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In LendingPatterns™ there are two methodologies to determine statistical evidence of redlining.  Both approaches use the z-test, the standard test to determine whether differences in proportions are statistically significant.  The p-value output from the z-test gets at the probability that redlining occurred.  LendingPatterns™ makes it easy: we highlight in bright yellow statistical evidence of redlining where the lender’s market share in majority minority tracts (MMT) is less than in white majority tracts, and the difference is statistically significant.

Methodologies

  1. Compares the lender’s share of lending in white majority tracts to its share in single and combined race/ethnicity majority tracts to the same proportions as the market (all lenders).
  2. Compares the percentage of a lender’s activity in a target tract demographic (i.e. MMT) to the aggregate market’s percentage of activity in that tract demographic.

Mitigating Factors

Both approaches ignore the fact that the underlying tract census demographics are not equal, nor is the percentage of white majority tracts to MMT equal in a specific geography.  If you factor in the competitiveness of the industry, you will find in hyper-competitive areas a lot of lenders competing for business in these hot zones.  These are factors that lenders should investigate in areas where their penetration is low compared to the market:

  1. Population Percent by Tract
  2. Occupied Housing Units by Tract
  3. Owner Occupancy Housing Units by Tract
  4. Owner Occupancy Rate by Tract
  5. Poverty Rate
  6. Families below Poverty Rate

LendingPatterns™ has demographic reports that can be filtered by census tract, enabling our users to drill down and determine whether these factors can explain unequal share results.