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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.

Satellite image of Vienna
Copernicus Sentinel-2, ESA · CC BY-SA 3.0 igo

The four pillars

Location

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.

Price

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.

Climate

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.

Commute

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.

Everyday & supplies
Supermarkets, bakeries, pharmacies and drugstores within walking distance: density and distance.
Family
Primary schools, kindergartens and playgrounds and how easy they are to reach.
Mobility
Connections and frequency of metro, tram and bus at the address.
Environment
Street trees, nearby water and the share of green and park areas: from the tree registers of Vienna, Graz, Salzburg and Linz and from OpenStreetMap.
Culture & nightlife
Bars, cafés, cultural venues and nightlife in the surrounding area.

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.

DataSource
Geodata & boundaries (districts, city districts, municipalities)
POI (supermarkets, doctors, pharmacies, schools, kindergartens)
Travel times by mode (transit, bike, car, foot)
Climate / heat load
Current weather (3D map)
Street and park trees
Tree canopy density (surroundings)
Flooding & natural hazards
Zoning
Water & green areas
Building heights & roofs (light & shadow, 3D map)
Aerial imagery (3D map, roof colours)
Terrain (horizon in the sun check, 3D map, elevation profiles)
Area types (built fabric of the area)
Street lighting (night view of the 3D map)
City hiking trails & walks
Broadband coverage
Addresses & geocoding
Rent & purchase prices (area)
Population (weighting of surrounding districts)
Photos & local history
Map tiles

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.

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Last updated: September 2026 · Map tiles © OpenStreetMap contributors