Forecasting hotel arrivals and occupancy using Monte Carlo simulation

Abstract

Forecasting hotel arrivals and occupancy is an important component in hotel revenue management systems. In this article, we propose a new Monte Carlo simulation approach for the arrivals and occupancy forecasting problem. In this approach, we simulate the hotel reservations process forward in time, and these future Monte Carlo paths will yield forecast densities. A key step for the faithful emulation of the reservations process is the accurate estimation of its parameters. We propose an approach for the estimation of these parameters from the historical data. Then, the reservations process will be simulated forward with all its constituent processes such as reservation arrivals, cancellations, length of stay, no shows, group reservations, seasonality, trend and so on. We considered as a case study the problem of forecasting room demand for Plaza Hotel, Alexandria, Egypt. The proposed model gives superior results compared to existing approaches.