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Study On The Influential Factors Of Individual Loan Rate Of Accumulation Fund In China

Posted on:2019-02-26Degree:MasterType:Thesis
Country:ChinaCandidate:M Z ZhangFull Text:PDF
GTID:2439330572498200Subject:Real Estate Finance
Abstract/Summary:PDF Full Text Request
Accumulation fund system started 1990s in China,which is a significant system innovation to solve the problem of housing in our country,which protects the basic housing needs,enhances the capacity of housing consumption adn makes a great contribution to improve the housing supply system.The supplyment and application of the housing accumulation fund is related to its value,which is related to whether the accumulation fund can meet the needs of the workers and the sustainable development of the accumulation fund system.The individual loan rate is the core index of the system of supplyment and application for housing accumulation fund.In recent years,with the rising of the real estate market in China and the demanding for extracting the accumulation fund loan,the individual loan rate of accumulation fund continued to rise in some regions,and some cities has reached 100%.The supply side has been unable to meet the needs of the use of the application,which must maintain the normal operation of accumulation fund center through external financing and other means.It is of practical significance to study the factors of the rate of individual loan and provide the theoretical guidance for the relevant departments to regulate the individual loan rate of the accumulation fund.In this paper,after combing the relevant theories and existed literature of accumulation fund and,we build a complete operation system of accumulation fund according to the actual circumstance of accumulation fund situation,to select the individual loan rate as the core index that has connected the supply side and application side.With constructing "accumulation fund operation system,operating mechanism analysis,based on the operating system to explore the influence factors of the individual loan rate" is given priority.Finally,the three kinds of influencing factors of the accumulation fund like supply category,the application category and macroeconomics category are selected.Based on this,the corresponding explanation variables will be converted to influencing factors.This paper joined innovatively the policy variables and weighted assignment according to actual situation and stepwise regression method for preliminary screening of variables.Finally,this paper treats the annual individual loan rate of accumulation fund in 33 large and medium-sized cities in our country in 2014 to 2016 as the main research object.Based on the corresponding test,the empirical study on the panel data is determined to base on the model of the variable intercept and the weighted estimation of the cross-section so that the real influence factors of the rate of the individual loan are studied.In this paper,The research results show that the increasing of accumulation fund loans,the rising of the average sales price of commercial house and the rising of real estate development investment will have a significant positive influence on individual loan rate;Such factors like increasing of deposite population,the increasing of the down-payment ratio of accumulation loans,the increasing of the interest rate of the accumulation fund loan and the increasing of proportion between the extraction and loan amount have a significant negative impact on individual loan rate.However,the change of the deposite contribution rate and base,and the change of the proportion of the total withdrawal rate of the household consumption are less obvious to the individual loan rate.In this paper,The author puts forward some pertinent suggestions on the supplyment part and the application part of the accumulation fund and provides theoretical support for the regulation of the individual loan rate to help the supervision department of the accumulation fund and the accumulation fund management center.
Keywords/Search Tags:accumulation fund, individual loan rate, operation system, influence factors, panel data model
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