# ImmobilienScout24.de Scraper (`one-api/immoscout24-scraper`) Actor

Scrape ImmobilienScout24.de (ImmoScout24), Germany's largest property marketplace. Get full listing details by id or URL, search homes for sale (kaufen) or rent (mieten) by location, coordinates + radius, or search URL, and resolve locations via autocomplete.

- **URL**: https://apify.com/one-api/immoscout24-scraper.md
- **Developed by:** [ONE API](https://apify.com/one-api) (community)
- **Categories:** Lead generation, Real estate
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $3.00 / 1,000 results

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## ImmobilienScout24.de Scraper

Scrape **ImmobilienScout24.de** (ImmoScout24), **Germany's largest real-estate marketplace**. Look up full listing details by id or `expose` URL, search apartments and houses for sale (*kaufen*) or rent (*mieten*) by city / district / postcode, by coordinates + radius, or by an immobilienscout24.de results URL with filters, and resolve locations via autocomplete.

### What it does

- **🏠 Property Details** — auto-detects each input as a numeric ImmoScout24 expose id (`/details/byid`) or an `immobilienscout24.de/expose/...` URL (`/details/byurl`); returns the full enriched record: price, living space, rooms, energy certificate, features, photos, description, agency + phone, costs, documents and project units. Comparable nearby listings the API attaches are added as extra rows.
- **🔎 Search Listings** — auto-detects a free-text location (`/search/bylocation`) or an immobilienscout24.de results URL (`/search/byurl`). Deal type, property type, price / rooms / living-area / plot / construction-year filters and sort all apply.
- **📍 Search by Coordinates** — radius search around a `latitude,longitude` point with an optional radius in km (`/search/bycoordinates`).
- **🔍 Location Autocomplete** — resolve free text (city / district / postcode) to the ImmoScout24 geocode id used by search (`/autocomplete`).

### Output

- **One flat dataset row per listing or location suggestion** — sortable, filterable, CSV / Excel / JSON exportable.
- Columns: `Mode`, `Input Given`, `ID`, `Title`, `Deal`, `Property Type`, `Price`, `Rooms`, `Living Space m²`, `Plot m²`, `Energy Class`, `Address`, `Latitude`, `Longitude`, `Scout ID`, `Agency`, `Agency Phone`, `Agency Rating`, `Cover Photo`, `Listing URL`.
- The complete upstream JSON for each row is kept in the `Raw` column for power users.

### Inputs

- Each section is independent — fill only the ones you need; multiple inputs per section, one per line.
- The Search filters (deal type, property type, ranges, sort, pages, results-per-page) apply to every location, coordinate and URL search.
- **Coordinates** entries use the format `latitude,longitude` or `latitude,longitude,radiusKm`, e.g. `48.137,11.575,3` (3 km around central Munich).

### Pricing

- Pay per result: **$3 per 1,000 dataset items** — the same rate on every Apify plan tier.
- Apify start fee: $0.00005 per run.

### Notes

- ImmoScout24 serves roughly **20 listings per page** — raise *Pages per search* to fetch more.
- Deal type is **Buy (kaufen)** or **Rent (mieten)** — sold listings are not available on ImmoScout24.
- Prices are in **EUR (€)**; living and plot area in **m²**. `Energy Class` is the German efficiency class (A+…H). For rentals `Price` is the total monthly rent.
- Detail records include energy certificate, costs, documents, project units and comparable similar listings; search results carry coordinates inline.

### Errors

- Failures land in the dataset as rows with `Mode = "ERROR"` and the upstream message in `Title` — nothing is silently dropped.

# Actor input Schema

## `property_inputs` (type: `array`):

Auto-detects each entry and calls the right endpoint:
• A numeric ImmoScout24 expose id (e.g. `167274972`) → `/details/byid`
• An `https://www.immobilienscout24.de/expose/...` URL → `/details/byurl`

Each row = one full property record (price, living space, rooms, energy certificate, features, photos, agency + phone, costs, documents, project units). Any comparable nearby listings the API attaches are added as extra rows.

## `search_inputs` (type: `array`):

Auto-detects each entry and routes to the right `/search/*` endpoint:
• An immobilienscout24.de results URL, e.g. `https://www.immobilienscout24.de/Suche/de/berlin/berlin/wohnung-kaufen` → `/search/byurl`
• Anything else (city, district or postcode, e.g. `Berlin`, `München`, `10115`) → `/search/bylocation`

The filters below apply to every `bylocation` query. Each hit returns one row per listing.

## `coordinate_inputs` (type: `array`):

Radius search around a point via `/search/bycoordinates`. One point per line as `latitude,longitude` or `latitude,longitude,radiusKm` (radius 0.5–50 km, default 5). Example: `48.137,11.575,3` searches 3 km around central Munich. The filters below also apply. One row per listing.

## `searchType` (type: `string`):

Buy (kaufen) or rent (mieten). Applies to every Search and Coordinates query.

## `propertyType` (type: `string`):

Type of real estate to search for. Leave as Any for all types.

## `priceRange` (type: `string`):

Format: `min:200000,max:600000` — or just `min:200000` / `max:600000`. For rent this filters the total monthly rent.

## `rooms` (type: `string`):

Number of rooms, e.g. `min:3` or `min:2,max:4` (half-rooms allowed).

## `areaRange` (type: `string`):

Living area in m², e.g. `min:60,max:140`.

## `plotRange` (type: `string`):

Plot or land area in m² (houses & land), e.g. `min:300`.

## `constructionYear` (type: `string`):

Year the property was built, e.g. `min:2010` or `min:1990,max:2020`.

## `sortOrder` (type: `string`):

Result ordering for `/search/bylocation` and `/search/bycoordinates`.

## `pages` (type: `integer`):

How many result pages to fetch for each location / coordinate / URL search (~20 listings per page).

## `resultCount` (type: `integer`):

Listings per page for every search (1–100; ~20/page upstream).

## `autocomplete_inputs` (type: `array`):

Resolve a German city / district / postcode to the ImmoScout24 geocode id used by Search, via `/autocomplete`. Each suggestion = one row (id, type, label).

## Actor input object example

```json
{
  "property_inputs": [
    "167274972",
    "https://www.immobilienscout24.de/expose/167274972"
  ],
  "search_inputs": [
    "Berlin"
  ],
  "coordinate_inputs": [
    "48.137,11.575,3"
  ],
  "searchType": "For_Sale",
  "propertyType": "apartment",
  "priceRange": "",
  "rooms": "",
  "areaRange": "",
  "plotRange": "",
  "constructionYear": "",
  "sortOrder": "Recommended",
  "pages": 1,
  "resultCount": 20,
  "autocomplete_inputs": [
    "Berlin"
  ]
}
```

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "property_inputs": [
        "167274972",
        "https://www.immobilienscout24.de/expose/167274972"
    ],
    "search_inputs": [
        "Berlin"
    ],
    "coordinate_inputs": [
        "48.137,11.575,3"
    ],
    "autocomplete_inputs": [
        "Berlin"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("one-api/immoscout24-scraper").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "property_inputs": [
        "167274972",
        "https://www.immobilienscout24.de/expose/167274972",
    ],
    "search_inputs": ["Berlin"],
    "coordinate_inputs": ["48.137,11.575,3"],
    "autocomplete_inputs": ["Berlin"],
}

# Run the Actor and wait for it to finish
run = client.actor("one-api/immoscout24-scraper").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "property_inputs": [
    "167274972",
    "https://www.immobilienscout24.de/expose/167274972"
  ],
  "search_inputs": [
    "Berlin"
  ],
  "coordinate_inputs": [
    "48.137,11.575,3"
  ],
  "autocomplete_inputs": [
    "Berlin"
  ]
}' |
apify call one-api/immoscout24-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=one-api/immoscout24-scraper",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/egZzBvo6qdYn0SaNs/builds/cYa321XdUaNzr4z57/openapi.json