We extract property listings, price per square metre, DPE/GES scores, and agency portfolios from Logic-Immo. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Property Listings objects from logic-immo.com. All fields typed and schema-versioned.
"property_id": "1849201A", "transaction_type": "buy", "property_type": "apartment", "price": 450000, "surface_area_m2": 85.5, "room_count": 4, "bedroom_count": 2, "city": "Lyon", "postal_code": "69003"
| # | property_id | transaction_type | property_type | price | surface_area_m2 | room_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Fees objects from logic-immo.com. All fields typed and schema-versioned.
"property_id": "1849201A", "price": 450000, "price_per_m2": 5263.15, "agency_fees_included": true, "agency_fee_percentage": 4.5, "condo_fees_annual": 1200, "scraped_at": "2026-05-12T09:14:00Z"
| # | property_id | price | price_per_m2 | agency_fees_included | agency_fee_percentage | notary_fees_estimated |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Energy & Diagnostics objects from logic-immo.com. All fields typed and schema-versioned.
"property_id": "1849201A", "dpe_score": "C", "dpe_value": 145, "ges_score": "D", "ges_value": 34, "heating_type": "individual", "heating_energy": "electric", "has_elevator": true
| # | property_id | dpe_score | dpe_value | ges_score | ges_value | heating_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Agency Data objects from logic-immo.com. All fields typed and schema-versioned.
"agency_id": "AG-74829", "agency_name": "Orpi Lyon Centre", "city": "Lyon", "postal_code": "69003", "phone_number": "+33472000000", "active_listings_count": 142
| # | agency_id | agency_name | agency_url | address | postal_code | city |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for New Builds (Neuf) objects from logic-immo.com. All fields typed and schema-versioned.
"program_id": "PRG-9921", "program_name": "Les Jardins de Lumiere", "developer_name": "Nexity", "delivery_quarter": "Q4", "delivery_year": 2027, "available_lots": 12, "pinel_eligible": true
| # | program_id | program_name | developer_name | delivery_quarter | delivery_year | total_lots |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our Logic-Immo scraper navigates complex search filters, paginates through thousands of regional listings, and extracts highly structured property attributes while bypassing Datadome bot protection.
Extract price, surface area, room configuration, location, descriptions, and high-resolution image URLs for every property.
Capture mandatory French energy performance certificates (DPE) and greenhouse gas emissions (GES) scores for compliance and valuation models.
Calculate price per square metre and separate agency fees from the net seller price to normalise valuation models.
Map properties to their listing agencies. Extract agency contact details, physical addresses, and total active portfolio sizes.
Track real estate development projects (Immobilier Neuf), including delivery dates, developer names, and available lot configurations.
Monitor properties over time to detect price reductions, calculating time-on-market and liquidity metrics per region.
Logic-Immo uses aggressive Datadome protection. Our infrastructure handles TLS fingerprinting and challenge-response cycles automatically.
Search and extract by region, department, postal code, or specific city boundaries with radius parameters.
Run daily diffs to identify newly added properties, sold listings, and price modifications without re-scraping the entire database.
Brief in. Clean data out.
Specify target regions, property types, and price brackets. We design the extraction schema together.
We configure Scrapy crawlers, residential proxy rotation, and Datadome bypass logic for logic-immo.com.
Schema validation, null-rate checks on DPE fields, and sample data reviews before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Logic-Immo protects its inventory with strict anti-bot measures. Here is how we maintain pipeline stability.
Logic-Immo relies on Datadome to block automated traffic. We use French residential proxies, perfectly spoofed TLS/JA3 fingerprints, and realistic browser headers to maintain trusted session scores and avoid CAPTCHA walls.
Property details and agency contact numbers are often rendered client-side or obfuscated. We execute full JavaScript rendering via Playwright to ensure complete data capture, including hidden phone numbers.
Search results on Logic-Immo are capped at a specific page depth. We programmatically segment searches by micro-geographies and price bands to extract the absolute total inventory without hitting pagination limits.
Agencies format descriptions and energy ratings inconsistently. Our pipeline applies regex-based normalisation to extract exact numeric values for surface area, room counts, and DPE scores from raw text blocks.
We maintain a database of all active Logic-Immo listings. Daily runs only extract new properties or those with modified attributes, drastically reducing compute costs and target server load.
PropTech companies feed clean price-per-metre and DPE data into machine learning models to generate instant property valuations.
Institutional investors monitor time-on-market and price drops to identify distressed sellers and high-yield acquisition targets.
Real estate networks track competitor inventory, market share by postal code, and average commission structures.
Construction and insulation firms target properties with low DPE/GES scores (F and G ratings) to offer mandatory renovation services.
Economists and banks aggregate housing supply metrics and pricing trends to forecast regional economic health.
Property developers analyse existing supply and pricing in target municipalities before launching new residential programmes.
"French real estate moves on energy efficiency and precise location data. Logic-Immo holds this intelligence, but accessing it requires bypassing enterprise-grade bot protection."
Datadome protects French property portals with aggressive fingerprinting and IP reputation checks. DataFlirt manages the residential proxy rotation, CAPTCHA solving, and session orchestration required to extract Logic-Immo listings at scale. You receive structured property data, not HTTP 403 errors.
Everything supported by our logic-immo.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles regional pagination and orchestration. Playwright manages JavaScript execution to reveal agency phone numbers and hydrate React components.
We route requests through localised French ISP proxies to satisfy geographic constraints and maintain high Datadome trust scores.
Pipelines run on Kubernetes with Airflow scheduling. State is stored in PostgreSQL to track property lifecycle events and compute price drops.
Data delivered to where your team already works — no new tooling required.
About logic-immo.com scraping, legality, and pipeline operations.
Ask us directly →We utilise dedicated French residential proxies, precise TLS fingerprinting, and automated challenge solvers. Our infrastructure mimics legitimate browser behaviour to maintain high session trust scores, preventing IP bans and CAPTCHA loops.
Yes. We extract both the alphabetic rating (A to G) and the specific numeric values for energy consumption and greenhouse gas emissions from every listing where provided.
Yes. By running stateful daily extractions, we compare current prices against historical records in our PostgreSQL database. We deliver the original price, current price, and the date of the reduction.
Raw text fields such as property descriptions remain in their native French. Categorical fields like property type, transaction type, and energy ratings are normalised into structured, predictable formats.
Yes. Logic-Immo often requires a user interaction to reveal phone numbers. We use Playwright to simulate this interaction and capture the unmasked contact details.
We support daily, weekly, or monthly cadences. For critical investment use cases, we can configure intraday pipelines targeting specific high-value postal codes.
20-minute scoping call. Pilot dataset within the week. Production within two. From single-city agency tracking to nationwide property valuations. We manage the proxies, the parsers, and the infrastructure. Tell us your data requirements.
