# Figaro Immobilier Scraper (`dadhalfdev/figaro-immobilier-scraper`) Actor

Scrape property listings from immobilier.lefigaro.fr. Use structured filters (listing type, property type, location, rooms, price — great for AI agents) or paste an input_url (great for humans). Returns price, surface, rooms, DPE, location, agency, and photos.

- **URL**: https://apify.com/dadhalfdev/figaro-immobilier-scraper.md
- **Developed by:** [Marco Rodrigues](https://apify.com/dadhalfdev) (community)
- **Categories:** Real estate
- **Stats:** 4 total users, 1 monthly users, 97.5% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $5.00 / 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.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

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

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
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.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## 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.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## 🏠 Figaro Immobilier Scraper

Want to pull property listings from [Figaro Immobilier](https://immobilier.lefigaro.fr/) or analyze the French buy/rent market? This scraper makes it super easy!

Use **structured filters** (listing type, property type, location, rooms, price) — structured filters work better for AI agents — **or paste an `input_url`** from your browser. When `input_url` is set, it overrides the filters. Either way, you get price, surface, rooms, DPE, agency, and photos as CSV or JSON.

### 💡 Perfect for...

- **Buyers & Renters:** Monitor apartments and houses by city, room count, and budget.
- **Agencies & Investors:** Track competitor inventory and pricing across France.
- **Market Research:** Analyze supply, €/m² signals, and DPE distributions.
- **Data Analysts:** Export clean structured rows for dashboards and models.
- **🤖 AI Agents:** Power bots and OpenClaw workflows with filter-based search (no brittle URL crafting).
- **📚 RAG Systems:** Feed listing specs and descriptions into retrieval pipelines.
- **🔗 AI Workflows:** Plug into LangChain, AutoGPT, CrewAI, and similar stacks.

### ✨ Why you'll love this scraper

- 🔗 **Input URL or Filters:** Paste a Figaro search URL (`input_url`) if you already filtered in the browser, or use structured filters (better for AI agents). `input_url` always wins when set.
- 🎯 **Structured Filters:** Sale/rent, property type, city, district, postal code, rooms, and price min/max.
- 📊 **Rich Listings:** Price, area, rooms/bedrooms, DPE, location, agency, images, and listing URL.
- 🔁 **Pagination:** Collects listings across result pages until `max_properties` is reached.

### 📦 What's inside the data?

For every listing you get:

- **Core:** `id`, `url`, `reference`, `transaction`, `property_type`, `description`
- **Pricing & Size:** `price`, `price_label`, `area_m2`, `rooms`, `rooms_label`, `bedrooms`
- **Location:** `city`, `postal_code`, `department`, `department_code`, `latitude`, `longitude`
- **Energy:** `dpe_energy`, `dpe_energy_category`, `dpe_ges`, `dpe_ges_category`
- **Agency & Media:** `agency_name`, `agency_id`, `image_url`, `images`
- **Meta:** `is_exclusive`, `is_boosted`, `first_publication_date`, `updated_at`, `origin_site`

### 🚀 Quick start

**Option A — Input URL**

1. Open [Figaro Immobilier](https://immobilier.lefigaro.fr/), apply filters in the browser, copy the results URL.
2. Paste it into `input_url`.
3. Set `max_properties` and click **Start**.

**Option B — Structured filters (better for AI agents)**

1. Leave `input_url` empty.
2. Set `listing_type`, `property_type`, `location` (e.g. `paris`), optional `district` / `postal_code` / `rooms` / price bounds.
3. Set `max_properties` and click **Start**.

Export JSON, CSV, or Excel when done.

***

#### Tech details for developers 🧑‍💻

**Input Example (filters)**

```json
{
  "listing_type": "vente",
  "property_type": "appartement",
  "location": "paris",
  "district": "11eme",
  "postal_code": "75011",
  "rooms": "2pieces",
  "price_max": 500000,
  "max_properties": 50
}
```

**Input Example (URL override)**

```json
{
  "input_url": "https://immobilier.lefigaro.fr/annonces/immobilier-vente-appartement-paris.html",
  "max_properties": 50
}
```

**Output Example**

```json
{
  "id": "103497843",
  "url": "https://immobilier.lefigaro.fr/annonces/annonce-103497843.html",
  "transaction": "vente",
  "property_type": "appartement",
  "price": 366000,
  "area_m2": 33.78,
  "rooms_label": "2 pièces",
  "bedrooms": 1,
  "city": "Paris 11ème (75)",
  "postal_code": "75011",
  "dpe_energy_category": "F",
  "agency_name": "Example Agency",
  "image_url": "https://..."
}
```

### 📋 Input reference (detailed)

| Parameter | Type | Required | Default | Description |
|-----------|------|----------|---------|-------------|
| `input_url` | string | No | — | Optional override. Paste any Figaro Immobilier search URL. When set, **overrides all filters below**. |
| `listing_type` | string enum | No | `vente` | `vente` (sale) or `location` (rent). |
| `property_type` | string enum | No | `appartement` | `appartement`, `maison`, `parking`, `terrain`, `divers`, `bien` (all). |
| `location` | string | No | `paris` | City/place name (e.g. `paris`, `lyon`, `toulouse`, `france`). |
| `district` | string | No | — | Optional district (e.g. `11eme`, `16eme`). |
| `postal_code` | string | No | — | Postal code — often helpful (`31000` Toulouse, `33000` Bordeaux). Auto-filled for common cities when empty. |
| `rooms` | string enum | No | any | `studio`, `2pieces` … `6pieces` (applied for apartments; ignored for maisons). |
| `price_min` / `price_max` | integer | No | — | Price bounds in euros. |
| `max_properties` | integer | No | `50` | How many listings to return (`1`–`500`). |

#### Notes

- `input_url` always wins over filters when provided.
- Each run returns at most **`max_properties`** listings.
- For complex browser-only criteria, paste the results URL into `input_url`.

# Actor input Schema

## `input_url` (type: `string`):

Optional. Paste any Figaro Immobilier search results URL to override the filters below. Leave empty to use structured filters instead (better for AI agents).

## `listing_type` (type: `string`):

Buy (vente) or rent (location). Ignored when input_url is set.

## `property_type` (type: `string`):

Property category. Ignored when input_url is set.

## `location` (type: `string`):

City or place name (e.g. paris, lyon, toulouse, france). Many cities work better with a postal code — common ones are auto-added if postal_code is empty. Ignored when input_url is set.

## `district` (type: `string`):

Optional district (e.g. 11eme, 16eme). Ignored when input_url is set.

## `postal_code` (type: `string`):

Postal code (e.g. 31000 for Toulouse, 33000 for Bordeaux). Helpful for many cities. Ignored when input_url is set.

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

Optional room filter. Ignored when input_url is set.

## `price_min` (type: `integer`):

Optional minimum price in euros. Ignored when input_url is set.

## `price_max` (type: `integer`):

Optional maximum price in euros. Ignored when input_url is set.

## `max_properties` (type: `integer`):

How many listings to return (1–500).

## Actor input object example

```json
{
  "input_url": "https://immobilier.lefigaro.fr/annonces/immobilier-vente-appartement-paris.html",
  "listing_type": "vente",
  "property_type": "appartement",
  "location": "paris",
  "rooms": "",
  "max_properties": 10
}
```

# Actor output Schema

## `overview` (type: `string`):

Table view using the dataset 'overview' view.

## `results` (type: `string`):

All items from the default dataset without view transformation.

# 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 = {
    "input_url": "https://immobilier.lefigaro.fr/annonces/immobilier-vente-appartement-paris.html",
    "max_properties": 10
};

// Run the Actor and wait for it to finish
const run = await client.actor("dadhalfdev/figaro-immobilier-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 = {
    "input_url": "https://immobilier.lefigaro.fr/annonces/immobilier-vente-appartement-paris.html",
    "max_properties": 10,
}

# Run the Actor and wait for it to finish
run = client.actor("dadhalfdev/figaro-immobilier-scraper").call(run_input=run_input)

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

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

```

## CLI example

```bash
echo '{
  "input_url": "https://immobilier.lefigaro.fr/annonces/immobilier-vente-appartement-paris.html",
  "max_properties": 10
}' |
apify call dadhalfdev/figaro-immobilier-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,dadhalfdev/figaro-immobilier-scraper"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/Q7ChWq2dkPpuoMegb/builds/IpdFZcWgcBcJjR1Pm/openapi.json