Increased incidence of “involuntary renters”
According to the Mortgage Bankers Association, one out of every 200 homes will be foreclosed. The incidence of “involuntary renters” will increase as a high foreclosure rate continues, in turn, fueling the current trend of consumers who rely solely on mobile service instead of landlines.
Implications for communications companies
Does it necessarily follow that foreclosure equals bad risk? I don’t think so. For example, many consumers who have undergone foreclosure were subjected to a readjusted ARM that doubled or even tripled their mortgage payments. While taking a mortgage out of a consumer’s credit file can negatively impact the overall credit score, it can also potentially generate a more positive cash flow. The consumer’s new rent payments would be lower than the readjusted mortgage would have been, making the consumer a potentially good customer for communications services.
Wireless companies, in particular, prefer to approve customers for regular installment plans (as opposed to prepaid plans). The goal, for nearly all communications companies, is to qualify customers for service without the need for a deposit. The key, when assessing credit risk, is to look at the total credit/payment history, not just the credit score alone.
Best Practices for qualifying involuntary renters:
- Validate ID/authenticate. Checking the credit application information against several data sources will help avoid potential fraud.
- Look at the overall credit picture, especially the current debt-to-income ratio.
- Review third-party data for payment history. Along with the typical payment data, Experian now offers rental histories through RentBureau. This data has the ability to increase credit report accuracy for renters.
- Consider the basic lender mentality. Consumers who have exhibited good payment history on utilities, credit cards, and other debt in the past are likely to continue that behavior despite having lost their house to foreclosure.
Considering the total credit picture allows you to rank-order customers and group them into populations that are lower risk, identifying, for example, those who can be serviced without an upfront deposit. In future posts, I’ll provide some guidance for rank-ordering customers as to their credit-worthiness.
