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San Diego County, CA — Clothing stores Saturation

Name: Clothing stores density by ZIP code in San Diego 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. 166 ZIP codes analyzed.

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

Measured density: Range spans bands — 1.11× the U.S. rate (between 1.11× and 1.69× 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. 135 ZIP cells in this county are withheld, 79 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 Diego County clothing stores — 111–169% of the U.S. rate

306 clothing stores are counted in San Diego County — 0.8 per 10,000 people present (residents + daytime workers), 111% of the U.S. rate of 0.7.

On the comparable ZIP set (ZIPs with a published count in every year), the total has stayed roughly flat between 2015 and 2023 (294 → 289).

Measured supply density is highest in 92173 (San Ysidro), 92008 (Carlsbad), 92122 (San Diego) — well above the national rate. On measured density alone, the lowest-density residential ZIPs with a published count are 92154 (San Diego), 92105 (San Diego), 92126 (San Diego).

Census County Business Patterns (2023) puts the county-wide count at 337 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 Diego County sits at 111% of the U.S. rate for clothing stores. 79 ZIPs with population have no published count (each 0–2 businesses), so the true per-capita rate is between 111% and 169% of the U.S. rate. Why. The highest measured density is in 92173 and 92008; the lowest measured density is in 92154 and 92105.

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

306–576

published count; upper bound counts each withheld ZIP cell as 2 (135 cells) (2023)

Per 10,000 present

0.8

residents + daytime workers

U.S. average

0.7

per 10,000 present

County index

1.11×

vs. U.S.

Latest year (CBP)

337

2023 — $294M annual payroll

Residents

3,282,423

110 ZIPs with data

Workers

1,385,217

jobs located in county

Is the area growing?

Population

3,298,799

Census PEP, July 1 2024

Change since 2020

−0.1%

−2,383 people

Net migration 2024

+292

international +24,226, domestic −23,934

Jobs located here

1,490,975

LODES 2022, +2.8% since 2019

San Diego County had an estimated 3,298,799 residents on July 1, 2024 — 0.1% fewer than in 2020 (−2,383). In the latest year it gained 12,909 people: births minus deaths +12,667, international migration +24,226, domestic migration −23,934. Components are published separately and may not sum exactly.

1,490,975 jobs were located in the county in 2022 (+2.8% since 2019), and 1,505,114 employed residents lived there (+2.6% 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

92101 San Diego

+25.9%

92108 San Diego

+20.2%

92071 Santee

+3.3%

ZIP losing residents

2015–19 → 2020–24

Change

92140 San Diego

−45.5%

92113 San Diego

−17.5%

92118 Coronado

−16.1%

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 — down 2% since 2015

Number of clothing stores in San Diego 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.

294

2015

294

2018

282

2020

264

2021

263

2022

289

2023

Highest measured density

ZIP

vs. U.S. average

Index

92173 San Ysidro

1613%

92008 Carlsbad

753%

92122 San Diego

522%

91950 National City

405%

92025 Escondido

351%

91915 Chula Vista

338%

91901 Alpine

281%

92020 El Cajon

277%

92118 Coronado

251%

92037 La Jolla

240%

92101 San Diego

224%

92054 Oceanside

221%

92029 Escondido

189%

92109 San Diego

182%

92009 Carlsbad

165%

Lowest measured density

ZIP

vs. U.S. average

Index

92154 San Diego

47%

92105 San Diego

58%

92126 San Diego

67%

91977 Spring Valley

67%

92024 Encinitas

70%

92130 San Diego

76%

92071 Santee

85%

91911 Chula Vista

105%

92078 San Marcos

117%

91941 La Mesa

120%

92128 San Diego

121%

92110 San Diego

123%

92117 San Diego

125%

92083 Vista

132%

91910 Chula Vista

163%

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
92108	San Diego	Employment hub	26,790	54,406	43	1152%
92121	San Diego	Destination	3,974	123,633	0–2	—
92123	San Diego	Employment hub	33,041	71,211	0–2	—
92134	San Diego	Employment hub	290	2,205	0–2	—
92147	San Diego	Employment hub	148	313	0–2	—
92155	San Diego	Employment hub	101	264	0–2	—
92161	San Diego	Destination	29	3,964	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
92173	San Ysidro	27,585	7,622	35	11.1	1613%	Very high density
92108	San Diego	26,790	54,406	43	8.0	1152%	Very high density
92008	Carlsbad	27,955	43,940	26	5.2	753%	Very high density
92122	San Diego	45,411	25,486	21	3.6	522%	Very high density
91950	National City	59,891	23,074	20	2.8	405%	Very high density
92025	Escondido	49,989	23,548	15	2.4	351%	Very high density
91915	Chula Vista	35,322	6,432	9	2.3	338%	Very high density
91901	Alpine	18,197	4,802	4	1.9	281%	Very high density
92020	El Cajon	57,842	30,445	14	1.9	277%	Very high density
92118	Coronado	18,929	8,169	4	1.7	251%	Very high density
92037	La Jolla	40,717	39,283	10	1.7	240%	High density
92101	San Diego	51,805	77,128	14	1.5	224%	High density
92054	Oceanside	38,417	14,957	7	1.5	221%	High density
92029	Escondido	20,726	19,941	4	1.3	189%	High density
92109	San Diego	44,820	21,935	7	1.3	182%	High density
92009	Carlsbad	44,739	15,535	6	1.1	165%	High density
91910	Chula Vista	77,123	22,822	10	1.1	163%	High density
92083	Vista	38,375	11,217	4	0.9	132%	Above average
92117	San Diego	52,275	11,535	5	0.9	125%	Above average
92110	San Diego	30,902	32,411	4	0.8	123%	Above average
92128	San Diego	48,992	21,804	5	0.8	121%	Above average
91941	La Mesa	33,218	6,032	3	0.8	120%	Above average
92078	San Marcos	53,605	16,140	5	0.8	117%	Above average
91911	Chula Vista	85,542	21,397	7	0.7	105%	Above average
92071	Santee	59,590	16,250	4	0.6	85%	Below average
92130	San Diego	60,768	30,708	4	0.5	76%	Below average
92024	Encinitas	50,617	23,621	3	0.5	70%	Below average
91977	Spring Valley	61,064	7,111	3	0.5	67%	Below average
92126	San Diego	76,059	21,618	4	0.5	67%	Below average
92105	San Diego	71,411	7,460	3	0.4	58%	Below average
92154	San Diego	80,445	23,535	3	0.3	47%	Far below average
91902	Bonita	17,698	3,059	0–2	—	—	Not published
91903	Alpine	—	32	0–2	—	—	Not published
91905	Boulevard	1,683	70	0–2	—	—	Not published
91906	Campo	3,325	418	0–2	—	—	Not published
91908	Bonita	—	30	0–2	—	—	Not published
91912	Chula Vista	—	27	0–2	—	—	Not published
91913	Chula Vista	57,721	3,016	0–2	—	—	Not published
91914	Chula Vista	16,877	6,952	0–2	—	—	Not published
91916	Descanso	1,710	99	0–2	—	—	Not published
91917	Dulzura	749	8	0–2	—	—	Not published
91921	Chula Vista	—	62	0–2	—	—	Not published
91931	Guatay	494	59	0–2	—	—	Not published
91932	Imperial Beach	25,741	2,543	0–2	—	—	Not published
91933	Imperial Beach	—	31	0–2	—	—	Not published
91934	Jacumba	1,200	14	0–2	—	—	Not published
91935	Jamul	9,114	2,186	0–2	—	—	Not published
91942	La Mesa	41,424	20,503	0–2	—	—	Not published
91943	La Mesa	—	12	0–2	—	—	Not published
91944	La Mesa	—	12	0–2	—	—	Not published
91945	Lemon Grove	27,745	6,571	0–2	—	—	Not published
91946	Lemon Grove	—	17	0–2	—	—	Not published
91948	Mount Laguna	93	28	0–2	—	—	Not published
91951	National City	—	96	0–2	—	—	Not published
91962	Pine Valley	2,574	167	0–2	—	—	Not published
91963	Potrero	689	26	0–2	—	—	Not published
91978	Spring Valley	10,014	1,678	0–2	—	—	Not published
91979	Spring Valley	—	12	0–2	—	—	Not published
91980	Tecate	176	216	0–2	—	—	Not published
92003	Bonsall	5,061	1,255	0–2	—	—	Not published
92004	Borrego Springs	3,007	920	0–2	—	—	Not published
92007	Cardiff By The Sea	11,100	3,005	0–2	—	—	Not published
92010	Carlsbad	18,489	11,630	0–2	—	—	Not published
92011	Carlsbad	23,190	13,073	0–2	—	—	Not published
92013	Carlsbad	—	69	0–2	—	—	Not published
92014	Del Mar	13,105	6,069	0–2	—	—	Not published
92018	Carlsbad	—	166	0–2	—	—	Not published
92019	El Cajon	45,351	7,932	0–2	—	—	Not published
92021	El Cajon	70,501	12,986	0–2	—	—	Not published
92022	El Cajon	—	242	0–2	—	—	Not published
92023	Encinitas	—	52	0–2	—	—	Not published
92026	Escondido	53,075	4,917	0–2	—	—	Not published
92027	Escondido	54,113	6,406	0–2	—	—	Not published
92028	Fallbrook	52,227	7,297	0–2	—	—	Not published
92030	Escondido	—	92	0–2	—	—	Not published
92033	Escondido	—	26	0–2	—	—	Not published
92036	Julian	3,218	764	0–2	—	—	Not published
92038	La Jolla	—	58	0–2	—	—	Not published
92039	La Jolla	—	36	0–2	—	—	Not published
92040	Lakeside	46,216	9,851	0–2	—	—	Not published
92046	Escondido	—	35	0–2	—	—	Not published
92049	Oceanside	—	29	0–2	—	—	Not published
92052	Oceanside	—	34	0–2	—	—	Not published
92055	Camp Pendleton	10,831	2,110	0–2	—	—	Not published
92056	Oceanside	53,616	20,198	0–2	—	—	Not published
92057	Oceanside	56,835	4,614	0–2	—	—	Not published
92058	Oceanside	49,834	5,427	0–2	—	—	Not published
92059	Pala	1,362	1,255	0–2	—	—	Not published
92060	Palomar Mountain	619	46	0–2	—	—	Not published
92061	Pauma Valley	1,543	688	0–2	—	—	Not published
92064	Poway	48,683	34,973	0–2	—	—	Not published
92065	Ramona	36,752	4,949	0–2	—	—	Not published
92066	Ranchita	290	31	0–2	—	—	Not published
92067	Rancho Santa Fe	7,938	5,528	0–2	—	—	Not published
92068	San Luis Rey	—	14	0–2	—	—	Not published
92069	San Marcos	47,335	16,881	0–2	—	—	Not published
92070	Santa Ysabel	607	282	0–2	—	—	Not published
92072	Santee	—	58	0–2	—	—	Not published
92074	Poway	—	142	0–2	—	—	Not published
92075	Solana Beach	11,944	15,387	0–2	—	—	Not published
92079	San Marcos	—	125	0–2	—	—	Not published
92081	Vista	29,002	22,411	0–2	—	—	Not published
92082	Valley Center	21,727	4,729	0–2	—	—	Not published
92084	Vista	49,727	8,164	0–2	—	—	Not published
92085	Vista	—	211	0–2	—	—	Not published
92086	Warner Springs	1,406	175	0–2	—	—	Not published
92088	Fallbrook	—	10	0–2	—	—	Not published
92091	Rancho Santa Fe	972	649	0–2	—	—	Not published
92092	La Jolla	528	111	0–2	—	—	Not published
92093	La Jolla	7,167	546	0–2	—	—	Not published
92096	San Marcos	0	27	0–2	—	—	Not published
92102	San Diego	38,901	9,141	0–2	—	—	Not published
92103	San Diego	34,836	30,239	0–2	—	—	Not published
92104	San Diego	47,410	9,070	0–2	—	—	Not published
92106	San Diego	21,651	10,774	0–2	—	—	Not published
92107	San Diego	28,827	4,498	0–2	—	—	Not published
92111	San Diego	45,249	37,056	0–2	—	—	Not published
92112	San Diego	—	238	0–2	—	—	Not published
92113	San Diego	48,215	14,861	0–2	—	—	Not published
92114	San Diego	65,745	3,115	0–2	—	—	Not published
92115	San Diego	66,264	8,129	0–2	—	—	Not published
92116	San Diego	31,843	4,036	0–2	—	—	Not published
92119	San Diego	24,456	2,223	0–2	—	—	Not published
92120	San Diego	31,915	13,814	0–2	—	—	Not published
92121	San Diego	3,974	123,633	0–2	—	—	Not published
92123	San Diego	33,041	71,211	0–2	—	—	Not published
92124	San Diego	30,474	2,191	0–2	—	—	Not published
92127	San Diego	50,555	25,038	0–2	—	—	Not published
92129	San Diego	52,965	10,606	0–2	—	—	Not published
92131	San Diego	35,503	21,633	0–2	—	—	Not published
92132	San Diego	0	35	0–2	—	—	Not published
92134	San Diego	290	2,205	0–2	—	—	Not published
92135	San Diego	849	1,192	0–2	—	—	Not published
92136	San Diego	4,573	704	0–2	—	—	Not published
92138	San Diego	—	180	0–2	—	—	Not published
92139	San Diego	34,703	1,385	0–2	—	—	Not published
92140	San Diego	2,038	148	0–2	—	—	Not published
92142	San Diego	—	102	0–2	—	—	Not published
92143	San Ysidro	—	85	0–2	—	—	Not published
92145	San Diego	2,349	538	0–2	—	—	Not published
92147	San Diego	148	313	0–2	—	—	Not published
92150	San Diego	—	99	0–2	—	—	Not published
92152	San Diego	—	295	0–2	—	—	Not published
92155	San Diego	101	264	0–2	—	—	Not published
92159	San Diego	—	14	0–2	—	—	Not published
92160	San Diego	—	16	0–2	—	—	Not published
92161	San Diego	29	3,964	0–2	—	—	Not published
92163	San Diego	—	260	0–2	—	—	Not published
92165	San Diego	—	9	0–2	—	—	Not published
92166	San Diego	—	5	0–2	—	—	Not published
92167	San Diego	—	9	0–2	—	—	Not published
92169	San Diego	—	53	0–2	—	—	Not published
92171	San Diego	—	20	0–2	—	—	Not published
92172	San Diego	—	12	0–2	—	—	Not published
92174	San Diego	—	13	0–2	—	—	Not published
92176	San Diego	—	47	0–2	—	—	Not published
92177	San Diego	—	8	0–2	—	—	Not published
92178	Coronado	—	16	0–2	—	—	Not published
92182	San Diego	0	4,825	0–2	—	—	Not published
92186	San Diego	—	233	0–2	—	—	Not published
92191	San Diego	—	97	0–2	—	—	Not published
92192	San Diego	—	8	0–2	—	—	Not published
92193	San Diego	—	87	0–2	—	—	Not published
92195	San Diego	—	3	0–2	—	—	Not published
92196	San Diego	—	9	0–2	—	—	Not published
92198	San Diego	—	5	0–2	—	—	Not published

Scroll the table sideways to see all columns.

Find an actual space in San Diego

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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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.

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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: 337 establishments and $294M 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.