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Research On The Relationship Between Real Estate Price And Household Consumption

Posted on:2012-08-15Degree:MasterType:Thesis
Country:ChinaCandidate:J F CaoFull Text:PDF
GTID:2219330371953616Subject:Statistics
Abstract/Summary:PDF Full Text Request
After the housing system marketization reform, the real estate market developed rapidly and as yet has been the pillar industry of the national economy. By the end of 2010, floor space of buildings completed already achieved 75,961 ten thousand sq.m which increased 4.5% and floor space of commercialized buildings sold already achieved 104,349 ten thousand sq.m which increased 10.1%. However, with the rapid development of real estate market, rapidly growing real estate price becomes an important problem which affects economic stability and social harmony. Real estate price rapidly grows from 2,063 yuan per square meters in 1998 to 4,681 yuan per square meters, which roses as high as 127%. Compared with the soaring real estate price, household consumption is falling. From 2000 to 2009, the final consumption rate drops in the ten years from 62.3% to 48.0% which cumulatively declines 14.3 percentage points and among them household consumption drops more serious than government consumption. In the year 2008 international financial crisis spread worldwide and expanding household consumer becomes the inevitable choice of solving economic growth problem. Twelfth Five-Year Plan first puts forward to goals of establishing long-term growth mechanism of household consumption. The current growth rate of real estate price is faster than household income. The high real estate price is far beyond the economic strength and psychology anticipated of ordinary residents and is very likely to become obstacles of expanding household consumption. So to research the relationship between real estate price and household consumption has great practical significance.On the basis of relevant research results at home and abroad, this paper finds that some relevant domestic research exists some weaknesses and deficiencies. Because capitalization time of China's real estate market is later, modeling methods based on the traditional time series exist the weakness of data volume. Some of the research extend time interval to the time before capitalization so that the reliability of the conclusion is influenced. Few studies touch on the profound background of social transformation in China. During the social transformation era, the change of basic livelihood security level, such as education, health care, social security and employment, is closely linked with household consumption. The real estate price and household consumption all have significant regional difference and group difference. At present, most of the studies are based on the whole country or individual districts and systematic and overall studies on regional difference and group difference almost can't be found. This paper will make efforts focusing on these weaknesses and deficiencies.The paper is divided into six chapters and the main content of each chapter introduces as follows:The first chapter is introduction. It expounds the profound background and significance of the study under the post-crisis era as Twelfth Five-Year Plan begins this year. Then it reviews the relevant literature home and abroad and illustrates the main work and innovations.Chapter 2 is the relationship between real estate price and household consumption and current situation of the two sides. It presents basic concepts and concludes nine different transmission mechanisms by which the real estate market can influence household consumer. It analyses fast growing situation of real estate price and the problems of establishing long-effect growth mechanism of household consumer.Chapter 3 is theory foundation of the empirical model and estimation method of panel data model. It summarizes representative modern consumption function model. It analyses the better place of panel data model than time series model and summarizes briefly the estimate method of the model.Chapter 4 is analysis of regional differences based on provincial panel data. It establishes a varying coefficient model to analyze the provincial differences of the relationship between real estate price and household consumption, which is doing linked to the basic livelihood security level in social transition period, such as education, health care, social security and employment and so on. And then the 31 provinces are divided into high, medium and low real estate price area to proceed more in-depth and macro analysis.Chapter 5 is analysis of group differences based on income grouping panel data. It establishes a varying coefficient model to analyze the group differences of the relationship between real estate price and household consumption. And then the 7 income classes are divided into high, middle and low income class to proceed more in-depth and macro analysis.Chapter 6 is summary and policy suggestions. It summarizes the thought, structure and conclusions of the paper and puts forward many measures by taking disposable income, real estate price, financial development and basic livelihood security level etc. into consideration.The paper works on the weaknesses and deficiencies of related literatures and makes some contributions in the respect of estimation method, research content and policy suggestions. First, the choice of data interval is more reasonable and the shortage that the amount of data is small is overcome; Second, the paper touches on the profound background of social transformation in China close; Third, the paper establishes varying coefficient model and analyses systematically regional differences and group differences; Finally, the paper draws some more specific conclusions and policy suggestions. However, there still exist shortages. Many other factors, such as consumption habits, population and geography, are not studied. Many models of other types are not compared. And some policy suggestions partly lack operability. These will be the focus of the follow-up research.
Keywords/Search Tags:real estate price, household consumption, panel data model
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