Airbnb Web Scraping in 2026: Methods and Tools

Table of Contents :

To Airbnb scraper, you have two options: one turnkey solution as Bright Data Where Apify, or your own script Python with Playwright.

Both allow you to’Extract data from Airbnb (prices, listings, reviews, availability). The choice depends on your coding skills and the volume you're aiming for.

How to Web Scrape Airbnb to Extract Prices and Listings — Find out how to scrap on Airbnb in 2025! ©Alexia for Alucare.fr

The different methods for scraping on Airbnb

the web scraping is a technique that allows you to’extract data automatically from websites. For Airbnb, there are two main approaches you can take: going through a turnkey solution, or write your own scraper in Python.

1. Use web scraping tools

Don't want to code? The web scraping tools are designed for that. They handle all the technical aspects for you: rotation of’IPs, management of proxies, rendered JavaScript and circumvention of the blockages.

Here are four scraping tools truly effective for collecting Airbnb data in just a few clicks:

Bright Data : the most powerful solution for extracting listings on a large scale, but also the most expensive for standard use.
Apify : the most accessible option for getting started without coding, thanks to its preconfigured Airbnb scraper.
ScrapingBee : a Scraping API a simple system that handles the rendering of dynamic pages.
ScraperAPI : designed for large-scale data extraction and bypassing security measures.

Bright Data

Bright Data is a comprehensive solution that offers Residential and data center proxies, a web scraping browser, and some Dedicated APIs. The platform includes a feature Airbnb Scraper API, specifically designed to extract listings and structured data from the platform.

This is the most reliable and the fastest, but also the most expensive option for entry-level use. Check out our full review of Bright Data.

Apify

Apify is a platform that lets you build, run, and share web scrapers. It offers a Airbnb scraper already ready, with IP rotation and built-in anti-blocking mechanisms. You can extract the listing data and host profiles without writing a single line of code, page by page.

This is clearly the best option a good place to start. Check out our full review of Apify.

ScrapingBee

ScrapingBee is a Scraping API which simplifies data extraction from dynamic websites. It handles rendering JavaScript and automatically rotates the proxies. You send a request to the API, and it returns the Rendered HTML from the page, ready to be analyzed to extract your listings.

ScraperAPI

ScraperAPI handles all the challenges of large-scale web scraping: proxies, headers, and user agents. It also offers a Optimized Airbnb API to bypass the site's security measures, allowing you to focus solely on extracting the data you're interested in.

Check out our full review of ScraperAPI.

2. Code scraping with Python and its libraries

Would you rather keep a total control About the process? Code-based scraping with Python is perfect for you. This language is ideal thanks to its powerful libraries, which let you create a web scraper 100 Custom %, tailored to Airbnb's structure.

Here are the essential libraries:

requests : To send HTTP requests to the target URL. This is the foundation of any scraping operation.
BeautifulSoup : to parse the content HTML Once you've retrieved it, navigate through the page's structure and extract the data you're interested in.
Selenium Where Playwright : to simulate a real browser with rendering JavaScript. This is crucial for Airbnb: the site is dynamic; without it, you'll just see a blank page.

Let's take a practical look at how to scrape the Titles, Awards, and Links listings available in a city (e.g., Paris) for specific dates.

Step 1: Analyze the Airbnb URL

The first step is to understand how Airbnb organizes its URLs. Here is a typical example:

https://www.airbnb.fr/s/Paris--France/homes?checkin=2025-09-01&checkout=2025-09-05&adults=2

Useful settings to look for:

s/Paris--France the location sought.
checkin and checkout : THE check-in and check-out dates.
adults : THE number of adults.

You can also add more settings manually (filters, maximum price, etc.) to refine your results. Airbnb pages its results: be sure to manage the page numbers in your browser to browse through multiple pages of listings and not limit yourself to just the first one.

Step 2: Set up the Python environment

Install the necessary libraries using the package manager pip :

pip install playwright requests beautifulsoup4

Step 3: The Python script

Here is an example script that includes:

Navigator simulation with Playwright.
Proxy Rotation through a dedicated service.
Header management (headers) and user agents.
CSS or XPath selectors for data extraction.
Random breaks to prevent deadlocks.

from playwright.sync_api import sync_playwright
import random
import time
import csv

def scrape_airbnb(city_url, proxy_list):
    with sync_playwright() as pw:
        browser = pw.chromium.launch(headless=True)
        page = browser.new_page()
        page.set_extra_http_headers({'User-Agent': '...'})
        proxy = random.choice(proxy_list)
        page.goto(city_url, proxy={'server': proxy})
        time.sleep(random.uniform(3, 6))
        # extraction via CSS or XPath
        titles = page.query_selector_all('._1c2n35az')
        prices = page.query_selector_all('._1p7iugi')
        data = [{'title': t.inner_text(), 'price': p.inner_text()} for t, p in zip(titles, prices)]
        browser.close()
    return data

Be careful with CSS selectors. Airbnb Classes (here ._1c2n35az and ._1p7iugi) are dynamically generated and change with every deployment of the site. Before running your script, inspect the page (press the F12 then «Inspector») to find the current selectors.

Step 4: Back up the data

You can export the extracted data in two common formats: the CSV (Comma-Separated Values) or the JSON (JavaScript Object Notation). The format JSON is especially useful when your data has a nested structure (for example, multiple reviews per listing).

To export to CSV :

with open('airbnb_prices.csv', 'w', newline='', encoding='utf-8') as f:
    writer = csv.DictWriter(f, fieldnames=['title', 'price'])
    writer.writeheader()
    writer.writerows(data)

To export to JSON :

import json

with open('airbnb_prices.json', 'w', encoding='utf-8') as f:
    json.dump(data, f, ensure_ascii=False, indent=2)

Why scrap on Airbnb?

Extracting data from Airbnb opens up practical applications, whether for professional or personal projects. Here are the most common use cases:

Market Analysis and Competitive Intelligence : The data collected helps you understand the state of the rental market and compare the listing prices and to analyze competition in a specific area.
Product Development : Using the extracted listings, you can create a price comparison tool or an analytics tool for travelers and hosts.
Research and Data Journalism : The retrieved data is used for urban or economic studies, and to investigate business practices.
Large-scale automation : With the right tools, you can set up a automatic monitoring and continuous extraction from thousands of pages of listings.

What data can you scrape on Airbnb?

On the public listings, you can extract most of the information displayed on each listing:

Listing Information: title, description, images, type of housing, number of bedrooms.
Host details : host's name and profile, number of reviews, listing history.
Price and availability : price per night, cleaning fee, available dates from reservation calendar.
Comments and notes: traveler reviews and overall score from the listing.
Geographic location: neighborhood, and approximate latitude and longitude of the residence.

This data covers most of what you need to analyze a market: prices, listing structures, reviews, and availability over several months.

Is scraping on Airbnb legal?

Collecting public data that are not protected by copyright is generally considered legal. That said, the Airbnb Terms of Service explicitly prohibit automated data extraction without written authorization.

We must distinguish between two levels. From a purely legal standpoint, the U.S. ruling hiQ v. LinkedIn (2022) limited the scope of the CFAA: scraping publicly available data does not constitute unauthorized access to a system. But this is not an absolute green light. Other remedies remain applicable: the contract law (the Terms of Service that you agree to), the copyright or database protection.

In practice, scraping Airbnb on a large scale mainly exposes you to Blocking Your IP Address, CAPTCHAs, or even a formal notice if use becomes widespread or commercial.

When it comes to ethics, a few simple principles will keep you on the right track: