南京城区住宅售租价格时空分异与影响因素

doi:10.18306/dlkxjz.2018.09.009

摘要/Abstract

摘要：

住宅价格空间分异是中国城市地理学和经济地理学近年来关注的热点前沿课题。以南京4560个居住小区为研究总样本,采集2009-2017年间30个季度各小区平均住宅售价和租金,选取6个特征时段和小区分布相对集中的重点研究区,采用克里格插值法分析研究区内住宅售租价格的空间分异与演变特征,发现售价空间分异明显加剧,高值区渐显于河西新城、江心洲和鼓楼名校学区;租金空间则从城市中心向外围递减格局转变为整体更加均衡的新老城区多中心结构。在此基础上,重点围绕住宅“区位”属性,构建售租价格分异影响因素指标体系,通过逐步多元回归分析发现,中心位势变量对售租价格的解释度最高,而配套服务类区位因素对售租价格的解释力在降低。南京城市房价快速增长背景下,常规“区位”因素对房价分异的重要性持续减弱,学区、政策偏向等特殊“区位”因子对房价的决定性作用则逐步突显,而“售租比”全面快速增长则预示着城市房价风险程度的整体提高。

关键词: 住宅, 区位, 售价, 租金, 时空分异, 影响因素, 售租比, 南京

Abstract:

Under the background of rapid urbanization and housing market reform, urban housing and housing price have become a major issue related to the national economy and people's livelihood in China. The spatial differentiation of housing prices has become an important topic of research, which has been concerned by Chinese urban geographer and economic geographer in recent years. Few of the existing studies in China relied on long time series data to compare the spatial pattern, location influencing factors, and mechanism of change of urban housing selling and rental prices. As Nanjing City can represent the general development path of China's big cities, this study took 4560 residential communities in Nanjing as the total sample, and collected the average housing selling and rental prices for 30 quarters of 2009-2017. Data analyses show that there is a causality between the selling and rental prices, and the selling price shows a clear pattern that "fast rise" alternates with "relatively smooth," price levels whereas the rental price shows a more stable trend. The study chose areas where the sample communities are concentrated as focused research area and 6 characteristic time intervals to analyze the spatiotemporal differentiation and characteristics of change of housing selling and rental prices by employing the Kriging interpolation method. The results show that the spatial differentiation of housing selling price significantly intensified and high-value areas are more concentrated in Hexi New Town, Jiangxinzhou, and elite school districts in Gulou District; and based on the current situation of development, the high-value areas continue to transfer to the periphery of the city. Meanwhile, the spatial pattern of rental price shifted from gradually decreasing from city center toward outskirts to a more balanced structure of multi-centers both in the old and the new city areas. On this basis, an indicator system of location influence coefficient on housing selling and rental prices was built, focusing on the characteristics of houses and the "location" property. Through multiple stepwise regression analysis, it was found that central geographical variable has the highest degree of explanation power on selling and rental prices, while the explanatory power of supporting services on selling and rental prices is decreasing. With rapidly rising housing selling price in Nanjing, the significance of normal "location" coefficient on housing price differentiation is continually abating, but the decisive effect of some special "location" coefficients (such as school district and policy preferences) on selling price is becoming more prominent, further expanding the price-to-rent ratio, which also indicates the overall increase of housing price "risk" in Nanjing.

Key words: housing, location, selling price, rental price, spatiotemporal differentiation, influencing factors, price-to-rent ratio, Nanjing

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.

Weixuan SONG, Yuzhu MA, Yanru CHEN. Spatiotemporal differentiation and influencing factors of housing selling and rental prices: A case study of Nanjing City[J]. PROGRESS IN GEOGRAPHY, 2018, 37(9): 1268-1276.

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参考文献 30

[1]	陈庚, 朱道林, 苏亚艺, 等. 2015. 大型城市公园绿地对住宅价格的影响: 以北京市奥林匹克森林公园为例[J]. 资源科学, 37(11): 2202-2210.
	[Chen G, Zhu D L, Su Y Y, et al.2015. The effects of large-scale urban park green spaces on residential prices exemplified by Olympic Forest Park in Beijing[J]. Resources Science, 37(11): 2202-2210.]
[2]	冯长春, 李维瑄, 赵蕃蕃. 2011. 轨道交通对其沿线商品住宅价格的影响分析: 以北京地铁5号线为例[J]. 地理学报, 66(8): 1055-1062.
	[Feng C C, Li W X, Zhao F F.2011. Influence of rail transit on nearby commodity housing prices: A case study of Beijing Subway Line Five[J]. Acta Geographica Sinica, 66(8): 1055-1062.]
[3]	谷一桢, 郑思齐. 2010. 轨道交通对住宅价格和土地开发强度的影响: 以北京市13号线为例[J]. 地理学报, 65(2): 213-223.
	[Gu Y Z, Zheng S Q.2010. The impacts of rail transit on property values and land development intensity: The case of No.13 Line in Beijing[J]. Acta Geographica Sinica, 65(2): 213-223.]
[4]	郭慧秀, 拓星星, 贾菲, 等. 2016. 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, 35(2): 58-64.
	[Guo H X, Ta X X, Jia F, et al.2016. Research progress of spatial-temporal differentiation and dynamic mechanism of domestic city housing price[J]. Areal Research and Development, 35(2): 58-64.]
[5]	况伟大. 2016. 租售比与中国城市住房泡沫[J]. 经济理论与经济管理, 36(2): 46-58.
	[Kuang W D.2016. Rent to price ratio and the housing bubble in urban China[J]. Economic Theory and Business Management, 36(2): 46-58.]
[6]	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.]
[7]	李颖丽, 刘勇, 刘秀华. 2017. 重庆市主城区住房价格影响因子的空间异质性[J]. 资源科学, 39(2): 335-345.
	[Li Y L, Liu Y, Liu X H.2017. Spatial heterogeneity in factors affecting Chongqing housing prices[J]. Resources Science, 39(2): 335-345].
[8]	刘康, 吴群, 王佩. 2015. 城市轨道交通对住房价格影响的计量分析: 以南京市地铁1、2号线为例[J]. 资源科学, 37(1): 133-141.
	[Liu K, Wu Q, Wang P.2015. Econometric analysis of the impacts of rail transit on property values: The number 1 and 2 lines in Nanjing[J]. Resources Science, 37(1): 133-141.]
[9]	吕江林. 2010. 我国城市住房市场泡沫水平的度量[J]. 经济研究, (6): 28-41.
	[Lv J L.2010. The measurement of the bubble of urban housing market in China[J]. Economic Research Journal, (6): 28-41.]
[10]	宋伟轩, 毛宁, 陈培阳, 等. 2017. 基于住宅价格视角的居住分异耦合机制与时空特征研究: 以南京为例[J]. 地理学报, 72(4): 589-602.
	[Song W X, Mao N, Chen P Y, et al.2017. Coupling mechanism and spatial-temporal pattern of residential differentiation from the perspective of housing prices: A case study of Nanjing[J]. Acta Geographica Sinica, 72(4): 589-602.]
[11]	苏亚艺, 朱道林, 耿槟. 2014. 北京市住宅租金空间结构及其影响因素[J]. 经济地理, 34(4): 64-69.
	[Su Y Y, Zhu D L, Geng B.2014. The spatial structure and affecting factors of the housing rental in Beijing[J]. Economic Geography, 34(4): 64-69.]
[12]	孙倩, 汤放华. 2015. 基于空间扩展模型和地理加权回归模型的城市住房价格空间分异比较[J]. 地理研究, 34(7): 1343-1351.
	[Sun Q, Tang F H.2015. The comparison of city housing price spatial variances based on spatial expansion and geographical weighted regression models[J]. Geographical Research, 34(7): 1343-1351.]
[13]	汤庆园, 徐伟, 艾福利. 2012. 基于地理加权回归的上海市房价空间分异及其影响因子研究[J]. 经济地理, 32(2): 52-58.
	[Tang Q Y, Xu W, Ai F L.2012. A GWR-based study on spatial pattern and structural determinants of Shanghai's housing price[J]. Economic Geography, 32(2): 52-58.]
[14]	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
[15]	王少剑, 王洋, 蔺雪芹, 等. 2016. 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, 71(8): 1329-1342.
	[Wang S J, Wang Y, Lin X Q, et al.2016. Spatial differentiation patterns and influencing mechanism of housing prices in China: Based on data of 2872 counties[J]. Acta Geographica Sinica, 71(8): 1329-1342.]
[16]	王洋, 李强, 王少剑, 等. 2014. 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, 33(3): 375-388. doi: 10.11820/dlkxjz.2014.03.009
	[Wang Y, Li Q, Wang S J, et al.2014. Determinants and dynamics of spatial differentiation of housing price in Yangzhou[J]. Progress in Geography, 33(3): 375-388.] doi: 10.11820/dlkxjz.2014.03.009
[17]	王洋, 王德利, 王少剑. 2013. 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, 33(10): 1157-1165.
	[Wang Y, Wang D L, Wang S J.2013. Spatial differentiation patterns and impact factors of housing prices of China's cities[J]. Scientia Geographica Sinica, 33(10): 1157-1165.]
[18]	温海珍, 李旭宁, 张凌. 2012. 城市景观对住宅价格的影响: 以杭州市为例[J]. 地理研究, 31(10): 1806-1814.
	[Wen H Z, Li X N, Zhang L.2012. Impacts of the urban landscape on the housing pricing: A case study in Hangzhou[J]. Geographical Research, 31(10): 1806-1814.]
[19]	杨海娟, 高二龙, 赵新正, 等. 2017. 西安市商品住宅价格空间格局的演化研究[J]. 测绘科学, 42(3): 86-92.
	[Yang H J, Gao E L, Zhao X Z, et al.2017. Research on the evolution of the spatial pattern of commodity housing price in Xi'an City[J]. Science of Surveying and Mapping, 42(3): 86-92.]
[20]	张少尧, 宋雪茜, 邓伟. 2017. 空间功能视角下的公共服务对房价的影响: 以成都市为例[J]. 地理科学进展, 36(8): 995-1005.
	[Zhang S Y, Song X Q, Deng W.2017. Impact of public services on housing prices in different functional spaces: A case study of metropolitan Chengdu[J]. Progress in Geography, 36(8): 995-1005.]
[21]	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
[22]	Chen J, Hao Q J, Stephens M.2010. Assessing housing affordability in Post-reform China: A case study of Shanghai[J]. Housing Studies, 25(6): 877-901. doi: 10.1080/02673037.2010.511153
[23]	Chiang S-h.2016. Rising residential rents in Chinese mega cities: The role of monetary policy[J]. Urban Studies, 53(16): 3493-3509. doi: 10.1177/0042098015613753
[24]	Cronina D, McQuinnb K.2016. Credit availability, macro-prudential regulations and the house price-to-rent ratio[J]. Journal of Policy Modeling, 38(5): 971-984. doi: 10.1016/j.jpolmod.2016.06.002
[25]	Feng Q, Wu G L.2015. Bubble or riddle: An asset-pricing approach evaluation on China's housing market[J]. Economic Modelling, 46(2): 376-383. doi: 10.1016/j.econmod.2015.02.004
[26]	Logan J R, Fang Y P, Zhang Z X.2009. Access to housing in urban China[J]. International Journal of Urban and Regional Research, 33(4): 914-935. doi: 10.1111/ijur.2009.33.issue-4
[27]	Shen L.2012. Are house prices too high in China?[J]. China Economic Review, 23(4): 1206-1210. doi: 10.1016/j.chieco.2012.03.008
[28]	Wang Z, Zhang Q.2014. Fundamental factors in the housing markets of China[J]. Journal of Housing Economics, 25(9): 53-61. doi: 10.1016/j.jhe.2014.04.001
[29]	Wu F L.2015. Commodification and housing market cycles in Chinese cities[J]. International Journal of Housing Policy, 15(1): 6-26. doi: 10.1080/14616718.2014.925255
[30]	Wu Q Y, Zhang X L, Waley P.2016. Jiaoyufication: When gentrification goes to school in the Chinese inner city[J]. Urban Studies, 53(16): 3510-3526. doi: 10.1177/0042098015613234

初始假设	F统计量	概率P值
住宅价格不是引起租金价格的原因	120.041	2.00E-52
租金价格不是引起住宅价格的原因	41.8429	8.00E-19

上涨期	房价 /(元/m²)	平稳期	房价 /(元/m²)	上涨期	房价 /(元/m²）	平稳期	房价 /(元/m²)	上涨期	房价 /(元/m²)
2009年4季度	10490	2011年2季度	14384	2012年4季度	14317	2014年2季度	17698	2015年4季度	17594
2010年1季度	11765	2011年3季度	14417	2013年1季度	15326	2014年3季度	17499	2016年1季度	18697
2010年2季度	13267	2011年4季度	14191	2013年2季度	15676	2014年4季度	17384	2016年2季度	20755
2010年3季度	13533	2012年1季度	14090	2013年3季度	16221	2015年1季度	16875	2016年3季度	22531
2010年4季度	13751	2012年2季度	14066	2013年4季度	17032	2015年2季度	17455	2016年4季度	23971
2011年1季度	14307	2012年3季度	14285	2014年1季度	17367	2015年3季度	17290	2017年1季度	25358

分异指数	2009年4季度	2011年2季度	2012年4季度	2014年2季度	2015年4季度	2017年1季度
售价	0.0228	0.0256	0.0270	0.0259	0.0289	0.0349
租金	0.0348	0.0335	0.0306	0.0271	0.0292	0.0380

特征类型	解释变量	变量描述
小区属性	住宅房龄	距离住宅建成年份的年数
小区属性	居住品质	绿化率(绿化面积与规划建设用地面积之比)
商业区位	生活配套	1 km半径内大型商场和各类超市数量
商业区位	商务配套	1 km半径内写字楼和银行网点数量
交通区位	交通便捷	与最近两个(已通车)不同线路地铁站距离之乘积(取对数)
交通区位	中心位势	与城市中心(新街口)和最近二级中心距离之乘积(取对数)
服务区位	教育医疗	1 km半径内中、小学和医院数量
服务区位	休闲娱乐	1 km半径内城市公园广场、大学校园和文化体育场馆数量
景观区位	环境景观	与城市大型山水景观资源的空间毗邻程度^*

	住宅房龄	居住品质	生活配套	商务配套	交通便捷	中心位势	教育医疗	休闲娱乐	环境景观	联合解释力(R²)
2009年1季度	3 / 4	6 / 8	- / 2	8 / 7	1 / 1	2 / 3	7 / -	5 / 6	4 / 5	0.605 / 0.593
2011年2季度	3 / 4	5 / -	- / 7	7 / 6	2 / 1	1 / 2	6 / -	8 / 3	4 / 5	0.558 / 0.598
2012年4季度	2 / 4	5 / -	- / -	7 / -	1 / 1	3 / 2	8 / -	6 / 3	4 / 5	0.548 / 0.622
2014年2季度	4 / 3	2 / -	8 / -	6 / 4	- / 6	1 / 1	7 / 5	5 / 2	3 / -	0.449 / 0.588
2015年4季度	3 / 3	2 / 6	9 / -	6 / 4	7 / -	1 / 1	8 / 5	5 / 2	4 / -	0.463 / 0.546
2017年1季度	2 / -	3 / 6	- / -	- / 4	- / 2	1 / 1	- / 5	- / 3	4 / -	0.359 / 0.338