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Do You Know Your Defect Rates?

November 1, 2013 By James Robinson

Fannie Mae announced new quality control guidelines on July 30, 2013 that include a requirement for lenders to track defect rates:

https://www.fanniemae.com/content/announcement/sel1305.pdf
https://www.fanniemae.com/content/tool/qc-defect-rate-tutorial.pdf

Do you know your defect rates?  If not, you will have to implement a process to track them in order to sell to Fannie Mae after January 1, 2014.

Surprisingly, Fannie’s new guidelines say that lenders should report both a “gross” defect rate and a “net” defect rate, (meaning “net” of defective loans that can be fixed.)  Really?  Loans that can be fixed after closing still cost the lender substantially more than loans done right the first time. And what about all the similarly defective loans in the population that weren’t sampled? Consider that an error that can be fixed 30-60 days after close may not be so fixable if the loan goes delinquent 10 months after close and is now a repurchase candidate.  This means you can’t reliably extrapolate from a “net” sample defect rate to “net” population defect rate (interval).

Fannie’s new guidelines also say that lenders should track defect rates by severity, such as “moderate defects” vs. “significant defects”.  This confuses ‘defects’, which are loan-level ratings, with ‘errors’, which are audit question-level ratings.  This is more than just semantics.  The final rating on a loan review should be a binary one:  acceptable or defective.  This is a requirement if statistical sampling is to be used.

Cogent has long asserted that the focus in QC reporting should be on the gross defect rate; this is the rate used to calculate sample sizes in our applications.  Ultimately, the objective of quality control is not to fix defective loans in your samples, but to understand where the defects are coming from and fix the process.

11-1-2013 12-31-41 PM

Cogent clients are able to track gross defect rates with the standard functionality built into both the ProductionQC and ServicingQC applications.  In Cogent’s applications, at the conclusion of each loan review, the QC auditor must assign an overall QC Decision.  The descriptions of the available QC Decisions are controlled by the System Administrator, but each will result in a Final Decision of either Acceptable or Defective, as shown in the screen shot.

Assigning this Final Decision enables users to generate the Cogent Management Reports, which show gross defect rate trends and comparisons, and also to calculate and select properly-sized statistical samples based on the recent 3-period average defect rate for each sample type.

Filed Under: Cogent, Cogent QC Systems, Cogent Software, FHLMC, FNMA, Loan Quality, Mortgage Compliance, Mortgage Industry, Mortgage Quality Control, Mortgage Servicing, Risk Management, Statistical Sampling, Statistics, Uncategorized

Cogent Quality Trend Reports Demystified – Infographic

October 23, 2013 By Kaan Etem

Cogent QC Systems ship with numerous standard reports, organized by category.  Loan Status reports help managers to track the progress of audit activities; Audit Findings reports show audit findings from various perspectives, from granular detail to summary overview, but always for a particular period; and Feedback and Letter reports are designed to track specific audit activities.

In contrast, most Management reports show quality trends across multiple periods for specific sample groups (such as the Statistical Sample or the Stratified Sample, each of which have specific definitions in Cogent.)  In addition, and perhaps most importantly, Cogent’s Quality Trend reports show quality levels for particular sample periods and specify the precision with which these may be inferred to the population.

 

CogentTrendReport-Infographic

It’s one thing to report the audit results of a selection of loans sampled from a population of loans (“we found 2 defective loans out of the 30 we audited, for a defect rate of 6.7%”); it’s another thing to make inferences, based on the results of the sample, to the population as a whole (“we are 95% confident that the population from which we sampled has a defect rate of 6.7%, plus or minus 2%.)  In order to make valid statements like this, sampling and auditing and reporting must be controlled to eliminate bias and maintain statistical integrity.  Cogent QC Systems do this for you automatically (while providing leeway to separately do non-random, non-statistical sampling.)  The results are presented in Cogent Quality Trend Reports.

Since most clients do not live the dream of statistical analysis on a daily basis, like we do at Cogent, it’s possible that statistical terminology is not top of mind.  So we have created this infographic to help with the interpretation of Cogent’s Quality Trend Reports (click on the image for a larger version).  Please pass the link around among users of Cogent in your organization.  We welcome insights and feedback at support@cogentqc.com.

 

 

Filed Under: Cogent, Cogent QC Systems, Data Visualization, Loan Audit Software, Loan Quality, Loan Review Software, Mortgage Auditing Software, Mortgage Quality Control, Mortgage Technology, Risk Management, Statistical Sampling, Statistics, Uncategorized

MBA and FHA and Statistical Sampling in Quality Control

September 25, 2013 By Kaan Etem

We are pleased to see the FHA proposing to introduce more statistical sophistication into its Quality Assurance Process (QAP) and to see the MBA responding with reasonable critiques.  There are a number of items under discussion which have been long-standing issues in the industry, including what defines a loan manufacturing defect, what are appropriate tolerance and severity levels for defects, and what are appropriate remedies.  “Loan quality” must have a standardized definition to be useful.  But the item that caught our attention was the discussion of statistical sampling.

FHA is proposing the following in its solicitation of information:

“Statistical sampling. FHA is also considering whether to establish a process to review a statistically significant random sample of loans for each mortgagee within a prescribed time frame after loan endorsement. Lenders would receive feedback on findings within an established timeframe.  FHA would use the statistical sample, to estimate the defect rate on each lender’s overall FHA portfolio and then extrapolate the origination defect rate to all lender originations during the sampled time period, and thus have the lender compensate FHA for the estimated total risk to FHA resulting from the lender’s origination processes.The purpose of this process would be to increase the efficiency of FHA’s post-endorsement review process. HUD invites comment on the use of and optimal methodology for a statistically significant random sample, including the nature of the loans that should be included or excluded from the sample.”

boyfriend-stat-signif

The MBA has responded with this:

“Most importantly, MBA has serious concerns about the impact of a sampling methodology on independent mortgage bankers and community banks and the number of lenders participating in the FHA program. While larger lenders may be able to originate enough loans to generate statistically significant sample sizes, many smaller lenders would be challenged in this regard. It is unclear how HUD would address this situation and what, if any, allowances would be made for small lenders. Moreover, depending on the structure of the penalty system, paying an upfront percentage could have a much greater impact on smaller lenders than larger lenders. The possibility of sampling bias that results in “overpaying” for smaller lenders has potentially devastating consequences reducing competition and increasing the price for consumers.  Companies could be forced out of business or cease originating FHA loans.”

Given the number of lenders we have seen who report only on the number of “findings” in their reviews, with no mention of defect rates or sampling method or population counts, it is encouraging to hear influential industry players talking about sample sizes and valid inferences to populations and statistical significance (albeit in a slightly different context.) If nothing else, it reminds us that the loan audits that take up so much of our time represent a small fraction of the loans we originate (or service).  And that what matters is the quality of the entire origination (or servicing) pool, not just the samples we draw (which are simply proxies for the population.)

We say let the discussion continue.  The more informed lenders are about what constitutes loan quality, the better they can do their jobs.

Filed Under: Business Process, Cogent, Loan Quality, Mortgage Compliance, Mortgage Industry, Mortgage Quality Control, Mortgage Servicing, Risk Management, Statistical Sampling, Statistics, Uncategorized

MBA’s Risk Management & Quality Assurance Forum 2013

September 10, 2013 By Kaan Etem

If you’re attending the MBA’s Risk Management & Quality Assurance Forum in Phoenix, AZ this week, stop by Cogent’s booth and say hello to our own Hakki Etem and James Robinson.  And if you’re a client, we look forward to seeing you at dinner on Wednesday evening.

MBA-RMQA2013

There are several interesting and topical sessions on the agenda this year.  One session we’ll be paying particular attention to is “Using Sampling Techniques to Manage Quality in Your Organization,” a topic that is near and dear to our hearts.

The rest of the schedule is here: https://events.mortgagebankers.org/RMQA2013/sessions/#INFO

More information about the event here: https://events.mortgagebankers.org/RMQA2013/default.html

See you there!

 

 

 

 

Filed Under: Cogent, Loan Quality, Mortgage Compliance, Mortgage Industry, Mortgage Quality Control, Risk Management, Servicing Management, Statistical Sampling, Statistics

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