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A Mass Appraisal Model on Residential Property with Random Forest Algorithm
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랜덤 포레스트 알고리즘을 통한 주택 대량평가모형 연구

논문 기본 정보

Type
Academic journal
Author
Jengei Hong (한동대학교)
Journal
한국부동산원 Journal of Real Estate Analysis Vol.7 No.1 KCI Accredited Journals
Published
2021.4
Pages
1 - 28 (28page)

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A Mass Appraisal Model on Residential Property with Random Forest Algorithm
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This paper discusses how to apply the random forest algorithm in building a mass appraisal system for residential property and analyzes issues related to modelling process. The paper investigates the relationship between the random forest model and the complexities of housing market. Based on the findings, various qualitative analyses has been attempted for effective model design. The findings are summarized as followed;-. First, the random forest model is performative in capturing the non-linearity from sub-market and locational effects. The random forest model has significantly low average percentage error (approx. 4%) compared to with a linear Hedonic model (approx. 11%). Second, the random forest model can efficiently capture the locational effects only with locational information (coordinates), without proxy variables. Third, using dummy variables may reduce explanatory power of the model, compared to label indexes because the number of variables included in an operation increases. Fourth, the advantage from model complexity seems overwhelm the disadvantage from overfitting. Fifth, modellers are not required to ensure consistency in the time periods contained in a dataset.
#Machine learning #Random forest #Mass appraisal #Housing sub-markets #Locational effect #기계학습 #랜덤 포레스트 #주택 평가 모형 #하부시장 #입지효과

Contents

Abstract
Ⅰ. 서론
Ⅱ. 선행연구
Ⅲ. 랜덤 포레스트 알고리즘의 소개
Ⅳ. 랜덤 포레스트와 주택 시장
Ⅴ. 정량분석
Ⅵ. 결론
참고문헌
국문초록

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