We extract residential and commercial listings, pricing histories, energy ratings, and agent intelligence from immonet.de. 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 Residential Rent objects from immonet.de. All fields typed and schema-versioned.
"property_id": "2948173", "title": "Altbauwohnung in Prenzlauer Berg", "cold_rent": 1250.0, "warm_rent": 1450.0, "living_space_sqm": 85.5, "rooms": 3, "location_plz": "10405", "location_city": "Berlin"
| # | property_id | title | cold_rent | warm_rent | utility_costs | deposit |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Residential Buy objects from immonet.de. All fields typed and schema-versioned.
"property_id": "8472910", "title": "Einfamilienhaus mit Garten", "purchase_price": 540000.0, "living_space_sqm": 145.0, "plot_area_sqm": 600.0, "construction_year": 2018, "energy_class": "A", "brokerage_fee": "3.57%"
| # | property_id | title | purchase_price | price_per_sqm | living_space_sqm | plot_area_sqm |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Commercial Real Estate objects from immonet.de. All fields typed and schema-versioned.
"property_id": "9938122", "title": "Bueroflaeche in Bestlage", "commercial_type": "Office", "usable_area_sqm": 320.0, "rent_per_month": 6400.0, "availability": "Immediately", "parking_spaces": 4, "location_city": "Muenchen"
| # | property_id | title | commercial_type | usable_area_sqm | rent_per_month | additional_costs |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Agent Profiles objects from immonet.de. All fields typed and schema-versioned.
"agent_id": "A-58291", "company_name": "Mueller Immobilien GmbH", "contact_person": "Hans Mueller", "city": "Hamburg", "plz": "20095", "active_listings_count": 42, "website_url": "www.mueller-immo-hamburg.de"
| # | agent_id | company_name | contact_person | phone_number | street_address | city |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Energy & Building Specs objects from immonet.de. All fields typed and schema-versioned.
"property_id": "2948173", "energy_certificate_type": "Bedarfsausweis", "energy_requirement_kwh": 85.4, "energy_class": "C", "primary_energy_source": "Gas", "heating_type": "Zentralheizung", "construction_year": 1995
| # | property_id | energy_certificate_type | energy_requirement_kwh | energy_class | primary_energy_source | heating_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Immonet scraper handles every layer of the platform: property listings, dynamic pricing, energy certificates, and broker intelligence. Built with JavaScript rendering, session management, and anti-bot circumvention.
Title, description, living space, rooms, bathrooms, condition, and every metadata field Immonet surfaces for rent, buy, and commercial listings.
Capture cold rent, warm rent, utility costs, deposit amounts, purchase prices, and brokerage fees, timestamped per crawl.
Extract energy class, kWh per square metre, heating type, primary energy source, and construction year for ESG compliance.
Capture PLZ, city, district, and street level data where available, enabling precise regional yield analysis.
Extract agent name, company details, contact information, and active portfolio size for every listing.
Collect high resolution image URLs, floor plan PDF links, and virtual tour URLs for automated property assessment.
Monitor price drops, listing duration, and delisting dates to identify stale inventory and motivated sellers.
Bypass Immonet 100-page search limits via automated PLZ and price bracketing to ensure zero missed listings.
Convert German numeric strings and date formats into clean, queryable floats and ISO timestamps automatically.
Run continuous pipelines that only push diffs, reducing compute cost and storage bloat in your warehouse.
Brief in. Clean data out.
Provide target cities, PLZ codes, property types, or broker IDs. We design the extraction schema together.
We configure Scrapy crawlers, proxy rotation, session management, and Datadome handling for immonet.de.
Schema validation, null-rate checks, price outlier detection, and numeric normalisation before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Immonet invests heavily in bot detection. Here is how we stay resilient, and why teams choose managed infrastructure over DIY.
Immonet uses aggressive bot protection. Our crawlers use German residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management to bypass Datadome challenges.
Immonet hard-limits search results to 100 pages. We circumvent this by dynamically splitting searches into a matrix of PLZ codes, radius parameters, and granular price brackets to extract the full catalogue.
Real estate data in Germany uses comma decimals and dot thousands separators. Our pipeline automatically normalises strings like 1.250,50 into clean float values before they reach your database.
Image galleries, interactive maps, and certain contact details on Immonet load via JavaScript. We run full Playwright browser sessions to capture data that headless HTTP clients miss entirely.
Brokers often post the same property multiple times. We generate unique hashes based on living space, price, PLZ, and floor to flag duplicate inventory across the platform.
Investors calculate gross rental yields by comparing cold rent listings against purchase prices in specific PLZ districts.
Valuation models and property aggregators ingest daily feeds of Immonet data to train their automated valuation models.
Agencies identify private sellers listing without a broker, or track competitor portfolios to recruit top performing agents.
Funds analyse energy efficiency ratings across regional portfolios to forecast renovation costs and regulatory compliance.
Municipalities and researchers track gentrification, rent indices, and vacancy durations to inform housing policy.
Buyers set automated alerts for properties priced below market average per square metre in high demand neighbourhoods.
"Immonet holds the pulse of the German property market, but extracting structured yield data requires bypassing aggressive bot protection and pagination limits."
Most teams fail at German real estate extraction due to Datadome blocks and strict 100-page pagination limits. DataFlirt orchestrates residential proxy pools, dynamic search grid splitting, and automated normalisation of German numeric formats. We handle the extraction layer so your engineers can focus on yield modelling.
Everything supported by our immonet.de 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, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
Our orchestration layer automatically subdivides broad regional searches into micro-queries based on PLZ and price increments, ensuring we never hit the hard pagination limits.
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 immonet.de scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available real estate listings is generally permissible. DataFlirt extracts only public, non-authenticated property and broker data. We do not extract personal data of private users or circumvent authentication walls. Clients must ensure their use of agent data complies with GDPR.
Immonet restricts search results to a maximum of 100 pages. We bypass this by generating a search matrix that breaks down queries by specific PLZ codes, radius parameters, and narrow price brackets, ensuring every sub-query returns fewer than 100 pages.
We use German residential ISP proxies, full Playwright browser sessions with realistic TLS fingerprints, and request timing modelled on human behaviour. This prevents IP bans and solves Datadome challenges transparently.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series record per property, allowing you to track price reductions and days on market.
Yes. Our pipeline automatically normalises German locale strings. Values like 1.250,50 EUR are converted to standard float formats, and dates are output as ISO timestamps.
We extract exact street addresses only when the broker or landlord has made them publicly visible on the listing. We cannot extract addresses that are intentionally hidden or obfuscated.
We configure pipelines to match your requirements. We support daily full-market refreshes or hourly delta syncs for specific high-velocity cities like Berlin or Munich.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a daily sync of all Berlin apartments or a nationwide commercial real estate feed, we scope, build, and operate the pipeline. Tell us what you need.
