House Prices, per Capita Income and Real Estate Planning Investment—An Empirical Study Based on Anhui Province

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:


Keywords
House Prices, Per Capita Disposable Income, Multiple Linear Regression

Introduction
With the development of the economy, housing prices in Chinese cities have experienced significant growth. By 2004 onwards to Beijing, Shanghai and other urban centers as the representative of more rapid rises in house prices, the annual average increase is far more than 20%. An investment tycoon seized market opportunities and turned to investing in real estate, the real estate industry soared unprecedentedly. At the same time, the increase in the per capita disposable income of urban residents far lags behind the growth rate of housing prices and real estate investment. The trend of rising house prices is not only in the central cities, and the growth rate of house prices in many second-tier and third-tier cities has also started to increase. The Chinese people have paid attention to the concept of home since ancient times. The rapid increase in house prices has also attracted the attention of the government and many scholars.
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. Gao et al. (2013) found that the excessive urban-rural income gap led to an increase in urban housing rents, and the per capita disposable income of residents was the most significant among all influencing factors. Li (2014) found that changes in housing demand, changes in the quality of housing supply, and differences in the housing environment will all lead to changes in housing prices. 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. Yuan et al. constructed a structural equation model to study the dynamic relationship between housing prices and land price changes in Beijing. Zhang and Li (2016) used house prices and land prices to predict house prices, proving that house price expectations have a greater impact on land prices. Li and Guo (2020) used GM (1,1) model and BP neural network model to analyze Nanjing short-term data. Zhu (2020) impact of disposable income on housing prices in Chongqing City is using the traditional linear regression method.
In summary, most scholars will refer to the supply and demand relationship of the housing market when studying the factors influencing housing prices. Residents' disposable income has a greater impact on housing demand. At the same time, 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.

Theoretical Models and Data Sources
Housing is a special consumer product, not only has practical value but also has investment value. From the consumer's point of view, no matter what type of product you get, you have to spend money to buy the product. The sales of the product depend in part on the purchasing power of the consumer. For consum- ers who have a demand for housing or housing, the higher the personal disposable income, the greater the demand and desire for housing purchases, and the price of goods depends on market demand rise. From an investor's perspective, the investor sees an increase in demand for housing in the market, and his desire for real estate investment will become more intense, which will also lead to rising housing prices. It can be seen that consumers "disposable income and investors" planned investment affect the selling price of houses by affecting the demand side of the real estate market. Among many data analysis methods, regression analysis has been favored by some scholars as a more widely used quantitative analysis method. It analyzes the statistical relationship between things while focusing on considering the law of quantitative change between variables, and finally through regression. The form of the equation directly describes this relationship. The general multiple linear regression equation format is:  Table 1. According to the data, it can be seen that the selling price per square meter of houses in Anhui Province is increasing year by year.

Building the Model
In different periods, house prices will be different, and many factors can affect the change in house prices. This article mainly studies the impact of residents "personal disposable income and investors" investment factors in real estate plans on house prices. By setting house prices as the dependent variable and per capita disposable income as the independent variable showing in Figure 1, it can be found that there is a more obvious linear fitting relationship between the house sales price (Y) and the per capita disposable income (X 1 ) and the real estate plan investment (X 2 ). Showing in Table 2, the correlation coefficient between the minimum index 0.9657, max 0.9929, indicating a correlation between the strength of the indicators.
Among them , , α β γ are the parameters of the linear regression equation, which t µ are random disturbance terms.
In order to ensure the authenticity and validity of the data with accuracy, reflecting the relationship between the variables facilitates, on Y, X two variables logarithmic transformation, can be obtained:

Heteroscedasticity Test
If the random error term of the linear regression model has heteroscedasticity, it will have a greater impact on the model parameter estimation and testing.
Therefore, the heteroscedasticity test needs to be used to ensure the practical application of the model.

Conclusion
According to the econometric model created, there is a clear positive correlation between house prices and personal disposable income and planned investment of real estate, and personal disposable income and planned investment of real estate both significantly affect changes in house prices. The functional relationship between the three is   

Conflicts of Interest
The author declares no conflicts of interest regarding the publication of this paper.