This paper studies the impact of a new policy implemented by the Federal Housing Finance Agency (FHFA) on December 1st, 2020, called the Adverse Market Refinance Fee (AMRF). This is a fee that is equal to 0.5% of the value of a refinancing loan. The AMRF was instituted by the FHFA as a mechanism to mitigate losses incurred from forbearance defaults and to support the Government Sponsored Enterprises (GSEs), Fannie Mae and Freddie Mac, in managing the increased risk associated with lending during the COVID-19 pandemic. Using data obtained by Fannie Mae, this paper measures the degree of bunching in response to a jump in interest rates for the treated group. I identify the effect of interest rates on borrower behavior by exploiting the exogenous variation in the relationship between loan size and interest rates that results from the threshold used by the new fee policy. Moreover, this paper also provides estimates of the interest rate elasticity of mortgage refinancing demand, resulting from the implementation of the AMRF.

In this paper, I investigate the consequences of the elimination of the Adverse Market Refinance Fee (AMRF) on mortgage refinancing behavior. Following the removal of the 0.5% fee, this study uses individual-level loan data to assess changes in borrower refinancing incentives and loan volumes. By analyzing the interest rate elasticity of refinancing demand, I estimate how borrowers responded to the policy reversal. A key focus is placed on assessing the welfare implications of the AMRF, particularly for low-income borrowers, and evaluating whether the policy effectively mitigated pandemic-related financial burdens for Government Sponsored Enterprises (GSEs) like Fannie Mae and Freddie Mac. Through a detailed analysis, the paper provides comprehensive insights into whether the AMRF created additional refinancing challenges for low-income households, while also exploring its broader efficacy in offsetting pandemic-related losses for the GSEs.

This paper examines how retirees strategically responded to a series of policy changes in the Home Equity Conversion Mortgage (HECM) program between 2013 and 2017. Specifically, it investigates whether borrowers adjusted the timing of their reverse mortgage applications in anticipation of these changes. Using the CoreLogic dataset, which contains detailed mortgage-level data from 2010 to 2020, this study examines borrower behavior in response to policy changes like mortgage insurance premium (MIP) adjustments and the introduction of financial assessments. By applying the bunching method to this dataset, this research uncovers how policy announcements shape borrower behavior and provide insights into the broader implications regarding the sustainability of the HECM program.