​According to a news article’s assessment of recent government data, Black women are leaving the corporate labor force due to economic uncertainty and current federal policies and are increasingly choosing self-employment. I will provide a basic quantitative framework on how to determine if this is indeed the trend for black women over time, and if true, is the trend consistent spatially. I will also introduce a high-level assessment of its impact on local economies. These results can be used to develop strategies to enhance employment opportunities not only for black women, but for the entire working class regardless of race, ethnicity, and gender.

The Issue: Finance companies are taking advantage of a relaxed regulatory environment to pursue “zombie” mortgage loans, i.e., 2nd mortgages that were previously written off by the original lender. These companies pay pennies on the dollar for the rights to these loans and pursue the borrowers for the unpaid principle and accrued interest.

The Problem for zombie loans borrowers: An increased financial burden in an economy already impacted by slow economic growth, geopolitical uncertainty, and escalated inflation of  goods and services.

The potential problem for local governments: Due to the aggressive tactics of these companies in a period of relaxed regulation, borrowers are struggling to reconcile this unpaid debt, placing existing household obligations in jeopardy. News articles have provided examples of borrowers falling behind on property taxes and ancillary household service payments to address this debt. This, in turn, increases the risk of local government agencies facing shortfalls in their largest revenue stream at a time when federal funding has been cut drastically.

The question: How can a local agency determine if these loans are a risk to their future revenue stream? I decided to evaluate the risk level of Mecklenburg County, which includes the city of Charlotte, North Carolina. Utilizing census microdata, and applied programming, I developed a logistic regression model to determine the likelihood of homeowners in the county having an existing second mortgage in three ways: by household type; educational attainment level; and years residing in the home, all by select race categories. The model generated statistical results which validates the model structure and results, which I will not address here at this time. My programming also generated summary charts, which provide the following information on the likelihood of possessing a 2nd mortgage in Mecklenburg County:

The overall likelihood of possessing a 2nd mortgage in Mecklenburg County is about 1.3%. Compare this to 13 percent for a home equity loan, 86 percent for no 2nd mortgage or home equity loan, and 0.2 percent of households having a home equity loan and a 2nd mortgage.

​​The likelihood decreases with the level of education attained; and

Household structure may play a role; single women with children have a higher likelihood of possessing a 2nd mortgage than other household types.

​These charts, along with the model statistical results, can be utilized to develop more comprehensive models, to develop strategies not only to mitigate any future risks to property tax revenue streams as well as create revenue enhancing opportunities. Finally, these models can be replicated and customized for other localities throughout the U.S.

This article will be updated in the future with enhanced modeling, an explanation of the statistical results in conjunction to the summary charts, and a comparison example of a different locality. If you have any questions about this summary or if my services can help your organization solve a quantitative or economic issue, don’t hesitate to contact me through the website, voice, or social media.