How to web scrap on Airbnb in 2025?

Table of Contents :

You are looking for collect data on Airbnb, but don't know how to do it? Web scraping is what you need!

Here's a clear guide to how to web scrap on Airbnb in 2025.

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 data to be extracted from websites automatically.

To scrape on Airbnb, you can choose between two effective methods: using scraping tools Where code your own scraper with Python. We tell you all about it!

1. Use web scraping tools

Don't want to code? Are you looking for a turnkey solution for scraper on Airbnb ?

You can use web scraping toolsDesigned to simplify the process, they take care of all the complex technical aspects for you!

Here are four really effective tools for collect data on Airbnb in just a few clicks :

💡 Bright Data
🤖 Apify
🍯 ScrapingBee
🧩 ScraperAPI

To compare these tools, see our article on best scraping tools in 2025.

Bright Data

Bright Data is a complete solution offering a full range of range of services To scrape any website efficiently: residential and data center proxies, scraping browser, and dedicated APIs.

Bright Data offers just such a feature Airbnb Scraper API which is specially designed to collect data on the Airbnb platform.

This dedicated API represents a excellent option if you're looking for a fast, reliable solution to scrape Airbnb.

👉 Discover our full review of Bright Data.

Apify

Apify is a development platform that lets you build, run and share scrapers. It also offers a Airbnb Scraperwith IP rotation and anti-lock mechanisms.

This is a preconceived solution that can be used to extract information from listings, host profiles, and more on Airbnb, without having to write complex code.

Apify's Airbnb Scraper is presented as a flexible, user-friendly option for developers.

👉 Discover our full review of Apify.

ScrapingBee

ScrapingBee is a Scraping API which simplifies data extraction from dynamic websites. The platform handles JavaScript rendering and proxy rotation for you.

The result: with ScrapingBee, scraping sites like Airbnb becomes much easier. simple and effective All you have to do is send a request to the API, and it will return the rendered HTML, ready to be parsed.

👉 Discover our full review of ScrapingBee.

ScraperAPI

ScraperAPI is another API solution that handles all the challenges of large-scale scraping. Proxies, headers, user agents... ScraperAPI takes care of everything for you!

ScraperAPI also offers a Airbnb API, optimized to bypass Airbnb's protections.

You will be able to focus solely on theAirbnb data extraction that interest you. Airbnb API also automates the collection of massive structured data.

👉 Discover our full review of ScraperAPI.

2. Code scraping with Python and its libraries

Would you prefer to have total control over the scraping process? Then the code scraping with Python is what you need.

Programming allows you to create a customized scraper 100 % and tailored to Airbnb's specific needs. Python is the ideal solution thanks to its powerful libraries.

Here are the essential libraries for scraping with Python :

✅ requests to send HTTP requests to the URLThis library is the basis for all scraping operations, allowing you to "request" the content of a web page.
✅ BeautifulSoup for parsing (analyze) HTML content retrieved and facilitate data extraction. It is the ideal tool for navigating the structure of a web page.

👉 Find out more in our article on the web scraping Python BeautifulSoup.

✅ Selenium or Playwright to simulate a real web browser, including JavaScript rendering and user interactions. This is the crucial part for scraping Airbnb, because Airbnb is a dynamic site, and without Selenium or Playwright, you will only retrieve a blank page.

But how do these tools work together? Let's take a look at how to scrape the titles, prices and links of available accommodations in a specific 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 an example of a typical search URL:

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

Here are some examples of useful parameters:

```
s/Paris--France
```
for location
```
checkin
```
and
```
checkout
```
for arrival and departure dates
```
adults
```
for the number of adults...

📌 You can add other parameters (filtering, max price...) manually.

⚙️ Step 2: Set up the Python environment

Then install the necessary libraries using the Python package management tool (pip):

pip install playwright requests beautifulsoup4

🐍 Step 3: The Python script (pseudocode)

Here is a sample script integrating the following processes :

👉 Navigator simulation with Playwright
👉 Proxy rotation (via a proxy service)
👉 Header management (headers) and user-agents
👉 CSS or XPath selectors for data extraction
👉 Random breaks to avoid blockage

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

💾 Step 4: Save data

You can then backup Airbnb data in a structured format such as CSV (Comma-Separated Values) or JSON (JavaScript Object Notation) files.

Here is an example of code for export as CSV or JSON :

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

Why scrap on Airbnb?

Web scraping on Airbnb offers many strategic and operational advantages:

👉 Market analysis and competitive intelligence : Scraped information can help you understand the state of the real estate market, analyze prices, and study the competition.
👉 Product and service development : By scraping Airbnb, you can create price comparison tools or analysis tools for travelers or hosts.
👉 Academic research and data journalism: The data collected can be used to conduct urban and economic studies or investigate commercial practices.
👉 Automation and efficiency : Scraping and its powerful tools enable you to set up large-scale automatic monitoring and data collection systems.

What data can you scrape on Airbnb?

Here are the main types of data you can extract by scraping on Airbnb:

🏡 Ad information : title, description, photos, type of accommodation, number of rooms
🧭 Host details : name and profile, number of reviews, listing history
💸 Price and availability : price per night, cleaning service, booking calendar
💬 Comments and notes: traveler reviews and overall rating
🌍 Geographical location : latitude and longitude of dwellings

Is scraping on Airbnb legal?

Generally, scraping public data (not protected by copyright) is considered legal. However, the Airbnb Terms of Service prohibit automated scraping.

Making scraping on Airbnb may result in the blocking of your IP address or legal action.

For more details, please see our article on legality of web scraping.

By 2025, the web scraping on Airbnb is more accessible than ever. Whether via tools like Bright Data and Apify, or by coding your own scraper with Python, there's no shortage of solutions.

Which method proposed in this article do you plan to try? Do you know of any other solutions for scraper Airbnb ? Tell us all about it in the comments!