Browsing Airbnb listings are the best way to find accommodation across the globe. Over a decade, it has become the largest marketplace for renting out local housing. It offers the utmost flexibility, price offerings, infinite wanderlust possibilities, and smooth UX.
Airbnb itself doesn’t own any of the listed properties. But, it offers a shared space for hosts and guests to easily find each other and fulfill the technicalities of lodging with the minimum headache. The most common listing includes an apartment with one or two rooms. Apart from house rentals, Airbnb Experience offers complete packages of guided tours that pull Airbnb into a digital travel agency format rather than a third-party rental service.
However, the significant power of Airbnb is in its filters. Its flexible filters offer ease to the user for finding the right place for themselves – be it a villa with a pool, in a city center, for one person or in a group, and more. They can also compare pricing by checking out the comments for negative experiences. All these data are essential and are available openly to collect and analyze for your business needs.
Whether looking for the perfect spot for yourself or wondering about getting better rent for your place for the upcoming tourist season, scraping Airbnb data will offer a better solution. Simply getting into the website, copy-pasting the relevant information, and comparing it on an Excel sheet is time-consuming and requires lots of effort. However, web scraping Airbnb zip codes data can ease your task in an instant.
When you observe the scraped Airbnb zip codes data in a broader context, you can find patterns in your results which will give detailed insights into your customers, a specific market segment, locality, zip codes, or ongoing trends.
We can easily set up a web scraping code to pull zip code data on an excel sheet at iWeb Data Scraping.
Have you ever found yourself in a situation where you are trying to find the perfect place to spend the holiday? Or do you want to find out how your listing competes with your competitors? In such an instance, the power of web scraping comes into play. A web scraper is a software that helps automate the complex process of collecting valuable data from third-party websites. The data scraper will allow you to search for the listed properties filtered by addresses, cities, countries, zip codes, etc.
To scrape Airbnb data, we will use Python as a programming language as it is perfect for prototyping and has a vast online community. Today, we are using two main tools:
BeautifulSoup: It helps in the easy extraction of data from HTML documents.
Selenium: It automates the web-browser action.
We need to implement two significant program parts
1. Scanning a search page
2. Extracting data from all detailed pages.
We are interested in parsing all vital information. So, we will process both types of pages – search, and detail. We will also look into the listings deep below the top search page. Per the search page, there are 20 results and up to 15 pages per destination. Scraping webpages is extremely simple with Python. Below is the function that extracts the HTML and places it into the BeautifulSoup object:
For easy navigation through an HTML tree and for accessing its elements, BeautifulSoup is a perfect option. For an Airbnb search page, our area of interest is individual listings. We must first define their tag type and class names to access them. For this, we will inspect the page with the Chrome tool.
All the listings are within a ‘div’ object with a class ‘_8s3ctt. As all search pages comprise 20 separate listings, all are available simultaneously using the Beautiful soup method ‘final.’
From the detail page, we can collect almost high-end information about listings like name, total price, ratings, country, zip codes, etc.
All these features are within different HTML objects with other classes. We can write multiple single extractions, one per feature:
Now, we get unified extraction information.
Everything is available to process the entire page with listings and extract their essential features. Below is an example of extracting only two components.
Generally, Airbnb gives access to about 300 listings per location, and we will scrape all of them.
Just add a parameter ‘items_offset’ to the initial URL. Below is the list with all links per location:
We will click on the corresponding elements to access the amenities and price details.
Let’s put everything into a Python function.
So, all the necessary information is at our disposal. After thoroughly inspecting the web page with a Chrome developer tool, note down all the names and classes of HTML elements. Feed them and see the results.
Instead of visiting 300 web pages, we have chosen a size of 8.
The raw datasets obtained after running the program are as follows:
Conclusion: Now that you know how to scrape Airbnb data, you can scrape any parameter and see the results obtained.
For more information, get in touch with iWeb Data Scraping now! You can also reach us for all your web scraping service and mobile app data scraping service requirements.