We extract holiday rental listings, dynamic availability calendars, pricing signals, and amenity metadata from Interhome. 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 interhome.com. All fields typed and schema-versioned.
"property_id": "CH3818.100.1", "title": "Chalet Alpenrose", "property_type": "Chalet", "capacity_max": 6, "bedrooms": 3, "location_city": "Grindelwald", "star_rating": 4.5, "review_count": 24
| # | property_id | title | property_type | capacity_max | bedrooms | bathrooms |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Fees objects from interhome.com. All fields typed and schema-versioned.
"property_id": "CH3818.100.1", "check_in_date": "2026-02-14", "check_out_date": "2026-02-21", "total_price": 1450.0, "currency": "CHF", "minimum_stay_days": 7, "cleaning_fee": 120.0, "price_timestamp": "2026-01-10T08:14:22Z"
| # | property_id | check_in_date | check_out_date | base_price | cleaning_fee | local_tax |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Availability Calendar objects from interhome.com. All fields typed and schema-versioned.
"property_id": "CH3818.100.1", "calendar_date": "2026-02-14", "status": "AVAILABLE", "price_if_available": 210.0, "check_in_allowed": true, "check_out_allowed": false, "updated_at": "2026-01-10T08:14:25Z"
| # | property_id | calendar_date | status | price_if_available | min_stay_override | check_in_allowed |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Amenities & Features objects from interhome.com. All fields typed and schema-versioned.
"property_id": "CH3818.100.1", "wifi_available": true, "pet_friendly": false, "parking_type": "Private Garage", "distance_to_ski_lift_m": 400, "kitchen_equipment": "['Dishwasher', 'Oven', 'Microwave']", "outdoor_space": "Balcony"
| # | property_id | wifi_available | pool_available | parking_type | air_conditioning | pet_friendly |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from interhome.com. All fields typed and schema-versioned.
"review_id": "REV-8849201", "property_id": "CH3818.100.1", "rating_overall": 5, "rating_cleanliness": 5, "rating_location": 4, "date_posted": "2025-12-28", "travel_group_type": "Family with children", "language": "EN"
| # | review_id | property_id | guest_name | rating_overall | rating_cleanliness | rating_location |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Interhome scraper handles dynamic calendar hydration, multi-currency pricing logic, and deep amenity structures, ensuring you receive normalised data ready for analysis.
Extract title, descriptions, capacity limits, bedroom counts, and exact coordinates for thousands of holiday homes across Europe.
Hydrate interactive calendar widgets to map booked versus available dates across 12-month rolling windows.
Calculate total stay costs including base rates, mandatory cleaning fees, local taxes, and seasonal discounts.
Capture exact latitude/longitude coordinates alongside distances to key POIs like ski lifts, beaches, and city centers.
Extract and normalise specific property features from unstructured lists into boolean and categorical fields.
Track dynamic booking constraints including minimum stay requirements and specific check-in/check-out day restrictions.
Scrape pricing in native local currencies or force a specific currency parameter across the entire extraction run.
Paginate through historical guest reviews, capturing sub-category ratings, text, and travel group demographics.
Run daily diffs on availability calendars to detect new bookings or cancellations without re-processing static property data.
Brief in. Clean data out.
Provide target regions, property URLs, or specific check-in date ranges. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for interhome.com.
Schema validation, null-rate checks, price-outlier detection, and sample property data before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting historical and future booking states requires navigating complex dynamic frontend logic. Here is how we build resilient pipelines.
Interhome loads availability and pricing dynamically via frontend JavaScript calls. We run full Playwright browser sessions to trigger date selections and hydrate calendar states, capturing data that headless HTTP clients miss entirely.
Travel sites deploy strict rate limits. Our crawlers use residential ISP proxies localized to target European markets, paired with realistic browser fingerprints and randomised request timing to prevent IP bans.
Frontend structures mutate frequently. Our selector strategy uses multiple fallback chains per field, combining CSS selectors, XPath, and JSON payload interception to ensure a layout change does not break your data feed.
For large property catalogues, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, such as calendar state changes, reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, schema drift, and coverage drops, responding before you notice.
Property managers monitor regional supply, occupancy rates, and competitor pricing to optimise their own daily rates.
Real estate investors analyse historical booking velocity and seasonal premium pricing to evaluate potential property acquisitions.
Travel metasearch engines ingest property availability and pricing to provide comprehensive cross-platform comparisons.
Revenue teams correlate forward-looking availability constraints with local events to model regional travel demand.
Competing rental agencies track Interhome inventory growth, new region expansion, and standard amenity offerings.
Algorithmic pricing engines ingest real-time market rates to automatically adjust listings on secondary platforms.
"Interhome holds decades of European holiday rental inventory and pricing trends, but extracting calendar states requires navigating complex dynamic frontend logic."
Most teams underestimate the investment required: reliable Interhome scraping requires residential proxies, full JavaScript rendering for calendar hydration, CAPTCHA handling, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our interhome.com 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.
We maintain pools of residential ISP proxies across EU regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). 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 interhome.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from property rental sites is generally permissible under applicable law. DataFlirt targets only public, non-authenticated property, pricing, and availability data. We do not extract personal user data or circumvent authentication walls.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for rate spikes in real time and trigger pool rotation automatically.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per property for availability status and pricing from the date your pipeline starts.
Real-time streaming pipelines achieve sub-60-minute latency for price and availability signals on a defined property set. Full regional refreshes complete within a 6-12 hour window.
Our smallest packages start at a defined region list (typically 1,000-10,000 properties) with weekly delivery. For larger catalogues, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 properties as part of the pre-engagement scoping process so you can validate schema fit, field completeness, and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off regional catalog dump or a continuous price-monitoring feed across 50K properties, we scope, build, and operate the pipeline. Tell us what you need.
