A Empirical Study Of Relationship Between House Price And Age Structure Of Population

While the demographic dividend disappears gradually, China begins to step into the period of aging society at the same time. The decrease of working-age population will not only lead to labor shortage and the increase of labor cost, but the recession in demand of some certain industry and even the whole society, which brings tremendous negative influence to China’s economic growth. Besides, there is always an argument in theory about the hot topic——house price. From the current position, that house price will rise or fall is still uncertain. Should the housing market be regulated and controlled to avoid the adverse effect generated by the drop of house price, or follow the market rules to get a low price, this is still a question for the government. As we all know, however, the housing market bubble is so much that the residents’living is affected in some extent. As the necessity of daily life, its price rises so rapidly that house turns to be a heavy burden for almost people in the whole life.This thesis aims at the age structure of population to research the relation with the house price. The relative and absolute amount of working-age population will both impact the consumer demand, thus the fluctuation of house price. The present study about the relation between demographic and housing market mainly concentrate on a broader level, such as the relation between house demand and age structure, or between house price and population structure. Thereinto the house demand house demand includes the specific types of commodity housing, such as the property types, house types and the position of building, while the structure of population includes age structure, social structure and regional structure. Nevertheless, the specific study about house price and age structure is rare. Therefore, this thesis chooses commercial house sale price and dependency ratio of population of Liaoning Province as the objects to start the empirical analysis in two steps. Firstly, we will see Liaoning Province in its entirety in the time series model. Secondly, we adopt the panel data model based on the statistics of the14cities of Liaoning Province, which covers9years from2004to2012.From the whole to the individual, we can not only acquire the relation between house price and age structure in Liaoning Province, but differences among all the14cities. In addition, the Statistical caliber of age structure which is used in the panel is more realistic. One more advantage about panel model is that it has more samples to ensure a more reliable result.This thesis covers four parts:In the first part, give an inductive description about the background, significance, relevant studies, the main content and method. Briefly introduce the innovations and shortcomings.In the second part, descriptively analyze the housing market and population of Liaoning Province over the years. In the housing market part, firstly, summarize the development of China’s real estate with the policies of promotion and restriction. Secondly, give a description of changes in the main indexes in Liaoning housing market after the Real Estate Reform, such as supply and sale volume. Lastly, it directly interprets the commodity residential price over the years in Liaoning Province. In the population part, firstly, organized descript the background and development in different stages of population of the whole country. Secondly, induce the dependency ratio of population of Liaoning Province on behalf of age structure. Thirdly, briefly analyze the social element of population related to this thesis——per capita disposable income, and select house price-to-income ratio to give a comparative analyze with30provinces in China. Lastly, expound the model of five stage of demographic transition by Charles Blacker, with which demographic transition in Liaoning Province is in accordance.In the third part, it is the empirical analysis based on time series model. Taking Liaoning Province as a whole, statistics of16years from1997to2012is collected and used in the model. The explanatory variables include dependency ratio of population, income, unemployment rate, interest, consumer price index. The relatively less important variables will be eliminated according to the goodness of fit T-statistics and other main statistics. Dependency ratio of population, income and employment rate are kept to explain dependent variable——house price, and the result conform to the experience prediction.In the fourth part, it is the empirical analysis based on panel model, in which statistics of14cities of Liaoning Province from2004to2012are selected and adopted.In the beginning, provide a brief introduction about the theory of panel model, then examine the samples and empirically analyze. The result keeps consistent with the one in time series model, extra providing the differences among all the14cites.In the fifth part, conclusions and policies are discussed. The conclusions include:negative correlation between dependency ratio of population and house price; severe population aging in Liaoning Province; irrationality of the difference between income and house price; more direct income impacting house price. The policies include:appropriately relax the one-child policy and timely to encourage fertility policy; improve residents’ income and narrow the income gap; improve the structure of housing and increase construction of indemnificatory housing; issue proper policies according to the real estate market, changes in population structure and economic development.