Roof Insights: Geospatial Analytics Reveal Risks, Opportunities at Scale
Roof insights from new geospatial analytics solutions may soon spell the difference between running a profitable enterprise and costly surprises in industries ranging from real estate and construction to insurance and solar energy.
Just look at Elon Musk. In recent weeks, the Tesla CEO admitted the company made “significant mistakes” in its solar roof tile project, leading to cost overruns, delays and, more recently, lawsuits. According to complaints, Tesla increased the cost of its photovoltaic tiles by two to three times initial estimates after buyers had already committed to expensive prep work.
According to Musk, Tesla ran into trouble “assessing the difficulty of certain roofs,” adding that the “complexity of roofs varies dramatically.”
Chalk it up to moving too fast to meet red-hot demand, inaccurate estimates from an online configurator, or a Cybertruck-level launch fiasco on steroids. Whatever the case, Tesla’s solar flare-up underscores how geospatial data can help organizations in a number of industries avoid expensive surprises with instant insights that allow them to price accurately, move quickly, and operate cost-efficiently.
Roof Insight: As Above, So Below
Take property and casualty insurance. Today, 45% of all homeowners claims are related to wind or hail damage to a home’s roof—and that percentage continues to increase year over year. That comes out to an average claim size of $10,182 per year, or $18 billion in average annual losses from wind and hail damage alone. So information about the characteristics and condition of the roof is kind of a big deal when it comes to making informed underwriting and policy decisions.
Damaged or deteriorating tiles or shingles are predictive of such losses. But along with deteriorating flashing, gutter issues, signs of protuberances or leakage, mold, and other factors aren’t just of concern by themselves. They can also impact the condition of the underlying structure. Likewise, tree overhangs and brush proximity can signal exposure to wildfires and other catastrophes.
As it happens, this data is also crucial in real estate valuations for origination and underwriting, investing, trading and portfolio management. And together with the type of materials, pitch, number of facets, square footage, and overall roof complexity, these kinds of insights are also essential to accurate estimates in roofing repairs and new construction, new solar installations, and more.
Moreover, a roof can act as a view lens into the rest of the home and other potential condition issues.
But getting this data can be difficult and costly using traditional methods like physical property inspections, leading to workarounds that come with their own limitations and risks.
The High Cost of Cutting Corners
In the insurance industry, for instance, information like roof age is often used as a proxy for overall condition, since deteriorated roofs exhibit lower performance when impacted by wind or hail. But different roof materials age differently, limiting the value of the metric. And besides, the age estimates provided by homeowners can be wildly off. According to BuildFax, more than two-thirds of homeowners underestimate roof age by more than five years. One in five are off by more than 15.
In turn, this kind of inaccurate or incomplete data leads to increased deductibles and actual cash value (ACV) policies in areas prone to hail and wind risks. It can also expose insurers to higher levels of claims fraud, resulting in scenarios like the one currently playing out in Florida.
The real estate business has information challenges of its own in this regard. Broker price opinions (BPOs) are pricey and time-consuming. And even today’s most sophisticated automated valuation models (AVMs) often miss critical and current property condition data that impact value. Decisions made without accurate insights on property condition and roof insights can be financially ruinous.
But today, new geospatial technologies that enable access to instant, accurate roof data, delivered via API and integrated with existing workflows, are starting to change all that.
Raising the Roof on Valuable Insights
CAPE’s own Roof Condition Rating (RCR), is a living property database encompassing over 70 million single-family homes across the continental United States. For each unique property, RCR is calculated over multiple points in time using aerial imagery with 3-inch per pixel resolution. The solution then uses this data to assign each property one of five condition labels—Severe, Poor, Fair, Good, and Excellent—to indicate the prevalence and severity of any visible roof defects.
“Severe” is defined as a roof with obvious, pronounced, and widespread signs of defects. “Excellent” is defined as a roof in pristine condition with no visible signs of defects. Visible roof defects caused by natural hazards such as wind or hail, aging impact, or human-related damage are predictive of unfavorable future claims performance, and are labeled accordingly.
Across the US, 15% to 20% of all single-family homes have roofs currently labeled “Severe” or “Poor,” while 40% to 50% have roofs labeled “Good” or “Excellent.”
These kinds of roof insights can be used by organizations in a multitude of different ways, depending on the industry. Insurers, for instance, can use the data to target the best risks, and price new policies and renewals competitively.
Firms in real estate can make informed decisions for valuation, trading, and portfolio management. For their part, organizations in construction and solar gain the information they need to prospect homes in need of roof repairs or replacement. And whether it’s through a live salesperson or an online configurator, they can provide accurate estimates in the blink of an eye.
That’s something Elon Musk could surely appreciate right about now.
To learn more about the importance of understanding roof condition, read “The Definitive Guide to Roof Condition for Property Insurers”
Aggregate Statistics Created Using Data Produced from Nearmap Imagery