Abstract
Purpose
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/approach
Articles 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.
Findings
Synthesis 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/value
To 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.
Subject
Tourism, Leisure and Hospitality Management,Geography, Planning and Development
Reference50 articles.
1. Forecasting hotel demand uncertainty using time series Bayesian VAR models;Tourism Economics,2018
2. A segmented machine learning modeling approach of social media for predicting occupancy;International Journal of Contemporary Hospitality Management,2021
3. Predicting hotel bookings cancellation with a machine learning classification model,2017
4. Big data in hotel revenue management: exploring cancellation drivers to gain insights into booking cancellation behavior;Cornell Hospitality Quarterly,2019
5. Forecasting occupancy rate with Bayesian compression methods;Annals of Tourism Research,2019
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