Thursday, November 20, 2014

California Municipal Default Probabilities and a Reply to Lumesis

Earlier this month, we published default probability scores for 490 California cities and counties using a municipal scoring model I developed during previous research. The scores and a description of the model can be found on the California Policy Center’s website. The methodology – which relies solely on financial statement data – is further justified in this academic paper.

The accompanying CPC study identified thirteen cities that had heightened risk of default or bankruptcy. The median city in the universe had a one year default probability of 0.11%, while cities in this highly distressed category had default probabilities of 0.74% and up.

Our findings were reported in the Los Angeles Times and produced rebuttals from two of the cities on the distressed list – Compton and San Fernando.

We also received a rebuttal from an unexpected source: a municipal bond analytics provider named Lumesis. In a November 10th commentary, they compared our county default probabilities to their Geo Scores, which measure relative economic health. Finding little correlation between the two sets of results, Lumesis concluded that our model “needs improvement”.

But this conclusion begs a fundamental question: are municipal bond investors better served by socioeconomic metrics like those provided by Lumesis or by metrics that rely upon financial statement data? Even our colleagues at Lumesis appear to recognize that economic health is not conclusive, noting the “ability of bad management to create a mediocre credit from a strong economy.”

Fortunately, we have some empirical evidence at hand to assess the relative strength of fiscal and economic predictors. Back in December 1994, Orange County California filed for bankruptcy. Thanks to the magic of the MSRB’s EMMA system, I was able to locate the County’s 1993 and 1994 financial statements –as appendices to old offering documents.

For the reader’s convenience, I have posted the statements here and here. Next I input relevant numbers from the statements into my fiscal scoring tool which you can see here and in the screenshot below.

The result was a default probability of 0.85%, well above the current median and comparable to the worst performing entities in the current universe. As of June 30, 1994, the County had a negative general fund balance and had experienced declining year-on-year governmental fund revenues – two harbingers of trouble we have seen in other default and bankruptcy cases.

Would the Geo score have singled out Orange County in this way? Perhaps Lumesis can run the numbers for us and report back. Short of that, I note that according to 1990 Census figures, Orange County had the 5th highest per capita income among California’s 58 counties. So it would seem that a methodology based solely on economic health (like the Geo score) would have missed this particular calamity.

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