Analysis of Development of Real Estate Industry Based on FA and CA

2013 ◽  
Vol 397-400 ◽  
pp. 2731-2734
Author(s):  
Xiao Chun Shi ◽  
Deng Ying Jiang

With the development of market economy, the real estate industry has become a driving force of the pillar industry of China's economic growth. The residential housing casing is used to construct the index system of real estate development. And on the combination of factor analysis and cluster analysis, this paper studies the real estate development status of China with the SPSS. By extracting three main factors that affect the development of residential housing industry, and with the factor score, the real estate development status of the 31 provinces and cities nationwide are classified and sorted. Last the causes and results of the classification are analyzed, and some targeted countermeasures are proposed.

2018 ◽  
Vol 10 (8) ◽  
pp. 2659 ◽  
Author(s):  
Jiangtao Li ◽  
Jianyue Ji ◽  
Huiwen Guo ◽  
Lei Chen

Private investment in China, as a developing country, is an important source of financing for Chinese SMEs (Small and Medium-Size Enterprises) and has played a major role in the development of the real economy. However, in 2016, the growth rate of private investment in China dropped from 10.18% to 3.17%, which had a significant impact on the real economy. At the same time, China’s real estate market has developed rapidly, attracting a large number of capital inflows. The relationship between real estate development and private investment in China is worth considering. This study first, theoretically analyzes the influence mechanism of real estate industry on private investment, pointing out that within a modest development range, the development of real estate industry can promote private investment through the industrial linkage, urbanization, and balance sheet effects, but when real estate is overdeveloped, it has an inhibitory effect on private investment through vampire effect, raising costs and reducing demand effect. In other words, real estate has different effects on private investment in different developmental periods. Therefore, there is a non-linear relationship between the two variables. Second, the relevant provincial panel data of 31 provinces in mainland China from 2003 to 2015 were selected. Using the dynamic panel system Generalized Method of Moments (GMM), this study estimated the correlation between real estate development and private investment. The empirical results showed that the development of the real estate industry has a significant impact on the level of private investment; the two showing an “inverted U-shaped” relationship. At present, in some provinces in China, the real estate industry has exceeded the inverted U-shaped threshold. To boost the vitality of private investment in promoting real economic growth, the development of the real estate industry should be restricted, and house prices should be properly regulated.


2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Ming Li ◽  
Yousong Wu

From reform and opening to the comprehensive construction of a well-off society, the rapid growth of the national economy and the advancement of urbanization have promoted the rapid development of China’s real estate industry. The real estate industry has become a pillar growth point in the development of the national economy. At the same time, China’s real estate markets also continue to mature. However, due to the short development time of my country’s real estate market, imperfect management mechanism, irregular organization, and other issues, coupled with the fierce competition and internationalization of the market investment environment, the risk of investment accumulation in the real estate industry is also increasing. Therefore, in real estate investment decision-making, it is of far-reaching significance to study how to control real estate investment risks and promote the healthy and stable development of the real estate industry. The purpose of this article is to build a set of investment portfolios based on the ant colony algorithm to diversify risks and obtain returns, so that the constructed investment portfolios will minimize the risk when the return reaches a certain amount of time. This article first gives a general introduction to wireless network communication and then analyzes the risk of real estate project investment. First, the variance is used as a measure of risk to establish a dynamic model of the real estate development project portfolio, and the ant colony algorithm is introduced to the investment risk of real estate development projects. In the dynamic analysis, an improved portfolio model was established, and the two were compared through case analysis. The experimental results show that under the condition of the same net present value and investment payback period, the ant colony algorithm based on variance is invested in lot H. The ratio is obviously higher, and the capital investment ratio of lot H based on the ant colony algorithm is obviously lower. The difference between the two is 30.1%.


Author(s):  
Igal Charney

This article provides an overview of the real estate development industry and evaluates its connection to urban planning. It reviews principal approaches used to study the real estate industry and explains the concept of development rhythms that encapsulate the unevenness of development over time. The article explains that the real estate development industry has not come to occupy an important position in urban planning because of the preference of urban scholars for social, political, and cultural issues. It also examines the workings of the core agents in the development process, real estate developers.


Author(s):  
Wenqin Gong ◽  
Yu Kong

Environmental pollution is a problem of universal concern throughout the globe. The development of real estate industry not only consumes huge resources, but also has close ties with high-consumption industries such as the construction industry. However, previous studies have rarely explored the impact of real estate development on environmental pollution. Therefore, this paper employs the entropy method to construct a comprehensive index of environmental pollution based on panel data of 31 provinces in China from 2000 to 2017, and empirically examines the impact of real estate development on environmental pollution. This article uses real estate investment to measure the development of the real estate industry. In view of the high spatial autocorrelation of environmental pollution, this paper selects a spatial econometric model. The empirical study found that: (1) By using the Spatial Durbin Model, real estate development has an inverted U-shaped impact on environmental pollution. Meanwhile, most cities have not yet reached the turning point; that is, with the continuous development of the real estate industry, environmental pollution will continue to increase. (2) Further regional heterogeneity found that the inverted U-shaped relationship still exists in coastal and inland areas. (3) Finally, this article used the Spatial Mediation Model to explain the nonlinear impact of real estate development on environmental pollution, with two important mediating variables: population density and industrial structure. Through the above analysis, it can be observed that real estate development has a significant impact on environmental pollution. Thus, the country and the government can reduce environmental pollution by improving the investment structure, using environmentally friendly building materials, guiding population flow and promoting industrial upgrading.


2014 ◽  
Vol 596 ◽  
pp. 136-140
Author(s):  
Xiao Ling Tang ◽  
Zhu Ling Tan

In this paper, on the basis of the effective data, build the evaluation index system of the real estate industry development level, using factor analysis and cluster analysis, analysis the real estate industry development level of 10 cities and 1 agricultural science and technology development zone in Shanxi province . According to factor score and cluster analysis, the level of real estate development in Shanxi Province is divided into four types, and ranking them, finally analysis the results of classification reasons.


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