BUILT2AFFORD: Machine-Learning-Driven Passive Retrofits for Affordable Housing
Ming Hu, Siavash Ghorbany, Siyuan Yao3, Chaoli Wang, Matthew Sisk
ARCC 2025
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.