基于户籍比较的特大城市住宅价格时空分异研究——以长沙市为例

doi:10.18306/dlkxjz.2023.01.003

Abstract

Abstract:

In order to reveal the differences in the spatial pattern of housing prices among different buyers in China's megacities, this study divided the household registration of housing buyers from Changsha City into two categories and depicted the spatial and temporal evolution of housing price patterns for these two groups of buyers respectively. A spatiotemporal auto regression model (STAR) was employed to analyze the influence of location, landscape, neighborhood, and other factors on the housing prices. We found that: 1) The housing price gradient of Changsha's non-registered household buyers has fallen faster than others while their space is more smooth. 2) The overall pattern of Changsha housing price space with "one river, two banks, and two centers" has gradually emerged. The influence of traditional central business district (CBD)—the Wuyi Square—on housing prices is declining, but it maintains a high level of housing price gradient and price elasticity for the locals. The sub-center Meixi Lake New City has a greater impact on the housing prices for outside buyer. 3) Outside buyers have a higher propensity to pay for housing quality and neighboring facilities. 4) In the period of prosperity and regulation of the housing market, the transaction prices of surrounding housing projects have different effects on the two types of housing buyers. This study explored the causes of the spatial differentiation of housing prices between the two types of buyers from four aspects: Functional requirements, subjective factors, differences of policy, and economic motivations. In the context of "housing for living not for speculation", paying attention to the spatial structure of housing price of different groups of people and the changing trends of influencing factors will help to develop a deeper understanding of the housing regulation policy in megacities.

Key words: household registration, spatial structure of housing price, housing price impact factor, spatiotemporal auto regression hedonic model, Changsha City

HUANG Tuofu, HE Tian, ZHU Xiang. Spatiotemporal variation of housing price in megacities based on household registration: Taking Changsha City as an example[J].PROGRESS IN GEOGRAPHY, 2023, 42(1): 27-41.

Figures/Tables 9

Fig.1

Fig.2

Tab.1

Fig.3

Tab.2

Fig.4

Tab.3

Results of the spatial price model for the residential housing buyers in 2013, 2016, and 2019

变量	2013年购房人群空间价格模型结果				2016年购房人群空间价格模型结果				2019年购房人群空间价格模型结果
	模型1：长沙本地人群		模型2：外来人群		模型3：长沙本地人群		模型4：外来人群		模型5：长沙本地人群		模型6：外来人群
	SAR	STAR	SAR	STAR	SAR	STAR	SAR	STAR	SAR	STAR	SAR	STAR
常数	8.684^***	24.733^***	8.678^***	34.669^***	8.797^***	16.913^***	8.816^***	12.363^***	9.175^***	24.604^***	8.998^***	8.278^***
户室面积	-0.011	-0.015	-0.014	-0.017	-0.003	-0.002	-0.010	-0.010	-0.017	-0.015	-0.021	-0.022
绿化率	0.001^*	0.001^*	0.002^***	0.003^***	0.002	0.002^*	0.003	0.004^**	0.002^**	0.005^***	0.004^**	0.006^**
车位配比	0.005^**	0.008^*	0.021^*	0.022^*	0.014^**	0.019^***	0.017^**	0.026^**	0.0019^**	0.026^**	0.027^***	0.033^**
物管等级	0.006^**	0.001^*	0.000^*	0.002^*	0.002^**	0.002^**	0.004^**	0.006^**	0.008^**	0.009^**	0.015^***	0.012^***
房龄	-0.007^*	-0.0012^*	-0.005	-0.002^*	-0.002^*	-0.006^*	-0.012^***	-0.012^***	-0.011^*	-0.013^*	-0.012^*	-0.016^*
知名小学	0.008	0.009	0.006	0.014	0.014^*	0.013^*	0.023^*	0.021^*	0.023^*	0.024^*	0.027^***	0.029^***
知名中学	0.029^**	0.053^***	0.042^***	0.056^***	0.042^***	0.057^***	0.046^***	0.061^***	0.050^***	0.063^***	0.061^***	0.089^***
健康休闲	0.004^**	0.009^*	0.001	0.001^*	0.001^**	0.001^**	0.001	0.002^*	0.005^***	0.006^***	0.001	0.001^*
医院	-0.021^**	-0.029^*	-0.016^*	-0.032^*	-0.009	-0.008	0.031^*	0.033^*	-0.029	-0.032^*	-0.015	-0.013
购物中心	0.012^**	0.012^*	0.001	0.001	0.034	0.040	0.012	0.015	-0.028	-0.037	-0.002	-0.003
公交站点数	-0.009^**	-0.011	-0.012	-0.013	-0.004	-0.003	-0.004	-0.004	-0.005	-0.002	-0.010^*	-0.009^**
地铁站点	0.034^**	0.047^***	0.050^***	0.064^***	0.043^***	0.039^***	0.057^***	0.084^***	0.054^***	0.059^***	0.066^**	0.069^**
CBD距离	-0.025^**	-0.048^***	-0.21^**	-0.037^**	-0.020^**	-0.034^***	-0.012^***	-0.019^***	-0.025^***	-0.032^***	-0.009^**	-0.014^**
高铁站距离	0.006^**	0.009^*	0.007^*	0.009^**	0.017^**	0.020^***	0.011	0.012	0.001	0.002	0.017	0.016
梅溪湖副中心距离	-0.012	-0.007^*	-0.006^*	-0.008^*	-0.007^**	-0.011^**	-0.008^**	-0.015^***	-0.013^*	-0.018^**	-0.024^***	-0.026^***
湘江距离	-0.018^**	-0.028^***	-0.032	-0.036^***	-0.033^***	-0.056^***	-0.038^***	-0.64^***	-0.055^**	-0.075^***	-0.069^**	-0.099^***
洋湖新城距离	-0.014	-0.016	-0.012	-0.014	-0.018	-0.021	-0.024^*	-0.017^*	-0.021^*	-0.019^*	-0.0027^*	-0.023^**
ρ	0.102^**		0.088^***		0.238^**		0.104^***		0.119^***		0.061^***
ψ		0.085^***		0.046^***		0.164^**		0.078^***		0.069^***		0.03^5***
AIC	223.205	219.001	161.246	149.966	31.263	28.83	-101.03	-112.1	162.837	158.974	67.24	64.166
R²	0.473	0.491	0.439	0.474	0.462	0.496	0.503	0.515	0.478	0.499	0.484	0.513
调整R²	0.449	0.467	0.421	0.432	0.403	0.418	0.481	0.492	0.444	0.457	0.469	0.481
Log-likelihood	-173.161	-166.433	-215.653	-199.911	-113.524	-104.339	-423.785	-395.211	-89.141	-66.335	-213.135	-202.171
N	15917	15917	32316	32316	15725	15725	42513	42513	16946	16946	24285	24285

Tab.3

Fig.5

Fig.6

References 34

[1]	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. ] doi: 10.11821/dlxb201704003
[2]	刘颖, 张平宇, 李静. 长春市区新建住宅价格的空间格局分析[J]. 地理科学, 2011, 31(1): 95-101.
	[Liu Ying, Zhang Pingyu, Li Jing. Spatial pattern of newly built housing's price in Changchun City. Scientia Geographica Sinica, 2011, 31(1): 95-101. ] doi: 10.13249/j.cnki.sgs.2011.01.95
[3]	邹利林, 杨俊, 胡学东. 中国城市住宅价格时空演变研究进展与展望[J]. 地理科学进展, 2013, 32(10): 1479-1489. doi: 10.11820/dlkxjz.2013.10.006
	[Zou Lilin, Yang Jun, Hu Xuedong. Research on temporal-spatial changes of urban residential housing price in China: Progress and prospects. Progress in Geography, 2013, 32(10): 1479-1489. ] doi: 10.11820/dlkxjz.2013.10.006
[4]	郭慧秀, 拓星星, 贾菲, 等. 国内城市房价时空分异及其动力机制研究进展[J]. 地域研究与开发, 2016, 35(2): 58-64.
	[Guo Huixiu, Ta Xingxing, Jia Fei, et al. Research progress of spatial-temporal differentiation and dynamic mechanism of domestic city housing price. Areal Research and Development, 2016, 35(2): 58-64. ]
[5]	王洋, 王德利, 王少剑. 中国城市住宅价格的空间分异格局及影响因素[J]. 地理科学, 2013, 33(10): 1157-1165. doi: 10.13249/j.cnki.sgs.2013.010.1157
	[Wang Yang, Wang Deli, Wang Shaojian. Spatial differentiation patterns and impact factors of housing prices of China's cities. Scientia Geographica Sinica, 2013, 33(10): 1157-1165. ] doi: 10.13249/j.cnki.sgs.2013.010.1157
[6]	王少剑, 王洋, 蔺雪芹, 等. 中国县域住宅价格的空间差异特征与影响机制[J]. 地理学报, 2016, 71(8): 1329-1342. doi: 10.11821/dlxb201608004
	[Wang Shaojian, Wang Yang, Lin Xueqin, et al. Spatial differentiation patterns and influencing mechanism of housing prices in China: Based on data of 2872 counties. Acta Geographica Sinica, 2016, 71(8): 1329-1342. ] doi: 10.11821/dlxb201608004
[7]	宋伟轩, 刘春卉. 长三角一体化区域城市商品住宅价格分异机理研究[J]. 地理研究, 2018, 37(1): 92-102. doi: 10.11821/dlyj201801007
	[Song Weixuan, Liu Chunhui. The price differentiation mechanism of commercial housing in the Yangtze River Delta. Geographical Research, 2018, 37(1): 92-102. ] doi: 10.11821/dlyj201801007
[8]	莫悦, 刘洋, 朱丽芳. 长江经济带城市土地价格空间分异特征及其影响因素[J]. 长江流域资源与环境, 2020, 29(1): 13-22.
	[Mo Yue, Liu Yang, Zhu Lifang. Spatial differentiation patterns and influencing factors of urban land prices in Yangtze River Economic Belt. Resources and Environment in the Yangtze Basin, 2020, 29(1): 13-22. ]
[9]	宋伟轩, 陈培阳, 陈浩, 等. 南京城市住宅“售租比”时空格局与分异机理[J]. 地理科学, 2018, 38(12): 2084-2092. doi: 10.13249/j.cnki.sgs.2018.12.017
	[Song Weixuan, Chen Peiyang, Chen Hao, et al. Spatial-temporal pattern and differentiation mechanism of price-to-rent ratio of Nanjing. Scientia Geographica Sinica, 2018, 38(12): 2084-2092. ] doi: 10.13249/j.cnki.sgs.2018.12.017
[10]	宋伟轩, 马雨竹, 李晓丽, 等. 南京城市住宅小区房价增长模式与效应[J]. 地理学报, 2018, 73(10): 1880-1895. doi: 10.11821/dlxb201810005
	[Song Weixuan, Ma Yuzhu, Li Xiaoli, et al. Housing price growth in different residences in urban Nanjing: Spatiotemporal pattern and social spatial effect. Acta Geographica Sinica, 2018, 73(10): 1880-1895. ] doi: 10.11821/dlxb201810005
[11]	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. ]
[12]	王洋, 李强, 王少剑, 等. 扬州市住宅价格空间分异的影响因素与驱动机制[J]. 地理科学进展, 2014, 33(3): 375-388. doi: 10.11820/dlkxjz.2014.03.009
	[Wang Yang, Li Qiang, Wang Shaojian, et al. Determinants and dynamics of spatial differentiation of housing price in Yangzhou. Progress in Geography, 2014, 33(3): 375-388. ] doi: 10.11820/dlkxjz.2014.03.009
[13]	薛冰, 肖骁, 李京忠, 等. 基于POI大数据的老工业区房价影响因素空间分异与实证[J]. 人文地理, 2019, 34(4): 106-114.
	[Xue Bing, Li Jingzhong, et al. POI-based analysis on the affecting factors of property prices' spatial distribution in the traditional industrial area. Human Geography, 2019, 34(4): 106-114. ]
[14]	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. ] doi: 10.18306/dlkxjz.2018.09.009
[15]	Wen H Z, Tao Y L. Polycentric urban structure and housing price in the transitional China: Evidence from Hangzhou[J]. Habitat International, 2015, 46: 138-146. doi: 10.1016/j.habitatint.2014.11.006
[16]	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 Shanggang, Li Zaijun, Song Weixuan, et al. Spatial differentiation and influence factors of residential rent in Nanjing based on Geographical Detector. Journal of Geo-information Science, 2018, 20(8): 1139-1149. ]
[17]	李卫民, 李同昇, 武鹏. 南京市住宅租金空间分异特征与影响因素分析[J]. 测绘科学, 2018, 43(5): 95-99, 104.
	[Li Weimin, Li Tongsheng, Wu Peng. Research on the spatial variation of housing rental and influence factors in Nanjing. Science of Surveying and Mapping, 2018, 43(5): 95-99, 104. ]
[18]	Kryvobokov M. What location attributes are the most important for market value: Extraction of attributes from regression models[J]. Property Management, 2007, 25(3): 257-286. doi: 10.1108/02637470710753639
[19]	Liao W C, Wang X Z. Hedonic house prices and spatial quantile regression[J]. Journal of Housing Economics, 2012, 21(1): 16-27. doi: 10.1016/j.jhe.2011.11.001
[20]	Yuan F, Wu J W, Wei Y D, et al. Policy change, amenity, and spatiotemporal dynamics of housing prices in Nanjing, China[J]. Land Use Policy, 2018, 75: 225-236. doi: 10.1016/j.landusepol.2018.03.045
[21]	Xiao Y, Hui E C M, Wen H Z. Effects of floor level and landscape proximity on housing price: A Hedonic analysis in Hangzhou, China[J]. Habitat International, 2019, 87: 11-26. doi: 10.1016/j.habitatint.2019.03.008
[22]	Wen H Z, Bu X Q, Qin Z F. Spatial effect of lake landscape on housing price: A case study of the West Lake in Hangzhou, China[J]. Habitat International, 2014, 44: 31-40. doi: 10.1016/j.habitatint.2014.05.001
[23]	Mei Y, Zhao X, Lin L, et al. Capitalization of urban green vegetation in a housing market with poor environmental quality: Evidence from Beijing[J]. Journal of Urban Planning and Development, 2018, 144(3): 05018011. doi: 10.1061/(ASCE)UP.1943-5444.0000458. doi: 10.1061/(ASCE)UP.1943-5444.0000458
[24]	Huang H, Yin L. Creating sustainable urban built environments: An application of hedonic house price models in Wuhan, China[J]. Journal of Housing and the Built Environment, 2015, 30(2): 219-235. doi: 10.1007/s10901-014-9403-8
[25]	Hui E C M, Li S M, Wong F K W, et al. Ethnicity, cultural disparity and residential mobility: Empirical analysis of Hong Kong[J]. Habitat International, 2012, 36(1). doi: 10.1016/j.habitatint.2011.08.003. doi: 10.1016/j.habitatint.2011.08.003
[26]	Frame D E, College B. International real estate review[J]. International Real Estate Review, 2008, 11(1): 96-112. doi: 10.53383/100092
[27]	Can A S, Megbolugbe I. Spatial dependence and house price index construction[J]. The Journal of Real Estate Finance and Economics, 1997, 14: 203-222. doi: 10.1023/A:1007744706720
[28]	Basu S, Thibodeau T. Analysis of spatial autocorrelation in house prices[J]. The Journal of Real Estate Finance and Economics, 1998, 17: 61-85. doi: 10.1023/A:1007703229507
[29]	Cohen J P, Coughlin C C. Spatial hedonic models of airport noise, proximity, and housing prices[J]. Journal of Regional Science, 2008, 48(5): 859-878. doi: 10.1111/j.1467-9787.2008.00569.x
[30]	Conway D, Li C Q, Wolch J, et al. A spatial autocorrelation approach for examining the effects of urban greenspace on residential property values[J]. The Journal of Real Estate Finance and Economics, 2010, 41(2): 150-169. doi: 10.1007/s11146-008-9159-6
[31]	Dubé J, Legros D. Spatial econometrics and the hedonic pricing model: What about the temporal dimension?[J]. Journal of Property Research, 2014, 31(4): 333-359. doi: 10.1080/09599916.2014.913655
[32]	Smith T E, Wu P. A spatio-temporal model of housing prices based on individual sales transactions over time[J]. Journal of Geographical Systems, 2009, 11(4): 333. doi: 10.1007/s10109-009-0085-9. doi: 10.1007/s10109-009-0085-9
[33]	Wen H Z, Xiao Y, Hui E C M. Quantile effect of educational facilities on housing price: Do homebuyers of higher-priced housing pay more for educational resources?[J]. Cities, 2019, 90: 100-112. doi: 10.1016/j.cities.2019.01.019
[34]	Gu P, Ma X M. Investigation and analysis of a floating population's settlement intention and environmental concerns: A case study in the Shawan River Basin in Shenzhen, China[J]. Habitat International, 2013, 39: 170-178. doi: 10.1016/j.habitatint.2012.12.005

变量		变量定义及测度方法
结构变量	户室面积	按房屋总面积分级赋值(60 m²以下为1，60~90 m²为2，90~120 m²为3，120~144 m²为4，144 m²以上为5)
	小区绿化	住宅小区绿地率(10%以下为0.5，10%~15%为1，15%~35%为2，35%~45%为3，45%~55%为4，55%以上为5)
	停车位配比	住宅小区内设计布置停车位总数与小区住宅总套数的比值(0.4以下为1，0.4~0.6为2，0.6~0.8为3，0.8~1为4，1以上为5)
	小区物管	住宅小区内物业管理的水平，按备案物业管理公司等级进行赋值(无物管为0，无级别物管为1，三级及以下物管为2，二级为3，一级为4，特级为5)
	房龄	距离住宅建成的年份(用研究时段年份减去住宅建造年份)
邻里变量	知名小学	住宅小区是否归属于长沙知名小学学区(是为1，否为0)
	知名中学	住宅小区是否归属于长沙知名初中学区(是为1，否为0)
	休闲	住宅小区半径范围1.5 km内公园广场及体育场馆的个数
	医院	住宅小区半径范围1.5 km内长沙市三甲医院个数
	商业	住宅小区半径范围1.5 km内商业购物中心个数
	公交	住宅小区半径范围1 km内公交站点个数
	有无地铁	住宅小区半径范围1 km内是否有地铁(是为1，否为0)
区位变量	CBD距离	住宅小区距离长沙市CBD五一广场的距离(单位：km)
	洋湖湿地距离	住宅小区距离洋湖湿地区的距离(单位：km)
	梅溪湖距离	住宅小区距离梅溪湖国际新城的距离(单位：km)
	湘江距离	住宅小区距离湘江沿江风光带的距离(单位：km)
	高铁站距离	住宅小区距离长沙高铁站的距离(单位：km)

类型	莫兰指数	Getis-Ord G_i*
2013年长沙本地人群购房价	0.117^***	0.35^***
2013年长沙外来人群购房价	0.121^***	0.43^***
2016年长沙本地人群购房价	0.236^***	0.47^***
2016年长沙外来人群购房价	0.173^***	0.38^***
2019年长沙本地人群购房价	0.027^***	0.36^***
2019年长沙外来人群购房价	0.103^***	0.37^***

Spatiotemporal variation of housing price in megacities based on household registration: Taking Changsha City as an example

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[2]	ZHU Lin, LIU Yansui. Influencing Factors behind Peasants'Desire for Urban Household Registration during Urbanization: A Case Study of Dancheng County [J]. PROGRESS IN GEOGRAPHY, 2012, 31(4): 461-467.