Vacasa sets the rate on every home it manages, so a Vacasa price scrape reads as one operator's revenue decisions repeated across tens of thousands of units, not as a crowd of owner asks.
You get CSV, JSON, XLSX or an API endpoint of ours, on whatever cadence the question needs. Rate and calendar work usually runs daily or weekly against a fixed query grid so the series stays comparable. Catalogue fields, amenities and review history move slowly, so a monthly pass covers them.
The site is defended, and the price quote in particular is guarded by an enterprise CAPTCHA. Anti-bot handling, proxy rotation and CAPTCHA solving are part of the service, run at a polite rate rather than a brute-force one, with the published crawl delay respected. The Canadian domain shares the same unit numbering, so one schema covers both markets without a second build.
One row per unit, keyed to the integer id in the unit path. Catalogue fields sit in the page markup; dated rates, calendar and reviews come from the site's own endpoints. The schema:
Column names are ours and hold steady between runs. Need a field that is on the page and not listed here? We add it.
A Vacasa rate is a decision, and it moves. The search card shows an average per night that excludes fees and taxes. The unit page publishes a low, a high and a base average for the same home. The dated quote returns a different figure again, and an off-season midweek night sits far below the yearly average. All three are real and none stands in for the others, so we fix the query grid with you first - arrival dates, stay lengths, party sizes - and stamp every row. A rate without its dates is not a rate.
The total is not the rent. Cleaning is a flat charge per stay, so its per-night weight falls as the stay lengthens. Lodging taxes stack by state, county and city, each on its own named line. The service fee is a percentage of rent and fees. On short stays in high-tax counties the load on top of the rent runs well into double digits as a share, so we keep every component in its own column, not one blended figure.
Coverage has to be sliced. The result set is capped, so a large market is broken down by destination, amenity facet, bedroom count and price band, then reconciled on unit id. The unit sitemap is the backbone: roughly 41,000 addresses in early September 2026.
Terms and personal data. Vacasa's terms of service prohibit robots, scrapers and spiders and automated collection of site content, with a narrow carve-out for search engines. We work from public pages only, never log in, never touch checkout, and ask clients to have their counsel sign off. Guest first names and initials from reviews are dropped; scores and text are kept.
Vacasa manages vacation homes on behalf of their owners. It does not carry third-party classifieds. Every home on vacasa.com sits under a management contract: Vacasa photographs it, writes the description, sets the nightly rate, runs the calendar, cleans between stays and answers the guest at three in the morning. That single fact changes what a Vacasa scraper collects. On an open marketplace you harvest thousands of independent asking prices from strangers. On Vacasa you harvest the pricing decisions of one professional operator, applied consistently across a whole portfolio.
Founded in Portland, Oregon in 2009, Vacasa listed on Nasdaq as VCSA through a 2021 SPAC combination with TPG Pace Solutions. Casago, a Phoenix-based manager founded in 2001, agreed on 30 December 2024 to buy out the public shares at USD 5.02 each, subject to adjustment, and closed on 1 May 2025, at which point Vacasa stock stopped trading and left the exchange. Proptech firm Roofstock invested alongside Casago and now appears as the contracting party in the rental agreement text served with a booking quote. The brand line in the page markup reads Vacasa, a Casago Company, and at closing the combined business was put at more than 40,000 managed homes.
Coverage follows that management footprint rather than a map of demand. City pages exist under the usa prefix for 49 states plus the District of Columbia, alongside separate pages for named resorts and condo communities such as Shipyard on Hilton Head. Outside the United States the published places sit under a vacation-rentals prefix for Mexico, Belize, Costa Rica and Aruba, and Canada runs on its own domain, vacasa.ca, sharing the same unit numbering.
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Benchmarking against a professional pricer. A Vacasa rate is not an owner's guess. It is what a manager with a revenue team decided to charge for a specific home on a specific night, and it moves when demand moves. For anyone pricing their own units, that is a reference series rather than noise.
Occupancy and pickup. Calendar flags say which nights have already gone. Run the same units week after week and the pickup curve appears: how far ahead a market fills, which weekends sell first, how thin the shoulder season really is. Nightly rate data next to calendar state is the closest a public source gets to an occupancy series.
Market sizing and supply tracking. Every destination page publishes its own home count and its low-to-high nightly band, so one sweep across the destination set returns supply and price spread by market, and a repeat sweep shows where managed stock is growing or thinning.
Acquisition and underwriting. Buyers of vacation homes, and buyers of management books, put rent, fee load, calendar fill and review history together to model revenue per home, then test it against the asking price of comparable stock.
Competitor watch for other managers. Units carry a local operating brand and a manager id, so a market can be grouped by operator to see who prices how, whose minimum stays run longest and what guests actually score them on.
Monitoring of real estate listings on funda.nl, pararius.com, rentberry.com, rentola.com, and zimmo.be to support the growth of a European property portal.
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Daily detection of new private property listings in Switzerland on Homegate.ch and ImmoScout24.ch, giving the agency first access to high-value leads.
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Scraping residential listings from Immobilienscout24.de in Germany and Mallorca, including complete data and resized images.
Learn MoreLearn how to use web scraping to solve data problems for your organization
Real estate teams work in a fragmented data landscape. The sites that drive demand in Boston look nothing like the ones that matter in Berlin, São Paulo, Dubai, or Mumbai.
Real estate teams are operating in a data environment that is bigger, faster, and more fragmented than ever. Listings go live and disappear in hours, price cuts happen quietly, and the portals that matter most in each country are rarely the same global “top 5.”
Real estate web scraping: a powerful tool for data collection and analysis. Learn how to choose the right data collection method and benefit from real estate web scraping
ScrapeIt is a managed web scraping agency. We are independent of Vacasa and of Casago. We scope the job, build the crawler, run it on your schedule, watch it when the site changes and repair it before you have to file a ticket.
You never touch the pipeline. What arrives is a file or an endpoint with the columns agreed at the start, in the same shape every run. Send us the markets and the date grid you care about and we will come back with a field list, a sample extract and a schedule.
No. There is no documented developer programme and no public Vacasa API you can register for and query. The site talks to its own front-end endpoints for price quotes, calendars and reviews, but those are undocumented, unversioned, bound to its own pages and guarded by an enterprise CAPTCHA on the quote path; they are not a supported interface and can change without notice. Vacasa also distributes its homes to Airbnb, Vrbo, Booking.com and Marriott Bonvoy Homes and Villas, so some inventory surfaces there, but that is channel distribution, not a data feed. Scraping the public pages into a fixed schema is the practical route, and our schema holds still when the site does not.
Yes, and separated rather than blended. For a stated arrival date, departure date and party size we capture the rent for each individual night, the rent subtotal, the cleaning fee and any other named fee, fees payable on arrival, security deposits, trip protection, the service fee, every tax line under its own name, and the quoted total. The payment schedule comes with it, so you can see what is due at booking and what is due later. Ask only for the headline nightly figure and you will get a number no guest ever pays, because on a short stay a flat cleaning charge and local lodging taxes move the effective nightly cost sharply.
Yes, at night-level granularity across a two-year forward horizon. Each date carries three flags: whether check-in is allowed, whether check-out is allowed, and whether the night is bookable at all. Read the caveat honestly, though: a night can be closed because it is sold, because the owner blocked it for personal use, or because a minimum-stay rule makes it unreachable from any legal arrival date. A single snapshot cannot always tell those apart. Repeated runs on a fixed schedule can, because a sold night behaves differently from a blocked one over time, and that is why occupancy work is sold as a series rather than a one-off pull.
The published inventory runs across 49 US states plus the District of Columbia, with city pages, named resort pages and condo-community pages sitting side by side under the same prefix, plus Mexico, Belize, Costa Rica and Aruba, and Canada on the vacasa.ca domain with matching unit numbering. In early September 2026 the unit sitemap listed roughly 41,000 addresses. Because the search result set is capped, we do not scrape a big market in one query; we slice it by destination, by amenity facet such as oceanfront, hot tub, pool, dog-friendly, lakefront or ski-in-ski-out, and by bedroom and price band, then reconcile everything on the unit id.
We work only from pages any visitor can open. We do not log in, we do not go through checkout, and we honour the crawl delay the site publishes. Be aware that the Vacasa terms of service forbid using a robot, script, scraper or spider to access, copy, crawl, collect, monitor or aggregate site content, with a narrow exception for general-purpose search engines, so we tell clients plainly to have their own counsel approve the use case before we start. On personal data we default to leaving it out: guest first names and last initials from reviews are dropped, homeowner details are not collected, and exact street addresses are not published before booking in any case.
Step 1 - Make a Request
You share your needs, expectations, and desired timeframe. We’ll suggest the best solution based on your request and budget.
Step 2 - Configuring Custom Web Crawlers
Our specialists configure the crawlers and extract a sample dataset for your review before proceeding with the full-scale extraction.
Step 3 - Collect and Deliver
Once you approve the sample, we launch the project and start full data collection. We gather, filter, and structure the data for easy use, delivering it on time in your preferred format.
Step 4 - Maintain and Support
Our team manages ongoing processes, monitors website changes, and supports all data extraction cycles. We can also help integrate data into your systems or create dashboards to simplify analysis.
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