One of our favorite websites is www.informationisbeautiful.net, where David McCandless, an independent London-based “visual & data journalist” (his words) practices the art of data visualization and information design. 

We like the way he conveys information, often multi-layered, with the minimum of words.  As he puts it, “I’m interested in how designed information can help us understand the world, cut through BS and reveal hidden connections, patterns and stories underneath. Or, failing that, it can just look cool!”

It’s that last bit that we want to talk about briefly here.  One of David’s recent creations is a graphic that compares, side by side, point by point, the assertions of “The Global Warming Skeptics” against “The Scientific Consensus”. 

This particular visualization is more wordy that most of his work.  For that reason, one is inclined to think that a lot of research went into it.  And that is the case.  Every familiar argument made by the “skeptics” seems to have been researched and refuted.  If you’re a believer in global warming, you might think that this is close to the final word on the debate. 

Yet one look at the comments section at the bottom of this page suggests otherwise.  It’s worth spending a few minutes examining the visualization and then the comments.  That should be enough to convince you that a beautifully presented argument is not proof.  It has to be backed by solid evidence.  And if there’s any debate that lacks conclusive data, it has to be the global warming issue, where complex meteorological phenomena meet millennial time spans in a cauldron of scant measurement.

We came across a well-written article recently titled “The New Rules For Compliance In The Post-Crash Environment” by Louis Pizante, CEO of Mavent, Inc. and a veteran of the industry. 

In addition to a concise synopsis of the events leading up to the current regulatory overhaul of the mortgage industry, the article outlines some of the changes we can expect in the way compliance reviews are performed.  In short: earlier in the origination process, more electronically and with greater automation. 

Mortgage compliance and quality control reviews can and should be performed on many levels, and at different points in the loan life cycle.  Tools such as automated compliance engines, automated fraud engines and automated valuation models can flag loans that merit further review.  The next step is to apply a rigorous methodology to digging deeper in order to:

1) determine whether a complete file review confirms the automated findings;2) fix the individual loans if possible;3) identify the source(s) of the issue(s);4) select additional loans from these source(s) and conduct complete file reviews to see if there is a pattern of issues;5) generate feedback to the field and document corrective actions to fix the flaw(s) in the origination or servicing processes and/or stop doing business with the identified sources.

We are seeing more tools for automating more aspects of the mortgage life cycle.  Don’t forget, though, that it was partially the over-reliance on automated underwriting tools that got us into our current mess.  Maybe tomorrow will be different, but today we still need human beings checking to see if it all makes sense.

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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