Decision Science for Housing and Community Development

MICHAEL P. JOHNSON, PhD, is Associate Professor in the Department of Public Policy and Public Affairs at the University of Massachusetts Boston. JEFFREY M. KEISLER, PhD, is Professor in the Department of Management Science and Information Systems at the University of Massachusetts Boston. SENAY SOLAK, PhD, is Associate Professor in the Department of Operations and Information Management at the University of Massachusetts Amherst. DAVID A. TURCOTTE, ScD, is Research Professor in the Department of Economics at the University of Massachusetts Lowell. ARMAGAN BAYRAM, PhD, is Assistant Professor in the Department of Industrial and Manufacturing Systems Engineering at University of Michigan – Dearborn. RACHEL BOGARDUS DREW, PhD, is a housing policy consultant.

"This book would be an excellent textbook for students who want to learn more about community-based operations research and are in advanced undergraduate or early graduate classes on the topic...The book�s cases and tools provide a wonderful reference for the broad spectrum of analytical tools available for students...Overall, the introductory sections provide a background and history of the various social issues and ills associated with urban crisis and sets an excellent foundation for the analytical models introduced later. We believe that the book contributes and advances CBOR, a topic that is meant to assist our most vulnerable regions and population, and we hope to see more topics related to this field in the future." (InterfacesJanuary 2017)

• Preface xiiiForeword xviiAcknowledgments xxiiiAuthor Biographies xxvList of Figures xxixList of Tables xxxv1 Introduction: Community-Based Organizations, Neighborhood-Level Development, and Decision Modeling 11.1 Challenges and Opportunities for Housing and Community Development in the US 11.2 Community Development in the United States 61.3 Big Data Analytics and Community Development 91.4 The Foreclosure Crisis: Problem Impacts and Responses 111.5 Community-Based Operations Research: A Novel Approach to Support Local Development 131.6 Why This Book Now? 191.7 Book Description 211.8 Conclusion 24Section 1 Policy and Practice in Foreclosed Housing and Community Development 272 Foreclosed Housing Crisis and Policy and Planning Responses 292.1 Roots of the Foreclosed Housing Crisis 292.2 Impacts of the Crisis 322.2.1 Foreclosure Rates 332.2.2 Home Equity and Wealth 342.2.3 Health, Education, and Household Mobility 362.2.4 Disamenities and Spillover Effects 372.2.5 Market-Level Consequences 382.3 Responses to the Crisis 392.4 Effectiveness of Foreclosure Responses 412.5 Opportunities for Decision Modeling Responses to the Foreclosed Housing Crisis 433 Community Partners and Neighborhood Characteristics 453.1 Introduction 453.2 Methodology 463.2.1 Data Gathering Summary 463.2.2 Triangulation 473.2.3 Analysis 483.3 Selection of Cases 493.4 Case 1: The Neighborhood Developers 503.4.1 Organization Type and Mission 503.4.2 Organization Service Area and Population 553.4.3 Organization Engagement with Foreclosure Crisis 553.4.4 Organization Technical Capacity and Familiarity with Project’s Analytic Methods 583.5 Case 2: Coalition for a Better Acre 593.5.1 Organization Type and Mission 593.5.2 Organization Service Area and Population Demographics 593.5.3 Organization Engagement with Foreclosure Crisis 613.5.4 Organization Technical Capacity and Familiarity with Project’s Analytic Methods 623.6 Case 3: Codman Square Neighborhood Development Corporation 633.6.1 Organization Type and Mission 633.6.2 Organization Service Area and Population Demographics 633.6.3 Organization Engagement with Foreclosure Crisis 643.6.4 Organization Technical Capacity and Familiarity with Project’s Analytic Methods 673.7 Case 4: Twin Cities Community Development Corporation 673.7.1 Organization Type and Mission 673.7.2 Organization Service Area and Population Demographics 683.7.3 Organization Engagement with Foreclosure Crisis 683.7.4 Organization Technical Capacity and Familiarity with Project’s Analytic Methods 703.8 Case Contrast and Discussion 713.8.1 Role of Community Partners 713.8.2 Adaptation of Case Study Theory for Our Project 733.9 Conclusion 744 Analytic Approaches to Foreclosure Decision Modeling 754.1 Introduction 754.2 Analysis of Community Partners and their Service Areas 814.3 Localized Foreclosure Response 944.4 Opportunities for Research-Based Analytic Responses to Foreclosures 974.5 Solution Design for Community Development using Community-Based Operations Research 1024.6 Where Do We Go From Here? 104Section 2 Values Metrics and Impacts for Decision Modeling 1075 Value-Focused Thinking: Defining, Structuring and Using CDC Objectives in Decision Making 1095.1 Introduction 1095.1.1 Overview 1095.1.2 Values and Objectives in Decisions 1095.1.3 Values and Objectives in Community-Based Organization/CDC Decisions 1105.1.4 Utility Functions and Decision Making 1115.1.5 Multiattribute Utility Functions 1125.1.6 Value-Focused Thinking 1145.1.7 VFT as Soft OR and Problem Structuring Method 1155.1.8 The Resource Allocation Decision Frame 1155.1.9 Plan 1185.2 Methods 1185.2.1 Linear Additive Assumption 1185.2.2 Defining the Mathematical Model as a Set of Linear Equations 1195.2.3 Structuring 1205.2.4 Obtaining Inputs 1225.3 Cases 1235.3.1 Simulated CDC 1235.3.2 Codman Square Neighborhood Development Corporation 1305.3.3 Twin Cities Community Development Corporation 1385.4 Common and Contingent Objectives for CDCs 1435.5 Lessons for Applying VFT to CBOs 1516 Characteristics of Acquisition Opportunities: Strategic Value 1536.1 Introduction 1536.2 Problem Description 1556.2.1 Policy Motivation 1556.2.2 Theoretical Foundations 1576.3 Model Development 1596.3.1 Sets and Indexes 1596.3.2 Parameters and Functions 1606.3.3 Individual Resident Frame 1606.3.4 CDC Frame 1616.4 Case Study: The Neighborhood Developers 1626.4.1 Site Description 1626.4.2 Model Computations 1666.5 Discussion 1706.5.1 Policy Analysis 1706.5.2 Implications for Modeling and Practice 1716.6 Conclusion 1727 Characteristics of Acquisition Opportunities: Property Value 1757.1 Introduction 1757.2 Property Value Changes as a Social Impact of Foreclosed Housing 1767.3 A Model of PVI for Foreclosed Housing 1787.4 The PVI Model 1807.4.1 The Foreclosure Process 1817.4.2 Modeling Foreclosure Phase Transitions with a Markov Chain 1827.4.3 Estimation of Proximate Property Value Impacts 1847.5 Case Study: The Neighborhood Developers 1867.5.1 Data and Model Specifications 1867.5.2 Computational Results 1907.5.3 Clustering Effects 1917.6 Discussion 1967.7 Model Validity and Limitations 1997.7.1 Nonlinearities in Aggregate Impacts 1997.7.2 Representativeness of Data Sources 2007.7.3 Sensitivity to Transition Probabilities 2007.7.4 Impacts of Multiple Foreclosures 2007.7.5 Wider Range of Social Impacts 2017.7.6 Model Validity 2017.8 Conclusion 202Section 3 Prescriptive Analysis and Findings 2058 Social Benefits of Decision Modeling for Property Acquisition 2078.1 Introduction 2078.2 CDC Practice in Foreclosed Housing Acquisition 2098.3 A Multiobjective Model of Foreclosed Housing Acquisition 2128.3.1 Decision Model 2128.3.2 Input Data 2158.4 Model Solutions 2208.4.1 Constraint on Number of Units Acquired 2218.4.2 Budget Constraint 2338.5 Discussion 2438.6 Conclusion and Next Steps 2449 Acquiring and Managing a Portfolio of Properties 2479.1 Introduction 2479.2 Dynamic Modeling of the Foreclosed Housing Acquisition Process 2489.3 Model Formulation 2519.4 Policy Analysis Under Different Fund Accessibility Cases 2539.4.1 Acquisition Policies Under No Fund Expiration 2539.4.2 Acquisition Policies Under Fund Expiration 2579.5 Case Study: Codman Square Neighborhood Development Corporation 2599.5.1 Data Description 2609.5.2 Implementation Under No Fund Expiration 2619.5.3 Implementation Under Fund Expiration 2659.6 Conclusion 26910 Strategic Acquisition Investments Across Neighborhoods 27310.1 Introduction 27310.2 General Framework of FHAP 27510.3 Model Formulation 27610.3.1 Methodology Overview 27610.3.2 FHAP with Simple Resource Allocation 27710.3.3 FHAP with Gradual Uncertainty Resolution 28210.3.4 Model Variations and Extensions 28610.4 Case Study: Codman Square Neighborhood Development Corporation 28910.4.1 Data Description and Parameter Justification 28910.4.2 Resource Allocations and Impacts of Model Parameters 29210.4.3 Policy Implications for CDCs 30310.5 Conclusion 30411 Conclusion: Findings and Opportunities in Decision Analytics for Foreclosure Response and Community Development 30711.1 Introduction 30711.2 Key Findings 30811.2.1 Foreclosure Crisis and Responses 30811.2.2 Engagement with Community-Based Organizations 30811.2.3 Decision-Modeling Fundamentals: Values and Attributes 30911.2.4 Foreclosed Property Strategy Design Using Decision Models 31011.3 Research Insights 31211.4 Lessons Learned 31411.5 Community-Based Operations Research: A Reassessment 31611.6 Research Extensions 31911.7 Conclusion 320AppendicesA Policy Analysis 323B Multicriteria Decision Modeling 329B.1 Multiobjective Decision Making 330B.2 Multiattribute Decision Models 333References 339Index 363