We extract property listings, price updates, energy ratings, and agent intelligence from Finn.no. 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 finn.no. All fields typed and schema-versioned.
"finn_kode": "312456789", "title": "Modern apartment in Grünerløkka", "property_type": "Leilighet", "price_suggestion": 4500000, "total_price": 4625000, "usable_area": 65, "energy_class": "C"
| # | finn_kode | title | property_type | address | city | zip_code |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Costs objects from finn.no. All fields typed and schema-versioned.
"finn_kode": "312456789", "price_suggestion": 4500000, "shared_costs": 3500, "municipal_taxes": 12000, "debt": 150000, "total_price": 4625000, "currency": "NOK"
| # | finn_kode | price_suggestion | total_price | shared_costs | municipal_taxes | debt |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Facilities & Details objects from finn.no. All fields typed and schema-versioned.
"finn_kode": "312456789", "bedrooms": 2, "floor": 3, "elevator": true, "balcony": true, "parking": false, "fireplace": true
| # | finn_kode | bedrooms | bathrooms | floor | elevator | balcony |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Agent & Agency Data objects from finn.no. All fields typed and schema-versioned.
"agent_name": "Ola Nordmann", "agency_name": "DNB Eiendom", "agency_branch": "Oslo Sentrum", "phone_number": "+47 912 34 567", "email": "ola@dnbeiendom.no", "active_listings_count": 14
| # | agent_name | agent_title | agency_name | agency_branch | phone_number | |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Viewing Schedules objects from finn.no. All fields typed and schema-versioned.
"finn_kode": "312456789", "viewing_date": "2026-05-20", "start_time": "17:00", "end_time": "18:00", "registration_required": true, "viewing_type": "Fellesvisning"
| # | finn_kode | viewing_date | start_time | end_time | registration_required | viewing_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Finn.no scraper handles every layer of the platform: property listings, dynamic pricing, agent intelligence, and viewing schedules, with Datadome bypass and session management built in.
Title, FINN-kode, address, primary area, usable area, bedrooms, year built, and every metadata field Finn.no surfaces.
Capture prisantydning, felleskostnader, omkostninger, and totalpris. Timestamped per crawl to track changes.
Extract visninger dates, times, and registration requirements to gauge market activity and demand.
Agent name, agency branch, contact details, and active listing counts to monitor market share.
Extract energikarakter and oppvarmingskarakter for ESG reporting and valuation models.
Extract gårdsnummer, bruksnummer, and latitude/longitude coordinates for GIS integration.
Monitor price drops, relistings, and time-on-market metrics across the Norwegian real estate sector.
Extract high-resolution image URLs and 3D tour links for property analysis and archival.
Run one-off bulk exports or configure continuous pipelines at hourly or daily cadences.
Brief in. Clean data out.
Provide region codes, property types, or specific agency targets. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and Datadome bypass for finn.no.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Schibsted employs strict anti-scraping measures on Finn.no. Here is how we maintain data flow without triggering IP bans.
Finn.no restricts access from non-Norwegian IPs and data centre ranges. Our crawlers use residential ISP proxies located in Norway with realistic browser fingerprints.
Schibsted uses Datadome for bot mitigation. We bypass this using TLS fingerprint spoofing, human-like interaction patterns, and automated CAPTCHA solving when challenged.
Property coordinates and historical price charts are loaded dynamically via JavaScript. We run full Playwright browser sessions to capture this data reliably.
We maintain a hash index of last-seen values per FINN-kode. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
To avoid triggering velocity-based bans, we distribute requests across thousands of IPs and implement randomised delays between page loads.
PropTech companies train Automated Valuation Models (AVMs) using historical price data, property features, and geographic trends.
Investors calculate gross yields, track time-on-market, and identify underpriced assets in specific Norwegian municipalities.
Real estate agencies track competitor market share, active listing volumes, and average time-to-sell metrics.
Municipalities and researchers analyse housing stock, population density indicators, and price development across regions.
Mortgage brokers and insurance providers enrich their platforms with accurate property data and energy ratings.
Analysts correlate energikarakter with property valuation to measure the market premium for energy-efficient homes.
"Finn.no holds the definitive dataset for Norwegian real estate, but extracting structured historical data requires navigating aggressive bot protection."
Most teams underestimate the investment required: reliable Finn.no scraping requires Norwegian residential proxies, full JavaScript rendering for maps, and Datadome bypass. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our finn.no 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 crawl orchestration and retry logic. Playwright handles JavaScript rendering and interaction flows for dynamic content.
We maintain pools of Norwegian residential ISP proxies to ensure access and prevent geoblocking by Schibsted.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling and dependency management. State stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About finn.no scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Finn.no is generally permissible for non-personal data. We target only public property, pricing, and agent data without circumventing authentication walls.
We use Norwegian residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and automated CAPTCHA solving to bypass Datadome protections.
We extract historical data visible on active or recently sold listings. For comprehensive historical datasets, we build continuous pipelines to track listings over time.
Pipelines can be configured for daily refreshes or sub-hourly monitoring for specific regions or property types.
Yes, we extract cadastral data (Gårdsnummer, Bruksnummer, Seksjonsnummer) from the property details section.
Yes, we extract data from Næringseiendom (commercial real estate) including office spaces, retail, and industrial properties.
Yes. We provide a sample run of up to 500 listings to validate schema fit and data quality before signing a contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily dump of Oslo apartments or a national feed of commercial listings — we scope, build, and operate the pipeline. Tell us what you need.
