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Updated: Nov 21, 2025

Methods for Presenting Real-world Objects Under Controlled Laboratory Conditions
Published on: June 21, 2019
Real estate listings and their usefulness for hedonic regressions
Jens Kolbe1, Rainer Schulz2, Martin Wersing2
1Chair for Econometrics and Business Statistics, Institute for Economics and Business Law, Technische Universität Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany.
Abstract:
Real estate platforms provide a new source of data which has already been used as a substitute for transaction data in hedonic regression applications. 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. It therefore compares listings and transaction data and regression results derived from them. We find that ask prices stochastically dominate sale prices, mainly because the composition of characteristics differs between the two data sets. But estimates of implicit prices also differ. As a result, willingness to pay estimates from listings data can be widely off when compared with estimates from transaction data. 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. We find, however, that an ask price index complements a sale price index, as it is useful for nowcasting.
Supplementary Information:
The online version contains supplementary material available at 10.1007/s00181-020-01992-3.
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