| Housing assets are an important part of household assets in China.According to the report of China Household Finance Survey,Chinese residents allocate 70% of their total assets to housing assets.After the central government set the direction of reforming the monetization,marketization and commercialization of urban housing in 1998,China's new housing system was gradually built up,and the real estate market has been booming since then.Since the monetization reform of the housing system,housing prices in China have been climbing,from 1,854 yuan in 1998 to 9,287 yuan in 2019,with housing prices rising more than four times in 20 years.The excessive rise in housing prices has caused the social problem of residents' difficulty in buying houses on the one hand,and exacerbated the uncertainty of the whole financial market on the other.China's high housing prices have become a serious socio-economic problem.Exploring the reasons behind the changes in housing prices helps to understand the operation rules of China's real estate market and to introduce targeted regulation and control policies to prevent risks in the real estate market.Existing studies have cut through the perspectives of land finance,monetary overdraft,rising resident income,and urbanization to explain house price movements.However,these factors have some limitations in explaining the long-term trend of house price changes.As the main consumer of the real estate market,the demand of residents is the key to influence the house price,and demographic factors will have a direct and far-reaching impact on the housing price.From the perspective of the population age structure,the third "baby boom" into adulthood and the intergenerational transfer behavior of the elderly group combined to cause housing prices to enter an upward path.However,the age structure of China's population continues to deteriorate,with the proportion of children aged0-14 declining from 36.3% in 1953 to 16.9% in 2018,while the proportion of the elderly population aged 65 or older has increased from 4.4% to 11.9%,reflecting the increasing prominence of the aging of China's young children,according to the population data published by the National Bureau of Statistics.In this context,the questions of how the effect of population age structure on house prices,the micro mechanism of population age structure affecting house prices,and the possible future trends of house prices with the change of population age structure are very important topics in the current economics field.The text develops sequentially through three parts in a progressive manner around the core issue of the effect of population age structure on house prices.The first part examines the impact of population age structure on house prices at the macro level.The paper first analyzes the impact of population age structure on housing prices at three levels: theoretical tracing,realistic description and empirical research.Specifically,at the theoretical analysis level,a theoretical analysis framework based on the overlapping generations model is constructed.From the life-cycle theory,the older population's own housing demand decreases,leading to the decline of house prices,but from the intergenerational transfer theory,the intergenerational transfer behavior of the older population drives up house prices,so the influence of the older dependency ratio on house prices relies on the combined effect of two forces.At the level of reality description,based on historical data of national,provincial and urban dimensions,we describe the changes of real estate prices,population age structure and the relationship between them in China.It is found that the "baby boom" adulthood is an important factor driving the structural increase in house prices after 2003,and that there is a negative relationship between the juvenile dependency ratio and house prices,while there is a positive relationship between the elderly dependency ratio and house prices.At the empirical level,the first step is to empirically test the effects of total population dependency ratio,juvenile dependency ratio,and old-age dependency ratio on house prices based on provincial panel data from 1999-2018 in China.In the second step,based on the fifth and sixth national census data in 2000 and 2010,we empirically test the effects of the population ratios of each age group on house prices by combining province-level and city-level data for the corresponding years.In the third step,we analyze the impact of population age structure on the future trend of house prices by combining the urban population age structure data obtained from the sixth national census in 2010 with the house price index of 70 large and medium-sized cities.Both province-and city-level macro data yield consistent conclusions that a declining juvenile dependency ratio drives house prices up,and an increasing elderly dependency ratio drives house prices up but the driving force is weakening.The second part examines the impact of population age structure on house prices at the micro-individual level.Starting from the housing demand decisions of different age groups,the reasons for the aforementioned influence of population age structure on house prices are explored based on a micro perspective,which helps to understand the operation of real estate prices more deeply.Using data from the 2005 National Population Sample Survey and the 2015 Sichuan Provincial Population Sample Survey,the paper calculates a stable "inverted U-shaped" relationship between age and housing demand through the M-W model.Further,using the data from the China Household Finance Survey,we provide evidence on the change of intergenerational transfer behavior of the elderly population based on the correction of the "cohort effect".It is found that the elderly population's own housing demand declines,but the intergenerational transfer behavior drives up house prices,while the power of intergenerational transfer is gradually weakening.What will be the future trend of the decreasing power of intergenerational transfer and the decreasing role of the increasing elderly dependency ratio in driving house prices? To answer this question,it is necessary to draw inspiration from the experience of developed countries such as OECD.On the one hand,the changing trend of China's population age structure is converging with that of OECD countries;on the other hand,the relatively mature real estate markets and long time series of population age structure changes in OECD countries make it advantageous to study the impact of their population age structure on house prices.Therefore,the paper uses data on population and house prices in OECD countries from 1970-2018 to provide more evidence for this study.The empirical results show a negative relationship between the total population dependency ratio,the juvenile dependency ratio,the elderly dependency ratio,and the housing price index.Notably,the negative effect of the old-age dependency ratio on the housing price index increases over time.Taken together,as the process of population aging progresses,the increase in the elderly dependency ratio in China will shift from driving the rise of housing prices to suppressing the rise of housing prices.The third part predicts the future house price trend based on the change of population age structure.From the above two parts of the discussion,it is clear that the age structure of China's population is an important factor affecting house prices,and it will show new trends in the future.Then,based on the inherent law of population age structure change,how will China's house prices change in the future?This is a common concern of the government,residents and academia.Therefore,the thesis firstly selects the population-development-environment analysis model,based on the data of the sixth national census in 2010,sets three sets of scenarios of high,medium and low fertility rates,and makes a forecast on the change of population age structure in China from 2021 to 2050.Secondly,we forecast the future housing demand and price trend based on the change of population age structure.The total housing demand will encounter an inflection point in the future,between 2030-2035 for the low/medium fertility scenario and between 2035-2040 for the high fertility scenario.Some characteristic trends in urban population and cross-regional mobility lead to differences in the timing of the inflection point in different tiers of cities.First-and second-tier cities are more stable and have a later inflection point,while third-and fourth-tier cities have a greater risk of house price decline and have a more advanced inflection point.Finally,by summarizing the findings of this paper and taking into account the future development trend of China's real estate market and population age structure,we propose policy recommendations to optimize the population structure,strengthen the housing function,adhere to the "city-based" policy,develop a new system of renting and purchasing,and strengthen real estate regulation and control.The innovations of this paper include: First,by unifying life cycle theory and intergenerational transfer theory,and by unifying domestic experience and international experience,we finally conclude that the relationship between the proportion of the elderly population and housing prices in China will be "inverted U-shaped".The life-cycle theory alone cannot explain the phenomenon that the aging population is driving up housing prices in China,because the demand for housing among the elderly population will decline according to the life-cycle theory.In order to explain this paradox,it is necessary to introduce the intergenerational transfer theory,because China's elderly population has experienced the real estate market reform and received the housing reform dividend,coupled with the lack of effective financial service products for the elderly in China's financial market,the unsound social pension system,and the high housing transaction costs,the elderly population has a strong intergenerational transfer ability and willingness.Therefore,aging instead pushes up housing prices,but this intergenerational transfer behavior is difficult to sustain.Along with the gradual weakening of the savings release process,the increasing abundance of pension financial service products,and the gradual improvement of the pension system,the power of intergenerational transfer will also weaken,which is confirmed by the empirical results.Further,combining with international experience,it is found that the impact of population aging on house prices is characterized by phases.As the aging process accelerates,its suppressive effect on house prices will become more and more obvious.In summary,the aging of population in China will first push up the house price,and then the driving force becomes less significant,and eventually the aging of population will suppress the house price.Second,the impact of population age structure on house prices and its mechanism are examined in a comprehensive manner by combining macro and micro perspectives and organically integrating data at the national and regional levels as well as at the micro-individual level.From the macro perspective,the population and house price data of 31 provinces,autonomous regions,municipalities directly under the Central Government and 35 large and medium-sized cities from the China Statistical Yearbook of previous years are selected to examine the impact of population age structure on house prices.To provide a broader perspective,the paper further selects the population and house price data of OECD countries released by the World Bank from 1970-2018 for analysis.The national,provincial,and city-level house price data,each with its own advantages and disadvantages,can better measure the time-series characteristics and regional variation characteristics of house price changes in China in different dimensions.From a micro perspective,the data from the Fifth National Census in2000,the Sixth National Census in 2010,the National Population Sample Survey in2005(2585,481 samples from 345 regions),the Population Sample Survey in Sichuan Province in 2015(907,238 samples),and the four periods of 2011,2013,2015,and 2017 were selected China Household Finance Survey data to provide a basis for dissecting the mechanism of population age structure's influence on house prices.The census data and household micro-survey data can better measure the change of population age structure and the change of housing assets.Through the organic combination of the two and the establishment of corresponding econometric models,this paper provides a relatively more accurate and comprehensive examination of the relationship between population age structure and house prices.Third,it predicts the future trend of housing price change more accurately based on the change of population age structure.At present,there are three main methods to predict the trend of house price change: the first one is to directly translate the population distribution obtained from the census to the future,and combine the corresponding housing demand of each age group to predict the future housing demand and price trend;the second one is based on a linear model,which substitutes the predicted values of house price influencing factors into the model to predict the future house price trend;the third one is based on a time series model,which predicts the future house price trend based on the historical change of house price The third one,based on a time series model,predicts the future house price trend based on the characteristics of the historical house price movement.The first method of directly translating the population distribution to predict the future evolution of population age structure dynamics has obvious shortcomings,while the latter two methods are suitable for predicting short-term house price changes and have limitations in predicting long-term house price changes.Therefore,this paper introduces a Population-Development-Environment Analysis model to forecast the change of population age structure in China from 2021 to 2050 based on the sixth national census data and three sets of scenarios of high,medium and low fertility rates,and to forecast the future trend of house price changes on this basis.At the same time,factors such as urbanization rate and cross-regional population movement are considered to analyze the trend of house price changes. |
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