# ImmoScout24 Scraper Cheap (`data_api/immoscout24-scraper-cheap`) Actor

ImmoScout24 listing scraper that pulls price, area, rooms, images, and descriptions from each expose page, so analysts and agents get clean property data without copying listings by hand.

- **URL**: https://apify.com/data\_api/immoscout24-scraper-cheap.md
- **Developed by:** [Data API](https://apify.com/data_api) (community)
- **Categories:** Real estate, Automation, Developer tools
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $3.99 / 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.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

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

## ImmoScout24 Scraper

![ImmoScout24 Scraper](cover.jpg)

Pulling property data off ImmoScout24 by hand is slow work: open each ad, copy the price, jot down the size, note the agent, repeat. This scraper skips all of that. Paste a search URL from immobilienscout24.de and every matching listing comes back as a clean row, with the price, floor area, location, photos, and contact details already split into separate fields. It pages through the results for you, so one search URL can fill a whole spreadsheet. Fast, cheap, no code, and you only pay for the listings you collect.

### What you get

Each listing becomes one row with a steady shape, so your columns line up when you load the data into a sheet, a database, or a model. The fields fall into three groups:

- **Property and price** — `propertyTitle`, `propertyCategory`, `priceAmount`, `currencyCode`, `pricePeriod`, `offerType`, `pricePerSquareMeter`, `floorAreaSqm`
- **Location** — `cityName`, `districtName`, `postalCode`, `addressLine`, plus the `listingLink` and `propertyId`
- **Advertiser and extras** — `advertiserName`, `advertiserPhone`, `privateSeller`, `commissionApplies`, `imageLinks`, `featureTags`, `publishedDate`, `updatedDate`, `sourceUrl`, `collectedAt`

### Quick start

1. Open immobilienscout24.de, run a search, and apply your filters (city, property type, price, size).
2. Copy the URL from your browser's address bar.
3. Hit **Try for free** and paste that URL into **Search page URLs**.
4. Set a **Results limit** to decide how many listings to pull.
5. Press **Start**, then export the results as JSON, CSV, Excel, or XML.

![How it works](how-it-works.jpg)

### Use cases

- **Rent and price tracking** — follow asking prices across German cities and catch changes early
- **Commercial space research** — gather office or retail availability in a region into one list
- **Market comparison** — line up price per m², size, and property type across districts
- **Investment screening** — build a database of for-sale or for-rent stock to score against your criteria
- **Agency and broker analysis** — see who lists what, where, and at what price
- **Dashboards and pipelines** — feed live ImmoScout24 data into your own sheet, BI tool, or app

### Input

| Field | Type | Required | Description |
|-------|------|----------|-------------|
| `targetUrls` | array of strings | Yes | One or more ImmoScout24 search result URLs. Run a search, apply filters, and copy the address bar link. Prefilled with a Hamburg for-sale search. |
| `resultsLimit` | integer | No | Total listings to collect across all URLs. Default `30`; max `1000`. |
| `timeoutSeconds` | integer | No | Seconds to wait on each request before timing out. Default `45`. |

#### Example input

```json
{
    "targetUrls": [
        "https://www.immobilienscout24.de/Suche/de/hamburg/wohnung-kaufen",
        "https://www.immobilienscout24.de/Suche/de/koeln/haus-mieten"
    ],
    "resultsLimit": 200,
    "timeoutSeconds": 45
}
```

### Output

Every listing on the search results becomes one row, paginated automatically until your `resultsLimit` is reached or the pages run out. A field that ImmoScout24 does not publish for a given property comes back as `null` rather than a guess, so the dataset stays rectangular.

#### Example output

```json
{
    "propertyId": "168447935",
    "listingLink": "https://www.immobilienscout24.de/expose/168447935",
    "propertyTitle": "Helle 3-Zimmer-Wohnung mit Balkon in Eppendorf",
    "propertyCategory": "Apartment",
    "priceAmount": 489000.0,
    "currencyCode": "EUR",
    "pricePeriod": "ONE_TIME_CHARGE",
    "offerType": "BUY",
    "pricePerSquareMeter": 5988.0,
    "floorAreaSqm": 81.65,
    "cityName": "Hamburg",
    "districtName": "Eppendorf",
    "postalCode": "20251",
    "addressLine": "Eppendorfer Weg, Hamburg",
    "advertiserName": "Nordstadt Immobilien GmbH",
    "advertiserPhone": "040 32819940",
    "privateSeller": false,
    "commissionApplies": true,
    "imageLinks": ["https://pictures.immobilienscout24.de/listings/xxx.jpg/ORIG/legacy_thumbnail/800x600/format/webp/quality/50"],
    "featureTags": ["Balkon", "Einbauküche"],
    "publishedDate": "2026-06-10T21:47:37.000+02:00",
    "updatedDate": "2026-06-10T21:47:50.729+02:00",
    "sourceUrl": "https://www.immobilienscout24.de/Suche/de/hamburg/wohnung-kaufen",
    "collectedAt": "2026-06-29T10:30:00+00:00",
    "errorMessage": null
}
```

#### Output fields

| Field | Type | Description |
|-------|------|-------------|
| `propertyId` | string | ImmoScout24's own identifier for the listing |
| `listingLink` | string | Direct link to the listing detail page |
| `propertyTitle` | string | Headline shown for the property |
| `propertyCategory` | string | Kind of property, such as Office, Apartment, or House |
| `priceAmount` | number | Monthly rent or sale price as a number |
| `currencyCode` | string | Currency the price is quoted in, for example EUR |
| `pricePeriod` | string | Billing period, such as MONTH for monthly rent |
| `offerType` | string | RENT or BUY |
| `pricePerSquareMeter` | number | Cost for each square meter of floor space |
| `floorAreaSqm` | number | Usable floor space in square meters |
| `cityName` | string | City where the property sits |
| `districtName` | string | District or neighbourhood within the city |
| `postalCode` | string | German postal code |
| `addressLine` | string | Full address text as printed on the listing |
| `advertiserName` | string | Agency company or private owner placing the ad |
| `advertiserPhone` | string | Phone number given for the advertiser |
| `privateSeller` | boolean | True for a private seller, false for an agency |
| `commissionApplies` | boolean | True when a broker commission (Courtage) is charged |
| `imageLinks` | array | Direct photo URLs at 800x600 resolution |
| `featureTags` | array | Highlight labels on the ad, such as Provisionsfrei or Aufzug |
| `publishedDate` | string | ISO date the listing first went online |
| `updatedDate` | string | ISO timestamp of the most recent change |
| `sourceUrl` | string | The search URL this listing was found through |
| `collectedAt` | string | ISO timestamp of when the row was captured |
| `errorMessage` | string | Reason a listing failed; `null` on success |

### Tips for best results

- **Filter on the site first.** A tighter search URL (city, type, price band, size) gives you a cleaner, more relevant dataset.
- **Cap test runs with `resultsLimit`.** Start at 20 to 50 to confirm the output fits your pipeline, then raise it for the full pull.
- **Add several URLs at once.** Drop in one URL per city or property type; the actor works through them in order until it hits your `resultsLimit`.
- **Both rent and buy work.** A rental search returns rentals and a sale search returns sale stock; `offerType` and `pricePeriod` tell them apart.
- **Raise `timeoutSeconds`** to around 60 if you see timeouts on large result sets or slow connections.

### How can I use ImmoScout24 property data?

**How can I use the ImmoScout24 Scraper to track German rental prices?**
Paste a search URL for your target city and run it on a schedule. Each row carries `priceAmount`, `pricePerSquareMeter`, `floorAreaSqm`, and `publishedDate`, so you can chart asking prices over time and flag changes as they land.

**How can I pull ImmoScout24 listings without copying them by hand?**
Run a search on immobilienscout24.de, copy the address bar URL, and paste it into `targetUrls`. The scraper pages through the results and returns every listing as a structured row, with price, size, location, and agent contact already in separate fields.

**How can I compare neighbourhoods on ImmoScout24 by price per square meter?**
Collect listings for several areas, then read `pricePerSquareMeter` straight from each row, or divide `priceAmount` by `floorAreaSqm` yourself. With `districtName`, `propertyCategory`, and `offerType` alongside, you get a clear side-by-side view of where the value sits.

**How can I build a German real estate dataset for investment research?**
Feed in one or more search URLs, set `resultsLimit` to the volume you need, and export to CSV or Excel. The result is a structured property database (address, price, size, advertiser) you can score against your own buy or rent criteria.

### Is it legal to scrape data?

Our actors are ethical and do not extract any private user data, such as email addresses or private contact information. They only extract what the user has chosen to share publicly. We therefore believe that our actors, when used for ethical purposes by Apify users, are safe.

However, you should be aware that your results could contain personal data. Personal data is protected by the GDPR in the European Union and by other regulations around the world. You should not scrape personal data unless you have a legitimate reason to do so. If you're unsure whether your reason is legitimate, consult your lawyers.

You can also read Apify's blog post on the [legality of web scraping](https://blog.apify.com/is-web-scraping-legal/).

### Support

Questions, feature requests, or a field you'd like added? Reach out at <data.apify@proton.me> and we'll get back to you.

# Actor input Schema

## `targetUrls` (type: `array`):

Paste one or more ImmoScout24 search result links here. Run a search on immobilienscout24.de, apply your filters, then copy the address bar URL. Example: https://www.immobilienscout24.de/Suche/de/hamburg/wohnung-kaufen

## `resultsLimit` (type: `integer`):

Total number of listings to gather across every search URL you provide. Use it to cap spend on test runs.

## `timeoutSeconds` (type: `integer`):

How long to wait on each request before giving up. Bump this higher if slow connections trigger timeout errors.

## Actor input object example

```json
{
  "targetUrls": [
    "https://www.immobilienscout24.de/Suche/de/hamburg/wohnung-kaufen",
    "https://www.immobilienscout24.de/Suche/de/koeln/haus-mieten"
  ],
  "resultsLimit": 30,
  "timeoutSeconds": 45
}
```

# 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 = {
    "targetUrls": [
        "https://www.immobilienscout24.de/Suche/de/hamburg/wohnung-kaufen"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("data_api/immoscout24-scraper-cheap").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 = { "targetUrls": ["https://www.immobilienscout24.de/Suche/de/hamburg/wohnung-kaufen"] }

# Run the Actor and wait for it to finish
run = client.actor("data_api/immoscout24-scraper-cheap").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 '{
  "targetUrls": [
    "https://www.immobilienscout24.de/Suche/de/hamburg/wohnung-kaufen"
  ]
}' |
apify call data_api/immoscout24-scraper-cheap --silent --output-dataset

```

## MCP server setup

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

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/acts/46K0MHDpobrMuiGzK/builds/41XuP35Ys816UZNBU/openapi.json