Upload
others
View
2
Download
0
Embed Size (px)
Citation preview
University of Massachusetts Boston
From the SelectedWorks of Michael P. Johnson
October 10, 2013
Parcel-Level Redevelopment Strategies forDistressed NeighborhoodsMichael P Johnson, Jr.Justin Hollander, Tufts University
Available at: https://works.bepress.com/michael_johnson/47/
Parcel-Level Redevelopment Strategies for Distressed
Neighborhoods: Implementing the Growing
Greener Strategy in Baltimore
INFORMS Fall National Conference, Minneapolis, MNOctober 8, 2013
Michael Johnson, University of Massachusetts BostonJustin Hollander, Tufts University
Image: theatlanticcities.com
Acknowledgements Funder: Abell Foundation, Baltimore, MD Sponsor: City of Baltimore Department of Planning
Thomas Stosur, Director Jill Lemke, Director, Research and Strategic Planning Beth Strommen, Director, Office of Sustainability
Research assistants: University of Massachusetts Boston: Merritt Hughes, Hyun-Jung
Lee, Buki Usidame Tufts University: Peter Ciurczak, Albert Engel, Elza Lambergs,
Kristine Keeney, Takayuki Suzuki, Jingyu Tu, Eliza Whiteman
October 8, 2013INFORMS Minneapolis 20132
Policy motivation
October 8, 2013INFORMS Minneapolis 20133
Population decline continues in many locales due to lower population growth rates, deindustrialization and sustained disinvestment, and the housing foreclosure crisis
Planners increasingly see ‘decline’ as something to plan for: a place may lose population while ensuring a high quality of life and enhanced social value (Delken 2008, Hollander 2010)
Vacancy and abandonment have a high cost to municipalities: studies have shown each vacant building can cost up to $1,472 per year in public safety costs (Winthrop 2009)
Vacancy and abandonment has been linked to negative social outcomes such as increased crime, decreased social capital, poor health outcomes, and fire injury (Garvin 2013)
What is Smart Shrinkage?
October 8, 2013INFORMS Minneapolis 20134
Smart shrinkage: ‘planning for less, fewer people, fewer buildings, fewer land uses’ (Popper and Popper 2002)
Reduction in level of public services (Popper and Popper 2002): Fixed assets: closure/consolidation/re-purposing of schools, fire stations,
libraries Services: reduced maintenance of infrastructure, outsourcing,
furloughs/layoffs
Transformative investments (Hollander 2010): Subdivision of owner-occupied single family homes into multi-family
rentals Demolition of homes Conversion of vacant lots to urban agriculture, parks and community
gardens and environmental remediation
What cities and regions face shrinkage?
October 8, 2013INFORMS Minneapolis 20135
Buffalo, New York (Hollander and Cahill 2011)
Flint, Michigan (Hollander 2010)
Great Plains region of the Midwest (Popper and Popper 2004)
Southwest US and central Florida (Hollander 2011)
Youngstown, Ohio (Hollander 2009)
Taranto, Italy; Porto, Portugal; Aberdeen, UK; Frankfurt/Oder, Germany and Tallinn, Estonia (Wolff, 2010)
Leipzig, Germany (Banzhaf, Kindler and Haase 2007)
Baltimore also faces issues of shrinkage and vacancy 34.6% population decline
1950-2012
Approximately 16,000 vacant homes
More than 33,000 home foreclosures 2000-2009, two-thirds of which were in Census tracts that were greater than 60% African American (Sangree 2009)
October 8, 2013INFORMS Minneapolis 20136
Vacancy and abandonment problem
October 8, 2013INFORMS Minneapolis 20137
Goal: Formulate and solve a decision problem in which certain residential parcels are selected for repurposing within distressed neighborhoods of Baltimore
Objectives: Explicitly consider framework of project sponsor Design decision problem to reflect Baltimore Planning’s procedures,
knowledge and organizational culture Identify and quantify social, environmental and economic impacts of vacant
lot redevelopment Identify and incorporate into model short and long term planning goals
and city- and neighborhood-level concerns
How can decision modeling apply to vacancy and abandonment?
October 8, 2013INFORMS Minneapolis 20138
Assumptions based on research and practice: Vacant lots and abandoned buildings can be transformed into assets to address
stormwater management, food justice, recreational needs and future development (Stosur and Strommen 2012; Hollander et al. 2009)
Multi-objective math programming has been used for strategy design: Neighborhood-level smart shrinkage (Johnson, Hollander and Hallulli 2013)
Parcel-level smart growth (Gabriel, Faria and Moglen 2006)
Assumptions based on client engagement: Individual parcels that are candidates for acquisition and redevelopment are
combined into clusters considered
Relocations, though common in practice, raise ethical and financial issues in modeling, and are to be discouraged
Model development according to policy analytic and planning principles Consider a wide range of attributes and objectives:
Social impacts: neighborhood health, property values, public safety, environmental quality, food access & security, aesthetic quality
Administrative concerns: acquisition, demolition, relocation and redevelopment costs, redevelopment targets
Modeling considerations: proximity, equity
Stakeholder engagement to identify highest-priority objectives, classes of policy alternatives, attributes
Quantify impacts associated with alternative land uses
Choose investment types and levels to optimize multiple social & administrative objectives over multiple time periods, incorporating uncertainty
October 8, 2013INFORMS Minneapolis 20139
Model development according to client concerns Political considerations place focus on acquisition levels of clusters for
alternative uses, as well as a classification category, rather than social impacts # acres of clusters acquired for urban agriculture # acres of clusters acquired for stormwater drainage # acres of clusters acquired for future development # acres of clusters that meet criteria for ‘blight elimination’
Single time period, certainty with respect to model parameters
Cost parameters address acquisition, demolition and relocation only; redevelopment fixed & variable costs borne by other actors
Model solutions intended to advise & support only
October 8, 2013INFORMS Minneapolis 201310
Baltimore Planning decision modelIndex and set:
1,… , : ∈ , , , : &
Decision variables:1,
0,
Parameters: ,
October 8, 2013INFORMS Minneapolis 201311
Baltimore Planning decision model, continued
October 8, 2013INFORMS Minneapolis 201312
Budget
Single land use
Cannot assign to classification category unless selected for land use
Study area represents blight, and opportunity Baltimore Planning selected 640
clusters citywide for potential redevelopment
Pilot study: Five neighborhoods in Northeast
Baltimore with highest vacancy rates 139 clusters Proximate ‘anchor institutions’: Johns
Hopkins University, Morgan State University, Coppin State University
Assume $3.5M available for acquisition and redevelopment
October 8, 2013INFORMS Minneapolis 201313
Cluster eligibility: results
October 8, 2013INFORMS Minneapolis 201315
Use/Classification Number of Clusters that Qualify
Urban Agriculture 10
Stormwater Drainage 38
Potential Development 23
Blight Elimination 7
Total clusters (combined) 139 (118)
A previous ‘toy’ model with 26 clusters had only a single one that qualified for urban agriculture
Eligibility sets: Urban agriculture and Stormwater drainage
October 8, 2013INFORMS Minneapolis 201316
Eligibility sets: Potential development and Blight elimination
October 8, 2013INFORMS Minneapolis 201317
Model solution Premium Solver Platform using Standard LSGRG
Nonlinear Engine 78 variables and 175 constraints Solution times were less than 10 seconds
October 8, 2013INFORMS Minneapolis 201318
Objective space results demonstrate potential impact of client preferences
Corner solutions show expected acquisition recommendations
Multiple compromise solutions demonstrate promising variation among objectives
October 8, 2013INFORMS Minneapolis 201319
Total area acquired demonstrates impacts of criteria Solution maximizing
urban agriculture features dominated by two large, expensive parcels
Maximizing future development results in all 23 eligible clusters being chosen
Only four of seven clusters eligible for blight elimination are also eligible for at least one land use
October 8, 2013INFORMS Minneapolis 201320
Decision space results demonstrate competing policy (and political) concerns
October 8, 2013INFORMS Minneapolis 201321
Compromise solutions provide opportunity for more nuanced client preferences
October 8, 2013INFORMS Minneapolis 201322
Lessons learned Eligible clusters Binary eligibility criteria are overly coarse Future development criteria did not reflect developers’
subjective assessments of market feasibility as well as other criteria based on physical characteristics
Small size of cluster-eligible sets, and tradeoffs between them, even after relaxing criteria, were a surprise
Model solution Objective function weights do not reflect actual stakeholder
preferences Clustering, equity and social impact objectives may generate
solutions more aligned with stakeholder values
October 8, 2013INFORMS Minneapolis 201323
Next steps Baltimore Planning project: Revise cluster eligibility sets Revise blight elimination constraints Use realistic objective function weights Deliver an end-product that supports daily use by planners and
GIS analysts
Research initiative Formulate social impact objectives using planning & policy
research Incorporate economic concerns (clustering) and fairness
(equity) Allow multiple periods and uncertainty
October 8, 2013INFORMS Minneapolis 201324
Questions?
October 8, 2013INFORMS Minneapolis 201326
Michael P. Johnson, PhDUniversity of Massachusetts Boston
Boston, MA 02125 USAtel: 617-287-6967
email: [email protected]://www.umb.edu/academics/mgs/faculty/michael_johnson/
Justin Hollander, Ph.D., AICPTufts University
Medford, MA 02155 USAtel: (617) 627-3394
Twitter: JustinHollanderemail: [email protected]
http://www.tufts.edu/~jholla03
The paper from which this presentation is derived is under development. A previous paper in this research stream is available at
http://works.bepress.com/michael_johnson/35
ReferencesBaltimore City Department of Planning. 2012a. The Baltimore City Growing Green Initiative. Prepared
by Thomas J. Stosur and Beth Strommen.Baltimore City Department of Planning. 2012b. “Decision Modeling Tool for Vacant Structure
Demolition and Redevelopment.” Proposal submitted to Abel Foundation. Prepared by Jill Lemke, Michael P. Johnson and Justin Hollander
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.
Hollander, J. 2011. Sunburnt Cities: The Great Recession, Depopulation and Urban Planning in the American Sunbelt. Abingdon, UK: Routledge.
Hollander, J. and J. Németh. 2010. The Bounds of Smart Decline: a Foundational Theory for Planning Shrinking Cities. Housing Policy Debate 21(3): 349 – 367.
Innes, J.E. and D.E. Booher. 2010. Planning with Complexity: An Introduction to Collaborative Rationality for Public Policy. New York: Routledge.
Johnson, M.P. (Ed.) 2011. Community‐Based Operations Research: Decision Modeling for Local Impact and Diverse Populations. New York: Springer.
Johnson, M.P., Hollander, J. and A. Hallulli. 2013. Maintain, Demolish, Re-purpose: Policy Design for Vacant Land Management using Decision Models. Cities: Special Issue: Vacant Land: The New Urban Green, to appear.
October 8, 2013INFORMS Minneapolis 201327