Methodology
How your fit works
The wohnwahn fit shows how well a property matches YOUR priorities. It weighs four pillars (location, price, climate and commute time) by what matters to you, and relies on open, traceable data: official datasets from federal, state and city authorities, OpenStreetMap and the real timetables of the transport associations. No paid ranking, no hidden assumptions.

The four pillars
Daily needs, family, mobility, environment and culture, from concrete places within walking distance. We compute quality across the whole area of a Grätzl (250 m grid) instead of a single centre point: the edge counts too, not just the middle.
Ratio of the price per square metre to the median of comparable listings on wohnwahn in the same district or municipality: rent against rent, purchase against purchase, split by building age and, for commercial space, by use. If there is too little to compare within the district, we compare with the whole city. Each development counts once, and the listing's own does not count. Fair, not just cheap. Where there is too little to compare or the comparison does not hold, we do not measure: the pillar then counts as in line with the market and is visibly marked as preliminary. We deliberately do not compare houses and parking spaces, nor special cases such as flats sold with a sitting tenant or fully furnished ones.
How cool the location is, plus street trees and nearby water. In Vienna the value comes from the city’s official urban climate analysis, in Graz from the City of Graz’s official climatope map (46 climatope classes, each area with its official description). Outside these two city areas no such map exists, so we calculate it from the official soil sealing data (Copernicus, 10 m): how much a location heats up depends first on how much ground around it is sealed. We calibrated this against 818 cells of the Vienna map and tested it on districts the model had never seen: the level is exactly right in 68 percent of cases and within one level in 90 percent. Every listing shows which of the two applies. Honest limit: the scale is calibrated on Vienna, so in markedly cooler regions the calculated value tends to come out slightly too warm.
Real travel times to your work address by mode: public transport, bike, car, on foot. Reachable metro/rail/tram/bus lines are counted from OpenStreetMap, the frequency (departures per weekday within walking distance) from the real timetables of the transport associations and the railways: both visible per Grätzl in the Atlas. For commercial spaces this pillar becomes "Accessibility" and measures footfall: how busy a location is for retail, computed per 250 m cell from the density of shops, food & drink and services, proximity to pedestrian zones and transit frequency. It carries the most weight; climate then counts for less.
The area in categories
The „Location“ pillar draws on five scannable categories, each from concrete places within walking distance. We count the figures per category from OpenStreetMap: you can see them per Grätzl in the Atlas.
Data sources
We read 64 open datasets from 25 publishers. Every row names the publisher, the dataset and the licence, and the link leads to the dataset. Entries marked “index only” are used solely to find the right tile, nothing from them is shown. The asking prices per district are published figures we copied by hand: not open data, hence without an open licence.
- Data source: City of Vienna – data.wien.gv.at · district boundaries Vienna (CC BY 4.0)
- Data source: City of Graz – data.graz.gv.at · administrative boundaries Graz (CC BY 4.0)
- Data source: City of Linz – data.linz.gv.at · statistical districts Linz since 1 Jan 2014 (CC BY 4.0)
- Data source: City of Salzburg – data.stadt-salzburg.at · city districts of Salzburg (CC BY 3.0 AT)
- Data source: Stadt Innsbruck · city districts (statistik_06) (geoHub Innsbruck terms of use)
- Data source: Statistik Austria – data.statistik.gv.at · statistical census districts of Austria, Bregenz, via VOGIS (Province of Vorarlberg) (CC BY 4.0)
- © OpenStreetMap contributors · OpenStreetMap, surrounding municipalities (Nominatim); St. Pölten: cadastral communities; Klagenfurt: municipal districts; Eisenstadt: cadastral communities (ODbL 1.0)
- © OpenStreetMap contributors · OpenStreetMap, facilities via Overpass (ODbL 1.0)
- Data source: Mobilitätsverbünde Österreich OG – data.mobilitaetsverbuende.at (Data Licence Mobilitätsverbünde Österreich)
- Timetable Data PTA Eastern Region (GTFS), no. 52
- Timetable Data PTA Styria (GTFS), no. 53
- Timetable Data PTA Salzburg (GTFS), no. 54
- Timetable Data PTA Carinthia (GTFS), no. 55
- Timetable Data PTA Upper Austria (GTFS), no. 56
- Timetable Data PTA Tyrol (GTFS), no. 57
- Timetable Data PTA Vorarlberg (GTFS), no. 58
- Railway Timetable Data (GTFS) - Current Reference Data, no. 66, ÖBB, Raaberbahn, Montafonerbahn, WESTbahn and ÖGEG
- Timetable Data Linz AG (GTFS), no. 69
- © OpenStreetMap contributors · OpenStreetMap, lines within walking distance (ODbL 1.0)
- © OpenStreetMap contributors · OpenStreetMap, path network for our own routing (ODbL 1.0)
- Data source: City of Vienna – data.wien.gv.at · urban climate analysis map Vienna (CC BY 4.0)
- Data source: City of Graz – data.graz.gv.at · climatope map (urban climate analysis), service outside the open data catalogue, use approved by the City of Graz (16 Aug 2026) (CC BY 4.0)
- Data source: GeoSphere Austria – data.hub.geosphere.at · SPARTACUS-v3: monthly climate data for Austria, climate series, caps the modelled value (CC BY 4.0)
- European Union’s Copernicus Land Monitoring Service information · Imperviousness Density 2018 (raster 10 m), derived: heat model outside the official climate maps (Copernicus data policy)
- Data source: GeoSphere Austria – data.hub.geosphere.at · TAWES weather stations, 10-minute data for Austria, weather at the nearest station, mood only, not a measurement at the house (CC BY 4.0)
- Data source: City of Vienna – data.wien.gv.at · tree register (CC BY 4.0)
- Data source: City of Graz – data.graz.gv.at · tree register, service outside the open data catalogue, use approved by the City of Graz (16 Aug 2026) (CC BY 4.0)
- Data source: City of Linz – data.linz.gv.at · tree register (CC BY 4.0)
- Data source: City of Salzburg – data.stadt-salzburg.at · tree register, location and species (CC BY 3.0 AT)
- Data source: Federal Office of Metrology and Surveying (BEV) (CC BY 4.0)
- ALS DSM elevation raster 1 m (surface model), height and crown of city trees where measurable, and the tree canopy
- ALS DTM elevation raster 1 m (terrain model), ground below the crowns
- © OpenStreetMap contributors · OpenStreetMap, trees where no register lists them (ODbL 1.0)
- European Union’s Copernicus Land Monitoring Service information (Copernicus data policy)
- Tree Cover Density 2018 (raster 10 m), derived: share of woody vegetation nearby
- Woody Vegetation Layer 2021 (raster 5 m), map layer and derived share of woody vegetation
- Dominant Leaf Type 2018 (raster 10 m), broadleaf or conifer, only to cross-check the laser-scan trees
- Data source: www.laerminfo.at (CC BY 4.0)
- noise zones of the 2022 environmental noise mapping: roads, Austria
- noise zones of the 2022 environmental noise mapping: motorways and expressways, Austria
- noise zones of the 2022 environmental noise mapping: rail, Austria
- noise zones of the 2022 environmental noise mapping: air traffic, Austria
- noise zones of the 2022 environmental noise mapping: industry (IPPC), Austria
- environmental noise Austria 2020 agglomerations, where the mapping covers every street
- Data source: Land Tirol – data.tirol.gv.at (CC BY 4.0)
- hazard zones BWV Tyrol, flooding, federal water engineering administration
- hazard zone plan of the torrent and avalanche control, avalanche, torrent, indication areas, via the province map service
- Data source: Federal Ministry of Agriculture, Forestry, Climate and Environmental Protection, Regions and Water Management (BMLUK) (CC BY 4.0)
- Data source: City of Vienna – data.wien.gv.at · generalised zoning plan Vienna (CC BY 4.0)
- Data source: Land Steiermark – data.steiermark.gv.at · zoning plan Styria (CC BY 4.0)
- Data source: Land Salzburg – data.salzburg.gv.at · zoning Province of Salzburg (CC BY 4.0)
- Data source: Land Oö., doris.at · zoning areas Upper Austria (CC BY 4.0)
- Data source: Land Tirol – data.tirol.gv.at · zoning Tyrol (CC BY 4.0)
- Data source: Land Vorarlberg – data.vorarlberg.gv.at · zoning plan Vorarlberg: areas (CC BY 4.0)
- Data source: Land Burgenland – GIS-Koordination · zoning Burgenland (CC BY 4.0)
- © OpenStreetMap contributors · OpenStreetMap, water, parks and green areas (ODbL 1.0)
- Data source: City of Vienna – data.wien.gv.at (CC BY 4.0)
- Data source: Federal Office of Metrology and Surveying (BEV) (CC BY 4.0)
- ALS DSM elevation raster 1 m (surface model), building heights outside Vienna
- ALS DTM elevation raster 1 m (terrain model), ground below the buildings outside Vienna
- © OpenStreetMap contributors · OpenStreetMap, building footprints outside Vienna (ODbL 1.0)
- Data source: City of Vienna – data.wien.gv.at · orthophoto 2025 Vienna (CC BY 4.0)
- Data source: basemap.at · orthophoto Austria (CC BY 4.0)
- Data source: City of Graz – data.graz.gv.at · orthophoto Graz 2022 (10 cm), service outside the open data catalogue, use approved by the City of Graz (16 Aug 2026) (CC BY 4.0)
- Data source: geoland.at (Austrian provinces) · Digital Terrain Model (DGM) Austria, 10 m, via the Terrain Tiles, zoom levels 11 to 14 (CC BY 4.0)
- Data source: Mapzen (Linux Foundation) via AWS Open Data · Terrain Tiles (Terrarium), coarser zoom levels from EU-DEM (Copernicus), SRTM and GMTED2010 (U.S. Geological Survey) and ETOPO1 (NOAA) (attribution per source)
- Data source: City of Vienna – data.wien.gv.at · area types 2021 Vienna (CC BY 4.0)
- Data source: City of Vienna – data.wien.gv.at (CC BY 4.0)
- Data source: City of Vienna – data.wien.gv.at (CC BY 4.0)
- street lights with LED exchange status, locations Vienna
- intermodal transport reference system (GIP.at) street graph Vienna, whether a light hangs above the carriageway
- © OpenStreetMap contributors · OpenStreetMap, street lamps where no official dataset lists them (ODbL 1.0)
- Data source: City of Vienna – data.wien.gv.at · city hiking trails and Rundumadum trail Vienna (CC BY 4.0)
- © OpenStreetMap contributors · OpenStreetMap, paths of the walks (ODbL 1.0)
- Data source: Broadband Office · broadband atlas, maximum download rate at the location (CC BY 3.0 AT)
- © OpenStreetMap contributors · OpenStreetMap, Nominatim and Photon (ODbL 1.0)
- Source: STATISTIK AUSTRIA (Statistics Austria terms of use)
- average real estate prices (flats, houses, building land), prices paid in purchase contracts
- housing costs (microcensus housing), rents paid per province
- Data source: immopreise.at / derStandard · price index: purchase asking prices per Vienna district (December 2025) (no open licence, copied by hand)
- Data source: IMMOXX · market survey: purchase asking prices Graz (June 2026) (no open licence, copied by hand)
- Data source: immomarktanalyse.at / ZT Datenforum · rents per Graz district (June 2026) (no open licence, copied by hand)
- Data source: Statistik Austria – data.statistik.gv.at · population at the start of the year (CC BY 4.0)
- Source: STATISTIK AUSTRIA · regional classifications: political districts and list of municipalities, index only (Statistics Austria terms of use)
- Data source: Wikimedia Commons · photos for atlas and guides (licence per photo, shown on the image)
- Data source: Wikipedia · article on the place, basis of the local history in the atlas, linked on each page (CC BY-SA 4.0)
- © OpenStreetMap contributors · OpenStreetMap, map data of the tiles (ODbL 1.0)
- © CARTO · Basemaps (CARTO basemap terms)
Licences in use: CC BY 4.0 (45), Data Licence Mobilitätsverbünde Österreich (9), Copernicus data policy (4), CC BY 3.0 AT (3), Statistics Austria terms of use (3), no open licence, copied by hand (3), geoHub Innsbruck terms of use (1), attribution per source (1), ODbL 1.0 (1), licence per photo, shown on the image (1), CC BY-SA 4.0 (1), CARTO basemap terms (1). In addition, every map names its sources directly in the attribution line.
How up to date
Location, transit, trees, water and building heights are refreshed automatically every month; the official climate maps (Vienna urban climate analysis and Graz climatope map) whenever a new release comes out. Listings arrive continuously, directly from agents, developers and private sellers, and the price comparison always works with the current listings. Every place and listing page visibly carries its „as of“ date.
Frequently asked questions
Is the fit objective?
The pillars rely on transparent, reproducible data (same data, same result). The weighting is deliberately YOURS: you decide what matters, and the fit shows how well a property matches, not a blanket „good/bad“, but a fit for you.
What is measured, what is preliminary?
Every pillar is calculated from real data: location, climate and commute time from open data (official datasets, OpenStreetMap, timetables), price from the asking prices of comparable listings on wohnwahn. A pillar only counts as measured, though, where this data exists for the individual property. Otherwise it is marked as preliminary in the breakdown, and price then counts as in line with the market. A price comparison needs enough comparable listings: the more there are, the more often the price is measured.
What do the arrows on the heat map mean?
The colours show heat load (red hot, blue/green cool). Arrows and lines show where fresh and cold air flows (air corridors): these cool the city at night. The source is the City of Vienna official urban climate analysis.
How current is the data?
Location, transit, trees and water refresh automatically every month, the official climate maps (Vienna urban climate analysis and Graz climatope map) whenever a new release comes out. New listings arrive continuously, and the price comparison always uses the current listings. Every page shows its update date.
Does paid placement play a role?
No. There is no bought ranking and no preferred listings. The fit depends solely on the data sources named here and your weighting.
Why is the location sometimes only „approximate“?
When a listing does not publish an exact address, we honestly show a soft radius instead of a sharp pin and mark it visibly.
Find your fit among real properties.
Start your own searchLast updated: September 2026 · Map tiles © OpenStreetMap contributors