1. Determination of Geographical Territories by Michael J. Miller EPIC Consulting, LLC 2004 CAS Ratemaking Seminar

2. 2 Risk Classification Definition Definition –A grouping of risks with similar risk characteristics so that differences in costs may be recognized. Purpose Purpose –Means by which data can be gathered so as to measure and quantify a specific risk characteristic’s relation to the propensity for loss. Example Example –Territory classes are a means to gather data so as to measure and quantify geographic risk factors relative to the propensity for loss.

3. 3 Homogeneity Definition Definition –A risk classification is homogeneous if all risks in the class have the same or similar degree of risk with respect to the specific risk factor being measured. Purpose Purpose –Homogeneity of the class increases the credibility of the loss data generated by the class. Example Example –A territory is considered homogeneous if all risks in the territory represent the same, or approximately the same, geographical risk.

4. 4 Statistical Test of Homogeneity Within Variance Within Variance: Based on the squared difference between each zip code pure premium in the cluster and the average pure premium for the specific cluster being tested Between Variance Between Variance: Based on the squared difference between each cluster’s pure premium and the statewide average pure premium Total Variance Total Variance = Within Variance + Between Variance Within Variance Percentage Within Variance Percentage = Within Variance divided by Total Variance Goals Goals: Low Percentage of Total Variance Within High Percentage of Total Variance Between

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7. 7 Basis to Group Areas County Largely stable over time Broad area ZIP Code Narrowly defined may be beneficial to define territories Useful for online rating Main disadvantage is need to deal with change over time Geo Coding Finest detail Static over time No predefined grouping

8. 8 Loss Indice Normalized Pure Premium Normalized Zip Code Pure Premium EQUALS State Ave. Prem.Zip Ave. Prem. State Ave. Base ÷ Zip Base

9. 9 Loss Indice Econometric Model Population Density Vehicle Density Accidents per Vehicle Injuries per Accident Thefts per Vehicle

10. 10 Credibility 3000 Claims Complement –Neighborhood Pure Premium –Within Two Miles –One Mile Extension

11. 11 Additional Credibility Considerations Distance formulas Discrete Continuous Choice of complements Use of distance based criteria Data grouped based on population density groups Combination of both distance based and population density Entire state

12. 12 Sigmoid Curve Characteristics S-shaped curve Flexible: can be fairly linear or approach step function Y = 1 / (1 + e -a(b-x-c) )

13. 13 Clustering Contiguous v. Non-Contiguous Absolute Dollar Difference Absolute Percentage Difference

14. 14 Other Clustering Ideas Group areas using contiguous constraints to broadly define a territory Group areas within a territory without contiguity constraints to refine territorial rating Consider treatment of catastrophe data Use of loss ratio data with premium at a common level to reflect only differences due to territory

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35. 35 Stability Predictive stability Choice of perils included in data Number of years of data Rating stability Limit movement between zones Use of capping Use of confidence intervals to help analyze changes

36. 36 Predictive Power & Stability Predictive Power – Test #1 1993/1994 v. 1995/1996 Correlation Coefficient Current = New Contiguous Non-Contiguous Better Predictive Power – Test #2 1993/1995 v. 1994/1996 Tested Boundaries Based on 1994/1996 Within Variance Only Marginally Better for 1994/1996 DataStability 1993/1995 Clusters v. 1994/1996 Clusters Compared Indicated Boundaries and Relativities Little Dislocation

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