Simple Models and Housing Prices
We are taking a new slant on modeling and forecasting. At the risk of over-simplifying, let us start with two schools of thought.
The professionals take classes in various modeling methods, learn techniques in real-life situations, and then go into the world to predict, to explain, and to advise.
The naysayers disparage the work of the professionals. They point out the errors in the forecasts, without seeming to make any of their own. They proclaim that all models include errors.
This represents a clear divergence of thought and a challenge for investors. If there is no expertise involved in modeling and forecasting, than anyone's opinion (or anecdote) is as good as anyone else's. It is the Wild West democracy of the blogosphere. Great fun, but is it profitable?
We recently read a comment from a journalist praising the economic blogs as "better than the economists." The reason? She saw a more accurate prediction of the housing situation on a blog than she could recall from a newspaper. The problem? You can find a blog predicting almost anything. Will her "winning blog" be right the next time?
There is an important concept which every investor should understand:
Every pundit does modeling. Every pundit makes forecasts.
The difference is that professional modelers have learned techniques that recognize and measure probable errors, determine an appropriate level of complexity for the model, and accept responsibility for the results.
An Illustrative Example
We have an excellent ARIMA model for volatility forecasting. It has a twist or two. It is not very popular with options traders, although we have used it successfully. Why not? The model results include an error band. The traders see the band and (correctly) say that the model is not very accurate. The inaccuracy is because volatility is difficult to forecast, not because the model is bad.
The options trader uses simple heuristics. He mentally parses some stock history, the recent market action, and a knowledge of upcoming events. The options market includes his opinions and the rest of the market makers in the pit. There is definitely a wisdom of the crowd.
But there is a problem: The crowd does not specify an error band or confidence interval. The actual agreement among peers, strongly influenced by the flow of paper, has no formal analytic basis and no "confidence interval." No one knows if it is correct, and no one keeps records on the performance of the pit forecasters.
A Current Example
There are many pundits — too many to cite — who believe that housing prices must decline to a certain level before any stability is possible. They typically use a simple heuristic like home prices as a ratio to income. They compare historic values with more recent ratios.
We applaud approaches like this. One can get a lot of mileage out of a single variable. Strong predictive models use as few variables as possible. The approach is supported by logic — the need the homeowner's paycheck to cover many things.
A great starting point.
So the question becomes, "Are any other variables relevant?"
Anyone who has bought a home knows that affordability is not merely the price of the home but the size of the payment. Much of the subprime lending problem came from loans that artificially reduced payments.
So it is obvious that a variable is missing. The rate of interest on a fixed, 30-year mortgage is certainly relevant. Comparing all of history with a time when rates are close to historic lows is quite misleading.
We are looking for an astute economist who examines historic affordability in terms of payments, not merely price-to-income. Meanwhile, we expect that housing sales will stabilize sooner than most believe, since no one seems to grasp this crucial point.