Mortgage Analytics Meets Fantasy Football

Posted By Bernard Nossuli on Oct 19, 2016

Fine, I admit it—I’m a fantasy football enthusiast. I have been playing for twenty years now and have seen it all—from touchdown-only leagues with traditional scoring to point-per-reception (PPR) leagues to everything in between.

I had never thought much about the parallel between mortgage analytics and fantasy football until I read this National Mortgage News piece. Although the piece is brief, it touches on some important characteristics that are similar between the two: data availability, data volume, agility, roster management, and unpredictability. I decided to examine those same characteristics in the analytics that we, at iEmergent, provide to lenders and the lessons we’ve learned helping them leverage our data.

Data Availability: The piece mentions how new technology allows lenders to access data in real time on their dashboard, which is similar to how fantasy football players can plug in a few characteristics about their league and get guidance on draft strategy. Through the dynamic maps in our Mortgage MarketSmart application, we too have seen how technology has impacted the way our clients find meaning in our forecast data. When lenders have the ability to not only add their origination data to our maps, but also zoom in and move around their markets of all sizes, they develop a clear understanding of how their lending patterns compare to our market forecast. Lenders can stay at the state and MSA-level or can dive deep into the county and census-tract level to see the mortgage opportunity, in terms of units and dollars, sitting in their very own backyards.

Data Volume: The piece comments on the sheer volume of data available, but also indicates that lenders can decide how much or how little to incorporate into their decision-making process. Fantasy football players can, likewise, do as much or as little analysis as they’d like to prepare for their fantasy drafts. iEmergent provides the same type of scalability. iEmergent provides five-year forecasts for 50 states, 942 MSAs, 3,142 counties, and 73,851 census tracts. This means lenders can use analytics at the appropriate level of detail for the type of strategic decision on the table.

Agility: Lenders use mortgage analytics to monitor their pipelines and make decisions in response to changes in the market. Fantasy footballers arrange lineups in response to player injuries or on-field player performances. iEmergent embodies agility by focusing not only on the size of every market, but also by incorporating speed and other metrics to show not only what happened yesterday or what’s happening now, but where the mortgage loans and dollars will be over the next five years. This type of information helps lenders anticipate change, so they are proactive—rather than reactive—in how they build strategies.

Roster Management: Managers use mortgage analytics to compare how loan originators handle workload relative to each another. Fantasy players decide whom to start and whom to sit on a weekly basis. In our work with lenders, we provide similar insight to identify issues with productivity, by allowing lenders to upload their individual loan officer's weekly funding data to our maps, where they can quickly see gaps in market coverage. The ability to visualize where loan officers are closing loans in comparison to where the opportunity will be opens up many possibilities for analyses, including drive times, product/market fit, and proper coverage ratios.

Unpredictability: Finally, the National Mortgage News piece mentions how savvy executives can study patterns to yield advantageous insights. Fantasy football players can look at points scored, matchups, and other metrics to make their starting lineup decisions. At iEmergent, we simplify how lenders can harness uncertainty by providing one metric as the ultimate guide to market health: the purchase mortgage generation rate (PMGR). Think of the PMGR as a compilation of key indicators (such as historical HMDA data, unemployment data, demographic data, home price data, and even federal and state legislative decisions), with its main benefit being that every market has a PMGR that is unique and predictive. In other words, each market behaves and creates mortgages at its own rate, and that rate is predictive over time.

So, while not every one of life’s answers lies in data, when lenders are faced with making sound mortgage decisions, they need sound mortgage analytics along the way. By looking ahead—in addition to what happened—iEmergent analytics mitigate the risk and challenges that accompany uncertainty.  And if you’re like me, and fantasy football is a part of your life every August through January, then mortgage analytics may make you feel right at home.  

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