  Mortgages account for the largest portion of household debt in the United
States, totaling around \$12 trillion nationwide. In times of financial
hardship, alleviating mortgage burdens is essential for supporting affected
households. The mortgage servicing industry plays a vital role in offering this
assistance, yet there has been limited research modelling the complex
relationship between households and servicers. To bridge this gap, we developed
an agent-based model that explores household behavior and the effectiveness of
relief measures during financial distress. Our model represents households as
adaptive learning agents with realistic financial attributes. These households
experience exogenous income shocks, which may influence their ability to make
mortgage payments. Mortgage servicers provide relief options to these
households, who then choose the most suitable relief based on their unique
financial circumstances and individual preferences. We analyze the impact of
various external shocks and the success of different mortgage relief strategies
on specific borrower subgroups. Through this analysis, we show that our model
can not only replicate real-world mortgage studies but also act as a tool for
conducting a broad range of what-if scenario analyses. Our approach offers
fine-grained insights that can inform the development of more effective and
inclusive mortgage relief solutions.
