The City of Toronto is the most populous city in Canada and is recognized as an international centre of business, finance, arts, and culture. With the large amount of visitors Toronto sees everyday, an increasing number of people have been turning to alternative forms of accommodations when they stop by. This analysis aims to examine the different factors that affect the prices of Airbnbs in Toronto such as the size of the listing, information about the hosts, review scores, and the neighbourhoods or districts they are located in. Using a hierarchical fixed effect model, we model the log price using a room type structure using a combination of these covariates and find that the most important factors that affect listing prices are the number of people it accommodates, the number of bedrooms and bathrooms, and the location of the listing. Across all models tried, it was found that entire homes/apartments had a higher starting price than any of the other room types, with hotel rooms having the lowest intercept.

Data Description

Although Airbnb does not have an official API available to the public, data is available from InsideAirbnb. Inside Airbnb is a mission-driven project with the objective to provide data that quantifies the impact of short-term rentals on the residential market.

The dataset is available here: http://insideairbnb.com/get-the-data/