打印

作者

来源

HABITAT INTERNATIONAL,Vol.81,P.1-11

语言

英文

关键字

Residential communities; Housing prices; Locational factors; Geographic field model; Geographically weighted regression; Spatially non-stationary; GEOGRAPHICALLY WEIGHTED REGRESSION; ORDINARY LEAST-SQUARES; PROPERTY PRICES; AMENITY VALUE; OPEN SPACES; LAN

作者单位

[Liang, Xiaojin; Liu, Yaolin; Qiu, Tianqi; Jing, Ying; Fang, Feiguo] Wuhan Univ, Sch Resources & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China. [Liu, Yaolin] Wuhan Univ, Minist Educ, Key Lab Geog Informat Syst, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China. [Liu, Yaolin] Wuhan Univ, Collaborat Innovat Ctr Geospatial Informat Techno, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China. Liu, YL (reprint author), Wuhan Univ, Sch Resources & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Hubei, Peoples R China. E-Mail: yaolin610@yeah.net

摘要

Residential communities are the basic living units in Chinese cities. Housing prices are closely associated with the community location and surrounding support facilities. When selecting satisfactory residential accommodation, potential real estate purchasers prioritize the community location in a city at the macro-level and then consider other micro-factors (i.e., the floor, orientation, structure, etc.). This paper attempts to explore the relationship between housing prices and locational factors at the community level. We collect the current market prices of 545 residential communities built in the last decade in Ningbo, the second largest city in Zhejiang Province. Then, thirteen locational factors of five dimensions are identified to research their influences on housing prices. In the process of selecting certain locational variables, both extant features and additional features (i.e., planned ones) are considered. The geographic field model is introduced to quantify the external effects of locational factors, due to its advantages of producing more accurate results than that of traditional distance-based measure methods. Then, regression analysis is performed based on the average housing prices of residential communities and explanatory variables by the ordinary least squares model and the geographically weighted regression. The regression coefficients demonstrate that the externalities of parks, lakes, department stores, banks, secondary schools and rail transit have significant but spatially non-stationary effects on housing prices. The results provide references for local real estate planning departments and potential real estate purchasers.