Modelling Airbnb Prices in the Maltese Islands

Abstract: The digital platform Airbnb has gained popularity in a number of countries particu- larly in the Maltese islands. Striking a balance in setting a price that is competitive and also renders a good profit can be a challenge. In this thesis a model is de- veloped to predict the price of a listing in the Maltese islands for September 2022 through a machine learning approach whereby five types of models are considered. K Nearest Neighbours sets a baseline, while linear regression, a random forest, gra- dient boosted trees and neural networks are assessed in search of the model that is most generalisable beyond training data. Findings from this research conclude that gradient boosting specifically CatBoost model gives the best performance achieving an R2 of 0.77. Additionally the same models are re-fitted but incorporating additional walkable distance features to carefully identified points of interest namely historical sites, beaches, nightclubs, the capital city and bus stops. The results attained indicate that none of of the walkable distance features heavily contribute to explain any variance in the price of listings in the Maltese islands and only a slight improve- ment in model performance in some of the models considered is reported. Further to this, while retaining the additional distance features, training of the neural net- work is leveraged by pre-training the model on data that corresponds to another Mediterranean touristic island of Crete and a slight improvement is reported in model performance over the model solely trained on data for Malta from an R2 of 0.66 to 0.67. This result opens a window for further research that seek to reap the benefits of transfer learning.