Deep Learning-Based Forecasting of Hotel KPIs: A Cross-City Analysis of Global Urban Markets
This provides a comparative framework for data-driven decision-making in tourism and urban planning, but it is incremental as it applies an existing method to new data.
This study tackled forecasting hotel KPIs like occupancy and revenue across five global cities using LSTM networks, achieving high predictive accuracy in Manchester and Mumbai while noting variability in Dubai and Bangkok due to seasonal factors.
This study employs Long Short-Term Memory (LSTM) networks to forecast key performance indicators (KPIs), Occupancy (OCC), Average Daily Rate (ADR), and Revenue per Available Room (RevPAR), across five major cities: Manchester, Amsterdam, Dubai, Bangkok, and Mumbai. The cities were selected for their diverse economic profiles and hospitality dynamics. Monthly data from 2018 to 2025 were used, with 80% for training and 20% for testing. Advanced time series decomposition and machine learning techniques enabled accurate forecasting and trend identification. Results show that Manchester and Mumbai exhibited the highest predictive accuracy, reflecting stable demand patterns, while Dubai and Bangkok demonstrated higher variability due to seasonal and event-driven influences. The findings validate the effectiveness of LSTM models for urban hospitality forecasting and provide a comparative framework for data-driven decision-making. The models generalisability across global cities highlights its potential utility for tourism stakeholders and urban planners.