Learning about the Neighborhood
We develop a model to analyze information aggregation and learning in housing markets. In the presence of pervasive informational frictions, housing prices serve as important signals to households and capital producers about the economic strength of a neighborhood. Our model provides a novel mechanism for amplification through learning in which noise from the housing market can propagate to the local economy, distorting not only migration into the neighborhood, but also the supply of capital and labor. We provide consistent evidence of our model implications for housing price volatility and new construction using data from the recent U.S. housing cycle.
We are grateful to Itay Goldstein, Laura Veldkamp and seminar participants of 2018 AEA Meetings, 2018 NBER Asset Pricing Meeting, Fordham University, McGill University and UC Berkeley for helpful comments. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.