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What Do Online Listings Tell Us about the Housing Market?

Author

Listed:
  • Michele Loberto

    (Banca d'Italia)

  • Andrea Luciani

    (Banca d'Italia)

  • Marco Pangallo

    (Sant'Anna School of Advanced Studies)

Abstract

Since the Great Recession, central banks and macroprudential authorities have been devoting much more attention to the housing market. To properly assess trends and risks, policymakers need detailed, timely, and granular information on demand, supply, and transactions. This information is hardly provided by traditional survey or administrative data. In this paper, we argue that data coming from housing sales advertisements (ads) websites can be used to overcome some existing deficiencies. Using a large data set of ads in Italy, we provide the first comprehensive analysis of the problems and potential of these data. We show how machine learning tools can correct a specific bias of online listings, namely the proliferation of duplicate ads that refer to the same housing unit, increasing the representativeness and reliability of these data. We then show how the timeliness, granularity, and online nature of these data make it possible to monitor in real-time housing demand, supply, and prices.

Suggested Citation

  • Michele Loberto & Andrea Luciani & Marco Pangallo, 2022. "What Do Online Listings Tell Us about the Housing Market?," International Journal of Central Banking, International Journal of Central Banking, vol. 18(4), pages 1-52, October.
    • Handle: RePEc:ijc:ijcjou:y:2022:q:4:a:8
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    References listed on IDEAS

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    Cited by:

    1. Michele Loberto, 2023. "Foreclosures and House Prices," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 9(1), pages 397-424, March.
    2. Guillaume Chapelle & Jean Benoît Eyméoud, 2022. "Can big data increase our knowledge of local rental markets? A dataset on the rental sector in France," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-21, January.

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    More about this item

    JEL classification:

    • C44 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Operations Research; Statistical Decision Theory
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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