Listings data are not very useful to predict market values of individual houses either, as these predictions suffer from upward bias and large error variance. As a result, willingness to pay estimates from listings data can be widely off when compared with estimates from transaction data. But estimates of implicit prices also differ. We find that ask prices stochastically dominate sale prices, mainly because the composition of characteristics differs between the two data sets. It therefore compares listings and transaction data and regression results derived from them. This paper asks whether it is valid to do so in the established research areas of (1) willingness to pay estimation, (2) automated valuations, and (3) price index construction. Real estate platforms provide a new source of data which has already been used as a substitute for transaction data in hedonic regression applications.
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