Credit Invisibles and the Unscored

Brevoort, Kenneth P.; Grimm, Philipp; Kambara, Michelle

doi:10.2139/ssrn.2743007

Cited by 45 publications

(58 citation statements)

References 4 publications

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“…Within the context of this study, Equifax calculated area-level credit score averages, but does not release information on how many households make up the average for a 9-digit zip code area or whether the average is based on a subset of households. Approximately 20% of the US population does not have a credit score: 11% have no credit accounts on record (“credit invisibles”) and 8% are not yet scoreable due to short or outdated histories (22, 23). This is more likely to affect young adults (age 25 or younger) and black and Hispanic (compared to white) persons (23), and thus may affect the accuracy of area-level averages in some areas more than others.…”

Section: Discussionmentioning

confidence: 99%

“…Approximately 20% of the US population does not have a credit score: 11% have no credit accounts on record (“credit invisibles”) and 8% are not yet scoreable due to short or outdated histories (22, 23). This is more likely to affect young adults (age 25 or younger) and black and Hispanic (compared to white) persons (23), and thus may affect the accuracy of area-level averages in some areas more than others. However, since credit scoring models vary based on which agency is computing the score (24), those who do not have enough information to be scored in one agency’s model may be able to be scored using another model.…”

Section: Discussionmentioning

confidence: 99%

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Consumer credit scores as a novel tool for identifying health in urban U.S. neighborhoods

Knapp

Dean

2018

Annals of Epidemiology

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Purpose: Credit scores may operate as a socioeconomic indicator of health: they represent cumulative financial history that directly influences ability to access financial and non-financial resources related to health. Yet little is known about the relationship of credit score and health, or to traditional measures of socioeconomic position. Our objectives were to: (1) evaluate the association between area-level credit score and individual self-rated health; and (2) compare credit score to traditional markers of area-level socioeconomic position (SEP) in predicting self-rated health. Methods: Equifax estimates of average household credit score in 2015 among 9-digit zip code regions were combined with a representative survey of 2,083 residents of Philadelphia to estimate the correlation with income, housing value, education, and occupational status then predict the odds of self-rated health for credit score and each SEP measure. Results: Credit score was moderately correlated with SEP markers (r=−0.78 to 0.49). After adjusting for area and individual level SEP and demographic factors, each standard deviation increase in credit score is associated with 26% greater odds of better self-rated health (OR=1.26, 95% CI: 1.09, 1.46). Credit score had a larger effect size than other SEP markers. Conclusions: Credit score may be a useful complement to traditional measures of SEP in assessing health outcomes.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Consumer credit scores as a novel tool for identifying health in urban U.S. neighborhoods

Knapp

Dean

2018

Annals of Epidemiology

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“…First, the CCP data only sample individuals with a credit history and a SSN, so individuals who have never applied for or qualified for a loan are not included. The Consumer Financial Protection Bureau reports that 26 million Americans (one in 10 adults) do not have any credit history with a nationwide consumer reporting agency as of 2015 (Brevoort, Grimm, and Kambara, 2016). However, the CCP data do include individuals with thin files, as well as individuals whose credit file only consists of a collection or public record item (such as bankruptcy) or only contains authorized user accounts or closed accounts.…”

Section: Datamentioning

confidence: 99%

Gentrification and residential mobility in Philadelphia

Ding

Hwang

Divringi

2016

Regional Science and Urban Economics

267

243

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Gentrification has provoked considerable controversy surrounding its effects on residential displacement. Using a unique individual-level, longitudinal data set, this study examines mobility rates and residential destinations of residents in gentrifying neighborhoods during the recent housing boom and bust in Philadelphia for various strata of residents and different types of gentrification. We find that vulnerable residents, those with low credit scores and without mortgages, are generally no more likely to move from gentrifying neighborhoods compared with their counterparts in nongentrifying neighborhoods. When they do move, however, they are more likely to move to lower-income neighborhoods. Residents in gentrifying neighborhoods at the aggregate level have slightly higher mobility rates, but these rates are largely driven by more advantaged residents. These findings shed new light on the heterogeneity in mobility patterns across residents in gentrifying neighborhoods and suggest that researchers should focus more attention on the quality of residential moves and nonmoves for less advantaged residents, rather than mobility rates alone.

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“…Over a loan with a three year amortization period, we find that the gains relative to no forecast 4 See Brevoort, Grimm, and Kambara (2016). For more information, see Section 6.1.1.…”

Section: Introductionmentioning

confidence: 91%

“…The second group are those consumers who do have credit records, but are considered "unscorable," that is they have insufficient credit histories to generate a credit score. Brevoort, Grimm, and Kambara (2016) find that 11% of the US population lacks credit records and an additional 8.3%…”

Section: Coveragementioning

confidence: 99%

Predicting Consumer Default: A Deep Learning Approach

Albanesi¹,

Vamossy²

2019

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We develop a model to predict consumer default based on deep learning. We show that the model consistently outperforms standard credit scoring models, even though it uses the same data. Our model is interpretable and is able to provide a score to a larger class of borrowers relative to standard credit scoring models while accurately tracking variations in systemic risk. We argue that these properties can provide valuable insights for the design of policies targeted at reducing consumer default and alleviating its burden on borrowers and lenders, as well as macroprudential regulation. JEL Codes: C45; D14; E27; E44; G21; G24.in the United States. They are proprietary measures designed to rank borrowers based on their probability of future default. Specifically, they target the probability of a 90 days past due delinquency in the next 24 months. 1 Despite their ubiquitous use in the financial industry, there is very little information on credit scores, and emerging evidence suggests that as currently formulated credit scores have severe limitations. For example, Albanesi, De Giorgi, and Nosal (2017) show that during the 2007-2009 housing crisis there was a marked rise in mortgage delinquencies and foreclosures among high credit score borrowers, suggesting that credit scoring models at the time did not accurately reflect the probability of default for these borrowers. Additionally, it is well known that credit scores and indiscriminately low for young borrowers, and a substantial fraction of borrowers are unscored, which prevents them from accessing conventional forms of consumer credit.The Fair Credit Reporting Act, a legislation passed in 1970, and the Equal Opportunity in Credit Access Act of 1984 regulate credit scores and in particular determine which information can be included and must be excluded in credit scoring models. Such models can incorporate information in a borrower's credit report, except age and location. These restrictions are intended to prevent discrimination by age and factors related to location, such as race. 2 The law also mandates that entities that provide credit scores make public the four most important factors affecting scores. In marketing information, these are reported to be payment history, which is stated to explain about 35% of variation in credit scores, followed by amounts owed, length of credit history, new credit and credit mix, explaining 30%, 15%, 10% and 10% of the variation in credit scores respectively. Other than this, there is very little public information on credit scoring models, though several services are now available that allow consumers to simulate how various scenarios, such as paying off balances or taking out new loans, will affect their scores.The purpose of our analysis is to propose a model to predict consumer default that uses the same data as conventional credit scoring models, improves on their performance, benefiting both lenders and borrowers, and provides more transparency and accountability. To do so, we resort to deep learning, a type of machine l...

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Credit Invisibles and the Unscored

Cited by 45 publications

References 4 publications

Consumer credit scores as a novel tool for identifying health in urban U.S. neighborhoods

Consumer credit scores as a novel tool for identifying health in urban U.S. neighborhoods

Gentrification and residential mobility in Philadelphia

Predicting Consumer Default: A Deep Learning Approach

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