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.
Tag Archives | House prices
Conventional wisdom held that housing prices couldn’t fall. But the spectacular boom and bust of the housing market during the first decade of the twenty-first century and millions of foreclosed homeowners have made it clear that housing is no different from any other asset in its ability to climb and crash.
Housing and the Financial Crisis looks at what happened to prices and construction both during and after the housing boom in different parts of the American housing market, accounting for why certain areas experienced less volatility than others. It then examines the causes of the boom and bust, including the availability of credit, the perceived risk reduction due to the securitization of mortgages, and the increase in lending from foreign sources. Finally, it examines a range of policies that might address some of the sources of recent instability.
Housing and the Financial Crisis, with Edward Glaeser, University of Chicago Press
We create reliable measures of the cost of owning and the cost of renting that enable us to compare the level of rents and ownership costs across MSAs. We show that households can predict whether renting or owning will end up being less expensive ex post. This exercise is more robust than trying to predict house price changes or housing returns because much of that uncertainty is inframarginal in the optimal own/rent decision, which depends only on the which tenure mode is cheaper. We show that households can profitably time the home ownership decision. Using several simple trading rules, we estimate that households can save as much as 50 percent of annual rental costs over a five-year period by timing the decision of when to buy a home. The potential savings varies across cities.
Timing the Housing Market with Cindy Soo, Mimeo, March 30, 2013
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)
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)
We construct measures of the annual cost of single-family housing for 46 metropolitan areas in the United States over the last 25 years and compare them with local rents and incomes as a way of judging the level of housing prices. Conventional metrics like the growth rate of house prices, the price-to-rent ratio, and the price-to-income ratio can be misleading because they fail to account both for the time series pattern of real long-term interest rates and predictable differences in the long-run growth rates of house prices across local markets. These factors are especially important in recent years because house prices are theoretically more sensitive to interest rates when rates are already low, and more sensitive still in those cities where the long-run rate of house price growth is high. During the 1980s, our measures show that houses looked most overvalued in many of the same cities that subsequently experienced the largest house price declines. We find that from the trough of 1995 to 2004, the cost of owning rose somewhat relative to the cost of renting, but not, in most cities, to levels that made houses look overvalued.
Assessing High House Prices: Bubbles, Fundamentals and Misperceptions with Charles Himmelberg and Christopher Mayer, Journal of Economic Perspectives vol. 19, number 4 (Fall 2005), pp. 67-92 (PDF)