Predicting daily hotel occupancy: a practical application for independent hotels

Apostolos Ampountolas () and Mark Legg ()
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Apostolos Ampountolas: Boston University
Mark Legg: Penn State Berks

Journal of Revenue and Pricing Management, 2024, vol. 23, issue 3, No 2, 197-205

Abstract: Abstract Accurately forecasting daily hotel occupancy is critical for revenue managers. Limited research focuses on predicting daily hotel occupancy by implementing traditional forecasting techniques, which only require a little statistical knowledge or expensive software for small independent properties. This study employs longitudinal daily occupancy data from multiple properties in urban settings within the United States to test four forecasting models for short-term (1–90 day) predictions. The results showed that Simple Exponential Smoothing (SES) was most accurate for four horizons, while Extreme Gradient Boosting (XGBoost) was better for shorter-term predictions in the other seven. In conclusion, these results demonstrate that small independent properties may successfully implement traditional forecasting methods for accurate daily occupancy forecasting.

Keywords: Linear regression forecasting; Exponential smoothing forecasting; Lodging forecast; Machine learning models; Time series analysis; Hospitality (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1057/s41272-023-00445-7

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