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Allegheny County, PA — Hotels Saturation

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

How many hotels 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: Hotels · Motels · Motor inns. Counted together because official statistics use one 721110 code.

Measured density: Range spans bands — 0.48× the U.S. rate (between 0.48× and 1.26× 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. 100 ZIP cells in this county are withheld, 84 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

Range spans the U.S. rate Allegheny County hotels — 48–126% of the U.S. rate

103 hotels are counted in Allegheny County — 0.7 per 10,000 people present (residents + daytime workers), 48% of the U.S. rate of 1.4.

On the comparable ZIP set (ZIPs with a published count in every year), the total has risen 22% between 2015 and 2023 (78 → 95). That set is smaller than today's county total. 2020–2021 were pandemic-distorted, and Census rounds counts, so small moves are within noise.

Measured supply density is highest in 15205 (Pittsburgh), 15238 (Pittsburgh), 15146 (Monroeville) — well above the national rate. On measured density alone, the lowest-density residential ZIPs with a published count are 15237 (Pittsburgh), 15236 (Pittsburgh), 15241 (Pittsburgh).

Census County Business Patterns (2023) puts the county-wide count at 138 establishments, including locations in ZIP codes that do not map to a ZCTA — so it can run slightly above the sum of the ZIP rows on this page. It is annual, so it lags more than QCEW.

Measured density: Allegheny County sits at 48% of the U.S. rate for hotels. 84 ZIPs with population have no published count (each 0–2 businesses), so the true per-capita rate is between 48% and 126% of the U.S. rate. Why. The highest measured density is in 15205 and 15238; the lowest measured density is in 15237 and 15236.

This is a supply-side read: it counts existing hotels 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.

Hotels

103–303

published count; upper bound counts each withheld ZIP cell as 2 (100 cells) (2023)

Per 10,000 present

0.7

residents + daytime workers

U.S. average

1.4

per 10,000 present

County index

0.48×

vs. U.S.

Latest year (CBP)

138

2023 — $150M annual payroll

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

Not shown. Census publishes hotels only combined with other traveler accommodation (NAICS 7211), so a hotels-and-motels-only nonemployer count is not published.

Census Nonemployer Statistics 2024 (NAICS 2022). We do not approximate this figure.

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 hotels 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 22% since 2015

Number of hotels 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.

2015

2018

2020

2021

2022

2023

Highest measured density

ZIP

vs. U.S. average

Index

15205 Pittsburgh

333%

15238 Pittsburgh

241%

15146 Monroeville

203%

15108 Coraopolis

200%

15203 Pittsburgh

128%

15206 Pittsburgh

101%

15106 Carnegie

94%

15241 Pittsburgh

82%

15236 Pittsburgh

56%

15237 Pittsburgh

39%

Lowest measured density

ZIP

vs. U.S. average

Index

15237 Pittsburgh

39%

15236 Pittsburgh

56%

15241 Pittsburgh

82%

15106 Carnegie

94%

15206 Pittsburgh

101%

15203 Pittsburgh

128%

15108 Coraopolis

200%

15146 Monroeville

203%

15238 Pittsburgh

241%

15205 Pittsburgh

333%

Low measured density is not evidence of opportunity. A ZIP with few hotels 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	Hotels	Index
15275	Pittsburgh	Destination	86	14,032	6	612%
15219	Pittsburgh	Employment hub	11,376	35,729	9	223%
15222	Pittsburgh	Employment hub	5,725	54,863	10	218%
15213	Pittsburgh	Employment hub	30,466	61,917	4	47%
15056	Leetsdale	Employment hub	1,041	2,242	0–2	—
15086	Warrendale	Destination	788	10,407	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 = hotels per 10,000 people present (residents + 0.5×workers) ÷ the U.S. average. 100% = exactly average.

ZIP	Neighborhood	Residents	Workers	Hotels	Per 10k	Index	Status
15275	Pittsburgh	86	14,032	6	8.4	612%	Very high density
15205	Pittsburgh	24,060	25,757	17	4.6	333%	Very high density
15238	Pittsburgh	12,868	22,426	8	3.3	241%	High density
15219	Pittsburgh	11,376	35,729	9	3.1	223%	High density
15222	Pittsburgh	5,725	54,863	10	3.0	218%	High density
15146	Monroeville	28,332	21,714	11	2.8	203%	High density
15108	Coraopolis	42,799	44,648	18	2.8	200%	High density
15203	Pittsburgh	10,370	13,271	3	1.8	128%	Above average
15206	Pittsburgh	29,056	13,514	5	1.4	101%	Above average
15106	Carnegie	19,244	7,724	3	1.3	94%	Below average
15241	Pittsburgh	22,294	8,502	3	1.1	82%	Below average
15236	Pittsburgh	31,313	14,397	3	0.8	56%	Below average
15213	Pittsburgh	30,466	61,917	4	0.7	47%	Far below average
15237	Pittsburgh	43,588	24,830	3	0.5	39%	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
15017	Bridgeville	17,782	12,008	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
15037	Elizabeth	10,417	1,813	0–2	—	—	Not published
15044	Gibsonia	27,370	7,035	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
15065	Natrona Heights	10,894	4,050	0–2	—	—	Not published
15071	Oakdale	12,663	3,697	0–2	—	—	Not published
15076	Russellton	1,010	208	0–2	—	—	Not published
15082	Sturgeon	275	18	0–2	—	—	Not published
15084	Tarentum	9,743	3,839	0–2	—	—	Not published
15086	Warrendale	788	10,407	0–2	—	—	Not published
15088	West Elizabeth	344	253	0–2	—	—	Not published
15090	Wexford	25,763	13,495	0–2	—	—	Not published
15091	Wildwood	—	210	0–2	—	—	Not published
15096	Warrendale	—	799	0–2	—	—	Not published
15101	Allison Park	26,036	5,012	0–2	—	—	Not published
15102	Bethel Park	30,344	8,287	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
15116	Glenshaw	14,461	2,584	0–2	—	—	Not published
15120	Homestead	18,029	7,125	0–2	—	—	Not published
15122	West Mifflin	18,816	11,920	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
15131	Mckeesport	8,209	1,556	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
15136	Mc Kees Rocks	22,623	7,187	0–2	—	—	Not published
15137	North Versailles	10,043	2,886	0–2	—	—	Not published
15139	Oakmont	6,702	3,252	0–2	—	—	Not published
15140	Pitcairn	3,032	395	0–2	—	—	Not published
15142	Presto	1,513	269	0–2	—	—	Not published
15143	Sewickley	21,851	9,684	0–2	—	—	Not published
15144	Springdale	3,771	998	0–2	—	—	Not published
15145	Turtle Creek	6,695	1,843	0–2	—	—	Not published
15147	Verona	15,798	3,529	0–2	—	—	Not published
15148	Wilmerding	2,351	891	0–2	—	—	Not published
15201	Pittsburgh	13,333	9,052	0–2	—	—	Not published
15202	Pittsburgh	19,564	3,606	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
15210	Pittsburgh	25,227	1,776	0–2	—	—	Not published
15211	Pittsburgh	10,728	1,208	0–2	—	—	Not published
15212	Pittsburgh	27,652	33,203	0–2	—	—	Not published
15214	Pittsburgh	14,048	1,077	0–2	—	—	Not published
15215	Pittsburgh	11,907	5,415	0–2	—	—	Not published
15216	Pittsburgh	21,471	3,797	0–2	—	—	Not published
15217	Pittsburgh	25,795	6,310	0–2	—	—	Not published
15218	Pittsburgh	13,379	3,354	0–2	—	—	Not published
15220	Pittsburgh	17,644	17,527	0–2	—	—	Not published
15221	Pittsburgh	29,374	7,950	0–2	—	—	Not published
15223	Pittsburgh	6,255	2,511	0–2	—	—	Not published
15224	Pittsburgh	10,665	9,923	0–2	—	—	Not published
15225	Pittsburgh	1,085	2,887	0–2	—	—	Not published
15226	Pittsburgh	13,999	2,282	0–2	—	—	Not published
15227	Pittsburgh	28,460	4,199	0–2	—	—	Not published
15228	Pittsburgh	18,236	3,247	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
15232	Pittsburgh	11,349	9,173	0–2	—	—	Not published
15233	Pittsburgh	2,596	6,678	0–2	—	—	Not published
15234	Pittsburgh	14,014	3,532	0–2	—	—	Not published
15235	Pittsburgh	33,951	9,999	0–2	—	—	Not published
15239	Pittsburgh	21,410	4,456	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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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 721110 — hotels (except casino hotels) and motels. Counts establishments with paid employees. ZBP is published annually with a ~2-year lag.

Fresh county signal. This industry has no clean QCEW series, so the freshest county signal is Census County Business Patterns 2023: 138 establishments and $150M in annual payroll county-wide. This count includes locations in ZIP codes that do not map to a ZCTA, so it can be slightly higher than the sum of the ZIP rows. CBP is annual and slower than QCEW. 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 (1.38 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.