Platform · Vrbo

Vrbo Data Scraping

Q: How do you compute an effective nightly figure?

For a stated stay length, with stay_length_basis naming it on every record. We do not publish a single effective nightly figure without that basis. Without it, the number would be an average across stay lengths nobody books.

Q: Do you identify professional hosts?

We record host_listing_count where the platform exposes it, and let you segment. We do not assert that a host is professional where the platform does not say so. The distinction matters because professional managers reprice frequently and individual owners often do not, so a market average blends two populations with different behaviour.

A nightly rate on a whole-home rental is close to meaningless. On a two-night stay the fees can cost more than the nights.

Vrbo data scraping collects whole-property rental listings, nightly rates, fee structures, minimum stays and calendar availability. The structural point: the fee stack is a large share of the total, and because cleaning fees are charged per stay rather than per night, the effective nightly cost depends entirely on stay length. A nightly rate on its own is not a price.

Whole-home rental is the travel category where the headline number is least representative of what is paid, and the gap is not a constant you can adjust for.

Get a free sample See sample data

Free pilot on your own Vrbo list, returned in 24 hours. No card, no trial clock — and you keep the sample data either way.

vrbo.jsonl LIVE FEED

{"property_id":"vr-44120","city":"Lisbon", "nightly_rate":110.00,"currency":"EUR", "cleaning_fee":95.00,"service_fee":31.00, "total_for_stay":346.00, "effective_nightly":173.00, "stay_length_basis":"2 nights", "note":"fees are 36% of a 2-night total. nightly rate says 110"} {"property_id":"vr-44120", "total_for_stay":896.00,"effective_nightly":128.00, "stay_length_basis":"7 nights", "note":"same property. cheapest for a week, among the dearest for a weekend"} {"calendar_state":"blocked", "occupancy_inferred":"not_produced", "caution":"blocked can mean booked, owner-occupied, maintenance or not released"}

3 of 2,440,110 property-date rows effective nightly requires a stated stay length · schema v1.0

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Vrbo or its owners. Vrbo and related marks belong to their respective owners, used here only to name the publicly accessible source this service collects from.

Our Data Powers

B2C Marketplace

amazon

D2C + Marketplace

NYKAA

D2C + Marketplace

Walmart

FMCG Marketplace

udaan

Food Delivery

Uber Eats

Quick Commerce

blinkit

Taxi Aggregator

Uber

E-Commerce

Tmall

Vrbo at a glance

How we handle Vrbo specifically

Platform-specific handling, not a generic retail template pointed at a different domain.

Platform: Vrbo — whole-property rentals, globally
The record: A property, not a room
The trap: Cleaning fees are per stay, not per night
So: Effective nightly cost depends on stay length
Minimum stays: Vary by property and by season. Part of the offer
Calendar: Availability is a calendar, not a nightly flag
Host type: Professional managers and individual owners behave differently
Never: Occupancy or revenue inferred from calendar blocks

Platform specifics

Why a nightly rate is not a price here

These are the reasons a Vrbo dataset needs its own handling rather than a shared retail schema.

Per-stay fees make stay length part of the price

A hotel charges per night and the total scales linearly. Whole-home rental does not.

Cleaning fees are per stay. On a two-night booking they are spread across two nights; on a fourteen-night booking, across fourteen.
Service fees are frequently a percentage, so they scale with the nightly total but not with the cleaning fee.
Some properties discount longer stays explicitly, compounding the non-linearity.

The practical result: the same property can be the cheapest option for a week and among the most expensive for two nights. A dataset reporting a nightly rate captures none of that.

What we deliver

nightly_rate, cleaning_fee, service_fee and any other charge as separate fields, plus total_for_stay and effective_nightly computed for a stated stay length — with stay_length_basis naming it.

We do not publish a single effective nightly figure without that basis, because it would be an average across stay lengths nobody books.

Minimum stays and calendars are part of the offer

A property with a seven-night minimum is not available for a weekend, regardless of what its calendar shows as open.

Minimum stay varies by property and by season, and frequently rises in peak periods.
An open calendar night with a minimum you cannot meet is not bookable, and a dataset treating it as available overstates supply.
Changeover-day rules at some properties restrict which nights a stay can start.

We record minimum_stay_nights against the date, and bookable_for_stay as a derived flag against your stated stay length — so an availability count reflects what a traveller could actually book.

Calendars are not occupancy

A blocked calendar night can mean booked, owner-occupied, blocked for maintenance, or simply not released.

We do not infer occupancy or revenue from calendar blocks. This is a widely sold inference and it rests on an assumption about why a night is blocked that nobody outside the host can verify. We deliver the calendar state and the block transitions. The inference, if you want one, is yours and should be labelled as one.

Host type changes the pricing behaviour

Vrbo carries listings from professional property managers and from individual owners, and they behave differently.

Professional managers reprice frequently, often algorithmically, and respond to demand.
Individual owners frequently set a rate and leave it, adjusting only seasonally.
A market's average nightly rate blends two populations with different behaviour.

Where the platform exposes a signal for management type — listing count under one host, for instance — we record it as host_listing_count and let you segment. We do not assert that a host is professional where the platform does not say so.

What we never collect

Host names, contact details, individual reviewer identities or any other personal data. Review counts and rating values are recorded; reviewers are not.

Scope

What we collect on Vrbo, and what we do not

The right column matters more than the left. Anyone can list fields; the limits are what tell you whether the dataset will hold up.

✅ What we collect

Property-level listings with the fee stack as separate fields
total_for_stay and effective_nightly computed for a stated stay length
stay_length_basis named on every derived nightly figure
minimum_stay_nights against the date, with seasonal variation captured
bookable_for_stay derived against your stated stay length
Calendar state and block transitions, as observations
host_listing_count where the platform exposes it
Review counts and rating values, without reviewer identity
Property attributes — bedrooms, capacity, amenities — as published

❌ What we do not, and why

A nightly rate presented as the price
An effective nightly figure without a stated stay length
Occupancy or revenue inferred from calendar blocks
A host asserted to be professional where the platform does not say
Host names, contacts, reviewer identities or any personal data

Core Vrbo fields

The full dictionary is agreed during scoping. These are the fields specific to this platform.

Field	What it is on this platform
property_id / city / region	The property and where it is
nightly_rate / currency	Before fees. Not the price
cleaning_fee / service_fee / other_fees	Each separately. Cleaning is per stay
total_for_stay / effective_nightly / stay_length_basis	Computed, with the basis named
minimum_stay_nights	Against the date. Varies by season
bookable_for_stay	Derived against your stated stay length
calendar_state / block_transition	Observations, not occupancy
host_listing_count	Where exposed. Not an assertion of host type
bedrooms / sleeps / property_type	As published
review_count / rating	Values only, no reviewer identity
observed_at / stay_date	Both dates, as on any travel source

Use cases

What teams do with Vrbo data

Whole-home price comparison that reflects the total

Fees as separate fields with effective nightly computed for a stated stay length, so a two-night and a seven-night comparison are both correct rather than one of them being badly wrong.

Supply analysis that reflects bookability

Minimum stays recorded against dates with bookable_for_stay derived, so an availability count reflects what a traveller could actually book rather than what the calendar shows open.

Host segmentation

Listing count per host where exposed, so a market average can be split between professionally managed and individually owned inventory, which price very differently.

Fee structure benchmarking

Cleaning and service fees as their own series, which on short stays move the total more than the nightly rate does.

The 24-hour sample — run on your sources, not ours

Send us a Vrbo item or category list. We run real collection against it and return the output within 24 hours, with the platform-specific fields populated so you can check them yourself rather than take our word for it.

Real extraction from your actual sources
Returned within 24 hours
Coverage and QA note included
You keep the data either way
No card, no trial clock
Named engineer on the call

Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.

How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

Dedicated engineer assigned to your account
Site changes fixed by us, not reported to you
Scheduled delivery to your warehouse or S3
Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

On-demand and scheduled endpoints
Rate limits agreed to your load profile
Sandbox keys for integration testing
Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

Fixed scope agreed in writing upfront
Single delivery with full QA report
Methodology documented for your records
Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

Vrbo is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Vrbo data becomes useful when it sits next to the competitor set on one schema, refreshed on one schedule, so a price index or availability comparison is genuinely like-for-like.

That is what travel & hospitality data covers, and a Vrbo-only engagement can be expanded into it without rebuilding. If you already know you need several platforms, start there instead — it is the same pipeline and usually the better scoping conversation.

FAQ

Vrbo data scraping: frequently asked questions

Platform-specific questions, including what cannot be collected here.

Because cleaning fees are charged per stay rather than per night. On a two-night booking they spread across two nights; on fourteen nights, across fourteen.

The result is that the same property can be the cheapest option for a week and among the most expensive for two nights. A nightly rate captures none of that.

For a stated stay length, with stay_length_basis naming it on every record. We do not publish a single effective nightly figure without that basis.

Without it, the number would be an average across stay lengths nobody books.

No, and this is worth being direct about because it is widely sold. A blocked calendar night can mean booked, owner-occupied, blocked for maintenance, or simply not released.

The inference rests on an assumption about why a night is blocked that nobody outside the host can verify. We deliver calendar state and block transitions; if you want the inference it is yours, and it should be labelled as one.

Because a property with a seven-night minimum is not available for a weekend, regardless of what its calendar shows open. A dataset treating that night as available overstates supply.

We record minimum stay against the date and derive bookable_for_stay against your stated stay length.

We record host_listing_count where the platform exposes it, and let you segment. We do not assert that a host is professional where the platform does not say so.

The distinction matters because professional managers reprice frequently and individual owners often do not, so a market average blends two populations with different behaviour.

We quote individually on properties times stay dates times observations. Markets and stay-length scenarios both affect it, since effective nightly figures are computed per stated stay length.

One scoping call, a free pilot within 24 hours, then a fixed monthly quote. Request a quote.

See real Vrbo data before you commit to anything

Send us an item or category list. We return the output within 24 hours with the platform-specific fields populated.

Get a free sample Talk to an engineer

Free pilot, no card, no obligation. If we cannot collect a field you need on this platform, the sample shows you that too.

Hear It Directly from Our Clients

Watch how businesses like yours are using Actowiz data to drive growth.

▶

1 min

★★★★★

"Actowiz Solutions offered exceptional support with transparency and guidance throughout. Anna and Saga made the process easy for a non-technical user like me. Great service, fair pricing!"

Thomas Galido

Co-Founder / Head of Product at Upright Data Inc.

▶

2 min

★★★★★

"Actowiz delivered impeccable results for our company. Their team ensured data accuracy and on-time delivery. The competitive intelligence completely transformed our pricing strategy."

Iulen Ibanez

CEO / Datacy.es

▶

1:30

★★★★★

"What impressed me most was the speed — we went from requirement to production data in under 48 hours. The API integration was seamless and the support team is always responsive."

Febbin Chacko

-Fin, Small Business Owner