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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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    References listed on IDEAS

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    1. Elliot Anenberg & Steven Laufer, 2017. "A More Timely House Price Index," The Review of Economics and Statistics, MIT Press, vol. 99(4), pages 722-734, July.
    2. Michele Loberto & Francesco Zollino, 2016. "Housing and credit markets in Italy in times of crisis," Temi di discussione (Economic working papers) 1087, Bank of Italy, Economic Research and International Relations Area.
    3. Liberati, Danilo & Loberto, Michele, 2019. "Taxation and housing markets with search frictions," Journal of Housing Economics, Elsevier, vol. 46(C).
    4. Paul E. Carrillo & Eric R. Wit & William Larson, 2015. "Can Tightness in the Housing Market Help Predict Subsequent Home Price Appreciation? Evidence from the United States and the Netherlands," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 43(3), pages 609-651, September.
    5. Honaker, James & King, Gary & Blackwell, Matthew, 2011. "Amelia II: A Program for Missing Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i07).
    6. Merlo, Antonio & Ortalo-Magne, Francois, 2004. "Bargaining over residential real estate: evidence from England," Journal of Urban Economics, Elsevier, vol. 56(2), pages 192-216, September.
    7. Dorinth W. van Dijk & Marc K. Francke, 2018. "Internet Search Behavior, Liquidity and Prices in the Housing Market," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 46(2), pages 368-403, June.
    8. Carrillo, Paul E. & Williams, Benjamin, 2019. "The repeat time-on-the-market index," Journal of Urban Economics, Elsevier, vol. 112(C), pages 33-49.
    9. Simone Emiliozzi & Elisa Guglielminetti & Michele Loberto, 2018. "Forecasting house prices in Italy," Questioni di Economia e Finanza (Occasional Papers) 463, Bank of Italy, Economic Research and International Relations Area.
    10. Allen Head & Huw Lloyd-Ellis & Hongfei Sun, 2014. "Search, Liquidity, and the Dynamics of House Prices and Construction," American Economic Review, American Economic Association, vol. 104(4), pages 1172-1210, April.
    11. Michele Loberto & Andrea Luciani & Marco Pangallo, 2018. "The potential of big housing data: an application to the Italian real-estate market," Temi di discussione (Economic working papers) 1171, Bank of Italy, Economic Research and International Relations Area.
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    Cited by:

    1. Michele Loberto, 2021. "Foreclosures and house prices," Temi di discussione (Economic working papers) 1325, Bank of Italy, Economic Research and International Relations Area.

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