Figure 4
|Klaas Westera
|Figure 1
|Figure 2
|Figure 3
The words “treasure” and “hunting” usually bring visions of dreamy-eyed fortune seekers with old maps digging endless holes in the sand. Their challenges are many. How does our hunter choose the right island? Have the winds and surf churned the landscape until it no longer bears any resemblance to the old map? Even if our treasure hunter finds a treasure chest, will it end up being empty, or full of fool’s gold? This picture is not a hopeful one for our fortune seeker, but it is a realistic one.
What does all of this have to do with insurance, you ask? Insurance has hidden treasure, but in a different form. Such treasure lies hidden within the vast amounts of data that describes and represents the risks and policies an insurer writes.
In the world of insurance, data is sand to the treasure hunter. As is with sand, data can be searched through for weeks, months and even years with no treasure found. Once in a while, the hunter may find what seems to be valuable information, which later turns out to be nothing more than fool’s gold. “Valuable” information provides an insurer with knowledge that can be used to proactively reshape the insurer’s business with a measurable and positive impact on the bottom line and its competitive position.
In insurance, maps have historically been “the way we always do things.” Why not? It worked in the past so it should still work today, right? Remember those winds and waves that changed the island’s landscape? Everything in insurance can change as easily and quickly as sand patterns.
Today’s insurance “treasure hunter” is a team of experts armed with valuation and statistical tools and the know-how to drive deep into a book of business. They mine through the data and detect hidden trends that provide “valuable” information and a big payback for the insurer.
Before we start treasure hunting, we will need a good shovel. To see if RCT can be that shovel, MSB has performed plate line (i.e. total loss) studies for our insurer clients. These studies confirm RCT accuracy to within 2% of actual loss costs across all postal codes, and a wide array of property styles, values, sizes and construction periods. Having validated RCT’s accuracy, we can now move on and use it as the shovel to uncover hidden treasure.
Over the last couple of years, MSB Canada has accumulated many thousands of property data records – including in-place property coverage – from various Canadian insurers. For the purpose of our treasure hunt, we have stripped any confidential information, randomly selected records and analyzed the resultant set, looking for a few hidden gems. Bear in mind this combined result is for illustrative purposes and does not necessarily represent an industry average or the results for a given insurer.
Let’s begin by searching for Regional anomalies and then move on to Total Living Area and Year of Construction.
REGIONAL TREASURES
In Figure 1, we can study how undervaluation varies from coast-to-coast by postal code. Distinct trends can readily be identified. We can see that certain regions, provinces and even large metropolitan areas are seriously undervalued while others are in much better shape.
This type of analytical information allows insurers to understand what to look for and take appropriate remedial action. Causes are probably a combination of historically poor valuations, weak local underwriting, high local competitive pressures and poor ongoing policy maintenance. Recent Canadian studies show that as many as 49% of Canadian residences are undergoing significant renovations every two years (CIBC 2005 Spring Homeowner’s Study).
TLA GEMS
When we compare undervalued percent against Total Living Area, some very interesting trends become visible (Figure 2). First, we find smaller homes of less than 500 square feet may be problematic, with an average underinsured amount of 51%. We also see that residences between 2,000 to 2,500 square feet, as well as those larger than 3,100 square feet, may also warrant further investigation.
YEAR OF CONSTRUCTION
In Figure 3, we see how an analysis of undervaluation by year built shows two clear trends. First, homes built prior to 1950 seem to be more undervalued than all others. This is not entirely unexpected: vintage homes built prior to 1940 have been consistently undervalued in the past. Construction techniques and materials of vintage homes are more costly to duplicate than those of more recent eras. The other trend shows that homes built between 1985 and 1996 are also undervalued by more than 30%.
HITTING THE JACKPOT
So far we have uncovered trends that will allow us to act on problematic and healthy business segments within the book. But why not see if we can take a shortcut and refine our analysis even further?
In Figure 4, we show the percentage undervalued by color against TLA and Year Built. This analysis provides some very interesting results. Specifically, the graph shows that:
* most pre-1950 homes are problematic regardless of TLA,
* most residences larger than 2,100 square feet are badly undervalued, and
* homes built between 1982 and 1996 with TLA between 1,475 and 2,100 square feet are also badly undervalued.
This deeper analysis allows the insurer to target those problem segments without wasting time on a lot of policies that are not likely to be problematic.
Treasure Hunting can be a lot of hard work with very little to show in the end. On the other hand, it can be a lot of fun and extremely rewarding – especially if you attack it with proven experience, a reliable map and quality tools. Today, we can treasure hunt in insurance and invite you along for what will prove to be a rewarding quest.