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Decision Science for Housing and Community Development: Localized and Evidence-Based Responses to Distressed Housing and Blighted Communities [Hardback]

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Decision Science for Housing and Community Development: Localized and Evidence-Based Responses to Distressed Housing and Blighted CommunitiesPalielināt
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Permanent link: https://www.kriso.lv/db/9781118974995.html
Keywords:
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.

 

Recenzijas

"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 books 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 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(26)
1.1 Challenges and Opportunities for Housing and Community Development in the US
1(5)
1.2 Community Development in the United States
6(3)
1.3 Big Data, Analytics and Community Development
9(2)
1.4 The Foreclosure Crisis: Problem, Impacts, and Responses
11(2)
1.5 Community-Based Operations Research: A Novel Approach to Support Local Development
13(6)
1.6 Why This Book Now?
19(2)
1.7 Book Description
21(3)
1.8 Conclusion
24(3)
SECTION 1 POLICY AND PRACTICE IN FORECLOSED HOUSING AND COMMUNITY DEVELOPMENT
27(80)
2 Foreclosed Housing Crisis and Policy and Planning Responses
29(16)
2.1 Roots of the Foreclosed Housing Crisis
29(3)
2.2 Impacts of the Crisis
32(7)
2.2.1 Foreclosure Rates
33(1)
2.2.2 Home Equity and Wealth
34(2)
2.2.3 Health, Education, and Household Mobility
36(1)
2.2.4 Disamenities and Spillover Effects
37(1)
2.2.5 Market-Level Consequences
38(1)
2.3 Responses to the Crisis
39(2)
2.4 Effectiveness of Foreclosure Responses
41(2)
2.5 Opportunities for Decision Modeling Responses to the Foreclosed Housing Crisis
43(2)
3 Community Partners and Neighborhood Characteristics
45(30)
3.1 Introduction
45(1)
3.2 Methodology
46(3)
3.2.1 Data Gathering Summary
46(1)
3.2.2 Triangulation
47(1)
3.2.3 Analysis
48(1)
3.3 Selection of Cases
49(1)
3.4 Case 1: The Neighborhood Developers
50(9)
3.4.1 Organization Type and Mission
50(5)
3.4.2 Organization Service Area and Population
55(1)
3.4.3 Organization Engagement with Foreclosure Crisis
55(3)
3.4.4 Organization Technical Capacity and Familiarity with Project's Analytic Methods
58(1)
3.5 Case 2: Coalition for a Better Acre
59(4)
3.5.1 Organization Type and Mission
59(1)
3.5.2 Organization Service Area and Population Demographics
59(2)
3.5.3 Organization Engagement with Foreclosure Crisis
61(1)
3.5.4 Organization Technical Capacity and Familiarity with Project's Analytic Methods
62(1)
3.6 Case 3: Codman Square Neighborhood Development Corporation
63(4)
3.6.1 Organization Type and Mission
63(1)
3.6.2 Organization Service Area and Population Demographics
63(1)
3.6.3 Organization Engagement with Foreclosure Crisis
64(3)
3.6.4 Organization Technical Capacity and Familiarity with Project's Analytic Methods
67(1)
3.7 Case 4: Twin Cities Community Development Corporation
67(4)
3.7.1 Organization Type and Mission
67(1)
3.7.2 Organization Service Area and Population Demographics
68(1)
3.7.3 Organization Engagement with Foreclosure Crisis
68(2)
3.7.4 Organization Technical Capacity and Familiarity with Project's Analytic Methods
70(1)
3.8 Case Contrast and Discussion
71(3)
3.8.1 Role of Community Partners
71(2)
3.8.2 Adaptation of Case Study Theory for Our Project
73(1)
3.9 Conclusion
74(1)
4 Analytic Approaches to Foreclosure Decision Modeling
75(32)
4.1 Introduction
75(6)
4.2 Analysis of Community Partners and their Service Areas
81(13)
4.3 Localized Foreclosure Response
94(3)
4.4 Opportunities for Research-Based Analytic Responses to Foreclosures
97(5)
4.5 Solution Design for Community Development using Community-Based Operations Research
102(2)
4.6 Where Do We Go From Here?
104(3)
SECTION 2 VALUES, METRICS AND IMPACTS FOR DECISION MODELING
107(98)
5 Value-Focused Thinking: Defining, Structuring and Using CDC Objectives in Decision Making
109(44)
5.1 Introduction
109(9)
5.1.1 Overview
109(1)
5.1.2 Values and Objectives in Decisions
109(1)
5.1.3 Values and Objectives in Community-Based Organization/CDC Decisions
110(1)
5.1.4 Utility Functions and Decision Making
111(1)
5.1.5 Multiattribute Utility Functions
112(2)
5.1.6 Value-Focused Thinking
114(1)
5.1.7 VFT as Soft OR and Problem Structuring Method
115(1)
5.1.8 The Resource Allocation Decision Frame
115(3)
5.1.9 Plan
118(1)
5.2 Methods
118(5)
5.2.1 Linear Additive Assumption
118(1)
5.2.2 Denning the Mathematical Model as a Set of Linear Equations
119(1)
5.2.3 Structuring
120(2)
5.2.4 Obtaining Inputs
122(1)
5.3 Cases
123(20)
5.3.1 Simulated CDC
123(7)
5.3.2 Codman Square Neighborhood Development Corporation
130(8)
5.3.3 Twin Cities Community Development Corporation
138(5)
5.4 Common and Contingent Objectives for CDCs
143(8)
5.5 Lessons for Applying VFT to CBOs
151(2)
6 Characteristics of Acquisition Opportunities: Strategic Value
153(22)
6.1 Introduction
153(2)
6.2 Problem Description
155(4)
6.2.1 Policy Motivation
155(2)
6.2.2 Theoretical Foundations
157(2)
6.3 Model Development
159(3)
6.3.1 Sets and Indexes
159(1)
6.3.2 Parameters and Functions
160(1)
6.3.3 Individual Resident Frame
160(1)
6.3.4 CDC Frame
161(1)
6.4 Case Study: The Neighborhood Developers
162(8)
6.4.1 Site Description
162(4)
6.4.2 Model Computations
166(4)
6.5 Discussion
170(2)
6.5.1 Policy Analysis
170(1)
6.5.2 Implications for Modeling and Practice
171(1)
6.6 Conclusion
172(3)
7 Characteristics of Acquisition Opportunities: Property Value
175(30)
7.1 Introduction
175(1)
7.2 Property Value Changes as a Social Impact of Foreclosed Housing
176(2)
7.3 A Model of PVI for Foreclosed Housing
178(2)
7.4 The PVI Model
180(6)
7.4.1 The Foreclosure Process
181(1)
7.4.2 Modeling Foreclosure Phase Transitions with a Markov Chain
182(2)
7.4.3 Estimation of Proximate Property Value Impacts
184(2)
7.5 Case Study: The Neighborhood Developers
186(10)
7.5.1 Data and Model Specifications
186(4)
7.5.2 Computational Results
190(1)
7.5.3 Clustering Effects
191(5)
7.6 Discussion
196(3)
7.7 Model Validity and Limitations
199(3)
7.7.1 Nonlinearities in Aggregate Impacts
199(1)
7.7.2 Representativeness of Data Sources
200(1)
7.7.3 Sensitivity to Transition Probabilities
200(1)
7.7.4 Impacts of Multiple Foreclosures
200(1)
7.7.5 Wider Range of Social Impacts
201(1)
7.7.6 Model Validity
201(1)
7.8 Conclusion
202(3)
SECTION 3 PRESCRIPTIVE ANALYSIS AND FINDINGS
205(118)
8 Social Benefits of Decision Modeling for Property Acquisition
207(40)
8.1 Introduction
207(2)
8.2 CDC Practice in Foreclosed Housing Acquisition
209(3)
8.3 A Multiobjective Model of Foreclosed Housing Acquisition
212(8)
8.3.1 Decision Model
212(3)
8.3.2 Input Data
215(5)
8.4 Model Solutions
220(23)
8.4.1 Constraint on Number of Units Acquired
221(12)
8.4.2 Budget Constraint
233(10)
8.5 Discussion
243(1)
8.6 Conclusion and Next Steps
244(3)
9 Acquiring And Managing A Portfolio Of Properties
247(26)
9.1 Introduction
247(1)
9.2 Dynamic Modeling of the Foreclosed Housing Acquisition Process
248(3)
9.3 Model Formulation
251(2)
9.4 Policy Analysis Under Different Fund Accessibility Cases
253(6)
9.4.1 Acquisition Policies Under No Fund Expiration
253(4)
9.4.2 Acquisition Policies Under Fund Expiration
257(2)
9.5 Case Study: Codman Square Neighborhood Development Corporation
259(10)
9.5.1 Data Description
260(1)
9.5.2 Implementation Under No Fund Expiration
261(4)
9.5.3 Implementation Under Fund Expiration
265(4)
9.6 Conclusion
269(4)
10 Strategic Acquisition Investments Across Neighborhoods
273(34)
10.1 Introduction
273(2)
10.2 General Framework of FHAP
275(1)
10.3 Model Formulation
276(13)
10.3.1 Methodology Overview
276(1)
10.3.2 FHAP with Simple Resource Allocation
277(5)
10.3.3 FHAP with Gradual Uncertainty Resolution
282(4)
10.3.4 Model Variations and Extensions
286(3)
10.4 Case Study: Codman Square Neighborhood Development Corporation
289(15)
10.4.1 Data Description and Parameter Justification
289(3)
10.4.2 Resource Allocations and Impacts of Model Parameters
292(11)
10.4.3 Policy Implications for CDCs
303(1)
10.5 Conclusion
304(3)
11 Conclusion: Findings and Opportunities in Decision Analytics for Foreclosure Response and Community Development
307(16)
11.1 Introduction
307(1)
11.2 Key Findings
308(4)
11.2.1 Foreclosure Crisis and Responses
308(1)
11.2.2 Engagement with Community-Based Organizations
308(1)
11.2.3 Decision-Modeling Fundamentals: Values and Attributes
309(1)
11.2.4 Foreclosed Property Strategy Design Using Decision Models
310(2)
11.3 Research Insights
312(2)
11.4 Lessons Learned
314(2)
11.5 Community-Based Operations Research: A Reassessment
316(3)
11.6 Research Extensions
319(1)
11.7 Conclusion
320(3)
APPENDICES
A Policy Analysis
323(6)
B Multicriteria Decision Modeling
329(10)
B.1 Multiobjective Decision Making
330(3)
B.2 Multiattribute Decision Models
333(6)
References 339(24)
Index 363
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.