NSF Forms

Collaborative Proposal: Decision Models for Foreclosed Housing Acquisition and Redevelopment

Proposal Summary Over the past three years, increased rates of mortgage foreclosures in the U.S. have resulted in widespread bankruptcies of financial institutions directly or indirectly linked to the U.S. housing market. Home foreclosures have resulted in massive losses of consumer wealth: U.S. households lost nearly $500 billion in home value in 2009, and $3.6 trillion in 2008. In response, municipalities, often with the aid of state and federal agencies, provide a variety of services to mitigate the effects of foreclosures. Purchases of foreclosed properties represent a particularly attractive strategy because they have the potential to minimize blight, reduce unanticipated housing mobility, and to provide affordable homeownership opportunities to lower-income families. However, the cost of all such purchases far exceeds the resources available in most urban centers. Thus, not-for-profit managers must dynamically solve the following decision problem: What subset of a large number of available foreclosed properties should be acquired for neighborhood stabilization and revitalization? Intellectual Merit: The objective of this project is to design, implement and evaluate decision models to assist community-based organizations in choosing foreclosed properties. These models will yield acquisition policies that are (1) more efficient, i.e. they make best use of organization resources and social subsidies, (2) more effective, i.e. they ensure 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) more equitable, i.e. they ensure that stakeholder groups and the communities see the foreclosure acquisition process as transparent, consistent and fair. Our project integrates multiple disciplinary perspectives and analytic methods in the service of a policyrelevant problem in order to achieve significant impact in theory and practice. First, we use interactive, participatory methods to build new theory about the process, decisions and impacts of foreclosed housing acquisition and redevelopment. Second, we adapt and extend current research to estimate values for attributes of the decision problem which are important to practitioners, such as the dollar-valued social impact of a foreclosed unit chosen for acquisition and redevelopment. Third, we develop innovative decision models that address the tactical question of the choice of specific foreclosed units to acquire and redevelop, and the strategic question of development of portfolios of foreclosed housing acquisition opportunities as a basis for longer-term planning. These models, intended for use by practitioners as well as researchers, balance verisimilitude and tractability. Fourth, we adapt methods of empirical operations management and other disciplines to assess the impact of the use of these decision models on practices of community-based organizations, as well as the communities they serve. Broader Impacts: This project will have a significant impact on community based operations research. Our work will enable practitioners to explicitly identify and quantify decision problems and solve these problems through user-friendly, lightweight applications to generate evidence-based recommendations for provision of key services. Policy-makers and funders will have increased resources to modify strategies, priorities and funding criteria based on the effectiveness of decision models for community-based service provision. Researchers will have increased ability to build value-based decision models, display results in intuitive ways to examine alternatives and build consensus, improve operations and strategy practice; and rigorously evaluate the impacts of community-based decision models on organizations and populations. Students who work on this project will be exposed to decision problems with community impact and will perform public service through operations research. The project will also improve university-community partnerships through collaborative projects using quantitative methods and information technology. NSF Proposal Summary

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National Science Foundation: Decision, Risk and Management Sciences COLLABORATIVE PROPOSAL: DECISION MODELS FOR FORECLOSED HOUSING ACQUISITION AND REDEVELOPMENT Project Description 1. Introduction Policy motivation and scope: The U.S. economic recession, which officially began in December 2007 (National Bureau of Economic Research 2008), and which has shown signs of conclusion by the close of 2009 (Zandi 2010), has had adverse effects in all sectors of the economy, particularly residential housing. A root cause of the recession is a dramatic increase in mortgage foreclosures since 2006, originating in decreases in home price appreciation that amplified the effects of increases in mortgage rates and the number of risky mortgage originations (Bernanke 2008). The negative impact of this crisis on the U.S. housing market has been broad and profound: since 2005 to the present, there have been substantial decreases in median housing values, home equity, existing home sales, mortgage refinances, total housing starts and new home sales (Joint Center for Housing Studies 2009). In the first quarter of 2009, mortgage actions had been initiated on 1.37% of all first mortgages, and the seasonally adjusted delinquency rate on mortgage loans for one-to-four unit properties was 9.12%. Both of these indicators are record highs (Mortgage Bankers Association 2009). Home foreclosures have resulted in massive losses of consumer wealth: U.S. households lost nearly $500 billion in home value in 2009, and $3.6 trillion in 2008 (CNNMoney.com 2009). The effects of the foreclosure crisis have been especially pronounced in economically vulnerable regions of the U.S.: 1 out of every 53 households in California and 1 out of every 56 households in Florida received a foreclosure filing during the third quarter of 2009 (RealtyTrac 2009). The worst of the foreclosed housing crisis is not yet over, and government and nonprofit resources are insufficient to meet the challenges of the foreclosure crisis (Mallach 2009). Federal efforts to address the problem of individual foreclosures by providing subsidies to lenders to modify troubled loans have been judged ineffective (Congressional Oversight Panel 2009). Federal efforts to address the neighborhood and regional impacts of the foreclosure crisis include the Housing and Economic Recovery Act of 2008, which has provided $3.92 billion to be administered by the U.S. Department of Housing and Community Development via the Neighborhood Stabilization Program. These funds are specifically designed to assist state and local governments in the acquisition and rehabilitation of foreclosed property (U.S. Department of Housing and Community Development 2008). Other Federal initiatives such as the Neighborhood Works HOPE hotline, the Federal Housing Administration (FHA) Home Secure program and the National Homeownership Sustainability Fund have emerged to help provide counseling, funds and other resources to keep borrowers in their homes (Carr 2007). In addition, $4.25 billion in funds authorized by the 2009 Emergency Economic Stabilization Act of 2008 is being targeted on construction of new, affordable rental housing. An example of state-level initiatives includes, in Massachusetts, a $20 million initiative to create an acquisition fund for the purchase and renovation of foreclosure properties (CHAPA 2008a). In November 2007, Massachusetts also enacted legislation that increases regulation of the lending industry and mandates third party counseling for first-time homebuyers receiving subprime financing with variable interest rates (CHAPA 2008b). In the nonprofit sector, community development corporations (CDCs) are key actors in foreclosure prevention as well as redevelopers of vacated foreclosed properties (Taylor 2008). CDCs have traditionally played a leading role in mitigating adverse market effects on inner cities though programs designed to increase community capacity. Chief amongst these have been the building and

NSF proposal: project description

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redevelopment of affordable housing for low-income families (McQuarrie, 2008). This mission has so consumed CDCs that in the quest to keep pace with the housing market boom, many CDCs, who generally have inadequate understanding of the private housing market (Bratt 2009), have themselves become victims of risky mortgage lending practices. However, CDCs are poised to play an important role in addressing the negative effects of foreclosure. In particular, their expertise in renovation and redevelopment make them prime candidates to tackle the increasing number of vacant properties threatening many neighborhoods. A research-based response to this crisis relies crucially on detailed data on the individual, local and regional impacts of the foreclosure crisis. Individual impacts can include displacement and housing instability, financial insecurity and economic hardship, personal and family stress, disrupted relationships and ill health; community impacts can include declining property values and physical deterioration, crime, social disorder and population turnover, and local government fiscal stress and deterioration of services (Kinglsey, Smith and Price 2009). However, there are significant gaps in the data available to measure these impacts, especially individual impacts. Significant changes in neighborhood quality can take two forms: stabilization, denoting preservation and potential increase in the value of a property-owner’s investment in a neighborhood, and revitalization, the process of increasing demand for housing which leads increased property values (Mallach 2008). Proposed policy responses for neighborhood stabilization and revitalization as a response to the foreclosure crisis include: a multi-year neighborhood stabilization program that increases the ability of CDCs to successfully acquire distressed units, Federal government-led land banking to pass foreclosed units to local actors, and Federal income tax credits to enable individuals to acquire foreclosed units that would otherwise be economically undesirable (Mallach 2009). Specific family support services and credit repair initiatives can mitigate the impacts of foreclosures on households (Kingsley, Smith and Price 2009), increasing the likelihood of neighborhood stabilization. This research proposal focuses on CDC-led initiatives in relatively small urban neighborhoods to acquire and redevelop foreclosed housing units for re-sale or rental to support neighborhood stabilization and revitalization. (Neighborhoods and regions, especially in suburban areas, whose greatly overheated housing markets have collapsed due to large numbers of foreclosures, such as the Inland Empire of southern California or the Miami-Dade region of Florida, will require market-level interventions that are far beyond the scope of particular CDCs.) Since the scale of the foreclosure crisis in most neighborhoods exceeds the response capacity of any particular CDC, our fundamental research question is the following: Given limited data on the impacts of the foreclosed housing crisis, what tactical and strategic decision processes should CDCs use to acquire a subset of a large number of available foreclosed properties in a distressed urban community to support neighborhood stabilization and revitalization? Answering this question requires expertise in multiple areas of decision modeling, as well as policy analysis and community-based housing development. We demonstrate that the team of researchers has the academic and practice expertise to develop practical solutions to assist CDCs in addressing neighborhood-level foreclosures. Previous work: For over thirty years, housing and community development has been a subject of inquiry by researchers in the decision sciences. A survey of work in this area (Johnson 2010) demonstrates a wide range of descriptive models, which explain identify evidence regarding policy initiatives, prescriptive models, which generate policy alternatives that balance multiple social objectives, and decision support systems to automate the process of policy analysis and recommendations. Examples of this work include cost-benefit analysis of housing mobility programs (Johnson, Ladd and Ludwig 2002), long-term policy modeling for housing mobility (Caulkins et al. 2005), multi-objective optimization for affordable and subsidized housing location (Johnson 2006, Johnson 2007) and housing mobility planning (Johnson 2003), and decision support for individual housing search (Johnson 2005). Johnson (2010) concludes, however, that much of the work in this area is disconnected, lacks evidence of real-world implementation,


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and is not grounded in a theory of housing and community development that is generalizeable to diverse regions and housing types. Decision modeling for housing and community development is situated in a long-standing literature of public-sector operations research (Larson and Odoni 1982, Pollock, Rothkopf and Barnett 1994) which has applied operations research/management science methods to different sectors at the national, regional and local levels. More recently, the notion of ‘community-based operations research’ has been coined (Johnson and Smilowitz 2007) to focus OR/MS inquiry on human stakeholders, disadvantaged, underserved and vulnerable populations and characteristics of well-defined communities that vary systematically in ways that are salient to decision models. Another tradition of inquiry relevant to this project is that of empirical operations management. Empirical OM seeks to develop theory that borrows judiciously from other academic traditions, reflects the empirical reality of OM practice and is relevant to OM practitioners (Meredith 1998). This subfield also addresses the appropriate balance of analytical methods and empirical methods (Wacker 1998). Empirical OM has increased in stature since the mid-1990s (Scudder and Hill 1998) and continues to be a robust area of research activity presently (The Wharton School 2009). The emphasis of empirical OM on primary data collection, theory-building, multiple methods and external as well as internal model validity is a fundamental motivation for the current project. The process of model-building is central to this proposal: there are a variety of qualitative methods to increase the likelihood that decision models reflect stakeholder values and are positioned to solve problems of core importance to decision-makers. Facilitated modeling places emphasis on the subjective, socially-constructed nature of decision problems and need for analysts to work cooperatively with clients, rather than experts, to develop decision models (Franco and Montibeller 2010). Problem structuring methods apply qualitative, iterative discussion-based methods in ways that are accessible to non-experts that address difficult, unstructured problems, including those arising from communities, as contrasted with large organizations (Taket and White 1997, Mingers and Rosenhead 2004). Value-focused thinking applies a systematic method to identify different types of objectives, or goals, of clients, and to decompose these goals, or build a chain of reasoning related to them, in order to build elements of prescriptive decision models (Keeney 1992). The focus of this proposal is housing, a physical entity subject to decisions regarding acquisition, construction and demolition which generates significant externalities. Public-sector facility location, the science of choosing sites to optimize objectives of access, coverage and equity as opposed to revenue, costs or profit (ReVelle 1987, Marianov and Serra 2004), is relevant to this study. In particular, concerns identified by these authors of appropriate social objectives, different classes of services and data limitations are central to our modeling efforts. There is no published work known to us that specifically addresses decision modeling for foreclosed housing redevelopment. However, a recent working paper describes the formulation and solution of a multi-objective integer program to guide long-term foreclosed housing acquisition (Johnson, Turcotte and Sullivan 2009a). Current efforts to generalize this work have focused on a single CDC in the Boston region, and field work at this organization has clarified notions of objectives to measure social impacts of foreclosed housing development, as well as new decision problem formulations that motivate this proposal (Johnson, Turcotte and Sullivan 2009b). Summary: The U.S. foreclosed housing crisis is broad, deep and shows no signs of abating. Current policy initiatives are limited by data available on impacts of the crisis, especially on individuals, and limited in their effectiveness to date. New academic research is well-positioned to produce theory, models and applications that assist local actors in urban neighborhoods to make improved choices regarding foreclosed housing units to acquire and redevelop to assist in neighborhood stabilization and revitalization. The remainder of this proposal describes the objectives and proposed significance of the research, a description of the decision problems to be solved, the research design, key project tasks,


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broader impacts of this research, qualifications of the investigators, the relation of the proposed project to the Principal Investigator’s research agenda, and prior support from the National Science Foundation. 2. Objectives and Proposed Significance The goal of this project is to design, implement and evaluate evidence-based decision models that leverage new theory about housing development processes and strategy design to assist community-based organizations to choose foreclosed housing properties to develop in the service of community stabilization and revitalization. These decision models address problems of strategy design, using multiobjective optimization, as well as operational guidance, using, respectively, multi-criteria decision models, where uncertainty is not central to the decision problem, and stochastic modeling, where it is. We provide additional details on these decision models below and in §3. Research impact and areas of emphasis: This research has five key areas of impact. First, using primary data collected from field observations of and interviews with community-based housing developers, it will result in new theory on the process of foreclosed housing acquisition and redevelopment. Second, it will apply this theory to develop practical, appropriate and parsimonious decision models built through collaborations with practitioners that can be generalized to other public sector applications. These models will represent, and optimize, where appropriate, measures of operational efficiency, policy effectiveness and social equity. Third, we will develop novel methods to solve challenging models that incorporate issues of scale, uncertainty, nonlinearities and idiosyncratic practitioner values and preferences. However, the focus on analytical methods is subservient to the emphasis on developing innovative and high-impact models for housing and community development, and community-based operations research more generally. Fourth, we will evaluate our efforts in theory-building, model development, solution impacts and policy insights through case studies of multiple community-based organizations. Last, we will generalize our findings to other housing development challenges such as vacant housing, new housing construction and economic decline and shrinking cities (see e.g. Hollander et al. 2009). To do research that may have the impacts listed above, we are mindful of certain considerations that guide our work. The focus of the project is advancing the field of community-based operations research through empirical methods. Also, our efforts in theory-building, model formulation, solution and implementation should be driven by specific problems and processes of real-world practitioners. And, we recognize that qualitative decision modeling is essential to gathering evidence to support and guide our model development and implementation; we expect to apply best practices from this field wherever possible. Research questions: As part of the proposed research, we will investigate the following research issues: (1) A fundamental issue we address is that of most-appropriate methods for model-building with community-based practitioners who may have some academic training in planning or real estate, and significant practical experience, but limited exposure to decision modeling and, therefore, less-thancomplete understanding of limitations in their understanding of problem definition and solution. Valuefocused thinking (Keeney 1992), facilitated modeling (Franco and Montibeller 2010) and problem structuring methods (Taket and White 1997, Mingers and Rosenhead 2004) are all well-documented as contributions to identifying and structuring complicated problems. Which of these methods, or combination of methods, is most appropriate to the context of urban foreclosed housing acquisition and redevelopment, especially by resource-limited community-based organizations serving in economicallydisadvantaged neighborhoods? (2) ‘Rationalist’ studies, as defined by researchers in empirical operations management, emphasize mathematical representation and analysis of objectively understood problems; ‘case’ studies emphasize subjective understanding of organizations and phenomena via direct observation (Meredith 1998). We propose to design ‘rationalist’ decision models for community-based foreclosed housing policy design, and evaluate the impact on organizations and communities of recommendations generated by these


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models. What relevant theory of research might guide this inquiry? Much of the literature on the application of case study methods is drawn from social science (Yin 2003) or operations management (Meredith 1998), which is focused primarily on private-market actors. What theory of case methods may be particularly useful to public-sector decision modeling, particularly community-based operations research? (3) As indicated in §1, there are significant gaps in research knowledge of the impacts of foreclosed housing on individuals and communities. What approximations are then appropriate for representing values of important parameters in models of foreclosed housing acquisition and redevelopment? The strategic problem, presented in Johnson, Turcotte and Sullivan (2009a), approximates the social benefit of foreclosed housing acquisition by the distance-weighted utility perceived by families associated with proximity to neighborhoods of varying amenity levels, and approximates the scale economies present in housing construction by the level of spatial proximity of units chosen for development. The tactical problem under certainty, developed from current research (Johnson, Turcotte and Sullivan 2009b), estimates property value impacts of foreclosed housing acquisition as a function of proximity of foreclosed units to non-distressed units and the administrative status of foreclosed units, and approximates the strategic value of foreclosed units as a function of foreclosed units to neighborhood assets such as parks, and liabilities, such as known areas of criminal offending. The tactical problem under uncertainty, also developed from current research, but in a very preliminary form, proposes, for a Markov decision process view of the foreclosed housing development problem, a ‘social return’ associated with specific development choices. How might such an indicator, and others, reflect current and best practices in community-level operations and planning for foreclosed housing development? (4) Visualization of decision model outputs is important in providing decision-makers with the broadest understanding of the range of actions available, and the impacts of changes in model parameters on those actions (Bregar, Györkös and Jurič 2009). What representations of outputs of the models we propose to develop will provide most value to community-based housing development practitioners? Sources of visualization challenges include: values of criteria or objectives (‘objective space’), values of decision variables (‘decision space’), and objective/criteria value tradeoffs between alternative solutions. These quantities may be displayed using value graphs (ReVelle 1987), tables, coordinate graphs, maps, animations or verbal explanations. (5) Within a particular model class, e.g. math programming, certain variants may better capture core concerns of decision-makers than others. What versions of the decision models that we will develop best balance accuracy of problem representation and ease of solution? Should the strategic problem, to be solved using multi-objective optimization, include nonlinear or strictly linear objectives, or stochastic as opposed to strictly deterministic elements? Should the tactical problem under certainty, to be solved using multi-criteria decision models, be solved using certain utility functions rather than others, or certain optimal solution methods rather than others, or perhaps using suboptimal heuristics rather than optimal methods? Should the tactical problem under uncertainty be solved using stochastic programming or Markov decision models? What representation of estimates of the success of a bid for a foreclosed unit, or the probability of successful development is best-suited for our application context? (6) Decision models intended for use by practitioners must have a defined use context. Which use contexts are most salient for foreclosed housing acquisition and redevelopment? One alternative is realtime decision-making. For the strategic decision model, one might use GIS to display model results, or full-featured spatial decision support systems (Malczewski 1999). For the tactical decision models, one might use spreadsheet-based decision support systems (Şeref, Ahuja and Winston 2007). Another alternative use of these models is expert support, i.e. heuristics or rules-of-thumb derived from repeated applications of decision models to realistic test data that support and complement practitioner expertise without prescribing specific courses of action. Finally, these models could be used as tools for auditing, i.e. assessing the quality of decisions made without explicit models by applying decision models to historical input data and documented decisions.


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(7) We assert that the decision models to be created in this project will have tangible positive impacts on the operations of community-based organizations as well as the neighborhoods they serve. How can impacts of our models be measured? Within the organization, one approach would be survey-based measures of satisfaction by practitioners with the decision models; another would be measures of changes in workflow processes and times. Within the community, we might measure the level of community input to model design and implementation, and reaction to specific foreclosed housing acquisition choices derived from model prescriptions1.

3. Description of Decision Problems In this section we describe three distinct decision problems to be addressed in this project that arise specifically from, and respond specifically to, problems in foreclosed housing acquisition and development that are current areas of inquiry (Johnson, Turcotte and Sullivan 2009a,b), and represent policy-relevant concerns within housing and community development more generally. An introduction to the foreclosed housing process provides context for our decision models. The foreclosure process consists of multiple stages (BiggerPockets 2009): After three to six months of nonpayment of the mortgage, a Notice of Default is registered by the lender at the county recorder’s office. At this point the owner is on notice that the home is subject to foreclosure until the home is auctioned off a short while later. If the homeowner does not bring the loan current, a sale date is set and a notice of sale is posted at the county recorder’s office and local newspapers. This trustee sale is actually an auction; the person whose bid, usually in cash, that exceeds the opening bid set by the lender and those of all other bidders, acquires title to the foreclosed property. If the auction is unsuccessful, ownership of the property formally reverts to the lender, and the property is deemed real estate-owned (REO). All junior liens other than property taxes are forgiven. A property may remain in REO status until purchased by public or private actors. Strategic problem: The goal of the strategic foreclosed housing redevelopment problem is to build portfolios of foreclosed (pre-REO and/or REO) multifamily housing units available for purchase from banks and mortgage companies for rehabilitation and resale or re-rental, from which decision-makers may choose one collection of units as the basis for strategy design. Interviews with Boston-area affordable housing developers and funders have yielded a consensus that strategy design for foreclosed housing acquisition is both highly important to ensure the best use of limited funds for neighborhood stabilization and revitalization, and yet often not an explicit concern of community-based organizations (CBOs) that perform such development, yielding the potential of ineffective social investments in distressed communities. This decision model optimizes measures of social impacts, redevelopment costs and equity, subject to a constraint on available resources. The planning horizon for this problem is one to three years, thus there is no guarantee that specific housing units that are input to the decision problem now will be available when the community-based organization selects a particular acquisition strategy. We model social impacts of redeveloped foreclosed housing as comprised of two components: those associated with the unit (borne jointly by the housing developer and the occupant of the housing) and those associated with the neighborhood in which the unit is located, as well as those nearby (borne primarily by the occupant of the housing). These impacts are approximated by four objectives. The first objective is to minimize the total subsidy required to acquire and redevelop foreclosed units, a proxy for social costs. The subsidy associated with each foreclosed unit acquired is the difference 1

In the longer term, likely beyond the scope of this project, one could assess the impacts of our decision models on community-level outcomes using experimental design in which a dependent variable might be property value changes, and independent variables might be level and type of use of decision support models and applications, controlling for CBO and community characteristics. NSF proposal: project description

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between the estimated purchase and redevelopment cost and the estimated market value upon completion of rehabilitation. The second objective is to maximize the aggregate utility associated with proximity to neighborhoods with high levels of amenities thought to appeal to renter occupants, a proxy for social benefit. The third objective is to maximize the proximity of acquired units to each other, a proxy for scale economies in development. The last objective is to minimize the perceived inequity in housing acquisitions across neighborhoods, a political concern that might restrict implementation of otherwise beneficial housing policies. The primary policy-substantive constraint is that the planning organization spends no more on acquisition and redevelopment than funds available. The goal of this model is to generate a collection of non-dominated solutions, especially those that balance concerns of clustering units with perceived equity. A full problem formulation is available in (Johnson, Turcotte and Sullivan 2009a). This decision problem is an example of the multi-objective knapsack problem (MOKP). Methods for solving multi-objective optimization problems include the weighting and constraint methods (Cohen 1978), as well as a variety of methods such as metaheuristics, interactive methods and fuzzy optimization (Ergot and Gandibleux 2002). Methods for solving MOKP specifically include: dynamic programming (Klamroth and Wiecek 2000), a memory-based metaheuristic (Beausoleil, Baldoquin and Montejo 2008), labeling algorithms applied to graph representations of MOKP (Rui Figueira, Tavares and Wiecek 2010) and interactive heuristics (Teghem, Tuyttens and Ulungu 2000). Applications of multi-objective optimization to housing strategy design, of which the current problem is a special case, include selections of parcels for land-use planning (Gabriel, Faria and Moglen 2006, Wright, ReVelle and Cohon 1983), choosing sites for development of affordable and assisted housing (Johnson 2006, 2007) and designing strategies for housing mobility counseling (Johnson 2003). The current version of the strategic foreclosed housing planning problem assumes certainty in the values of model parameters, linearizes all objectives, and is devoted to a single housing type, multi-family rental housing. In this project we will relax all of these constraints in order to formulate the model variant that is most reflective of practitioners’ needs while accommodating easily-implemented solution methods. In particular, we will (1) investigate the computational benefits and costs of representing certain objectives as linear versus nonlinear (see e.g. Isada, James and Nakagawa 2005), (2) build a theory of foreclosed housing policy design that accommodates a wide range of alternative equity measures (Marsh and Schilling 1994) and (3) explore the impacts on formulation, solution and policy design of incorporating uncertainty into the decision model (see e.g. Higle 2005). We will also generalize the decision model to address multiple housing types, such as owner-occupied versus rental housing, single-family versus multi-family housing, and single-use residential versus mixed-use commercial/residential (Johnson 2007). Tactical problem under certainty: The goal of the tactical problem under certainty is to select a particular REO foreclosed unit for acquisition and redevelopment in the short term, four to seven months or so, from a list of such units typically maintained by the community-based organization. In Massachusetts, the Citizens Housing and Planning Association (CHAPA) has negotiated an agreement with seven banks that control about 75% of foreclosed properties in Massachusetts to have 90 days of exclusive rights to market these properties to community-based organizations, who may acquire these properties directly from the lenders. Unfortunately, there are multiple administrative barriers that limit the ability of CBOs to purchase REO units, according to our interviews with a Boston-area CBO. First of these is the limited accessibility of Neighborhood Stabilization Plan funds to support these purchases; CBOs also face significant challenges in assembling commitments from lenders, funders and municipalities to secure financing to purchase these units. Finally, CBOs have limited ability to inspect these units to ensure their quality is acceptable to them. In addition, CBOs face the usual difficulties associated with local real estate development: ensuring demand exists for units to be redeveloped and ensuring that construction will be completed ontime and under budget. The description of the third decision problem, the tactical problem under uncertainty, addresses these issues more directly.


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Here, though, the acquisition problem can be simplified to that of choosing a most-preferred REO unit to acquire, under the assumption that funding is in place and enough is known about unit quality to make the purchase feasible by the CBO’s development standards. In practice, according to our interviews with a local community development organization, CBOs focus their energy on selecting a candidate unit known to have a strong potential for contribution to neighborhood stabilization and revitalization, ensuring that they have enough sources of funding to meet the total costs of redevelopment, via a sources and uses statement, and then managing the redevelopment process according to conventional professional standards. This process may be sufficient to ensure that a REO acquisition and redevelopment process is deemed successful. The decision problem, then, can be modeled as the choice of a most-preferred acquisition alternative, given knowledge of the values of attributes that are common to all candidate acquisitions. This is a singlestage multi-criteria decision problem (MCDM): the input data are represented as an evaluation matrix in which the rows are alternative properties, denoted as decision alternatives di, the columns are various property attributes, denoted as criteria ck, and the elements of the evaluation matrix A = [aik] represent the performance of decision alternative di according to criterion ck,. This MCDM has been extensively studied (Eiselt and Sandblom 2004, von Winterfeldt and Edwards 1986), and there are a wide variety of solution methods. Perhaps the most commonly-studied method uses multiattribute value functions (Keeney and Raiffa 1976), in which aggregation functions f are applied to decision-maker utilities associated with all elements aik associated with each alternative di to generate values for each decision, denoted v(di); the alternative with the greatest value is the most-preferred alternative. However, many other methods can be used to solve the MCDM. Reference point methods choose the alternative that is closest, in a geometric sense, to an ideal solution (see e.g. Hwang and Yoon 1981). Outranking methods such as PROMETHEE (Brans and Vincke 1985) define preferences between pairs of decision alternatives with respect to a particular criterion, computed using a variety of preference functions, which are aggregated to generate rankings of decision alternatives. Methods allowing inconsistent estimates, such as the Analytic Hierarchy Process (Saaty 1980), require decisionmaker to quantify tradeoffs between pairs of alternatives with respect to a given attribute, or between pairs of attributes with respect to a the problem goal; the matrices of values thus produced are transformed and aggregated to produce rankings of decision alternatives. There are a number of research issues that are salient to the tactical problem under certainty. The first of these is the choice and representation of foreclosed unit characteristics. Interviews with real estate developers at a Boston-area CBO have revealed that the social benefit of foreclosed housing redevelopment, though not quantified in practice, is a key criterion when choosing units to develop. With the consent of this CBO, we have proposed a social benefit measure derived from the foregone property value declines associated with redevelopment of a given unit. This measure is inspired by recent work in real estate economics that has estimated the property value impacts of nearby foreclosed units (Immergluck and Smith 2005, Harding, Rosenblatt and Yao 2010, Mikelbank 2008). We are particularly interested in the work of Harding and colleagues, who have estimated the percent discount on assessed value of non-distressed units associated with distance to the nearest foreclosed unit, and the stage of foreclosure of such a distressed unit. We have adapted the computed model parameters in this paper and applied some convenient assumptions to compute the total property value loss of non-distressed units associated with a given distance from a given REO unit. Current discussions, however, reveal that CBO professionals are uncomfortable with social impact estimates that ignore other property characteristics such as vacancy status or level of physical distress. Thus, the question of how to compute dollar-valued impacts of redevelopment of a particular foreclosed unit is unresolved. (An alternative approach would attempt to compute the benefit to residents of newly-redeveloped units using formulations of consumer surplus, see e.g. Johnson and Hurter (1999)). Another research question addresses a criterion revealed through CBO interviews to be salient to their decision process: quantifying the strategic nature of a particular foreclosed property with respect to the


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CBO’s mission. To date, we have proposed that the distances between a candidate unit for acquisition and neighborhood assets, such as schools or parks, be combined with distances between a candidate unit and neighborhood liabilities, such as known locations of criminal offending. However, it is not yet clear how these distances can be aggregated to produce a single scalar measure of strategic importance. CBO real estate developers seek to redevelop foreclosed units in a manner that is consistent with community standards of health, safety, code compliance, which contribute to neighborhood development. However, CBO professionals do not quantify these measures of project success. What is the most appropriate method for quantifying measures of project success? Doing so in the context of MCDM would require subjective probability assessments ranging from direct elicitations of likelihood, to more sophisticated methods using visual representations of various levels of housing quality. CBOs perform foreclosed housing acquisition and redevelopment without the benefit of formal decision models. Examples of heuristics include 'satisfying' and 'bounded rationality' (Simon 1977). Could heuristics produce rankings of alternatives that are consistent with those produced by different MCDMs? As is evident from the discussion in this section, there are many different MCDMs that might be applied to the tactical problem under certainty. How might one distinguish between the different MCDMs a mostpreferred solution method for this application? Guyton and Martel (1998) have developed a framework for comparing MCDMs; Hajkowicz and Higgins (2008) have applied multiple MCDMs to a particular set of problem instances. We hope to apply this literature learn which MCDM, if any, represents an optimal balance of concordance with problem characteristics, ease of use, data requirements and outputs visualization. Tactical problem under uncertainty: The introduction to this section distinguished between foreclosed properties that have not yet been offered for auction (“pre-REO”), and those that have not been claimed at auction and are controlled by the institutions which hold the mortgage (“REO”). We learned through interviews with CBO real estate developers that the decision problem they consider the most challenging is that of determining which pre-REO property, if any, to bid on in hopes of acquiring it for redevelopment. There are multiple administrative and financial barriers to pre-REO property acquisition. The financial constraints for this problem have been already discussed; another addresses the problem of significant uncertainty regarding whether a pre-REO property known to be available now will be available for purchase at the upcoming auction, and if the property is auctioned off, whether the CBO’s bid will be successful. The former problem addresses the possibility that a buyer with significant resources will purchase the unit before the auction takes place—a short sale (note, from the previous section, that in Massachusetts, CHAPA’s exclusive rights to foreclosed properties for CBO acquisition applies only to REO units); the latter problem addresses the significant limitations that Neighborhood Stabilization Program funds (among other funding sources) place on CBO acquisitions. Specifically, CBO interviews revealed that CBOs may not use funds provided by the National Community Stabilization Trust to purchase foreclosed units for more than 1% below the assessed value. In both cases, CBOs are at a significant disadvantage compared to private investors, who are often well-capitalized, can act quickly, purchase properties in bulk, and, having lower standards for redevelopment than CBOs, may be able to offer higher initial bids. If a CBO’s bid for an auctioned pre-REO unit is successful, it then faces the same concerns as discussed for the tactical problem under certainty: assessing the likelihood of successful project completion and estimating the social impacts of the project. To summarize, a CBO seeking to purchase a pre-REO unit faces a decision problem under uncertainty: should they prepare to place a bid for a given unit known to be available at auction in the near future, with uncertain probability of a successful bid, or should they wait to prepare a bid for another property with a higher likelihood of successful acquisition? These decision alternatives are complicated by the possibility that a property with a greater probability of high social returns may have a lower probability of bid success than one with a lower probability of high social returns. This problem is an example of a stochastic decision problem. Stochastic programming methods (Danzig 1955, Charnels and Cooper 1959;


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see also Higle 2005 for a tutorial introduction) are mostly appropriate for problems with fewer decision epochs but relatively complex interactions of decision variables that may require definition of several constraints. Dynamic programming or Markov Decision Process (MDP) approaches (Bellman 1957; Howard 1960; Puterman 2005; see also Toussaint 2009 for a lecture-style introduction) are typically appropriate where recursive relationships can be established between consecutive decision stages in a finite or an infinite horizon. There are a number of research issues relevant to this decision problem. Perhaps the most fundamental one is whether a policy-based result is possible and appropriate to the tactical decision problem under uncertainty, especially in light of the multiple objectives and the available modeling approaches such as stochastic programming and Markov decision models. Addressing this issue will require substantial discussions with CBO practitioners to capture the concordance between defined characteristics of the two stochastic modeling approaches and the practical concerns of practitioners, and the likelihood of tractable solutions given problem characteristics as understood by CBOs. Another issue is the representation of uncertain parameters such as the social return of housing development, the likelihood of project success, and uncertainty in funding. Another issue of concern is the representation of risk preferences are certainly salient given the difficulty of negotiations between CBOs and multiple stakeholders, and the perceptions of community residents who are directly affected by CBO acquisition decisions. Modeling this problem may be easier if we consider classes of foreclosed units, rather than individual units for acquisition; however, in practice the choice set may be small enough that all potential units for acquisition belong in the same class. Finally, it is not yet clear whether the tactical problem under uncertainty will yield analytical solutions, or whether solution heuristics will be necessary, and if solution methods can be implemented on a user-friendly platform, such as spreadsheets, that are easy for real estate professionals to use in daily practice.

4. Research design We will use the case study method (Yin 2003) as the fundamental organizing principle for this project. With the assistance of a community partner2, we will choose at least two community-based organizations (CBO) who will agree to work with us on model design, implementation and evaluation. One CBO will serve as the test-bed for the strategic model; another will serve as the test-bed for the two tactical models. We argue that it is appropriate to use one partner for two separate models because both are similar in temporal scope and intended use. CBOs will be chosen to reflect characteristics consistent with the decision models’ intended impacts. The CBO for the strategic model will have a budget, service area and programmatic focus appropriate for development and management of portfolios of foreclosed housing units for long-term planning purposes. The CBO for the tactical models will have a budget and programmatic focus appropriate for short-term acquisition decisions. Both CBOs will have adequate financial and technical resources to support longterm collaboration with academic researchers, such as: routine use of spreadsheets and GIS, active Real Estate committees that make planning and acquisition decisions, and financial resources to implement model-assisted acquisition decisions We will gather data from administrative records and first-hand observations to document the missions, primary services, fiscal and administrative resources and current practices of the two CBOs. The administrative data will be used to create a comprehensive profile of the communities served by these CBOs: properties, assessment and foreclosure data, political, administrative and organization-defined boundaries within and across neighborhoods, and socio-demographic and economic data from the U.S. Census 2

The Massachusetts Department of Housing and Community Development and the Massachusetts Association of Community Development Corporations have provided letters of support for this project. NSF proposal: project description

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For each of the two CBOs, we will develop strategic and tactical decision models, respectively, that achieve the following research goals: they will apply the qualitative, collaborative, iterative and theorybuilding methods described above; they will be the basis for modeling decisions that balance realism and tractability, and they will be validated using historical data on acquisition opportunities and decisions made. We will implement the decision models at each of the CBOs using appropriate technology: spreadsheets for model solutions, relational databases for CBO and community input data and model results, and GIS and spreadsheets for model results visualization. We will document the model development and implementation process through interviews, focus groups and administrative data. We will assess the impact of these decision models on CBO processes and, in a limited way (since there will be little time to observe housing markets for a long time after decision models have been developed) on CBO service areas. Finally, we will write case analyses that assess model development process within each community to answer a variety of questions related to broad research impact. Such questions include: Did we formulate the ‘right’ problem(s)? Did we select model solution strategies that could be adapted to CBOs in as straightforward way? Did the models make a difference in CBO operations and planning? In the community? We will also address the detailed research questions listed in §2(d) and for each of the three problem types in §3. 5. Task list Our project will be divided into nine overlapping tasks, starting with identifying community-based organizations to serve as case sites for our decision models, and ending with writing research results for submission to peer-reviewed journals. a. July – August 2010: Initiate collaborations with Boston-area CBOs and housing development entities We will work with a community partner to identify CBOs with whom we will collaborate over the course of the project. Candidates for community partners include: Massachusetts Department of Housing and Community Development, Massachusetts Association of Community Development Corporations, City of Boston Office of Neighborhood Development, Massachusetts Housing Partnership, and others. Our current funding has supported interviews with all of these organizations to define our project scope, with the exception of MACDC, as well as the choice of a particular CBO as a case site for model development. However, this particular CBO may not continue to work with us after June 2010 due to resource constraints. MACDC may provide us entrée to its 64 member organizations to identify a CBO for collaboration. We will also initiate and renew contacts with organizations that may provide policy and practice knowledge and technical assistance to our team, such as the Citizens Housing and Planning Association, Massachusetts Housing Investment Corporation, and other city agencies and NGOs. The University of Massachusetts Boston (UMB) and University of Massachusetts Lowell (UML) investigators and research assistants will lead on this task. b. July 2010 – June 2011: Develop model requirements The first element of this task is field research, which we will perform with two CBOs chosen in step (a). This step will require definitions of protocols and identification of qualitative methods for interviews, focus groups and field observations, drawing from the literature of value-focused thinking, facilitated modeling and problem structuring methods. The UMB and UML investigators and research assistants will lead on this step. The second element of this task is literature review to choose models and solution approaches that will represent viable opportunities for the tactical decision problem as well as algorithmic approaches that are most suitable for the strategic decision problem. The University of Massachusetts Amherst (UMA) investigator and research assistant will lead on this step.


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c. July – December 2010: Data collection Administrative and Census-based data will be gathered from CHAPA’s foreclosed housing database, the Massages website for Massachusetts spatial data, and other real estate data from municipalities and CBOs. Primary data will also be collected through field observations of CBOs, housing they develop and the neighborhoods they serve. The UMB and UML investigators and RAs will lead on this task. d. December 2010 – August 2011: Model development The essential step in this task is the choice of modeling approach for the tactical decision model under uncertainty, formulation of an initial decision model, and testing the model using ‘toy’ model instances to verify modeling and solution ideas. It will also be important to develop a revised strategic model, and to choose a most-appropriate MCDM to solve the tactical decision problem under certainty. The UMA investigator and RA will lead on the tactical decision model under uncertainty; the UMA investigator and RA will work with the UMB investigators and RA to refine the strategic decision model and the tactical model under certainty. e. July – September 2011: Model testing Model testing requires application of actual foreclosed housing development data from CBO partners where available, and construction of simulated datasets where real-life data are not available, to assess models’ internal validity, and to evaluate alternative solution algorithms. This will be a very laborintensive task, requiring close collaboration between investigators and RAs. The UMB and UMA investigators and RAs will lead on this task. f.

October 2011 – March 2012: Model evaluation

Model evaluation consists of two steps: process evaluation and outcomes evaluation. Process evaluation will answer the following questions: are the models internally consistent? Do they capture policy and practice issues uncovered in field research? Do they truly balance parsimony with explanatory power? Outcomes evaluation will address whether the models make a difference in organizations’ operations and/or in the communities served by organizations. This process will require close collaboration between investigators and RAs. The UMB and UML investigators and RAs will lead on this task. g. November 2010 – March 2012: Conference presentations Among the professional society conferences at which team members will present their research results, the following will be of particular importance: o

o

Institute for Operations Research and the Management Sciences (INFORMS):

San Diego, October 11-14, 2010

Austin, TX, November 7-10, 2010

Urban Affairs Association (UAA):

New Orleans, March 16-19, 2011

Pittsburgh, PA, April 18-21, 2012

h. March – June 2011: Research manuscript writing Writing will actually take place throughout the project, as investigators and RAs present interim results at various conferences. However, after the bulk of the model evaluation and case analysis is completed by March 2010, team members will collaborate to create manuscripts for consideration at a wide range of highly-ranked peer-reviewed journals, including: Environment and Planning B, Journal of Urban Affairs, Decision Support Systems, Decision Analysis, Housing Policy Debate, Journal of Policy Analysis and Management, Journal of Operations Management and Interfaces.


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6. Broader Impacts Our project will produce a variety of results of interest to researchers outside the domains and application interest areas associated with decision modeling for community-based foreclosed housing acquisition and development. Our findings regarding research methods will enable diverse researchers to build valuebased decision models, to display model results in intuitive ways to examine alternatives and build consensus, to improve the operations and strategy practice of nonprofit organizations, and to develop new methods for evaluating the impacts of community-based decision models on organizations and local populations. We will enable practitioners to explicitly identify and formulate decision models that heretofore may have been ignored, or addressed only in a qualitative way and not clearly solved to generate ‘best’ solutions. We will enable practitioners to solve decision problems through user-friendly, lightweight applications to generate evidence-based recommendations for provision of key services. We will disseminate our research results to benefit both academic researchers and practitioners and professionals. We will share our results with the academic and research community through Webaccessible working papers, professional society conferences such as INFORMS, the Urban Affairs Association, the Association for Policy Analysis and Management and the Federal Reserve Bank of Boston’s foreclosed housing researcher network. We will write articles for submission to top-tier peerreviewed journals as listed above. We may consider collecting our findings into a book. We will share our results with affordable/assisted housing decision-makers, community development practitioners and community members through community presentations of research findings, white papers, presentations to elected officials, presentations at conferences of practitioner organizations such as NeighborWorks America (http://www.nw.org/network/home.asp) and the National Association of Housing and Redevelopment Officials (http://www.nahro.org/index.cfm). We will also develop a website for research results that can be linked to by larger research-focused aggregation sites such as KnowledgePlex (http://www.knowledgeplex.org/), the U.S. Department of Housing and Community Development’s Office of Policy Development and Research (www.huduser.org) and Foreclosure-Response.org. Our project will result in the integration of research and education via service learning, dissertation topics and classroom teaching. Service learning at University of Massachusetts Boston’s Department of Public Policy and Public Affairs related to this project can be performed through a two-semester project course in the Public Policy PhD program or a one-semester capstone project in the Masters of Science in Public Affairs program in which the project client could be one of the community-based organizations serving as sites for our case study, or the community partner with whom we will work over the course of the project. Alternatively, service learning could be performed via volunteering and/or internships with a local CBO, our community partner, other community-based organizations in the Boston region, or technical assistance and umbrella/membership organizations serving CBOs. This project may also yield dissertation topics for students in housing, economic development and publicsector operations research. Classroom teaching opportunities arising from this project include real-world applications and case studies for Public Policy PhD courses such as Research Methods, Qualitative Methods and GIS for Public Policy, as well as the opportunity for a new graduate-level course in Community-Based Operations Research. University-community partnerships are a significant outcome of our project. There is a large research literature on this topic, often focusing on the difficulties in getting educators, local advocates and community members to cooperate, especially in low-income and minority communities (Cox 2000; Wiewel, Gaffican and Morrissey 2000; Lerner and Simon 1998). The organizations and neighborhoods that we will work with are low-to-moderate income and ethnically diverse. As a minority-serving university located in a predominately minority neighborhood of the city of Boston, this project may enable UMass Boston to increase ties with local community-based organizations, especially those that are minority-run and minority-serving, in a way that is consistent with its urban mission and which provides


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more specialized assistance than is typically the case in UMass Boston-directed community partnerships. These ties may take the form of research partnerships for technology transfer (Silka 2001, 2006), in which partner organizations may work closely with our team to modify policies, processes, rules, data and software to ensure that any efficiencies associated with improved foreclosed housing acquisition practices can be shared with the rest of the organization. This project will generate three societal benefits related to quantitative policy and administrative practices for community-based organizations that are potentially transformational. First, the project may result in increased resources for policy-makers and funders to modify strategies, priorities and funding criteria to accommodate novel planning and service provision initiatives that would not exist but for prescriptive decision models. Second, the project may influence national policy on support to local governments and nonprofits for addressing foreclosures, by demonstrating the positive impacts on neighborhood stabilization and revitalization that might occur if CBOs had adequate funding to support the foreclosed housing acquisition they wish to perform. Third, the project may increase the public profile of the decision sciences as a vehicle for community change and nonprofit organization management, in a manner similar to the increased understanding of the importance of information technology in supporting the missions of community-based organizations.

7. Qualifications of Research Team The research team for this project is multidisciplinary and well-qualified for this project. The University of Massachusetts Boston PI is trained in operations research, has spent his academic career in departments and schools of public policy and has a particular interest in urban, public-sector applications of operations research/management science. The University of Massachusetts Boston co-PI is a distinguished researcher in multi-attribute decision models and portfolio optimization, applied primarily to privatesector problems. The University of Massachusetts Lowell co-PI has extensive professional experience in community-based affordable housing development and local and regional economic development and has previously collaborated with the PI to develop the strategic foreclosed housing planning model. The University of Massachusetts Amherst PI specializes in stochastic decision modeling and is intimately familiar with a variety of solution methods for challenging decision problems.

8. Relation to Longer-Term Goals of PI’s Research Agenda The principal investigator’s primary mission is to develop quantitative models and methods that enable public organizations serving disadvantaged and vulnerable populations to provide programs and policies that jointly optimize economic efficiency, beneficial population outcomes and social equity. His research agenda to fulfill this mission is focused on operations research/management science planning models for public-sector facility location and service delivery, with applications to subsidized/affordable housing, senior services and community corrections. The PI also uses cost-benefit analysis to estimate impacts of public policies and information technology to design decision support systems. This project, then, is uniquely situated to advance the PI’s research agenda: it addresses decision modeling for housing and community development, community-based operations research, public-sector facility location, and public policy analysis for urban impacts. The project may generate publications in a wide disciplinary and application-area range of academic journals, as well as collaborations between the PI and Federal agencies. This project also furthers the research agendas of the various co-PIs as well: the co-PI at University of Massachusetts Boston’s College of Management seeks new public-sector application areas for his expertise in portfolio optimization and multi-criteria decision models, as does the co-PI at University of Massachusetts Amherst regarding his expertise in stochastic decision modeling. The co-PI at University


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of Massachusetts Lowell seeks research applications that can bridge the gap between community, city and regional planning and affordable real estate development so as to make explicit the decision criteria of different stakeholders. 9. Results From Prior NSF Support a. Michael Johnson Details: Award # SES-0134890, “CAREER: Public-Sector Decision Modeling for Facility Location and Service Delivery”, funded at $399,402, between August 2002 – July 2007 (no-cost extension through July 2008). Results: •

Developed models and applications for public-sector facility location in affordable and subsidized housing and senior services

•

Identified a novel domain within operations research and the management sciences called “community-based operations research”

•

Gained experience in wide range of analytical methods: GIS, optimal control theory, spatial interaction models

•

Build datasets of Pittsburgh metropolitan area addressing housing stock, community-based housing providers and physical and social characteristics

Publications (peer-reviewed journal articles and book chapters, and edited volumes): Caulkins et al. 2005a; Caulkins et al. 2005b; Johnson, Gorr and Roehrig 2005; Johnson 2003; Johnson 2006; Johnson 2007; Johnson and Smilowitz 2007; Johnson, Norman and Secomandi 2006. b. David Turcotte Details: Award # SES-0090256, “Using Democratic Criteria in Participatory Technology Decision Making”, funded at $210,166, between September 2000 – August 2003 (final report submitted March 2004). Results: •

Adapted the Scenario Workshop process by incorporating explicit democratic criteria for technology assessment and decision making

•

Tested a participatory process for decision making about technology in context of a community planning in the city of Lowell.

•

Criteria to evaluate social and political impacts of various planning decisions enabled researchers to provide community participants with the opportunity to evaluate a wide range of technology choices

•

Participants developed five visions and action plans in economic development; education and health; cultural resources and development; infrastructure (water, waste, energy); and housing and transportation.


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National Science Foundation: Decision, Risk and Management Sciences COLLABORATIVE PROPOSAL: DECISION MODELS FOR FORECLOSED HOUSING ACQUISITION AND REDEVELOPMENT References Cited Amundson, S.D. 1998. Relationships between Theory-Driven Empirical Research in Operations Management and Other Disciplines. Journal of Operations Management 16: 341 – 359. Beausoleil, R.P., Baldoquin, G. and R.A. Montejo. 2008. Multi-Start and Path Relinking Methods to Deal with Multiobjective Knapsack Problems. Annals of Operations Research 157: 105 – 133. Bellman, R.E. 1957. Dynamic Programming. Princeton, NJ: Princeton University Press Berman, O. and E. Kim. 2004. Dynamic Inventory Strategies for Profit Maximization in a Service Facility with Stochastic Service, Demand and Lead Time. Mathematical Methods of Operations Research 60(3): 497 – 521. Bernanke, B.S. 2008. “Housing, Mortgage Markets, and Foreclosures.” Speech at the Federal Reserve System Conference on Housing and Mortgage Markets, Washington, D.C., December 4, 2008. Web: http://www.federalreserve.gov/newsevents/speech/bernanke20081204a.htm. Accessed January 17, 2010. BiggerPockets. 2009. “The Foreclosure Process: Understanding How Foreclosures Work.” World Wide Web: http://www.biggerpockets.com/foreclosure-process.html. Accessed January 18, 2010. Brans, J.P. and Ph. Vincke. 1985. A Preference Ranking Organization Method. Management Science 31: 647 – 656. Bratt, R.G. 2009. Challenges for Nonprofit Housing Organizations Created by the Private Housing Market. Journal of Urban Affairs 31(1): 67 – 96. Bregar, A., Györkös, J. and M.B. Jurič. 2009. Robustness and Visualization of Decision Models. Informatica 33: 385 – 395. World Wide Web: http://www.informatica.si/PDF/33-3/16_A.Bregar - Robustness and Visualization of Decision M.pdf. Accessed January 19, 2010. Carr, J.H. 2007. Responding to the Foreclosure Crisis. Housing Policy Debate 18(4): 837-840. Caulkins, J.P., Feichtinger, G., Grass, D., Johnson, M.P., Tragler, G. and Y. Yegorov. 2005a. Placing the Poor While Keeping the Rich in Their Place: Separating Strategies for Optimally Managing Residential Mobility and Assimilation. Demographic Research 13(1): 1 – 34. Caulkins, J.P., Feichtinger, G., Johnson, M.P., Tragler, G. and Y. Yegorov. 2005b. Skiba Thresholds in a Model of Controlled Migration. Journal of Economic Behavior and Organization 57(4): 490 – 508. Charnes, A. and W.W. Cooper. 1959. Chance-Constrained Programming. Management Science 6(1): 73 – 79. Citizen Housing and Planning Association (CHAPA). 2008a. “State Roundup.” April 16, 2008. World Wide Web: http://www.chapa.org/?q=04-16-08. Accessed January 16, 2010. Citizen Housing and Planning Association (CHAPA). 2008b. “Addressing the Foreclosure Crisis: State and Federal Initiatives in Massachusetts.” Prepared by Janna Tetreault and Ann Verrilli. World Wide Web: http://www.chapa.org/pdf/StateandFederalForeclosureInitiatives08.pdf. Accessed January 16, 2010. Cohon, J.L. 1978. Multiobjective Programming and Planning New York: Academic Press.

NSF Proposal – References

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Congressional Oversight Panel. 2009. October Oversight Report: An Assessment of Foreclosure Mitigation Efforts After Six Months. World Wide Web: http://cop.senate.gov/documents/cop100909-report.pdf. Accessed January 17, 2010. Cox, D. 2000. Developing a Framework for Understanding University-Community Partnerships. Cityscape: A Journal of Policy Development and Research 5(1): 9 – 26. World Wide Web: http://www.huduser.org/periodicals/cityscpe/vol5num1/ch1.html. Accessed January 16, 2010. CNNMoney.com. 2009. “Home Values Plummet $500 Billion.” World Wide Web: http://money.cnn.com/2009/12/09/real_estate/home_value_loss/index.htm. Accessed January 15, 2010. Dantzig, G.B. 1955. Linear Programming under Uncertainty. Management Science 1(3-4): 197 -206. Ehrgott, M. and X. Gandibleux (Eds.) 2002. Multiple Criteria Optimization: State of the Art Annotated Bibliographic Surveys. Boston : Kluwer Academic Publishers. Eiselt, H.A. and C.-L. Sandblom. 2004. Decision Analysis, Location Models and Scheduling Problems. Berlin: Springer. Franco, L.A. and G. Montibeller. Facilitated Modelling in Operational Research. 2010. European Journal of Operational Research, to appear. Gabriel, S.A., Faria, J.A. and G. E. Moglen. 2006. A Multiobjective Optimization Approach to Smart Growth in Land Development. Socio-Economic Planning Sciences 40: 212 – 248. Gao, S. and I. Chabini. 2006. Optimal Routing Policy Problems in Stochastic Time-Dependent Networks. Transportation Research Part B 40: 93 – 122. Guitouni, A. and J.-M. Martel. 1998. Tentative Guidelines to Help Choosing an Appropriate MCDA Method. European Journal of Operational Research 109: 501 – 521. Hajkowicz, S. and A. Higgins. 2008. A Comparison of Multiple Criteria Analysis Techniques for Water Resource Management. European Journal of Operational Research 184: 225 – 265. Harding, J.P., Rosenblatt, E. and V.W. Yao. 2010. The Contagion Effect of Foreclosed Properties. Journal of Urban Economics, to appear. World Wide Web: http://ssrn.com/abstract=1160354. Accessed January 16, 2010. Higle, J.L. 2005. “Stochastic Programming: Optimization When Uncertainty Matters.” In (J. Cole Smith, Ed.) Tutorials in Operations Research 2005: Emerging Theory, Methods and Applications. Hanover, MD: Institute for Operations Research and the Management Sciences, pp. 30 – 53. Higle, J.L. and S. Sen. 1994. Finite Master Programs in Regularized Stochastic Decomposition. Mathematical Programming 67: 143 – 168. Hollander, J.B., Pallagst, K., Schwarz, T. and F. Popper. 2009. Planning Shrinking Cities. Progress in Planning 72(4): 223 – 232. Howard, R.A. 1960. Dynamic Programming and Markov Processes. Cambridge, MA: MIT Press. Hwang, C.L. and K. Yoon. 1981. Multiple-Attribute Decision Making: Methods and Applications. New York: Springer-Verlag. Immergluck, D. and G. Smith. 2005. “There Goes the Neighborhood: The Effect of Single-Family Mortgage Foreclosures on Property Values.” Woodstock Institute. Chicago, IL. Web: http://www.nw.org/foreclosuresolutions/reports/documents/TGTN_Report.pdf. Accessed January 16, 2010.


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Isada, Y., James, R.J.W. and Y. Nakagawa. 2005. An Approach for Solving Nonlinear Multi-Objective Separable Discrete Optimization Problem with One Constraint. European Journal of Operational Research 162: 503 – 513. Johnson, M.P. 2010. “Housing and Community Development”, under review at (J. Cochran, Ed.) Wiley Encyclopedia of Operations Research and Management Science. Johnson, M.P. 2007. Planning Models for Affordable Housing Development. Environment and Planning B: Planning and Design 34(3): 501 – 523. Johnson, M.P. 2006. Single-Period Location Models for Subsidized Housing: Project-Based Subsidies. Socio-Economic Planning Sciences 40(4): 249 – 274. Johnson, M.P. 2005. Spatial Decision Support for Assisted Housing Mobility Counseling. Decision Support Systems 41(1): 296 – 312. Johnson, M.P., Gorr, W.L. and S. Roehrig. 2005. Location of Elderly Service Facilities. Annals of Operations Research 136(1): 329 – 349. Johnson, M.P. 2003. Single-Period Location Models for Subsidized Housing: Tenant-Based Subsidies. Annals of Operations Research 123: 105 – 124. Johnson, M.P. and A.P. Hurter. 1999. Economic Impacts of Subsidized Housing Relocation. Papers in Regional Science 78(3): 265 - 295. Johnson, M.P., Ladd, H.F. and J. Ludwig. 2002. The Benefits and Costs of Residential-Mobility Programs for the Poor. Housing Studies 17(1): 125 – 138. Johnson, M.P., Norman, B. and N. Secomandi (Eds.) 2006. Tutorials in Operations Research: Models, Methods and Applications for Innovative Decisionmaking. Hanover, MD: Institute for Operations Research and the Management Sciences, Johnson, Michael P. and Karen Smilowitz. 2007. “Community-Based Operations Research”, in (T. Klastorin, Ed.) Tutorials in Operations Research 2007. Hanover, MD: Institute for Operations Research and the Management Sciences, p. 102 – 123. Johnson, M.P., Turcotte, D. and F.M. Sullivan. 2009a. “What Foreclosed Homes Should a Municipality Purchase to Stabilize Vulnerable Neighborhoods?”; under review at Networks and Spatial Economics. Johnson, M.P., Turcotte, D. and F.M. Sullivan. 2009b. Joseph P. Healey Grant Program, “Decision Modeling for Foreclosed Housing Acquisition in a Large Urban Area”, July 2009 – July 2010. Funded at $6,000. Joint Center for Housing Studies of Harvard University. 2009. The State of the Nation’s Housing 2009. Cambridge, MA. Web: http://www.jchs.harvard.edu/publications/markets/son2009/son2009.pdf. Accessed January 16, 2010. Keeney, R.L. 1992. Value-Focused Thinking: A Path to Creative Decisionmaking. Cambridge, MA: Harvard University Press. Keeney, R.L. and H. Raiffa. 1976. Decisions with Multiple Objectives, Preferences and Value Tradeoffs. Cambridge, MA: Cambridge University Press. Kingsley, G.T., Smith, R. and D. Price. 2009. “The Impacts of Foreclosures on Families and Communities”. Washington, D.C.: The Urban Institute. World Wide Web: www.urban.org/UploadedPDF/411909_impact_of_forclosures.pdf. Accessed January 16, 2010. Klamroth, K. and M.M. Wiecek. 2000. Dynamic Programming Approaches to the Multiple Criteria Knapsack Problem. Naval Research Logistics 47: 57 – 76. NSF Proposal – References

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Kuby, M. 1987. Programming Models for Facility Dispersion: the p-Dispersion and Maxisum Dispersion Problems. Geographical Analysis 19: 315 – 319. Larson, R.C. and A.R. Odoni. 1981. Urban Operations Research. Englewood Cliffs, NJ: Prentice-Hall. Lerner, R.M. and L.K. Simon. (Eds.). 1998. University-Community Collaborations for the Twenty-First Century. New York: Garland Publishing, Inc. Loch, C.H. and S. Kavadias, S. 2002. Dynamic Portfolio Selection of NPD Programs Using Marginal Returns. Management Science 48(10): 1227 – 1241. Malach, A. 2009. Stabilizing Communities: A Federal Response to the Secondary Impacts of the Foreclosure Crisis. Washington, D.C.: The Brookings Institution. World Wide Web: http://www.brookings.edu/~/media/Files/rc/reports/2009/02_foreclosure_crisis_mallach/02_forec losure_crisis_mallach_report.pdf. Accessed January 16, 2010. Malach, A. 2008. Managing Neighborhood Change: A Framework for Sustainable and Equitable Revitalization. Montclair, N.J.: National Housing Institute. World Wide Web: http://www.nhi.org/pdf/ManagingNeighborhoodChange.pdf. Accessed January 16, 2010. Malczewski, J. 1999. GIS and Multicriteria Decision Analysis. New York: John Wiley & Sons. Marianov, V. and D. Serra. 2004. “Location Problems in the Public Sector”, in (Zvi Drezner and Horst Hamacher, Eds.) Facility Location: Applications and Theory. Berlin: Springer, pp. 119 – 150. Marsh, M.T. and D.A. Schilling. 1994. Equity Measurement in Facility Location Analysis: a Review and Framework. European Journal of Operational Research 74:1 – 17. McQuarrie, M. 2008. “Running on Empty,” Shelterforce. Spring 2008: 19 – 21. Web: http://www.shelterforce.org/article/212/running_on_empty/. Accessed January 17, 2010. Meredith, J. 1998. Building Operations Management Theory through Case and Field Research. Journal of Operations Management 16: 441 – 454. Mikelbank, B.A. 2008. “Emerging Threats to Community Stability: A Spatial Hedonic Model.” Presented at “Moving Towards Solutions: Research and Policy on Vacancy and Abandonment”, Federal Reserve Bank of Cleveland, August 27, 2008. World Wide Web: http://www.clevelandfed.org/Our_Region/Community_Development/Events/Seminars/2008/2008 0827/Presentations/Mikelbank.ppt. Accessed January 16, 2010. Mingers, J. and J. Rosenhead. 2004. Problem Structuring Methods in Action. European Journal of Operations Research 152: 530 – 554. Mortgage Bankers Association. 2009. “Delinquencies and Foreclosures Continue to Climb in Latest MBA National Delinquency Survey”. World Wide Web: http://www.mbaa.org/NewsandMedia/PressCenter/69031.htm. Accessed January 15, 2010. National Bureau of Economic Research. 2008. “Determination of the December 2007 Peak in Economic Activity.” Business Cycle Dating Committee, December 11, 2008. Web: http://www.nber.org/cycles/dec2008.html. Accessed January 17, 2010. New York Times. 2009. “More Foreclosures to Come”. Editorial. November 11, 2009. World Wide Web: http://www.nytimes.com/2009/11/12/opinion/12thu2.html?_r=1. Accessed January 17, 2010. Pollock, S.M., Rothkopf, M.H. and A.Barnett (Eds.) 1994. Operations Research in the Public Sector. Amsterdam: North-Holland. Puterman, M.L. 2005. Markov Decision Processes: Discrete Stochastic Dynamic Programming. Hoboken, NJ: John Wiley & Sons.


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RealtyTrac. 2009. “Foreclosure Activity Hits Record High in Third Quarter”. World Wide Web: http://www.realtytrac.com/foreclosure/foreclosure-rates.html. Accessed January 15, 2010. ReVelle, Charles. 1987. “Urban Public Facility Location,” in E.S. Mills (ed.), Handbook of Regional and Urban Economics, Volume II. Amsterdam: North-Holland, pp. 703 – 714. Rui Figueira, J., Tavares, G. and M.M. Wiecek. 2010. Labeling Algorithms for Multiple Objective Integer Knapsack Problems. Computers and Operations Research 37: 700 – 711. Saaty, T.L. 1980. The Analytic Hierarchy Process. New York: McGraw-Hill. Scudder, G.D. and C.A. Hill. 1998. A Review and Classification of Empirical Research in Operations Management. Journal of Operations Management 16: 91 – 101. Şeref, M.M.H., Ahuja, R.K. and W.L. Winston. 2007. Developing Spreadsheet-Based Decision Support Systems Using Excel and VBA for Excel. Belmont, MA: Dynamic Ideas. Silka, L. 2001. “Addressing the Challenge of Community Collaborations.” In (R. Forrant, J. L. Pyle, W. Lazonick, & C. Levenstein Eds.) Approaches to Sustainable Development: The Public University in the Regional Economy. Amherst, MA: University of Massachusetts Press, pp. 358 – 382. Silka, L. 2006. “Reconfiguring Applied Research: Research Partnerships As Opportunities for Innovation. In (L. Silka, Ed.) Scholarship in Action: Applied Research and Community Change. Washington, DC: U. S. Department of Housing and Urban Development Office of University Partnerships, pp. 93 – 103. World Wide Web: http://www.oup.org/files/pubs/scholarship.pdf. Accessed January 16, 2010. Simon, H. 1977. The New Science of Management Decision. Englewood Cliffs, NJ: Prentice-Hall. Taket, A. and L. White. 1997. Wanted Dead OR Alive – Ways of Using Problem-Structuring Methods in Community OR. International Transactions in Operational Research 4: 99 – 108. Taylor, J. 2008. “Help Now, Not Later.” Shelterforce, Spring 2008: 16-18. Web: http://www.shelterforce.org/article/211/help_now_not_later. Accessed January 17, 2010. Teghem, J., Tuyttens, D. and E.L. Ulungu. 2000. An Interactive Heuristic Method for Multi-Objective Combinatorial Optimization. Computers and Operations Research 27: 621 – 634. The Boston Globe. 2009.”President Shifts Focus to Renting, Not Owning”. Joseph Williams, August 16, 2009. World Wide Web: http://www.boston.com/news/nation/washington/articles/2009/08/16/president_shifts_focus_to_r enting_not_owning?mode=PF. Accessed January 17, 2009. The Wharton School. 2009. “Workshop on Empirical Research in Operations Management.” November 12 – 13, 2009 (Agenda). University of Pennsylvania. World Wide Web: http://opim.wharton.upenn.edu/empom/program.php. Accessed January 15, 2010. Treharne, J.T. and C.R. Sox. 2002. Adaptive Inventory Control for Nonstationary Demand and Partial Information. Management Science 48(5): 607 – 624. Toussaint, M. 2009. “Lecture Notes: Markov Decision Processes.” Machine Learning & Robotics Group, TU Berlin. World Wide Web: www.marc-toussaint.net/notes/markov-decision-processes.pdf. Accessed January 15, 2010. U.S. Department of Housing and Urban Development. 2008. “Preston Allocates Nearly $4 Billion to Stabilize Neighborhoods in States and Local Communities Hard-Hit by Foreclosure.” September 26, 2008. Web: http://www.hud.gov/news/release.cfm?content=pr08-148.cfm. Accessed January 17, 2010.


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von Winterfeldt, D. and W. Edwards. 1986. Decision Analysis and Behavioral Research. Cambridge: Cambridge University Press. Wacker, J.G. 1998. A Definition of Theory: Research Guidelines for Different Theory-Building Research Methods in Operations Management. Journal of Operations Management 16: 361 – 385. Wiewel, W., Gaffikan, F. and M. Morrissey. 2000. Community-University Partnerships for Affordable Housing. Cityscape: A Journal of Policy Development and Research 5(1): 27 – 45. World Wide Web: http://www.huduser.org/periodicals/cityscpe/vol5num1/ch2.html. Accessed January 16, 2010. Wright, J., ReVelle, C. and J. Cohon. 1983. A Multiobjective Integer Programming Model for the Land Acquisition Problem. Regional Science and Urban Economics 13: 31 – 53. Wu, J., Wein, L. and A. Perelson. 2005. Optimization of Influenza Vaccine Selection. Operations Research 53(3): 456 – 476. Yin, R.K. 2003. Case Study Research: Design and Models, 3rd Edition. Thousand Oaks, CA: SAGE Publications. Zandi, M. 2010. “U.S. Macro Outlook: In Transition to Expansion. DismalScientist, January 12, 2010. World Wide Web: http://www.economy.com/dismal/article_free.asp?cid=120619&src=msnbc. Accessed January 17, 2010.


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NSF Bio Sketch : Michael P. Johnson Professional Preparation Morehouse College Georgia Institute of Technology University of California at Berkeley Northwestern University

Mathematics and French Electrical Engineering Operations Research Operations Research

Bachelor of Science, May 1987 Master of Science, Sept. 1987 Master of Science, May 1990 Doctor of Philosophy, Dec. 1997

Appointments Academic: Associate Professor (tenured) Department of Public Policy and Public Affairs, University of Massachusetts Boston, Boston, MA

September 2007 – present

Associate Professor of Management Science and Urban Affairs (untenured), H. John Heinz III School of Public Policy and Management, Carnegie Mellon University, Pittsburgh, PA

November 2004 – August 2007

Assistant Professor of Management Science and Urban Affairs H. John Heinz III School of Public Policy and Management, Carnegie Mellon University, Pittsburgh, PA

September 1997 - October 2004

Administrative: Chair, Department of Public Policy and Public Affairs, University of Massachusetts Boston, Boston, MA Director, Public Policy PhD Program, Department of Public Policy and Public Affairs, University of Massachusetts Boston, Boston, MA

September 2009 - present September 2008 – present

Publications Most closely related to project : [1] Johnson, M.P. 2010. “Housing and Community Development”, under review at (J. Cochran, Ed.) Wiley Encyclopedia of Operations Research and Management Science. [2] Johnson, M.P., Turcotte, D. and F.M. Sullivan. 2009. “What Foreclosed Homes Should a Municipality Purchase to Stabilize Vulnerable Neighborhoods?”; under review at Networks and Spatial Economics. [3] Johnson, M.P. 2007. Planning Models for Affordable Housing Development. Environment and Planning B: Planning and Design 34(3): 501 – 523. [4] Johnson, M.P. 2006. Single-Period Location Models for Subsidized Housing: Project-Based Subsidies. Socio-Economic Planning Sciences 40(4): 249 – 274. [5] Johnson, M.P. and K. Smilowitz. 2007. “Community-Based Operations Research”, in (T. Klastorin, Ed.) Tutorials in Operations Research 2007. Hanover, MD: Institute for Operations Research and the Management Sciences, p. 102 – 123. ISBN: 978-1-977640-22-3. (refereed) Other significant publications: [1] Xu, J., Johnson, M.P., Fischbeck, P., Small, M. and J. VanBriesen. 2010. Robust Optimization of Sensor Placement in Dynamic Water Distribution Systems. European Journal of Operational Research 202: 707 – 716. Web: http://dx.doi.org/10.1016/j.ejor.2009.06.010. [2] Johnson, M.P. 2006. Decision Models for Location of Community Corrections Centers. Environment and Planning B: Planning and Design 33(3): 393 – 412.

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[3] Caulkins, J.P., Feichtinger, G., Grass, D., Johnson, M.P., Tragler, G. and Y. Yegorov. 2005. Placing the Poor While Keeping the Rich in Their Place: Separating Strategies for Optimally Managing Residential Mobility and Assimilation. Demographic Research 13(1): 1 – 34. [4] Johnson, M.P., Gorr, W.L. and S. Roehrig. 2005. Location of Elderly Service Facilities. Annals of Operations Research 136(1): 329 – 349. [5] Johnson, M.P., Ladd, H.F. and J. Ludwig. 2002. The Benefits and Costs of Residential-Mobility Programs for the Poor. Housing Studies 17(1): 125 – 138. Synergistic Activities Service learning : University of Massachusetts Boston PhD course Policy Planning and Program Development I/II (Fall 2009/Spring 2010: “Design and Implementation of Strategies for Green Health Practice at Beth Israel Deaconness Medical Center”; Fall 2008/Spring 2009: “Assessment of Member Impact for the Massachusetts Association of Community Development Corporations (MACDC)”) Carnegie Mellon University master’s course Systems Synthesis I and II (Fall 2006: “Pittsburgh 2008: The Science and Technology High School” (with Gordon Lewis)) Professional service: Institute for Operations Research and Management Sciences (Doing Good with Good OR – Student Competition, May 2008 – present; Section on OR/MS Applied to Public Programs, Service and Needs: President-Elect, September 2009 – present, Secretary/Treasurer, January 2009 – August 2009; Pittsburgh 2006 National Conference Organizing Committee, March 2004 – November 2006; Section on Location Analysis: President , October 2004 – November 2006) Collaborators & Other Affiliations Collaborators and Co-Editors : Jon Caulkins (Carnegie Mellon University; co-author), Alan Clayton-Matthews (Northeastern University; co-principal investigator), Jacqueline Cohen (Carnegie Mellon University; principal investigator), Rachel Drew (University of Massachusetts Boston; doctoral research assistant), Paul Fischbeck (Carnegie Mellon University; co-author), Michael Goodman (University of Massachusetts Dartmouth; principal investigator), Wil Gorr (Carnegie Mellon University; co-author), Jen Mankoff (Carnegie Mellon University; co-author and principal investigator), Rema Padman (Carnegie Mellon University ; coauthor), Myra Esmeralda Silva (University of the Philippines Manila; co-author), Felicia Sullivan (University of Massachusetts Boston; doctoral research assistant), David Turcotte (University of Massachusetts Lowell; investigator), Jianhua Xu (Peking University; co-author, member of dissertation committee) Graduate Advisors and Postdoctoral Sponsors. Arthur P. Hurter (Northwestern University (retired); dissertation committee chair), Mark S. Daskin (Northwestern University ; dissertation committee), Edwin S. Mills (Northwestern University (retired); dissertation committee), Elizabeth Warren (Loyola University (retired); dissertation committee) Thesis Advisor and Postgraduate-Scholar Sponsor : Kai Zheng, University of Michigan (dissertation co-advisor, with Rema Padman) 1 doctoral student dissertation advised in past five years; 3 doctoral student dissertation committees served in past five years; no postdoctoral scholars sponsored in past five years.

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Biographical sketch Jeffrey M. Keisler (a) Professional preparation Undergraduate education: B.S.

1985 University of Wisconsin (Computer Sciences, Mathematics, with Honors and Distinction)

Graduate education: S.M 1988 Harvard University (Engineering Sciences) Ph.D. 1992 Harvard University (Decision Sciences) M.B.A. 1996 University of Chicago (with High Honors)

(b) Appointments Current appointment: University of Massachusetts Boston College of Management, Department of Management Science and Information Systems Associate Professor, 2007-Present Previous appointments University of Massachusetts Boston College of Management, Department of Management Science and Information Systems Assistant Professor, 2001-2007 Lecturer, 2000-2001 Strategic Decisions Group, Consultant, 1997-1999 Argonne National Laboratory, Assistant Engineer, Decision & Information Sciences Division, 1993-1997

(c) Publications related 1. Keisler, J., and I. Linkov, Managing a portfolio of risks. Wiley Encyclopedia of Operations Research and Management Sciences, Volume on Risk Analysis, 2010. 2. Keisler, J., Value of information in portfolio decision analysis, Decision Analysis, pp.177-189, Vol. 1, No. 3, September 2004. 3. Keisler, J., When to consider synergies in project portfolio decision analysis, UMass Boston College of Management Working Paper, 2005. 4. Keisler, J. and M. Brodfuhrer, An application of value-of-information to decision process reengineering. The Engineering Economist, Vol. 54, No. 3, pp. 197-221, July-September 2009. 5. Keisler, J., The value of assessing weights in multi-criteria portfolio decision analysis. Journal of Multi-Criteria Decision Analysis, Vol. 15, No. 5-6, pp. 111-123, September-December 2008. Other 6. Keisler, J., W. Buehring, P. McLaughlin, M. Robershotte and R. Whitfield, Allocating contractor risks in the Hanford waste cleanup, Interfaces, pp.180-190, Vol. 34-3, May-June 2004.

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7. Keisler, J. and P. Noonan, Communicating decision analysis results to various audiences. Wiley Encyclopedia of Operations Research and Management Sciences, Volume on Decision Analysis, 2010. 8. Baker, E., H. Chon, and J. Keisler, Carbon capture and storage: Combining economic analysis with expert elicitations to inform climate policy. Climatic Change, Vol. 96, No. 3, pp. 379-408, Oct. 2009. 9. Keisler, J., Additivity of information value in two-act linear loss decisions with normal priors, Risk Analysis, pp. 351-360, Vol. 25, No. 2, April 2005. 10. Wagner, J. and J. Keisler, Enhance your own research productivity using spreadsheets (From sin to salvation), INFORMS Tutorials in Operations Research: Models, Methods and Applications for Innovative Decision Making, pp.148-162, Vol. 2, 2006.

(d) Synergistic activities Developed new courses: Introduction to Management Information Systems (MSIS 105) Decision Analysis (MSIS 455) Editor, Decision Analysis Today (a publication of the INFORMS Decision Analysis Society) (e) Collaborators and other affiliations Collaborators and co-editors Erin Baker Hae Won Chon Igor Linkov Patrick Noonan Mark Brodfuehrer Wei Zhang Janet Wagner Roger Blake Graduate Advisors and Postdoctoral Sponsors Ph.D. Thesis committee: Howard Raiffa (Chair), David Bell, Daniel Raff.

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Biographical Sketch SENAY SOLAK Assistant Professor of Operations Management University of Massachusetts Amherst Amherst, MA 01003

[email protected] Voice: (413) 545-5681 Fax: (413) 545-3858

i. PROFESSIONAL PREPARATION United States Naval Academy, Annapolis, MD Electrical Engineering B.S. 1997 Georgia Institute of Technology, Atlanta, GA Industrial Engineering M.S. 2002 Georgia Institute of Technology, Atlanta, GA Industrial Engineering Ph.D. 2007 ii. APPOINTMENTS 8/08– 8/04–8/08 8/02–8/04 6/01–6/02 3/98–8/00 8/97–3/98

Assistant Professor of Operations Management, University of Massachusetts, Amherst, MA Assistant Professor of Industrial Engineering Technology, Southern Polytechnic State University, Marietta, GA Instructor of Industrial Engineering, Turkish Naval Academy, Istanbul, Turkey Analyst/Planner, Hartsfield-Jackson Atlanta International Airport, Atlanta, GA Division Officer/Department Head, Turkish Navy, Izmit, Turkey Operations Analyst, Turkish Naval Academy, Istanbul, Turkey

iii. (a). FIVE PUBLICATIONS RELATED TO PROPOSED TOPIC [1] A Stochastic Programming Model with Decision Dependent Uncertainty Realizations for Technology Portfolio Management, w/ J-P. Clarke, E. Johnson, E. Barnes (2007), Operations Research Proceedings: Selected Papers. S. Nickel, J.Kalcsics eds. Berlin, Germany: Springer. [2] Efficient Solution Procedures for Multistage Stochastic Programming Formulations of Two Problem Classes (2007), Ph.D. Thesis, Georgia Institute of Technology. [3] Airport Terminal Capacity Planning, w/ J-P. Clarke, E. Johnson (2009), Transportation Research Part B, 43(6), pp. 659-676. [4] Climate Change Energy Technology R&D Portfolio, w/ E. Baker (2009), Proceedings of International Energy Workshop 2009 [5] Optimization of Project Portfolios with Endogenous Uncertainty, w/ J-P. Clarke, E. Johnson, E. Barnes (2009), in review. iii. (b). FIVE ADDITIONAL PUBLICATIONS [1] SysML Modeling of Off-the-shelf-option Acquisition for Risk Mitigation in Military Programs, w/ J. Constantine (2009), Systems Engineering, article online in advance of print, http://dx.doi.org /10.1002/sys.20134. [2] Near Real-Time Fuel-Optimal En-Route Conflict Resolution, w/ A. Vela, J-P. Clarke, W. Singhose, E. Barnes, E. Johnson (2008), IEEE Transactions on Intelligent Transportation Systems accepted.

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[3] Implementing Uncertainty and Learning in Climate Change Policy Analysis, w/ E. Baker (2009), Proceedings of the Workshop on Uncertainty and Learning in the Management of Environmental and Natural Resources 2009 [4] Air Traffic Flow Management in the Presence of Uncertainty, w/ J-P. Clarke, Y. Chang, L. Ren, A. Vela (2009), Proceedings of the 8th USA/Europe Seminar on Air Traffic Management Research & Development 2009 [5] Combined Discrete-Continuous Simulation Modeling of an Autonomous Underwater Vehicle, w/ R. Jarnagin (2007), Extending the Horizons: Advances in Computing, Optimization and Decision Technologies. E. Baker, A. Joseph, A. Mehrotra, M. Trick eds. Norwell, MA: Springer. iv. SYNERGISTIC ACTIVITIES Member: Institute of Operations Research and the Management Sciences, Institute of Industrial Engineers, American Institute of Aeronautics and Astronautics, Airline Group of the Int. Fed. of Operational Research Societies, INFORMS Section on Transportation Science and Logistics, Board Member: INFORMS Section on Aviation Applications. Invited Presentations: CORS-INFORMS International Meeting, Toronto 2009; Air Transportation Laboratory Research Symposium, Atlanta 2008, 2009; Ellis Johnson Research Symposium, Grand Cayman 2008; INFORMS Annual Meeting, San Diego 2009, Washington, D.C. 2008, Seattle 2007, Pittsburgh 2006, San Francisco 2005; IIE Annual Meeting, Orlando 2006; AMS Annual Meeting, San Antonio 2006; University of Massachusetts Amherst, Drexel University, Purdue University, United States Naval Academy Researcher/Consultant: NASA Ames Research Center, Federal Aviation Administration, Department of Transportation, Georgia Institute of Technology Honors and Awards: Best Paper Award in Air Traffic Management Session, Digital Avionics System Conference 2009; Best Student Paper Award, Southeastern INFORMS 2007; The Supply Chain & Logistics Institute Global Logistics Scholar, Georgia Institute of Technology 2005; Distinctive Graduate, United States Naval Academy 1997 v. (a) COLLABORATORS (past 48 months) & CO-EDITORS (past 24 months) A. Ali (UMass Amherst) E. Barnes (Georgia Tech), E. Baker (UMass Amherst), S. Bolat (UT Knoxville), P. Collopy (DFM Consulting), D. Chang (UC Berkeley), J. Constantine (Raytheon), J-P. Clarke (Georgia Tech), E. Feron (Georgia Tech), A. Ghoniem (UMass Amherst), R. Jarnagin (Florida Int. Uni.), E. Johnson (Georgia Tech), M. Lowther (Metron Aviation) L. Ren (Georgia Tech), G. Sarpkaya (Auburn Uni.), C. Scherrer (Southern Polytechnic St. Uni.) W. Singhose (Georgia Tech), A. Vela (Georgia Tech). v. (b) GRADUATE AND POSTDOCTORAL ADVISORS J-P. Clarke (Georgia Tech), E. Johnson (Georgia Tech) v. (c) THESIS ADVISOR AND POSTGRADUATE SCHOLAR SPONSOR Doctoral Students: Zhuoxin Li. Masters Students (Southern Polytechnic St. Uni.): A. Adeniyi*, S. Belete*, D. Chang, J. Constantine, B. Croy, B. Hedspeth, K. Mobley, B. Mugambi*, A. Nagata, J. Perea, R. Jarnagin, R. Talley. Total number of masters students supervised: 11. Total number of doctoral students supervised: 1. ’*’ refers to underrepresented minority.

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NSF Biographical Sketch David A. Turcotte (a) Professional Preparation University of Lowell New Hampshire College University of Massachusetts Lowell

B.A. Sociology 1979 M.S. Community Economic Development 1999 Sc.D. Work Environment Policy 2007 Pollution Prevention

(b) Appointments University of Massachusetts Lowell: Department of Regional Economic and Social Development Research Professor, 2008-Present Merrimack Valley Housing Report Editor, 2008-Present Technical Assistance and Research Center for Housing Sustainability Director, 2007-Present Center for Family, Work, and Community Senior Program Manager/Project Director, 2006-Present Qualitative Research Network Steering Committee Member, Center for Family, Work, and Community Program Manager/Project Director, 2000-2006 Department of Regional Economic and Social Development Adjunct Professor, 2000-2008 Center for Family, Work, and Community Program Manager, 1998-2000 (c) Publications Related to Proposed Topic Turcotte, D. & Geiser, K. 2009. Cases in Sustainable Housing: Where do we go from here? Under review at Journal of Urban Affairs. Turcotte, D., Forrant, R., Fraser, J., & Shuwen, L. 2009. Foreclosures Cast Long Shadow Across Northeast Region. MassBenchmarks. 11(1), 19-26. Johnson, M.P., Turcotte, D. & Sullivan, F.M. 2009. What Foreclosed Homes Should a Municipality Purchase to Stabilize Vulnerable Neighborhoods? Under review at Networks and Spatial Economics.

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Turcotte, D. 2007. Developing Sustainable Housing: Moving Beyond Green. Progressive Planning 172, 34-37. Turcotte, D. 2007. A Framework for Sustainable Housing Development in the United States, Doctoral Dissertation, University of Massachusetts Lowell. Additional Publications Turcotte, D. 2010, pending publication. Waste Sites. Contributor to chapter in RA, Etzel, (Ed), Pediatrics Environmental Health. 3rd ed. Elk Grove Village, IL: American Academy of Pediatrics. Silka, L., Toof, R., Turcotte, D., Villareal, J., Buxbaum, L., & Renault-Caragianes, P. 2008. Community-University Partnerships: Achieving the Promise in the Face of Changing Goals, Changing Funding Patterns, and Competing Priorities. New Solutions, 18(2), 161175. Silka, L., Forrant, R., Bond, B., Coffey, Toof, R., Toomey, D., Turcotte, D., & West, C. 2008. Achieving Continuity in the Face of Change in Community-University Partnerships. Gateways: International Journal of Community Research and Engagement, Vol 1 (Inaugural Issue). Turcotte, D.& Silka, L. 2007. Social Capital in Refugee and Immigrant Communities. Chapter in J. Jennings (Ed), Race, Neighborhoods, and the Misuse of Social Capital. New York: Palgrave Macmillan. Turcotte, D. 2006. Sustainable Development: A Better Holistic View. New Solutions, 16 (4), 398-401. (d) Synergistic Activities Developed New Course: Sustainable Housing Development and Land Use: Policy and Practice (57.512) Developed holistic sustainable housing development framework (e) Collaborators & Other Affiliations L. Cowan (Lesley University), R. Drew (UMass Boston), J. Fraser (UMass Lowell), R. Forrant (UMass Lowell), K. Geiser (UMass Lowell), J. Gerson (UMass Lowell), J. Jennings (Tufts University), M. Johnson (UMass Boston), L. Pho (UMass Lowell), L. Shuwen (UMass Lowell), L. Silka (University of Maine), F. Sullivan(UMass Boston), R. Toof (UMass Lowell). Graduate and Postdoctoral Advisors UMass Lowell Dissertation Committee: Ken Geiser (chair), Jeffrey Gerson, Craig Slatin Thesis Advisor and Postgraduate-Scholar Sponsor Masters students supervised for capstone projects at University of Massachusetts Lowell: Tim Harrigan Keith Vaillancourt

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