3.2.6. Violent Crime Rate

Strong crime rates are also an important factor in predicting real estate land prices. The more dangerous the area, the poorer the image, and the more difficult it is to fix it in the residential area or commercial area. As a result, the commercial area and the residential area decline, the area becomes less active, and the influx of poor people and the number of criminals increase. From another perspective, it is true that, by taking risks and investing in a region, not only will land prices be cheaper than other regions but if the gamble succeeds, it will mean gaining an advantage in that region. Moreover, with the rise of outside investors, the crime rate will decrease, and real estate land prices can go up again. As was the case in the past, if there are rumors of rental apartments and houses around condominiums, a demonstration opposing it will most likely follow. The correlation between public rental housing and crime occurrence in four years was analyzed, and the rest excluding permanent rental housing were confirmed to have affected the crime rate. Figure 15 shows the results of using the prosecution's crime trend report to set the violent crime rate of 100,100 from 2015 to 2018. *Symmetry* **2021**, *13*, x FOR PEER REVIEW 16 of 25

**Figure 15.** Number of crimes per quarter nationwide. **Figure 15.** Number of crimes per quarter nationwide.

#### **4. Analysis of Factors from Macroscopic and Microscopic Perspectives for the Prediction of Future Land Prices in the Real Estate Market 4. Analysis of Factors from Macroscopic and Microscopic Perspectives for the Prediction of Future Land Prices in the Real Estate Market**

Prior to the data analysis, the land prices in the real estate market were analyzed based on apartments where land price transactions occurred smoothly in the living areas of each region. The land price of each regional living area was set as the independent variable, and the dependent variables are the economic activity participation rate of the young people, the economic growth rate, the unemployment rate of the young people, interest rates, the application rate of the comprehensive real estate tax and policy, local building regulations, foreign exchange reserves, the growth in the trading area, and violent crimes. The analysis was carried out with a total of 10 out of the 13 variables mentioned above; when we focused on the quarterly data from 2015 to 2018, the transportation, education, and public facilities sectors fluctuated. These were not used as variables because their effect was not big. As we all know, transportation, education, and public facilities are variables with a huge impact on real estate land prices. Unlike other areas, Gangnam-gu is a region which has been developed since the 1970s, so it has complete transportation, education, and public facilities. Thus, these three variables were not used in this analysis. Figure 16 shows the real estate data analysis through the R and Python Prior to the data analysis, the land prices in the real estate market were analyzed based on apartments where land price transactions occurred smoothly in the living areas of each region. The land price of each regional living area was set as the independent variable, and the dependent variables are the economic activity participation rate of the young people, the economic growth rate, the unemployment rate of the young people, interest rates, the application rate of the comprehensive real estate tax and policy, local building regulations, foreign exchange reserves, the growth in the trading area, and violent crimes. The analysis was carried out with a total of 10 out of the 13 variables mentioned above; when we focused on the quarterly data from 2015 to 2018, the transportation, education, and public facilities sectors fluctuated. These were not used as variables because their effect was not big. As we all know, transportation, education, and public facilities are variables with a huge impact on real estate land prices. Unlike other areas, Gangnam-gu is a region which has been developed since the 1970s, so it has complete transportation, education, and public facilities. Thus, these three variables were not used in this analysis. Figure 16 shows the real estate data analysis through the R and Python programs to determine which factors are related to land prices.
