Scrape Airbnb Data with Selenium and Beautiful Soup: Simple Steps for Non-Experts

Airbnb is a popular online marketplace that allows individuals to rent out their homes or apartments to travelers. One of the benefits of using Airbnb is that it provides a wealth of property data, including prices, availability, and reviews. However, this data is not easily accessible to the public. You will need to scrape Airbnb data to gather this information. In this article, we will explain how to scrape Airbnb data using the most effective tools to get the job done.

Airbnb Data Scraping: About the Platform

Airbnb is a short-term rental platform allowing people to rent their homes, apartments, or rooms to travelers. It was founded in 2008 and has become one of the largest home-sharing platforms in the world. Airbnb allows travelers to find and book unique, affordable accommodations in over 220 countries and regions worldwide. Hosts list their properties on Airbnb, and travelers can search for available listings, view photos and descriptions, and book the one that best suits their needs.

Thus, the Airbnb website can have massive data and statistics about local prices, the popularity of different offers depending on the region, and user reviews.

Airbnb Scraping: Website’s Architecture

To effectively scrape Airbnb’s website, it is essential to understand the architecture of the website. The information about properties, their listings, and reviews are stored in a database, and the website uses APIs to retrieve this information and display it on the website. Therefore, to scrape the information, you must interact with the APIs and retrieve the data in the desired format.

Benefits of Airbnb Data Scraping for Businesses

Airbnb scraping is typically done to collect data on listings, prices, reviews, and other information that can be useful for research, analysis, or competitive intelligence. The data can be used to study trends in the short-term rental market, identify popular locations and amenities, or compare prices and ratings for different properties. It can also be used to create custom applications, such as a price comparison tool for short-term rentals.

Here are a few reasons why someone might scrape Airbnb listings:

Data analysis: Scraped Airbnb data can be analyzed to understand consumer behavior and preferences, identify market trends, and improve marketing strategies.

Market research: Scraping Airbnb listings can provide valuable insights into the short-term rental market, such as pricing trends, popular locations, and property amenities. This data can be used to inform business decisions and stay ahead of the competition.

Competitor analysis: Scraping Airbnb listings can give businesses a better understanding of what their competitors are offering and how they are pricing their listings. This information can be used to make strategic decisions and improve their own offerings.

Price comparison: Scraping Airbnb listings can be used to compare prices and find the best deals for travelers. It can also be used to compare prices across different listings and identify any outliers or anomalies.

Airbnb Scraper Tools and Technologies

There are several tools and technologies available that you can use to scrape Airbnb’s website, including:

• Python libraries such as Beautiful Soup and Scrapy.
• Web scraping APIs such as Apify.
• Browser extensions.

Apify is a cloud-based web scraping platform that provides an easy-to-use interface for scraping websites and APIs. This article will use the Apify platform to show you how to scrape Airbnb data for listings and reviews.

Airbnb Scraper: Setting Up Apify Version

To set up a scraper on Apify, you need to create an account and set up a new scraper. The platform provides a visual interface for setting up the Airbnb scraper, and you can define the information you want to scrape and how it should be retrieved. To scrape Airbnb data using Apify, you need to follow these steps:

• Create an Apify account: If you do not already have an account, sign up for a free account on the Apify website.
• Start a new actor: In Apify, an actor is a program that runs on the platform and performs a specific task. To start a new actor, click on the “Actors” button in the top navigation bar and then click on the “Create new” button.
• Choose a scraping template: Apify provides several templates for different websites and use cases. To scrape Airbnb, choose the “Apify Scraping – Airbnb” template.
• Configure the scraping inputs: You need to specify the scraping inputs such as the Airbnb URL, the number of pages to scrape, the data fields to extract, etc. You can find these inputs in the “Input” tab of the actor.
• Launch the actor: Once you have configured the inputs, click on the “Run” button to launch the actor.
• Monitor the scraping progress: You can monitor the scraping progress and see the extracted data in the “Dataset” tab. You can also see the log output of the scraping process in the “Logs” tab.
• Download the data: Once the scraping is complete, you can download the data as a CSV or JSON file.

Note that Airbnb has anti-scraping measures in place, so it is possible that the scraping process might fail due to IP blocking or CAPTCHA challenges. You might need to use a proxy or use headless browser mode to avoid these issues.

Web Scraping Airbnb: Python Library Beautiful Soup

Beautiful Soup is a Python library for parsing HTML and XML documents. Here is a step-by-step guide to scraping Airbnb data:

Step 1: Install Required Libraries

Install Beautiful Soup and Requests using pip:

pip install beautifulsoup4 requests

Step 2: Send an HTTP Request

Use Requests to fetch the Airbnb page:

import requests
url = “https://www.airbnb.com/s/New-York–NY–United-States/homes”

headers = {“User-Agent”: “Your User Agent String”}

response = requests.get(url, headers=headers)

Step 3: Parse the HTML

Pass the response to Beautiful Soup:

from bs4 import BeautifulSoup
soup = BeautifulSoup(response.content, ‘html.parser’)

Step 4: Inspect the Page Structure

Use your browser’s Developer Tools (F12) to identify the HTML elements containing your target data. You can also use Beautiful Soup’s prettify() method:

print(soup.prettify())

Step 5: Extract the Data

Use Beautiful Soup methods to locate and extract data:

listings = soup.find_all(‘div’, class_=’listing-class-name’)

for listing in listings:

title = listing.find(‘span’, class_=’title-class’).text

price = listing.find(‘span’, class_=’price-class’).text

print(f”Title: {title}, Price: {price}”)

Step 6: Save the Data

Store extracted data in CSV or JSON format:

import csv
with open(‘airbnb_data.csv’, ‘w’, newline=”, encoding=’utf-8′) as file:

writer = csv.writer(file)

writer.writerow([‘Title’, ‘Price’])

writer.writerow([title, price])

Web Scraping Airbnb: Python Library Selenium

Selenium is a popular framework for automating web browsers and can be used for web scraping as well. Follow these steps to scrape Airbnb data with Selenium:

Step 1: Install Selenium

Install Selenium using pip:

pip install selenium

Step 2: Set Up WebDriver

Download the WebDriver for your browser. For Chrome, use ChromeDriver:

• Download from: https://sites.google.com/chromium.org/driver/
• Or install via webdriver-manager for automatic setup:
  pip install webdriver-manager
  

Step 3: Import Libraries and Initialize WebDriver

Set up Selenium with the necessary imports:

from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
from webdriver_manager.chrome import ChromeDriverManager

# Initialize the driver

driver = webdriver.Chrome(service=Service(ChromeDriverManager().install()))

Step 4: Navigate to Airbnb

Open the target Airbnb page:

url = “https://www.airbnb.com/s/New-York–NY–United-States/homes”
driver.get(url)

#Wait for page to load

WebDriverWait(driver, 10).until(
EC.presence_of_element_located((By.CSS_SELECTOR, “div[data-testid=’card-container’]”))
)

Step 5: Locate and Extract Elements

Use Selenium’s locator methods to find and extract data:

#Find all listing cards
listings = driver.find_elements(By.CSS_SELECTOR, “div[data-testid=’card-container’]”)
data = []
for listing in listings:
try:
title = listing.find_element(By.CSS_SELECTOR, “div[data-testid=’listing-card-title’]”).text
price = listing.find_element(By.CSS_SELECTOR, “span._tyxjp1”).text
data.append({ ‘title’: title, ‘price’: price }) except Exception as e: print(f”Error extracting listing: {e}”) continue

Step 6: Handle Scrolling (Optional)

For pages with infinite scroll, simulate scrolling to load more content:

import time

#Scroll down to load more listings

for _ in range(3):
driver.execute_script(“window.scrollTo(0, document.body.scrollHeight);”)
time.sleep(2)

Step 7: Save the Data

Export the scraped data to CSV or JSON:

import csv

with open(‘airbnb_selenium_data.csv’, ‘w’, newline=”, encoding=’utf-8′) as file:
writer = csv.DictWriter(file, fieldnames=[‘title’, ‘price’])
writer.writeheader()
writer.writerows(data)

Step 8: Close the Browser

Always close the WebDriver after scraping:

driver.quit()

Step 9: Run Your Script

Execute your Python file:

python airbnb_scraper.py

Airbnb Data Scraping: DataOx Contribution

It is important to note that web scraping Airbnb data Python or other coding language solutions can be difficult and time-consuming, as the website is constantly changing and updating. Therefore, it is important to use a scraping tool that can handle dynamic websites and can be easily updated to adapt to changes in the website’s structure.

Once you have collected the data, it is important to clean and organize it to make it usable. This will involve removing any irrelevant data and ensuring that the data is in a format that can be easily analyzed. In conclusion, Airbnb scraping can provide valuable information on properties, including prices, availability, and reviews. Web scraping and API scraping are both viable options for scraping Airbnb data, but web scraping is more widely used.

With the right scraping tool and a little bit of effort, you can easily collect and analyze data not only from Airbnb, but also from the most hospitality & travel platforms — for example, Tripadvisor. Our team’s portfolio includes work with such large sites as Facebook, YouTube, and LinkedIn, so we know perfectly well how to organize big data scraping properly.

Contact us for a free consultation and find out how to scrape Airbnb data efficiently, while considering your specific needs!