Topic 5: Regional Labor Market Dynamics and Housing Markets.

Topic 5:

Regional Labor Market Dynamicsand Housing Markets

Part A:Housing Data

U.S. Housing Data

• Housing price movements unconditionally

Census data

Transaction/deed data (provided by government agencies or available via public records)

Household data (PSID, Survey of Consumer Finances, etc.)

Mortgage data (appraised value of the home)

• Repeat sales indices

OFHEO

Case-Shiller

Repeat Sales vs. Unconditional Data

• House prices can increase either because the value of the land under the home increases or because the value of the structure increases.

* Is home more expensive because the underlying land is worth more or because the home has a fancy kitchen.

• Often want to know the value of the land separate from the value of the structure.

• New homes often are of higher quality than existing homes.

• Repeat sales indices try to difference out “structure” fixed effects – isolating the effect of changing land prices.

* Assumes structure remains constant (hard to deal with home improvements).

OFHEO/FHFA Repeat Sales Index

• OFHEO – Office of Federal Housing Enterprise OversightFHFA – Federal Housing Finance Agency

Government agencies that oversee Fannie Mae and Freddie Mac

• Uses the stated transaction price from Fannie and Freddie mortgages to compute a repeat sales index. (The price is the actual transaction price and comes directly from the mortgage document)

• Includes all properties which are financed via a conventional mortgage (single family homes, condos, town homes, etc.)

• Excludes all properties financed with other types of mortgages (sub prime, jumbos, etc.)

• Nationally representative – creates separate indices for all 50 states and over 150 metro areas.

Case Shiller Repeat Sales Index

• Developed by Karl Case and Bob Shiller

• Uses the transaction price from deed records (obtained from public records)

• Includes all properties regardless of type of financing (conventional, sub primes, jumbos, etc.)

• Includes only single family homes (excludes condos, town homes, etc.)

• Limited geographic coverage – detailed coverage from only 30 metro areas. Not nationally representative (no coverage at all from 13 states – limited coverage from other states)

• Tries to account for the home improvements when creating repeat sales index (by down weighting properties that increase by a lot relative to others within an area).

OFHEO vs. Case Shiller: National Index

OFHEO vs. Case Shiller: L.A. Index

OFHEO vs. Case Shiller: Denver Index

OFHEO vs. Case Shiller: Chicago Index

OFHEO vs. Case Shiller: New York Index

Conclusion: OFHEO vs. Case - Shiller

• Aggregate indices are very different but MSA indices are nearly identical.

• Does not appear to be the result of different coverage of properties included.

• I think the difference has to do with the geographic coverage.

• If using MSA variation, does not matter much what index is used.

• If calibrating aggregate macro models, I would use OFHEO data instead of Case-Shiller – I think it is more representative of the U.S.

A Note on Census Data

• To assess long run trends in house prices (at low frequencies), there is nothing better than Census data.

• Very detailed geographic data (national, state, metro area, zip code, census tract).

• Goes back at least to the 1940 Census.

• Have very good details on the structure (age of structure, number of rooms, etc.).

• Can link to other Census data (income, demographics, etc.).

Part B:Housing Cycles (Some Data)

Average Annual Real Price Growth By US State

State 1980-2000 2000-2007 2000-10 State 1980-2000 2000-2007 2000-2010AK -0.001 0.041 0.021 MT 0.003 0.049 0.024AL 0.000 0.024 0.012 NC 0.008 0.022 0.004AR -0.009 0.023 0.006 ND -0.010 0.033 0.018AZ -0.002 0.061 0.008 NE -0.002 0.007 -0.004CA 0.012 0.066 0.021 NH 0.014 0.041 0.015CO 0.012 0.012 0.002 NJ 0.015 0.058 0.027CT 0.012 0.044 0.018 NM -0.002 0.043 0.016DC 0.010 0.081 0.045 NV -0.005 0.060 -0.006DE 0.011 0.053 0.022 NY 0.020 0.051 0.024FL -0.002 0.068 0.016 OH 0.003 -0.001 -0.013GA 0.008 0.019 -0.003 OK -0.019 0.019 0.007HI 0.004 0.074 0.036 OR 0.009 0.051 0.016IA -0.001 0.012 0.001 PA 0.008 0.042 0.018ID -0.001 0.047 0.012 RI 0.017 0.059 0.027IL 0.010 0.030 0.004 SC 0.007 0.025 0.014IN 0.002 0.020 -0.010 SD 0.002 0.025 0.010

Average 0.011 0.036 0.01215

Typical “Country” Cycle (US – FHFA Data)

U.S. Real House Price Appreciation: 1976Q1 – 2010Q2

16

Typical “Local” Cycle: New York State

17

Typical “Local” Cycle: California

18

Housing Prices and Housing Cycles (Hurst and Guerrieri (2009))

• Persistent housing price increases are ALWAYS followed by persistent housing price declines

Some statistics about U.S. metropolitan areas 1980 – 2000

• 44 MSAs had price appreciations of at least 15% over 3 years during this period.

• Average price increase over boom (consecutive periods of price increases): 55%

• Average price decline during bust (the following period of price declines): 30%

• Average length of bust: 26 quarters (i.e., 7 years)

• 40% of the price decline occurred in first 2 years of bust 19

Typical “Country” Cycle (US – OFHEO Data)

U.S. Nominal House Price Appreciation: 1976 - 2008

20

Typical “Country” Cycle (US – OFHEO Data)

U.S. Real House Price Appreciation: 1976 - 2008

21

Country 1970-1999 2000-2006 Country 1970-1999 2000-2006

U.S. 0.012 0.055 Netherlands 0.023 0.027Japan 0.010 -0.045 Belgium 0.019 0.064

Germany 0.001 -0.029 Sweden -0.002 0.059France 0.010 0.075 Switzerland 0.000 0.019

Great Britain 0.022 0.068 Denmark 0.011 0.065Italy 0.012 0.051 Norway 0.012 0.047

Canada 0.013 0.060 Finland 0.009 0.040Spain 0.019 0.081 New Zealand 0.014 0.080

Australia 0.015 0.065 Ireland 0.022 0.059

Average 1970-1999 0.0122000-2006 0.046

Average Annual Real Price Growth By OECD Country

22

Country Cycles – The U.S. is Not Alone

23


24


25

26

Summary

• Long run house price appreciation runs from 0-2% real per year.

• Fact is consistent across time, countries, states, metro areas, etc.

• “Large” housing booms that occur over a relatively short period of time at country, state, and metro area levels almost always lead to substantial reversals.

• Questions:

- Why do housing prices cycle?

- What determines low frequency differences in house price appreciation across locations.

27

Part 2:Some Models of Spatial Equilibrium

Model Particulars (Baseline Model): The City• City is populated by N identical individuals.

• City is represented by the real line such that each point on the line (i) is a different location:

• : Measure of agents who live in i.• : Size of the house chosen by agents living in i.

• (market clearing condition)

• (maximum space in i is fixed and normalized to 1)

( , )i

( )tn i di N

( ) ( ) 1t tn i h i

29

( )tn i

( )th i

Household Preferences

Static model:

, ,

1

max ( ) ( ) > 0 and > 0

( ) ( ) ( ) normalize price of consumption to 1

Arbitrage implies:

1( ) ( ) ( )

1

t tc h i

t t

c i h i

c i R i h i Y

P i R i P ir

Construction

A continuum of competitive builders can always build a unit of housing

at constant marginal cost .

Profit maximization implies builders will build a unit of housing anytime:

P t

Demand Side of Economy

1

1

max ( ) ( ) [ ( ) ( ) ( )]

( ) ( )( ) ( ) (F.O.C. wrt c)

( )

( ) ( )( ) ( ) ( ) (F.O.C. wrt h)

( )

( ) ( ) 1

( ) ( ( ) ( )) ( )

c i h i Y c i R i h i

c i h ic i h i

c i

c i h ic i h i R i

h i

h i h i

c i Y R i h i R i

Housing and Consumption Demand Functions

1( )

( ) ( )

( )( )

h i YR i

c i Y

An Aside: Use of Cobb Douglas Preferences?

• Implication of Cobb Douglas Preferences:

0 1

1

(expenditure on housing)

Implication: Constant expenditure share on housing

Implication: Housing expenditure income elasticity = 1

ln(Rh) = l

h YR

Rh Y

1

n( )

Estimated should be 1

Y

Use CEX To Estimate Housing Income Elasticity

• Use individual level data from CEX to estimate “housing service” Engel curves and to estimate “housing service” (pseudo) demand systems.

Sample: NBER CEX files 1980 - 2003

Use extracts put together for “Deconstructing Lifecycle Expenditure” and “Conspicuous Consumption and Race”

Restrict sample to 25 to 55 year olds

Estimate:

(1) ln(ck) = α0 + α1 ln(tot. outlays) + β X + η (Engle Curve)

(2) sharek = δ0 + δ1 ln(tot. outlays) + γ X + λ P + ν (Demand)

* Use Individual Level Data

* Instrument total outlays with current income, education, and occupation.

* Total outlays include spending on durables and nondurables.

35

Engel Curve Results (CEX)

Dependent Variable Coefficient S.E.

log rent (renters) 0.93 0.014

log rent (owners) 0.84 0.001

log rent (all) 0.940.007

* Note: Rent for owners is “self reported” rental value of home

Selection of renting/home ownership appears to be important

36

Engel Curve Results (CEX)


log rent (renters) 0.93 0.014

log rent (owners) 0.84 0.001

log rent (all) 0.940.007

* Note: Rent for owners is “self reported” rental value of home


Other Expenditure Categories

log entertainment (all) 1.610.013

log food (all) 0.640.005

log clothing (all) 1.24 0.010

X controls include year dummies and one year age dummies

37

Demand System Results (CEX)


rent share (renters, mean = 0.242) -0.030 0.003

rent share (owners, mean = 0.275) -0.050 0.002

rent share (all, mean = 0.263) -0.0250.002

* Note: Rent share for owners is “self reported” rental value of home


38

Demand System Results (CEX)


rent share (renters, mean = 0.242) -0.030 0.003

rent share (owners, mean = 0.275) -0.050 0.002

rent share (all, mean = 0.263) -0.0250.002

* Note: Rent share for owners is “self reported” rental value of home


Other Expenditure Categories

entertainment share (all, mean = 0.033) 0.0120.001

food share (all, mean = 0.182) -0.0730.001

clothing share (all, mean = 0.062) 0.008 0.001

X controls include year dummies and one year age dummies

39

Spatial Equilibrium

Consider two locations i and i.

Spatial indifference implies that:

( ) ( ) ( ) ( )

1 1

( ) ( )

( ) ( ) for all and

c i h i c i h i

Y Y Y YR i R i

R i R i i i

%

% %

%

% %

Households have to be indifferent across locations:

Equilibrium

( ) ( )(1 )

Housing Demand Curve:

1 1( )= =

Housing Supply Curve:

P =

rR i P i

r

rh i h Y

r P

Graphical Equilibrium

ln(P)

ln(κ) =ln(P*)

ln(h)

hD(Y)

ln(h*)

Shock to Income

ln(P)

ln(κ) =ln(P*)

ln(h)

hD(Y)

ln(h*)

hD(Y1)

ln(h*1)

Shock to Income (with adjustment costs to supply)

ln(P)

ln(κ) =ln(P*)

ln(h)

hD(Y)

ln(h*)

hD(Y1)

ln(h*1)

Some Conclusions (Base Model)

• If supply is perfectly elastic in the long run (land is available and construction costs are fixed), then:

Prices will be fixed in the long run

Demand shocks will have no effect on prices in the long run.

Short run amplification of prices could be do to adjustment costs.

Model has “static” optimization. Similar results with dynamic optimization (and expectations – with some caveats)

• Notice – location – per se – is not important in this analysis. All locations are the same.

Equilibrium with Supply Constraints

Suppose city (area broadly) is of fixed size (2*I). For illustration, lets index the middle of the city as (0).

-I 0 I

Lets pick I such that all space is filled in the city with Y = Y and r = r.

2I = N (h(i)*)

1 12

1

2

rI N Y

r P

N rP Y

I r

Comparative Statics

What happens to equilibrium prices when there is a housing demand shock (Y increases or r falls).

Focus on income shock. Suppose Y increases from Y to Y1. What happens to prices?

With inelastic housing supply (I fixed), a 1% increase in income leads to a 1% increase in prices (given Cobb Douglas preferences)

1

2

1ln( ) ln ln( )

2

N rP Y

I r

N rP Y

I r

Shock to Income With Supply Constraints

The percentage change in income = the percentage change in price

ln(P1)

ln(κ) =ln(P)

ln(h)

hD(Y)

ln(h)=ln(h1)

hD(Y1)

Intermediate Case: Upward Sloping Supply

Cost of building in the city increases as “density” increases

ln(P1)

ln(κ) =ln(P)

ln(h)

hD(Y)

ln(h)=ln(h1)

hD(Y1)

Implication of Supply Constraints (base model)?

• The correlation between income changes and house price changes should be smaller (potentially zero) in places where density is low (N h(i)* < 2I).

• The correlation between income changes and house price changes should be higher (potentially one) in places where density is high.

• Similar for any demand shocks (i.e., decline in real interest rates).

Question: Can supply constraints explain the cross city differences in prices?

Topel and Rosen (1988)

“Housing Investment in the United States” (JPE)

• First paper to formally approach housing price dynamics.

• Uses aggregate data

• Finds that housing supply is relatively elastic in the long run

Long run elasticity is much higher than short run elasticity.

Long run was about “one year”

• Implication: Long run annual aggregate home price appreciation for the U.S. is small.

Siaz (2010)

“On Local Housing Supply Elasticity (QJE 2010)

• Estimates housing supply elasticities by city.

• Uses a measure of “developable” land in the city.

• What makes land “undevelopable”?

Gradient

Coverage of water

• Differences across cities changes the potential supply responsiveness across cities to a demand shock (some places are more supply elastic in the short run).

Are Housing Markets Efficient?

• Evidence is mixed

• Thing to read:

“The Efficiency of the Market for Single-Family Homes” (Case and Shiller, AER 1989)

“There is a profitable trading rule for persons who are free to time the purchase of their homes. Still, overall, individual housing price changes are not very forecastable.”

Subsequent papers find mixed evidence: Transaction costs?

Can Supply Constraints Explain Cycles?

“Housing Dynamics” (working paper 2007) by Glaeser and Gyrouko

Calibrated spatial equilibrium model

Match data on construction (building permits) and housing prices using time series and cross MSA variation.

Find that supply constraints cannot explain housing price cycles.

Their explanation: Negatively serially correlated demand shocks.

What Could Be Missing From Simple Model?

• Add in reasons for agglomeration.

• Long literature looking at housing prices across areas with agglomeration.

• Most of these focus on “production” agglomerations.

• We will lay out one of the simplest models – Muth (1969), Alonzo (1964), Mills (1967)

• Locations are no longer identical. There is a center business district in the area where people work (indexed as point (0) for our analysis).

• Households who live (i) distance from center business district must pay additional transportation cost of τi.

Same Model As Before – Except Add in Transport Costs

Static model:

, ,max ( ) ( ) > 0 and > 0

( ) ( ) ( )

Still no supply constraints (unlimited areas)

t tc h ic i h i

c i R i h i Y i

Demand Side of Economy

1

1

max ( ) ( ) [ ( ) ( ) ( )]

( ) ( )( ) ( ) (F.O.C. wrt c)

( )

( ) ( )( ) ( ) ( ) (F.O.C. wrt h)

( )

( ) ( )

( ) ( ( )

c i h i Y i c i R i h i

c i h ic i h i

c i

c i h ic i h i R i

h i

h i h i

c i Y i R i

1

( )) ( )h i R i

Housing and Consumption Demand Functions

1( ) ( )

( ) ( )

( ) ( )( )

h i Y iR i

c i Y i

Spatial Equilibrium

Consider two locations i and i.

Spatial indifference implies that:

( ) ( ) ( ) ( )

( ) ( )

When i > i, R(i) < R(i)

c i h i c i h i

Y iR i R i

Y i

%

% %

%%

% %

Households have to be indifferent across locations:

EquilibriumEquilibrium Result:

All occuppied neighborhoods i will be contained in [-I,I].

Define R(I) and P(I) as the rent and price, respectively,

at the boundary of the city.

Given arbitrage, we know that:

R(I)

= ( )(1 ) (1 )

Y ir rR i

r rY I

Complete Equilibrium: Size of City (Solve for I)

0

Remember: h(i)n(i) = 1 and ( )

12

( )

1 1( ) ( )

i

I

i

n i di N

di Nh i

rh i Y I Y i

r

Some Algebra (if my algebra is correct…)

0

0

12

1 1( )

1 1( )

2

1 11 1

21( )

1 11

2

I

i

I

i

di Nr

Y I Y ir

N rY i di Y I

r

N rr

I YN r

r

Prices By Distance (Initial Level of Y = Y0)

P

κ

0 I0 i

Linearized only for graphical illustration

Prices fall with distance. Prices in essentially all locations exceed marginal cost.

Suppose Y increases from Y0 to Y1

P

κ

0 I0 I1 i

Even when supply is completely elastic, prices can rise permanently with a permanent demand shock.

From Glaeser (2007): Suburb House Prices and Distance to Boston

From Glaeser (2007): Suburb Density and Distance to Boston

From Glaeser (2007): Cross City Income vs. House Prices

A Quick Review of Spatial Equilibrium Models

• Cross city differences?

Long run price differences across cities with no differential supply constraints.

Strength of the center business district (size of τ) drives long run price appreciations across city.

• Is it big enough?

• Fall in τ will lead to bigger cities (suburbs) and lower prices in center city (i = 0).

Part C:Gentrification and House Price Dynamics

(Some Within City Dynamics)

Endogenous Gentrification and Housing Price Dynamics

September 2011

Veronica Guerrieri, Daniel Hartley and Erik Hurst

70

Background

• NY Times (Jan 2010): Harlem got more expensive and richer during the last decade.

• Similar phenomenon occurred within many major cities:

o New York during late 1980s and 1990s: Greenwich Village, Soho, Tribecca

o Chicago during the late 1980s and early 1990s (Lakeview) and during the 2000s (Hyde Park, Wicker Park, South Loop)

o San Francisco during the 1980s and 1990s

• What is the relationship between gentrification and land price appreciation within cites? Moreover, how do we interpret cross city differences in housing price dynamics in light of the gentrification process.

71

Within City House Price Growth Appreciation

Midtown All

Manhattan Harlem NYC

2000 – 2006 45% 130% ~80%

Lincoln Hyde All

Park Park Chicago

2000 – 2006 20% 95% ~40%

Zip Zips All

28277 28203-7 Charlotte

2000 – 2006 8% 40% ~8%72

Within City House Price Growth Appreciation

Between MSA vs. Within MSA Variation in

House Price Appreciation

Mean Between S.D. Within S.D.

2000 – 2006 0.81 0.42 0.18 *

1990 – 1997 -0.07 0.21 0.17

• Data from Case Shiller Zip Code Data

• * Within city variation is 2-3 times larger for cities that experienced non-trivial property price appreciation.

73

What We Do In This Paper

• Present and empirical evaluate a model of within city house price growth heterogeneity during city wide housing price booms (and busts).

• Formalize the link between neighborhood gentrification and housing price dynamics in response to city wide housing demand shocks.

• Key ingredient of our model:

o Assume individual utility is increasing in the income of one’s neighbors (e.g., a spatial neighborhood externality).

o Such preferences have been empirically documented by:

Bayer et al. (2007) ; Rossi-Hansberg et al. (2010)

o Neighborhood amenities are endogenous74

Where Do the Preferences Come From

• Our preference structure is a catch all for many potential stories.

• As a result, we do not take a stand on what – in particular – people like about “rich” neighborhoods.

- Lower crime (dislike poor neighborhoods)

- Quality and extent of public goods (like schools) – could be through expenditures or peer effects.

- Increasing returns to scale in the provision of local service amenities (restaurants, entertainment options, etc.).

75

Mechanism for Within City Price Movements

• With the externality, any land occupied by rich people will be of higher value than land occupied by non-rich people.

– Can explain the within city differences in prices such that rich neighborhoods have higher land prices (Becker and Murphy (2003)).

• Anything that increases the demand for housing of rich people (i.e., an influx of new rich people) increases the value of the land onto which they move.

o New/expanding rich will migrate to the poor neighborhoods that directly border the existing rich neighborhoods (to maximize value

of the externality)

o The poor will get priced out of these border neighborhoods.

o We refer to this process as “endogenous” gentrification.76

Document Empirical Support for the Model

• Use variation from Bartik-type shocks across cities (cities that get an exogenous labor demand shock based on initial industry mix).

• For cities that get larger Bartik shocks:

1. House prices in the city as a whole appreciate more.

2. Poor neighborhoods that directly abut rich neighborhoods appreciate the most (both relative to rich neighborhoods and poor neighborhoods that are far from rich neighborhoods).

3. Poor neighborhoods that directly abut rich neighborhoods show much more signs of gentrification (income growth of residents) relative to other poor neighborhoods.

4. These patterns occur in the 1980s, 1990s, and 2000s.

77

Caveat 1: Other Stories For Within City Differences

1. Commuting costs (production agglomeration)

o Classic Urban Story: Muth (1967), Mills (1969), Alonzo (1962))

o Recent Work: Van Nieuwerburgh and Weill (2009), Moretti (2009)).

People pay a cost to commute to jobs.

2. Different fixed amenities

o Classic Urban Story: Rosen (1979), Roback (1982)

o Recent Work: Gyrouko et al. (2009)).

Fixed amenities include weather, beautiful vistas, ocean front property, etc.

Note: The mechanism we highlight could still go through in the presence of these other stories (even if neighborhood externality is zero).

Note: We attempt to distinguish among potential mechanisms in our empirical work. 78

Caveat 2: Booms vs. Busts

• Our data on within city house prices only extends through 2008.

o Do not have a lot of data on the recent bust.

o Have some data on housing price busts during the 1990s (New York, San Francisco, Boston).

o Working on getting more recent data (particularly 2010 data – not a lot of transactions in 2009).

Implication: Most of our empirical work today will focus on within city house price dynamics during city-wide housing booms.

79

Why We Care?

• Understand the nature of housing price movements within and across cities.

• Welfare implications of local demand shocks (e.g., Moretti 2010)

• Think about gentrification more broadly.

80

Organization of the Talk

1. Some background data on within city house price movements

2. Introduce dynamic model of spatial equilibrium with neighborhood externalities.

o Highlight the endogenous gentrification mechanism that arises during city wide housing demand shocks.

3. Empirically Evaluate Model With Respect to House Prices

o Descriptive relationship between border neighborhoods and house price dynamics.

o Use Bartik Variation

4. Empirically Evaluate Model with Respect to Gentrification

o Descriptive relationship between border neighborhoods and gentrification

o Use Bartik Variation81

Part 1: Background Facts

82

Main Data Sources

• We utilize three data sources for within city house prices:

– Case Shiller Zip Code Level Price Index: Repeat sales index

– Zillow Zip Code Level Price Index: Hedonic price index

– Census Median Neighborhood Price: Computed by us (simple hedonics).

• All the data have different plusses and minuses.

• Good news: Results are remarkably robust across the data sets.

83

Case-Shiller Data

• Zip code level price indices (quarterly) for roughly 30 cities.

• Repeat sale price index (get deed records and compute constant quality price indices within the zip code).

• Not publically available (provided to us by Fiserv – up through 2008)

• Data extends back to the late 1980s/early 1990s for most cities.

• Focuses exclusively on single family homes

• Does not cover all zip codes within the city

• Tries to account for remodeling/renovations

o Down-weights outliers in price movements, excludes houses held for less than 6 months, and down-weights properties that were held for a long time).

84

Zillow Data

• Zip code level price indices (monthly) for most zip codes in metropolitan areas.

• Uses same underlying deed records as Case Shiller.

• Data extends back only to about 2000.

• Uses hedonics to value characteristics from recent transactions then takes median vales of all units in the zip code.

• Gets control variables (characteristics) from a variety of places (assessor records, MLS, etc.)

• Has bigger samples than Case Shiller (does not rely on repeat sales).

• Identifies zip codes with not enough transactions to make a reliable index.

85

Census Data

• Median of reported home value for either zip code or census tract (finer geography).

• Available for 1980, 1990 and 2000.

• Self reported from owner-occupiers.

• Adjust for simple hedonics (based on neighborhood housing characteristics)

• Create measures at the zip code AND census tract level

• Has bigger sample than Case Shiller and Zillow.

• When we use it, we weight by number of owner occupied households.

86

Correlation Across Growth Rates of Price Indices

87

House Price Index Measure Correlation

2000 – 2006: Case-Shiller Index vs. Zillow Index (All Case-Shiller Zip Codes, # observations = 3,404)

0.95

2000 – 2006: Case-Shiller Index vs. Zillow Index (All “Main City” Case Shiller Zip Codes, # observations = 472)

0.96

1990 – 2000: Case-Shiller Index vs. Census Median(All Case-Shiller Zip Codes, # observations = 3,280)

0.78

1990 – 2000: Case-Shiller Index vs. Census Median(All “Main City” Case Shiller Zip Codes, # observations = 496)

0.82

Regression of Case-Shiller Growth Rates on Zillow or Census Growth Rates

88

2000-2006 1990-2000

Independent Var. Zillow Zillow Census Census

Coefficient 1.06 1.02 0.96 1.02

(0.01) (0.02) (0.03) (0.06)

Constant 0.04 0.09 0.02 0.07

(0.01) (0.01) (0.01) (0.03)

R-squared 0.92 0.92 0.66 0.71

Sample MSA Main City MSA Main City

Fact 1: Within City Dispersion

89

Between MSACross Zip Code

Within MSA or City Cross Tract

(Within City)

Time Period

FHFA Case-Shiller

Case-Shiller

(MSA)

Case-Shiller

(City)

Zillow

(City)

Census Median

(City)

Census Median

(CS Cities)

Census Median

(30+ Tracts Cities)

2000-2006 0.33 0.42 0.18 0.18 0.24 -

obs 384 20 1,602 472 472

1990-2000 0.17 0.21 0.16 0.17 - 0.15 0.33 0.54

obs 348 17 1,498 496 496 9,684 16,161

1980-1990 0.31 0.24 0.44

obs 158 4,640 8,729


90



(Within City)

Time Period

FHFA Case-Shiller

Case-Shiller

(MSA)

Case-Shiller

(City)

Zillow

(City)

Census Median

(City)

Census Median

(CS Cities)

Census Median

(30+ Tracts Cities)

2000-2006 0.33 0.42 0.18 0.18 0.24 -

obs 384 20 1,602 472 472

1990-2000 0.17 0.21 0.16 0.17 - 0.15 0.33 0.54

obs 348 17 1,498 496 496 9,684 16,161

1980-1990 0.31 0.24 0.44

obs 158 4,640 8,729


91



(Within City)

Time Period

FHFA Case-Shiller

Case-Shiller

(MSA)

Case-Shiller

(City)

Zillow

(City)

Census Median

(City)

Census Median

(CS Cities)

Census Median

(30+ Tracts Cities)

2000-2006 0.33 0.42 0.18 0.18 0.24 -

obs 384 20 1,602 472 472

1990-2000 0.17 0.21 0.16 0.17 - 0.15 0.33 0.54

obs 348 17 1,498 496 496 9,684 16,161

1980-1990 0.31 0.24 0.44

obs 158 4,640 8,729

Fact 2: Some of the Dispersion is Systematic

92Chicago Main City “Community Areas”: 2000-2006

Fact 2: “Poor” Neighborhoods Appreciate More

93New York Metro Area Zip Codes: 2000-2006


Boston, L.A., San Francisco, and Washington: β: -0.22 to -0.4994

Fact 2: Patterns are Robust Over Time/Space

95

MSA/Time PeriodTop Quartile

Initial House PriceBottom Quartile

Initial House Price

2000-2006 (Case Shiller)

Washington, D.C. 1.29 1.61

L.A. 1.21 1.76

San Francisco 0.35 0.61

1990-1997 (Case Shiller)

Portland 0.41 0.69

Denver 0.51 0.89

1984-1989 (Furman/Case Shiller)

New York City 0.33 1.06

Boston 0.65 0.84


• Estimate:

• Run this during the 80s, 90s, and 00-06 periods.

• Do this for Case-Shiller, Census, and Zillow indices.

• ω1 is always negative and statistically different from zero.

• ω1 = -0.23 (standard error 0.05) for Case Shiller data during 2000-2006.

• ω1 is more negative the larger the city wide house price boom.

96

Fact 3: More Variability Among Poor Neighborhoods

97

• Variability among neighborhoods in bottom quartile of 2000 house price distribution was 0.29.

• Variability among neighborhoods in bottom quartile of 2000 house price distribution was 0.05.

Fact 3: More Variability Among Poor Neighborhoods

98

• Variability difference increases with the size of the city wide property price boom.

Summary• Tremendous amount of within city house price variation.

• Variation across zip codes/census tracts within a city is of similar magnitude as the well studied cross city variation.

• Poor neighborhoods within a city appreciate most during city wide housing booms. The more the city as a whole appreciates, the bigger the differential between rich and poor neighborhoods within a city.

• There is much greater variation in house price appreciation rates among poor neighborhoods. The variation increases with the size of the city wide housing boom.

• All the facts are interesting and should be explored more fully in subsequent theoretical and empirical work.

• Our subsequent theory and empirical work only focuses on trying to explain the variation among the poor neighborhoods.

99

Part 2: A Spatial Equilibrium Model of Within City

Gentrification and House Price Dynamics

100

Model Particulars (Baseline Model): The City• City is populated by two types (indexed by s) of infinitely lived households; NR and NP (rich and

poor, respectively)

• City is represented by the real line such that each point on the line (i) is a different location:

• : Measure of agents of type s who live in i.• : Size of the house chosen by agents of type s living in i.

• (market clearing condition)

• (maximum space in i is fixed and normalized to 1)

( , )i

( )s stn i di N

( ) ( ) ( ) ( ) 1R R P Pt t t tn i h i n i h i

101

( )stn i

( )sth i

Model Particulars: Preferences

• Utility

• Neighborhood Externality:

• Preference Assumptions:

• Static budget constraint:

• Income (Exogenous)

, ,max ( ( ))

, , 0

s

tc h i

c h A H i

( ) ( ) ( )i R R

iH i h j n j dj

102

; can assume ( )R P R P

( ) ( ) ( )s s s sc i h i R i y+ £

R Py y y y

Comments on the Model1. No distinction between poor people and farm land (nothing interesting about the poor except they

are not rich).

- Could include a negative externality from living near the poor. We have not done that at this time.

2. No bounds on the city (or mechanisms to bound the city – like transport costs or location specific amenities).

3. Only two types of income (rich and poor).

4. Only one dimension of preference externality.

5. Neighborhoods are of fixed size (do not allow building up).

6. Externality is over space occupied by rich people (not amount of rich people).

7. No uncertainty (more on this later if time allows).

103

Housing Supply/Intermediaries

• Representative builder who builds poor houses in any location at marginal cost CP and who builds rich houses in any location at marginal cost CR.

• the price (per unit) of housing in location i at time t for household type s.

• Assume houses are owned by risk-neutral intermediaries

• Absence of arbitrage implies:

104

( )stp i

Equilibrium

An equilibrium is a sequence of:

• rent and price schedules:

• allocations:

• feasible locations:

Such that:

1. households maximize utility2. representative firm maximizes profits3. intermediaries maximize profits4. markets clear

105

Full Segregation

• Many equilibria (with full segregation)

• Focus on one of the equilibria.

• Rich live together at center of line (normalize i = 0 to be center of line).

• Symmetric city – restrict attention to positive side of line.

• Implications in other equilibria similar (as long as centers are far enough from each other). 106

107

Model Predictions: Neighborhoods, Externality, and Prices

108

Response to Increasing N keeping NR/NP constant(similar to lower r or increasing yR)

109


Poor NeighborhoodsThat Appreciate Substantially

110


Poor NeighborhoodsThat Do Not Appreciate

Implications of Model: Within City

• Lower priced neighborhoods are more price responsive than high priced neighborhoods to positive demand shocks.

• It is the low priced neighborhoods in close proximity to the high priced neighborhoods that appreciate the most when there is a positive housing demand shock.

• The low priced neighborhoods in close proximity to the high priced neighborhoods that appreciate the most do so because they gentrify (rich people move into those neighborhoods).

111

Implications of Model: Cross City

• Mechanism is relevant in that it can also explain differences in price appreciation across cities.

• Higher income growth (NR increase) within a city leads to higher house price appreciation (P) at the city level, all else equal.

- Define P as the weighted average of prices within the city.

- The city P just reflects the aggregation of the neighborhood p’s.

• The stronger the externality (δ), the larger the price growth at the city level (P), all else equal.

112

Part 3: House Price Dynamics Among Poor Neighborhoods

113

Part 3a: Some Descriptive Results

114

Proximity to Rich and House Price Changes

• Estimate the following:

• is distance for neighborhood i in city j to the nearest “rich neighborhood” (those in the top quarter of the period t house price distribution).

• X controls include initial house prices, initial income, initial fraction African-American, and initial fraction Hispanic.

• Z variables include controls for other prominent stories – average commuting times and distance to city’s center business district, distance to lake (if applicable), distance to ocean (if applicable), distance to river (if applicable), and initial age of housing stock.

• When dependent variable is Census Median Home Value Growth controls for changes in the area housing stock are included.

115

,i jtDist

Proximity to Rich and House Price Changes


• Estimate this for different periods (t, t+k = 2000 – 2006, 1990-2000, or 1980 – 1990).

• Estimate this for different measures of house prices growth (Case-Shiller, Zillow, or Census).

• Focus on only variation among poor neighborhoods (i.e., we restrict the sample to only include those neighborhoods that had period t median house prices within the bottom half of the city).

• Focus only on variation within the main city (not the whole MSA).

116

Distance to Rich and House Price Growth

117


118


119

Part 3b: Within City House Price Variation in Response to Exogenous Demand Shock

120

What We Do

• “Shock” the income of a given MSA.

• Look at spatial pattern of house price increases.

• What is the shock to income in MSA i between t and t+k?

Bartik-type instrument: Predicted change in income (between t and t+k) within the MSA based on the MSA’s industry mix in t.

Use census IPUMS data between 1980 and 1990, compute the average real growth in household income by 2 digit industry.

Impute predicted income growth for each MSA between 1980 and 1990 by multiplying the employment mix (by industry) of the MSA in 1980 and the national growth rate of per-worker, industry earnings.

• Similar to Blanchard and Katz (1992). 121

Some Preliminary Statistics (90 MSAs)

Large Variation Across Industries (1980 – 1990):

o Security, Commodity Brokerage, and Investment Company:59%o Trucking Services: 3%

Some Variation Across Cities:

o Income Shock: Median 0.20Mean 0.19Standard Deviation 0.0155th Percentile 0.1795th Percentile 0.22

Predictive Power of “Instrument”

Actual Income Growth on Predicted Income Growth: 1.95 (0.58)F-Stat of “Instrument”: ~11.0 122

Bartik Instrument: House Price Growth


• Broad Census Tract Sample:

o 1980 – 1990 sample as before (109 cities with at least 30 census tracts in 1980).o Again, focus only on those census tracts in the bottom half of the

initial house price distribution (i.e., variation among poor neighborhoods).o Controls are same as above.

• Coefficient of interest: β2 (interaction term)

123

Bartik Instrument: Distance to Rich and House Price Growth

124

Key Independent VariableSpecification

(1)Specification

(2)

Log Distance to Nearest Rich -2.27

* MSA Income Shock (β2) (0.53)

0 – 1 Miles to Nearest Rich 0.061* 1 SD MSA Income Shock (0.019)


Observations 4,251 4,251

1 SD Bartik Shock * Δdist from 1 to 4 miles 0.068Mean Dependent Variable 0.238

Bartik Instrument: Distance to Rich and House Price Growth

125

Key Independent VariableSpecification

(1)Specification

(2)

Log Distance to Nearest Rich -2.27

* MSA Income Shock (β2) (0.53)



Observations 4,251 4,251

1 SD Bartik Shock * Δdist from 1 to 4 miles 0.068Mean Dependent Variable 0.238

Part 4: House Price Dynamics Among Poor Neighborhoods and Gentrification

126

Part 4a: Some Descriptive Results

127

Proximity to Rich and Neighborhood Income Changes

• Focus on poorer neighborhoods (those in the bottom half of the house price distribution within a city at the initial period).


• Y is median household income.

• Same samples as used for house price growth.

• Can add all X and Z controls and results do not change.

128

Correlation of House Price and Income Growth

129

Another Descriptive Result

• Our model emphasizes a spatial dimension to gentrification.

• When faced with positive local demand shocks, poor neighborhoods abutting the wealthy neighborhoods will start to convert from poor to rich.

• Question: How many neighborhoods that are identified ex-post to have gentrified were in close proximity to rich

neighborhoods?

• Empirical Approach:

- Use all cities with at least 30 census tracts in initial year (same as before).

- ~170 cities for 1990 – 2000; ~100 cities for 1980 – 1990

- Look at all census tracts within the city that were in the bottom half of the house price distribution in initial year.

- Define “ex-post gentrification” as actual income growth among poor neighborhoods of (1) at least 50% or (2) at least 25% 130

Gentrification and Proximity to Rich Neighborhoods

131

Ex-post Gentrification Measure (Income Growth)

50% 25%

Time Period 80-90 90-00 80-90 90-00

Distance to Nearest Rich Neighborhood

0.0 - 0.5 miles 0.069(0.017)

0.057(0.027)

0.082(0.035)

0.109(0.040)

0.5 - 1.0 miles 0.015(0.007)

0.017(0.009)

0.092(0.020)

0.062(0.020)

1.0 - 2.0 miles 0.006(0.008)

0.018(0.007)

0.076(0.020)

0.029(0.014)

2.0 - 3.0 miles -0.005(0.007)

0.002(0.005)

0.024(0.019)

0.018(0.014)

City FE Yes Yes Yes Yes

Sample Size 4,251 7,981 4,251 7,981

Mean of Dependent Variable 0.110 0.059 0.302 0.197

Gentrification and Proximity to Rich Neighborhoods

132

Ex-post Gentrification Measure (Income Growth)

50% 25%

Time Period 80-90 90-00 80-90 90-00

Distance to Nearest Rich Neighborhood

0.0 - 0.5 miles 0.069(0.017)

0.057(0.027)

0.082(0.035)

0.109(0.040)

0.5 - 1.0 miles 0.015(0.007)

0.017(0.009)

0.092(0.020)

0.062(0.020)

1.0 - 2.0 miles 0.006(0.008)

0.018(0.007)

0.076(0.020)

0.029(0.014)

2.0 - 3.0 miles -0.005(0.007)

0.002(0.005)

0.024(0.019)

0.018(0.014)

City FE Yes Yes Yes Yes

Sample Size 4,251 7,981 4,251 7,981

Mean of Dependent Variable 0.110 0.059 0.302 0.197

Part 4b: Within City Gentrification in Response to Exogenous Demand Shock

133

“Bartik” Instrument: Income Growth


o Same sample and specification as above (poor neighborhoods in all cities with at least 30 census tracts in 1980; look at changes 1980 – 1990, etc.)

o Same Bartik shock and same controls.

o Measure of gentrification (G) takes one of the following:

- Percent growth in neighborhood income- Percentage point change in poverty rate in neighborhoods- Percentage point change in fraction of population with bachelors

degree or higher.134

“Bartik” Instrument: Distance to Rich and Income Growth

135

Sample 1980-1990109 Cities, 30 Tracts or more

Dependent Var. Census Median HH Income

Growth

Change in Poverty Rate

Change in Fraction with

BS Degree

Log Distance to Nearest Rich -0.57 0.23 -0.24* MSA Income Shock (0.27) (0.12) (0.08)

Observations 4,251 4,251 4,251

1 SD Shock * Delta from 4 to 1 Miles 0.021 -0.0069 0.0072

Mean Dependent Variable 0.149 0.029 0.028

Response to 1 SD Shock (1 to 4 miles) 14% -24% 26%


136



Growth



BS Degree







137



Growth



BS Degree






Other Thoughts

• Expectations and Gentrification

o Bubble-like behavioro Busts are unfulfilled expectations of gentrificationso Some antidotal evidence in Chicagoo Something we are working on

• Cross city variation?

• Subprime behavior or expectations?

• Rental prices vs. house prices?

138

Conclusions

• Endogenous gentrification is a first order explanation for within city housing price dynamics during city wide housing price booms.

• Data supports the existence of neighborhood externalities

• Important for welfare calculations of local demand shocks (amenities are endogenously changing).

• Use MSA industry shocks to see how neighborhood prices respond.

New facts about within city price movements:

1. Poorer neighborhoods are much more price responsive than richer neighborhoods during housing price booms and busts.

2. The poor neighborhoods that appreciate most during booms are spatially close to the rich neighborhoods.

Note: Future research can exploit within city dynamics of housing prices

139

Part D: Some Data on Recent Regional Variation in

Labor Markets

SD of Unemployment By State (Blue) and SD of Unemployment Change (1-yr) By State (Red)

142

Variation By Recession: 1980-1983

Total Increase in Unemployment U.S. As Whole: 4.5%

Top 10 States Increase in Unemployment: Average 6.4%

Illinois: 5.9% S. Carolina 5.4%

Ohio: 7.2% Mississippi: 5.5%

Michigan: 6.8% Alabama: 7.3%

West Virginia 8.7% Tennessee 5.8%

Wisconsin: 5.7% Arizona: 5.4%

Bottom 10 States Increase in Unemployment: Average 1.7%

New York: 1.8% Maryland: 2.0%

New Jersey: 2.5% Delaware: 0.8%

Connecticut: 1.7% Hawaii: 0.9%

Maine: 1.7% Alaska: 1.6%

Vermont: 2.5% S. Dakota: 2.0%

143




CA: 3.9% MA: 2.5%

NY: 3.7% WV: 2.8%

RI: 2.7% PA: 2.3%

FL: 2.6% OK 2.0%

NJ: 3.8% LA: 2.8%


MO: 0.3% UT: 0.6%

MT: 0.3% AR: 0.4%

KS: 0.3% MT: 0.5%

NE: 0.6% SD: -0.2%

IA: 0.0% ND: 0.6%

144




CA: 1.9% MA: 2.5%

NY: 2.1% OR: 2.0%

TX: 2.1% CT: 2.6%

OH: 2.0% OK 2.0%

NJ: 2.2% CO: 2.9%


MD: 0.8% HI: -0.2%

LA: 0.9% RI: 0.9%

NV: 0.5% MT: 0.0%

NE: 0.8% SD: 0.3%

ID: 0.8% ID: 0.8%

145

Variation By Recession: 2007-2009 (Update)



CA: 5.1% SC: 5.7%

FL: 4.8% AL: 5.2%

MI: 5.6% OR: 6.7%

NC: 5.8%

ID: 5.4% NV: 5.4%


NE: 1.7% WY: 1.6%

IA: 1.3% AK: 1.7%

UT: 2.2% MT: 2.2%

AR: 1.6% SD: 2.1%

NM: 2.2% ND: 0.9%

146

Current Unemployment Rate (March 2011)

147

House Price Growth (00-06) and Change in Construction Labor Share (00-06)

Construction Share from ACS – Prime Age Men (Out of All Men in Labor Force)

(R-squared=0.44)

AL

AK

AZ

AR

CA

CO CT

DE

FL

GA

HI

ID

IL

IN IA

KS

KY

LA

ME

MD

MA

MI

MN

MS

MO

MT

NE

NV

NHNJ

NMNYNC

NDOH

OK

OR

PA

RISC

SD

TN

TX

UT

VT

VAWAWV

WI

WY0.0

1.0

2.0

3.0

4.0

5

0 .2 .4 .6 .8hp_growth_00_06

delta_const_real_share_00_06 delta_const_real_share_00_06Fitted values

148

House Price Growth and Change in Construction Labor Share

Construction Share from ACS – Prime Age Men (Out of All Men in Labor Force)

(R-squared=0.44)

149

House Price Growth (006-06) and Change in Construction Labor Share (01-06)

Construction Share from BEA Employment Data (R-squared=0.52)

AL

AK

AZ

AR

CA

COCT

DE

FL

GA

HI

ID

ILIN

IA

KS

KY

LA

ME

MD

MA

MI

MN

MS

MO

MT

NE

NV

NH

NJ

NM

NYNC

ND

OH

OK

OR

PA

RISCSD

TN

TX

UT

VTVA

WA

WV

WI

WY

-.01

0.0

1.0

2.0

3

0 .2 .4 .6 .8hp_growth_00_06

delta_bea_house_share_01_06 delta_bea_house_share_01_06Fitted values

150

House Price Growth (00-06) vs Total Employment Growth (01-06)

Employment Data from BEA Employment Data (R-squared=0.11)

AL

AK

AZ

AR CACO

CT

DE

FL

GA

HI

ID

ILIN

IAKS

KY

LA ME

MD

MA

MI

MNMSMO

MT

NE

NV

NH NJ

NM

NY

NC

ND

OH

OK

OR

PA

RISC

SD

TN

TX

UT

VT

VA

WA

WVWI

WY

-.1

0.1

.2

0 .2 .4 .6 .8hp_growth_00_06

bea_totemp_gr_01_07 bea_totemp_gr_01_07Fitted values

151

Change in Construction Share (01-06) vs. Total Employment Growth (01-06)

All Data from BEA Employment Data (R-squared=0.46)

AL

AK

AZ

AR CA

CO

CT

DE

FL

GA

HI

ID

ILIN

IA

KSKY

LA ME

MD

MA

MN

MSMO

MT

NE

NV

NH NJ

NM

NY

NC

ND

OH

OK

OR

PA

RI

SC

SD

TN

TX

UT

VT

VA

WA

WVWI

WY

-.05

0.0

5.1

.15

.2

-.01 0 .01 .02 .03delta_bea_house_share_01_06


152

Change in Construction Share (01-06) vs Total Employment Growth (08-10)

All Data from BEA Employment Data (R-squared=0.45)

AL

AK

AZ

AR

CA

CO CT

DE

FL

GA

HI

ID

ILIN

IA

KS

KY

LA

ME

MD

MA

MN

MSMO

MT

NE

NV

NHNJ NM

NY

NC

ND

OH

OK

OR

PA

RI

SC

SD

TN

TX

UT

VTVA WA

WV

WI

WY

-.15

-.1

-.05

0.0

5



153

Change in Construction Share (01-06) vs Population Growth (00-06)

Construction Share Data from BEA Employment Data (R-squared=0.40)

AL

AK

AZ

AR

CACO

CT

DE

FL

GA

HI

ID

ILIN

IA

KSKY

LA

MEMD

MAMI

MN

MS

MO

MT

NE

NV

NHNJ

NM

NY

NC

ND

OH

OK

OR

PA

RI

SC

SDTN

TX

UT

VT

VAWA

WV

WI WY

0.1

.2.3


delta_total_num_00_06 delta_total_num_00_06Fitted values

154

Change in Construction Share (00-06) vs Population Growth (00-06)

Construction Share Data from ACS (R-squared=0.60)

AL

AK

AZ

AR

CACO

CT

DE

FL

GA

HI

ID

ILIN

IA

KSKY

LA

MEMD

MAMI

MN

MS

MO

MT

NE

NV

NHNJ

NM

NY

NC

ND

OH

OK

OR

PA

RI

SC

SDTN

TX

UT

VT

VAWA

WV

WIWY

0.1

.2.3

0 .01 .02 .03 .04 .05delta_const_real_share_00_06

delta_total_num_00_06 delta_total_num_00_06Fitted values

155

Change in Construction Share (00-06) vs Change in LFP (00-06)

Construction Share Data from ACS (R-squared=0.50)

AL

AK

AZ

AR

CA

CO CT

DE

FL

GA

HI

IDIL

IN

IAKS

KY

LA

ME

MD

MA

MI

MN

MSMO

MTNE

NV

NH

NJ

NMNY

NC

ND

OH

OK

ORPA

RI

SC

SD

TN

TXUT

VT

VA

WA

WV

WI

WY

-.0

4-.

02

0.0

2.0

4.0

6

0 .2 .4 .6 .8hp_growth_00_06

delta_labor_force_share_00_06 delta_labor_force_share_00_06Fitted values

156

Construction Labor Share (00-06 vs. 06-09)

Construction Share from ACS Data (R-squared=0.45)

ALAK

AZ

AR

CA

CO

CT

DE

FL

GA

HI

ID

ILIN

IA

KS

KY

LA

ME

MD

MA

MI MN

MS

MO

MT

NE

NV

NHNJ

NM

NY

NC

ND

OH

OK

OR

PA

RI

SC

SD

TN

TX

UT

VTVA

WA

WVWI

WY

-.06

-.04

-.02

0.0

2


delta_const_real_share_06_09 delta_const_real_share_06_09Fitted values

157

Construction Labor Share (01-06 vs. 06-09)


ALAK

AZ

AR

CACO

CT

DE

FL

GA

HI

ID

ILINIAKSKY

LA

MEMD

MAMIMN

MS

MO

MT

NE

NV

NHNJ

NM

NY

NC

NDOH

OK

OR

PARI

SC

SDTN

TX

UT

VT

VAWA

WVWI

WY

-.08

-.06

-.04

-.02

0


delta_bea_house_share_06_09 delta_bea_house_share_06_09Fitted values

158

House Price Growth and Change in Construction Labor Share

Unemployment Rate: BLS Statistics

159

Change in Construction Labor Share (01-06) vs. Change in Unemployment (06-10)


AL

AK

AZ

AR

CA

CO CTDE

FL

GA

HI

ID

ILIN

IAKS

KY LA

ME

MDMA

MI

MN

MS

MO

MT

NE

NV

NH

NJ NM

NY

NC

ND

OH

OK

OR

PA

RI

SC

SD

TN

TX

UT

VT

VA

WAWV

WI

WY

02

46

81

0


delta_bls_unemp_06_10 delta_bls_unemp_06_10Fitted values

160

Change in Construction Labor Share (01-06) vs. Share of Unemployment Coming From Construction (09)

Unemployment Share from ACS (R-squared=0.34)

AL

AK

AZ

AR

CA

CO CT DE

FL

GA

HI

ID

IL

IN

IA

KS

KY

LA

ME

MD

MA

MI

MN MS

MO

MT

NE

NV

NH

NJNM

NY

NC

ND

OH OK ORPA RI

SCSD

TNTX

UT

VT

VAWA

WV

WI

WY

.2.2

5.3

.35

.4


share_unemp_const_real_09 share_unemp_const_real_09Fitted values

161

Change in Construction Labor Share (01-06) vs. Change in Share of Unemployment Coming From Construction (Out of

Labor Force (06-09)

Unemployment Share from ACS (R-squared=0.50)

AL

AK

AZ

AR

CACO CT

DE

FL

GA

HI ID

IL

IN

IA

KS

KY LA

ME MDMAMI

MNMS

MOMT

NE

NV

NH

NJNM

NY

NC

ND

OH

OK

OR

PA

RI

SC

SDTN

TX

UT

VT

VA

WA

WVWI

WY

-.01

0.0

1.0

2.0

3


delta_share_unemp_lab_06_09 delta_share_unemp_lab_06_09Fitted values

162

Change in Construction Labor Share (01-06) vs. Change in Vacancies (07-10)

Vacancies From Conference Board’s HWOL Index (R-squared=0.31)

AL

AK

AZ

AR

CA

CO

CT

DE

FLGA

HI

ID

IL

INIA

KS

KY LAMEMDMA

MI

MN

MS

MO

MT

NE

NV

NH

NJ

NM

NYNC

ND

OH

OK

OR

PA

RI

SC

SD

TN

TX

UT

VT

VA

WA

WV

WI

WY

-1-.

50

.51


gr_vac_47_07_10 gr_vac_47_07_10Fitted values

163

Some Quick Conclusions

1. Large amount of regional variation during recent boom and bust

2. Strong relationship between size of employment boom and subsequent employment bust.

3. The boom/bust relationship seems correlated with share of workforce in housing. Does not identify causality!

4. Much of the unemployed in these booming construction states are coming from the construction sector.

5. Is there a structural component to current unemployment?

Even More Data

165

Change in Construction Labor Share (79-82avg - 89) vs. Change in Construction Share (89-92)

ME

NH

VT

MA

RI

CT

NY

NJ

PA

OH

IN

IL

MI WI

MN

IA

MOND

SDNE

KS

DE

MD

VA

WV NCSC

GA

FL

KYTN

AL

MS

AR

LA

OK

TX

MT

ID

WY CONM

AZ

UT

NV

WA

OR

CA

-.06

-.04

-.02

0.0

2

-.04 -.02 0 .02 .04 .06delta_const_real_share_79avg_89

delta_const_real_share_89_92 Fitted values

166

Change in Construction Labor Share (79-82avg - 89) vs. Change in Unemployment Rate (89-93)

ME

NHVT

MA

RICT

NY

NJ

PA

OH

IN

IL

MIWI

MN

IA

MO

ND

SD

NE

KS

DE

MD

VA

WV

NC

SC

GA

FL

KYTN

AL

MS

AR

LA

OKTX MT

ID

WY

CONM

AZ

UT

NV

WAOR

CA

-.02

0.0

2.0

4.0

6


delta_unemp_rate_89_93 Fitted values

167

Change in Construction Labor Share (79-82avg - 89) vs. Change in Share of Unemployed From Construction (91)

ME

NH

VT

MA

RI

CT

NY

NJPA

OH

IN

IL

MI

WI

MN

IA

MO

ND

SDNE

KS

DE

MD

VA

WV

NC

SC

GA

FL

KY

TNAL

MS

AR

LAOK

TX

MT

ID

WY

CO

NMAZ

UT

NV

WA

OR

CA

.15

.2.2

5.3

.35


share_unemp_const_real_91 Fitted values

Part E:Local Labor Market Adjustment

(Blanchard and Katz)

How Do Locations Respond to Local Shocks?

• Continue our theme about thinking about regional economics (house prices are one part of that).

• The direct mechanism: Mobility.

• What implications do mobility have on the response of labor supply, wages, and unemployment to local economic shocks?

• Some work:

Blanchard/Katz “Regional Evolutions” (Brookings, 1992)

Topel “Local Labor Markets” (JPE, 1986)

Consider the Following Labor Market (Inelastic Labor Supply)

0iW W

0iN

Labor Demand

Labor Supply


0iW W

0iN

Labor Demand

Labor Supply

1iW

In short run, adjustment takes place on wages (labor supply is less elastic in short run)


0iW W

0iN

Labor Demand

Labor Supply

In long run, adjustment takes place on N (labor supply is more elastic in long run)

2iN

What is the Mechanism?

• In/out migration of workers…..

Blanchard/Katz Facts: Persistence of Growth Rates

Blanchard/Katz Facts: Cumulative Declines (relative to trend)

Blanchard/Katz Facts: : Cumulative Declines (relative to trend)




Blanchard/Katz Facts: Persistence of Unemployment Rate?

Blanchard/Katz Facts: Convergence of Wages

Blanchard/Katz Facts: Unemployment vs. Growth

Blanchard/Katz Facts: Growth vs. Wages

Blanchard/Katz Facts: Unemployment vs. Wages

Blanchard/Katz Facts: VAR of Negative Regional Shock





Blanchard/Katz Facts VAR of Negative Regional Shock



Conclusions of Blanchard/Katz

• Regional Adjustments Take Place

• In short run, response occurs on unemployment and wage margins.

• In long run, it occurs on labor supply margin (via migration).

• Spatial equilibrium model has to make individuals indifferent to move across regions.

Part F:

Regional Convergence(Barro and Sali-Martin)

Cross-State Convergence in Y/N (R-squared ~ 0.91)

AL

AZ

AR

CA

CO

CT

DE

FL

GA

ID

IL

IN

IA

KSKY

LA

ME MD

MA

MI

MN

MS

MO

MT

NE

NV

NH

NJ

NM

NY

NC

ND

OH

OK

ORPA

RI

SC

SDTN

TX

UTVT

VA

WA

WVWI

WY

.51

1.5

2G

row

th in

Pe

r C

apita I

ncom

e 1

940

-1980

2000 4000 6000 8000 10000 12000Per Capita Income 1940

Fitted values gr_ipc_40_80

Unadjusted 1940-1980Historical Trends in Convergence


AL

AZ

AR

CA

CO

CT

DE

FL

GA

ID

IL

INIA

KSKY

LA

ME

MDMA

MI

MN

MS

MO

MT

NE

NV

NH

NJ

NM

NY

NC

ND

OH

OK

OR

PA

RI

SC

SD

TN

TX

UT

VT

VA

WA

WV WI

WY

.2.4

.6.8

1G

row

th in

Pe

r C

apita

Inc

ome

194

0-1

960





AL

AZ

AR

CA

CO

CT

DE

FLGA

ID

IL

IN

IA KS

KYLA

ME

MD

MAMI

MN

MS

MO

MT

NE

NV

NHNJNM

NY

NC

ND OH

OK

ORPA

RI

SC

SD

TN TX

UT

VT

VA

WA

WV

WI

WY

.3.4

.5.6

.7G

row

th in

Pe

r C

apita

Inco

me

1960

-1980




Cross-State Convergence

• Why did cross-state convergence decline. (I am looking for someone to work on this paper with me – there is low hanging fruit here – it is with Chang-Tai Hseih).

• Precursor: Why was there convergence?

Some Literature

o Barro/Sala-i-Martin: Document Some Facts (Brookings, 1991)

o Barro/Mankiw/Sala-i-Martin: Capital Mobility (AER, 1995)

Cross-State Convergence

More Literature

o Caselli and Coleman (JPE, 2001): U.S. Structural Transformation

- South had comparative advantage in producing unskilled labor intensive goods (agriculture).

- Declining education costs induce individuals to leave unskilled sector and move into the skilled sector.

- Ag wages increase AND composition shift – both increase income per capital of south relative to the north.

Part G:

Effect of Chinese Imports on U.S. Cities(Autor et al. 2011)

Read Autor, Dorn, and Hanson (2011)

o Look at the rise of imports to China on U.S. regional activity (wages, employment, population movements, transfer program response, etc.)

o Use a “Bartik”-like instrument. Use the initial share of manufacturing employment in specific industries in which China has grown.

- Identify within manufacturing variation

o Find it reduces local manufacturing employment

o Local unemployment and non-participation rise.

o Wage reductions in local non-manufacturing

o Large effect on local transfers!

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Topic 5: Regional Labor Market Dynamics and Housing Markets.

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