Tag: custom scores

Believe it or not, 2023 is underway, and the new year could prove to be a challenging one for apartment operators in certain ways. In 2021 and into the beginning of 2022, demand for apartment rentals approached record levels, which shrunk vacancy rates and increased monthly rents. The rest of the year remained stagnant while other regions saw some decline, but inflation and other economic factors have many apartment communities confronted with labor shortages, and other challenges which can certainly make leasing and operating properties difficult. Against that backdrop, here are some of the technologies and solutions operators should consider for optimizing their success and efficiencies in 2023 and beyond. Tools that allow prospective residents to have a fully digital and contactless leasing experience — During the pandemic, many operators rushed to implement virtual tours, onsite self-guided tours and other solutions that allowed prospects to apply for and finalize their leases remotely. Prospective renters have undoubtedly grown fond of navigating the leasing process from their homes and taking self-guided tours when onsite, and the demand for digital solutions will surely continue even after COVID distancing is no longer a factor. Therefore, apartment owners and operators should think of these capabilities as long-term investments and always seek ways to optimize the digital leasing experience they provide. Along those lines, forward-thinking operators are employing solutions that allow them to embed credit functionality into their websites and mobile apps using modern, RESTful APIs like the Experian ConnectSM API. Not only does it enhance the information included in a lease application with credit report data, but it also allows prospective renters to easily apply for more than one property at once, enhancing their experience at the same time. Automated lease application form fill — By using information entered by a lease applicant (such as first name, last name, postal code and the last four digits of a Social Security number), this technology uses information from credit files to automatically fill other data fields in a lease application. This tool reduces the effort required by prospective renters to complete the application process, resulting in a better user experience, faster completions, greater accuracy and reduced application abandonment. Automated verification of income, assets, and employment — These solutions eliminate the need for associates to manually verify these components of a lease application. Manual verification is both time-consuming and prone to human error. In addition, automated tools eliminate the opportunity for applicants to supply falsified supporting documentation. The best part about verification is the variety of options available; leasing managers can pick and choose verification options that meet their needs. Renter Risk Score™ and custom-built scores and models applying RentBureau data — These options offer a score designed expressly to predict the likelihood that an applicant will pay rent. Renter risk score can be purchased with preset score logic, or for high-volume decisions, a model can be built calibrated for your specific leasing decisioning needs. A rental payment history report — The RentBureau Consumer Profile tool can provide detailed insight into a lease applicant's history of meeting their lease obligations, which is invaluable information during the lease application process. Having a tool to report rental payment histories to credit bureaus can be a powerful financial amenity. By reporting these payments, operators can help residents build credit histories and improve financial well-being. Such an amenity can attract and retain residents and provide them with a powerful incentive to pay rent on time and in full. In the end, tools that seek to manage risk and create improved experiences for prospective renters have a multitude of benefits. They create meaningful efficiencies for onsite staff by greatly reducing the time, resources and paperwork required to process applications and verify applicant information. This gives overextended associates more time to handle their many other responsibilities. Beyond just efficiency savings, these technologies and solutions also can help operators avoid the complications and loss of income that result from evictions. In fact, the National Association of Realtors estimates that average eviction costs $7,685. Managing risk and providing the best possible customer experience should always be top of mind for rental housing operators. And with the solutions outlined above, they can effectively accomplish those goals in 2023 and beyond.

This is the third in a series of blog posts highlighting optimization, artificial intelligence, predictive analytics, and decisioning for lending operations in times of extreme uncertainty. The first post dealt with optimization under uncertainty and the second with predicting consumer payment behavior. In this post I will discuss how well credit scores will work for consumer lenders during and after the COVID-19 crisis and offer some recommendations for what lenders can be doing to measure and manage that model risk in a time like this. Perhaps no analytics innovation has created opportunity for more individuals than the credit score has. The first commercially available credit score was developed by MDS (now part of Experian) in 1987. Soon afterwards FICO® popularized the use of scores that evaluate the risk that a consumer would default on a loan. Prior to that, lending decisions were made by loan officers largely on the basis on their personal familiarity with credit applicants. Using data and analytics to assess risk not only created economic opportunity for millions of borrowers, but it also greatly improved the financial soundness of lending institutions worldwide. Predictive models such as credit scores have become the most critical tools for consumer lending businesses. They determine, among other things, who gets a loan and at what price and how an account such as a credit line is managed through its life cycle. Predictive models are in many cases critical for calculating loan and loss reserves, for stress testing, and for complying with accounting standards. Nearly all lenders rely on generic scores such as the FICO® score and VantageScore® credit score. Most larger companies also have a portfolio of custom scorecards that better predict particular aspects of payment behavior for the customers of interest. So how well are these scorecards likely to perform during and after the current pandemic? The models need to predict consumer credit risk even as: Nearly all consumers change their behaviors in response to the health crisis, Millions of people—in America and internationally—find their income suddenly reduced, and Consumers receive large numbers of accommodations from creditors, who have in turn temporarily changed some of their credit reporting practices in response to guidelines in the federal CARES Act. In an earlier post, I pointed out that there is good reason to believe that credit scores will tend to continue to rank order consumers from most likely to least likely to repay their debts even as we move from the longest economic expansion in history to a period of unforeseen and unexpected challenges. But the interpretation of the score (for example, the log odds or the bad rate) may need to be adjusted. Furthermore, that assumes that the model was working well on a lender’s population before this crisis started. If it has been a long time since a scorecard was validated, that assumption needs to be questioned. Because experts are considering several different scenarios regarding both the immediate and long-term economic impacts of COVID-19, it’s important to have a plan for ongoing monitoring as long as necessary. Some lenders have strong Model Risk Management (MRM) teams complying with requirements from the Federal Reserve, Federal Deposit Insurance Corporation (FDIC), the Office of the Comptroller of the Currency (OCC). Those resources are now stretched thin. Other institutions, with fewer resources for MRM, are now discovering gaps in their model inventories as they implement operational changes. In either case, now’s the time to reassess how well scorecards are working. Good model validation practices are especially critical now if lenders are to continue to make the sound data-driven decisions that promote fairness for consumers and financial soundness for the institution. If you’re a credit risk manager responsible for the generic or custom models driving your lending, servicing, or capital allocation policies, there are several things you can do--starting now--to be sure that your organization can continue to make fair and sound lending decisions throughout this volatile period: Assess your model inventory. Do you have good documentation showing when each of the models in your organization was built? When was it last validated? Assign a level of criticality to each model in use. Starting with your most critical models, perform a baseline validation to determine how the model was performing prior to the global health crisis. It may be prudent to conduct not only your routine validation (verifying that the model was continuing to perform at the beginning of the period) but also a baseline validation with a shortened performance window (such as 6-12 months). That baseline validation will be useful if the downturn becomes a protracted one—in which case your scorecard models should be validated more frequently than usual. A shorter outcome window will allow a timelier assessment of the relationship between the score and the bad rate—which will help you update your lending and servicing policies to prevent losses. Determine if any of your scorecards had deteriorated even before the global pandemic. Consider recalibrating or rebuilding those scorecards. (Use metrics such as the Population Stability Index, the K-S statistic and the Gini Coefficient to help with that decision.) Many lenders chose not to prioritize rebuilding their behavioral scorecards for account management or collections during the longest period of economic growth in memory. Those models may soon be among the most critical models in your organization as you work to maintain the trust of your accountholders while also maintaining your institution’s financial soundness. Once the CARES accommodation period has expired, it will be important to revalidate your models more frequently than in the past—for as long as it takes until consumer behavior normalizes and the economy finds its footing. When you find it appropriate to rebuild a scorecard model, consider whether now is the time to implement ethical and explainable AI. Some of our clients are finding that Machine Learned models are more predictive than traditional scorecards. Early Experian research using data from the last recession indicates this will continue to be true for the foreseeable future. Furthermore, Experian has invested in Research and Development to help these clients deliver FCRA-compliant Adverse Action reasons to their consumers and to make the models explainable and transparent for model risk governance and compliance purposes. The sudden economic volatility that has resulted from this global health crisis has been a shock to all organizations. It is important for lenders to take the pulse of their predictive models now and throughout the downturn. They are especially critical tools for making sound data-driven business decisions until the economy is less volatile. Experian is committed to helping your organization during times of uncertainty. For more resources, visit our Look Ahead 2020 Hub. Learn more