Resumen
House prices have long been closely related to the built environment of cities, yet whether the subjective perception (SP) of these environments has a differing effect on prices at multiple urban scales is unclear. This study sheds light on the impact of people?s SP of the urban environment on house prices in a multi-scale urban morphology analysis. We trained a machine learning (ML) model to predict people?s SP of the urban environment around properties across Greater London with survey response data from an online survey evaluating people?s SP of street view image (SVI) and linked this to house price data. This information was used to construct a hedonic price model (HPM) and to evaluate the association between SP and house price data in a series of linear regression models controlling location information and urban morphological characteristics such as street network centralities at multiple urban scales, quantified using space syntax (SS) methods. The findings show that SP influences house prices, but this influence differs depending on the urban scale of analysis. Particularly, a sense of ?enclosure? and ?comfort? are important factors influencing house price variation. This study contributes by introducing SP of the urban environment as a new dimension into the traditional HPM and by exploring the economic impact of SP on the house price market at multiple urban scales.