Statistical lease scoring increases acceptance levels and helps maximize occupancy with renters of better credit quality
Today’s rental property world is rapidly changing. For rental property owners and managers instability in the real estate market and an economic downturn has led to an increase in rental applicants. While occupancy levels and rent prices are up, the old rules—like evaluating applicants based only on credit scores, or turning down applicants with little or no credit bureau history—may no longer be sufficient to keep net operating income (NOI) at an optimum level. Instead, property owners and managers are increasingly turning to powerful, yet intuitive and cost-effective, statistical-based screening tools.
The benefits of these analytical screening models are two-fold: help maximize occupancy through the strategic lowering of criteria, and raise the overall quality of tenants by helping identify applicants most likely to stay longer, protect the property and absorb rent increases.
How is this possible? By using predictive data not available in credit reports. In fact, these statistically validated decision models are specifically designed to predict lease performance in place of generic, one-size-fits-all credit scores based only on narrower, credit bureau data.
Statistical-based lease risk models go beyond the traditional credit report to include more robust records from a multitude of sources previously unexamined. These include multifamily rental debt histories as well as records from payday loan companies, rental stores, subprime auto lenders, the courts and more. This deeper data is modeled to predict the likelihood of performance on a lease better than a generic “credit score.” The result? A richly layered snapshot of each applicant that better reflects overall renter credit quality.
For property owners and managers, this greater depth of insight and data allows users to set less restrictive applicant criteria without taking on additional risk, which in turn increases rental applicant acceptance. Taken out of context, this approach appears to defy conventional wisdom. But when current trends are more closely examined, this type of informed, intelligent decision-making is precisely what the rental market is lacking.
For example, say John Smith lost his job when the company he worked for suddenly closed. As a result of this unemployment, he was unable to make his mortgage payments—resulting in foreclosure and, ultimately, bankruptcy.
Under the old rule-of-thumb standards, John and his family could very well have been denied as renters based on a standard credit report. However, when multiple factors are taken into consideration—new employment has led to stable income, no other financial obligations and no previous rental evictions or collections—the picture changes dramatically. John and his family exceed the property’s applicant requirements while emerging as highly desirable long-term tenants.
Statistical-based screening tools are beneficial for applicants with thin credit files as well. With little or no credit history, these applicants are often quickly denied under old rule-of-thumb standards. Now, they can be scored on a wide range of “life” factors that increase their desirability as tenants.
Another benefit: statistical screening technologies can be used not only to screen out the least desirable candidates, but can actually help identify the “best of the best” candidates as well. Some methodologies, like the one used by Registry ScorePLUS® from CoreLogic® SafeRent®, generate easy-to-read three-digit scores that allow property owners and managers to instantly identify the applicants most likely to stay longer and pay more over time. Lower turnover rates and safer communities help improve renters’ overall quality of life—which in turn leads to increased rental values.
Statistical-based smart screening solutions can even be adjusted according to changing market conditions, such as seasonal changes in application levels and volatile rents.
Applying the best intelligence together with the best lease decision analytics not only takes the guesswork out of resident screening decisions, it also helps increase overall net operating income (NOI). It’s time to move beyond traditional pass-fail rental criteria in favor of more sophisticated, analytical statistical screening models.
By Jay Harris, Senior Director of Business Development, CoreLogic SafeRent
Jay Harris is the Senior Director of Business Development, CoreLogic SafeRent. For questions about this article, please email firstname.lastname@example.org or call 720-947-5589.