*4.2. Python Program Data Analysis*

In Section 4.1, multi-regression analysis was used to analyze the independent variables affecting the dependent variables using variable data from the macroscopic point of view and variable data from the microscopic point of view; this time, we tried to find the factors that correlate with the real estate price through correlation analysis. Correlation analysis is designed to understand the degree of association between two variables, not to explain causality. have an effect. Finally, in the case of the Suseo living areas, the youth economic activity participation rates, comprehensive real estate tax, and policy dependent variables were confirmed to have an effect.

As a result of analysis using the macroscopic variable data and microscopic variable data in Chapter 3, the regression model itself was found to be valid, but the overall independent variables did not significantly affect the dependent variable. Therefore, using the backward elimination method and the stepwise selection method, the analysis was con-

From the results above, it was confirmed that the dependent variable explains the large LP (Land Price) fluctuation of 92% and 99.8%. In addition, as a result of testing whether there is a significant relationship between the dependent variable and the set of all of the independent variables, the two were confirmed to be related at a significance

Based on Figure 17, we were able to ascertain the influence of the dependent variables and the independent variables. First, in the case of Apgujeong, we confirmed that dependent variables such as the unemployment rate, interest rate, comprehensive real estate tax, and violent crime rate affect land prices. In the case of the Second Samsung Living Area, dependent variables such as the economic growth rate, policies, land construction regulations, foreign exchange reserves, and violent crime rates were confirmed to have an effect. In the case of the third Daechi Living area, we confirmed that dependent variables such as the unemployment rate, interest rate, foreign exchange reserves, commercial growth, and violent crime rate have an effect. For the Yeoksam living sphere, eight dependent variables—the youth economic activity participation rate, economic growth rate, interest rate and policy, land construction regulation, foreign exchange reserves, commercial growth, and violent crime rate—have an effect. In the case of the fifth Gaepo living area, dependent variables such as the youth economic activity participation rate, interest rate, comprehensive real estate tax, policy, foreign exchange reserves, and commercial growth


*Symmetry* **2021**, *13*, x FOR PEER REVIEW 17 of 25

*4.1. R Program Data Analysis* 

level of 95%.

ducted based on the smallest AIC.

**Figure 17.** Checking the significant relationship through the stepwise selection method: (**a**) Apgujeong Area, (**b**) Daechi Area, (**c**) Samsung Area, (**d**) Yeoksam Area, (**e**) Gaepo Area, (**f**) Suseo Area. **Figure 17.** Checking the significant relationship through the stepwise selection method: (**a**) Apgujeong Area, (**b**) Daechi Area, (**c**) Samsung Area, (**d**) Yeoksam Area, (**e**) Gaepo Area, (**f**) Suseo Area.

As shown in Figure 18, there were differences in the factors relevant to each local living zone. Compared to other factors, the economic growth rate, unemployment rate, interest rate, policy, land building regulation, and violent crime rate were found to be relatively low.

In conclusion, the factors influencing the cost of living in each region through regression analysis in R were the amount of foreign exchange reserves, number of criminal activities, interest rates, and policies, and the factors correlated with the price of living in each region through correlation analysis in Python included the economic participation rate of the youth, rate of application of the comprehensive real estate tax bill, and amount of foreign exchange reserves, but the research results confirmed that fluctuations in foreign currency reserves are closely related to real estate land prices. As explained in Section 4.3, below, we would like to examine the impact of land prices associated with fluctuations in foreign exchange reserves in combination with the current data.
