Abstract: This paper examines the effects of seller uncertainty over their home value on the housing market. Using evidence from a new dataset on home listings and transactions, I first show that sellers do not have full information about current period demand conditions for their homes. I incorporate this type of uncertainty into a dynamic search model of the home selling problem with Bayesian learning. Simulations of the estimated model show that information frictions help explain short-run persistence in price appreciation rates and a positive (negative) correlation between price changes and sales volume (time on market).
Keywords: Learning, housing searchFull paper (427 KB PDF) | Full paper (Screen Reader Version)