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

1
School of Geographic Science, Nanjing Normal University, Nanjing 210097, China
2
Department of Geography, University of Maryland, College Park, MD 212000, USA
3
School of Geographic Science, Nantong University, Nantong 226000, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(10), 431; https://doi.org/10.3390/ijgi8100431
Received: 22 August 2019 / Revised: 8 September 2019 / Accepted: 9 September 2019 / Published: 29 September 2019
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. View Full-Text
Keywords: residential rent; housing price; price–rent ratio; mgwr; utility value; spatial non-stationarity residential rent; housing price; price–rent ratio; mgwr; utility value; spatial non-stationarity
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Zhang, S.; Wang, L.; Lu, F. Exploring Housing Rent by Mixed Geographically Weighted Regression: A Case Study in Nanjing. ISPRS Int. J. Geo-Inf. 2019, 8, 431.

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