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Your smart guide to finding a neighborhood in Madrid
VivirDonde is a free tool that helps you find the best neighborhood in Madrid based on your budget and personal preferences. We analyze official open data to score each area of the city across dimensions such as public transport, green spaces, safety and nightlife, and combine them with real rental prices to offer personalized recommendations. No registration, no cost, no hassle.
Each neighborhood's scores are calculated from official open public data sources:
Prices are refreshed automatically each month from the public reports of Idealista and Fotocasa. Scores (safety, transport, green spaces, family, nightlife, walkability) are revisited whenever the official sources publish new data: Madrid City Council's Quality of Life Survey (six-monthly cadence), CRTM GTFS feeds (continuous), Business Census and Municipal Register (yearly cadence).
Public Transport
Density of metro, bus, commuter rail and light rail stops per km² of district. Metro and commuter rail stations carry more weight due to their transport capacity.
Green Spaces
Square meters of parks and green areas per inhabitant, based on green surface data published by Madrid City Council.
Safety
Citizen perception by district (0-10 scale) according to the Quality of Life and Satisfaction with Public Services Survey by Madrid City Council, published every six months since 2022.
Nightlife
Density of bars, restaurants and entertainment venues per km², based on the City Council's Business Census (Division 56: food and beverage services).
Family
Combination of three indicators: 40% population aged 0-14 (Municipal Census), 35% density of schools and nurseries within 800m (Madrid Region — Educational Centers Directory), and 25% density of playgrounds within 500m (datos.madrid.es).
Walkability
Adaptation of the European Walkability Index: 30% commercial density (Business Census), 25% residential density (Municipal Census), 25% street connectivity (intersections/km² from OpenStreetMap) and 20% pedestrian infrastructure (linear meters of pedestrian streets and footways from OSM).
Update cadence: prices refresh on the 5th of every month via an automated pipeline; scores are revisited when the official sources publish new data.
Travel times from the points you enter (work, gym, school…) to each neighborhood are calculated with OpenRouteService on the real OpenStreetMap road network, not as the crow flies. Supported profiles are walking, cycling and driving. For public transport we apply an empirical factor over driving time (Madrid ≈ 1.4×) while we integrate GTFS routes from CRTM. If the routing service is unavailable, the system falls back to a Haversine estimate with a street correction factor.
Have suggestions, found a data error or want to collaborate? Write to us at [email protected]