Transparency is a key component in any relationship. In almost every instance, the more one knows and understand about the object of that relationship, the better off they are in the long run. When applying transparency to property insurance the same is true. The more transparency an insured can provide, the better the insurance marketplace can understand the exposure, extend appropriate terms and price the coverage accordingly.
Property underwriting data’s relevance has grown exponentially. Understanding the relationship between data, insurance terms and cost is vital when developing a best in class property submission, especially in a hard property market. Property underwriters use your property data, predictive analytics and technology platforms to determine how your property might perform when exposed to the perils you are looking to insure. Modeling enables an underwriter to weigh predictability against uncertainty to calculate the cost of insuring property for the covered peril. In today’s marketplace, greater uncertainty typically equates to lesser coverage terms and greater cost.
Historically, robust data was typically required for property exposed to higher risks like earthquakes and hurricanes. Now in many instances this is no longer the case. Climate change and more severe weather patterns bring into play additional perils like inland flood, winter / convective storms and wildfires that require additional exposure data to analyze risk properly. As insurers look to catastrophe modeling firms for more information to help address climate change, the subset of secondary property and exposure data that these models require is quickly becoming more relevant.
While there are a number of things that comprise a best in class property submission like claims data and engineering information, robust, well organized property data can make a significant difference in how insurance carriers analyze, extend terms and price your risk. When setting out to collect and organize your property data here are some things to keep in mind:
Ask your insurance professional what data you need.
Remember it is the quality of your data not the quantity that matters. Data collection efforts should focus on data that will provide an insurer with a transparent understanding of your property. Often, risk managers are under the impression that the more data they collect, the better off they will be. This can often lead to collecting an overabundance of exposure data that has little or no impact on the cost and terms and increases to the cost of the data collection effort.
Develop a plan.
Property data collection shouldn’t be a one-time event. Think of it as a program not project. Develop a systematic program for collecting and keeping your data current. Create a narrative around your program to include how data is collected, managed and updated. Your plan should result in a program that provides a comprehensive overview and supporting data for the property you want to insure. Remember, transparency about your program bolsters credibility and fosters great long term relationships with insurance carriers because they can understand the risk.
Set realistic goals.
Once you know what you need for your program, be realistic about how to meet your objectives. If you have internal resources to manage your program, make sure they understand the plan and can execute it in a timely manner. If you do not have the resources or expertise, consider engaging a professional firm to manage the program for your organization. Outsourcing to a professional valuation or consulting firm can typically deliver program cost management and the resources required to successfully implement and manage your program.
Weigh the costs.
An ounce of prevention is worth a pound of cure. The cost of a good property data program is miniscule when compared to property premiums. A well-administered program and a best in class property submission, place your organization and your broker in the position to negotiate advantageous cost and terms for property coverage year after year.
For More InformationA version of this article originally appeared on the Public Risk Management Association blog