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This study aimed to analyze the risk of deposit loss in the apartment market. Previous studies have neither adequately considered multi-homeowners nor analyzed the risk that home mortgages could suffer rent deposit loss. This study addressed the first issue by probabilistically linking landlord samples with real transaction samples with the probability distribution of the number of properties owned by landlords. The second issue caused by the infrequency of simultaneous sales and Jeonse transactions for individual apartments was handled by employing machine learning techniques which are capable of estimating prices in the absence of transactions. The analysis indicated that some landlords might not return rent deposit, particularly in the metropolitan area, though, the likelihood of underwater Jeonse could be low so that chance of deposit loss is minimal. Additionally, the method used in this study produced results that are closer to observations compared to existing methods. The results are expected to enhance the awareness about the risk of Jeonse.
Byung Chul Min (Mon,) studied this question.