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
- 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.
- 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.
- 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.