Pricing in the Sharing Economy—A Hybrid Approach Leveraging Econometrics, Machine Learning, and Artificial Intelligence

This study investigates the determinants of Airbnb prices in Athens and Thessaloniki, Greece, employing a hybrid approach combining econometric analysis, machine learning techniques, and artificial intelligence tools. Our findings highlight the significance of location, property type, host responsiveness, listing quality, and photograph quality in influencing rental prices. Notably, we leverage a publicly available AI tool to assess the esthetic and technical quality of listing photos, demonstrating its positive impact on rental prices. This underscores the increasing importance of visual marketing in the sharing economy and the democratization of AI tools for optimizing pricing strategies. We also conduct machine learning analysis, employing algorithms like Random Forest, k-Nearest Neighbors, Support Vector Machine, Neural Network, Gradient Boosting, and AdaBoost. Both AdaBoost and Gradient Boosting demonstrate strong performance across various metrics, with AdaBoost showing an advantage. The study offers valuable insights for Airbnb hosts, platform developers, and policymakers in understanding and optimizing pricing strategies within the short-term rental market.