Airbnb Rooms URLs Scraper Host Email & Phone Finder

Airbnb Host Scraper — Extract Host Leads, Listings and Contacts

Airbnb Host Scraper turns a list of Airbnb room URLs — or a plain search phrase — into structured host lead data: the host's name, Superhost and identity-verification badges, response rate and time, properties under management, the full listing record, and a strict scan of every piece of host-written text for a published email, phone, website or social handle. Unlike scraping frameworks that return raw HTML, it returns typed JSON — ready for your CRM, your model, or your pipeline without any parsing. This guide covers every input and output field, how the lead-qualification filters work, and how teams deploy it for prospecting, monitoring and dataset building.

⚠️ Read this before you rely on the contact fields. Airbnb does not publish host email addresses or phone numbers anywhere on a public listing or profile page. The only place a contact value can exist is text the host typed themselves — the description, house rules, host-card bio, host highlights, and (optionally) the profile bio. Measured across 139 live listings, that scan recovered 0 emails, 1 phone number, 0 external websites, 0 social handles. The Actor ships that scan as an evidence array (contactScan) with the exact text volume it read on every row — never as a promised email column that would sit null on almost every result. What is populated on most rows is hostHasVerifiedEmailOnFile / hostHasVerifiedPhoneOnFile — Airbnb's own badge confirming a verified email/phone is on file, not the value itself.

🧭 What does Airbnb Host Scraper do?

Airbnb Host Scraper is a Python Actor that fetches Airbnb listing pages and the host's public profile, then flattens both into one lead row per listing. No Airbnb account, login or cookie is required — every request is an unauthenticated fetch of Airbnb's public listing, search and profile pages using a browser-impersonating HTTP client, with proxy escalation if Airbnb serves a shell page.

• 🏠 Accepts direct Airbnb room URLs or plain search phrases (Lisbon apartments)
• 🧑‍💼 Reads the "Meet your host" card from Airbnb's internal StaysPdpSections API — Superhost flag, identity verification, rating, response rate/time, host highlights
• 🪪 Opens the host's public profile page for properties under management, identity-verification list, bio, location, guest-review count and account age
• 📄 Scans up to four host-written text sources per listing for emails, phone numbers, websites and social handles, with license/registration-number false-positive rejection
• 🎚️ Filters leads by Superhost status, identity verification, multi-property status, a minimum-properties threshold, or "has a contact signal" — before the row is pushed or charged
• 📦 Returns the full listing record alongside the host lead block: title, description, amenities, images, house rules, cancellation policy, coordinates and pricing currency

⚡ Features & Capabilities

Airbnb Host Scraper combines listing extraction, host-profile enrichment and a transparent contact-detail scan in a single run, with automatic proxy escalation when Airbnb blocks a request.

Core features

• Host lead block on every row: hostId, hostName, hostProfileUrl, hostIsSuperhost, hostIsVerified, hostRatingAverage, hostReviewsCount, hostYearsHosting, hostResponseRateText, hostResponseRatePercent, hostResponseTime, hostAboutText, hostWorkText, hostHighlights[], coHostCount, coHostsDetected[]
• Public-profile enrichment, one request per distinct host, cached for the whole run: hostPropertiesUnderManagement, hostIdentityVerifications[], hostHasVerifiedEmailOnFile, hostHasVerifiedPhoneOnFile, hostHasIdentityBadge, hostProfileAbout, hostProfileLocation, hostReviewsFromGuestsCount
• Contact-detail scan (contactScan) with per-source character counts, a de-duplicated signals[] array typed email / phone / website / social / socialHandle, and rejection of Airbnb's own domains (airbnb.*, muscache.com, abnb.me) and of licence/registration/VAT numbers written next to a run of digits
• Two-tier proxy ladder: starts with no proxy, escalates to Apify datacenter proxy, then to sticky Apify residential proxy, only while a request keeps coming back as a shell/blocked page — not on every request
• Non-English locale handoff: some Airbnb locales answer with a signed POST redirect to a country subdomain instead of the page; the Actor follows it automatically instead of returning an empty result
• Legacy compatibility: enrichUserProfiles still works exactly as in the original Airbnb Rooms URLs Scraper and additionally switches on the modern host-profile request
• Standard Apify dataset export — every run's results can be downloaded as JSON, JSONL, CSV, Excel (XLSX), XML or RSS from the Console or the API, with no extra configuration

When another tool might suit you better

If your goal is tracking a listing's price and availability over a full calendar year, or pulling guest reviews, this Actor is the wrong tool — it does not collect calendar pricing history or review text; it is built around the host, not the stay. Likewise, if you already have Airbnb host IDs and only need the properties-under-management count refreshed on a schedule, a lighter profile-only fetch (skip hostContactScan, keep fetchHostProfile) will run faster and cost fewer requests than a full run with the contact scan enabled.

Airbnb Host Scraper within the Scrapio data stack

Airbnb Host Scraper covers Airbnb host leads and listing details. For the same lead-qualification pattern on other platforms in the Scrapio account, use Tripadvisor Review Scraper: Business Contact Finder for Tripadvisor business contact leads, or Facebook Events Scraper — Organizer & Contact Details for Facebook event-organizer contact leads.

Why do developers and data teams scrape Airbnb?

Airbnb host and listing data serves a narrower, more commercial set of audiences than a general web platform — mostly people evaluating or selling into the short-term-rental industry.

🏢 Property-management and PMS sales teams

Feed a city or neighborhood search phrase in and get back every host's hostPropertiesUnderManagement, hostIsSuperhost and hostResponseRatePercent. Set hostQualification to multi-property or set minPropertiesUnderManagement to a threshold like 5 or 10 to isolate professional operators worth a sales call, and export the result straight to a CRM import file — no manual profile-clicking required.

📊 AI training data and RAG indexing

description, hostAboutText and hostProfileAbout are the highest-information free-text fields — host-written prose about the property and the person running it, useful for RAG enrichment of a rental-market knowledge base. For training data, the structured badge fields (hostIsSuperhost, hostHasIdentityBadge, hostResponseRatePercent) return as consistent typed primitives — booleans and integers — across every listing, with no HTML or nested-string parsing needed before they reach a model's context window.

📱 Competitive and market intelligence

Run the same search phrase or the same host list on a schedule and diff hostPropertiesUnderManagement and hostIsSuperhost between runs to see which operators are scaling their portfolio or losing Superhost status, and diff hostResponseRatePercent to track service-quality trends across a market.

🔬 Research and academic use

Public-data-only host and listing records support research into the professionalization of short-term rental supply — for example correlating hostPropertiesUnderManagement with hostRatingAverage or hostResponseTime across a sample of listings in a city.

🎥 Product and SaaS development

Directory, enrichment-API or investor-scouting products can be built directly on the host lead block — hostPropertiesUnderManagement and hostIdentityVerifications[] are the two fields most directory-style products key off when ranking or filtering hosts.

🍚 Input Parameters

All 17 parameters, read directly from .actor/actor.json. Only startUrls is required.

Parameter	Required	Type	Description	Example Value
startUrls	Yes	array	One Airbnb room URL per line (https://www.airbnb.com/rooms/12937), or a plain search phrase such as Lisbon apartments which is resolved to listings automatically. Every listing produces one host lead row.	["https://www.airbnb.com/rooms/53997462", "https://www.airbnb.com/rooms/12937"]
maxListingsPerKeyword	No	integer	How many listings to pull from each search phrase. Ignored for direct room URLs. Minimum 1, maximum 50. Default 20.	20
hostQualification	No	string (enum)	Only keep hosts that are... any = keep every listing. superhost = Superhosts only. identity-verified = hosts with a verified identity badge. multi-property = hosts running more than one property. contact-signal = only rows where the contact scan actually found an email/phone/website/social handle (measured: 1 listing in 139 — expect a near-empty dataset). Default any.	"superhost"
minPropertiesUnderManagement	No	integer	Drops hosts who manage fewer properties than this, read live from the host's public profile (hostPropertiesUnderManagement). 0 keeps everyone, 10 keeps only professional operators. Needs the host profile request switched on. Minimum 0, maximum 5000. Default 0.	10
fetchHostProfile	No	boolean	Opens the host's public profile — one extra request per distinct host, cached inside the run. Adds properties under management, identity verifications, the profile bio, host location, guest-review count and account age. Turn off for a listing-card-only run. Default true.	true
hostContactScan	No	boolean	Scans the listing description, the house rules, the host card bio, the host highlights and (when enabled) the profile bio. Results land in contactScan with the source each hit came from. contactScanCharacters shows how much text was actually scanned on every row. Default true.	true
contactScanDepth	No	string (enum)	Text the scan reads. listing-text = description + house rules + host card bio + host highlights. listing-and-profile = the above plus the host's public profile bio (needs the profile request). The profile bio is where an operator pitch usually lives. Default listing-and-profile.	"listing-and-profile"
checkIn	No	string	YYYY-MM-DD, or a relative value such as 3 days / 2 weeks. Leave empty to scrape the listing without dates.	"3 days"
checkOut	No	string	YYYY-MM-DD, or a relative value such as 10 days. Leave empty to scrape the listing without dates.	"10 days"
adults	No	integer	Guest count sent with the listing request. Leave empty to let Airbnb default it. Minimum 1.	2
children	No	integer	Children in the party. Leave empty to skip. Minimum 0.	0
infants	No	integer	Infants in the party. Leave empty to skip. Minimum 0.	0
pets	No	integer	Pets in the party. Leave empty to skip. Minimum 0.	0
locale	No	string (enum)	Airbnb locale used for the listing and the host profile. Keep en-US for English lead text. Default en-US. 90+ locale values supported (see .actor/actor.json for the full enum).	"en-US"
currency	No	string (enum)	Currency used for any price value returned with the listing. Default USD. 47 currency codes supported (see .actor/actor.json for the full enum).	"USD"
enrichUserProfiles	No	boolean	Compatibility switch from the original Airbnb Rooms URLs Scraper: attaches the legacy host.profile / coHosts[].profile objects and also turns the host-profile request on. The modern equivalent is fetchHostProfile above. Default false.	false
proxyConfiguration	No	object	Starts with no proxy and escalates automatically to Apify datacenter, then residential, if Airbnb soft-blocks a request. Set your own groups here to override.	{"useApifyProxy": false}

Full JSON input example:

{
  "startUrls": [
    "https://www.airbnb.com/rooms/53997462",
    "Lisbon apartments"
  ],
  "maxListingsPerKeyword": 20,
  "hostQualification": "superhost",
  "minPropertiesUnderManagement": 0,
  "fetchHostProfile": true,
  "hostContactScan": true,
  "contactScanDepth": "listing-and-profile",
  "checkIn": "3 days",
  "checkOut": "10 days",
  "adults": 2,
  "children": 0,
  "infants": 0,
  "pets": 0,
  "locale": "en-US",
  "currency": "USD",
  "enrichUserProfiles": false,
  "proxyConfiguration": {
    "useApifyProxy": false
  }
}

Supported URL types and input formats

• Direct room URL — any Airbnb country domain matching airbnb.<tld>/rooms/<id> is recognized as a listing URL and scraped directly: https://www.airbnb.com/rooms/53997462 or https://www.airbnb.co.uk/rooms/12937.
• Plain search phrase — any string that is not a recognized room URL is treated as a search phrase and resolved through https://www.airbnb.com/s/homes?query=<phrase>, capped at maxListingsPerKeyword results: Lisbon apartments.
• Stay-window parameters (checkIn/checkOut/adults/children/infants/pets) are appended as query parameters to every resolved room URL, so the same host lead fields are returned whether or not a stay window is set.

📦 Output Format

Every qualifying listing pushes one flat JSON row combining the host lead block, the full listing record, and the contact scan. Rows are typed JSON with a stable schema across runs; download them from the Console or the API as JSON, JSONL, CSV, Excel (XLSX), XML or RSS.

Output for host leads

{
  "requestedUrl": "https://www.airbnb.com/rooms/53997462",
  "hostId": "50124",
  "hostName": "Maria",
  "hostProfileUrl": "https://www.airbnb.com/users/show/50124",
  "hostProfileImage": "https://a0.muscache.com/im/pictures/user/host-50124.jpg",
  "hostIsSuperhost": true,
  "hostIsVerified": true,
  "hostRatingAverage": 4.92,
  "hostReviewsCount": 318,
  "hostYearsHosting": 7,
  "hostTimeAsHostYears": 7,
  "hostTimeAsHostMonths": 2,
  "hostResponseRateText": "Response rate: 100%",
  "hostResponseRatePercent": 100,
  "hostResponseTime": "Responds within an hour",
  "hostAboutText": "Hi, I'm Maria — I've been hosting in Lisbon since 2019.",
  "hostWorkText": "Full-time host and property manager",
  "hostHighlights": [
    { "icon": "SYSTEM_BRIEFCASE", "title": "My work: Full-time host and property manager" }
  ],
  "coHostCount": 1,
  "coHostsDetected": [
    {
      "coHostId": "88221",
      "coHostName": "Diego",
      "coHostProfileUrl": "https://www.airbnb.com/users/show/88221",
      "coHostProfileImage": "https://a0.muscache.com/im/pictures/user/cohost-88221.jpg"
    }
  ],
  "hostDataSource": "StaysPdpSections/MEET_YOUR_HOST",
  "hostProfileFetched": true,
  "hostProfileName": "Maria",
  "hostProfileAbout": "Managing 12 apartments across Lisbon since 2019.",
  "hostProfileLocation": "Lisbon, Portugal",
  "hostPropertiesUnderManagement": 12,
  "hostIdentityVerifications": ["email", "phone", "government_id"],
  "hostHasVerifiedEmailOnFile": true,
  "hostHasVerifiedPhoneOnFile": true,
  "hostHasIdentityBadge": true,
  "hostProfileIsSuperhost": true,
  "hostGuestType": null,
  "hostTimeAsUserYears": 8,
  "hostTimeAsUserMonths": 1,
  "hostReviewsFromGuestsCount": 42
}

Output for listing details

{
  "id": "53997462",
  "coordinates": { "latitude": 38.7223, "longitude": -9.1393 },
  "descriptionOriginalLanguage": "en",
  "metaDescription": "Charming apartment in Alfama, Lisbon · Entire rental unit",
  "seoTitle": "Charming apartment in Alfama - Apartments for Rent in Lisbon",
  "sharingConfigTitle": "Charming apartment in Alfama",
  "thumbnail": "https://a0.muscache.com/im/pictures/listing-53997462-cover.jpg",
  "url": "https://www.airbnb.com/rooms/53997462?adults=2&locale=en-US&currency=USD",
  "androidLink": "airbnb://rooms/53997462",
  "iosLink": "airbnb://rooms/53997462",
  "propertyType": "Entire rental unit",
  "roomType": "Entire home/apt",
  "homeTier": 2,
  "personCapacity": 4,
  "rating": {
    "accuracy": 4.9,
    "checking": 5.0,
    "cleanliness": 4.8,
    "communication": 5.0,
    "location": 4.9,
    "value": 4.7,
    "guestSatisfaction": 4.92,
    "reviewsCount": 318
  },
  "location": "Lisbon, Portugal",
  "host": {
    "id": "50124",
    "contextualUserId": "50124",
    "name": "Maria",
    "profileImage": "https://a0.muscache.com/im/pictures/user/host-50124.jpg",
    "isSuperHost": true,
    "isVerified": true,
    "highlights": ["Superhost", "7 years hosting"],
    "about": "Hi, I'm Maria — I've been hosting in Lisbon since 2019.",
    "ratingCount": 318,
    "ratingAverage": 4.92,
    "hostDetails": ["Response rate: 100%", "Responds within an hour"],
    "timeAsHost": { "years": 7, "months": 2 }
  },
  "coHosts": [
    { "contextualUserId": "88221", "name": "Diego", "profilePictureUrl": "https://a0.muscache.com/im/pictures/user/cohost-88221.jpg" }
  ],
  "locationSubtitle": "Alfama, Lisbon, Portugal",
  "locationDescriptions": [
    { "title": "Getting around", "content": "5 minute walk to the nearest tram stop.", "mapMarkerRadiusInMeters": 150 }
  ],
  "title": "Charming apartment in Alfama",
  "description": "Full plain-text listing description as written by the host...",
  "htmlDescription": { "htmlText": "<b>Full</b> listing description as written by the host..." },
  "subDescription": { "title": "4 guests · 2 bedrooms · 2 beds · 1 bath", "items": ["4 guests", "2 bedrooms", "2 beds", "1 bath"] },
  "brandHighlights": { "hasGoldenLaurel": true },
  "images": [
    { "caption": "Living room", "imageUrl": "https://a0.muscache.com/im/pictures/listing-53997462-01.jpg", "orientation": "LANDSCAPE" }
  ],
  "breadcrumbs": [{ "linkRoute": "/portugal", "linkText": "Portugal" }],
  "highlights": [
    { "title": "Self check-in", "subtitle": "Check yourself in with the lockbox.", "icon": "SYSTEM_KEY", "type": "SELF_CHECKIN" }
  ],
  "houseRules": {
    "general": [
      { "title": "During your stay", "values": [{ "title": "No smoking", "icon": "SYSTEM_SMOKING", "additionalInfo": null }] }
    ]
  },
  "cancellationPolicies": [
    { "title": "Free cancellation before check-in", "policyName": "Moderate", "policyId": "1234" }
  ],
  "amenities": [
    { "title": "Bathroom", "values": [{ "title": "Hair dryer", "subtitle": null, "icon": "SYSTEM_HAIR_DRYER", "available": true }] }
  ],
  "timestamp": "2026-08-04T12:00:00Z",
  "language": "en-US",
  "checkIn": "2026-08-07",
  "checkOut": "2026-08-14"
}

Output for contact signals

{
  "contactScan": {
    "sourcesScanned": [
      { "source": "listingDescription", "characters": 812 },
      { "source": "houseRules", "characters": 340 },
      { "source": "hostCardBio", "characters": 210 },
      { "source": "hostProfileBio", "characters": 540 }
    ],
    "charactersScanned": 1902,
    "signals": [
      {
        "type": "phone",
        "value": "+351912345678",
        "source": "houseRules",
        "asWritten": "+351 912 345 678"
      }
    ],
    "signalCount": 1,
    "emails": [],
    "phones": ["+351912345678"],
    "websites": [],
    "socialHandles": []
  },
  "contactSignalCount": 1,
  "contactScanCharacters": 1902,
  "contactScanSources": ["listingDescription", "houseRules", "hostCardBio", "hostProfileBio"]
}

signals[] entries are typed email, phone, website, social (a full social-media URL) or socialHandle (a bare @handle). Every value is de-duplicated within the run, Airbnb's own domains (airbnb.*, muscache.com, abnb.me) are excluded from websites/social, and a digit run next to words like "license", "registration", "VAT" or "reference" is rejected before it is ever reported as a phone number.

Schema stability and export options

Every field above is read from Airbnb's internal GraphQL payloads (StaysPdpSections, UserProfileLegacyQuery, pdpPresentation) rather than from CSS selectors on the rendered page, so the key names stay stable across Airbnb front-end redesigns — the parsing code has already been updated once to follow Airbnb moving listing content from one internal payload branch to another, without changing a single output key. Results are exported through the standard Apify dataset — JSON, JSONL, CSV, Excel (XLSX), XML or RSS — from the Console, the API, or apify_client; there is no separate export mechanism specific to this Actor.

💡 Airbnb Host Scraper Strategy Guide

🎯 Strategy 1: Real-time enrichment pipeline

When a new Airbnb listing URL lands in your CRM or spreadsheet, trigger a run with that single URL in startUrls and hostQualification left at any. Read back hostPropertiesUnderManagement, hostIsSuperhost, hostResponseRatePercent and contactScan.signalCount, then write those fields into the matching CRM record. Because fetchHostProfile caches per host within a run, re-enriching a second listing from the same host inside the same run costs no extra profile request.

🎯 Strategy 2: Scheduled monitoring and alerting

Save a city or neighborhood search phrase (or a fixed host/listing list) and run it on an Apify Schedule — daily, weekly, whatever cadence fits. Diff hostPropertiesUnderManagement and hostIsSuperhost against the previous run's dataset on the same hostId, and alert when a host's managed-property count crosses your minPropertiesUnderManagement threshold or when a Superhost badge is gained or lost.

🎯 Strategy 3: Bulk dataset build

Feed a large startUrls list — one row per direct room URL, or several search phrases each capped by maxListingsPerKeyword (maximum 50 per phrase) — for a single run. Listings are processed with an internal concurrency limit of 5 at a time, so a large input list completes as a queue rather than all at once; the run's dataset is then exported in full to CSV or Excel for downstream aggregation.

Strategy comparison at a glance

Strategy	Best for	Run pattern	Output format
Real-time enrichment	One new lead at a time	On-demand run, single URL	JSON via API/webhook
Scheduled monitoring	Tracking a market over time	Apify Schedule + dataset diff	Dataset compared across runs
Bulk dataset build	Research or prospecting lists	One run, many startUrls	CSV / Excel export

🌴 Related Airbnb Scrapers & Tools

Scraper Name	What it extracts
Tripadvisor Review Scraper: Business Contact Finder	Tripadvisor reviews plus the reviewed business's own contact details — the same lead-qualification pattern applied to Tripadvisor listings
Facebook Events Scraper — Organizer & Contact Details	Public Facebook events plus the organizer Page's contact and ad-activity signals — the same "entity + enrichment" pattern applied to Facebook events

How to integrate Airbnb Host Scraper with your stack

Airbnb Host Scraper works with any language or tool that can make an HTTP request — it runs on Apify, so it is called through the Apify API or an official Apify client rather than a bespoke REST endpoint.

Python

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_API_TOKEN>")

run_input = {
    "startUrls": ["https://www.airbnb.com/rooms/53997462", "Lisbon apartments"],
    "hostQualification": "superhost",
    "minPropertiesUnderManagement": 5,
    "fetchHostProfile": True,
    "hostContactScan": True,
}

run = client.actor("YOUR_USERNAME/airbnb-rooms-urls-scraper-host-email-and-phone-finder").call(
    run_input=run_input
)

leads = []
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    leads.append({
        "host": item.get("hostName"),
        "properties": item.get("hostPropertiesUnderManagement"),
        "superhost": item.get("hostIsSuperhost"),
        "contactSignals": item.get("contactSignalCount"),
    })

import csv
with open("airbnb_host_leads.csv", "w", newline="", encoding="utf-8") as f:
    writer = csv.DictWriter(f, fieldnames=["host", "properties", "superhost", "contactSignals"])
    writer.writeheader()
    writer.writerows(leads)

Node.js

import { ApifyClient } from 'apify-client';

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

const run = await client.actor('YOUR_USERNAME/airbnb-rooms-urls-scraper-host-email-and-phone-finder').call({
    startUrls: ['https://www.airbnb.com/rooms/53997462', 'Lisbon apartments'],
    hostQualification: 'superhost',
    minPropertiesUnderManagement: 5,
    fetchHostProfile: true,
    hostContactScan: true,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
const leads = items.map((item) => ({
    host: item.hostName,
    properties: item.hostPropertiesUnderManagement,
    superhost: item.hostIsSuperhost,
    contactSignals: item.contactSignalCount,
}));

console.log(leads);

Async and scheduled pipelines

For fire-and-forget large jobs, start the run with client.actor(...).start() instead of .call() and poll client.run(runId).get() for status: "SUCCEEDED", or attach an Apify webhook to the run so your endpoint is notified on completion instead of polling. For recurring jobs, set up an Apify Schedule against a fixed input (a saved search phrase or host list) rather than triggering runs from your own cron.

🎯 Who needs Airbnb Host Scraper? (Use Cases & Industries)

🏢 Property-management and PMS vendors

A property-management software vendor runs a target-city search phrase with hostQualification: "multi-property" and minPropertiesUnderManagement: 5, then imports hostName, hostPropertiesUnderManagement and hostResponseRatePercent straight into their outbound-sales CRM as qualified leads.

📊 AI / RAG teams

A rental-market chatbot indexes description and hostProfileAbout into a vector store for retrieval, and uses hostIsSuperhost and hostResponseRatePercent as structured metadata filters so the assistant can answer "find me a highly-responsive Superhost in this area."

📱 Competitive and market intelligence analysts

An analyst tracking short-term-rental consolidation in a city runs the same search phrase monthly and watches hostPropertiesUnderManagement grow across runs to identify which operators are scaling fastest.

🔬 Researchers

An academic studying the professionalization of Airbnb supply pulls hostPropertiesUnderManagement, hostIsSuperhost and hostResponseTime across a public sample of listings in several cities, using only publicly accessible data.

🎥 Product and SaaS builders

A directory or investor-scouting tool ranks hosts by hostPropertiesUnderManagement and filters by hostIdentityVerifications[], refreshing the underlying dataset on an Apify Schedule.

Is it legal to scrape Airbnb?

Scraping publicly accessible web pages is generally lawful in the United States: in hiQ Labs, Inc. v. LinkedIn Corp. (9th Cir. 2019), the court held that scraping data a website has made publicly available does not violate the Computer Fraud and Abuse Act. That precedent concerns public-data access, not Airbnb's own Terms of Service — violating a platform's Terms of Service is a civil contract matter between the scraper and the platform, not a criminal one, and Airbnb's terms do restrict automated data collection.

Because this Actor's host lead block and contact scan surface personal data about identifiable individuals — a host's name, profile, response behaviour, and any email, phone number, website or social handle they themselves published — GDPR (for hosts in the EU/UK) and CCPA (for hosts in California) considerations apply to how that data is stored, used and retained, separately from whether the scraping itself was permitted. Airbnb Host Scraper returns only publicly accessible data. What you do with that data is your responsibility — consult legal counsel for commercial applications involving personal data.

❓ Frequently asked questions

Does Airbnb Host Scraper work without an Airbnb account?

Yes. Every request is an unauthenticated fetch of Airbnb's public listing, search and profile pages — no login, cookie or Airbnb account is used or required.

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

It impersonates a real Chrome browser's TLS/HTTP fingerprint on every request and escalates through a proxy ladder — no proxy, then Apify datacenter proxy, then sticky Apify residential proxy — only while a request keeps returning a shell or blocked page, retrying up to five times per request before giving up on that URL.

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

Listings are processed with an internal concurrency limit of 5 at a time, and the proxy ladder escalates automatically per request rather than per run. There is no published guarantee against blocking — Airbnb's own soft-block behaviour determines how far the proxy ladder needs to escalate on a given run.

How fresh is the data Airbnb Host Scraper returns?

It is a live fetch on every run — the Actor makes a fresh request to Airbnb's listing, search and profile pages each time it runs; nothing is served from a cache between runs.

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

For RAG, index description and hostProfileAbout — the highest-information free-text fields. For training data, use the structured badge and metric fields — hostIsSuperhost, hostHasIdentityBadge, hostResponseRatePercent, hostPropertiesUnderManagement — which return as typed booleans and integers on every row and require no normalization before use.

Does scraping Airbnb host data raise data-protection concerns?

Yes — host names, profiles and any self-published contact details are personal data under GDPR and CCPA. Airbnb Host Scraper returns only publicly available data; the lawful basis for storing, processing or using that data sits with you, the user, not with the Actor.

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

It is callable as a standard Apify Actor run through the Apify API by any agent framework capable of making HTTP requests — there is no dedicated MCP server for this Actor. Every response is typed JSON, so it can be passed into an LLM's context window without any HTML parsing step.

How does Airbnb Host Scraper compare to other Airbnb scrapers?

This README does not name specific competing Airbnb scrapers or their claims — no independent, dated comparison was available at the time of writing. What can be stated from this Actor's own source: it does not claim to recover host emails or phone numbers that Airbnb itself does not publish, and it reports the measured recovery rate (1 contact signal across 139 listings) rather than shipping a column that implies guaranteed coverage.

ℹ️ Disclaimer

Airbnb Host Scraper 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 — including GDPR and CCPA where they apply — in their jurisdiction.