Amortizing Loans vs. Non-Amortizing Loans

Amortizing Loan

In an amortizing loan, the borrower makes regularly scheduled payments, called equated monthly installments, over the life of the loan to pay off the debt.  Each payment is consists of part interest and part principal.

The payment every month is fixed and is calculated in such a way that the interest portion of the payment is equal to the interest due for the month on the balance principal outstanding. The remaining portion of the payment is paid towards the principal. As more principal is paid every month, the outstanding principal balance keeps reducing. For this reason, this type of loan is also called a reducing-balance loan.

During the early years of the loan, the payment is primarily interest, as the principal outstanding is large. However, in the later years the payment is mostly principal.

Amortizing loans can usually be prepaid without penalty, so prepayment rates must be calculated for an ABS bond supported by amortizing loans.

Non-amortizing Loan

In a non-amortizing loan, the borrower does not have a schedule of payments to repay the debt, rather the borrower is only required to make minimum monthly payments. This is the common structure of a credit card in the U.S. Other examples are balloon mortgages and deferred interest programs.

When the borrower makes a periodic payment that is less than the accrued interest, the balance of the unpaid interest is added to the debt principal.

When the borrower makes a periodic payment that is greater than the accrued interest, the excess balance is applied to reduce the debt principal.

Since a non-amortizing loan has no determined repayment schedule, there is no clear definition for “prepayment”.

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.