Knowing your Neighborhood: Asymmetric Information in Real Estate Markets∗

We study equilibrium asset market outcomes when there is heterogeneity in information about asset values among both buyers and sellers. We argue that in residential real estate markets a key source of information heterogeneity pertains to hard-toobserve characteristics of the neighborhood that are subsequently capitalized in land prices. Sellers are usually better informed about neighborhood characteristics than buyers, but there are some sellers and some buyers that are better informed than their peers. Consistent with theoretical predictions, we find that changes in the seller composition towards (i) more informed sellers and (ii) sellers with a larger supply elasticity with respect to neighborhood trends predict subsequent price declines of houses in that neighborhood. This effect is larger for homes with a bigger neighborhood-β, and smaller for homes bought by more informed home buyers. In many markets, different market participants have differential information about important characteristics of heterogenous assets. Akerlof (1970), for example, analyzes a situation in which sellers of used cars have superior information relative to potential buyers. In other markets, sellers are better informed than buyers on average, but there exists important additional heterogeneity in the information sets of both buyers and sellers. We argue that the residential real estate market constitutes such a market with heterogenous assets and differentially informed buyers and sellers. Transaction prices for properties include payments for both the land and the structure, both of which might be hard to value and are plausibly the source of asymmetric information between different market participants. For example, the value of a house’s structure includes hard-to-observe aspects of construction quality (Stroebel, 2012). Similarly, local amenities such as crime rates or school quality that are capitalized in the value of the property’s land component are oftentimes in ∗June 2013. We thank Theresa Kuchler, Matteo Maggiori, Monika Piazzesi and Amit Seru, as well as seminar participants at Stanford University and Chicago Booth. We thank Trulia for providing data.