Overview of the ESPON Housing Affordability Resource
The dataset âUnequal housing affordability across European citiesâ is a publicâaccess dataâpaper produced by the ESPON EGTC, a European territorial planning agency. It was authored by Bartosz Bartosiewicz and published through the openâaccess journal Cybergeo (2021). The work consolidates harmonised spatial data on housing prices, incomes and related indicators for ten functional urban areas (FUAs) across France, Spain, Poland and Switzerland, and extends the coverage to 967 core cities and 676 FUAs Europeâwide via Eurostat indicators. The resource includes raw transaction records, webâscraped advertisement data, and a suite of derived affordability metrics, all documented for reproducibility.
Key Financial Indicators and Budgetary Context
The database provides priceâperâsquareâmeter values for both purchase and rental markets, transaction volumes, and debt information where available. National income deciles (first, median, ninth) are included to calculate months of income required to buy a square metre (local and national affordability). For example, in Paris the poorest households need over 3 months of full income per mÂČ, while in Geneva the same metric reaches 2.2 months for local residents. The dataset also contains profitability indexes (ratio of property price to rental price), highlighting investment opportunities across regions.
Geographic Coverage and CaseâStudy Selection
Ten FUAs were selected to represent diverse housing regimes: GenevaâAnnecyâAnnemasse (SwitzerlandâFrance), Avignon and Paris (France), Madrid, Barcelona and Palma de Mallorca (Spain), and Warsaw, ĆĂłdĆș and Krakow (Poland). These areas vary in sizeâfrom the 276 kâinhabitant Annecy to the 11.9 Mâinhabitant Paris FUAâallowing comparative analysis of both large metropolitan and mediumâsized urban contexts. The data are also aggregated to 1 km grid cells and LAU2 administrative units to capture intraâurban heterogeneity.
Methodology: Data Sources and Harmonisation
The study combines conventional institutional sources (Eurostat, national statistical offices, notarial transaction records) with unconventional bigâdata sources (webâscraped listings from platforms such as Leboncoin, Fotocasa, Idealista, and Airbnb). Transaction datasets include variables such as price paid, surface, rooms and debt. Webâscraped datasets provide advertised prices, surface and room counts for both sales and rentals. Spatial interpolation using Stewartâs potential method (distance decay parameter â2, span 5 km) smooths sparse data and mitigates the Modifiable Areal Unit Problem. Quality control compares scraped data with institutional records, revealing significant but understandable differences in distributions, especially for number of rooms.
Affordability Findings Across Cities
The analysis shows stark regional disparities. In Poland, Warsaw, ĆĂłdĆș and Krakow exhibit high daysâofâmedianâincome required to rent 1 mÂČ, reflecting lower incomes and rising rents. In Paris, affordability is highly uneven: central districts demand up to 3.8 months of income per mÂČ for purchase, while wealthier western arrondissements offset costs with higher local incomes. Genevaâs rental market appears less expensive centrally due to tenant protections, yet peripheral French municipalities display the highest rentâtoâincome ratios. Nationalâincomeâbased measures often contrast with localâincomeâbased measures, underscoring the importance of contextâspecific assessments.
Tools for Reuse and Further Research
All datasets are released in Excel and CSV formats with complete metadata, hosted on the ESPON database portal and the Nakala repository (DOI 10.34847/nkl.aaea911g). A reproducible RMarkdown example for the Barcelona caseâstudy demonstrates the workflow from data acquisition to indicator calculation. The resource is designed for policy analysts, urban planners and researchers focusing on sustainable housing, enabling extensions to additional cities or longitudinal studies as new data become available.
Implications for Sustainable Housing Policy
By providing comparable, fineâgrained affordability metrics, the ESPON housing database supports evidenceâbased policy aimed at reducing spatial inequality and improving access to decent housing. The inclusion of both purchase and rental markets, debt levels, and profitability indexes allows stakeholders to evaluate the impact of regulation, taxation and social housing programmes across diverse European contexts, fostering more sustainable and inclusive urban development.
