Decision Science for Housing and Community Development: Localized and Evidence-Based Responses to Distressed Housing and Blighted Communities(Wiley Series in Operations Research and Management Science)

A multidisciplinary approach to problem-solving in community-based organizations using decision models and operations research applications A comprehensive treatment of public-sector operations research and management science, Decision Science for Housing and Community Development: Localized and Evidence-Based Responses to Distressed Housing and Blighted Communities addresses critical problems in urban housing and community development through a diverse set of decision models and applications. The book represents a bridge between theory and practice and is a source of collaboration between decision and data scientists and planners, advocates, and community practitioners. The book is motivated by the needs of community-based organizations to respond to neighborhood economic and social distress, represented by foreclosed, abandoned, and blighted housing, through community organizing, service provision, and local development. The book emphasizes analytic approaches that increase the ability of local practitioners to act quickly, thoughtfully, and effectively. By doing so, practitioners can design and implement responses that reflect stakeholder values associated with healthy and sustainable communities; that benefit from increased organizational capacity for evidence-based responses; and that result in solutions that represent improvements over the status quo according to multiple social outcome measures. Featuring quantitative and qualitative analytic methods as well as prescriptive and exploratory decision modeling, the book also includes: Discussions of the principles of decision theory and descriptive analysis to describe ways to identify and quantify values and objectives for community development Mathematical programming applications for real-world problem solving in foreclosed housing acquisition and redevelopment Applications of case studies and community-engaged research principles to analytics and decision modeling Decision Science for Housing and Community Development: Localized and Evidence-Based Responses to Distressed Housing and Blighted Communities is an ideal textbook for upper-undergraduate and graduate-level courses in decision models and applications; humanitarian logistics; nonprofit operations management; urban operations research; public economics; performance management; urban studies; public policy; urban and regional planning; and systems design and optimization. The book is also an excellent reference for academics, researchers, and practitioners in operations research, management science, operations management, systems engineering, policy analysis, city planning, and data analytics.  

Table of Contents:
Preface xiii Foreword xvii Acknowledgments xxiii Author Biographies xxv List of Figures xxix List of Tables xxxv 1 Introduction: Community-Based Organizations, Neighborhood-Level Development, and Decision Modeling 1 1.1 Challenges and Opportunities for Housing and Community Development in the US 1 1.2 Community Development in the United States 6 1.3 Big Data Analytics and Community Development 9 1.4 The Foreclosure Crisis: Problem Impacts and Responses 11 1.5 Community-Based Operations Research: A Novel Approach to Support Local Development 13 1.6 Why This Book Now? 19 1.7 Book Description 21 1.8 Conclusion 24 Section 1 Policy and Practice in Foreclosed Housing and Community Development 27 2 Foreclosed Housing Crisis and Policy and Planning Responses 29 2.1 Roots of the Foreclosed Housing Crisis 29 2.2 Impacts of the Crisis 32 2.2.1 Foreclosure Rates 33 2.2.2 Home Equity and Wealth 34 2.2.3 Health, Education, and Household Mobility 36 2.2.4 Disamenities and Spillover Effects 37 2.2.5 Market-Level Consequences 38 2.3 Responses to the Crisis 39 2.4 Effectiveness of Foreclosure Responses 41 2.5 Opportunities for Decision Modeling Responses to the Foreclosed Housing Crisis 43 3 Community Partners and Neighborhood Characteristics 45 3.1 Introduction 45 3.2 Methodology 46 3.2.1 Data Gathering Summary 46 3.2.2 Triangulation 47 3.2.3 Analysis 48 3.3 Selection of Cases 49 3.4 Case 1: The Neighborhood Developers 50 3.4.1 Organization Type and Mission 50 3.4.2 Organization Service Area and Population 55 3.4.3 Organization Engagement with Foreclosure Crisis 55 3.4.4 Organization Technical Capacity and Familiarity with Project’s Analytic Methods 58 3.5 Case 2: Coalition for a Better Acre 59 3.5.1 Organization Type and Mission 59 3.5.2 Organization Service Area and Population Demographics 59 3.5.3 Organization Engagement with Foreclosure Crisis 61 3.5.4 Organization Technical Capacity and Familiarity with Project’s Analytic Methods 62 3.6 Case 3: Codman Square Neighborhood Development Corporation 63 3.6.1 Organization Type and Mission 63 3.6.2 Organization Service Area and Population Demographics 63 3.6.3 Organization Engagement with Foreclosure Crisis 64 3.6.4 Organization Technical Capacity and Familiarity with Project’s Analytic Methods 67 3.7 Case 4: Twin Cities Community Development Corporation 67 3.7.1 Organization Type and Mission 67 3.7.2 Organization Service Area and Population Demographics 68 3.7.3 Organization Engagement with Foreclosure Crisis 68 3.7.4 Organization Technical Capacity and Familiarity with Project’s Analytic Methods 70 3.8 Case Contrast and Discussion 71 3.8.1 Role of Community Partners 71 3.8.2 Adaptation of Case Study Theory for Our Project 73 3.9 Conclusion 74 4 Analytic Approaches to Foreclosure Decision Modeling 75 4.1 Introduction 75 4.2 Analysis of Community Partners and their Service Areas 81 4.3 Localized Foreclosure Response 94 4.4 Opportunities for Research-Based Analytic Responses to Foreclosures 97 4.5 Solution Design for Community Development using Community-Based Operations Research 102 4.6 Where Do We Go From Here? 104 Section 2 Values Metrics and Impacts for Decision Modeling 107 5 Value-Focused Thinking: Defining, Structuring and Using CDC Objectives in Decision Making 109 5.1 Introduction 109 5.1.1 Overview 109 5.1.2 Values and Objectives in Decisions 109 5.1.3 Values and Objectives in Community-Based Organization/CDC Decisions 110 5.1.4 Utility Functions and Decision Making 111 5.1.5 Multiattribute Utility Functions 112 5.1.6 Value-Focused Thinking 114 5.1.7 VFT as Soft OR and Problem Structuring Method 115 5.1.8 The Resource Allocation Decision Frame 115 5.1.9 Plan 118 5.2 Methods 118 5.2.1 Linear Additive Assumption 118 5.2.2 Defining the Mathematical Model as a Set of Linear Equations 119 5.2.3 Structuring 120 5.2.4 Obtaining Inputs 122 5.3 Cases 123 5.3.1 Simulated CDC 123 5.3.2 Codman Square Neighborhood Development Corporation 130 5.3.3 Twin Cities Community Development Corporation 138 5.4 Common and Contingent Objectives for CDCs 143 5.5 Lessons for Applying VFT to CBOs 151 6 Characteristics of Acquisition Opportunities: Strategic Value 153 6.1 Introduction 153 6.2 Problem Description 155 6.2.1 Policy Motivation 155 6.2.2 Theoretical Foundations 157 6.3 Model Development 159 6.3.1 Sets and Indexes 159 6.3.2 Parameters and Functions 160 6.3.3 Individual Resident Frame 160 6.3.4 CDC Frame 161 6.4 Case Study: The Neighborhood Developers 162 6.4.1 Site Description 162 6.4.2 Model Computations 166 6.5 Discussion 170 6.5.1 Policy Analysis 170 6.5.2 Implications for Modeling and Practice 171 6.6 Conclusion 172 7 Characteristics of Acquisition Opportunities: Property Value 175 7.1 Introduction 175 7.2 Property Value Changes as a Social Impact of Foreclosed Housing 176 7.3 A Model of PVI for Foreclosed Housing 178 7.4 The PVI Model 180 7.4.1 The Foreclosure Process 181 7.4.2 Modeling Foreclosure Phase Transitions with a Markov Chain 182 7.4.3 Estimation of Proximate Property Value Impacts 184 7.5 Case Study: The Neighborhood Developers 186 7.5.1 Data and Model Specifications 186 7.5.2 Computational Results 190 7.5.3 Clustering Effects 191 7.6 Discussion 196 7.7 Model Validity and Limitations 199 7.7.1 Nonlinearities in Aggregate Impacts 199 7.7.2 Representativeness of Data Sources 200 7.7.3 Sensitivity to Transition Probabilities 200 7.7.4 Impacts of Multiple Foreclosures 200 7.7.5 Wider Range of Social Impacts 201 7.7.6 Model Validity 201 7.8 Conclusion 202 Section 3 Prescriptive Analysis and Findings 205 8 Social Benefits of Decision Modeling for Property Acquisition 207 8.1 Introduction 207 8.2 CDC Practice in Foreclosed Housing Acquisition 209 8.3 A Multiobjective Model of Foreclosed Housing Acquisition 212 8.3.1 Decision Model 212 8.3.2 Input Data 215 8.4 Model Solutions 220 8.4.1 Constraint on Number of Units Acquired 221 8.4.2 Budget Constraint 233 8.5 Discussion 243 8.6 Conclusion and Next Steps 244 9 Acquiring and Managing a Portfolio of Properties 247 9.1 Introduction 247 9.2 Dynamic Modeling of the Foreclosed Housing Acquisition Process 248 9.3 Model Formulation 251 9.4 Policy Analysis Under Different Fund Accessibility Cases 253 9.4.1 Acquisition Policies Under No Fund Expiration 253 9.4.2 Acquisition Policies Under Fund Expiration 257 9.5 Case Study: Codman Square Neighborhood Development Corporation 259 9.5.1 Data Description 260 9.5.2 Implementation Under No Fund Expiration 261 9.5.3 Implementation Under Fund Expiration 265 9.6 Conclusion 269 10 Strategic Acquisition Investments Across Neighborhoods 273 10.1 Introduction 273 10.2 General Framework of FHAP 275 10.3 Model Formulation 276 10.3.1 Methodology Overview 276 10.3.2 FHAP with Simple Resource Allocation 277 10.3.3 FHAP with Gradual Uncertainty Resolution 282 10.3.4 Model Variations and Extensions 286 10.4 Case Study: Codman Square Neighborhood Development Corporation 289 10.4.1 Data Description and Parameter Justification 289 10.4.2 Resource Allocations and Impacts of Model Parameters 292 10.4.3 Policy Implications for CDCs 303 10.5 Conclusion 304 11 Conclusion: Findings and Opportunities in Decision Analytics for Foreclosure Response and Community Development 307 11.1 Introduction 307 11.2 Key Findings 308 11.2.1 Foreclosure Crisis and Responses 308 11.2.2 Engagement with Community-Based Organizations 308 11.2.3 Decision-Modeling Fundamentals: Values and Attributes 309 11.2.4 Foreclosed Property Strategy Design Using Decision Models 310 11.3 Research Insights 312 11.4 Lessons Learned 314 11.5 Community-Based Operations Research: A Reassessment 316 11.6 Research Extensions 319 11.7 Conclusion 320 Appendices A Policy Analysis 323 B Multicriteria Decision Modeling 329 B.1 Multiobjective Decision Making 330 B.2 Multiattribute Decision Models 333 References 339 Index 363

About the Author :
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.

Review :
"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)