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

Fatemeh Binesh (Department of Tourism, Hospitality and Event Management, University of Florida, Gainesville, Florida, USA)
Amanda Mapel Belarmino (William F. Harrah College of Hospitality, University of Nevada Las Vegas, Las Vegas, Nevada, USA)
Jean-Pierre van der Rest (Department of Business Studies, Leiden Law School, Leiden University, Leiden, The Netherlands)
Ashok K. Singh (William F. Harrah College of Hospitality, University of Nevada Las Vegas, Las Vegas, Nevada, USA)
Carola Raab (William F. Harrah College of Hospitality, University of Nevada Las Vegas, Las Vegas, Nevada, USA)

International Journal of Contemporary Hospitality Management

ISSN: 0959-6119

Article publication date: 27 June 2023

Issue publication date: 23 February 2024

391

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.

Keywords

Acknowledgements

This paper is derived from the first author’s PhD dissertation.

Citation

Binesh, F., Belarmino, A.M., van der Rest, J.-P., Singh, A.K. and Raab, C. (2024), "Forecasting hotel room prices when entering turbulent times: a game-theoretic artificial neural network model", International Journal of Contemporary Hospitality Management, Vol. 36 No. 4, pp. 1044-1065. https://doi.org/10.1108/IJCHM-10-2022-1233

Publisher

:

Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

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