Housing Quality

Research on housing quality focuses on developing efficient and accurate methods for assessing housing conditions, primarily to inform policy and investment decisions, particularly in underserved areas. Current approaches leverage deep learning models trained on various image sources, including street-level imagery (like Google Street View and Flickr) and aerial photography, to automatically extract relevant building characteristics and predict overall housing quality. These automated methods offer a cost-effective and scalable alternative to traditional, resource-intensive surveys, enabling more comprehensive and timely assessments of housing quality at both local and national levels. The resulting data can significantly improve understanding of regional disparities, vulnerability to hazards, and the effectiveness of housing policies.