Ideally we all would like to know the amount of risk involved before making a decision.
That’s why we evaluate the side-effects before trying out a new medication, check online reviews before eating at a restaurant or check a vehicle’s motor history before purchasing a used car. It’s safe to say that knowing certain information pertaining to risk helps us to understand what we’re really getting ourselves into, especially as it pertains to investing in rental property.
Knowing the true risk of a rental property reduces losses even when unexpected events occur. Historically, commercial underwriters have approached the problem of assessing risk with a mixture of tactics such as: Examining prior losses, reviewing ownership information, and weighing historical property characteristics in addition to other external risk factors that may play a role, such as local crime rates.
An underwriter may review the property using an internet search for pictures of the property and by obtaining residents’ reviews or complaints about property management. Underwriters may also issue a loss control inspection to review the roof, property maintenance and other potential hazards to assist in the overall property risk assessment.
However, without incorporating resident data into the underwriting process, it’s hard to paint a comprehensive picture. That said, insurers can gain valuable insight to assess the risk for every property by using predictive analytics. Here are some tips to consider during the underwriting process.
The Current Process
Some insurers will manually review rental rates to determine the quality of the resident risk. However, rental rates do not give a clear picture because they can vary significantly by region and do not precisely assess the residents’ overall risk profile. Reliance on benchmark pricing to determine an overall rate also requires understanding an area’s price points.
Today’s habitational insurance market is similar to the 1980s homeowners’ market when the industry relied on property characteristics and inspections for pricing and underwriting information. The homeowners’ industry learned that one of the most important underwriting factors, the resident owner, was missing from their pricing and underwriting process. As a result, the industry made huge segmentation gains from the creation of insurance scores based on the resident (or owner’s) credit.
New technology and real-time resident data can help commercial residential insurers aggregate information such as the occupants’ ages, the age distribution for the entire property and the average occupant tenure. Then, a tenure distribution can be performed to identify residents at a given address, enabling commercial underwriters to obtain a single aggregated risk profile of the residents. Now the insurer has the entire picture, which includes a risk score, average age and tenure to weigh into the model.
Determining Insurance Risk
A property usually displays a complex combination of insurance risks. A multi-resident property can include good insurance risks, poor insurance risks, or any mix of the two. Unlike personal credit lines that rely on a single report, the system must account for the overall mix of residents in determining the insurance risk. For example, 90% of the residents in an apartment building could have excellent credit-based insurance scores and 10% could have poor credit-based insurance scores compared to a property where all residents produce average scores. When comparing the risk factors for both, the question becomes – which of these apartments is riskier? A habitational risk score can segment these two different properties and provide a clear assessment of the insurance risk.
Based upon an internal analysis, policies scoring in the riskiest 10% score group have loss ratios approximately 50% higher than an average loss ratio, and they are two to three times higher than policies scoring in the best 10% score segment. The loss ratio results will vary by region and carrier depending on their overall rate adequacy and loss peril mix.
A habitational risk score is most effective at pricing for what appears to be similar properties on the outside but have wholly different risk factors on the inside.
An Example Of Hidden Risk
While the apartments pictured on the previous page appear to be very similar in age, construction and rental value, they present very different risks based on the resident data. The apartment that scored 840 has the best risk and could qualify for the best rate. If all three apartments were rated about the same, the insurer using the habitational risk score would be able to better price these risks, particularly if the benchmark pricing was being used previously. The insurer not using the habitational risk would write the insurance for the poor insurer scores, resulting in adverse selection.
How often do scores change? For renewal scores, three in four properties do not change radically year over year. The properties that are more likely to be subject to changes in scores are generally smaller properties.
A Comprehensive Picture Of Risk
Commercial residential property insurers have attempted to use loss control inspections as a means to assess risk caused by the behavior of occupants, but inspections are ineffective at measuring the total insurance loss potential. Residential data, flowing from improved technology tools, has a significant impact in the commercial residential market, in line with the impact that insurance scores had on personal lines in an earlier era. This trend allows insurers to more accurately price commercial risk, a substantial win for the industry and the customers they serve.
Source: Property Casualty 360