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 in negative direction –about in the positive direction Value Quantity VoVo QoQo Positive Direction More Elastic Negative Direction Less Elastic

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 =

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)

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 

1970s1980s1990s Dependent Var: Pct.   s - Pct.   c Constant Initial Suburban  s Initial Central City  c South Midwest Southwest Mountain/West SER R2R Instrumental Estimate – Equation 10

VariableCoefficient Std. Error.t-ratio Constant %  Sub  %  Sub Income %  Metro Pop Std. Error VariableCoefficient Std. Error.t-ratio Constant Pct.  Sub Value Std. Error Supply Demand Price Demand Income Demand Pop Table Instruments for  Demand Supply Elasticities

Three Decade Means Three Decades – 3SLS Estimators MeanMedian Supply Price Demand Price Demand Income Demand Pop

Regional Supply Elasticity Estimates B. Regions with Shift Terms Number Row Mean Row Median Northeast/North Central South/Southwest/ MW Column Weighted Mean

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 approx –Other regions approx 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 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 D S QoQo PoPo Q o x © A.C. Goodman, T. Thibodeau, 2007 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 (E D = -0.8) © A.C. Goodman, T. Thibodeau, 2007

Empirical Model Demand for Housing Units: Supply of Housing Units: Capital Market Equilibrium: User Cost: Product Market Equilibrium © 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 Measures

© A.C. Goodman, T. Thibodeau, 2007

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

Prices LOWER than Expected © 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 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

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 Inner Ring Outer Ring CC, Inner Rings, and Outer Rings are very different!