What question did this study set out to answer?

To identify key fundamentals, methodological evolution, and prospective research directions in hotel demand modeling and forecasting.

November 26, 2022Tourism Review

Hotel demand forecasting: a comprehensive literature review

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Authors

LHLiyao HuangMinzu University of China WZWeimin ZhengUniversity of South China

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Implication

Systematic review reveals an increasing reliance on AI-based and hybrid forecasting models utilizing booking and cancellation data in hospitality, highlighting key avenues for predictive accuracy.

Key Points

To identify key fundamentals, methodological evolution, and prospective research directions in hotel demand modeling and forecasting.
Screened literature on hotel demand modeling using explicit inclusion and exclusion criteria.
Performed a systematic content analysis on a final sample of 85 empirical studies.
Historical demand data remains the primary foundation for predictive models, though recent research increasingly incorporates reservation, cancellation, and combined datasets.
Time series and artificial intelligence methods represent the most prevalent modeling paradigms, with recent empirical work heavily emphasizing hybrid and AI-based architectures.

Cite This Study

Huang et al. (2022) studied this question.

synapsesocial.com/papers/6a7d23fcd685fe2c2d77577c https://doi.org/10.1108/tr-07-2022-0367

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Implication

Systematic review reveals an increasing reliance on AI-based and hybrid forecasting models utilizing booking and cancellation data in hospitality, highlighting key avenues for predictive accuracy.

Hotel demand forecasting: a comprehensive literature review

Discussion

Member takes

Implication

Key Points

Cite This Study

Hotel demand forecasting: a comprehensive literature review

Discussion

Member takes

Implication

Key Points

Cite This Study

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