How to Scrape Airbnb Listings with Proxies in 2026

Airbnb hosts over 7 million listings worldwide, making it an essential data source for real estate investors, hospitality analysts, travel startups, and market researchers. Scraping Airbnb provides insights into pricing strategies, occupancy patterns, and market supply that are not available through any official API.

However, Airbnb is one of the harder targets to scrape. It relies heavily on JavaScript rendering, deploys aggressive anti-bot technology, and uses dynamic content loading that defeats simple HTTP-based scrapers. This guide covers how to scrape Airbnb listings using Python with headless browsers and residential proxies.

Why Scrape Airbnb?

Airbnb data powers multiple business use cases:

Real estate investment — Analyze short-term rental yields by neighborhood before buying property
Dynamic pricing — Benchmark your own Airbnb pricing against nearby competitors
Market research — Track supply growth, new listings, and delisting patterns
Travel analytics — Monitor availability and pricing trends for travel planning
Regulatory compliance — Cities and regulators track Airbnb listings for housing policy enforcement
Hospitality benchmarking — Hotels compare Airbnb pricing and occupancy to their own performance

Airbnb’s Anti-Bot Protections

Airbnb’s defenses are among the strongest in the travel industry:

Heavy JavaScript rendering — Almost all listing data is loaded dynamically via React/JavaScript. Static HTML contains minimal useful data.
Akamai Bot Manager — Airbnb uses Akamai’s advanced bot detection, which analyzes browser fingerprints, mouse movements, and behavioral patterns.
Device fingerprinting — Canvas fingerprinting, WebGL detection, and AudioContext checks identify automated browsers.
Rate limiting — Strict per-IP and per-session request limits.
CAPTCHA challenges — hCAPTCHA deployed for suspicious sessions.
API encryption — GraphQL API payloads use obfuscated parameters and encrypted tokens.
Session binding — Sessions are bound to IP addresses; changing IPs mid-session triggers re-authentication.

Data Points to Extract

Data Point	Source	Notes
Listing title	Listing page	Property name
Price per night	Search results / listing	Dynamic pricing changes daily
Total price	Listing page	Includes fees and taxes
Location	Map / listing	Approximate area (Airbnb fuzzes exact coords)
Property type	Listing metadata	Entire home, private room, shared
Bedrooms/bathrooms	Listing details	Capacity information
Amenities	Listing page	WiFi, pool, kitchen, etc.
Reviews	Review section	Text, rating, reviewer info
Average rating	Listing card	Overall and category ratings
Host info	Host profile	Superhost status, response rate
Availability	Calendar widget	Available dates
Instant book	Listing badge	Booking without approval

Setting Up Your Environment

Since Airbnb requires JavaScript rendering, you need a headless browser:

pip install playwright beautifulsoup4 fake-useragent
playwright install chromium

Python Code: Scraping Airbnb with Playwright and Proxies

import asyncio
from playwright.async_api import async_playwright
from bs4 import BeautifulSoup
import json
import random
import logging

logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class AirbnbScraper:
    def __init__(self, proxy_list: list):
        self.proxy_list = proxy_list
        self.listings = []

    def get_random_proxy(self) -> dict:
        """Get a random proxy in Playwright format."""
        proxy_str = random.choice(self.proxy_list)
        # Expected format: user:pass@host:port
        auth, server = proxy_str.rsplit("@", 1)
        user, password = auth.split(":", 1)
        return {
            "server": f"http://{server}",
            "username": user,
            "password": password
        }

    async def scrape_search(self, location: str, checkin: str,
                            checkout: str, max_pages: int = 5):
        """Scrape Airbnb search results for a location."""
        async with async_playwright() as p:
            proxy = self.get_random_proxy()
            browser = await p.chromium.launch(
                headless=True,
                proxy=proxy
            )
            context = await browser.new_context(
                viewport={"width": 1920, "height": 1080},
                user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
                           "AppleWebKit/537.36 (KHTML, like Gecko) "
                           "Chrome/120.0.0.0 Safari/537.36",
                locale="en-US"
            )
            page = await context.new_page()

            for page_num in range(max_pages):
                offset = page_num * 20
                url = (
                    f"https://www.airbnb.com/s/{location}/homes"
                    f"?checkin={checkin}&checkout={checkout}"
                    f"&items_offset={offset}"
                )
                logger.info(f"Scraping page {page_num + 1}: {url}")

                try:
                    await page.goto(url, wait_until="networkidle", timeout=60000)
                    await page.wait_for_timeout(random.randint(2000, 4000))

                    # Scroll to trigger lazy loading
                    await self.scroll_page(page)

                    html = await page.content()
                    page_listings = self.parse_search_results(html)

                    if not page_listings:
                        logger.info("No more listings found")
                        break

                    self.listings.extend(page_listings)
                    logger.info(f"Found {len(page_listings)} listings on page {page_num + 1}")

                except Exception as e:
                    logger.error(f"Page scrape failed: {e}")
                    # Rotate proxy by creating new browser context
                    await browser.close()
                    proxy = self.get_random_proxy()
                    browser = await p.chromium.launch(headless=True, proxy=proxy)
                    context = await browser.new_context(
                        viewport={"width": 1920, "height": 1080},
                        user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
                                   "AppleWebKit/537.36",
                        locale="en-US"
                    )
                    page = await context.new_page()

                await page.wait_for_timeout(random.randint(3000, 6000))

            await browser.close()

    async def scroll_page(self, page):
        """Scroll page gradually to trigger lazy loading."""
        for i in range(5):
            await page.evaluate(f"window.scrollBy(0, {300 + i * 200})")
            await page.wait_for_timeout(random.randint(500, 1000))

    def parse_search_results(self, html: str) -> list:
        """Extract listing data from search results HTML."""
        soup = BeautifulSoup(html, "html.parser")
        listings = []

        # Look for listing cards in search results
        cards = soup.select("[itemprop='itemListElement'], [class*='listing']")

        for card in cards:
            listing = {}

            # Title
            title_el = card.select_one("[class*='title'], [id*='title']")
            if title_el:
                listing["title"] = title_el.get_text(strip=True)

            # Price
            price_el = card.select_one("[class*='price'], span[class*='_1y74zjx']")
            if price_el:
                listing["price"] = price_el.get_text(strip=True)

            # Rating
            rating_el = card.select_one("[class*='rating'], [aria-label*='rating']")
            if rating_el:
                listing["rating"] = rating_el.get_text(strip=True)

            # Property type
            type_el = card.select_one("[class*='type'], [class*='subtitle']")
            if type_el:
                listing["property_type"] = type_el.get_text(strip=True)

            # Link
            link_el = card.select_one("a[href*='/rooms/']")
            if link_el:
                listing["url"] = "https://www.airbnb.com" + link_el["href"]
                listing["listing_id"] = link_el["href"].split("/rooms/")[1].split("?")[0]

            if listing.get("title"):
                listings.append(listing)

        # Also try extracting from embedded JSON data
        scripts = soup.find_all("script", type="application/json")
        for script in scripts:
            try:
                data = json.loads(script.string)
                # Airbnb embeds listing data in various JSON structures
                self.extract_from_json(data, listings)
            except (json.JSONDecodeError, TypeError):
                continue

        return listings

    def extract_from_json(self, data, listings: list, depth: int = 0):
        """Recursively extract listing data from JSON."""
        if depth > 10:
            return
        if isinstance(data, dict):
            if "listing" in data and "id" in data.get("listing", {}):
                listing = data["listing"]
                listings.append({
                    "listing_id": listing.get("id"),
                    "title": listing.get("name"),
                    "price": data.get("pricingQuote", {}).get("rate", {}).get("amount"),
                    "lat": listing.get("lat"),
                    "lng": listing.get("lng"),
                    "property_type": listing.get("roomType"),
                    "bedrooms": listing.get("bedrooms"),
                    "bathrooms": listing.get("bathrooms"),
                    "rating": listing.get("avgRating"),
                    "reviews_count": listing.get("reviewsCount"),
                })
            for value in data.values():
                self.extract_from_json(value, listings, depth + 1)
        elif isinstance(data, list):
            for item in data:
                self.extract_from_json(item, listings, depth + 1)

    async def scrape_listing_detail(self, listing_id: str) -> dict:
        """Scrape detailed data from an individual listing page."""
        async with async_playwright() as p:
            proxy = self.get_random_proxy()
            browser = await p.chromium.launch(headless=True, proxy=proxy)
            context = await browser.new_context(
                viewport={"width": 1920, "height": 1080},
                user_agent="Mozilla/5.0 (Windows NT 10.0; Win64; x64) "
                           "AppleWebKit/537.36 (KHTML, like Gecko) "
                           "Chrome/120.0.0.0 Safari/537.36"
            )
            page = await context.new_page()

            url = f"https://www.airbnb.com/rooms/{listing_id}"
            detail = {}

            try:
                await page.goto(url, wait_until="networkidle", timeout=60000)
                await self.scroll_page(page)
                html = await page.content()

                soup = BeautifulSoup(html, "html.parser")

                # Description
                desc_el = soup.select_one("[class*='description'], [data-section-id='DESCRIPTION']")
                if desc_el:
                    detail["description"] = desc_el.get_text(strip=True)

                # Amenities
                amenities = []
                amenity_els = soup.select("[class*='amenity'], [class*='Amenity']")
                for a in amenity_els:
                    amenities.append(a.get_text(strip=True))
                detail["amenities"] = amenities

                # Host info
                host_el = soup.select_one("[class*='host'], [data-section-id='HOST_PROFILE']")
                if host_el:
                    detail["host_info"] = host_el.get_text(strip=True)

                # Reviews
                reviews = []
                review_els = soup.select("[class*='review'], [data-review-id]")
                for rev in review_els[:10]:
                    reviews.append(rev.get_text(strip=True))
                detail["reviews_sample"] = reviews

            except Exception as e:
                logger.error(f"Detail scrape failed: {e}")

            await browser.close()
            return detail


# Usage
if __name__ == "__main__":
    proxies = [
        "user:pass@residential1.proxy.com:8080",
        "user:pass@residential2.proxy.com:8080",
        "user:pass@residential3.proxy.com:8080",
    ]

    scraper = AirbnbScraper(proxy_list=proxies)

    asyncio.run(scraper.scrape_search(
        location="New-York",
        checkin="2026-04-01",
        checkout="2026-04-05",
        max_pages=3
    ))

    print(f"Total listings scraped: {len(scraper.listings)}")
    with open("airbnb_listings.json", "w") as f:
        json.dump(scraper.listings, f, indent=2)

Geo-Targeted Proxies for Different Markets

Airbnb shows different pricing, availability, and even different listings based on the viewer’s location:

Local pricing — Prices may be shown in local currency and reflect regional demand
Regulatory filtering — Some listings are hidden in regions with strict short-term rental laws
Search relevance — Results are influenced by the searcher’s location

For accurate data, use proxies from the target market:

Scraping Paris listings? Use French residential proxies
Analyzing Tokyo market? Use Japanese proxies
Studying New York inventory? Use US East Coast proxies

Verify your proxy location with our IP lookup tool before starting Airbnb scrapes.

Handling Airbnb’s Calendar and Pricing

Airbnb pricing is dynamic — it changes by date, demand, and viewing location. To capture pricing data:

async def scrape_calendar(self, listing_id: str, months: int = 3):
    """Scrape availability calendar for a listing."""
    # Airbnb uses a GraphQL API for calendar data
    calendar_url = (
        f"https://www.airbnb.com/api/v3/PdpAvailabilityCalendar"
        f"?listingId={listing_id}&month=4&year=2026&count={months}"
    )
    # This endpoint may require specific headers and cookies
    # Extract these from a browser session
    pass

Recommended Proxy Type

For Airbnb scraping:

Residential rotating proxies — Essential. Datacenter proxies are blocked instantly by Akamai.
Geo-targeted — Critical for accurate pricing and availability data.
Sticky sessions (10-15 minutes) — Airbnb binds sessions to IPs. Use sticky sessions for multi-page workflows.
High-quality providers — Akamai scores IP reputation aggressively. Use premium residential proxy providers with clean IP pools.

Estimate your costs with our proxy cost calculator.

Troubleshooting

Problem: Browser launches but page content is empty

Airbnb requires full JavaScript execution. Ensure you are using wait_until="networkidle" and adding sufficient wait time.
Scroll the page to trigger lazy loading of listing cards.

Problem: hCAPTCHA challenges on every request

Your proxy IPs have poor reputation. Switch to a higher-quality residential proxy provider.
Add random human-like delays (2-5 seconds) between page loads.
Ensure your browser fingerprint is consistent (viewport, locale, timezone should match proxy location).

Problem: Prices showing as zero or null

Pricing loads asynchronously. Wait longer after page load before extracting data.
Check for embedded JSON data in script tags, which often contains pricing before it renders in HTML.

Problem: Getting redirected to login page

Airbnb gates some data behind authentication for heavy scrapers.
Use fresher proxy IPs and reduce request frequency.
Consider maintaining authenticated sessions with valid accounts (be aware of ToS implications).

Problem: Different results than what browser shows

Ensure your headless browser timezone, locale, and geolocation match your proxy’s location.
Airbnb serves different content based on detected locale settings.

Legal and Ethical Considerations

Airbnb scraping raises significant legal questions:

Terms of Service — Airbnb explicitly prohibits scraping in their ToS. They have pursued legal action against scraping operations in the past.
CFAA implications — Accessing Airbnb data by circumventing technical measures (CAPTCHAs, bot detection) may raise CFAA concerns in the US.
GDPR — Host names, photos, and profile data are personal information under GDPR. European scraping operations must handle this data carefully.
Regulatory use — Governments and regulators may have stronger legal standing for scraping Airbnb data for policy enforcement.
Data freshness — Airbnb data changes constantly. Cached scraped data may be misleading if presented as current.
Server load — Large-scale scraping can impact Airbnb’s infrastructure. Always implement respectful rate limiting.

Consider alternatives like AirDNA, Mashvisor, or AllTheRooms that provide licensed Airbnb market data for commercial use.

Conclusion

Airbnb is a challenging but rewarding scraping target. The combination of Playwright for JavaScript rendering and residential proxies for IP rotation provides the best success rate. Focus on extracting embedded JSON data rather than parsing rendered HTML, as it is more reliable and contains richer data. Start with small geographic areas and specific date ranges, then scale your operation as you refine the approach.