Real estate listings and their usefulness for hedonic regressions

Jens Kolbe, Rainer Schulz, Martin Wersing, Axel Werwatz* (Corresponding Author)

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    1 Citation (Scopus)

    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 differ 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.
    Original languageEnglish
    Pages (from-to)1-31
    Number of pages31
    JournalEmpirical Economics
    Early online date13 Jan 2021
    DOIs
    Publication statusE-pub ahead of print - 13 Jan 2021

    Keywords

    • hedonic modelling
    • nowcasting
    • price prediction
    • Stochastic dominance
    • Hedonic modelling
    • Price prediction
    • Nowcasting

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