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| The MarketPulse g March 2019 g Volume 8, Issue 3
The MarketPulse
MARCH 2019
© 2019 CoreLogic — Proprietary. This material may not be reproduced in any form without express written permission.ii
Table of Contents | The MarketPulse March 2019 Volume 8, Issue 3
Table of Contents
Housing Recessions and Recoveries .............................................................1
Economic Observations, March 2019
Characteristics of Today’s Non-Qualifi ed Mortgages ................................2
Limited Documentation and High Debt-to-Income, but High Credit Score and Low Loan-to-Value
Younger Households Support Homeownership Growth for Eight Consecutive Quarters .....................................................................4New U.S. Census Bureau Data Shows Households of Under 44 Helped Raise the Homeownership Rate in 2017 and 2018
Home Price and Mortgage Rate Forecasts Suggest Smaller Gains in the Mortgage Payments Homebuyers Will Face This Year .....................5Annual Gain in “Typical Mortgage Payment” Last December Fell Six Percentage Points Compared With Prior Month, When Rates Hit Seven-Year High
In the News .................................................................................................................................... 6
10 Largest CBSA — Loan Performance Insights Report December 2018 ............................... 6
Home Price Index State-Level Detail — Combined Single Family Including DistressedJanuary 2019 ................................................................................................................................. 6
Home Price Index .......................................................................................................................... 7
Overview of Loan Performance .................................................................................................. 7
CoreLogic HPI® Market Condition Overview ............................................................................. 8January 2019January 2024 Forecast
National Home Equity Distribution ............................................................................................... 9
Map of Average Year-Over-Year Equity Gain per Borrower ................................................... 9
Variable Descriptions .................................................................................................................. 10
Housing Statistics
March 2019
HPI® YOY Chg 4.4%
HPI YOY Chg XD 4.1%
NegEq Share (Q4 2018) 5.4%
The MarketPulseVolume 8, Issue 3March 2019Data as of January 2019(unless otherwise stated)
News Media Contact
Alyson [email protected] 949.214.1414 (offi ce)
© 2019 CoreLogic — Proprietary. This material may not be reproduced in any form without express written permission. 1
The MarketPulse g March 2019 g Volume 8, Issue 3 | Articles
Housing Recessions and RecoveriesEconomic Observations, March 2019
By Ralph McLaughlin
Lately, it seems like the roof of the housing market might cave in. Several of the major housing market indicators have shown much volatility as of late. For instance, the S&P CoreLogic Case-Shiller Home Price Index fell for seven straight months, new home sales dropped a whopping 12 percent at the end of last year, and inventory is starting rise. On top of that, we’re six month’s short of the longest economic expansion. As such, it is understandable why some might think that the housing market and broader cycle is coming to an end.
Despite these troublesome signs, there are several reasons to be optimistic that the housing market is in good shape to weather a downturn.
First, broad and deep troughs in housing prices is the exception, rather than the rule, during recessions. If we look at the past five recessions, we see that home prices typically weather down turns quite well. For example, home prices grew 6.6 percent during the Dot-Com recession in 2001. And during the 1980 and 1981 recessions, prices grew by 6.1 percent and 3.5 percent, respectively. In fact, just two of the past five recessions brought decreases in home prices: a small 1.9 percent drop during the 1991 recession, and, of course, the massive 19.7 percent price drop during the Great Recession.
Second, housing inventory struggles to keep pace with demand. Total single-family inventory, which is the sum of newly-built and used homes, sits at just 15.7 homes per 1,000 household. This is up slightly from the record low of 14.9 set in December 2017. The fact that we’re at historically low inventory is important because it means we’re in a very different supply environment compared to the massive run up in inventory that appeared before the onset of the Great Recession. Today’s low supply environment means that prices are unlikely to fall far, if at all, during the next recession.
Lastly, the demographic structure of the United States should continue to support prime-household growth for over the next two decades. Currently, just under 46 percent of the U.S. population is under 35. As this cohort ages and gets their housing-market sea legs, we should expect them to form new households as they enter into their peak marital and child-bearing years. For example, The Harvard Joint Center for Housing Studies estimates Millennial households are expected to grow by 32 million over the next twenty years. That’s a lot of new homes that will be needed, regardless of whether they buy or rent. This surge in demand should continue to put upward pressure on the housing market until at least 2040. ■
FIGURE 2. TOTAL HOME INVENTORY (NEW+EXISTING)1988–2018
0
5
10
15
20
25
30
35
40
Oct
-88
Jun-
90
Feb-
92
Oct
-93
Jun-
95
Feb-
97
Oct
-98
Jun-
00
Feb-
02
Oct
-03
Jun-
05
Feb-
07
Oct
-08
Jun-
10
Feb-
12
Oct
-13
Jun-
15
Feb-
17
Oct
-18
Inve
ntor
y pe
r 1,0
00 H
ouse
hold
s
2001-04Refi Boom
2009-13Refi Boom
Tax ImplementedAugust 2016
Mclaughlin 1: fig 2Peak 18.4%
Source: NAR Existing Home Sales and U.S. Census New Residential Construction.
FIGURE 1. CORELOGIC NATIONAL HOUSE PRICE INDEXAnnual Change
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
1-O
ct-7
8
1-Ju
n-80
1-Fe
b-82
1-O
ct-8
3
1-Ju
n-85
1-Fe
b-87
1-O
ct-8
8
1-Ju
n-90
1-Fe
b-92
1-O
ct-9
3
1-Ju
n-95
1-Fe
b-97
1-O
ct-9
8
1-Ju
n-00
1-Fe
b-02
1-O
ct-0
3
1-Ju
n-05
1-Fe
b-07
1-O
ct-0
8
1-Ju
n-10
1-Fe
b-12
1-O
ct-1
3
1-Ju
n-15
1-Fe
b-17
1-O
ct-1
8
Recession National HPI
2001-04Refi Boom
2009-13Refi Boom
Tax ImplementedAugust 2016
Mclaughlin 1: fig 1Peak 18.4%
Source: CoreLogic HPI, (December 4, 2018 release)
Ralph McLaughlinDeputy Chief Economist
Ralph McLaughlin holds the title deputy chief economist for CoreLogic in the Office of the Chief Economist. He is responsible for leading economic research and using data and analytics to expand the visibility of the CoreLogic economic policy unit. He also works to enhance research capabilities and tools for clients, industry leaders, the public sector and news media.
Ralph has more than 15 years of experience in housing economics, applied econometrics, real estate development and investment, land use planning, spatial analysis, and economic geography. He previously worked at Trulia and Veritas Urbis Economics. He also served as an assistant professor at the San Jose State University. While at Trulia, he led the company’s housing economics research team, providing buyers with key insights about the economy, housing trends and public policy.
© 2019 CoreLogic — Proprietary. This material may not be reproduced in any form without express written permission.2
Articles | The MarketPulse March 2019 Volume 8, Issue 3
Continued on page 3
Characteristics of Today’s Non-Qualifi ed MortgagesLimited Documentation and High Debt-to-Income, but High Credit Score and Low Loan-to-Value
By Archana Pradhan
1 QM regulation went into effect in January 2014.2 QM regulations were used to identify pre-2014 non-QM
equivalent loans.3 Only conventional loans with valid values for the variables
needed to identify QM loans were included. 4 DTI for “non-QM equivalent” is likely too low in 2005–2006 or
missing many high DTI observations.
Archana PradhanEconomist
Archana Pradhan is an economist for CoreLogic in the Offi ce of the Chief Economist and is responsible for analyzing housing and mortgage markets trends.
FIGURE 1. NON-QM EQUIVALENT CONVENTIONAL HOME-PURCHASE LOANS BY COMPOSITION OF RISK FLAGSLoans exceeding 43 percent DTI threshold rising in recent years
0%
20%
40%
60%
80%
100%
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
DTI>43 for non-GSE and Gov loans Low/no doc Interest-only payments Balloon payments Term> 30 yrs Negative amortization
Source: CoreLogic March 2019
Five years have passed since the Consumer Financial Protection Bureau (CFPB) issued regulations to provide safer and more sustainable home loans for consumers, known as Qualifi ed Mortgages (QMs).1 The Dodd-Frank Wall Street Reform and Consumer Protection Act imposed an obligation on lenders to make a good-faith effort to determine that the applicants have the ability to repay the mortgage. This is known as the ability-to-repay (ATR) rule. The Act also mandates that QM loans cannot have risky loan features like negative amortization, interest-only, balloon payments, terms beyond 30 years or excessive points and fees. QM loans must also satisfy at least one of the following three criteria:
1. Borrower’s debt-to-income (DTI) ratio is 43 percent or less
2. Loan is eligible for purchase, guarantee or insurance through the Federal Housing Administration, Veterans Affairs, United States Department of Agriculture or a government-sponsored enterprise (GSE), regardless of the DTI ratio
3. Loan was originated by insured depositories with total assets less than $10 billion and must be held in portfolio for at least three years.
Any home loan that doesn’t comply with the QM rules is called non-QM. A non-QM loan is not necessarily a high-risk loan, it’s merely a loan that doesn’t meet the QM standards. Examples of a non-QM loan include interest-only or limited/alternative documentation loans. A non-QM loan still needs to satisfy the ATR requirements.
The non-QM market is expanding (up by 1 percentage point from 2017 to 2018) and represented about 4 percent of 2018 originations. Although the non-QM market is just a small piece of today’s mortgage market, it plays a key role in meeting the credit needs for homebuyers who are not able to obtain fi nancing through a GSE or government channels. Creditworthy borrowers not applying for GSE or government-insured loans may benefi t from non-QM options. These may include self-employed borrowers, fi rst-time homebuyers, borrowers with substantial assets but limited income, jumbo loan borrowers and investors.
Figure 1 compares the non-QM equivalent loans from 2001 to 2018 by composition of six major risk features.2 All conventional home-purchase loans not meeting at least one of these six QM-mandated criteria were included.3 The three main reasons why non-QM loans that originated in 2018 failed to fi t in the QM box were use of limited or alternative documentation, DTI above 43 percent and
© 2019 CoreLogic — Proprietary. This material may not be reproduced in any form without express written permission. 3
The MarketPulse g March 2019 g Volume 8, Issue 3 | Articles
Characteristics of Today’s Non-Qualified Mortgages continued from page 2
FIGURE 2. UNDERWRITING TRENDS FOR FIRST-LIEN HOME-PURCHASE LOANS: 2000 TO 2018(Averages of Credit Score, LTV and DTI)
Source: CoreLogic March 2019
30
34
38
42
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Average DTI
avgDTI_nonQM avgDTI avgDTI
archana: fig 2b
Mean: 1.6%Standard deviation: 5.7%Low appraisals: 9.8%
Leng
th o
f Sta
y
640
680
720
760
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Average Credit Score
avgFICO_nonQM avgFICO avgFICO
archana: fig 2a
Mean: 1.6%Standard deviation: 5.7%Low appraisals: 9.8%
Leng
th o
f Sta
y
70
80
90
100
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
Average LTV
Non-QM Government Loans QM Conventional
archana: fig 2c
Mean: 1.6%Standard deviation: 5.7%Low appraisals: 9.8%
Leng
th o
f Sta
y
interest-only loans. Almost 46 percent of the non-QM borrowers exceeded 43 percent DTI threshold, 44 percent used limited or alternative documentation and 13 percent of the non-QMs were interest-only loans. Share of non-QM loans exceeding 43 percent DTI threshold has increased by more than three times in 2018 compared with 2014. However, some of the riskier factors such as negative amortization and balloon payments have completely vanished.
Today’s non-QMs are high quality. They are vastly different and safer than their pre-crisis counterparts. Figure 2 shows the trend of three major variables of underwriting for first-lien home purchase loans: credit score, DTI and loan-to-value (LTV) ratio. In 2018, the average credit score of homebuyers with non-QMs was 760, compared to a score of 754 for homebuyers with QMs. Similarly, the average first-lien LTV for borrowers with non-QMs was 79 percent compared to 81 percent for borrowers with QMs. However, average DTI for homebuyers with non-QMs was higher compared with the DTI for borrowers with QMs.4
Despite having DTI ratios that are higher than conventional QM loans today, non-QMs are performing very well. Both the non-QM and QM conventional loans had low delinquency rates in 2018. In fact, the serious delinquency rate for non-QM loans is slightly lower than the rate for conventional QM loans and government-insured loans in 2018. Lenders are using high credit score and low LTV to help offset the added risk from high DTI, limited documentation and interest-only non-QM loans. ■
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Articles | The MarketPulse g March 2019 g Volume 8, Issue 3
1 In the event that the change for consecutive months is identical
to the previous months by one decimal point, we expand the
measure to multiple decimal points to break the tie.
Younger Households Support Homeownership Growth for Eight Consecutive Quarters New U.S. Census Bureau Data Shows Households of Under 44 Helped Raise the Homeownership Rate in 2017 and 2018
By Ralph McLaughlin
“…homeownership data shows the U.S. housing market continues on a healthy path of recovery.”
FIGURE 1. YEAR OVER YEAR HOUSEHOLD FORMATIONBy Tenure
-1,000
-500
0
500
1,000
1,500
2,000
2,500
2014
Q1
2014
Q2
2014
Q3
2014
Q4
2015
Q1
2015
Q2
2015
Q3
2015
Q4
2016
Q1
2016
Q2
2016
Q3
2016
Q4
2017
Q1
2017
Q2
2017
Q3
2017
Q4
2018
Q1
2018
Q2
2018
Q3
2018
Q4
New Owner Households New Renter Households
2001-04Refi Boom
2009-13Refi Boom
Tax ImplementedAugust 2016
Mclaughlin 3: fig 1Peak 18.4%
Source: HVS/CPS (February 28, 2019 release)
FIGURE 2: PRICE-TO-RENT RATIO FOR SELECT METROS
Source: CoreLogic Home Price Index (for homes valued at 75% to 100% of metro area median) and Single-Family Rent Index
Metro Areas
Millennial Mortgage Applicants
(Largest % of Total)
Pittsburgh, PA 57%
Provo, UT 56%
Rochester, NY 55%
Buffalo, NY 55%
Des Moines, IA 54%
Metro Areas
Millennial Mortgage Applicants
(Smallest % of Total)
Sarasota, FL 24%
Cape Coral, FL 30%
Ventura, CA 32%
Palm Beach, CA 33%
Miami, FL 35%
Continued on page 6
According to the latest U.S. Census Housing Vacancies and Homeownership data release, the homeownership rate grew to 64.8 percent in the fourth quarter of 2018. This is the eighth1 consecutive quarter of increasing year-over-year gains. Young households—44 and under—have seen the largest increase with those under 35 years old and those 35–44 seeing the largest gains, increasing from 36 and 58.9 percent to 36.5 and 61.1 percent, respectively over the past year.
Why is the homeownership rate on the rise? From a technical standpoint, it’s because of strong owner-occupied household
formation. The fourth quarter of 2018 was the fifth consecutive quarter that owner-occupied households grew by more than a million, at 1.7 million new owner households. At the same time, the number of new renter households either fell six out of the past seven quarters with a decrease of 167,000 households. This suggests that the increase in the homeownership rates is at least partly due to households making a switch from renting to owning. What’s more, total household growth has topped 1 percent for five straight quarters, which is positive news for the housing industry at large. This streak represents the longest and largest magnitude of household growth in more than 12 years.
While much of the recent growth in homeownership rates is due to young households buying homes, they aren’t doing so ubiquitously across the country. Using CoreLogic mortgage application data, we found millennials are buying homes at the highest rates in the more affordable Midwest, Mountain West and Northeast markets. Conversely, they are buying homes at the lowest rates in more expensive markets, like coastal California and Florida. For example, millennials make up the largest share of purchase mortgage applicants in Pittsburgh, Pennsylvania (57 percent), Provo, Utah (56 percent) and Rochester, New York
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The MarketPulse g March 2019 g Volume 8, Issue 3 | Articles
Continued on page 6
Home Price and Mortgage Rate Forecasts Suggest Smaller Gains in the Mortgage Payments Homebuyers Will Face This YearAnnual Gain in “Typical Mortgage Payment” Last December Fell Six Percentage Points Compared With Prior Month, When Rates Hit Seven-Year High
By Andrew LePage
1 Based on the average mortgage rate forecast from Freddie Mac,
Fannie Mae, Mortgage Bankers Association, National Association
of Realtors, National Association of Home Builders and IHS Markit.2 Inflation adjustments made with the U.S. Bureau of Labor Statistics
Consumer Price Index (CPI), Urban Consumer—All Items.
Andrew LePageResearch Analyst
Andrew LePage joined CoreLogic in 2015 as a research analyst working in the Office of the Chief Economist. Previously, Andrew was an analyst and writer for DQNews, a partner of DataQuick (acquired by CoreLogic in 2014). Andrew provided real estate data and trend analysis to journalists and issued a variety of housing market reports to the news media on behalf of DataQuick. Prior to that he was a staff writer at the Sacramento Bee newspaper covering residential real estate topics in the capital region and across California. He continues to monitor California’s housing market for CoreLogic in two monthly data briefs detailing trends in Southern California and the San Francisco Bay Area.
FIGURE 1. COMPARING MTG RATES TO THE YR/YR CHNG IN THE REAL MEDIAN PRICE & TYPICAL MTG PMTYoY % Change in Real Median Price and Real Typical Mtg Pmt Monthly Avg Rate for 30-Yr Fixed-Rate Mtg
Fo
reca
st
Dec-18: 9.9%
Dec-19: 3.5%
0
1
2
3
4
5
6
7
8
-30%
-20%
-10%
0%
10%
20%
30%
Jan-
01
Jul-0
2
Jan-
04
Jul-0
5
Jan-
07
Jul-0
8
Jan-
10
Jul-1
1
Jan-
13
Jul-1
4
Jan-
16
Jul-1
7
Jan-
19
Avg 30-year Mtg Rate YoY Change in Real Median$ YoY Change in Real Typical Mtg Pmt
lepage: fig 1
Source: CoreLogic’s Real Estate Analytics Suite, Bureau of Labor Statistics, Freddie Mac (for current and past mortgage rates), IHS Markit (for CPI forecast) and IHS, Freddie Mac, Fannie Mae, National Association of Home Builders, Mortgage Bankers Association and National Association of Realtors for averaging mortgage rate forecasts. Chart forecast period begins Feb-18.
While the nation’s median home sale price rose about 4 percent year over year in December 2018, the principal-and-interest mortgage payment on that median-priced home increased nearly three times as much because mortgage rates rose by more than half a percentage point over that period. However, some forecasts for home prices and mortgage rates indicate mortgage payments will rise at a much slower pace this year, which could help stoke home sales this spring.
One way to measure the impact of inflation, mortgage rates and home prices on affordability over time is to use what we call the “typical mortgage payment.” It’s a mortgage-rate-adjusted monthly payment based on each month’s U.S. median home sale price. It is calculated using Freddie Mac’s average rate on a 30-year fixed-rate mortgage with a 20 percent down payment. It does not include taxes or insurance. The typical mortgage payment is a good proxy for affordability because it shows the monthly amount that a borrower would have to qualify for to get a mortgage to buy the median-priced U.S. home.
The U.S. median sale price in December 2018—$220,305—was up 4.4 percent year over year, while the typical mortgage payment was up 12.1 percent because of a
0.6-percentage-point rise in mortgage rates over that one-year period. That 12.1 percent gain in the typical payment was down from a 17.8 percent increase in November 2018, when mortgage rates hit a seven-year high.
Looking ahead, the CoreLogic Home Price Index Forecast suggests a 4.5 percent annual gain in home prices by this December, while the average among six rate forecasts1 indicates a small increase—0.1 percentage points—in mortgage rates this December compared with December 2018.
The CoreLogic HPI Forecast suggests the median sale price will rise 2.1 percent in real, or inflation-adjusted, terms over the year ending December 2019 (or 4.5 percent in nominal, or not-inflation-adjusted, terms). Based on that projection, coupled with the aforementioned consensus mortgage rate forecast, the real
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Articles | The MarketPulse g March 2019 g Volume 8, Issue 3
Home Price and Mortgage Rate Forecasts continued from page 5
Younger Households continued from page 4
FIGURE 2. NATIONAL HOMEBUYERS’ “TYPICAL MORTGAGE PAYMENT”Inflation-Adjusted Monthly Mortgage Payment That Buyers Commit To
Jun-06: $1,275
Dec-18: $904
Dec-19: $935
$400
$500
$600
$700
$800
$900
$1,000
$1,100
$1,200
$1,300
$1,400
Jan-00 Jan-02 Jan-04 Jan-06 Jan-08 Jan-10 Jan-12 Jan-14 Jan-16 Jan-18
Forecast
lepage: fig 2
Source: CoreLogic’s Real Estate Analytics Suite, Bureau of Labor Statistics, Freddie Mac (for current and past mortgage rates), IHS Markit (for CPI forecast) and IHS, Freddie Mac, Fannie Mae, National Association of Home Builders, Mortgage Bankers Association and National Association of Realtors for averaging mortgage rate forecasts. Chart forecast period begins Feb-18.
In the News
U.S. News, March 14
Economic Indicators That Will Help You
Understand the Housing Market
“The U.S. real estate market looks like it is on solid
ground” this year, says Ralph McLaughlin, deputy chief
economist at business intelligence firm CoreLogic.
Houston Chronicle, March 14
Home rentals surge as buyers stay on
sidelines
Frank Nothaft, chief economist for the real estate
analytics company CoreLogic, also argued that, for
this winter at least, renting made more sense than
buying for many people.
CNBC, March 13
California housing seen cooling further
going into 2020: UCLA forecast
In Southern California and the San Francisco Bay
Area, home sales fell to an 11-year low in January,
according to CoreLogic.
HousingWire, March 12
Foreclosure rate falls to near 20-year low
CoreLogic said the overall delinquency rate has fallen
steadily since the beginning of 2018 to pre-crisis levels
not seen since early 2006.
Washington Post, March 8
It turns out Americans weren’t ready
to become a nation of renters.
Homeownership is back in
“When there’s very low unemployment, when there’s
been slow but steady wage growth, that tends to make
households confident in their ability to make what will
probably be their largest investment of their life,” said
Ralph McLaughlin. McLaughlin is an economist at the
real estate data outfit CoreLogic.
typical monthly mortgage payment would rise from $904 in December 2018 to $935 by December 2019, a 3.5 percent year-over-year gain (Figure 1), down from a 9.9 percent gain a year earlier. In nominal terms the typical mortgage payment’s year-over-year increase in December 2019 would be 6.0 percent, or about half the 12.1 percent gain a year earlier.
If forecasts for prices, rates and income hold, homebuyers will lose less purchasing power this year compared with 2018. An IHS Market forecast indicates a roughly 2.1 percent annual rise in real disposable income this December, while the rate and price forecasts suggest the real typical mortgage payment will rise 3.5 percent. The annual gain in real disposable income last December was about
3.3 percent while the real typical mortgage payment was up 9.9 percent.
When adjusted for inflation2 the typical mortgage payment puts homebuyers’ current costs in the proper historical context. Figure 2 shows that while the real typical mortgage payment has trended higher in recent years, in December 2018 it remained 29.2 percent below the all-time peak of $1,275 in June 2006. That’s because the average mortgage rate back in June 2006 was about 6.7 percent, compared with an average rate of about 4.6 percent in December 2018, and the real U.S. median sale price in June 2006 was $247,075 (or $197,100 in 2006 dollars), compared with a December 2018 median of $220,305. ■
(55 percent), but make up the lowest share of mortgage applicants in Sarasota, Florida (24 percent), Cape Coral, Florida (30 percent) and Ventura, California (32 percent).
More broadly, the homeownership data shows the U.S. housing market continues on a healthy path of recovery. This is for three reasons: first,
the upward tick in homeownership has been stubbornly persistent, despite the existence of low housing affordability and inventory; next, household formation has seen the strongest streak in over a decade; and lastly, young households, which represent the largest pool of potential homebuyers in the United States, are starting to enter the homeownership game. ■
© 2019 CoreLogic — Proprietary. This material may not be reproduced in any form without express written permission. 7
The MarketPulse g March 2019 g Volume 8, Issue 3 | Analysis
“The spike in mortgage interest rates last fall chilled buyer activity and led to a slowdown in home sales and price growth. Fixed-rate mortgage rates have dropped 0.6 percentage points since November 2018 and today are lower than they were a year ago. With interest rates at this level, we expect a solid home-buying season this spring.”
Dr. Frank Nothaft,
chief economist for CoreLogic
Home Price Index State-Level Detail — Combined Single Family Including Distressed January 2019
StateMonth-Over-Month
Percent ChangeYear-Over-Year Percent Change
Forecasted Month-Over-Month
Percent Change
Forecasted Year-Over-Year Percent Change
Alabama −0.4% 4.2% 0.2% 5.5%Alaska 0.3% 1.9% 0.4% 6.8%
Arizona 0.3% 7.1% 0.1% 5.5%Arkansas −0.3% 3.2% 0.1% 4.6%
California −0.3% 3.6% 0.0% 9.5%Colorado 0.3% 5.5% 0.0% 4.4%
Connecticut 0.8% 2.3% 0.3% 7.7%Delaware −0.1% 2.2% 0.1% 5.1%
District of Columbia −0.2% 2.9% 0.1% 4.2%Florida 0.2% 5.1% 0.2% 6.8%
Georgia −0.5% 5.4% 0.1% 4.8%Hawaii −0.2% 2.6% 0.0% 6.1%Idaho −0.3% 11.2% −0.1% 4.1%Illinois −0.4% 2.3% 0.1% 6.3%
Indiana −0.3% 5.7% 0.1% 5.3%Iowa −0.4% 4.0% 0.2% 5.9%
Kansas −0.1% 3.6% 0.3% 5.4%Kentucky −0.4% 4.0% 0.1% 4.7%Louisiana −0.6% −0.8% 0.0% 2.9%
Maine 0.4% 4.0% 0.6% 6.9%Maryland −0.5% 1.9% 0.0% 4.7%
Massachusetts 0.3% 4.1% 0.2% 6.3%Michigan −0.3% 6.1% 0.0% 7.1%
Minnesota −0.4% 5.0% 0.1% 4.8%Mississippi 0.6% 6.1% 0.2% 4.6%
Missouri −0.4% 5.0% 0.2% 5.7%Montana −0.1% 5.2% −0.3% 3.0%Nebraska −0.3% 4.8% 0.1% 5.0%
Nevada 0.1% 10.2% 0.0% 9.2%New Hampshire −0.2% 5.4% 0.2% 6.9%
New Jersey 0.0% 2.9% 0.3% 6.6%New Mexico −0.6% 3.1% −0.2% 4.1%
New York 1.7% 4.0% 0.3% 6.1%North Carolina −0.1% 4.2% 0.1% 4.9%North Dakota 0.5% −0.7% 0.2% 4.5%
Ohio 0.0% 5.6% 0.1% 5.0%Oklahoma −0.2% 2.6% 0.1% 4.2%
Oregon 0.2% 5.1% 0.2% 7.3%Pennsylvania −0.5% 3.9% 0.2% 5.7%Rhode Island −0.2% 6.1% −0.1% 4.8%
South Carolina 0.0% 3.6% 0.2% 5.3%South Dakota −0.2% 0.4% 0.1% 5.0%
Tennessee 0.2% 6.2% 0.1% 4.1%Texas −0.1% 4.1% 0.0% 2.4%Utah 0.8% 8.9% 0.2% 4.8%
Vermont 0.6% 3.0% 0.5% 6.9%Virginia −0.2% 2.6% 0.1% 5.0%
Washington −0.1% 5.5% 0.0% 5.9%West Virginia 3.0% 6.0% 0.0% 5.3%
Wisconsin −0.5% 5.4% 0.1% 5.2%Wyoming −1.0% 2.8% −0.2% 3.6%
Source: CoreLogic January 2019
10 Largest CBSA — Loan Performance Insights Report December 2018
CBSA
30 Days or More Delinquency Rate
December 2018 (%)
Serious Delinquency Rate
December 2018 (%)Foreclosure Rate
December 2018 (%)
30 Days or More Delinquent Rate
December 2017 (%)
Serious Delinquency Rate
December 2017 (%)Foreclosure Rate
December 2017 (%)
Boston-Cambridge-Newton MA-NH 3.2 1.0 0.3 3.9 1.3 0.5
Chicago-Naperville-Elgin IL-IN-WI 4.4 1.7 0.6 5.4 2.2 0.8
Denver-Aurora-Lakewood CO 1.8 0.4 0.1 2.1 0.5 0.1
Houston-The Woodlands-Sugar Land TX 5.2 1.8 0.3 9.8 5.1 0.3
Las Vegas-Henderson-Paradise NV 3.7 1.6 0.6 4.7 2.2 0.9
Los Angeles-Long Beach-Anaheim CA 2.5 0.7 0.1 3.0 0.9 0.2
Miami-Fort Lauderdale-West Palm Beach FL 5.3 2.3 0.9 11.6 6.7 0.8
New York-Newark-Jersey City NY-NJ-PA 5.4 2.7 1.3 6.8 3.7 1.8
San Francisco-Oakland-Hayward CA 1.4 0.4 0.1 1.8 0.6 0.1
Washington-Arlington-Alexandria DC-VA-MD-WV 3.5 1.2 0.3 4.3 1.6 0.5
Source: CoreLogic December 2018
© 2019 CoreLogic — Proprietary. This material may not be reproduced in any form without express written permission.8
Analysis | The MarketPulse g March 2019 g Volume 8, Issue 3
“On a national basis, income and home-price growth continue to support strong loan performance. Although things look good across most of the nation, areas that were impacted by hurricanes and other natural hazards are experiencing a sharp increase in the numbers of mortgages moving into 60-day delinquency or worse. One specific example is Panama City, Florida, which was devastated by Hurricane Michael, where 60-day delinquencies rose to 3.5 percent in December.”
Frank Martell,
president and CEO of CoreLogic
OVERVIEW OF LOAN PERFORMANCENational Delinquency Rates
Source: CoreLogic December 2018
4.1
2.0
0.7
0.3
1.1 1.2
0.4
5.3
2.4
0.8 0.6
1.5 1.5
0.6
0.0
1.0
2.0
3.0
4.0
5.0
6.0
30+ days 30 to 59 days 60 to 89 days 90 to 119 days 90+ days (not infcl)
120+ days In Foreclosure
Perc
enta
ge R
ate
2.61x5.11 / 2.69x4.98loan performance dec 2018: national overview
December 2017
December 201890-119 Days
Past Due120+ DaysPast Due
60-89 DaysPast Due
30-59 DaysPast Due
30 Days or MorePast Due
90+ Days(not in fcl)
HOME PRICE INDEXPercentage Change Year Over Year
Source: CoreLogic January 2019
-20%
-15%
-10%
-5%
0%
5%
10%
15%
20%
2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
2.58x4.99hpi as of jan 2019
Including DistressedIncluding Distressed
Charts & Graphs
© 2019 CoreLogic — Proprietary. This material may not be reproduced in any form without express written permission. 9
The MarketPulse g March 2019 g Volume 8, Issue 3 | Analysis
CORELOGIC HPI® MARKET CONDITION OVERVIEWJanuary 2019
Source: CoreLogic CoreLogic HPI Single Family Combined Tier, data through January 2019. CoreLogic HPI Forecasts Single Family Combined Tier, starting in February 2019.
Legend
Normal
Overvalued
Undervalued
CORELOGIC HPI® MARKET CONDITION OVERVIEWJanuary 2024 Forecast
Source: CoreLogic CoreLogic HPI Single Family Combined Tier, data through January 2019. CoreLogic HPI Forecasts Single Family Combined Tier, starting in February 2019.
Legend
Normal
Overvalued
Undervalued
© 2019 CoreLogic — Proprietary. This material may not be reproduced in any form without express written permission.10
Analysis | The MarketPulse g March 2019 g Volume 8, Issue 3
MAP OF AVERAGE YEAR-OVER-YEAR EQUITY GAIN PER BORROWERAs of Q4 2018
Vermont and South Dakota have insufficient equity data to report. Source: CoreLogic Q4 2018
$17K
$20K
$29K
$19K
$18K
$17K
$16K
$25K
$17K
$10K
$20K
$29K
$19K
$18K$9K
$17K
$16K
$16K –$10K
$8K
$8K
$2K
$7K–$3K
$3K
$8K
$2K
$10K
$9K
$4K
$9K
$9K$8K
$3K
$8K
$9K $8K $12K
$12K
$7K
$8K
$5K$1K
$7K
$15K
$10K
$16K
$4K
$27K
$1K$5K
$3K
$9K
–$400K$16K
$11K$25K
“Home equity generates wealth for homeowners and provides a cushion against foreclosure risk for lenders. Home equity creation has varied widely across the country. Homeowners in Hawaii, Nevada and Idaho experienced average gains of more than $20,000 in equity wealth during 2018, while owners in Connecticut, Louisiana and North Dakota had an average erosion of equity.”
Frank Martell,
president and CEO of CoreLogic
NATIONAL HOME EQUITY DISTRIBUTIONBy LTV Segment
Source: CoreLogic Q4 2018
0%
1%
2%
3%
4%
5%
6%
7%
8%
9%
10%
50%
to 5
4%
55%
to 5
9%
60%
to 6
4%
65%
to 6
9%
70%
to 7
4%
75%
to 7
9%
80%
to 8
4%
85%
to 8
9%
90%
to 9
4%
95%
to 9
9%
100%
to 1
04%
105%
to 1
09%
110%
to 1
14%
115%
to 1
19%
120%
to 1
24%
125%
+
Loan-to-Value Ratio
2.81x5.2q3 equity as of q3 2018
Including Distressed
Q2 2018
Q3 2018
© 2019 CoreLogic — Proprietary. This material may not be reproduced in any form without express written permission. 11
The MarketPulse g March 2019 g Volume 8, Issue 3 | Analysis
Variable Descriptions
Variable Definition
Total Sales The total number of all home-sale transactions during the month.
Total Sales 12-Month sum The total number of all home-sale transactions for the last 12 months.
Total Sales YoY Change 12-Month sum
Percentage increase or decrease in current 12 months of total sales over the prior 12 months of total sales
New Home Sales The total number of newly constructed residentail housing units sold during the month.
New Home Sales Median Price The median price for newly constructed residential housing units during the month.
Existing Home Sales The number of previously constucted homes that were sold to an unaffiliated third party. DOES NOT INCLUDE REO AND SHORT SALES.
REO Sales Number of bank owned properties that were sold to an unaffiliated third party.
REO Sales Share The number of REO Sales in a given month divided by total sales.
REO Price Discount The average price of a REO divided by the average price of an existing-home sale.
REO Pct The count of loans in REO as a percentage of the overall count of loans for the reporting period.
Short Sales The number of short sales. A short sale is a sale of real estate in which the sale proceeds fall short of the balance owed on the property's loan.
Short Sales Share The number of Short Sales in a given month divided by total sales.
Short Sale Price Discount The average price of a Short Sale divided by the average price of an existing-home sale.
Short Sale Pct The count of loans in Short Sale as a percentage of the overall count of loans for the month.
Distressed Sales Share The percentage of the total sales that were a distressed sale (REO or short sale).
Distressed Sales Share (sales 12-Month sum)
The sum of the REO Sales 12-month sum and the Short Sales 12-month sum divided by the total sales 12-month sum.
HPI MoM Percent increase or decrease in HPI single family combined series over a month ago.
HPI YoY Percent increase or decrease in HPI single family combined series over a year ago.
HPI MoM Excluding Distressed
Percent increase or decrease in HPI single family combined excluding distressed series over a month ago.
HPI YoY Excluding Distressed Percent increase or decrease in HPI single family combined excluding distressed series over a year ago.
HPI Percent Change from Peak
Percent increase or decrease in HPI single family combined series from the respective peak value in the index.
90 Days + DQ Pct The percentage of the overall loan count that are 90 or more days delinquent as of the reporting period. This percentage includes loans that are in foreclosure or REO.
Stock of 90+ Delinquencies YoY Chg Percent change year-over-year of the number of 90+ day delinquencies in the current month.
Foreclosure Pct The percentage of the overall loan count that is currently in foreclosure as of the reporting period.
Percent Change Stock of Foreclosures from Peak
Percent increase or decrease in the number of foreclosures from the respective peak number of foreclosures.
Pre-foreclosure Filings The number of mortgages where the lender has initiated foreclosure proceedings and it has been made known through public notice (NOD).
Completed ForeclosuresA completed foreclosure occurs when a property is auctioned and results in either the purchase of the home at auction or the property is taken by the lender as part of their Real Estate Owned (REO) inventory.
Negative Equity Share The percentage of mortgages in negative equity. The denominator for the negative equity percent is based on the number of mortgages from the public record.
Negative Equity
The number of mortgages in negative equity. Negative equity is calculated as the difference between the current value of the property and the origination value of the mortgage. If the mortgage debt is greater than the current value, the property is considered to be in a negative equity position. We estimate current UPB value, not origination value.
Months' Supply of Distressed Homes (total sales 12-Month avg)
The months it would take to sell off all homes currently in distress of 90 days delinquency or greater based on the current sales pace.
Price/Income Ratio CoreLogic HPI™ divided by Nominal Personal Income provided by the Bureau of Economic Analysis and indexed to January 1976.
Conforming Prime Serious Delinquency Rate
The rate serious delinquency mortgages which are within the legislated purchase limits of Fannie Mae and Freddie Mac. The conforming limits are legislated by the Federal Housing Finance Agency (FHFA).
Jumbo Prime Serious Delinquency Rate
The rate serious delinquency mortgages which are larger than the legislated purchase limits of Fannie Mae and Freddie Mac. The conforming limits are legislated by the Federal Housing Finance Agency (FHFA).
© 2019 CoreLogic — Proprietary. This material may not be reproduced in any form without express written permission. 12
The MarketPulse g March 2019 g Volume 8, Issue 3 | Analysis
corelogic.com
End Notes | The MarketPulse March 2019 Volume 8, Issue 3
© 2019 CoreLogic, Inc. All rights reserved.
CORELOGIC, the CoreLogic logo, CORELOGIC HPI and TRUESTANDINGS are trademarks of CoreLogic, Inc. and/or its subsidiaries. All other trademarks are the property of their respective holders.
17-MKTPLSE-0319-01
Source: CoreLogicThe data provided is for use only by the primary recipient or the primary recipient's publication or broadcast. This data may not be re-sold, republished or licensed to any other source, including publications and sources owned by the primary recipient's parent company without prior written permission from CoreLogic. Any CoreLogic data used for publication or broadcast, in whole or in part, must be sourced as coming from CoreLogic, a data and analytics company. For use with broadcast or web content, the citation must directly accompany fi rst reference of the data. If the data is illustrated with maps, charts, graphs or other visual elements, the CoreLogic logo must be included on screen or website. For questions, analysis or interpretation of the data, contact CoreLogic at [email protected]. Data provided may not be modifi ed without the prior written permission of CoreLogic. Do not use the data in any unlawful manner. This data is compiled from public records, contributory databases and proprietary analytics, and its accuracy is dependent upon these sources.
For more information please call 866.774.3282
The MarketPulse is a newsletter published by CoreLogic, Inc. ("CoreLogic"). This information is made available
for informational purposes only and is not intended to provide specifi c commercial, fi nancial or investment
advice. CoreLogic disclaims all express or implied representations, warranties and guaranties, including
implied warranties of merchantability, fi tness for a particular purpose, title, or non-infringement. Neither
CoreLogic nor its licensors make any representations, warranties or guaranties as to the quality, reliability,
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CoreLogic shall not be held responsible for any errors, inaccuracies, omissions or losses resulting directly or
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