基于空间分位数模型的住宅价格分异的影响因素研究——以武汉市为例

doi:10.18306/dlkxjz.2021.02.009

Abstract

Abstract:

The change of housing price is an important issue related to urbanization and high-quality social and economic development in China. In order to explore the influencing factors in terms of the quantiles of housing price, this study took Wuhan City as a case, which is the central city of central China, and used the spatial quantile regression (SQR) model for quantitative analysis. The two-stage least squares (2SLS) model result was compared with the SQR model output to reveal its superiority. The research shows that: 1) The SQR model not only can consider the spatial autocorrelation of the housing price, but also have the capability to embed the conditional distribution characteristics, which helps to better describe the driving effects of micro-factors on different housing prices. 2) In view of the quantiles, the spatial autocorrelation of the high prices is stronger than the low prices. The influencing factors show volatility and heterogeneity. Compared with the mean result of the 2SLS model, the influencing degree of each factor in the SQR model increases or decreases with the change of the quantile. There is a significant difference in the degree of influence on housing price of different levels. 3) Overall, age of the residential building and medical facilities are negative influencing factors, and building characteristics including floor area ratio, location, and neighborhood such as nearby educational facilities are positive influencing factors. Based on the results, reasonable increase of the floor area ratio and green space ratio of low- and middle-priced residential areas, and increased investment in rail transportation and educational facilities in low-priced residential areas can be taken as alternatives for the government to formulate differentiated policy measures for housing with different levels of prices.

Key words: housing price, differentiation, spatial quantile regression model, Wuhan City

LU Xinhai, CAI Dawei, ZENG Chen. Influencing factors of housing price differentiation based on the spatial quantile model: A case study of Wuhan City[J].PROGRESS IN GEOGRAPHY, 2021, 40(2): 283-292.

Figures/Tables 6

Fig.1

Tab.1

Fig.2

Fig.3

Fig.4

Tab.2

Estimation results of spatial quantile regression model"

变量	2SLS	SQR
变量	2SLS	0.1分位点	0.2分位点	0.3分位点	0.4分位点	0.5分位点	0.6分位点	0.7分位点	0.8分位点	0.9分位点
ln建筑年龄	-0.138^***	-0.112^***	-0.122^***	-0.124^***	-0.135^***	-0.147^***	-0.148^***	-0.150^***	-0.151^***	-0.144^***
	(0.007)	(0.011)	(0.011)	(0.010)	(0.008)	(0.009)	(0.011)	(0.012)	(1.186)	(0.018)
容积率	0.012^***	0.005	0.006	0.009^*	0.013^**	0.012^***	0.015^***	0.017^**	0.013^*	0.018^*
	(0.003)	(0.003)	(0.006)	(0.005)	(0.005)	(0.004)	(0.005)	(0.007)	(0.015)	(0.010)
绿地率	0.425^***	0.485^***	0.365^***	0.320^***	0.310^***	0.341^***	0.411^***	0.411^***	0.374^***	0.348^*
	(0.072)	(0.153)	(0.123)	(0.105)	(0.065)	(0.067)	(0.107)	(0.126)	(0.008)	(0.193)
ln交通区位	-0.013^*	-0.005	-0.005	-0.008	-0.015^**	-0.020^***	-0.020^***	-0.024^**	-0.016	-0.020
	(0.007)	(0.009)	(0.009)	(0.011)	(0.007)	(0.007)	(0.008)	(0.011)	(0.110)	(0.016)
环线区位	0.050^***	0.041^***	0.061^***	0.051^***	0.047^***	0.045^***	0.038^**	0.033^**	0.039^**	0.068^***
	(0.010)	(0.014)	(0.014)	(0.012)	(0.010)	(0.010)	(0.015)	(0.015)	(0.018)	(0.023)
教育配套	0.020^***	0.028^**	0.018^*	0.019^**	0.022^***	0.017^*	0.007	0.019^*	0.020^**	0.034^**
	(0.007)	(0.011)	(0.010)	(0.007)	(0.006)	(0.010)	(0.010)	(0.010)	(0.010)	(0.015)
生活配套	0.019^*	0.027	0.055^***	0.040^***	0.028^**	0.015	0.010	-0.006	0.001	-0.004
	(0.011)	(0.017)	(0.018)	(0.014)	(0.012)	(0.014)	(0.019)	(0.020)	(0.020)	(0.026)
ln环境设施	-0.032^***	-0.032^**	-0.026^*	-0.038^***	-0.037^***	-0.038^***	-0.034^***	-0.027^***	-0.028^***	-0.039^**
	(0.009)	(0.014)	(0.015)	(0.009)	(0.007)	(0.008)	(0.009)	(0.010)	(0.011)	(0.017)
ln医疗设施	0.014^**	0.011	0.015^**	0.022^***	0.021^***	0.014^*	0.010	0.011	0.012	0.019
	(0.006)	(0.008)	(0.008)	(0.006)	(0.006)	(0.007)	(0.008)	(0.008)	(0.012)	(0.015)
ln金融设施	-0.017^***	0.001	-0.014^*	-0.013^*	-0.014^**	-0.011^*	-0.015^*	-0.022^**	-0.030^***	-0.024^*
	(0.005)	(0.009)	(0.008)	(0.007)	(0.005)	(0.006)	(0.008)	(0.009)	(0.011)	(0.012)
常数项	3.145^***	3.400^***	4.892^***	4.699^***	4.347^***	3.538^***	2.600^*	2.574^*	1.975^*	3.826^***
	(0.785)	(1.255)	(1.314)	(0.963)	(0.905)	(1.033)	(1.423)	(1.364)	(1.186)	(1.323)
$ρ$	0.712^***	0.648^***	0.501^***	0.537^***	0.586^***	0.684^***	0.786^***	0.795^***	0.858^***	0.669^***
	(0.079)	(0.124)	(0.128)	(0.097)	(0.092)	(0.106)	(0.146)	(0.141)	(0.120)	(0.129)

Tab.2

References 29

[1]	张绍良, 李晶晶, 公云龙 . 基于特征价格模型的城市住宅价格影响因素研究[J]. 地域研究与开发, 2013,32(4):80-83.
	[ Zhang Shaoliang, Li Jingjing, Gong Yunlong . Study on the influencing factors of urban house price based on hedonic price model. Areal Research and Development, 2013,32(4):80-83. ]
[2]	尹上岗, 宋伟轩, 马志飞 , 等. 南京市住宅价格时空分异格局及其影响因素分析: 基于地理加权回归模型的实证研究[J]. 人文地理, 2018,33(3):68-77.
	[ Yin Shanggang, Song Weixuan, Ma Zhifei , et al. Spatial differentiation and influencing factors analysis of housing prices in Nanjing: Based on geographically weighted regression model. Human Geography, 2018,33(3):68-77. ]
[3]	崔娜娜, 古恒宇, 沈体雁 . 北京市住房价格和租金的空间分异与相互关系[J]. 地理研究, 2019,38(6):1420-1434. doi: 10.11821/dlyj020180352
	[ Cui Nana, Gu Hengyu, Shen Tiyan . The spatial differentiation and relationship between housing price and rents: Evidence from Beijing in China. Geographical Research, 2019,38(6):1420-1434. ]
[4]	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 Weixuan, Mao Ning, Chen Peiyang , et al. Coupling mechanism and spatial-temporal pattern of residential differentiation from the perspective of housing prices: A case study of Nanjing. Acta Geographica Sinica, 2017,72(4):589-602. ]
[5]	王福良, 冯长春, 甘霖 . 轨道交通对沿线住宅价格影响的分市场研究: 以深圳市龙岗线为例[J]. 地理科学进展, 2014,33(6):765-772. doi: 10.11820/dlkxjz.2014.06.005
	[ Wang Fuliang, Feng Changchun, Gan Lin . Impact of rail transit on the residential property prices of submarkets: A case of the Longgang Line of Shenzhen. Progress in Geography, 2014,33(6):765-772. ]
[6]	宋伟轩, 马雨竹, 陈艳如 . 南京城区住宅售租价格时空分异与影响因素[J]. 地理科学进展, 2018,37(9):1268-1276. doi: 10.18306/dlkxjz.2018.09.009
	[ Song Weixuan, Ma Yuzhu, Chen Yanru . Spatiotemporal differentiation and influencing factors of housing selling and rental prices: A case study of Nanjing City. Progress in Geography, 2018,37(9):1268-1276. ]
[7]	王健, 景霖霖, 彭山桂 , 等. 基于空间计量模型的商品住宅开发商定价互动影响及其空间分异研究: 以济南市为例[J]. 中国土地科学, 2019,33(1):24-31.
	[ Wang Jian, Jing Linlin, Peng Shangui , et al. Study on the interactive effect and spatial differentiation of commodity housing pricing based on spatial econometric models: A case study of Jinan City. China Land Science, 2019,33(1):24-31. ]
[8]	Zhang L, Yi Y M . What contributes to the rising house prices in Beijing? A decomposition approach[J]. Journal of Housing Economics, 2018,41:72-84. doi: 10.1016/j.jhe.2018.04.003
[9]	Koenker R, Bassett G . Regression quantiles[J]. Econometrica, 1978,46(1):33-50. doi: 10.2307/1913643
[10]	吴晓隽, 裘佳璐 . Airbnb房源价格影响因素研究: 基于中国36个城市的数据[J]. 旅游学刊, 2019,34(4):13-28.
	[ Wu Xiaojun, Qiu Jialu . A study of Airbnb listing price determinants: Based on data from 36 cities in China. Tourism Tribune, 2019,34(4):13-28. ]
[11]	程亚鹏 . 城市住房子市场价格差异的分位数分解方法与实证[J]. 中国管理科学, 2017,25(6):39-49.
	[ Cheng Yapeng . Quantile decomposition method and empirical analysis of price difference between urban housing sub-market. Chinese Journal of Management Science, 2017,25(6):39-49. ]
[12]	Zietz J, Zietz E N, Sirmans G S . Determinants of house prices: A quantile regression approach[J]. Journal of Real Estate Finance and Economics, 2008,37:317-333. doi: 10.1007/s11146-007-9053-7
[13]	Kostov P . A spatial quantile regression hedonic model of agricultural land prices[J]. Spatial Economic Analysis, 2009,4(1):53-72. doi: 10.1080/17421770802625957
[14]	Liao W C, Wang X Z . Hedonic house prices and spatial quantile regression[J]. Journal of Housing Economics, 2012,21:16-27. doi: 10.1016/j.jhe.2011.11.001
[15]	Mathur S . Impact of an urban growth boundary across the entire house price spectrum: The two-stage quantile spatial regression approach[J]. Land Use Policy, 2019,80:88-94. doi: 10.1016/j.landusepol.2018.09.011
[16]	Lancaster K J . A new approach to consumer theory[J]. Journal of Political Economy, 1996,74:132-157. doi: 10.1086/259131
[17]	Rosen S . Hedonic prices and implicit markets: Product differentiation in pure competition[J]. Journal of Political Economy, 1974,82(1):34-55. doi: 10.1086/260169
[18]	黄静, 崔光灿, 王诤诤 . 大型主题乐园对周边住宅价格的影响分析: 以上海迪士尼为例[J]. 城市发展研究, 2018,25(5):37-43.
	[ Huang Jing, Cui Guangcan, Wang Zhengzheng . Study on the impact of large theme resort on the surrounding housing price: Take Shanghai Disney as an example. Urban Development Studies, 2018,25(5):37-43. ]
[19]	毛德华, 吴亚菱, 袁周炎妍 , 等. 长沙市生态景观对住宅价格的影响分析[J]. 经济地理, 2018,38(8):76-82.
	[ Mao Dehua, Wu Yaling, Yuan Zhouyanyan , et al. Impact analysis of ecological landscape on housing price in Changsha City. Economic Geography, 2018,38(8):76-82. ]
[20]	陈建宝, 丁军军 . 分位数回归技术综述[J]. 统计与信息论坛, 2008(3):89-96.
	[ Chen Jianbao, Ding Junjun . A review of technologies on quantile regression. Statistics & Information Forum, 2008(3):89-96. ]
[21]	Heckman J J . Sample selection bias as a specification error[J]. Econometrica, 1979,47(1):153-161. doi: 10.2307/1912352
[22]	Stephen M, Lennon C, Winky H . Quantile regression estimates of Hong Kong real estate prices[J]. Urban Studies, 2010,47(11):2461-2472. doi: 10.1177/0042098009359032
[23]	Han X D, Fang W, Li H J , et al. Heterogeneity of influential factors across the entire air quality spectrum in Chinese cities: A spatial quantile regression analysis[J]. Environmental Pollution, 2020,262, 114259. doi: 10.1016/j.envpol.2020.114259. doi: 10.1016/j.envpol.2020.114259 pmid: 32120259
[24]	Zeren F, Özcan B, Menteşe E Y . Health care convergence analysis in Turkey on the province level: Spatial quantile method[J]. Procedia Economics and Finance, 2016,38:90-97. doi: 10.1016/S2212-5671(16)30181-2
[25]	Kim T H, Muller C . Two-stage quantile regression when the first stage is based on quantile regression[J]. The Econometrics Journal, 2004,7(1):218-231. doi: 10.1111/j.1368-423X.2004.00128.x
[26]	Chernozhukov V, Hansen C . Instrumental quantile regression inference for structural and treatment effect models[J]. Journal of Econometrics, 2006,132(2):491-525. doi: 10.1016/j.jeconom.2005.02.009
[27]	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 Fang, Gao Xiaolu, Yan Bingqiu . Research on urban spatial structure in Beijing based on housing prices. Progress in Geography, 2014,33(10):1322-1331. ]
[28]	王洪强, 李小雪, 张英婕 . 上海市住宅租金价格空间分异格局及其影响因素分析[J]. 管理现代化, 2019,39(5):95-100.
	[ Wang Hongqiang, Li Xiaoxue, Zhang Yingjie . Analysis on the spatial differentiation pattern of residential rent price in Shanghai and its influencing factors. Modernization of Management, 2019,39(5):95-100. ]
[29]	张凌, 常欣, 温海珍 . 重大事件与政策调整对杭州市住房市场价量波动的影响[J]. 浙江大学学报(理学版), 2017,44(3):363-368, 378.
	[ Zhang Ling, Chang Xin, Wen Haizhen . The influence of significant events and control policies on price and volume in housing market of Hangzhou. Journal of Zhejiang University (Science Edition), 2017,44(3):363-368, 378. ]

特征类型	变量名称	量化方式
建筑特征	建筑年龄	距小区建成年份
	容积率	小区的建筑总面积/小区宗地面积
	绿地率	小区绿化面积/小区宗地面积
区位特征	交通区位	小区距最近地铁站的距离
	环线区位	小区所处环线位置(一环内：3分;一环外二环内：2分;二环外三环内：1分)
邻里特征	教育配套	小区附近1 km内有无幼儿园、小学、中学、大学,每项设为1分,共4分
	生活配套	小区附近1 km内有无商场、超市、菜市,每项设为1分,共3分
	环境设施	小区距最近公园的距离
	医疗设施	小区距最近医院(不含诊所、卫生院)的距离
	金融设施	小区距最近银行(不含ATM机)的距离

Influencing factors of housing price differentiation based on the spatial quantile model: A case study of Wuhan City

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 29

Related Articles 15

Metrics

Comments

Recommended 0

[1]	GUO Qian, LIAO Heping, WANG Ziyi, LIU Yuanli, LI Tao. Measurement of rural poverty alleviation sustainability and return-to-poverty risk identification in Qinling-Bashan Mountains:A case study of Chengkou County, Chongqing Municipality [J]. PROGRESS IN GEOGRAPHY, 2021, 40(2): 232-244.
[2]	LI Zhixuan, ZHEN Feng, ZHANG Shanqi, YANG Yu. Seasonal and spatiotemporal differences in the public transport-based mobility of elderly population: A case study of Wuhu City in Anhui Province [J]. PROGRESS IN GEOGRAPHY, 2021, 40(2): 293-303.
[3]	QIAN Jiacheng, ZHANG Bailin, LIU Hongwu, GAO Yang, WANG Zhaoying, LIAN Xiaoyun. Development differentiation of rural areas with tourism characteristics in eastern mountainous areas and its driving forces: A case study of Pingyang County, Zhejiang Province [J]. PROGRESS IN GEOGRAPHY, 2020, 39(9): 1460-1472.
[4]	WANG Yahui, LI Xiubin, XIN Liangjie, TAN Minghong. Spatial differentiation of social security function of cultivated land assets: Comparison of different agricultural regions [J]. PROGRESS IN GEOGRAPHY, 2020, 39(9): 1473-1484.
[5]	HUANG Han, LI Xunhuan, ZHOU Yang. Geographical pattern and spatial differentiation mechanism of rural poverty in hilly and mountainous areas of China: A case study of Yudu County in Jiangxi Province [J]. PROGRESS IN GEOGRAPHY, 2020, 39(6): 938-950.
[6]	WU Yan, LI Hongbo. Spatial change and correlations of desakota regions in a metropolitan area using NPP/VIIRS nighttime light data: A case study of Wuhan City [J]. PROGRESS IN GEOGRAPHY, 2020, 39(1): 13-23.
[7]	LUO Jing,JIANG Liang,LUO Minghai,TIAN Lingling,CHEN Guolei,TIAN Ye,WU Yikun. Rural development level of villages in Wuhan City’s new urban districts and its hierarchical structure [J]. PROGRESS IN GEOGRAPHY, 2019, 38(9): 1370-1381.
[8]	Limin JIAO, Chen GONG, Gang XU, Ting DONG, Boen ZHANG, Zehui LI. Urban expansion dynamics and urban forms in three metropolitan areas—Tokyo, New York, and Shanghai [J]. PROGRESS IN GEOGRAPHY, 2019, 38(5): 675-685.
[9]	Chaofeng QIAN, Debin DU, Xuan HU, Dezhong DUAN. Does genetic difference influence inter-region technology transfer?Evidence from China’s provincial-level patent transfer data of 2001-2005 [J]. PROGRESS IN GEOGRAPHY, 2019, 38(5): 745-755.
[10]	Jinting ZHANG, Yudan ZHAO, Yangge TIAN, Qingqing HE, Yanhua ZHUANG, Yunxi PENG, Song HONG. Spatial non-coupling of air pollutant emissions and particulate matter-related air quality: A case study in Wuhan City, China [J]. PROGRESS IN GEOGRAPHY, 2019, 38(4): 612-624.
[11]	Cheng WANG, Haoying LI, Yanzhou HE, Xiaosu MA, Mingming ZHOU. Sustainable development ability and its spatiotemporal differentiations of rural human settlements in Chongqing Municipality from 1997 to 2015 [J]. PROGRESS IN GEOGRAPHY, 2019, 38(4): 556-566.
[12]	Gang LI, Weiyu CHEN, Lan YANG, Qian LIU, Xiliang CHEN. Spatial pattern and agglomeration mode of parcel collection and delivery points in Wuhan City [J]. PROGRESS IN GEOGRAPHY, 2019, 38(3): 407-416.
[13]	WANG Shaobo, GUO Jianke, LUO Xiaolong, GU Zongni. Spatial differentiation of the impact of high-speed rail on aviation passenger market in central cities of China [J]. PROGRESS IN GEOGRAPHY, 2019, 38(11): 1665-1674.
[14]	MAO Shuaiyong, JIAO Limin, XU Gang, LI Zehui. Identification of the polycentric spatial structure in Wuhan City based on multisource data [J]. PROGRESS IN GEOGRAPHY, 2019, 38(11): 1675-1683.
[15]	LI Zhixuan, HU Hong. Using the theory of planned behavior to understand the effects of urban residential differentiation on residents’ physical activities [J]. PROGRESS IN GEOGRAPHY, 2019, 38(11): 1712-1725.