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Allegheny County, PA — Beauty salons Saturation

Name: Beauty salons density by ZIP code in Allegheny County, PA
Creator: BizGapMap

How many beauty salons exist per person present — residents plus daytime workers — by ZIP code, compared with the U.S. average. Establishment data: Census ZBP 2023. Population: ACS 2024 5-year. 114 ZIP codes analyzed.

Includes: Hair salons · Beauty salons · Blow-dry & styling bars · Makeup & skincare studios. Counted together because official statistics use one 812112 code.

Measured density: High density — 1.74× the U.S. rate (between 1.74× and 2.02× once withheld ZIP cells are counted)

Why a range: Census does not publish a ZIP-by-industry count when that ZIP has 0–2 businesses. 61 ZIP cells in this county are withheld, 45 of them with residents. Each could hold 0, 1, or 2 businesses: counting them all as 0 gives the low end, counting them all as 2 gives the high end — the data cannot say where the truth sits. The density range uses only the populated cells, since ZIPs without residents are excluded from per-person rates. How ranges work

High density Allegheny County beauty salons — 174–202% of the U.S. rate

563 beauty salons are counted in Allegheny County — 3.6 per 10,000 people present (residents + daytime workers), 174% of the U.S. rate of 2.1.

On the comparable ZIP set (ZIPs with a published count in every year), the total has stayed roughly flat between 2015 and 2023 (529 → 539).

Measured supply density is highest in 15232 (Pittsburgh), 15228 (Pittsburgh), 15131 (Mckeesport) — well above the national rate. On measured density alone, the lowest-density residential ZIPs with a published count are 15212 (Pittsburgh), 15120 (Homestead), 15210 (Pittsburgh).

BLS QCEW puts the latest county-wide count (2026 Q1) at 553 establishments, the freshest available signal.

Measured density: Allegheny County sits at 174% of the U.S. rate for beauty salons. 45 ZIPs with population have no published count (each 0–2 businesses), so the true per-capita rate is between 174% and 202% of the U.S. rate. Why. The highest measured density is in 15232 and 15228; the lowest measured density is in 15212 and 15120.

This is a supply-side read: it counts existing beauty salons and the people present, not foot traffic, income, rent, or whether locals want more. Treat low density as a question to check, not an answer.

Beauty salons

563–685

published count; upper bound counts each withheld ZIP cell as 2 (61 cells) (2023)

Per 10,000 present

3.6

residents + daytime workers

U.S. average

2.1

per 10,000 present

County index

1.74×

vs. U.S.

Latest quarter (QCEW)

553

2026 Q1 — county total

Residents

1,213,050

98 ZIPs with data

Workers

682,711

jobs located in county

Is the area growing?

Population

1,231,814

Census PEP, July 1 2024

Change since 2020

−1.4%

−17,857 people

Net migration 2024

+3,356

international +8,548, domestic −5,192

Jobs located here

712,047

LODES 2022, −2.4% since 2019

Allegheny County had an estimated 1,231,814 residents on July 1, 2024 — 1.4% fewer than in 2020 (−17,857). In the latest year it gained 1,676 people: births minus deaths −1,693, international migration +8,548, domestic migration −5,192. Components are published separately and may not sum exactly.

712,047 jobs were located in the county in 2022 (−2.4% since 2019), and 582,425 employed residents lived there (−3.9% since 2019). LODES counts wage and salary jobs covered by unemployment insurance plus federal jobs; it excludes the self-employed and military, and counts jobs rather than unique people. 2019→2022 spans the pandemic, so read it as one change, not a trend.

Population and migration say whether the customer base is growing. They do not say whether people want another business of this type. Definitions and limits → · Full county detail →

ZIP gaining residents

2015–19 → 2020–24

Change

15205 Pittsburgh

+12.0%

15090 Wexford

+11.9%

15236 Pittsburgh

+9.6%

ZIP losing residents

2015–19 → 2020–24

Change

15233 Pittsburgh

−35.0%

15219 Pittsburgh

−31.2%

15132 Mckeesport

−10.6%

ACS 5-year estimates for 2015–2019 and 2020–2024. Only ZIPs with at least 2,000 residents in both windows and a change larger than the survey margin of error (90%) are listed — small ZIPs move a lot on paper without a real signal.

Nonemployer businesses (no paid employees)

Nonemployer businesses

1,646

no paid employees (2024)

Receipts

$47.8M

published gross receipts

Receipts per business

$29K

gross, not profit

Census Nonemployer Statistics counts businesses with no paid employees — a separate universe from the employer establishments counted above; a business is in one or the other, never both. They are not storefronts by definition: many are home-based, mobile, or part-time.

We show them beside the density index, not inside it. In categories such as nail and beauty salons, child care, and pet care, one-person businesses can outnumber employer establishments, so the employer-only count understates how many businesses exist. That is not evidence of higher demand.

Receipts are gross revenue reported for these businesses — not profit, and not the size of the local market. Census publishes this only at county level, so it is not shown on the ZIP map. See methodology.

Saturation map

Each ZIP area is shaded by its density of this business type per person present (residents + workers) vs. the U.S. average. Click any ZIP to zoom to its boundary, see its numbers, and switch to aerial imagery. Individual mapped shops appear as dots at street level — Show locations zooms you in to them (scroll or double-click to zoom).

Basemap:

Under 50% 50–100% 100–150% 150–250% 250%+ 0–2 (not published) / no data ZIP total (dot size) Mapped shop (OpenStreetMap, street level)

Map data © OpenStreetMap contributors, tiles by OpenFreeMap.

White dots are ZIP totals from the official Census count — the bigger the dot, the more beauty salons in that ZIP. They are aggregated, not individual shops. Turquoise dots are individual locations mapped in OpenStreetMap, shown only at street level; they are crowd-sourced and indicative, may be incomplete, and do not equal the official counts. Only the official ZIP totals feed the saturation index.

Market trend — up 2% since 2015

Number of beauty salons in Allegheny County across 2015–2023, on a consistent set of ZIP codes present in every year — so changes are real, not ZIP-boundary artifacts. 2015/2018 are pre-pandemic baselines; 2020/2021 are COVID-distorted.

529

2015

512

2018

516

2020

546

2021

548

2022

539

2023

Highest measured density

ZIP

vs. U.S. average

Index

15232 Pittsburgh

664%

15228 Pittsburgh

436%

15131 Mckeesport

428%

15090 Wexford

414%

15017 Bridgeville

384%

15237 Pittsburgh

352%

15102 Bethel Park

335%

15201 Pittsburgh

323%

15223 Pittsburgh

320%

15236 Pittsburgh

312%

15116 Glenshaw

305%

15146 Monroeville

295%

15238 Pittsburgh

280%

15136 Mc Kees Rocks

275%

15216 Pittsburgh

268%

Lowest measured density

ZIP

vs. U.S. average

Index

15212 Pittsburgh

54%

15120 Homestead

67%

15210 Pittsburgh

74%

15147 Verona

82%

15235 Pittsburgh

111%

15122 West Mifflin

116%

15108 Coraopolis

126%

15220 Pittsburgh

127%

15218 Pittsburgh

128%

15239 Pittsburgh

142%

15106 Carnegie

146%

15202 Pittsburgh

158%

15084 Tarentum

165%

15217 Pittsburgh

166%

15101 Allison Park

169%

Low measured density is not evidence of opportunity. A ZIP with few beauty salons may simply lack foot traffic, retail space, or demand. We rank only residential and mixed ZIPs here; employment hubs and visitor destinations are listed separately below. ZIPs where Census did not publish a count (0–2 businesses) are not ranked: they may be the least served of all, but the data cannot tell 0 from 1 or 2. Verify those locally.

Employment hubs & destinations

These ZIPs have far more workers (or visitors) than residents, so their counts are driven by daytime and visitor demand — not a place to read "shortage" from resident numbers alone.

ZIP	Neighborhood	Type	Residents	Workers	Beauty salons	Index
15086	Warrendale	Destination	788	10,407	5	401%
15222	Pittsburgh	Employment hub	5,725	54,863	16	232%
15275	Pittsburgh	Destination	86	14,032	3	203%
15219	Pittsburgh	Employment hub	11,376	35,729	4	66%
15213	Pittsburgh	Employment hub	30,466	61,917	4	31%
15056	Leetsdale	Employment hub	1,041	2,242	0–2	—
15123	West Mifflin	Destination	2	72	0–2	—
15225	Pittsburgh	Employment hub	1,085	2,887	0–2	—
15233	Pittsburgh	Employment hub	2,596	6,678	0–2	—

Scroll the table sideways to see all columns.

All ZIP codes

Click a column header to sort. Index = beauty salons per 10,000 people present (residents + 0.5×workers) ÷ the U.S. average. 100% = exactly average.

ZIP	Neighborhood	Residents	Workers	Beauty salons	Per 10k	Index	Status
15232	Pittsburgh	11,349	9,173	22	13.8	664%	Very high density
15140	Pitcairn	3,032	395	3	9.3	447%	Very high density
15228	Pittsburgh	18,236	3,247	18	9.1	436%	Very high density
15131	Mckeesport	8,209	1,556	8	8.9	428%	Very high density
15090	Wexford	25,763	13,495	28	8.6	414%	Very high density
15086	Warrendale	788	10,407	5	8.3	401%	Very high density
15017	Bridgeville	17,782	12,008	19	8.0	384%	Very high density
15237	Pittsburgh	43,588	24,830	41	7.3	352%	Very high density
15144	Springdale	3,771	998	3	7.0	338%	Very high density
15102	Bethel Park	30,344	8,287	24	7.0	335%	Very high density
15201	Pittsburgh	13,333	9,052	12	6.7	323%	Very high density
15223	Pittsburgh	6,255	2,511	5	6.7	320%	Very high density
15236	Pittsburgh	31,313	14,397	25	6.5	312%	Very high density
15116	Glenshaw	14,461	2,584	10	6.3	305%	Very high density
15146	Monroeville	28,332	21,714	24	6.1	295%	Very high density
15238	Pittsburgh	12,868	22,426	14	5.8	280%	Very high density
15136	Mc Kees Rocks	22,623	7,187	15	5.7	275%	Very high density
15216	Pittsburgh	21,471	3,797	13	5.6	268%	Very high density
15037	Elizabeth	10,417	1,813	6	5.3	255%	Very high density
15227	Pittsburgh	28,460	4,199	15	4.9	236%	High density
15241	Pittsburgh	22,294	8,502	13	4.9	236%	High density
15205	Pittsburgh	24,060	25,757	18	4.9	234%	High density
15044	Gibsonia	27,370	7,035	15	4.9	234%	High density
15222	Pittsburgh	5,725	54,863	16	4.8	232%	High density
15139	Oakmont	6,702	3,252	4	4.8	231%	High density
15065	Natrona Heights	10,894	4,050	6	4.6	223%	High density
15224	Pittsburgh	10,665	9,923	7	4.5	215%	High density
15206	Pittsburgh	29,056	13,514	16	4.5	215%	High density
15234	Pittsburgh	14,014	3,532	7	4.4	213%	High density
15137	North Versailles	10,043	2,886	5	4.4	209%	High density
15275	Pittsburgh	86	14,032	3	4.2	203%	High density
15071	Oakdale	12,663	3,697	6	4.1	199%	High density
15215	Pittsburgh	11,907	5,415	6	4.1	197%	High density
15143	Sewickley	21,851	9,684	10	3.7	180%	High density
15211	Pittsburgh	10,728	1,208	4	3.5	170%	High density
15203	Pittsburgh	10,370	13,271	6	3.5	170%	High density
15101	Allison Park	26,036	5,012	10	3.5	169%	High density
15217	Pittsburgh	25,795	6,310	10	3.5	166%	High density
15084	Tarentum	9,743	3,839	4	3.4	165%	High density
15202	Pittsburgh	19,564	3,606	7	3.3	158%	High density
15106	Carnegie	19,244	7,724	7	3.0	146%	Above average
15239	Pittsburgh	21,410	4,456	7	3.0	142%	Above average
15218	Pittsburgh	13,379	3,354	4	2.7	128%	Above average
15220	Pittsburgh	17,644	17,527	7	2.7	127%	Above average
15108	Coraopolis	42,799	44,648	17	2.6	126%	Above average
15122	West Mifflin	18,816	11,920	6	2.4	116%	Above average
15235	Pittsburgh	33,951	9,999	9	2.3	111%	Above average
15147	Verona	15,798	3,529	3	1.7	82%	Below average
15210	Pittsburgh	25,227	1,776	4	1.5	74%	Below average
15120	Homestead	18,029	7,125	3	1.4	67%	Below average
15219	Pittsburgh	11,376	35,729	4	1.4	66%	Below average
15212	Pittsburgh	27,652	33,203	5	1.1	54%	Below average
15213	Pittsburgh	30,466	61,917	4	0.7	31%	Far below average
15006	Bairdford	180	54	0–2	—	—	Not published
15007	Bakerstown	256	314	0–2	—	—	Not published
15014	Brackenridge	3,095	1,239	0–2	—	—	Not published
15015	Bradfordwoods	1,291	64	0–2	—	—	Not published
15018	Buena Vista	712	99	0–2	—	—	Not published
15020	Bunola	481	81	0–2	—	—	Not published
15024	Cheswick	9,325	3,635	0–2	—	—	Not published
15025	Clairton	16,939	4,349	0–2	—	—	Not published
15030	Creighton	1,018	491	0–2	—	—	Not published
15031	Cuddy	382	403	0–2	—	—	Not published
15032	Curtisville	—	9	0–2	—	—	Not published
15034	Dravosburg	1,643	455	0–2	—	—	Not published
15035	East Mc Keesport	2,002	248	0–2	—	—	Not published
15045	Glassport	4,368	615	0–2	—	—	Not published
15046	Crescent	2,141	508	0–2	—	—	Not published
15047	Greenock	203	17	0–2	—	—	Not published
15049	Harwick	974	18	0–2	—	—	Not published
15051	Indianola	329	387	0–2	—	—	Not published
15056	Leetsdale	1,041	2,242	0–2	—	—	Not published
15064	Morgan	360	183	0–2	—	—	Not published
15076	Russellton	1,010	208	0–2	—	—	Not published
15082	Sturgeon	275	18	0–2	—	—	Not published
15088	West Elizabeth	344	253	0–2	—	—	Not published
15091	Wildwood	—	210	0–2	—	—	Not published
15096	Warrendale	—	799	0–2	—	—	Not published
15104	Braddock	8,034	1,427	0–2	—	—	Not published
15110	Duquesne	5,149	1,129	0–2	—	—	Not published
15112	East Pittsburgh	3,655	856	0–2	—	—	Not published
15123	West Mifflin	2	72	0–2	—	—	Not published
15126	Imperial	7,276	3,386	0–2	—	—	Not published
15127	Ingomar	—	121	0–2	—	—	Not published
15129	South Park	10,789	696	0–2	—	—	Not published
15132	Mckeesport	18,898	5,663	0–2	—	—	Not published
15133	Mckeesport	6,112	222	0–2	—	—	Not published
15134	Mckeesport	—	65	0–2	—	—	Not published
15135	Mckeesport	4,859	505	0–2	—	—	Not published
15142	Presto	1,513	269	0–2	—	—	Not published
15145	Turtle Creek	6,695	1,843	0–2	—	—	Not published
15148	Wilmerding	2,351	891	0–2	—	—	Not published
15204	Pittsburgh	8,467	868	0–2	—	—	Not published
15207	Pittsburgh	10,961	1,567	0–2	—	—	Not published
15208	Pittsburgh	10,094	2,159	0–2	—	—	Not published
15209	Pittsburgh	12,328	1,896	0–2	—	—	Not published
15214	Pittsburgh	14,048	1,077	0–2	—	—	Not published
15221	Pittsburgh	29,374	7,950	0–2	—	—	Not published
15225	Pittsburgh	1,085	2,887	0–2	—	—	Not published
15226	Pittsburgh	13,999	2,282	0–2	—	—	Not published
15229	Pittsburgh	15,188	1,991	0–2	—	—	Not published
15230	Pittsburgh	—	644	0–2	—	—	Not published
15231	Pittsburgh	—	3,200	0–2	—	—	Not published
15233	Pittsburgh	2,596	6,678	0–2	—	—	Not published
15240	Pittsburgh	—	3,149	0–2	—	—	Not published
15243	Pittsburgh	13,456	4,145	0–2	—	—	Not published
15250	Pittsburgh	—	39	0–2	—	—	Not published
15258	Pittsburgh	—	5,143	0–2	—	—	Not published
15259	Pittsburgh	—	18	0–2	—	—	Not published
15260	Pittsburgh	0	758	0–2	—	—	Not published
15261	Pittsburgh	—	261	0–2	—	—	Not published
15262	Pittsburgh	—	2,010	0–2	—	—	Not published
15272	Pittsburgh	—	2,574	0–2	—	—	Not published
15276	Pittsburgh	—	3,445	0–2	—	—	Not published

Scroll the table sideways to see all columns.

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The numbers above tell you how the market looks. To find a specific storefront, these marketplaces list available retail space for lease.

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Census withholds some ZIP-by-industry cells to protect small businesses. 0–2 means the cell was not published (Census publishes industry counts only at 3+ per ZIP), so the ZIP is left unranked rather than counted as zero. ~ means the count is estimated from the broader industry group and can be off by a business or two.

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Methodology & sources

Establishments. U.S. Census ZIP Code Business Patterns (ZBP 2023), NAICS 812112 — beauty salons. Counts establishments with paid employees. ZBP is published annually with a ~2-year lag.

Fresh county signal. BLS QCEW (quarterly) is newer than ZBP — 2026 Q1 shows 553 establishments county-wide for this industry. It cannot be broken down by ZIP.

Population. U.S. Census American Community Survey 2024 5-year ZCTA population (table B01003). Workers are the ZIP's total employment from the same 2023 ZBP file.

Demand ("people present"). Demand comes from residents and daytime workers: residents + 0.5 × workers. This is the single documented assumption in the model and can be tuned.

Index. index = (ZIP establishments ÷ ZIP demand × 10,000) ÷ the U.S. average for this industry (2.08 per 10,000 present). It is a supply-side density measure; it does not capture income, rent, tourism, or whether locals actually want more.

Read the full methodology → · Data sources & vintages →

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BizGapMap — free, no-login small-business saturation reports for major U.S. counties. Built entirely from public U.S. Census, BLS, and IRS data (ZBP 2023, ACS 2024 5-year, PEP 2024, LODES, QCEW, IRS SOI). No forecasts, no revenue guesses — withheld cells are marked, and the few derived counts are labelled as estimates.