| After the reform of the real estate market in 1998,housing was both usable and investment in nature,and property prices across the country rose rapidly and fluctuated drastically,which seriously affected people’s demand for housing.The impact of "people" as the main users of housing on housing prices cannot be ignored,as people’s demand for housing varies at different stages of life,and therefore the impact on housing prices also shows different stage characteristics.The demographic structure of China is facing changes as the "demographic dividend" is slowly disappearing and the aging society is deepening,and the "two-child" policy was implemented in 2016 and the "three-child" policy will be liberalized in May 2021.The "three-child" policy was introduced in 2016,and the "three-child" policy will be liberalized in May 2021 to alleviate the current low fertility rate and the aging process.Based on the demographic perspective,the future trend of China’s total population and population structure from 2023 to 2050 is predicted by using the Logistic population projection and population cohort element model with MATLAB and PADIS software.The results show that the growth rate of China’s total population will gradually slow down,and the child dependency ratio will gradually decrease even after the population policy is liberalized,while the old-age dependency ratio will continue to increase in the future,and the aging and childlessness of China will become more and more serious.Based on the theoretical analysis of the effects of natural structure of population,geographic structure of population and social structure of population on house prices,a panel regression model is constructed based on panel data from 2008 to 2020,dividing 31provinces（cities）into Class I,II and III regions,adding dummy variables for population policy,and interaction terms between population policy and child dependency ratio and elderly dependency ratio,and empirically analyzing the effects of natural structure of population,geographic and social structure of population on house prices after the liberalization of population policy while controlling for variables such as level of economic development,population,degree of social security and land supply.The results show that: 1.the analysis of demographic structure and house prices shows that China is in the stage of decreasing child dependency ratio and increasing old-age dependency ratio,and there are high house prices and significant regional differences in house prices.2.the Logistic model and population cohort model predict the future trend of total population and demographic structure respectively,and the population peak will come soon,and the child dependency ratio will decrease and the old-age dependency ratio will increase.3.The theoretical and empirical analysis results show that the old-age dependency ratio will significantly contribute to the increase of house prices,while the young-age dependency ratio will suppress the increase of house prices.The correlation between the old-age dependency ratio and house prices in Class II and Class III areas has a significant positive correlation,while the correlation between the old-age dependency ratio and house prices in Class I areas has a weak positive correlation,while the correlation between the child dependency ratio and house prices in Classes I,II and III areas has a significant negative correlation,in which the geographical structure of the population and the social structure all play a positive role in promoting house prices.Differentiated policy recommendations are made for different regions.First,expand the efforts to liberalize population policies,improve the pension and maternity protection system,reduce the financial burden of childbirth,and thus increase people’s motivation to have children.The second is to strengthen the management and supervision of the real estate market,improve the multi-level housing system protection system,and ensure that housing prices fluctuate within a reasonable range.Thirdly,resources should be reasonably allocated according to local conditions to promote balanced development among different regions.Figure [10] table [12] reference [63]... |
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