Demand substitution across US cities: Observable similarity and home price correlation

This paper studies demand substitution in the context of US cities. Demand substitution occurs when individuals on the margin between certain city pairs affect demand patterns in the aggregate, causing certain cities to be better substitutes than others. Using a discrete model of city choice, I derive two predictions for migration flows and test them empirically using city-to-city migration data from the US Census. I show that cities which are similar on a variety of observable measures have higher levels of gross migration flows in the steady state and higher net migration flows in response to labor demand shocks. Finally, I propose pairwise correlation in metropolitan home prices as a price-based measure of substitutability and show that it contains substantial predictive power for migration flows relative to observable similarity.     

Home Price Risk, Local Market Shocks, and Index Hedging

All real estate markets are local, or so the conventional wisdom goes. But just how local is local? I address this question empirically using over 75,000 repeat-sales transactions from a large suburban county of Washington D.C.. I construct and evaluate a variety of local home price indices defined by geography, price, and home type. I also calculate ??house-specific?? indices using locally weighted regressions with maximized kernel bandwidths. On the whole, local indices add a moderate amount of explanatory power relative to metropolitan indices. In my sample, the metropolitan index explains 50?C75% of the variation in home price shocks, and local indices add 3?C7% more. In an index hedging framework, homeowners should be willing to pay 5?C10% to hedge with a local index versus a metropolitan index alone.