We extract active listings, kommande properties, slutpriser, bidding histories, and broker intelligence from Hemnet. 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 Active Listings objects from hemnet.se. All fields typed and schema-versioned.
"listing_id": "20543912", "address": "Sveavägen 104", "municipality": "Stockholm", "asking_price": 5495000, "living_area_sqm": 64.5, "rooms": 2.5, "property_type": "Bostadsrätt", "monthly_fee": 3120, "publish_date": "2023-10-14T08:30:00Z"
| # | listing_id | url | address | municipality | county | asking_price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Final Prices (Slutpriser) objects from hemnet.se. All fields typed and schema-versioned.
"sale_id": "1948271", "address": "Linnégatan 42", "final_price": 7200000, "asking_price": 6500000, "price_diff_pct": 10.7, "sold_date": "2023-10-12", "price_per_sqm": 110769, "property_type": "Bostadsrätt"
| # | sale_id | address | municipality | final_price | asking_price | price_diff_pct |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Bidding History objects from hemnet.se. All fields typed and schema-versioned.
"listing_id": "20543912", "bid_id": "b_84721", "bid_amount": 5600000, "bid_time": "2023-10-15T14:22:10Z", "bidder_alias": "Bidder 3", "bid_increase": 25000, "total_bids": 12, "active_bidders": 4
| # | listing_id | bid_id | bid_amount | bid_time | bidder_alias | bid_increase |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Broker Data objects from hemnet.se. All fields typed and schema-versioned.
"broker_id": "m_4921", "name": "Anna Svensson", "agency": "Fastighetsbyrån", "active_listings_count": 14, "total_sold_value_ytd": 142500000, "average_sale_time_days": 18, "region": "Stockholms innerstad"
| # | broker_id | name | agency | phone | active_listings_count | |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Property Costs & Specs objects from hemnet.se. All fields typed and schema-versioned.
"listing_id": "20543912", "construction_year": 1924, "energy_class": "D", "operating_cost_yearly": 5400, "plot_area_sqm": "None", "ancillary_area_sqm": 12, "brf_name": "Brf Svea 104", "brf_org_number": "769612-3456"
| # | listing_id | construction_year | energy_class | operating_cost_yearly | mortgage_deed_cost | title_deed_cost |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Hemnet scraper captures the full lifecycle of a property: from the initial 'Kommande' listing, through live bidding wars, to the final registered 'Slutpris'.
Capture asking prices, living area, room counts, monthly fees, and broker details for every active property on the market.
Track final sale prices, calculate bid premiums versus asking price, and compute price per square metre across municipalities.
Monitor properties marked as upcoming to forecast future market supply before official listings go live.
Extract bid timestamps, amounts, and bidder aliases to model market heat and demand velocity at the hyper-local level.
Aggregate listing volumes, average time on market, and final price premiums by individual broker and agency.
Extract housing society names, organisation numbers, and monthly fee structures to evaluate building financials.
Capture driftkostnad, heating, electricity, insurance, and municipal property tax estimates.
Extract precise latitude and longitude coordinates for spatial analysis and proximity modelling.
Identify price drops, relistings, and status changes without downloading the entire catalogue every run.
Brief in. Clean data out.
Specify target municipalities, property types, or data tiers (listings vs slutpriser). We design the extraction schema.
We configure Playwright crawlers, handle Cloudflare bypass, and set up pagination logic for hemnet.se.
Schema validation, null-rate checks, and coordinate verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Hemnet employs strict anti-scraping measures and complex frontend architectures. Here is how we maintain reliable extraction.
Hemnet relies heavily on Cloudflare to block automated traffic. We utilize Swedish residential proxies combined with specialized Playwright stealth configurations to bypass Turnstile challenges and maintain session validity.
Many listings and slutpriser are rendered dynamically via XHR requests as the user interacts with the map. We intercept these backend API payloads directly, extracting clean JSON before it hits the DOM.
Bidding wars move fast. To capture accurate bid histories, our pipelines can be configured for high-frequency polling on specific high-interest listings, ensuring no bid increments are missed.
Hemnet restricts search results to a maximum number of pages. We programmatically subdivide large search areas by micro-geographies and price brackets to ensure 100% extraction coverage without hitting pagination limits.
Properties frequently shift from 'Kommande' to 'Till salu' to 'Borttagen' (removed) to 'Slutpris'. We maintain persistent hash indexes to track these state changes across time, providing a complete historical ledger.
PropTech companies ingest daily slutpriser and active listings to train automated valuation models and estimate market prices.
Real estate funds track yield potentials by correlating asking prices, monthly fees, and historical appreciation rates in specific postcodes.
Real estate agencies monitor competitor performance, tracking market share, average sale times, and final price premiums.
Banks and macroeconomists track bidding velocity and price-to-asking ratios as leading indicators of Swedish economic health.
Municipalities analyse housing supply, demand hotspots, and price per square metre to inform zoning and development policies.
Analysts cross-reference property fees with housing society (BRF) data to identify undercapitalised or highly leveraged buildings.
"Hemnet holds the definitive pulse of the Swedish housing market, but extracting historical trends requires navigating aggressive bot protection and dynamic state changes."
Building a reliable Hemnet scraper is not just about parsing HTML. It requires Swedish residential proxies to bypass Cloudflare, intercepting undocumented internal APIs for map data, and maintaining state across thousands of properties as they transition from upcoming to sold. We handle the pipeline so you can focus on the real estate analysis.
Everything supported by our hemnet.se 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 manages JavaScript execution and Cloudflare clearance. Combined for maximum throughput.
We maintain pools of residential ISP proxies strictly within Sweden. This prevents geo-blocking and reduces the frequency of aggressive CAPTCHA challenges.
Pipelines run on Kubernetes clusters. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About hemnet.se scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available real estate listings is generally permissible under Swedish and EU law, provided it does not extract personal data in violation of GDPR or disrupt the target server. DataFlirt targets only public property data and broker business contact details. Clients should review Hemnet's ToS and consult legal counsel for specific commercial use cases.
We utilize localized Swedish residential proxies, realistic browser fingerprinting via Playwright, and automated Turnstile solvers. This ensures high success rates without triggering rate limits or IP bans.
Yes. We run distinct pipeline logic for active listings (Till salu), upcoming properties (Kommande), and final sale prices (Slutpriser), normalising the data into unified schemas.
For standard market monitoring, daily updates are sufficient. For active valuation or investment models, we can configure high-frequency polling on specific listing IDs to capture intra-day bidding increments.
We can extract all currently visible historical slutpriser on Hemnet. However, Hemnet routinely removes very old sale records from public view. For complete historical analysis, continuous pipeline execution is recommended.
Yes. Latitude and longitude are extracted from the map data payloads, enabling precise geospatial analysis and integration with GIS software.
Yes. We provide a sample run of up to 500 listings or slutpriser for a specific Swedish municipality during the scoping process, allowing you to validate data quality and schema fit.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily feed of active listings or a comprehensive database of Swedish slutpriser — we scope, build, and operate the pipeline. Tell us what you need.
