Identifying Key Attributes Associated with Short-Term Rental Occupancy Rates: Case of Airbnb

Short-term rental housing plays an important role in the housing market by increasing property utilization and generating income opportunities for property owners. This study investigates the key attributes associated with Airbnb occupancy rates using listing data from five major U.S. cities. Data mining and machine learning techniques, including Random Forest, XGBoost, Deep Neural Networks (DNN), and hierarchical cluster analysis, were applied to identify factors associated with occupancy rate variation. Random Forest achieved the best performance (R² = 23.92%). Feature importance analysis identified price, location (city), listing capacity, amenities, and host response rate as the variables most strongly associated with occupancy rates. Cluster analysis supported these findings by identifying a dominant group of moderately priced listings with smaller accommodation capacity, more amenities, higher host responsiveness, and an average occupancy rate of 57%. These findings provide data-driven insights that may help property owners optimize listing performance and improve occupancy rates.