Best Airbnb Scrapers

5 Best Airbnb Scrapers 2024: How To Scrape Data Using Python

Published on: May 24, 2023
Last Updated: May 24, 2023

5 Best Airbnb Scrapers 2024: How To Scrape Data Using Python

Published on: May 24, 2023
Last Updated: May 24, 2023

In a hurry?
The best Airbnb scraper in 2024, as found in our independent testing, is ScraperAPI!

Most of us are aware of Airbnb- it’s a popular service that allows tourists and travelers to rent private accommodations like rentals, cabins, homes, beach houses, etc, around the world- that too at affordable rates.

For most people looking for a comfortable resting space that doesn’t cost an arm and a leg, Airbnb is the best option. 

Since people who list their property on Airbnb want to earn through renting, they post all kinds of rental data which Airbnb makes accessible to the public.

This data includes everything from pricing to Airbnb amenities. 

While you can simply log onto the platform, search for a place, and write down the data you need, the process is really tedious and scattered.

There’s no simple way to look for a meaningful dataset that has relevant information like the number of listings in an area, the average price, variation in ratings, etc.

So if you’re interested in collecting Airbnb rental data for your new listings, recommendation system, data analysis project, or any other reason, your best bet is to opt for data scraping.

There are two main ways you can scrape Airbnb data and collect it in an organized fashion on an Excel sheet.

The first method is using an Airbnb scraper AKA web scraper, which is software dedicated to automating the scraping, processing and collecting relevant data.

This is usually a paid online service that both developers and non-developers can use. 

The second method is by creating your own web code in Python that can be stored and run on Selenium. This requires extensive knowledge and solid coding skills.

The favorable option is to go for a cost-effective Airbnb scraper tool, as Airbnb is anti-scraping and you will need to implement proxies in your scraping anyway, otherwise you run the risk of getting blocked by the site permanently.

As there are many Airbnb scrapers out there with varying degrees of user-friendliness and good or bad performance, we’ve compiled a list of what we find to be the best Airbnb scrapers of the bunch. So let’s dive in!

Best Airbnb Scrapers 2024

Here’s a quick look at the best Airbnb scrapers:

  1. ScraperAPI – 🏆 Winner!
  2. Oxylabs
  3. Bright Data
  4. Apify
  5. Octoparse

1. ScraperAPI


Recommended Guide: ScraperAPI Review

Like other Airbnb scraper options we’ve mentioned so far, ScraperAPI performs a plethora of important functions like running proxies, CAPTCHAs, built-in drivers, etc, and handles all browsers.

It also facilitates JavaScript rendering and makes the whole process smooth, perfect for an Airbnb scraper.

You don’t have to fetch webpages yourself as ScraperAPI does the job for you. You only have to worry about the data processing and management.

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There is zero-risk of losing progress or getting blocked as well, as ScraperAPI runs residential proxies.

You can choose between rotating and sticky ones, though rotating are better for Airbnb. You can customize request headers based on geolocation and parse all the data in-app.

Other than that, the software is malleable and developers can make various customizations through coding in Python and JS.

You can enable features like JS rendering through simple codes like add &render=true.

ScraperAPI gives you first 5,000 APIs free, then paid plans start at $29/mo, making it competitively low-priced.

2. Oxylabs

Oxylabs Web Scraper API

Recommended Guide: Oxylabs Review

Oxylabs is an efficient Airbnb scraper with rotating proxies taken from a massive pool, enabling you to bypass anti-scraping protocols every time.

Oxylabs renders JavaScript quite fast and lets you scrape using AngularJS, React, and other libraries. You can set custom wait times for clicks and scrolls too.

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If you don’t want JSON-formatted HTML data, then you can perform general scraping too.  Both are a single API call away.

Another nifty feature is the codeless scraping, which lets you simply run a scraper that puts all data into Google Sheets- you don’t even have to write a single command.

Lastly, if you’re confused about using Oxylabs or where and how to begin scraping, you can refer to the Blog section on their site.

3. Bright Data

Bright Data Web Scraper IDE

Recommended Guide: Bright Data Review

Bright Data is a comprehensive tool that does everything from collecting data to delivering ready-made datasets.

It has many high-end features and is coveted for the robust proxy service. 

It uses proprietary site unlocking, so crawling any data is easier than ever. It’s also industry-regulated and follows privacy laws (CCPA and GDPR).

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There are several reasons why we consider Bright Data to be the best Airbnb scraper, but it’s mostly because of the proxy aspect.

Airbnb tightly regulates the anti-scraping protocols, so software like Bright Data that retrieve relevant info while avoiding getting blocked are a must.

That’s why the app gives residential, datacenter, and mobile IP options that total to over a whopping 72 million IPs.

While Bright Data Scraper has a lot of additional features and abilities as well, Web Unlocker is the most notable one.

It automatically solves Captchas for sites and retries when failed without added wait time. Meanwhile, Search Engine Crawler gives geo-targeted results for all searches.

Another major pro about Bright Data are the multiple pricing options. You can either pay-as-you-go, which start at 90 cents per IP and 12 cents per GB.

If you go for residential IP (the best option of the three), then you’re effectively paying $25 per GB. Mobile IPs are the most expensive and cost $60 per GB.

If you use the app too often, then it’s best you commit to a monthly payment schedule for a good discount. 

4. Apify


Next up is Apify, a solid data extraction and Airbnb scraper tool that packs efficient automation.

However, unlike most other Airbnb scraper apps, you do have the option to add your own codes to processes and run them.

Because of this, Apify is good for all- non-coders, amateurs and coding pros.

Apify boasts a simple interface. There is a slight learning curve like Bright Data, but Apify has much more resources on its online site for you to learn from.

There’s an active developers’ community as well that discusses Airbnb scraping.

Apify developers offer turnkey projects to businesses too, custom built to mine specific Airbnb data. 

As for proxies, Apify lets you run them on any website like Airbnb that uses FTP, HTTP, and HTTPS.

The Proxy monitors your IP pool and rotates between addresses continuously to avoid blocking. To check your proxy settings, visit the Proxy page on the Apify Console.

Other than that, you get to download data of up to 1000 results, even though Airbnb displays only 300 listings. Info can be scraped in HTML, JSON, CSV, RSS, EXCEL, and XML.

Apify has a free plan for users, but it’s very restricted. We recommend trying the Personal paid plan that starts at $45/mo.

Businesses can go for different plans. Paid options have better support, retention, and RAM. Overall, Apify is a solid Airbnb scraper with good proxy protocols.

5. Octoparse


Our final pick as one or the best Airbnb scrapers is Octoparse. The software automates the data extraction completely and is super simple to use, thanks to the innovative point-and-click mechanisms.

You can crawl and export info in many formats including CSV, TXT, HTML, EXCEL, etc. Octoparse pairs with Zapier, Google Sheets, SQL Server, MySQL, and Oracle too.

A highlight for Octoparse is its brilliant support network. The website has an active FAQ and blog section where most common issues are listed.

If anything’s unavailable, you can reach out to customer support any time. Furthermore, Octoparse has excellent proxies that can bypass all anti-scraping protocols. 

Octoparse has several templates for mining Airbnb data, so you won’t have to create special tables with particular elements and fields either.

You can also schedule scraping and set auto tasks for each time. 

You can use Octoparse not only with windows but also on macOS. Just by a click data can be extracted, if you are using the designated browser it comes with.

You are not going to be blocked from any website as it mimics human behavior. The best part about Octoparse is that various pages can be scraped at once which saves a lot of time. 

Lastly, Octoparse is efficient at scraping from a JavaScript website like Airbnb and doesn’t need special settings.

There’s an Ad block too, to avoid lagging and low extraction speed.

Octoparse has subscriptions starting at $75/month, though you will get a free 14-day trial in the beginning. 

How To Scrape Airbnb Data From Using Python

Before we begin, there are a few things you must note. Firstly, we’re going to use Python as our programming language and will run the script on Selenium.

Python is just an example, so even if you don’t follow our guide or use a different language like C++ or JavaScript, you can gain benefit from reading the guide and getting an idea of how to program your custom scraping tool. 

Secondly, if you’re not a coder, it’s best to skip this part and stick to the web scrapers we mentioned above. However, if you’re planning to learn code, this guide can be a bit useful. 

Lastly, Aiirbnb isn’t easy to scrape and it has several anti-scraping techniques it implements to prevent you from scraping.

It’s a JavaScript-heavy site, meaning if you turn off JavaScript execution, you won’t gain access to the content available on the site. So you essentially can’t use Requests to render it.

You can use BeautifulSoup to navigate through the HTML elements if you want, which we’ll discuss later on. Mainly however, you just need the Selenium Web Driver.

Using Selenium

Most of the web scraping developers do is performed on Selenium, whether the subject is Airbnb or some other site.

Selenium is built with an API designed to replicate the clicks, scrolls, and other interactions humans do on websites.

You can load tons of data on Selenium by basic automated interactions, and the code you run will scrape it.

Another way to look at it is that Selenium is proficient at automating browsers, so if there’s any Ajaxified website like Airbnb, you can automate Chrome pages to open and run on Selenium by themselves.

The API will let you access the data you specify in the script.

Scraping Code

Before we jump into scraping, it’s good to look at what the typical user sees on the site for a better understanding.

As a visitor, you enter the site, then look for a particular location or destination at a desired date using a search button, which leads to the main search page.

There are multiple listings with brief info, but every listing has more details which you’re redirected to if you click on it.

As we need all the useful information we can get from Airbnb, we’ll need to process both the search and detail pages.

There are also listings hidden behind the top pages, and an automated scraping tool should be able to access all these.

So in short, we need to do both- scan the search page and extract info from the detail page, which the code focuses on.

Now onto the sample script, there are obvious changes you’re going to have to make if you’re scraping specific details of a selected Airbnb listing or are using a browser other than Google Chrome.

But we’ve kept our script short and easy to follow, and it neither handles exceptions nor does it incorporate any proxies you may want to protect your IP address.

This is for making the guide easy to follow- you can always add proxies to codes using other online guides.

The process is basically broken down into 3 simple steps:

  1. Initializing the webdriver from Chrome through Selenium.
  2. Using the webdriver to visit Airbnb and opening the location you want to scrape. You can also manually copy and paste the URL into the driver.
  3. Run the script we mentioned below. You can manually scroll as well as Selenium may not support direct scrolling.
from selenium import webdriver

class AirbnbScraper:


self.PATH = “chromedriver.exe”

self.driver = webdriver.Chrome(self.PATH)

self.hotel_list = []

self.hotel_info = {}

self.hotel_info[“name”] = “NA”

self.hotel_info[“about”] = “NA”

self.hotel_info[“price”] = “NA”

self.hotel_info[“verified”] = “NA”

defget_hotel_info(self, url):


# Parse data out of the page

self.hotel_info[“name”] = self.driver.find_element_by_class_name(“_fecoyn4”).text

self.hotel_info[“about”] = self.driver.find_element_by_class_name(“plmw1e5”)[0].find(“span”)

self.hotel_info[“price”] = self.driver.find_element_by_class_name(“_tyxjp1”).text

#add hotel info to hotel list


urls = [“”,]

AirbnbScraper = AirbnbScraper()

for urlin urls:



As you can see, the script takes the different listing URLs and presents them as details about each property. 

As you can see, the script takes the different listing URLs and presents them as details about each property. 

Using Proxies

Now that you’ve run the code as a trial, it’s important to discuss the need of proxies for scraping.

No matter whether the code you use is similar or different each time, Airbnb has protocls that will block you after a couple requests to scrape data.

Hence, you need to bypass the anti-scraping feature.

Proxies are important for this, as they bypass IP tracking each time and save you from getting blocked. 

There are many options amongst proxies, but we recommend using residential proxies.

These mask your activity really well by routing the scraping requests through regular local internet users.

Smartproxy, Shifter, and Bright Data are some of the best residential proxy providers who rotate between IP addresses continuously. 

Furthermore, if you’re going to scrape data on a bigger scale, then you’ll need a captcha solving app too.

Make sure to set random delays between each request as well, and rotate the request header values every time.

Other Tips and Tools

There are some tips and tricks that can be beneficial for when you’re modifying the script we’ve written above.

  • You can use BeautifulSoup in the coding process. If you want to specify a property, you can use the find_all method, then create div fields and choose elements. 
  • Execute a JavaScript code if you need specific pages or elements and don’t use a parser that specifies fields.
  • BeautifulSoup can also be used to navigate through the HTML tree on the site.
  • Add a wait function before scrolls.
  • If you’re looking to sort the listings, you can use XPath or CSS. They’re also good for highlighting specific fields.
  • If you want to define class names and tag types for individual listings, use a Chrome developer tool to inspect the page (key F12).
  • To grab all the listings on one page, use the findAll method on BeautifulSoup.
  • Loading detail pages can take a while, so to access price details and amenities click and wait on corresponding elements.

Parallel Execution

As we mentioned, with Python Selenium on Airbnb, we’re scraping both the search pages themselves and their listings.

Each location gives you around 15 search pages that load within seconds, in part thanks to the lack of JavaScript elements.

You get a dataset pretty fast, and it mentions all the basic listing features. URLs for detail pages are present too.

However, when it comes to the listing detail pages, scraping a single page takes 5-6 seconds for rendering and a few additional seconds for computing the script.

This process doesn’t take much CPU power but exhausts a lot of time, so the most efficient way of scraping is using parallel execution.

Instead of going to 200-300 or more web pages in a huge loop, try splitting the URLs into batches, then loop over the batches.

You can run 6-10 batches, depending on your CPU. To do parallel execution, use multiprocessing import Pool.

Running more batches or processes drastically slows down the CPU and can block or fail your scraping (if the proxies shut down), so being careful with your Airbnb scraper is key.

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Written by Jason Wise

Hello! I’m the editor at EarthWeb, with a particular interest in business and technology topics, including social media, privacy, and cryptocurrency. As an experienced editor and researcher, I have a passion for exploring the latest trends and innovations in these fields and sharing my insights with our readers. I also enjoy testing and reviewing products, and you’ll often find my reviews and recommendations on EarthWeb. With a focus on providing informative and engaging content, I am committed to ensuring that EarthWeb remains a leading source of news and analysis in the tech industry.