Statistics can serve as a good sleep aid, particularly when they’re presented as a simple row or table of numbers.  That’s the way most of us have encountered statistics, with the figures often morphing into zzzzzzzz’s.  But it is possible to make statistics come alive.  The secret is in helping the audience visualize the data in context so that they can quickly derive meaning.  And today’s media tools make this easier than ever.  When we find interesting visualizations of data or statistics, we will share them with you.  Here’s one to start us off: 

If you can’t see the video above, you can find it here:

https://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen.html

One of our favorite books is “How to Lie with Statistics”, a tongue-in-cheek primer on using statistics to make just about any argument you like.  It illustrates how easy it is to mislead people who are unaware of basic statistical principles.

For instance, what do you make of the headline “Median home price in Jefferson County falls by 27%”?  Sounds pretty dire when you read it in bold headline on a newspaper as you’re walking by.  You might think that home prices in Jefferson County have all fallen by 27%.  But it’s worth digging deeper.  What period are we talking about?  What are we comparing to?  What does ‘median’ mean (compared to ‘mean’, for instance)? How many homes were sold and does that make a difference in the statistic?  Are we including detached homes and condos?  None of this is clear from the headline, and there are many other questions to ask.

Among other things, basic statistical understanding gives you a sense of context, scale and precision.  In the knowledge economy, where information is coming at you from a variety of sources, that’s pretty important.  Whenever you see a statement involving statistics – or indeed, any measurement – it’s worth asking whether there is any ambiguity in the statement.  And if there is, dig deeper.

To help you get comfortable with some basics, we’ve compiled a short list of statistical concepts for mortgage quality control.  If there’s anything else you’d curious about, let us know.

Key Statistical Concepts for Mortgage Quality Control

CONTENTS:

• Sample inferences & statistical precision
• Random Selection
• Sample size estimation
• Qualitative analysis & defect rates
• Random variation & statistical control
• Sampling error & non-sampling error
• Correlation vs. Causation

Sample inferences & statistical precision

The fundamental purpose of sampling for Quality Control is to render judgments regarding quality of the overall loan portfolio, i.e., to infer general conclusions from the sample’s findings.  The degree to which those conclusions can be reliably inferred is measured by statistical precision. Keep in mind that the goal of Quality Control is to focus on the forest, not the trees. Accordingly, your objective is not to identify and correct errors or defects in specific loan files, but to use the incidence of such errors to infer conclusions about your loan origination process.

Critical issues:

1. Statistical inference must be based on random selection; the most common error is to draw conclusions from a non-random sample. To avoid this error, you should eliminate all non-random selections from any group used to make statistical inferences to the population.
2. Statistical precision (e.g., of two percent) must be demonstrated on the actual sample defect rate (i.e., the number of loans with defects divided by the number of loans reviewed). If you were unable to review some of your randomly sampled loan files, then the precision achieved by your process will be degraded.

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It’s encouraging to see the adoption of statistical methods in the world of mortgage quality control.  Done right, it can lead to enormous returns on your investment in qualiy control – what we call ‘return on quality’.  But you have to do it right.  There is plenty of misinformation about statistics on the Internet and a non-expert may have difficulty sifting through what’s right or wrong, especially as applied to mortgage quality control.

So we’d like to present the principles and methods that Cogent’s statisticians and QC experts have honed over the past 15 years.  We invite your comments.  We begin with an overview of statistical sampling in mortgage quality control, which is available as a white paper (PDF) here.

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Statistical Sampling in Mortgage Quality Control
By Hakki Etem, CEO, Cogent Economics

Introduction

Statistical methods are well-established tools for efficiently measuring and improving product quality in a variety of industries.  Unfortunately, statistical analysis has been slow to gain acceptance in the mortgage industry, although the ability to originate the best quality product at the lowest possible cost is just as valuable to mortgage originators as it is to automobile manufacturers.  There are many reasons why the mortgage industry has avoided statistical methods, but surely one reason is the subject itself:  few disciplines can be as mind-numbing as statistical theory.

Nevertheless the most effective way for QC managers to measure and improve loan quality — at the lowest possible cost — is to employ statistical methods.  Although this means that QC managers must necessarily become familiar with basic statistical concepts, with the right tools and professional support the process can be greatly simplified.

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Cogent clients who attended our October 28th Web Seminar, “What’s New in CogentQC.NET”, will have heard the announcement that Cogent is releasing Version 2.0 in January 2010.  For the rest of the world, this is our official announcement.

When it comes to versioniong, Cogent is more like Google than Microsoft. Microsoft releases a defined set of new functions on a particular date – like Windows 7 was released on Thursday, October 22 – and charges for the upgrade.  Google adds new functionality all the time behind the scenes; not only were they in beta for many years, but they still don’t have any version numbers on their search engine. Likewise, Cogent has been releasing features continuously without categorizing them into Version numbers – and without charging for upgrades.

Like most things Cogent, this is a soft release.  We’ve been on Version 1.x for 3.5 years now. We now see that the system, and the .NET/SQL Server platform as a whole, are stable. We also have several client upgrades under our belts and we have the conversion process down pretty well, including working with DTS data loads.

Look for a list of “official” Version 2.0 features shortly – but know that some early adopter clients already have some “2.0” features.

Cogent Economics has been commended by Mortgage Technology Magazine for its lasting impact on the mortgage industry.   In announcing its annual Tech Awards at last week’s Mortgage Banker’s Association Annual Convention, the magazine’s editors cited Cogent for “pioneering the use of statistical sampling [in mortgage quality control] to replace the straight 10% random sample with a calculated statistical random sample, which could be 1% or less of a lender’s origination volume, resulting in a 90% reduction in costs.”  More information here.

Although Cogent’s QC Systems do a lot more than intelligently reduce sample sizes, it’s the statistical sampling methodology that industry veterans remember.   Over the years, Cogent has been a Mortgage Technology Top 25 Vendor and a winner of the magazine’s Fix-It Award, but it’s particularly gratifying to be recognized for long term contributions to the industry.

This blog is part of our ongoing effort to continuously improve mortgage quality control.  Here, we’ll be talking about a lot more than just Cogent QC Systems.  We hope you’ll be part of the conversation.