Germany Neighborhood Profile – Census 2022 Rent, Vacancy & Demographics

Get official German Census 2022 (Zensus 2022) statistics for any address or coordinate in Germany – on a 100 m grid, with automatic fallback to 1 km where values are suppressed for privacy.

For every location you get:

Field	What it is	Unit
rent	Average net cold rent (Durchschnittliche Nettokaltmiete) of rented dwellings	€/m² per month
vacancy	Vacancy rate (Leerstandsquote)	% of dwellings
population	Residents (Einwohnerzahl)	persons
ownership	Owner-occupancy rate (Eigentümerquote)	%
age	Average age of residents (Durchschnittsalter)	years
construction	Buildings by construction decade (Gebäude nach Baujahr)	number of buildings
heating	Dwellings by main heating type (Heizungsart)	number of dwellings

Each value states the grid resolution it comes from (100m, 1km or 10km) and the official cell id (e.g. CRS3035RES100mN3273500E4552500).

Why use it

• Real estate investors & analysts: compare rent levels and vacancy around a property before you buy – the same check professional investors do, in one API call.
• PropTech, valuation and lead tools: enrich addresses with neutral, official neighbourhood data.
• AI agents: works as a tool via the Apify MCP server – ask your agent “What is the average rent and vacancy around Hauptstraße 5, Leipzig?”.
• Energy & heating businesses: see which heating types and building ages dominate a neighbourhood.

All data comes from the official Census 2022 grid tables of the German Federal Statistical Office (Destatis). The tables are packed into the actor at build time, so runs are fast, cheap and do not depend on scraping any website.

Input

{
  "locations": [
    { "label": "Berlin Alexanderplatz", "address": "Alexanderplatz 1, 10178 Berlin" },
    { "label": "Leipzig centre", "lat": 51.3397, "lon": 12.3731 },
    "Marienplatz 1, 80331 München"
  ],
  "minResolution": "100m"
}

• lat/lon (WGS84) are fastest. Addresses are geocoded with OpenStreetMap Nominatim at max. 1 request per second (usage policy) – for large batches please pass coordinates.
• minResolution: start at 100m (most local) or choose 1km for smoother neighbourhood averages.

Output (example, shortened)

{
  "label": "Leipzig centre",
  "status": "ok",
  "lat": 51.3397, "lon": 12.3731,
  "cell100m": "CRS3035RES100mN3139200E4486300",
  "rent": { "value": 6.84, "resolution": "100m", "cell": "CRS3035RES100mN3139200E4486300" },
  "vacancy": { "value": 3.9, "resolution": "1km", "cell": "CRS3035RES1kmN3139000E4486000" },
  "attribution": {
    "source": "Zensus 2022, © Statistisches Bundesamt (Destatis) und Statistische Ämter der Länder",
    "license": "Datenlizenz Deutschland – Namensnennung – Version 2.0 (dl-de/by-2-0)",
    "referenceDate": "2022-05-15"
  }
}

(The numbers above only illustrate the format.)

Pricing

Pay per result: you are only charged for successfully profiled locations. Failed lookups (address not found, outside Germany) are free.

Good to know

• Reference date 15 May 2022. Census values describe that day; rents have risen since. Use them for comparing places, not as today's market rent.
• Privacy suppression: the statistical offices suppress values for cells with very few dwellings or residents. The actor then falls back to the 1 km (or 10 km) cell and tells you which resolution was used.
• Small counts are statistically blurred: for privacy the census adds small random deviations to counts (cell key method). In cells with only a few buildings, the decades may therefore not add up exactly to the total. Averages and rates (rent, vacancy, age) are the most robust values.
• No valuation, no advice. The output is official statistics, not a property valuation.

Use with AI agents (MCP)

This Actor works as a tool for AI assistants and agents – Claude, ChatGPT, Cursor, VS Code, n8n and other MCP clients – through Apify's hosted MCP server. Add this server URL to your client:

https://mcp.apify.com?tools=stevenkramp/germany-neighborhood-profile

Sign in with your Apify account when asked. Your agent can then call the Actor in plain language, for example: "Give me a neighborhood profile for Leipziger Straße 1, 10117 Berlin – population, age structure and rents." – and gets clean, structured JSON back. Runs started by your agent are normal Actor runs on your Apify account at the same pay-per-event price.

Sources and licences

• Census 2022 grid data: © Statistisches Bundesamt (Destatis) and the statistical offices of the Länder, Datenlizenz Deutschland – Namensnennung – Version 2.0. Every result contains the required attribution.
• Geocoding: © OpenStreetMap contributors, ODbL, via Nominatim.

This actor is not affiliated with Destatis or OpenStreetMap.

---

Deutsch (kurz)

Zensus 2022 für jede Adresse in Deutschland: durchschnittliche Nettokaltmiete pro m², Leerstandsquote, Einwohner, Eigentümerquote, Durchschnittsalter, Gebäude nach Baujahr und Heizungsart – im 100-m-Gitter mit automatischem Rückfall auf 1 km. Ideal für Immobilien-Investoren (Ankaufsprüfung, Standortvergleich), PropTech und KI-Agenten. Amtliche Daten von Destatis (dl-de/by-2-0), Stichtag 15.05.2022.