Defining and Using Residential
Submarkets in Planning Work
Clifford A. Lipscomb, Ph.D.
Director of Economic Research
Greenfield Advisors, LLC
Seattle, WA and Atlanta, GA
United States of America
Greenfield Advisors
• 37-year-old firm headquartered in Seattle,
WA, USA
• Real estate valuation, economic research,
survey design and administration,
specialty in complex valuation issues (i.e.
we don’t do appraisals for banks/lenders)
• Most of our work is litigation support
• Current cool project: patent infringement
The Big Picture
• People and households are heterogeneous
(different)
• How different are they?
– Socioeconomic
– Racial
– Demographic
– Others
• How can we measure the differences? Are
they “systematic”?
Why is This Important?
• Price prediction (submarkets improve
model accuracy)
• Formulation of market strategy
• Understanding housing market structure
• Improving lenders’ and investors’ ability to
price risk associated with homeownership
• Public policy implications
– Policy tailored to one “type” may not be best
– Policy inertia (is a mid-course correction
possible after a policy is implemented?)
Terminology
• What is a market?
• What is a submarket?
• How can we define submarkets?
– Housing stock (similar packages of housing
services)
– Geography (traditional neighborhoods)
– Household characteristics (data intensive)
– Surveys that ask about who are your “peers”
– good for comparative studies
– Hybrid methods
Rest of the Presentation
• Focus on an Atlanta neighborhood
• Talk about external and internal factors
affecting the neighborhood
• Discuss how a change in land use can
affect the neighborhood
Home Park
Sales Price Distribution
North Home Park
South Home Park
Home Park green space
Less than $150K
$150K – 199,999
$200K – 249,999
$250K – 299,999
$300K +
Determining Submarkets
• Motivation – assumptions in the literature
• Publically available data was limited
(Census tract block group was smallest unit
available)
• Houses are the unit of analysis, so need
that level of detail in demographics
• Differences between renters and owners
• Door-to-door survey effort
• 51% response rate
Empirical Model
• Round 1: Cluster analysis establishes groups
• Round 2: Refines groups into “types” based on a
variation of linear regression model (SUR)
• Houses are sorted into types based on the
appraised value that minimizes error
• Determines the number of types without
researcher pre-determination! Because what if
the researcher draws an arbitrary boundary…
Original Household Types
Type A
Type B
Type C
Submarkets
• A: Undergraduate student renters
• B: Other student renters, young
professionals, and young married couples
• C: Owners and graduate students
• Note: Recent research has tightened the distinctions
between submarkets using different econometric
estimators (Belasco, Farmer, and Lipscomb 2012)
Dynamic Issues
• What happens to neighborhood if you put
in a pocket park?
• Simulation results
– Simulation 1: if preference for park access
stays same
– Simulation 2: if preference for park is ½ of
current estimate
Location of New Pocket Park
Type A
Type B
Type C
What Happens After
Re-sorting?
• Models predict that mix of residents will
change by 30% as a result of pocket park
• Student renters are “forced out” as new
owner-occupiers enter the neighborhood
• Change in amenity mix = change in
occupant
• Did pocket park simply accelerate resident
mix that was going to happen anyway?
Types After Pocket Park
Type A
Type B
Type C
Type A
Type B
Type C
Implications
• Policymakers and planners need to plan
with preference heterogeneity in mind
• A more granular level of data needed to
complete comprehensive plans
• E.g. new MARTA rail stop will be used by
what “type” of households?
• E.g. what “types” are attracted to TODs?
Implications (2)
• Balancing housing affordability with
housing construction (micro-apartments
targeting urban professionals living alone)
• Planners can influence the “types” of
residents attracted to a neighborhood –
influence can be latent or manifest
• Planners plan for change and can
influence that change
Summary
• Economists and planners often use data at one
geographic scale when analyzing phenomena at
a different scale
• Make sure statistical methods are an empirical
translation of your theory
• Beware of post hoc ergo propter hoc fallacy
(“after this, therefore because of this”)
• All methods have limitations; so seek to mitigate
them and show relevance relative to other
methods
Contact Information:
Clifford A. Lipscomb, Ph.D.
Director of Economic Research
Greenfield Advisors, LLC
1870 The Exchange SE, Suite 100
Atlanta, GA 30339
USA
E-mail: cliff@greenfieldadvisors.com
Web: www.greenfieldadvisors.com
Thank you