How does the built environment affect hotel prices? A study using multiscale GWR and deep learning

Abstract

This paper utilizes deep learning and street view images to extract visual variables and explore the interactive effects and spatial impacts of the built environment on hotel prices with GeoDetector and multiscale geographically weighted regression (MGWR) models. The results indicate that hotel prices are influenced by multiple factors in a coupled manner, with combined effects more substantial than individual drivers; the visual quality of the built environment can influence consumers’ perception and willingness to stay, thereby affecting hotel prices; MGWR, compared to traditional statistical models, demonstrates superior explanatory power for hotels, accurately identifying the spatial heterogeneity of factors influencing hotel prices; and by elucidating the bandwidth influence and spatial differentiation pattern of each factor, this study provides a reference for hotel site selection, price adjustment, and urban planning.

Keywords

• built environment
• visual quality
• hedonic price model
• mgwr
• deep learning