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Web Scraping Housing Prices in Real-time: the Covid-19 Crisis in the UK

Jean-Charles Bricongne (), Baptiste Meunier and Sylvain Pouget

Working papers from Banque de France

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 timelier and highly granular indicators. One originality of the dataset is to provide the sellers’ perspective, allowing to compute innovative indicators of the housing market such as the number of new posted offers or how prices fluctuate over time for existing offers. Matching selling prices in our dataset with transacted prices from the notarial database using machine learning techniques allows us to measure the negotiation margin of buyers – an innovation to the literature. 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 prices have been increasing in rural regions after the lockdown but experienced a continued decline in London.

Keywords: Housing; Real-time; Big Data; Web Scraping; High Frequency; United Kingdom (search for similar items in EconPapers)
JEL-codes: E01 R30 (search for similar items in EconPapers)
Pages: 42 pages
Date: 2021
New Economics Papers: this item is included in nep-big, nep-eur, nep-isf, nep-mac and nep-ure
References: View references in EconPapers View complete reference list from CitEc
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Persistent link: https://EconPapers.repec.org/RePEc:bfr:banfra:827

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