New solution uses connected and differentiated data to identify prospects early in the buying process to increase acquisition and retention; internal testing found significant increase in lead capture
As rising interest rates impact loan volume, competition among lenders is increasing. To help mortgage lenders better identify prospective buyers earlier in the process and retain them, Equifax announced its Mortgage Lead Generation Models – a new solution that uses connected and differentiated data to help predict the likelihood that a lead will apply for a mortgage within the next two to six months.
The solution uses credit, wealth/asset, property and demographic data, and includes four different models, which are segmented based on the consumer profile:
The Mortgage Lead Generation Models allow lenders to determine their own scenarios and desired number of leads for retention or acquisition. Equifax’s models create a score that appends to a name and address provided by the lender or even identify new leads for those likely to transact in specific geographic areas. The score is based on the lender’s requirements and ranges from 1 to 999; the higher the score, the more likely the consumer is to apply for a mortgage loan. Armed with this information, lenders can better execute their marketing campaigns.
In an internal test of the solution, Equifax found impactful lift across segments: The top 10 percent of the scores captured between 2.4 to 4 times more mortgage applicants than a randomly selected sample of equal size. Further proof of a tightening market (aside from rising interest rates), according to data from the latest Equifax National Consumer Credit Trends Report, is that just under four million first mortgage loans have been originated YTD, respresenting a decrease of 2.6 percent from the same time last year. Additionally, a survey conducted by SourceMedia finds that 30 percent of mortgage lenders will shift their investments to primarily respond to competitive pressure within the next 36 months.
The models are non-FCRA and exclude protected class data or data that could act as a proxy for protected class status. Additionally, credit and wealth/asset data are aggregated and anonymized.
*National Association of Realtors, 2018 Profile of Home Buyers and Sellers