머신러닝과 패널고정효과를 활용한 아파트 실거래가 예측

• 영문명: Predicting the Apartment Transaction Price by Using Machine Learning Methods and the Fixed Effects Model
• 발행기관: 한국주택학회
• 저자명: 김승현(Seunghyun Kim) 김원혁(Wonhyeok Kim) 이윤수(Yoonsoo Lee)
• 간행물 정보: 『주택연구』住宅硏究 第30卷 第1號, 43~69쪽, 전체 27쪽
• 주제분류: 경제경영 > 경제학
• 파일형태: PDF
• 발행일자: 2022.02.28

영문 초록

Predicting the prices of housing is a critical issue for all real estate market participants, including financial institutions and governments, as well as homeowners and potential buyers. To evaluate the performance of a housing price prediction analysis model, this study compares machine learning methods and the fixed effects model based on the panel data of actual transactions between January 2006 and July 2020 in Seoul. For this purpose, we are also considering the characteristics of the apartment complex, based on the hedonic pricing approach, as well as macroeconomic variables and policy variables. We find that the Random Forest (RF) model shows higher predictive accuracy than the Multivariate Adaptive Regression Splines (MARS) model in machine learning. Our study shows the potential of the wide use of the machine learning method by utilizing micro-data in predicting and forecasting housing prices more accurately.

목차

I. 서론

II. 선행연구 검토 및 차별성

III. 분석자료 및 모형

Ⅳ. 실증분석결과

참고문헌

키워드

머신러닝 랜덤 포레스트 MARS 패널고정효과 예측 아파트 실거래가 Machine Learning Random Forest MARS Fixed Effect Predicting Actual Apartment Transaction Price