Dynamic incentives for pro-social cities : an application to affordable housing
Author(s)
Luo, Kara(Kara F.)
Download1227276258-MIT.pdf (7.824Mb)
Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Kent Larson.
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With more than half of the world's population living in cities, there is increased emphasis on developing sustainable and resilient urban areas with pro-social characteristics. In particular, the issue of housing affordability has attracted the attention of many urban decision-makers, as housing plays an important role in urban resilience and social equity, and the rising housing prices in cities has created persistent social and economic pressure. A variety of incentive programs have been established across the U.S. to encourage private developers to produce greater amounts of affordable housing units. However, these programs are not frequently updated to reflect the changing demands of the city and have had varying levels of success in terms of producing affordable housing units. In this paper, I review the existing affordable housing programs, determine the crucial factors that would make such programs effective, and I propose a dynamic incentive model that incorporates these crucial factors, and more importantly, dynamically adjusts to reflect the conditions of the city. The proposed dynamic incentive model could facilitate the development of mixed-income residencies with affordable housing units targeting various income levels in the context of the dynamically shifting needs and circumstances of an urban community.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020 Cataloged from student-submitted PDF of thesis. Includes bibliographical references (pages 67-69).
Date issued
2020Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer SciencePublisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.