Impacts of peer-to-peer rental accommodation in Stockholm, Barcelona and Rio de Janeiro : An exploratory analysis of Airbnb’s data

Detta är en Master-uppsats från KTH/Maskinkonstruktion (Inst.)

Sammanfattning: As a part of the growing movement called the “peer-to-peer” economy, Airbnb has changed the short-stay rental market and has become one of the world’s largest booking websites for finding an accommodation to stay. The platform has also affected the economy of tourism around the world, so, given the importance of the subject, in this thesis study, the impacts that the Airbnb rental accommodation model has on clients of Stockholm, Barcelona and Rio de Janeiro is studied. In this way, it has been analyzed how factors such as price, location and seasonality affect Airbnb customers in these cities. To do this, the three cities were first analyzed individually and then compared, using data from the Inside Airbnb website from 2010 to now. This research has been carried out through an exploratory analysis using the R programming language. The study has been divided into three parts: First, the Spatial Data Analysis has shown that Airbnb´s presence in all three cities has increased significantly in the past decade, growing from the most touristy parts of the city to surrounding areas. In addition, it has been observed that the largest number of Airbnb properties are apartments located near the city center and touristic places, which also are the most valued areas by Airbnb customers and the most expensive to rent a property. Secondly, a Demand and Price Analysis has been carried out. In this part, the demand for Airbnb listings has been estimated over the years since 2010 and across months. A significant increase in demand has been appreciated in the last decade, which also shows a seasonal pattern. In the three cases, the demand graph follows the city´s climate, showing the highest demand during the summer months, which corresponds to the most expensive period. Finally, through User Review Mining, customer opinion has been studied by applying text mining to reviews. In this part of the research, word clouds have been used to have a visual representation of the text data, showing the most frequent words and analyzing what makes customers feel comfortable and uncomfortable.

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