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Decision Models for Foreclosed Housing Acquisition and Redevelopment: About

About Our Project

Our project, Decision Models for Foreclosed Housing and Redevelopment, developed decision analytic methods for community development corporations (CDC) to use in identifying foreclosed properties to acquire and rehabilitate for the purpose of neighborhood stabilization. Working closely with our community partner organizations, we drew on well-known theories and models from the field of operations research and the management science (OR/MS), and apply these to the unique challenge of helping communities recover from the foreclosure crisis of the late-2000s. The project was funded in large part by a grant from the National Science Foundation.


The objective of our project was to develop decision models that would yield acquisition strategies that are (1) efficient, making best use of organization resources and social subsidies,  (2) effective, ensuring that foreclosed properties that are acquired and redeveloped provide high-quality and affordable shelter for low/ moderate income families and assist in neighborhood social and economic development, and (3) equitable, ensuring that stakeholder groups and the communities see the foreclosure acquisition process as transparent, consistent and fair.


The foundations for this project were rooted in earlier work conducted by two of the principal investigators, Michael Johnson and David Turcotte. Having particular expertise in community-based operations research (CBOR), and non-profit housing and community development, respectively, Johnson and Turcotte had previously teamed up to study the role of CDCs acquiring foreclosed housing for redevelopment and neighborhood stabilization. Their work in this area included: 1) the development of a tactical single-period, multi-objective integer model of the hypothetical decision processes and outcomes for a CDC acquiring foreclosed housing units, 2) exploratory research funded through a seed grant at the University of Massachusetts Boston to apply the tactical model to the acquisition decisions of a local CDC, to assess whether and how decision models can inform CDC practice, and 3) a multi-site case study of the CDCs in analyzed in the first two projects, to chronicle their current decision making processes and the many limitations and challenges they face in acquiring foreclosed units. Based on the findings of these erfforts, Johnson and Turcotte sought to expand their work to include more organizations and to evaluate additional decision analytic techniques that could be applied to CDC housing acquisitions. A grant from the National Science Foundation, and additional expertise provided by two more principal investigators, Senay Solak and Jeffrey Keisler, supplied the resources needed to pursue this project.


Our analysis was based in interactive, participatory methods that make use of local CDC’s knowledge and resources, while providing practitioners with novel tools and perspectives that enable them to better achieve their organizations’ missions. We began this work by identifying potential community partner organizations, through interviews with key housing and community development stakeholders in Massachusetts, and quantitative analysis of organizational data. From these steps we developed a list of candidate organizations, all of which met the following criteria: 

  • Based in Massachusetts and serving Massachusetts communities hard hit by the foreclosure crisis
  • Actively engaged in the acquisition and redevelopment of foreclosed properties for the purposes of neighborhood stabilization
  • Using Federal Neighborhood Stabilization Program (NSP) funds for the purchase of foreclosed properties.

From our candidate list we chose two organizations: A large active CDC operating in a low-income neighborhood in the City of Boston, and a smaller organization serving a former industrial hub in the center of the state that has recently become a gateway city for immigrants.

Working with these CDCs, we used a combination of quantitative and qualitative methods to collect data on organizational objectives and activities, housing and population characteristics of their service areas, and their approaches to addressing the foreclosure crisis as it impacted their communities. We assessed this information in the context of known decision analytic techniques, settling on specific models that were appropriate to each organization and their stated values and challenges. We then adapted those models to assess the impact of the use of these decision models on practices of community-based organizations, as well as the communities they serve. 


The papers and presentations featured on this website describe in detail the results of our analytic endeavors, and how our findings contribute to different areas of study. Many of these results are also incorporated in our book, Decision Science for Housing and Community Development: Localized and EvidenceBased Responses to Distressed Housing and Blighted Communities, which ties together the insights from our different applications of decision analytics to address neighborhood revitalization, and demonstrates how this work can be extended to other areas of research and practice.

Generally, the application of decision analytics to addressing community-based issues, which is the foundation of the field of community-based operations research (CBOR), furthers CBOR as a branch of OR/MS, while incorporating insights from branches of the social sciences, such as housing and urban affairs. Specifically, our work demonstrates the importance of working with communities to identify core values and problems, develop solution approaches collaboratively, and quantify outcomes in terms that community members themselves define. Our results raise the possibility, for many potential projects, of measuring impacts of interventions not only through traditional efficiency measures but effectiveness measures associated with social welfare as well. By doing so we can better use OR/MS to address a range of social issues, for the benefit of both our field and society at large.

NSF Grant and Award Details

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