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

ArXiv ID: 2411.00563 “View on arXiv”

Authors: Unknown

Abstract

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.

Keywords: Multi-Agent Systems, Simulation, Mortgage Relief, Household Resilience, Exogenous Shocks

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 7.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced mathematical concepts including reinforcement learning (PPO, GAE) and agent-based modeling with utility maximization and product-conditioned policies, indicating significant math density. It demonstrates high empirical rigor by calibrating the simulation to real-world US census data, detailing agent financial profiles from public datasets, and focusing on backtest-ready simulation outputs for product design, though it lacks live trading code or explicit backtesting metrics.

  flowchart TD
    A["Research Goal<br>Design & Evaluate Novel<br>Mortgage Assistance Products"] --> B["Data Inputs<br>Census Data & Regulatory Guidelines<br>Calibrated to US Mortgage Market"]
    B --> C["Two-Layer Simulation<br>Multi-Agent System"]
    C --> D["Simulation Layer<br>Assess Household Resilience to<br>Exogenous Income Shocks"]
    C --> E["Optimization Layer<br>Explore Strategies & Design<br>New Mortgage Assistance Products"]
    D --> F["Household Learning<br>Agents adapt to maximize utility<br>balancing liquidity & equity"]
    E --> F
    F --> G["Key Findings<br>Successful Product Design<br>Improved Household Resilience<br>Cost-Benefit Analysis"]
    G --> H["Outcome<br>Validated Alternative to<br>Expensive Real-World Pilot Studies"]