We describe and evaluate the measures taken by the U.S. government to rescue Fannie Mae and Freddie Mac in September 2008. We begin by outlining the business model of these two firms and their role in the U.S. housing finance system. Our focus then turns to the sources of financial distress that the firms experienced and the events that ultimately led the government to take action in an effort to stabilize housing and financial markets. We describe the various resolution options available to policymakers at the time and evaluate the success of the choice of conservatorship, and other actions taken, in terms of five objectives that we argue an optimal intervention would have fulfilled. We conclude that the decision to take the firms into conservatorship and invest public funds achieved its short-run goals of stabilizing mortgage markets and promoting financial stability during a period of extreme stress. However, conservatorship led to tensions between maximizing the firms’ value and achieving broader macroeconomic objectives, and, most importantly, it has so far failed to produce reform of the U.S. housing finance system.

/pdf/the-rescue-of-fannie-mae-and-freddie-mac-1p2k292smi.pdf

The Rescue of Fannie Mae and Freddie Mac

We use quasi-random access to the Home Affordable Refinance Program (HARP) to identify the causal effect of refinancing into a lower-rate mortgage on borrower balance sheet outcomes. Refinancing substantially reduces borrower default rates on mortgages and other debt. Refinancing also causes borrowers to expand their use of debt instruments, such as auto loans, home equity lines, and other consumer debts that are proxies for spending. Borrowers that appear more constrained ex-ante grow these debts more strongly after refinancing but also pay down credit card balances by more. These borrowers also have lower take-up of the refinancing opportunity.

/pdf/how-do-mortgage-refinances-affect-debt-default-and-spending-20e0hx3kz4.pdf

How Do Mortgage Refinances Affect Debt, Default, and Spending? Evidence from HARP

Although "black box" models such as Artificial Neural Networks, Support Vector Machines, and Ensemble Approaches continue to show superior performance in many disciplines, their adoption in the sensitive disciplines (e.g., finance, healthcare) is questionable due to the lack of interpretability and explainability of the model. In fact, future adoption of "black box" models is difficult because of the recent rule of "right of explanation" by the European Union where a user can ask for an explanation behind an algorithmic decision, and the newly proposed bill by the US government, the "Algorithmic Accountability Act", which would require companies to assess their machine learning systems for bias and discrimination and take corrective measures. Top Bankruptcy Prediction Models are A.I.-based and are in need of better explainability -the extent to which the internal working mechanisms of an AI system can be explained in human terms. Although explainable artificial intelligence is an emerging field of research, infusing domain knowledge for better explainability might be a possible solution. In this work, we demonstrate a way to collect and infuse domain knowledge into a "black box" model for bankruptcy prediction. Our understanding from the experiments reveals that infused domain knowledge makes the output from the black box model more interpretable and explainable.

Infusing domain knowledge in AI-based "black box" models for better explainability with application in bankruptcy prediction.

Predicting loss severities for residential mortgage loans: A three-step selection approach

This paper examines the impact of refinancing on mortgage defaults based on an empirical investigation of the Home Affordable Refinance Program (HARP). We study a unique dataset from Freddie Mac which includes loans funded right before and after the HARP eligibility cutoff date, an exogenous event. Using a Fuzzy Regression Discontinuity Design method, we show that receiving a HARP refinance decreases the expected monthly default rate by about 48–62 percent using different bandwidth specifications.

Refinance and Mortgage Default: A Regression Discontinuity Analysis of HARP’s Impact on Default Rates

This article evaluates the effect of payment reduction on mortgage default within the context of the Home Affordable Refinance Program. We find that mortgage default is sensitive to payment reduction using univariate, duration and hazard modeling approaches. A relative risk Cox model of default with time-varying covariates estimates that a 10% reduction in mortgage payment is associated with about a 10–11% reduction in monthly default hazard for loans. This finding is robust to the inclusion of empirically important mortgage risk drivers (such as current loan-to-value and FICO score) as well as controlling for selection effects based on observables.

https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=2522749

The Effect of Mortgage Payment Reduction on Default: Evidence from the Home Affordable Refinance Program

Spillover effects of continuous forbearance mortgages

This paper examines the potential market impacts of continuous forbearance mortgages (CFM). This mortgage design embeds an insurance contract at origination that reduces the interest bearing balance to the smaller of the unpaid balance and an estimate of the current home value in exchange for an additional premium in the mortgage note rate. Thus the CFM mortgage payment is reduced in periods in which the estimated home value falls below the unpaid balance and is reduced fractionally based on the ratio of home value to loan balance. We consider a counterfactual in which all U.S. mortgages had this CFM feature at the start of 2006 and estimate the mortgage payment savings to borrowers at a loan level. Estimated mortgage payment savings are then used to estimate the effect on mortgage default. At the aggregate level, the sum of the mortgage payment savings at the state level is used to estimate potential impacts to regional employment.

Spillover Effects of Continuous Forbearance Mortgages

Automated valuation models (AVMs) are increasingly being used as a substitute for home appraisals in mortgage origination. This article examines whether there are differences in the credit risk of mortgages originated using AVMs relative to traditional appraisals. This question is explored through modeling the conditional default rates of loans originated through Freddie Mac’s automated collateral evaluation (ACE) program relative to those originated with appraisals. We show that ACE loans have about a 9.6% lower default risk in comparison to otherwise similar loans originated with appraisals. This superior default performance can be explained by the ACE decision logic employing data beyond that found in a traditional appraisal and the more efficient utilization of available information through statistical modeling. These findings are limited to Freddie Mac’s use of its AVM, the Home Value Explorer®, and the algorithmic decision logic in the ACE program.

Kadiri Karamon

Papers

The Effect of Mortgage Payment Reduction on Default: Evidence from the Home Affordable Refinance Program

Refinance and Mortgage Default: A Regression Discontinuity Analysis of HARP’s Impact on Default Rates

Spillover effects of continuous forbearance mortgages

Spillover Effects of Continuous Forbearance Mortgages

Does Automated Collateral Evaluation Lower Mortgage Credit Risk Relative to Home Appraisal Valuations?