Abstract
Rail transit is a significant measure for the comprehensive development of large cities, which influences the land use patterns and the spatial patterns of residential prices around the rail. This study considered Beijing Metro Line 10 and Line 13 as examples, based on a semi-logarithmic hedonic price model, combined with facility point-of-interest (POI) data and residential unit transaction data, to study how rail transit affects the spatial differentiation of urban residential prices. Within the 2 km study area along the line, factors such as community grade (property fee), living environment (park), and living convenience (shopping mall) significantly affected the residential prices. Factors influencing residential prices in different rail locations also differed. The residential prices within the fourth ring (Line 10) were correlated significantly with population density (plot ratio) and station distance, while residential prices outside the fourth ring (Line 13) were correlated with community environment (greening rate), community-built time (age of residence), and public transportation conditions. The conclusions of this study are as follows: (1) Within the urban area of a single central city, the average residential price on the inner side of the rail transit line adjacent to the city center is higher than on the outer side. (2) Neighborhood characteristics significantly affect residential prices along rail transit lines in urban areas, while the architectural and neighborhood characteristics have equally important effects on residential prices along suburban rail transit lines. (3) Urban residential patterns affect residential prices along rail transit lines, with rail transit in urban areas having lesser value-added effect on areas with higher residential prices and suburban rail transit having higher value-added effect on areas with lower residential prices. The innovation of this study is to analyze the spatial differentiation from two location perspectives: the residential price pattern of the city and the city’s own ring division, and to add new location characteristic variables at a unit distance of 200 m. This study confirms that the spatial effect of rail transit on residential prices in different locations of the same city is not the same, and it also provides policy suggestions for strengthening the combination of Transit-Oriented-Development (TOD) model and the layout of residential land.
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