Parcel-Level Redevelopment Strategies for Distressed ...

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/

http://www.umb.edu

https://works.bepress.com/michael_johnson/

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


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?


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?


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)


Vacancy and abandonment problem


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?


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


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


Baltimore Planning decision modelIndex and set:

1,… , : ∈ , , , : &

Decision variables:1,

0,

Parameters: ,


Baltimore Planning decision model, continued


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


Cluster eligibility: criteria


Cluster eligibility: results


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


Eligibility sets: Potential development and Blight elimination


Model solution Premium Solver Platform using Standard LSGRG

Nonlinear Engine 78 variables and 175 constraints Solution times were less than 10 seconds


Objective space results demonstrate potential impact of client preferences

Corner solutions show expected acquisition recommendations

Multiple compromise solutions demonstrate promising variation among objectives


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


Decision space results demonstrate competing policy (and political) concerns


Compromise solutions provide opportunity for more nuanced client preferences


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


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


Questions?


Source: Baltimore Sun

Questions?


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.


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