인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
DBpia AI
AI 에이전트
AI 아이디어
AI 리더
AI 뷰어
Citeasy
크롬 서지관리 다운로드
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.
학술저널
Full-text
오류 신고하기해당 페이지 내 제목·저자·목차·페이지정보가 잘못된 경우 알려주세요!
초록·키워드
As the real estates occupy major portion of domestic households assets, relevant issue has been dealt seriously by the Korean government. However, apartment prices in downtown Seoul, the capital city, have soared despite various policies. Forecasting the real estate market trend has become an important research topic in order to provide information for establishing policies. In the prediction of the real estate market in the previous studies, two research directions were classified as follows: quantitative economic models and machine learning models. Regarding this trend, there was a need for comparative research on machine learning models, emerging methods, that are used to compare and predict various real estate indices. In this study, the machine learning model RF(Random Forest), XGBoost(eXtreme Gradient Boosting), and LSTM (Long Short Term Memory) are used to select suitable machine learning models for selected real estate index and conduct a comparative study to validate predictive power of machine learning models. Apartment sales index, land price index, charter price index, and real estate psychological index using univariate variables are predicted. In addition, RF, XGBoost and LSTM models all tended to be generally marginal with RMSE values of 0.0268, 0.0296, and 0.0259 in charter(Jeonse), Korean traditional pre-deposit rental system, price index data with linear but small variants. This shows that the prediction of the real estate index is deviated from the prediction accuracy of machine learning models depending on the periodic characteristics and data characteristics of the real estate index.
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
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