In the past ten years, Airbnb has become the most significant global marketplace for renting out local housing. By searching Airbnb lists, finding lodging anywhere in the world has become quite simple. An Airbnb data scraper enables users to collect detailed information from the platform, including pricing data, customer reviews, rental listings, host profiles, and other relevant insights.
Despite having many competitors and a challenging business environment, Airbnb remains a profitable business strategy. Customers appreciate this platform because of the price options’ flexibility, the user-friendly UX, and the impression of limitless travel options.
In more than twelve years since its founding in 2008, Airbnb has developed into the most significant housing platform in the world. The business provides booking for more than 3.5 million lodgings throughout 33,000+ locations and 191 countries, and it has more than 160 million reviews. This post will teach you how to scrape data from Airbnb for your upcoming project effectively.
How Will Web Scraping Airbnb Data Change the Travel Industry?
Travelers looking for the ideal spot to stay for a brief period can make informed judgments thanks to the numerous filters and countless data points available. Most listings’ informative descriptions also clearly indicate how far the major city landmarks are from a specific listing. Listing the distance to the nearest train station, airport, or bus stop is standard. Digging further is made simpler by the availability of honest reviews written by anyone who has rented the home previously. Airbnb labels the top hosts on its website as “super hosts.” These are typically the accommodations that offer the best value and where the majority of visitors have previously had a positive experience.
Such a large number of data points are advantageous for anyone who wants to web-scrape Airbnb data and those who wish to reserve a place. From a market-research perspective, the comprehensive Airbnb dataset with all of the listings for any place worldwide is appealing.
According to projections, the website will have 150 million users by 2023, despite a decline due to the Coronavirus. As of 2023, the website has a magnificent 7 million listings. Bookings decreased significantly from 272 million in 2019 to 293 million in 2022.
As of 2019, the website has a magnificent 9 million listings. These enormous numbers imply that truckloads of data are available for anyone who wants to research the hotel and lodging sector or comprehend driving forces and myths in the travel sector.
What kind of Data will be available on Scraping Airbnb Data?
A marketplace called Airbnb allows users to book different properties, including apartments, homes, and bed and breakfasts, for brief stays. Nowadays, many people prefer to spend their vacations traveling and learning about new areas online. As a result, the website offers a wealth of helpful information while looking through its listings. It enables users to stay in private homes that are well-equipped for their needs and preferences and helps them choose the appropriate location.
You would anticipate scraping specific data points for each product when gathering information from any website that lists products. Similarly, Airbnb provides numerous data points for each of its listings. Among the most typical ones are:
- Images
- Location
- Reviews and Ratings
- Accommodation capacity
- Number of Bedrooms
- Number of Bathrooms
- Number of Beds
- Cost per night
- Details of amenities
- Check-in / Check-out timings
What are the Benefits of Airbnb Data Scraping?
You can gather a ton of data by Airbnb data scraping but different data points can be used differently and support different business decisions.
Choosing the Correct Price
If your pricing is off, the customer will still choose not to use your services, even if everything else is perfect. Depending on their price, different hotels can offer different amenities. Much information is needed to comprehend this correlation and identify the various elements that affect the pricing. Analyzing hotels and their acceptable costs may be done well using Airbnb’s “cost per night” data.
Choosing the Right Locations to Target with Airbnb Web Scraping
With the correct location, even the best accommodations can entice many tourists. Finding out where visitors would like to stay may be necessary. Many city landmarks, public plazas, and public transportation options may be nearby. It can be a remote hillside in a picturesque mountain range isolated from the main highways but still accessible to the market. Existing Airbnb listings and their locations can assist in determining which locations should be the focus of new enterprises.
Identifying the Most Wanted Characteristics
Different Airbnb listings have different features. Yet, the ones with the best ratings share some of them. It is possible to find inexpensive, premium models with these features for an additional cost. Costs may rise if kept immaculate, though people may book more expensive lodgings with pools if kept immaculate. Businesses that aim to entice customers to their establishments might examine customer expectations and preferences for particular characteristics and use this information to their advantage.
Recognizing customer feelings
One of the most critical information available on Airbnb is customer reviews. In addition, it is the most challenging to assess. Manually going through them could disclose a lot about consumer attitudes, what makes them happy, and what keeps them away from particular businesses. Yet it would take a lot of time and work. Instead, harnessing Natural Language Processing can help you achieve results much faster.
Locating Fewer Traveled Locations
Countless hotels and motels are available globally in the most well-known tourist locations, including Paris, Rome, London, and New York. Because there are so few hotels in these lesser-known locations, many communities there have seized the opportunity to use Airbnb to share their extra space with other travelers. See which more recent locations your hotel or accommodations can service by web scraping Airbnb data.
Performing Supporting Services
The hotel business does not run independently. It functions in concert with the entire travel sector. Any secondary service provider may use the travel and hotel listings data. Airlines can use Airbnb data to determine which locations should be added to their routes. An itinerary for a tour might include city landmarks and locations selected by a travel agency. Web scraping data from Airbnb opens up an equally limitless range of possibilities.
Final Thoughts
Due to the substantially more extensive selection of rentals, many individuals now favor Airbnb over other websites. Compared to other hotel booking websites, it offers better service and is less expensive. Airbnb’s ratings must reflect the time it takes to find a quality place to stay. A few excellent choices and reputable businesses are constantly available for you.
On the downside, due to infrastructural problems and prior fraud allegations, most tourists do not make their reservations through Airbnb. Despite all of this information, most individuals concur that one of the finest lodging options when traveling is Airbnb.
