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

TL;DR

It is shown 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.

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.

Figures (4)

• Figure 1: The proposed two-layer approach, featuring an outer (product) layer, and an inner (simulation) layer. The inner layer is conditioned on the output from the outer layer.
• Figure 2: The baseline delinquency rate $r$ and (negative) social index $\omega(r)$ under $\varphi_\varnothing$.
• Figure 3: Product analysis across configurations. Products sampled from the fixed (adaptive) outer layer are shown with $\circ$ ($\times$), with $100$ samples each. The region identified by the adaptive layer is indicated by the blue bounding box. In all cases darker colour indicates a better value.
• Figure 4: Pareto frontier of product configurations across the two metrics $C$, $\omega(r)$ for the products in Fig. \ref{['figMetricAffects']}. The social index (x-axis) is visualised on a log-scale. The (unattainable) ideal product is situated in the bottom left corner.