We extract property listings, dynamic pricing, availability calendars, and reviews from Vacasa. 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 vacasa.com. All fields typed and schema-versioned.
"property_id": "VA-84921", "title": "Oceanfront Getaway with Private Hot Tub", "property_type": "House", "bedrooms": 3, "bathrooms": 2.5, "max_guests": 8, "pet_friendly": true, "latitude": 44.9778, "longitude": -124.0153
| # | property_id | title | url | property_type | bedrooms | bathrooms |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Fees objects from vacasa.com. All fields typed and schema-versioned.
"property_id": "VA-84921", "check_in": "2026-07-10", "check_out": "2026-07-15", "base_rate": 350.0, "cleaning_fee": 150.0, "service_fee": 45.0, "taxes": 38.5, "total_price": 2333.5, "minimum_stay": 3, "currency": "USD"
| # | property_id | check_in | check_out | base_rate | cleaning_fee | service_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Availability Calendar objects from vacasa.com. All fields typed and schema-versioned.
"property_id": "VA-84921", "date": "2026-07-12", "available": false, "price": 375.0, "block_reason": "booked", "minimum_nights": 3, "updated_at": "2026-05-12T09:14:00Z"
| # | property_id | date | available | price | block_reason | updated_at |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Reviews objects from vacasa.com. All fields typed and schema-versioned.
"review_id": "REV-992813", "property_id": "VA-84921", "author_name": "Sarah J.", "rating": 5.0, "review_text": "Incredible views and the hot tub was perfect.", "review_date": "2026-04-18", "source_platform": "Vacasa"
| # | review_id | property_id | author_name | rating | review_text | review_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Results objects from vacasa.com. All fields typed and schema-versioned.
"location_query": "Lincoln City, OR", "position": 4, "property_id": "VA-84921", "nightly_price": 350.0, "rating": 4.8, "review_count": 142, "scraped_at": "2026-05-12T09:14:33Z"
| # | location_query | check_in | check_out | position | property_id | title |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Vacasa scraper handles the complexities of map-based pagination, dynamic calendar APIs, and complex fee structures to deliver structured property data ready for analysis.
Title, description, guest capacity, bedrooms, bathrooms, and high-resolution image URLs scraped at the property level.
Extract 12-month forward-looking availability calendars to calculate occupancy rates and booking velocity.
Capture nightly rates across different seasons, including weekend premiums and last-minute discounts.
Isolate base rates from cleaning fees, service charges, and local taxes for accurate total-cost calculations.
Extract latitude and longitude coordinates to map properties against local attractions and competitors.
Structured extraction of hot tubs, pools, pet policies, internet speeds, and parking availability.
Full review text, ratings, and management responses to gauge guest satisfaction and property quality.
Capture Matterport URLs and virtual tour links embedded within property listings.
Run continuous pipelines that only emit records when prices, availability, or property details change.
Brief in. Clean data out.
Provide target regions, property URLs, or coordinate bounding boxes. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, and calendar API parsing for vacasa.com.
Schema validation, null-rate checks, and calendar integrity verification before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Vacasa's architecture relies heavily on dynamic APIs and map-based interfaces. Here is how we build resilient pipelines to extract this data at scale.
Vacasa search results are constrained by map viewports. We use coordinate bounding boxes and granular zoom-level iteration to ensure complete property discovery without hitting pagination limits.
Instead of clicking through calendar widgets, our pipelines intercept and parse the underlying JSON API responses, allowing us to extract 12 months of daily pricing and availability in a single request.
To capture accurate cleaning fees and taxes, we simulate booking requests with specific check-in and check-out dates, forcing the platform to calculate and expose the final itemised receipt.
Aggressive calendar scraping triggers rate limits. We distribute requests across US-based residential proxy pools, ensuring our extraction runs continuously without IP bans or CAPTCHA blocks.
We utilise multiple fallback selectors and structured data (LD+JSON) extraction to maintain pipeline integrity even when Vacasa updates their frontend framework or property page layouts.
Property managers track Vacasa pricing strategies and occupancy rates to optimise their own nightly rates.
Investors analyse historical occupancy and revenue data to identify high-yield vacation rental markets.
Hospitality brands monitor inventory growth, amenity offerings, and guest reviews across target regions.
Analysts track the vacation rental supply side, measuring total available nights and seasonal demand fluctuations.
Machine learning teams use property descriptions and review text to train real estate pricing models and sentiment classifiers.
Alternative accommodation platforms ingest property details to enrich their own meta-search engines.
"Vacasa holds highly structured data on premium vacation rentals, but accessing their dynamic pricing and availability calendars requires navigating complex map-based pagination and API rate limits."
Extracting property data at scale demands more than simple HTTP requests. You need full JavaScript execution to render pricing calendars, residential proxies to bypass rate limits, and custom parsers for fee breakdowns. DataFlirt manages this entire extraction lifecycle.
Everything supported by our vacasa.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 and deduplication. Playwright handles JavaScript rendering for map interfaces and dynamic fee widgets.
We maintain pools of residential ISP proxies. Rotation happens per-request to prevent rate limiting on calendar API endpoints.
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 vacasa.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from Vacasa is generally permissible under applicable law. DataFlirt targets only public, non-authenticated property, pricing, and review data. We do not extract personal user data or circumvent authentication walls. Clients should review Vacasa's ToS and consult legal counsel for specific use cases.
We use residential ISP proxies and distribute requests across large IP pools. Our crawlers implement exponential backoff and request timing modelled on human behaviour to avoid triggering defensive blocks.
Yes. By simulating specific check-in and check-out dates, we force the platform to generate a complete fee breakdown, allowing us to extract base rates, cleaning fees, service charges, and local taxes separately.
We can configure pipelines to refresh calendar availability daily or weekly depending on your requirements. Change detection ensures we only deliver updates when a date transitions from available to booked, or when prices fluctuate.
Yes. We can target specific cities, states, or use coordinate bounding boxes to scrape all properties within a defined geographic radius, bypassing standard search pagination limits.
Absolutely. We provide a sample run of up to 100 properties in a target region as part of the pre-engagement scoping process, allowing you to validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a regional market snapshot or continuous calendar tracking across 40,000 properties — we scope, build, and operate the pipeline. Tell us what you need.
