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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.

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We document large long-run differences in average house price appreciation across metropolitan areas over the past 50 years, and show they can be explained by an inelastic supply of land in some unique locations combined with an increasing number of highincome households nationally. The resulting high house prices and price-to-rent ratios in those “superstar” areas crowd out lower income households. The same forces generate a similar pattern among municipalities within a metropolitan area. These facts suggest that disparate local house price and income trends can be driven by aggregate demand, not just changes in local factors such as productivity or amenities. (JEL R11, R23, R31, R52)

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We explore the dynamics of real house prices by estimating serial correlation and mean reversion coefficients from a panel data set of 62 metro areas from 1979-1995. The serial correlation and reversion parameters are then shown to vary cross sectionally with city size, real income growth, population growth, and real construction costs. Serial correlation is higher in metro areas with higher real income, population growth and real construction costs. Mean reversion is greater in large metro areas and faster-growing cities with lower construction costs. Empirically, substantial overshooting of prices can occur in high real construction cost areas, which have high serial correlation and low mean reversion, such as the coastal cities of Boston,