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Analysis About The Factors Which Influence The Housing Price In Suzhou And The Prediction

Posted on:2015-11-01Degree:MasterType:Thesis
Country:ChinaCandidate:J ZhangFull Text:PDF
GTID:2309330428999662Subject:Applied statistics
Abstract/Summary:PDF Full Text Request
The problem of housing price has always been the focus of the whole society. It ismuch closed with individuals, families and organizations, even to our country. Thedevelopment of China’s real estate market makes a great contribution to the economic. Payattention on the real estate market, much more policies have been introduced to inhibit thesoaring price, but the rising trend of the price still keeps on. But at the beginning of thisyear, the housing price in Hangzhou fell. Then, people have much more worry about thereal estate bubble and the collapse of the market. The trend of housing price in Suzhouwhich is not far from Hangzhou is worth studying. In this paper, qualitative, quantitativeanalysis and price forecast have been done.Then, we can understand the factors whichinfluence the housing price more comprehensive and more profound. The most useful isthat it also can provide references to everyone in the real estate market.This paper mainly based on supply and demand, combined with the house, economicenvironment, social status and policies.Then, think of the housing price in Suzhou as thedependent variable, and the9indicators (the region’s GDP, the urbanization ratio and so on)as variables. Multiple linear regression analysis is used to make the model. Finally, theoptimal model is established and it contains only two indicators: the urbanization ratio andthe cost of the construction projects. According to the model, the paper provides relatedsuggestions to government, real estate developers, and buyers. Using the regression model,the housing price in2013and2014are11257.92and12466.55Yuan per square meter. Atthis moment, the average housing price in2013has been published and it is11397Yuanper square. The predicted value is much closed with the actual value. Then, the regressionmodel can be sure.
Keywords/Search Tags:The housing price, Qualitative analysis, Multiple regressionanalysis, Price forecast
PDF Full Text Request
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