Determinants of Bank and Non-bank Household Loans and Short- and Long- Horizon Forecast

은행권 및 비은행권 가계대출 결졍요인 분석과 장단기 예측

The instability of the financial system is likely to occur when par-ticular types of loans surge rather than all types of loans surge at the same time. A preemptive policy response requires a monitoring system based on forecasts by different loan types. The purpose of this study is to forecast household loans by categorizing into four types : bank mortgage loan, bank credit loan, non-bank mortgage loan, and non-bank credit loan. Given the fact that there are numerous determinants and forecasting models for household loans, and that the determi-nants differ depending on the type of household loans, this study sets out the density forecasting algorithm based on Bayesian Machine Learning. which con-sists of a variable learning process, a model learning process, and a forecasting combination process. We find bank mortgage loans are largely predicted by the loan rates, the volume of apartments to be moved in, and the number of apart-ment units to be sold. while the key determinants of bank credit loans are the employment rate and Jeon-se price index. On the other hand, the non-bank mort-gage loans are largely determined by the loan rates and the ratio of apartment sales prices relative to Jeon-se prices. The non-bank credit loans are also influ-enced by not only the employment rate and the Jeon-se price index but also stock returns.