地理科学进展 ›› 2018, Vol. 37 ›› Issue (9): 1268-1276.doi: 10.18306/dlkxjz.2018.09.009

• 研究论文 • 上一篇    下一篇

南京城区住宅售租价格时空分异与影响因素

宋伟轩1,2(), 马雨竹1,3, 陈艳如1,3   

  1. 1. 中国科学院南京地理与湖泊研究所,南京 210008
    2. 中国科学院流域地理学重点实验室,南京 210008
    3. 中国科学院大学,北京 101407
  • 收稿日期:2017-11-15 修回日期:2018-01-12 出版日期:2018-09-28 发布日期:2018-09-28
  • 作者简介:

    作者简介:宋伟轩(1981-),男,吉林敦化人,博士,副研究员,主要从事城市社会空间研究,E-mail: wxsong@niglas.ac.cn

  • 基金资助:
    国家自然科学基金项目(41771184)

Spatiotemporal differentiation and influencing factors of housing selling and rental prices: A case study of Nanjing City

Weixuan SONG1,2(), Yuzhu MA1,3, Yanru CHEN1,3   

  1. 1. Nanjing Institute of Geography and Limnology, CAS, Nanjing 210008, China
    2. Key Laboratory of Watershed Geographic Sciences, CAS, Nanjing 210008, China
    3. University of Chinese Academy of Sciences, Beijing 101407, China
  • Received:2017-11-15 Revised:2018-01-12 Online:2018-09-28 Published:2018-09-28
  • Supported by:
    National Natural Science Foundation of China, No.41771184

摘要:

住宅价格空间分异是中国城市地理学和经济地理学近年来关注的热点前沿课题。以南京4560个居住小区为研究总样本,采集2009-2017年间30个季度各小区平均住宅售价和租金,选取6个特征时段和小区分布相对集中的重点研究区,采用克里格插值法分析研究区内住宅售租价格的空间分异与演变特征,发现售价空间分异明显加剧,高值区渐显于河西新城、江心洲和鼓楼名校学区;租金空间则从城市中心向外围递减格局转变为整体更加均衡的新老城区多中心结构。在此基础上,重点围绕住宅“区位”属性,构建售租价格分异影响因素指标体系,通过逐步多元回归分析发现,中心位势变量对售租价格的解释度最高,而配套服务类区位因素对售租价格的解释力在降低。南京城市房价快速增长背景下,常规“区位”因素对房价分异的重要性持续减弱,学区、政策偏向等特殊“区位”因子对房价的决定性作用则逐步突显,而“售租比”全面快速增长则预示着城市房价风险程度的整体提高。

关键词: 住宅, 区位, 售价, 租金, 时空分异, 影响因素, 售租比, 南京

Abstract:

Under the background of rapid urbanization and housing market reform, urban housing and housing price have become a major issue related to the national economy and people's livelihood in China. The spatial differentiation of housing prices has become an important topic of research, which has been concerned by Chinese urban geographer and economic geographer in recent years. Few of the existing studies in China relied on long time series data to compare the spatial pattern, location influencing factors, and mechanism of change of urban housing selling and rental prices. As Nanjing City can represent the general development path of China's big cities, this study took 4560 residential communities in Nanjing as the total sample, and collected the average housing selling and rental prices for 30 quarters of 2009-2017. Data analyses show that there is a causality between the selling and rental prices, and the selling price shows a clear pattern that "fast rise" alternates with "relatively smooth," price levels whereas the rental price shows a more stable trend. The study chose areas where the sample communities are concentrated as focused research area and 6 characteristic time intervals to analyze the spatiotemporal differentiation and characteristics of change of housing selling and rental prices by employing the Kriging interpolation method. The results show that the spatial differentiation of housing selling price significantly intensified and high-value areas are more concentrated in Hexi New Town, Jiangxinzhou, and elite school districts in Gulou District; and based on the current situation of development, the high-value areas continue to transfer to the periphery of the city. Meanwhile, the spatial pattern of rental price shifted from gradually decreasing from city center toward outskirts to a more balanced structure of multi-centers both in the old and the new city areas. On this basis, an indicator system of location influence coefficient on housing selling and rental prices was built, focusing on the characteristics of houses and the "location" property. Through multiple stepwise regression analysis, it was found that central geographical variable has the highest degree of explanation power on selling and rental prices, while the explanatory power of supporting services on selling and rental prices is decreasing. With rapidly rising housing selling price in Nanjing, the significance of normal "location" coefficient on housing price differentiation is continually abating, but the decisive effect of some special "location" coefficients (such as school district and policy preferences) on selling price is becoming more prominent, further expanding the price-to-rent ratio, which also indicates the overall increase of housing price "risk" in Nanjing.

Key words: housing, location, selling price, rental price, spatiotemporal differentiation, influencing factors, price-to-rent ratio, Nanjing