中国城市流动人口房租收入比时空格局及驱动因素

doi:10.13249/j.cnki.sgs.2020.01.013

地理科学 ›› 2020, Vol. 40 ›› Issue (1): 103-111.doi: 10.13249/j.cnki.sgs.2020.01.013

中国城市流动人口房租收入比时空格局及驱动因素

李在军¹, 尹上岗², 张晓奇³, 秦兴方⁴()

^1. 扬州大学苏中发展研究院, 江苏扬州 225009
^2. 南京师范大学地理科学学院, 江苏南京 210023
^3. 浙江财经大学金融学院, 浙江杭州 310018
^4. 扬州大学商学院, 江苏扬州 225009

收稿日期:2019-03-18 修回日期:2019-05-05 出版日期:2020-01-10 发布日期:2020-03-17
通讯作者: 秦兴方 E-mail:xfqin@yzu.edu.cn
作者简介:李在军（1989-）,男,山东临沂人,博士,助研,主要研究方向为区域经济发展。E-mail: junzailinyi@gmail.com
基金资助:
国家自然科学基金项目(41671155);城市群系统演化与可持续发展的决策模拟研究北京市重点实验室2019年度开放基金项目(MCR2019BS01)

The Spatial-temporal Evolution and Driving Factors of Floating Population's Rent Income Ratio in Prefectural City of China

Li Zaijun¹, Yin Shanggang², Zhang Xiaoqi³, Qin Xingfang⁴()

^1. Research Institute of Central Jiangsu Development, Yangzhou University, Yangzhou 225009, Jiangsu, China
^2. School of Geogra-phy Science, Nanjing Normal University, Nanjing 210023, Jiangsu, China
^3. School of Finance, Zhejiang University of Finance and Economics, Hangzhou 310018, Zhejiang, China
^4. Business School, Yangzhou University, Yangzhou 225009, Jiangsu, China

Received:2019-03-18 Revised:2019-05-05 Online:2020-01-10 Published:2020-03-17
Contact: Qin Xingfang E-mail:xfqin@yzu.edu.cn
Supported by:
National Natural Science Foundation of China(41671155);The Beijing Key Laboratory of Megaregions Sustainable Development Modelling, Capital University of Economics and Business(MCR2019BS01)

摘要/Abstract

摘要：

针对2012~2016年中国城市不同职业流动人口房租收入比时空变动特征及驱动力的研究表明：①流动人口房租收入比整体上呈“东高西低,南高北低”分异态势,较高及以上等级房租收入比地市集中于东部沿海发达地区及中西部省会城市。②各职业房租收入比逐渐形成金字塔形结构,但职业间房租收入比差距较大。③东部地区流动人口房租收入比多呈向下和平稳混杂分布,中部地区呈向下、平稳和向上镶嵌分布,西部地区以平稳为主,向上转移为辅。④经济、人口、社会及预期因素对房租收入比的解释力依次降低,消费水平、租赁户比例、地产投资密度、人口吸引力及收入水平是影响房租收入比的关键因素。

关键词: 城市流动人口, 房租收入比, 时空格局, 地理探测器

Abstract:

Floating population is the core subject and main contributor to China's urbanization process. However, due to poor employment stability and low income, floating population rely mainly on renting houses and becomes the main force of rental demand, which brings opportunities and challenges for Chinese real estate market. While the rent-to-income ratio is an important indicator describing the relationship between residential rent and income, and is also an important indicator reflecting the ability of residents to pay for rent and the operational status of the regional residential leasing market. In this context, this paper analyzes the spatio-temporal evolution characteristics and driving forces of the rent-to-income ratio of different occupational migrants in China's prefecture-level cities from 2012 to 2016. The results show: 1) The rent-to-income ratio of floating population shows obvious spatial differentiation pattern of ‘high east and low west, south high and low north’. The four occupational types of high and higher rent-to-income ratio of floating population are concentrated in the developed coastal areas and the capital cities of the central and western regions. 2) The rent-to-income ratio of four occupational types’ floating population has gradually formed a pyramid structure, and the inter-regional difference of the rent-to-income ratio of each occupational type has tended to narrow, but the rent-to-income ratio of commercial personnel, professional technicians, civil servants, production, transportation, construction personnel and service personnel has increased in a hierarchical manner. 3) The rent-to-income ratio the floating population has evolved toward the level of rationalization. However, the rent-to-income ratio of different types varies from one occupation to another. In all occupational types, the rent-to-income ratio in the eastern developed cities tends to shift downwards and change smoothly. The central region exhibits a mosaic pattern of downward transfer type, stationary type, and upward transfer type. The western region is dominated by the stationary type, and supplemented by the upward transfer type. 4) Economic factors, demographic factors, social factors and expectational factors exert an decreasing impact on the rent-to-income ratio of floating populations, while consumption levels, tenant households, real estate investment density, population attractiveness and income levels are the main influencing factors of the income rent ratio. However, the explanatory power of each influencing factor on the rent-to-income ratio decreases with the increase of occupational income level.

Key words: urban floating population, rent-to-income ratio, occupation type, geographical detector

中图分类号:

F299.23

李在军, 尹上岗, 张晓奇, 秦兴方. 中国城市流动人口房租收入比时空格局及驱动因素[J]. 地理科学, 2020, 40(1): 103-111.

Li Zaijun, Yin Shanggang, Zhang Xiaoqi, Qin Xingfang. The Spatial-temporal Evolution and Driving Factors of Floating Population's Rent Income Ratio in Prefectural City of China[J]. SCIENTIA GEOGRAPHICA SINICA, 2020, 40(1): 103-111.

图/表 6

表1

表2

图1

图2

图3

表3

中国流动人口各职业类型房租收入比影响因素探测结果"

变量	职业2		职业3		职业4		职业6
变量	$P D, U$	$Q$	$P D, U$	$Q$	$P D, U$	$Q$	$P D, U$	$Q$
经济发展水平	0.1096^***	0.0000	0.0798^***	0.0000	0.1238^***	0.0000	0.1406^***	0.0000
产业结构	0.1393^***	0.0000	0.0859^***	0.0000	0.1527^***	0.0000	0.1664^***	0.0000
消费水平	0.1963^***	0.0000	0.1730^***	0.0000	0.2674^***	0.0000	0.2681^***	0.0000
地产投资密度	0.2036^***	0.0000	0.1529^***	0.0000	0.2416^***	0.0000	0.2655^***	0.0000
收入水平	0.1692^***	0.0000	0.1387^***	0.0000	0.2130^***	0.0000	0.2116^***	0.0000
工资水平	0.1486^***	0.0000	0.0895^***	0.0000	0.1897^***	0.0000	0.1859^***	0.0000
城镇化水平	0.0581^***	0.0030	0.0522^***	0.0061	0.0860^***	0.0000	0.0866^***	0.0000
人口密度	0.1110^***	0.0000	0.0998^***	0.0000	0.1271^***	0.0000	0.1223^***	0.0000
人口吸引力	0.2026^***	0.0000	0.1465^***	0.0000	0.2405^***	0.0000	0.2498^***	0.0000
租赁户比例	0.2320^***	0.0000	0.1653^***	0.0000	0.2451^***	0.0000	0.2599^***	0.0000
房地产活跃度	0.0988^***	0.0000	0.0597^***	0.0024	0.1262^***	0.0000	0.1210^***	0.0000
人才潜力	0.1373^***	0.0000	0.1006^***	0.0000	0.1456^***	0.0000	0.1305^***	0.0000
公共服务投入	0.1551^***	0.0000	0.1368^***	0.0000	0.1792^***	0.0000	0.1932^***	0.0000
基础教育水平	0.1155^***	0.0000	0.0951^***	0.0000	0.0966^***	0.0000	0.1187^***	0.0000
医疗卫生水平	0.0278^*	0.0999	0.0223	0.1808	0.0263	0.1177	0.0511^***	0.0070
文化发展水平	0.0976^***	0.0000	0.0686^***	0.0000	0.1411^***	0.0000	0.1585^***	0.0000
绿化水平	0.0187	0.2593	0.0240	0.1504	0.0148	0.3833	0.0172	0.3030
经济预期	0.0495^***	0.0082	0.0848^***	0.0000	0.0892^***	0.0000	0.0769^***	0.0000
房价预期	0.0944^***	0.0000	0.0903^***	0.0000	0.1329^***	0.0000	0.1314^***	0.0000
房租预期	0.1046^***	0.0000	0.0820^***	0.0000	0.1417^***	0.0000	0.1479^***	0.0000
收入预期	0.0309^*	0.0705	0.0452^**	0.0136	0.0406^**	0.0232	0.0447^**	0.0145

表3

参考文献 29

[1]	段成荣, 吕利丹, 邹湘江 . 当前我国流动人口面临的主要问题和对策——基于2010年第六次全国人口普查数据的分析[J]. 人口研究, 2013,37(2):17-24.
	[ Duan Chengrong, Lv Lidan, Zou Xiangjiang . Major challenges for China’s floating population and policy suggestions: An analysis of the 2010 population census data. Population Research, 2013,37(2):17-24.]
[2]	林李月, 朱宇, 梁鹏飞 , 等. 基于六普数据的中国流动人口住房状况的空间格局[J]. 地理研究, 2014,33(5):887-898.
	[ Lin Liyue, Zhu Yu, Liang Pengfei et al. The spatial pattern of housing conditions of the floating population in China based on the sixth census data. Geographical Research, 2014,33(5):887-898.]
[3]	刘涛, 齐元静, 曹广忠 . 中国流动人口空间格局演变机制及城镇化效应——基于2000和2010年人口普查分县数据的分析[J]. 地理学报, 2015,70(4):567-581.
	[ Liu Tao, Qi Yuanjing, Cao Guangzhong . China's floating population in the 21st century: Uneven landscape, influencing factors, and effects on urbanization. Acta Geographica Sinica, 2015,70(4):567-581.]
[4]	Michael H C, Rebecca L H . Impact of accessibility on housing expenditure and affordability in Hong Kong's private rental sector[J]. Journal of Housing and the Built Environment, 2002,17(4):363-383.
[5]	Autor D H, Palmer C J, Pathak P A . Housing market spillovers: Evidence from the end of rent control in Cambridge, Massachusetts[J]. Journal of Political Economy, 2014,122(3):661-717.
[6]	Füss R, Koller J A . The role of spatial and temporal structure for residential rent predictions[J]. International Journal of Forecasting, 2016,32(4):1352-1368.
[7]	Haffner M, Boumeester H . Is renting unaffordable in the Netherlands?[J]. International Journal of Housing Policy, 2014,14(2):117-140.
[8]	Gan Q, Hill R J . Measuring housing affordability: Looking beyond the median[J]. Journal of Housing Economics, 2009,18(2):115-125.
[9]	刘海猛, 石培基, 潘竟虎 , 等. 中国城镇房价收入比时空演变的多尺度分析[J]. 地理科学, 2015,35(10):1280-1287.
	[ Liu Haimeng, Shi Peiji, Pan Jinghu et al. Spatio-temporal evolution on housing price to income ratio of China by multiscale analysis. Scientia Geographica Sinica, 2015,35(10):1280-1287.]
[10]	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.
	[ 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.]
[11]	高波, 王文莉, 李祥 . 预期、收入差距与中国城市房价租金“剪刀差”之谜[J]. 经济研究, 2013(6):100-112.
	[ Gao Bo, Wang Wenli, Li Xiang . Expectation, income inequality and the puzzle of city's housing price to rent scissors in China. Economic Research, 2013(6):100-112.]
[12]	Zhang D Y, Liu Z Y, Fan G Z et al. Price bubbles and policy interventions in the Chinese housing market[J]. Journal of Housing and the Built Environment, 2017,32(1):133-155.
[13]	董昕 . 住房支付能力与农业转移人口的持久性迁移意愿[J]. 中国人口科学, 2015(6):91-99.
	[ Dong Xin . Housing affordability and permanent migration desire of rural-urban migrants. Chinese Journal of Population Science, 2015 ( 6):91-99.]
[14]	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[J]. 经济地理, 2014,34(4):64-69.
	[ 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.]
[15]	韩娟, 金晓斌, 张志宏 , 等. 中国住宅出让地价发育特征及其影响因素分析[J]. 地理科学, 2017,37(4):573-584.
	[ Han Juan, Jin Xiaobin, Zhang Zhihong et al. Development characteristics and factors analysis of residential land price in China. Scientia Geographica Sinica, 2017,37(4):573-584.]
[16]	石忆邵, 范胤翡 . 上海市商务办公楼租金差异及其影响因素[J]. 地理研究, 2008,27(6):1427-1436.
	[ Shi Yishao, Fan Yinfei . Rent differentiation in commercial-office buildings and impact factors of Shanghai. Geographical Research, 2008,27(6):1427-1436.]
[17]	Chen Y M, Liu X P, Li X et al. Mapping the fine-scale spatial pattern of housing rent in the metropolitan area by using online rental listings and ensemble learning[J]. Applied Geography, 2016(75):31-40.
[18]	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.
	[ 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.]
[19]	叶剑平, 李嘉 . 完善租赁市场: 住房市场结构优化的必然选择[J]. 贵州社会科学, 2015(3):116-122.
	[ Ye Jianping, Li Jia . Improving the leasing market: The necessary choice of optimizing the housing market structure. Guizhou Social Science, 2015(3):116-122.]
[20]	何芳, 滕秀秀 . 德国住宅租赁管制与租金体系编制的借鉴与启示[J]. 价格理论与实践, 2017(3):93-96.
	[ He Fang, Teng Xiuxiu . Reference and enlightenment of German housing leasing management and rent system. Price: Theory and Practice, 2017(3):93-96.]
[21]	朱竑, 张博, 马凌 . 新型城镇化背景下中国流动人口研究: 议题与展望[J]. 地理科学, 2019,39(1):1-11.
	[ Zhu Hong, Zhang Bo, Ma Ling . A review of Chinese internal migration research under the background of new-type urbanization: Topics and prospects. Scientia Geographica Sinica, 2019,39(1):1-11.]
[22]	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. Scientia Geographica Sinica, 2013,33(10):1157-1165.]
[23]	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.
	[ Song Weixuan, Liu Chunhui . The price differentiation mechanism of commercial housing in the Yangtze River Delta. Geographical Research, 2018,37(1):92-102.]
[24]	中华人民共和国国家统计局. 中国城市统计年鉴[M]. 北京: 中国统计出版社, 2013-2017.
	[ National Bureau of Statistics of the People’s Republic of China. China city statistical yearbook. Beijing: China Statistics Press, 2013-2017.]
[25]	中华人民共和国国家统计局. 中国区域经济统计年鉴[M]. 北京: 中国统计出版社, 2013- 2014.
	[ National Bureau of Statistics of the People’s Republic of China. China statistical yearbook for regional economy. Beijing: China Statistics Press, 2013- 2014.]
[26]	Stone M E . What is housing affordability? The case for the residual income approach[J]. Housing Policy Debate, 2006,17(1):151-184.
[27]	方叶林, 黄震方, 李经龙 , 等. 中国省域旅游经济增长的时空跃迁及其趋同研究[J]. 地理科学, 2018,38(10):1616-1623.
	[ Fang Yelin, Huang Zhenfang, Li Jinglong et al. Space-time transition of tourism economic growth and its convergence in Chinese mainland. Scientia Geographica Sinica, 2018,38(10):1616-1623.]
[28]	张杰, 唐根年 . 浙江省制造业空间分异格局及其影响因素[J]. 地理科学, 2018,38(7):1107-1117.
	[ Zhang Jie, Tang Gennian . Spatial differentiation pattern of manufacturing industry in Zhejiang and its influencing factors. Scientia Geographica Sinica, 2018,38(7):1107-1117.]
[29]	张振海, 周向红 . 我国房屋租赁市场与租金泡沫研究——基于35个大中城市面板数据的实证分析[J]. 价格理论与实践, 2016(12):77-81.
	[ Zhang Zhenhai, Zhou Xianghong . Research on China's housing leasing market and rent bubble: An empirical analysis based on panel data of 35 large cities. Price: Theory and Practice, 2016(12):77-81.]

职业编号	职业类型	2012年			2016年
职业编号	职业类型	样本量（个）	占比（%）	月收入（元）	样本量（个）	占比（%）	月收入（元）
职业1	10	607	0.46	5214	746	0.53	5947
职业2	20、30	11314	8.52	3642	14156	10.10	4916
职业3	41、42	30386	22.90	3924	33660	24.01	4763
职业4	43、44、45、46、47、48	44958	33.87	2765	50642	36.13	3630
职业5	50	4207	3.17	2313	2867	2.05	2918
职业6	61、62、63、64	36090	27.19	2916	30872	22.02	3842
职业7	70、80、未填	5156	3.88	2434	7226	5.16	3052
总计	-	132718	100	3130	140169	100	4039

因素	变量	变量描述
城市经济实力	经济发展水平	人均GDP（元）
	产业结构	第三产业占GDP比重（%）
	消费水平	人均社会消费品零售额（元）
	地产投资密度	地均房地产开发投资额（万元/km²）
	收入水平	城镇居民可支配收入（元）
	工资水平	在岗职工平均工资（元）
城市活力	城镇化水平	城镇人口比重（%）
	人口密度	行政区域人口密度（人/km²）
	人口吸引力	城市外来人口比重（%）
	租赁户比例	租赁住房户数占总户数比重（%）
	房地产活跃度	房地产业从业人员比重（%）
城市社会公共服务	人才潜力	每万人在校大学生数（人）
	公共服务投入	地均公共财政支出（万元/km²)
	基础教育水平	中学生师生比（%）
	医疗卫生水平	万人卫生机构数（个）
	文化发展水平	百人公共图书馆藏书（册/100人）
	绿化水平	建成区绿化覆盖率（%）
城市社会经济发展预期	经济预期	地区生产总值增长率（%）
	房价预期	住宅价格增长率（%）
	房租预期	住宅租金增长率（%）
	收入预期	城镇居民可支配收入增长率（%）

中国城市流动人口房租收入比时空格局及驱动因素

The Spatial-temporal Evolution and Driving Factors of Floating Population's Rent Income Ratio in Prefectural City of China

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