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Research On Influencing Factors Of Commercial Housing Sales Price In China

Posted on:2020-09-10Degree:MasterType:Thesis
Country:ChinaCandidate:L TuFull Text:PDF
GTID:2439330596993446Subject:Applied statistics
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In 1998,China carried out a series of reforms to the real estate industry.The real estate transaction market has emerged.These houses enter the market in the form of commodities.The entire industry has begun to enter the market stage.Since then,China's real estate industry has entered a new period of development.The development of the real estate industry has greatly promoted the development of China's economy,stimulated fiscal growth,and became an important pillar industry of the national economy.To a certain extent,it effectively solved the people's demand for housing in the process of urbanization in China and improved the living and living conditions of the people.The real estate market activated the real estate industry.The Chinese real estate market is developing rapidly.A large amount of idle funds from the society poured into the real estate industry.The speculative heat is still high,causing China's housing prices to continue to rise.The ratio of house prices to income continues to rise,creating a widespread society.The moderate growth of commercial housing sales price is the inevitable development of the real estate market,but the excessively high commercial housing sales price will seriously hinder the healthy and sustainable development of the real estate industry.At the same time,it will hinder the development of our national economy and the improvement of people's living standards.It can be said that the price of commercial housing is an important issue affecting the country and the people's livelihood.Effectively solving the people's livelihood issue has always been the focus of attention of the party and the state.The most crucial point in solving the people's livelihood problem is to solve the problem of "residence".In this context,this paper takes the average selling price of commercial housing as the research object,and starts from the demand and supply to find out the factors affecting the price of commercial housing.First of all,based on the relevant ideas of economics,combined with the existing relevant conclusions,this paper studies and analyzes the paper from the two aspects of demand and supply,and combines the methods of qualitative and quantitative to conduct a holistic study.deal with.Based on the analysis and analysis of the relevant literature,select the residential consumer price index and population according to the demand,and choose the actual real estate fund,the number of real estate developmententerprises,the sales area and the completed area of commercial housing.Commercial housing.Then,through the “China Statistical Yearbook” and the “China Real Estate Yearbook”,relevant data from 31 regions from 2012 to 2016 were obtained,and a model based on longitudinal data was established and solved by generalized estimation equations.The results of this paper are as follows: Under the three different distribution hypotheses of normal distribution,inverse Gaussian distribution and gamma distribution,whether the variables obtained by selecting different optimal working correlation matrices are slightly different through significant results.Under the assumption of normal distribution,all variables passed the significance test,indicating that the six variables selected in this paper are closely related to the changes in the sales price of China's commercial housing market.Among them,the number of employees in real estate development enterprises,the area of commercial housing for sale,and the number of people who have undergone major tests under three different distributions are positive,and the calculation model coefficient is positive.Explain that the number of employees in real estate development enterprises,the sales area and number of commercial housing will lead to changes in housing prices in the Chinese commercial housing market.This change is positively related.
Keywords/Search Tags:commercial housing sales price, vertical data, demand factor, supply factor, generalized estimating equation
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