This paper describes six stylized patterns among housing markets in the United States that potential explanations of the housing boom and bust should seek to explain. First, individual housing markets in the U.S. experienced considerable heterogeneity in the amplitudes of their cycles. Second, the areas with the biggest boom-bust cycles in the 2000s also had the largest boom-busts in the 1980s and 1990s, with a few telling exceptions. Third, the timing of the cycles differed across housing markets. Fourth, the largest booms and busts, and their timing, seem to be clustered geographically. Fifth, the cross sectional variance of annual house price changes rises in booms and declines in busts. Finally, these stylized facts are robust to controlling for housing demand fundamentals – namely, rents, incomes, or employment – although changes in fundamentals are correlated with changes in prices.
Urban success increasingly has taken two different forms in the post-war era. One involves very high house price growth with relatively little population growth. The other pairs strong population expansion with mild house price appreciation. We document the heterogeneity across MSAs in the long-run house price growth rate and show that house price growth and housing unit growth tend to be inversely related. Income growth, too, varies widely across MSAs and high house price growth markets experience both high income growth and a right-shift of their entire income distribution. We then discuss four possible explanations for these relationships. One is differences the growth of urban amenities; another is changes in urban productivity; a third is differential growth in agglomeration economies; the last explanation relies on growth in the population of rich households at the national level. These households differentially sort by income into supply-constrained metropolitan areas, with the rich having to outbid other potential residents for the scarce slots available in supply-constrained metropolitan areas. The evidence suggests that this latter explanation is responsible for a significant portion of the urban outcomes we see, but it also is clear that much more work is needed to pin down the relative contributions of these basic factors.
Dispersion in House Price and Income Growth across Markets: Facts and Theories with Joseph Gyourko and Christopher Mayer, Agglomeration Economics Edward Glaeser, ed, University of Chicago Press (2009) (PDF)
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)
This paper documents the trends in the life-cycle profiles of net worth and housing equity between 1983 and 2004. The net worth of older households significantly increased during the housing boom of recent years. However, net worth grew by more than housing equity, in part because other assets also appreciated at the same time. Moreover, the younger elderly offset rising house prices by increasing their housing debt, and used some of the proceeds to invest in other assets. We also consider how much of their housing equity older households can actually tap, using reverse mortgages. This fraction is lower at younger ages, such that young retirees can consume less than half of their housing equity. These results imply that ‘consumable’ net worth is smaller than standard calculations of net worth.
Net Worth and Housing Equity in Retirement with Nicholas Souleles in J. Ameriks and O. S. Mitchell(eds.), Recalibrating Retirement Spending and Saving Oxford: Oxford University Press (2008), pp 46-77 (PDF)
Even though the top marginal income tax rate has fallen substantially and the tax code has become less progressive since 1979, the tax benefit to homeowners was virtually unchanged between 1979-1989, and then rose substantially between 1989-1999. Using tract-level data from the 1980, 1990, and 2000 censuses, we estimate how the income tax-related benefits to owner-occupiers are distributed spatially across the United States. Geographically, gross program benefits have been and remain very spatially targeted. At the metropolitan area level, tax benefits are spatially targeted, with a spatial skewness that is increasing over time. In 1979, owners in the top 20 highest subsidy areas received from 2.7 to 8.0 times the subsidy reaped by owners in the bottom 20 areas. By 1999, owners in the top 20 areas received from 3.4 to 17.1 times more benefits than owners in any of the 20 lowest recipient areas. Despite the increasing skewness, the top subsidy recipient areas tend to persist over time. In particular, the very high benefit per owner areas are heavily concentrated in California and the New York City to Boston corridor, with California owners alone receiving between 19 and 22 percent of the national aggregate gross benefits. While tax rates are somewhat higher in these places, it is high and rising house prices which appear most responsible for the large and increasing skewness in the spatial distribution of benefits.
The (Un)Changing Geographical Distribution of Housing Tax Benefits: 1980 to 2000 with Joseph Gyourko, Tax Policy and the Economy Volume 18, James Poterba, ed. (2004, Cambridge: MIT Press), pp. 175-208 [Revised version of NBER w10322, February 2004] (PDF)