Despite new capabilities through technological advancements, property management responsibilities are piling up and growing more complex, introducing new forms of risk in the process. What does it take for property owners and operators to reduce loss in the face of heightened risk exposure, especially when a recession looms?
Traditionally, loss protection strategies have focused on maintaining accurate records and vetting renters to identify trends and patterns that inform NOI growth and risk mitigation strategies. However, this is an imperfect process that often leads to human error, data inconsistencies, and subsequently, hidden risks. In other words, properties are left to rely on difficult-to-predict outcomes that leave them exposed and unable to make informed business decisions.
Apartment properties must control various risk factors (think: fraud, maintenance issues, damages, skipped rent). But without sophisticated AI, operators miss out on important data including historical trends in rent payments, damages, and other data points that are difficult to manually aggregate, track, and normalize in an actionable way. Unpredictable risk leads to unpredictable loss.
By building a multifamily risk mitigation strategy that leverages data and AI solutions, operators can bridge the gap between identifying hidden risks and proactively mitigating them. Specifically, predictive AI enables apartment operators to convert potential revenue loss into dependable net income.
Risk is an inherent part of multifamily rental properties but it comes in multiple forms. Fraud, for example, has become particularly rampant with rental applications and rent payments due to the increased use of digital services — approximately 97% of property management companies have experienced fraud.
When screening applicants, property managers need to be able to accurately determine whether or not they will be a good fit for their properties, meaning they need to be able to detect fraudulent renter contact details, applications, and rent payments. Similarly, standard information like income to rent ratio, current job status, and criminal records, doesn’t give the complete profile of a prospective renter. Without the ability to screen for particular renter behaviors, properties have only a partial understanding of the risk a property is taking on.
In this case, it becomes difficult to anticipate a resident’s willingness to make payments, and a security deposit or surety bond often will be insufficient in protecting against rent loss or excessive damage at move-out. Having insight into rent payment behavior is critical to reduce bad debt and financial loss.
To better anticipate and protect against loss, AI is a powerful tool in detecting hidden risks like fraud and predicting a resident’s willingness to prioritize their rent obligation. Pulling together data through AI-powered resident screening and ID verification gives multifamily operators an understanding of future performance across a larger group of renters, thus presenting a clearer picture of risk. As a result, properties can better protect themselves against renter behaviors like skipped rent, damages, fraudulent payments, and more.
What happens when a security deposit doesn’t cover leftover account balances? On top of revenue loss due to inadequate coverage, deposits create administrative headaches (i.e., debt recovery and deposit check refunds) and also increase regulatory risk (i.e., deposit disputes), leading to additional financial loss. Deposit alternatives like surety bonds and security deposit insurance compound the problem, sacrificing move-out protections for move-in expediency. What operators need is coverage tailored to the property’s risk profile.
That’s where AI comes in. Data-driven AI solutions like lease insurance predict the likelihood of residents owing high balances or leaving excessive damage after move-out. By assessing loss signals, evaluating claims data, and running thousands of coverage simulations, AI predictive analytics help properties set optimal pricing and coverage.
Rather than an incomplete risk assessment leading to arbitrary and insufficient coverage, AI helps uncover hidden risks to provide more accurate loss forecasts and generate precise coverage plans relative to the risk. This establishes predictability in the claims process, reducing the amount of time spent by site teams on claims and returning valuable hours to focus on leasing and customer service. In turn, AI optimizes back-end operations and improves revenue capture, driving NOI and asset value growth.
AI has demonstrated great success in identifying and mitigating hidden multifamily risks related to historical rent payment data, rental applications, and other renter behaviors. Through AI-powered predictive analytics (via resident screening, fraud detection, coverage optimization, and claims management), properties have the power to forecast previously unpredictable loss and turn it into predictable net income. Especially as operators look to protect against financial loss during the economic downturn, leveraging AI will go a long way in optimizing property performance and creating value—without adding risk.
Looking for more information? Check out our guide to Unlocking the Value of Risk Prediction for Better Loss Protection.
