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The potential of big housing data: an application to the Italian real-estate market

Author

Listed:
  • Michele Loberto

    (Bank of Italy)

  • Andrea Luciani

    (Bank of Italy)

  • Marco Pangallo

    (University of Oxford)

Abstract

We present a new dataset of housing sales advertisements (ads) taken from Immobiliare.it, a popular online portal for real estate services in Italy. This dataset fills a big gap in Italian housing market statistics, namely the absence of detailed physical characteristics for houses sold. The granularity of online data also makes possible timely analyses at a very detailed geographical level. We first address the main problem of the dataset, i.e. the mismatch between ads and actual housing units - agencies have incentives for posting multiple ads for the same unit. We correct this distortion by using machine learning tools and provide evidence about its quantitative relevance. We then show that the information from this dataset is consistent with existing official statistical sources. Finally, we present some unique applications for these data. For example, we provide first evidence at the Italian level that online interest in a particular area is a leading indicator of prices. Our work is a concrete example of the potential of large user-generated online databases for institutional applications.

Suggested Citation

  • 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.
  • Handle: RePEc:bdi:wptemi:td_1171_18
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    References listed on IDEAS

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    1. Donald R. Haurin & Jessica L. Haurin & Taylor Nadauld & Anthony Sanders, 2010. "List Prices, Sale Prices and Marketing Time: An Application to U.S. Housing Markets," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 38(4), pages 659-685, Winter.
    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. 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.
    4. 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.
    5. Federica Ciocchetta & Wanda Cornacchia & Roberto Felici & Michele Loberto, 2016. "Assessing financial stability risks from the real estate market in Italy," Questioni di Economia e Finanza (Occasional Papers) 323, Bank of Italy, Economic Research and International Relations Area.
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    Citations

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    Cited by:

    1. Pierluigi Bologna & Arianna Miglietta & Anatoli Segura, 2020. "Contagion in the CoCos Market? A Case Study of Two Stress Events," International Journal of Central Banking, International Journal of Central Banking, vol. 16(6), pages 137-184, December.
    2. Michele Loberto & Andrea Luciani & Marco Pangallo, 2020. "What do online listings tell us about the housing market?," Papers 2004.02706, arXiv.org.
    3. Jean-Charles Bricongne & Baptiste Meunier & Sylvain Pouget, 2021. "Web Scraping Housing Prices in Real-time: the Covid-19 Crisis in the UK," Working papers 827, Banque de France.
    4. Valeriia Budiakivska & Luca Casolaro, 2018. "Please in my back yard: the private and public benefits of a new tram line in Florence," Temi di discussione (Economic working papers) 1161, Bank of Italy, Economic Research and International Relations Area.
    5. Elisa Guglielminetti & Michele Loberto & Giordano Zevi & Roberta Zizza, 2021. "Living on my own: the impact of the Covid-19 pandemic on housing preferences," Questioni di Economia e Finanza (Occasional Papers) 627, Bank of Italy, Economic Research and International Relations Area.
    6. Valentina Aprigliano & Guerino Ardizzi & Alessia Cassetta & Alessandro Cavallero & Simone Emiliozzi & Alessandro Gambini & Nazzareno Renzi & Roberta Zizza, 2021. "Exploiting payments to track Italian economic activity: the experience at Banca d’Italia," Questioni di Economia e Finanza (Occasional Papers) 609, Bank of Italy, Economic Research and International Relations Area.
    7. Pangallo, Marco & Nadal, Jean-Pierre & Vignes, Annick, 2019. "Residential income segregation: A behavioral model of the housing market," Journal of Economic Behavior & Organization, Elsevier, vol. 159(C), pages 15-35.
    8. Davide Fantino, 2018. "Potential output and microeconomic heterogeneity," Temi di discussione (Economic working papers) 1194, Bank of Italy, Economic Research and International Relations Area.
    9. Alessio Anzuini & Luca Rossi, 2018. "Fiscal policy in the US: a new measure of uncertainty and its recent development," Temi di discussione (Economic working papers) 1197, Bank of Italy, Economic Research and International Relations Area.
    10. Corinna Ghirelli & Juan Peñalosa & Javier J. Pérez & Alberto Urtasun, 2019. "Some implications of new data sources for economic analysis and official statistics," Economic Bulletin, Banco de España, issue JUN, pages 1-12.

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    More about this item

    Keywords

    big data; machine learning; housing market;
    All these keywords.

    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
    • 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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