A new approach to computing hedonic equilibria and investigating the properties of locational sorting models

This paper outlines a new way to solve the traditional housing market assignment problem and uses it to investigate the properties of hedonic equilibria. Our approach to computing equilibria is based on Rosen's (1974) bid function. It has four desirable features: (i) convergence implies a hedonic equilibrium; (ii) convergence is guaranteed if a hedonic equilibrium exists; (iii) it can solve for a new equilibrium following a shock to the market; and (iv) if multiple equilibria exist, it can identify them. The algorithm is applied to micro data from San Joaquin County, California, where the choice of a home provides access to public schools in particular school districts. First we calibrate the algorithm to approximately reproduce actual housing prices in San Joaquin County as a hedonic equilibrium. Then we introduce a policy that improves school quality in selected school districts. We find that there are several possibilities for the new equilibrium. For each of these potential equilibria, we compare the marginal willingness to pay for school quality with the rate at which the improvement is capitalized into property values. The resulting capitalization rates differ substantially from marginal willingness to pay.