Download Building Assets, Building Credit: Creating Wealth in by Nicolas P. Retsinas, Eric S. Belsky, Edward Gramlich PDF

By Nicolas P. Retsinas, Eric S. Belsky, Edward Gramlich

Poor humans spend their funds dwelling day after day. How can they acquire wealth? within the usa, homeownership is usually the reply. houses not just offer look after but in addition are resources, and therefore a way to create fairness. loan credits turns into a very important issue. extra americans than ever now have a few entry to credits. despite the fact that. thank you largely to the expansion of world capital markets and larger use of "credit scores," no longer all owners have benefited both from the opened spigots. various phrases and prerequisites suggest that a few candidates are overpaying for loan credits, whereas a few are becoming in over their heads. And the door is left broad open for predatory creditors. during this very important new quantity, complete analysts research the placement, illustrate its ramifications, and suggest steps to enhance it. this day, low-income american citizens have extra entry to credits than ever sooner than. The problem is to extend the possibilities that homeownership turns into the recent pathway to asset-building that everybody hopes it's going to be.

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Read or Download Building Assets, Building Credit: Creating Wealth in Low-income Communities (James a. Johnson Metro Series) PDF

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Extra resources for Building Assets, Building Credit: Creating Wealth in Low-income Communities (James a. Johnson Metro Series)

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For example, GMAC’s Settle America program allows up to 30 percent of an applicant’s qualifying income to come from other resident members of the household. Meanwhile, Fannie Mae and Freddie Mac have both introduced mortgage products that reward credit impaired borrowers by lowering their mortgage interest rate by as much as 2 percent after twenty-four months of timely payments. Another promising effort is Pay Rent, Build Credit, Inc. 17 Software allows renters to enter up to thirty-six months of prior rental, utility, insurance, student loan, and other payments to build a historical credit file, provided they have a paper trail such as a signed lease, bills, cancelled checks, money order receipts, or bank statements that can be verified by a third party such as a real estate agent, tax preparation service, or credit counselor.

A recent study of mortgage discrimination by the Urban Institute demonstrated different treatment of white and minority testers (Turner and others, 2002). Many researchers are increasingly concerned with the apparent targeting of low-income, and especially low-income minority, communities by subprime lenders using aggressive and sometimes predatory lending practices (Goldstein, 1999; Gaberlavage and Hermanson, 2001; Renuart, 2002; Fishbein and Goldberg, 2003). Accurate Explanations Matter Greater clarity as to which of the above explanations are valid is a precondition for the development of sound public and business policy.

Compensating factors, such as the size of the down payment or stability of employment, were considered at the discretion of individual underwriters. Today, expanded information on loan activity and repayment history is statistically modeled to predict probabilities and severities of loan losses. Variables on race, ethnicity, religion, gender, and national origin are expressly prohibited from such models. That said, many of the variables in scoring models are correlated with race and ethnicity. Therefore, the application of these models has disparate effects.

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