Airbnb Full Year Price Tracker Scraper

Airbnb Price Tracker — Nightly Rates, Fees and Availability

The Airbnb Full Year Price Tracker Scraper returns one row per check-in date for any Airbnb listing, carrying the real nightly Primary Price, Total Price, Cleaning Fee, Service Fee, Taxes, and Booking availability. Each date is priced with its own live request, so weekday, weekend, and seasonal swings show up as genuinely different numbers instead of one repeated snapshot. Revenue managers, short-term-rental investors, and market analysts use it to benchmark competitor pricing and plan seasonal rates. Runs can simply extend further out as Airbnb's own booking calendar opens.

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What is Airbnb Full Year Price Tracker Scraper?

Airbnb Full Year Price Tracker Scraper is a Python-based Apify Actor that prices a specific Airbnb listing one calendar date at a time, across a date range you choose, and returns one structured row per date with the true nightly rate, fee breakdown, and availability. It works from a listing URL, a numeric listing ID, or a free-text search keyword. No Airbnb account, login, or API key is required — the Actor authenticates the same way a browser does, using Airbnb's own public web API key.

• Discovery by direct listing URL, numeric listing ID, or free-text keyword search
• Core price fields per date: Primary Price, Total Price, and Booking availability
• Secondary fee data: Cleaning Fee, Service Fee, Taxes, and Strike Through Price
• Temporal fields: Check-In Date and Check-Out Date written on every row, one per tracked day
• Real filters: stay length (stayDays), guest composition (adults/children/pets), currency, and total tracking window (numberOfDays)
• Exports through Apify's standard Dataset formats (JSON, CSV, Excel, and more) directly from the run's Dataset tab

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What data can I extract with Airbnb Full Year Price Tracker Scraper?

Each row groups into listing identity/details, guest-rating sub-scores, and per-date pricing. The table below lists every field the Actor writes, using its exact output key.

Field	Example Value	Use Case
Listing URL	https://www.airbnb.co.uk/rooms/7431972	Link back to the live listing for manual verification
Listing ID	7431972	Primary key for joining price rows across dates
Check-In Date	2026-08-01	Identify which calendar date this row's price applies to
Check-Out Date	2026-08-02	Pair with check-in to see the simulated booking window
Host	Sarah	Attribute pricing behavior to a specific host
Overview	Entire rental unit in London	Quick summary of the space type
Title	Bright 1BR flat near Camden Market	Display title for the listing
Canonical URL	https://www.airbnb.co.uk/rooms/7431972	De-duplicate listings scraped from different domains
Room Info	1 bedroom · 1 bed · 1 bath	Compact room summary for quick filtering
Property Type	Entire rental unit	Segment inventory by property category
Room Rating	4.85	Rank tracked listings by guest satisfaction
accuracyRating	4.9	Sub-rating for listing description accuracy
checkinRating	4.8	Sub-rating for check-in experience
cleanlinessRating	4.9	Sub-rating for cleanliness
communicationRating	4.95	Sub-rating for host communication
locationRating	4.7	Sub-rating for location quality
valueRating	4.6	Sub-rating for perceived value for price
guestSatisfactionOverall	4.85	Composite guest satisfaction score
visibleReviewCount	128	Weight rating confidence by review volume
reviewAccessibilityLabel	4.85 out of 5 stars from 128 reviews	Human-readable review summary for a UI
petsAllowed	true	Filter for pet-friendly listings
Guest_Capacity	4	Match a listing to a target party size
Location	London, England, United Kingdom	Geographic segmentation
Image URL	https://a0.muscache.com/im/pictures/...	Listing thumbnail for a UI or report
Meta Description	Aug 1 - 2 · Entire home for $150 night...	SEO meta text from the listing page
Price Breakdown Title	Price details	Header label above the fee section
Price Breakdown Subtitle	Includes taxes	Disclaimer copy trailing the price
Accessibility Label	$150 per night	Screen-reader price label from Airbnb's own UI
Strike Through Price	$175	Original price before any discount
Primary Price	$150	Nightly rate before fees — the core price signal
Cleaning Fee	$35	One-time fee added to the booking total
Service Fee	$22	Airbnb service fee added to the booking total
Taxes	$14	Occupancy/VAT tax added to the booking total
Total Price	$221	Full guest-facing price for the stay
Booking	Available	Whether the date is bookable
Unavailability Reason	Not available for these dates	Host-supplied reason when a date has no price
Bedrooms	1	Filter inventory by bedroom count
Beds	1	Match sleeping capacity to party size
Bathrooms	1	Filter inventory by bathroom count
Cancellation Policy Name	Moderate	Compare refund flexibility across listings
Cancellation Policy Subtitles	["Free cancellation before Jul 25"]	Exact refund cutoff copy for guest-facing text

Pricing and fee breakdown

Every row prices one exact date with Airbnb's own live booking response, not an estimate. Primary Price is the base nightly rate; Strike Through Price shows any discount Airbnb is displaying; Cleaning Fee, Service Fee, and Taxes are the add-ons Airbnb itemizes separately; Total Price is what a guest actually pays for that stay length. Price Breakdown Title and Price Breakdown Subtitle carry Airbnb's own disclaimer copy, and Accessibility Label mirrors the screen-reader price string from the live page. A common use case: compare a target listing's Total Price against a set of tracked competitors on the same dates to spot under- or over-pricing before adjusting your own rate.

Date and location targeting data

Check-In Date and Check-Out Date are written on every row, so a full run can be filtered, grouped, or charted by day, week, or month without any date math. Location and Guest_Capacity narrow which of several tracked listings match a target market and party size, while Bedrooms, Beds, and Bathrooms let you segment a basket of listings by unit size before comparing their date-by-date pricing. Together these fields turn a flat list of price rows into a market you can slice by place and time.

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Why not build this yourself?

Airbnb has no public, official API for third-party listings or pricing data, so there's no documented endpoint to compare against — anyone who wants full-year, per-date pricing has to reverse-engineer Airbnb's own web app.

Two things make that hard to build in-house. First, pagination complexity: Airbnb doesn't expose a single bulk endpoint for a listing's price calendar — each check-in date is its own live GraphQL request against the internal StaysPdpSections operation, identified by a persisted-query hash Airbnb rotates without notice. A full year of dates means driving that request 365 times per listing, discovering a fresh hash when the old ones stop returning price data, and reassembling the nightly rate from a deeply nested response that mixes booking-footer, sidebar, and calendar-sheet sections depending on which one the account happens to receive.

Second, anti-bot escalation: Airbnb sits behind Akamai, which serves an HTML deny page — not JSON — to any client whose TLS fingerprint doesn't match a real browser, on top of outright 403/429/rate-limit responses. A workable scraper needs a browser-accurate TLS impersonation layer and a proxy fallback ladder (direct → datacenter → residential) that escalates only when actually blocked, to avoid paying for residential proxy bandwidth on every request.

This Actor already ships the persisted-query hash rotation, the per-date request loop, and the none-to-residential proxy fallback, so a single run replaces that engineering rather than a single API call replacing a rate limit.

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How to use data extracted from Airbnb?

Revenue and pricing analysts

Revenue managers set checkInDate and numberOfDays to the exact booking window they're optimizing — typically the next 60-90 days — and stayDays to match their own minimum-night policy, then track their own listing alongside 3-5 comparable listings in the same startUrls array. Pulling Primary Price, Total Price, and Booking across every date builds a day-by-day rate calendar that shows exactly where a competitor charges a weekend premium, drops price to fill a gap, or goes fully unavailable. Feeding that into a spreadsheet or BI tool alongside the analyst's own calendar makes it possible to spot underpriced weekends before they book out, or to catch a competitor's last-minute discount and react the same day instead of a week later.

Property management and rental agencies

Agencies managing multiple owner units run a tracking job per property, listing every managed unit's Airbnb URL in startUrls with a shared checkInDate window, then compare each unit's Total Price and Cancellation Policy Name against a handful of tracked competitor listings in the same neighborhood. Because every date is priced independently, an agency can show an owner exactly which weekends underperform the local market rather than a single averaged rate. Repeating the same run monthly, with checkInDate rolled forward, turns this into a recurring rate-audit workflow owners can be shown directly.

Market research and investment analysts

Analysts scanning a market before acquisition pass a keyword search term (e.g. a neighborhood name) instead of a fixed URL, letting the Actor discover a handful of comparable listings automatically, then track all of them across the same numberOfDays window. Aggregating Primary Price by Bedrooms and Property Type builds a rough price-per-bedroom curve for that submarket, while Booking and Unavailability Reason across the date range hint at how tightly booked the area already is. That combination — real per-date price plus real availability — is a sturdier basis for a rental-yield estimate than a single scraped snapshot.

AI agents and automated pipelines

An agent framework can call this Actor as a tool whenever a user asks for the current or seasonal Airbnb rate on a specific listing, returning Primary Price and Booking for the requested dates instead of a stale, memorized figure. Automated repricing pipelines can run it nightly against a tracked competitor set and pipe Total Price straight into a host's own pricing rules engine, so rate adjustments respond to what competitors are actually charging that day.

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🔼 Input sample

Configure the run from the Apify Console UI or pass raw JSON. Only startUrls and checkInDate are required — every other field has a working default.

Parameter	Required	Type	Description	Example Value
startUrls	Yes	array	Airbnb URLs, listing IDs, or search keywords	["https://www.airbnb.co.uk/rooms/7431972"]
checkInDate	Yes	string	First check-in date to track (YYYY-MM-DD); past dates auto-shift to the near future	"2026-08-01"
stayDays	No	integer	Nights per simulated booking (1-365, default 1)	1
numberOfDays	No	integer	Total future days to generate price entries for (1-365, default 60)	60
adults	No	integer	Adult guests in the search (1-16, default 2)	2
children	No	integer	Children in the guest count (0-16, default 0)	0
pets	No	integer	Pets traveling with the guests (0-5, default 0)	0
currency	No	string	Currency for displayed prices: USD, GBP, EUR, CAD, AUD (default USD)	"USD"
proxyConfiguration	No	object	Apify Proxy settings for the run	{"useApifyProxy": false}

{
  "startUrls": ["https://www.airbnb.co.uk/rooms/7431972"],
  "checkInDate": "2026-08-01",
  "stayDays": 1,
  "numberOfDays": 60,
  "adults": 2,
  "children": 0,
  "pets": 0,
  "currency": "USD",
  "proxyConfiguration": { "useApifyProxy": false }
}

Pitfall: the most common zero-price run comes from a stayDays/checkInDate mismatch, not a bug. If stayDays is shorter than a host's own minimum-night requirement, or the dates fall in a genuinely unavailable window, Airbnb returns no price for any date in the run — the Actor still writes one row per date, but Primary Price and Total Price come back null and Unavailability Reason carries the host's own message. Try a longer stayDays or a checkInDate further out before assuming the run failed. Note also that every date row — priced or not — is billed as one row_result event, so a run over a mostly-unavailable window still consumes results.

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🔽 Output sample

Every run writes one typed JSON row per tracked date to the Actor's Dataset, exportable as JSON, CSV, Excel, or the other formats Apify's Dataset tab supports. Below is one complete record.

{
  "Listing URL": "https://www.airbnb.co.uk/rooms/7431972",
  "Listing ID": "7431972",
  "Host": "Sarah",
  "Overview": "Entire rental unit in London",
  "Title": "Bright 1BR flat near Camden Market · London, United Kingdom",
  "Canonical URL": "https://www.airbnb.co.uk/rooms/7431972",
  "Room Info": "1 bedroom · 1 bed · 1 bath",
  "Property Type": "Entire rental unit",
  "Room Rating": 4.85,
  "accuracyRating": 4.9,
  "checkinRating": 4.8,
  "cleanlinessRating": 4.9,
  "communicationRating": 4.95,
  "locationRating": 4.7,
  "valueRating": 4.6,
  "guestSatisfactionOverall": 4.85,
  "visibleReviewCount": 128,
  "reviewAccessibilityLabel": "4.85 out of 5 stars from 128 reviews",
  "petsAllowed": true,
  "Guest_Capacity": 4,
  "Location": "London, England, United Kingdom",
  "Image URL": "https://a0.muscache.com/im/pictures/miso/example.jpg",
  "Meta Description": "Aug 1 - 2 · Entire home for $150 night in London, United Kingdom.",
  "Price Breakdown Title": "Price details",
  "Price Breakdown Subtitle": "Includes taxes",
  "Accessibility Label": "$150 per night",
  "Strike Through Price": "$175",
  "Primary Price": "$150",
  "Cleaning Fee": "$35",
  "Service Fee": "$22",
  "Taxes": "$14",
  "Total Price": "$221",
  "Booking": "Available",
  "Unavailability Reason": null,
  "Bedrooms": "1",
  "Beds": "1",
  "Bathrooms": "1",
  "Cancellation Policy Name": "Moderate",
  "Cancellation Policy Subtitles": ["Free cancellation before 2:00 PM on Jul 25"],
  "Check-In Date": "2026-08-01",
  "Check-Out Date": "2026-08-02"
}

---

How do you filter and target specific listings?

Every filter here works against a single tracked listing, or a short explicit list, rather than a broad open-ended search — precision comes from picking the right combination of scope and stay parameters, not a category filter.

Direct ID/URL vs. keyword — passing a listing URL or numeric ID in startUrls pins the exact unit you want priced; passing free text runs a live Airbnb search and prices whatever comes back first, which is less exact but useful for a quick market scan when you don't have specific competitors picked yet.

Scope precision — checkInDate plus numberOfDays defines the exact calendar window tracked (up to 365 days), and stayDays (1-365) sets how long each simulated booking is. This matters beyond just computing the check-out date: a host's minimum-night policy can make a whole date range come back with no price if stayDays is set too short.

Quality thresholds — this Actor has none; it doesn't filter by rating or review count, so pair it with your own downstream filtering if you're tracking a keyword-discovered set of listings.

Volume controls — total rows written equal startUrls length × numberOfDays, so a 5-listing, 365-day run writes 1,825 rows in one job. Keep numberOfDays proportional to how far ahead you actually need visibility.

Three examples, each solving a different targeting problem:

{
  "startUrls": ["https://www.airbnb.co.uk/rooms/7431972"],
  "checkInDate": "2026-08-01",
  "numberOfDays": 365,
  "stayDays": 1
}

{
  "startUrls": ["123456789"],
  "checkInDate": "2026-09-01",
  "numberOfDays": 52,
  "stayDays": 7
}

{
  "startUrls": ["luxury villa Lisbon"],
  "checkInDate": "2026-12-20",
  "numberOfDays": 14,
  "adults": 2,
  "children": 2,
  "pets": 1,
  "currency": "EUR"
}

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▶️ Want to try other Airbnb scrapers?

Scraper	What it extracts
Airbnb Scraper: Host Full Listings	Airbnb search results enriched with the full host block (superhost, response rate/time, verifications), with optional per-host portfolio expansion
Airbnb Occupancy & Rate Analytics Scraper	Booked-vs-available days per listing from the public calendar, rolled up into a monthly occupancy rate
Airbnb Phone Number Scraper with Host Listing Leads Finder	Airbnb listings, host profiles, and experiences discovered via Google SERP by keyword and location
Google Hotels — Location Details Scraper	Google Hotels search results plus nearby attractions, neighborhood summary, and a walkability score per hotel
Zillow Search Scraper: Investment Analyzer	Zillow listings with cap rate, rental yield, mortgage payment, and cash-flow estimates per property
Zillow Agents Finder & Reviews Scraper	Zillow real-estate agent and lender directory profiles with individual customer reviews

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How to extract Airbnb data programmatically

This Actor runs like any other Apify Actor: one authenticated call starts a run, and results land in a structured JSON dataset you can pull with the same call pattern regardless of language.

Python example

from apify_client import ApifyClient

client = ApifyClient("<APIFY_API_TOKEN>")

run = client.actor("simpleapi/airbnb-full-year-price-tracker-scraper").call(
    run_input={
        "startUrls": ["https://www.airbnb.co.uk/rooms/7431972"],
        "checkInDate": "2026-08-01",
        "numberOfDays": 60,
        "stayDays": 1,
        "currency": "USD",
    }
)

for row in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(row["Check-In Date"], row["Primary Price"], row["Booking"])

MCP for AI agents

This Actor is reachable through Apify's official MCP server, which exposes any Apify Actor as a callable tool to an MCP-compatible agent. Register it locally with:

APIFY_TOKEN=<your-token> npx -y @apify/actors-mcp-server --actors simpleapi/airbnb-full-year-price-tracker-scraper

An agent in Claude Desktop, Claude Code, or any other MCP-compatible client can then call it directly — for example, answering "what will this Airbnb listing cost next weekend?" with a live per-date price instead of a memorized guess.

Export to spreadsheets or CRM

Every run's Dataset tab exports directly to CSV or Excel for a spreadsheet, or via the Apify API for a CRM import. Map Listing URL to a unique key, Check-In Date and Total Price to rate-calendar columns, and Booking to an availability flag — no reshaping needed, since the dataset is already one flat row per date.

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Is it legal to scrape Airbnb?

Scraping Airbnb's own publicly listed prices and availability is generally permissible when the data is public-facing and business/pricing in nature — these nightly rates, fees, and availability dates exist to be seen by any visitor to the listing page, not private information. This is business and product data (rates, fees, availability), not personal data about identifiable individuals, so the relevant framework is Airbnb's Terms of Service and database-rights law rather than GDPR/CCPA; the visible Host first name is publicly displayed by Airbnb itself as part of the listing. Running high-volume or continuous scraping can still conflict with a platform's Terms of Service, and legal treatment varies by jurisdiction. Consult legal counsel for commercial applications involving bulk storage of personal data.

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❓ FAQ

What happens when a tracked date has no price? The Actor still writes one row for that date — Primary Price and Total Price come back null, Booking is set from Airbnb's own availability flag, and Unavailability Reason carries the host's own message when Airbnb supplies one (e.g. below minimum nights, fully booked). This is the real "stale" signal for this data: an unpriced date, not a missing listing.

Can I get the full fee breakdown along with the nightly price? Yes — every row includes Cleaning Fee, Service Fee, Taxes, Strike Through Price, and Total Price alongside Primary Price, pulled from the same live response used to price that date. There's no separate paginated call for fees; they come from the identical request.

How accurate is the price and availability data? The Actor returns the exact price Airbnb's own booking API returns for that check-in date at request time — it isn't cached or estimated. Because Airbnb prices change with demand, treat older exported rows as a historical record rather than today's live rate, and re-run for a current quote.

How many date rows can I get per run? numberOfDays accepts 1-365 per listing, and every startUrls entry is tracked independently, so total rows = number of listings × numberOfDays. There is no separate result cap beyond that per-parameter limit.

What's the most reliable way to target the exact listing I care about? Pass the listing's exact URL or numeric ID in startUrls rather than a keyword — a direct ID/URL always prices that one unit, while a keyword runs a live search and prices whatever Airbnb's own ranking returns first, which can drift between runs.

Does this Actor work with Claude, ChatGPT, and AI agent frameworks? Yes — it's reachable through Apify's MCP server (npx -y @apify/actors-mcp-server --actors simpleapi/airbnb-full-year-price-tracker-scraper) for MCP-compatible clients such as Claude Desktop and Claude Code, and it's callable as a plain Apify API endpoint by any other agent framework.

How does this compare to other Airbnb price scrapers on Apify? As observed on their live Apify Store listings in July 2026: the Airbnb & Booking.com Price Scraper (inovaflow) returns market-wide median/average prices per city rather than a per-listing daily rate, and caps runs at 200 city×date-range combinations; the Airbnb Calendar & Availability Scraper (integrative_tangent) covers up to 12 months of calendar/price data per listing, priced per 1,000 calendar-days returned. This Actor differs by resolving one true live price per exact check-in date for a specific listing, rather than a market median, and bills per row_result row.

Can I use this without an Airbnb account or API key? Yes — no Airbnb login, cookies, or API key are required. The Actor authenticates the same way a browser does, using Airbnb's own long-standing public web API key, and only needs your Apify account to run.

Am I charged for dates where no price was found? Yes — every tracked date writes one row and is billed as one row_result event whether or not Airbnb returned a price, so a checkInDate/stayDays combination that lands on a mostly-unavailable window still consumes results. Adjust stayDays or checkInDate first if a run comes back mostly null.

Can I track guest-size-specific pricing, like families or pets? Yes — adults, children, and pets are sent with every date's live pricing request, so the returned Primary Price and Total Price reflect that exact party size and any pet fee Airbnb applies, not a generic 2-adult default.

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Conclusion

Airbnb Full Year Price Tracker Scraper turns a single listing, or a short list of them, into a genuine day-by-day price history — real nightly rates, fees, and availability, not one snapshot copied across a date range. It's built for revenue managers, short-term-rental investors, agencies, and market analysts who need to see how a rate actually moves across a season rather than guess at it. The output is a flat, schema-stable JSON row per date, so it drops straight into a spreadsheet, BI tool, or automated pricing pipeline. Start a run on Airbnb Full Year Price Tracker Scraper's Apify page to price your own date range today.