Data-driven approach to optimizing property management strategies: spatial modeling analytics of short-term rentals

Abstract

Purpose This study aims to explore the impact of locational and seasonal factors on the financial performance of short-term rental properties in Margaret River, Western Australia. It seeks to address the gap in understanding how these factors influence key financial metrics such as average daily rate (ADR) and occupancy rates, providing insights for property managers, investors and policymakers. Design/methodology/approach The research uses a mixed-method approach, integrating advanced predictive modeling techniques, such as Random Forests and Gradient Boosting, with spatial clustering algorithms like density-based spatial clustering of applications with noise (DBSCAN) and ordering points to identify the clustering structure (OPTICS). The study analyzes a comprehensive data set of short-term rental properties between 2012 and 2019. It focuses on locational attributes, seasonal variations and financial outcomes. Findings The findings reveal that properties located near tourist attractions and amenities consistently achieve higher ADRs and occupancy rates, confirming the critical role of location in driving rental demand. Seasonal analysis indicates significant fluctuations in both ADR and occupancy rates, with peaks during high tourist seasons and troughs in off-peak periods. The study underscores the importance of dynamic pricing strategies to optimize revenue and sustain occupancy across different seasons. In addition, it highlights the influence of property features, such as the number of bedrooms and bathrooms, on ADR, while noting that larger properties do not necessarily achieve higher occupancy rates. Research limitations/implications Future research could expand the scope to include different locations and explore the long-term impacts of locational and seasonal factors on property performance. Originality/value This research contributes to the literature by integrating spatial analysis with advanced predictive modeling techniques to provide a nuanced understanding of how locational and seasonal factors impact financial performance in the short-term rental market. It offers a novel application of data analytics within the context of tourism and hospitality management, bridging theoretical frameworks with practical insights.