Mapping Out A Smooth Voyage Into Mandatory Execution

[i]REQUIRED READING:[/i][/u] Lenders need to take a hard look in the mirror when they consider switching from best-efforts to mandatory loan executions. [/b] They might see that they have the required volume and net worth, but do they really know what's in their pipeline? This is essential, because accurate pipeline data is what allows lenders to hedge loans and maximize their profit opportunities. Pipeline data management should be an obsession for any lender that is hedging. Hedging models rely heavily on this data to guide lenders on what hedge positions to take, so having easy access to accurate pipeline data allows lenders to hedge more effectively and protect themselves better from market or interest-rate volatility. Given its importance, it's surprising how difficult it is for many lenders to put this data together and have confidence in it. It's not so much that the data is hard to produce – any lender with a competent workforce, a consistent process and a capable loan origination software should have pretty good data available to them (although this could always be improved). Instead, what we see are lenders having a hard time simply feeding this data into their hedging models. They subject themselves to a tedious and time-consuming process of exporting pipeline data, e-mailing the data to their risk manager, importing the data into the hedging model and then performing the analysis. For risk management firms that handle hedging activities for multiple lenders, this process becomes even more hazardous. Clients use an array of different systems and produce data that are not uniform. An additional data-normalization step is required, which extends the time and labor involved even further. There is a real cost to all this inefficiency. The longer it takes to analyze pipeline data, the less responsive a lender can be to market changes. And, because the process is so labor-intensive, there is a greater likelihood of error. Extra loans, mistyped information or even a busy day for your secondary marketing manager – it only takes one small mistake to have a big impact on your hedging effectiveness. Add to this the fact that pipelines are constantly in motion. Loans move from one stage to another, and the rate of change in pipeline composition is uneven. When it takes a lot of time to prepare data for analysis, the influence of pipeline changes becomes even more pronounced. In a perfect world, secondary marketing managers would have instantaneous access to pipeline data in real time. At a moment's notice, they could see the number of loans they have at each loan stage and plug that data into their pull-through models. They could then adjust their coverage accordingly. Even better, this data would flow directly into their hedging models and perform the analysis automatically, eliminating any manual processes and generating updated hedge recommendations in real time. No amount of market volatility could derail lenders' interest-rate neutral position, because they would know exactly where their risk lies. Here's another way to look at it: Imagine pipeline hedging as a voyage across the ocean. In this scenario, all lenders face the same risks. They have a hull filled with cargo (loan applications) that they know will not be 100% deliverable. Some goods will be washed overboard; others will spoil and be thrown away (fallout). External factors – such as winds, currents and weather conditions – also influence the success of the voyage, so lenders use pull-through models to estimate the amount of goods that will ultimately reach the other shore. Instead of analyzing pipelines and establishing hedge positions once a day – as many lenders and risk management firms do – real-time hedging allows lenders to continuously monitor pipelines and make small course corrections throughout the day. That keeps them on a tighter interest-rate neutral course and ensures that they are never too short or too long on their positions. [b][i]Pushing pull-through[/i] [/b] Pull-through is another critical determinant in a lender's mandatory-execution voyage. A hedging model is at its optimal effectiveness when lenders maintain a predictable level of pull-through. When actual pull-through rates closely match expected pull-through, lenders can make more precise trades and cover their positions more efficiently. The main threat to pull-through predictability is mistakes made during loan screening or underwriting, because there's no way to predict when an underwriter is going to miss a guideline or just have a bad day. Lenders need to eliminate these mistakes by screening loans before they're locked, in order to prevent bad approvals from happening in the first place. The earlier in the workflow process they can do this, the more consistent their overall pull-through rates will be. Automated underwriting technology is specifically designed to accomplish this task. These systems are typically used at the outset of loan origination, and they apply underwriting guidelines to loan applications and screen them properly for eligibility, prior to underwriting and prior to rate locking. Of course, not all automated underwriting systems are created equally -a poor-quality system will not be as effective in preventing bad loans from infiltrating pipelines. In our hypothetical voyage across the ocean, we've already equipped our ship with an enhanced ability to ‘see’ using real-time pipeline data. Automated underwriting technology would also help the ship becomes less prone to pitching or rolling due to rough conditions. This minimizes unexpected losses to cargo and makes it easier to predict how much gets delivered. When lenders make the decision to hedge their pipelines and sell loans via mandatory executions, they face greater risk in exchange for higher profit potential. But this risk does not imply that mandatory executions are risky. When done properly, real-time pipeline hedging allows lenders to always maintain an interest-rate neutral position in their open pipeline and position them to have 100% pull-through with investors. By understanding the value of good data and making use of technology that fosters the provision of good data, lenders can manage their risk and sleep well at night knowing that the uncertainties of the market will not capsize their business. [i]Linn Cook is director of marketing for PriceMyLoan, headquartered in Costa Mesa, Calif. He can be reached at (888) 285-3912. Curtis Richins is president of MCT Trading, based in San Diego. He can be reached at (619) 543-51


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