# Balance-to-Limit Ratio – Definition

### Balance-To-Limit Ratio

A Balance-to-limit ratio is a ratio used in the calculation of the credit scores of borrowers. This ratio compares the amount of credit being used by a borrower to the total amount of credit available. The balance-to-limit ratio is calculated by dividing the total credit balance of a borrower to the total credit being used. This ratio is otherwise called the credit utilization ratio, it tells a lender the amount of the available credit a borrower is using. A low Balance-to-limit ratio results in positive credit rating, it improves the credit score of borrowers.

### A Little More on What is the Balance-To-Limit Ratio

The Balance-to-limit ratio is one of the ratios credit scoring companies use to evaluate the credit quality of a borrower. A low balance-to-limit ratio shows that a borrower has carefully managed his available credit while a high balance-to-limit ratio tells a potential lender that a borrower has a huge amount of debt than the credit available to him. The lower the balance-to-limit ratio, the better the overall credit score of a borrower and vice versa.

Potential lenders pay attention to the balance-to-limit ratio and credit score of borrowers. Borrowers are often advised to maintain a ration below 20 percent on each of their credit cards so as to have a good credit score.

### Reference for “Balance-to-Limit Ratio”

https://www.investopedia.com/terms/b/balancetolimit-ratio.asp

https://www.experian.com/…/balance-to-limit-ratio-applies-only-to-revolving-account…

https://www.creditcards.com/glossary/term-balancetolimit-ratio.php

creditcard.bizcalcs.com/Calculator.asp?Calc=Balance-To-Limit-Ratio

https://heartlandcreditrestoration.com › Credit Restoration Tips

### Academic research on “Balance-to-Limit Ratio”

Does credit score really explain insurance losses? Multivariate analysis from a data mining point of view, Wu, C. S. P., & Guszcza, J. C. (2003, March). Does credit score really explain insurance losses? Multivariate analysis from a data mining point of view. In Proceedings of the Casualty Actuarial Society (pp. 113-138).

Consumer Credit Scoring: What Consumers Don’t Know Could Cost Them Thousands, Crenshaw-Kleve, C., & Baker, G. Consumer Credit Scoring: What Consumers Don’t Know Could Cost Them Thousands. Regional Business, 1.

Internet Appendix for “Moving to a Job: The Role of Home Equity, Debt, and Access to Credit,” published in American Economic Journal: Macroeconomics., Demyanyk, Y., Hryshko, D., Luengo-Prado, M. J., & Sørensen, B. E. (2016). Internet Appendix for “Moving to a Job: The Role of Home Equity, Debt, and Access to Credit,” published in American Economic Journal: Macroeconomics. This internet appendix has six sections with supplementary material. Section A provides more details on the data. Section B discusses econometric identification. Section C supplies the results from a number of empirical regressions which demonstrate the robustness of our results. Section D supplies the results from a number of regressions on simulated data which analyze the robustness of our results to the regression specification and certain modeling choices. Section E presents the household problem in recursive form and describes our computational procedure. Section F gives more details about the welfare analysis.

Moving to a new job: the role of home equity, debt, and access to credit, Demyanyk, Y., Hryshko, D., Luengo-Prado, M. J., & Sørensen, B. E. (2016). Moving to a new job: the role of home equity, debt, and access to credit (No. 16-1). Working Papers. Using individual-level credit reports merged with loan-level mortgage data, we estimate how mobility relates to home equity when labor markets are weak or strong. We control for constant individual-specific traits with fixed effects and find that homeowners with negative home equity move to other metropolitan areas more than other homeowners. We use a dynamic quantitative model of consumption, housing, employment, and mobility to interpret our findings. The model illustrates that the gain from accepting a job in another area outweighs the cost of disposing of underwater property and replicates the data well.

The Interplay Between Different Types of Unsecured Credit and Amplification of Consumer Default, Ionescu, F. (2011, February). The Interplay Between Different Types of Unsecured Credit and Amplification of Consumer Default. In 2011 Meeting Papers (No. 912). Society for Economic Dynamics. We analyze, theoretically and quantitatively, the interactions between different forms of unsecured credit and their implications for default behavior of young U.S. households. One type of credit mimics credit cards in the U.S. and the default option resembles a bankruptcy filing under Chapter 7 and the other type mimics student loans in the U.S. and the default option resembles Chapter 13 of the U.S. Bankruptcy Code. In the credit card market financial intermediary offers a menu of credit limits and interest rates based on individual credit scores. Scores evolve based on past borrowing and repayment behavior. In the student loan market default has no effect on credit scores. The government sets the interest rate and chooses a wage garnishment to pay for the cost associated with default. We prove the existence of a steady-state equilibrium and characterize the circumstances under which a household defaults on each of these loans depending on household characteristics as well as on the financial arrangements in both markets. Our model is consistent with the main facts regarding borrowing and default on both forms of unsecured credit for young U.S. households. We show that there are important cross-market effects: financial arrangements in one market nontrivially affect default in the other market. We plan to use the model to quantify the effects of increased college debt burdens and more severe credit card terms on the increase in default rates in recent years and to conduct policy analysis regarding loan terms and bankruptcy arrangements in both markets.