Airbnb has been the go-to application for tourists in the past few years. Within the last 11 years, it has grown meterolically and has grown from nothing to a 30 billion dollar firm (whew!). Eleven years on, Airbnb’s site lists more than six million rooms, flats and houses in more than 81,000 cities across the globe. On average, two million people rest their heads in an Airbnb property each night – half a billion since 2008. Last year, Forbes estimated the business to be worth 31 billion dollars (double whew!).
Naturally, with any multibillion dollar organization, exploring the inner workings and trends of their data would prove to be an exciting task. Especially with an organization that has uprooted the hotel business out from its very roots and challenged traditional thinking when it comes to housing and accomodation.
Obviously Airbnb doesn't provide us with open data or databases that we can work with but luckily, I found http://insideairbnb.com/ which provides us with public information compiled from the Airbnb web-site and analyzes publicly available information about a city's Airbnb's listings. Coincidentally, it also gives us very convenient filters to work with which made my Exploratory Data Analysis that much easier to work with.
I chose to work with the Toronto Airbnb data and perform EDA and run a few models on it. With the popularity of Airbnb growing by leaps and bounds (along with a few hiccups on the way), it seemed only natural to work on the data to glean more insights on it.