Post on 21-Dec-2015
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
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?’
© A.C. Goodman, T. Thibodeau, 2007
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.
© A.C. Goodman, T. Thibodeau, 2007
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?
© A.C. Goodman, T. Thibodeau, 2007
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
© A.C. Goodman, T. Thibodeau, 2007
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.
© A.C. Goodman, T. Thibodeau, 2007
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
© A.C. Goodman, T. Thibodeau, 2007
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%
© A.C. Goodman, T. Thibodeau, 2007
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%
© A.C. Goodman, T. Thibodeau, 2007
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.
© A.C. Goodman, T. Thibodeau, 2007
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.
© A.C. Goodman, T. Thibodeau, 2007
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.
© A.C. Goodman, T. Thibodeau, 2007