A Simple, Honest Proposition: Housing Data Interpretation First, Conclusions Later

by: Jeff Miller

Here is a simple and honest proposition: Data interpretation should lead to conclusions, not the reverse.

If an analyst believes that an indicator is important, and trumpets news about that indicator, it is intellectually dishonest to abandon the measure when it moves the other way.

If one endorses the Baltic Dry Freight index, Dr. Copper, or the inverted yield curve, then one should be willing to change forecasts when those indicators reverse.

Fair enough?

Some of these have recently rebounded, with little attention. We shall follow up in more detail.

The Application to Housing

Nearly everyone, even those who are not equity investors, is interested in the housing market. Calculated Risk covers this like a blanket. There are several key articles today. Our advice is to read them all.

It is a great job of telling us all what is at stake and where things stand.

Seasonal Adjustments

We are a bit confused by the analysis from The Big Picture. Barry Ritholtz correctly observes that there are important seasonal factors in home sales. This is, of course, the reason that everyone else uses the seasonally adjusted data to compare one month to another. Barry maintains [RealMoney article not yet available, but promised] that we should use unadjusted year-over-year data for comparisons. We do not understand the advantage of this approach.

Everyone agrees that things are much worse than a year ago. The seasonally adjusted pace of sales was up 2.9% last month and down 2% this month. The year-over-year was down 19.9% this month, actually a bit better than last month. Does that tell us something? Is it an improvement?

If that is the real test, the year-over-year is going to start looking better in September or October, just because last year was so bad. Will Barry call a turn in housing if year-over-year flattens out? That would seem to follow from his analysis, even if the month-to-month seasonal data shows a decline.

We still do not see the advantage of this approach over looking at the seasonally adjusted data. When one spots a big seasonal effect, as Barry demonstrates, doing the adjustment seems routine. (We note that he criticizes the WSJ article from last month as not recognizing seasonality, even though it employed the seasonally adjusted data, a 2.9% increase. Had they used the raw data, the increase would have been 12.3%. (Check out the data for yourself here).

Questions for Further Review

What does it mean for a home to be in "inventory?"

We have not yet seen a good answer to this question. Let us offer a simple comparison that everyone will understand. We hold various stock positions. Each day there is a point where we would buy more and a point where we would sell. We enter stock offers "away from the market." Are these offers part of inventory?

Furthermore, if the stock price were to move higher, more stock would be offered. If it were to move lower, more bids would appear. The market clearing mechanism involves price discovery, where the market-clearing price is found. This affects both price and volume.

Applying This to Housing

In our neighborhood, there are people, empty-nesters since this is a kid-friendly town, who are willing to sell - at a price that is not realistic. These homes are part of "inventory" but the offers are not really serious and the sellers are not motivated.

Meanwhile, CNBC reported today that there was a survey as part of the report [looking for the link]. 18% of homes offered were in foreclosure. We may assume that these are motivated sellers, as are those who have a job transfer or other personal needs.

Our point is that the concept of "inventory" in existing home sales is a bit elusive. If prices were to move higher, the "inventory" might increase dramatically. Things would be worse than we think. Meanwhile, the existing "inventory" may not really measure the motivated sellers.


We do not have a firm conclusion -- only questions.

We would like to see more analysis where economists looked at shifting demand and supply curves and talked about market-clearing prices. Instead, nearly all market commentators (mostly non-economists) view both supply and demand as black and white. This does not recognize that a buyer whose credit score does not qualify at one price may be good enough at another. This affects the demand curve.

Another question relates to the effect of government programs. We know that the liberalization of the conforming loan limit on jumbos has shifted the demand curve. How much?

We also know that efforts to help those threatened by foreclosure will shift the demand curve. How much?

No one is analyzing the problem in this way. At some point -- who knows when? - there will be some bottoming action. What indicator should we follow to observe this effect?

What is the difference between OFHEO prices and Shiller? Why?

It will be interesting to see if those who have been the most aggressive in pointing out problems use their methods and indicators that signal the onset of the solutions.