Over the past decades, China has built a large number of superblocks and gated residential areas, which have contributed to increased congestion on urban road traffic. Although some studies have found a correlation between residential environments and travel convenience, there is little quantitative evidence to explain which factors influence the connectivity of road networks. This paper aims to clarify the indicators of built environments in Chinese residential areas that affect travel accessibility and their correlation proportions through a more quantitative method and to provide a basis for planning and design decisions. To this end, this study took 204 residential areas in the three districts of Nanjing, China as a sample and calculated 16 built-environment indicators. The path distance (D) and pedestrian route directness (PRD) from the center of these residential areas to the nearest urban intersection were measured by GIS as outcome variables. First, correlation analysis was used to screen for significant variables, and multiple linear regression models were used to examine the significant influencing factors of D and PRD. Then, a binary logistic regression analysis was performed to provide a model that could determine whether the built environment of the residential area met the requirements for convenient travel. The results revealed that the length of the long side, the area size, and the total road length of the residential area were significantly related to D, and the number of entrances and exits, the intersection density, and the X ratio were significantly related to PRD. The indicators that were positively correlated with D were the size, network complexity, and the boundary shape of the residential area according to the degree of correlation. Meanwhile, other indicators such as the density and connectivity of the residential road network were negatively correlated with D.

The listed price of real estate is a subjective reflection of its value by sellers, usually related to structural, neighborhood, and environmental attributes. Although previous studies have proposed the hedonic pricing model, factors related to perception are rarely seen in explanatory variables. This study aims to explore the impact of street-level perceived qualities on the listed price per square meter of plot set by the seller of the real estate using the Geographically Weighted Regression (GWR)-based hedonic pricing model and analyzes the spatially heterogeneous effects of the coefficients. In the city of Eindhoven, the Netherlands, Google Street View photos collected at 200 m intervals were employed to calculate representative variables of perceptual quality via a validated convolutional neural network, alongside structural and neighborhood attributes. The final model includes eight explanatory variables, and the results indicate that, apart from the plot area and the number of rooms, the influencing mechanisms of other factors are different. The impact of perceived beautiful quality on listed real estate prices demonstrates obvious distinctions between the north and the south. Perceived livability (positive) and depressing (negative) qualities show similar heterogeneous characteristics. This study offers a comprehensive approach to promote diverse strategies for real estate development across urban areas and recommends a heightened emphasis on the design quality of residential streets.