When evaluating a financial institution's lending practices, it's crucial to look beyond the raw numbers. A key aspect of this is to understand how a bank's lending distribution compares to the aggregate data of all lenders in the same area. This type of comparative analysis can reveal important insights into a bank's lending behavior and fair lending risk. Here are some examples and why these analyses are essential:
- Geographic Distribution: A primary focus is on where the bank's lending activity occurs within its operational area. By examining the geographic distribution of loans, it becomes possible to see if there are any significant differences in lending penetration across different neighborhoods.
- Minority Geographies: One critical aspect is looking at lending activity in minority neighborhoods. A careful comparison of a bank's lending in these areas to aggregate lending data can help determine if the bank is serving all segments of the community equally.
- Statistical Significance: It is not enough to just look at raw numbers. Statistical tests should be applied to determine if any differences between a bank's lending and aggregate data are significant. This helps determine if any observed disparities are truly meaningful and not just due to chance.
- Market Share: Analysis at the individual tract level should also be considered. By comparing a bank's lending to the distribution of aggregate lending within each neighborhood, one can see if the bank is under- or over-represented in its lending activity in that specific neighborhood.
- Expected Range: Calculations of expected ranges of lending within each tract, based on market share and aggregate lending, will reveal any potential under-representation of a bank’s lending in any of the tracts.
- Focus on Relevant Data: When comparing a bank's lending to aggregate data, it's important to ensure the comparison focuses on the same type of lending. For example, if a bank primarily originates non-owner-occupied loans, the aggregate data used for comparison should also be limited to non-owner-occupied loans.
- Fair Lending Implications: These types of analysis are crucial for assessing a financial institution's compliance with fair lending practices. Analyzing the data with respect to minority geographies can help determine if there are any potential redlining issues.
- Beyond Raw Numbers: It's important to move past just looking at the numbers and consider the statistical significance of the data. A difference in lending behavior might not be statistically relevant if it falls within an expected range.
- Comprehensive Assessment: By comparing a bank's lending distribution with aggregate data, it provides a foundation for more in-depth analysis. These analyses can be expanded to the other metrics including comparisons of denial rates, pricing, steering, and other application outcomes.
In conclusion, a comparative analysis of a bank's lending distributions to aggregate data provides valuable insights into a bank's lending practices. This type of analysis not only helps in understanding a bank’s behavior, but more importantly, potential fair lending risk.
