Forecasting Hotel Demand using Machine Learning Approaches

Abstract

A critical aspect of revenue management is a firm's ability to predict future demand. Historically hotels have used pick-up based models owing to the complexities of trying to build casual models of demands. Machine learning approaches are slowly attracting attention owing to their outstanding predicting power and flexibility in modeling relationships. This study provides an overview of approaches to forecasting hospitality demand using machine learning models, including Neural Network, Nearest Neighbors, Tree, and Support Vector Machine. The out-of-sample performances of the above approaches are illustrated by using two sets of data: one from a single hotel with long booking windows up to 12 months, the other from 24 hotels with 14 days advanced bookings and additional information including pricing, location, etc. This research appears to be the first study in academia applying machine learning approaches in hotel demand forecast. The empirical findings prove that machine learning approaches outperform traditional models, especially given long booking history. The proposed models are valuable for practitioners in improving forecast accuracy and optimizing revenue, and lay the groundwork for future research into refining machine learning models in hotel revenue management.