Airbnb Market Intel Scraper — Listings, ADR, Occupancy & Revenue Estimate

Answer "what does a 2-bedroom in this market actually earn?" in one run. Scrape any Airbnb search (city, neighborhood, region or a pasted Airbnb URL with your filters) and get every listing's nightly price, rating, reviews, bedrooms/beds/baths, badges and coordinates — plus, per listing, the 12-month availability calendar turned into occupancy 30/60/90/365, estimated monthly revenue and RevPAR, superhost status, guest capacity, sub-ratings and host stats. A free market-summary row rolls it up: median / p25 / p75 nightly price, share of superhosts and guest favorites, median occupancy & revenue, and a breakdown by bedroom count.

HTTP-only (server-rendered pages + Airbnb's public calendar endpoint), no login, no proxy, no cookies. Pay per result: $4 per 1,000 listings, $6 per 1,000 enriched listings (calendar + details). Duplicates, failed listings and the summary row are never charged.

Try it free in under a minute. Press Start with the prefilled input (Austin, TX) — about 40 seconds later you have 54 listings plus a market-summary row. A run like that costs about $0.25, and Apify's free plan includes $5 of platform credit every month, no credit card, so your first ~800 enriched listings are effectively free.

Real rows from that prefilled run (23 Sep 2026):

listing	nightly	rating	BR	occupancy 30d	est. revenue/mo	superhost
Little bungalow in East Austin	$252	5.0 (49)	1	33 %	$2,517	✔
Spacious & Modern ~ Downtown ~ Zilker	$238	4.62 (103)	2	40 %	$2,856	—
Travis Heights Groover	$382	4.95 (21)	2	47 %	$5,343	✔
Easy Access to UT Campus + Pool	$212	4.57 (122)	1	57 %	$3,598	—

…and the free summary row answers the market question directly: median nightly $364, median occupancy 30d 47 %, with p25/p75 prices, superhost share and a per-bedroom breakdown.

Who uses this

• Short-term-rental investors comparing markets before buying — ADR, occupancy and revenue estimates per bedroom count, without a $100+/month AirDNA seat.
• Hosts & property managers pricing against the competition: who charges what, who is booked out, who is superhost, minimum nights.
• Real-estate agents & lenders building STR underwriting comps for a specific address or neighborhood.
• Analysts & AI agents that need a clean, structured snapshot of an Airbnb market on a schedule.

Input

Field	Default	What it does
location	Austin, TX	Anything you would type into Airbnb search.
locations	[]	Several markets in one run (one per line).
searchUrls	[]	Advanced: full airbnb.com/s/... URLs with your own filters (map area, amenities, instant book…).
checkIn / checkOut	4 weeks out, 3 nights	Stay dates drive the nightly price Airbnb shows.
adults	2	Guest count.
children / infants / pets	0	New in 0.0.3. Party details — same effect on price/availability as on airbnb.com; pets = pet-friendly listings only.
roomType	any	entire_home or private_room to filter.
minBedrooms	0	Minimum bedrooms.
minBeds / minBathrooms	0	New in 0.0.3. Airbnb min_beds / min_bathrooms.
minRating / minReviews / guestFavoriteOnly	0 / 0 / false	New in 0.0.3. Post-filters on the search card (rating, review count, Guest Favorite badge) — dropped listings are never charged. Use minReviews: 20 to keep only proven earners.
priceMin / priceMax	0	Nightly price filter in currency.
maxListings	54	Charged listings per location (18 per page, Airbnb caps a search at 270).
enrichCalendar	true	Add calendar KPIs + detail fields (2 extra requests per listing).
maxEnrich	30	Enrich only the first N listings; 270 = all.
currency	USD	Any ISO code (EUR, GBP, AUD, IDR…).
marketSummary	true	Append the free roll-up row (also saved as key-value record MARKET_SUMMARY).

Output — listing row

Field	Example	Notes
listingId, url	547878270470501768	Airbnb listing
title, name	Guest suite in Austin, Serene & Sunny SoCo Sanctuary…
propertyTypeShort, area	Guest suite, Austin	parsed from title
rating, reviewsCount, isNewListing	4.99, 114, false
isGuestFavorite, isTopGuestFavorite, badges	true, true, ["Guest favorite"]
bedrooms, beds, baths	1, 1, 1	studio = 0 bedrooms
nightlyPrice, nights, totalPrice, originalTotalPrice, discountPct, currency	274.33, 3, 823, null, null, USD	price for the searched dates, before taxes
checkIn, checkOut, freeCancellation	2026-10-09, 2026-10-12, true
latitude, longitude, pictureUrl, rank, searchLocation, scrapedAt
Enriched (enriched: true)
roomType, propertyType, personCapacity	Entire home/apt, Entire guest suite, 2
isSuperhost, hostName, hostRating, hostReviewCount, hostYears, hostIsVerified	true, Mary, 4.99, 114, 4.5, true
ratingCleanliness, ratingAccuracy, ratingCheckin, ratingCommunication, ratingLocation, ratingValue	5, 4.99, 4.98, 5, 4.97, 4.95
occupancy30, occupancy60, occupancy90, occupancy365	0.367, 0.417, 0.311, 0.269	share of calendar nights unavailable
bookedNights30/60/90/365, availableNights30/90/365	11, 25, 28, 97 …
estRevenue30, estRevenue90, estRevenue365	3018, 7681, 26610	nightlyPrice × unavailable nights
revPar30, revPar90	100.59, 85.35	nightlyPrice × occupancy
minNights, maxNights, nextAvailableDate, calendarFrom, calendarTo	2, 1125, 2026-09-13, …

How to read occupancy: Airbnb only exposes availability, so an unavailable night is either booked or blocked by the host. That is the same public-data proxy every STR analytics tool starts from — treat it as an upper bound and compare listings against each other rather than as an audited P&L.

Output — market-summary row (free)

type: "market-summary", listings, listingsEnriched, medianNightlyPrice, avgNightlyPrice, p25NightlyPrice, p75NightlyPrice, minNightlyPrice, maxNightlyPrice, avgRating, medianReviews, totalReviews, shareGuestFavorite, shareNewListings, shareSuperhost, medianOccupancy30, medianOccupancy90, medianEstRevenue30, medianRevPar30, medianMinNights, medianPersonCapacity, bedroomBreakdown[] (bedrooms, listings, medianNightlyPrice, medianOccupancy30, medianEstRevenue30, avgRating).

Example output (one real enriched row, 6 Sep 2026, "Austin, TX")

{
  "listingId": "547878270470501768",
  "url": "https://www.airbnb.com/rooms/547878270470501768",
  "title": "Guest suite in Austin",
  "name": "Serene & Sunny SoCo Sanctuary with Farmhouse Feel",
  "rating": 4.99, "reviewsCount": 114, "isGuestFavorite": true,
  "bedrooms": 1, "beds": 1, "baths": 1,
  "nightlyPrice": 274.33, "nights": 3, "totalPrice": 823, "currency": "USD",
  "roomType": "Entire home/apt", "personCapacity": 2, "isSuperhost": true,
  "hostName": "Mary", "hostRating": 4.99, "hostYears": 4.5,
  "occupancy30": 0.367, "occupancy90": 0.311, "occupancy365": 0.269,
  "estRevenue30": 3018, "estRevenue90": 7681, "revPar30": 100.59,
  "minNights": 2, "nextAvailableDate": "2026-09-13", "enriched": true
}

Default input (Austin, 54 listings, 30 enriched) costs about $0.28 and runs in under a minute.

Pricing

Event	Price
Listing (search data)	$0.004 ($4 / 1,000)
Listing enriched (calendar + details)	$0.006 ($6 / 1,000) — replaces the listing fee for that row
Actor start	$0.001

Compute is included. Duplicates across pages/locations, listings that fail to parse and the market-summary row are never charged. A failed enrichment falls back to the plain listing fee.

Schedule it

Markets move weekly. In Apify Console open the Actor → Schedules → e.g. every Monday 06:00 with your locations, then connect the dataset to Google Sheets, Airtable, Slack or a webhook via Integrations. Compare MARKET_SUMMARY records week over week to spot price drops and occupancy shifts before your competitors do.

Use it from code or AI agents (MCP)

curl -X POST "https://api.apify.com/v2/acts/tactful_anvil~airbnb-market-intel-scraper/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"location":"Canggu, Bali","roomType":"entire_home","minBedrooms":2,"maxListings":90,"maxEnrich":90,"currency":"USD"}'

Works out of the box with the Apify MCP server (https://mcp.apify.com) — add this Actor to your Claude, Cursor, ChatGPT or custom agent and ask "compare Airbnb occupancy and ADR for 2-bedroom homes in Austin vs Nashville". Python: ApifyClient(token).actor("tactful_anvil/airbnb-market-intel-scraper").call(run_input={...}).

Why this Actor

• Market intelligence, not just listings — occupancy, revenue estimate and RevPAR per listing plus a market roll-up; competitors stop at price and rating.
• HTTP-only & fast — ~1 s per page, ~0.7 s per enriched listing, no browser, no proxies to pay for.
• Fail-loud — if Airbnb changes its layout the run fails with a clear message instead of "succeeding" with 0 rows.
• Fair pricing — flat per-result fees, nothing charged for duplicates, failures or the summary.

Related Actors

• Google Ads Transparency Scraper — see which property managers and hosts advertise.
• Brand Ads Cross-Network Spy — ad creatives across Google and Meta for any brand.
• Contact Details Scraper — emails, phones and socials from any website.
• Kalshi Weather Markets Scraper — prediction-market weather odds for demand forecasting.

Found this useful? Bookmark & review

A bookmark or a short review on this page helps other hosts and investors find it — and tells me which fields to add next (reviews text, amenities, price history?). Issues tab is open.

Changelog

• 0.1.5 (24 Sep 2026) — Reliability: when Airbnb rate-limits the datacenter and proxy connections, pages now come through a residential connection instead of the run failing, and the first search page is retried patiently. A location with no listings ends as a normal (empty) run with a clear message, several locations are processed even if one is refused, and long runs stop cleanly before their timeout with everything collected so far. No price or field change.
• 0.1.4 (24 Sep 2026) — Store page now opens with real sample rows and the free-plan math; the run's status bar shows your market's median nightly price and occupancy at a glance. No code, price or field change to the data itself.
• 0.1.3 (24 Sep 2026) — Reliability on rate limits. (1) An HTTP 429/403 on the first search page used to end the run as "0 listings found — try a broader location"; the search now retries through the proxy automatically and, if Airbnb still refuses, the run says so plainly (rate limit, not your input; only the start fee charged). (2) A room page answering HTTP 429 during enrichment is retried once through the proxy after a short breather instead of downgrading the row to a plain listing. (3) dateLabel is now filled from your dates ("Oct 21 – 24") — Airbnb omits the card date line for dated searches, so the column was empty. (4) Unknown input fields are reported in the log (typo guard). No price or field removed.
• 0.1.2 (16 Sep 2026) — Fix: runs failed with "no embedded search payload" when Airbnb soft-blocked the datacenter IP (HTTP 200, no results). The Actor now retries the search through a proxy automatically (proxyConfiguration input added; empty = automatic fallback). Forensic snapshot in KV SEARCH_DEBUG if even the proxy gets no payload.
• 0.1.1 (16 Sep 2026) — Reliability: V8 heap capped at 70 % of run memory so garbage is collected before the container limit (prevents out-of-memory kills on big pages). No output change.
• 0.0.4 (13 Sep 2026) — bedrooms fill: Airbnb search cards sometimes omit the bedroom line (≈10–20 % of cards, mostly studios). When the listing name says “studio” the row now gets bedrooms: 0 instead of null; other cards without the line stay null until the room page fills it (fetchDetails). No other field changed.
• 0.0.3 (8 Sep 2026) — billing hardening: a charging hiccup can no longer abort a run that already returned listings (rows are delivered either way).
• 0.0.3 (8 Sep 2026) — party details (children, infants, pets), minBeds / minBathrooms, quality post-filters minRating / minReviews / guestFavoriteOnly (never charged when dropped). No existing field changed.
• 0.1 (6 Sep 2026) — launch: search pagination (15 pages), room details, 12-month calendar KPIs, market-summary row, multi-location, pasted search URLs.