Exploring Housing Rent by Mixed Geographically Weighted Regression: A Case Study in Nanjing

被引：24
|
作者：
Zhang, Shiwei [1 ,2 ]
Wang, Lin [3 ]
Lu, Feng [3 ]
机构：
[1] Nanjing Normal Univ, Sch Geog Sci, Nanjing 210097, Jiangsu, Peoples R China
[2] Univ Maryland, Dept Geog, College Pk, MD 20742 USA
[3] Nantong Univ, Sch Geog Sci, Nantong 226000, Peoples R China
来源：
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION | 2019年 / 8卷 / 10期
基金：
中国国家自然科学基金;
关键词：
residential rent; housing price; price-rent ratio; mgwr; utility value; spatial non-stationarity; PRICES; SHANGHAI;
D O I：
10.3390/ijgi8100431
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In China, the housing rent can clearly reveal the actual utility value of a house due to its low capital premium. However, few studies have examined the spatial variability of housing rent. Accordingly, this study attempted to determine the utility value of houses based on housing rent data. In this study, we applied mixed geographically weighted regression (MGWR) to explore the residential rent in Nanjing, the largest city in Jiangsu Province. The results show that the distribution of residential rent has a multi-center group pattern. Commercial centers, primary and middle schools, campuses, subways, expressways, and railways are the most significant influencing factors of residential rent in Nanjing, and each factor has its own unique characteristics of spatial differentiation. In addition, the MGWR has a better fit with housing rent than geographically weighted regression (GWR). These research results provide a scientific basis for local real estate management and urban planning departments.
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页数：12
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