December 23, 2020 8 min read

Commercial Property Condition: How to dramatically improve risk selection while reducing inspection expenses

Written By CAPE Analytics

This is the third post in our series, Bringing Innovation to Commercial Property Lines. Missed the previous posts? Read Part I here and Part II here.  

Earlier in this series, we wrote about the importance of innovation in commercial property underwriting, and how accurate building identification is the foundation for any subsequent advances.

This post dives into new, automated property condition features that Cape has built on top of its building identification technology. It also discusses how an understanding of property condition can help carriers and underwriters optimize, or even automate, risk selection decisions.  

How risk selection errors result in significant carrier losses 

Risk selection errors occur when an underwriter misses risk exposures and takes on—or keeps—risks they may have otherwise declined or non-renewed if more accurate information were available. 

But how often does this happen? Unfortunately, all too frequently. 

In speaking to our customers, at least 20% of commercial underwriting or inspection decisions would have been made differently if more accurate property data—and property condition data, more specifically—were available. 

Further, over a quarter of those policies, or 5-10% of a book, would have been declined or non-renewed. When these decisions are extrapolated across the industry and the higher likelihood of claims stemming from non-eligible risks, the subsequent losses are in the billions of dollars

So why do risk selection errors happen so frequently? In short: incomplete or wrong property risk information, as well as human error.  

Submissions can often be populated with incorrect, incomplete, or out-of-date property data, and carriers are careful to limit the questions they ask brokers for fear of losing out on business.  

Even if an underwriter goes the extra mile to order an in-person inspection for every submission, problems may arise because of human nature and constrained time or resources: 

  • Missing issues when inspecting building complexes: it’s easy to miss condition issues in multi-building commercial complexes where the inspector can’t get on every roof. Often an inspector or risk engineer inspects the largest buildings in a property complex. This makes intuitive sense (the largest buildings hold the most value), but, in reality, the building with the worst condition is statistically most likely to suffer a claim. 
  • Turnover in small commercial and BOP: Changes in tenancy often result in unpredictable changes, such as on-site construction, or occupancy type. But an underwriter does not have the resources or time to keep an eye on every property throughout its lifecycle. Unfortunately, if the property isn’t properly reassessed after a change in tenancy, a carrier may be losing out on premium or, inversely, risk a shopping event and lose a good, long-term customer. 
  • Identifying issues too late: Lastly, sometimes issues are not identified early enough in the underwriting cycle. Underwriters dislike canceling a policy post-bind in order to maintain good broker relationships and good customer experience; instead, underwriters tend to flag a problematic bound policy for nonrenewal, and just hope nothing happens in what remains of the policy’s life. 

Why commercial property condition is critical to underwriting 

After conducting studies with our commercial carrier partners, the answer is clear: external condition is linked to overall property risk and internal claims. Our customers have found that poor property condition, as defined by Cape Analytics, can be a signal of internal maintenance issues and potential safety concerns. 

Now, underwriters can access condition information instantly, at the point of submission: Cape Analytics is the first provider to offer roof condition and lot condition information, based on aerial imagery, for nearly every commercial property in the United States.

Studies across millions of residential records show a strong correlation between condition and losses, with poor and severe condition dwellings twice as likely to suffer a property claim. This is only compounded in commercial property insurance because of its multi-line nature. Early results show that worse external condition is linked to higher losses in workers’ comp and liability. 

In fact, different property condition components can have different risk signals:

  • Building roof condition is linked to higher potential wind/hail losses and business interruption claims
  • Parking lot condition is linked to liability, workers’ comp, and safety standards 
  • Lot debris is linked to property damage and liability losses 

How underwriters can leverage AI to instantly identify property condition

A topdown view can provide a sense of property condition; in fact, many underwriters do this today, using tools such as Google Maps to scan a location as part of submission review.

But to really compete over the near- to medium-term, carriers need to automate that analysis. The critical components of automation are high-resolution imagery and instant, high quality, and actionable condition data, which is only available through computer vision and machine learning algorithms.

AI-driven condition assessments and associated confidence scores give underwriters the objective and standardized information they need to automate where automation makes sense, and reserve their knowledge and judgment for those more complex risks that can make or break a loss ratio. 

Illuminating condition issues upfront can help avoid unnecessary inspections, post-bind pricing changes, and unexpected, downstream losses—all while highlighting potential property, liability, and workers’ comp risks. 

This predictive risk signal can also power granular workflows, allowing underwriters to move faster on risks that align with underwriting criteria while taking a closer look at risks that raise red flags. The Cape Analytics commercial property API  and underwriting app allow underwriters to do both, with condition information available in seconds, and the ability to manually review high-resolution imagery, if desired. A more speedy, competitive approach can even waterfall into better broker and customer experiences.  

Finally, AI and machine learning also allow underwriters to leverage exciting new capabilities such as automated property change detection. Now, underwriters can be alerted to changes across very large properties, in seconds, and reduce inspection expenses by focusing on the policies that have experienced some kind of significant change and then by deploying risk engineers to the buildings, rooftops, or portions of a property that need it most.

Key benefits of using property condition for underwriting

The Cape Analytics solution is the first to offer a hyper-accurate, instantaneous view of property condition and other features, in both an underwriting application as well as an API, for easy integration into underwriting workflows. Underwriters can make the right decision, faster, and supercharge their productivity from day 1. 

Imagine the impact if better underwriting decisions were made for 20% of a book: 

  1. More accurate assessment of true property risk and exposures
  2. Selection of the right risks, faster, with accurate property condition information
  3. Declination of risks that don’t align with eligibility guidelines, upfront 
  4. Reduction in inspection expenses by declining bad risks while performing more targeted inspections at new business and renewal 
  5. Increase in productivity by accelerating manual submission review  
  6. Offering more competitive pricing to those risks that align with underwriting criteria 

Are you a commercial underwriter who wants to learn more? Get in touch with us today.

Aggregate Statistics Created Using Data Produced from Nearmap Imagery