Airbnb Images Scraper With Captions & Room Tags

Airbnb Images Scraper — Extract Listings, Photos and Room Tags

Airbnb Images Scraper With Captions & Room Tags pulls the complete photo-tour gallery from any Airbnb room listing and returns it as typed JSON — every photo with its own caption, orientation, and a derived room/space tag such as Bedroom, Kitchen, or View, alongside the listing's title, location, coordinates, rating, and review count. Unlike scrapers that only parse the handful of images embedded in a listing page's structured data, this Actor calls the same photo-tour data the Airbnb site itself loads, so the gallery is complete, not partial. This guide covers every field returned and how teams deploy it in real pipelines.

🧭 What Does Airbnb Images Scraper Do?

It takes one or more Airbnb room URLs and, for each one, returns a single JSON row containing the listing's base details plus its full described photo-tour gallery — every photo, captioned, tagged by derived room/space, and optionally grouped by that tag. No Airbnb account or login is required: the Actor reads the same public listing page and the same photo-tour data Airbnb's own front end loads for any visitor.

• 🖼️ Full photo-tour gallery — every photo Airbnb includes in the listing's "Show all photos" view, not the smaller subset embedded in the page's basic structured data
• 💬 Per-photo captions — Airbnb's own accessibility label for each photo, never fabricated when absent
• 🗂️ Derived room/space tags — Bedroom, Bathroom, Kitchen, Living Room, View, and 12 more categories, parsed from the caption text
• 📐 Per-photo orientation — LANDSCAPE / PORTRAIT / SQUARE, as reported by Airbnb
• 🏠 Base listing details — title, location, coordinates, rating, and review count
• 🧮 Optional room-bucketed view — the same gallery regrouped into an object keyed by room tag

⚡ Features & Capabilities

Airbnb Images Scraper covers three practical needs: complete gallery extraction, per-photo description, and flexible input handling for teams migrating from a simpler image scraper.

Core features

• Complete photo-tour retrieval via Airbnb's StaysPdpSections data, not just the JSON-LD image subset
• Per-photo fields on every item in photos: index, imageUrl, caption, roomTag, and optionally orientation
• 18 keyword-derived room/space tag categories (Bedroom, Bathroom, Kitchen, Dining Area, Living Room, Pool / Spa, Balcony / Patio, Garden / Outdoor, View, Garage / Parking, Laundry, Office / Workspace, Gym / Fitness, Entrance / Hallway, Closet, Floor Plan, Exterior, and Other / Unspecified for uncaptioned or generically captioned photos)
• Optional photosByRoom grouping so downstream code doesn't need to re-bucket the flat array itself
• Legacy urls / requestTimeoutSecs input aliases and a legacy images output field, so input/output built for the original Airbnb Images Scraper keeps working unmodified
• Start-direct-then-escalate proxy handling: requests go out directly first and only switch to Apify Proxy if a response looks blocked or rate-limited

When another tool might suit you better

This Actor is scoped specifically to the photo-tour gallery and the base listing fields shown above — it does not return pricing, availability, amenities, house rules, or host details. If your workflow needs nightly rates, calendar availability, or host/contact information, a broader Airbnb listing-details scraper that covers those fields is the better fit. Reach for this Actor when the gallery itself — captioned, tagged, and complete — is the data you actually need.

Airbnb Images Scraper within the Scrapio data stack

Airbnb Images Scraper With Captions & Room Tags is currently the only Airbnb-focused Actor in the Scrapio lineup, and it covers listings and their photo-tour galleries specifically. For image and photo extraction on other platforms, see the related scrapers listed further below.

Why do developers and data teams scrape Airbnb?

🏢 Short-term rental and property management teams

Property managers and revenue-management teams pull a competing listing's photos and roomTag breakdown to audit how a rival property presents its bedrooms, bathrooms, and common areas, or to check imageCount and photoTourAvailable across their own portfolio to spot listings with a thin or incomplete gallery before a guest does. Results land as one row per URL, ready to join against an existing listing database on url.

📊 AI training data and RAG indexing

The caption field is the highest-information text on each photo — real, Airbnb-authored descriptions rather than generic alt text — which makes it usable directly as retrieval context for a RAG index built over property galleries, answering queries like "show me listings with a private pool" by matching against caption text. For training data, roomTag gives a consistent, small-vocabulary label per photo that's already structured for a classification or captioning dataset without further normalization.

📱 Competitive and market intelligence

Track how many bedroom, bathroom, or exterior photos a competing listing publishes, or watch imageCount over time to catch when a host adds or removes photos after a renovation or a review complaint. Because base fields (rating, reviews) return alongside the gallery, gallery depth can be correlated against rating trends in the same dataset.

🔬 Research and academic use

Housing and tourism researchers use captioned, room-tagged photo sets to study how short-term rental listings present different room types, without needing to manually review every image. This Actor only returns publicly accessible listing pages — no private or authenticated data.

🎥 Product and SaaS development

Directory, comparison, and listing-enrichment products can call this Actor to backfill a captioned, room-tagged gallery for any Airbnb URL a user submits, rather than building and maintaining their own photo-tour scraping logic.

🍚 Input Parameters

All fields are read directly from the input schema; none are required on their own, but at least one listing URL must be supplied via galleryUrls or the legacy urls field.

Parameter	Required	Type	Description	Example Value
galleryUrls	No	array of strings	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 (here, or in the legacy urls field).	["https://www.airbnb.com/rooms/782682596976136912"]
locale	No	string	Locale code Airbnb uses to localize photo captions and listing text when available, e.g. en, fr, de, es, pt, it, ja. Default en. If Airbnb has no translation for a photo caption, the original text is returned, never fabricated.	"en"
fetchTimeoutSecs	No	integer (min 5, max 120)	Maximum time in seconds to wait for each HTTP response before the request is skipped. Default 30.	30
groupByRoom	No	boolean	When enabled, adds a photosByRoom object to each row that buckets the same photos by their derived room/space tag. The flat photos array is always returned regardless of this setting. Default false.	false
includeOrientation	No	boolean	When enabled, each photo in photos includes an orientation field (LANDSCAPE, PORTRAIT, SQUARE, or null if Airbnb doesn't report one). Default true.	true
urls	No	array of strings	Legacy alias for galleryUrls from the original actor's input. If both are set, entries from both are merged and de-duplicated.	[]
requestTimeoutSecs	No	integer (min 5, max 120)	Legacy alias for fetchTimeoutSecs. Used only when fetchTimeoutSecs is not set.	30
proxyConfiguration	No	object	Apify Proxy settings for this run — proxy groups and, optionally, a country code.	{"useApifyProxy": true, "apifyProxyGroups": ["RESIDENTIAL"]}

Example input

{
  "galleryUrls": [
    "https://www.airbnb.com/rooms/782682596976136912"
  ],
  "locale": "en",
  "fetchTimeoutSecs": 30,
  "groupByRoom": false,
  "includeOrientation": true,
  "proxyConfiguration": {
    "useApifyProxy": true,
    "apifyProxyGroups": ["RESIDENTIAL"]
  }
}

Supported URL types and input formats

Every entry in galleryUrls (or the legacy urls) must match https://www.airbnb.com/rooms/ followed by the listing's ID or slug — the Actor checks this before any request goes out.

• Bare room URL: https://www.airbnb.com/rooms/782682596976136912
• Room URL with query parameters (check-in/out, currency, etc. are accepted and ignored): https://www.airbnb.com/rooms/782682596976136912?adults=2&currency=USD
• Multiple listings in one run, one per line in the input field, deduplicated automatically if the same URL appears in both galleryUrls and the legacy urls field

⚠️ URL validation happens once, up front, for the whole run: if any URL in the list doesn't start with https://www.airbnb.com/rooms/, the run fails immediately with a validation error listing the first three bad URLs — it does not skip just that one row and continue with the rest.

📦 Output Format

Every result is typed, normalized JSON with a stable field set — no HTML, no inline parsing required. Two shapes appear in the dataset: a full listing row for every URL that was reachable, and a minimal error row for the ones that weren't.

Output for a listing

{
  "url": "https://www.airbnb.com/rooms/782682596976136912",
  "title": "Perfect Getaway 2 Miles from MIA & 8 To UR Cruise",
  "location": "Hialeah",
  "coordinates": {
    "latitude": 25.8229,
    "longitude": -80.2665
  },
  "rating": 4.94,
  "reviews": 230,
  "imageCount": 44,
  "photoTourAvailable": true,
  "photos": [
    {
      "index": 1,
      "imageUrl": "https://a0.muscache.com/im/pictures/hosting/Hosting-782682596976136912/original/d2b52caa-5f58-4a8b-afe9-3df83eafb137.jpeg",
      "caption": "Living room image 1",
      "roomTag": "Living Room",
      "orientation": "LANDSCAPE"
    },
    {
      "index": 3,
      "imageUrl": "https://a0.muscache.com/im/pictures/hosting/Hosting-782682596976136912/original/13f6f3ab-9cf5-4a85-8ce0-1c0f8f8d99ef.jpeg",
      "caption": "Full kitchen image 1",
      "roomTag": "Kitchen",
      "orientation": "LANDSCAPE"
    },
    {
      "index": 12,
      "imageUrl": "https://a0.muscache.com/im/pictures/hosting/Hosting-782682596976136912/original/7a2e0b1e-91cf-4d7a-9e2b-2b8d6c1a5f90.jpeg",
      "caption": "Additional photos image 4",
      "roomTag": "Other / Unspecified",
      "orientation": "PORTRAIT"
    }
  ],
  "images": [
    "https://a0.muscache.com/im/pictures/c6cbda16-164d-481c-99d4-9d3c6344986c.jpg"
  ]
}

Field	Type	Notes
url	string	The listing URL as submitted
title	string	Listing title, whitespace-normalized
location	string or null	The listing's locality as published on the page — null if unavailable, never a fabricated region
coordinates	object or null	{ "latitude": number, "longitude": number } from the listing's own photo-tour response — null if unavailable
rating	number or null	Average rating — null if the listing has no rating yet, never a fake 0
reviews	integer or null	Review count — null if unavailable
imageCount	integer	Total number of items in photos for this listing
photoTourAvailable	boolean	true if the full photo-tour was retrieved; false if only the base fields could be retrieved
photos	array of objects	The full described gallery — see below
photosByRoom	object	Only present when groupByRoom is true
images	array of strings	Legacy field: the smaller image-URL subset from the page's basic structured data, kept for backward compatibility

Output for a photo

Each entry in photos (and inside photosByRoom, when enabled):

{
  "index": 1,
  "imageUrl": "https://a0.muscache.com/im/pictures/hosting/Hosting-782682596976136912/original/d2b52caa-5f58-4a8b-afe9-3df83eafb137.jpeg",
  "caption": "Living room image 1",
  "roomTag": "Living Room",
  "orientation": "LANDSCAPE"
}

Field	Type	Notes
index	integer	1-based position in the full photo-tour gallery
imageUrl	string or null	Direct image URL
caption	string or null	Airbnb's own accessibility label for this photo, never fabricated when Airbnb provides none
roomTag	string or null	Derived room/space label, or "Other / Unspecified" when the caption gives no signal; null only when there is no caption to derive from at all
orientation	string or null	LANDSCAPE, PORTRAIT, SQUARE, or null — omitted entirely from the object when includeOrientation is false

With groupByRoom enabled, photosByRoom groups the same photo objects by roomTag, e.g. { "Bedroom": [ {...}, {...} ], "Kitchen": [ {...} ], "Other / Unspecified": [ {...} ] }.

Output for a failed listing (unbilled)

{
  "url": "https://www.airbnb.com/rooms/000000000000000000",
  "error": "Could not retrieve this listing page."
}

The error text is one of two exact strings: "Could not retrieve this listing page." (the page couldn't be fetched at all) or "Listing data was not available in a readable format." (the page loaded but had no readable structured data). Filter these out of downstream processing with !row.error, or equivalently by checking for the presence of photos.

Schema stability and export options

Field names for the base listing (url, title, location, rating, reviews, images) match the original Airbnb Images Scraper's fields byte-for-byte, so they don't shift if you're migrating existing pipelines. The photo-tour fields (photos, photosByRoom, coordinates, imageCount, photoTourAvailable) are new and additive. Results are available through the Apify Console's dataset view, or as JSON, CSV, Excel, or XML export via the Console or the Apify API/apify-client. A RUN_SUMMARY.json record (totalUrls, success, withPhotoTour, finishedAt) is also written to the run's default key-value store for a quick per-run tally without scanning the dataset.

💡 Airbnb Images Scraper Strategy Guide

🎯 Strategy 1: Real-time enrichment pipeline

When a new listing URL enters your system — a lead form, a CRM record, a scraped search-results page from elsewhere — trigger a run with that single URL in galleryUrls. Append the returned photos, imageCount, roomTag values, and coordinates to the record, then write it back to your database or CRM. Because rows save to the dataset as each listing finishes, a single-URL run typically returns before a user-facing request would time out.

🎯 Strategy 2: Scheduled monitoring and alerting

For listings you track over time — your own portfolio, or competitors — set up an Apify Schedule to re-run the same galleryUrls list on a recurring cadence. Compare each new run's imageCount and photos array against the previous run's dataset for the same url; alert when imageCount drops (photos removed) or when new roomTag categories appear that weren't present before (e.g. a new "Pool / Spa" tag suggesting a recent renovation).

🎯 Strategy 3: Bulk dataset build

To build a research or training dataset across many listings, split your URL list into batches and give each batch to a separate Actor run via the Apify API — runs triggered this way execute independently of each other, so multiple batches can be in flight at once. Aggregate each run's dataset export to CSV or a database, keyed on url. No result cap is documented for galleryUrls; this Actor processes exactly the URLs it's given, sequentially within a run.

Strategy comparison at a glance

Strategy	Best for	Run pattern	Output format
Real-time enrichment	Adding a gallery to a record the moment it's created	Single on-demand run per URL	JSON row appended to your store
Scheduled monitoring	Tracking gallery changes on watched listings over time	Apify Schedule re-running the same URL list	Dataset per run, diffed externally on imageCount/photos
Bulk dataset build	Research or training datasets across many listings	Multiple independent runs, one per URL batch	Per-run datasets merged to CSV/DB

🌴 Related Airbnb Scrapers & Tools

There is currently no other Airbnb-focused Actor in the Scrapio lineup — this Actor is the account's dedicated tool for Airbnb listing photo-tour galleries. For image and photo extraction on other platforms available in Scrapio:

Scraper	What it extracts
Google Images Scraper (keyword search)	Bulk image search results by keyword, across the web
Instagram Posts Scraper (sponsored post finder)	Post images, captions, and sponsored-post detection from Instagram
Instagram Story Details Scraper (overlay text)	Story media plus its overlay text
Etsy Reviews Scraper (photos, translations, buyer trust)	Review photos, translated review text, and buyer-trust signals from Etsy

How to integrate Airbnb Images Scraper with your stack

Airbnb Images Scraper works with any language or tool that can make an HTTP request to the Apify API — the examples below use the official apify-client libraries.

Python

from apify_client import ApifyClient
import csv

client = ApifyClient("<APIFY_API_TOKEN>")

run_input = {
    "galleryUrls": [
        "https://www.airbnb.com/rooms/782682596976136912",
    ],
    "groupByRoom": False,
    "includeOrientation": True,
}

run = client.actor("<your-username>/airbnb-images-scraper-with-captions-and-room-tags").call(run_input=run_input)

rows = list(client.dataset(run["defaultDatasetId"]).iterate_items())

with open("airbnb_photos.csv", "w", newline="", encoding="utf-8") as f:
    writer = csv.writer(f)
    writer.writerow(["url", "title", "location", "rating", "reviews", "imageCount", "photoTourAvailable"])
    for row in rows:
        if row.get("error"):
            continue
        writer.writerow([
            row.get("url"), row.get("title"), row.get("location"),
            row.get("rating"), row.get("reviews"),
            row.get("imageCount"), row.get("photoTourAvailable"),
        ])

print(f"Wrote {len(rows)} rows to airbnb_photos.csv")

Node.js

import { ApifyClient } from 'apify-client';

const client = new ApifyClient({ token: '<APIFY_API_TOKEN>' });

const run = await client.actor('<your-username>/airbnb-images-scraper-with-captions-and-room-tags').call({
    galleryUrls: ['https://www.airbnb.com/rooms/782682596976136912'],
    groupByRoom: false,
    includeOrientation: true,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();

for (const row of items) {
    if (row.error) continue;
    console.log(row.url, row.imageCount, row.photoTourAvailable);
}

Async and scheduled pipelines

For fire-and-forget large batches, start the run via apify-client and poll client.run(runId).get() or the dataset's item count instead of blocking on .call(). For recurring collection, use an Apify Schedule to re-run a fixed galleryUrls list on a cron expression from the Console. Apify Console webhooks can also fire on run completion if you want a callback instead of polling.

🎯 Who Needs Airbnb Images Scraper? (Use Cases & Industries)

🏢 Short-term rental and property management teams

A revenue-management team pulls photos and roomTag for their own listings and a set of comparable competitor URLs, then reviews which of their properties have the fewest tagged Bedroom or Bathroom photos compared to top-rated comps — a concrete, low-effort audit before a photography refresh.

📊 Data engineers building enrichment pipelines

An engineer wires this Actor into an ingestion pipeline so that every new Airbnb URL a partner submits gets its photos, imageCount, and coordinates backfilled automatically, without maintaining a custom photo-tour parser against Airbnb's own frontend.

📱 Market intelligence analysts

An analyst schedules recurring runs against a watchlist of competitor URLs and tracks imageCount and rating together over several months to correlate gallery investment with review-score trends.

🔬 Researchers

Housing and tourism researchers use captioned, room-tagged photo sets to study how listings across a metro area present bedrooms, bathrooms, and shared spaces, working only from publicly accessible listing pages.

Is it legal to scrape Airbnb?

Scraping publicly accessible web pages is generally lawful in the United States — the Ninth Circuit's ruling in hiQ Labs, Inc. v. LinkedIn Corp. (9th Cir. 2019) supports that principle for data that isn't behind a login. That precedent concerns public-data access broadly; it did not rule on Airbnb specifically, and outcomes can differ by jurisdiction. Separately, scraping in violation of Airbnb's Terms of Service can expose the account or IP address involved to civil contract claims or access restrictions — a different question from whether scraping itself is a crime. This Actor returns only publicly visible listing details and photos of the property (no host identity, no reviewer names, no guest information), so it does not process personal data about identifiable individuals, and GDPR/CCPA obligations around personal data generally don't attach to its output. Airbnb Images Scraper With Captions & Room Tags returns only publicly accessible data. What you do with that data is your responsibility — consult legal counsel for commercial applications.

❓ Frequently asked questions

Does Airbnb Images Scraper work without an Airbnb account?

Yes. It makes plain HTTP requests to public listing pages and to the same photo-tour data Airbnb's own page loads for any visitor — no login, cookies, or Airbnb account are used or required.

How does Airbnb Images Scraper handle Airbnb's anti-scraping measures?

Every request starts direct. If a response comes back with a blocking status code (401/403/429/5xx) or block-page text (e.g. "verify you are a human", CAPTCHA references), the Actor escalates to Apify Proxy — using the Residential group by default, or the group/country set in proxyConfiguration — and retries up to 3 times before giving up on that request. Setting proxyConfiguration.useApifyProxy to false disables that escalation entirely: blocked requests fail immediately for that listing instead of retrying through proxy.

Can I run Airbnb Images Scraper at scale without getting blocked?

There's no published uptime or success-rate figure for this. What's documented is the behavior: requests are processed one at a time within a run (no internal concurrency), each listing that fails after retries is recorded as an unbilled error row, and processing continues to the next URL rather than stopping the run. For large URL lists, splitting the work across multiple parallel Actor runs is the scaling pattern described in the Strategy Guide above.

How fresh is the data Airbnb Images Scraper returns?

Every run performs a live fetch of the listing page and the photo-tour data at request time — nothing is cached or reused between runs.

Which Airbnb fields work best for AI training and RAG indexing?

For RAG, caption is the highest-information text field — real per-photo descriptions rather than generic alt text, useful for matching queries like "listings with a private pool" against gallery content. For training data, roomTag gives a small, consistent label vocabulary per photo. All fields return as typed primitives (strings, numbers, booleans, or null) requiring no normalization before use.

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

There's no MCP server for this Actor. It's callable as an HTTP endpoint through the Apify API by any agent framework that can make a request — every response is typed JSON, so no HTML parsing is needed before passing results into an LLM's context window.

How is this different from a scraper that just parses the images on the Airbnb page?

The images embedded in a listing page's basic structured data are a partial subset — this Actor's own legacy images field preserves that exact smaller list for backward compatibility. The photos field instead comes from the same photo-tour data Airbnb's front end calls when a visitor opens "Show all photos," which is why it can return the complete gallery with captions and orientation rather than a handful of preview images.

What happens if a listing URL is invalid or the page fails to load?

An invalid URL (anything not starting with https://www.airbnb.com/rooms/) fails the entire run immediately during input validation, before any request is made. A valid URL that fails to load, or loads without readable listing data, is instead recorded as its own unbilled { "url", "error" } row, and the run continues with the remaining URLs.

Does enabling groupByRoom or includeOrientation change what I'm charged for?

No. Charging is per successfully processed listing (the row_result event), independent of which optional fields are turned on. groupByRoom and includeOrientation only change the shape of a row that's already being billed.

How accurate are the derived room/space tags?

roomTag is keyword-matched against Airbnb's own caption text, not computer-vision image classification — its accuracy is bounded by how descriptively Airbnb captions each photo. Listings where Airbnb captions many photos generically (e.g. "Additional photos image 12") will show a higher share of photos tagged Other / Unspecified, by design, rather than a guessed label.

ℹ️ Disclaimer

Airbnb Images Scraper With Captions & Room Tags extracts only publicly available data from Airbnb. This tool is intended for lawful use cases only. Users are responsible for complying with Airbnb's terms of service and applicable data protection laws in their jurisdiction.