• Journal of Cadastre & Land InformatiX
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• v.49 no.1
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• pp.65-84
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• 2019

This study attempted to predict citizens' emotions regarding mortgage rates using machine learning algorithms. To accomplish the research purpose, I reviewed the related literature and then set up two research questions. To find the answers to the research questions, I classified emotions according to Akman's classification and then predicted citizens' emotions on mortgage rates using six machine learning algorithms. The results showed that AdaBoost was the best classifier in all evaluation categories. However, the performance level of Naive Bayes was found to be lower than those of other classifiers. Also, this study conducted a ROC analysis to identify which classifier predicts each emotion category well. The results demonstrated that AdaBoost was the best predictor of the residents' emotions on home mortgage rates in all emotion categories. However, in the sadness class, the performance levels of the six algorithms used in this study were much lower than those in the other emotion categories.

• 주택과사람들
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• s.200
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• pp.36-39
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• 2007

미국 부동산 시장이 연착륙 중이다. 모기지 금리가 40년 이래 최저 수준을 기록하고 부동산 경기 침체로 인해 주택 물량이 대거 쏟아져 주택을 구입할 수 있는 최대 기회라는 것이다. 워싱턴 D.C., 뉴욕 맨해튼, 뉴저지, 애틀랜타, 시애틀, 캘리포니아, 라스베이거스 등 미국의 주요 지역을 중심을 부동산 시장 상황을 점검해 보았다.