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San Bernardino County, CA — Clothing stores Saturation

Name: Clothing stores density by ZIP code in San Bernardino County, CA
Creator: BizGapMap

How many clothing stores 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. 109 ZIP codes analyzed.

Includes: Family clothing stores · General apparel shops. Counted together because official statistics use one 448140 code.

Measured density: Range spans bands — 1.09× the U.S. rate (between 1.09× and 1.83× 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. 87 ZIP cells in this county are withheld, 65 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 San Bernardino County clothing stores — 109–183% of the U.S. rate

192 clothing stores are counted in San Bernardino County — 0.8 per 10,000 people present (residents + daytime workers), 109% of the U.S. rate of 0.7.

On the comparable ZIP set (ZIPs with a published count in every year), the total has risen 6% between 2015 and 2023 (163 → 173). 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 91764 (Ontario), 92311 (Barstow), 91739 (Rancho Cucamonga) — well above the national rate. On measured density alone, the lowest-density residential ZIPs with a published count are 91730 (Rancho Cucamonga), 92345 (Hesperia), 92336 (Fontana).

Census County Business Patterns (2023) puts the county-wide count at 209 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: San Bernardino County sits at 109% of the U.S. rate for clothing stores. 65 ZIPs with population have no published count (each 0–2 businesses), so the true per-capita rate is between 109% and 183% of the U.S. rate. Why. The highest measured density is in 91764 and 92311; the lowest measured density is in 91730 and 92345.

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

Clothing stores

192–366

published count; upper bound counts each withheld ZIP cell as 2 (87 cells) (2023)

Per 10,000 present

0.8

residents + daytime workers

U.S. average

0.7

per 10,000 present

County index

1.09×

vs. U.S.

Latest year (CBP)

209

2023 — $153M annual payroll

Residents

2,195,107

87 ZIPs with data

Workers

711,262

jobs located in county

Is the area growing?

Population

2,214,281

Census PEP, July 1 2024

Change since 2020

+1.4%

+30,755 people

Net migration 2024

+3,724

international +9,984, domestic −6,260

Jobs located here

841,615

LODES 2022, +9.4% since 2019

San Bernardino County had an estimated 2,214,281 residents on July 1, 2024 — 1.4% more than in 2020 (+30,755). In the latest year it gained 13,476 people: births minus deaths +9,604, international migration +9,984, domestic migration −6,260. Components are published separately and may not sum exactly.

841,615 jobs were located in the county in 2022 (+9.4% since 2019), and 942,704 employed residents lived there (+7.2% 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

91708 Chino

+379.2%

92256 Morongo Valley

+35.8%

92394 Victorville

+19.2%

ZIP losing residents

2015–19 → 2020–24

Change

91710 Chino

−14.2%

92346 Highland

−12.0%

92324 Colton

−4.3%

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 now groups clothing with clothing accessories (NAICS 45811); a family-clothing-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 clothing stores 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 6% since 2015

Number of clothing stores in San Bernardino 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.

163

2015

163

2018

162

2020

158

2021

158

2022

173

2023

Highest measured density

ZIP

vs. U.S. average

Index

91764 Ontario

764%

92311 Barstow

582%

91739 Rancho Cucamonga

519%

91763 Montclair

494%

92374 Redlands

321%

92284 Yucca Valley

209%

92392 Victorville

199%

92410 San Bernardino

170%

91784 Upland

151%

91710 Chino

146%

92373 Redlands

125%

92376 Rialto

119%

92395 Victorville

111%

91786 Upland

106%

91709 Chino Hills

102%

Lowest measured density

ZIP

vs. U.S. average

Index

91730 Rancho Cucamonga

60%

92345 Hesperia

61%

92336 Fontana

65%

92399 Yucaipa

73%

92404 San Bernardino

85%

92335 Fontana

90%

91709 Chino Hills

102%

91786 Upland

106%

92395 Victorville

111%

92376 Rialto

119%

92373 Redlands

125%

91710 Chino

146%

91784 Upland

151%

92410 San Bernardino

170%

92392 Victorville

199%

Low measured density is not evidence of opportunity. A ZIP with few clothing stores 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	Clothing stores	Index
92408	San Bernardino	Employment hub	14,325	53,660	7	246%
92350	Loma Linda	Employment hub	344	2,352	0–2	—
92364	Nipton	Destination	7	97	0–2	—
92401	San Bernardino	Employment hub	1,659	3,378	0–2	—

Scroll the table sideways to see all columns.

All ZIP codes

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

ZIP	Neighborhood	Residents	Workers	Clothing stores	Per 10k	Index	Status
91764	Ontario	56,270	31,385	38	5.3	764%	Very high density
92311	Barstow	33,954	6,604	15	4.0	582%	Very high density
91739	Rancho Cucamonga	41,673	11,454	17	3.6	519%	Very high density
91763	Montclair	37,700	12,536	15	3.4	494%	Very high density
92374	Redlands	45,262	26,678	13	2.2	321%	Very high density
92408	San Bernardino	14,325	53,660	7	1.7	246%	High density
92284	Yucca Valley	25,415	4,558	4	1.4	209%	High density
92392	Victorville	65,429	14,518	10	1.4	199%	High density
92410	San Bernardino	47,869	6,508	6	1.2	170%	High density
91784	Upland	26,886	3,894	3	1.0	151%	High density
91710	Chino	78,754	40,333	10	1.0	146%	Above average
92373	Redlands	35,257	21,800	4	0.9	125%	Above average
92376	Rialto	84,394	25,088	8	0.8	119%	Above average
92395	Victorville	47,375	9,843	4	0.8	111%	Above average
91786	Upland	55,829	25,012	5	0.7	106%	Above average
91709	Chino Hills	78,006	14,381	6	0.7	102%	Above average
92335	Fontana	97,511	30,986	7	0.6	90%	Below average
92404	San Bernardino	63,243	9,026	4	0.6	85%	Below average
92399	Yucaipa	55,366	7,769	3	0.5	73%	Below average
92336	Fontana	105,517	12,653	5	0.4	65%	Below average
92345	Hesperia	88,706	12,884	4	0.4	61%	Below average
91730	Rancho Cucamonga	71,613	51,205	4	0.4	60%	Below average
91701	Rancho Cucamonga	39,287	3,424	0–2	—	—	Not published
91708	Chino	19,213	7,009	0–2	—	—	Not published
91729	Rancho Cucamonga	—	348	0–2	—	—	Not published
91737	Rancho Cucamonga	22,958	3,913	0–2	—	—	Not published
91743	Guasti	—	7	0–2	—	—	Not published
91758	Ontario	—	67	0–2	—	—	Not published
91759	Mt Baldy	341	76	0–2	—	—	Not published
91761	Ontario	62,935	80,635	0–2	—	—	Not published
91762	Ontario	64,235	11,224	0–2	—	—	Not published
91785	Upland	—	62	0–2	—	—	Not published
92242	Earp	1,359	185	0–2	—	—	Not published
92252	Joshua Tree	7,538	1,190	0–2	—	—	Not published
92256	Morongo Valley	4,209	243	0–2	—	—	Not published
92267	Parker Dam	0	151	0–2	—	—	Not published
92268	Pioneertown	434	137	0–2	—	—	Not published
92277	Twentynine Palms	26,277	1,626	0–2	—	—	Not published
92278	Twentynine Palms	3,913	401	0–2	—	—	Not published
92285	Landers	2,764	58	0–2	—	—	Not published
92286	Yucca Valley	—	24	0–2	—	—	Not published
92301	Adelanto	38,577	3,763	0–2	—	—	Not published
92304	Amboy	0	35	0–2	—	—	Not published
92305	Angelus Oaks	533	112	0–2	—	—	Not published
92307	Apple Valley	41,180	9,065	0–2	—	—	Not published
92308	Apple Valley	42,709	5,955	0–2	—	—	Not published
92309	Baker	343	391	0–2	—	—	Not published
92310	Fort Irwin	8,419	1,906	0–2	—	—	Not published
92312	Barstow	—	28	0–2	—	—	Not published
92313	Grand Terrace	13,078	2,241	0–2	—	—	Not published
92314	Big Bear City	9,334	601	0–2	—	—	Not published
92315	Big Bear Lake	5,215	5,701	0–2	—	—	Not published
92316	Bloomington	32,765	13,511	0–2	—	—	Not published
92317	Blue Jay	916	379	0–2	—	—	Not published
92318	Bryn Mawr	—	38	0–2	—	—	Not published
92321	Cedar Glen	724	100	0–2	—	—	Not published
92322	Cedarpines Park	770	28	0–2	—	—	Not published
92324	Colton	57,365	20,473	0–2	—	—	Not published
92325	Crestline	9,065	995	0–2	—	—	Not published
92327	Daggett	576	123	0–2	—	—	Not published
92329	Phelan	—	31	0–2	—	—	Not published
92331	Fontana	—	280	0–2	—	—	Not published
92332	Essex	105	42	0–2	—	—	Not published
92333	Fawnskin	302	52	0–2	—	—	Not published
92334	Fontana	—	258	0–2	—	—	Not published
92337	Fontana	36,026	21,209	0–2	—	—	Not published
92338	Ludlow	0	73	0–2	—	—	Not published
92339	Forest Falls	1,302	140	0–2	—	—	Not published
92340	Hesperia	—	188	0–2	—	—	Not published
92341	Green Valley Lake	406	98	0–2	—	—	Not published
92342	Helendale	7,606	345	0–2	—	—	Not published
92344	Hesperia	21,349	1,246	0–2	—	—	Not published
92346	Highland	56,179	14,141	0–2	—	—	Not published
92347	Hinkley	766	89	0–2	—	—	Not published
92350	Loma Linda	344	2,352	0–2	—	—	Not published
92352	Lake Arrowhead	6,246	1,449	0–2	—	—	Not published
92354	Loma Linda	22,051	19,065	0–2	—	—	Not published
92356	Lucerne Valley	6,543	1,007	0–2	—	—	Not published
92357	Loma Linda	—	3,094	0–2	—	—	Not published
92358	Lytle Creek	672	179	0–2	—	—	Not published
92359	Mentone	9,284	754	0–2	—	—	Not published
92363	Needles	5,531	1,076	0–2	—	—	Not published
92364	Nipton	7	97	0–2	—	—	Not published
92365	Newberry Springs	2,192	178	0–2	—	—	Not published
92368	Oro Grande	906	356	0–2	—	—	Not published
92369	Patton	2,575	2,856	0–2	—	—	Not published
92371	Phelan	21,580	1,155	0–2	—	—	Not published
92372	Pinon Hills	5,149	151	0–2	—	—	Not published
92375	Redlands	—	140	0–2	—	—	Not published
92377	Rialto	20,814	5,807	0–2	—	—	Not published
92378	Rimforest	315	182	0–2	—	—	Not published
92382	Running Springs	5,215	876	0–2	—	—	Not published
92385	Skyforest	198	170	0–2	—	—	Not published
92386	Sugarloaf	2,042	140	0–2	—	—	Not published
92391	Twin Peaks	2,254	171	0–2	—	—	Not published
92393	Victorville	—	263	0–2	—	—	Not published
92394	Victorville	41,614	5,012	0–2	—	—	Not published
92397	Wrightwood	5,854	1,152	0–2	—	—	Not published
92398	Yermo	2,609	332	0–2	—	—	Not published
92401	San Bernardino	1,659	3,378	0–2	—	—	Not published
92402	San Bernardino	—	97	0–2	—	—	Not published
92405	San Bernardino	31,217	2,538	0–2	—	—	Not published
92406	San Bernardino	—	225	0–2	—	—	Not published
92407	San Bernardino	71,711	10,988	0–2	—	—	Not published
92411	San Bernardino	27,249	3,739	0–2	—	—	Not published
92415	San Bernardino	0	106	0–2	—	—	Not published
92423	San Bernardino	—	84	0–2	—	—	Not published
92427	San Bernardino	—	88	0–2	—	—	Not published
93562	Trona	1,849	500	0–2	—	—	Not published

Scroll the table sideways to see all columns.

Find an actual space in Ontario

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

LoopNet

Largest commercial marketplace

Browse Ontario retail space →

Crexi

Free listings + market data

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CityFeet

Retail space for lease

Browse Ontario retail space →

Open vacancy data (actual empty units) is only published by some cities; where it exists we show it directly. Listings above are third-party.

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.

Follow San Bernardino County and clothing stores

When a data vintage changes or a section of this report is revised, we post it to a public update feed. No email address, no account, no tracking — and nothing is sent unless something actually changed.

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Methodology & sources

Establishments. U.S. Census ZIP Code Business Patterns (ZBP 2023), NAICS 448140 — family clothing stores. 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: 209 establishments and $153M 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 (0.69 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.