topic 4: regional economics. part a: measuring house prices

Topic 4:

Regional Economics

Part A:Measuring House Prices

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.)

• Repeat sales indices

FHFA (Google it – government agency)

Case-Shiller

Zillow

CoreLogic

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.

o 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.

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

FHFA Repeat Sales Index

• FHFA – 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 a large amount of 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.

• 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.).

• NOTE: The lower the level of geographic area in which house prices are measured (in all data sets), the more likely the data is either noisy or imputed.

Part B:Some More Data: Housing Cycles

Some Housing Facts

1. Long run house price appreciation averages only 0-2% per year.

o These patterns are consistent across timeo These patterns are consistent across all levels of geographic

aggregation (e.g., countries, state, cities)

2. Big booms are always followed by big busts

o These patterns are consistent across timeo These patterns are consistent across all levels of geographic

aggregation (e.g., countries, state, cities)

3. Supply and demand determine housing prices

o Housing supply is very elastic in the long run (as demand goes up, we build more houses).

Average Annual Real Price Growth By US State (FHFA Data)

16

State 1980-2000 2000-2007 2000-13 State 1980-2000 2000-2007 2000-2013AK -0.001 0.041 0.015 MT 0.003 0.049 0.016AL 0.000 0.024 -0.001 NC 0.008 0.022 -0.003AR -0.009 0.023 0.001 ND -0.010 0.033 0.021AZ -0.002 0.061 0.001 NE -0.002 0.007 -0.003CA 0.012 0.066 0.013 NH 0.014 0.041 0.007CO 0.012 0.012 0.001 NJ 0.015 0.058 0.013CT 0.012 0.044 0.006 NM -0.002 0.043 0.004DC 0.010 0.081 0.038 NV -0.005 0.060 -0.016DE 0.011 0.053 0.009 NY 0.020 0.051 0.014FL -0.002 0.068 0.005 OH 0.003 -0.001 -0.016GA 0.008 0.019 -0.013 OK -0.019 0.019 0.005HI 0.004 0.074 0.025 OR 0.009 0.051 0.006IA -0.001 0.012 0.001 PA 0.008 0.042 0.010ID -0.001 0.047 0.002 RI 0.017 0.059 0.011IL 0.010 0.030 -0.006 SC 0.007 0.025 -0.001IN 0.002 0.020 -0.010 SD 0.002 0.025 0.009

Average 0.011 0.036 0.005


17


Average 0.011 0.036 0.005


18


Average 0.011 0.036 0.005

Inflation Adjusted Housing Price Growth in the U.S.

19

Housing Market: New York

20

Typical “Local” Cycle: California

21

Typical “Local” Cycle: Nevada

22

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

23

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

24

Country Cycles – The U.S. is Not Alone

25


26


27

Part C: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).

Can Supply Constraints Explain Cycles?

“Housing Dynamics” by Glaeser et al.

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 some of differences in 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).

Many Urban Models Have Similar Feature

• In model we just outlined, land is made special because of center city where travel costs = 0.

• Land could be made special for a variety of reasons:

o Production agglomeration effects (endogenize center city)

o Export reasons (proximity to ports)

o Fixed natural amenities (sunshine, nice weather, beautiful vistas, etc.)

o Locally provided public goods (school districts, crime)

o Consumption agglomeration effects (endogenous provision of amenities).

Part D:“Endogenous Gentrification and House

Price Dynamics” (Guerrieri, Hartley, and Hurst)

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%70

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.

71

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 endogenous72

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.).

73

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.74

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.

75

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. 76

Fact 1: Within City Dispersion is Almost as Large as Cross City Dispersion

77

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

Fact 2: “Poor” Neighborhoods Appreciate More

78New York Metro Area Zip Codes: 2000-2006

Fact 2: “Poor” Neighborhoods Appreciate More

Boston, L.A., San Francisco, and Washington: β: -0.22 to -0.4979

Fact 2: Patterns are Robust Over Time/Space

80

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

Fact 3: More Variability Among Poor Neighborhoods

81

• 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

82

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

Summary of Facts• 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.

83

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

84

( )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

85

; 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).

86

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:

87

( )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

88

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). 89

90

Model Predictions: Neighborhoods, Externality, and Prices

91

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

92


Poor NeighborhoodsThat Appreciate Substantially

93


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).

94

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.

95

Rest of Paper

• Test predictions of model using:

o Within city price movements

o Exogenous “Bartik” shocks to city as a whole (i.e., manufacturing declines, finance booms, etc.).

o Show strong support for the model

Poor neighborhoods on the border of rich neighborhoods are more likely to appreciate in response to a city wide labor demand shock (relative to other equally poor neighborhoods).

These neighborhoods also experience a rapid turnover in population type (i.e., they got richer).

96

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

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

Part G:Some Facts (I think) – Based on ongoing work I am doing with Martin Beraja and

Juan Ospina

Question

o Is the persistence across locations that starts during a recession a feature of recessions?

o Yes!

Question

o Is the persistence across locations that starts during a recession a feature of recessions?

o Yes!

o Why does such persistence exist? Is it inconsistent with Blanchard and Katz adjustments? Does it show up in other labor market outcomes in earlier recessions? Why does the convergence take place at other recessions?

o Ripe area for potential research. Something I am pursuing now.

A Word on the Price Index

o Based on goods in the Nielsen dataset.

- Mostly food

- Some non-food (stuff sold in grocery stores or Target).

o Have detailed observations on prices and quantities.

o Have detailed location measures.

o Dataset is massive!

o Make a price index akin to BLS for these goods.

o Does not take into account store effects! (Coibion, Gorodnichenko, and Hong show store effects could be important).

A Word on Wage Adjustments

o Can one use regional data to test macro models of wage adjustments?

o Yes – no one has done this now.

Bottom Line

o Lot of regional variation during the recession in macro variables.

o Can we use regional relationships to help discipline macro models.

o Growing area of research (which we will turn to shortly).

Part H:Some Facts (I know)

Now it is Time For Me to Bring You a Fact

o I have had this idea for about 18 years!

o It was one of the original ideas I was kicking around for my dissertation.

o Very little work on the topic to this day.

o Big question:

“Does monetary policy help those regions that need the help the least (i.e., increase regional dispersion)”

o Sub-title

“Does monetary policy disproportionately help Las Vegas (doing relatively bad) or Dallas (doing relatively well).

Mechanism

o Monetary policy often works through bank lending.

o Bank lending is dependent on borrower collateral.

o Borrower collateral is highly pro-cyclical.

o I think I finally found the empirical approach to get a handle on this issue.

o New paper I am working on with Martin Beraja (chicago grad student) and Andreas Fuester (NY Fed).

o (As an aside – Martin will be presenting this in the student workshop next thursday night).

Some Data

Experiment

o Use QE1 as a natural experiment

o First QE by Fed was designed to target the mortgage market (buy up mortgage back securities).

o Lower mortgage rates and stimulate economic activity via refinancing

o Occurred in December of 2008 (three-four months after Lehman).

o Examine loan origination activity (primarily refinancing activity) in December 2008 across regions.

- Control for pre and post December 2008 trends

Loan Volume Growth in 12/08 By Unemployment Increase (Early 2007 through 11/08)

Loan Volume Growth in 11/08 By Unemployment Increase (Early 2007 through 11/08)

Loan Volume Growth in 12/08 Relative to 11/08 By Unemployment Increase (Early 2007 through 11/08)

Loan Volume Growth in 12/08 Relative to 11/08 By LTV Increase (Early 2007 through 11/08)

Estimated Cross Sectional Differences

Cash-Out Refinance Share

o Cash out refinancing share only slightly higher in bad regions.

o Total cash out refinancing is still much higher in good places.

What’s Next

o Write down a model where monetary policy can differ across space

- Desire to tap into home equity (collateral) – driven by consumption smoothing motives.

- Constraints to tapping into home equity (collateral) – driven by falling collateral values.

o Otherwise, model is a pretty standard New Keynesian model with multiple islands (assuming no labor mobility across islands, sticky wages on islands, tradable goods across the islands, monetary authority controlling money supply, island specific productivity shocks).

o Goal is to calibrate the model to assess regional impacts of monetary policy shocks. Also discuss potential “optimal” monetary policy decisions.

Part I:

Recent Literature Using Regional Variation for Macro Questions

Caution: Pitfalls of Regional Studies

• Often not designed to assess general equilibrium effects!

o Compares outcomes in some region (region 1) with some other region (region 2).

o Any effect on the outcome that is the same for both region 1 and region 2 (i.e., aggregate effect) gets differenced out.

What are some potential candidates:

Future tax rate increases (from government spending shock today),

Interest rate changes (due to changing supply and demand of money),

Mobility of capital and labor across regions (Blanchard and Katz type adjustments),

Effect of local shocks on tradable demand (which effects goods produced in other regions).

Mian and Sufi (2012)

o Huge literature exploring effect of housing market (and increase in leverage in particular) on local labor markets.

Mian and Sufi (2012) (First slide of their talk)

• The decline in aggregate demand, driven by the household balance sheet channel, is responsible for 65% of the jobs lost from 2007 to 2009

• We are confident this represents a separate channel from the uncertainty channel or the construction-related structural employment channel

• We provide suggestive evidence on the frictions that would translate demand shocks into employment losses

The Shock

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Groceries

High leverage counties, 2006Low leverage counties, 2006

The Effect on Employment: First Pass(Figure 2)

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Motivating Example:Auto Retail versus Auto Manufacturing

(Figure 3)

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Employment Growth: Non-Tradable and Tradable Industries(Figure 4)

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Employment Growth: Non-Tradable and Tradable Industries:Herfindahl-Based Definition

(Figure 5)

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Non-tradable (excluding construction)

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Tradable

Household balance sheet channel explains 65% of jobs lost

Nakamura and Steinsson (2011)

o Look regional variation in military spending and show how it affects local economic activity.

o They refer to this as a “local multiplier”

o I stress that it has little to do with aggregate multiplier. It is about regional redistribution.

o However, as they show, you can use local multiplier to learn about what type of models match the regional data.

Nakamura and Steinsson (2013)

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!

Charles, Hurst and Notowidigdo (2013)

o Already looked at this in class.

o Assess housing price booms and manufacturing declines on local labor markets during the 2000s.

o Try to assess the deterioration of U.S. labor market prior to recession due to declining manufacturing.

o Show that the housing boom masked the deteriorating labor market during this period (particularly for low skilled workers).

o Tries to adjust for migration in estimates.

topic 4: regional economics. part a: measuring house prices

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