How to Scrape Airbnb: A 2026 Guide with ProxyTee

This tutorial guides you through manually scraping public data from Airbnb using Python. The data collected can help analyze market trends, develop competitive pricing strategies, perform sentiment analysis from guest reviews, or build recommendation systems.

Additionally, explore advanced solutions from ProxyTee. Our specialized residential proxies and easy-to-use tools simplify and enhance the data extraction process.

How to Scrape Airbnb

Before we dive in, having some basic knowledge of web scraping and HTML is recommended. Additionally, make sure to install Python on your computer if you don’t already have it. The official Python guide provides detailed instructions on how to do this. If you already have Python installed, make sure it’s updated to Python 3.7.9 or newer.

Once Python is installed, launch your terminal or command line interface and initiate the creation of a new project directory with the following commands:

mkdir airbnb-scraper && cd airbnb-scraper

After you’ve created a new project directory, you need to set up some additional libraries that you’ll use for web scraping. Specifically, you’ll utilize Requests, a library enabling HTTP requests in Python; pandas, a robust library dedicated to data manipulation and analysis; Beautiful Soup (BS4) for parsing HTML content; and Playwright for automating browser-based tasks.

To install these libraries, open your terminal or shell and execute the following commands:

pip3 install beautifulsoup4
pip3 install requests
pip3 install pandas
pip3 install playwright
playwright install

Ensure that the installation process is completed without errors before moving on to the next step of this tutorial.

Note: The last command (ie playwright install) is necessary to install the browser binaries.

Airbnb Structure and Data Objects

Before you begin scraping Airbnb, it’s crucial to familiarize yourself with its structure. Airbnb’s main page features a user-friendly search bar, allowing you to look for accommodation options, experiences, and even adventures.

Upon entering your search criteria, the results are presented in a list format, displaying properties with their names, prices, locations, ratings, and other pertinent details. It’s worth noting that these search results can be filtered based on various parameters, such as price range, property type, and availability dates.

If you want more search results than what’s initially presented, you can utilize the pagination buttons located at the bottom of the page. Each page typically hosts numerous listings, enabling you to browse additional properties. The filters found at the top of the page provide an opportunity to refine your search according to your needs and preferences.

To help you understand the HTML structure of Airbnb’s website, follow these steps:

1. Navigate to the Airbnb website.
2. Input a desired location, date range, and guest count in the search bar, and hit Enter.
3. Initiate the browser’s developer tools by right-clicking on a property card and choosing Inspect.
4. Explore the HTML layout to pinpoint the tags and attributes that encompass the data you’re interested in scraping.

Scrape an Airbnb Listing

Now that you know more about Airbnb’s structure, set up Playwright to navigate to an Airbnb listing and scrape data. In this example, you’ll gather the listing’s name, location, pricing details, owner details, and reviews.

Create a new Python script, airbnb_scraper.py, and add the following code:

import asyncio
from playwright.async_api import async_playwright
import pandas as pd

async def scrape_airbnb():
    async with async_playwright() as pw:
        # Launch new browser
        browser = await pw.chromium.launch(headless=False)
        page = await browser.new_page()
        # Go to Airbnb URL
        await page.goto('https://www.airbnb.com/s/homes', timeout=600000)
        # Wait for the listings to load
        await page.wait_for_selector('div.g1qv1ctd.c1v0rf5q.dir.dir-ltr')
        # Extract information
        results = []
        listings = await page.query_selector_all('div.g1qv1ctd.c1v0rf5q.dir.dir-ltr')
        for listing in listings:
            result = {}
            # Property name
            name_element = await listing.query_selector('div[data-testid="listing-card-title"]')
            if name_element:
                result['property_name'] = await page.evaluate("(el) => el.textContent", name_element)
            else:
                result['property_name'] = 'N/A'
            # Location
            location_element = await listing.query_selector('div[data-testid="listing-card-subtitle"]')
            result['location'] = await location_element.inner_text() if location_element else 'N/A'
            # Price
            price_element = await listing.query_selector('div._1jo4hgw')
            result['price'] = await price_element.inner_text() if price_element else 'N/A'
            results.append(result)
        
        # Close browser
        await browser.close()
       
        return results
# Run the scraper and save results to a CSV file
results = asyncio.run(scrape_airbnb())
df = pd.DataFrame(results)
df.to_csv('airbnb_listings.csv', index=False)

The function scrape_airbnb() asynchronously opens a browser, visits Airbnb’s home listings page, and gathers details such as property name, location, and price from each listing. If an element is not found, it’s marked as N/A. After processing, the acquired data is stored in a pandas DataFrame and saved as a CSV file named airbnb_listings.csv.

To run the script, run python3 airbnb_scraper.py in your terminal or shell. Your CSV file should look like this:

property_name,location,price
"Brand bei Bludenz, Austria",343 kilometers away,"€ 2,047 \nnight"
"Saint-Nabord, France",281 kilometers away,"€ 315 \nnight"
"Kappl, Austria",362 kilometers away,"€ 1,090 \nnight"
"Fraisans, France",394 kilometers away,"€ 181 \nnight"
"Lanitz-Hassel-Tal, Germany",239 kilometers away,"€ 185 \nnight"
"Hohentannen, Switzerland",291 kilometers away,"€ 189 \nNight"
…output omitted…

Enhance Web Scraping with ProxyTee

Scraping websites can sometimes pose challenges, such as IP bans and geoblocking. This is where ProxyTee residential proxies come in handy, enabling you to bypass these hurdles and enhance your data scraping efforts.

After you run the previous script a few times, you may notice that you stop receiving data. This may happen if your IP is detected by Airbnb and they block you from scraping their website.

To mitigate the associated challenges, implementing proxies for scraping is a practical approach. Here are some of the advantages of employing ProxyTee for web scraping:

• Geoblocking restricts access to content based on the user’s geographical location. A proxy can help bypass IP restrictions by providing you with an IP address from a specific location. ProxyTee offers over 20 million IPs in 100+ countries, allowing for extensive global coverage.
• IP rotation involves rotating IP addresses to prevent your scraper from being banned by the website. It’s especially useful when you need to make a large number of requests to a single website. With ProxyTee’s auto-rotation feature, IP addresses change automatically at intervals you specify (3 to 60 minutes), greatly reducing the risk of bans.
• Load balancing ensures the distribution of network or application traffic across many resources, preventing any single component from becoming a bottleneck and providing redundancy in case of failure. ProxyTee provides unlimited bandwidth, ensuring smooth operations without concerns about data overages, even during high-traffic tasks.

How to Integrate ProxyTee into Your Python Script

With the previously mentioned benefits, you can see why one may want to incorporate ProxyTee in a Python script. The good news is that it’s easy to do. It just involves setting up a ProxyTee account, configuring your proxy settings, and then implementing these within your Python code.

To get started, you need to create a ProxyTee account. To do so, go to the ProxyTee website and select Pricing; then proceed as directed. Once your account is set up, you'll find your unique credentials and details needed for proxy setup.

Create a new airbnb_scraping_proxy.py Python script and add the following code:

from playwright.sync_api import sync_playwright
import pandas as pd

def run(playwright):
    browser = playwright.chromium.launch()
    context = browser.new_context()

    # Set up proxy
    proxy_username='YOUR_MYPROXY_USERNAME'
    proxy_password='YOUR_MYPROXY_password'
    proxy_host = 'YOUR_MYPROXY_HOST'
    proxy_auth=f'{proxy_username}:{proxy_password}'
    proxy_server = f'http://{proxy_auth}@{proxy_host}'

    context = browser.new_context(proxy={
        'server': proxy_server,
        'username': proxy_username,
        'password': proxy_password
    })

    page = context.new_page()
    page.goto('https://www.airbnb.com/s/homes')

    # Wait for the page to load
    page.wait_for_load_state("networkidle")

    # Extract the data
    results = page.eval_on_selector_all('div.g1qv1ctd.c1v0rf5q.dir.dir-ltr', '''(listings) => {
        return listings.map(listing => {
            return {
                property_name: listing.querySelector('div[data-testid="listing-card-title"]')?.innerText || 'N/A',
                location: listing.querySelector('div[data-testid="listing-card-subtitle"]')?.innerText || 'N/A',
                price: listing.querySelector('div._1jo4hgw')?.innerText || 'N/A'
            }
        })
    }''')

    df = pd.DataFrame(results)
    df.to_csv('airbnb_listings_scraping_proxy.csv', index=False)

    # Close the browser
    browser.close()

with sync_playwright() as playwright:
    run(playwright)

This code uses the Playwright library to launch a Chromium browser with a specific proxy server. It navigates to Airbnb’s home page; extracts details such as property names, locations, and prices from the listings; and saves the data into a CSV file using pandas. After data extraction, the browser is closed.

Note: Replace the proxy_username, proxy_password, and proxy_host with your ProxyTee access parameters.

To run the script, run python3 airbnb_scraping_proxy.py in your terminal or shell. The scraped data is saved in a CSV file named airbnb_listings_scraping_proxy.csv. Your CSV file should look like this:

property_name,location,price
"Sithonia, Greece",Lagomandra,"$3,305 \nnight"
"Apraos, Greece","1,080 kilometers away","$237 \nnight"
"Magnisia, Greece",Milopotamos Paralympic,"$200 \nnight"
"Vourvourou, Greece",861 kilometers away,"$357 \nnight"
"Rovies, Greece","1,019 kilometers away","$1,077 \nnight"
…output omitted…

Leveraging ProxyTee for Enhanced Scraping

While residential proxies are crucial for avoiding blocks, ProxyTee also provides user-friendly interface. Our simple API allows for easy integration of proxies with your existing workflows and applications, simplifying your overall process.

Scraping a Single Airbnb Listing

Now, scrape some data related to a single listing. Create a new Python script, airbnb_scraping_single_listing.py, and add the following code:

import asyncio
from playwright.async_api import async_playwright
from bs4 import BeautifulSoup
import pandas as pd
username='YOUR_MYPROXY_USERNAME'
password='YOUR_MYPROXY_password'
auth=f'{username}:{password}'
host = 'YOUR_MYPROXY_HOST'
browser_url = f'wss://{auth}@{host}'

async def scrape_airbnb_listing():
    async with async_playwright() as pw:
        # Launch new browser
        print('connecting')
        browser = await pw.chromium.connect_over_cdp(browser_url)
        print('connected')
        page = await browser.new_page()
        # Go to Airbnb URL
        await page.goto('https://www.airbnb.com/rooms/26300485', timeout=120000)
        print('done, evaluating')
        # Wait for content to load
        await page.wait_for_selector('div.tq51prx.dir.dir-ltr h2')
        # Get the entire HTML content
        html_content = await page.evaluate('()=>document.documentElement.outerHTML')
        # Parse the HTML with Beautiful Soup
        soup = BeautifulSoup(html_content, 'html.parser')
        # Extract host name
        host_div = soup.select_one('div.tq51prx.dir.dir-ltr h2')
        host_name = host_div.text.split("hosted by ")[-1] if host_div else 'N/A'
        print(f'Host name: {host_name}')
        # Extract reviews
        reviews_span = soup.select_one('span._s65ijh7 button')
        reviews = reviews_span.text.split(" ")[0] if reviews_span else 'N/A'
        print(f'Reviews: {reviews}')
        # Close browser
        await browser.close()
        return {
            'host_name': host_name,
            'reviews': reviews,
        }
# Run the scraper and save results to a CSV file
results = asyncio.run(scrape_airbnb_listing())
df = pd.DataFrame([results]) # results is now a dictionary
df.to_csv('scrape_airbnb_listing.csv', index=False)

In this code, you navigate to the desired listing URL, extract the HTML content, parse it with Beautiful Soup to retrieve the host’s name and number of reviews, and finally, save the extracted details to a CSV file using pandas.

Run the code from your terminal or shell:

python3 airbnb_scraping_single_listing.py

And you should see a new CSV file named scrape_airbnb_listing.csv in your project. The content of this file should look like this:

host_name,reviews
Amelia,88

Key Features of ProxyTee

ProxyTee is your go-to for reliable rotating residential proxies. It offers a comprehensive suite of features:

• Unlimited Bandwidth: No more worrying about data overages. Scrape and stream without restrictions.
• Global IP Coverage: Access a massive pool of IPs from over 100 countries, perfect for geo-targeting.
• Multiple Protocol Support: Enjoy compatibility with various applications, supporting both HTTP and SOCKS5.
• User-Friendly Interface: Easily navigate the platform with its intuitive GUI; no tech expertise required.
• Auto Rotation: Automatically rotate IPs at customizable intervals, vital for avoiding detection.
• API Integration: Integrate ProxyTee into your projects seamlessly, automating proxy-related tasks efficiently.

Conclusion

In this tutorial, you learned how to pull data from Airbnb listings using Python, and you saw how ProxyTee, specifically our residential proxies, can make this job even easier. Our commitment to competitive pricing makes it a superior alternative to pricier competitors like Bright Data and Oxylabs, providing you with the power of residential proxies at up to 50% cheaper than others.

ProxyTee offers a range of solutions for collecting data from any website, not just Airbnb. These tools turn difficult web scraping tasks into simple ones, saving you time and effort. Explore our options and find the best solution for your data needs today!