지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
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1. CONTENTS (1) RESEARCH OBJECTIVES The purpose of this paper is to analyze housing price index predictabilities by using ARIMA and artificial neural network models in Korean metropolitan areas. The data of housing price index were collected for the seven metropolitan areas such as Seoul, Incheon, Daejeon, Daegu, Ulsan, Busan, and Gwangju, and categorized into groups with similar changing patterns by cluster analysis. The data of housing price index from January 1986 to May 2008 were used to analyze the changing patterns of housing price index for the metropolitan areas.
(2) RESEARCH METHOD In this study, housing price index predictabilities were analyzed by using the analytic techniques such as ARIMA, artificial neural network model, cluster analysis and ANOVA.
(3) RESEARCH RESULTS The result of this empirical study showed that there were the three changing pattern groups of housing price index - 1) Seoul and Incheon, 2) Daejeon, Daegu, Ulsan and Busan, and 3) Gwangju.
2. RESULTS The results of this paper can be summarized as follows. First, the result of the cluster analysis showed that there were the three changing pattern groups of housing price index - 1) Seoul and Incheon, 2) Daejeon, Daegu, Ulsan and Busan, and 3) Gwangju. The first group showed the highest rising pattern in housing price index, while the third one showed the lowest rising pattern. Second, artificial neural network model was more excellent than ARIMA model in a stable pattern of housing price index, while the former model was worse than the latter in a steep pattern of housing price index.
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