Collaborative and professional accommodations on Airbnb: Exploring patterns for sustainable tourism management in Spain

This study analyses the characteristics of accommodation types (collaborative and professional) in Madrid, Barcelona, Seville, and Valencia. It examines location factors, including proximity to top tourist spots, concentration of Airbnb accommodations, and population density, to identify which accommodation type contributes to negative impacts in these cities. Additionally, it delves into pricing features, reviews, length of stay, accommodation services, and host characteristics to distinguish between collaborative and professional accommodations. Logistic regression analysis and Random Forest are used, employing 500 decision trees to explore non-linear and complex relationships. Partial dependence plots offer insights into how predictions evolve across different ranges, providing valuable information about the locations, population density, tenure, and reviews associated with the two accommodation types. The findings reveal that professional accommodations tend to be concentrated in areas with high population density and near major tourist attractions, exacerbating tourist overcrowding issues. Conversely, collaborative accommodations are situated more than 4 km away from the main tourist spots, potentially contributing to a more balanced distribution of tourists and avoiding excessive concentration. These findings have significant implications for urban tourism dynamics, offering valuable insights that can inform urban planning, policy development, and sustainable tourism practices. • This study analyzes the characteristics of accommodation types (collaborative or professionalized) in Madrid, Barcelona, Seville, and Valencia. • The research examines location factors, including proximity to top tourist spots, concentration of Airbnb accommodations, and population density, to identify the accommodation type contributing to negative impacts in these cities. • Additionally, the study delves into pricing features, reviews, length of stay, accommodation services, and host characteristics to distinguish between collaborative and professional accommodations. • The study utilizes logistic regression and Random Forest, employing 500 decision trees to explore non-linear and complex relationships. • Partial dependence plots offer insights into how predictions evolve across different ranges, providing valuable information about the locations, population density, tenure, and reviews associated with two different accommodations. • Key findings reveal that professional accommodations tend to concentrate in areas with high population density and near major tourist attractions, exacerbating issues of tourist overcrowding. • Conversely, collaborative accommodations are situated more than 4 km away from the main tourist spots, potentially contributing to a more balanced distribution of tourists and avoiding excessive concentration. • These results carry significant implications for urban tourism dynamics, offering insights that can inform urban planning, policy development, and sustainable tourism practices.