A machine learning model for occupancy rates and demand forecasting in the hospitality industry

Abstract

Occupancy rate forecasting is a very important step in the decision-making process of hotel planners and managers. Popular strategies as Revenue Management feature forecasting as a vital activity for dynamic pricing, and without accurate forecasting, errors in pricing will negatively impact hotel financial performance. However, having accurate enough forecasts is no simple task for a wealth of reasons, as the inherent variability of the market, lack of personnel with statistical skills, and the high cost of specialized software. In this paper, several machine learning techniques were surveyed in order to construct models to forecast daily occupancy rates for a hotel, given historical records of bookings and occupation. Several approaches related to dataset construction and model validation are discussed. The results obtained in terms of the Mean Absolute Percentage Error (MAPE) are promising, and support the use of machine learning models as a tool to help solve the problem of occupancy rates and demand forecasting.