From downturn to upside: AI helps retain renters

First off, a little bad news. 

According to Freddie Mac’s multifamily Midyear outlook, apartment rental growth is slowing across the country while vacancies are on the rise.

In the report, Freddie Mac speculates that apartments could weather a 9% drop in collections before they breach any covenant headroom on DSCR (that’s assuming 1.25x). 

Okay, that’s not so bad. 

What’s more, falling interest rates are creating a double whammy on both higher cashflows (through lower debt payments) as well as tighter cap rates on real estate.

But this begs the question: in this market, with a wildly uncertain future, does retaining customers even make sense? 

Absolutely, for these three reasons.

(1) Applicant credit quality: In previous recessions (take 2008 for example), consumer credit quality was massively impacted. In fact, FICO data shows that consumers with higher scores saw their scores drop for up to five years after the recession. So it pays to keep heads in beds.

(II)Concessions on new leases: These are only going to rise as we’ve already seen in Q2 due to poor demand and oversupply.

(III) Turn costs: With new amenities (e.g. smarthomes) and increased sanitization, “turning” a unit will require both a higher capital expenditure and time than in years past.

With a recession in mind, Beekin developed Leasemax. Utilizing a multivariate model, Leasemax allows apartment operators to accurately predict who will stay, and helps retain your best customers at the right price. Over Jan through June 2020, in the midst of the COVID-19 crisis, Leasemax has picked longer-staying renters – and retained them! Check out these results for yourself:

Expected Beekin renewal probability band% of residents who actually renewed No. of happy residents

Retention Study: How 1,000 leases performed in the midst of the COVID-19 outbreak.

As with all machine learning approaches, stability and objectivity matter.

That’s why our data science team spent time ensuring that our model didn’t violate fair housing. That meant eliminating variables which were considered “protected class.” Of course, when we did this, a fair amount of our model accuracy was lost. Maybe having kids does make people move, we’ll never know!

To recoup some of this accuracy loss, our team feature-engineered variables while augmenting the datasets with internal data that was purchased, curated and normalized. This included neighborhood, mobility, and demographic data across thousands of neighborhoods in the US. 

The Beekin datastore, painstakingly constructed over the past 18 months, began to deliver big results, with our feature-engineered model performing flawlessly across MSAs!

Play offense – or defense.

Think of Leasemax as the ultimate data strategy play. It’s built to optimize pricing by overlaying strategies, much like stock market strategies.

Or for fans of Moneyball, we empower data to help transform you from a regular “scout” to a pennant-winning, come-from-behind underdog. In fact, our resident node which predicts whether a resident will stay or pay, is codenamed “Billy” for that very reason!

Get closer than ever to your customers. So close, in fact, that you tell them what they need, well before they realize it themselves.

Use Leasemax to find the perfect customer for your assets. It’s a natural win-win for landlord and resident, alike. Thanks for reading.

To learn more about what Beekin can do for you, we invite you to reach out personally at for a free portfolio analysis. 

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