Lots of really clever ideas there - good job!
We discussed this offline, might as well add my comments here 
This is the part I’m really wondering about. My understanding is that you calculated a frequency for each city, then “pulled” toward the global average. Cities with less exposure would be pulled more than cities with big exposure. What’s the formula for that?
I was thinking about using a “poor man’s bayesian” approach. Basically I would calculate a frequency for all the cities, but for each city I would add (say) 50 years of exposure and the number of claims I would get at the global market frequency. If the global frequency is 0.10, then I would add 50 years of exposure and 509 * 0.1 = 5 claims to every city. A small city with 4 years of exposure and 1 claim would have a “bayesian” frequency of 1+5 claims over4+ 50 years, so 0.11 , while a larger city with 400 years of exposure and 100 claims would have a “bayesian” frequency of 100+5 claims over 400+50 years, so 0.233, aymptotically trending towards 0.25.
Oh! I just had another idea. I’ve been looking for a way to incorporate that my loss function is asymetric: I don’t mind overcharging ( I just lose a sale), but I really don’t want to undercharge and be the victim of adverse selection because the only person from that city in my portfolio never made a claim. In other words, I really want to charge you more if you are in a “risky neighbourhood with small exposure” but I dont necessarily want to give you a rebate if you are in a “safe neighbourhood with a small exposure”.
One way to do this might be to have a larger offset when I your frequency is lower than the market average. Like if you are over the average frequency my offset is 5 claims and 50 years (like above), but if you are under the average my offset will be 10 claims and 100 years, so it’s easier to bring my price down.