南京市住宅价格影响因素分析

doi:10.12082/dqxxkx.2021.200285

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.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. 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/Abstract

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

秦佳睿, 盛业华, 王燕锋, 何育枫. 南京市住宅价格影响因素分析[J]. 地球信息科学学报, 2021, 23(5): 882-890.DOI:10.12082/dqxxkx.2021.200285

QIN Jiarui, SHENG Yehua, WANG Yanfeng, HE Yufeng. Analysis of the Influencing Factors of Housing Price in Nanjing[J]. Journal of Geo-information Science, 2021, 23(5): 882-890.DOI:10.12082/dqxxkx.2021.200285

图/表 8

图1

表1

图2

表2

表3

表4

表5

表6

参考文献 22

[1]	王芳, 高晓路, 颜秉秋. 基于住宅价格的北京城市空间结构研究[J]. 地理科学进展, 2014,33(10):1322-1331. doi: 10.11820/dlkxjz.2014.10.004
	[ Wang F, Gao X L, Yan B Q. Research on urban spatial structure in Beijing based on housing prices[J]. Progress in Geography, 2014,33(10):1322-1331. ]
[2]	梅志雄, 徐颂军, 欧阳军, 等. 广州地铁三号线对周边住宅价格的时空影响效应[J]. 地理科学, 2011,31(7):836-842.
	[ Mei Z X, Xu S J, Ouyang J, et al. Spatio-temporal impact effects of Guangzhou metro 3rd line on housing prices[J]. Scientia Geographica Sinica, 2011,31(7):836-842. ]
[3]	宋伟轩, 毛宁, 陈培阳, 等. 基于住宅价格视角的居住分异耦合机制与时空特征——以南京为例[J]. 地理学报, 2017,72(4):589-602. doi: 10.11821/dlxb201704003
	[ Song W X, Mao N, Chen P Y, et al. Coupling mechanism and spatial-temporal pattern of residential differentiation from the perspective of housing prices: A case study of Nanjing[J]. Acta Geographica Sinica, 2017,72(4):589-602. ]
[4]	温海珍, 李旭宁, 张凌. 城市景观对住宅价格的影响——以杭州市为例[J]. 地理研究, 2012,31(10):1806-1814.
	[ Wen H Z, Li X N, Zhang L. Impacts of the urban landscape on the housing price: A case study in Hangzhou[J]. Geographical Research, 2012,31(10):1806-1814. ]
[5]	Lancaster K J. A new approach to consumer theory[J]. Journal of Political Economy, 1966,74:132-157. doi: 10.1086/259131
[6]	Rosen S. Hedonic prices and implicit markets:product differentiation in pure competition[J]. Journal of Political Economy, 1974,82:1,34-55. doi: 10.1086/260168
[7]	Noor N M, Asmawi M Z, Abdullah A. Sustainable urban regeneration: GIS and hedonic pricing method in determining the value of green space in housing area[J]. Procedia Social & Behavioral Sciences, 2015,170:669-679.
[8]	Liang X, Liu Y, Qiu T, et al. The effects of locational factors on the housing prices of residential communities: The case of Ningbo, China[J]. Habitat International, 2018,81:1-11. doi: 10.1016/j.habitatint.2018.09.004
[9]	Lan F, Wu Q, Zhou T, et al. Spatial effects of public service facilities accessibility on housing prices: A case study of Xi'an, China[J]. Sustainability, 2018,10(12):4503. doi: 10.3390/su10124503
[10]	Rong L H, Sun Y M. The analysis of second-hand housing price influencing factors based on hedonic model and WEB information[J]. Applied Mechanics and Materials, 2014,587-589:2285-2289. doi: 10.4028/www.scientific.net/AMM.587-589
[11]	许基伟, 马欣. 基于Hedonic模型的小区房价影响因素研究——以南京市鼓楼区为例[J]. 工程经济, 2020,30(1):61-64.
	[ Xu J W, Ma X. Study on the factors affecting the housing price based on the hedonic model: A case study of gulou district, Nanjing[J]. Engineering Economy, 2020,30(1):61-64. ]
[12]	刘俊豪. 住宅特征对城市二手房挂牌价的影响研究——以徐州市为例[D]. 徐州:中国矿业大学, 2016.
	[ Liu J H. Study on the impact of house characteristics on the asking price of urban second-hand house: A case of Xuzhou City[D]. Xuzhou: China University of Mining and Technology, 2016. ]
[13]	南京市规划和自然资源局. . 《南京市城市总体规划(2018—2035)》草案[EB]. http://ghj.nanjing.gov.cn/njsgtzyj/201812/t20181225_1347362.html.
	[ Nanjing Bureau of Planning and Natural Resources. Draft of "Nanjing City Master Plan (2018-2035)"[EB]. http://ghj.nanjing.gov.cn/njsgtzyj/201812/t20181225_1347362.html. ]
[14]	尹上岗, 李在军, 宋伟轩, 等. 基于地理探测器的南京市住宅租金空间分异格局及驱动因素研究[J]. 地球信息科学学报, 2018,20(8):1139-1149. doi: 10.10282/dqxxkx.2018.180072
	[ Yin S G, Li Z J, Song W X, et al. Spatial differentiation and influence factors of residential rent in Nanjing based on geographical detector[J]. Geo-Information Science, 2018,20(8):1139-1149. ]
[15]	钟芬. 棕地对周边住宅价格的影响研究[D]. 合肥:合肥工业大学, 2017.
	[ Zhong F. The impact of brownfields on residential property values[D]. Hefei: Hefei University of Technology, 2017. ]
[16]	刘倩倩. 基于3C定价理论的上海商品住宅定价策略的实证研究[D]. 上海:华东师范大学, 2012.
	[ Liu Q Q. Empiricalresearch of the new housing pricing strategy in Shanghai based on 3C pricing theory[D]. Shanghai: East China Normal University, 2012. ]
[17]	刘立祥. 线性回归模型中自变量的选择与逐步回归方法[J]. 统计与决策, 2015(21):80-82.
	[ Liu L X. Selection of independent variables and stepwise regression method in linear regression model[J]. Statistics & Decision, 2015,21:80-82. ]
[18]	游士兵, 严研. 逐步回归分析法及其应用[J]. 统计与决策, 2017,14:31-35.
	[ You S B, Yan Y. Stepwise regression analysis method and its application[J]. Statistics & Decision, 2017(14):31-35. ]
[19]	彭保发, 石忆邵, 单玥, 等. 上海市三甲医院对周边地区住房价格的空间影响效应分析[J]. 地理科学, 2015,35(7):860-866. doi: 10.13249/j.cnki.sgs.2015.07.860
	[ Peng B F, Shi Y S, Shan Y, et al. The spatial impacts of class 3A comprehensive hospitals on A peripheral residential property prices in Shanghai[J]. Scientia Geographica Sinica, 2015,35(7):860-866. ]
[20]	石忆邵, 王伊婷. 上海市学区房价格的影响机制[J]. 中国土地科学, 2014,28(12):47-55.
	[ Shi Y S, Wang Y T. The impacting mechanism of housing prices in the school districts in Shanghai City[J]. China Land Science, 2014,28(12):47-55. ]
[21]	Liu F, Min M, Zhao K, et al. Spatial-temporal variation in the impacts of urban infrastructure on housing prices in Wuhan, China[J]. Sustainability, 2020,12(3):1281. doi: 10.3390/su12031281
[22]	董藩, 董文婷. 学区房价格及其形成机制研究[J]. 社会科学战线, 2017(1):43-51.
	[ Dong F, Dong W T. Study on the price of school district and its formation mechanism[J]. Social Science Front, 2017(1):43-51. ]

变量类型	特征变量	变量描述	量化表达
区位特征	地铁站距离(Subway)	与最近地铁站距离	1 km内赋为5, 1~2 km赋为4, 2~3 km赋为3, 3~5 km赋为2, 5 km以上赋为1
	公交站点距离(Bus Station)	与最近公交站点距离	50 m内赋为5, 50~100 m赋为4, 100~500 m赋为3, 500~1000 m赋为2, 1000 m以上赋为1
	城市中心 (Urban Center)	与南京市新街口的距离	5 km内赋为1, 5 km以上赋为0
	二级商业中心(Second Center)	各行政区内的二级商业中心,如河西、城南、城南路、东山等	1 km内赋为4, 1~3 km内赋为3, 3 ~5 km内赋为2, 5 km以上赋为1
建筑特征	绿化率(Green)	小区绿化面积/小区规划面积	小区绿化率
	物业费用(Cost)	住宅小区的物业管理费用(元/月/m²)	具体数值
	住宅房龄(Age)	住宅建成的时间	当前时间与建成时间差值
	建筑类型(Style)	低层、多层、小高层、高层、超高层	低层赋为1,多层赋为2,小高层赋为3,高层赋为4,超高层赋为5
邻里特征	生活配套设施(Market)	与最近大型超市、商场的距离	200 m内赋为5, 200~500 m赋为4, 500 m~1000 m赋为3, 1~3 km赋为2, 3 km以上赋为1
	医疗设施(Hospital)	与最近的三甲医院的距离	500 m内赋为5, 500~1 km赋为4, 1~3 km赋为3, 3~5 km赋为2, 5 km以上赋为1
	教育配备(Key Primary)	与重点小学的距离	根据政府实际文件,位于重点学区内的赋为1,反之赋为0
	休闲设施(Park)	与最近的城市公园的距离	1 km内赋为5, 1~2 km赋为4, 2~3 km赋为3, 3~5 km赋为2, 5 km以上赋为1

模型	包含的特征变量
1	Key Primary
2	Key Primary、Cost
3	Key Primary、Cost、Urban Center
4	Key Primary、Cost、Urban Center 、Subway
5	Key Primary、Cost、Urban Center 、Subway、Bus Station
6	Key Primary、Cost、Urban Center 、Subway、Bus Station、Green
7	Key Primary、Cost、Urban Center 、Subway、Bus Station、Green、Park
8	Key Primary、Cost、Urban Center 、Subway、Bus Station、Green、Park、Hospital
9	Key Primary、Cost、Urban Center 、Subway、Bus Station、Green、Park、Hospital、Age

模型	R	R²	调整后的R²	标准估计的误差	更改统计
模型	R	R²	调整后的R²	标准估计的误差	R²变化量	F变化量	自由度1	自由度2	Sig.F 变化量
1	0.612	0.374	0.374	0.3768	0.374	1854.534	1	3100	0.000
2	0.641	0.411	0.411	0.3655	0.037	195.276	1	3099	0.000
3	0.669	0.447	0.447	0.3542	0.036	201.154	1	3098	0.000
4	0.685	0.470	0.469	0.3470	0.023	132.067	1	3097	0.000
5	0.697	0.486	0.485	0.3418	0.016	94.619	1	3096	0.000
6	0.704	0.496	0.495	0.3385	0.010	63.119	1	3095	0.000
7	0.709	0.503	0.502	0.3362	0.007	42.205	1	3094	0.000
8	0.711	0.505	0.504	0.3355	0.002	14.328	1	3093	0.000
9	0.712	0.507	0.506	0.3352	0.001	7.090	1	3092	0.000

模型	自变量	t	Sig.t	F	Sig.F	共线性统计
模型	自变量	t	Sig.t	F	Sig.F	Tolerance	VIF
9	Key Primary	24.126	0.000	1854.534	0.000	0.534	1.872
	ln(Cost)	13.922	0.000	2.98	0.000	0.643	1.556
	Urban Center	10.249	0.000	1059.049	0.000	0.552	1.812
	Subway	8.857	0.000	126.168	0.000	0.830	1.205
	Bus Station	8.899	0.000	204.262	0.000	0.762	1.313
	ln(Green)	8.749	0.000	1.308	0.018	0.871	1.148
	Park	4.873	0.000	91.752	0.000	0.759	1.318
	Hospital	3.725	0.000	107.512	0.000	0.657	1.523
	ln(Age)	2.754	0.001	7.438	0.000	0.515	1.942

自变量	非标准化系数
自变量	B	标准误差
(常量)	9.482	0.055
Key Primary	0.405	0.017
ln(Cost)	0.147	0.011
Urban Center	0.173	0.017
Subway	0.057	0.007
Bus Station	0.062	0.007
ln(Green)	0.163	0.019
Park	0.040	0.008
Hospital	0.030	0.008
ln(Age)	0.030	0.011

南京市住宅价格影响因素分析

Analysis of the Influencing Factors of Housing Price in Nanjing

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 8

参考文献 22

相关文章 15

Metrics

本文评价

推荐阅读 0

[1]	李大伟, 黄薇薇, 沈非, 程煜, 陈铭杨. 基于栅格的安徽省人居环境人文适宜性评价[J]. 地球信息科学学报, 2021, 23(6): 1017-1027.
[2]	雷雪, 周毅, 李阳, 王泽涛. 基于DEM的黄土地貌逼近度因子构建及特征分析[J]. 地球信息科学学报, 2020, 22(3): 431-441.
[3]	乔富伟,白永平,周亮,车磊,曹鹏,陈志杰. 雅鲁藏布江中游地区城乡居民点多尺度空间分异及自然因素影响分析[J]. 地球信息科学学报, 2019, 21(9): 1341-1351.
[4]	符海月, 张祎婷. 时间尺度重构EEMD-GRNN改进模型预测PM_2.5的研究[J]. 地球信息科学学报, 2019, 21(7): 1132-1142.
[5]	陈杰, 李昂, 符峥, 李思倩, 王结臣. 公交模式对公共服务设施可达性的影响[J]. 地球信息科学学报, 2019, 21(7): 983-993.
[6]	曹阳, 葛军莲, 龙毅, 张翎. 时空协同的城市旅游行程规划模型构建[J]. 地球信息科学学报, 2019, 21(6): 814-825.
[7]	柳林, 纪佳楷, 宋广文, 廖薇薇, 余洪杰, 刘文娟. 基于犯罪空间分异和建成环境的公共场所侵财犯罪热点预测[J]. 地球信息科学学报, 2019, 21(11): 1655-1668.
[8]	尹上岗, 李在军, 宋伟轩, 马志飞. 基于地理探测器的南京市住宅租金空间分异格局及驱动因素研究[J]. 地球信息科学学报, 2018, 20(8): 1139-1149.
[9]	金星星, 祁新华, 陆玉麒, 叶士琳, 王毅. 福建省高温热浪风险评估与空间分异研究[J]. 地球信息科学学报, 2018, 20(12): 1820-1829.
[10]	周洋, 祝善友, 华俊玮, 刘祎, 向嘉敏, 丁文. 南京市高温热浪时空分布研究[J]. 地球信息科学学报, 2018, 20(11): 1613-1621.
[11]	张琬卿, 赵书宁, 张冬韵, 董卫华. 尺度对人口地图空间分异规律认知的影响[J]. 地球信息科学学报, 2018, 20(10): 1396-1402.
[12]	王琛智, 张朝, 周脉耕, 殷鹏, 陶福禄, 金月雄. 低温对中国居民健康影响的空间差异性分析[J]. 地球信息科学学报, 2017, 19(3): 336-345.
[13]	翟召坤, 卢善龙, 王萍, 马丽娟, 李多, 任玉玉, 武胜利. 基于NSIDC海冰产品的FY北极海冰数据集优化[J]. 地球信息科学学报, 2017, 19(2): 143-151.
[14]	乔伟峰, 毛广雄, 王亚华, 陈月娇. 近32年来南京城市扩展与土地利用演变研究[J]. 地球信息科学学报, 2016, 18(2): 200-209.
[15]	朱寿佳, 甄峰, 秦萧, 何伊伊. 基于核密度估计的南京二手房活跃度特征及影响机制研究[J]. 地球信息科学学报, 2015, 17(6): 698-704.