Homeowners¶

Catastrophes¶

\[ (\text{Averaged})\dfrac{\text{Non-modelled Cat}}{AIY} \times \dfrac{\text{AIY}}{\text{Exposures}} (\text{Projected}) \]

Cat-to-AIY is the nature of the year… in this year, this portion of coverage went to catastrophic losses… there's not levelling or trending required. This is only with respect to the amount of coverage so \(\dfrac{\text{Loss}}{\text{Loss}}\), no premium pricing involved in this ratio.
AIY-to-earned exposures. This ratio tells us, how much amount of insurance (coverage) was being provided on an average per exposure. We are comparing this ratio across years to answer "Are exposures worth more coverage in the future or less?"
For a linear fit,
- \(m \times x + b\)
For exponential fit
- \(bm^x\)
Need the future average earned date for Projected Average AIY = average earned date of future policy.
- Future policy avg earned date = 1/1/2018
- We have AIY/earned data about CY2017 and CY2018
- Their average earned dates are 7/1/2017 and 7/1/2018
- So we give 50%-50% interpolation to find our required avg AIY, you can also think if it was not 50%-50% interpolation would be different.