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Decision Models for Affordable Housing and Sustainable Community Development
journal contributionposted on 01.01.2006, 00:00 by Michael P. Johnson
Researchers in urban housing and community development face significant challenges in evaluating the success of efforts to improve urban neighborhoods, and identifying underlying theories that might predict the success of future initiatives. Practitioners in this field confront political considerations, restrictive administrative guidelines and limited funding. In the face of these challenges, prescriptive decision models have the potential to improve policy responses to challenges such as affordable housing, race and class segregation, ineffective and/or inequitable economic development, and urban sprawl. This paper first reviews previous research in these areas across multiple disciplines and identify important limitations and modeling opportunities. It then describes recent work on tools and methods to assist policymakers to design effective and sustainable housing and community development strategies. These strategies are based on explicit values of maximization of social welfare and social equity and use best-available evidence regarding impacts of housing and community development policies on program participants and non-participants. The discussion is animated by a case study of a hypothetical affordable housing policy initiative for a diverse metropolitan area that we analyze from three perspectives. The first is a long-term and national perspective in which stylized policy models provide insight into large-scale implementation of this program. The second perspective is the medium- term and regional, in which detailed planning models and decision support systems provide specific guidance to housing providers. The third perspective is the short-term and local, in which decision support systems assist individuals’ choice of specific alternatives as defined by housing initiatives. These complementary modeling perspectives are shown to provide technical and policy insights that differ significantly from, and improve on, those associated with conventional methods.