BizGapMap

Home / Car washes / South Carolina / Horry County, SC

Horry County, SC — Car washes Saturation

How many car washes 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. 21 ZIP codes analyzed.

Includes: Car wash tunnels & bays · Self-serve & automatic washes · Detailing (wash-attached). Counted together because official statistics use one 811192 code.

Measured density: Range spans bands — 0.67× the U.S. rate (between 0.67× and 1.74× 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. 17 ZIP cells in this county are withheld, 12 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   Horry County car washes — 67–174% of the U.S. rate

15 car washes are counted in Horry County — 0.3 per 10,000 people present (residents + daytime workers), 67% of the U.S. rate of 0.5.

On the comparable ZIP set (ZIPs with a published count in every year), the total has fallen 17% between 2015 and 2023 (6 → 5). 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 29575 (Myrtle Beach), 29566 (Little River), 29576 (Murrells Inlet) — well above the national rate. On measured density alone, the lowest-density residential ZIPs with a published count are 29577 (Myrtle Beach), 29576 (Murrells Inlet), 29566 (Little River).

Census County Business Patterns (2023) puts the county-wide count at 21 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: Horry County sits at 67% of the U.S. rate for car washes. 12 ZIPs with population have no published count (each 0–2 businesses), so the true per-capita rate is between 67% and 174% of the U.S. rate. Why. The highest measured density is in 29575 and 29566; the lowest measured density is in 29577 and 29576.

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

Car washes
15–49
published count; upper bound counts each withheld ZIP cell as 2 (17 cells) (2023)
Per 10,000 present
0.3
residents + daytime workers
U.S. average
0.5
per 10,000 present
County index
0.67×
vs. U.S.
Latest year (CBP)
21
2023 — $3M annual payroll
Residents
395,678
16 ZIPs with data
Workers
127,407
jobs located in county

Is the area growing?

Population
413,391
Census PEP, July 1 2024
Change since 2020
+16.9%
+59,612 people
Net migration 2024
+16,311
international +1,746, domestic +14,565
Jobs located here
135,830
LODES 2022, +4.7% since 2019

Horry County had an estimated 413,391 residents on July 1, 2024 — 16.9% more than in 2020 (+59,612). In the latest year it gained 15,017 people: births minus deaths −1,409, international migration +1,746, domestic migration +14,565. Components are published separately and may not sum exactly.

135,830 jobs were located in the county in 2022 (+4.7% since 2019), and 142,912 employed residents lived there (+6.9% 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
29544 Galivants Ferry
+48.0%
29566 Little River
+36.1%
29579 Myrtle Beach
+33.3%
ZIP losing residents
2015–19 → 2020–24
Change

No ZIP had a statistically significant decrease.

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 only a broader group that also includes oil-change and other auto service shops, so a car-wash-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.

Basemap:
Under 50% 50–100% 100–150% 150–250% 250%+ 0–2 (not published) / no data ZIP total (dot size)

Map data © OpenStreetMap contributors, tiles by OpenFreeMap.

White dots are ZIP totals from the official Census count — the bigger the dot, the more car washes in that ZIP.

Market trend — down 17% since 2015

Number of car washes in Horry 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.

6
2015
8
2018
8
2020
8
2021
6
2022
5
2023

Highest measured density

ZIP
vs. U.S. average
Index
29575 Myrtle Beach
257%
29566 Little River
235%
29576 Murrells Inlet
203%
29577 Myrtle Beach
193%

Lowest measured density

ZIP
vs. U.S. average
Index
29577 Myrtle Beach
193%
29576 Murrells Inlet
203%
29566 Little River
235%
29575 Myrtle Beach
257%
Low measured density is not evidence of opportunity. A ZIP with few car washes 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.

All ZIP codes

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

ZIPNeighborhood ResidentsWorkersCar washes Per 10kIndexStatus
29575Myrtle Beach 20,7216,350 3 1.3 257% Very high density
29566Little River 23,8064,518 3 1.2 235% High density
29576Murrells Inlet 34,69511,245 4 1.0 203% High density
29577Myrtle Beach 35,44634,946 5 0.9 193% High density
29511Aynor 6,8741,015 0–2 — — Not published
29526Conway 54,13018,947 0–2 — — Not published
29527Conway 28,4511,945 0–2 — — Not published
29528Conway —549 0–2 — — Not published
29544Galivants Ferry 8,252328 0–2 — — Not published
29545Green Sea 1,76731 0–2 — — Not published
29568Longs 17,0972,072 0–2 — — Not published
29569Loris 17,7883,884 0–2 — — Not published
29572Myrtle Beach 12,12410,851 0–2 — — Not published
29578Myrtle Beach —559 0–2 — — Not published
29579Myrtle Beach 56,91510,615 0–2 — — Not published
29581Nichols 4,845162 0–2 — — Not published
29582North Myrtle Beach 20,15812,815 0–2 — — Not published
29587Myrtle Beach —145 0–2 — — Not published
29588Myrtle Beach 52,6097,683 0–2 — — Not published
29597North Myrtle Beach —89 0–2 — — Not published
29598North Myrtle Beach —11 0–2 — — Not published

Scroll the table sideways to see all columns.

Find an actual space in Myrtle Beach

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 Myrtle Beach retail space →
Crexi
Free listings + market data
Browse Myrtle Beach retail space →
CityFeet
Retail space for lease
Browse Myrtle Beach 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 Horry County and car washes

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.

Subscribe to updates (RSS) How these numbers are built →

Methodology & sources

Establishments. U.S. Census ZIP Code Business Patterns (ZBP 2023), NAICS 811192 — car washes. 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: 21 establishments and $3M 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 (0.49 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 →

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.