SPATIAL DIFFERENTIATION PATTERNS AND INFLUENCING FACTORS ANALYSIS OF HOUSING PRICES IN SHENYANG
XU Dan-meng1, LI Xin1, ZHANG Su-wen2
1. School of Public Administration, Nanjing Agricultural University, Nanjing 210014, China;
2. School of Geographical Sciences, Northeast Normal University, Changchun 130024, China
Abstract：Affordable housing plays a significant role for the wellbeing of people all over the world. However, against the background of housing commodification and market reforms since 1978 in China, housing price in many cities especially mega cities such as Beijing, Shanghai, Shenzhen and Guanghzou in China has undergone rapidly increasing. The fact negatively affects housing accessibility of many residents and leads to socio-spatial polarization of many cities. Driven by this concern, this research explores the spatial distribution pattern of housing prices and the influencing factors of Shenyang, a typical old industrial city in China. Based on POI data and the Kriging method, we firstly simulated the spatial distribution pattern of housing prices in Shenyang. Then, 11 independent variables were selected (consisting of community characteristics, public facilities and public transportations) to investigate mechanisms underlying the spatial differential pattern of housing prices of Shenyang, based on the Geographically Weighted Regression model (GWR). The results are as following. First, the housing price of different communities in Shenyang spatially forms a multi-center structure. Changbai region has replaced Shenhe and Heping districts as the new peak price area. Second, the independent variables show significant spatial heterogeneity. Variables related to community characteristics, such as ratio of green space, parking lot ratio and neighbourhoods management fees, have significant positive effects on housing price in general. Third, we found that urban housing market development of old industrial cities such as Shenyang has long been featured by the "strong government, weak market" development strategies.
Liu C H, Song W X. Perspectives of socio-spatial differentiation from soaring housing prices:A case study in Nanjing, China[J]. Sustainability, 2019,11(9):1-16.
[2]
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.[Wang Yang, Wang Deli, Wang Shaojian. Spatial differentiation patterns and impact factors of housing prices of China's cities[J]. Scientia Geographica Sinica, 2013,33(10):1157-1165.]
[3]
Luciani M. Monetary policy and the housing market:A structural factor analysis[J]. Journal of Applied Econometrics, 2013,30(2):199-218.
[4]
Du Z C, Zhang L. Home-purchase restriction, property tax and housing price in China:A counterfactual analysis[J]. Journal of Econometrics, 2015,188(2):558-568.
[5]
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.[Wang Fang, Gao Xiaolu, Yan Bingqiu. Research on urban spatial structure in Beijing based on housing prices[J]. Progress in Geography, 2014,33(10):1322-1331.]
[6]
孙倩,汤放华.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,2015,34(7):1343-1351.[Sun Qian, Tang Fanghua. The comparison of city housing price spatial variances based on spatial expansion and geographical weighted regression models[J]. Geographical Research, 2015,34(7):1343-1351.]
[7]
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.[Wang Yang, Li Qiang, Wang Shaojian, et al. Determinants and dynamics of spatial differentiation of housing price in Yangzhou[J]. Progress in Geography, 2014,33(3):375-388.]
[8]
薛冰,肖骁,李京忠,等.基于POI大数据的沈阳市住宅与零售业空间关联分析[J].地理科学,2019,39(3):442-449.[Xue Bing, Xiao Xiao, Li Jingzhong, et al. POI-based spatial correlation of the residences and retail industry in Shenyang city[J]. Scientia Geographica Sinica, 2019,39(3):442-449.]
[9]
尹上岗,胡信,马志飞,等.基于教育公平视角的城市学区房价格时空效应——以南京主城区公办小学为例[J].经济地理,2019,39(9):82-93.[Yin Shanggang, Hu Xin, Ma Zhifei, et al. Spatial and temporal effects of housing price in urban school districts based on education equity:A case study of public pimary school in the main urban area of Nanjing[J]. Economic Geography, 2019,39(9):82-93.]
[10]
斯子文,石忆邵.三甲医院对人口分布及房价影响的研究——以复旦大学附属儿科医院为例[J].经济地理,2013,33(10):74-81.[Si Ziwen, Shi Yishao.The impacts of class 3A comprehensive hospitals on population distribution and housing price:Take children's hospital of Fudan university for example[J]. Economic Geography, 2013,33(10):74-81.]
[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 of housing price in Nanjing city and its influencing factors:Based on geographical weighted regression model[J]. Human Geography, 2018,33(3):68-77.]
[12]
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.[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[J]. Acta Geographica Sinica, 2017,72(4):589-602.]
[13]
吴文佳,张晓平,李媛芳.北京市景观可达性与住宅价格空间关联[J].地理科学进展,2014,33(4):488-498.[Wu Wenjia, 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.]
[14]
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.[Song Weixuan, Ma Yuzhu, Li Xiaoli, et al. Housing price growth in different residences in urban Nanjing:Spatiotemporal pattern and social spatial effect[J]. Acta Geographica Sinica, 2018,73(10):1880-1895.]
[15]
赵梓渝,王雪微,王士君.长春市住宅价格空间分异与影响因素研究[J]. 人文地理,2019,34(4):97-105.[Zhao Ziyu, Wang Xuewei, Wang Shijun. Study on spatial differentiation of housing price distribution in Changchun and its influencing factors in Changchun[J]. Human Geography, 2019,34(4):97-105.]
[16]
Bitter C, Mulligan G F, Dall'erba S. Incorporating spatial variation in housing attribute prices:A comparison of geographically weighted regression and the spatial expansion method[J]. Journal of Geographical Systems, 2007,9(1):7-27.
[17]
Brunsdon C, Fotheringham S, Carlton M. Geographically weighted regression-modelling spatial non-stationarity[J]. Journal of the Royal Statistical Society. Series D (The Statistician), 1998,47(3):431-443.
[18]
Lu B, Charlton M, Harris P, et al. Geographically weighted regression with a non-euclidean distance metric:A case study using hedonic house price data[J]. International Journal of Geographical Information Science, 2014,28(4):660-681.
[19]
李志,周生路,张红富,等.基于GWR模型的南京市住宅地价影响因素及其边际价格作用研究[J].中国土地科学,2009,23(10):20-25.[Li Zhi, Zhou Shenglu, Zhang Hongfu, et al. Exploring the factors impacting on the residential land price and measuring their marginal effects based on geographically weighted regression model:A case study of Nanjing[J]. China Land Science, 2009,23(10):20-25.]
[20]
吕萍,甄辉.基于GWR模型的北京市住宅用地价格影响因素及其空间规律研究[J].经济地理,2010,30(3):472-478.[Lv Ping, ZhenHui. Affecting factors research of Beijing residential land price based on GWR model[J]. Economic Geography, 2010,30(3):472-478.]
[21]
中华人民共和国住房和城乡建设部.住房和城乡建设部强调:落实城市主体责任, 确保实现稳地价、稳房价、稳预期目标[EB/OL].(2020-08-28)[2020-11-17]. http://www.mohurd.gov.cn/zxydt/202008/t20200828_246982. Ministry of Housing and Urban-Rural Development,PRC. The Ministry of Housing and Urban-Rural Development stressed that cities should beresponsible for stabilizing land prices, housingprices and expectations.[EB/OL].(2020-08-28)[2020-11-17]. http://www.mohurd.gov.cn/zxydt/202008/t20200828_246982.
[22]
李苗裔,龙瀛.中国主要城市公交站点服务范围及其空间特征评价[J].城市规划学刊,2015,59(6):30-37.[Li Miaoyi, Long Ying. The coverage ratio of bus stations and an evalution of spatial parrerns of major Chinese cities[J]. Urban Planning Forum, 2015, 59(6):30-37.]
[23]
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.[Cui Nana, Gu Hengyu, Shen Tiyan. The spatial differentiation and relationship between housing prices and rents:Evidence from Beijing in China[J]. Geographical Research, 2019,38(6):1420-1434.]
[24]
孟斌,张景秋,王劲峰,等.空间分析方法在房地产市场研究中的应用——以北京市为例[J].地理研究,2005,24(6):956-964.[Meng Bin, Zhang Jingqiu, Wang Jingfeng, et al. Application of spatial analysis to the research of real estate:Taking Beijing as a case[J]. Geographical Research, 2005,24(6):956-964.]
[25]
Brunsdon C, Fotheringham A S, Charlton M. Geographically weighted regression:A method for exploring spatial nonstationarity[J]. Geographical Analysis, 1996,28(4):281-298.
[26]
Li Q Y, Wang J W, Callanan J, et al. The spatial varying relationship between services of the train network and residential property values in melbourne, Australia[J]. Urban Studies, 2020,58(2):335-354.
[27]
汤庆园,徐伟,艾福利.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,2012,32(2):52-58.[Tang Qingyuan, Xu Wei, Ai Fuli. A GWR-based study on spatial pattern and structural determinants of Shanghai's housing price[J]. Economic Geography, 2012,32(2):52-58.]
[28]
李广娣,沈昊婧.城市住房价格的空间分布格局研究——以沈阳市为例[J].现代城市研究,2014,29(2):80-84.[Li Guangdi, Shen Haojing. Research on spatial distribution pattern of urban housing prices:A case study in Shenyang[J]. Modern Urban Research, 2014, 29(2):80-84.]
[29]
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, Xiao Xiao, Li Jingzhong, et al. POI-based analysis on the affecting factors of property prices' spatial distribution in the traditional industrialarea[J]. Human Geography, 2019,34(4):106-114.]
[30]
李团胜.沈阳市城市景观演化及其驱动因素分析[J].人文地理, 2005,20(4):5-8.[Li Tuansheng. The landscape evolution of Shenyang city and its driving factors[J]. Human Geography, 2005,20(4):5-8.]
[31]
延善玉,张平宇,马延吉,等.沈阳市工业空间重组及其动力机制[J]. 人文地理,2007,22(3):107-111.[Yan Shanyu, Zhang Pingyu, Ma Yanji, et al. Spatial restructuring of industry in Shenyang city and its mechanisms[J]. Human Geography, 2007,22(3):107-111.]
