One concept · Applied economics lesson
Measuring Local Market Concentration
How do economists measure whether a market is concentrated—and how much can the answer change when the researcher cleans names or redraws the market?
Why this matters
Market shares and the Herfindahl–Hirschman Index (HHI) are common starting points for describing competition. The arithmetic is simple. The consequential work often comes first: deciding which observations belong together, which names identify the same economic organization, and which geography is the market.
Research
Yelowitz and coauthors measured brokerage concentration across 90 local markets and then examined six metropolitan areas in greater detail.
Teaching
This lesson makes the data work visible. Generated spelling variants fragment one firm until you clean them; offices can then be analyzed separately or grouped by franchise.
Policy
Official U.S. merger-guideline thresholds changed in 1992/1997, 2010, and 2023. The same synthetic market can therefore receive a different screening label across vintages.
Interpretation boundary: An HHI or structural screen is evidence about market structure, not a legal conclusion. A ZIP comparison is a sensitivity exercise, not an automatic antitrust market definition.
The basic idea
Calculate each firm's percentage share, square it, and add the squared shares.
HHI = Σ sᵢ²
Four equal firms each have a 25 percent share, so HHI = 25² + 25² + 25² + 25² = 2,500. A monopoly has HHI = 100² = 10,000. Squaring gives more weight to large shares.
The synthetic data
The lab contains 1,620 deterministic synthetic listings anchored to the six metropolitan areas studied in the 2013 Cityscape paper: Atlanta, Boston, Chicago, Dallas, Los Angeles, and Washington, DC. The 24 ZIP labels and period brokerage names come from the source materials; every row-level assignment and value is generated. Version hhi-synthetic-v1 uses seed 20130411.
Synthetic teaching exercise. No address, agent, listing URL, sale, or property record was copied. The generated shares do not state any organization's historical or current market share.
Measure one market
Hold the generated rows fixed and change one analytical choice at a time. Start with Chicago ZIP 60614, one of the source paper's explicit ZIP examples.
Loading the synthetic listings…
Selected market
- Synthetic listings
- —
- Identities
- —
- HHI
- —
- CR4
- —
—
Hypothetical top-two combination
- Firm A
- — ()
- Firm B
- — ()
- ΔHHI = 2ab
- —
- Post-combination HHI
- —
- Vintage HHI screen
- —
This mechanical illustration combines the two largest synthetic identities. It does not evaluate competitive effects or reach a legal conclusion. Official guideline source.
Same rows, three identity rules
Cleaning duplicate spellings reduces artificial fragmentation. Combining offices under a common organization answers a broader firm-identity question.
HHI ranges from 0 to 10,000. The bars zoom to 0–1,500 because every scenario in this synthetic release falls between 145.1 and 1,219.0; the table gives exact values.
| Identity rule | Identities | HHI |
|---|
Look inside the calculation
| Rank | Synthetic identity | Listings | Share | HHI contribution |
|---|---|---|---|---|
| All identities | — | 100.0% | — | |
See what cleaning does
These generated variants are deliberately messy. Treating them as separate firms inflates the identity count and biases this exercise's measured HHI downward.
| Generated raw string | Cleaned office | Combined firm |
|---|
Test a synthetic submarket definition
Price tiers are generated. Their purpose is to show how specialization can change market shares, not to describe the real ZIPs or firms.
Version 1 design: each ZIP is assigned to one generated price tier. These tier results regroup ZIP-based rows; they are not separate within-ZIP specialization estimates.
| Tier | N | Firms | HHI | Largest identity | Share |
|---|
What changed?
Try this: keep Chicago selected and compare raw strings, cleaned offices, and combined firms. Then switch between the metro and its ZIPs. Explain whether the number moved because the underlying rows changed, the economic unit changed, or the geographic market changed.
Yelowitz evidence
The lesson simplifies the workflow. The published studies supply the historical questions, samples, and findings.
Concentration and Market Structure in Local Real Estate Markets
Jason Beck, Frank Scott, and Aaron Yelowitz, Real Estate Economics 40(3), 2012. The study examines 90 markets and longitudinal Louisville data.
The Market for Real Estate Brokerage Services in Low- and High-Income Neighborhoods: A Six-City Study
Aaron Yelowitz, Frank Scott, and Jason Beck, Cityscape 15(1), 2013. The article makes firm-name cleaning and alternative geographic definitions central to the analysis.