Missing ‘Middle Scenarios’: Uncovering Nuanced Conditions in Latin America’s Housing Crisis

Kira Intrator
Massachusetts Institute of Technology

Kaustubh Shivdikar
Veermata Jijabai Technological Institute

This article proposes a novel approach to capturing housing deficiencies in rapidly urbanizing regions that is more suitable for data capture, policymaking, and redevelopment. Housing deficit data need to be accurately captured and categorized to adequately act on them. As underscored in the New Urban Agenda, urban policymakers need to be able to set accurate and realizable targets to address the housing crisis. Local governments require precise knowledge of ground realities to strategically allocate money and resources, target solutions, and avoid needless waste. Planners and architects require detailed data and plans to respond to complex conditions. Citizens and nongovernmental organizations should be informed of their communities’ needs to engage and collaborate in enhancing local urban service delivery. The goal of this article is threefold: (1) to explore a new method to rapidly capture high-quality housing data in the region; (2) to discuss how these deficits and spatial patterns could be clustered into a new form of place-based deficiency typologies; and (3) to contribute to a more precise analysis of housing shortfalls for planners, policymakers, and local governments using the Latin American region as a case-study scenario.

The proposed solution has the potential to be lower cost, more accurate, rapid, and scalable compared with currently applied techniques and technologies such as census data and satellite imagery. This research serves as a call to action and an exploration in the potential to leverage unmanned aircraft systems and a leading artificial intelligence machine learning algorithm applied to its output data to test, program, and study geospatial data for the purpose of capturing and categorizing rapidly changing qualitative housing typologies in Latin America.