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Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model

YANG Jun BAO Yajun ZHANG Yuqing LI Xueming GE Quansheng

YANG Jun, BAO Yajun, ZHANG Yuqing, LI Xueming, GE Quansheng. Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model[J]. 中国地理科学, 2018, 28(3): 505-515. doi: 10.1007/s11769-018-0954-6
引用本文: YANG Jun, BAO Yajun, ZHANG Yuqing, LI Xueming, GE Quansheng. Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model[J]. 中国地理科学, 2018, 28(3): 505-515. doi: 10.1007/s11769-018-0954-6
YANG Jun, BAO Yajun, ZHANG Yuqing, LI Xueming, GE Quansheng. Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model[J]. Chinese Geographical Science, 2018, 28(3): 505-515. doi: 10.1007/s11769-018-0954-6
Citation: YANG Jun, BAO Yajun, ZHANG Yuqing, LI Xueming, GE Quansheng. Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model[J]. Chinese Geographical Science, 2018, 28(3): 505-515. doi: 10.1007/s11769-018-0954-6

Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model

doi: 10.1007/s11769-018-0954-6
基金项目: Under the auspices of National Natural Science Foundation of China (No. 41471140, 41771178), Liaoning Province Outstanding Youth Program (No. LJQ2015058)
详细信息
    通讯作者:

    YANG Jun.E-mail:yangjun@lnnu.edu.cn

Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model

Funds: Under the auspices of National Natural Science Foundation of China (No. 41471140, 41771178), Liaoning Province Outstanding Youth Program (No. LJQ2015058)
More Information
    Corresponding author: YANG Jun.E-mail:yangjun@lnnu.edu.cn
  • 摘要: This paper studies the relationship between accessibility and housing prices in Dalian by using an improved geographically weighted regression model and house prices, traffic, remote sensing images, etc. Multi-source data improves the accuracy of the spatial differentiation that reflects the impact of traffic accessibility on house prices. The results are as follows:first, the average house price is 12 436 yuan (RMB)/m2, and reveals a declining trend from coastal areas to inland areas. The exception was Guilin Street, which demonstrates a local peak of house prices that decreases from the center of the street to its periphery. Second, the accessibility value is 33 minutes on average, excluding northern and eastern fringe areas, which was over 50 minutes. Third, the significant spatial correlation coefficient between accessibility and house prices is 0.423, and the coefficient increases in the southeastern direction. The strongest impact of accessibility on house prices is in the southeastern coast, and can be seen in the Lehua, Yingke, and Hushan communities, while the weakest impact is in the northwestern fringe, and can be seen in the Yingchengzi, Xixiaomo, and Daheishi community areas.
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  • 收稿日期:  2017-09-08
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Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model

doi: 10.1007/s11769-018-0954-6
    基金项目:  Under the auspices of National Natural Science Foundation of China (No. 41471140, 41771178), Liaoning Province Outstanding Youth Program (No. LJQ2015058)
    通讯作者: YANG Jun.E-mail:yangjun@lnnu.edu.cn

摘要: This paper studies the relationship between accessibility and housing prices in Dalian by using an improved geographically weighted regression model and house prices, traffic, remote sensing images, etc. Multi-source data improves the accuracy of the spatial differentiation that reflects the impact of traffic accessibility on house prices. The results are as follows:first, the average house price is 12 436 yuan (RMB)/m2, and reveals a declining trend from coastal areas to inland areas. The exception was Guilin Street, which demonstrates a local peak of house prices that decreases from the center of the street to its periphery. Second, the accessibility value is 33 minutes on average, excluding northern and eastern fringe areas, which was over 50 minutes. Third, the significant spatial correlation coefficient between accessibility and house prices is 0.423, and the coefficient increases in the southeastern direction. The strongest impact of accessibility on house prices is in the southeastern coast, and can be seen in the Lehua, Yingke, and Hushan communities, while the weakest impact is in the northwestern fringe, and can be seen in the Yingchengzi, Xixiaomo, and Daheishi community areas.

English Abstract

YANG Jun, BAO Yajun, ZHANG Yuqing, LI Xueming, GE Quansheng. Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model[J]. 中国地理科学, 2018, 28(3): 505-515. doi: 10.1007/s11769-018-0954-6
引用本文: YANG Jun, BAO Yajun, ZHANG Yuqing, LI Xueming, GE Quansheng. Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model[J]. 中国地理科学, 2018, 28(3): 505-515. doi: 10.1007/s11769-018-0954-6
YANG Jun, BAO Yajun, ZHANG Yuqing, LI Xueming, GE Quansheng. Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model[J]. Chinese Geographical Science, 2018, 28(3): 505-515. doi: 10.1007/s11769-018-0954-6
Citation: YANG Jun, BAO Yajun, ZHANG Yuqing, LI Xueming, GE Quansheng. Impact of Accessibility on Housing Prices in Dalian City of China Based on a Geographically Weighted Regression Model[J]. Chinese Geographical Science, 2018, 28(3): 505-515. doi: 10.1007/s11769-018-0954-6
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