The purpose of this paper is to find out how each districts(Gu) of Seoul are related based on the apartment price trends. All the data used in this paper comes from a public data sources, Seoul apartments transaction data provided by ‘Ministry of Land Infrastructure and Transport Korea’ and the apartments properties from NAVER’s real estate service. To analyze the similarities between the price trends of each apartments, this study uses FastDTW algorithm which is quite popular in time series analysis domain. After figured out the distance matrix from FastDTW, this study uses Hierarchical Clustering algorithm and Chi-squared test to compare each districts’ relationship. The analysis result shows that which districts in Seoul are similar and which districts are not.

• 이홍석(서울과학기술대학교 IT정책대학원, Seoul National University of Science & Technology) | Hong-Seok Lee
• 이강원(서울과학기술대학교 IT정책대학원, Seoul National University of Science & Technology) | Kang-Won Lee