We extract property listings, pricing models, viewing schedules, and agent intelligence from Eiendomsmegler 1. 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 eiendomsmegler1.no. All fields typed and schema-versioned.
"address": "Storgata 42, 0182 Oslo", "property_type": "Leilighet", "bedrooms": 2, "usable_area_bra": 74, "primary_area_prom": 70, "construction_year": 2018, "energy_grade": "B", "finn_code": "294817263"
| # | address | property_type | bedrooms | usable_area_bra | primary_area_prom | construction_year |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Financials & Pricing objects from eiendomsmegler1.no. All fields typed and schema-versioned.
"asking_price": 5490000, "shared_debt": 240000, "shared_costs": 4250, "registration_fee": 137250, "total_price": 5867250, "municipal_taxes": 12400, "estimated_value": 5500000
| # | asking_price | shared_debt | shared_costs | registration_fee | total_price | municipal_taxes |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Cadastral & Location objects from eiendomsmegler1.no. All fields typed and schema-versioned.
"municipality": "Oslo", "municipality_number": "0301", "farm_number_gnr": 208, "title_number_bnr": 412, "section_number_snr": 14, "latitude": 59.9154, "longitude": 10.7562, "plot_size": 1245.5
| # | municipality | municipality_number | farm_number_gnr | title_number_bnr | section_number_snr | lease_number_fnr |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Agent Details objects from eiendomsmegler1.no. All fields typed and schema-versioned.
"agent_name": "Kari Nordmann", "agent_title": "Eiendomsmegler MNEF", "phone_number": "+47 987 65 432", "email_address": "kari.nordmann@em1.no", "office_name": "Eiendomsmegler 1 Oslo Sentrum", "active_listings": 12, "past_sales": 148
| # | agent_name | agent_title | phone_number | email_address | office_name | active_listings |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Viewings & Status objects from eiendomsmegler1.no. All fields typed and schema-versioned.
"listing_status": "Til salgs", "viewing_date": "2026-08-14", "viewing_time": "17:00 - 18:00", "registration_required": true, "published_date": "2026-08-01T10:15:00Z", "last_modified_date": "2026-08-05T14:22:00Z", "open_house": false
| # | listing_status | viewing_date | viewing_time | registration_required | published_date | last_modified_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our scraper handles the complexities of the Norwegian real estate market: dynamic price models, cadastral mapping, agent directories, and viewing schedules, complete with anti-bot circumvention.
Capture primary area (P-rom), usable area (BRA), bedrooms, construction year, and energy grades for every listed property.
Extract asking price (prisantydning), shared debt (fellesgjeld), monthly costs (felleskostnader), and total price calculations.
Map properties to official Norwegian cadastral formats: Gnr (gårdsnummer), Bnr (bruksnummer), and Snr (seksjonsnummer).
Monitor viewing dates, times, and registration requirements to gauge market activity and property interest levels.
Scrape agent profiles, contact details, active listing counts, and associated branch offices across Norway.
Extract complex Nybygg (new build) project pages, mapping individual units to their parent development project.
Separate extraction logic for Fritidsbolig (cabins/leisure), capturing specific amenities like water access and road conditions.
Capture the Finn-kode where available, allowing you to join Eiendomsmegler 1 data with existing Finn.no datasets.
Run daily or hourly pipelines to catch status changes from 'Til salgs' to 'Solgt', capturing the exact time on market.
Brief in. Clean data out.
Provide target municipalities, property types, or specific branch offices. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and session management tailored for eiendomsmegler1.no.
Schema validation, null-rate checks, and geospatial coordinate verification before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Real estate portals use dynamic rendering and rate limiting to protect their inventory. Here is how we maintain stable extraction.
Eiendomsmegler 1 relies on modern JavaScript frameworks for property search and filtering. We run full Playwright browser sessions to execute JavaScript, trigger lazy-loaded image galleries, and hydrate financial widgets.
Property locations are often obfuscated or loaded via third-party map providers. We intercept the underlying API responses to extract precise latitude, longitude, and cadastral identifiers (Gnr/Bnr) directly from the network layer.
To avoid IP bans and geo-blocking, our crawlers route requests through residential ISP proxies located within Norway, ensuring request patterns mimic legitimate local home buyers.
Cabins, commercial properties, and standard apartments use different DOM templates. Our selector strategy uses conditional logic based on property type, falling back to structured JSON-LD data when visual layouts change.
We maintain a hash index of last-seen values per property URL. Subsequent runs only push diffs, reducing compute cost and providing a clean changelog of price adjustments and status changes.
PropTech companies feed historical listing prices, P-rom data, and location coordinates into machine learning models to predict property values.
Financial institutions track inventory levels, time-on-market, and price reductions across specific Norwegian municipalities to gauge macroeconomic health.
Competing real estate agencies monitor Eiendomsmegler 1 market share, agent performance, and regional dominance to optimise their own recruitment and marketing.
Developers track Nybygg (new build) project absorption rates and pricing strategies to inform future land acquisition and project phasing.
Property investors screen the market for mispriced assets, high-yield rental opportunities, and distressed sales using automated filters on the data feed.
Researchers correlate energy grades (Energimerking) with property premiums to study the financial impact of green upgrades in the housing sector.
"Norwegian real estate data is highly structured, but extracting it consistently across thousands of dynamic listings requires dedicated infrastructure."
Most teams underestimate the investment required to maintain a real estate scraper. Handling dynamic SPA frameworks, residential proxy rotation, and daily selector maintenance drains engineering resources. DataFlirt absorbs that complexity so your team can focus on valuation models and market analysis, not DOM parsing.
Everything supported by our eiendomsmegler1.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 deduplication. Playwright handles JavaScript rendering for dynamic real estate listings and map widgets.
We maintain pools of residential ISP proxies specifically located in Norway. Rotation happens per-request to prevent rate limiting from property portals.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling for daily market updates, storing state in managed Postgres with PostGIS for spatial queries.
Data delivered to where your team already works — no new tooling required.
About eiendomsmegler1.no scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available real estate information is generally permissible. DataFlirt targets only public, non-authenticated property and agent data. We do not extract personal data behind BankID logins or circumvent authentication walls. Clients should review local regulations and terms of service for their specific use cases.
Yes. We frequently build unified pipelines that normalise data across Eiendomsmegler 1, Finn.no, Krogsveen, and Privatmegleren into a single, queryable schema.
Our change detection system logs the exact run when a property URL returns a 404 or changes status to 'Solgt'. You receive a status update record rather than a silent deletion.
We extract all currently available data on the site. For historical data, we begin building a time-series archive from the day your pipeline is commissioned, tracking price drops and market duration.
Yes. We extract the exact latitude and longitude coordinates embedded in the map widgets, alongside official cadastral identifiers (Gnr/Bnr) for precise mapping.
Pipelines can be configured for hourly, daily, or weekly runs. For most market analysis use cases, a daily sweep of new and modified listings provides the optimal balance of freshness and compute cost.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily sweep of Oslo apartments or a continuous feed of all active Norwegian listings, we scope, build, and operate the pipeline. Tell us what you need.
