Banka örneğinde konut kredilerinde erken ödeme modellemesi

Home loans are one of the longest-term products in the Banking sector and are exposed to multiple macroeconomic cycles throughout their maturity. Each home loan contract includes the right of the loan to pay at any time during the term of the loan which causes the risk of changes in the contractual cash flows of the Banks. When the literature for the Turkish market is analyzed, studies are done on calculating home loan option prices and most of the calculations are based on classical option techniques. In previous studies, market spot interest rate and house price levels are used as variables. Aside from other studies in the literature, this thesis is based on prepayment probability of the home loans. Borrower-specific, loan-specific and macro economic specific factors are chosen as the variables affecting prepayment probability. To analyze the cyclical effects, the study is carried out in one of the top 10 banks with asset sizes by selecting a 12-year data observation interval between 01.01.2010 and 31.12.2021. The study aims to model customer prepayment behavior by estimating a logistic regression using 694.778 fixed-rate home loans and 247.572 prepayment events. The data set has about 30 million observations where each home loan has monthly observations until each home loan is closed. The logistic regression model can accurately predict the prepayment behavior of contracts with an Area-under-the-Curve (AUC) statistic of 0.921 and Gini coefficient of 0.843. In studies investigating home loans out of Turkey, interest rate, borrower income, loan to value ratio, borrower age, loan age and region are the variables that affect prepayment in the loan portfolios. This thesis shows that the most effective variables in the prepayment behaviors are the interest rate level changes, reference market interest rates, current risk of home loan and customer total debt which affect home loans payment schedule. Interestingly, original and current Loan to Value ratio, loan maturity, loan age, customer age, customer education status and customer income have limited impact on the prepayments as indicated by the model. The most plausible for this observation is the fact that people in Turkey buy their houses for residential purposes, not for trade and do not sell their houses in a short time unless it is compulsory can be interpreted.