  In light of intense hurricane activity along the U.S. Atlantic coast,
attention has turned to understanding both the economic impact and behaviour of
these storms. The compound Poisson-lognormal process has been proposed as a
model for aggregate storm damage, but does not shed light on regional analysis
since storm path data are not used. In this paper, we propose a fully Bayesian
regional prediction model which uses conditional autoregressive (CAR) models to
account for both storm paths and spatial patterns for storm damage. When fitted
to historical data, the analysis from our model both confirms previous findings
and reveals new insights on regional storm tendencies. Posterior predictive
samples can also be used for pricing regional insurance premiums, which we
illustrate using three different risk measures.
