August 31, 2016 Escrow Overcharges and Delayed Refunds Can Lead to Increased CFPB Complaints Refunds from escrow accounts are Granger-causing CFPB complaints Dingxi Qiu | Mortgage Performance


Consumer Financial Protection Bureau (CFPB) complaints are difficult to track and address, but they can potentially cause significant reputational damage to mortgage lenders and servicers if common issues in the complaints are not handled properly. Using CoreLogic tax servicing data to evaluate the reasons for CFPB complaints, we found that the number of overpayment-related escrow account refunds is one of the main factors of these complaints.

Escrow accounts are often required by the lender or servicer to pay taxes and hazard insurance premiums for the borrower on top of monthly mortgage payments. The servicers typically evaluate the PITI[1] on an annual basis and charge borrowers on a monthly basis. If the borrowers overpay, they will receive a refund at the end of the year. The overpayment can be due to various reasons such as borrower overpayment during closing, double payments by both the borrower and the servicer and inaccurate annual bill estimation. Borrowers can become unhappy if they learn that they have been overpaying for months without the opportunity for a refund. If the borrowers have a non-escrow account, on the other hand, they will be responsible for all the tax and hazard insurance payments and can potentially encounter penalties from the taxing agency for late payments. CoreLogic processes both refunds and penalties for the mortgage servicers.

For this study, CoreLogic extracted complaints data from the CFPB’s official website[2] from January 2013 through December 2015 for four relevant complaint categories: loan servicing, payments, escrow account; application, originator, mortgage broker; settlement process and costs; and credit decision/underwriting. The complaints data was merged with CoreLogic penalty data, refund data and portfolio size data by servicers to evaluate the potential factors for CFPB complaints, but only refunds were found to be relevant.

Figure 1 shows the relationship between the lagged one-month refunds and the average number of relevant CFPB complaints. The higher level of monthly refunds in the previous month is correlated with the higher level of monthly complaints in the current month.

 What Title of figure 2 is

 What Title of figure 2 is

More rigorously, CoreLogic performed a statistical hypothesis test, known as Granger-causality test[3], to determine whether certain factors, such as the number of monthly penalties, the number of monthly refunds and the monthly level portfolio size, were useful in forecasting the monthly relevant CFPB complaint counts. The results showed that only the monthly refund counts are Granger-causing the number of relevant CFPB complaints. Figure 2 shows the Granger-causality model outputs, where the target variable is the number of relevant monthly CFPB complaints. In this model, numComplaints_1 and numComplaints_2 denote the one-month and two-month lagged values of the target variable — the number of monthly CFPB complaints; while lender_A, lender_B and lender_C are dummy variables for three mortgage servicers and refund_1 and refund_2 are the one-month and two-month lagged values of the number of monthly refunds. One-month lagged refunds, refund_1, has a significant coefficient (p_value= 0.0032 ≤ 0.05) on the number of monthly CFPB complaints, which implies that the number of refunds is Granger-causing the number of relevant CFPB complaints,[4] so the higher number of processed refunds due to overpayments, the more complaints.

In summary, the refunds the CoreLogic Tax Servicing team processed for the mortgage servicers span a variety of reasons, and borrowers can become frustrated if they do not receive their refunds in a timely manner. Therefore, mortgage servicers can achieve a significant reduction in borrower complaints by improving the accuracy of their yearly mortgage bill estimation and more efficiently processing refunds to the borrowers.

Mark Liu and Dominique Lalisse contributed to this blog.

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