Acta Geographica Sinica ›› 2021, Vol. 76 ›› Issue (8): 1924-1938.doi: 10.11821/dlxb202108008
• Urban and Human Health • Previous Articles Next Articles
WANG Yang1,2(), WU Kangmin1,2(
), ZHANG Hong'ou1,2
Received:
2020-05-06
Revised:
2021-03-26
Online:
2021-08-25
Published:
2021-10-25
Contact:
WU Kangmin
E-mail:wyxkwy@163.com;kangmwu@163.com
Supported by:
WANG Yang, WU Kangmin, ZHANG Hong'ou. The core influencing factors of housing rent difference in Guangzhou's urban district[J].Acta Geographica Sinica, 2021, 76(8): 1924-1938.
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Tab. 1
The analysis system of impact factors of housing rents based on 4 aspects of hedonic rent model
要素 | 细分特征领域 | 涉及的主要因素 |
---|---|---|
建筑特征 | 单户住宅特有特征 | 建筑面积、所在楼层、朝向、装修程度、视野、通风、采光、住宅设施、户型格局 |
整栋住宅(或小区)共有特征 | 房龄、是否有电梯、物业管理水平、楼盘(或小区)档次、绿化水平、容积率、小区设施配套、停车位、开发商品牌、社会文化特征 | |
便利性特征 | 交通出行便利性 | 地铁便利性、公交站点便利性、道路交通便利性 |
就业便利性 | 主要就业地点的可达性 | |
就学便利性 | 幼儿园、小学、中学、大学的可达性 | |
商业服务便利性 | 商场、购物中心、超市、餐饮、娱乐、市场等商业服务场所的可达性 | |
公共服务与休闲游憩便利性 | 文化、体育、医疗等各类公共服务设施可达性、各类休闲游憩场所可达性 | |
环境特征 | 优质环境或景观 | 公园、水域、自然景区可达性,位于或亲近优质的建成环境或社会环境区域 |
消极环境或景观 | 受到工厂、物流中心、批发中心、厌恶型交通设施(例如飞机场、港口、火车站、汽车站、高速公路或高架路、加油站)、厌恶型市政设施(例如污水处理厂、垃圾处理场、燃气站、发电厂、变电站、高压走廊、殡仪馆、墓地、信号发射塔)的影响程度,受到环境污染、较差的建成环境、不安全的社会环境影响 | |
区位特征 | 地理位置 | 距市中心距离、所处圈层、板块、区域、方位 |
Tab. 2
The appraisal system of impact factors of housing rents in Guangzhou's urban district
特征租金因素视角 | 影响因素指标 | 计算方法(分数赋值标准) | 预期影响方向 |
---|---|---|---|
HR1建筑特征 | F1 建筑面积 | 住宅的建筑面积数值 | 负向 |
F2 朝向与楼层 | 好朝向且优越楼层(9分);好朝向且一般楼层,或者一般朝向且优越楼层(7分);一般朝向且一般楼层(5分);差朝向且优越楼层(3分),差朝向且一般楼层(1分)。对朝向定义:好朝向为南、东南、西南,差朝向为北,其他情况为一般朝向;对楼层定义:对于楼梯楼,处于低楼层为优越楼层,中高层为一般楼层;对于电梯楼,中高层为优越楼层,低层为一般楼层。 | 正向 | |
F3 房龄 | 2020年减去住房建成时的年份 | 负向 | |
F4 电梯与物业 | 有电梯且有物业公司管理(9分);有电梯且为业主自筹/私人承办/单位代管物业(7分);有电梯但无物业管理,或者无电梯但有物业公司管理(5分);无电梯且为业主自筹/私人承办/单位代管物业(3分);无电梯且无物业管理(1分) | 正向 | |
HR2 便利性特征 | F5 地铁便利性 | 距地铁站200 m范围内(9分),距地铁站200~400 m范围内(7分),距地铁站400~800 m范围内(5分),距地铁站800~1500 m范围内(3分),距1500 m范围外(1分) | 正向 |
F6 办公便利性 | 将主要办公场所(写字楼、政府机关、事业单位、科技园)点数据生成核密度,并根据标准差均值面进行分级。住宅位于核密度3个标准差以上(含3个标准差)范围内(9分),位于2~3个标准差之间(7分),位于1~2个标准差之间(5分),位于0~1个标准差之间(3分),其他,即位于低于标准差均值的范围内(1分) | 正向 | |
F7 基础教育便利性 | 将小学、中学的点数据生成核密度,并根据标准差均值面进行分级。赋分方式与办公便利性相同 | 正向 | |
F8 商业服务便利性 | 将主要商业服务网点(市场、超市、商场、餐饮场所、娱乐场所)的点数据生成核密度,并根据标准差均值面进行分级。赋分方式与办公便利性相同 | 正向 | |
HR3环境特征 | F9 公园可达性 | 位于公园边界100 m范围内(9分),位于公园边界100~200 m范围内(8分),…,距公园800m范围外(1分) | 正向 |
F10 厌恶型市政设施影响 | 各类交通或市政类厌恶型设施及其影响范围设定如下:机场(半径5000 m),火车站(半径500 m),长途汽车站(半径500 m),高速公路和高架路(单侧200 m),铁路(单侧80 m),加油站(半径80 m),殡仪馆(半径1000 m),污水处理厂(半径2000 m),垃圾处理场(半径4000 m),变电站(半径500 m),高压走廊(单侧100 m)。在此基础上,赋分如下:未受到厌恶型设施影响(1分),受到1种设施影响(3分),受到2种设施影响(5分),受到3种设施影响(7分),受到4种及4种以上设施影响(9分) | 负向 | |
F11 工业污染影响 | 将工厂点数据生成核密度,并根据标准差均值面进行分级。赋分方式与办公便利性相同 | 负向 | |
HR4区位特征 | F12 距城市中心的距离 | 距广州国际金融中心大厦(珠江新城西塔)的距离 | 负向 |
Tab. 3
The quantity and proportion of different levels of housing rent in Guangzhou's urban district in March 2020
租金等级 | 租金区间 (元/(m2·月)) | 研究区全部 | 旧城 | 核心区 | 外围城区 | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
数量(套) | 占比(%) | 数量(套) | 占比(%) | 数量(套) | 占比(%) | 数量(套) | 占比(%) | |||||
高房租 | >100.00 | 2834 | 12.25 | 306 | 11.42 | 2254 | 25.21 | 274 | 2.38 | |||
中高房租 | 80.01~100.00 | 3199 | 13.83 | 513 | 19.14 | 1889 | 21.13 | 797 | 6.93 | |||
中等房租 | 60.01~80.00 | 5898 | 25.50 | 923 | 34.44 | 2903 | 32.47 | 2072 | 18.01 | |||
中低房租 | 40.01~60.00 | 8424 | 36.43 | 835 | 31.16 | 1735 | 19.41 | 5854 | 50.88 | |||
低房租 | ≤ 40.00 | 2771 | 11.98 | 103 | 3.84 | 159 | 1.78 | 2509 | 21.81 | |||
总计 | 全部 | 23126 | 100.00 | 2680 | 100.00 | 8940 | 100.00 | 11506 | 100.00 |
Tab. 5
The regression coefficient of housing rents model based on SEM in Guangzhou's urban district
因素类别(自变量) | 回归系数 | 标准差 | z统计值 | P值 |
---|---|---|---|---|
F1建筑面积 | -0.3494** | 0.0034 | -101.4040 | 0.0000 |
F2朝向与楼层 | 0.0196** | 0.0029 | 6.8631 | 0.0000 |
F3房龄 | -0.2807** | 0.0126 | -22.2924 | 0.0000 |
F4电梯与物业 | 0.1191** | 0.0094 | 12.6365 | 0.0000 |
F5地铁便利性 | 0.0484** | 0.0092 | 5.2342 | 0.0000 |
F6办公便利性 | 0.0983** | 0.0174 | 5.6511 | 0.0000 |
F7基础教育便利性 | 0.0391* | 0.0157 | 2.4926 | 0.0127 |
F8商业服务便利性 | -0.0246 | 0.0131 | -1.8787 | 0.0603 |
F9公园可达性 | 0.0240** | 0.0057 | 4.2099 | 0.0000 |
F10厌恶型市政设施影响 | 0.0130 | 0.0070 | 1.8574 | 0.0633 |
F11工业污染影响 | -0.0555** | 0.0070 | -7.9185 | 0.0000 |
F12距城市中心的距离 | -0.2060** | 0.0088 | -23.3809 | 0.0000 |
CONSTANT | 7.7150** | 0.0974 | 79.2189 | 0.0000 |
LAMBDA | 0.7314** | 0.0041 | 176.5310 | 0.0000 |
R2:0.7356;调整R2:0.7354;AIC:-4040.23;对数似然数:2033.11 |
[1] |
Gilbert A. Rental housing: The international experience. Habitat International, 2016, 54:173-181.
doi: 10.1016/j.habitatint.2015.11.025 |
[2] |
Cui N N, Gu H Y, Shen T Y, et al. The impact of micro-level influencing factors on home value: A housing price-rent comparison. Sustainability, 2018, 10(12):4343. DOI: 10.3390/su10124343.
doi: 10.3390/su10124343 |
[3] |
Nishi H, Asami Y, Shimizu C. Housing features and rent: Estimating the microstructures of rental housing. International Journal of Housing Markets and Analysis, 2019, 12(3):210-225.
doi: 10.1108/IJHMA-09-2018-0067 |
[4] | Jia Shijun, Zhou Chunshan. Reference rent measurement and the spatial distribution for urban real estate: A case of Guangzhou. Economic Geography, 2009, 29(4):618-623. |
[ 贾士军, 周春山. 城市房屋参考租金测定与空间分布: 以广州为例. 经济地理, 2009, 29(4):618-623.] | |
[5] |
Su Yayi, Zhu Daolin, Geng Bin. The spatial structure and affecting factors of the housing rental in Beijing. Economic Geography, 2014, 34(4):64-69.
doi: 10.2307/142337 |
[ 苏亚艺, 朱道林, 耿槟. 北京市住宅租金空间结构及其影响因素. 经济地理, 2014, 34(4):64-69.] | |
[6] |
Du Chao, Wang Jiao'e, Liu Binquan, et al. Impacts of street and public transport network centralities on housing rent: A case study of Beijing. Progress in Geography, 2019, 38(12):1831-1842.
doi: 10.18306/dlkxjz.2019.12.001 |
[ 杜超, 王姣娥, 刘斌全, 等. 城市道路与公共交通网络中心性对住宅租赁价格的影响研究: 以北京市为例. 地理科学进展, 2019, 38(12):1831-1842.] | |
[7] | 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. |
[ 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. 测绘科学, 2018, 43(5):95-99, 104.] | |
[8] |
Zhang S W, Wang L, Lu F. Exploring housing rent by mixed geographically weighted regression: A case study in Nanjing. ISPRS International Journal of Geo-Information, 2019, 8(10):431. DOI: 10.3390/ijgi8100431.
doi: 10.3390/ijgi8100431 |
[9] | Wang Jiali, Ji Minhe, Deng Zhongwei. Factors behind residential rent distribution in outer ring of Shanghai: A GWR-based hedonic price analysis. Areal Research and Development, 2016, 35(5):72-80. |
[ 汪佳莉, 季民河, 邓中伟. 基于地理加权特征价格法的上海外环内住宅租金分布成因分析. 地域研究与开发, 2016, 35(5):72-80.] | |
[10] | Wang Hongqiang, Li Xiaoxue, Zhang Yingjie. Analysis on the spatial differentiation pattern of residential rent price in Shanghai and its influencing factors. Modernization of Management, 2019, 39(5):95-100. |
[ 王洪强, 李小雪, 张英婕. 上海市住宅租金价格空间分异格局及其影响因素分析. 管理现代化, 2019, 39(5):95-100.] | |
[11] | Zhang Shensheng, Zhang Lulu. Comparative analysis of spatial differentiation rules and influential factors of residential rent in two urban areas of Shenyang. Journal of Shenyang Jianzhu University (Social Science), 2019, 21(4):365-370. |
[ 张沈生, 张露露. 沈阳市两城区住宅租金空间分异规律及其影响因素比较分析. 沈阳建筑大学学报(社会科学版), 2019, 21(4):365-370.] | |
[12] | Zhang Shiwei, Wang Lin, Lu Feng. Study on the influencing factors of housing rent in Nanjing based on MGWR. Modern Urban Research, 2019, 34(11):97-103. |
[ 张世伟, 王琳, 鲁凤. 基于MGWR的南京市住宅租金影响因素研究. 现代城市研究, 2019, 34(11):97-103.] | |
[13] |
Cao K, Diao M, Wu B. A big data-based geographically weighted regression model for public housing prices: A case study in Singapore. Annals of the American Association of Geographers, 2019, 109(1):173-186.
doi: 10.1080/24694452.2018.1470925 |
[14] |
Gan X L, Zuo J, Chang R D, et al. Exploring the determinants of migrant workers' housing tenure choice towards public rental housing: A case study in Chongqing, China. Habitat International, 2016, 58:118-126.
doi: 10.1016/j.habitatint.2016.10.007 |
[15] |
Leung K M, Yiu C Y. Rent determinants of sub-divided units in Hong Kong. Journal of Housing and the Built Environment, 2019, 34(1):133-151.
doi: 10.1007/s10901-018-9607-4 |
[16] |
Nakagawa M, Saito M, Yamaga H. Earthquake risk and housing rents: Evidence from the Tokyo Metropolitan Area. Regional Science and Urban Economics, 2007, 37(1):87-99.
doi: 10.1016/j.regsciurbeco.2006.06.009 |
[17] | Zhang Ruoxi, Jia Shijun. Study on the influencing factors of Guangzhou city's residential housing rent. Journal of Engineering Management, 2014, 28(6):118-123. |
[ 张若曦, 贾士军. 广州市住宅租金影响因素的研究. 工程管理学报, 2014, 28(6):118-123.] | |
[18] |
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.
doi: 10.10282/dqxxkx.2018.180072 |
[ 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. 地球信息科学学报, 2018, 20(8):1139-1149.] | |
[19] | Lu Yuxi, Zhan Changgen, Dai Yun. Research on influencing factors of housing rent based on characteristic price model: Taking Wuhan City as an example. China Real Estate, 2019(4):58-63. |
[ 鲁羽西, 詹长根, 戴云. 基于特征价格模型的住宅租金影响因素研究: 以武汉市主城区为例. 中国房地产, 2019(4):58-63.] | |
[20] |
Efthymiou D, Antoniou C. How do transport infrastructure and policies affect house prices and rents? Evidence from Athens, Greece. Transportation Research Part A: Policy and Practice, 2013, 52:1-22.
doi: 10.1016/j.tra.2013.04.002 |
[21] | Feng Youjian, Chen Tianyi. The spatial effect of rail transit on residential prices based on SEM model: A case study of Hangzhou. Journal of Zhejiang University (Science Edition), 2020, 47(1):115-122. |
[ 冯友建, 陈天一. 基于SEM模型的轨道交通对住宅价格的空间效应: 以杭州市为例. 浙江大学学报(理学版), 2020, 47(1):115-122.] | |
[22] |
D'Arcangelo F M, Percoco M. Housing rent and road pricing in Milan: Evidence from a geographical discontinuity approach. Transport Policy, 2015, 44:108-116.
doi: 10.1016/j.tranpol.2015.07.004 |
[23] |
Haurin D R, Brasington D. School quality and real house prices: Inter- and intrametropolitan effects. Journal of Housing Economics, 1996, 5(4):351-368.
doi: 10.1006/jhec.1996.0018 |
[24] |
Zambrano-Monserrate M A, Ruano M A. Does environmental noise affect housing rental prices in developing countries? Evidence from Ecuador. Land Use Policy, 2019, 87:104059. DOI: 10.1016/j.landusepol.2019.104059.
doi: 10.1016/j.landusepol.2019.104059 |
[25] |
Muhammad I. Disamenity impact of Nala Lai (open sewer) on house rent in Rawalpindi city. Environmental Economics and Policy Studies, 2017, 19(1):77-97.
doi: 10.1007/s10018-015-0136-z |
[26] | Kemiki O A, Odumosu J O, Popoola N L, et al. Empirical model for determination of rent within M. I. Wushishi housing estate, Minna, Niger-State. American Journal of Economics 2015, 5(5):449-457. |
[27] |
Cui Nana, Gu Hengyu, Shen Tiyan. The spatial differentiation and relationship between housing prices and rents: Evidence from Beijing in China. Geographical Research, 2019, 38(6):1420-1434.
doi: 10.11821/dlyj020180352 |
[ 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. 地理研究, 2019, 38(6):1420-1434.] | |
[28] |
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 |
[ 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. 地理科学进展, 2018, 37(9):1268-1276.] | |
[29] | Feng Changchun, Li Weixuan, Zhao Fanfan. Influence of rail transit on nearby commodity housing prices: A case study of Beijing subway line five. Acta Geographica Sinica, 2011, 66(8):1055-1062. |
[ 冯长春, 李维瑄, 赵蕃蕃. 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. 地理学报, 2011, 66(8):1055-1062.] | |
[30] |
Niu Fangqu, Liu Weidong, Feng Jianxi. Modeling urban housing price: The perspective of household activity demand. Acta Geographica Sinica, 2016, 71(10):1731-1740.
doi: 10.11821/dlxb201610006 |
[ 牛方曲, 刘卫东, 冯建喜. 基于家庭区位需求的城市住房价格模拟分析. 地理学报, 2016, 71(10):1731-1740.] | |
[31] | Shi Yishao, Li Muxiu. The analysis of the housing price gradient and its impact factors of Shanghai city. Acta Geographica Sinica, 2006, 61(6):604-612. |
[ 石忆邵, 李木秀. 上海市住房价格梯度及其影响因素分析. 地理学报, 2006, 61(6):604-612.] | |
[32] |
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 |
[ 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. 地理学报, 2018, 73(10):1880-1895.] | |
[33] |
Hill R J, Rambaldi A N, Scholz M . Higher frequency hedonic property price indices: A state-space approach. Empirical Economics, 2020: 1-25. DOI: 10.1007/s00181-020-01862-y.
doi: 10.1007/s00181-020-01862-y |
[34] |
Wen H Z, Xiao Y, Wang X R, et al. Land-transfer events' effects on the housing market: Empirical evidence from Hangzhou, China. Journal of Urban Planning and Development, 2019, 145(2):04019003. DOI: 10.1061/(ASCE)UP.1943-5444.0000505.
doi: 10.1061/(ASCE)UP.1943-5444.0000505 |
[35] |
Lancaster K J. A new approach to consumer theory. Journal of Political Economy, 1966, 74(2):132-157.
doi: 10.1086/259131 |
[36] |
Rosen S. Hedonic prices and implicit markets: Product differentiation in pure competition. Journal of Political Economy, 1974, 82(1):34-55.
doi: 10.1086/260169 |
[37] | Ohta M, Griliches Z. Hedonic price indexes and the measurement of capital and productivity: Some historical reflections//Fifty Years of Economic Measurement. The Jubilee of the Conference on Research in Income and Wealth, 1976. |
[38] |
Xiao Y, Xui E C M, XWen H Z. Effects of floor level and landscape proximity on housing price: A hedonic analysis in Hangzhou, China. Habitat International, 2019, 87:11-26.
doi: 10.1016/j.habitatint.2019.03.008 |
[39] | Shen Tiyan, Yu Hanchen, Zhou Lin, et al. On hedonic price of second-hand houses in Beijing based on multi-scale geographically weighted regression: Scale law of spatial heterogeneity. Economic Geography, 2020, 40(3):75-83. |
[ 沈体雁, 于瀚辰, 周麟, 等. 北京市二手住宅价格影响机制: 基于多尺度地理加权回归模型(MGWR)的研究. 经济地理, 2020, 40(3):75-83.] | |
[40] |
Wang Yang, Jin Lixia, Zhang Hong'ou, et al. Pattern and model of residential criminal risk based on social space in Guangzhou, China. Geographical Research, 2017, 36(12):2465-2478.
doi: 10.11821/dlyj201712016 |
[ 王洋, 金利霞, 张虹鸥, 等. 社会空间视角下广州居住地犯罪风险的格局与模式. 地理研究, 2017, 36(12):2465-2478.] | |
[41] | Wang Yang, Zhang Hong'ou, Ye Yuyao, et al. Comprehensive evaluation and distribution pattern of social space quality in Guangzhou, China. Tropical Geography, 2017, 37(1):25-32. |
[ 王洋, 张虹鸥, 叶玉瑶, 等. 广州市社会空间质量的综合评价与分布格局. 热带地理, 2017, 37(1):25-32.] | |
[42] |
Yu W H, Ai T H, Shao S W. The analysis and delimitation of central business district using network kernel density estimation. Journal of Transport Geography, 2015, 45:32-47.
doi: 10.1016/j.jtrangeo.2015.04.008 |
[43] |
Wu Kangmin, Zhang Hong'ou, Wang Yang, et al. Identify of the multiple types of commercial center in Guangzhou and its spatial pattern. Progress in Geography, 2016, 35(8):963-974.
doi: 10.18306/dlkxjz.2016.08.005 |
[ 吴康敏, 张虹鸥, 王洋, 等. 广州市多类型商业中心识别与空间模式. 地理科学进展, 2016, 35(8):963-974.] | |
[44] |
Tian G, Wei Y D, Li H. Effects of accessibility and environmental health risk on housing prices: A case of Salt Lake County, Utah. Applied Geography, 2017, 89:12-21.
doi: 10.1016/j.apgeog.2017.09.010 |
[45] |
Wang Y, Wang S J, Li G D, et al. Identifying the determinants of housing prices in China using spatial regression and the geographical detector technique. Applied Geography, 2017, 79:26-36.
doi: 10.1016/j.apgeog.2016.12.003 |
[46] |
Anselin L, Syabri I, Kho Y. GeoDa: An introduction to spatial data analysis. Geographical Analysis, 2006, 38(1):5-22.
doi: 10.1111/gean.2006.38.issue-1 |
[47] | LeSage J, Pace R K. Introduction to Spatial Econometrics. London: Chapman and Hall/CRC, 2009. |
[48] | Anselin L. Spatial Econometrics: Methods and Models. Dordrecht: Springer, 1988. |
[49] | Arbia G. Spatial Econometrics: Statistical Foundations and Applications to Regional Economic Growth. Berlin and Heidelberg: Springer-Verlag, 2006. |
[50] |
Brasington D M, Hite D. Demand for environmental quality: A spatial hedonic analysis. Regional Science and Urban Economics, 2005, 35(1):57-82.
doi: 10.1016/j.regsciurbeco.2003.09.001 |
[51] | Cliff A D, Ord J K. Spatial Processes: Model and Application. London: Pion, 1981. |
[52] |
Anselin L. Local indicators of spatial association: LISA. Geographical Analysis, 1995, 27(2):93-115.
doi: 10.1111/gean.1995.27.issue-2 |