Airbnb Images Scraper With Metadata Enrichment

Airbnb Images Scraper — Extract Listings, Photos & Geo Metadata

Airbnb Images Scraper With Metadata Enrichment turns a list of Airbnb room URLs into structured JSON — listing details, photo URLs, and enrichment metadata (room type, coordinates, guest capacity) read straight from each room page. There is no HTML to parse and no selectors to maintain: every response is typed, normalized JSON with the same field names on every run. Give it one or more https://www.airbnb.com/rooms/... URLs and get back one row per listing, ready to drop into a spreadsheet, a database, or an LLM context window.

What is Airbnb Images Scraper With Metadata Enrichment?

Airbnb Images Scraper With Metadata Enrichment is an Apify Actor that fetches individual Airbnb room listing pages and extracts the listing's title, location, rating, review count, and photo URLs, then enriches the row with room type, coordinates, and guest capacity parsed from data Airbnb already embeds in the page. No Airbnb account or login is required — it reads the same public page a browser loads. Because it runs as a standard Apify Actor, it is callable over HTTP by any script, pipeline, or AI agent, not only from the Apify Console.

• 🏠 Scrape core listing details — title, location, rating, review count
• 🖼️ Pull the room's photo URLs from the page's own structured data
• 🗺️ Enrich each row with room type, coordinates and guest capacity
• 🆔 Get a stable listingId and query-stripped canonicalUrl for every room
• 📦 Export results as JSON, CSV, or any format the Apify dataset supports

What data does Airbnb Images Scraper With Metadata Enrichment collect?

Every run returns one row per room URL, combining four kinds of data pulled from the listing page itself:

Data Type	Key Fields	JSON Field Names
Identifiers	Canonical listing URL, stable listing ID	url, canonicalUrl, listingId
Listing details	Title, location, average rating, review count	title, location, rating, reviews
Images	Photo URLs from the listing's structured data	images
Context metadata	Room type, latitude/longitude, guest capacity	roomType, coordinates, personCapacity

Why not build this yourself?

Writing your own Airbnb room scraper means maintaining more than a simple HTTP fetch. Airbnb serves listing photos and rating counts inside an application/ld+json structured-data block, while room type, coordinates, and guest capacity live in a separate embedded JSON payload (data-deferred-state-0 → niobeClientData) that isn't part of any documented, versioned API — it can shift shape without notice. A DIY script also has to detect and react to blocking on its own: this Actor treats HTTP 401/403/429/5xx responses and CAPTCHA/"verify you are a human" pages as a block signal, retries, and escalates from a direct request to Apify Residential proxy automatically. Reproducing that — plus proxy sourcing, retry logic, and re-parsing every time Airbnb changes its markup — is ongoing engineering work, not a one-time script.

Why do developers and teams scrape Airbnb?

🏘️ For real estate investors and market analysts

Short-term rental data is a direct proxy for local market conditions. Feeding room URLs from a target neighborhood through the Actor produces a structured dataset of rating, reviews, roomType, and personCapacity per listing, which analysts join against location (coordinates) to compare inventory type and guest capacity across areas — without touching a browser or writing a parser.

🤖 For AI engineers and agent builders

An agent that answers questions about a specific Airbnb listing — "what's the guest capacity and where is it located?" — can call this Actor with the listing URL and get back typed fields (personCapacity, coordinates, title) instead of raw HTML it would otherwise have to chunk and extract from inside the agent loop.

🔬 For researchers and analysts

Because the Actor only reads publicly served room pages, it fits studies of publicly accessible short-term rental listings — comparing guest capacity, room type distribution, or review counts across a defined set of listings — without requiring any authenticated access.

👩‍💻 For developers building data products

Products that track a fixed portfolio of listings (a property manager's own units, or a curated comparison set) can schedule this Actor against the same URL list on a recurring basis and get a stable JSON shape back every time, since output field names don't change between runs.

How to scrape Airbnb room listings (step by step)

1. Open Airbnb Images Scraper With Metadata Enrichment on its Apify Store listing and click Try for free (or Run, if already added to your account).
2. Paste one or more Airbnb room URLs into roomUrls — each must start with https://www.airbnb.com/rooms/. This is the only input the Actor needs; nothing else is required.
3. Optionally toggle includeListingContext (default: on) to control whether roomType, coordinates, and personCapacity are added, and configure proxyConfiguration if you want proxy requests from the first attempt.
4. Click Start and let the run process each URL — one row is pushed to the dataset per listing as soon as it's extracted.
5. Download the results from the Dataset tab as JSON, CSV, or another supported export format, or pull them via the Apify API.

What to do when Airbnb changes its structure

The Actor is maintained, and the output field names stay stable on your end. If Airbnb changes the layout of the embedded metadata block, context fields (roomType, coordinates, personCapacity) degrade to null rather than breaking the run — the core listing fields are extracted independently and are unaffected.

⬇️ Input

The Actor takes a list of Airbnb room URLs and a few optional settings. Nothing is marked required at the schema level, but at least one URL (in roomUrls or the legacy urls field) must be present or the run fails with a validation error.

Parameter	Required	Type	Description	Example Value
roomUrls	No	array (stringList)	One or more Airbnb room page URLs, e.g. https://www.airbnb.com/rooms/782682596976136912. Add one per line. At least one URL is required (here or in the legacy urls field below).	["https://www.airbnb.com/rooms/782682596976136912"]
urls	No	array (stringList)	Legacy alias for "Airbnb room URLs" — kept for compatibility with the original actor's input field. If both are set, entries from both lists are merged and de-duplicated.	[]
fetchTimeoutSecs	No	integer (min 5, max 120)	Maximum time in seconds to wait for each page response before the request is skipped. Default: 30.	30
requestTimeoutSecs	No	integer (min 5, max 120)	Legacy alias for "Request timeout (seconds)" — kept for compatibility with the original actor's input field. Used only if fetchTimeoutSecs is not set.	45
includeListingContext	No	boolean (default true)	Adds roomType, coordinates (latitude/longitude) and personCapacity to each row, read from the listing's own page data. Also always adds a query-stripped canonicalUrl and a stable listingId regardless of this toggle. Turn off to skip the extra parsing step and get only the base fields.	true
proxyConfiguration	No	object (proxy editor)	Optional Apify Proxy settings. Configure proxy groups and country below if you want to use a proxy.	{"useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"]}

Example input:

{
  "roomUrls": ["https://www.airbnb.com/rooms/782682596976136912"],
  "urls": [],
  "fetchTimeoutSecs": 30,
  "requestTimeoutSecs": 45,
  "includeListingContext": true,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"],
    "apifyProxyCountry": "US"
  }
}

⚠️ Common pitfall: the URL must start with https://www.airbnb.com/rooms/ exactly. Search-result pages, wishlists, and Experience URLs fail input validation and are rejected before any request is made — only individual room-listing URLs are supported.

⬆️ Output

Results are stored in an Apify dataset as typed JSON, with the same field names on every run. Export as JSON, CSV, Excel, or another format supported by the Apify dataset, or read the data directly via the Apify API.

Scraped listing

{
  "url": "https://www.airbnb.com/rooms/782682596976136912",
  "canonicalUrl": "https://www.airbnb.com/rooms/782682596976136912",
  "listingId": "782682596976136912",
  "title": "Cozy Loft in Downtown Austin",
  "location": "Austin",
  "roomType": "Entire home/apt",
  "personCapacity": 4,
  "coordinates": { "lat": 30.2672, "lng": -97.7431 },
  "rating": 4.87,
  "reviews": 213,
  "images": [
    "https://a0.muscache.com/im/pictures/example-front-view.jpg",
    "https://a0.muscache.com/im/pictures/example-living-room.jpg",
    "https://a0.muscache.com/im/pictures/example-bedroom.jpg"
  ]
}

Any field the source page doesn't expose is returned as null — rating, reviews, roomType, personCapacity, and coordinates are never fabricated or defaulted to 0. Setting includeListingContext to false returns the same shape with roomType, coordinates, and personCapacity always null; canonicalUrl and listingId are still populated regardless of that setting.

⚠️ images reflects the photo URLs present in the listing's structured-data block on the page — a representative set, not a guaranteed complete photo tour of every image in the room's gallery.

Non-billed error rows: if a URL can't be fetched or the page has no parseable listing data, the Actor pushes a row with only url and error ("Fetch failed after retries" or "Extraction failed (missing or invalid listing data)") and does not charge for it — these rows are pushed without the row_result charged event. Filter them out downstream by keeping only rows where error is absent (e.g. !item.error, or simply check for the presence of images).

How does Airbnb Images Scraper With Metadata Enrichment compare to other Airbnb scrapers?

Feature	Airbnb Images Scraper With Metadata Enrichment	Generic alternative
Output format	Typed JSON straight from the page's own structured data ✅	Raw HTML you parse and normalize yourself ❌
Failed-request accounting	Failed fetch/parse rows are pushed uncharged, flagged with an error key ✅	No built-in distinction between billed and failed requests ❌
Input flexibility	Accepts roomUrls and a legacy urls alias, merged and de-duplicated automatically ✅	You maintain your own URL de-duplication logic ❌
Anti-bot handling	Detects block responses (401/403/429/5xx, CAPTCHA pages) and escalates to Apify Residential proxy with retries ✅	You build and fund your own proxy pool and retry logic ❌
Schema stability	Same JSON keys every run; a page-structure change nulls context fields instead of breaking the row ✅	A selector change silently breaks the parser ❌

If you're building an AI agent or RAG pipeline, the output format row is the decision-maker — parsing HTML inside an agent loop is a reliability failure mode, not a feature.

How many results can you scrape with Airbnb Images Scraper With Metadata Enrichment?

There is no hard result cap in the Actor itself: the number of output rows tracks the number of room URLs you supply, since each roomUrls/urls entry yields exactly one dataset row. Pagination doesn't apply because the input is an explicit URL list rather than a search query — there's no "next page" to walk. URLs that fail to fetch or don't yield parseable listing data are pushed as uncharged error rows rather than counted against your successful extractions. Overall run time depends on the number of URLs, fetchTimeoutSecs, and how many listings trigger the retry/proxy-escalation path.

Integrate Airbnb Images Scraper With Metadata Enrichment and automate your workflow

Airbnb Images Scraper With Metadata Enrichment works with any language or tool that can send an HTTP request.

REST API integration

from apify_client import ApifyClient

client = ApifyClient("<APIFY_API_TOKEN>")

run = client.actor("<your-account>/airbnb-images-scraper-with-metadata-enrichment").call(
    run_input={
        "roomUrls": ["https://www.airbnb.com/rooms/782682596976136912"],
        "includeListingContext": True,
    }
)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item["title"], item["personCapacity"], item["images"])

Works in Python, Node.js, Go, Ruby, cURL — any client that can call the Apify API with your API token.

MCP for AI agents

The Actor can be exposed to MCP-compatible clients (Claude Desktop, Claude Code, and other MCP clients) through Apify's own Actors MCP Server by referencing its Actor ID as one of the tools the server loads — no separate MCP deployment for this Actor specifically.

Automation platforms (n8n, Make, LangChain)

In n8n, the community-maintained Apify node can run any Actor by ID and pass its dataset output to downstream nodes. In Make, the Apify app's "Run Actor" module does the same inside a scenario. In LangChain, the ApifyWrapper/ApifyActorsTool integration calls an Actor by ID and loads its dataset directly into a chain or agent tool.

Is it legal to scrape Airbnb?

Scraping publicly accessible web pages is generally legal, and Airbnb Images Scraper With Metadata Enrichment only returns data already published on a room's public listing page — no login-gated content, no private messages, no host account data. This is business/product data (a rental listing and its published details), not personal data scraped from an individual's profile, so the relevant framework is Airbnb's own Terms of Service and applicable database-rights law, not data-protection law like GDPR. How you use the output — republishing it, combining it with other datasets, or scraping at scale — is your responsibility under those terms. Consult legal counsel for commercial use cases involving large-scale data collection.

Frequently asked questions

Does Airbnb Images Scraper With Metadata Enrichment work without an Airbnb account?

Yes. The Actor fetches the public room page over a plain HTTP request — no Airbnb login, session, or account credentials are used or required.

How often is the scraped data updated?

Every run performs a live fetch of each room URL at run time. There is no caching layer — the data reflects whatever the room page returns at the moment the request is made.

What happens if a room listing has been removed or the request is blocked?

The Actor pushes a row containing only url and an error message ("Fetch failed after retries" for a blocked/failed request, or "Extraction failed (missing or invalid listing data)" if the page loads but has no listing data) and does not charge for that row.

Can I scrape private or login-gated Airbnb listings?

No. Only room pages reachable at a public https://www.airbnb.com/rooms/{id} URL are supported — there is no mechanism for authenticated or restricted content.

Can I pass Airbnb search-result or Experience URLs instead of room URLs?

No. Input validation only accepts URLs matching https://www.airbnb.com/rooms/...; search-result pages, wishlists, and Experience listings are rejected before any request is made.

Does Airbnb Images Scraper With Metadata Enrichment work for AI agent workflows and LLM pipelines?

Yes — it's callable as a standard HTTP endpoint via the Apify API from any agent framework, or through Apify's Actors MCP Server. Every response is typed JSON, so there's no HTML-parsing step before passing results to an LLM.

How does it handle Airbnb's anti-bot system?

It treats HTTP 401/403/429/5xx responses and block-page indicators (CAPTCHA, "verify you are a human", "access denied") as a block signal. The first request for a run is sent directly; on a detected block it switches to Apify Residential proxy and retries, then stays on proxy for the rest of the run.

How does it compare to other Airbnb scrapers?

See the comparison table above — the concrete, checkable differences are typed JSON output versus raw HTML, uncharged failed rows, automatic proxy escalation, and a schema that degrades gracefully instead of breaking when Airbnb changes its page structure.

Does it return data in a format LLMs can use directly?

Yes. Every field is typed, normalized JSON with stable key names — no HTML, no CSS selectors, no parsing step. Pass rows directly into an LLM context window, a vector store, or an agent tool.

Can I use it without managing proxies?

Yes. Proxy handling is built in — the Actor starts direct and escalates to Apify Residential proxy automatically on a detected block. You can also pre-configure proxyConfiguration to start on proxy immediately and set proxy groups or country.

What happens when Airbnb changes its structure or blocks the scraper?

The Actor is maintained, and its output schema stays stable on your end — field names and types don't change. If Airbnb alters the embedded metadata block specifically, context fields (roomType, coordinates, personCapacity) fall back to null instead of the run failing.

Your feedback

Found a bug or missing a field? We want to know. Reach out through the Actor's page on the Apify Store — issues and suggestions submitted there are how this Actor gets maintained and improved.