**7. Conclusions**

vided by real estate applications.

**7. Conclusions**  In real estate, there are various variables in addition to the macro and micro factors discussed in the previous study as factors which change the land price. Therefore, it was In real estate, there are various variables in addition to the macro and micro factors discussed in the previous study as factors which change the land price. Therefore, it was not difficult to obtain statistical data related to land prices, and analysis sites related to this

not difficult to obtain statistical data related to land prices, and analysis sites related to

method of grasping the current real estate market by grasping the trends of apartment sales prices and charter prices using past data, and the situation uses an analysis tool prowere also increased. As a representative method of real estate analysis, there is a method of grasping the current real estate market by grasping the trends of apartment sales prices and charter prices using past data, and the situation uses an analysis tool provided by real estate applications.

Therefore, in this paper, the focus was on finding out which factors of the past data have a significant influence on the change in land prices. In the case of factor data, the data from fiscal years 15 to 18 were used for the analysis, because the data was public up to a certain point, and as a result, it was confirmed that the change in foreign exchange reserves was the most influential factor. Subsequently, as a result of substituting the data for the year 19, it was confirmed that it had an influence on the land price. In addition, in order to check whether the landscape district correlates with the real estate market, we investigated the factors influencing price fluctuations; as a result, it was confirmed that the way to interact with real estate is the introduction of smart cities.

Since the current study focused on the living areas of Gangnam-gu, it was less accurate than expected. Therefore, in future studies, if the relationship between the ten factors and the land price is explained in more detail by an administrative unit, a more accurate result will be obtained. This is believed to be possible. In addition, 14 factors were explained, especially six micro factors, but since only two were used, we would like to find a way to convert the four data points that could not be used as data. Finally, after explaining the correlation between real estate and landscape districts, we came to the conclusion that the introduction of a smart city is the way to interact. Based on the case of smart city construction, we want to confirm the relationship with real estate land prices.

**Author Contributions:** Conceptualization, S.-H.L., J.-H.K. and J.-H.H.; Data curation, S.-H.L. and J.-H.K.; Formal analysis, J.-H.H.; Funding acquisition, J.-H.K.; Investigation, S.-H.L., J.-H.K. and J.-H.H.; Methodology, S.-H.L., J.-H.K. and J.-H.H.; Project administration, J.-H.K. and J.-H.H.; Resources, S.-H.L., J.-H.K. and J.-H.H.; Software, S.-H.L., J.-H.K. and J.-H.H.; Supervision, J.-H.K.; Validation, S.-H.L.; Visualization, S.-H.L.; Writing—original draft, S.-H.L., J.-H.K. and J.-H.H.; Writing review and editing, J.-H.K. and J.-H.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.
