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Web-scraping housing prices in real-time: The Covid-19 crisis in the UK

Author

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  • Jean-Charles Bricongne

    (Centre de recherche de la Banque de France - Banque de France, LEO - Laboratoire d'Économie d'Orleans [2022-...] - UO - Université d'Orléans - UT - Université de Tours - UCA - Université Clermont Auvergne, LIEPP - Laboratoire interdisciplinaire d'évaluation des politiques publiques (Sciences Po) - Sciences Po - Sciences Po)

  • Baptiste Meunier

    (Centre de recherche de la Banque Centrale européenne - Banque Centrale Européenne, AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique)

  • Sylvain Pouget

    (Grenoble INP ENSIMAG - École nationale supérieure d'informatique et de mathématiques appliquées - UGA - Université Grenoble Alpes - Grenoble INP - Institut polytechnique de Grenoble - Grenoble Institute of Technology - UGA - Université Grenoble Alpes)

Abstract

While official statistics provide lagged and aggregate information on the housing market, extensive information is available publicly on real-estate websites. By web-scraping them for the UK on a daily basis, this paper extracts a large database from which we build timely and highly granular indicators. One originality of the dataset is to focus on the supply side of the housing market, allowing to compute innovative indicators reflecting the sellers' perspective such as the number of new listings posted or how prices fluctuate over time for existing listings. Matching listing prices in our dataset with transacted prices from the notarial database, using machine learning, also measures the negotiation margin of buyers. During the Covid-19 crisis, these indicators demonstrate the freezing of the market and the "wait-and-see" behaviour of sellers. They also show that listing prices after the lockdown experienced a continued decline in London but increased in other regions.

Suggested Citation

  • Jean-Charles Bricongne & Baptiste Meunier & Sylvain Pouget, 2023. "Web-scraping housing prices in real-time: The Covid-19 crisis in the UK," SciencePo Working papers Main hal-04064185, HAL.
    • Handle: RePEc:hal:spmain:hal-04064185
    DOI: 10.1016/j.jhe.2022.101906
    Note: View the original document on HAL open archive server: https://amu.hal.science/hal-04064185
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    2. Federica Ciocchetta & Elisa Guglielminetti & Alessandro Mistretta, 2024. "What Drives House Prices in Europe?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(5), pages 1089-1121, October.
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    4. Cedric Behler & Philip Gärtner & Hans-Joachim Linke, 2023. "Marktdominanz von Maklern auf Onlineimmobilienportalen in Deutschland und Kalifornien [Market dominance of real estate brokers on online real estate platforms in Germany and California]," Zeitschrift für Immobilienökonomie (German Journal of Real Estate Research), Springer;Gesellschaft für Immobilienwirtschaftliche Forschung e. V., vol. 9(1), pages 31-61, April.
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    More about this item

    Keywords

    Housing; Real time; Big data; Web-scraping; High frequency; United Kingdom;
    All these keywords.

    JEL classification:

    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General

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