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Can big data increase our knowledge of local rental markets? A dataset on the rental sector in France

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  • Guillaume Chapelle
  • Jean Benoît Eyméoud

Abstract

Social Scientists and policy makers need precise data on market rents. Yet, while housing prices are systematically recorded, few accurate data sets on rents are available. In this paper, we present a new data set describing local rental markets in France based on online ads collected through to webscraping. Comparison with alternate sources reveals that online ads provide a non biased picture of rental markets and allow coverage of the whole territory. We then estimate hedonic models for prices and rents and document the spatial variations in rent-price ratios. We show that rents do not increase as much as prices in the tightest housing markets. We use our dataset to estimate the market rent of each transaction and of social dwellings. In the latter case,this allows us to estimate the in-kind benefit received by social tenants which is mainly driven by the level of private rent in their municipality.

Suggested Citation

  • Guillaume Chapelle & Jean Benoît Eyméoud, 2022. "Can big data increase our knowledge of local rental markets? A dataset on the rental sector in France," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-21, January.
    • Handle: RePEc:plo:pone00:0260405
    DOI: 10.1371/journal.pone.0260405
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    1. 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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