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

Overview of the ESPON Housing Affordability Resource

The dataset “Unequal housing affordability across European cities” is a public‑access data‑paper produced by the ESPON EGTC, a European territorial planning agency. It was authored by Bartosz Bartosiewicz and published through the open‑access 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 Europe‑wide via Eurostat indicators. The resource includes raw transaction records, web‑scraped advertisement data, and a suite of derived affordability metrics, all documented for reproducibility.

Key Financial Indicators and Budgetary Context

The database provides price‑per‑square‑meter 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 m², 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 Case‑Study Selection

Ten FUAs were selected to represent diverse housing regimes: Geneva‑Annecy‑Annemasse (Switzerland‑France), Avignon and Paris (France), Madrid, Barcelona and Palma de Mallorca (Spain), and Warsaw, Łódź and Krakow (Poland). These areas vary in size—from the 276 k‑inhabitant Annecy to the 11.9 M‑inhabitant Paris FUA—allowing comparative analysis of both large metropolitan and medium‑sized urban contexts. The data are also aggregated to 1 km grid cells and LAU2 administrative units to capture intra‑urban heterogeneity.

Methodology: Data Sources and Harmonisation

The study combines conventional institutional sources (Eurostat, national statistical offices, notarial transaction records) with unconventional big‑data sources (web‑scraped listings from platforms such as Leboncoin, Fotocasa, Idealista, and Airbnb). Transaction datasets include variables such as price paid, surface, rooms and debt. Web‑scraped datasets provide advertised prices, surface and room counts for both sales and rentals. Spatial interpolation using Stewart’s potential method (distance decay parameter −2, 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, Łódź and Krakow exhibit high days‑of‑median‑income required to rent 1 m², reflecting lower incomes and rising rents. In Paris, affordability is highly uneven: central districts demand up to 3.8 months of income per m² for purchase, while wealthier western arrondissements offset costs with higher local incomes. Geneva’s rental market appears less expensive centrally due to tenant protections, yet peripheral French municipalities display the highest rent‑to‑income ratios. National‑income‑based measures often contrast with local‑income‑based measures, underscoring the importance of context‑specific 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 case‑study 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, fine‑grained affordability metrics, the ESPON housing database supports evidence‑based 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.