White Papers & Reports

To benchmark CRA and HMDA lending against peers, pick a group of lenders that compete in the same markets with a similar size and product mix, pull their public HMDA and CRA data for the same year and the same geography, and compare market share, lending by tract income level, denial rates and pricing. The comparison only holds up if the peer group does. Your own numbers mean little on their own. Say you hold a 6% share of home purchase originations in moderate-income tracts. That could be strong or weak, and nobody can tell which until it sits next to what lenders like you did in the same tracts in the same year. Examiners make that comparison as a matter of course, so it makes sense to make it first. This guide covers where the data comes from, how to build a peer group that will…

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Every fair lending and CRA software evaluation starts the same way: a shortlist of vendors, a run of demos that all look competent, and a decision that has to be defended internally on something firmer than which interface felt nicer. The difficulty is that the differences between these products are mostly invisible in a demo. They show up eighteen months later, in an examination, when someone asks how a number was produced and the honest answer turns out to be that nobody knows. This is a guide to testing for that in advance. It is written from our side of the table — we sell one of these products — so treat the last section as the interested party it is. The first six are the questions we would ask any vendor in this category, including ourselves. What does fair lending…

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Although geocoding is only one part of the examination accuracy testing, it is substantially different from the other submittable data that is tested. The geocode is probably the most important data element because of its demographic impact. There is no such thing as 100% accuracy for geocodes on a complete LAR due to the complexities and subtleties of the subject, but there is at least one proven solution that can achieve greater than 98% accuracy. This document contains statistical and anecdotal evidence describing how to achieve the highest precision level possible to help avoid a time-consuming resubmission process or a costly penalty. See the geocoding engine behind these accuracy figures. If you want to check a hard-to-place address yourself before it becomes a resubmission problem,…

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