시계열분석모형과 머신러닝을 이용한 주택가격 예측력 연구

• 영문명: Prediction of Housing Price Using Time Series Analysis and Machine Learning Methods
• 발행기관: 한국주거환경학회
• 저자명: 전해정(Chun, Hae Jung)
• 간행물 정보: 『주거환경(한국주거환경학회논문집)』住居環境 제18권 제1호 (통권 제47호), 49~65쪽, 전체 17쪽
• 주제분류: 사회과학 > 지역개발
• 파일형태: PDF
• 발행일자: 2020.03.30

영문 초록

This study compares the ARIMA, VAR and BVAR models of the time series analysis model and the RNN and LSTM models of machine learning methods for predicting housing price. The variables were apartment sales price index, apartment Chonsei price index, corporate bond yield, industrial production index and construction permit area. The temporal range was from January 2001 to December 2018, divided into the whole period and the period before and after the global financial crisis, and the spatial ranges were nationwide, Seoul, Gangnam, and Gangbuk. As a result, it was found that the predictive power of house price using machine learning was better than the time series analysis model. Overall, the estimation based on the machine learning methods showed that the predicted value and the actual value were very similar. Therefore, The government needs to develop a housing market prediction model using artificial intelligence such as machine learning for accurate housing price prediction.

목차

Ⅰ. 서 론

Ⅱ. 선행연구 고찰

Ⅲ. 머신러닝 기법

Ⅳ. 실증 분석

Ⅴ. 결 론

참고문헌

키워드

시계열분석모형 머신러닝 비선형 주택가격 예측력 Time Series Analysis Model Machine Learning Non-linear Housing Price Prediction