Differences in house price and income growth rates between 1950 and 2000 across metropolitan areas have led to an ever-widening gap in housing values and incomes between the typical and highest-priced locations. We show that the growing spatial skewness in house prices and incomes are related and can be explained, at least in part, by inelastic supply of land in some attractive locations combined with an increasing number of high-income households nationally. Scarce land leads to a bidding-up of land prices and a sorting of high-income families relatively more into those desirable, unique, low housing construction markets, which we label “superstar cities.” Continued growth in the number of high-income families in the U.S. provides support for ever-larger differences in house prices across inelastically supplied locations and income-based spatial sorting. Our empirical work confirms a number of equilibrium relationships implied by the superstar cities framework and shows that it occurs both at the metropolitan area level and at the sub-MSA level, controlling for MSA characteristics.
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Conventional wisdom holds that one of the riskiest aspects of owning a house is the uncertainty surrounding its sale price, especially if one moves to another housing market. However, households who sell a house typically buy another house, whose purchase price is also uncertain. We show that for such households, home owning often hedges their net exposure to housing market risk, because their sale price covaries positively with house prices in their likely new market. That expected covariance is much higher than previously recognized because there is considerable heterogeneity across city pairs in how much house prices covary and households tend to move between the highly correlated housing markets. Taking these two considerations into account increases the estimated median expected correlation in real house price growth across MSAs from 0.35 to 0.60. Moreover, we show that households’ decisions whether to own or rent are sensitive to this “moving-hedge” value. We find that the likelihood of home owning for a mobile household is more than one percentage point higher when the expected house price covariance rises by 38 percent (one standard deviation). This effect attenuates as a household’s probability of moving diminishes and thus the moving-hedge value declines.
Spatial Variation in the Risk of Home Owning in Edward Glaeser and John Quigley(eds.),, Housing Markets and the Economy, Risk Regulation, and Policy Lincoln Institute of Land Policy(2009), pp 83-112 (PDF)
We examine the relative roles of fundamentals and psychology in explaining U.S. house price dynamics. Using metropolitan area data, we estimate how the house price-rent ratio responds to fundamentals such as real interest rates and taxes (via a user cost model) and availability of capital, and behavioral conjectures such as backwards-looking expectations of house price growth and inflation illusion. We find that user cost and lagged five-year house price appreciation rate are the most important determinants of changes in the price-rent ratio and lending market efficiency also is capitalized into house prices, with higher prices associated with lower origination costs and a greater use of subprime mortgages. We find no evidence in favor of behavioral explanations based on the one-year lagged house price growth rate or the inflation rate. The causes of a house price boom appear to vary over time, with interest rate fundamentals mattering more than backwards-looking price expectations in the house price run-up of the 2000s and vice versa during the 1980s boom.
U.S. House Price Dynamics and Behavorial Finance with Christopher Mayer in C. Foote, L. Goette and S Meier(eds.),, Policymaking Insights from Behavorial Economics Federal Reserve Bank of Boston, 2000, pp 261-308 (PDF)