Some geographies are consistent. Consistency over time is not sufficient in non-bankruptcy loss patterns to predict these losses geographically. One must have separate predictors of bankruptcy and nonbankruptcy experience and then combine them properly to forecast future good and bad credit performance. Bankruptcy patterns by geography are both predictable and show great variance from one place to another. That is, whatever the bankruptcy trend might be, you can tell which places will be better or worse than others.
Huge differences exist in per capita filing rates from one place to another.
This predicts the nonbankruptcy experience that our bankruptcy data, by itself, had failed to forecast. When we combine the two predictive data sets using an optimal formula, we can tell which local markets will have what credit quality in the future. In an agreement with a Top 10 card issuer, SMR received that issuer's customer credit risk data on more than 20 million accounts. Data received were from 1991-1996. Using solely our own data, we created risk scores for counties for each year.