You've probably heard the buzz phrase big data. But what is big data — and what does it have to do with bad debt at rental properties? Well, to start, big data is a dataset that can't easily fit in a spreadsheet or be grasped at a glance but requires special tools for analysis to create a meaningful story out of it.
And what does it mean for property managers? Big data tools can help you, for example, find those residents who appear unqualified at first glance but are really diamonds in the rough. Or big data can help you sniff out fraudulent IDs used in rental applications. That's the topic of "Using Big Data Analytics to Reduce Bad Debt," a webinar from TransUnion Consultant Ryan Nichols.
In the webinar, which is available on demand, Nichols explains that TransUnion provides both the tools and the data that help you ask meaningful questions about residents and applicants while also cutting down on bad debt. Let’s look at three different ways to approach the problem.
Credit scores: One size that doesn't fit all
Property managers now use credit scores near-universally as proxies for how reliable a person is to do business with. But credit scoring systems are designed with a specific purpose in mind: to determine how suitable a person is for a loan. Renting someone an apartment isn't the same as loaning them money, so if you're using a person’s credit score to figure out whether you should a sign a lease with them, you're asking the wrong question for the answer you want.
Someone with a mediocre credit score, for example, might be a fine potential resident — or not. The point is, you need to use a dataset that goes beyond the information that goes into credit scores and an algorithm that can parse that data with the goal of giving meaningful answers.
There's one case where a good credit score isn’t a sign of a good applicant: When it's a score for someone who doesn't exist. Synthetic fraud is form of identity fraud that involves not stealing a real person's ID, but building an entirely fictional identity piece by piece, then cashing in on its high credit score.
If you rent to someone who's pulling this kind of scam, in all likelihood you'll end up with debt that you'll never see paid back. You need broad reach and data analytical ability to identify these fraudsters up front.
Not all evictions are equal
Some property managers treat any past eviction as a scarlet letter for a potential renter. But managers of properties may have difficulties filling all their units if they decline out of hand everyone who's ever been evicted.
Fortunately, with the right data and the right tools, you can look beyond the obvious to see if applicants with past evictions still have the makings of good residents. Instead of using a simple rules-based decision process, such as "don't rent to anyone who's been evicted," you need analytical tools that can assess an applicant's whole history, to see if their circumstances have changed and if they're less likely to be a risk today.
With such tools, you can simultaneously lower the chance that you'll need to evict a renter and increase the number of applicants that you accept.
TransUnion with the answers
In "Using Big Data Analytics to Reduce Bad Debt," Nichols outlines the big data tools and services TransUnion offers that can help property management companies do all this and more. Finding the right residents can keep bad debt at bay, and big data is an important weapon in your arsenal in that quest.