PROGRESS IN GEOGRAPHY ›› 2023, Vol. 42 ›› Issue (1): 27-41.doi: 10.18306/dlkxjz.2023.01.003
• Articles • Previous Articles Next Articles
HUANG Tuofu1(), HE Tian2,*(
), ZHU Xiang2
Received:
2022-06-07
Revised:
2022-08-23
Online:
2023-01-28
Published:
2023-03-28
Contact:
HE Tian
E-mail:toffhtf1025@gmail.com;hetian99@hunnu.edu.cn
Supported by:
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.
Tab.1
Influencing factors of residential housing price differentiation in Changsha City
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) |
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*** | 0.035*** | |||||||||||
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 |
[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 |
[1] | ZHANG Bailin, YANG Qingyuan, SU Kangchuan, WANG Zhaolin, FENG Yingbin. Heterogeneous households’decision on household registration transfer and farmland relinquishment: From livelihood perspective [J]. PROGRESS IN GEOGRAPHY, 2013, 32(2): 170-180. |
[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. |
|