The Other Side of Eight Mile
*
Suburban Housing Supply
Allen C. Goodman
Wayne State University
September 2004
Presented 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
Positive Direction
More Elastic
Vo
Negative Direction
Less Elastic
Qo
Quantity
Direct Estimates of Change
Populationt = (Dwel. Units)t (Occupancy Rate)t (HH Size/Occupied Dwel. Unit)t
and:
Pt = Ut Ot St
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
 Population = Pt+1 - Pt =
and:
U O ( S t 1  S t )  S O (U t 1  U t )  U S (Ot 1  Ot )
%  Population = Pt 1  Pt  S t 1  S t  U t 1  U t  Ot 1  Ot
P
S
U
O
Supply and Demand Model
Housing Services Demand:
ln QtD  a ln Yt  b ln Rt  d ln N t  e tD
(3)
Supply of Housing Stock:
ln QtS  g ln Vt  h k Gtk  e tS
(4)
k
Product Market Equilibrium
ln QtS  ln QtD
(5)
Capital Market Equilibrium
ln Rt  ln Vt  ln r t
(6)
Solving for Q and V yields:
ln Vt 
a
g b
ln Yt 
b
g b
ln r t 
d
g b
ln N t  
ln Vt  J1 ln Yt  J 2 ln r t  J3 ln N t   Jk Gtk
k
ln Qt  g ln Vt  h k Gtk .
k
k
hk
g b
Gtk
,or
(7)
(7´)
(8)
Instrument for user cost r
D  %r s  %rc  0  s r s  c rc   k Gk (10)
k
• This follows the expectations implicit in value-rent
ratios. An initially high rs (low suburban value/rent
ratio) would be expected to predict a decrease
(s < 0) in D.
• Similarly an initially high central city rc 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
Instrumental Estimate – Equation 10
1970s
1980s
1990s
-0.0629
0.2471
0.0764
0.0520
0.0499
0.0370
-61.4445
-209.9906
-156.9625
7.3276
9.8411
10.7593
36.8553
179.6729
110.7284
6.4661
6.5060
14.1687
0.0492
-0.0679
0.1622
0.0224
0.0223
0.0276
-0.0342
-0.0770
0.1117
0.0220
0.0212
0.0289
-0.0320
-0.0763
0.1468
0.0245
0.0225
0.0289
0.0885
-0.1092
0.1267
0.0232
0.0269
0.0290
0.1275
0.1266
0.1554
0.3330
0.7593
0.6387
Dependent Var: Pct.  rs - Pct.  rc
Constant
Initial Suburban rs
Initial Central City rc
South
Midwest
Southwest
Mountain/West
SER
R2
Table 6
1970-1980
Instruments for r
Demand
Supply
Elasticities
Variable
Coefficient
Std.
Error.
Constant
0.2488
0.0151
16.53
-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
%  Sub r
Variable
Coefficient
Std.
Error.
Constant
-0.1424
0.0500
Pct.  Sub
Value
1.3662
Std. Error
0.2238
Supply
1.3662
Demand Price
Demand
Income
Demand Pop
-0.1453
0.0302
1.0225
t-ratio
t-ratio
-2.85
0.1310 10.43
Three Decade Means
Three Decades – 3SLS Estimators
Mean
1.2585
-0.0547
Median
1.3662
-0.0697
Demand Income
0.1311
0.1280
Demand Pop
0.9893
1.0225
Supply Price
Demand Price
Regional Supply Elasticity Estimates
B. Regions with Shift Terms
Number
Northeast/North
Central
South/Southwest/
MW
Column Weighted
Mean
144
173
19701980
19801990
19902000
Row
Mean
Row
Median
1.5983 0.6252
0.4468
0.8901
0.6252
0.3572 0.1113
0.2651
1.7872 1.5352
2.2663
1.8629
1.7872
0.3645 0.2863
0.7083
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)
Elasticity (Central City – increasing)
Elasticity (Suburbs)
Northeast quadrant
Other regions
+0.0 - +0.1
+1.0 - +1.1
+1.3 - +1.5
approx. +0.9
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 University
Thomas G. Thibodeau – University of Colorado
AREUEA Meetings – Chicago
January 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 owneroccupied 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
D
S
Po
Especially when it is
clear that the Supply
curve is indicating
higher costs
© A.C. Goodman, T. Thibodeau, 2007
Qo
Qox 1.127
Q
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)
ES
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.5
2.0
5.0
10.0
Demand Shift
Quantity
Price
63.50% 127.00%
35.28% 63.50%
25.87% 42.33%
21.17% 31.75%
18.34% 25.40%
16.46% 21.17%
15.12% 18.14%
14.11% 15.88%
13.33% 14.11%
12.70% 12.70%
10.82%
8.47%
9.88%
6.35%
8.18%
2.54%
7.62%
1.27%
D+S
Shift
Price
151.00%
87.50%
66.33%
55.75%
49.40%
45.17%
42.14%
39.88%
38.11%
36.70%
32.47%
30.35%
26.54%
25.27%
© A.C. Goodman, T. Thibodeau, 2007
Empirical Model
Demand for Housing Units:
ln QtD  a ln Yt  b ln Rt  d ln H t  e tD
Supply of Housing Units:
ln Q  g ln Vt  h j G jt  e tS
jJ
S
t
j 1
Capital Market Equilibrium:
User Cost:
Rt  Vt [i  d  tr  E{ p}]
Product Market Equilibrium
ln QtS  ln QtD
© A.C. Goodman, T. Thibodeau, 2007
ln Rt  ln Vt  ln r t
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 Measures
Variable
Place Information
Central City Dummy
Density/square kilometer
Distance to CBD (in kilometers)
Number of Places in MSA
Number of gov’t per capita
Name
N
Mean
Std Dev
CC
density
distance
nplaces
Numgov
9180
9180
9180
9180
9180
5.90%
974
27.92
83.21
0.0243
23.57%
1283
42.48
83.78
0.0425
Decadal Changes
Change in Population
Change in Total Units
Change in Occupied Units
Change in Owner-occupied Units
Change in Occupancy Rate
Change in Household Size
Change in Minority Households
Change in Median Rents
Change in Median Values
Change in Median Incomes
Change in User Cost
popch
totunch
occunch
ownoccch
occratch
hhsizech
minoritych
medrntch
medvalch
medincch
rhoch
9180
9180
9180
9179
9180
9175
9180
9150
9146
9179
9117
12.36%
13.90%
14.67%
16.35%
0.81%
-2.33%
0.41%
0.59%
5.01%
4.96%
-7.36%
24.22%
22.78%
23.33%
26.59%
4.80%
6.65%
0.57%
15.79%
23.27%
12.94%
22.17%
Table 5 - Elasticities Within and Among Metropolitan Areas
Mean
Median
Supply Price (all)
Supply Price (+ only)
Supply Price
(neg. set to 0)
0.3457
0.6181
0.3050
0.5960
0.4508
0.3050
Demand Price
Demand Income
-0.4430
0.3559
-0.4030
0.3237
Pct.
Pct Correct Significant
Sign
10% Sig.
Within Metropolitan Areas
71.40%
63.2
Among Metropolitan Areas
Supply Price
0.3457
Demand Price
Demand Income
-0.2193
0.4250
© A.C. Goodman, T. Thibodeau, 2007
Prices HIGHER than
Expected
© A.C. Goodman, T. Thibodeau, 2007
Expected
nominal
appreciation
Fort Myers
Sacramento
Riverside
San Diego
Orange
Los Angeles
Monmouth NJ
Oakland
Las Vegas
Santa Rosa
Atlantic City
Washington DC
Fresno
Nassau-Suffolk
Orlando
Tampa
Phoenix
Middlesex NJ
Miami
Poughkeepsie
Honolulu CDP
Baltimore
Newburgh
54.19%
57.64%
66.21%
53.56%
62.73%
73.20%
68.16%
66.32%
49.95%
63.48%
59.29%
78.30%
100.98%
66.21%
58.56%
66.21%
59.27%
67.73%
102.00%
69.23%
66.21%
66.21%
68.11%
Observed
appreciation
Observed expected
151.69%
154.17%
160.76%
147.72%
149.66%
151.32%
135.94%
133.27%
115.31%
127.68%
118.04%
136.49%
155.68%
118.90%
110.29%
113.37%
106.41%
114.71%
146.01%
111.73%
108.37%
107.49%
106.38%
97.49%
96.53%
94.55%
94.16%
86.93%
78.13%
67.78%
66.96%
65.36%
64.20%
58.76%
58.19%
54.70%
52.69%
51.73%
47.16%
47.14%
46.98%
44.01%
42.50%
42.16%
41.28%
38.28%
Prices LOWER than
Expected
© A.C. Goodman, T. Thibodeau, 2007
Exp nominal
appreciation
Seattle
Madison
Syracuse
Austin
Nashville-Davidson
Portland OR
Houston
Birmingham
McAllen
Dallas
Memphis
Kansas City
Springfield MA
Raleigh
Lancaster
Rochester NY
Chicago
Columbus OH
Ann Arbor
Charlotte
Hartford
Greensboro
Denver
Fort Worth
Salt Lake City
Fort Wayne
Dayton
Rockford
Appleton
Indianapolis
Atlanta
Bergen-Passaic
Tacoma
Providence
Omaha
Louisville
Detroit
83.74%
70.88%
66.21%
58.90%
58.69%
87.31%
59.17%
66.21%
57.01%
60.07%
54.59%
70.28%
114.09%
56.80%
84.84%
66.21%
100.41%
69.10%
74.68%
66.21%
111.97%
70.47%
90.78%
76.25%
92.62%
79.36%
82.17%
94.57%
100.22%
93.60%
115.59%
203.03%
187.17%
245.43%
157.32%
247.92%
286.22%
Observed
Observed appreciation Expected
63.46%
49.64%
43.96%
33.03%
31.76%
59.52%
31.12%
36.22%
24.89%
27.44%
21.57%
37.22%
80.59%
22.37%
48.84%
28.05%
61.42%
29.73%
34.67%
25.01%
68.81%
23.12%
41.68%
26.98%
33.38%
19.83%
22.10%
32.42%
34.84%
24.41%
35.99%
97.67%
73.24%
117.93%
29.26%
30.46%
29.47%
-20.28%
-21.24%
-22.25%
-25.87%
-26.93%
-27.79%
-28.05%
-29.99%
-32.12%
-32.63%
-33.02%
-33.06%
-33.50%
-34.43%
-36.00%
-38.17%
-38.99%
-39.37%
-40.01%
-41.20%
-43.15%
-47.36%
-49.09%
-49.27%
-59.24%
-59.52%
-60.07%
-62.15%
-65.37%
-69.18%
-79.60%
-105.36%
-113.93%
-127.50%
-128.07%
-217.46%
-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.
© 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