Tag: credit scores

The New Mortgage Fee Structure

Mary Pat Campbell

Link: https://rajivsethi.substack.com/p/the-new-mortgage-fee-structure

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Excerpt:

Notice that credit scores below 639 have been consolidated into a single row, and those above 740 have been split into three. In addition, loan-to-value ratios at 60% and below are now in two separate columns rather than one. This makes direct visual comparisons a little difficult, but one thing is clear—at no level of the loan-to-value ratio does someone with a lower credit score pay less than someone with a higher score. That is, one cannot gain by sabotaging one’s own credit rating.

In general, there are two types of borrowers who stand to gain under the new fee structure: those with low credit scores, and those with low down payments. In fact, those at the top of the credit score distribution gain under the new structure if they have down payments less than 5% of the value of the home. The following table shows gains and losses relative to the old structure, with reduced fees in green and elevated ones in red (a comparable color-coded chart with somewhat different cells may be found here):

What might the rationale be for rewarding those making especially low down payments? Perhaps the goal is to make housing more affordable for people without substantial accumulated savings or inherited wealth. But there will be an unintended consequence as those with reasonably high credit scores and substantial wealth choose to lower their down payments strategically in order to benefit from lower fees. They may do so by simply borrowing more for any given property, or buying more expensive properties relative to their accumulated savings. The incentive to do so will be strongest for those hit hardest by the changes, with credit scores in the 720-760 range and down payments between 15% and 20%.

Author(s): Rajiv Sethi

Publication Date: 23 Apr 2023

Publication Site: Imperfect Information at Substack

Using First Name Information to Improve Race and Ethnicity Classification

Mary Pat Campbell

Link: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2763826

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Abstract:

This paper uses a recent first name list to improve on a previous Bayesian classifier, the Bayesian Improved Surname Geocoding (BISG) method, which combines surname and geography information to impute missing race and ethnicity. The proposed approach is validated using a large mortgage lending dataset for whom race and ethnicity are reported. The new approach results in improvements in accuracy and in coverage over BISG for all major ethno-racial categories. The largest improvements occur for non-Hispanic Blacks, a group for which the BISG performance is weakest. Additionally, when estimating disparities in mortgage pricing and underwriting among ethno-racial groups with regression models, the disparity estimates based on either BIFSG or BISG proxies are remarkably close to those based on actual race and ethnicity. Following evaluation, I demonstrate the application of BIFSG to the imputation of missing race and ethnicity in the Home Mortgage Disclosure Act (HMDA) data, and in the process, offer novel evidence that race and ethnicity are somewhat correlated with the incidence of missing race/ethnicity information.

Author(s):

Ioan Voicu
Office of the Comptroller of the Currency (OCC)

Publication Date: February 22, 2016

Publication Site: SSRN

Suggested Citation:

Voicu, Ioan, Using First Name Information to Improve Race and Ethnicity Classification (February 22, 2016). Available at SSRN: https://ssrn.com/abstract=2763826 or http://dx.doi.org/10.2139/ssrn.2763826

Biological and Psychobehavioral Correlates of Credit Scores and Automobile Insurance Losses: Toward an Explication of Why Credit Scoring Works

Mary Pat Campbell

Link: https://www.jstor.org/stable/4138424

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Abstract:

The most important new development in the past two decades in the personal lines of insurance may well be the use of an individual’s credit history as a classification and rating variable to predict losses. However, in spite of its obvious success as an underwriting tool, and the clear actuarial substantiation of a strong association between credit score and insured losses over multiple methods and multiple studies, the use of credit scoring is under attack because there is not an understanding of why there is an association. Through a detailed literature review concerning the biological, psychological, and behavioral attributes of risky automobile drivers and insured losses, and a similar review of the biological, psychological, and behavioral attributes of financial risk takers, we delineate that basic chemical and psychobehavioral characteristics (e.g., a sensation-seeking personality type) are common to individuals exhibiting both higher insured automobile loss costs and poorer credit scores, and thus provide a connection which can be used to understand why credit scoring works. Credit scoring can give information distinct from standard actuarial variables concerning an individual’s biopsychological makeup, which then yields useful underwriting information about how they will react in creating risk of insured automobile losses.

Author(s): Patrick L. Brockett and Linda L. Golden

Publication Date: originally 2007

Publication Site: jstor, The Journal of Risk and Insurance

Cite: Brockett, Patrick L., and Linda L. Golden. “Biological and Psychobehavioral Correlates of Credit Scores and Automobile Insurance Losses: Toward an Explication of Why Credit Scoring Works.” The Journal of Risk and Insurance, vol. 74, no. 1, 2007, pp. 23–63. JSTOR, http://www.jstor.org/stable/4138424. Accessed 22 May 2022.