the other side of eight mile * suburban housing supply allen c. goodman wayne state university...

The Other Side of Eight Mile*

Suburban Housing Supply

Allen C. GoodmanWayne State University

September 2004Presented at AREUEA Meetings, Philadelphia PA

January 2005

Housing Supply

• Estimates have been all over the map.

• Depends on whether it is new housing or existing housing.

• For central cities stock, Goodman (2004) finds:– +0 to +0.10 in negative

direction– about +1.00 in the

positive direction

Value

Quantity

Vo

Qo

Positive DirectionMore Elastic

Negative DirectionLess Elastic

Direct Estimates of Change

Populationt = (Dwel. Units)t (Occupancy Rate)t (HH Size/Occupied Dwel. Unit)t

Pt = Ut Ot St and:Populationt+1 = (Dwel. Units)t+1 (Occupancy Rate)t+1 (HH Size/Occupied Dwel.

Unit)t+1

Pt+1 = Ut+1 Ot+1 St+1 and:

Population = Pt+1 - Pt = )( 1 tt SSOU )( 1 tt UUOS )( 1 tt OOSU

% Population =O

OO

U

UU

S

SS

P

PP tttttttt

1111

Supply and Demand ModelHousing Services Demand: D

ttttDt NRYQ lnlnlnln

(3)

Supply of Housing Stock: k

St

ktkt

St GVQ lnln

(4)

Product Market Equilibrium Dt

St QQ lnln

(5)

Capital Market Equilibrium ttt VR lnlnln

(6)

Solving for Q and V yields:

kt

k

ktttt GNYV

lnlnlnln ,or

(7)

k

ktktttt GNYV lnlnlnln 321

(7´)

k

ktktt GVQ lnln .

(8)

• This follows the expectations implicit in value-rent ratios. An initially high s (low suburban value/rent ratio) would be expected to predict a decrease (s < 0) in D.

• Similarly an initially high central city c would predict a central city user cost decrease relative to the CC, or a rise (c > 0) through the decade in D.

• Predicted value from equation (10) is then used as an alternative measure of user cost in the supply-demand regressions

)10( %% 0 k

kkccsscs GD

Instrument for user cost

1970s 1980s 1990s

Dependent Var: Pct. s - Pct. c

Constant -0.0629 0.2471 0.0764

0.0520 0.0499 0.0370

Initial Suburban s -61.4445 -209.9906 -156.9625

7.3276 9.8411 10.7593

Initial Central City c 36.8553 179.6729 110.7284

6.4661 6.5060 14.1687

South 0.0492 -0.0679 0.1622

0.0224 0.0223 0.0276

Midwest -0.0342 -0.0770 0.1117

0.0220 0.0212 0.0289

Southwest -0.0320 -0.0763 0.1468

0.0245 0.0225 0.0289

Mountain/West 0.0885 -0.1092 0.1267

0.0232 0.0269 0.0290

SER 0.1275 0.1266 0.1554

R2 0.3330 0.7593 0.6387

Instrumental Estimate – Equation 10

Variable CoefficientStd.

Error. t-ratio

Constant 0.2488 0.0151 16.53

% Sub -0.0961 0.0499 -1.93

% Sub Income 0.0200 0.0165 1.21

% Metro Pop 0.6993 0.0584 11.97

Std. Error 0.1488

Variable CoefficientStd.

Error. t-ratio

Constant -0.1424 0.0500 -2.85

Pct. Sub Value 1.3662 0.1310 10.43

Std. Error 0.2238

Supply 1.3662

Demand Price -0.1453

Demand Income 0.0302

Demand Pop 1.0225

Table 61970-1980Instruments for

Demand

Supply

Elasticities

Three Decade Means

Three Decades – 3SLS Estimators

Mean Median

Supply Price 1.2585 1.3662

Demand Price -0.0547 -0.0697

Demand Income 0.1311 0.1280

Demand Pop 0.9893 1.0225

Regional Supply Elasticity EstimatesB. Regions with Shift Terms

Number1970-1980

1980-1990

1990-2000

Row Mean

Row Median

Northeast/North Central 144 1.5983 0.6252 0.4468 0.8901 0.6252

0.3572 0.1113 0.2651

South/Southwest/MW 173 1.7872 1.5352 2.2663 1.8629 1.7872

0.3645 0.2863 0.7083

Column Weighted Mean 1.7014 1.1218 1.4398 1.4210 1.2594

Metropolitan Elasticities

Conclusions

• Direct method to estimate housing stock elasticity.• Results are plausible.

– Elasticity (Central City – decreasing) +0.0 - +0.1– Elasticity (Central City – increasing) +1.0 - +1.1– Elasticity (Suburbs) +1.3 - +1.5– Northeast quadrant approx. +0.9– Other regions approx.

+1.9.

• Further directions– Compare older and newer suburbs.– Decompose changes in values into changes in quantities and

changes in prices

Where is the Speculative Bubble in US House Prices?

Allen C. Goodman – Wayne State UniversityThomas G. Thibodeau – University of Colorado

AREUEA Meetings – ChicagoJanuary 2007

© A.C. Goodman, T. Thibodeau, 2007

Questions to Address

• How much real appreciation in house prices is justified by the economic fundamentals of local housing markets?

• How much real appreciation is attributable to speculation?’


What’s Our Approach?

1. We examine real house price appreciation using a simple simulation of long-run housing market behavior. The simulation model demonstrates that the key explanation for the observed spatial variation in house price appreciation rates is spatial variation in supply elasticities.

2. The empirical model of the paper attempts to estimate supply elasticities for 133 metropolitan areas across the US. We then use the estimated elasticities to estimate how much of each metropolitan area’s appreciation can be attributed to economic fundamentals and, by inference, how much is attributable to speculation.


Simulation Model – 2 Questions

• Over the 2000-2005 period what shift in aggregate demand was required for owner-occupied housing to observe a 12.7% increase in the number of owner-occupied housing units in the US over this period?

• What was the corresponding increase in the equilibrium house price?


Evaluate Supply and Demand Shifts

• What shifts must occur for quantity to increase by 12.7%?

P

Q

DS

Qo

Po

Qox 1.127© A.C. Goodman, T. Thibodeau, 2007

Especially when it is clear that the Supply curve is indicating higher costs

Especially when it is clear that the Supply curve is indicating higher costs

Table 1: Increases in Real House Prices Necessary to Achieve 12.7% Increase in the Number of Owner-Occupied Housing Units for

Alternative Housing Supply Elasticities (ED = -0.8)

Demand Shift D + S Shift

ES Quantity Price Price

0.1 63.50% 127.00% 151.00%0.2 35.28% 63.50% 87.50%0.3 25.87% 42.33% 66.33%0.4 21.17% 31.75% 55.75%0.5 18.34% 25.40% 49.40%0.6 16.46% 21.17% 45.17%0.7 15.12% 18.14% 42.14%0.8 14.11% 15.88% 39.88%0.9 13.33% 14.11% 38.11%1.0 12.70% 12.70% 36.70%1.5 10.82% 8.47% 32.47%2.0 9.88% 6.35% 30.35%5.0 8.18% 2.54% 26.54%

10.0 7.62% 1.27% 25.27% © A.C. Goodman, T. Thibodeau, 2007

Empirical Model

Demand for Housing Units:ln ln ln lnD D

t t t t tQ Y R H

Supply of Housing Units:1

ln lnj J

S St t j jt t

j

Q V G

Capital Market Equilibrium:

User Cost:[ { }]t tR V i d tr E p

ttt VR lnlnln

Product Market EquilibriumDt

St QQ lnln


Data

• HUD’s State of the Cities Database augmented by,

• Location (latitude and longitude) obtained from the 1990 Census;

• Metropolitan area construction costs from RS Means;

• Agricultural land prices obtained from the US Department of Agriculture;

• BLS data on the CPI.


Table 2: Descriptive MeasuresName N Mean Std Dev

Central City Dummy CC 9180 5.90% 23.57%Density/square kilometer density 9180 974 1283Distance to CBD (in kilometers) distance 9180 27.92 42.48Number of Places in MSA nplaces 9180 83.21 83.78Number of gov’t per capita Numgov 9180 0.0243 0.0425

Change in Population popch 9180 12.36% 24.22%Change in Total Units totunch 9180 13.90% 22.78%Change in Occupied Units occunch 9180 14.67% 23.33%Change in Owner-occupied Units ownoccch 9179 16.35% 26.59%Change in Occupancy Rate occratch 9180 0.81% 4.80%Change in Household Size hhsizech 9175 -2.33% 6.65%Change in Minority Households minoritych 9180 0.41% 0.57%Change in Median Rents medrntch 9150 0.59% 15.79%Change in Median Values medvalch 9146 5.01% 23.27%Change in Median Incomes medincch 9179 4.96% 12.94%Change in User Cost rhoch 9117 -7.36% 22.17%

Decadal Changes

VariablePlace Information

Pct.Pct Correct Significant

Mean Median Sign 10% Sig.

0.3457 0.3050 71.40% 63.20.6181 0.5960

0.4508 0.3050

Demand Price -0.4430 -0.4030Demand Income 0.3559 0.3237

0.3457

-0.21930.4250

Demand PriceDemand Income

(neg. set to 0)

Among Metropolitan Areas

Supply Price

Table 5 - Elasticities Within and Among Metropolitan Areas

Within Metropolitan Areas

Supply Price (all)Supply Price (+ only)Supply Price


Prices HIGHER than Expected

Expected nominal

appreciationObserved

appreciationObserved - expected

Fort Myers 54.19% 151.69% 97.49%Sacramento 57.64% 154.17% 96.53%Riverside 66.21% 160.76% 94.55%San Diego 53.56% 147.72% 94.16%Orange 62.73% 149.66% 86.93%Los Angeles 73.20% 151.32% 78.13%Monmouth NJ 68.16% 135.94% 67.78%Oakland 66.32% 133.27% 66.96%Las Vegas 49.95% 115.31% 65.36%Santa Rosa 63.48% 127.68% 64.20%Atlantic City 59.29% 118.04% 58.76%Washington DC 78.30% 136.49% 58.19%Fresno 100.98% 155.68% 54.70%Nassau-Suffolk 66.21% 118.90% 52.69%Orlando 58.56% 110.29% 51.73%Tampa 66.21% 113.37% 47.16%Phoenix 59.27% 106.41% 47.14%Middlesex NJ 67.73% 114.71% 46.98%Miami 102.00% 146.01% 44.01%Poughkeepsie 69.23% 111.73% 42.50%Honolulu CDP 66.21% 108.37% 42.16%Baltimore 66.21% 107.49% 41.28%Newburgh 68.11% 106.38% 38.28%


Prices LOWER than Expected

Exp nominal appreciation

Observed appreciation

Observed - Expected

Seattle 83.74% 63.46% -20.28%Madison 70.88% 49.64% -21.24%Syracuse 66.21% 43.96% -22.25%Austin 58.90% 33.03% -25.87%Nashville-Davidson 58.69% 31.76% -26.93%Portland OR 87.31% 59.52% -27.79%Houston 59.17% 31.12% -28.05%Birmingham 66.21% 36.22% -29.99%McAllen 57.01% 24.89% -32.12%Dallas 60.07% 27.44% -32.63%Memphis 54.59% 21.57% -33.02%Kansas City 70.28% 37.22% -33.06%Springfield MA 114.09% 80.59% -33.50%Raleigh 56.80% 22.37% -34.43%Lancaster 84.84% 48.84% -36.00%Rochester NY 66.21% 28.05% -38.17%Chicago 100.41% 61.42% -38.99%Columbus OH 69.10% 29.73% -39.37%Ann Arbor 74.68% 34.67% -40.01%Charlotte 66.21% 25.01% -41.20%Hartford 111.97% 68.81% -43.15%Greensboro 70.47% 23.12% -47.36%Denver 90.78% 41.68% -49.09%Fort Worth 76.25% 26.98% -49.27%Salt Lake City 92.62% 33.38% -59.24%Fort Wayne 79.36% 19.83% -59.52%Dayton 82.17% 22.10% -60.07%Rockford 94.57% 32.42% -62.15%Appleton 100.22% 34.84% -65.37%Indianapolis 93.60% 24.41% -69.18%Atlanta 115.59% 35.99% -79.60%Bergen-Passaic 203.03% 97.67% -105.36%Tacoma 187.17% 73.24% -113.93%Providence 245.43% 117.93% -127.50%Omaha 157.32% 29.26% -128.07%Louisville 247.92% 30.46% -217.46%Detroit 286.22% 29.47% -256.74%


Conclusions – 1

• We attempt to identify how much of the recent appreciation in house prices can be attributable to economic fundamentals and how much can be attributed to speculation.

• After reviewing the relevant literature, we investigate the relationship between house price appreciation rates and supply elasticities using a simulation model of the housing market.

• The model illustrates that the expected rate of appreciation in house prices is very sensitive to the assumed supply elasticity.


Conclusions – 2 • We then produce estimates of metropolitan area supply

elasticities using cross-sectional place data obtained from HUD’s State of the Cities Data System.

• Our empirical analyses yield statistically significant supply elasticities for 84 MSAs. We then compute expected rates of appreciation for these places and compare the expected appreciation rates to the rates observed over the 2000-2005 period.

• We find that speculation has driven house prices well above levels that can be justified by economic fundamentals in less than half of the areas examined.


Conclusions – 3

• Establishing “20% over the expected increase” as a housing bubble threshold, we find that only 23 of the 84 metropolitan areas with positive supply elasticities exceed this threshold.

• Moreover, with the exception of Las Vegas, Phoenix, and Honolulu, every single one of these areas is either within 50 miles of the Atlantic coast or California’s Pacific coast.

• This suggests that extreme speculative activity, so prominently publicized, has been extraordinarily localized.


the other side of eight mile * suburban housing supply allen c. goodman wayne state university...

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