Platform · Airbnb

Airbnb Data Scraping

A blocked night can mean booked, owner-occupied, under maintenance, or simply not released. The industry sells it as occupancy anyway.

Airbnb data scraping collects listings, nightly rates, fee structures, calendars and review counts. The position that defines this page: a blocked calendar night is not a booked night. Occupancy and revenue estimates built on that inference are the most widely sold unobservable in this category, and we do not produce them.

This is the clearest case in the whole project of a number that is easy to produce, looks authoritative, and rests on an assumption nobody outside the host can check.

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airbnb.jsonl LIVE FEED

{"listing_id":"ab-44120","city":"Example city", "location_precision":"area_as_published", "nightly_rate":128.00,"cleaning_fee":85.00, "service_fee":41.00, "effective_nightly":191.00,"stay_length_basis":"2 nights"} {"effective_nightly":146.00,"stay_length_basis":"7 nights", "minimum_stay_nights":3,"bookable_for_stay":false, "note":"calendar shows open. a 2-night stay cannot book it"} {"calendar_state":"blocked", "block_transition":"2026-08-21T09:14Z", "occupancy_estimate":"not_produced", "caution":"booked, owner-occupied, maintenance, or unreleased. the estimate calls all four booked"}

3 of 8,884,220 listing-date rows · globalcalendar state, NOT occupancy · location never sharpened · schema v1.0

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

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Airbnb at a glance

How we handle Airbnb specifically

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

Platform: Airbnb — global short-term rental
The central point: A blocked night is not a booked night
Possible meanings: Booked, owner-occupied, maintenance, or not released
So: We deliver calendar state and transitions, not occupancy
Fees: Cleaning is per stay, so effective nightly depends on stay length
Minimum stays: Part of the offer. An open night may not be bookable
Host type: Professional managers and individual hosts behave differently
Never: Host names, contacts or reviewer identities

Platform specifics

The inference we decline, and why

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

Four meanings, one calendar state

A calendar shows a night as available or blocked. It does not show why a night is blocked, and there are at least four reasons:

It is booked.
The owner is using it.
It is blocked for maintenance or cleaning buffer.
It has not been released for sale.

Occupancy estimates treat all four as the first. The error is directional — it always overstates occupancy, because every non-booking reason is counted as a booking.

And it is undetectable downstream. An occupancy percentage in a column looks like a measurement, and nothing in the dataset says it was assembled from an assumption.

What we deliver instead

calendar_state per night and block_transition events with timestamps, so you can see the calendar move. occupancy_estimate is a constant not_produced with the reason.

If you want the inference, build it — on an assumption you can state and defend. That is the observed-versus-derived position, and this is the platform where it matters most because the inference is sold so widely.

Fees, minimum stays and what a nightly rate hides

Cleaning fees are per stay

So effective nightly cost depends entirely on stay length. A property can be the cheapest option for a week and among the dearest for two nights — the argument our Vrbo page sets out in full, and it applies identically here.

effective_nightly is computed only for a stated stay length, with stay_length_basis named. A single effective nightly figure without that basis is an average across stay lengths nobody books.

Minimum stays

An open calendar night with a minimum you cannot meet is not bookable. We record minimum_stay_nights against the date and derive bookable_for_stay against your stated stay length, so an availability count reflects what a traveller could actually book.

Host type

Professional managers reprice frequently, often algorithmically. Individual hosts frequently set a rate and leave it. A market average blends two populations with different behaviour.

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

What we never collect, and it matters more here

Host names, photographs, contact details or profile text. Never, in any market.
Reviewer identities or review text that could identify an individual. Counts and ratings only.
Guest data of any kind.
Exact addresses. Where the platform obfuscates location to an area, we record it at the precision published and do not attempt to resolve it further.

Why the address point is not negotiable

A short-term rental listing is frequently somebody's home. The platform deliberately obfuscates location before a booking, and resolving that obfuscation — by triangulating from images, descriptions or map approximations — would defeat a privacy measure the platform put there on purpose.

We record location_precision as published. We do not sharpen it, and we decline requests to, which does happen.

Scope

What we collect on Airbnb, 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

calendar_state per night and block_transition events with timestamps
occupancy_estimate as a constant not_produced, with the reason
effective_nightly only for a stated stay length, with the basis named
minimum_stay_nights against the date, with bookable_for_stay derived
Cleaning, service and other fees as separate fields
host_listing_count where the platform exposes it
Review counts and ratings, without reviewer identity
location_precision as published, never sharpened
Property attributes, capacity and amenities as published

❌ What we do not, and why

Occupancy or revenue inferred from calendar blocks
An effective nightly figure without a stated stay length
A host asserted to be professional where the platform does not say
Location resolved beyond the precision the platform publishes
Host names, contacts, profile text, reviewer identities or guest data

Core Airbnb fields

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

Field	What it is on this platform
listing_id / city / region	The listing and where it is, at published precision
location_precision	As published. Never sharpened
nightly_rate / currency	Before fees. Not the price
cleaning_fee / service_fee / other_fees	Each separately. Cleaning is per stay
effective_nightly / stay_length_basis	Computed, with the basis named
minimum_stay_nights / bookable_for_stay	Against the date, and your stated stay
calendar_state / block_transition	Observations, not occupancy
occupancy_estimate	Constant not_produced, with the reason
host_listing_count	Where exposed. Not an assertion of host type
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 Airbnb data

Short-term rental pricing 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 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.

Calendar movement as an observation

Block transitions with timestamps, which is the real signal underneath occupancy estimates — delivered as what it is rather than converted into what it is not.

Host segmentation

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

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

Send us a Airbnb 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

REST endpoints on the same pipeline, with sandbox keys for testing.

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 fixed-scope extraction for a single project or research question.

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

Pricing is quoted individually, based on category scope, number of markets, cadence and ongoing identity maintenance. After a scoping call and free pilot, we quote a fixed monthly figure, with no per-request metering.

Book a 20-min scoping call Request a quote Talk to an engineer

Airbnb is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Airbnb 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 Airbnb-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

Airbnb data scraping: frequently asked questions

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

Because a blocked calendar night can mean booked, owner-occupied, blocked for maintenance, or simply not released — and the estimate treats all four as the first.

The error is directional: it always overstates, because every non-booking reason is counted as a booking. And it is undetectable downstream, because an occupancy percentage in a column looks like a measurement.

Because being widely sold does not make it observable. This is the clearest case in our whole coverage of a number that is easy to produce, looks authoritative, and rests on an assumption nobody outside the host can check.

We deliver the calendar state and the transitions. If you want the inference, build it on an assumption you can state and defend.

Because cleaning fees are charged per stay rather than per night. A property can be the cheapest option for a week and among the dearest for two nights.

We compute effective nightly only for a stated stay length, with the basis named. A single figure without it is an average across stay lengths nobody books.

No, and we decline requests to — which do happen. The platform deliberately obfuscates location before a booking, and resolving that by triangulating from images or map approximations would defeat a privacy measure put there on purpose.

A short-term rental listing is frequently somebody's home. We record location at the precision published and do not sharpen it.

No. Host names, photographs, contacts and profile text are never collected, and reviews are captured as counts and ratings only — never reviewer identity or text that could identify someone.

We quote individually on listings times stay dates times observations. Stay-length scenarios affect it too, 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 Airbnb 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.

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