1. 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

2. 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 VoVo QoQo Positive Direction More Elastic Negative Direction Less Elastic

3. Direct Estimates of Change Population t = (Dwel. Units) t (Occupancy Rate) t (HH Size/Occupied Dwel. Unit) t P t = U t O t S t and : Population t+1 = (Dwel. Units) t+1 (Occupancy Rate) t+1 (HH Size/Occupied Dwel. Unit) t+1 P t+1 = U t+1 O t+1 S t+1 and:  Population = P t+1 - P t = %  Population =

5. Supply and Demand Model Housing Services Demand: D tttt D t NRYQ  ln (3) Supply of Housing Stock:   k S t k tkt S t GVQ  ln (4) Product Market Equilibrium D t S t QQln  (5) Capital Market Equilibrium ttt VR  ln  (6) Solving forQ andV yields: k t k k tttt GNYV                   ln,or (7)   k k tktttt GNYV  ln 321 (7´)   k k tktt GVQ  ln. (8)

6. 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 Instrument for user cost 

7. 1970s1980s1990s Dependent Var: Pct.   s - Pct.   c Constant -0.06290.24710.0764 0.05200.04990.0370 Initial Suburban  s -61.4445-209.9906-156.9625 7.32769.841110.7593 Initial Central City  c 36.8553179.6729110.7284 6.46616.506014.1687 South 0.0492-0.06790.1622 0.02240.02230.0276 Midwest -0.0342-0.07700.1117 0.02200.02120.0289 Southwest -0.0320-0.07630.1468 0.02450.02250.0289 Mountain/West 0.0885-0.10920.1267 0.02320.02690.0290 SER 0.12750.12660.1554 R2R2 0.33300.75930.6387 Instrumental Estimate – Equation 10

8. VariableCoefficient Std. Error.t-ratio Constant 0.24880.015116.53 %  Sub  -0.09610.0499-1.93 %  Sub Income 0.02000.01651.21 %  Metro Pop 0.69930.058411.97 Std. Error 0.1488 VariableCoefficient Std. Error.t-ratio Constant -0.14240.0500-2.85 Pct.  Sub Value 1.36620.131010.43 Std. Error 0.2238 Supply 1.3662 Demand Price -0.1453 Demand Income 0.0302 Demand Pop1.0225 Table 6 1970-1980 Instruments for  Demand Supply Elasticities

9. Three Decade Means Three Decades – 3SLS Estimators MeanMedian Supply Price1.25851.3662 Demand Price-0.0547-0.0697 Demand Income0.13110.1280 Demand Pop0.98931.0225

10. Regional Supply Elasticity Estimates B. Regions with Shift Terms Number 1970- 1980 1980- 1990 1990- 2000 Row Mean Row Median Northeast/North Central 1441.59830.62520.44680.89010.6252 0.35720.11130.2651 South/Southwest/ MW 1731.78721.53522.26631.86291.7872 0.36450.28630.7083 Column Weighted Mean 1.70141.12181.43981.42101.2594

11. Metropolitan Elasticities

12. 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

13. 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

14. 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

15. 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

16. 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

17. Evaluate Supply and Demand Shifts What shifts must occur for quantity to increase by 12.7%? P Q D S QoQo PoPo Q o x 1.127 © A.C. Goodman, T. Thibodeau, 2007 Especially when it is clear that the Supply curve is indicating higher costs

18. 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 (E D = -0.8) © A.C. Goodman, T. Thibodeau, 2007

19. Empirical Model Demand for Housing Units: Supply of Housing Units: Capital Market Equilibrium: User Cost: Product Market Equilibrium © A.C. Goodman, T. Thibodeau, 2007

20. 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

21. Table 2: Descriptive Measures

22. © A.C. Goodman, T. Thibodeau, 2007

23. Prices HIGHER than Expected © A.C. Goodman, T. Thibodeau, 2007

24. Prices LOWER than Expected © A.C. Goodman, T. Thibodeau, 2007

25. 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

26. 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

27. 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

28. An Example 1970s1980s1990s Metropolitan Area  Pop HH Size Effect Unit Effect  Pop HH Size Effect Unit Effect  Pop HH Size Effect Unit Effect Baltimore CC -118984-96479-7301-50761-373082515-84860-37013-10167 Inner Ring -68504-761296490-9353-29230248338112-683314615 Outer Ring -1549-26197243601481-11395132217209-528613037 CC, Inner Rings, and Outer Rings are very different!