Real estate listings and their usefulness for hedonic regressions

Jens Kolbe (), Rainer Schulz (), Martin Wersing and Axel Werwatz ()
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Jens Kolbe: Technische Universität Berlin
Rainer Schulz: University of Aberdeen Business School
Axel Werwatz: Technische Universität Berlin

Empirical Economics, 2021, vol. 61, issue 6, No 10, 3239-3269

Abstract: 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.

Keywords: Hedonic modelling; Nowcasting; Price prediction; Stochastic dominance (search for similar items in EconPapers)
JEL-codes: C14 C81 R31 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (12)

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DOI: 10.1007/s00181-020-01992-3

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