We extract residential listings, commercial real estate, housing company financials, and agent profiles from Oikotie. 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 oikotie.fi. All fields typed and schema-versioned.
"listing_id": "16894321", "property_type": "Kerrostalo", "city": "Helsinki", "neighborhood": "Kallio", "rooms": "2h+kt", "living_area_m2": 45.5, "price_debt_free": 285000.0, "year_built": 1938
| # | listing_id | url | title | property_type | city | neighborhood |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Financials & Fees objects from oikotie.fi. All fields typed and schema-versioned.
"listing_id": "16894321", "price_sales": 270000.0, "debt_share": 15000.0, "maintenance_fee": 210.5, "financing_fee": 145.0, "water_fee": 22.0, "total_monthly_cost": 377.5
| # | listing_id | price_debt_free | price_sales | maintenance_fee | financing_fee | water_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Housing Company objects from oikotie.fi. All fields typed and schema-versioned.
"company_name": "As Oy Helsingin Kallionkulma", "heating_system": "Kaukolämpö", "energy_class": "E2018", "plot_ownership": "Oma", "plot_area_m2": 1250.0, "past_renovations": "['Putkiremontti 2015', 'Julkisivu 2018']", "upcoming_renovations": "['Kattoremontti 2027']"
| # | company_name | property_manager | year_built | heating_system | energy_class | plot_ownership |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Agent & Agency objects from oikotie.fi. All fields typed and schema-versioned.
"agent_name": "Matti Meikäläinen", "agent_title": "Kiinteistönvälittäjä, LKV", "agency_name": "Helsingin Koti LKV", "phone": "+358 40 123 4567", "active_listings_count": 14, "agency_address": "Mannerheimintie 12, 00100 Helsinki"
| # | agent_name | agent_title | agency_name | agency_id | phone | |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from oikotie.fi. All fields typed and schema-versioned.
"keyword": "Helsinki", "position": 3, "listing_id": "16894321", "featured_badge": true, "price": 285000.0, "rooms": "2h", "scraped_at": "2026-05-12T09:14:33Z"
| # | keyword | location_id | position | listing_id | featured_badge | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Oikotie scraper handles complex housing company data, separate financial fee structures, and agent metadata with JavaScript rendering and session management built in.
Title, location, square metres, room configurations, and detailed text descriptions for apartments, houses, and plots.
Extract debt-free price, sales price, maintenance fees, financing fees, and water charges accurately separated.
Capture renovation history, upcoming pipeline, plot ownership status, energy class, and heating systems.
Extract specific parameters for rental listings including security deposits, availability dates, and pet policies.
Map agents to their active listings and track agency market share across different Finnish municipalities.
Extract office space, retail units, and warehouse listings with commercial-specific pricing structures.
Capture dates, times, and registration requirements for upcoming property showings.
Extract high-resolution URLs for property image galleries and architectural floorplans.
Run continuous pipelines at daily cadences with change-detection diffing to track price drops and status changes.
Brief in. Clean data out.
Provide target cities, property types, or specific agencies. We design the extraction schema together.
We configure Scrapy crawlers, EU proxy rotation, and session management for Oikotie.
Schema validation, null-rate checks, and price-outlier detection before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage.
Real estate portals actively block automated collection. Here is how we maintain stable pipelines and deliver clean, normalised data.
Oikotie employs regional blocking and bot detection heuristics. Our crawlers use Finnish ISP proxies with realistic browser fingerprints and full cookie session management to ensure uninterrupted access.
Map views, image galleries, and certain contact details require JavaScript execution. We run full Playwright browser sessions to capture data that standard HTTP clients miss.
An apartment listing has a different DOM structure than a commercial plot. Our extraction logic normalises these variations into a unified schema, ensuring your downstream database receives clean, typed records.
We maintain a hash index of last-seen values per listing. Subsequent runs push diffs, allowing you to track exactly when a property drops its price or is removed from the market.
Every run emits structured logs. We alert on null-rate spikes in critical fields like price or area, responding to site layout changes before they impact your data warehouse.
PropTech companies use historical listing data, debt-free prices, and area metrics to train automated valuation models (AVMs).
Institutional investors track gross yields, maintenance fees, and rent-vs-buy ratios across specific Helsinki neighbourhoods.
Real estate brokerages monitor competitor listing volumes, time-on-market, and agent performance to inform regional strategy.
Developers identify underutilised plots, teardown candidates, and upcoming zoning changes by parsing listing descriptions.
Financial analysts monitor housing supply, average listing prices, and transaction velocity as leading economic indicators.
B2B service providers target housing companies planning upcoming renovations, extracted directly from the housing company data.
"Oikotie holds the definitive dataset for Finnish real estate, but accessing structured historical and active listing data requires dedicated extraction infrastructure."
Most teams underestimate the investment required: reliable Oikotie extraction requires EU residential proxies, full JavaScript rendering for maps, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis.
Everything supported by our oikotie.fi 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 required for complex real estate portals.
We maintain pools of residential ISP proxies specifically for the Nordic region. Rotation happens per-request with sticky sessions where required.
Pipelines run on AWS Lambda and ECS. 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 oikotie.fi scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available property listings is generally permissible under EU law, provided it does not extract personal data in violation of GDPR or circumvent authentication walls. We target only public listing data. Clients should consult legal counsel regarding their specific commercial use case.
We use Finnish residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour to ensure reliable extraction without triggering rate limits.
Yes. We parse the unstructured text often found in the past and upcoming renovations fields, delivering it as structured arrays within the housing company data object.
Full catalogue refreshes for specific municipalities can be configured at daily or sub-daily cadences. Change detection ensures you receive updates on price drops or status changes quickly.
We begin accumulating historical time-series data from the day your pipeline is commissioned. We do not maintain a pre-scraped historical database of off-market Oikotie listings.
Our packages start at defined municipality lists or property types with weekly delivery. Contact us with your specific volume requirements for a scoped quote.
Yes. We provide a sample run of up to 500 listings as part of the scoping process, allowing you 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 one-off export of Helsinki apartments or a continuous feed of national property data, we scope, build, and operate the pipeline. Tell us what you need.
