Forecasting hotel room prices when entering turbulent times: a game-theoretic artificial neural network model

Abstract

Purpose This study aims to propose a risk-induced game theoretic forecasting model to predict average daily rate (ADR) under COVID-19, using an advanced recurrent neural network. Design/methodology/approach Using three data sets from upper-midscale hotels in three locations (i.e. urban, interstate and suburb), from January 1, 2018, to August 31, 2020, three long-term, short-term memory (LSTM) models were evaluated against five traditional forecasting models. Findings The models proposed in this study outperform traditional methods, such that the simplest LSTM model is more accurate than most of the benchmark models in two of the three tested hotels. In particular, the results show that traditional methods are inefficient in hotels with rapid fluctuations of demand and ADR, as observed during the pandemic. In contrast, LSTM models perform more accurately for these hotels. Research limitations/implications This study is limited by its use of American data and data from midscale hotels as well as only predicting ADR. Practical implications This study produced a reliable, accurate forecasting model considering risk and competitor behavior. Theoretical implications This paper extends the application of game theory principles to ADR forecasting and combines it with the concept of risk for forecasting during uncertain times. Originality/value This study is the first study, to the best of the authors’ knowledge, to use actual hotel data from the COVID-19 pandemic to determine an appropriate neural network forecasting method for times of uncertainty. The application of Shapley value and operational risk obtained a game-theoretic property-level model, which fits best.