Backend Ratio – Definition

Cite this article as:"Backend Ratio – Definition," in The Business Professor, updated April 28, 2019, last accessed May 26, 2020, https://thebusinessprofessor.com/lesson/backend-ratio-definition/.

Back to: BANKING, LENDING, & CREDIT INDUSTRY

Back-End Ratio Definition

The  debt-to-income ratio, is also known as the back-end ratio is indicative of how much of a person’s income monthly paid toward outstanding debts. Expenses such as child support, credit card payments, mortgage payments (insurance, taxes, interest, and principal) and other types of loan payments equal total monthly debt. Back-end ratio equals (Total monthly expense debts / Gross Income Monthly) x 100 is a formula used by lenders for the approval of mortgages in conjunction with  front-end ratio.

A Little More on What is Back-End Ratio

Mortgage underwriters use the back-end ratio as one of many tools used in the prospective borrower’s assessment of the level of risk related to lending money. This ratio is important because it is related to the amount of the borrower’s income that is owed to another company or someone else. Applicant’s are considered to be high-risk borrowers if a high percentage of their paycheck goes to pay off debt each month. Potential income reduction or job loss could cause bills that are unpaid to pile up quickly.

The addition of a borrower’s monthly debt payments and dividing them by the monthly income of the borrower reveals the back-end ratio. For instance, a borrower with a $5,000 monthly income (5,000×12=$60,000 annually) and a $2,000 monthly debt payment has a 40% back-end ratio ($2000 / $5000).

If a borrower has good credit but a ratio up to 50%, some lenders will make exceptions, however a back-end ratio that is not in excess of 36% is generally what lenders prefer to work with. When it comes to approving mortgages, some lenders only consider this ratio, however others may use it along with the front-end ratio.

Similar to the back-end ratio, another debt-to-income comparison mortgage underwriters use is the front-end ratio, but the biggest difference is that only the mortgage payment is considered as debt. By dividing the mortgage payment of the borrower by their monthly income the front-end ratio can be calculated. Based on the earlier example, let’s assume that $1200 of the borrowers $2000 monthly debt is comprised of the monthly mortgage debt. 24% is what the borrower’s front-end ratio would be ($1200 / $5000). Mortgage companies commonly impose an upper limit of 28% for a front-end ratio. If a borrower is known to possess other mitigating factors such as large cash reserves, reliable income, or good credit, some lenders will offer more flexibility on the front-end ratio in the same fashion as the back-end ratio.

A borrower’s back-end ratio can be lowered in two ways: (1) selling a car they financed, or (2) paying off credit card balances. If the borrower is applying for a refinance mortgage loan, and there is enough equity using a cash-out refinance to consolidate outstanding debt can reduce the back-end ratio. As a compensation for the higher risk that lender potentially face on a cash-out refinance, more often the interest rate is higher over the standard term rate. To avoid borrowers running up paid off debt balances in the future, most lenders will require that any revolving debt accounts be closed if a borrower was using a cash-out refinance for payoff.

Academic Research on Back-End Ratio

Do loan-to-value and debt-to-income limits work? Evidence from Korea, Igan, D., & Kang, H. (2011). Do loan-to-value and debt-to-income limits work? Evidence from Korea. With the world economy facing another real estate boom-bust, the search has begun for boom-busts toolkits to better deal with impending woes. Such a toolkit could be macroprudential measures, although we don’t know very much about the impact they can have to date. The analyzation of these measures with the Korean experience is one step toward filling in this gap. The decline in transaction activities and house price appreciation is associated with debt-to-income limits and loan-to-value. Additionally, expectations are altered by the limits where bubble dynamics play key roles.

The growth of US household debt: Demand-side influences, Pollin, R. (1988). The growth of US household debt: Demand-side influences. Journal of Macroeconomics, 10(2), 231-248.  Since 1974, household debt-to-income ratio has been on a steady rise. Enthoven created an accounting framework that is shown in this paper and the household debt-to-income on the rise corresponds to increased borrowing-to-income ratio net. To explain the rise of net borrowing-to-income, four factors of demand-side should be considered: demographic changes, increased implicit attitudes toward debt, variations in real asset yields versus real cost of credit, and the increase in necessity borrowing related to rising housing costs and declining median incomes. Results of regression reveal that the main causes for the net borrowing-to-income has increased due to credit cost variations and the demand for credit increases.

Household debt and income inequality, 1963–2003, Iacoviello, M. (2008). Household debt and income inequality, 1963–2003. Journal of Money, Credit and Banking, 40(5), 929-965. From 1963 to 2003 the earnings distribution in the U.S. is mimicked through heterogeneous agents in the time-series behavior in a constructed economy. The accumulation of financial and real assets are related to idiosyncratic and aggregate shocks facing agents. It is estimated that measures of financial liberalization, aggregate income, and income inequality data is utilized to drive the model by these shocks. This paper will show two empirical facts on how a model economy can be replicated: the cyclical and trend behavior of debts of the household and the pattern divergence over time in wealth inequality and consumption. The concurrent increase in income inequality is the only quantitative explanation that accounts for household debts short-run changes during the prolonged rise of business cycle fluctuations throughout the 1980’s and 1990’s.

Recent trends in household wealth in the United States: Rising debt and the middle-class squeeze-an update to 2007, Wolff, E. N. (2010). Recent trends in household wealth in the United States: Rising debt and the middle-class squeeze-an update to 2007. Between 2001 to 2007, a consequent “middle-class squeeze” and exploding debt were witnessed early and in the mid-aughts. In the late 1990’s, median wealth grew quickly. In the aughts, the growth was even faster during which time the net worth inequality was slightly up. Late in the 1990’s, indebtedness made a substantial fall, and then early in the mid-aughts it skyrocketed and among the middle class, the highest level of debt-to-income ratio was seen in 24 years. Just like the prior two decades, investment type assess concentration was high in the year 2007. Between 1998 to 2001, after stabilizing during most of the 1990’s, the ethnic and racial disparity in wealth holdings widened, but early and toward the mid-aughts it narrowed. In young households under age 45, wealth began to shift away and move toward the age group of 55 to 75. The Gini coefficient advanced from 0.8434 to 0.865 and the rise in wealth inequality was fairly steep and stock and housing prices projected in July 2009 plunged by 36%.

The sustainability of budget deficits in a stochastic economy, Bohn, H. (1995). The sustainability of budget deficits in a stochastic economy. Journal of Money, Credit and Banking, 27(1), 257-271. In recent years, the long-run sustainability of the  U.S. fiscal policy has raise considerable concerns due to the huge Federal deficits in recent years. The consistency of U.S. fiscal policies have been examined by a number of empirical studies to reveal whether or not the consistency throughout intertemporal budget constraints has been maintained.

Portfolio choice and the debt-to-income relationship, Friedman, B. M. (1985). Portfolio choice and the debt-to-income relationship. In the United States, for a period of measurement not using years, but instead uses decades, the outstanding debt to the gross national product ratio reveals no time trend essentially. This paper’s research reports the portfolio of a lender has behavior that exhibits and explanation that has plausible characteristics of this phenomenon. Given the wealth of the U.S. economy has a long-running stability related to income, the behavior of the lenders comes into question and the debt-to-income ratio is explained as a stable aggregate that is turned on depending on how the investors treat other assets and debt either as distant or close substitutes within their portfolio. Within relatively narrow limits, the debt-to-income ratio is confined to the lenders’ behavior and further analysis of the respective risk properties of financial assets is indicative that equity and debt are indeed sufficient distant substitutes. Particularly, securities of equity and debt substituted in sufficient limits of expected return differentials within large movements on the presumption that it can elicit responses to offset from borrowers, through the aggregate portfolio of the investors required to go through major changes, thus effecting the debt-to-income ratio in the aggregate economy.

Debt and the consumption response to household income shocks, Baker, S. R. (2014). Debt and the consumption response to household income shocks. SSRN Research Paper, 2541142, 1-46. A detailed new dataset is explained in this paper that includes a comprehensive look at millions of U.S. households to investigate the interaction during the Great Recession ranging from consumption, balance sheets, and income. Particularly, the consumption rate being more sensitive to households with larger debt levels versus those with a change in income. To derive unanticipated and persistent changes in income, employer shocks are used, and households are matched to their employers. The study reveals that households that are higher in debt are more sensitive to fluctuation in income than those that face a standard deviation increase. The elasticity of consumption is debt-to-asset ratios is increased up to 25%.

House prices, home equity-based borrowing, and the US household leverage crisis, Mian, A., & Sufi, A. (2011). House prices, home equity-based borrowing, and the US household leverage crisis. American Economic Review, 101(5), 2132-56. Between 2002 to 2006 in the US a significant fraction for the rise in household leverage was existing homeowners borrowing against their increased home equity, and between 2006 to 2008 an increase in defaults was noticed. In home equity, .25 cents for every dollar increased was extracted by homeowners as revealed in instrumental estimation variables. Households with lower credit scores and younger households were stronger in-home equity-based borrowing. Real outlays as suggested by the evidence, is what the borrowed funds were used for. Between 2002 to 2008, $1.25 trillion dollars in household debt was created by home equity based borrowing, and from 2006 to 2008 it accounted for upwards of 39% of the new defaults.

Do debt service costs affect macroeconomic and financial stability?, Drehmann, M., & Juselius, M. (2012). Do debt service costs affect macroeconomic and financial stability?. Economic stability can be undermined by excessive private sector debts. In this study, the debt service ratio (DSR) is proposed as a measurement of the imposed financial constraints created by the indebtedness of the private sector and investigate the association with financial crisis and recessions. The size of subsequent output losses are related to DSR prior to economic slumps. Up to two years in advance, DSR can provide an accurate warning signal early on that there will be an impending banking crisis ahead. As a useful supplementary indicator, DSR can warn the real economy about the build up of certain vulnerabilities.

Need or want: what explains the run-up in consumer debt?, Weller, C. E. (2007). Need or want: what explains the run-up in consumer debt?. Journal of Economic Issues, 41(2), 583-592. Consumer debt has been on the increase in the past few years. Between 2001 to 2006, consumer debt skyrocketed four times faster than seen in the 1990’s. This surge in debt is shared widely across demographic groups. According to the data provided, the run up in debt is accounted for by a consequence of economic necessity rather than dissolute spending.

Household insolvency: A review of household debt repayment, delinquency, and bankruptcy, DeVaney, S. A., & Lytton, R. H. (1995). Household insolvency: A review of household debt repayment, delinquency, and bankruptcy. Financial Services Review, 4(2), 137-156. Within the domain of household finance, this paper explores issues surrounding insolvency as a meaning of measurement. It reviews the onset of insolvency as explained through empirical and conceptual evidence. Insolvent households are identified through the techniques of financial ratios and predictive models. Responses to insolvency are presented as are the implications or monitoring the solvency of households.

Was this article helpful?