Real estate listings and their usefulness for hedonic regressions

Jens Kolbe; Rainer Schulz; Martin Wersing; Axel Werwatz

doi:10.1007/s00181-020-01992-3

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 journal › Article › peer-review

11 Citations (Scopus)

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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 language	English
Pages (from-to)	3239–3269
Number of pages	31
Journal	Empirical Economics
Volume	61
Issue number	6
Early online date	13 Jan 2021
DOIs	https://doi.org/10.1007/s00181-020-01992-3
Publication status	Published - Dec 2021

Keywords

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

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Cite this

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title = "Real estate listings and their usefulness for hedonic regressions",

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.",

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note = "Acknowledgements: We are grateful to the two anonymous referees and the associate editor for suggestions and comments that helped to improve the paper. We thank Robert Hill, Helmut L¨utkepohl, Bryan MacGregor, Aleksandar Petreski, Verity Watson, and audiences at University of Aberdeen Business School, KTH Royal Institute of Technology Stockholm, and AREUEA 2019 International Conference for helpful comments. The usual disclaimer applies. Kolbe and Werwatz thank the DFG Research Unit 2569: Agricultural Land Markets - Efficiency and Regulation for financial support. Funding: Open Access funding enabled and organized by Projekt DEAL.",

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N2 - 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.

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Real estate listings and their usefulness for hedonic regressions

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