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What do online listings tell us about the housing market?

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  • Michele Loberto
  • Andrea Luciani
  • Marco Pangallo

Abstract

Traditional data sources for the analysis of housing markets show several limitations, that recently started to be overcome using data coming from housing sales advertisements (ads) websites. In this paper, using a large dataset of ads in Italy, we provide the first comprehensive analysis of the problems and potential of these data. The main problem is that multiple ads ("duplicates") can correspond to the same housing unit. We show that this issue is mainly caused by sellers' attempt to increase visibility of their listings. Duplicates lead to misrepresentation of the volume and composition of housing supply, but this bias can be corrected by identifying duplicates with machine learning tools. We then focus on the potential of these data. We show that the timeliness, granularity, and online nature of these data allow monitoring of housing demand, supply and liquidity, and that the (asking) prices posted on the website can be more informative than transaction prices.

Suggested Citation

  • Michele Loberto & Andrea Luciani & Marco Pangallo, 2020. "What do online listings tell us about the housing market?," Papers 2004.02706, arXiv.org.
    • Handle: RePEc:arx:papers:2004.02706
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    Cited by:

    1. 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.
    2. 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.

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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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