The Impact Of Housing Prices On Total Factor Productivity

Since the beginning of the new century,the rapid growth of housing prices in China has driven the development of the economy to a certain extent.The notice of the state council on promoting the sustained and healthy development of the real estate market,no.18 document of the state council issued in 2003,makes it clear that real estate has become the pillar industry of the national economy.However,some studies show that the rapid rise of housing price leads to the misallocation of social resources and the crowding out of technological innovation investment,and the rise of housing price and the growth of total factor productivity show an inverse relationship,which restricts the sustainable growth of economy.Since the reform and opening up forty years,China's economy has developed rapidly and made remarkable achievements,but China's development depends on factor input to a large extent,and the growth of total factor productivity contributes little to economic growth.As China enters the new normal of economic development,the development advantages brought by demographic dividend and institutional dividend gradually weaken,and the extensive economic growth relying on factor input lacks sustained power.Therefore,this paper studies the impact of rising housing prices on total factor productivity,explores the mechanism of housing prices restraining total factor productivity growth,removes the growth barrier of total factor productivity,further releases the potential of China's economic development,and promotes the healthy and sustainable development of the economy.In the first chapter in this paper on China's economic growth from the highway to the rapid growth in the era background and the development of the real estate market is presented,at home and abroad about the total factor productivity calculation,influencing factors and house prices and the study of the relationship between total factor productivity comb literature summary,is also described in this paper,the research content and method,finally introduced the features and deficiency.The second chapter is the theoretical analysis of housing price and total factor productivity,which defines the main concepts of this paper,introduces the basic theories: economic growth theory and resource allocation theory,and analyzes the mechanism of housing price affecting total factor productivity.The third chapter,first using the stochastic frontier beyond the logarithmic function model calculation of China's 31 provinces,cities and autonomous regions in 2000-2017 provincial total factor productivity growth,and decomposition: technical efficiency(TE),frontier technology progress rate(TP)and scale efficiency(SE),and then to set the variables of this study,indicating the data source.The fourth chapter designs the econometric model to empirically analyze the influence of housing price on total factor productivity.According to the empirical analysis results,the housing price has a negative impact on the growth rate of total factor productivity,and influences total factor productivity through the path of reducing technical efficiency(TE),advanced technology progress rate(TP)and scale efficiency(SE).House prices rose out of the enterprise innovation not only venture capital,management,capital and other inputs,resulting in frontier technology progress and technical efficiency are affected by negative,and house prices rising capital of the real economy continuously in the real estate market,to seek profit maximization,does not result in a mismatch of social resources and scale economy,affect the sustained economic development.The last chapter puts forward four policy Suggestions based on the research results :(1)From focusing on economic policies to focusing on social policies;(2)Adjust the industrial structure and promote industrial adjustment and upgrading;(3)Optimize the structure of the tax system and promote the reform of the fiscal and taxation systems;(4)Better promote the implementation of national strategies such as innovation-driven development.