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

Author

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  • Guillaume Chapelle

    (THEMA - Théorie économique, modélisation et applications - CNRS - Centre National de la Recherche Scientifique - CY - CY Cergy Paris Université)

  • Jean-Benoît Eymeoud

    (LIEPP - Laboratoire interdisciplinaire d'évaluation des politiques publiques (Sciences Po) - Sciences Po - Sciences Po, Banque de France)

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 Eymeoud, 2022. "Can big data increase our knowledge of local rental markets? A dataset on the rental sector in France," SciencePo Working papers Main hal-03592434, HAL.
    • Handle: RePEc:hal:spmain:hal-03592434
    DOI: 10.1371/journal.pone.0260405
    Note: View the original document on HAL open archive server: https://hal.science/hal-03592434v1
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