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.

Highlights

  • The internet is a new source of data for economic research

  • We find that an ask price index is not a substitute for a sale price index but a complement, as we obtain statistical evidence that it is useful for nowcasting

  • We examine the extent of the bias that is introduced when sale data are substituted with ask data for the three economic research areas: (1) estimating the willingness to pay for non-traded amenities, (2) automated valuation applications, (3) construction of price indexes

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Summary

Introduction

The internet is a new source of data for economic research. Such data make it possible to answer research questions that could not be answered before and may make it easier to answer established questions (Edelman 2012). We examine whether it is valid to use listings as substitute for transaction data in the three applications of housing market research. We approach this question in three steps. They find that sellers who experienced nominal losses since the purchase of the property they want to sell, set high ask prices and are willing to wait for a long time for a good offer If such an offer does not arrive, the house will eventually be taken off the market. We use the listings and transaction data for the regression applications of hedonic pricing, automated valuation, and index construction and examine what the selectivity implies in economic terms.

Data sources
Data cleaning
Comparative analysis of the two data sets
Decomposition analysis
Hedonic regression applications
The semiparametric hedonic model
Willingness to pay
Automated valuation
Price index construction and nowcasts
Price indices and nowcasts
Robustness checks
Findings
Conclusion
Full Text
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