By Susan Saldibar
In an industry struggling to keep occupancy above water, the fight for move-ins can result in some corner cutting to be sure. A dangerous area to skim over, however, is determining if a new resident is willing to pay. Notice I say “willing” because in this climate you need to know more than if they “can” pay.
I learned about the importance of screening for willingness after speaking recently with Michael Mauseth, SVP, Screening Group, for RealPage® (a Senior Living Foresight partner). RealPage has rolled out a pretty sophisticated AI layer to their screening platform which, by all appearances, seems to have filled some important gaps in the screening process.
If the Demographic Has Changed, Shouldn’t the Screening Tools Change as Well?
There are plenty of reasons to need better screening tools. “It isn’t hard for new residents to get in over their heads in making a decision to move into a senior living community,” Mike tells me. “It can become a very expensive proposition. As their needs change, they need more services. That adds more expense.”
We also need to face the fact that the attributes of our target resident have changed. And it’s not good. According to a 2019 white paper published by the Insured Retirement Institute, “Boomer Expectations for Retirement 2019”, nearly 50% of boomer aged adults have no retirement savings. Of those that do, about one third have less than $100k in savings. That means a greater reliance by residents on other family members to pay. That also means greater risk for senior living communities.
I asked Mike to share some of the features of their AI-enabled screening platform. Without giving away all the secret sauce, here are some key attributes:
- AI-based algorithm, along with behavior data for deeper screening
- Analyzes an applicant using a pool of 30 million rental history records
- Goes beyond credit scoring to identify both the capability to pay and willingness to pay
- Early returns show that it’s reducing bad debt and financial loss by an average of $31/unit/year
Willingness to Pay Is a Valuable Screening Add. Who Wouldn’t Want That?
Mike further explained the methodology behind willingness to pay. “We have actual outcomes of payment in our database”, he says. “Using AI, as well as access to extensive rental histories, our screening tool can say, ‘These types of renters have the capacity to pay, but do not pay.’” That’s different from the traditional credit scoring model.
It’s an important difference, and one that Mike wants operators to understand. “We built a model from our vast dataset of over 30 million rental histories, with machine learning as an underlying, ongoing process. To give an analogy, consider driving someplace new. The old way to consider risk is similar to using a printed-out map or set of directions. AI Screening is akin to using your smartphone that is powered by consumers’ feedback on detours and slowdowns. Our model’s predictive power increases as clients use it.”
There’s one more thing that I think is cool about RealPage’s AI screening ability, especially given the “just say no” mentality often associated with financial screening. It can help operators identify low-risk residents who might otherwise be unfairly branded as high risk. “These are people who may look riskier when using traditional scoring methodologies, and yet the history shows that they do pay their rent on time, every month. So, conceivably, an operator can say yes to someone who may look like a higher risk if not evaluated through our AI Screening model.”
As Mike puts it, instead of saying, “We’re getting good at saying no”, it gives operators the ability to get better at saying “yes”. That’s a solid win-win. Something we can use in this industry.
There is much more to learn about this new AI screening capability. RealPage has written a white paper which you can access here. For more information about RealPage Senior Living please visit their website.