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 prop-
The current process
erty using an internet search for pictures
of the property and by obtaining resi-
dents’ reviews or complaints about prop-
erty 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 resi-
dent data into the under writing 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.
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
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