Scraping is the best solution when we need information from websites, but obtaining such information from a web source takes time and effort.
One of the most significant websites that offer accommodations for tourists is Airbnb. Due to its vastness, the accommodation service may be split into different types, including guest houses, resorts, tree houses, cottages, desert houses, and many more listed in the categories.
While the website offers a plethora of hotel-related information, its layout might need to be revised for new users. However, your scraping process can be complex because Airbnb uses different java script types to extract information. Learn how to use Beautiful Soup to extract hotel information from the Airbnb website.
The website offers different hotel data, however a website's architecture is very complex for beginners. In contrast, Airbnb utilizes different Java scripts for accessing data, so that your scraping procedure may take so much of work.
When we started coding and looking for types in each section, we assumed that scraping hotel information from the Airbnb website would be as easy as extracting other hotel sites. However, as we continued, we discovered that our assumptions were wrong. Let us follow the instructions described below.
Installing the required libraries, especially the beautiful Soup library, should be your first step. Alternatively, you can use syntax.
It is essential to remember that installing other frameworks, including Requests, will enable you to execute HTTP requests and get the HTML of the website.
After that, you need to import other basic modules, as shown in the following script (if you don't have them yet, install them right away).
The fundamentals of Beautiful Soup are simple, and you can quickly grasp it after just a few lines of code. The scraping we show you here involves a single page. In this example, the scraping is demonstrated on a single page (we do not want to promote any particular hotel, after all).
We shall use the villa Dewi Laksmi as an example. An essential thing to remember is the item names and category names we will get from the web page.
In this example, we will retrieve the page's title in an h1 with the category "_fecoyn4". You can experiment with the following syntax:
However, the outcome will be as follows:
As we attempt to get an element of a "NoneType," it follows that the class we aim to achieve has not been able to obtain anything properly. Trying to print(title) will likewise get a null result.
What could be done, then? We'll get the site's raw form HTML. To accomplish this, we will be using the syntax shown below.
And the result will be as follows:
However, it will be useless if this core HTML isn't used. Regex can be utilized to obtain the necessary info (in this example, we will scrape the title). Finding the term, we're searching for—in this example, "Villa Dewi Laksmi"—is the first step.
Next, use CTRL+F to locate the phrase in the core HTML result. You'll find it by searching for "__typename": "PdpTitleSection" and "Villa Dewi Laksmi." From this, we can build a regex with the following syntax:
If it is included in the HTML raw content, the same technique could be used to get the cost, area, IP address, and other details. Since the headline/title coding will be the same on all product pages, the above syntax can also be applied there.
The implication is that regardless of whether a website is difficult to extract, there is always a method to get the information you need. However, if you have the opportunity and desire, you should contact the professionals and get a quote for the services you need.
Are you curious about learning how to scrape hotel information from the Airbnb website using Beautiful Soup? Visit our website for detailed information.
You can also contact us for your mobile app scraping and web scraping services requirements.
Our web scraping expertise is relied on by 4,000+ global enterprises including Zomato, Tata Consumer, Subway, and Expedia — helping them turn web data into growth.
Watch how businesses like yours are using Actowiz data to drive growth.
From Zomato to Expedia — see why global leaders trust us with their data.
Backed by automation, data volume, and enterprise-grade scale — we help businesses from startups to Fortune 500s extract competitive insights across the USA, UK, UAE, and beyond.
We partner with agencies, system integrators, and technology platforms to deliver end-to-end solutions across the retail and digital shelf ecosystem.
Complete guide to scraping Shopify store data in 2026. Extract product prices, reviews, and inventory from Shopify stores for competitive intelligence.
Discover how Natural Grocers achieved a 23% increase in promotional ROI using real-time organic product pricing intelligence. Learn how data-driven pricing strategies enhance promotions and retail performance.
Track UK Grocery Products Daily Using Automated Data Scraping across Morrisons, Asda, Tesco, Sainsbury’s, Iceland, Co-op, Waitrose, and Ocado for insights.
Whether you're a startup or a Fortune 500 — we have the right plan for your data needs.