In the News
This section is reprinted from the 224 page Annie E. Casey 2003 Kids Count Data BookOriginally available at the following URL:
http://www.aecf.org/kidscount/databook/pdfs/e_entire_book.pdf
Credit Building
Financial literacy and a greater range of available mainstream financial services certainly can help low-income families spend and save more shrewdly. However, real asset development will depend on their ability to build a positive credit history and access fair and affordable borrowing opportunities. Otherwise, their chances to invest in homes, transportation, business, and education - investments with the asset-building potential that can advance family economic security and help halt the spiral of intergenerational poverty that permeates so many communities - will be severely compromised.
Currently, credit-reporting systems focus almost exclusively on the failures of low income families to pay their bills on time; such systems ignore other evidence of regular, responsible payment. Thus, a delinquent utility fee can permanently damage a family's credit rating, but no amount of consistent, timely payment can be recorded as positive credit behavior in the existing system.
One promising idea to address this issue is the Pay Rent, Build Credit Data Network, which will function as a consumer reporting agency under the Fair Credit Reporting Act and make rental payment data available to authorized subscribers. The potential value of this effort is significant, since rental histories are overlooked as predictors of future ability to pay a mortgage, despite the fact that rents often are as high or higher than monthly mortgage payments. It's been shown that good payment habits can reduce interest rates by 25 to 30 basis points and save a low-income family $30,000 over the life of a typical home loan.
Another approach, which also helps guard against potential discrimination toward low-income borrowers, is the use of advanced, computerized risk-assessment technology (automated underwriting). Although they don't totally eliminate income and racial bias, technological advances in mortgage lending have demonstrated that the risk of default among low-income borrowers is nowhere near as widespread as lenders traditionally have supposed. Automated underwriting uses a much broader range of variables to evaluate a loan applicant's credit worthiness; income is only one among many other factors. Since the mid- 1990s, this process has enhanced lenders' ability to identify good and bad credit risks in their applicant pool, and loan approval rates have risen for low-income and minority customers.
For example, tests of automated underwriting demonstrate that this system can increase loan approvals substantially for many Essay low-income and minority loan applicants compared to manual systems. However, this innovation can help only a limited sector of low-income borrowers because the data source that fuels the assessment program is bank records. This is one more reason why it is critical to help the 'unbanked' connect to mainstream financial institutions and products.
