Mortgage Availability and Personal Consumption: Round Three
During my vacation, Doug Kass posted again on the topic of housing and personal consumption. His analysis can be divided into two parts. Part one "replied" to our analysis and part two elaborated further his reasoning about the connection, the causal model, and what he expected to happen.
Here is what he wrote:
like "Mad Money’s" Jim "El Capitan" Cramer, "Kudlow & Company’s" Larry
Kudlow and others, readily dismiss the potential spending consequences of
substantially less capacity in the subprime mortgage lending market and the
emerging trend by mainstream originators and lenders to reduce lending in the
primary mortgage market and for refinancing cashouts. Indeed, Jim takes the
subprime issue one step further, noting that the mortgage house of pain will
have a salutary market and economic result, as it will hasten the Federal
Reserve’s path toward monetary ease. Shockingly (at least to me), many
others can’t comprehend the link between mortgage availability and consumer
spending, claiming that the correlation between the two variables (seen below)
As you can see, "A Dash" is the unnamed "many" in Doug’s statement. Well, we have been in worse company than Kudlow and Cramer!
There is a serious flaw in Doug’s analysis, and it plays upon the perceptions of both traders and individual investors. The concept of correlation has a measurable statistical basis. It is not a matter of opinion. It is a fact. We believe that the chart is an optical illusion. Here is a familiar example, one that most readers have probably already seen:
Readers looking at the Kass chart see with their eyes a strong correlation. This is because the key elements of the chart have been pushed together to emphasize two key points.
To our surprise, readers at Calculated Risk commented both on our site and theirs that the areas we described as "no fit" in our chart actually showed a strong correlation. Take a look again at our "no fit" chart.
Readers saw what they called the "Batman" segment as quite similar. They also were undisturbed by the periods where the two lines ratcheted along, out of phase. This is a trick played by the human mind, the most powerful computer. Our minds take two patterns and look for similarity. Sometimes that leads us astray. Correlation means that the movement in one variable corresponds in both direction and magnitude to the other variable–and at the same time. The fact that the two lines both have "Batman" characteristics is unhelpful to the trader unless the movements directly correspond.
Since this is difficult to see visually, it is important to use statistical techniques to measure correlation.
To put this in the proper perspective, we should remind readers of our mission at "A Dash." We review and analyze Wall Street Research where we find interesting errors. We have no shortage of candidates. We do not have a mission linked to a particular market viewpoint. We choose our market stance based upon the evidence we see.
In the Doug Kass case there is a proposition that historical data show a strong correlation suggesting that personal consumption expenditure declines are imminent. Using traditional statistical methods, we find that the data cited do not support this conclusion. It does not mean that Doug is wrong in his ultimate conclusion — just that his frequently-cited chart does not support his viewpoint.
In the areas of the chart that we cited as "no fit" the correlation is much lower or non-existent. The entire strength of the chart depends upon a few data points. If readers do not see this, then we have failed in our mission to educate about the danger of visual chart interpretation.
Going Back to the Classroom
There will always be some who make up their minds first and then see everything as evidence. We cannot reach them. It was surprising to us how many readers insisted on the correlation, based upon this chart. What were we seeing that they were not? To improve on our analysis in the first two posts on this topic, let us turn back to the yellowed notes of classroom instruction from decades ago. To see why this is not a strong or useful correlation, one must start by understanding what that means.
The concept of correlation is based upon a linear relationship between two variables. With modern software, it is easy for anyone with access to the underlying data to calculate the correlation and statistical significance. But that is not enough!
The concepts are from an undergraduate introductory statistics course where correlation and regression are two of many topics.
We present here four different data sets, all showing the same statistical characteristics — averages, standard deviations, slope coefficients, etc. The dramatic demonstration for students was that it was not enough to look at the numbers, one also had to consider a scatterplot of the data. If the data did not fit a linear model, the entire concept of correlation was called into question.
In only the first case is the regression equation a good specification of the model — a way of saying that the description and correlation really reflect the data. In the case where the relationship is curvilinear, this tells the researcher that there is a missing independent variable. The bottom two cases show a very strong relationship that is distorted by the presence of a single outlier, an unusual case deserving further study. It is quite obvious that the regression equation is not a good description of the data in the bottom cases.
In real life, examples are never as clear as the pure cases from the classroom, but the Doug Kass chart comes pretty close. We showed, in the chart repeated below, the scatterplot for the hypothesized relationship.
When the optical illusion of the time series is stripped away, the reality is clear. This is mostly a cloud of data with a few outliers that have a strong effect on the equation. It is not really a good candidate for a linear model, and that means that the term "correlation" is meaningless. (Those who really want to understand this should also read the prior two articles on the topic.)
The Real Conclusion
The data show that for a few quarters in the early 90’s a decline of over 15% in mortgage availability occurred at the same time as a decline of about one percent in personal consumption expenditures. There was a recession that probably caused both effects. The data include no other examples of big declines, and the smaller moves are all part of a data cloud.
If someone told you this in words, you would not find it very persuasive. Somehow the chart has an overpowering visual effect. Do not bet real money on what you think you see.