Case Studies

Predictive Modeling Grows Reverse Mortgage Business

As the baby boomer generation begins to retire, businesses are looking for ways to serve the growing senior population. Once such company, a west coast-based mortgage provider with a reverse mortgage product, had been successful in its local market, but wanted to expand its reverse mortgage customer base to other regions. The company knew that it needed to target the over-62 population of homeowners, but beyond that it needed to know who among this group would be likely to use the value of their home to generate cash through a reverse mortgage. The mortgage company approached us to help determine and target its best prospective customers for expansion beyond the local region.

Discover What You’re Best Prospects Look Like

Applying genetic algorithms to predictive modeling creates targeted prospect universes that are customized for every campaign. By applying over 750 variables to data on more than 120 million households, we are able to generate models that are scored to produce highly-targeted mailing lists that can improve response rates by 7.5-15%.

A reverse mortgage is a kind of home equity loan intended for the senior population of homeowners aged 62+. Rather than taking out a loan on which payments are made, a reverse mortgage essentially "pays" the borrower the equity of the home in cash, but requires no repayment for as long as the owner lives in the home. The borrower does not need an income to qualify, and cannot lose his home as long as he maintains the property and pays property taxes. With such a large portion of the population approaching retirement age, there is a potentially large market for reverse mortgages.

The mortgage company was familiar with its west coast market, but needed to know what its customers in other areas of the country looked like. To accurately predict who would be responsive to the company's mail campaign, we built a model using the basic qualifying requirements for a reverse mortgage, targeting owners of single-family homes with equity, aged 62 and older, with no federal debt. In addition to these factors, the model also discovered that people between the ages of 65-74, without a college or graduate degree, who had lived in their homes for a certain length of time (i.e., were not considered "new movers"), were most likely to be interested in a reverse mortgage. The inclusion of these characteristics generated lists that resulted in several successful campaigns, enabling the company to achieve its expansion goal.

The Results

To break even on its direct marketing campaign, the mortgage company needed a .5% response rate to the mailing. Using the lists generated from our model to send approximately two million mailers in six months, the response rate increased four-fold, to 2%, turning an estimated profit of $16 million. The mortgage company has successfully expanded its business to 12 regions throughout the U.S., and continues to rent lists each year for unlimited annual use.

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