空间功能视角下的公共服务对房价的影响——以成都市为例

doi:10.18306/dlkxjz.2017.08.008

Abstract

Abstract:

In China, the rise of housing prices has become one of the most pressing issues that residents encountered in recent years and received considerable attention. Regulating housing prices and preventing them from overheating have become an urgent question in some regions. This study examined the impact of public service provision on the spatiotemporal patterns of housing prices and its explanatory power in Chengdu City, Sichuan Province. A dataset of Chengdu housing prices and selected public service factors were constructed for the year 2016, and geographical detector technique was adopted in this study. The results indicate that the average housing price was 8480 yuan/m² in Chengdu in 2016, and it showed a progressive decrease from the city center to the suburbs between the traffic circles and along the radial arteries, forming the circular and radial patterns. In general, housing prices were high in the western and southern parts while low in the eastern and north parts of the city. The impact of public service provision (mainly health care, financing, and education services) on housing prices was most pronounced in the third circular zone and southwest of the downtown area, where it was higher than the overall impact in Chengdu downtown areas. The impact of public services on housing prices was significantly affected by the difference of spatial functions in different zones. Differences in public service provision and spatial functions will further deepen the differentiation pattern of housing prices, and promote the overheating of local property prices. We argue that a better understanding of the influence of public services on housing prices will help Chinese policymakers not only to formulate traditional financial and economic policies, but also to optimize spatial functions and public services.

Key words: housing price, public service, spatial function, geographical detector technique, Chengdu City

Shaoyao ZHANG, Xueqian SONG, Wei DENG. Impact of public services on housing prices in different functional spaces:A case study of metropolitan Chengdu[J].PROGRESS IN GEOGRAPHY, 2017, 36(8): 995-1005.

Figures/Tables 11

Fig.1

Fig.2

Table.1

Fig.3

Fig.4

Table.2

Table.3

Fig.5

Table.4

Housing prices explained by the provision of public service factors in different circular zones of Chengdu City and confidence level"

第一圈层			第二圈层			第三圈层			整体区域
因子	q	p	因子	q	p	因子	q	p	因子	q	p
公司企业	0.0250^*	0.0000	专科医院	0.0318^*	0.0000	银行	0.1387^*	0.0000	ATM	0.0810^*	0.0000
文化设施	0.0193^*	0.0112	公园	0.0240^*	0.0325	诊所	0.1307^*	0.0032	银行	0.0435	0.3649
政府部门	0.0188^*	0.0024	商场	0.0157^*	0.0067	专科医院	0.1301^*	0.0000	诊所	0.0381	0.8911
酒店	0.0140	0.1699	公司企业	0.0147^*	0.0040	金融机构	0.0680^*	0.0000	专科医院	0.0381	0.7273
商场	0.0112	0.0751	文化设施	0.0137^*	0.0026	大学	0.0625^*	0.0000	公园	0.0379^*	0.0000
诊所	0.0084	0.1949	综合医院	0.0120	0.6489	商场	0.0611^*	0.0000	企业	0.0368^*	0.0000
道路密度	0.0079	0.7662	道路密度	0.0116	0.0924	ATM	0.0540^*	0.0000	道路密度	0.0306^*	0.0000
公园	0.0071	0.1805	诊所	0.0109^*	0.0390	公司企业	0.0502^*	0.0000	小学	0.0267^*	0.0000
综合医院	0.0057	0.6585	金融机构	0.0106	0.0718	综合医院	0.0493^*	0.0000	商场	0.0215^*	0.0043
中学	0.0047	0.1748	大学	0.0098	0.3616	小学	0.0484^*	0.0000	大学	0.0134^*	0.0023
停车场	0.0033	0.7667	公交站	0.0066	0.1657	幼儿园	0.0444^*	0.0000	金融机构	0.0115^*	0.0025
ATM	0.0025	0.7138	中学	0.0056	0.2890	餐馆	0.0443^*	0.0000	政府部门	0.0093^*	0.0000
金融机构	0.0024	0.7978	ATM	0.0052	0.3843	中学	0.0404^*	0.0000	停车场	0.0088^*	0.0000
大学	0.0016	0.8776	餐馆	0.0031	0.4899	酒店	0.0365^*	0.0000	餐馆	0.0076	0.4029
小学	0.0013	0.8761	幼儿园	0.0025	0.7851	道路密度	0.0337^*	0.0000	中学	0.0073	0.1348
公交站	0.0012	0.9573	政府机构	0.0009	0.9166	文化设施	0.0300^*	0.0000	幼儿园	0.0045	0.0891
幼儿园	0.0008	0.9817	停车场	0.0006	0.9915	停车场	0.0286^*	0.0000	酒店	0.0045^*	0.0120
餐馆	0.0002	0.9733	银行	0.0002	0.9878	政府机构	0.0236^*	0.0000	综合医院	0.0040	0.3767
银行	0.0000	0.9551	小学	0.0001	0.9934	公交站	0.0190^*	0.0000	文化设施	0.0034	0.4094
专科医院	0.0000	0.9871	酒店	0.0001	0.9620	公园	0.0051	0.8104	公交站	0.0026	0.1869

Table.4

Table.5

Housing prices explained by the provision of public services in different directions along the main radiating roads of Chengdu City and confidence level"

西南			东南			东北			西北
因子	q	p	因子	q	p	因子	q	p	因子	q	p
专科医院	0.1394^*	0.0067	公园	0.0393	0.0740	大学	0.0960^*	0.0000	金融机构	0.0575^*	0.0000
诊所	0.1266	0.1117	道路密度	0.0360^*	0.0076	综合医院	0.0737^*	0.0000	大学	0.0303	0.1072
商场	0.1241	0.0642	综合医院	0.0353	0.0872	停车场	0.0713^*	0.0277	公园	0.0275	0.2393
银行	0.1236	0.0878	大学	0.0236	0.3519	公司企业	0.0279^*	0.0000	综合医院	0.0264^*	0.0310
大学	0.1166	0.2988	公司企业	0.0192	0.1489	道路密度	0.0255	0.0552	政府机构	0.0236^*	0.0000
中学	0.1142	0.1982	诊所	0.0181^*	0.0241	文化设施	0.0088	0.2071	诊所	0.0231^*	0.0437
小学	0.0966	0.6664	停车场	0.0170	0.5457	ATM	0.0082	0.7248	幼儿园	0.0228^*	0.0304
ATM	0.0649^*	0.0000	金融机构	0.0129	0.3790	小学	0.0077	0.3561	银行	0.0200^*	0.0000
综合医院	0.0600^*	0.0000	诊所	0.0119	0.1267	中学	0.0074	0.5692	文化设施	0.0180^*	0.0128
餐馆	0.0509^*	0.0000	银行	0.0116	0.1543	银行	0.0068	0.2857	酒店	0.0172^*	0.0046
金融机构	0.0495^*	0.0227	幼儿园	0.0108	0.2363	公园	0.0062	0.8610	小学	0.0141	0.5092
公园	0.0441^*	0.0000	餐馆	0.0106	0.2605	酒店	0.0054	0.3398	停车场	0.0116	0.2723
公司企业	0.0385^*	0.0000	文化设施	0.0096	0.4849	幼儿园	0.0052	0.8092	公司企业	0.0098	0.6152
幼儿园	0.0311^*	0.0000	酒店	0.0078	0.5019	商场	0.0027	0.7411	诊所	0.0090	0.3210
公交站	0.0264^*	0.0000	ATM	0.0076	0.6185	诊所	0.0021	0.8423	ATM	0.0082	0.4160
文化设施	0.0232	0.0672	小学	0.0059	0.6026	金融机构	0.0021	0.9336	道路密度	0.0063	0.5144
酒店	0.0216^*	0.0000	政府机构	0.0029	0.8794	公交站	0.0015	0.8648	商场	0.0041	0.7936
停车场	0.0164	0.0915	中学	0.0026	0.8737	政府机构	0.0009	0.8824	餐馆	0.0028	0.7482
道路密度	0.0140	0.1179	公交站	0.0018	0.8382	专科医院	0.0008	0.8557	中学	0.0023	0.9385
政府机构	0.0095	0.0507	商场	0.0016	0.9417	餐馆	0.0007	0.9168	公交站	0.0018	0.8545

Table.5

Fig.6

References 39

1	安体富, 任强. 2008. 中国公共服务均等化水平指标体系的构建: 基于地区差别视角的量化分析[J]. 财贸经济, (6): 79-82.
	[An T F, Ren Q.2008. Construction of index system of equalization of public service: Based on regional difference from the perspective of quantitative analysis[J]. Finance & Trade Economics, (6): 79-82.]
2	蔡秀云, 李雪, 汤寅昊. 2012. 公共服务与人口城市化发展关系研究[J]. 中国人口科学, (6): 58-65.
	[Cai X Y, Li X, Tang Y H.2012. China's public services and population urbanization[J]. Chinese Journal of Population Science, (6): 58-65.]
3	陈岚, 曾坚, 周波. 2009. 成都城市空间形态的生态进程与可持续发展研究[J]. 建筑学报, (12): 14-17. doi: 10.3969/j.issn.0529-1399.2009.12.004
	[Chen L, Zeng J, Zhou B.2009. Study on the ecological course and sustainable development of Chengdu urban spatial form[J]. Architectural Journal, (12): 14-17.] doi: 10.3969/j.issn.0529-1399.2009.12.004
4	董冠鹏, 张文忠, 武文杰, 等. 2011. 北京城市住宅土地市场空间异质性模拟与预测[J]. 地理学报, 66(6): 750-760.
	[Dong G P, Zhang W Z, Wu W J, et al.2011. Spatial heterogeneity in determinants of residential land price: Simulation and prediction[J]. Acta Geographica Sinica, 66(6): 750-760.]
5	樊淑云. 2014. 基于GIS和RS的成都市LUCC与城市扩展动态特征研究[D]. 成都: 四川师范大学. [Fan S Y. 2014. Study on dynamic characteristics of LUCC and urban expansion in Chengdu City based on GIS and RS[D]. Chengdu, China: Sichuan Normal University.]
6	高敏. 2009. 成都城市空间形态扩展时空演化过程及其规律分析[D]. 成都: 西南交通大学. [Gao M. 2009. The analysis of spatial and temporal evolution of the urban spatial form of Chengdu City in economies transition[D]. Chengdu, China: Southwest Jiaotong University.]
7	洪世键, 周玉. 2016. 基于特征价格法的学区房价格外溢效应探讨: 以厦门岛为例[J]. 建筑经济, (2): 65-69. doi: 10.14181/j.cnki.1002-851x.201602065
	[Hong S J, Zhou Y.2016. Research on price spillover effect of the school district housing based on hedonic price model: A case study of Xiamen Island[J]. Construction Economy, (2): 65-69.] doi: 10.14181/j.cnki.1002-851x.201602065
8	胡奇志. 2014. 近现代成都区域空间结构演变研究[D]. 成都: 西南交通大学. [Hu Q Z. 2014. The evolvement research of modern Chengdu region spatial structure[D]. Chengdu, China: Southwest Jiaotong University.]
9	冷璞. 2015. 成都未来五年房地产发展趋势分析[J]. 知识经济, (7): 95. doi: 10.3969/j.issn.1007-3825.2015.07.076
	[Leng P.2015. Chengdu weilai wunian fangdichan fazhan qushi fenxi[J]. Knowledge Economy, (7): 95.] doi: 10.3969/j.issn.1007-3825.2015.07.076
10	李威, Joh K, Lee C, 等. 2016. 社区步行通达性对独栋住宅房价的效益评估: 美国德克萨斯州奥斯丁市空间特征分析[J]. 城市交通, 14(4): 75-91.
	[Li W, Joh K, Lee C, et al.2016. Assessing benefits of neighborhood walkability to single-family property values: A spatial hedonic study in Austin, Texas[J]. Journal of Planning Education and Research, 14(4): 75-91.]
11	李祥, 高波, 王维娜. 2012. 公共服务资本化与房价租金背离: 基于南京市微观数据的实证研究[J]. 经济评论, (5): 78-88.
	[Li X, Gao B, Wang W N.2012. Capitalization of public services and deviation of housing price and rent: An empirical research based on micro data of Nanjing[J]. Economic Review, (5): 78-88.]
12	梁军辉, 林坚, 吴佳雨. 2016. 北京市公共服务设施配置对住房价格的影响[J]. 城市发展研究, 23(9): 82-87, 124. doi: 10.3969/j.issn.1006-3862.2016.09.012
	[Liang J H, Lin J, Wu J Y.2016. Study on the influence of the allocation of public service facilities on housing price in Beijing City[J]. Urban Development Studies, 23(9): 82-87, 124.] doi: 10.3969/j.issn.1006-3862.2016.09.012
13	刘华富. 2013. 天府新区的产业选择与空间布局研究[J]. 中外企业家, (22): 27-29. doi: 10.3969/j.issn.1000-8772.2013.22.010
	[Liu H F.2013. Tianfu xinqu de chanye xuanze yu kongjian buju yanjiu[J]. Chinese and Foreign Entrepreneurs, (22): 27-29.] doi: 10.3969/j.issn.1000-8772.2013.22.010
14	牛方曲, 刘卫东, 冯建喜. 2016. 基于家庭区位需求的城市住房价格模拟分析[J]. 地理学报, 71(10): 1731-1740. doi: 10.11821/dlxb201610006
	[Niu F Q, Liu W D, Feng J X.2016. Modeling urban housing price: The perspective of household activity demand[J]. Acta Geographica Sinica, 71(10): 1731-1740.] doi: 10.11821/dlxb201610006
15	王芳, 高晓路, 颜秉秋. 2014. 基于住宅价格的北京城市空间结构研究[J]. 地理科学进展, 33(10): 1322-1331. doi: 10.11820/dlkxjz.2014.10.004
	[Wang F, Gao X L, Yan B Q.2014. Research on urban spatial structure in Beijing based on housing prices[J]. Progress in Geography, 33(10): 1322-1331.] doi: 10.11820/dlkxjz.2014.10.004
16	王松涛, 郑思齐, 冯杰. 2007. 公共服务设施可达性及其对新建住房价格的影响: 以北京中心城为例[J]. 地理科学进展, 26(6): 78-85.
	[Wang S T, Zheng S Q, Feng J.2007. Spatial accessibility of housing to public services and its impact on housing price: A case study of Beijing's inner city[J]. Progress in Geography, 26(6): 78-85.]
17	王延松. 2010. 成都市锦江区乡村旅游发展研究[D]. 成都: 电子科技大学. [Wang Y S. 2010. The rural tourism development research in Jinjang District, Chengdu[D]. Chengdu, China: University of Electronic Science and Technology of China.]
18	王洋洋. 2014. 物流节点布局对城市空间结构的影响分析: 以成都市为例[D]. 成都: 西南交通大学. [Wang Y Y. 2014. The analysis for the influence of logistics nodes layout to the urban spatial structure: Taking Chengdu as an example[D]. Chengdu, China: Southwest Jiaotong University.]
19	武力超, 林子辰, 关悦. 2014. 我国地区公共服务均等化的测度及影响因素研究[J]. 数量经济技术经济研究, (8): 72-86.
	[Wu L C, Lin Z C, Guan Y.2014. Study on measurement of equalization of basic public service and its influencing factors[J]. Journal of Quantitative & Technical Economics, (8): 72-86.]
20	夏怡然, 陆铭. 2015. 城市间的“孟母三迁”: 公共服务影响劳动力流向的经验研究[J]. 管理世界, (10): 78-90.
	[Xia Y R, Lu M.2015.Chengshi jian de "Mengmu Sanqian": Gonggongfuwu yingxiang laodongli liuxiang de jingyan yanjiu[J]. Management World, (10): 78-90.]
21	肖夏. 2014. 成都市都市区空间结构及演化机制研究[D]. 成都: 四川师范大学. [Xiao X. 2014. Research on the spatial structure and evolutionary mechanism of the Chengdu metropolitan area[D]. Chengdu, China: Sichuan Normal University.]
22	杨林川, 常延聚, 马晴, 等. 2015. 公共服务对住宅价格的影响: 以厦门岛为例[J]. 城乡规划: 城市地理学术版, (2): 32-41.
	[Yang L C, Chang Y J, Ma Q, et al.2015. The impact of public services on housing price: A case study of Xiamen Island, China[J]. Urban and Rural Planning, (2): 32-41.]
23	杨林川, 张衔春, 洪世键, 等. 2016. 公共服务设施步行可达性对住宅价格的影响: 基于累积机会的可达性度量方法[J]. 南方经济, 34(1): 57-70.
	[Yang L C, Zhang X C, Hong S J, et al.2016. The impact of walking accessibility of public services on housing prices: Based on the cumulative opportunities measure[J]. South China Journal of Economics, 34(1): 57-70.]
24	邹金凤. 2013. 城市双核空间结构形成中的产业布局研究: 以成都为例[D]. 成都: 西南财经大学. [Zou J F. 2013. Research on industry layout in urban dual space structure formation: In Chengdu as an example[D]. Chengdu, China: Southwestern University of Finance and Economics.]
25	邹利林, 杨俊, 胡学东. 2013. 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, 32(10): 1479-1489. doi: 10.11820/dlkxjz.2013.10.006
	[Zou L L, Yang J, Hu X D.2013. Research on temporal-spatial changes of urban residential housing price in China: Progress and prospects[J]. Progress in Geography, 32(10): 1479-1489.] doi: 10.11820/dlkxjz.2013.10.006
26	Bertram C, Meyerhoff J, Rehdanz K, et al.2017. Differences in the recreational value of urban parks between weekdays and weekends: A discrete choice analysis[J]. Landscape and Urban Planning, 159: 5-14. doi: 10.1016/j.landurbplan.2016.10.006
27	Cao F, Ge Y, Wang J F.2013. Optimal discretization for geographical detectors-based risk assessment[J]. Giscience & Remote Sensing, 50(1): 78-92. doi: 10.1080/15481603.2013.778562
28	Getis A, Ord J K.1992. The analysis of spatial association by use of distance statistics[J]. Geographical Analysis, 24(3): 189-206. doi: 10.1111/j.1538-4632.1992.tb00261.x
29	Hu N, Legara E F, Lee K K, et al.2016. Impacts of land use and amenities on public transport use, urban planning and design[J]. Land Use Policy, 57: 356-367. doi: 10.1016/j.landusepol.2016.06.004
30	Husin H N, Nawawi A H, Ismail F, et al.2015. Correlation analysis of occupants' satisfaction and safety performance level in low cost housing[J]. Procedia-Social and Behavioral Sciences, 168: 238-248. doi: 10.1016/j.sbspro.2014.10.229
31	Kooshali A D, Parvizi R, Azeri A R K, et al.2015. A comparative study on the effect of nature on satisfaction with residence at detached houses (single unit) and residential building complexes (apartment)[J]. Procedia-Social and Behavioral Sciences, 201: 243-254. doi: 10.1016/j.sbspro.2015.08.173
32	Lankila T, Näyhä S, Rautio A, et al.2015. Is geographical distance a barrier in the use of public primary health services among rural and urban young adults? Experience from Northern Finland[J]. Public Health, 131: 82-91. doi: 10.1016/j.puhe.2015.10.020 pmid: 26715323
33	Li H B, Liu Y L.2016. Neighborhood socioeconomic disadvantage and urban public green spaces availability: A localized modeling approach to inform land use policy[J]. Land Use Policy, 57: 470-478. doi: 10.1016/j.landusepol.2016.06.015
34	Ouyang W, Wang B Y, Tian L, et al.2017. Spatial deprivation of urban public services in migrant enclaves under the context of a rapidly urbanizing China: An evaluation based on suburban Shanghai[J]. Cities, 60: 436-445. doi: 10.1016/j.cities.2016.06.004
35	Silverman B W.1986. Density estimation for statistics and data analysis[M]. London, UK: Chapman and Hall.
36	Wang J F, Li X H, Christakos G, et al.2010. Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region, China[J]. International Journal of Geographical Information Science, 24(1): 107-127. doi: 10.1080/13658810802443457
37	Wang Y, Wang S, Li G, et al.2017. Identifying the determinants of housing prices in China using spatial regression and the geographical detector technique[J]. Applied Geography, 79: 26-36. doi: 10.1016/j.apgeog.2016.12.003
38	Zhan X X, Zhang Z X, Sun F, et al.2016. The attitudes of primary healthcare providers towards web-based training on public health services in rural China: A cross-sectional study[J]. Public Health, 141: 153-162. doi: 10.1016/j.puhe.2016.09.007
39	Zhang Z, Tang W.2016. Analysis of spatial patterns of public attention on housing prices in Chinese cities: A web search engine approach[J]. Applied Geography, 70: 68-81. doi: 10.1016/j.apgeog.2016.03.004

圈层结构	圈层	一圈层	二圈层	三圈层	整体区域
	均价	10287	9748	7022	8480
	标准差	3699	5328	4120	4628

放射结构	方向	西南	东南	东北	西北
	均价	9191	7990	7533	6744
	标准差	5782	4620	4051	3756

[1]	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.
[2]	Dongsheng ZHAN, Wenzhong ZHANG, Li Chen, Xiaofen YU, Yunxiao DANG. Research progress and its enlightenment of urban public service facilities allocation [J]. PROGRESS IN GEOGRAPHY, 2019, 38(4): 506-519.
[3]	HAN Zenglin, LI Yuan, LIU Tianbao, DONG Mengru. Spatial differentiation of public service facilities’ configuration in community life circle: A case study of Shahekou District in Dalian City [J]. PROGRESS IN GEOGRAPHY, 2019, 38(11): 1701-1711.
[4]	Haibing JIANG, Wenzhong ZHANG, Sheng WEI. Public service facility accessibility as influenced by public transportation in Beijing [J]. PROGRESS IN GEOGRAPHY, 2017, 36(10): 1239-1249.
[5]	Yong ZHAO, Hao ZHANG, Yuling WU, Yang LIU. Public service demand of residents for smart city construction: Shijiazhuang City in Hebei Province as an example [J]. PROGRESS IN GEOGRAPHY, 2015, 34(4): 473-481.
[6]	Jiemin LIU, Bin LV. Characteristics of change in integrated rural-urban development: a case study of Sichuan Province and Chongqing Municipality [J]. PROGRESS IN GEOGRAPHY, 2015, 34(10): 1266-1274.
[7]	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 [J]. PROGRESS IN GEOGRAPHY, 2014, 33(6): 765-772.
[8]	WUWenjia, ZHANG Xiaoping, LI Yuanfang. Spatial correlation analysis of landscape accessibility and residential housing price in Beijing [J]. PROGRESS IN GEOGRAPHY, 2014, 33(4): 488-498.
[9]	WANG Yang, LI Qiang, WANG Shaojian, QIN Jing. Determinants and dynamics of spatial differentiation of housing price in Yangzhou [J]. PROGRESS IN GEOGRAPHY, 2014, 33(3): 375-388.
[10]	Fang WANG, Xiaolu GAO, Bingqiu YAN. Research on urban spatial structure in Beijing based on housing prices [J]. PROGRESS IN GEOGRAPHY, 2014, 33(10): 1322-1331.
[11]	ZOU Lilin, YANG Jun, HU Xuedong. Research on temporal-spatial changes of urban residential housing price in China： Progress and prospects [J]. PROGRESS IN GEOGRAPHY, 2013, 32(10): 1479-1489.
[12]	HUANG Xing, YANG Yongchun. The Characteristics and Forming Mechanisms of Gentrification in Cities of Western China: The Case Study in Chengdu City [J]. PROGRESS IN GEOGRAPHY, 2010, 29(12): 1532-1540.
[13]	SONG Zhengna, CHEN Wen, ZHANG Guixiang, ZHANG Lei. Spatial Accessibility to Public Service Facilities and Its Measurement Approaches [J]. PROGRESS IN GEOGRAPHY, 2010, 29(10): 1217-1224.

Impact of public services on housing prices in different functional spaces:A case study of metropolitan Chengdu

RichHTML

PDF (PC)

Like

Knowledge

Cited

Abstract

Cite this article

share this article

Figures/Tables 11

References 39

Related Articles 13

Metrics

Comments

Recommended 0