*4.3. Analysis of the Relationship between Foreign Exchange Reserves and Land Prices*

Through the R program regression analysis and the correlation analysis of Python, it was confirmed in Sections 4.1 and 4.2 that the most relevant factor for the land prices is the foreign exchange reserves. Next, we sought to analyze the fluctuations in foreign currency reserves and the relationship with land prices from 2019 to the present, and to investigate how the influence is applied as it is.

Figure 19, below, shows the fluctuations in foreign exchange reserves from the first quarter of 2019 to the first quarter of 2020, and the average land price fluctuations in each region's living area. The foreign exchange reserves can be checked in parentheses next to the year; the unit is 100 million dollars, and the land price is also 100 million.

As a result, we were able to confirm whether the ten factors affect land prices. Nonetheless, four factors policy, interest rate, violent crime rate, and foreign exchange reserves were found to have more influence than the other factors. In addition, the F TEST confirmed that the dependent variables of the six regional living spaces were significant to-

In Chapter 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

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

gether with the independent variables.

*4.2. Python Program Data Analysis* 

to explain causality.

relatively low.

**Figure 18. Figure 18.**  Relationship between real estate prices and variables. Relationship between real estate prices and variables.

The foreign currency reserve was set as an independent variable through a Python program. The representative apartment area in the living area of each region was set as the dependent variable. The results were then confirmed through correlation analysis and regression analysis. It was confirmed that, among the six regional living areas, the representative apartments in the regional living area of Samsung have a significant relationship with the fluctuation of foreign exchange reserves. The correlation was also confirmed. In addition, the fluctuation of foreign currency reserves was confirmed to be related to the land prices of each regional living area. Unfortunately, the real estate transaction is active, and it was set at 120 square yards, so there was not much data, and the relevance between foreign currency reserves and land prices increased. However, other studies confirming fluctuations in foreign exchange reserves and their relationship to the stock market have confirmed that an increase in the amount of currency has led to overinvestment in real estate and the stock market, leading to price increases [41,42]. Foreign exchange reserves in the first quarter of 2020 showed a decline due to the increase in the dollar due to the foreign exchange market stabilization measures and COVID19, but has been on the rise since. Korea's foreign exchange reserves at the end of December last year were \$443.1 billion, an increase of \$6.7 billion compared to November, indicating that securities will also have a

significant increase in return on investment, and that real estate land prices will also rise significantly. This research also confirmed the data of 2019 using the data of 2015 to 2018, but for this research, it was also confirmed that the fluctuation of real estate land prices due to the fluctuation of foreign currency reserves has a correlation [42–44]. The result of this can be valid. Figure 20, below, shows the transactions for a representative apartment in 120 square yards in the Samsung area of Gangnam-gu, where the blue line denotes the quotes for the apartment. The red dot shows the actual transaction price. The green line shows the current price of the property, and the red line at the end is the speculation that the market price will rise due to the increase in foreign currency reserves. Through the R program regression analysis and the correlation analysis of Python, it was confirmed in Sections 4.1 and 4.2 that the most relevant factor for the land prices is the foreign exchange reserves. Next, we sought to analyze the fluctuations in foreign currency reserves and the relationship with land prices from 2019 to the present, and to investigate how the influence is applied as it is. Figure 19, below, shows the fluctuations in foreign exchange reserves from the first quarter of 2019 to the first quarter of 2020, and the average land price fluctuations in each region's living area. The foreign exchange reserves can be checked in parentheses next to the year; the unit is 100 million dollars, and the land price is also 100 million.

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

*Symmetry* **2021**, *13*, x FOR PEER REVIEW 19 of 25

in foreign exchange reserves in combination with the current data.

*4.3. Analysis of the Relationship between Foreign Exchange Reserves and Land Prices* 

**Figure 19.** Changes in the land price of living areas in each region due to changes in the foreign exchange reserves. **Figure 19.** Changes in the land price of living areas in each region due to changes in the foreign exchange reserves. denotes the quotes for the apartment. The red dot shows the actual transaction price. The green line shows the current price of the property, and the red line at the end is the speculation that the market price will rise due to the increase in foreign currency reserves.

**Figure 20.** Prediction of changes in the market prices due to fluctuations in foreign exchange reserves. **Figure 20.** Prediction of changes in the market prices due to fluctuations in foreign exchange reserves.

#### **5. Impact on Landscape Districts of Real Estate 5. Impact on Landscape Districts of Real Estate**

tricts.

In addition to the macroscopic and microscopic perspectives in the real estate market, the landscape district is also a desirable tool for the prediction of future land prices. Landscape districts are districts required for the preservation, management, and formation of landscapes, and they are determined by the National Land Planning and Utilization Act. According to their designated purpose, landscape districts are classified into natural land-In addition to the macroscopic and microscopic perspectives in the real estate market, the landscape district is also a desirable tool for the prediction of future land prices. Landscape districts are districts required for the preservation, management, and formation of landscapes, and they are determined by the National Land Planning and Utilization Act. According to their designated purpose, landscape districts are classified into natural

scapes, urban landscapes, and waterside landscape districts. The natural landscape dis-

the natural scenery of the city. The urban landscape district is a district required for protecting and maintaining urban landscapes such as residential areas or centers, and it is a waterside landscape district and specialized landscape district. Finally the waterside landscape district is a district designed to protect and maintain the landscape around the main

There are four major factors influencing land price fluctuations in these landscape areas. First, the city planning ordinance can be changed. For example, the green belt in Haeundae-gu, Busan has recently been enhanced. Second is a case wherein changes are made through development projects such as residential land development projects. Third are the environmental factors which affect real estate or policies related to restrictions and business promotion related to the national landscape, such as the Second Framework Plan for Landscape Policy recently established in Korea. Lastly, due to factors such as capital inflow, there are various factors which can vary real estate prices, such as landscape dis-

waterfront or buildings with great cultural conservation value.

landscapes, urban landscapes, and waterside landscape districts. The natural landscape district is necessary for the protection of mountainous and hilly areas, and for maintaining the natural scenery of the city. The urban landscape district is a district required for protecting and maintaining urban landscapes such as residential areas or centers, and it is a waterside landscape district and specialized landscape district. Finally the waterside landscape district is a district designed to protect and maintain the landscape around the main waterfront or buildings with great cultural conservation value.

There are four major factors influencing land price fluctuations in these landscape areas. First, the city planning ordinance can be changed. For example, the green belt in Haeundae-gu, Busan has recently been enhanced. Second is a case wherein changes are made through development projects such as residential land development projects. Third are the environmental factors which affect real estate or policies related to restrictions and business promotion related to the national landscape, such as the Second Framework Plan for Landscape Policy recently established in Korea. Lastly, due to factors such as capital inflow, there are various factors which can vary real estate prices, such as landscape districts.

Because these landscape districts were formed in order to preserve and manage landscapes, it was difficult for landowners to retain property rights; the release of the landscape districts could affect real estate prices because it attracts investment and creates jobs in the area. Nonetheless, with the release of the landscape district, industrial factors are becoming positive, but the analysis of environmental factors does not yield good results. Because the ecosystem elements in the area are destroyed, and because there is a risk of environmental damage, the current landscape policy is called the watch landscape district or highest altitude district in order to protect the environment and urban landscape in the residential area, or to prevent overcrowding. It is a situation which sets a minimum limit for the height of a building [45].

Therefore, the current landscape district development has a policy of preventing environmental damage; for want of a better word, it is called a multifunctional landscape, providing various functions to maintain the current environmental ecosystem services [46]. To put this into the real estate market, a smart city is to be built [47–49]. In the case of the current smart city construction in the Republic of Korea, construction is roughly divided into two areas. First, in the case of the Sejong Smart City, it is a policy promoted by the nation. Before the introduction of the Sejong Smart City, it was in a remote state. After focusing on artificial-intelligence–based cities, however, the building of elements of seven major innovations—such as mobility and healthcare—was sought to transform them into nature-friendly cities [50].

The second area is the Busan Eco-Delta City. Busan City plans to build a robot city unlike Sejong City: one that can be utilized in daily life. There was also an attempt to change to a waterfront city in the future based on teenage innovation. Overseas cases include Hangzhou, which decided to realize a paper-free society by using blockchain technology, and Toronto, which decided to improve the problem of major cities and build eco-friendly cities. The purpose of introducing smart city construction is to solve various urbanization problems and build a convenient, comfortable life. In addition, the land prices in the surrounding area are likely to increase with the introduction of smart cities [51,52].

Therefore, the method of introducing a smart city in Gangnam-gu, the target area of the current study, can be explained as follows. Of the six living areas in Gangnam-gu, the southern area of Gaepo area and the western area of Suseo area are currently limited to green belt development. Therefore, it is desirable to develop a smart city that can interact with real estate and landscape in this area. Through this, various uses such as welfare, transportation, and education will be created. In addition, it would be desirable to develop real estate and landscape by benchmarking the introduction of overseas smart cities in the region. Therefore, it is to realize self-driving public transportation or to establish a nature-friendly city through a paperless society. As a result, it is expected that through the introduction of smart cities, various effects such as a nature-friendly life and a satisfying life will be enjoyed. Therefore, it is considered that the introduction of a smart city is

a way to make good use of the effect of interaction between landscape and real estate. Therefore, it is necessary to develop real estate and landscape based on the design plan for the introduction of smart cities in Korea through Figure 21 below [53]. *Symmetry* **2021**, *13*, x FOR PEER REVIEW 22 of 25

**Figure 21.** Smart city illustration. **Figure 21.** Smart city illustration.

#### **6. Discussion 6. Discussion**

Recently, people's interest in the real estate market has increased [13,14]. Among them, what attracts attention is the exploration of the factors that cause changes in real estate land prices [19]. Various factors, such as transportation access, financial stability, and stocks exist as factors which can change the land prices [26,27,54,55], but in the current study, based on macro and micro factors—which are frequently used terms in the economy—we analysed which ones have a greater influence. In addition, the technologies used for real estate Big Data analysis include artificial neural network analysis, data mining, and machine learning, but in the current study, through regression analysis and correlation analysis, we investigated which factors are influential, and explained the correlation well. As a result, it was found that the fluctuation of foreign exchange holdings among macro factors exerts influence on real estate land prices, and it was found that future real estate prices also have an influence and explain the correlation well. In addition, it was confirmed that changes in foreign exchange holdings exert an influence on changes in land prices in China and Taiwan, as well as in Korea. Based on this, it can be seen that it is an effective method to predict future land prices through changes in foreign Recently, people's interest in the real estate market has increased [13,14]. Among them, what attracts attention is the exploration of the factors that cause changes in real estate land prices [19]. Various factors, such as transportation access, financial stability, and stocks exist as factors which can change the land prices [26,27,54,55], but in the current study, based on macro and micro factors—which are frequently used terms in the economy—we analysed which ones have a greater influence. In addition, the technologies used for real estate Big Data analysis include artificial neural network analysis, data mining, and machine learning, but in the current study, through regression analysis and correlation analysis, we investigated which factors are influential, and explained the correlation well. As a result, it was found that the fluctuation of foreign exchange holdings among macro factors exerts influence on real estate land prices, and it was found that future real estate prices also have an influence and explain the correlation well. In addition, it was confirmed that changes in foreign exchange holdings exert an influence on changes in land prices in China and Taiwan, as well as in Korea. Based on this, it can be seen that it is an effective method to predict future land prices through changes in foreign exchange reserves among macro and micro factors in real estate Big Data analysis.

exchange reserves among macro and micro factors in real estate Big Data analysis. In addition to the above-described factors, it can be confirmed that the factor of the landscape is also closely related to the real estate market. Since the landscape district is a district formed for the conservation and management of the landscape, there are positive In addition to the above-described factors, it can be confirmed that the factor of the landscape is also closely related to the real estate market. Since the landscape district is a district formed for the conservation and management of the landscape, there are positive aspects for industrial factors, but negative influences for environmental factors also exist.

aspects for industrial factors, but negative influences for environmental factors also exist. In fact, in the Republic of Korea, Busan and Sejong are introducing smart cities to build a city, and technologies such as Blockchain, Big Data, and IoT are being used [36,37]. Therefore, in this paper, the factor that can interact in the landscape district and the real estate market is the establishment of a smart city. When the construction is completed, it is believed that this could also be a big factor in fluctuations in land prices. In fact, in the Republic of Korea, Busan and Sejong are introducing smart cities to build a city, and technologies such as Blockchain, Big Data, and IoT are being used [36,37]. Therefore, in this paper, the factor that can interact in the landscape district and the real estate market is the establishment of a smart city. When the construction is completed, it is believed that this could also be a big factor in fluctuations in land prices.
