The Impact Of Resident Income Gap On Housing Price

The rapid rise of housing prices and the widening income gap have become two important issues affecting the well-being of residents and the healthy development of the economy.Housing prices are the weathervane of the prosperity of the national economy,and the excessive rise in housing prices is often seen as a sign of economic bubbles.Statistical data show that the average price of commercial housing in China rose from 1 806 yuan per square meter in 1998 to 7 203 yuan per square meter in 2016,with an increasing of 300%.Price of high-income cities rose faster,such as Shanghai's commercial housing price rose by 349.74% in 2016 compared with 2004.The increase of middle-income cities and lowincome cities was relatively low,such as Wuhan and Yichang in 2016 compared with 2004.The degrees were 298.66% and 240.57% respectively.Scholars generally believe that the acceleration of urbanization and the growth of people's income have led to the high housing prices in China to a certain extent.However,it is worth noting that the income gap of Chinese residents is expanding,and the polarization of income and wealth is serious.According to statistical data,since 2003,the Gini coefficient of our country has exceeded the internationally recognized warning line of 0.4.Income gap is too large,social class differentiation is serious and easy to lead to social welfare losses and social unrest.The rapid rise of house prices and the widening of income gap are not independent phenomena.Excessive house prices may be related to the widening of income gap.In this context,this paper uses the combination of theoretical analysis and empirical analysis to comprehensively analyze the impact of the widening income gap on the price of commodity housing in China.There are three main ways that the income gap of residents influences the price of commodity housing: one is the crowding-out effect of high-income people to low-income people which is caused by the difference of income constraint;the other is the crowding-out effect caused by the different housing demand elasticity between high-income people and low-income people;the third is the crowding-out effect caused by the credit difference between high-income people and low-income people.Micro-level,these three paths will squeeze the housing demand of low-income people,and push up housing prices.In terms of empirical analysis,this paper uses the panel data of 42 large and mediumsized cities in China from 2004 to 2016 to conduct unit root test on relevant variables,and establishes a fixed-effect regression model of house price(HP)and income gap(GINI).The regression results show that,on the whole,the expansion of the income gap will promote the rise of housing prices,but there are regional differences in the impact of the income gap on housing prices.This paper divides the data of 42 large and medium-sized cities into high,medium and low grades according to income level,and returns to the sub-regions.It is found that the impact of income gap on housing prices is more significant in cities with lowermiddle income levels.It is not significant in cities with high income levels.Secondly,among middle-income cities,the most significant impact on housing prices is the middle-income level.In low-income cities,the highest impact on housing prices is the high-income level.In order to improve the income distribution pattern and promote the healthy development of the real estate market,this paper puts forward the following suggestions: First,expand the middle income group to improve the income and living standards of the low income level;Second,establish and improve the taxable income monitoring system for high income earners,improve The personal income tax law;fourth,establish a diversified investment philosophy;fifth,curb speculation in the housing market.There are three main innovations in this paper: one is to take the income gap of residents as the starting point,combine the economic problems of our country with the social hotspots,and the research perspective has certain innovation;the second is that the paper considers the extrusion effect.The difference in income constraints caused by the income gap,the difference in housing demand elasticity,the credit difference,and the impact of these differences on the crowding out effect are complementary to the existing theory;the third is the use of urban-level data,the sample range is broader,more Representative.The shortcomings of this paper are mainly the lack of data in some cities,resulting in limited sample capacity,and the calculation of the Gini coefficient is only an approximate calculation method,and the results are not accurate enough.