Scraping Airbnb listing data with Python

Web scraping airbnb python and scraping Airbnb data is crucial for analyzing the real estate market, researching rental price dynamics, conducting competitive analysis, and assessing reviews and ratings. This can be accomplished by scraping techniques. However, accessing this information can be challenging as scraping may violate the terms of use of the site.

Next, we will explore a step-by-step guide on how to develop a web scraper using Python and Selenium to scrape airbnb listings. This guide will also cover how to avoid potential blocks and restrictions imposed by the platform.

Understanding the architecture of Airbnb's website

The first step in creating a web scraper is understanding how to access the web pages you're interested in, since the structure of websites can often change. To familiarize yourself with the structure of a site, you can use the browser's developer tools to inspect the HTML of the web page.

To access Developer Tools, right-click on the webpage and select “Inspect” or use the shortcut:

• CTRL+SHIFT+I for Windows;
• Option + ⌘ + I on Mac.

Each listing container is wrapped in a div element with the following attribute: class="g1qv1ctd”.

By clicking on "location" and typing "London, UK" we can access the location offered in London. The website suggests adding check-in and check-out dates. It allows them to calculate the price of the rooms.

The URL for this page would look something like this:

url = "https://www.airbnb.com/s/London--United-Kingdom/homes?tab_id=home_tab&refinement_paths%5B%5D=%2Fhomes&flexible_trip_lengths%5B%5D=one_week&monthly_start_date=2024-01-01&monthly_length=3&price_filter_input_type=0&channel=EXPLORE&query=London%2C%20United%20Kingdom&place_id=ChIJdd4hrwug2EcRmSrV3Vo6llI&date_picker_type=calendar&source=structured_search_input_header&search_type=autocomplete_click"

From the search page, we will scrape the following attributes of the product listing data from Airbnb:

• URL;
• Title;
• Description;
• Rating;
• Price;
• Additional Information (No of beds and available dates).

Step-by-step guide on building an Airbnb scraping program

To start web scraping airbnb python data, you need to set up your development environment first. Here are the steps to do that:

Step 1: Creating a virtual environment

Virtual environments allow you to isolate Python packages and their dependencies for different projects. Every project’s dependencies are guaranteed to be accurate when no cross project interference exists.

Creating a virtual environment on Windows

Windows users can create a virtual environment titled “venv” by opening a command prompt with administrator privileges and running the command:

python -m venv venv

To activate the newly created virtual environment, run the command:

venv\Scripts\activate

Creating a virtual environment on macOS/Linux

Open a terminal and execute the command below to set up a new virtual environment dubbed “venv”:

sudo python3 -m venv venv

Activate the virtual environment:

source venv/bin/activate

To deactivate the virtual environment, simply run the following command:

deactivate

Step 2: Installing the required libraries

Now that you have a virtual environment set up, you can install the necessary libraries.

Understanding the libraries:

• Selenium: Is an excellent tool for scraping Airbnb with Python, allowing you to control a web browser programmatically. It lets you interact with pages—clicking buttons, filling out forms, and navigating like a real user.
• Seleniumwire: This library extends Selenium by allowing you to intercept and inspect HTTP requests and integrate proxies with your scraping operations.
• BeautifulSoup4: Its aim is to help process HTML and XML documents. It helps you extract specific information from web pages in a structured and efficient way. This will help scrape data from airbnb.
• lxml: A fast and robust HTML and XML parser that complements BeautifulSoup.

Within your activated virtual environment, run the following command to install the required libraries:

pip install selenium beautifulsoup4 lxml seleniumwire

Selenium drivers

Selenium requires a driver to interface with the chosen browser. We will use Chrome for this guide. However, please ensure you have installed the appropriate WebDriver for the browser of your choice.

Once downloaded, ensure the driver is placed in a directory accessible by your system's PATH environment variable. This will allow Selenium to find the driver and control the browser.

Step 3: Import libraries

As mentioned above, the first thing to do is to import the Seleniumwire and BeautifulSoup libraries into your Python file. This is how it looks like:

from seleniumwire import webdriver
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.chrome.options import Options
from bs4 import BeautifulSoup
import time
import csv
import random

We will also import the `random`, `time`, and `csv` libraries for various utilities.

Step 4: Proxy integration

Next, we define a list of proxies to avoid being blocked by Airbnb. When trying to send a request without a premium proxy, you may encounter an "Access Denied" response.

You can set up a proxy as follows:

# List of proxies
proxies = [
     "username:password@Your_proxy_IP_Address:Your_proxy_port1",
     "username:password@Your_proxy_IP_Address:Your_proxy_port2",
     "username:password@Your_proxy_IP_Address:Your_proxy_port3",
     "username:password@Your_proxy_IP_Address:Your_proxy_port4",
     "username:password@Your_proxy_IP_Address:Your_proxy_port5",

]

Make sure you customize “YourproxyIPAddress” and ”Yourproxy_port” fields with the corresponding information modulus your proxy address from Proxy-seller. Also do not forget to change the placeholders “username” and “password” with your actual credentials.

Step 5: Rotating proxies

Rotating proxies is a vital step in web scraping. Websites often block or restrict access to bots and scrapers when they receive multiple requests from the same IP address. By rotating through different proxy IP addresses, you can avoid detection, appear as multiple organic users, and bypass most anti-scraping measures implemented on the website.

To set up rotation, import the “random” Library. We also define a function `get_proxy()` to select a proxy from our list. This function randomly selects an item from the list of proxies using the random.choice() method.

def get_proxy():
    return random.choice(proxies)

Step 6: Set up WebDriver

Next, we define the main function called `listings()`. This is where we’ll set-up our “ChromeDriver”. This function uses Selenium to navigate the property listings page, waits for the page to load, and parses the HTML using Beautiful Soup.

def listings(url):

    proxy = get_proxy()
    proxy_options = {
        "proxy": {
            "http": f"http://{proxy}",
            "https": f"http://{proxy}",
            "no_proxy": "localhost,127.0.0.1",
        }
    }

    chrome_options = Options()
    chrome_options.add_argument("--headless")
  

    s = Service(
        "C:/Path_To_Your_WebDriver"
    )  # Replace with your path to ChromeDriver
    driver = webdriver.Chrome(
        service=s, seleniumwire_options=proxy_options, chrome_options=chrome_options
    )

    driver.get(url)

    time.sleep(8)  # Adjust based on website's load time

    soup = BeautifulSoup(driver.page_source, "lxml")

    driver.quit()

To scrape Airbnb using Python, we start by selecting a random proxy and setting up the options. These options will be used to configure the webdriver. Next, we set up the Chrome options. Add the --headless argument to run the browser in headless mode, which means that the browser will run in the background without a graphical user interface.

Then initialize the webdriver with the service, seleniumwire options, and Chrome options. The webdriver is then used to navigate to the given URL. We add a sleep time of 8 seconds to allow the page to load completely, and then parse the returned HTML using Beautiful Soup. After the parsing is done, it closes the webdriver.

Step 7: Finding and extracting the listing data

Once you have successfully obtained the HTML content, the next step is to extract relevant data for each property. Using BeautifulSoup, we can easily navigate through the structure and locate the sections containing the necessary information.

Extracting listing elements

First, we identify all the blocks on the page that hold property details. These sections include the URL, title, description, rating, price, and any additional info.

listing_elements = soup.find_all("div", class_="g1qv1ctd")
for listing_element in listing_elements:

This code uses BeautifulSoup's find_all() method to locate all div tags with the class “g1qv1ctd”. Each of these represents a single property on the Airbnb page. It then loops through them to gather the relevant data.

Extracting listing URL

For each block found, we extract the URL.

URL_element = soup.find("a", class_="rfexzly")
listing_data["Listing URL"] = (
    "https://www.airbnb.com" + URL_element["href"] if URL_element else ""
)

We search within our soup object for an anchor tag with the class “rfexzly”. If found, it extracts the 'href' attribute (which contains the relative URL) and combines it with the base URL to create the full address. If not found, an empty string is used to avoid errors.

Extracting the listing title

Next, we grab the title, which is inside a div tag with the class “t1jojoys”. We retrieve and clean the text. If the tag isn’t present, we simply store an empty string.

title_element = listing_element.find("div", class_="t1jojoys")
listing_data["Title"] = (
    title_element.get_text(strip=True) if title_element else ""
)

Extracting the listing description

Description_element = listing_element.find("span", class_="t6mzqp7")
listing_data["Description"] = (
    Description_element.get_text(strip=True) if Description_element else ""
)

Similar to how we get the title, this part finds a span tag with the class "t6mzqp7". We extract and clean the text content, which holds a short description of the property.

Extracting the listing rating

rating_element = listing_element.find("span", class_="ru0q88m")
listing_data["Rating"] = (
    rating_element.get_text(strip=True) if rating_element else ""
)

As shown in the code above, a span tag with the class “ru0q88m” holds the rating value. We extract and clean it to remove extra spaces.

Extracting the listing price

Finally, we extract the price.

price_element = listing_element.select_one("._1y74zjx")
listing_data["Price"] = (
    f"{price_element.get_text(strip=True)} per night" if price_element else ""
)

This code locates the element with the class "_1y74zjx" within the current listing_element. If this element, which typically contains the price information, is found, its text content is extracted, cleaned, and appended with "per night" to form a more informative price string.

Extracting additional listing information

Some properties may include extra details.

listing_info_element = listing_element.find("span", {"aria-hidden": "true"})
listing_data["Additional Listing information"] = (
    listing_info_element.get_text(strip=True) if listing_info_element else ""
)

We search for a span tag with the attribute aria-hidden="true" to locate this extra information. After gathering all the relevant data from a single property, we add it to our list:

listings.append(listing_data)

Once all listings have been processed, we return the list of listings, each represented as a dictionary containing the extracted data.

return listings

Step 8: Writing data to a CSV file

After successfully scraping data from Airbnb's pages, the next important step is storing this valuable information for future analysis and reference. We use the csv library for this task. We open a CSV file in write mode and create a csv.DictWriter object. We then write the header and the data to the file.

airbnb_listings = listings(url)

csv_file_path = "proxy_web_listings_output.csv"

with open(csv_file_path, "w", encoding="utf-8", newline="") as csv_file:
    fieldnames = [
        "Listing URL",
        "Title",
        "Description",
        "Rating",
        "Price",
        "Additional Listing information",
    ]
    writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
    writer.writeheader()
    for listing in airbnb_listings:
        writer.writerow(listing)

print(f"Data has been exported to {csv_file_path}")

Here is a complete python code to scrape airbnb we used for this tutorial:

from seleniumwire import webdriver
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.chrome.options import Options
from bs4 import BeautifulSoup
import time
import csv
import random

# List of proxies
proxies = [ 
 "username:password@Your_proxy_IP_Address:Your_proxy_port1",
 "username:password@Your_proxy_IP_Address:Your_proxy_port2",
 "username:password@Your_proxy_IP_Address:Your_proxy_port3",
 "username:password@Your_proxy_IP_Address:Your_proxy_port4",
 "username:password@Your_proxy_IP_Address:Your_proxy_port5",
]

def get_proxy():
    return random.choice(proxies)


def listings(url):

    proxy = get_proxy()
    proxy_options = {
        "proxy": {
            "http": f"http://{proxy}",
            "https": f"http://{proxy}",
            "no_proxy": "localhost,127.0.0.1",
        }
    }

    chrome_options = Options()
    chrome_options.add_argument("--headless")
  

    s = Service(
        "C:/Path_To_Your_WebDriver"
    )  # Replace with your path to ChromeDriver
    driver = webdriver.Chrome(
        service=s, seleniumwire_options=proxy_options, chrome_options=chrome_options
    )

    driver.get(url)

    time.sleep(8)  # Adjust based on website's load time

    soup = BeautifulSoup(driver.page_source, "lxml")

    driver.quit()

    listings = []

    # Find all the listing elements on the page
    listing_elements = soup.find_all("div", class_="g1qv1ctd")

    for listing_element in listing_elements:
        # Extract data from each listing element
        listing_data = {}

        # Listing URL
        URL_element = soup.find("a", class_="rfexzly")
        listing_data["Listing URL"] = (
            "https://www.airbnb.com" + URL_element["href"] if URL_element else ""
        )

        # Title
        title_element = listing_element.find("div", class_="t1jojoys")
        listing_data["Title"] = (
            title_element.get_text(strip=True) if title_element else ""
        )

        # Description
        Description_element = listing_element.find("span", class_="t6mzqp7")
        listing_data["Description"] = (
            Description_element.get_text(strip=True) if Description_element else ""
        )

        # Rating
        rating_element = listing_element.find("span", class_="ru0q88m")
        listing_data["Rating"] = (
            rating_element.get_text(strip=True) if rating_element else ""
        )

        # Price
        price_element = listing_element.select_one("._1y74zjx")
        listing_data["Price"] = (
            f"{price_element.get_text(strip=True)} per night" if price_element else ""
        )

        # Additional listing info
        listing_info_element = listing_element.find("span", {"aria-hidden": "true"})
        listing_data["Additional Listing information"] = (
            listing_info_element.get_text(strip=True) if listing_info_element else ""
        )

        # Append the listing data to the list
        listings.append(listing_data)

    return listings


url = "https://www.airbnb.com/s/London--United-Kingdom/homes?tab_id=home_tab&refinement_paths%5B%5D=%2Fhomes&flexible_trip_lengths%5B%5D=one_week&monthly_start_date=2024-01-01&monthly_length=3&price_filter_input_type=0&channel=EXPLORE&query=London%2C%20United%20Kingdom&place_id=ChIJdd4hrwug2EcRmSrV3Vo6llI&date_picker_type=calendar&source=structured_search_input_header&search_type=autocomplete_click"


airbnb_listings = listings(url)

csv_file_path = "proxy_web_listings_output.csv"

with open(csv_file_path, "w", encoding="utf-8", newline="") as csv_file:
    fieldnames = [
        "Listing URL",
        "Title",
        "Description",
        "Rating",
        "Price",
        "Additional Listing information",
    ]
    writer = csv.DictWriter(csv_file, fieldnames=fieldnames)
    writer.writeheader()
    for listing in airbnb_listings:
        writer.writerow(listing)

print(f"Data has been exported to {csv_file_path}")

The code segment guarantees that any data collected by the scraper is kept in a file called "proxyweblistings_output.csv" in CSV format.

Results

The results of our scraper are saved to a CSV file called “proxy_web_listings_output.csv” as seen below.

This guide effectively explains how to scrape airbnb data listings using Python, enabling the extraction of key details such as prices, availability, and reviews. It emphasizes the importance of using proxies and rotating them to prevent being blocked by Airbnb's anti-bot measures.

Conclusion

Scrape airbnb data python and Selenium gives you direct access to valuable information like pricing, availability — key insights for market research, investment analysis, or even building your own real estate tools. While the process comes with technical challenges (and some legal gray areas), setting up the right environment, understanding how the website works, and using tools like proxies and headless browsers can help you get around most obstacles. Just be sure to respect the platform’s terms of use and always handle the data responsibly.