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Accurately forecasting daily hotel occupancy is critical for Revenue Managers. Ampountolas and Legg (2023) observe limited research has focused on predicting daily hotel occupancy by implementing traditional forecasting techniques, which only require a little statistical knowledge or expensive software for small independent properties. There paper 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. Blengini and Heo (2023) study focuses on the role of the exchange rate on hotelier’s pricing decision and business performance. There study explores the way hospitality industry practitioners …