# Leboncoin Real Estate Scraper — Prix, Surface & Annonces FR (`totaka/leboncoin-real-estate-scraper`) Actor
Scrape real estate listings from Leboncoin (France's #1 classifieds) — title, price, surface, rooms, location, agency, and listing URL. The most reliable Leboncoin scraper on Apify.
- **URL**: https://apify.com/totaka/leboncoin-real-estate-scraper.md
- **Developed by:** [Thomas Gharbi](https://apify.com/totaka) (community)
- **Categories:** Real estate, Marketing
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet
## Pricing
Pay per usage
This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have.
Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
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.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md).
If your project is in a different language, use 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
## Leboncoin Real Estate Scraper — Prix, Surface & Annonces Immobilières
Scrape **real estate listings from Leboncoin** (France's #1 classifieds platform) for any city or region. Get the listing title, sale or rental price, surface area, number of rooms, location, agency name, and direct URL. Exports to JSON, CSV, or Excel.
**This is the most reliable Leboncoin real estate scraper on Apify.**
---
### What you get
For every listing found on Leboncoin:
| Field | Description |
|-------|-------------|
| `title` | Listing title (e.g. "Appartement 3 pièces — Paris 11e") |
| `price` | Listed price (sale or monthly rent) |
| `surface` | Surface area in m² |
| `rooms` | Number of rooms |
| `location` | City and postal code |
| `agency` | Agency name (if listed by an agency; null for individuals) |
| `url` | Direct link to the listing on Leboncoin |
| `searchQuery` | The location slug used for this search |
| `scrapedAt` | Scrape timestamp (ISO 8601) |
---
### How to use
1. Click **Try for free**
2. Enter location slugs — one per line (format: `City_DeptCode`, e.g. `Paris_75`)
3. Set **Max results per location** (default: 20)
4. Click **Run** — results appear in your dataset in real time
5. Export to CSV, JSON, or Excel from the dataset tab
---
### Location slug format
Leboncoin uses department codes in its URLs. Common examples:
| Location | Slug |
|----------|------|
| Paris | `Paris_75` |
| Marseille | `Marseille_13` |
| Lyon | `Lyon_69` |
| Bordeaux | `Bordeaux_33` |
| Toulouse | `Toulouse_31` |
| Nice | `Nice_06` |
| Nantes | `Nantes_44` |
| Strasbourg | `Strasbourg_67` |
| Lille | `Lille_59` |
| Montpellier | `Montpellier_34` |
---
### Input parameters
| Parameter | Type | Description |
|-----------|------|-------------|
| `searchQueries` | array | Location slugs (City_DeptCode format) |
| `maxResultsPerQuery` | integer | Max listings per location (default: 20) |
| `proxyConfiguration` | object | Proxy settings (residential recommended) |
#### Input example
```json
{
"searchQueries": [
"Paris_75",
"Lyon_69",
"Marseille_13"
],
"maxResultsPerQuery": 100
}
````
***
### Output example
```json
{
"title": "Appartement 3 pièces — Lumineux, proche métro",
"price": "1 250 € / mois CC",
"surface": "68 m²",
"rooms": "3 pièces",
"location": "Paris 11e (75011)",
"agency": "Cabinet Immobilier Bastille",
"url": "https://www.leboncoin.fr/annonce/123456789",
"searchQuery": "Paris_75",
"scrapedAt": "2026-06-18T10:15:00.000Z"
}
```
***
### Use cases
**Real estate investment research** — Track price-per-m² trends across French cities and neighborhoods for buy-to-let analysis.
**Rental market analysis** — Monitor supply, average rents, and agency density in any French city or arrondissement.
**Property price tracking** — Scrape the same location weekly to detect price changes and identify deals before they're gone.
**Agency prospecting** — Identify agencies active in a market to target for partnership or competitive analysis.
**Urban planning research** — Analyze listing density, surface sizes, and room counts by arrondissement or commune.
**Relocation research** — Compare rental prices and availability across multiple French cities before relocating.
***
### Why this scraper is reliable
Leboncoin has a React-based frontend that changes frequently. This scraper targets `data-qa-id` and `data-testid` attributes — Leboncoin's own stable test hooks — rather than fragile CSS class names that change with every UI update. Residential proxies are used to avoid rate limiting.
***
### Limitations
- **France only** — Leboncoin is a French platform; listings are exclusively in France and French-speaking territories
- **Real estate only** — this scraper targets the property (category 9) and uses real estate type filters (apartment + house). For other Leboncoin categories, a different scraper configuration is needed
- **~200 listings per search** — Leboncoin paginates results; use specific city slugs rather than regional slugs for complete coverage of a market
- **No price history** — each run captures current listings; historical price tracking requires scheduled runs
***
### Support
Open a ticket in the **Issues tab** on the actor page. We monitor all issues and respond quickly. If a location slug doesn't return results, open an issue and we'll verify the correct format for that area.
# Actor input Schema
## `searchQueries` (type: `array`):
Enter one search query per line — exactly as you would type it into Google Maps.
Examples: Paris\_75, restaurant Paris 7ème, dentist New York
## `maxResultsPerQuery` (type: `integer`):
Maximum number of results to scrape per search query.
Default: 20 | Max: 100
⚠️ Higher values mean longer runs and higher costs.
## `proxyConfiguration` (type: `object`):
Residential proxies are recommended for reliable scraping.
## Actor input object example
```json
{
"searchQueries": [
"Paris_75"
],
"maxResultsPerQuery": 3,
"proxyConfiguration": {
"useApifyProxy": true
}
}
```
# Actor output Schema
## `results` (type: `string`):
Scraped data items stored in the default dataset
# 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 '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"searchQueries": [
"Paris_75"
],
"maxResultsPerQuery": 3
};
// Run the Actor and wait for it to finish
const run = await client.actor("totaka/leboncoin-real-estate-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 '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = {
"searchQueries": ["Paris_75"],
"maxResultsPerQuery": 3,
}
# Run the Actor and wait for it to finish
run = client.actor("totaka/leboncoin-real-estate-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 '{
"searchQueries": [
"Paris_75"
],
"maxResultsPerQuery": 3
}' |
apify call totaka/leboncoin-real-estate-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=totaka/leboncoin-real-estate-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Leboncoin Real Estate Scraper — Prix, Surface & Annonces FR",
"description": "Scrape real estate listings from Leboncoin (France's #1 classifieds) — title, price, surface, rooms, location, agency, and listing URL. The most reliable Leboncoin scraper on Apify.",
"version": "1.0",
"x-build-id": "J9K1Zdyr3imOxIz6t"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/totaka~leboncoin-real-estate-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-totaka-leboncoin-real-estate-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/totaka~leboncoin-real-estate-scraper/runs": {
"post": {
"operationId": "runs-sync-totaka-leboncoin-real-estate-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/totaka~leboncoin-real-estate-scraper/run-sync": {
"post": {
"operationId": "run-sync-totaka-leboncoin-real-estate-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"required": [
"searchQueries"
],
"properties": {
"searchQueries": {
"title": "🔍 Search queries",
"minItems": 1,
"type": "array",
"description": "Enter one search query per line — exactly as you would type it into Google Maps.
Examples: Paris_75, restaurant Paris 7ème, dentist New York",
"items": {
"type": "string"
}
},
"maxResultsPerQuery": {
"title": "📊 Max results per query",
"minimum": 1,
"maximum": 100,
"type": "integer",
"description": "Maximum number of results to scrape per search query.
Default: 20 | Max: 100
⚠️ Higher values mean longer runs and higher costs.",
"default": 20
},
"proxyConfiguration": {
"title": "🌐 Proxy configuration",
"type": "object",
"description": "Residential proxies are recommended for reliable scraping.",
"default": {
"useApifyProxy": true
}
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```