We extract property details, availability calendars, host profiles, and review corpora from Flipkey. 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 flipkey.com. All fields typed and schema-versioned.
"property_id": "FK3910482", "title": "Oceanfront Villa with Private Pool", "property_type": "Villa", "bedrooms": 4, "bathrooms": 3.5, "max_guests": 8, "latitude": 25.0343, "longitude": -77.3963
| # | property_id | title | url | property_type | bedrooms | bathrooms |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Fees objects from flipkey.com. All fields typed and schema-versioned.
"property_id": "FK3910482", "base_price": 450.0, "currency": "USD", "cleaning_fee": 150.0, "service_fee": 45.0, "total_price": 645.0, "date_scraped": "2026-05-12T10:14:00Z"
| # | property_id | base_price | currency | cleaning_fee | service_fee | tax |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Availability Calendar objects from flipkey.com. All fields typed and schema-versioned.
"property_id": "FK3910482", "date": "2026-12-24", "available": false, "min_stay": 5, "price_for_date": 650.0, "blocked_by_host": false, "updated_at": "2026-05-12T10:15:22Z"
| # | property_id | date | available | min_stay | price_for_date | blocked_by_host |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews & Ratings objects from flipkey.com. All fields typed and schema-versioned.
"review_id": "RV938471", "property_id": "FK3910482", "rating": 5.0, "review_text": "Incredible views and spotless property.", "stay_date": "2026-04-10", "publish_date": "2026-04-15", "helpful_votes": 12
| # | review_id | property_id | author_name | rating | review_text | stay_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Host Profiles objects from flipkey.com. All fields typed and schema-versioned.
"host_id": "HST49201", "name": "Bahamas Retreats LLC", "join_date": "2018-03-12", "response_rate": 98.5, "response_time": "within an hour", "properties_listed": 14, "superhost_status": true
| # | host_id | name | join_date | response_rate | response_time | total_reviews |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Flipkey scraper handles dynamic date-based pricing, calendar hydration, and pagination across TripAdvisor's infrastructure — with session management and anti-bot circumvention built in.
Titles, descriptions, precise coordinates, amenity lists, and house rules — scraped at the listing level.
Extract 12-month forward-looking availability calendars, minimum stay requirements, and host-blocked dates.
Capture base rates, cleaning fees, service charges, and seasonal modifiers for specific check-in/check-out parameters.
Full review text, ratings, stay dates, and host responses — paginated across the entire property history.
Identify property managers vs individual hosts. Extract response rates, portfolio size, and join dates.
Extract exact latitude/longitude coordinates and neighbourhood descriptors for spatial analysis.
Monitor property visibility for specific destination searches, dates, and guest counts.
Capture high-resolution image URLs, photo captions, and virtual tour links.
Run daily calendar updates or weekly full-catalogue refreshes with change-detection diffing.
Brief in. Clean data out.
Provide target geographies, property URLs, or host IDs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for flipkey.com.
Schema validation, null-rate checks, price-outlier detection, and calendar accuracy before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
TripAdvisor's network employs strict rate limiting and complex API structures for availability. Here's how we stay resilient.
TripAdvisor's perimeter relies on TLS fingerprints and IP reputation. Our crawlers use residential ISP proxies with realistic browser fingerprints and full cookie session management to bypass rate limits.
Flipkey calendars are populated via complex GraphQL/XHR requests. We intercept and replicate these API calls directly, extracting full 12-month availability arrays faster than rendering the DOM.
DOM structures for vacation rentals change frequently. Our selector strategy uses multiple fallback chains — CSS selectors, XPath, and JSON state hydration — so layout changes do not break pipelines.
For large property portfolios, we maintain a hash index of last-seen calendar states. Subsequent runs only push diffs — reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, calendar extraction failures, and coverage drops — responding before you notice.
Property managers ingest competitor rates and availability to optimise their own pricing algorithms.
Funds analyse yield, occupancy rates, and seasonal demand to identify profitable acquisition targets.
Tourism boards and analysts track supply growth, average daily rates (ADR), and market saturation.
OTAs and vacation rental platforms monitor Flipkey inventory overlap and fee structures.
NLP models process review corpora to identify trending amenities, common complaints, and guest preferences.
B2B SaaS companies identify multi-property hosts and professional managers for lead generation.
"Flipkey holds a massive repository of vacation rental supply and demand signals — but the calendar and pricing APIs require serious infrastructure to query at scale."
Most teams underestimate the investment required: reliable Flipkey scraping demands reverse-engineering undocumented calendar endpoints, managing residential proxy rotation to bypass TripAdvisor's perimeter, and normalising complex fee structures. DataFlirt absorbs that complexity so your engineers can focus on yield management — not infrastructure.
Everything supported by our flipkey.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.
We maintain pools of residential ISP proxies. 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 flipkey.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Flipkey is generally permissible under applicable law. DataFlirt targets only public, non-authenticated property, pricing, and review data. We do not extract PII 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 503/CAPTCHA rate spikes in real time.
Real-time streaming pipelines achieve sub-60-minute latency for availability signals on a defined property set. Full market refreshes at daily cadence complete within a 6-12 hour window.
Yes. We can submit specific check-in/check-out date combinations and guest counts to extract the exact base rate, cleaning fee, and service fees applied.
Our smallest packages start at a defined market or property list (typically 1,000-10,000 properties) with weekly delivery. We price based on volume and delivery frequency.
Yes. We extract public host profiles, including total properties managed, response rates, join dates, and superhost status, which is critical for identifying professional property managers.
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 and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off market extraction or a continuous availability feed across 100K properties — we scope, build, and operate the pipeline. Tell us what you need.
