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

Michele Loberto, Andrea Luciani () and Marco Pangallo ()
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Andrea Luciani: Bank of Italy
Marco Pangallo: University of Oxford

No 1171, Temi di discussione (Economic working papers) from Bank of Italy, Economic Research and International Relations Area

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.

Keywords: big data; machine learning; housing market (search for similar items in EconPapers)
JEL-codes: C44 C81 R31 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-eur, nep-pay and nep-ure
Date: 2018-04
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