Fargo Diversity Within and Outside Research Paper

Pages: 10 (3358 words)  ·  Bibliography Sources: 8  ·  File: .docx  ·  Level: College Junior  ·  Topic: Economics

SAMPLE EXCERPT . . .
Likewise credit is distributed intentionally across population and industry classes in the attempt to diversify risk. Since past credit delinquency is a major indicator underlying both FICO and Wells' eligibility criteria (Annual Report 136), if FICO ratings lower than 600 and the Wells appraisal indicate risk too high for retail lenders, or conversely if retail lenders would have to charge rates prohibited by law or too high for borrowers to pay in order to justify lending, the result is that the borrower is prevented from obtaining credit by their own historical payment performance and not by demographics of race, ethnicity, sex or age. While these social determinants may demonstrably affect past payment performance, delinquency could occur even at high wealth. Ultimately the bank does not control who chooses to apply for credit, and so measuring credit volume compared to share of national income levels distorts a picture that may be significantly impacted by these demographic characteristics, but which take place in the culture outside the sole responsibility of even the largest single bank. Race, ethnicity and sex undoubtedly affect U.S. credit ratings but these characteristics do not show up explicitly in FICO or apparently Wells Fargo's risk appraisals. The result is that while Wells Fargo cannot be held directly or solely responsible for macroeconomic demographic effects of discrimination, individual consumer demographic markers besides perhaps age do not show up explicitly in the bank's risk analysis and law protects consumers from discrimination where those markers can be determined by the interviewer, the bank allocates credit based on microeconomic individual financial characteristics having nothing to do with heritage or diversity, usually. Usually.

Research Paper on Fargo Diversity Within and Outside Assignment

Note 14, "Guarantees and Legal Actions," explains that the Illinois Attorney General filed a civil suit for damages on behalf of a class of minority customers who allege the bank steered them into higher-cost subprime mortgages at higher rates than other consumers "with similar incomes" and that a subsidiary misled customers regarding mortgage loan terms and engaged in "reverse redlining" (Annual Report 168), which the bank leaves undefined. Interesting in the terms the bank uses to frame the complaint is that the plaintiffs use income to benchmark credit appraisal while the bank uses delinquency, FICO scores and other financial indicators like loan-to-value (Annual Report 136) alongside income, which could indicate higher risk even in the face of higher income if the applicant had serious history of late payments. At the time of publication the firm had filed a motion to dismiss and were awaiting adjudication, which has itself since been rejected (Golobay 2011).

A jury in the Superior Court of California found Wells Fargo did actually violate the civil rights of 880 minority homebuyers March 24 of this year in Opal Jones, et al. Vs. Wells Fargo, et al. (Howard Law 2011). In an apparently as-yet unpublished opinion, the jury found that Wells Fargo did not act with malice but their use of the computer program "Loan Economics" was discriminatory because the program was not used to allocate home loans fairly across all areas, particularly in minority-dense Los Angeles neighborhoods, with the result that minorities were 'steered' into higher-risk, higher-cost mortgages at a higher rate than non-minority buyers in other markets (Prior 2011). This lender liability class outcome will undoubtedly affect the Illinois verdict and all such future class actions like similar cases Wells faces in Baltimore and Memphis (Andrew 2011).

Indeed under "Credit Risk" in Wells' 2010 annual report we find that the bank uses automated valuation models, "AVMs," "to estimate the market value of homes" (56). These programs, probably now including Loan Economics, are usually used according to the bank "only where the loan amount is under $250,000" (Annual Report 56), above which they "require property visitation appraisals by qualified independent appraisers" (Annual Report 56). The bank justifies this procedure as "a lower-cost alternative to appraisals" to "support valuations of large numbers of properties in a short period of time" (Annual Report 56). The bank admits there is a risk that the value of individual properties may deviate significantly from the average value the program uses for the specific market (Annual Report 56). Where would properties below this threshold value cluster? Lower-income neighborhoods. If lower-income neighborhoods are primarily or even heavily minority-dense, and this type of automated program was used primarily for lower-value homes, it seems plausible there could be higher deviation from average values in minority neighborhoods than in whiter, middle and upper class neighborhoods appraised by certified professionals.

The Federal Reserve Bank of New York staff found in 2009 that subprime home loans were cheaper in minority neighborhoods at the high-water mark of subprime lending, August 2005, perhaps because of prior credit shortage and high density (Haughwout, Mayer and Tracy 5). Although statistically significant, the difference was "economically insignificant" (Haughwout, Mayer & Tracy 4). The N.Y. Fed however qualifies this finding with the disclaimer that these results do not indicate whether the consumers who purchased these '2-28' adjustable rate mortgages (ARMs) would have qualified for a less-risky, standard loan (5) and that their model cannot incorporate fees and points assigned at the onset of these mortgages, with the result that "it is possible that we are missing data that might show disparate treatment in loan origination costs" (Haughwout, Mayer & Tracy 4). The overall outcome is that subprime loans were more highly clustered in lower-income, higher-unemployment neighborhoods with lower price than in areas with higher home values and loan products with lower rates of default (Haughwout, Mayer & Tracy 5). These subprime 2/28 ARMs at least were also assigned instead of prime loans partially based on FICO credit scores, for which Blacks consistently rank far lower than Asians or Hispanics (Haughwout, Mayer & Tracy 11), clustered particularly around home values just below Wells' $250,000 appraisal threshold (Wells Fargo 56).

The Federal Reserve Board of Governors reported to Congress in 2007 that credit scoring has increased credit availability uniformly enough across demographic groups so as not to constitute violation of housing discrimination laws (U.S. Federal Reserve System S-2) even though Blacks and Hispanics have lower credit scores and credit outcomes than non-Hispanic whites and Asians (U.S. Federal Reserve System S-2). The Fed however was unable to fully explain these differences because their data lacked specific information describing individual wealth, education or employment (U.S. Federal Reserve System S-2). Wells Fargo indeed uses FICO credit scores, along with delinquency and loan-to-value as indicators of individual creditworthiness (Annual Report 138). If minorities, particularly Blacks, have lower FICO scores, and those scores along with delinquency and loan-to-value ratios are used to assign mortgage credit, but the homes in the target neighborhoods were below the threshold for AVM evaluation, the result is a conflict according to Wells Fargo's published creditworthiness ranking where the home values indicate automated valuation but the FICO scores indicate individual assessment per applicant. Opal Jones vs. Wells Fargo et al. On the other hand found Wells at fault for not using the software in these low-income, low-scoring neighborhoods like they did in neighborhoods with midrange home values and higher recent appreciation rates, but rather that individual associates 'steered' minorities into subprime mortgages when they were eligible for lower-cost and less risky prime mortgages (Prior 2011). This conflict between whether Wells should have used the software to rank mortgage applications, which their credit risk policy indicates should have been the case given home values below the $250,000 benchmark, when at the same time applicants' lower FICO scores indicated higher risk thus subprime mortgages and appraisal by individual Wells associates by the bank's published policy, goes unremarked in the press coverage. Perhaps the apparently as-yet unpublished (on LexisNexis or Google at least as of 10 Dec. 2011) judicial opinion speaks to this conflict but that does not seem available as of this writing.

Wider questions unanswered by the Federal Reserve and so far the courts include why minorities are concentrated in lower-income, lower home value and higher-unemployment credit rating clusters that apparently result in higher incidence of subprime mortgage allocation, or 'reverse redlining.' Distribution across these categories takes place across the markets within which banks like Wells Fargo operate, and while banks undoubtedly have contributed to these existing conditions by allocating credit risk, Congress and the Federal Reserve which regulates all U.S. banks, find these specific credit risk factors nondiscriminatory as far as is measurable given available information. Considering that the courts found absence of malice in Opal Jones vs. Wells Fargo (Prior 2011), and given Wells' demonstrated effort employing, purchasing from, and giving and lending to minorities and low-income demographic sectors, the bank in fact seems to be working to address those wider macroeconomic forces that drive minority borrowers into lower credit ratings and thus higher-risk, albeit historically cheaper and more available, subprime mortgage lending clusters. Considering that the borrowers' characteristics in this case may have indicated the bank by its own published policies both should and should not have employed AVM software ranking these borrowers' credit risk at the same time, the ruling… [END OF PREVIEW] . . . READ MORE

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