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中国城市流动人口房租收入比时空格局及驱动因素
引用本文: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.
作者姓名:李在军  尹上岗  张晓奇  秦兴方
作者单位:扬州大学苏中发展研究院, 江苏 扬州 225009
南京师范大学地理科学学院, 江苏 南京 210023
浙江财经大学金融学院, 浙江 杭州 310018
扬州大学商学院, 江苏 扬州 225009
基金项目: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)
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  
收稿时间:2019-03-18
修稿时间:2019-05-05

The Spatial-temporal Evolution and Driving Factors of Floating Population's Rent Income Ratio in Prefectural City of China
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.
Authors:Li Zaijun  Yin Shanggang  Zhang Xiaoqi  Qin Xingfang
Institution:Research Institute of Central Jiangsu Development, Yangzhou University, Yangzhou 225009, Jiangsu, China
School of Geogra-phy Science, Nanjing Normal University, Nanjing 210023, Jiangsu, China
School of Finance, Zhejiang University of Finance and Economics, Hangzhou 310018, Zhejiang, China
Business School, Yangzhou University, Yangzhou 225009, Jiangsu, China
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.
Keywords:urban floating population  rent-to-income ratio  occupation type  geographical detector  
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