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Hotel demand forecasting: a comprehensive literature review

Hotel demand forecasting: a comprehensive literature review This study aims to provide a comprehensive review of hotel demand forecasting to identify its key fundamentals and evolution and future research directions and trends to advance the field.Design/methodology/approachArticles on hotel demand modeling and forecasting were identified and rigorously selected using transparent inclusion and exclusion criteria. A final sample of 85 empirical studies was obtained for comprehensive analysis through content analysis.FindingsSynthesis of the literature highlights that hotel forecasting based on historical demand data dominates the research, and reservation/cancellation data and combined data gradually attracted research attention in recent years. In terms of model evolution, time series and AI-based models are the most popular models for hotel demand forecasting. Review results show that numerous studies focused on hybrid models and AI-based models.Originality/valueTo the best of the authors’ knowledge, this study is the first systematic review of the literature on hotel demand forecasting from the perspective of data source and methodological development and indicates future research directions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Tourism Review Emerald Publishing

Hotel demand forecasting: a comprehensive literature review

Huang, Liyao; Zheng, Weimin
Tourism Review , Volume 78 (1): 27 – Jan 20, 2023
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27 pages

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1660-5373
eISSN
1660-5373
DOI
10.1108/tr-07-2022-0367
Publisher site
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Abstract

This study aims to provide a comprehensive review of hotel demand forecasting to identify its key fundamentals and evolution and future research directions and trends to advance the field.Design/methodology/approachArticles on hotel demand modeling and forecasting were identified and rigorously selected using transparent inclusion and exclusion criteria. A final sample of 85 empirical studies was obtained for comprehensive analysis through content analysis.FindingsSynthesis of the literature highlights that hotel forecasting based on historical demand data dominates the research, and reservation/cancellation data and combined data gradually attracted research attention in recent years. In terms of model evolution, time series and AI-based models are the most popular models for hotel demand forecasting. Review results show that numerous studies focused on hybrid models and AI-based models.Originality/valueTo the best of the authors’ knowledge, this study is the first systematic review of the literature on hotel demand forecasting from the perspective of data source and methodological development and indicates future research directions.

Journal

Tourism Review – Emerald Publishing

Published: Jan 20, 2023

Keywords: Hotel demand; Modeling and forecasting; Data source; Methodological development; Literature review; 酒店需求; 建模与预测; 数据来源; 方法发展; 文献综述; Demanda hotelera; Modelado y pronóstico; Fuente de datos; Desarrollo metodológico; Revisión de la literatura

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