Abstract
In order to explore the relationship between housing prices, per capita disposable income and real estate planning investment, this article uses the data of average sales price of Anhui Province from 2000 to 2018, per capita disposable income and real estate planning investment as samples to establish multiple linear regression. The model found a significant positive correlation between house prices and income. By using EVIEWS9.0 analysis software,three quantitative relationship between those who came to the conclusion functional relationship is: In Yt∧ = 5.1649 + 0.0803X1t∧ + 0.4266X2t∧.
Highlights
With the development of the economy, housing prices in Chinese cities have experienced significant growth
In order to explore the relationship between housing prices, per capita disposable income and real estate planning investment, this article uses the data of average sales price of Anhui Province from 2000 to 2018, per capita disposable income and real estate planning investment as samples to establish multiple linear regression
It can be seen that the planned investment in real estate and the change in house prices are relatively close, but the growth rate of personal disposable income is slower than the rise in house prices
Summary
With the development of the economy, housing prices in Chinese cities have experienced significant growth. In the research on the factors affecting house prices, Kuang (2010) analyzed panel data and found that interest rate changes and the speed of population growth have a more significant impact on housing price fluctuations. In addition to studying the relationship between housing supply and demand and the factors affecting housing prices, Wang et al (2015) found that economic growth and population growth will increase housing prices based on panel data. Scholars did not mention the effect of planned investment on house price changes, so this article will choose two factors: average disposable income of residents and planned investment of housing to establish a multiple linear regression equation and quantitative analysis
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