 346.1 347.7 Total Employment (000) 342.7 346.6 356.3 369.7 380.6-1.1 0.8 1.4 1.5 2.4 1.5 0.5 % Change -1.4 1.1 2.8 3.8 3.04.8 5.1 5.0 4.6 4.7 4.6 4.2 Unemployment Rate 5.4 6.3 5.0 3.6 2.84.6 3.7 6.4 7.0 7.6 7.3 3.6 Personal Income Growth 0.4 3.5 5.3 6.6 5.9

622.7 629.2 636.7 645.0 656.2 665.4 673.5 Population (000) 681.3 689.6 697.5 705.4 713.12,764 3,515 3,590 4,196 3,767 2,877 1,753 Single-Family Permits 1,198 1,355 2,029 2,680 2,7251,772 583 1,935 1,552 408 600 943 Multifamily Permits 110 14 12 69 128

3.4 4.1 5.1 6.6 6.0 4.3 0.9 Case-Shiller House Price Growth -0.0 0.8 1.5 1.6 1.53,269 4,799 3,149 3,322 3,375 3,474 1,851 Mortgage Originations ($Mil) 1,490 1,056 1,109 1,294 1,335


Pittsburgh’s housing market is holding up better than most, but it is not completely immune from what ails regional markets. It will dodge a major decline in prices because of its high affordability and manageable foreclosure pipeline. However, the slow-down in economic growth and tough credit conditions are hurting home sales. Single-family home sales have steadily declined

to their lowest level since the early 1990s. Homebuilders are responding: Single-family permits and construction starts are off their previous highs, but a bottom is forming.

According to Fiserv, Pittsburgh’s house price growth never escaped the mid-single digits during the national housing boom. This is nothing new for the area, where house price growth has held within a very

narrow range for more than 20 years. Ac-cording to Moody’s Economy.com, local house prices are undervalued by approxi-mately 4.5%.

House prices peaked in the fourth quarter of 2007 but have been relatively unchanged since. House prices in Pittsburgh are down less than 1% from their peak, compared with 18% nationally, and even in-



Pittsburgh, PA

The housing market in Houston is doing better than most. Median-priced homes have held their value, and are, if anything, slightly undervalued. Price-to-income ratios are in balance, and far lower than in many other areas. The inventory of homes for sale has risen modestly to about seven months, far less than the big increases, to well over a year of inven-tory, seen in many other areas. In terms of credit conditions, although local de-linquency rates on home mortgages have risen somewhat over the past two years,

the 0.5 percentage point increase has been only one-fifth the erosion nationally. Like-wise, the foreclosure rate is little changed since 2006, and now is below the average for the top 100 metro areas in the country.

Nonetheless, construction has yet to im-prove, in large measure due to the reduced availability of credit. Permits for both single-family and multifamily units have continued to decline, the former since 2006 and the latter since the end of 2007. Moreover, sales of existing single-family homes have been falling since their peak in early 2007.

On balance, Houston’s recent hous-ing market performance reflects its strong economic fundamentals, which place it among the better performing metropolitan areas in the country. Despite the national recession, payroll employment has con-tinued to increase and is up significantly from year-ago levels. Every sector, includ-ing manufacturing, has contributed to the gains. Although data from the household survey are less positive, the unemployment rate has risen less than nationally, and now stands a full percentage point lower, at 5%.

creased slightly in the third quarter of 2008. Speculative residential construction was ab-sent in the metro area, which prevented the market from becoming saturated. Though there is excess inventory, even the current sluggish pace of home sales is sufficient to draw down the supply.

The national housing boom passed right over Pittsburgh because of weak underly-ing economic fundamentals, namely below average employment growth and a declining population. Its major advantage is that it is extremely affordable, especially for a large northeastern metro area. For nearly 15 years, income growth outpaced house prices, rais-ing residents’ purchasing power for houses. With housing affordability at its highest level in the past 30 years, the pool of potential homebuyers is large.

A major factor limiting the severity of the decline in local house prices is the com-paratively low exposure to investor interest and subprime lending. Since 2005, subprime mortgages’ share of total mortgage origina-tions has averaged 19% per year, compared with 23% nationally. Low exposure to sub-prime mortgages and steady house price appreciation is paying dividends, as the fore-closure pipeline is not filling quickly.

The measurable national deterioration in mortgage credit quality has been absent in

Pittsburgh. The mortgage delinquency rate has increased modestly over the past year but is just above the rate in 2002 and 2007, according to Equifax and Moody’s Economy.com. Further, the delinquency rate on mort-gages 120 days past due has been relatively unchanged for five years and is well below the national average. This suggests that the foreclosure pipeline is not filling rapidly, a positive for consumer credit and house prices. If Pittsburgh continues to dodge a surge in foreclosures, it will bode well for the outlook. Foreclosures add houses to an al-ready-bloated market, suppress house prices, extend the housing downturn, and weigh on household wealth accumulation.

The housing market will not turn around quickly. The local economy will suf-fer through several more quarters of weak performance. Demand will remain sluggish, but a substantial decline in house prices will be absent in Pittsburgh, and home prices will start growing at a faster rate in 2010. Home-building will hit bottom in the first quarter of 2009 but remain depressed until addi-tional progress is made in working off excess inventories. Sales of existing single-family homes will bottom out in early 2009, for a peak-to-trough decline of 38%. Prices will be unchanged throughout next year before gradually appreciating in 2010.

The near-term outlook for Pittsburgh is for a mild recession through the end of this year. Peak-to-trough declines in em-ployment will not be as severe as during the 2001 recession because manufactur-ing payrolls were much leaner heading into this downturn. The unemployment rate will peak near 7.5%, compared with its current 5.5%. The drag from declining final demand will force local manufac-turers to cut production. The banking and finance industry will be tested by consolidation and reduced loan demand. Pittsburgh enjoys a large and safe base of education and healthcare employers, which will provide much-needed support during the recession.

The risks to the outlook are heavily weighted to the downside. If mortgage credit conditions erode more than ex-pected, they will increase foreclosures and put downward pressure on house prices. Further, potential homebuyers’ less than pristine balance sheets will hurt their abil-ity to qualify for mortgages in the tough credit environment. Also, the national re-cession is broad-based and few industries are immune. Therefore, if Pittsburgh’s critical education and healthcare industries weaken, it will be a major blow to the la-bor market and household income.

2002 2003 2004 2005 2006 2007 2008 Indicators 2009 2010 2011 2012 201382.1 83.8 84.3 84.7 86.1 87.3 87.0 Gross Metro Product, C$B 84.9 84.5 86.5 89.7 92.12.0 2.0 0.6 0.4 1.7 1.3 -0.4 % Change -2.4 -0.5 2.3 3.7 2.6

1,140.5 1,132.4 1,132.0 1,132.6 1,137.1 1,145.5 1,150.6 Total Employment (000) 1,138.3 1,134.2 1,151.9 1,184.9 1,210.3-1.0 -0.7 -0.0 0.1 0.4 0.7 0.4 % Change -1.1 -0.4 1.6 2.9 2.25.7 5.9 5.7 5.2 4.7 4.3 5.3 Unemployment Rate 7.8 8.7 7.3 5.5 4.71.8 2.2 4.6 2.7 6.5 5.9 4.3 Personal Income Growth 1.6 2.9 4.3 5.6 5.3

2,408.7 2,399.9 2,387.6 2,372.0 2,360.4 2,353.3 2,351.7 Population (000) 2,349.6 2,347.9 2,345.6 2,343.0 2,340.45,296 5,294 5,548 4,672 4,367 3,844 2,803 Single-Family Permits 2,457 2,413 2,457 2,454 2,3941,763 1,221 1,308 929 1,266 836 478 Multifamily Permits 833 937 918 906 947

5.0 4.7 4.7 3.4 3.4 1.6 0.1 Case-Shiller House Price Growth 0.6 1.8 4.0 4.6 4.512,115 18,055 10,292 9,172 8,909 8,668 5,541 Mortgage Originations ($Mil) 6,850 7,286 7,285 7,425 7,653

-3.4 -3.0 -6.8 -9.4 -7.7 -4.1 1.8 Net Migration (000) 1.1 1.4 0.6 0.3 0.111,474 13,110 13,965 19,849 6,460 7,747 10,858 Personal Bankruptcies 12,652 17,407 17,771 15,952 15,534



There are risks to the overall local economic outlook. Houston remains an energy town, and the abrupt about-face in energy prices since the beginning of July has put the energy-led expansion at risk. Since peaking at $145, oil has slid to the $50 range. The Baker Hughes rotary rig count for Texas has declined about 6%, though this is still nearly double the level in the early years of this decade. Further slowing in exploration is likely. Moreover, the deepening of the recessions in the U.S. and in Europe will put additional down-ward pressure on Houston manufacturing, particularly its petroleum refining and pet-rochemicals industries.

However, provided energy prices do not take another quantum leap downward, the outlook is not dire for several reasons. First, prices remain in the range seen in 2004 and 2005, twice as high as in the 1990s, which is high enough to keep the bulk of exploration activity profitable. Second, improvements in technology have made some of the more difficult drilling increasingly cost-effective.

Third, Houston is a whole lot more diversified than it was in the early 1980s. To-day, besides its energy credentials, Houston can boast of being both a national biomedi-cal research hub and also a regional transpor-tation hub. Regarding biomedical research, three of the four top institutions in Texas are located in Houston. Regarding trans-portation and distribution, Port of Houston terminal facilities expanded in February 2007 upon the opening of the Bayport Con-tainer Terminal. Earlier, in 2005, Walmart made the decision to open one of its largest distribution centers in the entire country in Houston. Driving expansion of distribution industries is the trend growth in trade, both with expanding Latin America, and also with Pacific nations following the ongoing expan-sion of the Panama Canal. Further, Houston is the home of the Johnson Space Center where most of the work on the Orion project is taking place.

Solid economic fundamentals will keep Houston from following the national hous-ing downturn. While its housing market will not be strong, it will remain much more

stable than the nation. House prices in the forecast will dip down imperceptibly—less than half a percentage point by mid-2009 compared with the current level of prices. Once the current housing downturn finally ends, Houston residential construction should grow strongly for a number of rea-sons. First, among the largest metropoli-tan areas in the country, over a multiyear period, Houston ranks near the top in both population growth and employment growth, which has prevented any funda-mental buildup of excess housing supply. Second, its large supply of available land compared with other major metros means that growth in demand tends to drive con-struction rather than home price increases. Third, local law favors development. Devel-opers elsewhere in the county must connect to a city’s water and sewer lines, which confines new projects to nearby land be-cause building lengthy lines is prohibitively expensive. In contrast, in Houston a devel-oper can create a municipal utility district to provide these services on the property and finance with tax-free bonds.

Houston, TX


2.9 1.2 5.2 0.6 4.8 4.2 3.2 % Change 0.8 2.2 4.8 5.9 4.52,288.7 2,274.3 2,289.8 2,348.4 2,446.0 2,549.4 2,612.6 Total Employment (000) 2,626.0 2,665.1 2,752.5 2,869.4 2,964.4

-0.2 -0.6 0.7 2.6 4.2 4.2 2.5 % Change 0.5 1.5 3.3 4.2 3.36.0 6.7 6.2 5.6 5.0 4.3 4.8 Unemployment Rate 6.9 7.9 6.9 5.2 4.3

-0.0 4.0 9.0 11.0 10.0 7.7 6.5 Personal Income Growth 3.1 5.0 6.9 8.1 7.14,984.5 5,092.9 5,202.0 5,313.8 5,498.1 5,613.8 5,748.4 Population (000) 5,869.3 5,993.3 6,118.0 6,234.6 6,354.634,685 42,053 45,103 51,205 55,162 42,217 28,637 Single-Family Permits 24,069 22,095 31,763 41,796 40,34112,407 16,761 10,933 10,920 16,557 21,057 15,094 Multifamily Permits 10,373 12,769 16,068 18,092 17,946

4.2 3.8 3.6 4.6 5.8 5.4 4.2 Case-Shiller House Price Growth 0.8 0.6 1.1 1.0 0.929,277 42,557 29,906 29,762 32,842 31,887 25,473 Mortgage Originations ($Mil) 29,493 29,797 30,227 31,724 33,162



90 Moody’s Economy.com, Inc. • www.economy.com • [email protected] • Internal use only, please do not distribute


91Moody’s Economy.com, Inc. • www.economy.com • [email protected] • Internal use only, please do not distribute

The unprecedented depth and breadth of the housing correction have dragged the na-tion into the worst economic downturn since the Great Depression. The end is in sight for housing’s travails, but the events of the last year will have long-lasting negative implica-tions for the housing market as well as the broader economy. The housing crisis will leave an indelible mark on financial markets and on the collective psyche of homebuyers and inves-tors. Demand for homes will remain below its long-run average for a prolonged period, as will the pace of residential construction, and the nation’s homeownership rate will stay stuck at its preboom rate. Not until the next decade will home sales, housing starts, and house price growth return to their historical norms, and it will be even longer before these metrics return to their housing boom peak. Below-potential housing activity, combined with greater caution among lenders, will also keep the economy’s output growth below potential.

This housing recession is the worst in modern economic history, both with regard to the severity of the house price declines and to their reach across the country. Nationwide, nominal house prices will plunge some 36% peak to trough, with nearly 40% of metro ar-eas experiencing double-digit, peak-to-trough percentage price declines. Housing markets in California, Florida, much of the Mountain West, parts of the Northeast Corridor, and across the industrial Midwest are crashing the hardest, but no region will escape unscathed.

The end of this debacle is in sight. Be-cause house prices have descended so sharply and because homebuilders are building at the slowest pace in decades, the imbalances that caused the correction are abating. Housing is affordable again relative to household income, and the stock of unsold homes is leveling off. The main constraints on housing are job losses, foreclosures and sinking confidence. Strong policy measures will help on all three fronts. Indeed, the Moody’s Economy.com outlook for housing depends highly on Presi-dent Obama and the new Congress acting swiftly and effectively to push the economy out of recession and to prevent foreclosures from piling up even faster. Under these as-sumptions, the housing market should sta-

bilize at the end of this year. If policymakers are unable to implement an effective plan, the housing market could easily weaken further, with house prices depreciating another 20%, for a total 45% peak-to-trough decline.

A large economic stimulus is expected soon. The package will total more than $800 billion over 2009-2010 and include aid to state and local governments, greater infra-structure spending, tax cuts for lower- and middle-income households, and some busi-ness tax cuts as well. The package equals approximately 5% of GDP, ensuring that the economy stops contracting by the end of 2009. Some sort of foreclosure mitiga-tion plan is also likely, whether by allowing so-called mortgage cram-downs that enable bankruptcy courts to alter loan payments for homeowners or by using some of the remain-ing TARP funds to aid distressed homeowners.

The longer-term implications of the hous-ing correction are manifold. Housing may hit bottom at the end of this year, but it will remain there for several quarters. The reces-sion, more sober lending practices, a lingering overhang of distressed properties, and, finally, confidence that has been deeply shaken will keep many measures of housing trailing below equilibrium into the next decade. Even with a strong fiscal stimulus, the economy will not add jobs on net until 2010. Although mortgage availability is improving, lenders will remain cautious because the industry is still absorbing losses. Moreover, lenders are sorting out how to position themselves and their products in a world in which regula-tors will likely scrutinize them more carefully. The many foreclosures will have a lingering depressing impact on the housing market. No mitigation plan will prevent what will be a significant number of future foreclosures. Although the downward pressure will lessen, distress sales will dampen house price ap-preciation and homebuilding for several years. Confidence in the housing market has been severely damaged, and it will take time, in addition to some solid price appreciation, to encourage buyers back into the market.

These forces will keep measures of hous-ing treading below equilibrium until 2011 or 2012. Until then, sales of new single-family

homes and existing homes and condos will remain below the approximately 6.8 million annual sales that would be consistent with underlying demographic and income trends. Similarly, homebuilding will fall short of the 1.8 million homes per year that would satisfy long-run demand. House price appreciation will be weak, rising solidly on a year-over-year basis only by mid-2010. Even then, price appreciation will fall below 3%. It will take a full decade before house prices return to their 2006 peak. As a consequence of the still-weak housing market, the homeowner-ship rate will not regain its 2004 peak of 69% but settle at around 67%, where it stood at the beginning of 2000.

The fallout from the housing market cor-rection also hurts the broader economy. Not only does slower residential construction and wealth growth depress national output growth, but Washington’s eroding fiscal situ-ation also threatens the economy’s long-term growth prospects. The federal government has committed nearly $9 trillion to address the crisis, and although not all of it will end up as part of the national debt, taxpayers’ ultimate bill could very well exceed $2 trillion. By com-parison, the savings and loan crisis of the early 1990s cost taxpayers $275 billion in today’s dollars. With Social Security, Medicare and Medicaid costs set to increase substantially in coming years, policymakers will have to make very difficult tax and spending decisions.

Although it is easy to be pessimistic, it is worth noting that the problems at the root of the economic crisis are being addressed: Falling house prices make homes more afford-able, idled construction thins the inventory of unsold houses, the personal saving rate is rising rapidly, and the trade deficit is nar-rowing as consumers retrench. It was hoped that these imbalances would be corrected gradually over years, even decades, rather than months. But although working through them in such a short period is painful, this ordeal could catalyze fundamental changes in our financial system and regulatory framework, and perhaps even spark an effort to address the nation’s long-term fiscal challenges. Some economic good can still come out of all the bad that has befallen the nation.

Chapter 11: Conclusions


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Table of Contents - AppendixesAppendix 1-1: CSI Home Price Index ..........................................95

Appendix 2-1: Non-Owner Occupied Share of Purchase

Originations by $ Volume, States ........................103

Appendix 2-2: Non-Owner Occupied Share of Purchase

Originations by $ Volume, Metros Areas .............104

Appendix 2-3: Nonprime Share of Total Purchase Originations

by $ Volume, States ............................................110

Appendix 2-4: Nonprime Share of Total Purchase Originations

by $ Volume, Metro Areas ...................................111

Appendix 6-1: CSI Home Price-to-Household Income Ratio .........117

Appendix 6-2: Housing Affordability Index ................................123

Appendix 6-3: Price-to-Net Rent Ratio .......................................129

Appendix 7-1: Metropolitan Area Pool Classifications ................130

Appendix 7-2: House Price Over/Undervaluation Validation ......135

Appendix 7-3: House Price Over/Undervaluation Validation,

Current ...............................................................140

Appendix 8-1A: Moody’s Economy.com State NAR Median

Existing-House Price Forecast Accuracy ..............145

Appendix 8-1B: Moody’s Economy.com State FHFA House

Price Index Forecast Accuracy .............................146

Appendix 8-1C: Moody’s Economy.com State CSI House Price

Index Forecast Accuracy .....................................147

Appendix 8-2A: Moody’s Economy.com Metro Area NAR Median

Existing-House Price Forecast Accuracy ..............148

Appendix 8-2B: Moody’s Economy.com Metro Area FHFA

House Price Index Forecast Accuracy ..................150

Appendix 8-2C: Moody’s Economy.com Metro Area CSI House

Price Forecast Accuracy .......................................152

Appendix 8-3A: Moody’s Economy.com Metro Area NAR

Forecast Accuracy ...............................................154

Appendix 8-3B: Moody’s Economy.com Metro Area FHFA


Appendix 8-3C: Moody’s Economy.com Metro Area CSI


Appendix 8-4A: Moody’s Economy.com State NAR Median

Existing-House Price Forecast Consistency .........160



Appendix 8-4B: Moody’s Economy.com State FHFA Price Index

Forecast Consistency .........................................161

Appendix 8-4C: Moody’s Economy.com State CSI Price Index


Appendix 8-5A: Moody’s Economy.com Metro NAR Median

Existing-House Price Forecast Consistency ........ 163

Appendix 8-5B: Moody’s Economy.com Metro Area FHFA Price

Index Forecast Consistency ................................165

Appendix 8-5C: Moody’s Economy.com Metro CSI Price Index


Appendix 9-1: Metro Area House Price Outlook .......................169

Appendix 9-2: Shape of the Recovery ........................................173

Appendix 9-3: Supply Balance Indicator ...................................179

Table of Contents - Appendixes (cont.)



Appendix 1-1: CSI Home Price Index, Average Annual % Change

00Q1 to Peak Peak to 08Q3

United States 88.3 -21.3

Abilene, TX 48.2 -0.3Akron, OH 20.8 -7.4Albany, GA 43.2 -3.1Albany-Schenectady-Troy, NY 95.4 -1.5Albuquerque, NM 59.5 -2.1Alexandria, LA 52.6 -1.6Allentown-Bethlehem-Easton, PA-NJ 88.4 -3.0Altoona, PA 53.5 0.0Amarillo, TX 40.1 -0.6Ames, IA 33.2 -3.0Anchorage, AK 75.2 -0.4Anderson, IN 20.2 -3.5Anderson, SC 42.3 -1.7Ann Arbor, MI 31.4 -22.0Anniston-Oxford, AL 54.1 0.0Appleton, WI 34.6 -5.9Asheville, NC 81.5 -0.2Athens-Clarke County, GA 49.2 -3.4Atlanta-Sandy Springs-Marietta, GA 32.9 -10.1Atlantic City, NJ 144.5 -15.9Auburn-Opelika, AL 61.0 0.0Augusta-Richmond County, GA-SC 60.7 0.0Austin-Round Rock, TX 58.3 0.0Bakersfield, CA 211.0 -44.6Baltimore-Towson, MD 123.1 -10.0Bangor, ME 87.0 -1.0Barnstable Town, MA 116.5 -11.6Baton Rouge, LA 54.0 -0.3Battle Creek, MI 22.0 -7.3Bay City, MI 30.6 -10.6Beaumont-Port Arthur, TX 48.6 -1.3Bellingham, WA 112.3 -2.7Bend, OR 132.2 -12.2Bethesda-Frederick-Rockville, MD 138.6 -16.6Billings, MT 75.7 -0.4Binghamton, NY 75.6 0.0Birmingham-Hoover, AL 48.1 -0.3Bismarck, ND 63.8 -2.4Blacksburg-Christiansburg-Radford, VA 62.6 -0.3Bloomington, IN 41.9 -1.3Bloomington-Normal, IL 28.3 -0.2Boise City-Nampa, ID 85.8 -6.0Boston-Quincy, MA 85.0 -11.2Boulder, CO 43.1 0.0Bowling Green, KY 32.9 -2.0



Appendix 1-1: CSI Home Price Index, Average Annual % Change (cont.)

00Q1 to Peak Peak to 08Q3Bradenton-Sarasota-Venice, FL 151.0 -38.2Bremerton-Silverdale, WA 109.0 -5.5Bridgeport-Stamford-Norwalk, CT 77.5 -6.7Brownsville-Harlingen, TX 38.5 -3.0Brunswick, GA 86.3 -1.8Buffalo-Niagara Falls, NY 45.5 0.0Burlington, NC 19.0 -2.3Burlington-South Burlington, VT 82.3 -1.8Cambridge-Newton-Framingham, MA 64.3 -8.6Camden, NJ 105.1 -8.9Canton-Massillon, OH 18.5 -7.4Cape Coral-Fort Myers, FL 189.3 -48.6Carson City, NV 121.5 -15.4Casper, WY 112.9 -2.3Cedar Rapids, IA 28.7 -0.2Champaign-Urbana, IL 45.2 0.0Charleston, WV 36.5 -2.6Charleston-North Charleston-Summerville, SC 76.5 -8.7Charlotte-Gastonia-Concord, NC-SC 32.9 -3.6Charlottesville, VA 109.9 -2.3Chattanooga, TN-GA 49.9 -1.1Cheyenne, WY 62.6 -0.9Chicago-Naperville-Joliet, IL 67.4 -13.3Chico, CA 151.9 -23.3Cincinnati-Middletown, OH-KY-IN 25.0 -6.1Clarksville, TN-KY 42.6 0.0Cleveland, TN 51.1 -2.0Cleveland-Elyria-Mentor, OH 21.5 -12.0Coeur d'Alene, ID 102.6 -4.4College Station-Bryan, TX 48.7 0.0Colorado Springs, CO 44.4 -3.3Columbia, MO 40.9 -0.3Columbia, SC 47.2 0.0Columbus, GA-AL 53.6 -2.2Columbus, IN 27.9 -4.8Columbus, OH 25.5 -6.6Corpus Christi, TX 52.5 -0.8Corvallis, OR 71.5 -1.1Cumberland, MD-WV 82.3 0.0Dallas-Plano-Irving, TX 35.1 0.0Dalton, GA 51.0 -5.1Danville, IL 43.7 -7.5Danville, VA 41.3 -6.6Davenport-Moline-Rock Island, IA-IL 37.9 -2.4Dayton, OH 21.1 -4.7Decatur, AL 37.1 -0.5Decatur, IL 33.4 0.0Deltona-Daytona Beach-Ormond Beach, FL 143.6 -30.4Denver-Aurora-Broomfield, CO 35.2 -7.1




00Q1 to Peak Peak to 08Q3Des Moines-West Des Moines, IA 38.7 -3.0Detroit-Livonia-Dearborn, MI 25.1 -31.4Dothan, AL 54.5 -0.1Dover, DE 91.9 -2.0Dubuque, IA 45.2 -2.8Duluth, MN-WI 74.8 -1.3Durham-Chapel Hill, NC 45.2 -1.1Eau Claire, WI 44.3 -1.4Edison-New Brunswick, NJ 117.3 -11.8El Centro, CA 141.1 -42.5El Paso, TX 73.1 -1.0Elizabethtown, KY 52.2 -3.9Elkhart-Goshen, IN 30.5 -3.8Elmira, NY 45.8 -6.6Erie, PA 32.4 -0.4Eugene-Springfield, OR 83.1 -4.1Evansville, IN-KY 28.4 -0.8Fairbanks, AK 63.3 -6.4Fargo, ND-MN 59.0 -0.8Farmington, NM 82.6 -2.8Fayetteville, NC 41.8 0.0Fayetteville-Springdale-Rogers, AR-MO 59.1 -5.0Flagstaff, AZ 121.1 -10.9Flint, MI 26.0 -20.3Florence, SC 42.4 0.0Florence-Muscle Shoals, AL 49.5 0.0Fond du Lac, WI 38.4 -1.7Fort Collins-Loveland, CO 36.2 -1.3Fort Lauderdale-Pompano Beach-Deerfield Beach, FL 176.2 -36.4Fort Smith, AR-OK 43.8 0.0Fort Walton Beach-Crestview-Destin, FL 139.2 -28.4Fort Wayne, IN 20.3 -0.2Fort Worth-Arlington, TX 34.3 -0.3Fresno, CA 191.4 -39.7Gadsden, AL 49.3 0.0Gainesville, FL 96.5 -10.1Gainesville, GA 39.1 -9.1Gary, IN 40.3 -2.1Glens Falls, NY 110.4 -0.5Goldsboro, NC 38.9 -2.8Grand Forks, ND-MN 57.6 -2.9Grand Junction, CO 101.0 0.0Grand Rapids-Wyoming, MI 27.0 -8.9Great Falls, MT 64.6 -0.6Greeley, CO 32.8 -10.0Green Bay, WI 33.5 -2.1Greensboro-High Point, NC 30.0 -0.2Greenville, NC 35.0 0.0Greenville-Mauldin-Easley, SC 40.6 -0.3




00Q1 to Peak Peak to 08Q3Gulfport-Biloxi, MS 70.8 -4.0Hagerstown-Martinsburg, MD-WV 117.1 -8.9Hanford-Corcoran, CA 154.2 -30.0Harrisburg-Carlisle, PA 58.2 -1.1Harrisonburg, VA 89.2 -5.0Hartford-West Hartford-East Hartford, CT 69.4 -5.3Hattiesburg, MS 54.2 -3.0Hickory-Lenoir-Morganton, NC 38.9 0.0Hinesville-Fort Stewart, GA 84.6 -6.4Holland-Grand Haven, MI 25.9 -6.0Honolulu, HI 131.7 -1.9Hot Springs, AR 67.4 -1.3Houma-Bayou Cane-Thibodaux, LA 70.7 -1.0Houston-Sugar Land-Baytown, TX 49.6 0.0Huntington-Ashland, WV-KY-OH 46.9 -0.7Huntsville, AL 49.7 0.0Idaho Falls, ID 67.2 -2.6Indianapolis-Carmel, IN 25.3 -0.7Iowa City, IA 37.6 -3.5Ithaca, NY 78.9 0.0Jackson, MI 29.2 -16.3Jackson, MS 41.7 -2.4Jackson, TN 24.7 -3.9Jacksonville, FL 101.5 -18.7Jacksonville, NC 75.0 0.0Janesville, WI 38.6 -3.9Jefferson City, MO 35.3 -0.4Johnson City, TN 42.4 -1.2Johnstown, PA 51.7 -1.5Jonesboro, AR 25.5 0.0Joplin, MO 39.3 -2.7Kalamazoo-Portage, MI 30.8 -5.1Kankakee-Bradley, IL 48.7 0.0Kansas City, MO-KS 38.2 -3.0Kennewick-Pasco-Richland, WA 43.6 0.0Killeen-Temple-Fort Hood, TX 43.5 0.0Kingsport-Bristol-Bristol, TN-VA 56.3 0.0Kingston, NY 122.1 -4.3Knoxville, TN 53.7 -1.6Kokomo, IN 17.5 -9.6La Crosse, WI-MN 49.6 -3.7Lafayette, IN 14.4 -1.8Lafayette, LA 56.9 -1.8Lake Charles, LA 52.1 0.0Lake County-Kenosha County, IL-WI 53.8 -3.3Lake Havasu City-Kingman, AZ 145.4 -20.3Lakeland-Winter Haven, FL 116.7 -31.9Lancaster, PA 68.3 -2.0Lansing-East Lansing, MI 33.2 -19.1




00Q1 to Peak Peak to 08Q3Laredo, TX 50.6 -4.5Las Cruces, NM 75.8 -0.7Las Vegas-Paradise, NV 130.4 -38.6Lawrence, KS 43.6 -1.2Lawton, OK 53.4 -4.4Lebanon, PA 81.7 -1.6Lewiston, ID-WA 86.4 0.0Lewiston-Auburn, ME 91.9 0.0Lexington-Fayette, KY 41.7 0.0Lima, OH 34.0 -3.0Lincoln, NE 28.5 -1.4Little Rock-North Little Rock-Conway, AR 43.3 0.0Logan, UT-ID 47.6 0.0Longview, TX 62.5 -2.7Longview, WA 71.8 -3.9Los Angeles-Long Beach-Glendale, CA 176.5 -34.4Louisville-Jefferson County, KY-IN 37.0 -1.5Lubbock, TX 38.4 0.0Lynchburg, VA 68.2 -1.3Macon, GA 40.0 0.0Madera-Chowchilla, CA 165.7 -30.2Madison, WI 48.9 -2.7Manchester-Nashua, NH 92.3 -13.3Mansfield, OH 28.5 -5.5McAllen-Edinburg-Mission, TX 42.5 0.0Medford, OR 116.2 -12.5Memphis, TN-MS-AR 17.7 -12.5Merced, CA 211.5 -60.5Miami-Miami Beach-Kendall, FL 205.9 -36.4Michigan City-La Porte, IN 40.0 -3.0Midland, TX 100.0 -1.3Milwaukee-Waukesha-West Allis, WI 55.9 -2.8Minneapolis-St. Paul-Bloomington, MN-WI 67.0 -19.1Missoula, MT 92.2 -3.4Mobile, AL 63.2 0.0Modesto, CA 198.2 -52.8Monroe, LA 43.3 0.0Monroe, MI 28.0 -16.4Montgomery, AL 40.5 -1.7Morgantown, WV 67.9 0.0Morristown, TN 52.0 -0.9Mount Vernon-Anacortes, WA 97.4 -1.7Muncie, IN 24.8 -7.9Muskegon-Norton Shores, MI 24.5 -4.3Myrtle Beach-North Myrtle Beach-Conway, SC 77.6 -11.3Napa, CA 139.7 -35.3Naples-Marco Island, FL 188.4 -42.3Nashville-Davidson-Murfreesboro-Franklin, TN 45.6 -2.5Nassau-Suffolk, NY 126.4 -10.3




00Q1 to Peak Peak to 08Q3New Haven-Milford, CT 85.1 -8.5New Orleans-Metairie-Kenner, LA 65.4 -2.6New York-White Plains-Wayne, NY-NJ 120.3 -11.8Newark-Union, NJ-PA 103.7 -11.6Niles-Benton Harbor, MI 50.3 -7.0Norwich-New London, CT 89.4 -5.3Oakland-Fremont-Hayward, CA 114.0 -38.8Ocala, FL 121.4 -14.8Ocean City, NJ 163.1 -12.6Odessa, TX 94.3 0.0Ogden-Clearfield, UT 52.1 -2.1Oklahoma City, OK 49.0 -0.6Olympia, WA 98.4 -3.1Omaha-Council Bluffs, NE-IA 31.1 -2.4Orlando-Kissimmee, FL 132.3 -31.7Oshkosh-Neenah, WI 36.3 0.0Owensboro, KY 26.1 -1.9Oxnard-Thousand Oaks-Ventura, CA 147.6 -35.6Palm Bay-Melbourne-Titusville, FL 146.4 -27.4Palm Coast, FL 135.8 -25.2Panama City-Lynn Haven-Panama City Beach, FL 116.9 -11.4Parkersburg-Marietta-Vienna, WV-OH 39.8 0.0Pascagoula, MS 72.3 -3.6Peabody, MA 71.5 -14.5Pensacola-Ferry Pass-Brent, FL 83.3 -18.5Peoria, IL 37.0 -0.5Philadelphia, PA 92.6 -0.5Phoenix-Mesa-Scottsdale, AZ 123.4 -38.9Pine Bluff, AR 46.6 -2.3Pittsburgh, PA 36.0 -0.4Pittsfield, MA 81.7 -4.3Pocatello, ID 61.6 -2.9Port St. Lucie, FL 167.1 -42.3Portland-South Portland-Biddeford, ME 88.7 -1.4Portland-Vancouver-Beaverton, OR-WA 85.1 -7.9Poughkeepsie-Newburgh-Middletown, NY 108.7 -5.9Prescott, AZ 113.8 -21.2Providence-New Bedford-Fall River, RI-MA 117.3 -14.5Provo-Orem, UT 62.4 -4.0Pueblo, CO 34.0 -7.9Punta Gorda, FL 168.1 -47.1Racine, WI 54.7 -3.3Raleigh-Cary, NC 41.9 0.0Rapid City, SD 61.1 0.0Reading, PA 75.0 -3.6Redding, CA 167.6 -27.4Reno-Sparks, NV 119.3 -22.5Richmond, VA 87.5 -1.4Riverside-San Bernardino-Ontario, CA 189.4 -47.1




00Q1 to Peak Peak to 08Q3Roanoke, VA 66.9 -0.5Rochester, MN 42.4 -3.5Rochester, NY 34.2 -2.1Rockford, IL 41.9 -2.4Rockingham County-Strafford County, NH 86.2 -11.7Rocky Mount, NC 32.4 0.0Rome, GA 38.8 0.0Sacramento-Arden-Arcade-Roseville, CA 161.9 -43.5Saginaw-Saginaw Township North, MI 23.6 -13.0Salem, OR 71.6 -3.0Salinas, CA 168.6 -54.2Salisbury, MD 96.0 -10.5Salt Lake City, UT 72.0 -2.7San Angelo, TX 62.5 0.0San Antonio, TX 57.8 0.0San Diego-Carlsbad-San Marcos, CA 143.0 -35.6San Francisco-San Mateo-Redwood City, CA 78.8 -15.8San Jose-Sunnyvale-Santa Clara, CA 70.9 -25.6San Luis Obispo-Paso Robles, CA 161.2 -24.2Sandusky, OH 29.5 -14.4Santa Ana-Anaheim-Irvine, CA 162.3 -30.9Santa Barbara-Santa Maria-Goleta, CA 162.1 -43.3Santa Cruz-Watsonville, CA 102.3 -33.2Santa Fe, NM 88.5 -2.8Santa Rosa-Petaluma, CA 121.3 -39.7Savannah, GA 81.2 -2.2Scranton-Wilkes-Barre, PA 63.2 -2.0Seattle-Bellevue-Everett, WA 86.2 -8.5Sebastian-Vero Beach, FL 139.5 -35.4Sheboygan, WI 43.4 -3.5Sherman-Denison, TX 44.4 0.0Shreveport-Bossier City, LA 53.5 -0.9Sioux City, IA-NE-SD 27.9 -2.5Sioux Falls, SD 43.4 -1.1South Bend-Mishawaka, IN-MI 34.0 -1.8Spartanburg, SC 22.8 -1.5Spokane, WA 86.8 -2.5Springfield, IL 28.7 -2.0Springfield, MA 77.9 -6.0Springfield, MO 43.2 -2.3Springfield, OH 21.0 -5.9St. Cloud, MN 65.4 -3.3St. George, UT 95.8 -10.4St. Joseph, MO-KS 39.9 -4.8St. Louis, MO-IL 53.6 -1.8State College, PA 60.8 -0.7Stockton, CA 171.8 -54.0Sumter, SC 55.0 -1.7Syracuse, NY 55.7 -0.1




00Q1 to Peak Peak to 08Q3Tacoma, WA 83.8 -11.5Tallahassee, FL 93.4 -3.1Tampa-St. Petersburg-Clearwater, FL 130.6 -28.7Terre Haute, IN 28.6 -3.2Texarkana, TX-Texarkana, AR 38.4 -1.9Toledo, OH 21.2 -12.1Topeka, KS 40.6 -0.2Trenton-Ewing, NJ 105.7 -9.0Tucson, AZ 100.2 -20.7Tulsa, OK 37.8 -0.1Tuscaloosa, AL 49.3 0.0Tyler, TX 48.9 -2.2Utica-Rome, NY 75.6 -2.2Valdosta, GA 55.7 -4.5Vallejo-Fairfield, CA 159.1 -47.1Victoria, TX 54.7 0.0Vineland-Millville-Bridgeton, NJ 103.1 -2.5Virginia Beach-Norfolk-Newport News, VA-NC 123.7 -2.4Visalia-Porterville, CA 159.8 -39.0Waco, TX 45.2 0.0Warner Robins, GA 36.5 -3.2Warren-Troy-Farmington Hills, MI 22.9 -29.8Washington-Arlington-Alexandria, DC-VA-MD-WV 145.6 -27.3Waterloo-Cedar Falls, IA 46.6 -0.4Wausau, WI 44.5 -1.8Weirton-Steubenville, WV-OH 39.9 -7.8Wenatchee-East Wenatchee, WA 90.1 -3.0West Palm Beach-Boca Raton-Boynton Beach, FL 162.0 -36.7Wheeling, WV-OH 51.3 -8.6Wichita Falls, TX 40.1 -3.6Wichita, KS 32.2 -1.5Williamsport, PA 51.4 -4.6Wilmington, DE-MD-NJ 88.8 -6.2Wilmington, NC 87.8 -1.3Winchester, VA-WV 124.2 -16.8Winston-Salem, NC 33.8 -3.1Worcester, MA 80.2 -15.6Yakima, WA 51.4 -0.8York-Hanover, PA 75.4 -1.3Youngstown-Warren-Boardman, OH-PA 19.8 -10.3Yuba City, CA 164.4 -34.7Yuma, AZ 113.1 -8.8

Sources: Fiserv Lending Solutions, FHFA, Moody's Economy.com



Appendix 2-1: Non-Owner Occupied Share of Purchase Originations by $ Volume, States, %

2005 2006 2007 % Change2006 to 2007

Alaska 8.8 8.7 6.9 -20.7Alabama 14.4 15.5 14.4 -7.1Arkansas 13.4 14.6 14.4 -1.4Arizona 25.8 19.8 17.4 -12.1California 11.8 10.3 10.9 5.8Colorado 16.0 18.4 19.6 6.5Connecticut 8.9 8.4 7.2 -14.3District of Columbia 14.3 12.7 10.1 -20.5Delaware 27.3 23.7 24.4 3.0Florida 31.2 28.0 26.5 -5.4Georgia 13.8 15.5 14.4 -7.1Hawaii 33.4 31.4 30.2 -3.8Iowa 8.3 9.0 8.9 -1.1Idaho 26.6 25.6 21.1 -17.6Illinois 10.0 10.2 9.8 -3.9Indiana 8.9 10.0 9.4 -6.0Kansas 8.3 8.5 8.1 -4.7Kentucky 10.7 12.9 12.5 -3.1Louisiana 11.4 11.1 10.9 -1.8Massachusetts 12.7 11.8 12.1 2.5Maryland 10.6 9.1 8.6 -5.5Maine 23.6 22.5 24.5 8.9Michigan 10.8 11.5 11.4 -0.9Minnesota 11.5 11.5 11.4 -0.9Missouri 14.2 14.4 15.4 6.9Mississippi 12.3 14.3 14.9 4.2Montana 23.5 24.1 23.1 -4.1North Carolina 19.9 19.4 17.8 -8.2North Dakota 7.2 7.0 7.3 4.3Nebraska 7.4 7.3 6.8 -6.8New Hampshire 13.3 12.6 14.5 15.1New Jersey 14.0 11.7 11.8 0.9New Mexico 21.6 21.1 16.7 -20.9Nevada 25.0 22.2 20.9 -5.9New York 10.9 10.4 11.2 7.7Ohio 8.6 8.5 8.2 -3.5Oklahoma 10.8 11.7 10.8 -7.7Oregon 18.5 19.1 15.6 -18.3Pennsylvania 9.6 9.6 9.0 -6.3Rhode Island 15.8 14.1 16.0 13.5South Carolina 32.5 30.3 26.5 -12.5South Dakota 10.7 10.0 10.5 5.0Tennessee 12.1 14.6 13.2 -9.6Texas 11.1 13.0 11.3 -13.1Utah 18.7 19.8 17.8 -10.1Virginia 10.9 9.1 8.3 -8.8Vermont 28.0 27.4 24.8 -9.5Washington 12.2 13.1 11.6 -11.5Wisconsin 15.3 15.8 15.2 -3.8West Virginia 15.4 15.9 13.7 -13.8Wyoming 14.9 14.6 13.9 -4.8

Source: Federal Financial Institutions Examination Council: Home Mortgage Disclosure Act



2005 2006 2007 % Change2006 to 2007

Abilene, TX 8.0 10.1 10.0 -1.0Akron, OH 6.9 6.6 6.3 -4.5Albany, GA 13.9 13.7 14.9 8.8Albany, NY 11.5 10.7 8.4 -21.5Albuquerque, NM 19.6 19.0 12.8 -32.6Alexandria, LA 13.3 13.5 11.8 -12.6Allentown, PA 9.9 10.3 8.2 -20.4Altoona, PA 9.7 6.8 6.5 -4.4Amarillo, TX 8.0 8.5 7.4 -12.9Ames, IA 9.2 10.2 5.6 -45.1Anchorage, AK 8.7 8.7 6.4 -26.4Anderson, IN 7.6 7.1 7.2 1.4Anderson, SC 15.5 18.7 16.5 -11.8Ann Arbor, MI 8.4 7.8 7.0 -10.3Anniston, AL 7.4 7.4 10.3 39.2Appleton, WI 5.6 6.6 7.4 12.1Asheville, NC 24.2 28.8 27.2 -5.6Athens, GA 22.4 23.1 25.5 10.4Atlanta, GA 12.1 14.1 12.0 -14.9Atlantic City, NJ 34.9 28.6 26.8 -6.3Auburn, AL 16.7 21.4 22.0 2.8Augusta, GA 9.9 10.9 10.7 -1.8Austin, TX 16.7 18.9 14.8 -21.7Bakersfield, CA 17.0 13.6 10.6 -22.1Baltimore, MD 9.6 8.9 8.1 -9.0Bangor, ME 12.1 12.2 11.3 -7.4Barnstable Town, MA 47.4 46.4 51.4 10.8Baton Rouge, LA 13.6 13.4 11.4 -14.9Battle Creek, MI 9.1 9.8 7.4 -24.5Bay City, MI 5.5 6.8 5.3 -22.1Beaumont, TX 4.7 6.0 6.1 1.7Bellingham, WA 19.3 20.3 16.9 -16.7Bend, OR 35.6 36.5 30.1 -17.5Bethesda, MD 6.6 4.8 4.5 -6.3Billings, MT 12.2 11.0 11.7 6.4Binghamton, NY 9.0 10.5 8.5 -19.0Birmingham, AL 6.6 7.9 7.7 -2.5Bismarck, ND 5.7 6.8 6.3 -7.4Blacksburg, VA 15.1 17.2 17.6 2.3Bloomington, IN 13.3 10.8 13.2 22.2Bloomington, IL 7.0 5.8 6.8 17.2Boise City, ID 24.7 23.5 15.5 -34.0Boston, MA 10.9 10.3 9.5 -7.8Boulder, CO 13.9 14.8 14.5 -2.0Bowling Green, KY 16.0 14.4 20.6 43.1Bradenton, FL 41.0 34.8 33.3 -4.3Bremerton, WA 15.9 14.9 12.3 -17.4Bridgeport, CT 7.1 6.7 6.1 -9.0Brownsville, TX 29.6 30.8 30.1 -2.3Brunswick, GA 39.7 36.7 38.5 4.9Buffalo, NY 6.4 7.3 8.5 16.4Burlington, NC 11.7 12.2 9.8 -19.7Burlington, VT 12.5 13.2 12.8 -3.0Cambridge, MA 7.4 6.8 6.9 1.5Camden, NJ 6.8 6.8 6.4 -5.9Canton, OH 7.9 7.4 7.6 2.7Cape Coral, FL 45.5 43.9 42.6 -3.0Carson City, NV 18.3 15.0 14.1 -6.0Casper, WY 8.9 6.3 6.2 -1.6Cedar Rapids, IA 6.4 6.4 6.4 0.0Champaign, IL 12.0 14.8 13.7 -7.4Charleston, WV 7.0 11.6 6.9 -40.5Charleston, SC 30.9 27.3 25.5 -6.6Charlotte, NC 10.1 13.9 12.8 -7.9Charlottesville, VA 20.6 23.6 20.6 -12.7Chattanooga, TN 10.5 12.1 12.7 5.0

Appendix 2-2: Non-Owner Occupied Share of Purchase Originations by $ Volume, Metro Areas, %



Appendix 2-2: Non-Owner Occupied Share of Purchase Originations by $ Volume, Metro Areas, % (cont.)

2005 2006 2007 % Change2006 to 2007

Cheyenne, WY 9.2 6.5 6.7 3.1Chicago, IL 10.5 10.5 10.0 -4.8Chico, CA 22.2 18.7 14.0 -25.1Cincinnati, OH 8.3 8.1 9.4 16.0Clarksville, TN 9.2 8.3 6.6 -20.5Cleveland, TN 16.6 17.7 17.5 -1.1Cleveland, OH 8.5 8.9 6.9 -22.5Coeur d'Alene, ID 34.5 29.3 26.9 -8.2College Station, TX 24.2 26.3 24.6 -6.5Colorado Springs, CO 11.5 12.1 10.0 -17.4Columbia, MO 15.9 15.9 16.8 5.7Columbia, SC 11.7 14.9 12.2 -18.1Columbus, GA 9.6 11.3 10.1 -10.6Columbus, IN 8.1 6.5 6.4 -1.5Columbus, OH 8.6 8.5 8.3 -2.4Corpus Christi, TX 22.4 21.3 20.9 -1.9Corvallis, OR 16.6 19.6 15.5 -20.9Cumberland, MD 16.5 17.3 15.4 -11.0Dallas, TX 8.7 10.5 9.2 -12.4Dalton, GA 8.8 12.1 12.3 1.7Danville, IL 10.2 14.8 13.3 -10.1Danville, VA 13.6 15.5 13.4 -13.5Davenport, IL 8.4 9.6 9.4 -2.1Dayton, OH 8.6 8.1 6.8 -16.0Decatur, AL 8.1 10.7 7.9 -26.2Decatur, IL 6.7 6.5 10.4 60.0Deltona, FL 35.3 33.1 30.4 -8.2Denver, CO 9.7 11.6 10.9 -6.0Des Moines, IA 6.6 7.1 7.8 9.9Detroit, MI 11.6 12.8 11.1 -13.3Dothan, AL 11.9 12.1 11.9 -1.7Dover, DE 9.2 9.7 8.8 -9.3Dubuque, IA 8.0 11.0 9.3 -15.5Duluth, MN 19.8 17.9 16.9 -5.6Durham, NC 11.6 12.4 12.4 0.0Eau Claire, WI 15.8 12.8 16.0 25.0Edison, NJ 14.6 12.4 14.3 15.3El Centro, CA 14.3 9.7 8.4 -13.4El Paso, TX 10.9 14.0 8.6 -38.6Elizabethtown, KY 13.4 20.5 16.1 -21.5Elkhart, IN 6.5 6.7 5.5 -17.9Elmira, NY 6.5 8.0 8.5 6.3Erie, PA 5.8 6.8 6.8 0.0Eugene, OR 16.8 17.0 14.4 -15.3Evansville, IN 7.2 8.5 6.0 -29.4Fairbanks, AK 7.3 5.0 5.0 0.0Fargo, ND 8.9 7.0 7.9 12.9Farmington, NM 12.5 11.4 8.8 -22.8Fayetteville, NC 7.5 10.5 6.9 -34.3Fayetteville, AR 15.0 16.5 14.9 -9.7Flagstaff, AZ 39.4 39.1 38.8 -0.8Flint, MI 7.3 7.0 7.0 0.0Florence, SC 10.5 11.8 10.1 -14.4Florence, AL 11.9 11.8 15.1 28.0Fond du Lac, WI 8.1 9.4 9.5 1.1Fort Collins, CO 16.5 18.8 18.3 -2.7Fort Lauderdale, FL 23.6 19.7 19.3 -2.0Fort Smith, AR 15.0 14.1 12.7 -9.9Fort Walton Beach, FL 42.0 35.2 37.4 6.2Fort Wayne, IN 6.6 9.0 5.5 -38.9Fort Worth, TX 10.5 11.9 9.8 -17.6Fresno, CA 18.5 12.8 9.8 -23.4Gadsden, AL 9.0 8.7 8.9 2.3Gainesville, FL 22.8 24.3 18.6 -23.5Gainesville, GA 12.8 13.8 16.9 22.5Gary, IN 8.1 8.2 7.6 -7.3



2005 2006 2007 % Change2006 to 2007

Glens Falls, NY 29.1 28.7 30.4 5.9Goldsboro, NC 8.0 6.6 8.3 25.8Grand Forks, ND 8.8 10.0 10.6 6.0Grand Junction, CO 12.3 13.3 14.1 6.0Grand Rapids, MI 8.7 9.2 8.7 -5.4Great Falls, MT 12.0 12.2 12.3 0.8Greeley, CO 11.0 13.8 12.1 -12.3Green Bay, WI 10.0 10.9 10.6 -2.8Greensboro, NC 10.1 10.2 10.2 0.0Greenville, NC 12.4 14.8 12.8 -13.5Greenville, SC 13.6 16.0 15.4 -3.8Gulfport, MS 23.9 28.4 30.1 6.0Hagerstown, MD 15.9 12.9 11.7 -9.3Hanford, CA 16.0 11.1 6.0 -45.9Harrisburg, PA 7.8 7.5 8.7 16.0Harrisonburg, VA 21.1 17.9 15.6 -12.8Hartford, CT 7.7 7.2 5.8 -19.4Hattiesburg, MS 16.4 14.3 14.5 1.4Hickory, NC 12.6 13.5 14.4 6.7Hinesville, GA 14.0 11.3 9.8 -13.3Holland, MI 10.2 11.2 10.6 -5.4Honolulu, HI 24.3 21.6 18.9 -12.5Hot Springs, AR 29.4 29.4 32.4 10.2Houma, LA 8.6 6.5 9.3 43.1Houston, TX 8.5 10.1 9.5 -5.9Huntington, WV 11.3 11.8 10.9 -7.6Huntsville, AL 8.0 12.0 8.9 -25.8Idaho Falls, ID 13.3 15.2 10.8 -28.9Indianapolis, IN 8.4 10.2 8.5 -16.7Iowa City, IA 10.9 13.2 11.4 -13.6Ithaca, NY 12.5 11.6 11.0 -5.2Jackson, MI 11.1 14.3 11.0 -23.1Jackson, MS 7.7 10.9 9.5 -12.8Jackson, TN 18.3 12.5 12.0 -4.0Jacksonville, FL 22.5 20.1 15.7 -21.9Jacksonville, NC 25.7 16.6 13.3 -19.9Janesville, WI 11.6 12.8 10.5 -18.0Jefferson City, MO 10.7 14.9 11.2 -24.8Johnson City, TN 11.3 13.5 13.5 0.0Johnstown, PA 7.6 8.1 8.0 -1.2Jonesboro, AR 14.7 19.3 19.5 1.0Joplin, MO 10.1 12.9 10.5 -18.6Kalamazoo, MI 13.1 13.2 13.1 -0.8Kankakee, IL 14.2 14.0 15.4 10.0Kansas City, MO 9.9 10.2 11.2 9.8Kennewick, WA 9.6 12.4 8.9 -28.2Killeen, TX 8.0 12.2 8.8 -27.9Kingsport, TN 11.1 13.9 12.0 -13.7Kingston, NY 16.2 13.3 17.9 34.6Knoxville, TN 9.7 13.7 12.0 -12.4Kokomo, IN 5.5 4.8 6.0 25.0La Crosse, WI 11.5 11.1 16.0 44.1Lafayette, IN 9.8 12.2 9.4 -23.0Lafayette, LA 8.7 7.3 6.8 -6.8Lake Charles, LA 6.6 7.6 6.4 -15.8Lake County, IL 7.1 7.5 6.7 -10.7Lake Havasu, AZ 49.8 45.4 40.9 -9.9Lakeland, FL 36.0 32.5 23.1 -28.9Lancaster, PA 8.6 8.6 8.7 1.2Lansing, MI 7.6 7.3 7.7 5.5Laredo, TX 6.9 6.0 7.6 26.7Las Cruces, NM 20.9 24.7 18.9 -23.5Las Vegas, NV 25.8 23.5 22.0 -6.4Lawrence, KS 16.8 18.0 14.6 -18.9Lawton, OK 7.8 10.6 8.2 -22.6Lebanon, PA 9.3 8.8 16.6 88.6




Appendix 2-2: Non-Owner Occupied Share of Purchase Originations by $ Volume, Metro Areas, % (cont.)2005 2006 2007 % Change

2006 to 2007Lewiston, ID 10.4 9.6 9.3 -3.1Lewiston, ME 17.8 11.9 11.6 -2.5Lexington, KY 15.0 15.8 15.3 -3.2Lima, OH 9.7 7.7 6.5 -15.6Lincoln, NE 9.4 8.5 8.1 -4.7Little Rock, AR 8.3 9.4 9.6 2.1Logan, UT 11.7 12.5 12.7 1.6Longview, TX 12.7 11.0 9.6 -12.7Longview, WA 12.2 14.6 12.8 -12.3Los Angeles, CA 10.2 9.3 9.9 6.5Louisville, KY 8.8 11.1 10.7 -3.6Lubbock, TX 14.8 12.7 13.7 7.9Lynchburg, VA 21.6 18.3 18.3 0.0Macon, GA 9.9 13.3 13.6 2.3Madera, CA 20.3 16.6 15.1 -9.0Madison, WI 10.3 11.9 11.3 -5.0Manchester, NH 7.2 5.8 6.2 6.9Mansfield, OH 9.0 11.8 9.5 -19.5McAllen, TX 14.8 17.6 18.0 2.3Medford, OR 22.7 19.0 14.7 -22.6Memphis, TN 8.4 10.2 10.4 2.0Merced, CA 18.3 12.7 10.3 -18.9Miami, FL 24.2 19.9 22.9 15.1Michigan City, IN 22.1 18.3 17.8 -2.7Midland, TX 4.9 6.9 6.3 -8.7Milwaukee, WI 12.2 12.5 9.6 -23.2Minneapolis, MN 10.0 9.8 9.4 -4.1Missoula, MT 17.4 19.6 17.7 -9.7Mobile, AL 13.6 10.3 11.7 13.6Modesto, CA 11.4 8.5 8.3 -2.4Monroe, LA 9.0 9.6 9.3 -3.1Monroe, MI 2.8 5.0 4.0 -20.0Montgomery, AL 12.1 12.3 9.6 -22.0Morgantown, WV 17.2 24.5 21.7 -11.4Morristown, TN 18.8 20.9 19.3 -7.7Mount Vernon, WA 17.3 17.1 13.3 -22.2Muncie, IN 12.9 8.7 11.0 26.4Muskegon, MI 11.1 7.4 11.0 48.6Myrtle Beach, SC 66.3 63.1 60.4 -4.3Napa, CA 15.2 18.3 21.2 15.8Naples, FL 49.7 45.1 49.3 9.3Nashville, TN 9.7 12.7 10.9 -14.2Nassau, NY 11.5 10.7 13.3 24.3New Haven, CT 11.5 9.8 8.0 -18.4New Orleans, LA 13.2 12.8 13.9 8.6New York, NY 9.8 9.2 9.6 4.3Newark, NJ 7.7 7.5 6.1 -18.7Niles, MI 33.5 29.8 30.9 3.7Norwich, CT 13.2 13.0 11.8 -9.2Oakland, CA 7.2 6.1 5.9 -3.3Ocala, FL 29.6 33.1 29.7 -10.3Ocean City, NJ 83.3 77.6 75.5 -2.7Odessa, TX 11.6 8.0 7.0 -12.5Ogden, UT 10.6 13.6 11.4 -16.2Oklahoma City, OK 10.4 11.0 10.6 -3.6Olympia, WA 11.2 11.4 9.9 -13.2Omaha, NE 6.5 7.1 6.8 -4.2Orlando, FL 28.0 27.7 24.5 -11.6Oshkosh, WI 10.9 13.1 10.8 -17.6Owensboro, KY 8.5 8.5 10.3 21.2Oxnard, CA 8.4 9.2 10.3 12.0Palm Bay, FL 30.6 28.4 21.5 -24.3Palm Coast, FL 53.1 49.7 45.0 -9.5Panama City, FL 54.3 50.6 55.0 8.7Parkersburg, WV 9.3 9.2 12.4 34.8Pascagoula, MS 14.5 19.2 15.4 -19.8



2005 2006 2007 % Change2006 to 2007

Peabody, MA 8.1 7.6 7.6 0.0Pensacola, FL 26.0 25.9 19.5 -24.7Peoria, IL 7.3 8.5 7.9 -7.1Philadelphia, PA 9.4 9.0 8.1 -10.0Phoenix, AZ 24.5 17.7 15.2 -14.1Pine Bluff, AR 10.4 9.2 6.7 -27.2Pittsburgh, PA 6.2 6.3 6.0 -4.8Pittsfield, MA 30.7 28.5 28.6 0.4Pocatello, ID 18.2 17.9 12.0 -33.0Port St. Lucie, FL 35.7 31.6 27.0 -14.6Portland, ME 22.0 21.3 23.1 8.5Portland, OR 13.6 14.6 11.5 -21.2Poughkeepsie, NY 8.5 8.3 7.3 -12.0Prescott, AZ 35.2 28.9 25.7 -11.1Providence, RI 13.7 12.3 13.5 9.8Provo, UT 13.7 16.5 15.6 -5.5Pueblo, CO 12.1 13.3 12.4 -6.8Punta Gorda, FL 44.1 45.0 45.3 0.7Racine, WI 12.0 9.4 10.9 16.0Raleigh, NC 9.1 11.6 9.9 -14.7Rapid City, SD 11.4 10.7 9.0 -15.9Reading, PA 8.5 9.0 7.9 -12.2Redding, CA 22.2 18.4 13.9 -24.5Reno, NV 20.6 15.1 18.3 21.2Richmond, VA 9.9 9.5 8.7 -8.4Riverside, CA 14.3 11.5 13.3 15.7Roanoke, VA 16.6 14.9 14.0 -6.0Rochester, MN 6.6 7.0 8.0 14.3Rochester, NY 8.7 8.1 8.3 2.5Rockford, IL 11.3 11.6 10.7 -7.8Rockingham County, NH 8.9 8.2 9.0 9.8Rocky Mount, NC 11.8 12.4 10.9 -12.1Rome, GA 14.0 15.4 12.2 -20.8Sacramento, CA 14.6 12.1 12.0 -0.8Saginaw, MI 6.5 6.8 4.8 -29.4Salem, OR 11.6 13.5 12.1 -10.4Salinas, CA 14.5 13.7 20.1 46.7Salisbury, MD 21.7 22.0 18.9 -14.1Salt Lake City, UT 20.0 20.8 18.7 -10.1San Angelo, TX 13.3 14.1 10.2 -27.7San Antonio, TX 11.9 16.3 11.6 -28.8San Diego, CA 13.1 11.7 14.2 21.4San Francisco, CA 9.2 7.8 8.4 7.7San Jose, CA 6.6 5.5 5.1 -7.3San Luis Obispo, CA 25.6 22.8 26.6 16.7Sandusky, OH 27.4 19.3 24.1 24.9Santa Ana, CA 10.0 9.8 10.5 7.1Santa Barbara, CA 16.7 19.8 19.1 -3.5Santa Cruz, CA 16.6 15.0 20.2 34.7Santa Fe, NM 29.2 28.1 26.6 -5.3Santa Rosa, CA 14.0 14.3 18.3 28.0Savannah, GA 20.6 19.7 21.1 7.1Scranton, PA 13.4 13.8 12.6 -8.7Seattle, WA 9.4 10.4 9.4 -9.6Sebastian, FL 41.0 35.3 37.7 6.8Sheboygan, WI 17.6 11.4 17.7 55.3Sherman, TX 16.4 16.7 18.0 7.8Shreveport, LA 5.9 6.1 6.8 11.5Sioux City, IA 9.0 8.0 6.9 -13.8Sioux Falls, SD 7.9 7.1 8.1 14.1South Bend, IN 14.9 15.1 12.3 -18.5Spartanburg, SC 12.1 14.6 13.3 -8.9Spokane, WA 15.0 15.6 11.2 -28.2Springfield, IL 9.1 9.8 8.8 -10.2Springfield, MA 11.6 9.3 7.8 -16.1Springfield, MO 16.0 15.3 15.5 1.3




2005 2006 2007 % Change2006 to 2007

Springfield, OH 7.5 9.5 7.4 -22.1St. Cloud, MN 11.4 11.8 13.0 10.2St. George, UT 33.6 29.0 28.8 -0.7St. Joseph, MO 11.2 11.0 13.2 20.0St. Louis, MO 11.6 11.0 10.7 -2.7State College, PA 10.7 12.0 13.6 13.3Stockton, CA 9.4 6.9 6.9 0.0Sumter, SC 8.3 9.9 8.4 -15.2Syracuse, NY 8.3 8.9 9.0 1.1Tacoma, WA 12.7 12.8 10.2 -20.3Tallahassee, FL 19.7 21.7 13.9 -35.9Tampa, FL 25.5 25.1 20.4 -18.7Terre Haute, IN 10.3 8.4 7.2 -14.3Texarkana, TX 16.1 15.1 11.9 -21.2Toledo, OH 9.3 10.9 10.6 -2.8Topeka, KS 9.3 9.7 8.3 -14.4Trenton, NJ 8.8 9.3 6.5 -30.1Tucson, AZ 24.0 20.5 17.8 -13.2Tulsa, OK 10.3 11.4 9.8 -14.0Tuscaloosa, AL 11.8 15.9 20.5 28.9Tyler, TX 13.1 12.4 9.3 -25.0Utica, NY 10.6 10.9 10.8 -0.9Valdosta, GA 18.7 21.1 16.9 -19.9Vallejo, CA 9.5 7.5 6.9 -8.0Victoria, TX 13.7 13.1 12.8 -2.3Vineland, NJ 11.3 11.7 9.3 -20.5Virginia Beach, VA 16.4 13.6 11.8 -13.2Visalia, CA 19.3 13.7 9.9 -27.7Waco, TX 11.4 14.1 10.5 -25.5Warner Robins, GA 6.8 7.2 9.0 25.0Warren, MI 5.5 6.1 5.9 -3.3Washington, DC 8.9 6.4 5.2 -18.8Waterloo, IA 12.4 8.9 7.5 -15.7Wausau, WI 9.8 10.6 12.7 19.8Weirton, OH 7.3 6.7 6.4 -4.5Wenatchee, WA 27.7 31.3 28.1 -10.2West Palm Beach, FL 29.7 27.7 27.5 -0.7Wheeling, WV 8.5 10.4 7.6 -26.9Wichita Falls, TX 8.8 10.3 10.5 1.9Wichita, KS 8.3 9.2 7.8 -15.2Williamsport, PA 12.8 16.0 17.1 6.9Wilmington, DE 8.4 8.8 7.4 -15.9Wilmington, NC 46.6 42.3 38.7 -8.5Winchester, VA 13.7 12.9 11.0 -14.7Winston, NC 8.3 9.9 9.4 -5.1Worcester, MA 6.7 6.5 6.8 4.6Yakima, WA 12.5 14.8 16.4 10.8York, PA 6.1 6.6 6.3 -4.5Youngstown, OH 7.1 6.9 6.5 -5.8Yuba City, CA 10.4 9.2 7.1 -22.8Yuma, AZ 20.4 17.0 10.5 -38.2





Appendix 2-3: Nonprime Share of Total Purchase Originations by $ Volume, States, %

2005 2006 2007 % Change2006 to 2007

Alabama 19.6 21.1 15.1 -28.4Alaska 15.5 19.2 10.4 -45.8Arizona 20.7 26.2 15.7 -40.1Arkansas 17.8 20.3 14.6 -28.1California 19.6 23.4 13.5 -42.3Colorado 17.1 17.6 9.8 -44.3Connecticut 17.4 19.9 11.7 -41.2Delaware 16.2 20.0 13.2 -34.0District of Columbia 14.1 20.4 11.3 -44.6Florida 23.1 30.8 20.1 -34.7Georgia 21.4 22.7 14.4 -36.6Hawaii 16.4 21.2 10.6 -50.0Idaho 15.9 20.2 13.0 -35.6Illinois 23.3 26.0 15.5 -40.4Indiana 20.5 23.3 13.8 -40.8Iowa 17.6 18.8 12.4 -34.0Kansas 17.7 18.2 12.0 -34.1Kentucky 18.8 20.1 13.8 -31.3Louisiana 22.3 23.4 18.0 -23.1Maine 19.2 21.8 13.9 -36.2Maryland 22.8 27.6 15.6 -43.5Massachusetts 16.4 20.4 10.4 -49.0Michigan 22.4 25.7 15.5 -39.7Minnesota 20.0 22.4 13.1 -41.5Mississippi 25.3 26.4 19.3 -26.9Missouri 21.4 23.9 15.5 -35.1Montana 13.7 16.1 10.5 -34.8Nebraska 17.3 18.5 12.8 -30.8Nevada 22.7 27.9 16.3 -41.6New Hampshire 17.9 20.0 12.2 -39.0New Jersey 19.4 24.3 14.7 -39.5New Mexico 17.0 20.8 13.6 -34.6New York 21.3 24.6 13.9 -43.5North Carolina 15.3 17.5 11.6 -33.7North Dakota 12.9 15.6 11.2 -28.2Ohio 20.9 22.5 13.3 -40.9Oklahoma 21.2 21.7 15.4 -29.0Oregon 16.5 19.5 11.7 -40.0Pennsylvania 16.4 19.4 13.2 -32.0Rhode Island 25.4 26.8 14.2 -47.0South Carolina 15.8 18.7 13.1 -29.9South Dakota 13.6 14.4 10.5 -27.1Tennessee 21.5 22.7 15.2 -33.0Texas 21.8 23.4 14.3 -38.9Utah 19.1 21.3 15.7 -26.3Vermont 13.2 16.3 10.5 -35.6Virginia 16.0 20.9 12.2 -41.6Washington 16.4 19.2 11.9 -38.0West Virginia 17.1 20.8 16.3 -21.6Wisconsin 16.8 19.8 12.1 -38.9Wyoming 17.6 19.0 14.6 -23.2




2005 2006 2007 % Change2006 to 2007

Abilene, TX 21.5 19.7 11.4 -42.1Akron, OH 21.5 23.7 13.6 -42.6Albany, GA 23.9 24.1 17.9 -25.7Albany-Schenectady-Troy, NY 18.9 21.9 14.3 -34.7Albuquerque, NM 17.3 21.8 13.1 -39.9Alexandria, LA 24.8 29.3 24.9 -15.0Allentown-Bethlehem-Easton, PA-NJ 18.2 21.2 14.1 -33.5Altoona, PA 19.1 20.7 15.0 -27.5Amarillo, TX 16.2 18.1 13.7 -24.3Ames, IA 11.2 11.6 6.7 -42.2Anchorage, AK 16.1 20.0 10.2 -49.0Anderson, IN 29.2 31.3 18.3 -41.5Anderson, SC 19.2 20.1 15.9 -20.9Ann Arbor, MI 14.0 16.0 8.9 -44.4Anniston-Oxford, AL 27.1 26.5 19.5 -26.4Appleton, WI 13.5 16.4 10.4 -36.6Asheville, NC 14.8 17.5 12.1 -30.9Athens-Clarke County, GA 16.2 15.2 12.2 -19.7Atlanta-Sandy Springs-Marietta, GA 22.2 23.9 14.2 -40.6Atlantic City, NJ 21.8 26.1 16.3 -37.5Auburn-Opelika, AL 12.5 15.6 10.4 -33.3Augusta-Richmond County, GA-SC 15.7 17.4 12.8 -26.4Austin-Round Rock, TX 13.2 15.3 9.4 -38.6Bakersfield, CA 32.2 34.7 19.8 -42.9Baltimore-Towson, MD 20.4 25.1 14.9 -40.6Bangor, ME 24.2 27.7 19.6 -29.2Barnstable Town, MA 13.2 16.4 9.3 -43.3Baton Rouge, LA 23.0 24.8 16.7 -32.7Battle Creek, MI 26.2 28.3 18.1 -36.0Bay City, MI 19.9 23.7 15.2 -35.9Beaumont-Port Arthur, TX 21.4 24.7 18.2 -26.3Bellingham, WA 12.2 14.7 8.9 -39.5Bend, OR 11.9 17.7 11.4 -35.6Bethesda-Frederick-Rockville, MD 16.1 20.6 10.1 -51.0Billings, MT 13.2 14.0 8.7 -37.9Binghamton, NY 19.4 23.0 15.4 -33.0Birmingham-Hoover, AL 20.8 21.3 15.1 -29.1Bismarck, ND 10.8 14.3 12.0 -16.1Blacksburg-Christiansburg-Radford, VA 11.5 13.8 9.5 -31.2Bloomington, IN 14.7 18.9 10.8 -42.9Bloomington-Normal, IL 12.6 12.5 7.1 -43.2Boise City-Nampa, ID 16.5 20.7 13.5 -34.8Boston-Quincy, MA 17.0 21.7 10.4 -52.1Boulder, CO 8.2 9.8 5.2 -46.9Bowling Green, KY 15.7 15.8 11.6 -26.6Bradenton-Sarasota-Venice, FL 16.1 22.4 13.9 -37.9Bremerton-Silverdale, WA 15.6 18.7 11.1 -40.6Bridgeport-Stamford-Norwalk, CT 13.2 16.4 9.4 -42.7Brownsville-Harlingen, TX 31.9 33.1 23.9 -27.8Brunswick, GA 13.8 15.5 12.3 -20.6Buffalo-Niagara Falls, NY 16.2 18.6 13.1 -29.6Burlington, NC 20.8 18.8 13.2 -29.8Burlington-South Burlington, VT 11.8 12.9 7.9 -38.8Cambridge-Newton-Framingham, MA 12.8 15.6 7.8 -50.0Camden, NJ 22.0 26.3 16.5 -37.3Canton-Massillon, OH 25.0 25.8 15.9 -38.4Cape Coral-Fort Myers, FL 23.9 33.7 20.3 -39.8Carson City, NV 15.4 19.3 7.9 -59.1Casper, WY 21.2 20.5 15.1 -26.3Cedar Rapids, IA 14.4 15.7 10.3 -34.4Champaign-Urbana, IL 11.3 12.5 9.0 -28.0Charleston, WV 14.2 17.1 14.8 -13.5Charleston-North Charleston-Summerville, SC 13.2 17.7 11.3 -36.2Charlotte-Gastonia-Concord, NC-SC 17.2 18.6 11.7 -37.1Charlottesville, VA 10.9 14.5 9.8 -32.4

Appendix 2-4: Nonprime Share of Total Purchase Originations by $ Volume, Metro Areas, %



2005 2006 2007 % Change2006 to 2007

Chattanooga, TN-GA 23.6 24.6 17.1 -30.5Cheyenne, WY 17.0 17.6 12.2 -30.7Chicago-Naperville-Joliet, IL 24.7 27.4 16.0 -41.6Chico, CA 17.8 21.2 12.1 -42.9Cincinnati-Middletown, OH-KY-IN 18.4 19.9 11.9 -40.2Clarksville, TN-KY 17.0 17.3 9.2 -46.8Cleveland, TN 25.5 22.5 18.3 -18.7Cleveland-Elyria-Mentor, OH 21.4 23.6 13.4 -43.2Coeur d'Alene, ID 16.3 20.7 11.7 -43.5College Station-Bryan, TX 16.0 16.1 9.5 -41.0Colorado Springs, CO 17.3 17.8 10.7 -39.9Columbia, MO 12.0 14.9 9.4 -36.9Columbia, SC 20.4 20.6 14.2 -31.1Columbus, GA-AL 20.6 22.1 15.0 -32.1Columbus, IN 15.4 19.6 10.6 -45.9Columbus, OH 18.3 20.4 11.5 -43.6Corpus Christi, TX 19.6 23.5 14.5 -38.3Corvallis, OR 9.3 11.4 7.4 -35.1Cumberland, MD-WV 24.5 29.5 20.5 -30.5Dallas-Plano-Irving, TX 21.2 22.0 12.8 -41.8Dalton, GA 22.3 21.8 18.6 -14.7Danville, IL 22.4 23.5 19.7 -16.2Danville, VA 23.6 27.3 20.1 -26.4Davenport-Moline-Rock Island, IA-IL 21.4 22.4 13.3 -40.6Dayton, OH 20.9 21.5 12.9 -40.0Decatur, AL 24.2 25.3 16.8 -33.6Decatur, IL 17.2 22.6 16.5 -27.0Deltona-Daytona Beach-Ormond Beach, FL 24.3 31.1 17.3 -44.4Denver-Aurora-Broomfield, CO 19.3 19.6 10.1 -48.5Des Moines-West Des Moines, IA 17.6 17.8 11.6 -34.8Detroit-Livonia-Dearborn, MI 35.3 39.7 25.2 -36.5Dothan, AL 19.5 22.8 19.9 -12.7Dover, DE 20.5 24.8 16.2 -34.7Dubuque, IA 13.3 13.6 9.4 -30.9Duluth, MN-WI 18.4 21.8 14.3 -34.4Durham-Chapel Hill, NC 13.2 14.8 9.5 -35.8Eau Claire, WI 15.9 18.7 12.4 -33.7Edison-New Brunswick, NJ 16.5 21.1 12.8 -39.3El Centro, CA 35.5 39.4 24.5 -37.8El Paso, TX 24.5 32.7 21.5 -34.3Elizabethtown, KY 22.2 23.6 16.0 -32.2Elkhart-Goshen, IN 22.2 26.2 15.1 -42.4Elmira, NY 22.4 23.2 15.6 -32.8Erie, PA 16.9 18.6 12.3 -33.9Eugene-Springfield, OR 16.9 19.5 10.5 -46.2Evansville, IN-KY 17.2 18.8 12.6 -33.0Fairbanks, AK 16.1 16.7 11.0 -34.1Fargo, ND-MN 12.6 15.4 9.5 -38.3Farmington, NM 23.6 23.1 15.7 -32.0Fayetteville, NC 18.5 17.8 12.2 -31.5Fayetteville-Springdale-Rogers, AR-MO 13.9 17.2 12.1 -29.7Flagstaff, AZ 11.1 16.6 8.5 -48.8Flint, MI 26.2 30.4 20.6 -32.2Florence, SC 24.7 29.1 20.3 -30.2Florence-Muscle Shoals, AL 21.1 21.4 17.1 -20.1Fond du Lac, WI 16.9 18.6 11.3 -39.2Fort Collins-Loveland, CO 14.3 14.8 7.5 -49.3Fort Lauderdale-Pompano Beach-Deerfield Beach, FL 26.9 35.2 24.3 -31.0Fort Smith, AR-OK 22.0 22.1 16.2 -26.7Fort Walton Beach-Crestview-Destin, FL 12.3 17.1 12.6 -26.3Fort Wayne, IN 19.9 23.1 13.4 -42.0Fort Worth-Arlington, TX 21.5 23.6 13.9 -41.1Fresno, CA 27.2 30.7 18.5 -39.7Gadsden, AL 26.6 26.9 21.2 -21.2Gainesville, FL 14.2 18.5 11.2 -39.5Gainesville, GA 17.8 17.4 13.0 -25.3

Appendix 2-4: Nonprime Share of Total Purchase Originations by $ Volume, Metro Areas, % (cont.)



2005 2006 2007 % Change2006 to 2007

Gary, IN 24.0 27.9 17.1 -38.7Glens Falls, NY 21.3 24.2 16.4 -32.2Goldsboro, NC 16.0 18.5 10.6 -42.7Grand Forks, ND-MN 14.6 18.6 10.4 -44.1Grand Junction, CO 18.8 21.8 13.4 -38.5Grand Rapids-Wyoming, MI 21.7 24.0 13.4 -44.2Great Falls, MT 14.1 16.0 10.0 -37.5Greeley, CO 22.4 22.6 12.6 -44.2Green Bay, WI 13.8 17.0 10.4 -38.8Greensboro-High Point, NC 18.8 20.4 12.1 -40.7Greenville, NC 16.3 16.7 12.8 -23.4Greenville-Mauldin-Easley, SC 16.3 18.1 11.6 -35.9Gulfport-Biloxi, MS 18.2 22.5 15.1 -32.9Hagerstown-Martinsburg, MD-WV 20.4 25.0 14.8 -40.8Hanford-Corcoran, CA 28.0 31.2 18.1 -42.0Harrisburg-Carlisle, PA 14.5 17.2 12.3 -28.5Harrisonburg, VA 13.9 18.0 11.9 -33.9Hartford-West Hartford-East Hartford, CT 18.8 20.7 12.4 -40.1Hattiesburg, MS 22.8 21.8 16.7 -23.4Hickory-Lenoir-Morganton, NC 19.3 22.0 14.3 -35.0Hinesville-Fort Stewart, GA 21.7 16.6 13.3 -19.9Holland-Grand Haven, MI 14.8 16.1 9.5 -41.0Honolulu, HI 16.2 20.2 9.4 -53.5Hot Springs, AR 18.3 20.5 13.1 -36.1Houma-Bayou Cane-Thibodaux, LA 21.9 23.2 22.0 -5.2Houston-Sugar Land-Baytown, TX 25.8 27.0 15.4 -43.0Huntington-Ashland, WV-KY-OH 18.5 19.7 14.8 -24.9Huntsville, AL 16.1 16.3 10.5 -35.6Idaho Falls, ID 16.9 20.6 14.0 -32.0Indianapolis-Carmel, IN 19.1 21.7 12.3 -43.3Iowa City, IA 7.8 9.6 6.4 -33.3Ithaca, NY 8.0 8.1 6.0 -25.9Jackson, MI 28.6 30.4 15.7 -48.4Jackson, MS 24.3 24.1 15.7 -34.9Jackson, TN 25.6 28.5 20.3 -28.8Jacksonville, FL 19.4 24.1 15.1 -37.3Jacksonville, NC 11.8 13.2 8.3 -37.1Janesville, WI 21.9 23.4 13.6 -41.9Jefferson City, MO 15.3 17.7 13.5 -23.7Johnson City, TN 18.0 20.1 13.7 -31.8Johnstown, PA 17.5 19.9 13.4 -32.7Jonesboro, AR 17.1 21.3 17.2 -19.2Joplin, MO 22.6 26.5 18.1 -31.7Kalamazoo-Portage, MI 19.5 21.0 13.4 -36.2Kankakee-Bradley, IL 24.6 28.0 16.2 -42.1Kansas City, MO-KS 21.5 22.4 13.9 -37.9Kennewick-Pasco-Richland, WA 16.7 18.6 10.8 -41.9Killeen-Temple-Fort Hood, TX 14.4 17.9 10.2 -43.0Kingsport-Bristol-Bristol, TN-VA 18.8 22.4 18.6 -17.0Kingston, NY 19.9 23.1 15.0 -35.1Knoxville, TN 18.1 19.8 13.7 -30.8Kokomo, IN 24.2 27.8 17.8 -36.0La Crosse, WI-MN 14.0 14.6 11.7 -19.9Lafayette, IN 13.7 15.5 10.0 -35.5Lafayette, LA 18.6 18.9 14.2 -24.9Lake Charles, LA 22.7 23.7 20.4 -13.9Lake County-Kenosha County, IL-WI 16.8 20.7 12.4 -40.1Lake Havasu City-Kingman, AZ 22.0 28.0 15.7 -43.9Lakeland-Winter Haven, FL 28.2 35.4 23.5 -33.6Lancaster, PA 14.2 15.5 10.6 -31.6Lansing-East Lansing, MI 24.2 25.7 14.3 -44.4Laredo, TX 35.1 38.0 24.4 -35.8Las Cruces, NM 18.7 21.1 14.1 -33.2Las Vegas-Paradise, NV 24.5 30.0 17.9 -40.3Lawrence, KS 11.3 11.6 6.4 -44.8Lawton, OK 21.0 20.1 11.9 -40.8





2005 2006 2007 % Change2006 to 2007

Lebanon, PA 15.7 17.4 11.0 -36.8Lewiston, ID-WA 15.1 13.4 11.2 -16.4Lewiston-Auburn, ME 25.7 28.4 18.1 -36.3Lexington-Fayette, KY 14.7 15.4 10.2 -33.8Lima, OH 23.9 24.1 15.4 -36.1Lincoln, NE 12.8 15.3 10.8 -29.4Little Rock-North Little Rock-Conway, AR 17.0 18.9 11.3 -40.2Logan, UT-ID 15.0 15.2 12.6 -17.1Longview, TX 23.1 24.2 15.7 -35.1Longview, WA 24.0 26.2 16.5 -37.0Los Angeles-Long Beach-Glendale, CA 21.5 25.4 15.2 -40.2Louisville-Jefferson County, KY-IN 18.7 20.0 12.6 -37.0Lubbock, TX 18.0 17.6 10.5 -40.3Lynchburg, VA 16.1 20.4 13.7 -32.8Macon, GA 26.8 27.0 15.9 -41.1Madera-Chowchilla, CA 29.5 33.6 20.8 -38.1Madison, WI 10.3 11.8 7.4 -37.3Manchester-Nashua, NH 17.5 19.3 12.0 -37.8Mansfield, OH 21.7 24.2 16.7 -31.0McAllen-Edinburg-Mission, TX 37.7 41.8 32.1 -23.2Medford, OR 16.3 18.9 10.8 -42.9Memphis, TN-MS-AR 28.0 28.6 19.3 -32.5Merced, CA 31.5 33.7 21.9 -35.0Miami-Miami Beach-Kendall, FL 31.4 40.4 28.3 -30.0Michigan City-La Porte, IN 21.1 23.2 17.0 -26.7Midland, TX 21.1 20.9 15.3 -26.8Milwaukee-Waukesha-West Allis, WI 18.7 23.1 13.2 -42.9Minneapolis-St. Paul-Bloomington, MN-WI 20.2 22.7 12.8 -43.6Missoula, MT 11.2 13.7 9.7 -29.2Mobile, AL 23.4 26.6 17.7 -33.5Modesto, CA 29.0 32.0 19.1 -40.3Monroe, LA 22.4 25.9 17.2 -33.6Monroe, MI 20.6 23.0 13.7 -40.4Montgomery, AL 17.1 18.4 13.2 -28.3Morgantown, WV 8.9 14.6 10.5 -28.1Morristown, TN 22.0 25.5 18.3 -28.2Mount Vernon-Anacortes, WA 16.6 20.5 11.6 -43.4Muncie, IN 23.6 25.8 15.2 -41.1Muskegon-Norton Shores, MI 26.6 29.2 17.6 -39.7Myrtle Beach-North Myrtle Beach-Conway, SC 14.6 20.0 15.0 -25.0Napa, CA 12.4 14.9 11.4 -23.5Naples-Marco Island, FL 13.3 23.6 15.7 -33.5Nashville-Davidson-Murfreesboro-Franklin, TN 18.1 19.0 11.9 -37.4Nassau-Suffolk, NY 23.6 27.3 15.6 -42.9New Haven-Milford, CT 23.1 25.5 15.3 -40.0New Orleans-Metairie-Kenner, LA 20.5 20.2 16.2 -19.8New York-White Plains-Wayne, NY-NJ 20.7 24.6 13.3 -45.9Newark-Union, NJ-PA 21.9 26.9 16.4 -39.0Niles-Benton Harbor, MI 18.3 18.9 11.3 -40.2Norwich-New London, CT 19.7 20.3 11.9 -41.4Oakland-Fremont-Hayward, CA 16.4 20.4 11.2 -45.1Ocala, FL 25.0 31.9 21.7 -32.0Ocean City, NJ 8.8 14.8 11.0 -25.7Odessa, TX 34.6 34.9 28.0 -19.8Ogden-Clearfield, UT 19.8 19.4 13.9 -28.4Oklahoma City, OK 19.4 19.2 13.5 -29.7Olympia, WA 17.0 19.9 11.2 -43.7Omaha-Council Bluffs, NE-IA 17.6 18.4 11.7 -36.4Orlando-Kissimmee, FL 24.4 31.4 20.4 -35.0Oshkosh-Neenah, WI 16.0 17.6 10.7 -39.2Owensboro, KY 16.4 19.3 15.8 -18.1Oxnard-Thousand Oaks-Ventura, CA 14.9 16.4 11.9 -27.4Palm Bay-Melbourne-Titusville, FL 19.8 25.1 15.2 -39.4Palm Coast, FL 20.9 27.6 16.5 -40.2Panama City-Lynn Haven-Panama City Beach, FL 13.2 19.5 11.2 -42.6Parkersburg-Marietta-Vienna, WV-OH 16.1 17.9 14.9 -16.8




2005 2006 2007 % Change2006 to 2007

Pascagoula, MS 21.1 21.9 16.8 -23.3Peabody, MA 17.8 21.6 10.7 -50.5Pensacola-Ferry Pass-Brent, FL 18.3 20.9 12.9 -38.3Peoria, IL 15.1 17.3 11.2 -35.3Philadelphia, PA 13.9 17.6 11.7 -33.5Phoenix-Mesa-Scottsdale, AZ 21.6 27.4 16.4 -40.1Pine Bluff, AR 26.3 32.3 22.6 -30.0Pittsburgh, PA 18.2 19.7 13.2 -33.0Pittsfield, MA 11.2 13.3 9.0 -32.3Pocatello, ID 16.7 19.4 13.3 -31.4Port St. Lucie, FL 24.7 32.6 20.8 -36.2Portland-South Portland-Biddeford, ME 16.5 18.5 10.8 -41.6Portland-Vancouver-Beaverton, OR-WA 17.4 20.3 11.6 -42.9Poughkeepsie-Newburgh-Middletown, NY 23.2 26.4 16.6 -37.1Prescott, AZ 15.0 19.4 13.1 -32.5Providence-New Bedford-Fall River, RI-MA 23.4 25.7 13.7 -46.7Provo-Orem, UT 19.3 21.5 16.3 -24.2Pueblo, CO 30.3 30.3 18.2 -39.9Punta Gorda, FL 20.3 29.9 15.6 -47.8Racine, WI 19.2 23.6 13.8 -41.5Raleigh-Cary, NC 13.7 15.2 9.4 -38.2Rapid City, SD 15.7 16.4 12.7 -22.6Reading, PA 18.8 21.0 14.6 -30.5Redding, CA 20.1 21.8 12.2 -44.0Reno-Sparks, NV 15.5 18.4 10.1 -45.1Richmond, VA 19.5 23.5 15.4 -34.5Riverside-San Bernardino-Ontario, CA 29.9 34.3 20.3 -40.8Roanoke, VA 17.5 19.7 12.7 -35.5Rochester, MN 13.8 15.1 9.4 -37.7Rochester, NY 16.3 17.2 12.1 -29.7Rockford, IL 26.0 28.1 18.2 -35.2Rockingham County-Strafford County, NH 16.8 18.9 10.9 -42.3Rocky Mount, NC 26.2 28.5 18.5 -35.1Rome, GA 18.0 19.7 12.8 -35.0Sacramento-Arden-Arcade-Roseville, CA 20.8 23.4 12.4 -47.0Saginaw-Saginaw Township North, MI 23.2 25.1 16.7 -33.5Salem, OR 21.0 22.9 14.6 -36.2Salinas, CA 18.5 21.3 12.3 -42.3Salisbury, MD 22.2 26.7 16.6 -37.8Salt Lake City, UT 19.0 21.9 15.9 -27.4San Angelo, TX 20.1 22.1 15.4 -30.3San Antonio, TX 18.4 20.7 12.6 -39.1San Diego-Carlsbad-San Marcos, CA 14.7 18.7 11.4 -39.0San Francisco-San Mateo-Redwood City, CA 7.8 11.2 7.7 -31.3San Jose-Sunnyvale-Santa Clara, CA 11.8 15.6 10.0 -35.9San Luis Obispo-Paso Robles, CA 9.9 12.6 8.8 -30.2Sandusky, OH 17.4 19.1 10.8 -43.5Santa Ana-Anaheim-Irvine, CA 14.9 18.7 11.3 -39.6Santa Barbara-Santa Maria-Goleta, CA 11.1 13.9 8.3 -40.3Santa Cruz-Watsonville, CA 11.0 14.0 8.4 -40.0Santa Fe, NM 11.5 14.6 11.6 -20.5Santa Rosa-Petaluma, CA 12.5 14.9 8.8 -40.9Savannah, GA 16.8 17.5 10.8 -38.3Scranton-Wilkes-Barre, PA 22.1 25.3 18.1 -28.5Seattle-Bellevue-Everett, WA 14.1 16.8 10.7 -36.3Sebastian-Vero Beach, FL 16.7 22.4 14.7 -34.4Sheboygan, WI 16.5 19.0 12.3 -35.3Sherman-Denison, TX 23.9 26.0 20.7 -20.4Shreveport-Bossier City, LA 23.9 26.3 18.1 -31.2Sioux City, IA-NE-SD 19.6 22.9 16.4 -28.4Sioux Falls, SD 11.6 12.7 8.7 -31.5South Bend-Mishawaka, IN-MI 22.6 24.8 14.8 -40.3Spartanburg, SC 21.7 23.3 15.4 -33.9Spokane, WA 16.8 19.9 12.8 -35.7Springfield, IL 13.7 14.9 10.5 -29.5Springfield, MA 22.0 26.4 14.3 -45.8




2005 2006 2007 % Change2006 to 2007

Springfield, MO 19.5 21.6 12.3 -43.1Springfield, OH 28.0 29.1 17.6 -39.5St. Cloud, MN 18.7 21.0 13.0 -38.1St. George, UT 19.1 23.3 18.0 -22.7St. Joseph, MO-KS 26.8 26.1 19.1 -26.8St. Louis, MO-IL 20.2 23.4 15.0 -35.9State College, PA 9.5 11.8 8.6 -27.1Stockton, CA 29.2 32.1 19.3 -39.9Sumter, SC 25.8 27.5 19.2 -30.2Syracuse, NY 15.1 18.5 12.8 -30.8Tacoma, WA 23.2 26.0 16.1 -38.1Tallahassee, FL 17.6 21.4 13.4 -37.4Tampa-St. Petersburg-Clearwater, FL 23.3 29.6 18.5 -37.5Terre Haute, IN 22.7 25.0 14.4 -42.4Texarkana, TX-Texarkana, AR 19.2 22.1 19.2 -13.1Toledo, OH 19.9 21.0 13.1 -37.6Topeka, KS 18.7 18.6 13.9 -25.3Trenton-Ewing, NJ 17.1 21.4 13.3 -37.9Tucson, AZ 16.6 21.8 13.0 -40.4Tulsa, OK 21.3 22.6 14.8 -34.5Tuscaloosa, AL 15.1 17.7 11.2 -36.7Tyler, TX 17.2 18.8 11.6 -38.3Utica-Rome, NY 19.0 22.7 17.2 -24.2Valdosta, GA 16.8 21.4 14.2 -33.6Vallejo-Fairfield, CA 24.8 28.7 16.3 -43.2Victoria, TX 18.3 20.4 14.1 -30.9Vineland-Millville-Bridgeton, NJ 30.7 34.5 22.2 -35.7Virginia Beach-Norfolk-Newport News, VA-NC 18.4 23.1 14.6 -36.8Visalia-Porterville, CA 29.3 34.7 19.0 -45.2Waco, TX 21.5 22.6 16.9 -25.2Warner Robins, GA 17.8 16.5 9.7 -41.2Warren-Troy-Farmington Hills, MI 18.3 22.2 13.7 -38.3Washington-Arlington-Alexandria, DC-VA-MD-WV 19.4 25.0 13.2 -47.2Waterloo-Cedar Falls, IA 14.4 14.4 9.4 -34.7Wausau, WI 14.3 15.1 10.6 -29.8Weirton-Steubenville, WV-OH 20.9 25.7 18.7 -27.2Wenatchee-East Wenatchee, WA 15.9 18.2 12.0 -34.1West Palm Beach-Boca Raton-Boynton Beach, FL 20.1 27.2 18.3 -32.7Wheeling, WV-OH 18.2 20.8 14.4 -30.8Wichita Falls, TX 16.9 18.6 12.9 -30.6Wichita, KS 18.7 18.5 12.2 -34.1Williamsport, PA 18.1 18.2 16.5 -9.3Wilmington, DE-MD-NJ 19.4 23.4 15.4 -34.2Wilmington, NC 10.1 14.8 10.6 -28.4Winchester, VA-WV 20.2 24.1 14.2 -41.1Winston-Salem, NC 17.1 18.2 11.6 -36.3Worcester, MA 20.2 25.1 12.9 -48.6Yakima, WA 21.3 22.8 14.1 -38.2York-Hanover, PA 18.0 21.2 14.1 -33.5Youngstown-Warren-Boardman, OH-PA 26.0 26.5 17.3 -34.7Yuba City, CA 26.9 28.5 13.0 -54.4Yuma, AZ 21.8 25.0 15.4 -38.4




20-Year % DeviationAverage From

1980 1985 1990 1995 2000 2005 2008Q3 1984-2004 2008Q3United States 2.3 1.9 2.0 1.8 1.9 2.8 2.0 2.0 -1.5

Abilene, TX 1.7 1.6 1.0 1.1 1.0 1.0 1.0 1.2 17.1Akron, OH 1.7 1.3 1.4 1.5 1.4 1.5 1.2 1.4 16.7Albany, GA 2.1 1.8 1.5 1.4 1.4 1.5 1.4 1.5 4.6Albany-Schenectady-Troy, NY 1.5 1.5 1.9 1.6 1.4 1.9 1.9 1.7 -12.1Albuquerque, NM 2.6 2.3 2.2 2.3 2.0 2.0 2.3 2.2 -5.1Alexandria, LA 2.2 1.7 1.2 1.2 1.3 1.4 1.2 1.3 12.9Allentown-Bethlehem-Easton, PA-NJ 1.8 1.5 2.0 1.7 1.5 2.1 2.0 1.7 -15.4Altoona, PA 1.0 1.0 1.3 1.3 1.3 1.4 1.4 1.2 -12.5Amarillo, TX 1.8 1.4 1.1 1.3 1.2 1.3 1.2 1.3 4.3Ames, IA 2.3 1.7 1.6 1.7 1.7 1.7 1.6 1.7 5.3Anchorage, AK 1.9 2.2 1.5 1.8 1.8 2.2 2.2 1.8 -17.6Anderson, IN 1.4 1.1 1.2 1.3 1.4 1.4 1.2 1.3 5.6Anderson, SC 1.9 1.7 1.6 1.6 1.6 1.7 1.7 1.6 -6.0Ann Arbor, MI 2.1 1.5 1.9 1.7 2.0 2.4 1.7 1.9 7.2Anniston-Oxford, AL 1.8 1.5 1.4 1.4 1.5 1.5 1.5 1.5 -1.8Appleton, WI 1.7 1.4 1.3 1.3 1.3 1.4 1.2 1.3 5.9Asheville, NC 2.1 1.9 1.9 1.9 2.1 2.6 2.8 2.0 -29.1Athens-Clarke County, GA 2.7 2.2 1.9 1.9 2.1 2.3 2.3 2.1 -9.7Atlanta-Sandy Springs-Marietta, GA 2.1 1.7 1.6 1.4 1.4 1.7 1.5 1.6 4.7Atlantic City, NJ 1.8 1.4 1.6 1.4 1.4 2.8 2.3 1.5 -32.8Auburn-Opelika, AL 2.6 2.2 2.2 2.2 2.3 2.5 2.7 2.2 -17.2Augusta-Richmond County, GA-SC 2.2 1.7 1.6 1.6 1.5 1.6 1.7 1.6 -5.3Austin-Round Rock, TX 2.6 2.6 1.7 1.9 1.7 1.9 2.1 2.0 -7.3Bakersfield, CA 2.8 2.5 2.3 2.0 1.6 3.7 2.1 2.1 2.4Baltimore-Towson, MD 1.6 1.4 1.8 1.6 1.5 2.4 2.0 1.6 -18.3Bangor, ME 1.4 1.4 1.8 1.6 1.5 1.9 2.0 1.6 -18.8Barnstable Town, MA 2.5 2.6 2.8 2.4 2.9 4.9 3.6 2.9 -18.2Baton Rouge, LA 2.4 2.0 1.5 1.6 1.6 1.7 1.6 1.7 4.8Battle Creek, MI 1.4 1.1 1.2 1.2 1.3 1.4 1.2 1.2 -0.4Bay City, MI 1.3 1.0 1.1 1.2 1.4 1.6 1.3 1.2 -10.0Beaumont-Port Arthur, TX 1.9 1.6 1.3 1.3 1.2 1.2 1.2 1.3 11.4Bellingham, WA 2.5 2.0 2.4 2.8 2.5 3.6 3.7 2.5 -33.5Bend, OR 2.4 1.6 1.8 2.2 2.3 3.2 3.5 2.0 -40.8Bethesda-Gaithersburg-Frederick, MD 2.4 1.9 2.4 2.0 1.8 3.3 2.5 2.1 -15.1Billings, MT 2.1 1.8 1.5 1.7 1.6 1.8 1.8 1.7 -7.5Binghamton, NY 1.4 1.6 1.7 1.4 1.3 1.5 1.7 1.5 -9.7Birmingham-Hoover, AL 2.3 1.9 1.8 1.8 1.7 1.8 1.8 1.8 3.1Bismarck, ND 2.1 1.7 1.4 1.5 1.5 1.5 1.5 1.5 -0.2Blacksburg-Christiansburg-Radford, VA 2.0 1.8 1.9 2.0 2.1 2.4 2.5 2.0 -20.2Bloomington, IN 2.2 1.7 1.7 1.9 1.8 2.0 1.9 1.8 -6.9Bloomington-Normal, IL 2.2 1.8 1.7 1.8 1.7 1.8 1.6 1.8 11.0Boise City-Nampa, ID 2.4 2.0 1.7 1.8 1.6 2.0 2.3 1.8 -23.4Boston-Quincy, MA 1.8 2.5 2.5 2.2 2.5 3.7 2.8 2.6 -7.0Boulder, CO 3.0 2.7 2.4 3.0 3.1 3.4 3.2 2.9 -9.3Bowling Green, KY 2.4 1.9 1.7 1.8 1.7 1.8 1.7 1.8 4.3Bremerton-Silverdale, WA 3.0 2.4 2.0 2.0 2.0 3.5 2.0 2.2 7.8Bridgeport-Stamford-Norwalk, CT 2.5 2.0 2.0 2.2 2.0 2.6 2.6 2.1 -19.4Brownsville-Harlingen, TX 2.3 2.5 2.5 2.2 2.2 3.2 2.5 2.5 1.8Brunswick, GA 1.8 1.7 1.2 1.2 1.1 1.2 1.1 1.3 21.3Buffalo-Niagara Falls, NY 2.1 1.8 1.7 1.6 1.8 2.1 2.2 1.8 -21.1Burlington, NC 1.4 1.2 1.5 1.4 1.2 1.3 1.2 1.4 9.6Burlington-South Burlington, VT 1.9 1.7 1.9 1.8 1.8 1.9 1.7 1.8 6.0Cambridge-Newton-Framingham, MA 1.9 1.6 1.9 1.8 1.7 2.4 2.1 1.8 -13.1Camden, NJ 2.0 2.6 2.6 2.3 2.5 3.5 2.6 2.6 0.4Canton-Massillon, OH 1.7 1.5 1.9 1.6 1.4 2.2 1.9 1.7 -9.7Cape Coral-Fort Myers, FL 1.9 1.4 1.5 1.6 1.6 1.7 1.4 1.5 7.5Carson City, NV 2.4 2.0 1.9 1.7 1.6 2.8 1.6 1.8 15.5Casper, WY 2.5 2.0 2.0 2.1 1.8 3.0 2.3 2.1 -11.7Cedar Rapids, IA 1.7 1.3 0.9 1.2 1.0 1.4 1.3 1.1 -15.5Champaign-Urbana, IL 2.0 1.5 1.5 1.7 1.5 1.7 1.6 1.6 0.5Charleston, WV 1.9 1.6 1.5 1.6 1.5 1.8 1.7 1.5 -7.0Charleston-North Charleston, SC 2.6 1.9 1.6 1.7 1.6 1.6 1.6 1.7 9.4Charlotte-Gastonia-Concord, NC-SC 1.9 1.8 1.7 1.7 2.0 2.5 2.4 1.9 -20.5Charlottesville, VA 2.5 2.1 1.9 1.8 1.7 1.7 1.7 1.9 7.0Chattanooga, TN-GA 2.5 2.1 2.1 2.0 1.9 2.8 2.8 2.1 -25.7Cheyenne, WY 1.7 1.6 1.5 1.5 1.5 1.7 1.7 1.5 -8.2Chicago-Naperville-Joliet, IL 1.7 1.7 1.4 1.6 1.5 1.6 1.4 1.6 14.5Chico, CA 1.7 1.5 1.8 1.8 1.8 2.3 1.9 1.8 -3.2Cincinnati-Middletown, OH-KY-IN 4.5 3.7 3.9 3.5 3.2 6.2 4.3 3.7 -14.8Clarksville, TN-KY 2.0 1.6 1.7 1.7 1.6 1.7 1.5 1.7 13.0Cleveland, TN 1.9 1.7 1.6 1.5 1.4 1.3 1.4 1.5 11.0

Appendix 6-1: CSI Home Price-to-Household Income Ratio



Appendix 6-1: CSI Home Price-to-Household Income Ratio (cont.)20-Year % Deviation

Average From1980 1985 1990 1995 2000 2005 2008Q3 1984-2004 2008Q3

Cleveland-Elyria-Mentor, OH 2.2 1.8 1.6 1.6 1.7 1.8 1.8 1.7 -9.5Coeur d'Alene, ID 1.8 1.4 1.5 1.6 1.6 1.7 1.3 1.5 18.9College Station-Bryan, TX 2.6 2.2 1.9 2.3 2.0 2.7 2.9 2.1 -27.3Colorado Springs, CO 3.3 2.7 1.9 2.0 1.8 1.9 1.8 2.1 16.8Columbia, MO 2.5 2.1 1.8 2.1 2.0 2.3 2.2 2.0 -7.0Columbia, SC 2.3 1.8 1.7 1.7 1.6 1.8 1.8 1.7 -4.6Columbus, GA-AL 2.2 1.7 1.7 1.6 1.6 1.7 1.7 1.7 -3.9Columbus, IN 2.0 1.6 1.5 1.5 1.4 1.5 1.4 1.5 8.1Columbus, OH 1.7 1.3 1.4 1.5 1.4 1.5 1.4 1.4 -0.8Corpus Christi, TX 2.1 1.7 1.7 1.7 1.7 1.8 1.5 1.7 12.7Corvallis, OR 2.0 1.8 1.4 1.4 1.3 1.4 1.3 1.5 18.2Cumberland, MD-WV 2.8 1.9 1.9 2.5 2.3 2.6 2.9 2.2 -25.5Dallas-Plano-Irving, TX 1.4 1.3 1.6 1.6 1.4 1.6 1.7 1.5 -16.5Dalton, GA 2.5 2.2 1.7 1.5 1.3 1.5 1.4 1.7 19.2Danville, IL 2.0 1.7 1.6 1.5 1.5 1.7 1.6 1.6 -4.4Danville, VA 0.8 0.7 1.0 1.0 1.0 1.1 1.0 1.0 0.5Davenport-Moline-Rock Island, IA-IL 1.5 1.4 1.7 1.6 1.6 1.7 1.5 1.6 3.6Dayton, OH 1.7 1.2 1.1 1.2 1.2 1.3 1.2 1.2 3.4Decatur, AL 1.7 1.3 1.5 1.5 1.4 1.5 1.3 1.5 10.7Decatur, IL 2.3 1.8 1.6 1.5 1.5 1.5 1.5 1.6 7.0Deltona-Daytona Beach-Ormond Beach, FL 1.3 1.1 1.2 1.2 1.1 1.1 1.0 1.2 11.7Denver-Aurora, CO 2.4 1.9 1.8 1.6 1.5 2.6 1.8 1.7 -4.3Des Moines, IA 2.4 2.0 1.6 1.9 2.0 2.3 1.9 1.9 1.4Detroit-Livonia-Dearborn, MI 2.0 1.6 1.4 1.5 1.5 1.6 1.5 1.5 0.9Dothan, AL 1.3 0.9 1.2 1.3 1.5 1.7 1.1 1.3 18.0Dover, DE 2.0 1.6 1.5 1.5 1.5 1.5 1.6 1.5 -4.1Dubuque, IA 1.7 1.5 1.8 1.7 1.5 2.1 2.3 1.7 -26.8Duluth, MN-WI 1.7 1.3 1.3 1.5 1.4 1.5 1.5 1.4 -8.1Durham, NC 1.2 1.1 1.1 1.1 1.3 1.7 1.6 1.2 -24.9Eau Claire, WI 2.0 1.9 1.8 1.8 1.7 1.9 1.9 1.8 -2.4Edison, NJ 1.6 1.3 1.2 1.4 1.5 1.7 1.6 1.4 -12.6El Centro, CA 1.9 1.9 2.2 1.9 1.9 3.2 2.5 2.1 -14.7El Paso, TX 1.1 1.1 1.4 1.6 1.6 2.7 1.6 1.5 -10.8Elizabethtown, KY 2.2 2.0 1.7 1.6 1.3 1.3 1.5 1.6 8.6Elkhart-Goshen, IN 1.6 1.3 1.4 1.6 1.5 1.6 1.5 1.4 -4.9Elmira, NY 2.0 1.5 1.4 1.4 1.4 1.4 1.4 1.4 1.8Erie, PA 0.9 1.2 1.4 1.2 1.1 1.1 1.0 1.2 17.9Eugene-Springfield, OR 1.5 1.5 1.5 1.6 1.5 1.6 1.5 1.5 1.4Evansville, IN-KY 2.2 1.6 1.8 2.1 2.1 2.7 2.9 2.0 -31.3Fairbanks, AK 1.8 1.4 1.2 1.2 1.3 1.3 1.2 1.3 7.3Fargo, ND-MN 1.6 2.0 1.5 1.8 1.8 2.1 2.0 1.8 -10.0Farmington, NM 2.5 1.9 1.6 1.6 1.4 1.7 1.5 1.6 5.7Fayetteville, NC 1.7 1.9 1.6 1.7 1.8 2.1 2.1 1.8 -14.8Fayetteville-Springdale-Rogers, AR-MO 1.8 1.6 1.7 1.6 1.5 1.3 1.3 1.6 22.2Flagstaff, AZ 2.6 1.8 1.7 1.7 1.6 1.8 1.8 1.7 -2.7Flint, MI 2.3 1.8 1.9 2.3 2.2 3.3 3.0 2.1 -29.1Florence, SC 1.4 1.0 1.2 1.1 1.5 1.7 1.3 1.3 -1.4Florence-Muscle Shoals, AL 1.9 1.6 1.5 1.5 1.5 1.5 1.4 1.5 6.7Fond du Lac, WI 2.2 1.9 1.7 1.7 1.7 1.7 1.8 1.8 -0.9Fort Collins-Loveland, CO 1.6 1.3 1.2 1.4 1.4 1.5 1.5 1.3 -7.0Fort Lauderdale-Pompano Beach-Deerfield Beach, FL 2.6 2.2 1.9 2.3 2.3 2.6 2.4 2.2 -8.7Fort Smith, AR-OK 2.7 2.2 2.2 2.0 1.9 3.7 2.3 2.2 -4.7Fort Walton Beach-Crestview-Destin, FL 2.0 1.6 1.4 1.4 1.3 1.3 1.3 1.4 9.6Fort Wayne, IN 3.1 2.4 1.9 2.0 1.8 3.1 1.8 2.0 12.2Fort Worth-Arlington, TX 1.9 1.4 1.3 1.2 1.2 1.3 1.3 1.3 4.2Fresno, CA 2.4 2.1 1.6 1.5 1.4 1.4 1.4 1.6 20.4Gadsden, AL 2.8 2.4 2.2 2.0 1.8 3.9 2.2 2.2 -3.3Gainesville, FL 1.6 1.4 1.3 1.4 1.5 1.5 1.6 1.4 -9.3Gainesville, GA 3.0 2.3 1.9 1.9 1.9 2.5 2.2 2.0 -6.9Gary, IN 2.6 2.1 1.9 1.8 1.9 2.2 2.3 1.9 -15.2Glens Falls, NY 1.7 1.4 1.4 1.5 1.4 1.6 1.5 1.4 -5.4Goldsboro, NC 1.6 1.9 2.2 1.8 1.5 2.2 2.4 1.9 -20.4Grand Forks, ND-MN 2.0 1.8 1.8 1.7 1.7 1.7 1.6 1.8 11.6Grand Junction, CO 2.3 1.7 1.4 1.6 1.4 1.6 1.5 1.5 4.3Grand Rapids-Wyoming, MI 2.6 1.6 1.6 1.9 1.9 2.4 2.8 1.8 -35.1Great Falls, MT 1.6 1.3 1.4 1.4 1.5 1.7 1.4 1.5 5.2Greeley, CO 1.7 1.5 1.3 1.6 1.6 1.7 1.7 1.5 -9.8Green Bay, WI 2.4 2.1 1.7 2.1 2.1 2.6 2.2 2.1 -6.4Greensboro-High Point, NC 2.0 1.5 1.4 1.6 1.6 1.8 1.6 1.6 -2.1Greenville, NC 2.2 2.0 1.9 1.8 1.8 1.9 1.9 1.9 2.5Greenville, SC 2.2 2.0 1.8 1.7 1.7 1.8 1.7 1.8 8.0




Average From1980 1985 1990 1995 2000 2005 2008Q3 1984-2004 2008Q3

Gulfport-Biloxi, MS 2.3 1.9 1.8 1.8 1.8 1.9 1.9 1.8 -3.9Hagerstown-Martinsburg, MD-WV 2.1 1.9 1.5 1.5 1.5 1.9 1.7 1.6 -3.9Hanford-Corcoran, CA 1.7 1.5 2.0 1.9 1.7 2.6 2.5 1.8 -25.6Harrisburg-Carlisle, PA 1.5 1.6 1.9 1.9 1.6 2.8 2.0 1.8 -9.0Harrisonburg, VA 1.9 1.6 1.6 1.6 1.5 1.7 1.7 1.6 -10.2Hartford-West Hartford-East Hartford, CT 2.4 2.0 2.1 2.1 2.0 2.5 2.8 2.1 -25.0Hattiesburg, MS 2.1 2.0 2.4 1.9 1.7 2.3 1.9 2.1 6.2Hickory-Lenoir-Morganton, NC 2.3 2.1 1.5 1.5 1.5 1.5 1.6 1.6 3.4Hinesville-Fort Stewart, GA 1.8 1.6 1.6 1.6 1.6 1.8 1.8 1.6 -9.7Holland-Grand Haven, MI 1.8 1.6 1.7 1.5 1.7 1.6 1.8 1.6 -10.6Honolulu, HI 1.8 1.4 1.5 1.5 1.7 1.9 1.7 1.6 -7.3Hot Springs, AR 3.4 2.8 4.2 4.2 3.2 4.9 5.0 3.6 -28.1Houma-Bayou Cane-Thibodaux, LA 2.5 2.0 1.7 1.7 1.7 1.9 2.0 1.8 -9.2Houston-Sugar Land-Baytown, TX 1.8 1.7 1.3 1.4 1.4 1.7 1.4 1.5 8.3Huntington-Ashland, WV-KY-OH 2.3 1.8 1.4 1.3 1.1 1.2 1.2 1.4 16.0Huntsville, AL 2.2 1.7 1.4 1.5 1.5 1.6 1.6 1.5 -2.9Idaho Falls, ID 2.6 1.9 1.7 1.7 1.6 1.6 1.7 1.7 3.2Indianapolis, IN 2.1 1.7 1.4 1.5 1.4 1.4 1.6 1.5 -6.9Iowa City, IA 1.8 1.5 1.5 1.5 1.4 1.4 1.4 1.5 6.9Ithaca, NY 2.5 1.8 1.8 1.9 1.8 2.0 1.8 1.8 1.1Jackson, MI 1.5 1.6 2.1 1.8 1.6 2.1 2.1 1.8 -14.7Jackson, MS 1.3 1.0 1.2 1.3 1.5 1.7 1.4 1.3 -7.1Jackson, TN 2.2 2.1 1.7 1.5 1.4 1.5 1.4 1.6 15.6Jacksonville, FL 2.2 1.9 1.6 1.5 1.4 1.4 1.3 1.6 19.6Jacksonville, NC 1.8 1.6 1.4 1.3 1.4 1.9 1.6 1.5 -8.0Janesville, WI 1.6 1.2 1.4 1.3 1.3 1.3 1.5 1.3 -14.2Jefferson City, MO 1.7 1.3 1.2 1.4 1.5 1.7 1.6 1.4 -15.2Johnson City, TN 1.9 1.6 1.4 1.4 1.4 1.4 1.4 1.4 -0.4Johnstown, PA 2.4 2.2 1.8 1.8 1.8 1.9 1.9 1.9 -0.8Jonesboro, AR 0.9 1.0 1.3 1.1 1.1 1.2 1.3 1.2 -7.9Joplin, MO 2.2 1.8 1.5 1.5 1.5 1.5 1.4 1.6 11.3Kalamazoo-Portage, MI 1.8 1.5 1.3 1.3 1.3 1.4 1.3 1.3 0.6Kankakee-Bradley, IL 2.1 1.5 1.5 1.5 1.7 1.8 1.6 1.6 -4.7Kansas City, MO-KS 1.1 0.9 1.2 1.3 1.2 1.4 1.4 1.2 -13.3Kennewick-Richland-Pasco, WA 2.2 1.7 1.6 1.5 1.5 1.8 1.6 1.6 1.3Killeen-Temple-Fort Hood, TX 2.1 1.6 1.7 1.8 1.6 1.8 1.8 1.7 -5.4Kingsport-Bristol-Bristol, TN-VA 2.0 1.7 1.4 1.4 1.3 1.2 1.1 1.4 26.7Kingston, NY 2.1 1.8 1.6 1.7 1.7 1.9 2.0 1.7 -12.4Knoxville, TN 1.6 1.8 2.2 1.8 1.8 2.8 2.7 2.0 -25.1Kokomo, IN 2.1 1.7 1.7 1.8 1.7 1.9 2.0 1.7 -11.7La Crosse, WI-MN 1.6 1.2 1.2 1.3 1.3 1.4 1.2 1.3 7.1Lafayette, IN 1.9 1.5 1.2 1.4 1.5 1.7 1.6 1.4 -9.3Lafayette, LA 1.9 1.6 1.7 1.8 1.7 1.7 1.6 1.7 6.2Lake Charles, LA 2.3 2.0 1.5 1.6 1.5 1.8 1.4 1.6 14.0Lake County-Kenosha County, IL-WI 2.0 1.9 1.4 1.4 1.4 2.0 1.4 1.5 10.7Lake Havasu City - Kingman, AZ 1.8 1.6 1.8 1.7 1.6 2.1 1.9 1.7 -7.6Lakeland, FL 2.9 2.3 2.3 2.5 2.2 3.6 3.1 2.4 -22.1Lancaster, PA 2.0 1.9 1.6 1.5 1.3 1.9 1.5 1.6 7.6Lansing-East Lansing, MI 2.1 1.7 1.8 1.7 1.5 1.9 2.0 1.7 -14.3Laredo, TX 1.8 1.4 1.5 1.4 1.6 1.8 1.3 1.5 15.2Las Cruces, NM 2.9 2.8 1.9 1.6 1.4 1.4 1.4 1.8 31.9Las Vegas-Paradise, NV 2.4 2.1 1.9 1.9 1.8 1.9 2.1 1.9 -10.1Lawrence, KS 2.5 2.1 2.0 1.8 1.7 3.0 1.7 2.0 14.8Lawton, OK 2.3 1.8 1.9 1.9 1.9 2.3 2.1 1.9 -9.4Lebanon, PA 2.5 1.9 1.4 1.5 1.3 1.3 1.1 1.5 29.4Lewiston, ID-WA 1.4 1.3 1.6 1.6 1.5 1.7 1.8 1.5 -12.3Lewiston-Auburn, ME 2.1 1.8 1.6 2.0 1.8 2.1 2.5 1.8 -27.8Lexington-Fayette, KY 1.4 1.5 2.0 1.7 1.6 2.1 2.1 1.8 -17.3Lima, OH 2.1 1.8 1.6 1.6 1.6 1.8 1.8 1.7 -7.2Lincoln, NE 1.7 1.2 1.3 1.3 1.3 1.5 1.3 1.3 -2.1Little Rock-North Little Rock, AR 2.1 1.6 1.5 1.5 1.5 1.6 1.5 1.5 -1.4Logan, UT-ID 1.9 1.7 1.4 1.4 1.3 1.4 1.3 1.4 9.7Longview, TX 2.4 1.9 1.6 2.1 2.1 2.2 2.4 2.0 -18.5Longview, WA 1.8 1.6 1.3 1.3 1.2 1.2 1.2 1.4 10.9Los Angeles-Long Beach-Glendale, CA 1.9 1.8 1.7 2.2 2.2 2.5 2.8 2.0 -29.8Louisville, KY-IN 2.4 2.0 2.9 1.9 2.1 4.2 2.7 2.3 -12.0Lubbock, TX 1.9 1.5 1.5 1.6 1.6 1.7 1.6 1.6 -4.0Lynchburg, VA 1.8 1.5 1.3 1.3 1.2 1.3 1.2 1.3 9.0Macon, GA 2.0 1.7 1.8 1.8 1.8 2.0 2.2 1.8 -16.8Madera, CA 1.7 1.7 1.6 1.5 1.4 1.5 1.4 1.5 7.4




Average From1980 1985 1990 1995 2000 2005 2008Q3 1984-2004 2008Q3

Madison, WI 1.9 2.1 2.1 2.3 1.9 3.6 2.7 2.1 -22.1Manchester-Nashua, NH 2.0 1.7 1.6 1.8 1.8 2.1 2.0 1.7 -10.7Mansfield, OH 1.7 1.8 1.9 1.5 1.6 2.6 2.0 1.8 -7.3McAllen-Edinburg-Mission, TX 1.7 1.2 1.3 1.4 1.5 1.6 1.4 1.4 -3.3Medford, OR 2.1 1.8 1.4 1.4 1.2 1.2 1.2 1.4 23.0Memphis, TN-MS-AR 2.5 1.9 2.0 2.2 2.3 3.6 3.0 2.2 -29.0Merced, CA 2.3 1.9 1.7 1.4 1.4 1.4 1.1 1.6 40.9Miami-Miami Beach-Kendall, FL 2.4 2.1 2.4 2.1 2.0 4.5 1.7 2.2 27.0Michigan City-La Porte, IN 2.5 1.9 1.8 1.9 1.9 3.5 2.5 2.0 -19.9Midland, TX 1.4 1.2 1.3 1.5 1.5 1.7 1.6 1.4 -13.7Milwaukee-Waukesha-West Allis, WI 2.0 1.7 1.1 1.1 0.8 0.9 1.0 1.2 12.7Minneapolis-St. Paul-Bloomington, MN-WI 2.0 1.6 1.6 1.7 1.7 2.1 1.9 1.7 -11.5Missoula, MT 2.0 1.6 1.5 1.4 1.6 2.1 1.6 1.6 1.0Mobile, AL 2.5 2.2 1.7 2.4 2.3 2.9 2.9 2.2 -24.1Modesto, CA 2.4 1.9 1.6 1.6 1.7 1.7 2.0 1.7 -15.5Monroe, LA 2.7 2.0 2.7 2.0 1.9 4.2 2.0 2.2 13.5Monroe, MI 2.3 1.8 1.4 1.3 1.4 1.6 1.4 1.5 10.3Montgomery, AL 1.6 1.3 1.6 1.6 1.8 2.0 1.6 1.6 1.9Morgantown, WV 2.3 1.9 1.6 1.6 1.5 1.5 1.4 1.6 13.3Morristown, TN 3.3 2.3 1.9 1.9 1.8 2.0 2.0 1.9 -2.4Mount Vernon-Anacortes, WA 2.5 2.1 1.7 1.6 1.7 1.9 2.0 1.8 -10.6Muncie, IN 2.3 1.9 2.2 2.5 2.3 2.9 3.1 2.2 -28.7Muskegon-Norton Shores, MI 1.5 1.2 1.2 1.3 1.3 1.4 1.1 1.3 12.6Myrtle Beach-Conway-North Myrtle Beach, SC 1.6 1.2 1.3 1.3 1.4 1.6 1.4 1.3 -7.3Napa, CA 3.0 2.5 2.2 2.2 2.3 2.9 3.3 2.4 -27.5Naples-Marco Island, FL 2.5 2.2 2.9 2.3 2.7 4.7 2.9 2.6 -8.3Nashville-Davidson-Murfreesboro, TN 4.0 3.0 2.8 2.6 2.7 4.7 2.7 2.8 5.2Nassau-Suffolk, NY 2.2 1.8 1.6 1.5 1.5 1.6 1.6 1.6 -0.5New Haven-Milford, CT 1.1 1.7 1.8 1.6 1.7 2.9 2.2 1.8 -17.9New Orleans-Metairie-Kenner, LA 1.7 1.8 2.2 1.7 1.7 2.5 2.0 2.0 -1.0New York-White Plains-Wayne, NY-NJ 2.3 1.9 1.4 1.5 1.6 0.4 1.4 1.6 12.4Newark-Union, NJ-PA 1.9 2.3 2.7 2.3 2.4 4.0 3.2 2.7 -17.1Niles-Benton Harbor, MI 2.1 2.3 2.4 2.2 2.1 3.4 2.6 2.4 -6.6Norwich-New London, CT 1.6 1.2 1.4 1.4 1.5 1.8 1.6 1.4 -10.5Oakland-Fremont-Hayward, CA 1.9 1.9 2.4 1.9 1.8 2.7 2.2 2.1 -4.9Ocala, FL 3.7 3.0 4.0 3.0 3.5 5.8 3.4 3.5 4.6Ocean City, NJ 2.3 1.8 1.6 1.4 1.4 1.9 1.9 1.6 -16.0Odessa, TX 1.2 1.3 1.8 1.5 1.8 3.6 2.7 1.8 -35.8Ogden-Clearfield, UT 1.5 1.4 1.0 1.0 0.9 0.9 1.1 1.1 -2.9Oklahoma City, OK 2.4 1.9 1.5 1.9 1.9 1.9 2.3 1.8 -19.5Olympia, WA 2.0 1.8 1.3 1.3 1.2 1.3 1.2 1.4 19.1Omaha-Council Bluffs, NE-IA 2.0 1.8 1.9 2.3 2.0 2.6 2.8 2.1 -26.4Orlando-Kissimmee, FL 2.0 1.6 1.4 1.5 1.5 1.5 1.4 1.5 6.5Oshkosh-Neenah, WI 2.4 2.1 1.9 1.7 1.6 2.5 1.9 1.8 -3.8Owensboro, KY 1.8 1.4 1.3 1.4 1.4 1.5 1.4 1.4 -1.2Oxnard-Thousand Oaks-Ventura, CA 1.7 1.4 1.4 1.5 1.4 1.4 1.3 1.5 8.3Palm Bay-Melbourne-Titusville, FL 3.1 2.6 3.4 2.4 2.6 4.8 3.0 2.9 -3.1Palm Coast, FL 2.0 1.8 1.7 1.6 1.4 2.5 1.9 1.7 -9.8Panama City-Lynn Haven, FL 3.7 2.8 3.0 2.4 2.3 3.9 3.2 2.7 -14.8Parkersburg-Marietta-Vienna, WV-OH 2.7 2.1 1.7 1.9 1.8 2.7 2.4 1.9 -21.1Pascagoula, MS 2.0 1.4 1.4 1.5 1.5 1.6 1.6 1.5 -5.7Peabody, MA 2.0 1.9 1.5 1.4 1.4 1.6 1.6 1.5 -3.5Pensacola-Ferry Pass-Brent, FL 1.9 2.6 2.7 2.3 2.6 3.7 2.7 2.7 0.8Peoria, IL 2.2 1.9 1.6 1.6 1.6 2.3 1.7 1.7 1.4Philadelphia, PA 1.7 1.2 1.2 1.3 1.3 1.3 1.2 1.2 3.9Phoenix-Mesa-Scottsdale, AZ 1.4 1.2 1.5 1.3 1.2 1.8 1.6 1.4 -11.8Pine Bluff, AR 2.4 2.0 1.7 1.7 1.8 2.9 1.9 1.8 -3.0Pittsburgh, PA 1.7 1.5 1.2 1.2 1.2 1.2 1.2 1.3 9.1Pittsfield, MA 1.6 1.3 1.4 1.4 1.3 1.3 1.2 1.3 12.9Pocatello, ID 1.7 2.2 2.6 2.1 2.0 2.7 2.3 2.3 -1.1Port St. Lucie-Fort Pierce, FL 1.9 1.6 1.4 1.6 1.6 1.7 1.8 1.6 -13.6Portland-South Portland-Biddeford, ME 2.5 1.9 1.8 1.5 1.4 2.8 1.5 1.7 9.9Portland-Vancouver-Beaverton, OR-WA 1.9 1.9 2.1 1.7 1.8 2.6 2.4 2.0 -18.7Poughkeepsie-Newburgh-Middletown, NY 2.3 1.7 1.7 2.0 2.0 2.8 2.8 1.9 -29.8Prescott, AZ 1.8 1.7 2.2 1.9 1.8 2.9 2.7 2.1 -22.5Providence-New Bedford-Fall River, RI-MA 2.7 2.1 2.4 2.8 2.8 4.3 3.5 2.6 -25.4Provo-Orem, UT 1.9 1.9 2.5 2.0 2.0 3.3 2.4 2.3 -7.0Pueblo, CO 2.6 2.1 1.7 2.3 2.2 2.4 3.0 2.1 -29.9Punta Gorda, FL 1.9 1.6 1.5 1.6 1.6 1.8 1.6 1.6 0.4Racine, WI 2.5 1.9 2.0 1.8 1.6 3.2 1.6 1.9 19.1




Average From1980 1985 1990 1995 2000 2005 2008Q3 1984-2004 2008Q3

Raleigh-Cary, NC 1.6 1.4 1.4 1.5 1.5 1.8 1.6 1.4 -11.2Rapid City, SD 2.3 2.0 1.9 1.9 1.7 1.9 2.0 1.9 -7.4Reading, PA 1.6 1.3 1.1 1.3 1.4 1.5 1.4 1.3 -8.3Redding, CA 1.5 1.3 1.5 1.3 1.2 1.5 1.6 1.3 -13.7Reno-Sparks, NV 3.7 2.8 3.0 2.9 2.7 5.4 3.6 3.0 -15.7Richmond, VA 2.5 2.2 2.0 1.9 1.7 2.9 2.2 2.0 -10.5Riverside-San Bernardino-Ontario, CA 2.1 1.8 1.8 1.7 1.6 2.1 2.2 1.7 -21.0Roanoke, VA 3.0 2.2 2.8 2.0 2.0 4.2 2.3 2.3 1.7Rochester, MN 1.7 1.7 1.7 1.7 1.7 2.0 2.1 1.7 -17.3Rochester, NY 2.0 1.6 1.4 1.4 1.5 1.6 1.5 1.5 -2.2Rockford, IL 1.4 1.4 1.5 1.3 1.2 1.2 1.1 1.3 21.6Rockingham County-Strafford County, NH 1.5 1.1 1.1 1.1 1.0 1.3 1.2 1.1 -10.9Rocky Mount, NC 2.0 2.0 2.2 1.7 1.9 2.9 2.2 2.1 -6.9Rome, GA 2.1 1.9 1.7 1.6 1.5 1.5 1.5 1.6 8.3Sacramento-Arden-Arcade-Roseville, CA 1.8 1.5 1.4 1.4 1.5 1.6 1.4 1.5 5.8Saginaw-Saginaw Township North, MI 2.5 2.0 2.6 1.8 1.8 3.6 1.9 2.1 11.6Salem, OR 1.4 1.1 1.2 1.2 1.3 1.5 1.2 1.2 2.5Salinas, CA 2.2 1.6 1.6 1.9 2.0 2.3 2.6 1.8 -28.9Salisbury, MD 3.3 2.9 3.8 3.0 3.8 7.3 3.1 3.6 14.7Salt Lake City, UT 2.4 1.9 1.9 1.8 1.6 2.3 2.0 1.8 -10.9San Angelo, TX 1.9 1.6 1.3 1.8 1.7 1.7 2.2 1.6 -26.0San Antonio, TX 1.8 1.6 1.3 1.2 1.1 1.2 1.2 1.3 5.1San Diego-Carlsbad-San Marcos, CA 2.3 2.0 1.5 1.4 1.2 1.4 1.4 1.5 7.5San Francisco-San Mateo-Redwood City, CA 3.6 2.7 3.6 2.7 2.9 5.3 3.1 3.1 0.3San Jose-Sunnyvale-Santa Clara, CA 3.4 2.8 4.0 3.1 3.7 5.1 3.7 3.5 -4.9San Luis Obispo-Paso Robles, CA 3.0 2.6 3.8 2.8 3.4 5.2 3.7 3.3 -9.9Sandusky, OH 4.0 3.2 4.4 3.1 3.4 6.5 4.3 3.6 -14.7Santa Ana-Anaheim-Irvine, CA 1.7 1.3 1.4 1.5 1.6 1.7 1.3 1.5 12.2Santa Barbara-Santa Maria, CA 3.4 2.8 3.7 2.7 2.8 5.4 3.4 3.2 -5.9Santa Cruz-Watsonville, CA 3.3 2.7 3.6 2.9 3.2 6.4 3.2 3.4 4.9Santa Fe, NM 5.1 3.9 5.4 4.0 4.9 8.1 5.0 4.8 -4.1Santa Rosa-Petaluma, CA 3.1 2.5 2.4 3.0 2.7 3.2 3.3 2.7 -19.2Sarasota-Bradenton-Venice, FL 4.1 3.0 4.2 3.4 3.7 6.4 3.6 3.7 1.8Savannah, GA 1.6 1.6 1.4 1.5 1.5 1.9 1.9 1.5 -18.9Scranton-Wilkes-Barre, PA 1.3 1.2 1.6 1.5 1.4 1.6 1.6 1.5 -8.2Seattle-Bellevue-Everett, WA 2.2 1.8 2.2 2.0 2.0 2.7 2.6 2.0 -23.1Sebastian-Vero Beach, FL 2.3 1.8 1.6 1.4 1.4 2.2 1.3 1.6 24.3Sheboygan, WI 1.7 1.4 1.3 1.5 1.5 1.6 1.5 1.4 -3.1Sherman-Denison, TX 2.0 1.7 1.4 1.3 1.3 1.4 1.3 1.4 8.6Shreveport-Bossier City, LA 2.2 1.7 1.3 1.3 1.3 1.5 1.2 1.4 18.9Sioux City, IA-NE-SD 1.7 1.2 1.1 1.2 1.2 1.2 1.1 1.2 3.7Sioux Falls, SD 2.0 1.6 1.3 1.4 1.4 1.4 1.4 1.4 3.3South Bend-Mishawaka, IN-MI 1.4 1.2 1.2 1.2 1.2 1.2 1.2 1.2 2.6Spartanburg, SC 1.9 1.6 1.5 1.6 1.5 1.6 1.6 1.6 -2.1Spokane, WA 2.1 1.7 1.6 1.9 1.6 2.0 2.3 1.7 -24.2Springfield, IL 1.7 1.5 1.4 1.4 1.3 1.3 1.2 1.4 14.4Springfield, MA 1.4 1.7 2.3 1.7 1.7 2.4 2.0 1.9 -2.7Springfield, MO 2.1 1.7 1.5 1.5 1.4 1.5 1.6 1.5 -1.2Springfield, OH 1.5 1.3 1.3 1.4 1.4 1.5 1.3 1.4 8.3St. Cloud, MN 1.8 1.5 1.4 1.5 1.5 2.0 1.8 1.5 -16.0St. George, UT 3.3 2.7 2.2 2.7 2.5 3.2 3.6 2.5 -29.6St. Joseph, MO-KS 1.4 1.1 1.2 1.2 1.3 1.4 1.2 1.2 -1.0St. Louis, MO-IL 1.8 1.5 1.4 1.4 1.4 1.7 1.6 1.4 -8.6State College, PA 1.6 1.5 1.7 1.8 1.8 2.0 2.0 1.8 -11.9Stockton, CA 2.7 2.3 3.0 2.3 2.3 5.0 2.1 2.6 20.5Sumter, SC 2.1 1.7 1.5 1.5 1.5 1.6 1.6 1.6 -1.3Syracuse, NY 1.3 1.4 1.5 1.3 1.1 1.3 1.3 1.4 5.6Tacoma, WA 2.0 1.7 1.8 2.0 2.0 2.5 2.3 1.9 -17.7Tallahassee, FL 2.8 2.4 2.0 2.1 1.9 2.6 2.7 2.1 -19.7Tampa-St. Petersburg-Clearwater, FL 2.5 2.1 1.9 1.7 1.6 2.6 1.9 1.8 -0.8Terre Haute, IN 1.5 1.3 1.3 1.3 1.3 1.3 1.2 1.3 6.4Texarkana TX-Texarkana, AR 2.1 1.8 1.4 1.3 1.2 1.2 1.1 1.4 24.8Toledo, OH 1.8 1.3 1.4 1.4 1.5 1.6 1.3 1.4 12.3Topeka, KS 1.9 1.4 1.3 1.2 1.2 1.4 1.3 1.3 -0.3Trenton-Ewing, NJ 1.6 1.6 1.8 1.6 1.5 2.4 1.8 1.7 -4.0Tucson, AZ 2.6 2.0 1.9 2.0 1.9 2.7 2.1 2.0 -7.6Tulsa, OK 2.2 1.8 1.4 1.4 1.3 1.3 1.2 1.5 25.8Tuscaloosa, AL 2.4 2.0 1.8 1.8 1.9 1.9 1.9 1.9 -0.2Tyler, TX 2.2 1.9 1.4 1.4 1.2 1.4 1.3 1.5 15.2Utica-Rome, NY 1.1 1.4 1.8 1.5 1.3 1.5 1.6 1.5 -6.2




Average From1980 1985 1990 1995 2000 2005 2008Q3 1984-2004 2008Q3

Valdosta, GA 2.4 2.0 1.8 1.6 1.6 1.7 1.8 1.7 -2.8Vallejo-Fairfield, CA 3.6 2.6 3.2 2.6 2.8 5.1 2.5 2.9 16.0Victoria, TX 1.9 1.8 1.3 1.3 1.1 1.2 1.2 1.4 18.4Vineland-Millville-Bridgeton, NJ 1.0 1.0 1.6 1.5 1.3 1.8 2.0 1.4 -30.5Virginia Beach-Norfolk-Newport News, VA-NC 2.0 1.8 1.8 1.8 1.6 2.4 2.4 1.8 -26.1Visalia-Porterville, CA 2.9 2.4 2.1 2.0 1.7 3.2 2.0 2.0 3.7Waco, TX 1.9 1.6 1.4 1.3 1.2 1.3 1.2 1.4 14.6Warner Robins, GA 2.1 1.7 1.6 1.6 1.5 1.5 1.5 1.6 7.8Warren-Farmington Hills-Troy, MI 1.8 1.3 1.6 1.6 1.8 2.0 1.3 1.6 24.6Washington-Arlington-Alexandria, DC-VA-MD-WV 1.9 1.6 2.0 1.7 1.6 2.8 1.9 1.8 -7.0Waterloo-Cedar Falls, IA 1.5 1.0 1.0 1.1 1.2 1.3 1.3 1.1 -12.6Wausau, WI 1.7 1.4 1.3 1.4 1.4 1.5 1.4 1.4 -3.6Weirton-Steubenville, WV-OH 1.1 1.0 1.1 1.2 1.3 1.4 1.2 1.2 -3.5Wenatchee, WA 2.3 2.1 1.9 2.5 2.2 2.5 3.1 2.2 -28.8West Palm Beach-Boca Raton-Boynton Beach, FL 2.4 1.8 1.6 1.5 1.4 2.6 1.5 1.6 10.7Wheeling, WV-OH 1.9 1.4 1.3 1.2 1.3 1.3 1.3 1.3 -1.0Wichita Falls, TX 1.5 1.3 1.0 1.1 1.0 1.0 0.9 1.1 25.0Wichita, KS 1.9 1.6 1.3 1.3 1.3 1.2 1.2 1.4 17.9Williamsport, PA 1.2 1.2 1.6 1.6 1.5 1.6 1.6 1.5 -4.3Wilmington, DE-MD-NJ 1.7 1.7 2.1 1.9 1.7 2.5 2.1 1.9 -12.4Wilmington, NC 2.2 2.3 2.2 2.3 2.3 2.9 3.3 2.3 -29.2Winchester, VA-WV 2.4 2.0 2.3 2.0 1.8 3.0 2.5 2.1 -17.4Winston-Salem, NC 2.1 1.8 1.7 1.7 1.7 1.8 1.6 1.7 6.8Worcester, MA 1.5 1.8 2.0 1.6 1.6 2.5 1.8 1.9 4.6Yakima, WA 2.0 1.7 1.4 1.8 1.7 1.8 1.9 1.7 -14.3York-Hanover, PA 1.6 1.6 1.8 1.7 1.5 1.9 2.1 1.7 -18.9Youngstown-Warren-Boardman, OH-PA 1.2 1.0 1.1 1.1 1.2 1.2 1.0 1.1 14.3Yuba City, CA 1.9 1.8 2.2 2.0 1.7 3.6 2.3 2.0 -12.9Yuma, AZ 2.0 1.7 1.6 1.5 1.8 2.4 2.6 1.7 -34.8

Note: The CSI index has been converted into a dollar value by benchmarking it to the 2000Q1 median existing-house price.Sources: Fiserv Lending Solutions, Moody's Economy.com



Appendix 6-2: Housing Affordability Index20-Year % Deviation

Average, From1980 1985 1990 1995 2000 2005 2008Q3 1984-2004 2008Q3

United States 96.7 113.0 125.6 149.0 142.4 131.3 151.2 139.0 -8.1

Abilene, TX 108.9 126.1 202.6 232.1 224.2 246.8 306.3 211.7 -30.9Akron, OH 117.8 152.4 158.5 167.5 170.9 208.1 247.3 174.2 -29.6Albany, GA 113.4 128.2 158.2 193.4 172.2 193.5 202.3 171.3 -15.3Albany-Schenectady-Troy, NY 134.6 134.0 115.2 157.2 184.3 153.9 147.9 155.2 4.9Albuquerque, NM 82.4 99.3 115.3 137.0 128.2 139.5 122.0 128.0 5.0Alexandria, LA 107.9 110.1 157.4 186.0 166.3 192.8 227.7 165.6 -27.3Allentown-Bethlehem-Easton, PA-NJ 119.0 136.6 117.7 151.8 174.9 112.1 121.2 146.2 20.6Altoona, PA 191.0 173.2 162.6 179.3 198.8 211.5 220.3 190.6 -13.5Amarillo, TX 103.2 124.3 165.0 198.5 181.5 191.8 188.8 181.4 -3.9Ames, IA 112.8 138.7 157.5 172.4 166.6 197.9 227.7 172.0 -24.4Anchorage, AK 73.2 83.2 162.9 161.2 141.0 143.1 151.1 143.3 -5.2Anderson, IN 140.2 189.7 199.6 224.6 207.7 242.7 277.1 219.1 -20.9Anderson, SC 125.0 136.2 159.2 184.1 167.5 178.1 212.4 170.1 -19.9Ann Arbor, MI 99.0 148.9 139.4 173.3 146.8 149.6 188.5 159.1 -15.6Anniston-Oxford, AL 119.5 151.3 161.3 193.0 174.1 205.0 211.3 181.8 -14.0Appleton, WI 114.8 147.4 185.5 207.2 207.6 212.0 228.2 203.2 -11.0Asheville, NC 95.4 109.0 124.5 147.7 124.3 120.4 125.8 132.1 5.0Athens-Clarke County, GA 83.3 102.7 132.3 150.5 132.8 146.6 171.8 136.8 -20.4Atlanta-Sandy Springs-Marietta, GA 107.7 132.0 146.6 173.6 169.3 168.5 197.0 163.8 -16.9Atlantic City, NJ 97.0 123.5 113.3 156.1 161.7 110.2 107.7 145.5 35.1Auburn-Opelika, AL 95.1 120.2 129.9 154.4 145.9 152.5 171.0 147.0 -14.0Augusta-Richmond County, GA-SC 91.8 123.8 148.8 171.0 171.4 193.4 197.6 162.0 -18.0Austin-Round Rock, TX 96.8 95.5 152.2 156.9 151.5 174.4 152.3 147.6 -3.0Bakersfield, CA 78.6 93.7 94.2 133.5 142.1 68.6 112.3 118.8 5.7Baltimore-Towson, MD 130.4 154.2 143.9 188.7 171.2 116.9 123.7 171.7 38.8Bangor, ME 159.9 138.2 120.2 151.5 188.0 178.2 176.3 159.6 -9.5Barnstable Town, MA 63.8 61.0 61.0 87.9 90.8 77.4 99.9 79.1 -20.8Baton Rouge, LA 88.9 99.8 152.0 168.3 150.8 160.9 151.4 154.7 2.2Battle Creek, MI 166.0 200.1 219.1 256.2 204.6 224.1 259.6 235.9 -9.1Bay City, MI 169.8 210.0 229.6 243.3 206.7 220.7 246.9 233.7 -5.3Beaumont-Port Arthur, TX 114.7 128.6 177.9 217.1 186.4 205.1 173.7 190.3 9.5Bellingham, WA 81.2 114.4 123.4 120.0 116.6 98.9 107.7 126.2 17.2Bend, OR 94.4 121.9 133.2 132.8 123.8 111.3 106.1 133.3 25.6Bethesda-Gaithersburg-Frederick, MD 90.7 113.3 94.4 130.5 135.4 90.7 119.9 121.1 1.0Billings, MT 84.4 123.2 155.0 160.2 156.7 165.8 199.9 155.3 -22.3Binghamton, NY 115.9 126.0 126.6 173.7 224.9 226.8 226.6 177.6 -21.6Birmingham-Hoover, AL 84.6 104.0 121.2 131.8 132.5 148.0 149.2 130.4 -12.6Bismarck, ND 106.1 138.9 167.6 207.6 188.3 215.9 188.8 189.5 0.4Blacksburg-Christiansburg-Radford, VA 93.8 113.8 120.9 136.2 121.4 131.4 144.0 129.5 -10.0Bloomington, IN 93.3 113.6 151.2 167.0 176.5 187.2 187.5 165.8 -11.6Bloomington-Normal, IL 102.9 133.0 147.6 165.6 167.2 175.6 189.5 165.6 -12.6Boise City-Nampa, ID 79.9 121.7 142.3 162.2 157.2 157.9 129.5 153.4 18.5Boston-Quincy, MA 99.6 82.8 90.7 116.5 86.4 73.9 90.2 95.7 6.1Boulder, CO 65.0 84.7 109.1 93.1 94.1 99.1 97.7 101.4 3.9Bowling Green, KY 90.7 111.4 135.0 162.4 147.2 185.2 206.9 152.2 -26.4Bremerton-Silverdale, WA 83.6 104.7 124.4 120.8 118.3 66.9 95.7 120.7 26.2Bridgeport-Stamford-Norwalk, CT 73.2 114.0 140.9 136.9 138.1 124.4 132.9 139.9 5.3Brownsville-Harlingen, TX 58.9 60.5 57.3 78.6 87.9 83.1 91.2 74.4 -18.5Brunswick, GA 101.5 96.8 146.1 176.2 172.4 166.6 213.0 157.7 -25.9Buffalo-Niagara Falls, NY 106.7 126.5 144.3 163.5 141.4 137.5 155.2 150.0 -3.4Burlington, NC 129.5 150.9 136.1 181.4 218.9 252.8 244.8 187.7 -23.3Burlington-South Burlington, VT 106.2 121.5 128.7 153.9 145.3 160.3 192.1 144.0 -25.0Cambridge-Newton-Framingham, MA 117.1 113.4 109.1 132.4 162.4 145.9 159.5 140.6 -11.8Camden, NJ 92.5 79.5 86.7 110.1 96.1 85.1 104.5 95.8 -8.3Canton-Massillon, OH 114.7 140.2 122.6 152.6 190.3 147.5 151.8 159.0 4.7Cape Coral-Fort Myers, FL 110.9 136.9 160.8 171.7 164.0 197.4 243.7 168.9 -30.7Carson City, NV 83.9 115.0 144.7 177.9 165.4 85.5 139.8 156.0 11.6Casper, WY 68.0 87.4 114.8 123.5 121.9 89.2 128.8 116.2 -9.8Cedar Rapids, IA 65.7 114.9 189.0 181.0 207.1 200.7 222.9 186.9 -16.1Champaign-Urbana, IL 143.0 168.9 188.6 181.2 174.5 203.6 197.6 189.6 -4.0Charleston, WV 100.2 122.1 163.4 186.7 201.0 191.6 178.2 185.6 4.1Charleston-North Charleston, SC 103.5 116.5 129.2 139.0 122.4 116.5 116.6 132.8 13.9Charlotte-Gastonia-Concord, NC-SC 115.0 105.8 136.7 157.4 143.0 164.9 157.3 145.5 -7.5Charlottesville, VA 95.6 114.4 124.8 154.5 145.6 139.8 130.0 140.3 8.0Chattanooga, TN-GA 73.5 90.7 95.1 115.5 112.7 104.1 119.2 110.0 -7.7Cheyenne, WY 106.2 120.3 137.2 163.7 165.2 167.1 164.0 155.5 -5.2Chicago-Naperville-Joliet, IL 75.6 106.4 137.4 150.9 152.0 169.3 238.7 150.8 -36.8Chico, CA 107.6 115.0 113.6 123.5 131.8 106.4 115.0 124.6 8.4Cincinnati-Middletown, OH-KY-IN 40.7 56.7 62.7 90.7 88.6 52.9 74.3 79.2 6.6



Appendix 6-2: Housing Affordability Index (cont.)20-Year % Deviation

Average, From1980 1985 1990 1995 2000 2005 2008Q3 1984-2004 2008Q3

Clarksville, TN-KY 98.7 126.1 141.9 158.2 154.1 179.8 203.5 157.1 -22.8Cleveland, TN 94.2 122.7 128.6 171.5 165.5 196.7 199.8 159.7 -20.1Cleveland-Elyria-Mentor, OH 102.9 128.9 140.1 149.6 152.3 177.5 228.2 151.7 -33.5Coeur d'Alene, ID 104.0 135.1 148.5 169.7 150.1 179.2 173.0 161.0 -7.0College Station-Bryan, TX 75.2 100.4 142.6 145.1 128.1 109.4 119.1 137.8 15.7Colorado Springs, CO 80.0 90.3 139.9 165.9 170.0 188.8 221.0 153.0 -30.8Columbia, MO 73.9 97.7 127.2 133.5 134.5 133.4 139.8 131.1 -6.2Columbia, SC 93.3 117.3 151.5 172.7 173.8 181.4 195.0 168.6 -13.6Columbus, GA-AL 101.7 120.5 134.3 160.8 158.6 180.1 169.7 156.1 -8.0Columbus, IN 96.9 123.1 141.0 159.5 159.1 167.9 216.1 154.8 -28.4Columbus, OH 109.8 153.5 166.2 176.8 172.6 199.8 219.6 179.8 -18.1Corpus Christi, TX 99.6 126.4 138.2 161.6 154.5 174.2 193.4 154.4 -20.2Corvallis, OR 87.1 99.6 141.5 169.2 165.1 148.9 148.3 153.4 3.4Cumberland, MD-WV 95.8 124.1 148.8 128.0 125.1 143.3 131.6 142.2 8.0Dallas-Plano-Irving, TX 141.1 146.0 158.5 190.8 192.4 205.8 212.4 191.4 -9.9Dalton, GA 78.2 87.1 121.1 162.5 164.0 165.2 173.6 141.9 -18.3Danville, IL 106.3 127.0 149.0 183.7 166.9 162.6 197.5 165.0 -16.5Danville, VA 197.2 236.1 248.5 299.6 287.1 289.0 366.3 284.9 -22.2Davenport-Moline-Rock Island, IA-IL 128.5 146.2 154.2 185.2 169.2 192.6 233.0 174.6 -25.1Dayton, OH 131.1 163.8 221.6 225.2 212.2 212.2 264.6 220.3 -16.8Decatur, AL 109.6 143.2 154.6 179.0 175.0 205.5 225.7 173.7 -23.1Decatur, IL 93.2 120.2 146.8 184.5 165.7 200.3 199.9 165.4 -17.2Deltona-Daytona Beach-Ormond Beach, FL 149.6 171.3 208.0 225.6 234.3 282.4 269.4 229.3 -14.9Denver-Aurora, CO 92.5 118.1 142.0 183.3 179.8 106.9 133.4 161.5 21.1Des Moines, IA 87.6 111.6 139.7 134.5 120.9 118.2 131.0 132.6 1.2Detroit-Livonia-Dearborn, MI 117.1 149.0 186.6 187.0 170.2 193.8 181.6 184.1 1.4Dothan, AL 126.4 185.3 178.2 204.1 158.1 187.7 247.2 187.4 -24.2Dover, DE 112.5 137.2 142.7 173.5 173.5 210.2 197.5 170.8 -13.5Dubuque, IA 106.4 113.6 132.8 174.3 171.5 134.9 114.2 156.7 37.3Duluth, MN-WI 137.1 155.6 181.4 191.3 182.3 215.6 217.0 192.2 -11.4Durham, NC 186.3 192.0 236.0 247.9 222.0 201.7 207.7 235.1 13.2Eau Claire, WI 113.8 119.4 136.7 161.9 153.5 153.1 149.7 149.8 0.0Edison, NJ 126.9 155.4 193.7 205.6 185.9 180.6 207.6 197.6 -4.8El Centro, CA 115.7 121.1 107.2 141.9 146.5 96.6 108.1 130.2 20.5El Paso, TX 100.5 115.8 110.5 122.8 135.5 95.0 150.5 128.8 -14.4Elizabethtown, KY 97.2 97.3 111.4 141.7 146.5 132.4 119.8 133.1 11.1Elkhart-Goshen, IN 88.8 118.3 134.0 161.5 164.7 184.3 195.7 159.1 -18.7Elmira, NY 92.4 132.9 158.2 192.2 176.4 202.3 200.8 179.4 -10.7Erie, PA 184.3 169.2 157.3 207.2 227.9 274.6 236.3 203.8 -13.8Eugene-Springfield, OR 127.8 125.3 138.8 149.6 170.6 224.3 232.9 160.8 -31.0Evansville, IN-KY 96.2 141.8 138.1 131.9 124.8 111.7 107.5 141.7 31.8Fairbanks, AK 115.7 146.7 190.7 217.8 203.8 241.4 252.6 208.3 -17.6Fargo, ND-MN 88.5 105.9 155.4 162.8 152.7 171.7 180.2 151.2 -16.1Farmington, NM 88.6 120.2 162.0 189.5 187.1 201.6 204.8 177.9 -13.1Fayetteville, NC 89.6 89.6 131.8 157.0 125.9 121.3 110.7 128.6 16.2Fayetteville-Springdale-Rogers, AR-MO 95.6 117.5 142.0 165.1 160.2 167.9 182.7 156.1 -14.5Flagstaff, AZ 90.1 113.2 118.7 155.1 150.6 177.4 218.5 145.8 -33.3Flint, MI 74.4 101.2 114.7 123.1 114.7 97.9 107.3 120.7 12.5Florence, SC 148.2 218.1 200.7 259.5 188.9 210.6 254.5 224.8 -11.7Florence-Muscle Shoals, AL 106.3 117.9 147.4 170.0 161.1 184.4 207.2 158.8 -23.4Fond du Lac, WI 114.9 123.1 149.3 177.6 163.9 181.5 214.6 163.7 -23.7Fort Collins-Loveland, CO 127.3 152.0 182.6 201.7 187.0 207.9 218.6 194.9 -10.8Fort Lauderdale-Pompano Beach-Deerfield Beach, FL 76.4 100.6 141.2 133.8 134.7 140.7 183.9 136.3 -25.9Fort Smith, AR-OK 77.6 111.2 121.7 149.0 125.1 66.6 101.2 128.2 26.7Fort Walton Beach-Crestview-Destin, FL 119.0 134.7 166.3 204.9 186.3 205.2 223.0 182.2 -18.3Fort Wayne, IN 83.4 108.2 147.1 156.7 139.7 111.7 142.4 147.8 3.8Fort Worth-Arlington, TX 97.7 134.3 167.6 209.8 205.5 243.9 245.3 195.9 -20.2Fresno, CA 90.2 100.8 149.4 187.4 182.6 197.2 221.3 165.7 -25.1Gadsden, AL 66.0 83.8 90.1 119.3 123.1 60.5 95.2 106.1 11.4Gainesville, FL 132.4 152.3 171.1 197.6 173.9 180.8 191.1 184.3 -3.5Gainesville, GA 85.6 111.5 133.5 155.8 145.5 124.5 132.8 145.1 9.3Gary, IN 88.6 111.9 129.4 155.5 141.1 144.0 150.4 140.0 -6.9Glens Falls, NY 121.4 145.3 174.0 173.0 174.7 188.1 204.2 177.7 -13.0Goldsboro, NC 99.8 105.0 98.9 137.5 186.6 167.8 145.1 144.6 -0.3Grand Forks, ND-MN 98.8 113.8 135.9 161.5 154.7 179.7 215.7 150.4 -30.3Grand Junction, CO 97.1 135.2 152.7 186.9 185.7 212.0 221.2 179.7 -18.8Grand Rapids-Wyoming, MI 70.8 115.7 141.6 139.6 131.9 131.8 134.7 140.9 4.6Great Falls, MT 132.0 158.3 161.3 199.1 168.5 177.8 225.1 181.3 -19.4Greeley, CO 86.3 121.1 143.5 159.3 152.8 190.7 202.4 154.9 -23.5Green Bay, WI 88.1 109.5 142.3 139.9 139.2 155.2 220.2 141.8 -35.6Greensboro-High Point, NC 110.6 143.3 175.5 183.0 166.4 167.6 180.9 177.6 -1.8




Average, From1980 1985 1990 1995 2000 2005 2008Q3 1984-2004 2008Q3

Greenville, NC 86.0 101.8 121.9 148.0 138.4 150.6 165.8 135.8 -18.1Greenville, SC 83.8 100.7 130.2 164.8 152.1 165.8 209.9 145.2 -30.9Gulfport-Biloxi, MS 112.6 121.7 135.4 154.2 138.2 152.7 150.2 144.8 -3.6Hagerstown-Martinsburg, MD-WV 87.2 104.2 133.5 153.5 154.7 154.1 140.2 147.6 5.3Hanford-Corcoran, CA 110.0 122.0 119.5 157.0 166.6 112.8 147.3 148.5 0.8Harrisburg-Carlisle, PA 78.7 112.5 123.5 144.4 154.5 111.8 159.0 141.2 -11.2Harrisonburg, VA 113.1 134.0 149.7 163.0 178.3 189.6 194.4 170.1 -12.5Hartford-West Hartford-East Hartford, CT 76.0 89.1 92.8 116.3 113.2 108.5 116.7 110.1 -5.7Hattiesburg, MS 84.9 101.9 93.5 147.1 149.6 134.2 145.7 130.8 -10.3Hickory-Lenoir-Morganton, NC 91.3 101.7 144.9 175.4 160.6 181.1 198.0 157.0 -20.7Hinesville-Fort Stewart, GA 109.1 128.9 151.1 179.2 161.2 172.0 186.6 162.7 -12.8Holland-Grand Haven, MI 91.1 113.7 116.7 158.3 150.5 180.8 186.9 145.8 -22.0Honolulu, HI 111.0 137.6 151.9 184.3 160.9 179.7 197.9 170.6 -13.8Hot Springs, AR 41.9 59.5 42.4 59.9 74.4 52.6 55.3 65.8 18.9Houma-Bayou Cane-Thibodaux, LA 85.2 92.6 131.9 159.4 145.0 152.8 149.3 139.8 -6.4Houston-Sugar Land-Baytown, TX 130.8 113.3 157.0 180.0 169.3 187.2 203.9 166.2 -18.5Huntington-Ashland, WV-KY-OH 104.1 121.9 158.9 195.2 165.1 164.1 168.8 167.8 -0.6Huntsville, AL 148.1 134.6 154.5 183.3 165.0 201.9 228.1 172.9 -24.2Idaho Falls, ID 82.1 116.2 141.3 169.4 173.7 211.5 203.3 163.1 -19.8Indianapolis, IN 96.9 140.1 172.5 193.1 187.2 204.1 207.9 183.7 -11.6Iowa City, IA 109.0 134.2 151.8 170.4 176.7 210.7 222.8 171.8 -22.9Ithaca, NY 100.6 124.7 147.3 161.2 164.1 168.1 198.5 162.1 -18.4Jackson, MI 124.0 143.4 109.8 152.9 191.4 183.5 196.0 161.2 -17.8Jackson, MS 163.6 195.7 196.1 223.3 189.9 208.1 235.6 213.8 -9.3Jackson, TN 77.9 99.0 132.0 172.7 160.0 166.9 165.6 153.3 -7.4Jacksonville, FL 98.3 118.7 141.9 174.7 170.9 193.5 231.3 162.1 -29.9Jacksonville, NC 102.0 125.5 145.7 180.2 183.2 142.0 154.2 168.0 8.9Janesville, WI 82.1 121.7 101.6 148.4 137.6 158.2 154.5 135.2 -12.5Jefferson City, MO 121.2 166.3 200.9 225.4 190.9 203.1 217.2 210.5 -3.1Johnson City, TN 111.6 137.6 184.8 208.6 198.1 227.4 230.0 200.4 -12.9Johnstown, PA 96.6 114.8 140.4 154.9 142.2 172.9 168.9 148.0 -12.4Jonesboro, AR 199.0 159.2 153.8 205.0 218.1 231.1 237.2 195.8 -17.5Joplin, MO 93.5 103.1 156.1 194.3 174.4 205.1 243.2 168.0 -30.9Kalamazoo-Portage, MI 107.3 139.6 174.7 206.8 197.6 212.2 217.4 193.3 -11.1Kankakee-Bradley, IL 114.9 148.9 179.9 195.9 167.9 194.7 217.9 186.1 -14.6Kansas City, MO-KS 167.6 193.8 204.6 222.2 211.4 203.3 195.1 219.0 12.2Kennewick-Richland-Pasco, WA 89.5 122.5 154.8 177.4 163.1 175.0 198.3 164.2 -17.2Killeen-Temple-Fort Hood, TX 99.1 160.0 175.8 167.9 162.2 165.7 155.5 173.6 11.6Kingsport-Bristol-Bristol, TN-VA 84.6 100.3 133.2 175.0 174.7 215.6 241.1 158.2 -34.4Kingston, NY 95.1 117.9 137.2 161.1 143.6 158.1 168.0 148.1 -11.8Knoxville, TN 98.1 114.9 102.6 143.1 154.2 107.3 109.3 133.0 21.7Kokomo, IN 90.0 112.7 128.2 151.8 153.4 156.8 158.1 147.0 -7.0La Crosse, WI-MN 125.6 174.2 199.5 229.3 208.2 235.2 270.5 216.6 -19.9Lafayette, IN 116.7 140.9 185.0 199.5 188.5 207.2 222.7 195.2 -12.3Lafayette, LA 109.3 135.5 155.1 172.3 168.8 204.9 210.9 171.2 -18.8Lake Charles, LA 85.7 101.2 152.8 163.7 152.8 169.8 191.9 155.1 -19.2Lake County-Kenosha County, IL-WI 126.3 109.0 160.3 191.7 167.3 182.6 211.9 169.1 -20.2Lake Havasu City-Kingman, AZ 112.5 113.8 118.8 137.1 143.6 129.5 141.8 134.8 -4.9Lakeland, FL 65.0 83.5 113.4 135.9 136.8 105.4 133.8 124.5 -6.9Lancaster, PA 85.6 105.5 133.9 191.7 189.1 130.8 157.1 162.4 3.3Lansing-East Lansing, MI 88.8 110.5 109.2 133.1 164.7 159.3 159.0 141.1 -11.3Laredo, TX 123.8 168.2 184.7 221.9 182.8 187.6 256.0 198.0 -22.7Las Cruces, NM 64.4 63.4 98.0 127.4 130.8 143.8 167.2 115.2 -31.1Las Vegas-Paradise, NV 71.7 92.2 114.3 139.8 130.8 122.7 136.1 126.2 -7.2Lawrence, KS 77.5 101.7 114.7 139.7 131.0 82.1 119.9 124.3 3.7Lawton, OK 109.3 140.2 155.2 179.3 171.3 167.2 179.1 173.2 -3.3Lebanon, PA 80.2 105.2 139.6 165.5 176.0 200.2 234.3 161.3 -31.2Lewiston, ID-WA 162.8 146.5 126.5 153.0 177.9 185.9 163.5 163.7 0.1Lewiston-Auburn, ME 92.8 122.3 167.8 166.6 153.9 139.2 132.4 161.6 22.1Lexington-Fayette, KY 155.1 132.1 100.6 141.7 175.8 152.9 159.0 146.5 -7.8Lima, OH 90.0 106.2 143.5 168.4 156.8 170.1 170.7 156.2 -8.5Lincoln, NE 117.1 159.0 176.6 201.7 192.8 216.5 258.0 197.1 -23.6Little Rock-North Little Rock, AR 130.7 152.9 189.3 194.8 176.3 193.1 180.6 189.7 5.1Logan, UT-ID 113.6 126.4 157.8 190.8 188.7 191.4 180.6 173.5 -3.9Longview, TX 83.0 107.3 153.0 131.6 131.1 145.6 154.8 139.3 -10.0Longview, WA 121.7 128.8 167.4 200.4 196.4 207.5 243.1 183.6 -24.5Los Angeles-Long Beach-Glendale, CA 94.7 133.4 167.0 142.7 136.3 139.8 130.2 152.3 17.0Louisville, KY-IN 52.6 73.0 60.3 89.0 98.3 51.2 76.4 80.9 5.9Lubbock, TX 114.8 144.0 163.0 171.5 150.1 174.1 179.8 168.4 -6.3Lynchburg, VA 116.2 130.4 173.7 204.9 203.2 230.9 271.0 190.4 -29.7Macon, GA 96.3 111.4 120.2 136.8 131.1 135.4 146.7 131.0 -10.7




Average, From1980 1985 1990 1995 2000 2005 2008Q3 1984-2004 2008Q3

Madera, CA 119.6 125.4 143.3 177.3 173.5 187.2 230.6 165.1 -28.4Madison, WI 64.6 88.1 112.7 120.2 132.8 89.6 116.2 119.1 2.5Manchester-Nashua, NH 105.4 127.2 157.6 149.2 148.1 139.3 146.8 156.5 6.7Mansfield, OH 96.3 105.8 103.8 154.8 176.3 144.6 189.7 142.7 -24.8McAllen-Edinburg-Mission, TX 118.9 161.7 182.4 196.4 183.1 208.0 227.9 193.7 -15.0Medford, OR 108.0 109.7 148.1 157.0 155.2 153.5 201.1 149.9 -25.5Memphis, TN-MS-AR 73.6 104.1 122.0 123.7 117.6 92.8 109.9 125.0 13.7Merced, CA 85.4 104.4 122.2 159.2 143.7 154.5 181.7 138.6 -23.7Miami-Miami Beach-Kendall, FL 70.0 87.2 75.6 114.0 120.9 58.0 127.6 102.6 -19.6Michigan City-La Porte, IN 72.9 101.4 106.7 118.7 104.5 53.0 73.5 109.6 49.1Midland, TX 146.6 166.4 180.5 195.3 189.5 207.2 214.5 195.7 -8.8Milwaukee-Waukesha-West Allis, WI 100.1 122.8 172.1 212.4 213.8 226.0 200.2 194.1 -3.0Minneapolis-St. Paul-Bloomington, MN-WI 100.4 123.9 140.9 146.3 145.5 125.4 134.8 145.2 7.7Missoula, MT 98.3 124.9 150.4 176.4 163.0 142.8 162.8 160.4 -1.5Mobile, AL 73.4 108.4 143.4 121.5 117.9 113.8 119.1 125.3 5.3Modesto, CA 97.4 121.9 145.7 162.2 143.0 145.4 129.3 152.8 18.2Monroe, LA 59.4 92.1 85.1 123.4 143.8 92.5 184.4 116.9 -36.6Monroe, MI 103.4 107.8 152.6 188.8 165.9 168.2 206.3 163.2 -20.9Montgomery, AL 114.2 160.8 159.0 185.0 160.9 179.6 214.9 179.3 -16.6Morgantown, WV 91.5 116.2 139.4 157.1 156.0 160.9 168.8 152.0 -10.0Morristown, TN 112.5 110.3 122.0 163.7 152.0 156.3 190.8 148.0 -22.5Mount Vernon-Anacortes, WA 87.9 106.7 138.2 164.7 146.8 159.1 165.2 148.6 -10.0Muncie, IN 87.7 117.2 125.5 119.8 116.8 113.3 107.9 127.2 17.9Muskegon-Norton Shores, MI 148.0 184.1 195.7 225.3 211.9 209.1 276.8 214.4 -22.5Myrtle Beach-Conway-North Myrtle Beach, SC 135.2 171.5 180.2 214.0 194.3 219.8 232.3 203.3 -12.5Napa, CA 71.4 85.9 104.3 127.3 117.3 111.7 108.6 114.0 4.9Naples-Marco Island, FL 56.5 81.7 77.0 101.7 99.5 70.2 106.5 94.1 -11.6Nashville-Davidson-Murfreesboro, TN 55.2 69.1 81.6 106.1 85.2 53.6 56.8 91.0 60.3Nassau-Suffolk, NY 98.1 118.1 126.9 147.4 150.1 153.8 143.0 145.7 1.9New Haven-Milford, CT 139.0 101.5 105.9 145.8 133.6 84.6 98.4 122.2 24.2New Orleans-Metairie-Kenner LA 84.2 94.5 91.8 141.0 148.9 111.7 124.1 122.3 -1.5New York-White Plains-Wayne, NY-NJ 81.6 86.5 129.1 163.0 135.5 134.9 151.4 137.5 -9.2Newark-Union, NJ-PA 73.1 62.8 64.7 80.5 76.4 52.1 56.4 71.0 25.9Niles-Benton Harbor, MI 86.8 83.6 89.1 107.4 108.4 83.7 87.5 99.0 13.1Norwich-New London, CT 175.3 167.4 169.2 193.3 172.9 174.6 198.7 186.4 -6.2Oakland-Fremont-Hayward, CA 105.1 109.3 87.0 135.5 150.4 124.9 145.1 125.9 -13.2Ocala, FL 49.8 73.3 68.9 93.9 77.0 53.6 68.0 80.2 18.0Ocean City, NJ 89.9 117.1 144.7 176.0 193.6 123.4 147.0 165.8 12.8Odessa, TX 102.6 110.0 98.7 131.0 139.8 95.5 117.3 125.8 7.2Ogden-Clearfield, UT 132.6 149.1 212.2 255.9 258.4 287.3 278.2 236.5 -15.0Oklahoma City, OK 75.2 114.5 168.0 147.0 148.6 173.7 161.0 153.5 -4.7Olympia, WA 107.8 122.1 187.2 191.4 189.8 188.1 182.8 181.8 -0.5Omaha-Council Bluffs, NE-IA 99.4 125.8 140.1 138.1 148.5 129.5 129.5 146.2 12.9Orlando-Kissimmee, FL 125.4 138.0 178.2 190.5 171.5 192.8 185.5 181.1 -2.4Oshkosh-Neenah, WI 80.0 106.0 126.1 156.2 156.0 96.1 119.5 142.7 19.4Owensboro, KY 117.5 148.8 185.8 206.3 197.4 212.6 230.9 199.1 -13.8Oxnard-Thousand Oaks-Ventura, CA 131.1 140.9 166.2 194.4 180.3 210.4 267.0 182.2 -31.8Palm Bay-Melbourne-Titusville, FL 53.2 77.0 68.8 107.3 102.7 67.1 104.0 92.2 -11.3Palm Coast, FL 103.4 126.9 150.8 184.2 190.9 113.1 185.6 173.4 -6.6Panama City-Lynn Haven, FL 64.8 86.3 87.2 128.2 123.2 88.5 125.7 111.4 -11.4Parkersburg-Marietta-Vienna, WV-OH 90.3 119.7 153.0 169.0 139.7 99.1 119.7 154.3 28.9Pascagoula, MS 163.9 162.0 165.3 189.3 164.6 189.5 226.6 180.9 -20.2Peabody, MA 126.1 144.3 168.3 196.2 179.1 195.5 198.9 183.8 -7.6Pensacola-Ferry Pass-Brent, FL 97.6 78.3 78.0 108.3 101.6 86.8 109.9 94.8 -13.8Peoria, IL 86.4 112.1 137.6 162.3 157.2 135.0 161.0 154.6 -4.0Philadelphia, PA 132.1 174.6 219.0 219.5 216.1 242.5 219.7 225.0 2.4Phoenix-Mesa-Scottsdale, AZ 121.5 135.9 115.8 146.0 178.7 136.0 131.6 148.2 12.6Pine Bluff, AR 82.7 111.4 125.1 161.0 142.0 102.3 140.5 141.1 0.4Pittsburgh, PA 116.0 122.3 187.1 236.6 219.8 241.7 274.1 203.4 -25.8Pittsfield, MA 115.9 128.0 144.1 173.6 184.1 206.0 226.4 170.5 -24.7Pocatello, ID 93.8 86.9 79.2 114.7 136.0 129.9 136.8 112.8 -17.6Port St. Lucie-Fort Pierce, FL 104.6 148.1 179.5 181.7 178.1 186.0 213.0 181.3 -14.9Portland-South Portland-Biddeford, ME 82.0 103.6 125.1 176.1 166.2 85.8 163.2 149.2 -8.6Portland-Vancouver-Beaverton, OR-WA 102.9 102.0 98.7 137.0 138.6 108.8 122.9 123.8 0.7Poughkeepsie-Newburgh-Middletown, NY 89.6 132.3 142.4 136.4 130.0 110.5 103.0 139.9 35.8Prescott, AZ 100.7 113.1 94.2 130.9 154.8 118.6 131.7 127.9 -2.9Providence-New Bedford-Fall River, RI-MA 58.0 79.1 94.5 111.5 108.0 94.3 105.7 105.8 0.1Provo-Orem, UT 106.5 102.7 87.3 127.1 134.4 98.3 123.3 114.3 -7.3Pueblo, CO 72.3 94.3 145.3 124.4 128.7 136.3 132.1 129.0 -2.4Punta Gorda, FL 95.9 118.9 161.0 171.4 156.8 186.9 233.1 163.7 -29.8Racine, WI 106.1 126.7 148.8 179.4 171.2 94.0 161.1 160.9 -0.1




Average, From1980 1985 1990 1995 2000 2005 2008Q3 1984-2004 2008Q3

Raleigh-Cary, NC 122.3 142.4 169.0 187.5 179.0 170.4 184.7 183.2 -0.8Rapid City, SD 86.0 105.1 129.2 152.1 159.6 152.5 143.0 145.9 2.0Reading, PA 113.5 132.3 173.9 186.2 174.3 186.1 182.1 181.3 -0.4Redding, CA 116.0 141.7 132.1 176.0 224.6 191.6 163.0 181.0 11.0Reno-Sparks, NV 36.1 58.0 68.2 96.5 95.3 56.8 75.1 84.4 12.4Richmond, VA 73.4 95.1 104.7 127.0 126.2 77.0 110.9 117.7 6.2Riverside-San Bernardino-Ontario, CA 98.0 119.9 131.4 155.4 151.0 138.8 134.5 148.6 10.5Roanoke, VA 59.2 90.6 89.5 122.3 136.0 68.0 113.9 112.8 -0.9Rochester, MN 108.4 122.5 132.4 156.9 142.9 151.0 160.4 145.6 -9.2Rochester, NY 109.1 139.9 176.9 202.3 191.0 217.2 226.0 194.1 -14.1Rockford, IL 152.5 146.0 153.3 200.3 221.6 227.7 230.0 195.0 -15.2Rockingham County-Strafford County, NH 144.3 177.9 214.1 246.1 265.5 201.1 204.0 240.6 17.9Rocky Mount, NC 99.0 105.3 103.6 152.4 142.0 121.1 152.6 131.2 -14.0Rome, GA 90.4 103.9 127.7 162.2 160.7 185.0 208.5 150.2 -28.0Sacramento-Arden-Arcade-Roseville, CA 125.8 143.6 176.0 191.9 164.4 175.4 214.8 177.2 -17.5Saginaw-Saginaw Township North, MI 72.5 105.9 88.6 141.1 151.0 76.9 146.2 124.2 -15.0Salem, OR 131.2 186.1 203.0 236.5 203.8 179.4 307.3 213.2 -30.6Salinas, CA 86.9 131.9 165.6 147.6 132.2 130.1 120.9 151.2 25.1Salisbury, MD 53.4 64.8 54.1 75.3 57.6 40.2 100.3 65.5 -34.7Salt Lake City, UT 96.4 114.4 132.5 167.0 164.8 144.1 157.8 153.2 -2.9San Angelo, TX 88.3 115.2 154.0 143.0 151.5 143.7 117.9 149.0 26.3San Antonio, TX 111.1 125.3 165.8 207.5 208.6 247.0 269.2 191.5 -28.9San Diego-Carlsbad-San Marcos, CA 88.2 95.8 138.1 164.8 169.4 166.1 151.3 151.7 0.3San Francisco-San Mateo-Redwood City, CA 53.9 76.0 67.3 92.9 81.9 49.4 85.4 80.5 -5.8San Jose-Sunnyvale-Santa Clara, CA 42.7 61.0 49.5 69.7 64.1 47.5 60.4 61.9 2.5San Luis Obispo-Paso Robles, CA 52.7 77.2 61.9 89.4 77.1 54.5 65.9 76.3 15.9Sandusky, OH 45.8 64.1 56.5 91.6 90.7 63.2 89.4 81.3 -9.0Santa Ana-Anaheim-Irvine, CA 118.8 158.0 163.9 184.6 165.8 162.6 247.7 175.7 -29.1Santa Barbara-Santa Maria, CA 50.3 74.3 66.1 96.5 82.6 48.5 69.5 80.7 16.1Santa Cruz-Watsonville, CA 41.7 61.7 53.6 77.9 75.6 47.3 76.5 69.0 -9.8Santa Fe, NM 49.4 65.1 51.0 72.9 57.4 48.0 79.4 63.1 -20.5Santa Rosa-Petaluma, CA 61.2 86.1 103.6 105.7 93.1 81.6 98.6 100.8 2.2Sarasota-Bradenton-Venice, FL 49.9 77.1 67.2 86.3 74.6 45.2 75.1 78.5 4.5Savannah, GA 121.2 124.9 154.4 174.8 160.6 157.5 165.4 162.4 -1.8Scranton-Wilkes-Barre, PA 155.1 165.1 148.0 164.1 184.3 185.9 194.8 174.0 -10.7Seattle-Bellevue-Everett, WA 92.5 132.0 96.0 119.0 115.4 95.4 90.8 123.3 35.8Sebastian-Vero Beach, FL 73.4 89.8 119.5 142.5 145.8 101.4 153.1 130.6 -14.7Sheboygan, WI 119.8 144.8 178.8 189.9 181.3 202.5 209.5 188.8 -9.9Sherman-Denison, TX 105.1 118.5 178.2 224.0 221.0 251.4 305.5 198.7 -35.0Shreveport-Bossier City, LA 90.7 101.5 138.1 179.2 172.4 163.6 156.3 158.0 1.1Sioux City, IA-NE-SD 137.5 160.3 199.0 224.6 210.3 247.1 249.0 212.0 -14.8Sioux Falls, SD 108.4 124.1 181.0 177.5 172.2 184.5 170.0 177.1 4.2South Bend-Mishawaka, IN-MI 131.9 163.6 177.8 217.5 212.2 241.0 282.2 205.5 -27.2Spartanburg, SC 124.0 135.4 161.0 188.3 168.3 171.7 174.3 172.0 -1.3Spokane, WA 106.0 147.6 174.4 149.6 176.8 158.9 127.3 169.9 33.4Springfield, IL 115.8 136.4 180.0 193.9 224.4 249.1 248.4 205.1 -17.4Springfield, MA 115.2 101.1 90.2 136.4 152.7 129.9 141.1 127.1 -9.9Springfield, MO 68.5 85.7 124.2 164.9 172.7 177.3 183.7 154.0 -16.2Springfield, OH 131.5 154.9 170.0 196.2 187.5 201.9 239.8 188.9 -21.2St. Cloud, MN 105.9 128.5 162.0 191.4 186.8 175.4 187.7 179.7 -4.3St. George, UT 54.9 77.5 114.2 109.3 115.9 108.0 105.6 110.2 4.4St. Joseph, MO-KS 143.9 172.8 194.8 224.4 210.2 235.3 255.8 215.8 -15.6St. Louis, MO-IL 94.3 117.2 140.7 180.2 176.9 185.1 196.2 166.7 -15.0State College, PA 127.0 121.3 117.2 139.6 160.1 155.2 176.5 143.1 -18.9Stockton, CA 70.8 89.1 70.5 112.0 115.3 60.4 138.6 99.1 -28.5Sumter, SC 101.4 119.4 142.6 175.1 161.1 168.5 185.2 156.5 -15.5Syracuse, NY 131.8 137.1 141.9 187.8 223.3 228.5 219.3 188.7 -14.0Tacoma, WA 93.2 130.8 135.9 139.7 129.8 113.7 114.9 141.7 23.3Tallahassee, FL 75.8 99.1 135.3 151.0 145.6 142.6 158.0 142.8 -9.6Tampa-St. Petersburg-Clearwater, FL 90.1 110.1 134.9 174.2 151.5 110.1 139.7 151.1 8.2Terre Haute, IN 145.2 173.4 189.7 215.8 212.8 247.3 251.2 212.6 -15.4Texarkana TX-Texarkana, AR 98.9 114.9 168.6 209.7 196.7 192.9 278.2 183.4 -34.1Toledo, OH 108.6 155.8 170.1 195.8 171.7 201.7 248.2 185.3 -25.3Topeka, KS 110.8 149.2 183.4 230.5 230.4 234.4 229.4 210.5 -8.2Trenton-Ewing, NJ 97.7 106.3 105.5 156.2 174.9 138.8 112.8 140.4 24.4Tucson, AZ 72.0 103.2 117.1 140.2 131.6 95.8 114.0 128.0 12.3Tulsa, OK 96.9 118.8 159.4 168.6 165.4 179.1 174.5 164.3 -5.9Tuscaloosa, AL 88.3 109.1 131.8 144.0 135.6 145.7 176.9 137.8 -22.1Tyler, TX 92.7 108.8 154.4 180.9 177.1 185.6 218.8 165.7 -24.3Utica-Rome, NY 133.7 134.4 118.7 165.8 218.7 214.7 199.1 172.8 -13.2Valdosta, GA 95.8 120.2 134.7 163.2 155.6 167.9 175.1 152.5 -12.9



20-Year % DeviationAverage, From

1980 1985 1990 1995 2000 2005 2008Q3 1984-2004 2008Q3Vallejo-Fairfield, CA 59.5 94.1 94.9 123.6 106.4 61.1 111.2 107.4 -3.5Victoria, TX 112.9 116.1 161.9 207.3 211.6 212.2 248.9 184.1 -26.0Vineland-Millville-Bridgeton, NJ 149.3 148.9 126.9 151.3 187.0 159.0 161.3 163.4 1.3Virginia Beach-Norfolk-Newport News, VA-NC 102.0 116.1 122.3 138.7 151.2 131.2 120.9 141.6 17.0Visalia-Porterville, CA 70.5 85.5 89.2 116.8 130.7 67.0 95.1 108.8 14.4Waco, TX 107.7 128.8 166.9 216.4 198.6 232.3 264.5 190.8 -27.9Warner Robins, GA 115.6 141.7 169.3 196.3 190.3 228.0 273.3 184.7 -32.4Warren-Farmington Hills-Troy, MI 102.7 147.4 145.6 167.5 140.3 165.0 209.0 156.5 -25.1Washington-Arlington-Alexandria, DC-VA-MD-WV 106.2 123.4 116.8 152.2 162.3 92.4 132.4 139.5 5.4Waterloo-Cedar Falls, IA 156.9 191.4 243.1 261.8 213.4 231.2 222.6 245.7 10.4Wausau, WI 124.8 153.1 193.5 208.5 195.7 214.6 218.4 202.9 -7.1Weirton-Steubenville, WV-OH 200.8 192.0 225.1 240.4 214.6 238.8 252.9 230.6 -8.8Wenatchee, WA 86.9 105.6 132.8 115.5 117.6 114.0 108.3 124.2 14.7West Palm Beach-Boca Raton-Boynton Beach, FL 67.0 98.8 112.5 133.0 145.1 64.1 93.6 124.5 33.1Wheeling, WV-OH 181.0 157.2 172.2 217.9 186.5 223.9 257.1 196.4 -23.6Wichita Falls, TX 127.1 142.5 195.4 234.1 227.9 252.0 305.3 215.1 -29.5Wichita, KS 108.8 131.9 175.0 201.5 201.6 215.4 194.6 192.5 -1.1Williamsport, PA 147.6 135.9 129.1 144.8 179.1 187.0 197.7 157.2 -20.5Wilmington, DE-MD-NJ 121.2 123.7 122.3 153.4 146.4 125.2 145.8 141.2 -3.2Wilmington, NC 100.6 98.0 117.6 130.3 120.3 117.7 120.4 123.6 2.7Winchester, VA-WV 81.5 97.4 108.1 145.3 154.4 119.1 162.0 132.7 -18.1Winston-Salem, NC 101.8 111.6 131.3 163.3 154.5 167.4 203.7 148.8 -26.9Worcester, MA 98.0 87.7 90.4 129.1 170.6 107.8 149.8 119.1 -20.5Yakima, WA 99.3 125.3 162.9 137.9 135.8 145.8 130.3 147.6 13.3York-Hanover, PA 114.8 116.4 115.1 141.5 173.3 156.2 167.0 147.8 -11.5Youngstown-Warren-Boardman, OH-PA 139.6 156.1 182.4 216.8 213.6 248.7 303.6 202.8 -33.2Yuba City, CA 74.2 100.2 111.3 131.2 150.0 99.6 179.9 129.1 -28.2Yuma, AZ 65.0 90.5 120.3 152.8 142.5 122.7 134.5 139.3 3.6Source: Moody's Economy.comCalculating Housing Affordability

The housing affordability index is designed to measure the degree to which a typical middle-income family can afford the mortgage payments on the typical home.To interpret the index, a value of 100 means that the typical family has just enough income to qualify for an 80% mortgage on a median-priced home. The higher the index, the more affordable the housing.

Moody’s Economy.com’s estimate of housing affordability for states and metropolitan and county areas is an index that relates what proportion of a median-priced home a family earning the median income can afford. It is based on the median sale price for existing single-family homes, mortgage interest rates, median family income, and assumptions concerning down payments and the share of income that should be used for mortgage costs.

Moody’s Economy.com assumes a maturity of 30 years. Moody’s Economy.com assumes a 25% coverage ratio, which is the proportion of minimum qualifying family income allocated to the monthly payment.

Calculation of affordability indices is dependent on several published data sources and assumptions. The primary building block is the median existing-house sale price published by the National Association of Realtors. The NAR price estimates are available for the U.S., Census regions, and approximately 132 metropolitan areas.Moody’s Economy.com estimates house prices for counties and states in addition to the metropolitan areas not published by the NAR.

Published median family income data for the U.S., regions, states, and metropolitan and county areas are used to determine the income available for a home purchase.Since the Census Bureau publishes median family income for metropolitan and county areas on a decennial basis, Moody’s Economy.com estimates the intercensal years.

The affordability indices use the state-level “effective” interest rates released on an annual basis by the Federal Housing Finance Board. Effective rates are higher than contract rates because they include fees and charges (points) amortized over the typical seven-year life of a mortgage.

A 20% down payment is assumed, being a standard of the housing industry. This implies a loan amount of 80% of the median sale price.

Appendix 6-2: Housing Affordability Index (cont.)



Appendix 6-3: Price-to-Rent Ratio

% Deviation Sep-90 Sep-95 Sep-00 Sep-05 Sep-08 Avg. 86-04 From Sep-08

United States 7.8 8.3 9.9 17.0 12.0 9.0 -25.5Average of Metro Areas 14.6 14.2 15.5 24.2 19.8 15.2 -23.2

Atlanta-Sandy Springs-Marietta, GA 13.6 12.1 13.8 18.6 17.0 13.5 -20.8Austin-Round Rock, TX 14.8 14.9 17.4 19.8 22.6 16.0 -29.1Baltimore-Towson, MD 11.7 11.0 11.0 20.2 17.8 11.5 -35.1Boston-Quincy, MA 15.9 15.4 16.7 26.5 21.5 17.3 -19.9Bridgeport-Stamford-Norwalk, CT 17.4 17.2 21.5 33.5 30.4 19.9 -34.5Charlotte-Gastonia-Concord, NC-SC 15.7 16.2 16.2 22.2 22.8 16.2 -29.0Chicago-Naperville-Joliet, IL 15.7 15.8 15.6 22.4 18.4 15.7 -14.6Cincinnati-Middletown, OH-KY-IN 13.4 14.2 15.4 17.5 15.6 14.7 -5.5Cleveland-Elyria-Mentor, OH 10.4 13.1 14.1 16.3 13.8 13.4 -2.2Columbus, OH 13.0 15.1 16.9 20.8 18.3 16.0 -12.6Dallas-Plano-Irving, TX 18.7 16.3 15.4 18.2 18.2 17.4 -4.4Denver-Aurora, CO 14.2 14.7 19.1 25.1 21.4 16.4 -23.4Detroit-Livonia-Dearborn, MI 6.8 8.8 11.8 13.9 9.6 10.1 5.2Fort Lauderdale-Pompano Beach-Deerfield Beach, FL 14.7 12.1 12.8 27.3 18.3 13.9 -23.9Hartford-West Hartford-East Hartford, CT na 12.4 13.7 20.1 19.5 13.9 -28.6Honolulu, HI 24.4 23.9 21.7 35.3 33.9 23.6 -30.5Houston-Sugar Land-Baytown, TX 13.2 13.2 13.8 15.9 16.8 13.5 -19.7Indianapolis-Carmel, IN 12.9 13.8 14.8 17.3 16.6 14.3 -14.1Jacksonville, FL 10.8 11.5 12.8 19.9 17.8 12.4 -30.2Kansas City, MO-KS 11.7 13.7 14.1 17.9 16.8 13.8 -17.6Las Vegas-Paradise, NV 13.4 14.5 15.3 30.0 17.6 15.8 -10.3Los Angeles-Long Beach-Glendale, CA 14.0 10.2 11.7 24.4 15.4 12.4 -19.4Memphis, TN-MS-AR na 14.8 16.1 19.1 16.8 16.0 -4.9Miami-Miami Beach-Kendall, FL 11.2 10.8 12.7 27.7 20.0 12.7 -36.5Milwaukee-Waukesha-West Allis, WI 11.2 14.6 17.3 22.6 22.4 15.5 -30.8Minneapolis-St. Paul-Bloomington, MN-WI 12.3 12.2 14.5 21.3 16.2 13.5 -16.3Nashville-Davidson--Murfreesboro--Franklin, TN 14.2 16.2 18.5 22.7 23.7 17.2 -27.4Nassau-Suffolk, NY 10.9 10.5 12.9 24.0 21.3 12.7 -40.4New Orleans-Metairie-Kenner, LA 10.7 12.8 14.0 21.3 16.4 13.4 -18.5New York-White Plains-Wayne, NY-NJ 9.1 8.2 9.9 17.9 14.7 9.8 -32.8Oakland-Fremont-Hayward, CA 33.4 25.9 29.4 53.3 29.6 29.9 1.2Oklahoma City, OK 10.2 12.0 11.1 13.4 13.6 11.4 -16.2Orlando-Kissimmee, FL 13.9 11.8 11.9 22.6 16.4 12.8 -22.1Philadelphia, PA 9.4 9.4 9.2 16.5 16.1 9.8 -39.4Phoenix-Mesa-Scottsdale, AZ 12.7 10.8 12.6 25.1 15.3 12.2 -20.3Pittsburgh, PA 9.0 9.6 10.2 12.2 12.3 10.0 -18.4Portland-Vancouver-Beaverton, OR-WA 11.4 16.7 19.7 29.6 28.5 17.9 -37.3Raleigh-Cary, NC na 16.0 17.4 22.3 24.7 17.8 -28.1Richmond, VA 14.1 14.5 15.4 21.9 23.6 14.5 -38.6Riverside-San Bernardino-Ontario, CA 20.1 16.3 14.4 27.6 14.5 16.8 15.8Sacramento-Arden-Arcade-Roseville, CA 19.1 13.7 14.6 30.6 16.2 15.7 -2.5Salt Lake City, UT 11.6 13.9 16.0 19.6 21.9 14.6 -33.4San Antonio, TX 13.9 12.1 12.1 14.7 16.4 12.7 -22.4San Diego-Carlsbad-San Marcos, CA 18.0 15.4 19.3 37.2 21.2 18.6 -12.5San Francisco-San Mateo-Redwood City, CA 26.8 21.7 26.0 43.2 31.3 25.0 -20.0San Jose-Sunnyvale-Santa Clara, CA 24.5 20.8 26.6 46.2 29.3 24.3 -16.8Santa Ana-Anaheim-Irvine, CA 22.4 17.5 20.5 42.8 27.4 21.4 -21.9Seattle-Bellevue-Everett, WA 17.0 16.9 20.3 30.5 28.3 17.7 -37.4St. Louis, MO-IL 11.0 13.2 13.2 17.4 17.3 13.2 -23.5Tampa-St. Petersburg-Clearwater, FL 14.4 12.8 12.7 23.3 17.4 13.5 -22.6Trenton-Ewing, NJ 14.2 13.8 12.7 22.3 19.4 14.2 -26.7Virginia Beach-Norfolk-Newport News, VA-NC 17.0 16.6 16.1 23.5 24.4 16.7 -31.4Washington-Arlington-Alexandria, DC-VA-MD-WV 11.5 11.3 11.4 24.7 16.2 12.0 -25.9West Palm Beach-Boca Raton-Boynton Beach, FL 12.6 12.6 12.6 26.5 16.6 13.6 -18.4Sources: Fiserv Lending Solutions, PPR, Moody's Economy.com

Housing Price-Rent Ratio



Region 1: East CoastAllentown-Bethlehem-Easton, PA-NJ New York-White Plains-Wayne, NY-NJ Atlantic City, NJ Newark-Union, NJ-PA Baltimore-Towson, MD Norwich-New London, CT Bangor, ME Ocean City, NJ Barnstable Town, MA Peabody, MABethesda-Gaithersburg-Frederick, MD Philadelphia, PA Boston-Quincy, MA Pittsfield, MA Bridgeport-Stamford-Norwalk, CT Portland-South Portland-Biddeford, ME Burlington-South Burlington, VT Poughkeepsie-Newburgh-Middletown, NY Cambridge-Newton-Framingham, MA Providence-New Bedford-Fall River, RI-MA Camden, NJ Rockingham County-Strafford County, NH Cumberland, MD-WV Salisbury, MD Dover, DE Springfield, MA Edison, NJ Trenton-Ewing, NJ Hagerstown-Martinsburg, MD-WV Vineland-Millville-Bridgeton, NJ Hartford-West Hartford-East Hartford, CT Virginia Beach-Norfolk-Newport News, VA-NC Kingston, NY Washington-Arlington-Alexandria, DC-VA-MD-WV Lewiston-Auburn, ME Wilmington, DE-MD-NJ Manchester-Nashua, NH Winchester, VA-WV Nassau-Suffolk, NY Worcester, MA New Haven-Milford, CT

Region 2: Mountain WestAlbuquerque, NM Las Cruces, NM Billings, MT Las Vegas-Paradise, NV Boise City-Nampa, ID Lewiston, ID-WA Boulder, CO Logan, UT-ID Carson City, NV Missoula, MTCasper, WY Ogden-Clearfield, UT Cheyenne, WY Phoenix-Mesa-Scottsdale, AZ Coeur d'Alene, ID Pocatello, IDColorado Springs, CO Prescott, AZ Denver-Aurora, CO Provo-Orem, UT Farmington, NM Pueblo, CO Flagstaff, AZ Reno-Sparks, NV Fort Collins-Loveland, CO Salt Lake City, UT Grand Junction, CO Santa Fe, NM Great Falls, MT St. George, UT Greeley, CO Tucson, AZ Idaho Falls, ID Yuma, AZ Lake Havasu City, AZ

Region 3: FloridaBradenton-Sarasota-Venice, FL Orlando-Kissimmee, FL Cape Coral-Fort Myers, FL Palm Bay-Melbourne-Titusville, FL Deltona-Daytona Beach-Ormond Beach, FL Palm Coast, FLFort Lauderdale-Pompano Beach-Deerfield Beach, FL Panama City-Lynn Haven, FL Fort Walton Beach-Crestview-Destin, FL Pensacola-Ferry Pass-Brent, FL Gainesville, FL Port St. Lucie-Fort Pierce, FL Jacksonville, FL Punta Gorda, FL Lakeland, FL Tallahassee, FL Miami-Miami Beach-Kendall, FL Tampa-St. Petersburg-Clearwater, FL Naples-Marco Island, FL Vero Beach, FL Ocala, FL West Palm Beach-Boca Raton-Boynton Beach, FL

Appendix 7-1: Metropolitan Area Pool Classifications



Appendix 7-1: Metropolitan Area Pool Classifications (cont.)Region 4: InlandAkron, OH Kansas City, MO-KS Albany-Schenectady-Troy, NY Kokomo, IN Altoona, PA La Crosse, WI-MN Ames, IA Lafayette, IN Anderson, IN Lake County-Kenosha County, IL-WI Ann Arbor, MI Lancaster, PA Appleton, WI Lansing-East Lansing, MI Battle Creek, MI Lawrence, KS Bay City, MI Lebanon, PA Binghamton, NY Lima, OH Bismarck, ND Lincoln, NE Bloomington, IN Madison, WI Bloomington-Normal, IL Mansfield, OH Buffalo-Niagara Falls, NY Michigan City-La Porte, IN Canton-Massillon, OH Milwaukee-Waukesha-West Allis, WI Cedar Rapids, IA Minneapolis-St. Paul-Bloomington, MN-WI Champaign-Urbana, IL Monroe, MI Chicago-Naperville-Joliet, IL Muncie, IN Cincinnati-Middletown, OH-KY-IN Muskegon-Norton Shores, MI Cleveland-Elyria-Mentor, OH Niles-Benton Harbor, MI Columbia, MO Omaha-Council Bluffs, NE-IA Columbus, IN Oshkosh-Neenah, WI Columbus, OH Peoria, IL Danville, IL Pittsburgh, PA Davenport-Moline-Rock Island, IA-IL Racine, WI Dayton, OH Rapid City, SD Decatur, IL Reading, PA Des Moines, IA Rochester, MN Detroit-Livonia-Dearborn, MI Rochester, NY Dubuque, IA Rockford, IL Duluth, MN-WI Saginaw-Saginaw Township North, MI Eau Claire, WI Sandusky, OH Elkhart-Goshen, IN Scranton-Wilkes-Barre, PA Elmira, NY Sheboygan, WI Erie, PA Sioux City, IA-NE-SD Evansville, IN-KY Sioux Falls, SD Fargo, ND-MN South Bend-Mishawaka, IN-MI Flint, MI Springfield, ILFond du Lac, WI Springfield, MOFort Wayne, IN Springfield, OHGary, IN St. Cloud, MNGlens Falls, NY St. Joseph, MO-KSGrand Forks, ND-MN St. Louis, MO-ILGrand Rapids-Wyoming, MI State College, PAGreen Bay, WI Syracuse, NYHarrisburg-Carlisle, PA Terre Haute, INHolland-Grand Haven, MI Toledo, OHIndianapolis, IN Topeka, KSIowa City, IA Utica, NYIthaca, NY Warren-Farmington Hills-Troy, MI Jackson, MI Waterloo-Cedar Falls, IA Janesville, WI Wausau, WI Johnstown, PA Weirton-Steubenville, WV-OH Jefferson City, MO Wichita, KSJoplin, MO Williamsport, PA Kalamazoo-Portage, MI York-Hanover, PAKankakee-Bradley, IL Youngstown-Warren-Boardman, OH-PA



Appendix 7-1: Metropolitan Area Pool Classifications (cont.)

Region 5: CaliforniaBakersfield, CA Sacramento-Arden-Arcade-Roseville, CA Chico, CA Salinas, CA El Centro, CA San Diego-Carlsbad-San Marcos, CA Fresno, CA San Francisco-San Mateo-Redwood City, CA Hanford-Corcoran, CA San Jose-Sunnyvale-Santa Clara, CA Los Angeles-Long Beach-Glendale, CA San Luis Obispo-Paso Robles, CA Madera, CA Santa Ana-Anaheim-Irvine, CA Merced, CA Santa Barbara-Santa Maria, CA Modesto, CA Santa Cruz-Watsonville, CA Napa, CA Santa Rosa-Petaluma, CA Oakland-Fremont-Hayward, CA Stockton, CAOxnard-Thousand Oaks-Ventura, CA Vallejo-Fairfield, CARedding, CA Visalia-Porterville, CARiverside-San Bernardino-Ontario, CA Yuba, CA



Appendix 7-1: Metropolitan Area Pool Classifications (cont.)Region 6: South, Excluding FloridaAbilene, TX Jackson, MS Albany, GA Jackson, TN Alexandria, LA Jacksonville, NC Amarillo, TX Johnson City, TN Anderson, SC Jonesboro, AR Anniston-Oxford, AL Killeen-Temple-Fort Hood, TX Asheville, NC Kingsport-Bristol-Bristol, TN-VA Athens-Clarke County, GA Knoxville, TN Atlanta-Sandy Springs-Marietta, GA Lafayette, LA Auburn-Opelika, AL Lake Charles, LA Augusta-Richmond County, GA-SC Laredo, TX Austin-Round Rock, TX Lawton, OK Baton Rouge, LA Lexington-Fayette, KY Beaumont-Port Arthur, TX Little Rock-North Little Rock-Conway, AR Birmingham-Hoover, AL Longview, TX Blacksburg-Christiansburg-Radford, VA Louisville-Jefferson County, KY-IN Bowling Green, KY Lubbock, TX Brownsville-Harlingen, TX Lynchburg, VA Brunswick, GA Macon, GA Burlington, NC McAllen-Edinburg-Mission, TX Charleston, WV Memphis, TN-MS-AR Charleston-North Charleston-Summerville, SC Midland, TX Charlotte-Gastonia-Concord, NC-SC Mobile, AL Charlottesville, VA Monroe, LA Chattanooga, TN-GA Montgomery, AL Clarksville, TN-KY Morgantown, WV Cleveland, TN Morristown, TN College Station-Bryan, TX Myrtle Beach-North Myrtle Beach-Conway, SC Columbia, SC Nashville-Davidson-Murfreesboro-Franklin, TN Columbus, GA-AL New Orleans-Metairie-Kenner, LA Corpus Christi, TX Odessa, TX Dallas-Plano-Irving, TX Oklahoma City, OK Dalton, GA Owensboro, KY Danville, VA Parkersburg-Marietta-Vienna, WV-OH Decatur, AL Pascagoula, MS Dothan, AL Pine Bluff, AR Durham-Chapel Hill, NC Raleigh-Cary, NC El Paso, TX Richmond, VA Elizabethtown, KY Roanoke, VA Fayetteville, NC Rocky Mount, NC Fayetteville-Springdale-Rogers, AR-MO Rome, GA Florence, SC San Angelo, TX Florence-Muscle Shoals, AL San Antonio, TX Fort Smith, AR-OK Savannah, GA Fort Worth-Arlington, TX Sherman-Denison, TX Gadsden, AL Shreveport-Bossier City, LA Gainesville, GA Spartanburg, SC Goldsboro, NC Sumter, SC Greensboro-High Point, NC Texarkana, TX-Texarkana, AR Greenville, NC Tulsa, OK Greenville-Mauldin-Easley, SC Tuscaloosa, AL Gulfport-Biloxi, MS Tyler, TX Harrisonburg, VA Valdosta, GA Hattiesburg, MS Victoria, TX Hickory-Lenoir-Morganton, NC Waco, TX Hinesville-Fort Stewart, GA Warner Robins, GA Hot Springs, AR Wheeling, WV-OH Houma-Bayou Cane-Thibodaux, LA Wichita Falls, TX Houston-Sugar Land-Baytown, TX Wilmington, NC Huntington-Ashland, WV-KY-OH Winston-Salem, NC Huntsville, AL



Appendix 7-1: Metropolitan Area Pool Classifications (cont.)

Region 7: Pacific, Excluding CaliforniaAnchorage, AK Medford, OR Bellingham, WA Mount Vernon-Anacortes, WA Bend, OR Olympia, WA Bremerton-Silverdale, WA Portland-Vancouver-Beaverton, OR-WA Corvallis, OR Salem, OR Eugene-Springfield, OR Seattle-Bellevue-Everett, WA Fairbanks, AK Spokane, WA Honolulu, HI Tacoma, WA Kennewick-Pasco-Richland, WA Wenatchee-East Wenatchee, WA Longview, WA Yakima, WA



Appendix 7-2: House Price Over/Undervaluation ValidationValuation Cumulative % change Valuation Cumulative % change Valuation Cumulative % change

as of 1987Q4 in Price as of 1989Q4 in Price as of 2006Q1 in Price% 1989-1991 % 1990-1993 % 2006Q1-2008Q3

Abilene, TX 8.9 0.9 -14.7 24.8 -0.9 10.7Akron, OH 3.6 15.6 -0.9 16.3 0.8 -7.4Albany, GA 8.1 9.4 5.4 11.1 4.8 7.6Albany-Schenectady-Troy, NY 26.6 5.4 25.0 3.1 11.9 7.2Albuquerque, NM 13.2 4.7 7.9 15.1 -8.1 13.1Alexandria, LA -0.3 1.7 -7.4 13.6 -3.1 14.5Allentown-Bethlehem-Easton, PA-NJ 26.3 -1.7 21.9 -0.2 2.5 4.1Altoona, PA 6.7 2.6 7.6 13.0 16.7 13.9Amarillo, TX 1.0 4.5 -11.1 24.0 5.6 9.9Ames, IA -7.4 16.3 -8.6 25.2 -0.7 2.4Anchorage, AK -7.7 4.1 -7.0 42.4 -4.6 7.4Anderson, IN -9.6 18.6 -10.1 21.9 2.6 0.6Anderson, SC -1.3 12.1 -0.9 11.7 8.4 11.8Ann Arbor, MI -2.6 9.3 0.7 7.5 11.8 -21.5Anniston-Oxford, AL 5.3 8.0 -0.8 17.7 5.4 12.5Appleton, WI 4.2 11.9 -6.7 15.4 2.9 -1.8Asheville, NC 0.0 12.9 -4.0 13.9 25.9 18.4Athens-Clarke County, GA -3.6 7.3 -7.5 8.5 20.4 6.3Atlanta-Sandy Springs-Marietta, GA 0.7 2.6 -1.6 7.1 16.0 -7.9Atlantic City, NJ -0.4 -0.8 7.0 -4.7 44.6 -14.5Auburn-Opelika, AL 7.5 9.6 -8.9 11.3 17.3 12.0Augusta-Richmond County, GA-SC 6.6 10.6 -1.0 12.5 8.7 16.2Austin-Round Rock, TX -4.8 7.2 -15.5 30.2 12.2 21.9Bakersfield, CA 11.9 18.1 -1.5 4.9 25.3 -44.6Baltimore-Towson, MD 6.1 8.7 9.2 4.7 20.4 -8.9Bangor, ME 21.3 -0.9 15.4 8.2 -2.1 7.9Barnstable Town, MA 39.5 -6.9 21.2 -10.7 11.7 -10.9Baton Rouge, LA 5.3 9.7 -6.3 19.4 1.3 14.6Battle Creek, MI -13.5 7.6 -13.9 9.3 4.9 -6.9Bay City, MI -12.9 15.0 -13.5 24.2 20.3 -10.6Beaumont-Port Arthur, TX 7.8 13.2 -0.1 18.8 -1.4 14.3Bellingham, WA -13.8 54.0 -5.6 41.7 7.5 7.7Bend, OR -7.5 41.8 -4.1 37.2 14.8 -0.3Bethesda-Frederick-Rockville, MD 0.5 4.1 11.2 -1.8 29.5 -16.6Billings, MT -10.8 15.8 -14.0 32.8 -0.8 15.5Binghamton, NY 26.5 2.1 17.2 2.1 -4.7 19.7Birmingham-Hoover, AL 0.5 10.0 -4.2 16.7 10.8 8.0Bismarck, ND 8.6 7.5 0.7 18.3 -1.2 13.9Blacksburg-Christiansburg-Radford, VA 1.5 7.5 -5.1 9.3 25.2 13.9Bloomington, IN -1.4 11.5 -0.8 20.2 2.2 7.0Bloomington-Normal, IL 8.7 7.7 -2.6 16.4 -1.9 5.0Boise City-Nampa, ID -6.5 19.4 -12.3 34.4 14.5 7.3Boston-Quincy, MA 31.8 -12.8 11.1 -7.5 14.5 -9.5Boulder, CO -8.2 14.5 -17.2 37.6 7.0 6.0Bowling Green, KY 14.4 8.5 -0.6 17.8 0.0 4.7Bradenton-Sarasota-Venice, FL 10.9 5.5 4.5 8.0 29.6 -38.2Bremerton-Silverdale, WA -2.0 42.5 -5.5 35.2 9.5 6.1Bridgeport-Stamford-Norwalk, CT 38.3 -8.3 6.9 -0.3 8.7 -6.7Brownsville-Harlingen, TX 16.9 7.7 8.9 17.7 -17.4 6.0Brunswick, GA 4.5 12.7 4.1 10.9 11.3 14.6Buffalo-Niagara Falls, NY 7.5 16.1 11.4 13.5 -3.7 7.9Burlington, NC 0.2 4.9 -0.5 11.1 12.3 0.4Burlington-South Burlington, VT 9.7 2.9 12.0 0.0 -1.2 0.7Cambridge-Newton-Framingham, MA 32.2 -12.6 10.8 -5.3 6.3 -7.8Camden, NJ 20.3 -6.0 19.7 -4.1 8.8 -7.2Canton-Massillon, OH 2.7 12.1 -3.9 18.2 0.6 -7.4Cape Coral-Fort Myers, FL 10.4 8.8 11.8 4.7 32.3 -48.6Carson City, NV -7.3 20.5 -8.5 23.7 38.7 -15.4Casper, WY -18.4 25.8 -26.0 35.9 7.0 17.4Cedar Rapids, IA -3.1 13.3 -5.8 20.9 -1.3 4.6Champaign-Urbana, IL 2.3 4.2 -0.5 7.6 6.4 5.5Charleston, WV 6.6 8.8 -1.1 18.9 -1.7 6.8Charleston-North Charleston-Summerville, SC 4.1 12.1 4.7 11.6 4.1 -1.2Charlotte-Gastonia-Concord, NC-SC -3.0 4.5 -3.8 4.8 2.0 5.4Charlottesville, VA -2.6 15.4 3.6 11.5 35.9 7.4Chattanooga, TN-GA 3.3 6.0 -2.7 11.6 14.4 7.1Cheyenne, WY 2.7 15.5 -9.6 17.9 1.3 7.1Chicago-Naperville-Joliet, IL -1.9 14.1 4.5 13.3 36.2 -11.8Chico, CA -1.0 32.2 -5.8 9.2 13.7 -22.8Cincinnati-Middletown, OH-KY-IN 5.5 9.5 2.2 13.3 -0.5 -6.1Clarksville, TN-KY 11.5 2.6 2.2 9.6 -4.4 9.3Cleveland, TN 4.2 0.4 -3.9 7.9 13.9 10.8Cleveland-Elyria-Mentor, OH -2.3 16.3 -2.0 17.4 4.7 -11.7Coeur d'Alene, ID -7.1 22.3 -16.9 45.7 25.8 2.3College Station-Bryan, TX 16.4 5.1 3.6 18.7 -4.6 16.5Colorado Springs, CO -1.8 6.7 -13.3 22.4 4.2 0.6Columbia, MO 11.7 6.6 -1.5 9.0 1.4 5.7Columbia, SC 6.9 8.7 2.2 10.5 4.3 10.2Columbus, GA-AL 5.7 1.5 0.8 10.6 10.7 7.2Columbus, IN -5.3 15.1 -1.3 23.3 -1.5 1.7



Appendix 7-2: House Price Over/Undervaluation Validation (cont.)Valuation Cumulative % change Valuation Cumulative % change Valuation Cumulative % change


Columbus, OH 4.1 9.6 -1.2 13.7 0.8 -6.4Corpus Christi, TX 13.0 4.1 4.4 14.2 -8.1 7.7Corvallis, OR -12.1 34.1 -12.2 52.6 -6.4 17.8Cumberland, MD-WV 2.3 7.7 7.1 2.4 -6.0 20.9Dallas-Plano-Irving, TX 9.5 3.8 -2.0 6.9 -1.4 7.8Dalton, GA -0.3 8.8 -6.2 6.4 18.8 4.2Danville, IL -2.1 14.2 5.8 9.1 12.5 0.5Danville, VA 5.6 -2.1 14.6 0.3 8.8 3.4Davenport-Moline-Rock Island, IA-IL -6.7 16.9 -9.9 20.9 0.0 5.2Dayton, OH 1.4 10.7 0.5 15.4 1.6 -4.7Decatur, AL 9.7 4.9 -5.7 12.3 -0.4 14.0Decatur, IL 6.1 12.4 3.6 15.5 -2.8 7.8Deltona-Daytona Beach-Ormond Beach, FL 5.6 4.7 -0.6 3.2 30.7 -30.2Denver-Aurora-Broomfield, CO -3.5 8.2 -19.8 31.0 7.8 -7.1Des Moines-West Des Moines, IA -1.1 11.7 -7.2 17.2 -0.6 2.5Detroit-Livonia-Dearborn, MI -13.7 21.0 -12.0 20.1 24.8 -31.4Dothan, AL 5.2 9.9 1.2 13.1 3.2 13.1Dover, DE 4.9 20.8 4.9 11.8 1.3 9.0Dubuque, IA -8.3 13.8 -7.9 23.3 3.0 4.5Duluth, MN-WI 7.1 18.4 2.8 29.0 3.5 4.0Durham-Chapel Hill, NC 8.3 4.9 -2.8 7.6 12.9 10.6Eau Claire, WI -7.0 10.3 -9.4 17.6 14.7 3.4Edison-New Brunswick, NJ 43.5 -13.5 12.4 -5.0 24.6 -11.4El Centro, CA -9.6 28.3 -2.4 19.1 44.3 -41.9El Paso, TX 7.5 6.9 -1.6 12.2 4.2 19.1Elizabethtown, KY -5.0 8.1 -6.3 14.8 13.1 5.7Elkhart-Goshen, IN -4.4 9.5 -5.2 11.4 -2.4 3.5Elmira, NY 33.3 -1.4 17.7 2.3 -1.7 4.3Erie, PA 3.5 17.7 -2.8 26.0 2.7 6.1Eugene-Springfield, OR -12.8 28.3 -14.1 33.6 21.2 7.6Evansville, IN-KY 5.9 5.1 -2.4 9.5 -5.6 4.1Fairbanks, AK -12.1 -14.9 -23.8 53.4 -10.8 5.8Fargo, ND-MN 6.7 6.6 -1.8 10.4 -2.1 6.6Farmington, NM 15.0 12.6 -3.9 22.6 3.1 9.8Fayetteville, NC 4.8 8.2 2.1 12.9 0.3 14.4Fayetteville-Springdale-Rogers, AR-MO -4.0 10.3 -1.6 15.5 16.3 -0.4Flagstaff, AZ 1.3 23.2 -4.1 23.5 17.2 -10.2Flint, MI -8.9 15.3 -9.1 16.4 18.1 -18.8Florence, SC 1.3 9.0 -1.9 14.6 5.0 11.8Florence-Muscle Shoals, AL 11.7 10.1 3.1 17.4 -3.1 18.8Fond du Lac, WI 0.1 12.7 -10.8 19.7 11.4 2.2Fort Collins-Loveland, CO -2.9 9.4 -13.4 26.3 1.2 0.6Fort Lauderdale-Pompano Beach-Deerfield Beach, FL 8.6 7.5 4.4 2.3 49.4 -35.5Fort Smith, AR-OK 1.7 1.6 -3.8 10.7 1.9 10.3Fort Walton Beach-Crestview-Destin, FL -4.1 6.2 -12.2 8.2 26.6 -27.7Fort Wayne, IN 10.0 6.3 -0.8 9.4 -8.4 2.8Fort Worth-Arlington, TX 6.1 3.0 -3.1 6.1 -3.7 7.5Fresno, CA -0.5 22.1 -9.1 10.6 29.7 -39.7Gadsden, AL -2.6 8.0 -7.9 15.0 18.5 12.3Gainesville, FL 9.4 -1.1 -2.9 7.9 18.3 -5.9Gainesville, GA -1.9 2.8 -5.4 6.3 21.6 -3.4Gary, IN -6.2 24.8 -3.7 26.0 1.6 5.3Glens Falls, NY 65.1 -1.5 52.6 -5.7 -16.8 11.5Goldsboro, NC 4.7 10.1 -1.7 13.4 11.4 5.4Grand Forks, ND-MN 1.4 6.9 -4.6 15.4 -3.2 5.1Grand Junction, CO -20.7 27.5 -19.4 31.0 14.3 30.4Grand Rapids-Wyoming, MI -1.3 12.2 -1.0 10.8 5.5 -7.7Great Falls, MT -11.5 16.7 -14.2 32.2 1.6 16.2Greeley, CO 0.6 8.5 -23.0 23.2 16.8 -10.0Green Bay, WI 1.9 9.8 -5.1 19.4 -1.8 0.6Greensboro-High Point, NC 5.3 5.6 0.4 10.4 8.6 6.4Greenville, NC 9.6 3.4 0.4 9.8 5.8 9.2Greenville-Mauldin-Easley, SC -0.2 9.0 -4.1 11.0 7.2 12.5Gulfport-Biloxi, MS -3.1 5.4 -5.5 14.6 32.7 13.3Hagerstown-Martinsburg, MD-WV -7.4 20.6 3.8 11.0 23.3 -4.5Hanford-Corcoran, CA 1.6 24.3 -11.4 22.8 25.7 -29.8Harrisburg-Carlisle, PA 4.2 11.2 -0.7 11.2 5.2 11.4Harrisonburg, VA 1.6 9.0 -1.5 12.8 34.6 7.6Hartford-West Hartford-East Hartford, CT 44.8 -16.9 23.1 -15.5 -5.8 -2.7Hattiesburg, MS 4.1 6.2 -1.8 6.8 4.5 11.2Hickory-Lenoir-Morganton, NC -2.2 8.5 -5.4 14.9 12.9 13.0Hinesville-Fort Stewart, GA 2.4 3.4 -2.7 7.3 39.0 13.1Holland-Grand Haven, MI -2.8 12.5 -6.0 9.3 12.5 -3.7Honolulu, HI -13.6 50.1 7.7 21.3 23.9 5.7Hot Springs, AR 10.7 5.0 2.3 13.6 0.7 17.1Houma-Bayou Cane-Thibodaux, LA 9.9 3.9 -0.4 18.1 0.1 20.5Houston-Sugar Land-Baytown, TX 6.4 12.1 3.5 11.6 -4.3 13.5Huntington-Ashland, WV-KY-OH 0.6 4.6 -5.8 14.3 6.8 8.3Huntsville, AL 3.8 5.4 -3.0 11.4 -1.6 15.9Idaho Falls, ID 1.7 16.5 -8.5 14.0 -4.0 15.5Indianapolis-Carmel, IN 6.0 11.1 1.1 14.5 -6.7 2.8





Iowa City, IA -5.0 10.0 -6.4 18.9 1.4 2.3Ithaca, NY 25.0 6.6 21.6 0.4 12.4 10.0Jackson, MI -13.0 21.6 -10.8 18.5 17.2 -15.1Jackson, MS 7.1 5.8 -1.5 9.1 -2.4 5.3Jackson, TN 8.4 6.5 8.4 11.8 1.8 0.4Jacksonville, FL 2.7 1.8 -7.1 4.3 18.5 -17.1Jacksonville, NC -2.9 6.0 -3.4 11.5 0.0 19.1Janesville, WI -9.9 13.5 -15.2 24.5 12.0 2.2Jefferson City, MO 10.2 6.0 0.4 6.4 -0.1 6.2Johnson City, TN 6.8 7.2 0.9 13.0 4.7 9.3Johnstown, PA 26.5 2.6 28.2 0.8 -1.0 10.9Jonesboro, AR 4.3 9.7 -5.9 14.5 -1.6 4.3Joplin, MO 11.9 8.4 0.5 13.4 0.9 4.9Kalamazoo-Portage, MI -6.1 14.2 -6.8 14.5 2.5 -3.1Kankakee-Bradley, IL -6.6 23.8 -1.7 27.2 16.2 10.3Kansas City, MO-KS 4.7 2.9 -3.1 6.5 4.3 0.0Kennewick-Pasco-Richland, WA 5.3 42.5 9.1 44.2 -7.1 8.3Killeen-Temple-Fort Hood, TX 9.4 0.3 -7.0 10.4 -5.9 11.1Kingsport-Bristol-Bristol, TN-VA -6.4 11.2 -2.7 9.1 11.3 14.2Kingston, NY 33.8 -0.1 21.8 -7.7 15.9 -1.4Knoxville, TN 3.3 12.8 -2.1 13.0 15.4 6.8Kokomo, IN -3.7 11.1 -6.4 16.1 -1.4 -7.0La Crosse, WI-MN -7.1 13.1 -13.5 14.7 14.4 3.0Lafayette, IN 4.2 15.2 -0.7 18.6 -5.7 1.8Lafayette, LA -9.1 26.8 -9.4 27.9 1.1 11.2Lake Charles, LA 6.3 6.7 6.2 9.0 5.1 18.4Lake County-Kenosha County, IL-WI 0.6 14.9 0.5 15.6 18.8 0.1Lake Havasu City-Kingman, AZ -10.1 30.8 -9.5 17.5 39.0 -16.4Lakeland-Winter Haven, FL 16.5 4.0 2.8 7.2 8.6 -30.3Lancaster, PA 0.4 6.9 2.7 1.3 15.5 8.2Lansing-East Lansing, MI -0.5 8.7 -4.3 8.6 9.1 -19.1Laredo, TX 19.7 4.9 3.3 7.9 -8.0 10.7Las Cruces, NM 5.2 6.4 0.0 8.8 -1.9 14.1Las Vegas-Paradise, NV -1.1 17.3 -5.6 13.9 45.6 -38.6Lawrence, KS -2.9 4.8 -8.5 10.2 14.6 4.6Lawton, OK 3.7 7.2 -1.0 11.1 -5.4 4.2Lebanon, PA -7.7 18.6 3.2 18.1 17.0 13.9Lewiston, ID-WA -9.0 19.7 -16.0 36.7 11.8 24.5Lewiston-Auburn, ME 17.4 7.2 31.2 -3.5 5.0 3.8Lexington-Fayette, KY 3.1 5.1 -2.8 7.2 11.9 5.6Lima, OH -8.1 9.0 -5.1 11.2 7.2 1.0Lincoln, NE -0.9 9.7 -5.4 16.9 2.1 1.8Little Rock-North Little Rock-Conway, AR 6.2 4.2 -5.1 13.9 2.9 7.5Logan, UT-ID -12.5 12.9 -20.2 22.8 3.3 19.7Longview, TX 15.6 3.0 3.8 13.0 -0.4 16.5Longview, WA -16.8 29.1 -14.5 37.6 14.9 13.5Los Angeles-Long Beach-Glendale, CA 0.9 3.0 22.6 -25.0 38.4 -34.1Louisville-Jefferson County, KY-IN -5.2 12.0 -5.7 16.1 14.2 5.0Lubbock, TX 7.1 3.1 -5.7 13.0 -2.6 7.3Lynchburg, VA 3.6 11.7 -1.4 15.7 15.6 15.7Macon, GA 6.5 2.2 2.2 5.7 3.9 9.9Madera-Chowchilla, CA -8.3 36.4 -8.1 24.7 35.3 -28.3Madison, WI -8.4 14.8 -12.8 22.5 16.0 2.4Manchester-Nashua, NH 35.6 -19.7 18.0 -18.7 12.9 -13.2Mansfield, OH -11.2 15.1 -10.7 15.6 8.0 0.2McAllen-Edinburg-Mission, TX 11.7 7.2 9.9 14.1 -14.4 11.0Medford, OR -17.6 30.8 -15.0 29.1 42.0 -11.6Memphis, TN-MS-AR 12.3 -0.4 3.1 3.7 -4.2 -12.0Merced, CA -11.0 25.9 -9.0 2.3 55.0 -60.5Miami-Miami Beach-Kendall, FL -8.8 5.1 -13.8 10.6 76.6 -30.6Michigan City-La Porte, IN -2.0 27.3 -3.0 21.9 4.7 4.6Midland, TX 16.0 -3.2 0.3 8.6 -7.2 36.0Milwaukee-Waukesha-West Allis, WI -8.5 16.4 -11.1 21.6 20.7 1.4Minneapolis-St. Paul-Bloomington, MN-WI -1.1 4.5 -8.1 11.4 30.6 -19.1Missoula, MT -15.6 24.7 -18.5 49.1 19.0 9.7Mobile, AL -0.2 7.0 -7.5 18.1 9.6 20.5Modesto, CA -4.5 16.6 2.1 -11.0 46.9 -52.8Monroe, LA 2.4 12.3 -8.1 8.1 1.5 10.8Monroe, MI -9.6 25.3 -8.7 13.9 8.1 -14.9Montgomery, AL 9.1 6.0 1.3 11.2 -2.4 8.1Morgantown, WV 5.7 9.6 0.9 10.2 2.0 12.1Morristown, TN 17.8 0.4 4.6 5.0 10.8 12.6Mount Vernon-Anacortes, WA -8.9 53.7 -8.7 36.6 9.5 12.4Muncie, IN 0.2 11.5 -4.1 19.3 3.7 -6.1Muskegon-Norton Shores, MI -5.5 13.6 -3.6 10.8 6.5 -3.3Myrtle Beach-North Myrtle Beach-Conway, SC 30.8 6.3 23.4 9.2 -16.0 -5.5Napa, CA 7.0 29.4 7.6 2.7 21.6 -35.3Naples-Marco Island, FL 4.7 12.8 11.7 6.3 72.7 -42.3Nashville-Davidson-Murfreesboro-Franklin, TN 1.8 -1.5 -5.4 8.6 14.9 6.3Nassau-Suffolk, NY 29.1 -9.2 6.9 -5.0 37.6 -8.6New Haven-Milford, CT 45.2 -13.9 19.3 -13.7 10.9 -8.4





New Orleans-Metairie-Kenner, LA -0.6 4.2 -13.3 18.1 49.4 6.1New York-White Plains-Wayne, NY-NJ 32.8 -9.2 14.4 -5.8 37.9 -10.9Newark-Union, NJ-PA 40.2 -10.4 11.8 -0.1 18.6 -11.2Niles-Benton Harbor, MI 3.2 14.7 7.8 15.4 4.1 -0.1Norwich-New London, CT 39.3 -15.6 27.0 -11.2 6.2 -5.1Oakland-Fremont-Hayward, CA 1.2 11.2 13.5 -9.7 23.1 -38.8Ocala, FL 5.0 -0.1 -3.8 5.5 16.7 -7.8Ocean City, NJ 16.8 -0.8 26.8 -7.0 47.0 -12.3Odessa, TX 19.1 7.2 7.3 17.2 -7.2 37.3Ogden-Clearfield, UT -3.4 9.4 -15.8 24.6 -3.0 21.3Oklahoma City, OK 2.6 8.3 -6.6 14.8 -0.1 9.2Olympia, WA -4.0 40.8 -9.0 41.9 14.0 9.5Omaha-Council Bluffs, NE-IA 4.1 9.6 -3.8 15.6 -4.0 1.0Orlando-Kissimmee, FL 5.6 4.6 -4.5 -0.9 32.1 -30.4Oshkosh-Neenah, WI 2.2 8.6 -7.3 18.2 5.0 5.0Owensboro, KY 5.9 -3.8 0.3 14.9 2.3 3.7Oxnard-Thousand Oaks-Ventura, CA 7.6 -5.3 28.0 -21.6 24.8 -35.6Palm Bay-Melbourne-Titusville, FL -3.2 6.7 -6.3 9.0 35.4 -27.4Palm Coast, FL 0.3 6.3 -15.8 3.5 28.5 -22.8Panama City-Lynn Haven-Panama City Beach, FL 4.1 9.1 -3.5 12.8 -1.2 -10.5Parkersburg-Marietta-Vienna, WV-OH 1.6 5.5 -5.4 14.1 -0.7 9.4Pascagoula, MS 6.5 3.5 -2.7 9.5 23.3 16.4Peabody, MA 36.0 -13.5 15.6 -7.0 11.3 -13.8Pensacola-Ferry Pass-Brent, FL 8.1 -1.1 -5.7 9.5 15.8 -18.0Peoria, IL -7.0 16.1 -8.5 18.9 -1.0 7.7Philadelphia, PA 22.6 0.2 18.2 -0.2 7.6 1.6Phoenix-Mesa-Scottsdale, AZ 1.5 -2.1 -10.3 4.2 53.4 -38.6Pine Bluff, AR 7.0 5.0 -1.6 12.1 0.3 7.4Pittsburgh, PA 8.1 17.4 4.9 20.1 -2.9 2.4Pittsfield, MA 50.1 -19.8 33.7 -16.3 -5.2 -4.0Pocatello, ID -6.3 15.5 -16.7 28.7 4.6 14.9Port St. Lucie, FL 9.5 4.8 4.7 -5.5 31.9 -42.3Portland-South Portland-Biddeford, ME 27.0 -6.0 18.2 -7.8 6.6 2.4Portland-Vancouver-Beaverton, OR-WA -8.6 36.0 -10.5 40.5 9.8 -0.2Poughkeepsie-Newburgh-Middletown, NY 30.0 -3.2 15.8 -6.5 21.0 -4.3Prescott, AZ -17.0 24.9 -16.8 29.9 59.7 -20.9Providence-New Bedford-Fall River, RI-MA 33.7 -6.2 22.1 -7.2 20.3 -14.6Provo-Orem, UT -13.5 15.8 -19.3 37.0 10.7 22.3Pueblo, CO 3.1 8.3 -7.6 16.4 2.5 -2.6Punta Gorda, FL -4.6 15.8 -2.9 0.1 69.5 -47.1Racine, WI -6.1 16.2 -8.4 19.5 18.0 1.0Raleigh-Cary, NC 2.1 5.0 -4.2 8.8 7.2 14.8Rapid City, SD -7.1 9.8 -13.6 27.5 7.8 11.5Reading, PA 3.8 10.3 9.2 8.0 14.5 6.8Redding, CA 7.2 35.4 -4.5 23.8 15.2 -27.4Reno-Sparks, NV 1.4 16.4 -5.6 17.3 37.0 -22.5Richmond, VA -1.0 7.3 -3.8 7.9 31.6 9.0Riverside-San Bernardino-Ontario, CA -5.8 12.8 -4.1 -13.5 31.0 -46.9Roanoke, VA 4.8 9.2 1.4 13.0 15.1 13.2Rochester, MN -1.1 10.4 -7.8 13.2 4.3 0.4Rochester, NY 25.2 4.3 14.3 5.7 -11.4 3.2Rockford, IL -0.5 14.6 -5.0 16.9 5.0 5.2Rockingham County-Strafford County, NH 42.4 -18.6 19.4 -17.5 6.8 -11.5Rocky Mount, NC 6.6 9.6 -2.8 5.9 -0.4 10.6Rome, GA -5.6 2.8 -1.6 18.2 18.5 3.5Sacramento-Arden-Arcade-Roseville, CA 4.0 31.5 2.7 -6.7 12.1 -42.8Saginaw-Saginaw Township North, MI -4.2 13.8 -7.5 16.4 9.2 -12.2Salem, OR -14.6 33.1 -17.2 38.1 13.2 13.9Salinas, CA -9.1 15.1 7.9 -2.5 60.2 -54.2Salisbury, MD 5.4 4.1 3.9 7.2 8.0 -8.1Salt Lake City, UT -12.5 15.9 -20.2 38.5 6.5 22.3San Angelo, TX 9.1 2.9 2.2 12.0 3.2 15.5San Antonio, TX 6.0 3.6 -6.8 18.4 -3.7 14.9San Diego-Carlsbad-San Marcos, CA -3.6 10.5 6.5 -12.1 19.8 -35.6San Francisco-San Mateo-Redwood City, CA 10.9 5.3 25.0 -11.4 8.4 -14.0San Jose-Sunnyvale-Santa Clara, CA 1.8 3.6 28.1 -14.2 17.7 -25.3San Luis Obispo-Paso Robles, CA -4.8 22.4 22.6 -18.0 16.7 -24.2Sandusky, OH 0.4 16.6 1.9 18.6 -7.3 -10.4Santa Ana-Anaheim-Irvine, CA 1.5 1.7 16.3 -18.8 31.0 -30.9Santa Barbara-Santa Maria-Goleta, CA -5.5 5.3 7.9 -11.9 42.5 -42.9Santa Cruz-Watsonville, CA 3.6 11.5 33.9 -8.9 11.4 -32.6Santa Fe, NM -5.2 24.7 -7.9 36.3 -0.7 10.1Santa Rosa-Petaluma, CA -4.3 22.2 7.8 -5.2 25.2 -39.6Savannah, GA 0.0 6.2 -6.3 11.6 34.2 8.9Scranton-Wilkes-Barre, PA 1.9 19.4 11.8 17.6 6.2 14.5Seattle-Bellevue-Everett, WA -5.1 31.4 6.8 4.4 6.1 2.1Sebastian-Vero Beach, FL 5.3 3.1 4.9 1.6 29.3 -35.3Sheboygan, WI -4.6 15.1 -11.5 20.4 5.8 5.0Sherman-Denison, TX 13.8 5.9 0.7 10.1 -5.8 11.4Shreveport-Bossier City, LA 7.6 4.5 -4.3 11.0 -0.7 8.6Sioux City, IA-NE-SD -5.3 17.3 -5.9 24.8 -2.6 7.1





Sioux Falls, SD 3.1 11.2 -8.1 27.3 -0.5 7.7South Bend-Mishawaka, IN-MI 4.7 9.9 3.4 12.2 -4.1 4.9Spartanburg, SC -0.5 10.8 -5.6 13.1 5.8 2.8Spokane, WA -1.9 28.7 -5.0 40.8 -1.6 16.0Springfield, IL 4.0 13.5 -0.5 16.0 -5.9 4.4Springfield, MA 44.2 -9.0 30.9 -13.6 3.3 -4.9Springfield, MO 7.8 5.7 -0.2 13.4 -0.7 6.0Springfield, OH -5.1 7.7 -2.2 19.8 3.8 -5.1St. Cloud, MN -2.7 11.8 -8.9 12.2 19.0 -0.8St. George, UT -1.3 9.2 -4.4 28.4 27.6 -6.3St. Joseph, MO-KS 0.2 12.6 -3.8 10.8 14.2 0.9St. Louis, MO-IL 6.7 4.3 -0.3 6.2 9.9 3.3State College, PA 10.8 9.7 5.2 16.6 10.3 11.4Stockton, CA -1.1 16.0 9.8 -5.3 41.4 -54.0Sumter, SC -2.4 8.4 -3.9 10.1 8.2 14.2Syracuse, NY 23.3 6.3 18.6 3.2 -6.9 7.2Tacoma, WA -5.1 40.6 -2.5 38.6 2.9 -5.1Tallahassee, FL 4.5 10.4 -6.4 13.8 12.3 4.9Tampa-St. Petersburg-Clearwater, FL 7.8 0.3 3.1 4.4 24.5 -27.6Terre Haute, IN 3.6 9.6 0.8 18.3 -0.9 -1.4Texarkana, TX-Texarkana, AR 4.8 4.9 -2.4 9.1 -5.2 6.0Toledo, OH 7.3 6.5 6.8 9.9 -11.8 -12.1Topeka, KS -2.6 6.2 -2.7 7.6 0.8 5.7Trenton-Ewing, NJ 42.6 -8.4 12.4 -0.9 15.9 -9.1Tucson, AZ -0.3 4.1 -10.7 16.1 34.7 -20.6Tulsa, OK 5.9 6.3 -2.2 13.1 -0.6 9.9Tuscaloosa, AL 0.5 10.9 -6.8 16.3 6.3 11.0Tyler, TX 0.0 3.2 -4.2 19.9 -1.6 8.2Utica-Rome, NY 29.4 11.3 32.4 7.3 -8.2 14.8Valdosta, GA 8.3 3.4 3.5 6.4 10.3 8.4Vallejo-Fairfield, CA -8.1 26.4 -10.6 -0.9 39.6 -47.1Victoria, TX 23.7 3.9 14.3 8.5 -16.4 20.3Vineland-Millville-Bridgeton, NJ 16.5 8.7 20.1 5.7 22.6 6.1Virginia Beach-Norfolk-Newport News, VA-NC 8.7 4.5 1.8 6.3 13.2 6.4Visalia-Porterville, CA 7.9 25.3 -1.6 19.6 13.2 -39.0Waco, TX 2.1 -0.6 -4.8 10.9 1.9 9.3Warner Robins, GA 10.7 4.9 -0.7 9.3 0.2 3.8Warren-Troy-Farmington Hills, MI -0.5 11.5 -4.3 13.3 12.2 -28.7Washington-Arlington-Alexandria, DC-VA-MD-WV 9.4 -1.0 12.8 -3.6 32.3 -27.3Waterloo-Cedar Falls, IA -17.6 27.6 -9.1 23.6 12.2 6.5Wausau, WI -2.8 18.0 -10.2 22.5 2.6 8.5Weirton-Steubenville, WV-OH -0.9 11.4 -3.8 17.2 11.8 -0.1Wenatchee-East Wenatchee, WA -9.7 35.5 -8.0 47.3 -2.6 29.8West Palm Beach-Boca Raton-Boynton Beach, FL 6.8 3.5 5.6 -3.0 39.9 -36.7Wheeling, WV-OH 4.7 9.6 -2.4 17.9 1.6 4.8Wichita Falls, TX 11.5 4.0 -4.4 15.1 -0.3 4.7Wichita, KS 7.0 3.4 -2.8 9.8 -10.2 7.6Williamsport, PA 11.4 10.7 12.4 17.6 1.3 5.4Wilmington, DE-MD-NJ 10.7 5.2 11.0 0.6 6.3 -4.5Wilmington, NC 19.7 11.4 21.2 14.5 4.9 11.9Winchester, VA-WV 1.3 8.3 7.5 6.4 22.4 -14.6Winston-Salem, NC 4.4 5.1 -1.4 9.2 8.4 2.5Worcester, MA 38.5 -17.8 20.6 -12.7 7.6 -15.0Yakima, WA -6.4 18.5 -17.0 43.9 -0.2 14.6York-Hanover, PA 4.3 13.4 4.1 11.6 20.0 8.8Youngstown-Warren-Boardman, OH-PA -4.4 16.5 -2.6 22.8 3.4 -9.5Yuba City, CA 3.0 40.0 -1.4 17.2 18.1 -34.7Yuma, AZ -11.5 10.5 -5.3 12.2 40.8 -6.2



Appendix 7-3: House Price Over/Undervaluation, CurrentPrice % Peak Trough % Peak-to-

Balance Overpriced Quarter Quarter Trough Decline

Abilene, TX balanced 1.1 2007Q4 2010Q1 -2.2Akron, OH highly underpriced -11.4 2006Q1 2009Q4 -9.0Albany, GA highly overpriced 7.7 2008Q1 2009Q4 -5.5Albany-Schenectady-Troy, NY overpriced 9.1 2008Q1 2009Q3 -2.2Albuquerque, NM balanced 1.6 2008Q1 2010Q3 -13.5Alexandria, LA overpriced 5.4 2008Q1 2009Q4 -2.7Allentown-Bethlehem-Easton, PA-NJ overpriced 4.0 2007Q2 2010Q1 -9.3Altoona, PA highly overpriced 23.2 na na naAmarillo, TX overpriced 11.1 2008Q1 2009Q4 -1.9Ames, IA balanced -3.5 2008Q1 2009Q4 -4.7Anchorage, AK underpriced -8.6 2007Q2 2007Q3 -0.8Anderson, IN underpriced -6.3 2005Q3 2006Q3 -8.3Anderson, SC highly overpriced 12.5 2008Q1 2009Q2 -3.2Ann Arbor, MI highly underpriced -13.8 2005Q4 2009Q4 -24.7Anniston-Oxford, AL highly overpriced 12.1 2008Q3 2009Q2 -0.9Appleton, WI balanced -5.3 2008Q2 2009Q4 -7.7Asheville, NC highly overpriced 42.8 2008Q2 2010Q4 -9.7Athens-Clarke County, GA highly overpriced 22.5 2008Q2 2010Q3 -7.0Atlanta-Sandy Springs-Marietta, GA overpriced 3.8 2007Q2 2009Q4 -14.1Atlantic City, NJ overpriced 19.8 2006Q2 2010Q4 -37.0Auburn-Opelika, AL highly overpriced 20.0 2008Q3 2009Q3 -1.2Augusta-Richmond County, GA-SC highly overpriced 20.1 2008Q3 2010Q3 -2.0Austin-Round Rock, TX highly overpriced 26.0 2008Q3 2009Q4 -1.3Bakersfield, CA underpriced -28.3 2006Q2 2009Q4 -55.5Baltimore-Towson, MD overpriced 3.2 2007Q1 2010Q3 -31.6Bangor, ME balanced 2.6 2008Q2 2009Q4 -4.0Barnstable Town, MA balanced -6.5 2005Q4 2010Q1 -25.1Baton Rouge, LA overpriced 9.1 2008Q2 2010Q1 -2.0Battle Creek, MI balanced -5.7 2006Q3 2010Q1 -11.0Bay City, MI balanced 2.5 2006Q1 2009Q4 -13.9Beaumont-Port Arthur, TX overpriced 4.4 2008Q2 2009Q4 -2.6Bellingham, WA balanced 0.9 2008Q1 2009Q4 -11.6Bend, OR overpriced 6.0 2007Q1 2009Q4 -23.7Bethesda-Frederick-Rockville, MD balanced 3.5 2006Q2 2010Q1 -30.0Billings, MT balanced 2.8 2008Q1 2009Q3 -2.8Binghamton, NY balanced 2.5 na na naBirmingham-Hoover, AL highly overpriced 11.6 2008Q1 2009Q2 -1.1Bismarck, ND overpriced 4.1 2007Q4 2010Q1 -4.8Blacksburg-Christiansburg-Radford, VA highly overpriced 34.6 2008Q1 2009Q4 -2.6Bloomington, IN balanced -3.7 2008Q2 2009Q3 -2.0Bloomington-Normal, IL balanced 1.7 2008Q2 2009Q3 -1.0Boise City-Nampa, ID highly overpriced 19.3 2007Q2 2010Q2 -25.2Boston-Quincy, MA balanced -3.2 2005Q3 2010Q2 -26.1Boulder, CO overpriced 5.9 2008Q3 2009Q4 -2.4Bowling Green, KY balanced -3.5 2008Q2 2009Q2 -2.1Bradenton-Sarasota-Venice, FL underpriced -18.0 2006Q1 2010Q3 -62.1Bremerton-Silverdale, WA overpriced 3.1 2007Q4 2009Q4 -9.0Bridgeport-Stamford-Norwalk, CT balanced -7.1 2006Q1 2010Q2 -21.6Brownsville-Harlingen, TX underpriced -17.9 2008Q1 2009Q2 -3.9Brunswick, GA highly overpriced 23.5 2008Q2 2009Q2 -3.1Buffalo-Niagara Falls, NY underpriced -8.4 na na naBurlington, NC overpriced 8.2 2007Q2 2009Q3 -3.9Burlington-South Burlington, VT highly overpriced 49.0 2007Q4 2010Q4 -18.9Cambridge-Newton-Framingham, MA underpriced -7.0 2005Q2 2009Q4 -20.3Camden, NJ balanced -2.6 2006Q3 2010Q2 -26.7Canton-Massillon, OH highly underpriced -12.4 2006Q1 2009Q4 -9.0Cape Coral-Fort Myers, FL highly underpriced -31.3 2006Q1 2010Q2 -66.4Carson City, NV overpriced 8.1 2006Q1 2009Q4 -30.6Casper, WY overpriced 8.9 2008Q2 2010Q1 -4.5Cedar Rapids, IA balanced -2.1 2007Q4 2009Q3 -1.0Champaign-Urbana, IL overpriced 4.4 2008Q3 2009Q3 -1.0Charleston, WV overpriced 6.2 2008Q2 2009Q2 -2.7Charleston-North Charleston-Summerville, SC balanced -1.7 2007Q1 2009Q2 -10.3Charlotte-Gastonia-Concord, NC-SC highly overpriced 9.4 2007Q3 2009Q3 -6.0Charlottesville, VA highly overpriced 39.5 2007Q4 2009Q4 -5.5Chattanooga, TN-GA highly overpriced 17.7 2008Q1 2009Q2 -2.3Cheyenne, WY balanced -7.0 2008Q2 2009Q4 -6.6Chicago-Naperville-Joliet, IL overpriced 12.4 2007Q1 2010Q1 -17.1Chico, CA underpriced -12.4 2005Q4 2009Q4 -36.2Cincinnati-Middletown, OH-KY-IN highly underpriced -9.6 2006Q1 2009Q4 -8.3Clarksville, TN-KY balanced -6.0 2008Q3 2009Q2 -1.1Cleveland, TN highly underpriced -12.4 2008Q1 2010Q1 -4.4Cleveland-Elyria-Mentor, OH highly overpriced 19.3 2005Q4 2009Q4 -13.4Coeur d'Alene, ID highly overpriced 20.4 2007Q3 2010Q2 -20.2College Station-Bryan, TX balanced 5.3 na na naColorado Springs, CO balanced -3.6 2008Q1 2009Q4 -7.9Columbia, MO balanced 0.7 2008Q1 2008Q2 -1.5



Appendix 7-3: House Price Over/Undervaluation, Current (cont.)Price % Peak Trough % Peak-to-

Balance Overpriced Quarter Quarter Trough DeclineColumbia, SC highly overpriced 7.4 2008Q3 2009Q2 -0.5Columbus, GA-AL highly overpriced 11.0 2008Q1 2009Q2 -3.5Columbus, IN underpriced -6.8 2008Q2 2009Q3 -5.8Columbus, OH highly underpriced -9.4 2006Q2 2009Q4 -7.9Corpus Christi, TX balanced -9.3 2008Q2 2009Q2 -1.6Corvallis, OR balanced -4.2 2007Q4 2009Q4 -6.7Cumberland, MD-WV overpriced 9.0 2008Q3 2010Q1 -8.6Dallas-Plano-Irving, TX balanced 0.6 2008Q3 2009Q3 -1.1Dalton, GA highly overpriced 18.4 2008Q1 2010Q2 -9.3Danville, IL balanced -0.5 2007Q3 2009Q3 -9.1Danville, VA overpriced 3.1 2008Q2 2009Q2 -8.1Davenport-Moline-Rock Island, IA-IL balanced -5.1 2008Q1 2009Q4 -3.5Dayton, OH highly underpriced -8.6 2006Q1 2009Q4 -5.8Decatur, AL overpriced 6.2 2008Q2 2009Q3 -1.2Decatur, IL underpriced -5.8 na na naDeltona-Daytona Beach-Ormond Beach, FL balanced -8.5 2006Q2 2010Q3 -55.3Denver-Aurora-Broomfield, CO balanced -3.3 2006Q1 2010Q2 -18.9Des Moines-West Des Moines, IA balanced -1.2 2008Q1 2009Q4 -4.9Detroit-Livonia-Dearborn, MI balanced -15.5 2006Q1 2010Q1 -36.6Dothan, AL highly overpriced 8.9 2007Q4 2008Q2 -3.1Dover, DE highly overpriced 13.6 2008Q1 2010Q3 -12.0Dubuque, IA balanced 0.8 2008Q2 2009Q4 -3.7Duluth, MN-WI balanced -4.1 2008Q1 2008Q3 -1.3Durham-Chapel Hill, NC highly overpriced 17.3 2008Q2 2009Q2 -1.9Eau Claire, WI overpriced 10.3 2008Q1 2009Q4 -2.6Edison-New Brunswick, NJ balanced 5.3 2006Q2 2010Q3 -28.0El Centro, CA balanced 7.1 2006Q2 2009Q4 -56.6El Paso, TX highly overpriced 14.3 2008Q1 2009Q4 -2.7Elizabethtown, KY highly overpriced 12.8 2008Q2 2009Q4 -6.2Elkhart-Goshen, IN balanced -2.8 2008Q2 2010Q1 -6.0Elmira, NY underpriced -7.1 2007Q2 2008Q1 -7.0Erie, PA balanced 1.0 2008Q2 2008Q3 -0.4Eugene-Springfield, OR highly overpriced 12.8 2008Q1 2010Q3 -17.1Evansville, IN-KY underpriced -8.0 2008Q1 2009Q3 -1.9Fairbanks, AK underpriced -13.8 2008Q1 2009Q2 -6.5Fargo, ND-MN balanced -1.0 2008Q1 2010Q1 -3.4Farmington, NM overpriced 3.4 2008Q1 2009Q4 -8.1Fayetteville, NC highly overpriced 12.9 2008Q3 2009Q2 -1.0Fayetteville-Springdale-Rogers, AR-MO highly overpriced 9.3 2007Q2 2010Q2 -8.9Flagstaff, AZ balanced 1.9 2006Q2 2010Q2 -28.3Flint, MI balanced -7.7 2005Q3 2010Q1 -23.7Florence, SC balanced 5.0 2008Q2 2009Q2 -0.7Florence-Muscle Shoals, AL highly overpriced 11.2 2008Q3 2008Q4 -0.1Fond du Lac, WI overpriced 5.4 2008Q1 2009Q4 -2.9Fort Collins-Loveland, CO underpriced -5.4 2007Q4 2009Q1 -2.1Fort Lauderdale-Pompano Beach-Deerfield Beach, FL balanced 3.8 2006Q2 2010Q4 -64.4Fort Smith, AR-OK overpriced 5.0 2008Q3 2009Q1 -0.6Fort Walton Beach-Crestview-Destin, FL balanced -0.7 2005Q4 2010Q3 -55.0Fort Wayne, IN underpriced -11.3 2007Q1 2007Q4 -2.5Fort Worth-Arlington, TX balanced -2.1 2008Q2 2009Q3 -1.1Fresno, CA underpriced -19.3 2006Q1 2009Q4 -53.6Gadsden, AL highly overpriced 22.0 2008Q3 2009Q3 -1.6Gainesville, FL overpriced 13.2 2007Q1 2011Q2 -39.4Gainesville, GA highly overpriced 27.2 2007Q1 2010Q4 -12.3Gary, IN overpriced 0.5 2008Q1 2009Q4 -3.7Glens Falls, NY underpriced -16.8 2008Q1 2009Q4 -3.4Goldsboro, NC highly overpriced 10.6 2008Q2 2009Q2 -4.2Grand Forks, ND-MN underpriced -10.7 2007Q4 2009Q4 -4.1Grand Junction, CO highly overpriced 38.0 2008Q3 2010Q4 -12.2Grand Rapids-Wyoming, MI underpriced -9.1 2005Q3 2009Q4 -10.7Great Falls, MT overpriced 4.0 2008Q1 2009Q4 -4.4Greeley, CO balanced 0.9 2006Q1 2009Q4 -14.4Green Bay, WI underpriced -8.5 2008Q1 2009Q4 -6.7Greensboro-High Point, NC highly overpriced 8.0 2008Q2 2009Q2 -1.2Greenville, NC overpriced 7.8 2008Q3 2009Q2 -1.1Greenville-Mauldin-Easley, SC highly overpriced 11.8 2008Q2 2009Q2 -0.8Gulfport-Biloxi, MS highly overpriced 42.4 2008Q1 2010Q2 -7.7Hagerstown-Martinsburg, MD-WV highly overpriced 14.8 2007Q1 2010Q3 -20.3Hanford-Corcoran, CA balanced -7.3 2006Q2 2009Q4 -43.8Harrisburg-Carlisle, PA highly overpriced 9.0 2008Q1 2009Q4 -2.6Harrisonburg, VA highly overpriced 34.9 2008Q2 2009Q4 -8.5Hartford-West Hartford-East Hartford, CT underpriced -13.7 2007Q1 2010Q2 -16.8Hattiesburg, MS highly overpriced 9.0 2008Q2 2009Q4 -4.9Hickory-Lenoir-Morganton, NC highly overpriced 21.3 2008Q3 2009Q2 -1.1Hinesville-Fort Stewart, GA highly overpriced 55.3 2008Q2 2009Q4 -8.8Holland-Grand Haven, MI overpriced 4.3 2005Q3 2010Q1 -9.9Honolulu, HI overpriced 11.8 2008Q1 2010Q2 -24.2Hot Springs, AR overpriced 9.0 2008Q2 2009Q2 -2.1




Balance Overpriced Quarter Quarter Trough DeclineHouma-Bayou Cane-Thibodaux, LA highly overpriced 14.1 2008Q2 2010Q1 -3.0Houston-Sugar Land-Baytown, TX balanced 3.1 2008Q3 2009Q3 -0.2Huntington-Ashland, WV-KY-OH overpriced 8.8 2008Q1 2009Q4 -1.5Huntsville, AL overpriced 5.9 2008Q3 2009Q3 -1.0Idaho Falls, ID overpriced 6.4 2008Q1 2010Q1 -8.5Indianapolis-Carmel, IN underpriced -7.7 2008Q1 2009Q3 -1.5Iowa City, IA balanced -1.0 2008Q1 2010Q1 -5.8Ithaca, NY overpriced 18.1 na na naJackson, MI balanced -2.2 2005Q4 2010Q1 -20.3Jackson, MS balanced -2.2 2008Q1 2009Q2 -3.5Jackson, TN balanced -3.9 2007Q2 2009Q3 -5.7Jacksonville, FL highly overpriced 22.2 2006Q2 2011Q1 -45.9Jacksonville, NC highly overpriced 17.3 2008Q3 2009Q1 -0.9Janesville, WI overpriced 9.4 2008Q1 2010Q1 -6.3Jefferson City, MO balanced -2.4 2008Q2 2009Q3 -0.7Johnson City, TN overpriced 8.2 2007Q4 2009Q3 -2.7Johnstown, PA balanced 0.4 2008Q2 2008Q3 -1.5Jonesboro, AR balanced -3.5 na na naJoplin, MO balanced -1.7 2008Q1 2009Q4 -3.9Kalamazoo-Portage, MI balanced -6.0 2008Q1 2009Q4 -7.0Kankakee-Bradley, IL overpriced 14.7 2008Q3 2009Q2 -0.4Kansas City, MO-KS balanced -2.4 2008Q1 2010Q1 -5.1Kennewick-Pasco-Richland, WA highly underpriced -10.5 na na naKilleen-Temple-Fort Hood, TX balanced -2.5 2008Q3 2009Q3 -0.8Kingsport-Bristol-Bristol, TN-VA highly overpriced 18.1 2008Q3 2010Q3 -0.4Kingston, NY overpriced 7.9 2008Q1 2010Q1 -14.8Knoxville, TN highly overpriced 16.5 2007Q2 2009Q4 -3.9Kokomo, IN highly underpriced -13.7 2005Q4 2009Q3 -10.8La Crosse, WI-MN overpriced 10.5 2008Q2 2010Q1 -5.9Lafayette, IN highly underpriced -10.3 2007Q1 2009Q2 -2.1Lafayette, LA overpriced 5.6 2008Q2 2010Q4 -4.4Lake Charles, LA overpriced 7.7 2008Q3 2009Q4 -0.7Lake County-Kenosha County, IL-WI highly overpriced 12.2 2007Q4 2009Q4 -6.1Lake Havasu City-Kingman, AZ overpriced 13.9 2007Q1 2010Q2 -33.6Lakeland-Winter Haven, FL underpriced -25.4 2006Q2 2010Q3 -54.0Lancaster, PA highly overpriced 16.7 2008Q1 2010Q3 -6.2Lansing-East Lansing, MI highly underpriced -15.0 2006Q1 2009Q4 -21.6Laredo, TX balanced -1.5 2007Q4 2010Q1 -7.7Las Cruces, NM balanced 5.3 2007Q4 2010Q1 -5.9Las Vegas-Paradise, NV balanced -10.8 2006Q1 2010Q2 -55.7Lawrence, KS highly overpriced 15.7 2008Q1 2010Q4 -5.3Lawton, OK balanced -10.5 2008Q2 2009Q4 -5.1Lebanon, PA highly overpriced 24.0 2008Q2 2009Q3 -1.7Lewiston, ID-WA highly overpriced 31.8 2008Q3 2010Q3 -9.2Lewiston-Auburn, ME balanced 4.7 2008Q3 2009Q4 -1.9Lexington-Fayette, KY overpriced 12.3 2008Q3 2009Q3 -0.8Lima, OH balanced 0.5 2008Q1 2009Q4 -4.8Lincoln, NE balanced -2.0 2008Q1 2009Q4 -2.9Little Rock-North Little Rock-Conway, AR overpriced 5.3 2007Q4 2008Q1 -0.6Logan, UT-ID overpriced 13.1 2008Q3 2010Q1 -13.6Longview, TX overpriced 8.4 2008Q2 2009Q2 -2.9Longview, WA highly overpriced 13.5 2007Q4 2010Q4 -10.0Los Angeles-Long Beach-Glendale, CA balanced -6.4 2006Q2 2010Q3 -53.0Louisville-Jefferson County, KY-IN highly overpriced 12.9 2008Q2 2009Q2 -2.6Lubbock, TX balanced -0.1 2008Q3 2009Q3 -0.4Lynchburg, VA highly overpriced 23.5 2008Q2 2010Q3 -2.8Macon, GA highly overpriced 10.1 2008Q3 2009Q1 -1.0Madera-Chowchilla, CA overpriced 2.0 2006Q3 2010Q1 -49.9Madison, WI highly overpriced 12.4 2008Q1 2010Q1 -5.5Manchester-Nashua, NH balanced -6.1 2005Q4 2010Q2 -30.2Mansfield, OH balanced 0.3 2005Q4 2009Q4 -7.1McAllen-Edinburg-Mission, TX underpriced -10.2 2008Q3 2009Q3 -1.4Medford, OR overpriced 9.5 2007Q1 2010Q3 -23.4Memphis, TN-MS-AR highly underpriced -20.0 2006Q2 2009Q3 -14.9Merced, CA underpriced -35.4 2006Q1 2009Q4 -69.6Miami-Miami Beach-Kendall, FL overpriced 25.1 2007Q1 2011Q2 -66.4Michigan City-La Porte, IN balanced 1.3 2008Q1 2009Q3 -4.1Midland, TX overpriced 16.4 2008Q2 2010Q4 -4.9Milwaukee-Waukesha-West Allis, WI highly overpriced 15.7 2008Q1 2010Q1 -5.5Minneapolis-St. Paul-Bloomington, MN-WI balanced -1.0 2006Q1 2010Q1 -23.6Missoula, MT highly overpriced 19.2 2008Q1 2010Q1 -9.4Mobile, AL highly overpriced 18.5 2008Q3 2009Q1 -0.2Modesto, CA balanced -24.6 2006Q1 2010Q2 -67.4Monroe, LA overpriced 8.7 2008Q3 2009Q4 -0.5Monroe, MI balanced -10.5 2005Q3 2009Q4 -20.4Montgomery, AL balanced -2.1 2008Q1 2009Q2 -2.5Morgantown, WV overpriced 7.4 2008Q3 2009Q4 -1.0Morristown, TN highly overpriced 17.9 2008Q1 2010Q1 -3.2




Balance Overpriced Quarter Quarter Trough DeclineMount Vernon-Anacortes, WA highly overpriced 12.4 2008Q1 2010Q3 -12.0Muncie, IN underpriced -9.4 2005Q3 2009Q4 -10.4Muskegon-Norton Shores, MI balanced -2.7 2005Q3 2009Q4 -6.5Myrtle Beach-North Myrtle Beach-Conway, SC balanced -17.5 2006Q4 2009Q2 -12.8Napa, CA underpriced -20.1 2006Q1 2009Q4 -48.2Naples-Marco Island, FL balanced 6.6 2006Q1 2010Q4 -70.1Nashville-Davidson-Murfreesboro-Franklin, TN highly overpriced 15.6 2007Q2 2009Q4 -4.5Nassau-Suffolk, NY overpriced 14.6 2006Q2 2010Q3 -31.4New Haven-Milford, CT balanced -5.4 2006Q2 2010Q2 -22.8New Orleans-Metairie-Kenner, LA highly overpriced 33.9 2007Q3 2011Q3 -9.8New York-White Plains-Wayne, NY-NJ overpriced 10.5 2006Q3 2010Q3 -33.3Newark-Union, NJ-PA balanced -0.4 2006Q2 2010Q2 -26.1Niles-Benton Harbor, MI balanced -5.2 2008Q1 2009Q2 -8.0Norwich-New London, CT balanced -7.3 2006Q2 2010Q2 -17.1Oakland-Fremont-Hayward, CA underpriced -25.0 2006Q1 2009Q4 -54.0Ocala, FL overpriced 7.1 2007Q1 2010Q3 -35.0Ocean City, NJ overpriced 13.7 2005Q4 2010Q4 -42.1Odessa, TX overpriced 15.3 2008Q3 2010Q3 -7.5Ogden-Clearfield, UT overpriced 11.0 2008Q1 2010Q1 -15.0Oklahoma City, OK balanced 2.5 2008Q2 2010Q1 -1.8Olympia, WA overpriced 9.6 2008Q1 2010Q1 -12.5Omaha-Council Bluffs, NE-IA underpriced -9.5 2008Q1 2009Q2 -2.9Orlando-Kissimmee, FL balanced -1.7 2006Q2 2011Q1 -62.1Oshkosh-Neenah, WI balanced 1.7 2008Q3 2009Q3 -0.7Owensboro, KY balanced -0.8 2008Q1 2009Q2 -2.9Oxnard-Thousand Oaks-Ventura, CA balanced -14.9 2006Q2 2009Q4 -50.7Palm Bay-Melbourne-Titusville, FL balanced 0.2 2006Q2 2010Q1 -47.4Palm Coast, FL overpriced 7.8 2007Q1 2010Q1 -42.5Panama City-Lynn Haven-Panama City Beach, FL underpriced -13.0 2007Q1 2010Q2 -32.3Parkersburg-Marietta-Vienna, WV-OH balanced 0.6 2008Q3 2008Q4 0.0Pascagoula, MS highly overpriced 37.8 2008Q1 2011Q3 -8.2Peabody, MA underpriced -10.1 2005Q3 2010Q2 -30.8Pensacola-Ferry Pass-Brent, FL balanced 0.4 2006Q2 2010Q4 -48.2Peoria, IL balanced -2.6 2008Q2 2009Q3 -1.0Philadelphia, PA balanced 4.3 2007Q2 2010Q2 -16.3Phoenix-Mesa-Scottsdale, AZ balanced -5.8 2006Q2 2010Q2 -57.9Pine Bluff, AR balanced 3.2 2008Q1 2009Q1 -3.1Pittsburgh, PA highly underpriced -8.2 2007Q4 2008Q2 -0.7Pittsfield, MA underpriced -15.4 2006Q2 2009Q4 -15.3Pocatello, ID highly overpriced 15.3 2008Q2 2010Q1 -9.5Port St. Lucie, FL underpriced -23.6 2006Q1 2010Q2 -60.8Portland-South Portland-Biddeford, ME overpriced 4.8 2008Q1 2010Q1 -5.6Portland-Vancouver-Beaverton, OR-WA highly overpriced 5.7 2007Q2 2010Q3 -23.0Poughkeepsie-Newburgh-Middletown, NY overpriced 10.5 2006Q3 2010Q2 -16.0Prescott, AZ overpriced 23.6 2006Q2 2010Q4 -46.3Providence-New Bedford-Fall River, RI-MA balanced -3.9 2006Q1 2010Q2 -31.5Provo-Orem, UT highly overpriced 28.6 2008Q1 2010Q4 -22.7Pueblo, CO underpriced -9.3 2007Q1 2009Q3 -10.9Punta Gorda, FL balanced -7.2 2006Q1 2010Q2 -65.4Racine, WI highly overpriced 11.5 2007Q1 2009Q4 -5.4Raleigh-Cary, NC highly overpriced 16.4 2008Q3 2009Q2 -0.5Rapid City, SD overpriced 4.2 na na naReading, PA highly overpriced 14.5 2008Q2 2010Q2 -7.2Redding, CA balanced -0.4 2006Q1 2009Q4 -44.4Reno-Sparks, NV balanced 0.8 2006Q1 2010Q2 -47.9Richmond, VA highly overpriced 36.1 2008Q1 2010Q3 -9.5Riverside-San Bernardino-Ontario, CA underpriced -23.7 2006Q2 2010Q2 -62.7Roanoke, VA highly overpriced 21.2 2008Q1 2008Q2 -1.1Rochester, MN balanced -0.3 2008Q1 2009Q4 -4.7Rochester, NY underpriced -16.6 2008Q1 2008Q3 -2.1Rockford, IL overpriced 2.7 2008Q1 2010Q1 -3.9Rockingham County-Strafford County, NH balanced -6.8 2006Q2 2010Q1 -26.4Rocky Mount, NC balanced 4.8 na na naRome, GA highly overpriced 18.7 2008Q3 2009Q3 -2.2Sacramento-Arden-Arcade-Roseville, CA highly underpriced -31.3 2005Q4 2009Q4 -54.2Saginaw-Saginaw Township North, MI balanced -8.9 2005Q4 2009Q4 -16.3Salem, OR highly overpriced 12.0 2008Q1 2010Q4 -16.7Salinas, CA underpriced -28.4 2006Q1 2009Q4 -67.9Salisbury, MD balanced -6.6 2007Q3 2010Q2 -31.3Salt Lake City, UT highly overpriced 22.7 2008Q1 2010Q2 -20.4San Angelo, TX highly overpriced 12.4 2008Q3 2010Q1 -1.5San Antonio, TX balanced 3.2 2008Q3 2010Q1 -1.6San Diego-Carlsbad-San Marcos, CA highly underpriced -21.7 2006Q1 2009Q4 -50.5San Francisco-San Mateo-Redwood City, CA underpriced -13.5 2007Q3 2009Q4 -27.0San Jose-Sunnyvale-Santa Clara, CA underpriced -18.1 2007Q1 2009Q3 -34.0San Luis Obispo-Paso Robles, CA highly underpriced -13.1 2006Q1 2009Q1 -33.4Sandusky, OH highly underpriced -25.4 2006Q4 2009Q2 -15.9Santa Ana-Anaheim-Irvine, CA balanced -3.3 2006Q1 2009Q4 -40.3




Balance Overpriced Quarter Quarter Trough DeclineSanta Barbara-Santa Maria-Goleta, CA underpriced -20.9 2005Q3 2009Q4 -57.7Santa Cruz-Watsonville, CA highly underpriced -32.7 2005Q4 2009Q3 -44.3Santa Fe, NM balanced -0.1 2008Q1 2009Q4 -8.0Santa Rosa-Petaluma, CA highly underpriced -26.6 2005Q4 2009Q4 -57.1Savannah, GA highly overpriced 37.5 2008Q2 2010Q4 -5.5Scranton-Wilkes-Barre, PA highly overpriced 10.0 2008Q2 2009Q3 -2.6Seattle-Bellevue-Everett, WA balanced -2.9 2007Q2 2009Q4 -17.5Sebastian-Vero Beach, FL balanced -14.8 2005Q4 2010Q3 -60.2Sheboygan, WI overpriced 3.1 2008Q1 2009Q4 -5.0Sherman-Denison, TX balanced -3.6 na na naShreveport-Bossier City, LA balanced 2.0 2008Q2 2010Q1 -2.4Sioux City, IA-NE-SD balanced -1.9 2008Q1 2009Q4 -4.2Sioux Falls, SD balanced -3.1 2008Q2 2008Q3 -1.1South Bend-Mishawaka, IN-MI underpriced -7.7 2008Q2 2009Q2 -2.7Spartanburg, SC overpriced 9.6 2007Q4 2009Q2 -2.5Spokane, WA balanced -2.2 2008Q1 2009Q4 -9.2Springfield, IL balanced -8.8 2008Q2 2009Q2 -2.7Springfield, MA balanced -8.1 2007Q1 2010Q1 -20.9Springfield, MO balanced -2.6 2008Q2 2009Q4 -4.2Springfield, OH highly underpriced -10.1 2005Q4 2008Q2 -7.4St. Cloud, MN overpriced 11.1 2007Q2 2009Q4 -5.2St. George, UT overpriced 14.9 2007Q1 2010Q3 -23.7St. Joseph, MO-KS overpriced 3.9 2007Q3 2009Q4 -6.8St. Louis, MO-IL overpriced 6.0 2008Q1 2009Q4 -3.5State College, PA highly overpriced 14.9 2007Q4 2008Q2 -1.7Stockton, CA underpriced -28.3 2006Q1 2009Q4 -67.1Sumter, SC highly overpriced 21.0 2008Q1 2008Q2 -4.5Syracuse, NY underpriced -10.0 2008Q1 2008Q2 -1.3Tacoma, WA balanced -8.8 2007Q2 2009Q4 -17.8Tallahassee, FL highly overpriced 20.3 2007Q2 2010Q1 -18.7Tampa-St. Petersburg-Clearwater, FL balanced -7.2 2006Q2 2010Q4 -49.9Terre Haute, IN highly underpriced -9.6 2008Q2 2009Q3 -3.7Texarkana, TX-Texarkana, AR balanced -4.8 2007Q4 2009Q3 -2.5Toledo, OH highly underpriced -28.3 2006Q1 2009Q2 -12.7Topeka, KS balanced -2.6 2008Q1 2009Q4 -1.6Trenton-Ewing, NJ balanced 0.0 2006Q1 2010Q1 -28.1Tucson, AZ overpriced 5.0 2006Q2 2010Q3 -44.0Tulsa, OK balanced 0.8 2008Q2 2009Q4 -0.6Tuscaloosa, AL overpriced 6.9 2008Q3 2009Q2 -0.2Tyler, TX balanced -0.3 2008Q1 2009Q4 -3.7Utica-Rome, NY balanced -4.4 2008Q1 2008Q3 -2.2Valdosta, GA highly overpriced 14.5 2008Q1 2009Q4 -7.1Vallejo-Fairfield, CA balanced -22.8 2006Q1 2010Q2 -63.9Victoria, TX balanced -10.5 na na naVineland-Millville-Bridgeton, NJ highly overpriced 27.7 2007Q3 2010Q2 -12.4Virginia Beach-Norfolk-Newport News, VA-NC highly overpriced 16.2 2007Q4 2011Q2 -25.3Visalia-Porterville, CA underpriced -28.8 2006Q1 2009Q4 -51.9Waco, TX balanced 4.7 2008Q3 2009Q4 -1.5Warner Robins, GA overpriced 1.9 2007Q4 2009Q3 -5.3Warren-Troy-Farmington Hills, MI highly underpriced -21.6 2005Q3 2009Q4 -34.1Washington-Arlington-Alexandria, DC-VA-MD-WV balanced -7.1 2006Q1 2010Q1 -38.3Waterloo-Cedar Falls, IA overpriced 11.6 2008Q1 2011Q3 -2.9Wausau, WI overpriced 4.4 2008Q1 2009Q3 -2.6Weirton-Steubenville, WV-OH overpriced 1.8 2008Q2 2009Q4 -9.6Wenatchee-East Wenatchee, WA highly overpriced 7.5 2008Q1 2010Q1 -11.7West Palm Beach-Boca Raton-Boynton Beach, FL balanced -8.0 2006Q1 2010Q3 -59.2Wheeling, WV-OH balanced 0.9 2008Q2 2009Q2 -9.2Wichita Falls, TX balanced -0.7 2008Q1 2009Q4 -6.2Wichita, KS underpriced -10.4 2008Q1 2009Q4 -2.5Williamsport, PA balanced -2.6 2008Q2 2008Q3 -4.6Wilmington, DE-MD-NJ balanced 0.9 2007Q1 2010Q2 -26.9Wilmington, NC highly overpriced 15.6 2008Q1 2009Q2 -2.5Winchester, VA-WV overpriced 0.2 2006Q2 2010Q2 -30.9Winston-Salem, NC overpriced 3.4 2008Q1 2009Q4 -5.1Worcester, MA underpriced -14.7 2005Q4 2009Q4 -29.5Yakima, WA balanced -3.2 2008Q2 2009Q4 -6.5York-Hanover, PA highly overpriced 22.6 2008Q1 2010Q2 -4.8Youngstown-Warren-Boardman, OH-PA highly underpriced -12.1 2006Q3 2009Q4 -11.2Yuba City, CA underpriced -20.3 2006Q1 2010Q2 -49.6Yuma, AZ highly overpriced 34.6 2007Q1 2010Q3 -25.5



Appendix 8-1A: Moody’s Economy.com State NAR Median Existing-House Price Forecast Accuracy

Growth 2007 Rank Order3/07 Forecast Actual 3/07 Forecast Actual

New EnglandConnecticut -6.7 2.0 50 13Maine -2.6 -0.5 36 30Massachusetts -3.2 -1.4 40 36New Hampshire -5.0 -3.8 47 47Rhode Island -5.3 0.1 48 26Vermont -2.2 -0.3 33 27Middle AtlanticNew Jersey -4.1 1.4 45 18New York -3.6 1.0 42 22Pennsylvania -1.8 2.8 29 8South AtlanticDelaware -3.1 -1.7 39 39District Of Columbia 6.8 0.7 2 23Florida -4.1 -4.7 44 49Georgia 1.6 -0.6 6 32Maryland -2.3 -1.1 34 34North Carolina 3.2 3.8 3 4South Carolina 0.3 1.4 14 19Virginia -3.0 2.1 38 12West Virginia -1.2 -2.5 27 44East North CentralIllinois -0.7 0.3 22 25Indiana -1.8 -0.5 31 29Michigan -1.9 -6.9 32 51Ohio -1.8 -1.9 30 40Wisconsin -2.7 0.4 37 24East South CentralAlabama 0.2 -1.4 15 37Kentucky -0.5 -0.6 20 31Mississippi 1.3 -2.1 7 42Tennessee 0.7 1.1 13 21West North CentralIowa -1.8 1.7 28 16Kansas 1.1 1.2 9 20Minnesota -3.6 -3.9 41 48Missouri -1.1 -2.1 25 43Nebraska -0.5 -0.4 21 28North Dakota 0.0 3.2 17 6South Dakota -0.4 1.9 19 14West South CentralArkansas -0.9 -1.9 24 41Louisiana -0.8 -1.6 23 38Oklahoma 0.8 2.9 12 7Texas 0.8 2.4 11 10MountainArizona -6.1 -3.7 49 46Colorado -4.9 -1.3 46 35Idaho 2.8 1.4 4 17Montana 0.1 2.3 16 11Nevada -6.8 -6.9 51 50New Mexico 1.1 6.2 8 2Utah 8.6 9.0 1 1Wyoming -0.1 1.9 18 15PacificAlaska -1.2 -1.1 26 33California -3.7 -3.0 43 45Hawaii -2.6 2.5 35 9Oregon 1.0 3.6 10 5Washington 1.6 5.7 5 3

Mean Absolute Error 2.3Rank Correlation Coefficient 0.52



Appendix 8-1B: Moody’s Economy.com State FHFA House Price Index Forecast Accuracy


New EnglandConnecticut -2.4 1.8 47 40Maine -0.7 3.0 39 34Massachusetts -1.9 -1.6 45 48New Hampshire -2.5 0.2 49 45Rhode Island -2.4 -1.0 48 47Vermont 0.1 4.3 33 22Middle AtlanticNew Jersey -2.6 1.7 50 41New York -0.6 2.2 38 39Pennsylvania 0.7 4.4 32 21South AtlanticDelaware 0.0 3.9 35 25District Of Columbia 9.3 4.7 2 19Florida -1.1 -0.3 41 46Georgia 4.9 4.1 6 24Maryland -1.1 3.6 42 28North Carolina 3.8 6.7 12 9South Carolina 3.0 5.7 17 15Virginia -1.0 3.2 40 33West Virginia 1.8 4.3 25 23East North CentralIllinois 1.8 3.3 23 32Indiana 1.3 2.4 28 38Michigan -1.5 -2.7 43 50Ohio 0.8 0.4 31 44Wisconsin 0.9 2.9 30 35East South CentralAlabama 4.7 6.0 7 14Kentucky 1.7 3.7 26 27Mississippi 4.4 6.3 8 11Tennessee 3.5 6.0 14 13West North CentralIowa 1.7 3.4 27 29Kansas 3.1 3.9 16 26Minnesota 0.0 0.8 36 43Missouri 2.5 3.4 21 31Nebraska 1.1 2.5 29 37North Dakota 1.8 7.0 24 8South Dakota 2.5 5.4 19 16West South CentralArkansas 2.5 4.6 20 20Louisiana 5.0 6.2 5 12Oklahoma 2.2 5.4 22 18Texas 4.1 6.4 11 10MountainArizona -1.8 1.1 44 42Colorado -0.5 2.5 37 36Idaho 3.5 8.0 13 6Montana 4.4 8.9 9 3Nevada -4.6 -2.3 51 49New Mexico 4.3 8.3 10 5Utah 10.5 13.8 1 1Wyoming 5.1 11.2 4 2PacificAlaska 2.7 5.4 18 17California -2.0 -2.7 46 51Hawaii 0.0 3.4 34 30Oregon 3.3 7.1 15 7Washington 5.3 8.4 3 4




Appendix 8-1C: Moody’s Economy.com State CSI House Price Index Forecast Accuracy

Growth 2007Q4 Rank Order4/07 Forecast Actual 4/07 Forecast Actual

New EnglandConnecticut -1.1 -1.3 36 34Maine -2.0 2.0 38 17Massachusetts -3.0 -2.8 41 36New Hampshire -2.8 -3.3 40 37Rhode Island -4.2 -3.5 46 38Vermont -0.1 1.2 33 25Middle AtlanticNew Jersey -3.5 -5.7 43 42New York -3.3 1.5 42 24Pennsylvania 0.7 -2.1 30 35South AtlanticDelaware -0.9 -0.1 35 31District Of Columbia -6.9 -0.8 50 32Florida -5.6 -15.7 48 49Georgia 2.2 -1.1 20 33Maryland -3.5 -5.7 44 43North Carolina 2.6 3.3 14 11South Carolina 2.4 2.0 19 19Virginia -3.7 -9.9 45 46West Virginia 3.9 4.4 4 5East North CentralIllinois 2.2 -4.5 21 41Indiana 3.1 0.0 8 30Michigan 0.6 -10.7 32 47Ohio 2.0 -4.5 23 40Wisconsin 1.3 0.2 28 29East South CentralAlabama 4.6 2.4 2 15Kentucky 2.5 1.8 17 21Mississippi 2.9 1.2 11 26Tennessee 3.2 2.8 7 13West North CentralIowa 2.6 2.0 15 18Kansas 2.8 2.3 12 16Minnesota 0.7 -7.8 31 45Missouri 2.0 0.4 24 28Nebraska 2.5 1.0 18 27North Dakota 2.7 4.1 13 6South Dakota 2.1 3.8 22 7West South CentralArkansas 3.0 1.5 9 23Louisiana 5.2 2.8 1 14Oklahoma 3.6 6.0 5 2Texas 4.3 3.5 3 9MountainArizona -6.6 -14.1 49 48Colorado -2.7 -3.6 39 39Idaho -1.6 3.5 37 10Montana 3.2 5.1 6 3Nevada -8.3 -15.9 51 50New Mexico 1.8 2.9 26 12Utah 1.9 6.0 25 1Wyoming 2.5 5.0 16 4PacificAlaska 2.9 3.5 10 8California -5.1 -18.1 47 51Hawaii 0.9 -6.1 29 44Oregon -0.8 1.6 34 22Washington 1.4 1.9 27 20




Appendix 8-2A: Moody’s Economy.com Metro Area NAR Median Existing-House Price Forecast Accuracy


New EnglandBoston, MA -1.5 -0.4 72 58Bridgeport, CT -2.8 2.2 89 37Hartford, CT 0.9 2.1 44 39Manchester, NH -1.9 -5.0 76 90New Haven, CT -2.1 -0.5 81 59Portland, ME 2.0 -0.8 31 66Providence, RI -1.8 -2.1 75 74Springfield, MA -1.7 1.5 74 45Middle AtlanticAlbany, NY 3.6 2.4 18 36Buffalo, NY 0.6 4.3 51 19Camden, NJ 1.0 0.3 43 54Edison, NJ -6.4 -2.2 97 76Harrisburg, PA 1.2 2.0 41 41Nassau, NY -0.7 -0.6 65 63New York, NY -6.6 3.9 98 23Newark, NJ 3.7 3.0 17 33Philadelphia, PA 1.1 3.4 42 31Pittsburgh, PA 2.4 3.5 26 30Rochester, NY 0.1 3.0 57 34Syracuse, NY 2.7 4.5 25 17Trenton, NJ 12.0 6.8 2 11South AtlanticAtlanta, GA -0.5 0.0 63 55Baltimore, MD 3.2 2.1 22 38Bethesda, MD -3.8 -5.7 93 91Charlotte, NC 5.8 7.2 7 9Fort Lauderdale, FL -1.2 -1.8 68 70Greensboro, NC 1.8 2.0 35 40Greenville, SC 0.5 1.2 52 48Jacksonville, FL -2.1 -0.9 80 67Miami, FL -1.4 0.7 71 52Orlando, FL 2.7 -3.3 24 82Raleigh, NC 10.8 7.7 3 7Richmond, VA 4.5 3.7 11 27Tampa, FL 4.5 -6.2 12 93Virginia Beach, VA 2.4 3.9 27 24Washington, DC -2.2 0.8 82 50West Palm Beach, FL -4.9 -4.4 96 89Wilmington, DE -0.2 -3.1 59 80East North CentralAkron, OH -1.6 3.4 73 32Ann Arbor, MI -2.3 -9.6 83 99Chicago, IL 0.8 0.8 48 51Cincinnati, OH -1.4 -1.7 70 69Cleveland, OH 0.1 -3.3 56 83Columbus, OH -2.5 -0.5 86 61Dayton, OH 6.1 -3.0 6 78Detroit, MI -6.8 -7.5 100 97Fort Wayne, IN 4.0 -3.3 14 81Gary, IN -2.7 4.6 88 16Grand Rapids, MI -1.1 -4.0 67 86Indianapolis, IN 0.9 -0.1 46 56Lansing, MI -0.4 -7.4 61 96Madison, WI 0.9 1.7 45 43Milwaukee, WI -2.4 0.5 84 53



Appendix 8-2A: Moody’s Economy.com Metro Area NAR Median Existing-House Price Forecast Accuracy (cont.)


Peoria, IL -2.0 5.5 78 14Toledo, OH -1.3 -3.1 69 79East South CentralBirmingham, AL 0.6 -2.4 50 77Louisville, KY 2.0 -0.3 32 57Memphis, TN 2.3 -4.0 28 87Nashville, TN 2.0 3.9 30 25West North CentralDes Moines, IA 0.5 3.8 53 26Fargo, ND 1.4 3.5 40 29Kansas City, MO -0.3 -1.8 60 71Minneapolis, MN -2.5 -3.6 87 85Omaha, NE 0.6 -0.5 49 62Sioux Falls, SD 0.4 3.9 54 22St. Louis, MO -0.8 -2.2 66 75Wichita, KS 1.6 4.2 37 21West South CentralAustin, TX 4.8 6.1 9 13Dallas, TX -0.4 1.1 62 49Fort Worth, TX 0.0 -0.6 58 65Houston, TX 1.9 2.0 33 42Little Rock, AR 2.1 1.4 29 47New Orleans, LA -6.7 -7.2 99 95Oklahoma City, OK 3.4 6.4 20 12San Antonio, TX 2.9 8.2 23 3MountainAlbuquerque, NM 4.8 7.9 10 5Boise City, ID 8.8 1.5 4 46Boulder, CO 3.5 2.7 19 35Colorado Springs, CO 1.6 -0.5 36 60Denver, CO -1.9 -2.0 77 73Las Vegas, NV -2.1 -6.5 79 94Phoenix, AZ -3.0 -4.2 90 88Provo, UT 6.3 9.0 5 2Salt Lake City, UT 16.7 13.8 1 1Tucson, AZ -3.5 -0.6 91 64PacificEugene, OR 3.4 4.4 21 18Honolulu, HI -0.6 1.6 64 44Los Angeles, CA 0.3 -1.0 55 68Oakland, CA 1.9 6.9 34 10Oxnard, CA -3.7 -10.8 92 100Portland, OR 4.2 5.2 13 15Riverside, CA 0.8 -5.7 47 92Sacramento, CA -3.8 -9.5 94 98San Diego, CA -4.1 -3.4 95 84San Francisco, CA 1.6 4.2 39 20San Jose, CA 1.6 7.9 38 4Santa Ana, CA -2.4 -1.9 85 72Seattle, WA 3.9 7.4 16 8Spokane, WA 5.6 7.9 8 6Tacoma, WA 3.9 3.6 15 28




Appendix 8-2B: Moody’s Economy.com Metro Area FHFA House Price Index Forecast Accuracy


New EnglandBoston, MA -2.7 -2.5 93 92Bridgeport, CT -1.8 0.3 86 74Hartford, CT 1.3 2.7 58 50Manchester, NH -1.5 -0.6 84 82New Haven, CT -0.1 2.1 71 60Portland, ME -0.2 2.3 74 56Providence, RI -1.0 -1.3 83 89Springfield, MA -0.3 2.6 77 51Middle AtlanticAlbany, NY 3.8 5.0 23 21Buffalo, NY 5.2 3.2 11 43Camden, NJ 1.4 3.6 56 40Edison, NJ -2.3 0.1 92 77Harrisburg, PA 4.8 6.7 13 14Nassau, NY -3.5 0.5 100 72New York, NY -1.0 2.6 82 52Newark, NJ -0.6 1.9 80 62Philadelphia, PA 0.6 3.7 68 35Pittsburgh, PA 3.0 3.7 34 38Rochester, NY 3.1 2.3 31 57Syracuse, NY 3.5 3.3 28 41Trenton, NJ 0.8 1.0 64 67South AtlanticAtlanta, GA 3.0 3.1 37 44Baltimore, MD 0.6 4.4 67 27Bethesda, MD -2.7 -0.2 96 80Charlotte, NC 5.6 8.0 9 8Fort Lauderdale, FL -2.7 -2.0 95 91Greensboro, NC 2.9 3.7 38 36Greenville, SC 3.0 6.0 33 17Jacksonville, FL 1.5 3.6 55 39Miami, FL -0.2 5.9 73 18Orlando, FL -0.1 2.1 72 61Raleigh, NC 4.2 7.0 19 11Richmond, VA 3.7 6.3 25 16Tampa, FL 0.5 -0.1 70 79Virginia Beach, VA 1.0 5.3 61 20Washington, DC -2.2 0.5 91 71West Palm Beach, FL -2.0 -5.7 89 99Wilmington, DE 1.7 3.7 53 34East North CentralAkron, OH 1.8 -0.6 52 83Ann Arbor, MI 1.0 -4.9 63 96Chicago, IL 3.0 3.1 36 47Cincinnati, OH 3.1 1.4 30 66Cleveland, OH 1.1 -1.0 60 87Columbus, OH 1.6 0.6 54 70Dayton, OH 2.3 0.8 48 69Detroit, MI -0.9 -5.0 81 97Fort Wayne, IN 2.9 1.9 40 64Gary, IN 3.0 4.2 35 30Grand Rapids, MI 2.4 -1.0 47 86Indianapolis, IN 2.9 2.2 41 59Lansing, MI 0.6 -0.9 66 85Madison, WI 2.7 2.8 43 49



Appendix 8-2B: Moody’s Economy.com Metro Area FHFA House Price Index Forecast Accuracy (cont.)


Milwaukee, WI 2.1 2.5 51 53Peoria, IL 4.0 4.3 20 29Toledo, OH 1.0 -0.7 62 84East South CentralBirmingham, AL 4.2 3.9 18 32Louisville, KY 2.5 3.1 45 46Memphis, TN 4.5 2.9 15 48Nashville, TN 5.1 6.7 12 15West North CentralDes Moines, IA 2.8 3.1 42 45Fargo, ND 2.9 3.7 39 37Kansas City, MO 2.6 2.4 44 55Minneapolis, MN 0.7 -0.3 65 81Omaha, NE 2.1 1.8 50 65Sioux Falls, SD 2.5 4.7 46 23St. Louis, MO 3.0 3.3 32 42Wichita, KS 3.8 4.6 24 25West South CentralAustin, TX 5.3 10.1 10 3Dallas, TX 3.9 3.9 21 33Fort Worth, TX 4.7 4.0 14 31Houston, TX 4.4 5.4 16 19Little Rock, AR 3.6 4.6 26 26New Orleans, LA 6.6 4.3 3 28Oklahoma City, OK 3.3 4.7 29 24San Antonio, TX 5.8 9.1 7 5MountainAlbuquerque, NM 5.9 8.1 6 7Boise City, ID 3.8 7.0 22 12Boulder, CO 1.3 2.4 57 54Colorado Springs, CO 2.3 2.3 49 58Denver, CO -0.3 0.2 76 76Las Vegas, NV -3.2 -1.9 98 90Phoenix, AZ -2.1 0.3 90 73Provo, UT 8.7 15.9 2 1Salt Lake City, UT 9.3 14.6 1 2Tucson, AZ -0.4 1.9 79 63PacificEugene, OR 4.3 7.0 17 13Honolulu, HI 1.1 4.8 59 22Los Angeles, CA -0.4 1.0 78 68Oakland, CA -2.9 -4.2 97 94Oxnard, CA -1.9 -5.1 87 98Portland, OR 3.6 7.4 27 10Riverside, CA -1.5 -1.1 85 88Sacramento, CA -3.5 -7.7 99 100San Diego, CA -1.9 -4.6 88 95San Francisco, CA -0.3 0.0 75 78San Jose, CA 0.5 0.2 69 75Santa Ana, CA -2.7 -2.6 94 93Seattle, WA 5.8 9.1 8 6Spokane, WA 6.5 10.1 4 4Tacoma, WA 6.1 7.9 5 9




Appendix 8-2C: Moody’s Economy.com Metro Area CSI House Price Index Forecast AccuracyGrowth 2007 Rank Order

4/07 Forecast Actual 4/07 Forecast ActualNew EnglandBoston, MA -4.4 -3.2 88 70Bridgeport, CT -1.6 -1.0 75 60Hartford, CT 0.2 0.7 61 51Manchester, NH -3.1 -3.7 81 75New Haven, CT -1.5 -1.3 74 63Portland, ME 0.1 2.3 62 44Providence, RI -3.5 -4.3 83 78Springfield, MA -0.3 -0.9 65 58Middle AtlanticAlbany, NY 4.3 5.0 16 17Buffalo, NY 4.8 3.2 10 35Camden, NJ -0.7 -0.7 70 56Edison, NJ -3.8 -4.4 87 80Harrisburg, PA 4.4 6.7 13 11Nassau, NY -5.0 -3.4 92 71New York, NY -2.0 -3.5 77 72Newark, NJ -0.6 -3.6 68 73Philadelphia, PA 0.7 1.4 56 50Pittsburgh, PA 2.0 1.5 49 49Rochester, NY 2.7 2.3 34 43Syracuse, NY 3.3 3.3 26 33Trenton, NJ -3.5 -4.5 82 83South AtlanticAtlanta, GA 2.2 0.6 43 52Baltimore, MD -0.7 -0.9 69 57Bethesda, MD -3.8 -4.4 86 81Charlotte, NC 5.5 8.0 7 7Fort Lauderdale, FL -5.6 -9.4 96 95Greensboro, NC 2.8 3.7 31 31Greenville, SC 3.2 6.0 27 14Jacksonville, FL -1.8 -3.7 76 74Miami, FL 2.7 -0.1 35 55Orlando, FL -3.7 -6.4 84 86Raleigh, NC 4.1 7.0 17 9Richmond, VA 4.1 6.3 18 13Tampa, FL -5.5 -8.6 95 93Virginia Beach, VA 1.4 5.3 52 16Washington, DC -6.0 -7.5 98 87West Palm Beach, FL -6.6 -12.6 100 98Wilmington, DE 0.6 0.4 57 53East North CentralAkron, OH 0.3 -2.6 60 68Ann Arbor, MI -1.4 -8.0 72 89Chicago, IL 2.0 -1.4 48 64Cincinnati, OH 1.5 -1.9 51 66Cleveland, OH -0.1 -4.3 64 77Columbus, OH 0.5 -3.0 58 69Dayton, OH 0.5 -1.0 59 62Detroit, MI -1.5 -9.4 73 96Fort Wayne, IN 2.7 1.9 32 47Gary, IN 3.0 4.2 29 27Grand Rapids, MI 2.1 -0.9 47 59Indianapolis, IN 2.7 2.2 37 46Lansing, MI -0.6 -5.1 66 84Madison, WI 2.7 2.8 33 39



Appendix 8-2C: Moody’s Economy.com Metro Area CSI House Price Index Forecast Accuracy (cont.)


Milwaukee, WI 2.1 2.5 45 40Peoria, IL 3.9 4.3 20 26Toledo, OH 1.0 -4.3 55 79East South CentralBirmingham, AL 4.4 3.9 14 29Louisville, KY 2.5 3.1 39 37Memphis, TN 1.6 -1.8 50 65Nashville, TN 5.0 4.7 9 19West North CentralDes Moines, IA 2.7 3.1 36 36Fargo, ND 2.5 3.7 42 32Kansas City, MO 2.5 2.4 41 42Minneapolis, MN -0.1 -4.1 63 76Omaha, NE 2.1 1.8 44 48Sioux Falls, SD 2.5 4.7 40 20St. Louis, MO 3.0 3.3 30 34Wichita, KS 3.5 4.6 23 22West South CentralAustin, TX 5.4 10.1 8 3Dallas, TX 4.1 3.9 19 30Fort Worth, TX 4.5 4.0 12 28Houston, TX 4.7 5.4 11 15Little Rock, AR 3.6 4.6 22 23New Orleans, LA 6.5 4.3 5 25Oklahoma City, OK 3.1 4.7 28 21San Antonio, TX 5.6 9.1 6 5MountainAlbuquerque, NM 6.7 8.1 3 6Boise City, ID 3.4 7.0 25 8Boulder, CO 1.2 2.4 54 41Colorado Springs, CO 2.1 2.3 46 45Denver, CO -2.7 -2.1 78 67Las Vegas, NV -4.7 -8.0 91 90Phoenix, AZ -5.3 -8.4 94 92Provo, UT 9.2 16.0 2 1Salt Lake City, UT 9.2 14.6 1 2Tucson, AZ -4.5 -4.5 89 82PacificEugene, OR 3.7 7.0 21 10Honolulu, HI 1.3 4.8 53 18Los Angeles, CA -2.9 -6.1 79 85Oakland, CA -3.0 -8.3 80 91Oxnard, CA -5.7 -9.3 97 94Portland, OR 2.5 4.6 38 24Riverside, CA -3.8 -13.1 85 99Sacramento, CA -6.5 -14.6 99 100San Diego, CA -5.1 -9.4 93 97San Francisco, CA -0.6 0.4 67 54San Jose, CA -0.8 -1.0 71 61Santa Ana, CA -4.6 -7.7 90 88Seattle, WA 4.3 6.3 15 12Spokane, WA 6.5 10.1 4 4Tacoma, WA 3.4 3.0 24 38




Appendix 8-3A: Moody’s Economy.com Metro Area NAR Forecast AccuracyProjection of falling house prices in 2007

3/07 Forecast Actual

New EnglandBoston, MA Yes YesBridgeport, CT Yes NoHartford, CT No NoManchester, NH Yes YesNew Haven, CT Yes YesPortland, ME No YesProvidence, RI Yes YesSpringfield, MA Yes NoMiddle AtlanticAlbany, NY No NoBuffalo, NY No NoCamden, NJ No NoEdison, NJ Yes YesHarrisburg, PA No NoNassau, NY Yes YesNew York, NY Yes NoNewark, NJ No NoPhiladelphia, PA No NoPittsburgh, PA No NoRochester, NY No NoSyracuse, NY No NoTrenton, NJ No NoSouth AtlanticAtlanta, GA Yes NoBaltimore, MD No NoBethesda, MD Yes YesCharlotte, NC No NoFort Lauderdale, FL Yes YesGreensboro, NC No NoGreenville, SC No NoJacksonville, FL Yes YesMiami, FL Yes NoOrlando, FL No YesRaleigh, NC No NoRichmond, VA No NoTampa, FL No YesVirginia Beach, VA No NoWashington, DC Yes NoWest Palm Beach, FL Yes YesWilmington, DE Yes YesEast North CentralAkron, OH Yes NoAnn Arbor, MI Yes YesChicago, IL No NoCincinnati, OH Yes YesCleveland, OH No YesColumbus, OH Yes YesDayton, OH No YesDetroit, MI Yes YesFort Wayne, IN No YesGary, IN Yes NoGrand Rapids, MI Yes YesIndianapolis, IN No YesLansing, MI Yes YesMadison, WI No No



Appendix 8-3A: Moody’s Economy.com Metro Area NAR Forecast Accuracy (cont.)Projection of falling house prices in 2007


Milwaukee, WI Yes NoPeoria, IL Yes NoToledo, OH Yes YesEast South CentralBirmingham, AL No YesLouisville, KY No YesMemphis, TN No YesNashville, TN No NoWest North CentralDes Moines, IA No NoFargo, ND No NoKansas City, MO Yes YesMinneapolis, MN Yes YesOmaha, NE No YesSioux Falls, SD No NoSt. Louis, MO Yes YesWichita, KS No NoWest South CentralAustin, TX No NoDallas, TX Yes NoFort Worth, TX No YesHouston, TX No NoLittle Rock, AR No NoNew Orleans, LA Yes YesOklahoma City, OK No NoSan Antonio, TX No NoMountainAlbuquerque, NM No NoBoise City, ID No NoBoulder, CO No NoColorado Springs, CO No YesDenver, CO Yes YesLas Vegas, NV Yes YesPhoenix, AZ Yes YesProvo, UT No NoSalt Lake City, UT No NoTucson, AZ Yes YesPacificEugene, OR No NoHonolulu, HI Yes NoLos Angeles, CA No YesOakland, CA No NoOxnard, CA Yes YesPortland, OR No NoRiverside, CA No YesSacramento, CA Yes YesSan Diego, CA Yes YesSan Francisco, CA No NoSan Jose, CA No NoSanta Ana, CA Yes YesSeattle, WA No NoSpokane, WA No NoTacoma, WA No No

% of MSAs Where Price Decline Forecast Is Accurate 73%% of MSAs Where Forecast Misses a Price Decline 15%% of MSAs Where Forecast Falsely Predicts Price Decline 12%



Appendix 8-3B: Moody’s Economy.com Metro Area FHFA Forecast AccuracyProjection of falling house prices in 2007


New EnglandBoston, MA Yes YesBridgeport, CT Yes NoHartford, CT No NoManchester, NH Yes YesNew Haven, CT Yes NoPortland, ME Yes NoProvidence, RI Yes YesSpringfield, MA Yes NoMiddle AtlanticAlbany, NY No NoBuffalo, NY No NoCamden, NJ No NoEdison, NJ Yes YesHarrisburg, PA No NoNassau, NY Yes NoNew York, NY Yes NoNewark, NJ Yes NoPhiladelphia, PA No NoPittsburgh, PA No NoRochester, NY No NoSyracuse, NY No NoTrenton, NJ No NoSouth AtlanticAtlanta, GA No NoBaltimore, MD No NoBethesda, MD Yes YesCharlotte, NC No NoFort Lauderdale, FL Yes YesGreensboro, NC No NoGreenville, SC No NoJacksonville, FL No NoMiami, FL Yes NoOrlando, FL Yes NoRaleigh, NC No NoRichmond, VA No NoTampa, FL No YesVirginia Beach, VA No NoWashington, DC Yes NoWest Palm Beach, FL Yes YesWilmington, DE No NoEast North CentralAkron, OH No YesAnn Arbor, MI No YesChicago, IL No NoCincinnati, OH No NoCleveland, OH No YesColumbus, OH No NoDayton, OH No NoDetroit, MI Yes YesFort Wayne, IN No NoGary, IN No NoGrand Rapids, MI No YesIndianapolis, IN No NoLansing, MI No YesMadison, WI No No




Milwaukee, WI No NoPeoria, IL No NoToledo, OH No YesEast South CentralBirmingham, AL No NoLouisville, KY No NoMemphis, TN No NoNashville, TN No NoWest North CentralDes Moines, IA No NoFargo, ND No NoKansas City, MO No NoMinneapolis, MN No YesOmaha, NE No NoSioux Falls, SD No NoSt. Louis, MO No NoWichita, KS No NoWest South CentralAustin, TX No NoDallas, TX No NoFort Worth, TX No NoHouston, TX No NoLittle Rock, AR No NoNew Orleans, LA No NoOklahoma City, OK No NoSan Antonio, TX No NoMountainAlbuquerque, NM No NoBoise City, ID No NoBoulder, CO No NoColorado Springs, CO No NoDenver, CO Yes NoLas Vegas, NV Yes YesPhoenix, AZ Yes YesProvo, UT No NoSalt Lake City, UT No NoTucson, AZ Yes NoPacificEugene, OR No NoHonolulu, HI No NoLos Angeles, CA Yes NoOakland, CA Yes YesOxnard, CA Yes YesPortland, OR No NoRiverside, CA Yes YesSacramento, CA Yes YesSan Diego, CA Yes YesSan Francisco, CA Yes YesSan Jose, CA No YesSanta Ana, CA Yes YesSeattle, WA No NoSpokane, WA No NoTacoma, WA No No

% of MSAs Where Price Decline Forecast Is Accurate 78%% of MSAs Where Forecast Misses a Price Decline 9%% of MSAs Where Forecast Falsely Predicts Price Decline 13%

Appendix 8-3B: Moody’s Economy.com Metro Area FHFA Forecast Accuracy (cont.)Projection of falling house prices in 2007



Appendix 8-3C: Moody’s Economy.com Metro Area CSI Forecast AccuracyProjection of falling house prices in 2007


New EnglandBoston, MA Yes YesBridgeport, CT Yes YesHartford, CT No NoManchester, NH Yes YesNew Haven, CT Yes YesPortland, ME No NoProvidence, RI Yes YesSpringfield, MA Yes YesMiddle AtlanticAlbany, NY No NoBuffalo, NY No NoCamden, NJ Yes YesEdison, NJ Yes YesHarrisburg, PA No NoNassau, NY Yes YesNew York, NY Yes YesNewark, NJ Yes YesPhiladelphia, PA No NoPittsburgh, PA No NoRochester, NY No NoSyracuse, NY No NoTrenton, NJ Yes YesSouth AtlanticAtlanta, GA No NoBaltimore, MD Yes YesBethesda, MD Yes YesCharlotte, NC No NoFort Lauderdale, FL Yes YesGreensboro, NC No NoGreenville, SC No NoJacksonville, FL Yes YesMiami, FL No YesOrlando, FL Yes YesRaleigh, NC No NoRichmond, VA No NoTampa, FL Yes YesVirginia Beach, VA No NoWashington, DC Yes YesWest Palm Beach, FL Yes YesWilmington, DE No NoEast North CentralAkron, OH No YesAnn Arbor, MI Yes YesChicago, IL No YesCincinnati, OH No YesCleveland, OH Yes YesColumbus, OH No YesDayton, OH No YesDetroit, MI Yes YesFort Wayne, IN No NoGary, IN No NoGrand Rapids, MI No YesIndianapolis, IN No NoLansing, MI Yes YesMadison, WI No NoMilwaukee, WI No No



Appendix 8-3C: Moody’s Economy.com Metro Area CSI Forecast Accuracy (cont.)Projection of falling house prices in 2007


New EnglandBoston, MA Yes YesBridgeport, CT Yes YesHartford, CT No NoManchester, NH Yes YesNew Haven, CT Yes YesPortland, ME No NoProvidence, RI Yes YesSpringfield, MA Yes YesMiddle AtlanticAlbany, NY No NoBuffalo, NY No NoCamden, NJ Yes YesEdison, NJ Yes YesHarrisburg, PA No NoNassau, NY Yes YesNew York, NY Yes YesNewark, NJ Yes YesPhiladelphia, PA No NoPittsburgh, PA No NoRochester, NY No NoSyracuse, NY No NoTrenton, NJ Yes YesSouth AtlanticAtlanta, GA No NoBaltimore, MD Yes YesBethesda, MD Yes YesCharlotte, NC No NoFort Lauderdale, FL Yes YesGreensboro, NC No NoGreenville, SC No NoJacksonville, FL Yes YesMiami, FL No YesOrlando, FL Yes YesRaleigh, NC No NoRichmond, VA No NoTampa, FL Yes YesVirginia Beach, VA No NoWashington, DC Yes YesWest Palm Beach, FL Yes YesWilmington, DE No NoEast North CentralAkron, OH No YesAnn Arbor, MI Yes YesChicago, IL No YesCincinnati, OH No YesCleveland, OH Yes YesColumbus, OH No YesDayton, OH No YesDetroit, MI Yes YesFort Wayne, IN No NoGary, IN No NoGrand Rapids, MI No YesIndianapolis, IN No NoLansing, MI Yes YesMadison, WI No NoMilwaukee, WI No No



Appendix 8-4A: Moody’s Economy.com State NAR Median Existing House Price Forecast Consistency House price growth, %

2007Q1 2007Q2 2007Q3 2007Q4 2007Q1 2007Q2 2007Q3 2007Q4New EnglandConnecticut -1.0 -5.6 -8 -11.7 38 44 44 43Maine 0.0 -4.6 -5.9 -5.7 31 38 40 23Massachusetts -1.4 -4.5 -5.7 -8.9 39 37 38 35New Hampshire 0.7 -3.3 -4.8 -7.6 28 34 32 31Rhode Island -1.8 -4.4 -6.9 -12.3 42 36 42 47Vermont 0.8 -2.9 -5.3 -6.7 27 32 33 28Middle AtlanticNew Jersey -2.2 -5.5 -8.0 -9.0 43 43 46 37New York -0.7 -5.2 -5.3 -8.2 36 40 34 32Pennsylvania 1.1 -0.6 -0.4 -4.5 25 29 26 18South AtlanticDelaware -0.9 -5.4 -5.7 -8.9 37 42 39 36District Of Columbia -6.3 -8.4 -9.6 -14.9 50 49 49 48Florida -1.5 -5.8 -7.8 -15.8 40 45 45 49Georgia 2.8 2.8 2.7 -4.6 9 6 8 19Maryland -2.9 -6.7 -9.6 -12.1 44 47 48 46North Carolina 2.3 2.2 2.0 -1.3 13 13 13 7South Carolina 2.2 2.2 2.4 -2.8 15 14 10 15Virginia -4.3 -5.9 -7.1 -11.7 48 46 43 44West Virginia 4.2 3.9 3.9 -2.5 1 1 1 14East North CentralIllinois 1.1 0.6 -0.2 -7.6 24 24 25 30Indiana 2.7 2.7 2.8 -4.7 10 7 6 20Michigan 1.0 -0.2 -2.7 -8.5 26 25 28 34Ohio 2.3 2.5 1.1 -6.5 14 9 17 27Wisconsin 0.2 -0.3 0.6 -2.1 30 27 22 13East South CentralAlabama 3.8 2.9 2.9 -1.6 2 5 4 10Kentucky 1.4 1.2 0.8 -6.2 23 21 21 25Mississippi 2.1 1.6 0.9 -10.6 17 17 20 41Tennessee 2.6 2.2 2.4 -4.9 11 12 12 21West North CentralIowa 1.5 1.5 0.4 -0.6 21 19 23 4Kansas 1.7 1.9 1.4 -2.0 19 15 15 12Minnesota 0.0 -0.6 -1.4 -6.3 32 28 27 26Missouri 1.5 1.1 2.8 -5.6 22 23 7 22Nebraska 2.2 1.8 1.0 -2.0 16 16 19 11North Dakota 1.8 1.6 1.0 -0.5 18 18 18 3South Dakota 1.5 1.2 1.3 0.0 20 22 16 2West South CentralArkansas 2.9 2.5 2.4 -3.3 7 10 11 17Louisiana 3.1 1.5 1.5 -1.5 6 20 14 9Oklahoma 2.6 2.6 0.3 0.1 12 8 24 1Texas 3.1 3.1 3.1 -1.4 5 3 2 8MountainArizona -6.0 -8.6 -10.4 -11.8 49 50 50 45Colorado -3.9 -5.2 -5.6 -10.9 47 41 37 42Idaho -1.8 -4.0 -5.4 -9.6 41 35 36 40Montana 3.2 3.1 2.9 -0.7 4 4 5 5Nevada -7.3 -9.9 -12.0 -16.9 51 51 51 51New Mexico -0.5 -2.1 -4.7 -6.1 35 30 31 24Utah -0.5 -3.2 -3.6 -7.5 34 33 30 29Wyoming 3.4 2.4 2.6 -1.0 3 11 9 6PacificAlaska 2.9 3.4 3.1 -3.0 8 2 3 16California -3.8 -6.8 -8.6 -16.1 46 48 47 50Hawaii 0.3 -0.3 -5.3 -9.0 29 26 35 38Oregon -2.9 -4.8 -6.3 -9.2 45 39 41 39Washington -0.4 -2.3 -2.9 -8.5 33 31 29 33

Sum of States -1.0 -3.3 -4.5 -9.7

Rank Correlation Coefficient with 0.84 0.81 0.82Most Recent Forecast

Forecast of 2008, history through: Rank order for 2008 Forecast, history through:



Appendix 8-4B: Moody’s Economy.com State FHFA Price Index Forecast Consistency House price growth, %

2007Q1 2007Q2 2007Q3 2007Q4 2007Q1 2007Q2 2007Q3 2007Q4New EnglandConnecticut -1.9 -0.9 -2 -1.8 34 32 31 33Maine -2.6 -1.2 -2.8 0.0 37 33 34 27Massachusetts -3.8 -2.9 -4.1 -2.0 42 39 39 34New Hampshire -2.8 -2.5 -3.5 -2.4 40 37 38 37Rhode Island -2.6 -4.8 -6.5 -7.3 36 46 48 46Vermont -2.7 -1.7 -3.5 -1.7 38 35 37 32Middle AtlanticNew Jersey -4.2 -3.9 -4.7 -3.9 45 42 43 43New York -4.1 -3.8 -4.3 -2.1 43 41 40 35Pennsylvania 0.0 1.4 0.8 0.8 30 28 26 25South AtlanticDelaware -2.7 -1.4 -1.6 -0.5 39 34 29 28District Of Columbia -7.4 -7.8 -7.3 -5.2 49 49 49 44Florida -4.4 -5.8 -6.4 -9.5 46 48 47 49Georgia 1.8 2.0 2.0 1.2 22 23 21 21Maryland -5.2 -4.6 -4.6 -3.2 48 45 42 41North Carolina 2.1 2.5 2.2 2.3 18 17 17 12South Carolina 1.8 2.3 2.3 1.9 21 20 16 15Virginia -5.0 -4.6 -4.4 -3.5 47 44 41 42West Virginia 3.4 4.7 4.0 3.2 5 2 3 7East North CentralIllinois 1.6 2.3 1.7 -0.8 24 22 23 29Indiana 3.1 2.9 2.6 1.7 6 14 13 17Michigan 1.4 1.6 -3.4 -6.0 25 26 36 45Ohio 2.7 2.7 1.8 -2.5 9 16 22 38Wisconsin 1.0 1.5 1.1 0.8 26 27 25 26East South CentralAlabama 4.3 4.4 4.1 3.6 2 3 2 4Kentucky 2.3 2.3 2.2 2.3 15 21 18 11Mississippi 2.5 3.4 2.8 2.9 12 7 11 10Tennessee 3.0 3.2 3.4 2.0 7 9 7 14West North CentralIowa 2.2 2.8 2.5 1.8 17 15 14 16Kansas 2.7 2.5 2.4 2.2 10 18 15 13Minnesota 0.5 0.8 -2.3 -3.0 28 29 30 39Missouri 1.7 1.7 1.6 1.1 23 25 24 22Nebraska 2.6 2.5 2.1 1.4 11 19 20 19North Dakota 2.3 2.9 2.9 3.6 16 12 10 5South Dakota 1.8 3.6 3.4 3.7 20 4 6 3West South CentralArkansas 2.9 2.9 2.7 1.7 8 13 12 18Louisiana 4.7 3.5 2.9 3.3 1 6 9 6Oklahoma 3.9 2.9 2.2 3.1 4 11 19 8Texas 4.0 3.5 3.5 3.0 3 5 4 9MountainArizona -8.0 -12.6 -12.2 -10.8 51 51 50 50Colorado -1.6 -2.3 -2.6 -1.1 32 36 33 30Idaho -3.4 -4.5 -4.8 -3.2 41 43 44 40Montana 2.3 5.4 5.1 4.2 14 1 1 1Nevada -7.9 -12.1 -13.1 -13.2 50 50 51 51New Mexico 0.0 -0.8 -1.5 1.0 31 31 28 23Utah -1.9 -3.2 -3.0 -1.2 33 40 35 31Wyoming 2.0 3.3 3.4 3.8 19 8 5 2PacificAlaska 2.5 3.0 2.9 1.4 13 10 8 20California -4.1 -5.1 -6.3 -9.0 44 47 46 48Hawaii 0.9 1.8 -6.2 -8.7 27 24 45 47Oregon -2.5 -2.6 -2.5 -2.2 35 38 32 36Washington 0.2 0.2 -0.3 0.9 29 30 27 24

Sum of States -1.6 -1.8 -2.6 -3.1





Appendix 8-4C: Moody’s Economy.com State CSI Price Index Forecast Consistency House price growth, %

2007Q1 2007Q2 2007Q3 2007Q4 2007Q1 2007Q2 2007Q3 2007Q4New EnglandConnecticut -0.2 -1.5 -7 -10.1 31 29 42 44Maine -1.2 -2.1 -2.7 -0.2 33 33 34 20Massachusetts -1.2 -1.8 -2.0 -5.1 34 31 32 34New Hampshire -1.6 -1.7 -1.6 -5.1 37 30 31 33Rhode Island -3.3 -5.8 -9.0 -5.8 41 45 44 38Vermont -1.4 -3.9 -0.9 -1.5 36 40 26 25Middle AtlanticNew Jersey -2.9 -4.5 -9.8 -13.3 39 43 46 46New York -3.5 -3.4 -3.4 -4.7 43 39 38 32Pennsylvania 0.9 -0.8 -1.4 -3.5 29 27 30 30South AtlanticDelaware -1.2 -1.9 -1.3 -3.5 35 32 28 29District Of Columbia -4.5 -7.5 -9.8 -15.1 46 47 47 47Florida -7.8 -9.9 -15.1 -19.2 49 49 49 49Georgia 1.9 1.7 1.8 -1.2 23 22 22 24Maryland -3.6 -4.2 -9.4 -8.3 45 41 45 41North Carolina 2.5 2.3 3.1 1.0 16 19 10 12South Carolina 2.4 2.6 2.7 0.0 19 15 16 19Virginia -3.2 -4.6 -5.4 -8.8 40 44 41 42West Virginia 4.7 4.2 4.7 3.2 2 3 2 1East North CentralIllinois 2.0 0.8 -1.3 -6.1 22 25 29 39Indiana 2.9 3.1 2.9 -0.3 13 9 11 21Michigan 1.7 -3.3 -3.3 -9.2 25 38 37 43Ohio 2.3 1.1 0.8 -5.2 21 24 24 36Wisconsin 1.5 1.6 1.6 -0.6 27 23 23 22East South CentralAlabama 4.4 4.3 3.6 1.6 3 2 8 10Kentucky 2.3 2.6 2.5 1.0 20 14 17 11Mississippi 3.3 2.7 2.4 0.6 7 13 18 14Tennessee 2.6 3.2 2.7 0.4 15 6 15 16West North CentralIowa 2.8 2.5 2.8 0.7 14 17 14 13Kansas 2.5 2.6 2.9 1.7 17 16 12 8Minnesota 1.1 -2.3 -2.4 -7.6 28 35 33 40Missouri 1.7 2.0 1.9 -1.1 26 21 19 23Nebraska 2.5 2.4 1.9 0.1 18 18 21 18North Dakota 2.9 3.5 3.6 1.7 11 4 9 9South Dakota 3.6 3.0 4.2 2.5 4 11 4 4West South CentralArkansas 2.9 3.0 2.8 0.4 12 10 13 15Louisiana 3.5 2.9 3.8 2.3 6 12 7 5Oklahoma 2.9 2.2 3.9 2.6 10 20 5 3Texas 3.6 3.5 3.9 1.8 5 5 6 6MountainArizona -8.3 -11.3 -15.5 -17.9 50 50 50 48Colorado -3.5 -3.0 -3.1 -5.7 44 36 36 37Idaho -4.5 -4.4 -4.7 -4.5 47 42 40 31Montana 5.4 5.6 5.4 2.9 1 1 1 2Nevada -11.3 -14.2 -20.2 -22.2 51 51 51 51New Mexico -0.7 -1.3 -0.5 -2.0 32 28 25 26Utah -3.3 -3.0 -2.9 -3.3 42 37 35 28Wyoming 3.3 3.2 4.3 1.8 8 7 3 7PacificAlaska 3.0 3.1 1.9 0.3 9 8 20 17California -6.1 -8.1 -13.8 -20.7 48 48 48 50Hawaii 1.8 -6.6 -7.2 -12.8 24 46 43 45Oregon -2.1 -2.3 -4.2 -5.2 38 34 39 35Washington 0.2 -0.4 -1.1 -2.8 30 26 27 27

Sum of States -1.6 -2.7 -4.6 -7.9





Appendix 8-5A: Moody’s Economy.com Metro NAR Median Existing-House Price Forecast Consistency House price growth, %

2007Q1 2007Q2 2007Q3 2007Q4 2007Q1 2007Q2 2007Q3 2007Q4New EnglandBoston, MA -5.8 -6.9 -7.5 -9.3 80 80 69 64Bridgeport, CT -6.0 -4.9 -5.8 -9.0 85 60 58 61Hartford, CT -6.0 -11.6 -12.1 -12.4 83 99 93 81Manchester, NH -2.5 -4.3 -5.9 -6.9 54 57 59 48New Haven, CT -4.7 -6.2 -9.3 -13.3 68 73 78 86Portland, ME -5.6 -6.6 -6.7 -6.3 76 76 64 43Providence, RI -4.4 -6.9 -9.4 -13.0 65 79 79 84Springfield, MA -2.5 -2.1 -6.8 -5.6 53 48 65 39Middle AtlanticAlbany, NY 2.3 -1.1 0.0 -2.4 21 43 36 25Buffalo, NY 3.6 6.6 8.2 4.5 2 1 1 1Camden, NJ -3.8 -5.1 -5.6 -7.1 62 62 57 49Edison, NJ -8.6 -8.4 -9.8 -10.4 97 84 81 72Harrisburg, PA 2.2 1.8 2.3 -1.0 24 22 17 13Nassau, NY -7.9 -9.7 -10.4 -11.0 94 92 86 75New York, NY -5.5 -5.0 -8.3 -8.8 75 61 74 58Newark, NJ -3.6 -10.6 -12.2 -7.4 61 94 94 52Philadelphia, PA -2.7 -2.7 -6.1 -6.5 55 51 62 45Pittsburgh, PA 4.0 4.0 5.3 0.7 1 4 2 8Rochester, NY 3.0 3.5 2.8 1.0 10 8 12 6Syracuse, NY 3.3 4.9 1.9 1.8 5 2 19 4Trenton, NJ -0.5 -3.0 -5.4 -1.0 48 53 55 14South AtlanticAtlanta, GA 3.0 2.9 2.0 -5.1 8 11 18 38Baltimore, MD -6.2 -10.2 -12.5 -10.0 86 93 95 69Bethesda, MD -7.5 -9.4 -10.5 -12.6 91 90 87 82Charlotte, NC 1.1 2.3 3.5 -2.2 38 18 8 22Fort Lauderdale, FL -5.7 -8.0 -10.9 -13.3 79 82 90 85Greensboro, NC 2.1 2.5 1.7 -2.7 27 16 23 28Greenville, SC 1.3 1.7 4.0 0.4 35 24 6 9Jacksonville, FL -4.8 -5.8 -10.7 -11.5 70 71 89 77Miami, FL -6.0 -8.7 -10.6 -14.9 84 85 88 92Orlando, FL -7.3 -11.1 -13.1 -18.9 89 96 97 98Raleigh, NC 1.9 1.4 1.7 1.8 30 28 24 2Richmond, VA -0.3 -1.6 -1.1 -6.1 46 45 43 41Tampa, FL -6.9 -5.4 -8.7 -15.4 88 66 75 94Virginia Beach, VA -5.4 -4.0 -4.8 -10.6 74 56 53 73Washington, DC -7.8 -9.7 -10.0 -14.8 93 91 84 91West Palm Beach, FL -4.5 -5.7 -9.2 -15.0 67 70 77 93Wilmington, DE -4.9 -5.7 -7.6 -9.3 71 68 70 63East North CentralAkron, OH 2.4 -3.0 -4.2 -9.0 17 52 51 59Ann Arbor, MI 0.2 -1.4 -5.4 -8.1 42 44 56 55Chicago, IL 0.5 -0.4 -3.3 -8.3 41 40 48 56Cincinnati, OH 2.3 1.8 2.3 -6.3 22 23 16 44Cleveland, OH 2.5 -0.2 -0.2 -9.7 16 39 37 67Columbus, OH 2.2 1.7 1.5 -4.6 25 25 29 35Dayton, OH 2.4 2.4 3.2 -7.3 19 17 11 50Detroit, MI -3.6 -6.4 -7.6 -14.2 60 75 71 88Fort Wayne, IN 2.5 2.7 1.6 -8.5 15 14 25 57Gary, IN 2.6 3.6 4.0 -7.5 13 6 5 53Grand Rapids, MI 1.7 1.2 -0.5 -0.7 32 30 40 12Indianapolis, IN 3.3 3.7 1.5 -4.2 3 5 28 33Lansing, MI 0.0 -6.3 -6.7 -14.4 44 74 63 90




Appendix 8-5A: Moody’s Economy.com Metro NAR Median Existing-House Price Forecast Consistency (cont.) House price growth, %

2007Q1 2007Q2 2007Q3 2007Q4 2007Q1 2007Q2 2007Q3 2007Q4Forecast of 2008, history through: Rank order for 2008 Forecast, history through:

Madison, WI 0.1 -0.6 1.6 -3.1 43 42 26 30Milwaukee, WI -1.3 1.5 3.4 -1.0 50 27 9 16Peoria, IL 1.8 0.7 1.4 -0.3 31 36 30 11Toledo, OH 2.7 1.2 -0.9 -1.2 12 29 42 17East South CentralBirmingham, AL 2.5 2.8 4.7 -3.6 14 13 3 31Louisville, KY 1.1 1.0 1.7 -4.8 37 34 22 37Memphis, TN 2.1 2.1 0.3 -11.5 26 20 35 76Nashville, TN 2.2 2.9 2.4 -2.7 23 12 15 27West North CentralDes Moines, IA 1.4 0.0 1.9 0.1 34 38 20 10Fargo, ND 1.7 0.4 1.5 -1.9 33 37 27 20Kansas City, MO 0.6 1.6 1.8 -4.7 40 26 21 36Minneapolis, MN -0.9 -1.7 -0.3 -6.7 49 46 38 47Omaha, NE 2.1 1.2 3.3 -2.3 29 31 10 24Sioux Falls, SD 1.0 1.2 4.7 0.8 39 32 4 7St. Louis, MO 1.3 3.6 -0.3 -7.9 36 7 39 54Wichita, KS 2.1 0.9 1.0 -2.4 28 35 31 26West South CentralAustin, TX 3.0 2.7 3.8 1.4 9 15 7 5Dallas, TX 3.0 4.0 -0.8 -2.9 7 3 41 29Fort Worth, TX 3.1 3.1 0.8 -2.2 6 10 34 23Houston, TX 2.4 2.1 2.7 -1.9 20 19 14 21Little Rock, AR 2.4 3.4 2.8 -1.8 18 9 13 19New Orleans, LA -0.4 1.1 0.9 -1.6 47 33 33 18Oklahoma City, OK 2.8 -0.5 -1.7 1.8 11 41 44 3San Antonio, TX 3.3 2.1 1.0 -1.0 4 21 32 15MountainAlbuquerque, NM -1.4 -5.2 -2.7 -6.5 51 64 46 46Boise City, ID -5.0 -6.7 -8.7 -12.0 72 78 76 80Boulder, CO -3.3 -4.4 -7.1 -4.1 58 59 68 32Colorado Springs, CO -4.7 -6.0 -6.0 -10.0 69 72 61 70Denver, CO -5.9 -5.7 -6.0 -12.9 81 69 60 83Las Vegas, NV -10.0 -12.1 -14.7 -17.0 100 100 98 95Phoenix, AZ -8.8 -11.0 -12.8 -11.6 99 95 96 78Provo, UT -2.9 -3.4 -3.6 -6.0 56 55 49 40Salt Lake City, UT -3.3 -3.3 -2.5 -9.0 57 54 45 60Tucson, AZ -8.2 -8.9 -10.0 -11.7 95 87 83 79PacificEugene, OR -4.4 -5.7 -7.8 -7.4 66 67 73 51Honolulu, HI -0.3 -5.4 -7.1 -9.4 45 65 66 65Los Angeles, CA -6.6 -9.3 -12.1 -18.4 87 89 92 96Oakland, CA -5.9 -9.0 -9.5 -10.9 82 88 80 74Oxnard, CA -5.6 -7.9 -9.9 -14.3 77 81 82 89Portland, OR -5.1 -6.6 -7.7 -9.1 73 77 72 62Riverside, CA -8.8 -11.4 -16.4 -23.1 98 98 100 100Sacramento, CA -7.4 -11.3 -16.1 -22.9 90 97 99 99San Diego, CA -7.7 -8.0 -11.3 -18.8 92 83 91 97San Francisco, CA -3.4 -1.9 -2.7 -6.2 59 47 47 42San Jose, CA -5.7 -4.4 -3.6 -4.4 78 58 50 34Santa Ana, CA -8.5 -8.7 -10.3 -13.8 96 86 85 87Seattle, WA -2.1 -2.6 -4.7 -9.8 52 50 52 68Spokane, WA -3.9 -2.6 -4.8 -9.6 63 49 54 66Tacoma, WA -4.3 -5.1 -7.1 -10.2 64 63 67 71

Sum of Large MSAs -4.3 -5.4 -7.2 -10.5




Appendix 8-5B: Moody’s Economy.com Metro FHFA Price Index Forecast Consistency House price growth, %

2007Q1 2007Q2 2007Q3 2007Q4 2007Q1 2007Q2 2007Q3 2007Q4New EnglandBoston, MA -3.7 -5.1 -7.1 -2.5 76 77 77 62Bridgeport, CT -1.7 -2.8 -5.5 -1.8 62 60 69 55Hartford, CT 1.2 -0.8 -4.7 -1.4 47 51 68 50Manchester, NH -2.5 -3.6 -5.9 -2.6 68 70 73 63New Haven, CT -2.0 -3.3 -6.1 -2.5 64 66 74 61Portland, ME -1.9 -3.4 -3.9 -0.5 63 68 59 45Providence, RI -4.8 -6.5 -11.3 -7.1 85 86 90 83Springfield, MA -2.0 -2.7 -2.7 0.5 65 58 50 37Middle AtlanticAlbany, NY 2.9 2.9 2.8 2.6 19 16 21 10Buffalo, NY 4.0 4.1 3.8 2.5 4 4 6 15Camden, NJ -2.3 -3.5 -5.8 -2.8 66 69 72 67Edison, NJ -4.9 -5.6 -8.5 -4.8 86 82 82 78Harrisburg, PA 3.1 2.8 2.7 2.5 12 18 22 16Nassau, NY -7.1 -7.5 -8.3 -4.5 92 92 81 76New York, NY -4.8 -5.4 -5.6 -2.8 84 80 71 66Newark, NJ -3.3 -3.9 -6.5 -2.7 73 73 76 65Philadelphia, PA -1.1 -1.9 -2.5 -0.8 57 56 49 47Pittsburgh, PA 4.2 3.7 4.0 2.7 2 5 3 9Rochester, NY 4.1 4.4 4.5 4.4 3 2 1 1Syracuse, NY 3.8 4.4 3.2 1.1 5 1 12 33Trenton, NJ -1.3 -1.1 -6.4 -3.0 59 53 75 68South AtlanticAtlanta, GA 1.9 1.7 1.3 0.7 38 33 34 36Baltimore, MD -4.0 -3.9 -4.7 -2.5 77 72 67 60Bethesda, MD -6.2 -6.7 -7.1 -4.7 88 87 78 77Charlotte, NC 2.2 2.1 3.2 2.1 35 25 13 19Fort Lauderdale, FL -8.2 -8.0 -13.4 -13.0 96 95 94 98Greensboro, NC 2.4 1.7 1.9 2.5 30 32 27 14Greenville, SC 1.9 2.1 2.7 2.0 37 26 23 24Jacksonville, FL -6.1 -5.7 -9.4 -6.5 87 83 84 81Miami, FL -9.2 -8.7 -13.1 -11.4 97 97 93 95Orlando, FL -7.7 -7.7 -12.0 -10.6 94 94 92 93Raleigh, NC 2.2 2.4 2.9 2.5 33 21 19 11Richmond, VA 0.9 1.2 1.6 0.3 48 43 28 38Tampa, FL -4.7 -5.4 -10.0 -7.2 83 79 87 84Virginia Beach, VA -4.2 -4.2 -4.1 -1.7 80 74 63 53Washington, DC -7.3 -7.4 -7.7 -6.6 93 91 80 82West Palm Beach, FL -4.6 -5.7 -13.8 -12.3 82 84 96 97Wilmington, DE -1.4 -1.0 -1.7 -0.5 60 52 46 43East North CentralAkron, OH 1.7 1.7 -3.6 -3.5 41 34 56 71Ann Arbor, MI 1.7 0.6 -3.1 -3.3 42 45 54 70Chicago, IL 2.2 1.7 -1.5 -1.5 34 36 44 51Cincinnati, OH 2.6 1.8 0.0 -0.5 25 30 39 46Cleveland, OH 2.6 1.8 -4.2 -4.0 26 31 65 74Columbus, OH 2.5 1.6 -3.4 -3.0 28 39 55 69Dayton, OH 2.2 1.7 -3.1 -2.7 32 35 51 64Detroit, MI 0.4 -7.6 -8.5 -11.2 52 93 83 94Fort Wayne, IN 2.7 1.5 1.2 -0.5 22 41 35 44Gary, IN 2.9 3.5 3.2 2.8 16 6 14 6Grand Rapids, MI 3.4 2.5 0.7 0.1 6 20 37 41Indianapolis, IN 2.9 2.3 2.5 1.9 17 22 25 26Lansing, MI 1.3 -3.0 -4.4 -5.6 45 61 66 79Madison, WI 1.3 1.0 1.4 0.9 46 44 31 35Milwaukee, WI 0.5 0.3 0.1 0.0 50 48 38 42




Appendix 8-5B: Moody’s Economy.com Metro FHFA Price Index Forecast Consistency (cont.) House price growth, %


Peoria, IL 2.9 2.9 3.3 2.2 18 15 10 17Toledo, OH 3.1 2.2 -3.1 -3.7 13 23 53 72East South CentralBirmingham, AL 4.6 4.2 3.6 3.1 1 3 7 3Louisville, KY 2.1 2.1 2.0 2.0 36 24 26 21Memphis, TN 3.0 3.2 3.1 1.6 15 9 17 29Nashville, TN 3.0 3.2 3.8 2.0 14 8 4 22West North CentralDes Moines, IA 1.8 1.6 1.6 1.3 40 37 29 31Fargo, ND 2.4 1.9 1.6 2.1 29 27 30 18Kansas City, MO 1.5 1.3 1.0 0.3 44 42 36 39Minneapolis, MN 0.3 -3.4 -3.8 -4.0 54 67 57 73Omaha, NE 2.5 1.9 1.4 1.1 27 29 32 34Sioux Falls, SD 2.8 2.6 2.6 2.7 20 19 24 8St. Louis, MO 1.6 1.5 1.3 1.2 43 40 33 32Wichita, KS 2.7 2.8 3.3 2.9 24 17 9 4West South CentralAustin, TX 2.7 3.2 3.5 2.5 23 11 8 12Dallas, TX 3.2 3.4 3.3 2.1 7 7 11 20Fort Worth, TX 3.2 3.0 4.0 2.8 11 14 2 7Houston, TX 3.2 3.1 3.1 2.9 9 12 15 5Little Rock, AR 3.2 3.0 2.9 2.0 10 13 20 23New Orleans, LA 2.8 1.9 3.0 1.8 21 28 18 27Oklahoma City, OK 2.3 1.6 3.1 2.5 31 38 16 13San Antonio, TX 3.2 3.2 3.8 3.5 8 10 5 2MountainAlbuquerque, NM -1.2 -1.9 -1.6 0.2 58 57 45 40Boise City, ID -6.5 -7.0 -7.5 -6.0 91 89 79 80Boulder, CO 0.6 0.4 -0.4 1.7 49 46 42 28Colorado Springs, CO -1.0 -1.5 -3.1 -1.7 56 54 52 52Denver, CO -2.9 -3.2 -4.0 -1.7 69 64 60 54Las Vegas, NV -12.3 -13.4 -15.9 -13.6 99 100 99 99Phoenix, AZ -13.8 -13.4 -14.0 -12.1 100 99 97 96Provo, UT -4.4 -3.7 -3.8 -2.1 81 71 58 57Salt Lake City, UT -3.4 -3.0 -2.4 -1.0 75 62 48 48Tucson, AZ -10.6 -10.2 -10.8 -7.6 98 98 89 86PacificEugene, OR -1.4 -0.7 -1.8 -1.3 61 50 47 49Honolulu, HI 1.8 -6.2 -5.5 -8.1 39 85 70 90Los Angeles, CA -6.4 -7.0 -10.2 -8.0 89 90 88 89Oakland, CA -4.2 -5.5 -9.8 -7.4 79 81 86 85Oxnard, CA -4.0 -5.3 -9.4 -7.7 78 78 85 88Portland, OR -3.3 -3.1 -4.1 -2.4 74 63 64 59Riverside, CA -8.1 -8.4 -16.4 -13.6 95 96 100 100Sacramento, CA -3.1 -5.0 -13.6 -10.1 72 76 95 91San Diego, CA -3.0 -4.5 -11.9 -7.7 71 75 91 87San Francisco, CA -0.7 -1.7 -1.2 -1.9 55 55 43 56San Jose, CA -2.9 -3.2 -4.0 -4.1 70 65 62 75Santa Ana, CA -6.5 -7.0 -14.6 -10.1 90 88 98 92Seattle, WA 0.5 0.3 0.0 1.4 51 47 40 30Spokane, WA 0.3 0.1 0.0 1.9 53 49 41 25Tacoma, WA -2.4 -2.7 -4.0 -2.3 67 59 61 58

Sum of Large MSAs -2.7 -3.3 -5.4 -4.0




Appendix 8-5C: Moody’s Economy.com Metro CSI Price Index Forecast Consistency House price growth, %

2007Q1 2007Q2 2007Q3 2007Q4 2007Q1 2007Q2 2007Q3 2007Q4New EnglandBoston, MA -2.0 -2.7 -2.6 -6.1 67 63 58 61Bridgeport, CT -1.7 -3.4 -6.8 -9.0 65 70 72 74Hartford, CT 2.1 -0.2 -6.5 -9.6 33 50 71 76Manchester, NH -2.1 -2.3 -2.0 -4.7 68 60 52 55New Haven, CT -1.4 -2.2 -7.2 -10.1 61 59 74 80Portland, ME -1.9 -3.3 -3.8 -0.6 66 69 64 37Providence, RI -3.3 -5.8 -9.1 -5.8 74 80 81 59Springfield, MA -0.5 -2.1 -2.3 -5.3 56 58 57 58Middle AtlanticAlbany, NY 2.9 2.9 2.5 3.2 17 13 21 3Buffalo, NY 4.0 4.0 3.7 2.0 3 4 3 19Camden, NJ -1.4 -1.4 -8.4 -12.1 60 54 79 82Edison, NJ -3.3 -4.8 -10.6 -14.1 75 75 86 86Harrisburg, PA 3.1 2.7 2.5 2.2 12 16 19 16Nassau, NY -6.3 -7.1 -8.0 -9.9 88 87 76 78New York, NY -4.9 -5.3 -5.6 -9.9 83 79 69 79Newark, NJ -2.7 -5.1 -10.2 -13.8 71 78 84 85Philadelphia, PA -1.7 -3.0 -2.1 -3.8 64 65 55 51Pittsburgh, PA 3.1 2.6 3.2 0.4 13 17 9 33Rochester, NY 4.1 4.2 4.3 4.2 2 1 1 1Syracuse, NY 3.8 4.2 3.1 1.2 4 3 13 28Trenton, NJ -1.5 -2.3 -6.9 -6.3 63 61 73 64South AtlanticAtlanta, GA 1.6 1.5 1.6 -2.0 42 35 30 44Baltimore, MD -3.1 -3.5 -8.8 -7.3 73 71 80 68Bethesda, MD -4.9 -4.8 -9.3 -7.3 84 76 82 69Charlotte, NC 2.2 2.1 3.2 1.8 30 25 11 23Fort Lauderdale, FL -8.5 -11.8 -16.8 -23.6 96 96 95 99Greensboro, NC 2.4 1.9 1.9 2.5 26 26 26 11Greenville, SC 1.9 2.1 2.5 2.3 37 24 20 14Jacksonville, FL -6.1 -6.1 -10.3 -9.6 87 83 85 75Miami, FL -10.1 -12.1 -19.3 -19.0 99 97 99 91Orlando, FL -8.4 -10.3 -17.3 -20.2 95 94 97 95Raleigh, NC 2.2 2.4 3.0 2.4 29 21 16 12Richmond, VA 0.9 1.1 1.7 0.6 48 39 27 32Tampa, FL -8.0 -9.4 -12.8 -16.5 94 93 90 87Virginia Beach, VA -4.2 -4.3 -4.3 -1.5 80 74 67 40Washington, DC -4.9 -7.3 -8.3 -11.8 85 88 78 81West Palm Beach, FL -7.2 -10.9 -15.1 -21.9 92 95 92 97Wilmington, DE -1.1 -1.4 -0.7 -4.1 59 52 51 52East North CentralAkron, OH 2.3 1.6 1.6 -3.4 27 31 28 49Ann Arbor, MI 1.1 0.2 0.0 -4.6 47 48 45 54Chicago, IL 1.8 0.5 -2.1 -7.5 39 44 54 70Cincinnati, OH 2.1 1.5 0.9 -4.9 32 36 39 57Cleveland, OH 2.0 1.3 0.2 -6.6 35 38 43 65Columbus, OH 1.9 0.6 1.6 -4.8 38 42 29 56Dayton, OH 2.1 0.6 0.2 -3.7 34 41 44 50Detroit, MI -0.3 -7.6 -8.2 -12.5 55 89 77 83Fort Wayne, IN 2.7 1.6 1.2 -0.7 20 33 36 38Gary, IN 2.9 3.5 2.9 2.9 14 5 17 6Grand Rapids, MI 3.4 2.6 0.6 -0.3 5 18 41 36Indianapolis, IN 2.9 2.4 2.3 1.8 15 22 22 25Lansing, MI 1.1 -1.9 -2.1 -6.9 46 56 53 67Madison, WI 1.3 1.0 1.1 0.9 45 40 37 31




Appendix 8-5C: Moody’s Economy.com Metro CSI Price Index Forecast Consistency (cont.) House price growth, %


Milwaukee, WI 0.5 0.3 -0.2 -0.1 51 46 47 35Peoria, IL 2.9 3.0 3.3 2.1 16 10 8 17Toledo, OH 3.3 0.5 0.2 -4.3 6 43 42 53East South CentralBirmingham, AL 4.6 4.2 3.7 3.2 1 2 5 4Louisville, KY 2.1 2.1 1.9 2.1 31 23 24 18Memphis, TN 2.4 3.0 1.9 -1.8 25 12 25 42Nashville, TN 1.9 2.4 1.6 -2.4 36 20 31 47West North CentralDes Moines, IA 1.8 1.6 1.5 1.1 41 30 32 29Fargo, ND 2.4 1.8 1.3 1.8 24 28 34 24Kansas City, MO 1.5 1.4 1.0 0.1 44 37 38 34Minneapolis, MN 0.6 -3.0 -3.1 -6.1 49 66 60 63Omaha, NE 2.5 1.8 1.2 1.3 23 29 35 27Sioux Falls, SD 2.8 2.4 2.3 2.7 18 19 23 8St. Louis, MO 1.6 1.6 1.4 1.1 43 32 33 30Wichita, KS 2.7 2.7 3.3 2.8 22 15 7 7West South CentralAustin, TX 2.7 3.0 3.4 2.6 21 9 6 10Dallas, TX 3.2 3.4 3.2 2.2 7 6 10 15Fort Worth, TX 3.2 2.9 3.9 2.7 11 14 2 9Houston, TX 3.2 3.1 3.2 3.2 9 7 12 5Little Rock, AR 3.2 3.0 2.8 1.9 10 11 18 21New Orleans, LA 2.8 1.9 3.1 1.6 19 27 14 26Oklahoma City, OK 2.3 1.5 3.0 2.4 28 34 15 13San Antonio, TX 3.2 3.1 3.7 3.4 8 8 4 2MountainAlbuquerque, NM -1.0 -2.1 -0.3 -2.0 58 57 48 45Boise City, ID -6.5 -6.9 -7.8 -6.1 90 86 75 62Boulder, CO 0.6 0.4 -0.5 2.0 50 45 50 20Colorado Springs, CO -1.0 -1.4 -3.2 -1.9 57 53 61 43Denver, CO -5.1 -3.8 -3.4 -7.7 86 73 62 73Las Vegas, NV -11.3 -14.6 -21.2 -23.6 100 100 100 98Phoenix, AZ -9.1 -12.9 -17.1 -19.4 97 99 96 92Provo, UT -4.4 -3.8 -4.0 -2.2 81 72 66 46Salt Lake City, UT -3.4 -3.2 -2.6 -1.2 76 68 59 39Tucson, AZ -6.3 -8.5 -12.9 -13.3 89 91 91 84PacificEugene, OR -1.4 -0.7 -2.2 -1.7 62 51 56 41Honolulu, HI 1.8 -6.1 -5.9 -7.6 40 81 70 71Los Angeles, CA -7.5 -8.9 -15.4 -19.7 93 92 93 94Oakland, CA -4.0 -6.3 -12.3 -18.1 78 84 88 90Oxnard, CA -4.4 -6.1 -9.5 -19.7 82 82 83 93Portland, OR -2.5 -2.7 -4.4 -6.1 69 64 68 60Riverside, CA -9.3 -12.5 -17.6 -29.9 98 98 98 100Sacramento, CA -4.1 -6.5 -11.4 -21.4 79 85 87 96San Diego, CA -3.4 -4.9 -12.6 -16.6 77 77 89 88San Francisco, CA 0.1 -1.5 0.8 -7.7 54 55 40 72San Jose, CA -2.8 -3.1 -3.8 -9.6 72 67 63 77Santa Ana, CA -6.7 -8.1 -16.2 -18.0 91 90 94 89Seattle, WA 0.4 0.2 0.0 -3.3 52 47 46 48Spokane, WA 0.3 0.0 -0.4 1.9 53 49 49 22Tacoma, WA -2.6 -2.7 -3.8 -6.7 70 62 65 66

Sum of Large MSAs -2.7 -3.9 -6.3 -9.3




Appendix 9-1: Metro Area House Price OutlookPeak-to-Trough Rank Peak to 2008Q3 Peak Trough Near-Term Outlook (a) Long-Term Outlook (b)

% change Worst % change Quarter QuarterCorrection = 369

United States -36.2 -21.3 2006Q1 2009Q4 -9.4 -0.3

Abilene, TX -2.2 75 -0.3 2007Q4 2010Q1 -0.8 0.4Akron, OH -9.0 210 -7.4 2006Q1 2009Q4 -0.2 2.5Albany, GA -5.5 161 -3.1 2008Q1 2009Q4 -0.9 0.6Albany-Schenectady-Troy, NY -2.2 77 -1.5 2008Q1 2009Q3 0.5 1.8Albuquerque, NM -13.5 245 -2.1 2008Q1 2010Q3 -6.0 0.6Alexandria, LA -2.7 100 -1.7 2008Q1 2009Q4 -0.3 0.6Allentown-Bethlehem-Easton, PA-NJ -9.3 218 -3.0 2007Q2 2010Q1 -2.9 1.1Altoona, PA na na na na na 0.6 1.2Amarillo, TX -1.9 65 -0.7 2008Q1 2009Q4 -0.4 0.4Ames, IA -4.7 143 -3.0 2008Q1 2009Q4 -0.6 0.9Anchorage, AK -0.8 23 -0.4 2007Q2 2007Q3 1.8 2.7Anderson, IN -8.3 203 -3.5 2005Q3 2006Q3 0.7 2.2Anderson, SC -3.2 113 -1.7 2008Q1 2009Q2 -0.2 0.8Ann Arbor, MI -24.7 286 -22.0 2005Q4 2009Q4 -1.1 2.4Anniston-Oxford, AL -0.9 25 0.0 2008Q3 2009Q2 0.2 1.1Appleton, WI -7.7 195 -5.9 2008Q2 2009Q4 -0.4 1.5Asheville, NC -9.7 223 -0.2 2008Q2 2010Q4 -4.6 -1.9Athens-Clarke County, GA -7.0 187 -3.5 2008Q2 2010Q3 -1.9 -0.7Atlanta-Sandy Springs-Marietta, GA -14.1 248 -10.1 2007Q2 2009Q4 -1.9 0.5Atlantic City, NJ -37.0 319 -16.0 2006Q2 2010Q4 -13.4 -4.3Auburn-Opelika, AL -1.2 45 0.0 2008Q3 2009Q3 -0.2 0.7Augusta-Richmond County, GA-SC -2.0 67 0.0 2008Q3 2010Q3 -1.0 0.5Austin-Round Rock, TX -1.3 47 0.0 2008Q3 2009Q4 -0.5 -0.1Bakersfield, CA -55.5 348 -44.7 2006Q2 2009Q4 -7.6 0.5Baltimore-Towson, MD -31.6 309 -10.0 2007Q1 2010Q3 -12.8 -1.8Bangor, ME -4.0 130 -1.0 2008Q2 2009Q4 -0.7 2.2Barnstable Town, MA -25.1 287 -11.6 2005Q4 2010Q1 -7.4 1.0Baton Rouge, LA -2.0 69 -0.3 2008Q2 2010Q1 -0.8 0.0Battle Creek, MI -11.0 232 -7.3 2006Q3 2010Q1 -1.7 1.8Bay City, MI -13.9 247 -10.6 2006Q1 2009Q4 -1.4 0.9Beaumont-Port Arthur, TX -2.6 89 -1.3 2008Q2 2009Q4 -0.5 0.6Bellingham, WA -11.6 234 -2.7 2008Q1 2009Q4 -3.8 0.7Bend, OR -23.7 284 -12.2 2007Q1 2009Q4 -6.3 -0.4Bethesda-Frederick-Rockville, MD -30.0 301 -16.6 2006Q2 2010Q1 -7.9 -0.2Billings, MT -2.8 101 -0.4 2008Q1 2009Q3 0.2 3.0Binghamton, NY na na na na na 1.2 1.8Birmingham-Hoover, AL -1.1 39 -0.3 2008Q1 2009Q2 0.6 1.5Bismarck, ND -4.8 146 -2.4 2007Q4 2010Q1 -1.1 0.6Blacksburg-Christiansburg-Radford, VA -2.6 93 -0.3 2008Q1 2009Q4 -0.8 0.2Bloomington, IN -2.0 68 -1.3 2008Q2 2009Q3 0.3 1.6Bloomington-Normal, IL -1.0 31 -0.2 2008Q2 2009Q3 0.4 1.8Boise City-Nampa, ID -25.2 288 -6.0 2007Q2 2010Q2 -10.8 -3.1Boston-Quincy, MA -26.1 291 -11.2 2005Q3 2010Q2 -8.5 -0.1Boulder, CO -2.4 80 0.0 2008Q3 2009Q4 0.1 2.6Bowling Green, KY -2.1 72 -2.0 2008Q2 2009Q2 1.4 2.5Bradenton-Sarasota-Venice, FL -62.1 358 -38.2 2006Q1 2010Q3 -21.7 -3.8Bremerton-Silverdale, WA -9.0 212 -5.5 2007Q4 2009Q4 -1.4 4.2Bridgeport-Stamford-Norwalk, CT -21.6 276 -6.7 2006Q1 2010Q2 -8.1 -0.3Brownsville-Harlingen, TX -3.9 126 -3.0 2008Q1 2009Q2 1.1 3.1Brunswick, GA -3.1 112 -1.8 2008Q2 2009Q2 -0.1 0.8Buffalo-Niagara Falls, NY na na na na na 1.3 2.5Burlington, NC -3.9 128 -2.3 2007Q2 2009Q3 0.2 2.0Burlington-South Burlington, VT -18.9 266 -1.8 2007Q4 2010Q4 -9.1 -2.1Cambridge-Newton-Framingham, MA -20.3 271 -8.6 2005Q2 2009Q4 -6.0 1.3Camden, NJ -26.7 294 -8.9 2006Q3 2010Q2 -10.2 -1.0Canton-Massillon, OH -9.0 211 -7.4 2006Q1 2009Q4 -0.3 2.4Cape Coral-Fort Myers, FL -66.4 363 -48.6 2006Q1 2010Q2 -18.9 -0.2Carson City, NV -30.6 303 -15.4 2006Q1 2009Q4 -8.4 0.4Casper, WY -4.5 140 -2.3 2008Q2 2010Q1 -0.9 0.8Cedar Rapids, IA -1.0 34 -0.2 2007Q4 2009Q3 0.2 1.4Champaign-Urbana, IL -1.0 33 0.0 2008Q3 2009Q3 0.1 1.2Charleston, WV -2.7 96 -2.6 2008Q2 2009Q2 0.5 1.6Charleston-North Charleston-Summerville, SC -10.3 227 -8.7 2007Q1 2009Q2 0.9 3.2Charlotte-Gastonia-Concord, NC-SC -6.0 171 -3.6 2007Q3 2009Q3 -0.5 1.4Charlottesville, VA -5.5 163 -2.3 2007Q4 2009Q4 -1.4 -0.2Chattanooga, TN-GA -2.3 79 -1.1 2008Q1 2009Q2 0.0 0.7Cheyenne, WY -6.6 181 -0.9 2008Q2 2009Q4 -1.5 2.2Chicago-Naperville-Joliet, IL -17.1 261 -13.3 2007Q1 2010Q1 -1.9 1.3Chico, CA -36.2 317 -23.4 2005Q4 2009Q4 -6.8 -0.8Cincinnati-Middletown, OH-KY-IN -8.3 204 -6.1 2006Q1 2009Q4 -0.7 1.9Clarksville, TN-KY -1.1 36 0.0 2008Q3 2009Q2 0.7 2.0Cleveland, TN -4.4 137 -2.0 2008Q1 2010Q1 -1.2 -0.3Cleveland-Elyria-Mentor, OH -13.4 244 -12.0 2005Q4 2009Q4 -0.1 2.8Coeur d'Alene, ID -20.2 268 -4.4 2007Q3 2010Q2 -8.6 -2.3College Station-Bryan, TX na na na na na 0.5 1.1Colorado Springs, CO -7.9 196 -3.3 2008Q1 2009Q4 -1.1 3.2Columbia, MO -1.5 52 -0.3 2008Q1 2008Q2 0.0 0.8Columbia, SC -0.5 12 0.0 2008Q3 2009Q2 0.8 1.4Columbus, GA-AL -3.5 118 -2.2 2008Q1 2009Q2 0.5 1.2Columbus, IN -5.8 167 -4.8 2008Q2 2009Q3 0.3 1.9Columbus, OH -7.9 196 -6.6 2006Q2 2009Q4 -0.1 2.2Corpus Christi, TX -1.6 58 -0.8 2008Q2 2009Q2 0.5 2.1Corvallis, OR -6.7 183 -1.1 2007Q4 2009Q4 -1.1 3.1Cumberland, MD-WV -8.6 207 0.0 2008Q3 2010Q1 -4.0 0.4Dallas-Plano-Irving, TX -1.1 39 0.0 2008Q3 2009Q3 -0.2 0.7Dalton, GA -9.3 217 -5.1 2008Q1 2010Q2 -2.2 -0.7Danville, IL -9.1 213 -7.5 2007Q3 2009Q3 -0.1 1.9Danville, VA -8.1 200 -6.6 2008Q2 2009Q2 0.2 2.2Davenport-Moline-Rock Island, IA-IL -3.5 120 -2.4 2008Q1 2009Q4 0.0 1.6Dayton, OH -5.8 166 -4.7 2006Q1 2009Q4 0.2 2.7Decatur, AL -1.2 44 -0.5 2008Q2 2009Q3 0.1 1.2Decatur, IL na na na na na 1.7 3.1Deltona-Daytona Beach-Ormond Beach, FL -55.3 347 -30.4 2006Q2 2010Q3 -19.9 -4.6Denver-Aurora-Broomfield, CO -18.9 266 -7.1 2006Q1 2010Q2 -6.6 0.3Des Moines-West Des Moines, IA -4.9 149 -3.0 2008Q1 2009Q4 -0.7 0.6Detroit-Livonia-Dearborn, MI -36.6 318 -31.4 2006Q1 2010Q1 -3.4 2.0Dothan, AL -3.1 111 -0.1 2007Q4 2008Q2 0.2 1.3

Annualized % change



Appendix 9-1: Metro Area House Price Outlook (cont.)Peak-to-Trough Rank Peak to 2008Q3 Peak Trough Near-Term Outlook (a) Long-Term Outlook (b)


Annualized % change

Dover, DE -12.0 237 -2.0 2008Q1 2010Q3 -5.3 -0.8Dubuque, IA -3.7 121 -2.9 2008Q2 2009Q4 0.0 1.1Duluth, MN-WI -1.3 48 -1.3 2008Q1 2008Q3 2.1 3.8Durham-Chapel Hill, NC -1.9 64 -1.1 2008Q2 2009Q2 -0.1 0.5Eau Claire, WI -2.6 89 -1.4 2008Q1 2009Q4 -0.2 1.0Edison-New Brunswick, NJ -28.0 297 -11.8 2006Q2 2010Q3 -9.7 -1.1El Centro, CA -56.6 350 -42.5 2006Q2 2009Q4 -10.7 -1.0El Paso, TX -2.7 96 -1.0 2008Q1 2009Q4 -0.7 0.3Elizabethtown, KY -6.2 175 -3.9 2008Q2 2009Q4 -0.9 0.4Elkhart-Goshen, IN -6.0 172 -3.8 2008Q2 2010Q1 -0.8 0.6Elmira, NY -7.0 185 -6.6 2007Q2 2008Q1 2.0 3.6Erie, PA -0.4 7 -0.4 2008Q2 2008Q3 0.7 1.8Eugene-Springfield, OR -17.1 262 -4.1 2008Q1 2010Q3 -7.0 0.2Evansville, IN-KY -1.9 63 -0.8 2008Q1 2009Q3 0.3 2.0Fairbanks, AK -6.5 178 -6.5 2008Q1 2009Q2 1.8 1.8Fargo, ND-MN -3.4 116 -0.8 2008Q1 2010Q1 -1.2 0.4Farmington, NM -8.1 201 -2.9 2008Q1 2009Q4 -2.1 1.9Fayetteville, NC -1.0 30 0.0 2008Q3 2009Q2 0.1 1.0Fayetteville-Springdale-Rogers, AR-MO -8.9 209 -5.0 2007Q2 2010Q2 -2.0 -0.3Flagstaff, AZ -28.3 299 -11.0 2006Q2 2010Q2 -10.0 0.3Flint, MI -23.7 282 -20.3 2005Q3 2010Q1 -1.7 1.2Florence, SC -0.7 18 0.0 2008Q2 2009Q2 0.4 1.0Florence-Muscle Shoals, AL -0.1 2 0.0 2008Q3 2008Q4 1.5 2.1Fond du Lac, WI -2.9 104 -1.7 2008Q1 2009Q4 0.0 1.4Fort Collins-Loveland, CO -2.1 74 -1.3 2007Q4 2009Q1 1.7 4.1Fort Lauderdale-Pompano Beach-Deerfield Beach, FL -64.4 361 -36.4 2006Q2 2010Q4 -24.9 -7.4Fort Smith, AR-OK -0.6 15 0.0 2008Q3 2009Q1 0.4 1.4Fort Walton Beach-Crestview-Destin, FL -55.0 346 -28.4 2005Q4 2010Q3 -20.7 -3.8Fort Wayne, IN -2.5 87 -0.2 2007Q1 2007Q4 1.0 2.3Fort Worth-Arlington, TX -1.1 35 -0.3 2008Q2 2009Q3 0.1 0.9Fresno, CA -53.6 342 -39.7 2006Q1 2009Q4 -11.7 -2.7Gadsden, AL -1.6 57 0.0 2008Q3 2009Q3 -0.5 0.5Gainesville, FL -39.4 321 -10.1 2007Q1 2011Q2 -17.2 -6.0Gainesville, GA -12.3 239 -9.1 2007Q1 2010Q4 -1.8 0.0Gary, IN -3.7 123 -2.1 2008Q1 2009Q4 -0.3 1.2Glens Falls, NY -3.4 115 -0.5 2008Q1 2009Q4 0.9 4.3Goldsboro, NC -4.2 134 -2.8 2008Q2 2009Q2 -0.1 1.2Grand Forks, ND-MN -4.1 131 -2.9 2007Q4 2009Q4 0.1 2.2Grand Junction, CO -12.2 238 0.0 2008Q3 2010Q4 -6.2 -1.7Grand Rapids-Wyoming, MI -10.7 229 -8.9 2005Q3 2009Q4 -0.2 2.0Great Falls, MT -4.4 136 -0.6 2008Q1 2009Q4 -1.1 2.1Greeley, CO -14.4 249 -10.0 2006Q1 2009Q4 -1.6 1.6Green Bay, WI -6.7 182 -2.1 2008Q1 2009Q4 -1.5 1.3Greensboro-High Point, NC -1.2 43 -0.2 2008Q2 2009Q2 0.3 1.2Greenville, NC -1.1 42 0.0 2008Q3 2009Q2 0.0 0.9Greenville-Mauldin-Easley, SC -0.8 21 -0.3 2008Q2 2009Q2 0.3 0.7Gulfport-Biloxi, MS -7.7 194 -4.0 2008Q1 2010Q2 -2.0 0.3Hagerstown-Martinsburg, MD-WV -20.3 269 -8.9 2007Q1 2010Q3 -6.5 -1.7Hanford-Corcoran, CA -43.8 325 -30.0 2006Q2 2009Q4 -8.2 -0.7Harrisburg-Carlisle, PA -2.6 93 -1.1 2008Q1 2009Q4 -0.5 0.8Harrisonburg, VA -8.5 205 -5.0 2008Q2 2009Q4 -1.6 0.2Hartford-West Hartford-East Hartford, CT -16.8 259 -5.3 2007Q1 2010Q2 -5.9 0.9Hattiesburg, MS -4.9 150 -3.0 2008Q2 2009Q4 -0.7 0.5Hickory-Lenoir-Morganton, NC -1.1 39 0.0 2008Q3 2009Q2 -0.1 0.4Hinesville-Fort Stewart, GA -8.8 208 -6.4 2008Q2 2009Q4 -1.1 0.1Holland-Grand Haven, MI -9.9 225 -6.0 2005Q3 2010Q1 -2.0 -0.2Honolulu, HI -24.2 285 -1.9 2008Q1 2010Q2 -11.8 -3.5Hot Springs, AR -2.1 72 -1.3 2008Q2 2009Q2 0.1 1.1Houma-Bayou Cane-Thibodaux, LA -3.0 109 -1.0 2008Q2 2010Q1 -0.9 0.1Houston-Sugar Land-Baytown, TX -0.2 5 0.0 2008Q3 2009Q3 0.3 0.7Huntington-Ashland, WV-KY-OH -1.5 55 -0.8 2008Q1 2009Q4 -0.2 0.6Huntsville, AL -1.0 27 0.0 2008Q3 2009Q3 0.2 1.5Idaho Falls, ID -8.5 206 -2.6 2008Q1 2010Q1 -2.5 1.7Indianapolis-Carmel, IN -1.5 53 -0.7 2008Q1 2009Q3 0.5 1.9Iowa City, IA -5.8 167 -3.5 2008Q1 2010Q1 -1.0 0.3Ithaca, NY na na na na na 1.3 1.9Jackson, MI -20.3 270 -16.3 2005Q4 2010Q1 -2.1 0.4Jackson, MS -3.5 117 -2.4 2008Q1 2009Q2 0.5 1.8Jackson, TN -5.7 165 -3.9 2007Q2 2009Q3 -0.3 1.2Jacksonville, FL -45.9 329 -18.7 2006Q2 2011Q1 -18.1 -5.0Jacksonville, NC -0.9 26 0.0 2008Q3 2009Q1 0.0 0.6Janesville, WI -6.3 177 -3.9 2008Q1 2010Q1 -1.0 0.3Jefferson City, MO -0.7 17 -0.4 2008Q2 2009Q3 0.5 1.7Johnson City, TN -2.7 95 -1.2 2007Q4 2009Q3 -0.1 1.1Johnstown, PA -1.5 54 -1.5 2008Q2 2008Q3 2.1 2.9Jonesboro, AR na na na na na 1.2 1.9Joplin, MO -3.9 125 -2.7 2008Q1 2009Q4 -0.2 1.2Kalamazoo-Portage, MI -7.0 185 -5.1 2008Q1 2009Q4 -0.4 1.5Kankakee-Bradley, IL -0.4 7 0.0 2008Q3 2009Q2 0.7 1.5Kansas City, MO-KS -5.1 153 -3.0 2008Q1 2010Q1 -0.8 0.9Kennewick-Pasco-Richland, WA na na na na na 3.8 3.5Killeen-Temple-Fort Hood, TX -0.8 24 0.0 2008Q3 2009Q3 0.1 1.0Kingsport-Bristol-Bristol, TN-VA -0.4 6 0.0 2008Q3 2010Q3 -0.2 0.9Kingston, NY -14.8 250 -4.3 2008Q1 2010Q1 -5.1 1.5Knoxville, TN -3.9 126 -1.6 2007Q2 2009Q4 -0.7 0.7Kokomo, IN -10.8 230 -9.6 2005Q4 2009Q3 0.4 2.7La Crosse, WI-MN -5.9 169 -3.7 2008Q2 2010Q1 -1.0 0.4Lafayette, IN -2.1 71 -1.8 2007Q1 2009Q2 1.0 2.5Lafayette, LA -4.4 138 -1.8 2008Q2 2010Q4 -1.3 0.2Lake Charles, LA -0.7 18 0.0 2008Q3 2009Q4 -0.2 1.2Lake County-Kenosha County, IL-WI -6.1 173 -3.3 2007Q4 2009Q4 -1.2 0.4Lake Havasu City-Kingman, AZ -33.6 313 -20.3 2007Q1 2010Q2 -8.7 -1.9Lakeland-Winter Haven, FL -54.0 343 -31.9 2006Q2 2010Q3 -17.8 -2.7Lancaster, PA -6.2 175 -2.0 2008Q1 2010Q3 -2.2 -0.2Lansing-East Lansing, MI -21.6 275 -19.1 2006Q1 2009Q4 -1.0 2.2Laredo, TX -7.7 193 -4.5 2007Q4 2010Q1 -1.6 0.0Las Cruces, NM -5.9 170 -0.7 2007Q4 2010Q1 -2.2 1.1Las Vegas-Paradise, NV -55.7 349 -38.6 2006Q1 2010Q2 -14.9 -1.0Lawrence, KS -5.3 156 -1.3 2008Q1 2010Q4 -2.0 -0.3Lawton, OK -5.1 154 -4.4 2008Q2 2009Q4 0.1 1.4





Annualized % change

Lebanon, PA -1.7 60 -1.6 2008Q2 2009Q3 0.3 0.6Lewiston, ID-WA -9.2 215 0.0 2008Q3 2010Q3 -4.7 -1.4Lewiston-Auburn, ME -1.9 66 0.0 2008Q3 2009Q4 0.0 2.1Lexington-Fayette, KY -0.8 21 0.0 2008Q3 2009Q3 -0.1 0.3Lima, OH -4.8 145 -3.0 2008Q1 2009Q4 -0.4 1.2Lincoln, NE -2.9 107 -1.4 2008Q1 2009Q4 -0.4 1.1Little Rock-North Little Rock-Conway, AR -0.6 13 0.0 2007Q4 2008Q1 0.3 1.1Logan, UT-ID -13.6 246 0.0 2008Q3 2010Q1 -6.7 -0.7Longview, TX -2.9 107 -2.7 2008Q2 2009Q2 0.3 1.1Longview, WA -10.0 226 -3.9 2007Q4 2010Q4 -3.1 1.2Los Angeles-Long Beach-Glendale, CA -53.0 341 -34.4 2006Q2 2010Q3 -15.4 -4.4Louisville-Jefferson County, KY-IN -2.6 92 -1.5 2008Q2 2009Q2 0.0 0.9Lubbock, TX -0.4 9 0.0 2008Q3 2009Q3 0.1 0.8Lynchburg, VA -2.8 102 -1.3 2008Q2 2010Q3 -0.8 0.3Macon, GA -1.0 27 0.0 2008Q3 2009Q1 0.1 0.8Madera-Chowchilla, CA -49.9 336 -30.2 2006Q3 2010Q1 -14.3 1.2Madison, WI -5.5 162 -2.7 2008Q1 2010Q1 -1.3 0.0Manchester-Nashua, NH -30.2 302 -13.3 2005Q4 2010Q2 -10.1 -0.4Mansfield, OH -7.1 189 -5.5 2005Q4 2009Q4 -0.5 0.9McAllen-Edinburg-Mission, TX -1.4 49 0.0 2008Q3 2009Q3 0.2 1.8Medford, OR -23.4 280 -12.5 2007Q1 2010Q3 -6.4 0.9Memphis, TN-MS-AR -14.9 251 -12.5 2006Q2 2009Q3 0.1 2.8Merced, CA -69.6 368 -60.5 2006Q1 2009Q4 -10.8 0.0Miami-Miami Beach-Kendall, FL -66.4 364 -36.4 2007Q1 2011Q2 -26.0 -9.5Michigan City-La Porte, IN -4.1 132 -3.0 2008Q1 2009Q3 0.1 1.6Midland, TX -4.9 148 -1.3 2008Q2 2010Q4 -1.7 -0.5Milwaukee-Waukesha-West Allis, WI -5.5 159 -2.8 2008Q1 2010Q1 -1.3 0.1Minneapolis-St. Paul-Bloomington, MN-WI -23.6 281 -19.1 2006Q1 2010Q1 -2.5 1.3Missoula, MT -9.4 219 -3.4 2008Q1 2010Q1 -2.9 0.6Mobile, AL -0.2 3 0.0 2008Q3 2009Q1 0.9 1.6Modesto, CA -67.4 366 -52.8 2006Q1 2010Q2 -16.8 -2.3Monroe, LA -0.5 11 0.0 2008Q3 2009Q4 -0.1 0.4Monroe, MI -20.4 272 -16.4 2005Q3 2009Q4 -1.8 2.1Montgomery, AL -2.5 83 -1.7 2008Q1 2009Q2 0.7 2.4Morgantown, WV -1.0 31 0.0 2008Q3 2009Q4 -0.3 0.7Morristown, TN -3.2 114 -0.9 2008Q1 2010Q1 -1.1 -0.3Mount Vernon-Anacortes, WA -12.0 236 -1.7 2008Q1 2010Q3 -5.4 1.0Muncie, IN -10.4 228 -7.9 2005Q3 2009Q4 -0.6 2.0Muskegon-Norton Shores, MI -6.5 180 -4.3 2005Q3 2009Q4 -0.6 1.3Myrtle Beach-North Myrtle Beach-Conway, SC -12.8 243 -11.3 2006Q4 2009Q2 1.2 3.5Napa, CA -48.2 334 -35.3 2006Q1 2009Q4 -8.7 -0.5Naples-Marco Island, FL -70.1 369 -42.3 2006Q1 2010Q4 -27.7 -8.4Nashville-Davidson-Murfreesboro-Franklin, TN -4.5 139 -2.5 2007Q2 2009Q4 -0.7 0.2Nassau-Suffolk, NY -31.4 307 -10.3 2006Q2 2010Q3 -12.6 -3.4New Haven-Milford, CT -22.8 278 -8.5 2006Q2 2010Q2 -8.0 -0.8New Orleans-Metairie-Kenner, LA -9.8 224 -2.6 2007Q3 2011Q3 -3.4 -0.1New York-White Plains-Wayne, NY-NJ -33.3 311 -11.9 2006Q3 2010Q3 -13.0 -2.7Newark-Union, NJ-PA -26.1 291 -11.6 2006Q2 2010Q2 -8.3 0.6Niles-Benton Harbor, MI -8.0 199 -7.0 2008Q1 2009Q2 1.0 3.3Norwich-New London, CT -17.1 260 -5.3 2006Q2 2010Q2 -6.1 0.3Oakland-Fremont-Hayward, CA -54.0 344 -38.8 2006Q1 2009Q4 -11.8 -0.3Ocala, FL -35.0 316 -14.8 2007Q1 2010Q3 -12.7 -1.5Ocean City, NJ -42.1 323 -12.6 2005Q4 2010Q4 -18.6 -5.2Odessa, TX -7.5 192 0.0 2008Q3 2010Q3 -3.8 -0.8Ogden-Clearfield, UT -15.0 252 -2.1 2008Q1 2010Q1 -6.6 -0.7Oklahoma City, OK -1.8 62 -0.6 2008Q2 2010Q1 -0.5 0.2Olympia, WA -12.5 241 -3.1 2008Q1 2010Q1 -4.9 0.9Omaha-Council Bluffs, NE-IA -2.9 106 -2.4 2008Q1 2009Q2 0.8 2.2Orlando-Kissimmee, FL -62.1 357 -31.7 2006Q2 2011Q1 -24.9 -8.1Oshkosh-Neenah, WI -0.7 20 0.0 2008Q3 2009Q3 0.2 1.4Owensboro, KY -2.9 103 -1.9 2008Q1 2009Q2 0.5 1.8Oxnard-Thousand Oaks-Ventura, CA -50.7 339 -35.6 2006Q2 2009Q4 -11.6 -1.7Palm Bay-Melbourne-Titusville, FL -47.4 331 -27.4 2006Q2 2010Q1 -13.8 -0.4Palm Coast, FL -42.5 324 -25.2 2007Q1 2010Q1 -11.9 -1.0Panama City-Lynn Haven-Panama City Beach, FL -32.3 310 -11.4 2007Q1 2010Q2 -12.6 -0.9Parkersburg-Marietta-Vienna, WV-OH 0.0 1 0.0 2008Q3 2008Q4 1.2 1.8Pascagoula, MS -8.2 202 -3.6 2008Q1 2011Q3 -2.4 -0.7Peabody, MA -30.8 304 -14.5 2005Q3 2010Q2 -9.8 0.5Pensacola-Ferry Pass-Brent, FL -48.2 333 -18.5 2006Q2 2010Q4 -20.2 -5.4Peoria, IL -1.0 27 -0.5 2008Q2 2009Q3 0.5 1.9Philadelphia, PA -16.3 257 -0.6 2007Q2 2010Q2 -8.0 -1.1Phoenix-Mesa-Scottsdale, AZ -57.9 353 -38.9 2006Q2 2010Q2 -16.7 -1.9Pine Bluff, AR -3.1 110 -2.3 2008Q1 2009Q1 0.5 1.8Pittsburgh, PA -0.7 16 -0.4 2007Q4 2008Q2 1.3 3.2Pittsfield, MA -15.3 253 -4.3 2006Q2 2009Q4 -4.2 3.9Pocatello, ID -9.5 221 -2.9 2008Q2 2010Q1 -3.3 0.1Port St. Lucie, FL -60.8 356 -42.3 2006Q1 2010Q2 -17.5 -1.4Portland-South Portland-Biddeford, ME -5.6 164 -1.4 2008Q1 2010Q1 -1.7 1.3Portland-Vancouver-Beaverton, OR-WA -23.0 279 -7.9 2007Q2 2010Q3 -8.5 -1.4Poughkeepsie-Newburgh-Middletown, NY -16.0 255 -5.9 2006Q3 2010Q2 -5.4 -0.1Prescott, AZ -46.3 330 -21.2 2006Q2 2010Q4 -17.3 -5.2Providence-New Bedford-Fall River, RI-MA -31.5 308 -14.6 2006Q1 2010Q2 -10.4 -1.0Provo-Orem, UT -22.7 277 -4.1 2008Q1 2010Q4 -10.2 -3.4Pueblo, CO -10.9 231 -7.9 2007Q1 2009Q3 0.3 4.6Punta Gorda, FL -65.4 362 -47.1 2006Q1 2010Q2 -18.9 -2.0Racine, WI -5.4 158 -3.3 2007Q1 2009Q4 -0.7 0.8Raleigh-Cary, NC -0.5 10 0.0 2008Q3 2009Q2 0.4 0.6Rapid City, SD na na na na na 1.1 2.1Reading, PA -7.2 190 -3.6 2008Q2 2010Q2 -1.9 -0.4Redding, CA -44.4 328 -27.4 2006Q1 2009Q4 -9.5 -1.2Reno-Sparks, NV -47.9 332 -22.5 2006Q1 2010Q2 -17.6 -3.7Richmond, VA -9.5 220 -1.4 2008Q1 2010Q3 -4.2 -1.4Riverside-San Bernardino-Ontario, CA -62.7 359 -47.1 2006Q2 2010Q2 -15.8 -4.1Roanoke, VA -1.1 37 -0.5 2008Q1 2008Q2 0.8 1.3Rochester, MN -4.7 144 -3.5 2008Q1 2009Q4 -0.1 1.2Rochester, NY -2.1 70 -2.1 2008Q1 2008Q3 1.7 3.4Rockford, IL -3.9 129 -2.4 2008Q1 2010Q1 -0.6 0.5Rockingham County-Strafford County, NH -26.4 293 -11.7 2006Q2 2010Q1 -8.3 0.1Rocky Mount, NC na na na na na 1.4 1.5Rome, GA -2.2 75 0.0 2008Q3 2009Q3 -0.7 0.4





Annualized % change

Sacramento-Arden-Arcade-Roseville, CA -54.2 345 -43.5 2005Q4 2009Q4 -7.3 0.6Saginaw-Saginaw Township North, MI -16.3 256 -13.0 2005Q4 2009Q4 -1.4 1.4Salem, OR -16.7 258 -3.0 2008Q1 2010Q4 -7.3 -0.7Salinas, CA -67.9 367 -54.2 2006Q1 2009Q4 -15.4 -2.1Salisbury, MD -31.3 306 -10.5 2007Q3 2010Q2 -12.4 -1.5Salt Lake City, UT -20.4 273 -2.7 2008Q1 2010Q2 -9.5 -1.9San Angelo, TX -1.5 51 0.0 2008Q3 2010Q1 -0.7 0.1San Antonio, TX -1.6 58 0.0 2008Q3 2010Q1 -0.8 -0.1San Diego-Carlsbad-San Marcos, CA -50.5 338 -35.6 2006Q1 2009Q4 -11.0 -0.6San Francisco-San Mateo-Redwood City, CA -27.0 296 -15.8 2007Q3 2009Q4 -4.2 1.1San Jose-Sunnyvale-Santa Clara, CA -34.0 314 -25.6 2007Q1 2009Q3 -4.3 2.3San Luis Obispo-Paso Robles, CA -33.4 312 -24.2 2006Q1 2009Q1 -2.2 1.4Sandusky, OH -15.9 254 -14.4 2006Q4 2009Q2 1.1 4.4Santa Ana-Anaheim-Irvine, CA -40.3 322 -30.9 2006Q1 2009Q4 -4.9 1.0Santa Barbara-Santa Maria-Goleta, CA -57.7 352 -43.3 2005Q3 2009Q4 -12.3 0.1Santa Cruz-Watsonville, CA -44.3 327 -33.2 2005Q4 2009Q3 -6.6 1.4Santa Fe, NM -8.0 198 -2.8 2008Q1 2009Q4 -1.8 2.3Santa Rosa-Petaluma, CA -57.1 351 -39.7 2005Q4 2009Q4 -14.3 -1.7Savannah, GA -5.5 160 -2.2 2008Q2 2010Q4 -1.7 -0.3Scranton-Wilkes-Barre, PA -2.6 89 -2.0 2008Q2 2009Q3 0.4 1.7Seattle-Bellevue-Everett, WA -17.5 263 -8.5 2007Q2 2009Q4 -3.8 2.2Sebastian-Vero Beach, FL -60.2 355 -35.4 2005Q4 2010Q3 -21.5 -4.9Sheboygan, WI -5.0 151 -3.5 2008Q1 2009Q4 -0.2 1.5Sherman-Denison, TX na na na na na 0.9 1.5Shreveport-Bossier City, LA -2.4 81 -0.9 2008Q2 2010Q1 -0.6 0.4Sioux City, IA-NE-SD -4.2 135 -2.5 2008Q1 2009Q4 -0.6 0.9Sioux Falls, SD -1.1 37 -1.1 2008Q2 2008Q3 0.9 1.9South Bend-Mishawaka, IN-MI -2.7 98 -1.8 2008Q2 2009Q2 0.7 2.6Spartanburg, SC -2.5 83 -1.5 2007Q4 2009Q2 0.4 1.0Spokane, WA -9.2 214 -2.5 2008Q1 2009Q4 -2.4 2.0Springfield, IL -2.7 99 -2.0 2008Q2 2009Q2 0.7 2.3Springfield, MA -20.9 274 -6.0 2007Q1 2010Q1 -7.7 0.9Springfield, MO -4.2 133 -2.3 2008Q2 2009Q4 -0.6 1.0Springfield, OH -7.4 191 -5.9 2005Q4 2008Q2 0.3 3.2St. Cloud, MN -5.2 155 -3.3 2007Q2 2009Q4 -0.7 0.7St. George, UT -23.7 282 -10.4 2007Q1 2010Q3 -7.7 -2.6St. Joseph, MO-KS -6.8 184 -4.8 2007Q3 2009Q4 -0.7 1.1St. Louis, MO-IL -3.5 119 -1.8 2008Q1 2009Q4 -0.5 1.0State College, PA -1.7 61 -0.7 2007Q4 2008Q2 -0.1 1.1Stockton, CA -67.1 365 -54.0 2006Q1 2009Q4 -14.4 -2.1Sumter, SC -4.5 141 -1.7 2008Q1 2008Q2 -0.8 0.2Syracuse, NY -1.3 46 -0.1 2008Q1 2008Q2 1.8 2.8Tacoma, WA -17.8 264 -11.5 2007Q2 2009Q4 -1.1 4.1Tallahassee, FL -18.7 265 -3.1 2007Q2 2010Q1 -8.0 -1.6Tampa-St. Petersburg-Clearwater, FL -49.9 337 -28.7 2006Q2 2010Q4 -16.2 -3.5Terre Haute, IN -3.7 121 -3.2 2008Q2 2009Q3 0.7 2.4Texarkana, TX-Texarkana, AR -2.5 82 -1.9 2007Q4 2009Q3 0.3 1.4Toledo, OH -12.7 242 -12.1 2006Q1 2009Q2 0.6 3.3Topeka, KS -1.6 56 -0.3 2008Q1 2009Q4 -0.3 1.0Trenton-Ewing, NJ -28.1 298 -9.1 2006Q1 2010Q1 -10.8 -1.5Tucson, AZ -44.0 326 -20.7 2006Q2 2010Q3 -16.0 -3.1Tulsa, OK -0.6 13 -0.1 2008Q2 2009Q4 0.1 0.9Tuscaloosa, AL -0.2 3 0.0 2008Q3 2009Q2 1.1 1.9Tyler, TX -3.7 124 -2.2 2008Q1 2009Q4 -0.4 0.8Utica-Rome, NY -2.2 78 -2.2 2008Q1 2008Q3 2.7 3.4Valdosta, GA -7.1 188 -4.5 2008Q1 2009Q4 -1.0 0.4Vallejo-Fairfield, CA -63.9 360 -47.1 2006Q1 2010Q2 -17.3 -2.6Victoria, TX na na na na na 1.8 2.8Vineland-Millville-Bridgeton, NJ -12.4 240 -2.5 2007Q3 2010Q2 -5.2 -0.5Virginia Beach-Norfolk-Newport News, VA-NC -25.3 289 -2.4 2007Q4 2011Q2 -12.3 -4.6Visalia-Porterville, CA -51.9 340 -39.0 2006Q1 2009Q4 -8.5 0.4Waco, TX -1.5 50 0.0 2008Q3 2009Q4 -0.5 0.4Warner Robins, GA -5.3 156 -3.2 2007Q4 2009Q3 -0.5 0.9Warren-Troy-Farmington Hills, MI -34.1 315 -29.8 2005Q3 2009Q4 -2.3 2.7Washington-Arlington-Alexandria, DC-VA-MD-WV -38.3 320 -27.3 2006Q1 2010Q1 -7.1 1.0Waterloo-Cedar Falls, IA -2.9 104 -0.4 2008Q1 2011Q3 -1.2 -0.4Wausau, WI -2.6 88 -1.8 2008Q1 2009Q3 0.2 1.4Weirton-Steubenville, WV-OH -9.6 222 -7.8 2008Q2 2009Q4 -0.5 1.4Wenatchee-East Wenatchee, WA -11.7 235 -3.0 2008Q1 2010Q1 -4.6 0.8West Palm Beach-Boca Raton-Boynton Beach, FL -59.2 354 -36.7 2006Q1 2010Q3 -19.8 -3.7Wheeling, WV-OH -9.2 216 -8.6 2008Q2 2009Q2 0.1 1.5Wichita Falls, TX -6.2 174 -3.6 2008Q1 2009Q4 -1.0 0.9Wichita, KS -2.5 86 -1.5 2008Q1 2009Q4 -0.1 1.5Williamsport, PA -4.6 142 -4.6 2008Q2 2008Q3 1.6 3.1Wilmington, DE-MD-NJ -26.9 295 -6.2 2007Q1 2010Q2 -11.7 -2.0Wilmington, NC -2.5 83 -1.3 2008Q1 2009Q2 0.6 1.7Winchester, VA-WV -30.9 305 -16.8 2006Q2 2010Q2 -8.8 -0.5Winston-Salem, NC -5.1 152 -3.1 2008Q1 2009Q4 -0.7 0.7Worcester, MA -29.5 300 -15.6 2005Q4 2009Q4 -7.7 2.1Yakima, WA -6.5 178 -0.8 2008Q2 2009Q4 -1.9 2.9York-Hanover, PA -4.8 147 -1.3 2008Q1 2010Q2 -1.8 -0.6Youngstown-Warren-Boardman, OH-PA -11.2 233 -10.3 2006Q3 2009Q4 0.1 2.7Yuba City, CA -49.6 335 -34.7 2006Q1 2010Q2 -11.6 -0.2Yuma, AZ -25.5 290 -8.8 2007Q1 2010Q3 -9.6 -2.8

(a) 2-year average annual growth (2008Q3 to 2010Q3)(b) 5-year average annual growth (2008Q3 to 2013Q3)



Appendix 9-2: Shape of RecoveryCorrection

% change peak to Shape of Recovery

Length of Downturn

Length of Recovery

PeakQuarter

TroughQuarter

Peak to 2008Q3

trough Quarters Quarters % change

United States -36.2 U-shape 12 40 2006Q1 2009Q4 -21.3

Abilene, TX -2.2 U-shape 9 10 2007Q4 2010Q1 -0.3Akron, OH -9.0 V-shape 15 11 2006Q1 2009Q4 -7.4Albany, GA -5.5 U-shape 7 16 2008Q1 2009Q4 -3.1Albany-Schenectady-Troy, NY -2.2 L-shape 6 5 2008Q1 2009Q3 -1.5Albuquerque, NM -13.5 U-shape 10 12 2008Q1 2010Q3 -2.1Alexandria, LA -2.7 U-shape 7 10 2008Q1 2009Q4 -1.7Allentown-Bethlehem-Easton, PA-NJ -9.3 U-shape 11 12 2007Q2 2010Q1 -3.0Altoona, PA na na na na na na naAmarillo, TX -1.9 U-shape 7 10 2008Q1 2009Q4 -0.7Ames, IA -4.7 U-shape 7 13 2008Q1 2009Q4 -3.0Anchorage, AK -0.8 L-shape 1 8 2007Q2 2007Q3 -0.4Anderson, IN -8.3 U-shape 4 19 2005Q3 2006Q3 -3.5Anderson, SC -3.2 U-shape 5 10 2008Q1 2009Q2 -1.7Ann Arbor, MI -24.7 U-shape 16 28 2005Q4 2009Q4 -22.0Anniston-Oxford, AL -0.9 U-shape 3 5 2008Q3 2009Q2 0.0Appleton, WI -7.7 U-shape 6 13 2008Q2 2009Q4 -5.9Asheville, NC -9.7 U-shape 10 37 2008Q2 2010Q4 -0.2Athens-Clarke County, GA -7.0 U-shape 9 31 2008Q2 2010Q3 -3.5Atlanta-Sandy Springs-Marietta, GA -14.1 U-shape 10 35 2007Q2 2009Q4 -10.1Atlantic City, NJ -37.0 U-shape 18 57 2006Q2 2010Q4 -16.0Auburn-Opelika, AL -1.2 U-shape 4 6 2008Q3 2009Q3 0.0Augusta-Richmond County, GA-SC -2.0 L-shape 8 7 2008Q3 2010Q3 0.0Austin-Round Rock, TX -1.3 U-shape 5 20 2008Q3 2009Q4 0.0Bakersfield, CA -55.5 U-shape 14 61 2006Q2 2009Q4 -44.7Baltimore-Towson, MD -31.6 U-shape 14 26 2007Q1 2010Q3 -10.0Bangor, ME -4.0 V-shape 6 5 2008Q2 2009Q4 -1.0Barnstable Town, MA -25.1 U-shape 17 22 2005Q4 2010Q1 -11.6Baton Rouge, LA -2.0 U-shape 7 17 2008Q2 2010Q1 -0.3Battle Creek, MI -11.0 V-shape 14 13 2006Q3 2010Q1 -7.3Bay City, MI -13.9 U-shape 15 23 2006Q1 2009Q4 -10.6Beaumont-Port Arthur, TX -2.6 U-shape 6 11 2008Q2 2009Q4 -1.3Bellingham, WA -11.6 U-shape 7 15 2008Q1 2009Q4 -2.7Bend, OR -23.7 U-shape 11 29 2007Q1 2009Q4 -12.2Bethesda-Frederick-Rockville, MD -30.0 U-shape 15 29 2006Q2 2010Q1 -16.6Billings, MT -2.8 L-shape 6 5 2008Q1 2009Q3 -0.4Binghamton, NY na na na na na na naBirmingham-Hoover, AL -1.1 L-shape 5 4 2008Q1 2009Q2 -0.3Bismarck, ND -4.8 U-shape 9 14 2007Q4 2010Q1 -2.4Blacksburg-Christiansburg-Radford, VA -2.6 U-shape 7 11 2008Q1 2009Q4 -0.3Bloomington, IN -2.0 U-shape 5 6 2008Q2 2009Q3 -1.3Bloomington-Normal, IL -1.0 L-shape 5 4 2008Q2 2009Q3 -0.2Boise City-Nampa, ID -25.2 U-shape 12 53 2007Q2 2010Q2 -6.0Boston-Quincy, MA -26.1 U-shape 19 27 2005Q3 2010Q2 -11.2Boulder, CO -2.4 L-shape 5 3 2008Q3 2009Q4 0.0Bowling Green, KY -2.1 U-shape 4 5 2008Q2 2009Q2 -2.0Bradenton-Sarasota-Venice, FL -62.1 U-shape 18 58 2006Q1 2010Q3 -38.2Bremerton-Silverdale, WA -9.0 U-shape 8 9 2007Q4 2009Q4 -5.5Bridgeport-Stamford-Norwalk, CT -21.6 U-shape 17 24 2006Q1 2010Q2 -6.7Brownsville-Harlingen, TX -3.9 U-shape 5 6 2008Q1 2009Q2 -3.0Brunswick, GA -3.1 U-shape 4 11 2008Q2 2009Q2 -1.8Buffalo-Niagara Falls, NY na na na na na na naBurlington, NC -3.9 L-shape 9 7 2007Q2 2009Q3 -2.3Burlington-South Burlington, VT -18.9 U-shape 12 28 2007Q4 2010Q4 -1.8Cambridge-Newton-Framingham, MA -20.3 U-shape 18 18 2005Q2 2009Q4 -8.6Camden, NJ -26.7 U-shape 15 29 2006Q3 2010Q2 -8.9Canton-Massillon, OH -9.0 V-shape 15 12 2006Q1 2009Q4 -7.4Cape Coral-Fort Myers, FL -66.4 U-shape 17 59 2006Q1 2010Q2 -48.6Carson City, NV -30.6 U-shape 15 31 2006Q1 2009Q4 -15.4Casper, WY -4.5 U-shape 7 12 2008Q2 2010Q1 -2.3Cedar Rapids, IA -1.0 L-shape 7 4 2007Q4 2009Q3 -0.2Champaign-Urbana, IL -1.0 U-shape 4 4 2008Q3 2009Q3 0.0Charleston, WV -2.7 U-shape 4 9 2008Q2 2009Q2 -2.6Charleston-North Charleston-Summerville, SC -10.3 U-shape 9 11 2007Q1 2009Q2 -8.7Charlotte-Gastonia-Concord, NC-SC -6.0 U-shape 8 10 2007Q3 2009Q3 -3.6Charlottesville, VA -5.5 U-shape 8 26 2007Q4 2009Q4 -2.3Chattanooga, TN-GA -2.3 U-shape 5 8 2008Q1 2009Q2 -1.1Cheyenne, WY -6.6 U-shape 6 6 2008Q2 2009Q4 -0.9Chicago-Naperville-Joliet, IL -17.1 U-shape 12 24 2007Q1 2010Q1 -13.3



Appendix 9-2: Shape of Recovery (cont.)Correction


Length of Downturn

Length of Recovery

PeakQuarter

TroughQuarter

Peak to 2008Q3

trough Quarters Quarters % change Chico, CA -36.2 U-shape 16 61 2005Q4 2009Q4 -23.4Cincinnati-Middletown, OH-KY-IN -8.3 V-shape 15 12 2006Q1 2009Q4 -6.1Clarksville, TN-KY -1.1 U-shape 3 4 2008Q3 2009Q2 0.0Cleveland, TN -4.4 U-shape 8 28 2008Q1 2010Q1 -2.0Cleveland-Elyria-Mentor, OH -13.4 V-shape 16 15 2005Q4 2009Q4 -12.0Coeur d'Alene, ID -20.2 U-shape 11 46 2007Q3 2010Q2 -4.4College Station-Bryan, TX na na na na na na naColorado Springs, CO -7.9 U-shape 7 7 2008Q1 2009Q4 -3.3Columbia, MO -1.5 L-shape 1 11 2008Q1 2008Q2 -0.3Columbia, SC -0.5 L-shape 3 2 2008Q3 2009Q2 0.0Columbus, GA-AL -3.5 U-shape 5 7 2008Q1 2009Q2 -2.2Columbus, IN -5.8 U-shape 5 10 2008Q2 2009Q3 -4.8Columbus, OH -7.9 V-shape 14 11 2006Q2 2009Q4 -6.6Corpus Christi, TX -1.6 U-shape 4 5 2008Q2 2009Q2 -0.8Corvallis, OR -6.7 V-shape 8 6 2007Q4 2009Q4 -1.1Cumberland, MD-WV -8.6 U-shape 6 11 2008Q3 2010Q1 0.0Dallas-Plano-Irving, TX -1.1 U-shape 4 6 2008Q3 2009Q3 0.0Dalton, GA -9.3 U-shape 9 38 2008Q1 2010Q2 -5.1Danville, IL -9.1 U-shape 8 15 2007Q3 2009Q3 -7.5Danville, VA -8.1 U-shape 4 13 2008Q2 2009Q2 -6.6Davenport-Moline-Rock Island, IA-IL -3.5 U-shape 7 8 2008Q1 2009Q4 -2.4Dayton, OH -5.8 L-shape 15 8 2006Q1 2009Q4 -4.7Decatur, AL -1.2 U-shape 5 5 2008Q2 2009Q3 -0.5Decatur, IL na na na na na na naDeltona-Daytona Beach-Ormond Beach, FL -55.3 U-shape 17 58 2006Q2 2010Q3 -30.4Denver-Aurora-Broomfield, CO -18.9 U-shape 17 20 2006Q1 2010Q2 -7.1Des Moines-West Des Moines, IA -4.9 U-shape 7 15 2008Q1 2009Q4 -3.0Detroit-Livonia-Dearborn, MI -36.6 U-shape 16 40 2006Q1 2010Q1 -31.4Dothan, AL -3.1 L-shape 2 9 2007Q4 2008Q2 -0.1Dover, DE -12.0 U-shape 10 29 2008Q1 2010Q3 -2.0Dubuque, IA -3.7 U-shape 6 10 2008Q2 2009Q4 -2.9Duluth, MN-WI -1.3 L-shape 2 6 2008Q1 2008Q3 -1.3Durham-Chapel Hill, NC -1.9 U-shape 4 12 2008Q2 2009Q2 -1.1Eau Claire, WI -2.6 U-shape 7 8 2008Q1 2009Q4 -1.4Edison-New Brunswick, NJ -28.0 U-shape 17 30 2006Q2 2010Q3 -11.8El Centro, CA -56.6 U-shape 14 61 2006Q2 2009Q4 -42.5El Paso, TX -2.7 U-shape 7 14 2008Q1 2009Q4 -1.0Elizabethtown, KY -6.2 U-shape 6 21 2008Q2 2009Q4 -3.9Elkhart-Goshen, IN -6.0 U-shape 7 16 2008Q2 2010Q1 -3.8Elmira, NY -7.0 U-shape 3 13 2007Q2 2008Q1 -6.6Erie, PA -0.4 L-shape 1 6 2008Q2 2008Q3 -0.4Eugene-Springfield, OR -17.1 U-shape 10 15 2008Q1 2010Q3 -4.1Evansville, IN-KY -1.9 L-shape 6 5 2008Q1 2009Q3 -0.8Fairbanks, AK -6.5 U-shape 5 10 2008Q1 2009Q2 -6.5Fargo, ND-MN -3.4 U-shape 8 12 2008Q1 2010Q1 -0.8Farmington, NM -8.1 U-shape 7 9 2008Q1 2009Q4 -2.9Fayetteville, NC -1.0 U-shape 3 5 2008Q3 2009Q2 0.0Fayetteville-Springdale-Rogers, AR-MO -8.9 U-shape 12 33 2007Q2 2010Q2 -5.0Flagstaff, AZ -28.3 U-shape 16 21 2006Q2 2010Q2 -11.0Flint, MI -23.7 U-shape 18 34 2005Q3 2010Q1 -20.3Florence, SC -0.7 U-shape 4 4 2008Q2 2009Q2 0.0Florence-Muscle Shoals, AL -0.1 L-shape 1 1 2008Q3 2008Q4 0.0Fond du Lac, WI -2.9 U-shape 7 7 2008Q1 2009Q4 -1.7Fort Collins-Loveland, CO -2.1 U-shape 5 5 2007Q4 2009Q1 -1.3Fort Lauderdale-Pompano Beach-Deerfield Beach, FL -64.4 U-shape 18 57 2006Q2 2010Q4 -36.4Fort Smith, AR-OK -0.6 L-shape 2 5 2008Q3 2009Q1 0.0Fort Walton Beach-Crestview-Destin, FL -55.0 U-shape 19 58 2005Q4 2010Q3 -28.4Fort Wayne, IN -2.5 U-shape 3 8 2007Q1 2007Q4 -0.2Fort Worth-Arlington, TX -1.1 U-shape 5 5 2008Q2 2009Q3 -0.3Fresno, CA -53.6 U-shape 15 61 2006Q1 2009Q4 -39.7Gadsden, AL -1.6 U-shape 4 8 2008Q3 2009Q3 0.0Gainesville, FL -39.4 U-shape 17 49 2007Q1 2011Q2 -10.1Gainesville, GA -12.3 U-shape 15 43 2007Q1 2010Q4 -9.1Gary, IN -3.7 U-shape 7 9 2008Q1 2009Q4 -2.1Glens Falls, NY -3.4 L-shape 7 3 2008Q1 2009Q4 -0.5Goldsboro, NC -4.2 U-shape 4 12 2008Q2 2009Q2 -2.8Grand Forks, ND-MN -4.1 V-shape 8 7 2007Q4 2009Q4 -2.9Grand Junction, CO -12.2 U-shape 9 25 2008Q3 2010Q4 0.0Grand Rapids-Wyoming, MI -10.7 V-shape 17 15 2005Q3 2009Q4 -8.9Great Falls, MT -4.4 V-shape 7 6 2008Q1 2009Q4 -0.6Greeley, CO -14.4 U-shape 15 23 2006Q1 2009Q4 -10.0





Length of Downturn

Length of Recovery

PeakQuarter

TroughQuarter

Peak to 2008Q3

trough Quarters Quarters % change Green Bay, WI -6.7 U-shape 7 10 2008Q1 2009Q4 -2.1Greensboro-High Point, NC -1.2 U-shape 4 5 2008Q2 2009Q2 -0.2Greenville, NC -1.1 U-shape 3 5 2008Q3 2009Q2 0.0Greenville-Mauldin-Easley, SC -0.8 U-shape 4 4 2008Q2 2009Q2 -0.3Gulfport-Biloxi, MS -7.7 U-shape 9 16 2008Q1 2010Q2 -4.0Hagerstown-Martinsburg, MD-WV -20.3 U-shape 14 58 2007Q1 2010Q3 -8.9Hanford-Corcoran, CA -43.8 U-shape 14 61 2006Q2 2009Q4 -30.0Harrisburg-Carlisle, PA -2.6 U-shape 7 9 2008Q1 2009Q4 -1.1Harrisonburg, VA -8.5 U-shape 6 24 2008Q2 2009Q4 -5.0Hartford-West Hartford-East Hartford, CT -16.8 U-shape 13 15 2007Q1 2010Q2 -5.3Hattiesburg, MS -4.9 U-shape 6 19 2008Q2 2009Q4 -3.0Hickory-Lenoir-Morganton, NC -1.1 U-shape 3 6 2008Q3 2009Q2 0.0Hinesville-Fort Stewart, GA -8.8 U-shape 6 28 2008Q2 2009Q4 -6.4Holland-Grand Haven, MI -9.9 U-shape 18 27 2005Q3 2010Q1 -6.0Honolulu, HI -24.2 U-shape 9 32 2008Q1 2010Q2 -1.9Hot Springs, AR -2.1 U-shape 4 8 2008Q2 2009Q2 -1.3Houma-Bayou Cane-Thibodaux, LA -3.0 U-shape 7 17 2008Q2 2010Q1 -1.0Houston-Sugar Land-Baytown, TX -0.2 L-shape 4 2 2008Q3 2009Q3 0.0Huntington-Ashland, WV-KY-OH -1.5 U-shape 7 8 2008Q1 2009Q4 -0.8Huntsville, AL -1.0 U-shape 4 4 2008Q3 2009Q3 0.0Idaho Falls, ID -8.5 U-shape 8 9 2008Q1 2010Q1 -2.6Indianapolis-Carmel, IN -1.5 L-shape 6 4 2008Q1 2009Q3 -0.7Iowa City, IA -5.8 U-shape 8 20 2008Q1 2010Q1 -3.5Ithaca, NY na na na na na na naJackson, MI -20.3 U-shape 17 60 2005Q4 2010Q1 -16.3Jackson, MS -3.5 U-shape 5 7 2008Q1 2009Q2 -2.4Jackson, TN -5.7 U-shape 9 13 2007Q2 2009Q3 -3.9Jacksonville, FL -45.9 U-shape 19 55 2006Q2 2011Q1 -18.7Jacksonville, NC -0.9 L-shape 2 6 2008Q3 2009Q1 0.0Janesville, WI -6.3 U-shape 8 21 2008Q1 2010Q1 -3.9Jefferson City, MO -0.7 L-shape 5 3 2008Q2 2009Q3 -0.4Johnson City, TN -2.7 U-shape 7 7 2007Q4 2009Q3 -1.2Johnstown, PA -1.5 L-shape 1 4 2008Q2 2008Q3 -1.5Jonesboro, AR na na na na na na naJoplin, MO -3.9 U-shape 7 10 2008Q1 2009Q4 -2.7Kalamazoo-Portage, MI -7.0 U-shape 7 12 2008Q1 2009Q4 -5.1Kankakee-Bradley, IL -0.4 U-shape 3 3 2008Q3 2009Q2 0.0Kansas City, MO-KS -5.1 U-shape 8 12 2008Q1 2010Q1 -3.0Kennewick-Pasco-Richland, WA na na na na na na naKilleen-Temple-Fort Hood, TX -0.8 U-shape 4 4 2008Q3 2009Q3 0.0Kingsport-Bristol-Bristol, TN-VA -0.4 L-shape 8 2 2008Q3 2010Q3 0.0Kingston, NY -14.8 U-shape 8 12 2008Q1 2010Q1 -4.3Knoxville, TN -3.9 L-shape 10 9 2007Q2 2009Q4 -1.6Kokomo, IN -10.8 V-shape 15 14 2005Q4 2009Q3 -9.6La Crosse, WI-MN -5.9 U-shape 7 19 2008Q2 2010Q1 -3.7Lafayette, IN -2.1 L-shape 9 5 2007Q1 2009Q2 -1.8Lafayette, LA -4.4 U-shape 10 14 2008Q2 2010Q4 -1.8Lake Charles, LA -0.7 U-shape 5 5 2008Q3 2009Q4 0.0Lake County-Kenosha County, IL-WI -6.1 U-shape 8 18 2007Q4 2009Q4 -3.3Lake Havasu City-Kingman, AZ -33.6 U-shape 13 59 2007Q1 2010Q2 -20.3Lakeland-Winter Haven, FL -54.0 U-shape 17 58 2006Q2 2010Q3 -31.9Lancaster, PA -6.2 U-shape 10 22 2008Q1 2010Q3 -2.0Lansing-East Lansing, MI -21.6 U-shape 15 26 2006Q1 2009Q4 -19.1Laredo, TX -7.7 U-shape 9 26 2007Q4 2010Q1 -4.5Las Cruces, NM -5.9 V-shape 9 8 2007Q4 2010Q1 -0.7Las Vegas-Paradise, NV -55.7 U-shape 17 59 2006Q1 2010Q2 -38.6Lawrence, KS -5.3 U-shape 11 18 2008Q1 2010Q4 -1.3Lawton, OK -5.1 U-shape 6 11 2008Q2 2009Q4 -4.4Lebanon, PA -1.7 U-shape 5 9 2008Q2 2009Q3 -1.6Lewiston, ID-WA -9.2 U-shape 8 26 2008Q3 2010Q3 0.0Lewiston-Auburn, ME -1.9 L-shape 5 3 2008Q3 2009Q4 0.0Lexington-Fayette, KY -0.8 U-shape 4 6 2008Q3 2009Q3 0.0Lima, OH -4.8 U-shape 7 11 2008Q1 2009Q4 -3.0Lincoln, NE -2.9 U-shape 7 8 2008Q1 2009Q4 -1.4Little Rock-North Little Rock-Conway, AR -0.6 L-shape 1 8 2007Q4 2008Q1 0.0Logan, UT-ID -13.6 U-shape 6 21 2008Q3 2010Q1 0.0Longview, TX -2.9 U-shape 4 11 2008Q2 2009Q2 -2.7Longview, WA -10.0 V-shape 12 10 2007Q4 2010Q4 -3.9Los Angeles-Long Beach-Glendale, CA -53.0 U-shape 17 58 2006Q2 2010Q3 -34.4Louisville-Jefferson County, KY-IN -2.6 U-shape 4 8 2008Q2 2009Q2 -1.5Lubbock, TX -0.4 U-shape 4 4 2008Q3 2009Q3 0.0





Length of Downturn

Length of Recovery

PeakQuarter

TroughQuarter

Peak to 2008Q3

trough Quarters Quarters % change Lynchburg, VA -2.8 U-shape 9 12 2008Q2 2010Q3 -1.3Macon, GA -1.0 L-shape 2 6 2008Q3 2009Q1 0.0Madera-Chowchilla, CA -49.9 U-shape 14 60 2006Q3 2010Q1 -30.2Madison, WI -5.5 U-shape 8 22 2008Q1 2010Q1 -2.7Manchester-Nashua, NH -30.2 U-shape 18 26 2005Q4 2010Q2 -13.3Mansfield, OH -7.1 U-shape 16 18 2005Q4 2009Q4 -5.5McAllen-Edinburg-Mission, TX -1.4 U-shape 4 4 2008Q3 2009Q3 0.0Medford, OR -23.4 U-shape 14 18 2007Q1 2010Q3 -12.5Memphis, TN-MS-AR -14.9 U-shape 13 16 2006Q2 2009Q3 -12.5Merced, CA -69.6 U-shape 15 61 2006Q1 2009Q4 -60.5Miami-Miami Beach-Kendall, FL -66.4 U-shape 17 55 2007Q1 2011Q2 -36.4Michigan City-La Porte, IN -4.1 U-shape 6 9 2008Q1 2009Q3 -3.0Midland, TX -4.9 U-shape 10 22 2008Q2 2010Q4 -1.3Milwaukee-Waukesha-West Allis, WI -5.5 U-shape 8 20 2008Q1 2010Q1 -2.8Minneapolis-St. Paul-Bloomington, MN-WI -23.6 U-shape 16 31 2006Q1 2010Q1 -19.1Missoula, MT -9.4 U-shape 8 15 2008Q1 2010Q1 -3.4Mobile, AL -0.2 L-shape 2 2 2008Q3 2009Q1 0.0Modesto, CA -67.4 U-shape 17 59 2006Q1 2010Q2 -52.8Monroe, LA -0.5 U-shape 5 5 2008Q3 2009Q4 0.0Monroe, MI -20.4 U-shape 17 23 2005Q3 2009Q4 -16.4Montgomery, AL -2.5 U-shape 5 6 2008Q1 2009Q2 -1.7Morgantown, WV -1.0 U-shape 5 5 2008Q3 2009Q4 0.0Morristown, TN -3.2 U-shape 8 25 2008Q1 2010Q1 -0.9Mount Vernon-Anacortes, WA -12.0 U-shape 10 11 2008Q1 2010Q3 -1.7Muncie, IN -10.4 V-shape 17 14 2005Q3 2009Q4 -7.9Muskegon-Norton Shores, MI -6.5 L-shape 17 13 2005Q3 2009Q4 -4.3Myrtle Beach-North Myrtle Beach-Conway, SC -12.8 U-shape 10 13 2006Q4 2009Q2 -11.3Napa, CA -48.2 U-shape 15 59 2006Q1 2009Q4 -35.3Naples-Marco Island, FL -70.1 U-shape 19 57 2006Q1 2010Q4 -42.3Nashville-Davidson-Murfreesboro-Franklin, TN -4.5 U-shape 10 33 2007Q2 2009Q4 -2.5Nassau-Suffolk, NY -31.4 U-shape 17 44 2006Q2 2010Q3 -10.3New Haven-Milford, CT -22.8 U-shape 16 35 2006Q2 2010Q2 -8.5New Orleans-Metairie-Kenner, LA -9.8 V-shape 16 11 2007Q3 2011Q3 -2.6New York-White Plains-Wayne, NY-NJ -33.3 U-shape 16 32 2006Q3 2010Q3 -11.9Newark-Union, NJ-PA -26.1 U-shape 16 20 2006Q2 2010Q2 -11.6Niles-Benton Harbor, MI -8.0 U-shape 5 10 2008Q1 2009Q2 -7.0Norwich-New London, CT -17.1 U-shape 16 19 2006Q2 2010Q2 -5.3Oakland-Fremont-Hayward, CA -54.0 U-shape 15 61 2006Q1 2009Q4 -38.8Ocala, FL -35.0 U-shape 14 58 2007Q1 2010Q3 -14.8Ocean City, NJ -42.1 U-shape 20 39 2005Q4 2010Q4 -12.6Odessa, TX -7.5 U-shape 8 21 2008Q3 2010Q3 0.0Ogden-Clearfield, UT -15.0 U-shape 8 23 2008Q1 2010Q1 -2.1Oklahoma City, OK -1.8 U-shape 7 10 2008Q2 2010Q1 -0.6Olympia, WA -12.5 U-shape 8 13 2008Q1 2010Q1 -3.1Omaha-Council Bluffs, NE-IA -2.9 U-shape 5 7 2008Q1 2009Q2 -2.4Orlando-Kissimmee, FL -62.1 U-shape 19 56 2006Q2 2011Q1 -31.7Oshkosh-Neenah, WI -0.7 U-shape 4 4 2008Q3 2009Q3 0.0Owensboro, KY -2.9 U-shape 5 7 2008Q1 2009Q2 -1.9Oxnard-Thousand Oaks-Ventura, CA -50.7 U-shape 14 61 2006Q2 2009Q4 -35.6Palm Bay-Melbourne-Titusville, FL -47.4 U-shape 15 60 2006Q2 2010Q1 -27.4Palm Coast, FL -42.5 U-shape 12 38 2007Q1 2010Q1 -25.2Panama City-Lynn Haven-Panama City Beach, FL -32.3 U-shape 13 35 2007Q1 2010Q2 -11.4Parkersburg-Marietta-Vienna, WV-OH 0.0 L-shape 1 1 2008Q3 2008Q4 0.0Pascagoula, MS -8.2 U-shape 14 23 2008Q1 2011Q3 -3.6Peabody, MA -30.8 U-shape 19 23 2005Q3 2010Q2 -14.5Pensacola-Ferry Pass-Brent, FL -48.2 U-shape 18 57 2006Q2 2010Q4 -18.5Peoria, IL -1.0 L-shape 5 4 2008Q2 2009Q3 -0.5Philadelphia, PA -16.3 U-shape 12 24 2007Q2 2010Q2 -0.6Phoenix-Mesa-Scottsdale, AZ -57.9 U-shape 16 59 2006Q2 2010Q2 -38.9Pine Bluff, AR -3.1 U-shape 4 9 2008Q1 2009Q1 -2.3Pittsburgh, PA -0.7 L-shape 2 4 2007Q4 2008Q2 -0.4Pittsfield, MA -15.3 V-shape 14 9 2006Q2 2009Q4 -4.3Pocatello, ID -9.5 U-shape 7 21 2008Q2 2010Q1 -2.9Port St. Lucie, FL -60.8 U-shape 17 59 2006Q1 2010Q2 -42.3Portland-South Portland-Biddeford, ME -5.6 V-shape 8 7 2008Q1 2010Q1 -1.4Portland-Vancouver-Beaverton, OR-WA -23.0 U-shape 13 40 2007Q2 2010Q3 -7.9Poughkeepsie-Newburgh-Middletown, NY -16.0 U-shape 15 30 2006Q3 2010Q2 -5.9Prescott, AZ -46.3 U-shape 18 57 2006Q2 2010Q4 -21.2Providence-New Bedford-Fall River, RI-MA -31.5 U-shape 17 40 2006Q1 2010Q2 -14.6Provo-Orem, UT -22.7 U-shape 11 45 2008Q1 2010Q4 -4.1Pueblo, CO -10.9 V-shape 10 8 2007Q1 2009Q3 -7.9



Correction% change peak to

Shape of Recovery

Length of Downturn

Length of Recovery

PeakQuarter

TroughQuarter

Peak to 2008Q3

trough Quarters Quarters % change Punta Gorda, FL -65.4 U-shape 17 59 2006Q1 2010Q2 -47.1Racine, WI -5.4 U-shape 11 14 2007Q1 2009Q4 -3.3Raleigh-Cary, NC -0.5 U-shape 3 3 2008Q3 2009Q2 0.0Rapid City, SD na na na na na na naReading, PA -7.2 U-shape 8 28 2008Q2 2010Q2 -3.6Redding, CA -44.4 U-shape 15 59 2006Q1 2009Q4 -27.4Reno-Sparks, NV -47.9 U-shape 17 58 2006Q1 2010Q2 -22.5Richmond, VA -9.5 U-shape 10 34 2008Q1 2010Q3 -1.4Riverside-San Bernardino-Ontario, CA -62.7 U-shape 16 59 2006Q2 2010Q2 -47.1Roanoke, VA -1.1 L-shape 1 7 2008Q1 2008Q2 -0.5Rochester, MN -4.7 U-shape 7 11 2008Q1 2009Q4 -3.5Rochester, NY -2.1 L-shape 2 7 2008Q1 2008Q3 -2.1Rockford, IL -3.9 U-shape 8 15 2008Q1 2010Q1 -2.4Rockingham County-Strafford County, NH -26.4 U-shape 15 33 2006Q2 2010Q1 -11.7Rocky Mount, NC na na na na na na naRome, GA -2.2 U-shape 4 9 2008Q3 2009Q3 0.0Sacramento-Arden-Arcade-Roseville, CA -54.2 U-shape 16 61 2005Q4 2009Q4 -43.5Saginaw-Saginaw Township North, MI -16.3 U-shape 16 23 2005Q4 2009Q4 -13.0Salem, OR -16.7 U-shape 11 17 2008Q1 2010Q4 -3.0Salinas, CA -67.9 U-shape 15 61 2006Q1 2009Q4 -54.2Salisbury, MD -31.3 U-shape 11 31 2007Q3 2010Q2 -10.5Salt Lake City, UT -20.4 U-shape 9 32 2008Q1 2010Q2 -2.7San Angelo, TX -1.5 U-shape 6 11 2008Q3 2010Q1 0.0San Antonio, TX -1.6 U-shape 6 16 2008Q3 2010Q1 0.0San Diego-Carlsbad-San Marcos, CA -50.5 U-shape 15 61 2006Q1 2009Q4 -35.6San Francisco-San Mateo-Redwood City, CA -27.0 U-shape 9 30 2007Q3 2009Q4 -15.8San Jose-Sunnyvale-Santa Clara, CA -34.0 U-shape 10 38 2007Q1 2009Q3 -25.6San Luis Obispo-Paso Robles, CA -33.4 U-shape 12 59 2006Q1 2009Q1 -24.2Sandusky, OH -15.9 U-shape 10 14 2006Q4 2009Q2 -14.4Santa Ana-Anaheim-Irvine, CA -40.3 U-shape 15 56 2006Q1 2009Q4 -30.9Santa Barbara-Santa Maria-Goleta, CA -57.7 U-shape 17 61 2005Q3 2009Q4 -43.3Santa Cruz-Watsonville, CA -44.3 U-shape 15 59 2005Q4 2009Q3 -33.2Santa Fe, NM -8.0 U-shape 7 8 2008Q1 2009Q4 -2.8Santa Rosa-Petaluma, CA -57.1 U-shape 16 61 2005Q4 2009Q4 -39.7Savannah, GA -5.5 U-shape 10 23 2008Q2 2010Q4 -2.2Scranton-Wilkes-Barre, PA -2.6 U-shape 5 6 2008Q2 2009Q3 -2.0Seattle-Bellevue-Everett, WA -17.5 U-shape 10 13 2007Q2 2009Q4 -8.5Sebastian-Vero Beach, FL -60.2 U-shape 19 58 2005Q4 2010Q3 -35.4Sheboygan, WI -5.0 U-shape 7 10 2008Q1 2009Q4 -3.5Sherman-Denison, TX na na na na na na naShreveport-Bossier City, LA -2.4 U-shape 7 10 2008Q2 2010Q1 -0.9Sioux City, IA-NE-SD -4.2 U-shape 7 12 2008Q1 2009Q4 -2.5Sioux Falls, SD -1.1 L-shape 1 8 2008Q2 2008Q3 -1.1South Bend-Mishawaka, IN-MI -2.7 U-shape 4 6 2008Q2 2009Q2 -1.8Spartanburg, SC -2.5 U-shape 6 7 2007Q4 2009Q2 -1.5Spokane, WA -9.2 U-shape 7 10 2008Q1 2009Q4 -2.5Springfield, IL -2.7 U-shape 4 6 2008Q2 2009Q2 -2.0Springfield, MA -20.9 U-shape 12 16 2007Q1 2010Q1 -6.0Springfield, MO -4.2 U-shape 6 10 2008Q2 2009Q4 -2.3Springfield, OH -7.4 U-shape 10 14 2005Q4 2008Q2 -5.9St. Cloud, MN -5.2 U-shape 10 16 2007Q2 2009Q4 -3.3St. George, UT -23.7 U-shape 14 58 2007Q1 2010Q3 -10.4St. Joseph, MO-KS -6.8 U-shape 9 15 2007Q3 2009Q4 -4.8St. Louis, MO-IL -3.5 U-shape 7 10 2008Q1 2009Q4 -1.8State College, PA -1.7 L-shape 2 11 2007Q4 2008Q2 -0.7Stockton, CA -67.1 U-shape 15 61 2006Q1 2009Q4 -54.0Sumter, SC -4.5 L-shape 1 25 2008Q1 2008Q2 -1.7Syracuse, NY -1.3 L-shape 1 2 2008Q1 2008Q2 -0.1Tacoma, WA -17.8 V-shape 10 9 2007Q2 2009Q4 -11.5Tallahassee, FL -18.7 U-shape 11 36 2007Q2 2010Q1 -3.1Tampa-St. Petersburg-Clearwater, FL -49.9 U-shape 18 57 2006Q2 2010Q4 -28.7Terre Haute, IN -3.7 U-shape 5 7 2008Q2 2009Q3 -3.2Texarkana, TX-Texarkana, AR -2.5 U-shape 7 7 2007Q4 2009Q3 -1.9Toledo, OH -12.7 U-shape 13 15 2006Q1 2009Q2 -12.1Topeka, KS -1.6 L-shape 7 6 2008Q1 2009Q4 -0.3Trenton-Ewing, NJ -28.1 U-shape 16 35 2006Q1 2010Q1 -9.1Tucson, AZ -44.0 U-shape 17 43 2006Q2 2010Q3 -20.7Tulsa, OK -0.6 L-shape 6 3 2008Q2 2009Q4 -0.1Tuscaloosa, AL -0.2 L-shape 3 1 2008Q3 2009Q2 0.0Tyler, TX -3.7 U-shape 7 11 2008Q1 2009Q4 -2.2Utica-Rome, NY -2.2 L-shape 2 5 2008Q1 2008Q3 -2.2

Appendix 9-2: Shape of Recovery (cont.)





Length of Downturn

Length of Recovery

PeakQuarter

TroughQuarter

Peak to 2008Q3

trough Quarters Quarters % change Valdosta, GA -7.1 U-shape 7 22 2008Q1 2009Q4 -4.5Vallejo-Fairfield, CA -63.9 U-shape 17 59 2006Q1 2010Q2 -47.1Victoria, TX na na na na na na naVineland-Millville-Bridgeton, NJ -12.4 U-shape 11 37 2007Q3 2010Q2 -2.5Virginia Beach-Norfolk-Newport News, VA-NC -25.3 U-shape 14 50 2007Q4 2011Q2 -2.4Visalia-Porterville, CA -51.9 U-shape 15 61 2006Q1 2009Q4 -39.0Waco, TX -1.5 U-shape 5 8 2008Q3 2009Q4 0.0Warner Robins, GA -5.3 U-shape 7 13 2007Q4 2009Q3 -3.2Warren-Troy-Farmington Hills, MI -34.1 U-shape 17 36 2005Q3 2009Q4 -29.8Washington-Arlington-Alexandria, DC-VA-MD-WV -38.3 U-shape 16 49 2006Q1 2010Q1 -27.3Waterloo-Cedar Falls, IA -2.9 U-shape 14 18 2008Q1 2011Q3 -0.4Wausau, WI -2.6 U-shape 6 7 2008Q1 2009Q3 -1.8Weirton-Steubenville, WV-OH -9.6 U-shape 6 17 2008Q2 2009Q4 -7.8Wenatchee-East Wenatchee, WA -11.7 U-shape 8 13 2008Q1 2010Q1 -3.0West Palm Beach-Boca Raton-Boynton Beach, FL -59.2 U-shape 18 58 2006Q1 2010Q3 -36.7Wheeling, WV-OH -9.2 U-shape 4 20 2008Q2 2009Q2 -8.6Wichita Falls, TX -6.2 U-shape 7 14 2008Q1 2009Q4 -3.6Wichita, KS -2.5 U-shape 7 7 2008Q1 2009Q4 -1.5Williamsport, PA -4.6 L-shape 1 10 2008Q2 2008Q3 -4.6Wilmington, DE-MD-NJ -26.9 U-shape 13 31 2007Q1 2010Q2 -6.2Wilmington, NC -2.5 U-shape 5 6 2008Q1 2009Q2 -1.3Winchester, VA-WV -30.9 U-shape 16 41 2006Q2 2010Q2 -16.8Winston-Salem, NC -5.1 U-shape 7 14 2008Q1 2009Q4 -3.1Worcester, MA -29.5 U-shape 16 19 2005Q4 2009Q4 -15.6Yakima, WA -6.5 U-shape 6 7 2008Q2 2009Q4 -0.8York-Hanover, PA -4.8 U-shape 9 30 2008Q1 2010Q2 -1.3Youngstown-Warren-Boardman, OH-PA -11.2 U-shape 13 13 2006Q3 2009Q4 -10.3Yuba City, CA -49.6 U-shape 17 59 2006Q1 2010Q2 -34.7Yuma, AZ -25.5 U-shape 14 46 2007Q1 2010Q3 -8.8



Appendix 9-3: Supply Balance Indicator, 2008Q3

Months Index Q/Q change Y/Y changeAbilene, TX 7.1 10 down downAkron, OH 10.5 7 no change no changeAlbany, GA 4.2 17 no change no changeAlbany, NY -0.9 60 no change downAlbuquerque, NM -2.5 78 no change upAlexandria, LA 102.4 0 down downAllentown, PA -0.5 56 no change no changeAltoona, PA 7.3 9 no change no changeAmarillo, TX 3.9 18 down downAmes, IA -1.3 65 no change upAnchorage, AK -6.0 97 no change no changeAnderson, IN 3.4 20 no change upAnderson, SC 1.4 34 no change no changeAnn Arbor, MI -2.9 82 up upAnniston, AL 17.1 5 down downAppleton, WI 0.2 47 no change upAsheville, NC 0.2 47 no change upAthens, GA 2.0 28 no change no changeAtlanta, GA -0.1 51 no change no changeAtlantic City, NJ 6.0 12 no change no changeAuburn, AL 2.4 25 down downAugusta, GA 3.3 20 no change no changeAustin, TX -0.7 58 down downBakersfield, CA 0.2 48 no change downBaltimore, MD 0.2 48 up upBangor, ME 1.9 29 no change no changeBarnstable Town, MA 4.0 18 up upBaton Rouge, LA 74.8 0 down downBattle Creek, MI 22.8 4 no change no changeBay City, MI 33.4 2 no change no changeBeaumont, TX 46.1 1 no change no changeBellingham, WA -1.6 69 up upBend, OR 0.8 40 up upBethesda, MD [Metropolitan Division] -2.6 79 up upBillings, MT -3.1 83 no change upBinghamton, NY -3.5 86 no change upBirmingham, AL 5.2 13 down downBismarck, ND -1.1 63 no change no changeBlacksburg, VA 1.6 32 down downBloomington, IL -0.1 51 no change upBloomington, IN -1.7 70 no change upBoise City, ID 0.4 45 no change no changeBoston, MA [Metropolitan Division] -3.0 83 up upBoulder, CO -7.7 99 no change upBowling Green, KY -0.7 58 up upBradenton, FL 15.0 6 no change no changeBremerton, WA 3.3 21 no change upBridgeport, CT 4.0 18 down no changeBrownsville, TX 2.6 24 no change no changeBrunswick, GA 8.7 8 no change no changeBuffalo, NY 2.0 28 no change no changeBurlington, NC -0.2 52 up upBurlington, VT -4.1 90 no change upCambridge, MA [Metropolitan Division] 4.4 16 no change no changeCamden, NJ [Metropolitan Division] 6.9 10 no change no changeCanton, OH 7.2 9 no change downCape Coral, FL 35.6 2 no change no change

Excess Supply Trend of Excess Supply



Appendix 9-3: Supply Balance Indicator, 2008Q3 (cont.)

Months Index Q/Q change Y/Y changeExcess Supply Trend of Excess Supply

Carson City, NV -2.4 78 no change no changeCasper, WY -2.7 80 no change upCedar Rapids, IA 4.5 16 no change no changeChampaign, IL 2.1 28 up upCharleston, SC 3.2 21 no change no changeCharleston, WV -2.1 75 no change no changeCharlotte, NC 4.8 14 no change no changeCharlottesville, VA 2.6 24 no change downChattanooga, TN 3.0 22 no change upCheyenne, WY 0.6 43 no change upChicago, IL [Metropolitan Division] -0.8 59 up upChico, CA 2.6 24 no change no changeCincinnati, OH -2.5 78 up upClarksville, TN 3.4 20 no change downCleveland, OH 32.3 2 no change no changeCleveland, TN 0.9 40 up upCoeur d'Alene, ID 6.5 10 down downCollege Station, TX 0.4 45 up upColorado Springs, CO 4.3 16 no change downColumbia, MO 1.8 30 no change upColumbia, SC 15.2 5 no change no changeColumbus, GA -2.4 78 no change no changeColumbus, IN -1.8 71 no change upColumbus, OH 11.4 7 no change no changeCorpus Christi, TX -4.1 90 no change upCorvallis, OR 6.9 10 no change upCumberland, MD -1.7 70 down no changeDallas, TX [Metropolitan Division] 1.0 38 no change upDalton, GA 3.6 19 no change upDanville, IL 6.8 10 no change upDanville, VA -0.6 57 no change upDavenport, IL 14.3 6 no change no changeDayton, OH 1.4 34 down downDecatur, AL 17.9 5 no change no changeDecatur, IL 0.3 47 up upDeltona, FL -3.6 87 no change upDenver, CO -0.5 56 no change no changeDes Moines, IA 56.8 0 no change no changeDetroit, MI [Metropolitan Division] 0.3 46 down downDothan, AL 1.8 30 no change no changeDover, DE 0.5 44 up upDubuque, IA 5.1 14 no change upDuluth, MN -2.0 74 no change upDurham, NC -0.3 53 no change upEau Claire, WI -0.7 58 up upEdison, NJ [Metropolitan Division] 2.6 24 no change no changeEl Centro, CA 1.0 38 down no changeEl Paso, TX 14.2 6 no change no changeElizabethtown, KY 0.3 47 no change downElkhart, IN 0.8 40 no change upElmira, NY 1.6 32 no change upErie, PA -3.3 85 no change upEugene, OR 3.6 19 no change upEvansville, IN -7.3 99 no change no changeFairbanks, AK 2.1 27 no change upFargo, ND -1.6 69 up upFarmington, NM 8.8 8 no change no change





Fayetteville, AR 2.3 26 no change downFayetteville, NC -0.3 54 up upFlagstaff, AZ 28.9 3 no change no changeFlint, MI 4.9 14 no change downFlorence, AL 5.8 12 down downFlorence, SC 2.4 26 no change upFond du Lac, WI -3.4 86 no change upFort Collins, CO 56.9 0 no change no changeFort Lauderdale, FL [Metropolitan Division] -2.9 82 no change no changeFort Smith, AR 75.6 0 no change no changeFort Walton Beach, FL -1.9 72 up upFort Wayne, IN -1.3 65 down no changeFort Worth, TX [Metropolitan Division] -0.9 60 up upFresno, CA 9.8 7 down downGadsden, AL 0.0 50 down downGainesville, FL -2.5 78 no change no changeGainesville, GA 4.1 17 no change no changeGary, IN [Metropolitan Division] 2.2 26 no change no changeGlens Falls, NY 2.6 24 up upGoldsboro, NC 1.8 30 up upGrand Forks, ND -2.7 81 no change no changeGrand Junction, CO 0.1 49 up upGrand Rapids, MI 11.6 7 no change no changeGreat Falls, MT -1.0 61 up upGreeley, CO 5.3 13 no change no changeGreen Bay, WI -0.7 58 no change upGreensboro, NC 1.3 35 no change no changeGreenville, NC 0.1 49 no change no changeGreenville, SC 78.6 0 no change no changeGulfport, MS -0.5 56 up upHagerstown, MD -0.5 56 up upHanford, CA -0.5 56 no change no changeHarrisburg, PA 2.4 25 no change no changeHarrisonburg, VA 0.5 44 no change upHartford, CT -3.3 85 down downHattiesburg, MS -2.9 82 up upHickory, NC 13.2 6 no change no changeHinesville, GA 4.7 15 no change no changeHolland, MI 9.2 8 no change downHonolulu, HI -6.3 98 no change no changeHot Springs, AR 111.5 0 down downHouma, LA 1.3 35 down downHouston, TX 0.1 49 up upHuntington, WV 2.7 24 down downHuntsville, AL 3.2 21 no change downIdaho Falls, ID -0.6 57 no change upIndianapolis, IN -1.1 63 no change upIowa City, IA -4.1 90 no change upIthaca, NY 6.4 11 no change no changeJackson, MI 6.4 11 down downJackson, MS 4.7 15 no change no changeJackson, TN 4.3 16 no change no changeJacksonville, FL 5.8 12 no change upJacksonville, NC 0.8 40 no change upJanesville, WI -1.2 64 no change no changeJefferson City, MO 1.0 39 no change no changeJohnson City, TN 7.1 10 no change up





Johnstown, PA -0.1 52 down downJonesboro, AR -5.6 96 no change no changeJoplin, MO 4.2 17 no change upKalamazoo, MI 0.9 40 no change no changeKankakee, IL -0.3 53 up upKansas City, MO -0.2 52 up upKennewick, WA 4.6 15 down downKilleen, TX 1.4 34 no change upKingsport, TN 2.3 26 no change upKingston, NY 0.2 47 no change no changeKnoxville, TN 8.6 8 no change upKokomo, IN 2.2 26 no change no changeLa Crosse, WI -2.7 80 no change upLafayette, IN 92.9 0 down downLafayette, LA -10.1 <1 NC NCLake Charles, LA -1.5 68 up upLake County, IL [Metropolitan Division] 1.3 36 up upLake Havasu, AZ 3.7 19 no change no changeLakeland, FL -1.8 71 no change upLancaster, PA 8.2 9 no change no changeLansing, MI 2.5 24 down no changeLaredo, TX 0.6 43 no change no changeLas Cruces, NM 0.8 41 no change upLas Vegas, NV -1.7 70 no change no changeLawrence, KS 2.2 27 no change upLawton, OK 0.2 47 no change downLebanon, PA -2.4 77 no change upLewiston, ID 2.3 26 no change upLewiston, ME -0.1 51 up upLexington, KY 6.7 10 no change no changeLima, OH -1.9 73 no change upLincoln, NE 2.2 27 no change no changeLittle Rock, AR 2.4 25 no change no changeLogan, UT -1.8 71 down downLongview, TX -3.2 84 no change no changeLongview, WA 0.1 49 up upLos Angeles, CA [Metropolitan Division] -2.2 75 up upLouisville, KY 6.5 10 down no changeLubbock, TX 1.5 33 down downLynchburg, VA 11.6 7 no change no changeMacon, GA 2.1 27 up upMadera, CA -1.9 73 no change upMadison, WI 5.9 12 no change no changeManchester, NH 22.8 4 no change no changeMansfield, OH 3.2 21 down no changeMcAllen, TX -0.9 61 up upMedford, OR 5.5 12 no change no changeMemphis, TN 4.0 17 no change upMerced, CA 21.6 4 no change no changeMiami, FL [Metropolitan Division] 8.7 8 no change no changeMichigan City, IN -5.1 95 no change no changeMidland, TX -1.0 61 up upMilwaukee, WI -2.5 78 up upMinneapolis, MN -4.1 90 no change upMissoula, MT -4.4 92 no change upMobile, AL 2.0 28 up upModesto, CA 138.6 0 down down





Monroe, LA 2.0 28 up upMonroe, MI -1.2 65 down downMontgomery, AL -5.7 97 no change no changeMorgantown, WV -1.5 68 down downMorristown, TN -1.7 70 no change upMount Vernon, WA 24.1 3 no change no changeMuncie, IN 1.9 29 up upMuskegon, MI 3.9 18 no change downMyrtle Beach, SC -2.5 78 up upNapa, CA 6.0 11 no change downNaples, FL 0.0 50 no change no changeNashville, TN 10.5 7 no change no changeNassau, NY [Metropolitan Division] -1.4 67 no change upNew Haven, CT -9.1 100 up upNew Orleans, LA -4.7 93 no change no changeNew York, NY [Metropolitan Division] 5.7 12 no change upNewark, NJ [Metropolitan Division] 14.1 6 no change no changeNiles, MI 3.5 20 no change upNorwich, CT -2.8 81 up upOakland, CA [Metropolitan Division] 1.7 31 no change no changeOcala, FL 18.0 5 no change no changeOcean City, NJ -7.4 99 no change no changeOdessa, TX -0.1 51 no change upOgden, UT -0.2 52 no change no changeOklahoma City, OK 1.0 38 no change downOlympia, WA -1.6 69 up upOmaha, NE 5.0 14 no change downOrlando, FL -0.5 56 no change upOshkosh, WI -2.4 78 up upOwensboro, KY -2.9 82 up upOxnard, CA 5.9 12 no change no changePalm Bay, FL -2.3 76 up upPalm Coast, FL 29.3 3 no change no changePanama City, FL 1.0 38 up upParkersburg, WV 67.5 0 no change no changePascagoula, MS -3.7 88 up upPeabody, MA 3.4 20 no change upPensacola, FL 3.0 22 no change no changePeoria, IL 0.8 40 up upPhiladelphia, PA [Metropolitan Division] -0.7 58 no change upPhoenix, AZ 46.7 1 no change no changePine Bluff, AR 14.5 6 no change no changePittsburgh, PA 3.4 20 up upPittsfield, MA 7.9 9 no change no changePocatello, ID 4.1 17 no change no changePort St. Lucie, FL 5.3 13 no change no changePortland, ME -3.5 86 no change upPortland, OR -0.4 55 no change no changePoughkeepsie, NY -0.5 56 up upPrescott, AZ 9.9 7 no change no changeProvidence, RI 2.9 22 no change downProvo, UT -0.7 58 up upPueblo, CO 28.6 3 no change no changePunta Gorda, FL 3.2 21 no change upRacine, WI -2.1 75 no change no changeRaleigh, NC 3.1 22 no change upRapid City, SD -3.9 89 no change no change





Reading, PA 0.7 42 up upRedding, CA 0.5 45 up upReno, NV -0.8 59 no change upRichmond, VA -0.2 53 up upRiverside, CA 0.3 46 no change upRoanoke, VA -1.8 72 up upRochester, MN 4.4 16 no change no changeRochester, NY -3.5 86 no change upRockford, IL 0.9 40 no change upRockingham County, NH [Metropolitan Division] 1.7 31 no change upRocky Mount, NC 7.0 10 no change no changeRome, GA -1.9 72 up upSacramento, CA 38.1 2 no change no changeSaginaw, MI -4.4 92 no change upSalem, OR 0.5 44 up upSalinas, CA 1.1 37 no change upSalisbury, MD -2.2 76 no change no changeSalt Lake City, UT 6.5 11 down downSan Angelo, TX -0.8 59 down no changeSan Antonio, TX -1.9 73 up upSan Diego, CA -8.8 100 no change no changeSan Francisco, CA [Metropolitan Division] -7.8 99 no change no changeSan Jose, CA 0.0 50 up upSan Luis Obispo, CA 12.8 6 no change no changeSandusky, OH -1.1 63 up upSanta Ana, CA [Metropolitan Division] -3.7 88 up upSanta Barbara, CA -1.5 68 up upSanta Cruz, CA -6.0 97 no change upSanta Fe, NM 1.7 31 up upSanta Rosa, CA 4.9 14 no change no changeSavannah, GA 2.6 24 no change downScranton, PA 2.5 24 no change no changeSeattle, WA [Metropolitan Division] -2.4 77 no change upSebastian, FL 9.4 8 no change no changeSheboygan, WI 0.6 43 up upSherman, TX 0.3 46 down downShreveport, LA 106.2 0 down downSioux City, IA -5.2 95 no change upSioux Falls, SD -2.3 76 no change no changeSouth Bend, IN 4.7 15 no change upSpartanburg, SC -0.1 51 no change upSpokane, WA -0.6 57 up upSpringfield, IL 0.5 44 no change upSpringfield, MA -1.3 65 up upSpringfield, MO -0.8 59 no change upSpringfield, OH 4.3 16 no change upSt. Cloud, MN 2.0 29 no change upSt. George, UT 1.0 38 no change no changeSt. Joseph, MO -4.8 93 no change upSt. Louis, MO 2.8 23 no change upState College, PA -0.4 55 no change upStockton, CA -0.4 54 up upSumter, SC 19.0 4 no change no changeSyracuse, NY 2.9 22 no change upTacoma, WA [Metropolitan Division] -0.3 54 no change upTallahassee, FL 4.1 17 no change no changeTampa, FL 1.8 30 no change no change





Terre Haute, IN 7.5 9 no change no changeTexarkana, TX 1.9 29 down downToledo, OH 18.5 4 no change no changeTopeka, KS 0.2 48 up upTrenton, NJ 2.9 22 no change upTucson, AZ 0.0 50 up upTulsa, OK 0.8 41 down no changeTuscaloosa, AL 5.3 13 down downTyler, TX -5.2 95 no change no changeUtica, NY 0.3 46 no change no changeValdosta, GA 2.3 26 no change no changeVallejo, CA 13.8 6 no change no changeVictoria, TX -0.7 58 down upVineland, NJ -0.2 52 no change downVirginia Beach, VA 12.9 6 no change no changeVisalia, CA -1.2 65 up upWaco, TX 5.3 13 down no changeWarner Robins, GA 4.3 16 no change no changeWarren, MI [Metropolitan Division] 3.8 18 up upWashington, DC [Metropolitan Division] 0.5 44 up upWaterloo, IA 1.5 33 no change upWausau, WI 5.6 12 no change no changeWeirton, OH 32.2 2 no change no changeWenatchee, WA -1.1 63 no change no changeWest Palm Beach, FL [Metropolitan Division] 14.6 6 no change no changeWheeling, WV 14.2 6 no change no changeWichita Falls, TX 62.9 0 no change no changeWichita, KS -1.9 73 up upWilliamsport, PA 6.5 10 no change upWilmington, DE [Metropolitan Division] -2.3 76 up upWilmington, NC 1.9 29 no change no changeWinchester, VA 2.3 26 no change no changeWinston, NC -0.7 58 no change upWorcester, MA -0.3 54 up upYakima, WA -1.5 67 no change upYork, PA -0.4 55 no change downYoungstown, OH 33.2 2 no change no changeYuba City, CA -0.6 57 up upYuma, AZ 0.5 44 no change up

Notes:

"No Change" otherwise.

Months of Excess Supply equals the level of excess supply divided by annual demand.Index of Excess Supply is a value from 0 to 100 indicating the current excess supply condition relative to conditions over the past 10 years in all metros.A value of 0 is extreme excess supply, and a value of 100 is extreme excess demand.

Trend of Excess Supply."Up" means Index of Excess Supply has risen 5 or more points in last four quarters."Down" means Index of Excess Supply has fallen 5 or more points in last four quarters.

Excess Supply equals supply less demand.Single-family housing supply is defined as the three-year annual average of single-family household completions.Single-family housing demand is defined as the three-year annual average of single-family household formations plus estimated obsolescence plus vacation- and second-home demand.

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