Airbnb Images Scraper

Airbnb Images Scraper — Listing Photos, Ratings and Location Data

Airbnb Images Scraper pulls the title, address, coordinates, rating, review count and every listing photo URL from any public Airbnb room page — you paste one or more https://www.airbnb.com/rooms/... URLs and get back structured JSON. It's built for people who already know which listings they care about: short-term rental hosts checking their own photo sets, agencies auditing client listings, and researchers or AI pipelines that need labeled property images tied to location and rating. Results write to the dataset row by row as each URL finishes, so a run that stops partway still keeps what it already scraped.

🏠 What is Airbnb Images Scraper?

Airbnb Images Scraper is an Apify Actor that fetches one or more Airbnb room listing pages directly and extracts the listing's structured data and photo URLs — no Airbnb account or login is required, since it reads the same public page any visitor sees. Given a list of room URLs, it returns one record per listing containing the title, address, GPS coordinates, star rating, review count, and every photo URL found on the page.

Key features:

• URL-driven, not search-driven — you supply the exact room URLs to scrape; there's no category or keyword discovery step
• Full image harvesting, not just the hero shot — every listing photo referenced on the page is collected and deduplicated, not just the images inside the page's structured-data block
• Live, incremental writes — each listing is pushed to the dataset as soon as it's scraped, so a long batch or a stopped run doesn't lose completed listings
• Automatic block handling — requests that come back blocked (403/429/5xx or an anti-bot page) automatically retry through Apify Proxy
• Configurable request timeout — control how long the Actor waits on a slow or unresponsive listing page before giving up
• JSON output — every record lands in the Apify dataset, exportable to JSON, CSV, Excel, or XML from the Console

📊 What data can I extract with Airbnb Images Scraper?

Every scraped listing returns eight fields: the listing URL, its title, a locality-level address, GPS coordinates, its star rating, its review count, and the full array of photo URLs.

Field	Example value	Use case
url	https://www.airbnb.com/rooms/782682596976136912	The listing you requested — the join key back to your input list
title	Cozy Modern Loft in Downtown Austin	Listing name for reports, catalogues, or matching to other sources
address	Austin, Texas, United States	Locality-level location for grouping listings by city or region
latitude	30.2672	Plotting listings on a map or measuring distance to a point of interest
longitude	-97.7431	Paired with latitude for mapping and geofencing
rating	4.92	Filtering or ranking listings by guest satisfaction
reviews	128	Weighting rating by review volume, or flagging low-review listings
images	["https://a0.muscache.com/im/pictures/...", "..."]	The full photo set for a listing — content review, ML training data, or portfolio audits

⭐ Rating and review data

rating and reviews come straight off the listing's own structured data — the same aggregateRating block Airbnb embeds in the page for search engines. There's no separate reviews endpoint or per-review breakdown; you get the listing's current aggregate score and review count, one pair per listing, alongside its photos. That's enough to spot a well-reviewed listing with a thin photo set, or the reverse, without opening the page yourself.

📍 Location data

address, latitude and longitude place each listing on the map. address is a locality-level string (city, region, country) — not a street address, since Airbnb doesn't expose exact addresses on the public listing page either. Coordinates come from the page's own geo block or, if that's missing, from its map meta tags, so you can cluster a batch of listings by city or compute distance to a landmark without geocoding the address text yourself.

🛠️ Why not build this yourself?

Airbnb does not offer a public, self-service API for listing data — access to its official API is limited to approved property-management partners, not individual developers or businesses (confirmed via search, July 2026). That leaves scraping the public listing page as the only route to a listing's own title, coordinates, rating, and photo set from just a URL.

Doing that yourself means writing an HTML/JSON-LD parser for Airbnb's structured-data blocks, a regex or DOM walk to catch the photo URLs that aren't in that block, retry logic for the 403/429/5xx responses Airbnb returns to obvious bot traffic, and a proxy fallback so a blocked request doesn't just fail silently. Airbnb Images Scraper already does all of that: it parses every JSON-LD block on the page (not just the first), falls back to og:title and geo meta tags when a block is missing a field, unions photo URLs from both the structured data and the raw page markup with resolution-aware deduplication, and retries through Apify Proxy automatically when a request looks blocked.

If you already have a compliant data-sharing agreement with Airbnb as a listing owner or certified partner, use that channel for your own inventory. For pulling structured data and photos off public listing pages you already have the URLs for, this Actor does the parsing and retry work for you.

💡 How to use data extracted from Airbnb

🏨 Short-term rental hosts and property managers

If you manage several Airbnb listings, feed their room URLs in and get back each listing's current title, rating, review count, and full photo set in one batch. Compare images counts across your portfolio to spot listings with thin photo sets, or track rating and reviews over repeated runs to see which properties are slipping before it shows up in bookings.

🎯 Agencies and marketing partners

Photography, staging, and STR marketing agencies working across multiple client accounts can re-run the same URL list on a schedule to re-check a client's listings after a photo shoot or a listing refresh, confirming the new photos are live and the rating/review numbers are still what the client reported.

📈 Market research and intelligence

Given a list of comparable listings — pulled from a manual search or another source — feed their URLs through the Actor to build a comp set: rating, reviews, and address line up listings by neighborhood and guest satisfaction, while images lets you eyeball how competing listings present themselves photographically.

🤖 AI agents and automated pipelines

Computer-vision and interior-design models need labeled property photos. This Actor turns a list of room URLs into a JSON feed of photo arrays tied to location, rating, and title — a straightforward ingestion step ahead of a training pipeline, RAG index, or automated listing-monitoring agent.

🔼 Input sample

Parameter	Required	Type	Description	Example value
urls	Yes	array of string	One or more Airbnb room URLs to scrape. Each must be a valid Airbnb room page, e.g. https://www.airbnb.com/rooms/782682596976136912. Add one URL per line. At least one URL is required.	["https://www.airbnb.com/rooms/782682596976136912"]
requestTimeoutSecs	No	integer (5–120, default 30)	Maximum time in seconds to wait for each page response before the request is skipped.	30
proxyConfiguration	No	object	Optional Apify Proxy settings for this run. You can choose proxy groups and optionally a country code.	see below
proxyConfiguration.useApifyProxy	No	boolean	Enable Apify Proxy for this run.	true
proxyConfiguration.apifyProxyGroups	No	array of string	Apify proxy groups to use (e.g. RESIDENTIAL). Default is RESIDENTIAL when proxy is enabled.	["RESIDENTIAL"]
proxyConfiguration.apifyProxyCountry	No	string	Optional ISO-2 country code for proxy (e.g. US, GB). Leave empty for no country restriction.	"US"

{
  "urls": [
    "https://www.airbnb.com/rooms/782682596976136912",
    "https://www.airbnb.com/rooms/12345678"
  ],
  "requestTimeoutSecs": 30,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"]
  }
}

⚠️ Common pitfall: every URL in urls must match https://www.airbnb.com/rooms/<id> exactly — search-result URLs, experience URLs, and host-profile URLs don't match and the whole run fails validation before anything is scraped, not just the bad line. Check your list for stray search or category links before starting the run.

🔽 Output sample

Each successfully scraped listing is pushed to the dataset as its own row, in the same shape used above — no nested nulls, no nesting beyond the images array. The dataset exports to JSON, CSV, Excel, or XML from the Apify Console.

{
  "url": "https://www.airbnb.com/rooms/782682596976136912",
  "title": "Cozy Modern Loft in Downtown Austin",
  "address": "Austin, Texas, United States",
  "latitude": 30.2672,
  "longitude": -97.7431,
  "rating": 4.92,
  "reviews": 128,
  "images": [
    "https://a0.muscache.com/im/pictures/miso/Hosting-782682596976136912/original/1a2b3c4d-original.jpeg",
    "https://a0.muscache.com/im/pictures/miso/Hosting-782682596976136912/original/5e6f7g8h-original.jpeg"
  ]
}

If a URL can't be fetched or the page has no usable listing data, the Actor writes a row with just url and error instead — and that row is not charged, since row_result (the billed event) only fires on a successful extraction. Filter these out with !item.error or by excluding rows where the error field is present:

{ "url": "https://www.airbnb.com/rooms/000000000000", "error": "Fetch failed: page could not be retrieved" }

The Actor also writes a small run summary — totalUrls, success, and proxyUsed — to the run's key-value store under RUN_SUMMARY.json, useful for confirming how many of your input URLs actually produced a result.

🎯 How do you filter and target specific listings?

Targeting here is direct rather than filter-based: since the Actor takes exact room URLs instead of a search query or category, you get exactly the listings you ask for and nothing else — the precision comes from curating your input list, not from narrowing results after the fact. The only run-level controls are requestTimeoutSecs, for skipping a listing page that's taking too long to respond, and proxyConfiguration, for routing requests through a specific Apify Proxy group or country from the first request rather than only after a block is detected. There's no result limit, category filter, or location radius to configure — scope your run by how many and which URLs you put in urls.

▶️ Want to try other Airbnb scrapers?

Scraper	What it extracts
Google Images Scraper	Image URLs from Google Images search results
Realtor.com Agents By ZIP Code Scraper	Real estate agent listings by ZIP code
eBay Product Details Scraper	Product listing details from eBay
Ebay Search Scraper	Product search results from eBay
Etsy Reviews Scraper	Product reviews from Etsy listings

💻 How to extract Airbnb data programmatically

Airbnb Images Scraper runs as a standard Apify Actor: one API call starts the run, and results are readable as structured JSON from the dataset the moment each listing finishes — no separate scraping infrastructure to maintain.

Python example

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_API_TOKEN>")

run_input = {
    "urls": ["https://www.airbnb.com/rooms/782682596976136912"],
    "requestTimeoutSecs": 30,
}

run = client.actor("<YOUR_USERNAME>/airbnb-images-scraper").call(run_input=run_input)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    if item.get("error"):
        continue
    print(item["title"], item["rating"], len(item["images"]))

Export to spreadsheets or CRM

From the Apify Console, export the dataset directly to CSV or Excel — title, address, rating, and reviews map cleanly to spreadsheet columns, and images exports as a single delimited cell you can split or link out from for a photo-review sheet or CRM import.

⚖️ Is it legal to scrape Airbnb?

Scraping publicly accessible Airbnb listing pages for the data covered here — title, address at the locality level, coordinates, rating, review count, and photo URLs — is generally permissible, since these listings are published precisely so the public can find and view them. The fields this Actor returns describe the listing itself, not individuals, so this falls under Airbnb's terms of service and database-rights considerations rather than personal-data law like GDPR or CCPA. Review Airbnb's terms of service before scraping at scale, particularly around commercial reuse, and consult legal counsel for commercial applications involving bulk storage of listing data.

❓ Frequently asked questions

Does the Actor tell me if a listing has been removed or delisted?

Yes, indirectly — if a room URL no longer resolves to a live listing, the fetch or extraction step fails and the Actor writes an error row instead of a data row for that URL, without charging for it. It doesn't distinguish "removed" from "temporarily unavailable" or "blocked request"; check the error message text for the specific failure reason.

Can I get ratings and reviews along with the photos?

Yes — rating and reviews are returned on every successfully scraped listing alongside images, pulled from the same structured-data block on the page. There's no separate per-review breakdown; you get the listing's current aggregate rating and review count, not individual review text.

How accurate is the listing data?

The Actor returns exactly what's published on the public listing page at the time of the request — the same title, rating, and photos a visitor would see. Accuracy depends on how current Airbnb's own page is; re-run the URL if you need a fresh snapshot before relying on the numbers.

How many listings can I scrape per run?

There's no built-in cap in the input schema — you can list as many room URLs in urls as you want, and each is scraped and written to the dataset independently. Practical limits come from your run's time and memory allowance on the Apify platform, not from a limit the Actor imposes itself.

Can I target specific listings, or only search results?

Only specific listings — this Actor takes exact https://www.airbnb.com/rooms/... URLs, not a search query, location, or category. If you need to discover which room URLs to scrape in the first place, you'll need a separate search step; this Actor is the extraction step once you have the URLs.

Does Airbnb Images Scraper work with Claude, ChatGPT, and AI agent frameworks?

It's callable as a standard Apify Actor run through the Apify API or apify-client, so any agent framework that can make an HTTP call or use the Apify SDK can trigger a run and read back the dataset. There is no dedicated MCP server documented for this Actor.

Can I use this without an Airbnb account or API key?

Yes — the Actor fetches the same public listing page any visitor sees, so no Airbnb login, account, or API key is required. You only need an Apify account to run the Actor itself.

What happens if Airbnb blocks the request?

The Actor checks every response for block signals — 401/403/429/5xx status codes, or page text like "access denied," "verify you are a human," or a CAPTCHA prompt — and automatically retries through Apify Proxy (defaulting to the RESIDENTIAL group) when it detects one, even if you didn't enable proxy in your input. If every attempt still fails, that URL gets an uncharged error row instead of blocking the rest of the run.

Does the Actor use a headless browser?

No — it fetches each page with a direct HTTP request and parses the returned HTML and JSON-LD with parsel, rather than rendering the page in a browser. That keeps runs lighter, but it also means any data that only appears after client-side JavaScript execution beyond the page's own structured-data blocks and embedded photo URLs won't be captured.

Conclusion

Airbnb Images Scraper turns a list of Airbnb room URLs into structured JSON — title, address, coordinates, rating, review count, and every photo URL on the page — written to the dataset in real time so partial progress survives an interrupted run. It's built for hosts, agencies, researchers, and AI pipelines that already know which listings they care about and need the photos and metadata pulled reliably, with automatic retries when Airbnb pushes back. Paste in your room URLs and start a run from the Apify Console to get your first dataset in minutes.