# Simulate and Optimise: A two-layer mortgage simulator for designing novel mortgage assistance products

arXiv: https://arxiv.org/abs/2411.00563 · HTML: https://mlsanity.com/paper/2411.00563/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 23 references that are in the map.
- First posted on arXiv 1 Nov 2024.

## Builds on (most cited first, 6 of 6)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [A Practical Guide to Multi-Objective Reinforcement Learning and Planning](https://mlsanity.com/paper/2103.09568/) (2021)
- [Convergence of Learning Dynamics in Stackelberg Games](https://mlsanity.com/paper/1906.01217/) (2019)
- [Calibration of Shared Equilibria in General Sum Partially Observable Markov Games](https://mlsanity.com/paper/2006.13085/) (2020)
- [The impact of social influence in Australian real-estate: market forecasting with a spatial agent-based model](https://mlsanity.com/paper/2009.06914/) (2020)
- [A Heterogeneous Agent Model of Mortgage Servicing: An Income-based Relief Analysis](https://mlsanity.com/paper/2402.17932/) (2024)

## Abstract (arXiv)

We develop a novel two-layer approach for optimising mortgage relief products through a simulated multi-agent mortgage environment. While the approach is generic, here the environment is calibrated to the US mortgage market based on publicly available census data and regulatory guidelines. Through the simulation layer, we assess the resilience of households to exogenous income shocks, while the optimisation layer explores strategies to improve the robustness of households to these shocks by making novel mortgage assistance products available to households. Households in the simulation are adaptive, learning to make mortgage-related decisions (such as product enrolment or strategic foreclosures) that maximize their utility, balancing their available liquidity and equity. We show how this novel two-layer simulation approach can successfully design novel mortgage assistance products to improve household resilience to exogenous shocks, and balance the costs of providing such products through post-hoc analysis. Previously, such analysis could only be conducted through expensive pilot studies involving real participants, demonstrating the benefit of the approach for designing and evaluating financial products.