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PROCEEDINGS May 15, 16, 17, 18, 2005 - Casualty Actuarial Society

PROCEEDINGS May 15, 16, 17, 18, 2005 - Casualty Actuarial Society

PROCEEDINGS May 15, 16, 17, 18, 2005 - Casualty Actuarial Society

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ARCHITECTURE FOR RESIDENTIAL PROPERTY INSURANCE RATEMAKING 513premium. Each partial base premium will be calculated with aperil-specific partial base rate, territory factor, class factor, key(amount of insurance) factor, and deductible factor. This modernizationof the rating plan streamlines many aspects of propertyinsurance ratemaking.6. DEFINITIONS FOR RATEMAKINGTerritory Boundary DefinitionsAppropriate territory definitions are a critical companion toperil-specific rating. Given a Cartesian surface or geographicmap where loss costs are expressed as a function of latitude andlongitude, risk classification principles [1] imply that territorydefinitions should correspond to loss cost gradients (contours onthe map). Previous authors have explored the use of loss cost gradientsand GIS software to define territories, but their approacheshave generally been based on data organized at the ZIP code level[5, 13]. <strong>15</strong> Unfortunately, the public purpose of ZIP codes is suchthat they do not represent a sufficiently granular starting pointfor the analysis of hurricane loss potential. <strong>16</strong> The basic problemin property insurance is that loss cost gradients may vary widelyby peril, and in fact the direction of the gradient for one perilmay frequently be opposite that for another. In plain English, thecontour maps by peril may not “match up” very well.In Florida, there is significant conflict among meteorologicalindications, as well as conflict between meteorological and politicalboundaries. Modeled hurricane loss cost gradients largelyreflect proximity to the coastline, meaning the optimal set of territorieswould make a contour map of the state look somewhatlike an onion, with concentric closed polygons. In addition, the<strong>15</strong> To be fair, Kozlowski and Mathewson advocated the use of square-mile loss densitiesgiven that the data is available.<strong>16</strong> ZIP codes are based on urban demographics and tend to be convex polygons ratherthan thin “strips” parallel to coastlines, which is the general pattern of hurricane loss costgradients.

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