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http://dx.doi.org/10.6109/jkiice.2021.25.12.1739

Analsis Of Outliers In Real Estate Prices Using Autoencoder  

Kim, Yoonseo (Department of Business Administration, Sungkyunkwan University)
Park, Jongchan (Department of Economics, Sungkyunkwan University)
Oh, Hayoung (College of Computing and Informatics, Sungkyunkwan University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.12, 2021 , pp. 1739-1748 More about this Journal
Abstract
Real estate prices affect countries, businesses, and households, and many studies have been conducted on the real estate bubble in recent soaring real estate prices. However, if the real estate bubble prediction simply compares the real estate price, or if it does not reflect key psychological variables in real estate sales, it can be judged that the accuracy of the bubble prediction model is poor. The purpose of this study is to design a predictive model that can explain the real estate bubble situation by region using the autoencoder technique. Existing real estate bubble analysis studies failed to set various types of variables that affect prices, and most of them were conducted based on linear models. Thus, this study suggests the possibility of introducing techniques and variables that have not been used in existing real estate bubble studies.
Keywords
Real estate; Machine learning; Autoencoder; Bubble; Anomaly detection;
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