Beyond Rates: Optimizing Hotel Profits with Machine Learning and Simulation

Carolina Costa and Nuno Antonio ()
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Carolina Costa: NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa
Nuno Antonio: NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa

Chapter 52 in Information and Communication Technologies in Tourism 2026, 2026, pp 769-784 from Springer

Abstract: Abstract Dynamic pricing is crucial to hotel competitiveness as demand patterns shift and technology continues to reshape the sector. We present a hybrid model that integrates Machine Learning (XGBoost) with Monte Carlo simulation to optimize room prices dynamically. The model forecasts demand based on seasonality, holidays, and market signals, then incorporates these forecasts into simulations to determine prices that balance revenue and room occupancy. The model was evaluated using data from four Portuguese hotels (two city hotels and two resort hotels), and the approach delivered tangible gains: an average €4 increase in Revenue Per Available Room (RevPAR) and a 9% rise in profit. By combining predictive analytics with simulation, the framework enables adaptive pricing decisions that are responsive to market changes. The study contributes to smart tourism and revenue management by illustrating the practical value of interdisciplinary methods that link information systems, machine learning, and data-driven simulation. It also highlights the importance of ethical data management and strategic Artificial Intelligence (AI) utilization, demonstrating how IS-driven solutions can modernize revenue management and enhance AI-powered tourism ecosystems.

Keywords: Revenue management; Pricing; Monte Carlo simulation; Demand forecasting; Machine learning (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-032-23925-9_52

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DOI: 10.1007/978-3-032-23925-9_52

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