A comparison of forecasting methods for hotel revenue management

Abstract

The arrivals forecast is one of the key inputs for a successful hotel revenue management system, but no research on the best forecasting method has been conducted. In this research, we used data from Choice Hotels and Marriott Hotels to test a variety of forecasting methods and to determine the most accurate method. Preliminary results using the Choice Hotel data show that pickup methods and regression produced the lowest error, while the booking curve and combination forecasts produced fairly inaccurate results. The more in-depth study using the Marriott Hotel data showed that exponential smoothing, pickup, and moving average models were the most robust.

A Study of Time Series Model for Predicting Jute Yarn Demand: Case Study

“…Time series analysis is also used in tourism sector. Weatherford and Kimes [13] applied different forecasting methods on hotel revenue management and recommended that exponential smoothing, pickup, and moving average models were the most suitable for forecasting as well as revenue generation.…”

Intelligent System to Support Judgmental Business Forecasting: The Case of Hotel Room Demand in Hotel Revenue Management System

“…Combined models use either regression or a weighted average of historical data and advanced booking models to develop forecasts. A review of forecasting methods for all three types is found in Weatherford andKimes, 2008 andFrechtling, 2001. In this study particular interest is devoted to reservation data as it is very rich and contains very useful information indicating the actual demand to come.…”

Hotel Revenue Management – A Critical Literature Review

“…Successful application of revenue management requires hotels being able to forecast demand. Th erefore, a high proportion of the research literature is dedicated to forecasting from theoretical and methodological perspective (Burger, Dohnal, Kathrada & Law, 2001;Frechtling, 2001;Tranter et al, 2008; Weatherford, Kimes & Scott, 2001;Weatherford & Kimes, 2003, among others), summarized in Table 4.…”