地球信息科学学报 ›› 2021, Vol. 23 ›› Issue (5): 882-890.doi: 10.12082/dqxxkx.2021.200285

• 地理空间分析综合应用 • 上一篇    下一篇

南京市住宅价格影响因素分析

秦佳睿1,2,3(), 盛业华1,2,3,*(), 王燕锋1,2,3, 何育枫1,2,3   

  1. 1.南京师范大学地理科学学院,南京210023
    2.虚拟地理环境教育部重点实验室,南京 210023
    3.江苏省地理信息资源开发与利用协同创新中心, 南京210023
  • 收稿日期:2020-06-05 修回日期:2020-08-15 出版日期:2021-05-25 发布日期:2021-07-25
  • 通讯作者: 盛业华
  • 作者简介:秦佳睿(1997— ),女,河南洛阳人,硕士生,主要从事城市与区域经济研究。E-mail:qinjiarui1997@foxmail.com
  • 基金资助:
    国家自然科学基金项目(41631175);国家重点研发计划项目(2017YFB0503500)

Analysis of the Influencing Factors of Housing Price in Nanjing

QIN Jiarui1,2,3(), SHENG Yehua1,2,3,*(), WANG Yanfeng1,2,3, HE Yufeng1,2,3   

  1. 1. School of Geography, Nanjing Normal University, Nanjing 210023, China
    2. Key Laboratory of Virtual Geographic Environment, Ministry of Education, Nanjing 210023, China
    3. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
  • Received:2020-06-05 Revised:2020-08-15 Online:2021-05-25 Published:2021-07-25
  • Contact: SHENG Yehua
  • Supported by:
    National Natural Science Foundation of China(41631175);National Key Research and Development Program of China(2017YFB0503500)

摘要:

住宅价格影响因素的研究对于购房者购买住房及政府制定相关政策具有重要影响。然而,目前对于住宅价格问题的研究大多从宏观角度进行,而从微观角度入手的相对较少。此外,对住宅价格的研究通常需要建立多元回归方程,但这样会造成多重共线性问题,导致伪回归,不能准确地分析各因素对住宅价格的影响。针对该问题,本文从微观角度出发,运用定量与定性相结合的方法,建立特征价格模型,并且选择逐步回归法对其进行修正,将其中显著性不强、经济意义不明显的变量逐步剔除,从而更准确地探究影响住宅价格的主要因素。本文以南京市住宅价格为实验数据,并初步选择12个影响因素对该方法进行验证。结果表明,该方法能够有效地剔除二级商业中心、建筑类型、生活配套设施3个显著性不强的影响因素,保留重点学校等9个影响较大的因素。本文方法更为精准地分析了住宅价格的影响因素,为购房者购买住房及政府制定相关政策提供了一定的理论基础。

关键词: 住宅价格, 特征价格模型, 逐步回归法, 南京, 空间分异, 价格影响因素, 共线性检验, 微观角度

Abstract:

With the urbanization and the rapid growth of economy, the housing price of cities continues to rise and the spatial differentiation of housing price increases, which has attracted a widespread attention from the public and the government. Exploring the influencing factors of housing price is of great significance for understanding house-buying of the public and provides useful support for the government to formulate relevant policies. Until now, many researches have studied the influencing factors of housing price from a macro perspective and only a few studies are conducted from the micro perspective, which leads to inadequate understanding of the influencing factors of housing price. In addition, the method of establishing multiple regression equations is usually adopted to study the relationship between housing price and multiple influencing factors. However, the selection of influencing factors are usually subjective, which leads to an artificial regression that cannot characterize the influence of various factors accurately. To solve these problems, a hedonic price model combining both quantitative and qualitative methods is proposed from the micro perspective of structure characteristics, location characteristics, and neighborhood characteristics in this study. Moreover, a stepwise regression method is applied to modify this model by eliminating non-significant factors. To validate the proposed method, a case study in Nanjing city, Jiangsu is conducted, and 12 primary influencing factors are selected. The results show that the proposed method eliminates three insignificant influencing factors including secondary commercial centers, building types, and living facilities, and keeps the rest nine factors. Compared with traditional methods, this paper analyzes the influencing factors of housing price more quantitatively and provides a theoretical basis for house-buying of the public and policy making of the government. What's more, it is conducive to the real estate management department to have an in-depth understanding of the distribution of housing price, so as to strengthen the management of the real estate market.

Key words: housing price, hedonic price model, stepwise regression method, Nanjing, spatial differentiation, price influencing factors, collinearity test, micro perspective