Unequal housing affordability across European cities. The ESPON Housing Database, Insights on Affordability in Selected Cities in Europe

Renaud Le Goix1, Ronan Ysebaert1, Timothée Giraud1, Marc Lieury1, Guilhem Boulay2, Mathieu Coulon2, Sébastien Rey-Coyrehourcq3, Rémi Lemoy3, José J. Ramasco4, Mattia Mazzoli4, Pere Colet4, Thierry Theurillat5, Alain Segessemann6, Szymon Marcińczak1 and Bartosz Bartosiewicz1

1Université de Paris Diderot-CNRS, UMS 2414 RIATE, UMR 8504 Géographie-cités, France.
2Université d'Avignon-CNRS, UMR 7300 ESPACE, France.
3UMR 6266 IDEES, CNRS, Université de Rouen, France.
4Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), 07122 Palma, Spain.
5Institut du Management des Villes et du Territoire (IMVT) de la Haute Ecole de Gestion Arc (HEG-Arc), Switzerland.
6Haute École de Gestion Arc, Switzerland.
7Institute of Urban Geography and Tourism Studies, University of Łódź, Poland.

(April 2021)

This data-paper presents and describes a consolidated, harmonized, internationally comparable database to quantify the impacts of the housing affordability crisis. Local harmonized indicators allow to examine the unequal spatial patterns of housing affordability across a selection of European cities. This study seeks at informing and mapping the increased and unequal affordability gap, a critical issue for social cohesion and sustainability in metropolitan areas in Europe. We characterize affordability with measures of price (property and rent) and income in a selection of European Functional Urban Areas (FUAs). The methodological goal was to cope with a data gap, i.e. a lack of harmonized spatial data to map and analyze affordability in Europe. This research, conducted in 2018-19 by a European consortium for the ESPON agency, covers 4 countries and one cross-border region: Geneva (Switzerland), Annecy-Annemasse, Avignon and Paris (France), Madrid, Barcelona and Palma de Majorca (Spain) and Warsaw, Łódź and Krakow (Poland). We bring insights on how institutional data (i.e. transactions data), can be bridged with unconventional data ("big data" harvested on line) to provide a cost-effective and harmonized data collection effort that can contribute to compare affordability within cities (between neighborhoods) and across cities, using various geographical levels (1km square-grid, municipalities, FUA). We present the structure of the database, how it has been constructed in a reproducible manner; we document the validation process, the strengths and limitations of the data provided, and document the reproducibility of the workflow.