IDEAS home Printed from https://ideas.repec.org/p/bdi/wptemi/td_1171_18.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: http://www.bancaditalia.it/pubblicazioni/temi-discussione/2018/2018-1171/en_tema_1171.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. Piazzesi, M. & Schneider, M., 2016. "Housing and Macroeconomics," Handbook of Macroeconomics, in: J. B. Taylor & Harald Uhlig (ed.), Handbook of Macroeconomics, edition 1, volume 2, chapter 0, pages 1547-1640, Elsevier.
    3. 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.
    4. 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.
    5. 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.
    6. Donald Haurin, 1988. "The Duration of Marketing Time of Residential Housing," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 16(4), pages 396-410, December.
    7. 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.
    8. Atif Mian & Kamalesh Rao & Amir Sufi, 2013. "Household Balance Sheets, Consumption, and the Economic Slump," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 128(4), pages 1687-1726.
    9. 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.
    10. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Boeri, Filippo & Di Cataldo, Marco & Pietrostefani, Elisabetta, 2022. "Localised effects of re-allocated real estate mafia assets," LSE Research Online Documents on Economics 116682, London School of Economics and Political Science, LSE Library.
    3. 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.
    4. Trojanek, Radoslaw & Gluszak, Michal, 2022. "Short-run impact of the Ukrainian refugee crisis on the housing market in Poland," Finance Research Letters, Elsevier, vol. 50(C).
    5. 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.
    6. Bricongne, Jean-Charles & Meunier, Baptiste & Pouget, Sylvain, 2023. "Web-scraping housing prices in real-time: The Covid-19 crisis in the UK," Journal of Housing Economics, Elsevier, vol. 59(PB).
    7. 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.
    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. 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.
    10. Michele Cascarano & Filippo Natoli, 2023. "Temperatures and search: evidence from the housing market," Temi di discussione (Economic working papers) 1419, Bank of Italy, Economic Research and International Relations Area.
    11. 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.
    12. 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.
    13. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Dorinth van Dijk, 2019. "Local Constant-Quality Housing Market Liquidity Indices," DNB Working Papers 637, Netherlands Central Bank, Research Department.
    3. Han, Lu & Strange, William C., 2016. "What is the role of the asking price for a house?," Journal of Urban Economics, Elsevier, vol. 93(C), pages 115-130.
    4. Lu Han & William C. Strange, 2014. "Bidding Wars for Houses," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 42(1), pages 1-32, March.
    5. Velma Zahirovic-Herbert & Bennie D. Waller & Geoffrey K. Turnbull, 2020. "Properties that Sell at or above Listing Price: Strategic Pricing, Better Broker or Just Dumb Luck?," The Journal of Real Estate Finance and Economics, Springer, vol. 60(1), pages 53-76, February.
    6. Liu, Xiaolong & van der Vlist, Arno J., 2019. "Listing strategies and housing busts: Cutting loss or cutting list price?," Journal of Housing Economics, Elsevier, vol. 43(C), pages 102-117.
    7. Galesi, Alessandro & Mata, Nuria & Rey, David & Schmitz, Sebastian & Schuffels, Johannes, 2020. "Regional Housing Market Conditions in Spain," Research Memorandum 029, Maastricht University, Graduate School of Business and Economics (GSBE).
    8. de Wit, Erik R. & van der Klaauw, Bas, 2013. "Asymmetric information and list-price reductions in the housing market," Regional Science and Urban Economics, Elsevier, vol. 43(3), pages 507-520.
    9. Pfeifer Norbert & Steurer Miriam, 2022. "Early Real Estate Indicators during the COVID-19 Crisis," Journal of Official Statistics, Sciendo, vol. 38(1), pages 319-351, March.
    10. Han, Lu & Strange, William C., 2015. "The Microstructure of Housing Markets," Handbook of Regional and Urban Economics, in: Gilles Duranton & J. V. Henderson & William C. Strange (ed.), Handbook of Regional and Urban Economics, edition 1, volume 5, chapter 0, pages 813-886, Elsevier.
    11. Shen, Lily & Ross, Stephen, 2021. "Information value of property description: A Machine learning approach," Journal of Urban Economics, Elsevier, vol. 121(C).
    12. Liu, Crocker H. & Nowak, Adam & Rosenthal, Stuart S., 2016. "Housing price bubbles, new supply, and within-city dynamics," Journal of Urban Economics, Elsevier, vol. 96(C), pages 55-72.
    13. Simon Stevenson & James Young, 2015. "The Role of Undisclosed Reserves in English Open Outcry Auctions," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 43(2), pages 375-402, June.
    14. Waxman, Andrew & Liang, Yuanning & Li, Shanjun & Barwick, Panle Jia & Zhao, Meng, 2020. "Tightening belts to buy a home: Consumption responses to rising housing prices in urban China," Journal of Urban Economics, Elsevier, vol. 115(C).
    15. Jun Chen & Ronald Rutherford, 2012. "Quality & Time-on-the-Market in Residential Markets," The Journal of Real Estate Finance and Economics, Springer, vol. 44(3), pages 414-428, April.
    16. Francisco Gomes & Michael Haliassos & Tarun Ramadorai, 2021. "Household Finance," Journal of Economic Literature, American Economic Association, vol. 59(3), pages 919-1000, September.
    17. Eric Smith & Zoe Xie & Lei Fang, 2022. "The Short and the Long of It: Stock-Flow Matching in the US Housing Market," CESifo Working Paper Series 10035, CESifo.
    18. Justin Benefield & Ronald Rutherford & Marcus Allen, 2012. "The Effects of Estate Sales of Residential Real Estate on Price and Marketing Time," The Journal of Real Estate Finance and Economics, Springer, vol. 45(4), pages 965-981, November.
    19. Liang, Cong & Hui, Eddie C.M. & Yip, Tsz Leung, 2018. "Time on market (TOM): The impact of new residential stamp duty," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1117-1130.
    20. Herrin, William E. & Knight, John R. & Sirmans, C.F., 2004. "Price cutting behavior in residential markets," Journal of Housing Economics, Elsevier, vol. 13(3), pages 195-207, September.

    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

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bdi:wptemi:td_1171_18. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/bdigvit.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.