Car Insurance Rates and Restaurants

Location Intelligence has a blog post talking about how lame zip codes can be for measuring auto-insurance risk. For example, I live in 55406, which is a considerably different zip code if you happen to live along the East of West side of the zip.

“Factors that affect risk can include the street types, the presence of mass transit, the population density, commuter patterns, the adjacent businesses and even the weather. The table below shows the effect of different traffic generators on risk.”

They included this pretty table from ISO Innovative Analytics that shows correlations with other things:

Auto Insurance Risks

When I look at that table, I realize that anywhere that’s worth living (aka, places within walking distance of the things on the list outside of a racetrack or airport), is going to have higher insurance rates. I guess that’s the cost of convenience.

3 thoughts on “Car Insurance Rates and Restaurants”

  1. Where is ‘High School and College’ on the list?

    By segmenting out Elementary/Secondary Schools, with other schools no where to be found, is this telling us that youthful drivers are being ripped off on insurance rates?

    It would seem to me that mini-van moms should have the highest rates given this listing?

  2. It looks like people are more distracted when doing routine things. Possible jabbin on the phone while jockying for position at the grocery store.

  3. Right, or have young kids distracting them…so instead of nailing youthful drivers (so are in high school and college), they should be asking parents of young kids to step up and pay, right?

    Or, does this chart simply show the places that attract cars, especially the ‘within one mile’ rule. Within one mile of an airport is nothing but highways and open areas with little activity. Within a mile of the local grocery store is usually 50 other stores, banks, restaurants, etc…all churning traffic and all guilty by association in the accident stats.

    Maybe what this shows more than anything, is how statistics can be misleading.

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