The Decision Maker

Sharpen Collateral Valuation through Data Integration

Posted by Nate Wentzlaff on Jul 13, 2016 11:30:00 AM

Sharpen Collateral Valuation Through Data Integration

Collateral Valuations are essential while serving members and maintaining a healthy credit union. However, credit unions are relying on inaccurate valuations of their members’ collateral values because of disintegrated data.

Vehicles

Members own many different kinds of vehicles. The values of their vehicles can fluctuate greatly depending on a plethora of factors.  Since vehicles are usually the second most valuable asset members own (next to their house), net worth calculations heavily depend on the value of members’ vehicles. Credit unions should begin gathering data about all their members’ vehicles and store all the history of these assets.

Housing

A house is usually the most valuable asset a member will ever own. As we saw in the Great Recession, housing values can fluctuate quickly. Housing valuations are becoming more common with companies like Zillow beginning to use statistical models to determine worth (sometimes very inaccurately due to disintegrated data). Credit unions have an advantage in valuing members’ housing (especially if they originated their mortgage) because they have historical data about all their members from many. Utilizing internal and external data sources, credit unions can sharpen their valuation of all their members’ (and potential members) homes.

Businesses

Many members own their own business and may therefore have collateral that could be tied to a loan. There are many different ways to value a member’s business and credit unions can begin putting a value on every business owned by their members. Using net income, total assets, and many other financial metrics, credit unions can ascribe a value to all their members. Business values can be used to continue developing a complete picture of the credit unions’ membership.

Data Integration

Gathering an extensive amount of historical data in an analytic data model is the first step. Once data sources are integrated, credit unions can begin gleaning deeper insights about their members’ assets. For example, members who a own home should have a higher collateral value on their car since they likely have a safe place to protect their vehicle and will retain value for longer than vehicles that are parked on the street outside of an apartment. Data points should be gathered from multiple sources to more accurately determine the value of any asset a member owns. Calculating their travel distance from work, crime patterns in their neighborhood, employer, and many other data fields will empower credit unions to sharpen their valuation of members’ assets and act on opportunities previously unseen.

Trending Collateral Values

Establishing the total collateral value of every member will give the credit union a much better picture of their members. Knowing what every member owns (and frequently updating the collateral values) will give the credit union a much better picture of members’ ability to repay new debts, along with many other data-driven opportunities. Data integration and establishing a member-centric data model are essential to sharpen the collateral values and build analytics into the credit union.New Call-to-action

Topics: Big Data, Credit Unions, Data Integration, predictive analytics

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