Reap the benefits of improving your property data now.
A Clear Separation Between Portfolios Being Submitted for Underwriting
The current state of property data in higher education is a cause for concern in the insurance and risk transfer industry. Lack of quality data is an emerging risk as limited capacity, climate change, and the hard market puts pressure on risk professionals and insurers to improve their practices. While poor property data quality is nothing new, the market pressures are beginning to show clear separations between high-data quality portfolios submitted for underwriting and those lacking organized, standardized, and complete property data.
What Causes Poor Property Data
Across the industry, lack of standardization has been known to be a primary cause of poor-quality data. As risk managers and their teams collect property data from different campuses, building managers, and real estate teams, they often find the data inconsistent in syntax, classification, and level of detail.
This results in incomplete, unclear, or non-evidenced data, which must somehow be transferred into a statement of values (SOV) for the insurance placement process. This non-standardized data is often shoehorned into the SOV and handed over to a broker, who then attempts to clean up the data the best they can before taking the portfolio to market. Brokers understand that they need to format the SOV data in a way that underwriters' catastrophe modeling software can understand. Still, there is only so much they can do before submitting the data to insurers.
Insurers often look at this SOV data with skepticism, especially when there is little or no supporting documentation for the assets presented in the SOV. This makes it difficult for underwriters to understand a portfolio’s risk fully. To protect their interests and manage against limited capacity, underwriters often disregard the non-standardized or incomplete information they receive and assume the worst, leading to increased premiums for the insured.
Unfortunately, insurers tend not to give feedback to the insurance buyer about data quality issues. In a 2022 Advisen survey sponsored by Archipelago, 73% of risk managers said they do not receive comprehensive and actionable feedback from their insurance partners. Furthermore, only 19% of those risk managers understand how insurers use their statement of values data.
The lack of feedback and understanding of the underwriting process creates a market where a lack of transparency leads to a lack in data quality, and the vicious cycle continues.
Why Focus on Improving Property Data Now
As the insurance market continues to become more challenging for all players, now is the time to start focusing on organizing, standardizing, and improving your property data.
While ultimately, making improvements to your property data will lead to better outcomes in your insurance placement, this is not the only reason to make this change.
Institutions with clean, complete, and organized data use that data to make better decisions across their property portfolios. When your data is accurate and trustworthy, you can leverage it to:
- Better understand your risk and losses across your portfolio, helping in decision-making regarding investment in new properties or divestment of properties with high-risk exposures
- Deploy capital expenditure funds to upgrade properties to guard against losses, improve safety, and protect your long-term investments
- Identify climate-related risks across your property portfolio and assist in environmental reporting for ESG initiatives
- Monitor and update property valuations to make sure your portfolio is adequately covered
- Assist in setting up alternative forms of risk transfer, such as a captive or catastrophe bonds
The longer you put off systematically improving your property data, the further behind you will fall. While “doing your best” to organize your SOV data was acceptable in the past, insurers are already putting high-quality submissions ahead of the pack, giving them preferential pricing, and deploying their capacity to portfolios that have better quantifiable risk.
Where Can You Start Improving Your Property Data
While not always easy, improving your property data on your own is possible. Starting with your SOV, look at your properties with the highest total insured value (TIV) and focus on them first. What data is available and what is missing? Work across your teams, with your property managers or real estate team, to fill in the primary and secondary modifiers in your SOV to provide the highest level of detail on your high TIV properties.
Next, pull all supporting documentation for those high TIV properties and make sure to submit these reports with your submission. The easier you make it for underwriters to see evidence of your building's composition and risk mitigations, the more likely you are to receive a positive outcome in your next renewal.
Another critical but more complicated step to improving your data quality is to focus on making your SOV “cat model ready.” This means your SOV uses the specific nomenclature used in insurers’ cat models, making it easy for them to ingest and model your SOV. While you can do this in-house, it’s often best to enlist the help of your broker or a third party to help normalize your primary and secondary property attributes.
The good news is that you no longer have to do it alone when trying to improve your property data. A new breed of technology companies are making organizing and maintaining your property data easier and, in turn, making the commercial insurance buying process better for insurance buyers and their insurers. These companies can digitize SOVs, use artificial intelligence (AI) to pull data from supporting documentation to fill in blanks on an SOV, and transform your data into the syntax of insurers. Major brokerages are seeing the value in automating the renewal process and are partnering with these software providers to offer data improvement to their clients.
No matter what you do, now is the time to make incremental, if not significant, improvements to your property data. Doing so will help your institution reduce risk, improve safety, and potentially find financial savings and opportunities within your current portfolio.
By Erin Ashley, Risk Engineering Director, Archipelago Analytics, Inc.