MReport May 2017

TheMReport — News and strategies for the evolving mortgage marketplace.

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24 | TH E M R EP O RT FEATURE F inancial professionals in the digital age are able to do something that was once impossible: see into the future. Using predictive analytics, a blend of statistical analysis and computer science, today's financial profes - sionals solve problems, improve processes, and understand trends in consumer behavior. For example, they can process credit card behavior to identify potential fraud before approving a charge, reduce churn by targeting unhappy customers, and identify customers for new products and services based on what they have purchased in the past. Predictive analytics is not complicated; it just requires data and the right tools. The mortgage industry has tre - mendous amounts of data. There is housing data, mortgage origina- tion data, and servicing data, to name a few. The conditions are ripe for using predictive analytics. To illustrate the benefits of predictive analytics, we'll examine one case example from publicly available Government National Mortgage Association (GNMA or Ginnie Mae) data in the servicing space. Once a borrower is 90 days past due on his or her mortgage payment, the approved Ginnie Mae issuer and loan servicer has the option to buy out the nonperforming loan at par—100 percent of the unpaid principal balance (UPB)—from a GNMA pool. Then, according to the GNMA mortgage backed securities (MBS) issuer handbook, the issuer can employ loss mitigation tools on those loans to cure the mortgage back to performing status. If the loans re-perform, the issuer can re-securitize the loans into a new issue pool. If the price is greater than par, or 100 percent of UPB, the issuer can get an immediate gain on the sale of the loans, earning revenue. In many cases, the issuer has to buy out the loan to pursue loss mitigation strategies that change the terms of the mortgage (such as term extension or interest rate re - duction). However, the handbook does not require a partial claim to alter the terms of the mortgage. In a rising interest rate environment, partial claims are likely to become a more prevalent loss mitigation strategy, leaving the issuer with more choices on whether or not to buy out nonperforming loans for a security. There is an opportunity to buy out loans in the early stages of delinquency if the issuer expects them to re-perform. The opportu - nity can be profitable if the issuer can effectively identify which loans to buy out and which loans to move through the foreclosure process. In the latter cases, issuers need to evaluate the cost of interest advances and property mainte - nance expenses. Also, GNMA issuers must be mindful of the delinquency ratios on pools that are monitored by GNMA. The delinquency ratio is the fraction of the loans in the issuer's GNMA portfolio that are either in foreclo - sure or 90 or more days delin- quent. Buyouts can be an effective way to manage delinquency ratios. These metrics in some cases in- centivize the issuer to pursue early buyouts to reduce those ratios. To Buy Out or Not to Buy Out S o how can predictive analyt- ics help GNMA servicers make a decision on whether or not to buy out a given mortgage? There is ample data on GNMA loan performance, and the data is available for download from GNMA's website. All that remains is to predict which loans will turn around from nonperforming to performing. There are a variety of techniques that can be used for this analysis, including a relatively new algorithm called XGBoost. XGBoost has been drawing a lot of interest in the predictive analyt - ics community, winning several international data competitions. The XGBoost algorithm works by systematically iterating through possible predictive models while reducing unexplained error at each step. At the end, the result is an estimated value with low error and outstanding predictive ability. To perform the analysis, the GNMA data was downloaded and modified to extract a list of all loans that were 90 days delinquent and received a loan modification. The outcome of the loan modifica - tion (i.e., re-perform or re-default) was added to the data as a binary indicator (0 or 1). Several economic variables were also included. The XGBoost algorithm was then ap - plied to the data to create a model that predicted which loans would re-default following modification. If an issuer can effectively evaluate which loans will re-perform, then the issuer can target those loans to buy out from the pool and per - form loss mitigation. Then, if the loans cure, the issuer can re-securi- tize them into new issue pools. After running XGBoost on the performance data, the tool revealed insights that weren't accessible with more traditional techniques such as logistic regression. The graph in Figure 1 depicts the importance of various features for the GNMA algorithm. Figure 1 highlights the most predictive features on the top, with the less predictive features on the bottom. (The features here are shown in isolation, but the algorithm does take into consideration correlations between variables.) Home price appreciation from loan origination to first delinquen - cy was the most important feature for predicting loan performance after modification. In addition, changes in the local housing affordability index and unemploy - ment rate had modest effects, while variables like number of Enhanced Vision With predictive analytics, mortgage professionals can see further into the cycle of a loan to determine the best course of action to take. By Jonathan B. Glowacki and Makho Mashoba

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