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Founded in 2008, more than 12 years ago, Airbnb has become the largest marketplace for lodging across the world. While it does not own or operate any of the properties or experiences offered on its website, it makes money by taking a commission on every booking on its platform. Common listings do include villas or a single bedroom, but there are also exotic stays listed on their site such as cabins and treehouses in far-flung picturesque locations. Along with the lodging-on-rent, Airbnb also offers “experiences” which are usually guided tours of historical locations or entire planned trips that cover days or weeks. The owners who rent out their places also vary. While some rent out their empty rooms, others have set up an entire condo to rent out on Airbnb. You could very well get a cheap bed-and-breakfast in London or even a house with a swimming pool in Bali, all on Airbnb. One of the best features offered is the innumerable filters that allow users to pinpoint exactly which type of lodging one is interested in, and the price range that is desired. Web scraping Airbnb data can be a game changer for travel market giants.
Thanks to the multiple filters and innumerable data points available, travellers can make informed decisions when trying to find the perfect place to stay for a short duration. The vivid description on most listings also gives a clear idea of how far the important locations in a city are from a particular listing. The distance from the closest train station, airport, or bus stand is also often listed. Thanks to the availability of transparent reviews by anyone who has rented the property earlier, digging deeper is also easier. Airbnb also tags the top listings (hosts) on its websites as “super hosts”, and these are usually the most bang for the buck lodging where most travellers have had an excellent experience earlier. Such a multitude of data points not only benefit those who want to book a place but also anyone web scraping Airbnb data. The detailed Airbnb dataset with all the listings for any location worldwide would be very attractive from a market-research standpoint.
There have been 150 million users on the site as of 2018, and the numbers are expected to grow despite the tumble caused by the Coronavirus. The number of bookings in 2020 stood at 193 million, which was quite a bit under the 272 million of 2019. The number of listings on its website stood at a magnanimous 7 million as of 2019. These ginormous figures all mean one thing- truckloads of data for anyone who wants to analyze the hotels and lodging industry or to understand underlying factors and fallacies in the travel industry.
When scraping data from any website that has listings of any product, you would expect to scrape certain data points for each. Similarly, Airbnb too offers multiple data points for all its listings. Some of the most common ones are:
PicturesHeadingLocationRatingNumber of guests that can be accommodatedNumber of BedroomsNumber of Bathrooms (private/shared)Number of BedsReviewsPrice per nightAbout this Place sectionList of available amenitiesList of unavailable amenitiesHouse rulesCancellation policy (which includes check-in and check-out timings)
There are other data points or features available for listings that stand out due to their quality offerings. These can be “enhanced cleanliness” or a “seamless check-in” experience, or a “great location”. A lot of important information is also hidden in plain text in the about us section where the host usually explains add-on services that he or she might be offering for extra charges. One such offering is food. While Airbnb does not include any sort of food in its offering, or features, many hosts do offer free breakfast or paid meals based on requirements.
Scraping listings from Airbnb can help you get a ton of data, but different data points can be used in different ways and can help in various business decisions. Some key problems that can be solved are:
Even if you have everything else right, unless your pricing hits the bull’s eye, the customer would refuse to avail of your services. This is because different hotel rooms or lodgings come with some specific offerings at each price point. To understand this correlation and find out the different factors that influence the price, a lot of data is required. Using the “cost per night” data from Airbnb can be a great way to analyze lodgings and their appropriate prices. This information can then be used by hotel chains or those who want to rent out their own spaces or even campsite owners.
Setting up the perfect lodgings may not draw too many travellers unless you get the location right. This may mean figuring out where guests want to stay. In a city, this may be near different modes of public transports or popular monuments or plazas. In scenic mountains, it might be a far-flung hillside that would be far from the main roads but still close enough to get to the market. Existing Airbnb listings and their locations can greatly help in understanding which locations should be targeted for setting up new establishments.
Not all Airbnb listings offer the same features. But some are common among the ones with the highest ratings. These features may come at a price or may even be common among the cheaper and the more expensive ones. For example, high levels of cleanliness may draw more visitors to all types of lodgings, whereas among the more expensive ones, those with a swimming pool may see higher bookings. Customer expectations and their preference towards certain features can be analyzed and this information can be put to use by businesses who want to draw the crowd to their establishments.
Customer reviews are one of the most important data points available on Airbnb. It is also the most difficult to analyze. Going through them manually may reveal a lot about customer sentiments and what makes them happy and what draws them away from certain establishments. This would, however, take a lot of time and effort. Instead, harnessing Natural Language Processing can help in providing you with results much faster.
The most popular tourist destinations in the world, Paris, Rome, London, or New York, are already served by hundreds and thousands of hotels and lodgings. But how many hotels, do you think, are present around the Pangong Tso Lake in Ladakh, India, or the Hitachi Seaside Park in Japan? Chances are, not too many. These lesser-known destinations are served by very few hotels and many localities in these unconventional travel destinations have taken the advantage of Airbnb to offer their extra space to fellow travellers. By web scraping Airbnb data, figure out which newer destinations can be served by your hotel or lodgings.
The hotel industry does not operate on its own. It works in sync with the entire travel industry. Travel and hotel listings data can be used by any secondary service provider. An airline company can use data from Airbnb to figure out which locations need to be added to their route. A travel agency can decide which monuments and places in the city can be offered to tourists on a trip. The possibilities are just as endless with web scraping Airbnb data.
Enterprises that need to scrape data from the web to empower their business decisions are usually held back due to the fear of complexity. Our team at PromptCloud helps in the heavy lifting so that our customers can focus on what’s really important–the data. Our fully-managed cloud-based solution helps simplify the entire process of web scraping Airbnb data and integration of data streams.
Step 1: You give us the requirements (websites, filters, etc.)Step 2: We give you the data (in the format and means that would suit your business integration best).
Our highly customizable real-time web scraping services help companies decide how much data they want, how frequently they want it to be updated, and where they want it from. Our pay-per-use model, where you are only charged based on the data you consume, helps make our services attractive to companies of all sizes. We believe that data holds the key to success in any industry today, and it can also benefit the travel industry immensely and help it in reaching greater heights as the effects of this pandemic wear down.
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