Defining and Using Residential Submarkets in Planning Work

Clifford A. Lipscomb, Ph.D.Director of Economic Research

Greenfield Advisors, LLCSeattle, 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

Less than $150K$150K – 199,999$200K – 249,999$250K – 299,999$300K +

Home Park green space

North Home Park

South Home Park

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 ResearchGreenfield Advisors, LLC1870 The Exchange SE, Suite 100Atlanta, GA 30339USAE-mail: cliff@greenfieldadvisors.com Web: www.greenfieldadvisors.com

Thank you