A data storytelling project exploring how affordable renting in Berlin really is — and for whom. The app brings together rent listings, income, population, and social/subsidized housing data to show how affordability evolved from 2013 to 2023, and lets you test affordability for different incomes and flat sizes.
This project was built as a Data Analytics bootcamp capstone and combines data analysis, mapping, and UX to communicate a complex topic accessibly.
Berlin’s reputation for “cheap living” has eroded. Rents rose fast; incomes rose too, but unevenly; and the stock of subsidized housing shrank. Behind citywide averages are stark spatial differences. This project asks: Who can still afford to rent, where, and how has that changed over time?
- Rent market evolution (2013–2023): changes in €/m² and areas where prices doubled
- Demand signals: population growth, net migration, income trends
- Affordability: rent burden maps (share of income spent on rent)
- Access to social/subsidized housing: WBS & public housing trends
- Your rent: interactive map to test affordability by income and apartment size
Rent burden = (median rent €/m² × apartment size m²) ÷ monthly income
Baseline sizes: 1-room 50 m², 2-room 65 m², 3-room 80 m²
Affordability threshold: 30% of income (clearly marked in the color scale)
PLR = Planungsräume (LOR planning areas), the unit used for spatial analysis
This project aggregates publicly available statistics and official geodata for Berlin.
| Source | Description |
|---|---|
| Amt für Statistik Berlin-Brandenburg (AfS) | Population by PLR, Income 2022–2024, Household data 2023 |
| Investitions Bank Berlin Housing Market Reports (IBB) | Median and Average Rent prices by PLR 2013–2014, Rent Trends |
| Bundesagentur für Arbeit (AfA) | Unemployment rates, Bürgergeld caps |
| Wohnatlas 2022 Senatsverwaltung für Stadtentwicklung, Bauen und Wohnen (SenStadt) | Social housing %, Social Welfare Recipients %, Geographic Information |
| Verband Berlin-Brandenburgischer Wohnungsunternehmen (BBU) | Social Housing data for 2017–2023 |
Created as the capstone project for a Data Analytics bootcamp. The goal is to demonstrate end-to-end skills: data collection/cleaning, spatial analysis, interactive visualization, and narrative design.