Predicting prices of Airbnb listings via Graph Neural Networks and Document Embeddings: The case of the island of Santorini

Nikos Kanakaris, Nikos I. Karacapilidis

Abstract: We propose a new approach for predicting prices of Airbnb listings for touristic destinations such as the island of Santorini using graph neural networks and document embeddings. The already existing methods rely only on the features of each individual listing, ignoring any topological or neighborhood properties. Our approach represents the listings of a given area as a graph, where each node corresponds to a listing and each edge connects two similar neighboring listings. This enables us to not only exploit the features of each individual listing, but also to take into consideration information related to its neighborhood. Our preliminary experiments demonstrate that the proposed approach outperforms a list of classical regression models as far as the coefficient of determination (R2) is concerned and decreases the Mean Squared Error (MSE). The data of the experimentations reported in this paper have been retrieved from the insideairbnb.com platform and describe the Airbnb listings of the island of Santorini.