Devastating earthquakes can cause affected households to relocate. Postearthquake relocation disrupts impacted households' social ties and, in some instances, their access to affordable services. Simulation-based approaches that model postearthquake relocation decision-making can be valuable tools for supporting the development of related disaster risk reduction (DRR) policies. Yet, existing versions of these models focus particularly on housing-related factors, which are not the sole driver of postearthquake relocation. We integrate data-driven approaches and local data to account for postearthquake household relocation decision-making within an existing simulation-based framework for policy-related risk-sensitive decision support on future urban development. We use household survey data related to the 2015 Gorkha earthquakes in Nepal to develop a random forest model that estimates the postearthquake relocation inclination of disaster-affected households. The developed model holistically captures various context-specific factors important to postearthquake household relocation decision-making. We leverage the framework to quantitatively assess the effectiveness of various DRR policies in reducing positive postearthquake relocation inclination, with an explicit focus on low-income households. We demonstrate it using "Tomorrowville," a hypothetical expanding urban extent that reflects important social and physical characteristics of Kathmandu, Nepal. Our analyses suggest that the provision of livelihood assistance funds is more successful when it comes to mitigating positive postearthquake relocation inclination than hard policies focused on strengthening buildings (at least in the context of the examined case study). They also suggest viable pro-poor pathways for mitigating disaster relocation impacts without the need to create potentially politically sensitive income-based restrictions on policy remits.