It’s a moving target subject to economic factors, new purchases, development, replacement and sale, shifting requirements and, the most frustrating and unpredictable element of all: human error.
What is it? Your property data, the information you gather yearly for your insurance Statement of Values (SOV). And by auditing your property data before you submit it, you help mitigate your risk in many important ways.
Why Audit Your Property Data
There are plenty of benefits to doing a thorough, regular audit of your property data. The most important to your organization is that a careful audit helps make sure your property is properly insured, right-sizing your insurance coverage and your rates. A regular audit helps you avoid incorrect or “worst case scenario” assumptions when the data is run through modeling programs. It removes the guesswork found in blank insurance data fields. And by caring for the quality of your insurance data, you demonstrate a commitment to the accuracy and completeness that data — which gets the positive attention of insurers, increasing your credibility. Best of all, you empower yourself with a holistic view of your organization’s property, enabling more strategic, data-backed decisions. It supports decisions about risk mitigation as well as more creative valuation strategies, allowing you to target the best strategies for the property you own.
Step One: Collect All of Your Property Data
You begin by verifying that all assets you wish to insure are listed on your SOV. This can include:
Buildings
- Property-in-the-open/land improvements
- Movable equipment
- Licensed vehicles
You’ll want to confirm that all buildings have, at a minimum, the following attributes identified:
- Name
- Square footage
- Address
- Occupancy
- Replacement cost new
Based on your own property program, determine what other details might be critical to gather for your organization. This could be any one or more of the following—and then some…
- Contents values
- Construction years
- ISO classifications
- COPE, security, and fire protection data
- Catastrophe modeling (CAT) data, such as seismic, windstorm, or convective storm modeling information
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Step Two: Look for Red Flags in Your Property Data
Now that you have all your property data pulled together, you’ll want to check that data for some common red flags. These are the clues that a sharp appraiser is likely to notice that lead to potential short-cuts and other problems lurking within your data. Check your…
Square Footages
Begin your focus on any square footages with rounded values. It’s very rare that an actual square footage would come out as a rounded number, such as 10,000 square feet. Generally, this means an estimate was provided instead of any actual building measurements, leaving significant room for error — and potentially leaving you over- or under-covered. You’ll also want to double-check any cost per square foot values that are abnormally high or low, as well as cost per square foot values that are inconsistent for like structures. For instance, if one school building is valued far higher than average, you’ll want to question why and verify those numbers.
Historical Structures
Look at the data for any historical buildings or other structures you might have. Are these valued by reproduction cost or replacement cost? Reproduction cost means all of the historic elements are recreated with like kind and quality, using the same historic materials and often involving artisans and specialized techniques to rebuild. Replacement costs refer to the amount that your insurance would have to pay to replace an asset at the present time with modern materials, according to its current worth. It doesn’t take into account recreating the workmanship of the past or using materials from that time period. (For more information on replacement costs and reproduction costs in historical structures, click here.)
Discrepancies between ISO Classification and Frame Type
You can use tools like Excel to compare ISO Construction Classifications to Frame Types you have listed. An example of a big red flag would be an ISO Class 6 Fire Resistive building with a wood frame. Look for the combinations that simply don’t match.
Multi-structure Properties Listed as a Single Line Item
Verify that multiple buildings within a property are listed individually, and not batched into one line item. Each structure on a property should be individually measured and valued to ensure your property has the full insurance coverage you need.
Non-Buildings Listed as Buildings
Check to see if you have any property-in-the-open assets listed as individual buildings. One example of this might be a play structure categorized as a building.
Building Additions or Capital Improvements Listed Separately
Items like a new roof or a building addition shouldn’t be listed separately from the buildings they’re a part of. It’s good to check your data for this.
Inconsistent Information
Audit time is the right time to clean up your numbering and naming conventions for your property data. Make sure all your buildings are labeled consistently and accurately across your property portfolio.
Leased Buildings
If you occupy a building but only own your own personal property or contents within it, it shouldn’t be included in your property data for valuation. So make sure to check that no leased buildings are in your SOV data.
City/County Buildings Listed Twice
Avoid listing buildings in duplicate for multiple agencies. A building serving both the city and county only needs to be listed on one property schedule.
Step Three: Organize Your Data for Further Investigation
Now that you’ve identified the trouble areas in your data and noted the red flags, examine those trouble areas for commonalities. Identify which subsets of data or particular data elements need improvement. Is it buildings above a specific threshold? Properties in a specific region? Buildings of a certain occupancy? Are you missing key information that your insurer/reinsurer thinks will really help your ratings?
By answering these questions, you help determine your strategy for further data collection through prioritization and batching.
Step Four: Look for Risk Mitigation Opportunities
Since you have all this great property data collected in one place, now is the time to take advantage of it. Check your data for areas where you can head off potential risk situations. For example, do you track the age of the roof of each structure? Do you have any roofs that are getting up there in age and may need to be replaced? Now is the time to consider scheduling a refresh. And what about sprinkler systems? Are there a bunch of buildings that have no sprinklers? How would adding sprinklers to these structures impact their exposure and insurance rates?
So now, after you’ve identified the opportunities for improvement and for risk mitigation, it’s time to…
Step Five: Put a Plan in Place and Roll it Out!
Decide how your property appraisals should be done. Will these property insurance valuations be on-site or will they be virtual valuations? Who is your third-party appraisal firm? Do you perform your own preliminary virtual valuations or get your appraisal firm to assist? Do you leverage tools to generate property values when you’re confident in the data? Do you put programs in place encouraging improvements that mitigate risk/exposure? Do you put a proposal together to take to your leadership to suggest opportunities to mitigate risk?
Step Six: Maintaining Data for the Future
By this time, with all the effort you’ve put into getting your data ship-shape, you’ll need to formulate a plan to maintain it. So ask yourself: are you comfortable with spreadsheets? Do you have access to a shared drive? Do you need to be able to track the history of changes made? Do you still need to facilitate the collection of additional data elements? Do you need property changes to be approved before going live on the schedule?
The Effort that Pays Off
By taking the time to go through these auditing steps, you can increase your property data confidence and ensure you’re getting the best rates and the right coverage for your organization.