BUILT2AFFORD: Machine-Learning-Driven Passive Retrofits for Affordable Housing

The persistent shortage of affordable housing in the United States, coupled with aging infrastructure and rising energy costs, disproportionately impacts low-income households, particularly in historically disinvested communities. Addressing this challenge requires innovative, scalable solutions that balance affordability, energy efficiency, and climate resilience. This study introduces the BUILT2AFFORD dashboard, an integrated tool leveraging machine learning (ML) and Google Street View (GSV) imagery to pre-identify low-cost passive retrofit strategies for preserving and improving affordable housing. The dashboard assesses existing building conditions, evaluates energy retrofit potential, and mitigates heat-related health risks. The research involved on-site audits of single-family homes and Section 8 apartments, combined with continuous monitoring of indoor environmental conditions such as temperature, humidity, and CO2 levels. Preliminary results reveal significant opportunities for energy savings and improved thermal comfort, with scenarios like infiltration reduction and insulation upgrades achieving substantial reductions in energy use. The dashboard’s validation through eight testbeds demonstrates its potential to address housing challenges in South Bend, Indiana while advancing carbon-neutral and equity goals. This paper highlights how integrating advanced technologies into retrofit planning can enhance housing quality, reduce displacement risk, and foster climate resilience in vulnerable communities.