RLTO Resources

The Chicagoland Apartment Association publishes this pamphlet as a public service.  It is meant to inform and not to advise.  Before enforcing any rights or remedies you may want to seek the advice of an attorney who can better analyze the specific facts of your case. Click Here to see this pamphlet in Adobe Acrobat (PDF) format.

 

The importance of resident screening cannot be emphasized enough, because when a good screening method is used to its fullest potential, it can help apartment properties financially manage portfolios by increasing Net Operating Income (NOI). This factsheet will explain the financial impact resident screening may have on properties, provides guidelines on how to choose a screening method, and includes screening tools that properties can use to leverage screening data to its fullest potential.

By analyzing several variables and comparing them to the performance of other applicants with similar profiles, resident screening helps predict how likely applicants will pay rent on time and fulfill other lease obligations. This prediction should help property owners and managers pick the best residents which may reduce bad debt, balance risk and occupancy, and improve Net Operating Income (NOI).

Most applicant screening methods are similar in that they examine a few applicant attributes, such as rent-to-income ratio or debt levels, and make a judgment of an applicant’s likelihood to meet their rental obligations. Such methods, however, do not consider the hundreds of variables that impact an applicant’s ability and willingness to pay rent. Nor do they consider the interactions of such variables. The following best practices provide guidelines that can be used when choosing which resident screening method to employ:

Consider selecting a statistical resident screening model. Results-based, statistical modeling has been used for decades in the mortgage industry, credit card industry, and numerous other industries because it is the only technique that can objectively determine which variables are predictive of the performance being modeled and appropriately weigh these variables. This methodology is deemed to be more accurate and delivers better bottom line financial performance. Statistical scoring models built specifically for resident screening deliver a score that estimates the credit risk that an applicant may not satisfactorily fulfill his/her lease obligations.

It is important for a screening model to include landlord-tenant data in addition to credit payment history to get a complete picture of prospective resident lease performance. This data should include past court actions, prior landlord inquiries, and landlord-reported history information regarding lease performances. Please ensure that the screening model uses the largest landlord-tenant data set because doing so should provide the most accurate score and prediction.

Use a resident screening method that improves Fair Housing compliant resident selection. A mathematically derived score and standard acceptance levels ensure more consistent interpretation and execution of acceptance policies than other screening methods. Elimination of manual screening decision overrides should improve Fair Housing compliance even further.

Resident screening models with adjustable acceptance levels provide property managers with a powerful tool for fine-tuning and controlling the balance between loss rate and vacancy rate. It is best if the screening model gives each property within a portfolio the flexibility to adjust its own acceptance level based on the financial risk that is acceptable for that specific property.This allows a property to adjust levels as economic conditions, vacancy rates, and other market considerations change. This flexibility also accommodates portfolios that have a variety of property classifications because it allows acceptance levels to be adjusted to accommodate risks associated with specific property classifications. For example, a Class A property may require a higher acceptance score than a Class B property in the same portfolio. Consider a screening model that delivers three possible decisions: accept applicant, decline applicant, or accept applicant with conditions, such as an increased security deposit. Adding a third decision of accepting applicants with conditions may help increase occupancy rates.