Improving Hotel Room Demand Forecasts for Vienna across Hotel Classes and Forecast Horizons: Single Models and Combination Techniques Based on Encompassing Tests

Ulrich Gunter

doi:10.3390/forecast3040054

Abstract

The present study employs daily data made available by the STR SHARE Center covering the period from 1 January 2010 to 31 January 2020 for six Viennese hotel classes and their total. The forecast variable of interest is hotel room demand. As forecast models, (1) Seasonal Naïve, (2) Error Trend Seasonal (ETS), (3) Seasonal Autoregressive Integrated Moving Average (SARIMA), (4) Trigonometric Seasonality, Box–Cox Transformation, ARMA Errors, Trend and Seasonal Components (TBATS), (5) Seasonal Neural Network Autoregression (Seasonal NNAR), and (6) Seasonal NNAR with an external regressor (seasonal naïve forecast of the inflation-adjusted ADR) are employed. Forecast evaluation is carried out for forecast horizons h = 1, 7, 30, and 90 days ahead based on rolling windows. After conducting forecast encompassing tests, (a) mean, (b) median, (c) regression-based weights, (d) Bates–Granger weights, and (e) Bates–Granger ranks are used as forecast combination techniques. In the relative majority of cases (i.e., in 13 of 28), combined forecasts based on Bates–Granger weights and on Bates–Granger ranks provide the highest level of forecast accuracy in terms of typical measures. Finally, the employed methodology represents a fully replicable toolkit for practitioners in terms of both forecast models and forecast combination techniques.

Highlights

IntroductionAccurate hotel room demand forecasts ( daily forecasts) are crucial for successful hotel revenue management (e.g., for revenue-maximizing pricing) in a fastpaced and competitive industry [4,5,6,7,8]
Prior to consulting the forecast accuracy measures, a mere visual inspection of this graph shows that none of the employed forecast models are widely off track and that they are able to pick up the seasonal drop in hotel room demand after New Year
With respect to the single models, the Error Trend Seasonal (ETS) model is able to achieve the lowest sum of ranks in four cases (‘luxury’ for h = 1, 7, 30, ‘economy’ for h = 7), the Seasonal NNAR model in three cases (‘luxury’ for h = 90, ‘upscale’ for h = 90, ‘midscale’ for h = 90), and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model in two cases (‘upper upscale’ for h = 1, ‘all’ for h = 30)

Summary

Introduction

Accurate hotel room demand forecasts ( daily forecasts) are crucial for successful hotel revenue management (e.g., for revenue-maximizing pricing) in a fastpaced and competitive industry [4,5,6,7,8] Besides their hotel’s absolute performance, (revenue) managers are typically interested in the relative performance of their hotel with respect to the relevant peer group ( known as the competitive set; [9]) within or beyond the same destination: other hotels from the same hotel class, those that cater to the same type of travelers [10], or those belonging to the same hotel chain [11,12]. Another advantage of aggregated data per hotel class is that these do not suffer from the lack of representativity that individual hotel-level data would

Methods

Results

Conclusion