Seller’s (Mis)Fortune in the Housing Market: Directed Search in Online Real Estate Platforms

Algorithm-based market valuations for houses, such as the Zillow’s Zestimate, impact trading outcomes in the housing market. Sellers who advertise an asking price below their Zestimate increase buyers’ search intensity, shorten their time on market, and reduce their sales price, irrespectively of sellers’ preferences and home characteristics. Using data about the home selling process on Zillow in the Seattle metropolitan area, we estimate the cost associated with this tradeoff is $3,600 (0.75%) of the house sales price for one fewer day on market. Despite this high cost, we show that it is still rational for a seller to advertise an asking price below the Zestimate if there exists a distance between his reservation value and the Zestimate. Our model implies that heterogeneity in house trading outcomes can arise from homogenous sellers’ (mis)fortune in receiving a low or a high Zestimate.