Essays on housing markets: Dynamics, fundamentals, and measurement

This dissertation addresses the behavior of housing markets in three ways. First, it examines the dynamics of owner-occupied housing prices both at the level of the individual dwelling and in aggregate. Using a unique data set, a model of individual dwelling prices is estimated; the model represents features of housing markets more accurately than standard models of housing prices. Statistical tests strongly reject the hypothesis that individual housing prices follow a random walk in favor of the alternative hypothesis that housing prices are mean reverting. This result also holds in aggregate, offering an additional explanation for the "inertia" commonly found in housing returns. Finally, the paper shows that excess returns are forecastable, but demonstrates, using realized returns to individual dwellings, that the large transactions costs associated with home ownership prevent profitable speculation in owner-occupied housing markets.; Second, the dissertation examines the influence of common fundamentals across metropolitan housing markets. Specifically, if metropolitan areas are viewed as small open economies, they should share shocks to the prices of common imports and exports---shocks that may spill over to housing markets. This dissertation demonstrates that the correlation of returns to residential housing between two metropolitan areas is a function not only of their physical proximity, but also of the similarity of their industrial composition. This implies that as local economies evolve, so will the covariance of housing returns, which suggests that the benefits derived from diversification are maximized by considering the industry risk inherent in the current metropolitan areas, not just the correlation of past returns.; Third, the dissertation considers the process by which individual dwellings are sold and the influence this process has upon measured aggregate housing prices. Estimates of the prices of housing and the value of its stock are derived from observations on housing transactions. These transactions may well be a nonrandom sample of the underlying population of dwellings. For example, it is widely thought that smaller "starter homes" sell more frequently than more expensive properties and that the frequency of transactions on high-valued properties varies over the business cycle.; This dissertation considers the importance of these selectivity issues in making inferences about housing price trends by estimating a model of housing price determination and of the nonrandom selection of observed transactions. The factors affecting the probabilities that transactions on different houses will be observed are analyzed, and their effect upon housing prices is estimated. The analysis considers a variety of plausible selection models, using non-parametric as well as parametric methods. For each of the alternatives, the estimated effect of selectivity upon housing price calculations is substantial.