This project investigated mortgage lending decisions through the lens of protected class data. It described the recent history of mortgage lending for the last century and analyzed HMDA data from 2018-2022 using ANOVA and Chi-squared tests for analyzing variances between groups; and random forest and boosted tree classifiers and regressors for descriptive modeling. The author used model outputs to describe equity between protected class population subset decisions specific industries, geographies, and financial institutions. In larger datasets where bias was detected, the study delved into subcategories of data to determine the true scope of iniquity. The results found statistically significant differences between protected class subsets within specific financial institutions and underwriting systems.