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Lecture 3 Fall 2009 Referee Reports Referee Reports 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.)

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Lecture 3 l.jpg

Lecture 3

Fall 2009





U s housing data l.jpg

  • 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

U.S. Housing Data


Repeat sales vs unconditional data l.jpg

  • 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).

Repeat Sales vs. Unconditional Data


Ofheo fhfa repeat sales index l.jpg

  • OFHEO – Office of Federal Housing Enterprise Oversight land under the home increases or because the value of the structure increases.

  • 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 over 150 metro areas.

OFHEO/FHFA Repeat Sales Index


Case shiller repeat sales index l.jpg

  • Developed by Karl Case and Bob Shiller land under the home increases or because the value of the structure increases.

  • 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).

Case Shiller Repeat Sales Index


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OFHEO vs. Case Shiller: National Index land under the home increases or because the value of the structure increases.


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OFHEO vs. Case Shiller: L.A. Index land under the home increases or because the value of the structure increases.


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OFHEO vs. Case Shiller: Denver Index land under the home increases or because the value of the structure increases.


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OFHEO vs. Case Shiller: Chicago Index land under the home increases or because the value of the structure increases.


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OFHEO vs. Case Shiller: New York Index land under the home increases or because the value of the structure increases.


Conclusion ofheo vs case shiller l.jpg

  • 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.

Conclusion: OFHEO vs. Case - Shiller


A note on census data l.jpg

  • 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.).

A Note on Census Data


Housing cycles l.jpg

Housing Cycles frequencies), there is nothing better than Census data.


Slide17 l.jpg

Average Annual frequencies), there is nothing better than Census data.Real Housing Price Growth By US State


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Typical “Local” Cycle frequencies), there is nothing better than Census data.


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Typical “Local” Cycle frequencies), there is nothing better than Census data.


Housing prices and housing cycles hurst and guerrieri 2009 l.jpg

  • 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

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


Typical country cycle us ofheo data l.jpg

U.S. persistent housing price declinesNominal House Price Appreciation: 1976 - 2008

Typical “Country” Cycle (US – OFHEO Data)


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U.S. persistent housing price declinesReal House Price Appreciation: 1976 - 2008

Typical “Country” Cycle (US – OFHEO Data)


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Average Annual Real Price Growth By OECD Country persistent housing price declines


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Country Cycles – The U.S. is Not Alone persistent housing price declines


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Country Cycles – The U.S. is Not Alone persistent housing price declines


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Country Cycles – The U.S. is Not Alone persistent housing price declines


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Change in Total Housing Units Against Change in Housing Price

Adjusted for Population Changes (2000-2005, State Level)

Do Supply Factors Explain 2000-2008 Cycle


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Change in Total Housing Units Against Change in Housing Price

Adjusted for Population Changes (2005-2009, State Level)

Do Supply Factors Explain 2000-2008 Cycle


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Homework PriceWhy Do Housing Prices Cycle?


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A Spatial Equilibrium PriceModelPart 1


Model particulars baseline model the city l.jpg
Model Particulars (Baseline Model): The City Price

  • 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)


Household preferences l.jpg

Household Preferences Price

Static model:







Graphical equilibrium l.jpg

h PriceD(Y)

ln(P)

Graphical Equilibrium

ln(κ) =

ln(P*)

ln(h*)

ln(h)


Shock to income similar to shock to interest rate l.jpg

h PriceD(Y1)

hD(Y)

ln(P)

Shock to Income (similar to shock to interest rate)

ln(κ) =

ln(P*)

ln(h*)

ln(h*1)

ln(h)


Shock to income with adjustment costs to supply l.jpg

h PriceD(Y1)

hD(Y)

ln(P)

Shock to Income (with adjustment costs to supply)

ln(κ) =

ln(P*)

ln(h*)

ln(h*1)

ln(h)


Some conclusions base model l.jpg

Some Conclusions (Base Model) Price

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 l.jpg

Equilibrium with Supply Constraints Price

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)*)


Comparative statics l.jpg

Comparative Statics Price

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)


Shock to income with supply constraints l.jpg

ln(P Price1)

Shock to Income With Supply Constraints

ln(κ) =

ln(P)

hD(Y1)

hD(Y)

ln(h)=ln(h1)

ln(h)

The percentage change in income = the percentage change in price


Intermediate case upward sloping supply l.jpg

Intermediate Case: Upward Sloping Supply Price

ln(P1)

ln(κ) =

ln(P)

hD(Y1)

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

hD(Y)

ln(h)=ln(h1)

ln(h)


Implication of supply constraints base model l.jpg

Implication of Supply Constraints (base model)? Price

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 l.jpg

Topel and Rosen (1988) Price

“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.


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Comment 1: Cobb Douglas Preferences? Price

Implication of Cobb Douglas Preferences:


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Use CEX To Estimate Housing Income Elasticity Price

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.


Engel curve results cex l.jpg

Engel Curve Results (CEX) Price

Dependent Variable Coefficient S.E.

log rent (renters) 0.93 0.014

log rent (owners) 0.84 0.001

log rent (all) 0.94 0.007

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

Selection of renting/home ownership appears to be important


Demand system results cex l.jpg

Demand System Results (CEX) Price

Dependent Variable Coefficient S.E.

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) -0.025 0.002

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

Selection of renting/home ownership appears to be important


Engel curve results cex54 l.jpg

Engel Curve Results (CEX) Price

Dependent Variable Coefficient S.E.

log rent (renters) 0.93 0.014

log rent (owners) 0.84 0.001

log rent (all) 0.94 0.007

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

Selection of renting/home ownership appears to be important

Other Expenditure Categories

log entertainment (all) 1.61 0.013

log food (all) 0.64 0.005

log clothing (all) 1.24 0.010

X controls include year dummies and one year age dummies


Demand system results cex55 l.jpg

Demand System Results (CEX) Price

Dependent Variable Coefficient S.E.

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) -0.025 0.002

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

Selection of renting/home ownership appears to be important

Other Expenditure Categories

entertainment share (all, mean = 0.033) 0.012 0.001

food share (all, mean = 0.182) -0.073 0.001

clothing share (all, mean = 0.062) 0.008 0.001

X controls include year dummies and one year age dummies


Comment 1 conclusion l.jpg
Comment 1: Conclusion Price

  • Cannot reject constant income elasticity (estimates are pretty close to 1 for housing expenditure share).

  • Consistent with macro evidence (expenditure shares from NIPA data are fairly constant over the last century).

  • If constant returns to scale preferences (α+β = 1), β ≈ 0.3 (share of expenditure on housing out of total expenditure).


Comment 2 cross city differences l.jpg
Comment 2: Cross City Differences Price

“On Local Housing Supply Elasticity” Albert Siaz (QJE Forthcoming)

  • 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).


Comment 3 are housing markets efficient l.jpg
Comment 3: Are Housing Markets Efficient? Price

  • Evidence is mixed

  • Things 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?


Comment 4 can supply constraints explain cycles l.jpg
Comment 4: Can Supply Constraints Explain Cycles? Price

“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 l.jpg
What Could Be Missing? Price

  • 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.









Prices by distance initial level of y y 0 l.jpg

P Price

κ

0 I0 i

Linearized only for graphical illustration

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

Prices By Distance (Initial Level of Y = Y0)


Suppose y increases from y 0 to y 1 l.jpg

P Price

κ

0 I0 I1 i

Even when supply is completely elastic, prices can rise permanently with a

permanent demand shock.

Suppose Y increases from Y0 to Y1


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A Quick Review of Spatial Equilibrium Models Price

  • 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).


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