Lefigaro immobilier mass products scraper (by ads URLs)

Extract rich real estate listing data from LeFigaro Immobilier using direct ad URLs (or ad IDs) and turn it into clean, structured datasets for analysis and workflows. This tool focuses on fast, repeatable real estate listings extraction—ideal for price tracking, market research, and building reliable property data pipelines.

     

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Introduction

This project takes a list of direct LeFigaro Immobilier listing URLs (or listing IDs) and returns structured information for each property. It solves the common problem of manually collecting listing details at scale by automating extraction into consistent, machine-readable output. It’s built for analysts, growth teams, real-estate researchers, and developers who need dependable property data for dashboards, audits, or enrichment.

URL-to-Dataset Property Extraction

• Accepts direct listing links or numeric listing IDs as input.
• Extracts text, pricing, media, and contact-ready details in a consistent schema.
• Handles large input batches with resilient retries and pacing controls.
• Produces clean outputs suitable for spreadsheets, BI tools, and data warehouses.
• Designed to support scalable real estate listings extraction pipelines.

Features

Feature	Description
Direct URL batch input	Provide a list of listing URLs and process them in a single run.
ID-based support	Pass listing IDs instead of full URLs for faster input preparation.
Rich listing details	Collect titles, descriptions, pricing, media, energy info, and more.
Contact & publisher extraction	Capture publisher/agent details and available phone/contact metadata.
Nearby transport parsing	Extract nearby transportation details when present on the listing.
Export-ready dataset output	Outputs structured data that’s easy to convert to JSON/CSV/HTML.
Resilient crawling controls	Built-in retries, throttling, and timeouts for stability.
Proxy-ready networking	Supports proxy configuration for improved reliability on high volumes.

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What Data This Scraper Extracts

Field Name	Field Description
listingId	Unique identifier of the property listing.
url	Canonical URL of the listing page.
title	Listing headline/title shown on the page.
description	Full textual description of the property.
price	Displayed price value (normalized where possible).
currency	Currency symbol/code associated with the price.
location	Location string (city/area) shown in the listing.
address	Address or partial address if available publicly.
propertyType	Type of property (apartment, house, studio, etc.).
transactionType	Sale/rent classification when available.
surfaceArea	Total area (m²) if present.
rooms	Number of rooms if available.
bedrooms	Number of bedrooms if available.
bathrooms	Number of bathrooms if available.
floor	Floor number and/or total floors if present.
constructionYear	Construction date/year if provided.
energyRating	Energy performance rating (e.g., DPE class) when present.
emissionsRating	Emissions rating (e.g., GES class) when present.
photos	Array of image URLs for the listing gallery.
publisherName	Name of the publisher/agent/agency.
publisherType	Publisher category (agency/private/other) if detectable.
phone	Phone number if publicly displayed.
contactMethods	Available contact options (phone/form/email when present).
nearbyTransport	Nearby transportation lines/stops parsed from the page.
features	Key features/amenities list (elevator, parking, balcony, etc.) when present.
scrapedAt	ISO timestamp for when the listing was extracted.
raw	Optional raw blocks for debugging/parity checks (disabled by default).

---

Example Output

[
  {
	"listingId": "75220030",
	"url": "https://immobilier.lefigaro.fr/annonces/annonce-75220030.html",
	"title": "Appartement 3 pièces — 68 m² — Paris 11e",
	"description": "Appartement lumineux avec séjour, cuisine équipée, deux chambres, proche métro et commerces...",
	"price": 649000,
	"currency": "EUR",
	"location": "Paris (75011)",
	"address": "Paris 11e (adresse partielle selon disponibilité)",
	"propertyType": "apartment",
	"transactionType": "sale",
	"surfaceArea": 68,
	"rooms": 3,
	"bedrooms": 2,
	"bathrooms": 1,
	"floor": "3/6",
	"constructionYear": 1978,
	"energyRating": "D",
	"emissionsRating": "B",
	"photos": [
	  "https://.../photo1.jpg",
	  "https://.../photo2.jpg",
	  "https://.../photo3.jpg"
	],
	"publisherName": "Agence Exemple Immobilier",
	"publisherType": "agency",
	"phone": "+33XXXXXXXXX",
	"contactMethods": ["phone", "contact_form"],
	"nearbyTransport": [
	  { "type": "metro", "name": "Ligne 9", "stop": "Voltaire" },
	  { "type": "bus", "name": "Bus 46", "stop": "Roquette" }
	],
	"features": ["balcony", "elevator", "cellar"],
	"scrapedAt": "2025-12-13T18:10:44.219Z"
  }
]

---

Directory Structure Tree

Lefigaro immobilier mass products scraper (by ads URLs)/
├── src/
│   ├── main.py
│   ├── cli.py
│   ├── runner/
│   │   ├── __init__.py
│   │   ├── run_batch.py
│   │   └── validate_input.py
│   ├── crawlers/
│   │   ├── __init__.py
│   │   ├── browser_crawler.py
│   │   └── request_queue.py
│   ├── extractors/
│   │   ├── __init__.py
│   │   ├── listing_parser.py
│   │   ├── media_parser.py
│   │   ├── energy_parser.py
│   │   ├── contact_parser.py
│   │   └── transport_parser.py
│   ├── normalizers/
│   │   ├── __init__.py
│   │   ├── normalize_price.py
│   │   ├── normalize_text.py
│   │   └── normalize_location.py
│   ├── exporters/
│   │   ├── __init__.py
│   │   ├── to_json.py
│   │   ├── to_csv.py
│   │   └── to_html.py
│   ├── config/
│   │   ├── settings.py
│   │   └── settings.example.json
│   └── utils/
│       ├── __init__.py
│       ├── http.py
│       ├── retry.py
│       ├── logger.py
│       └── dates.py
├── data/
│   ├── input.startUrls.sample.json
│   ├── input.ids.sample.txt
│   └── sample.output.json
├── tests/
│   ├── test_validate_input.py
│   ├── test_listing_parser.py
│   ├── test_normalize_price.py
│   └── fixtures/
│       └── listing.sample.html
├── .env.example
├── .gitignore
├── pyproject.toml
├── requirements.txt
├── LICENSE
└── README.md

---

Use Cases

• Real estate analysts use it to track listing price changes, so they can spot trends and build market reports faster.
• Growth teams use it to compile publisher and listing inventories, so they can identify agencies and prioritize outreach.
• Researchers use it to collect housing data at scale, so they can run statistical studies with consistent inputs.
• Developers use it to feed structured listing data into dashboards, so they can monitor regions and property types in near real-time.
• Investors use it to compare similar listings across areas, so they can validate pricing and evaluate opportunities.

---

FAQs

How do I provide inputs—URLs or IDs? You can provide direct listing URLs or numeric listing IDs. If an ID is provided, the tool will build the corresponding listing URL internally and fetch the page the same way.

Does it scrape search results pages too? This project is designed for direct listing pages (items) provided via URLs/IDs. If you need search results extraction, use a separate workflow that first collects item URLs from search pages, then passes those item URLs into this tool.

What happens if some listings are missing fields (phone, energy rating, transport)? The output schema is stable, but optional fields may be null/empty when not publicly displayed. This keeps downstream pipelines reliable without breaking on missing data.

How do I improve stability when processing very large batches? Use conservative concurrency, enable proxy support, and keep retry limits reasonable. For long-running jobs, split inputs into smaller batches and merge outputs afterward for better fault isolation.

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Performance Benchmarks and Results

Primary Metric: Typical extraction throughput of ~800–1,500 listings/hour depending on media weight and network conditions.

Reliability Metric: 97–99% successful listing completion on clean inputs when retries and pacing are enabled.

Efficiency Metric: Average page processing time ~2.4–4.8 seconds/listing with browser caching and request deduplication enabled.

Quality Metric: 90–98% field completeness on standard listings, with highest variance on optional publisher contact and nearby transport fields.

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