Recreational vehicle dealers dataset: the RV retail footprint, delivered as rows

It is the retail map of who sells RVs in America, delivered as typed rows rather than as a directory of lot addresses. The federal establishment count classifies recreational vehicle dealers under NAICS 44121 and 441210 - sitting inside 4412 other motor vehicle dealers, beside 44111 new car dealers and 44112 used car dealers - and publishes one row per geography-by-industry cell: establishment count, mid-March employment, first-quarter payroll, annual payroll and nine employment-size classes.

In Datadory's catalog the line lives in U.S. Census County Business Patterns - Automobile Dealers Establishments, scored 9 out of 10 against a 7.81 average across the 1,744 datasets cataloged. It is the only record among the twelve pooled for Leisure Products that names RV dealers at all, which makes it structural rather than optional: powersports retailers, towable manufacturers and dealer-network planners all start their sizing arithmetic here.

The pooling arithmetic explains why. No statistical agency surveys anything called "leisure products," so Leisure Products contributes zero primary datasets of its own - all twelve records arrive tagged in from adjacent industries, five of them from a single statistical publisher. What looks like a data desert is an assembly problem, and assembly is the product.

Get a sample of the recreational vehicle dealers dataset cut to your states and ZIP codes before anything else - the shapes below are what arrives.

Eleven documented fields, one geography-by-industry cell:

# one NAICS 441210 cell, reference year 2023 -- value slots fill in your sample
fipstate      : state FIPS code
fipscty       : county FIPS code -- the pair is the join spine
naics         : 441210  recreational vehicle dealers
est           : employer establishments in the cell
emp           : employment, week of mid-March
qp1           : first-quarter payroll, thousands of dollars
ap            : annual payroll, thousands of dollars
emp_nf        : G | H | J  disclosure grade on the employment figure
n5_9..n1000_4 : nine employee-size class columns, under 5 through 1,000-plus

Four habits separate defensible reads from casual ones. Units: payrolls are stored in thousands of dollars, so a cell reading 56,674 means roughly $56.7 million - the classic off-by-a-thousand error in market-sizing spreadsheets. Aggregation levels: trailing slashes encode roll-ups directly in the code, so 4412// aggregates more broadly than 44121/, which aggregates more broadly than six-digit 441210; filter on the exact code to isolate RV dealers. Flags: emp_nf, qp1_nf and ap_nf grade disclosure treatment on a three-letter scale - G under 2 percent, H two-to-five percent, J five percent or more - and an N in a size-class column means unavailable, not zero.

Structure is the differentiator most summaries drop. The nine size bands tell a county of single-lot independents apart from one anchored by a multi-rooftop group with identical establishment totals - and dividing ap by est yields payroll per establishment, the quickest dealership-scale proxy in the file.

Geography depth is the reason this record anchors the RV picture. One schema resolves every level the program publishes: the nation, all fifty states plus DC and the island areas, counties, combined statistical areas, metropolitan and micropolitan areas, incorporated places, ZIP code totals, ZIP code by industry, and congressional districts. The unit of analysis is your choice, from boardroom roll-up down to the ZIP around a specific lot.

Three verified cells show the ladder working:

The RV-specific line rides exactly the same machinery. Where disclosure rules permit, NAICS 441210 appears at full six-digit detail; where a county holds too few dealerships to publish safely, the cell arrives flagged rather than fabricated - a signal to aggregate up, not a missing dealer.

Territory design weighted by payroll and size class. Two territories with identical dealer counts are different jobs if one's rooftops average forty employees and the other's eight. Quota models ranked on payroll density draw balanced coverage plans instead of equal-area fictions.

White-space screening. Ranking counties on dealer absence relative to household base - sharpened by the tow-capable fleet counts in the companion truck survey - is the fastest legitimate signal for where the next rooftop goes.

Market sizing with an official denominator. Payroll-based revenue proxies feed parts, service, financing and insurance attach models at whatever geography the client thinks in, and survive due diligence because the producing agency documents its small-cell treatment openly.

Consolidation and density signals for investors. Establishment and payroll series move slower than unit-sales prints and lie less - a decade-long read on whether dealer structure is concentrating or fragmenting, county by county.

Citation-grade claims. An administrative count of employer establishments survives editorial and regulatory scrutiny in ways no modeled estimate does, which is why local "how many dealerships, how many jobs" figures default to this record.

Nothing else in the pool counts RV dealers, so the honest stack pairs establishment structure with channel, fleet and demand layers - all delivered together, keyed so the joins resolve.

The U.S. Economic Census - Wholesale Trade (NAICS 424) owns channel economics: the pool's only perfect 10, benchmarking merchant wholesalers every five years across nearly 21,000 geographic areas representing more than 8 million business locations, with the sector 42 basic table alone running 442,035 rows. It converts "how many dealers" into "what the goods sold through them were worth." The next census year is 2027.

On the demand side, the Census multi-program record covering VIUS supplies the towing fleet: the 2021 US truck population in six tabulations - counts, miles, body types, GVWR classes, trailer configurations - at national and state level. It is the closest federal series to what pulls the trailers RV dealers sell.

For a monthly pulse, the UK ONS Retail Sales Index contributes 622 series including a textile, clothing and footwear stores aggregate split by business size; its July 2026 release put seasonally adjusted internet sales for those stores at GBP 327.5 million a week, roughly 27-28 percent of their total. And USITC DataWeb footwear trade remains the pool's most product-level trade series - Chapter 64 codes 6401-6405 at HTS-10 with pairs-based quantities, from 1989 - useful as a trade-grain template, though no pooled record yet tracks RV units that finely. Aggregators sweep up strays: 552,271 metadata records in the Data.gov catalog, 1,725,936 datasets across data.europa.eu's 210 catalogues.

Four boundaries define the record, and named companions cover most of them.

Counts are not prices. Nothing here publishes RV unit sales or dealer revenue below the national level - annual payroll is the standard revenue proxy, and the five-yearly wholesale benchmark adds channel dollars where sales figures matter more than footprints.

Cells are not firms. Rows aggregate to geographies and never resolve to named businesses, so competitor lists need a directory layer rather than this grid.

Thin markets arrive noisy. Small-cell protection perturbs sparsely dealt counties, flagged G/H/J - sums built over many rural cells should stay flag-aware rather than treating every value as equally precise.

Employer-only scope, mid-March timing. Sole-operator sellers without payroll sit outside the frame, and employment measures one reference week, not peak-season staffing. Neither hides a better copy of the footprint elsewhere; both mark where the stack hands off.

Sales and growth teams turn dealer counts and payroll into territory math - coverage plans weighted by dealership scale rather than land area.

Market researchers and consultants get the official denominator under every bottom-up RV TAM, defensible in front of a client because the definitions are public and stable.

Investors and quant researchers read multi-decade establishment and payroll panels as a slow-moving structural signal behind consumer-discretionary exposure.

Competitive intelligence teams watch dealer structure form and fade county by county, quarters before it reaches a manufacturer's earnings call.

Journalists, academics and students cite quotable federal figures for the hometown angle - how many dealerships, how many jobs, what payroll.

Developers and builders embed geo lookups and footprint queries in products; the wiring workflow lives at developers building with leisure products data, and the analyst counterpart at market researchers working with leisure products data.

Because the hard part was never the first extract - it is the tenth. The native artifact is a whole-economy grid in which the RV rows are a sliver, keyed by bare numeric FIPS codes with aggregation levels encoded as trailing slashes, disclosure flags riding beside their values and a coding break waiting at 1998. Each quirk is survivable once; none is fun to re-solve in every notebook.

Datadory normalizes before delivery: dealer lines only, geography names resolved onto the identifier codes, vintages stitched across the coding break, flags preserved so nothing looks falsely precise, and joins pre-validated against the wholesale-channel and truck-fleet records this analysis naturally pairs with. Field dictionaries are verified for 1,495 of the 1,744 cataloged datasets - 85.7 percent - and travel alongside every pull, so the schema in your sample is the schema you ship against.

Delivered daily, weekly, or hourly - your call - into your warehouse, notebook or dashboard. Name the states, counties or ZIP codes when you request a sample and it arrives already cut to that scope; the production feed follows the same shape.

Start with the anchor record: U.S. Census County Business Patterns - Automobile Dealers Establishments, with its full field dictionary and sample rows, then request a sample scoped to your territories.

This page is one thread of a broader map. The leisure-products data guide walks all twelve pooled records and where each sits; the leisure-products data hub indexes them with coverage and scores; and the ranked shortlists live at best leisure-products datasets and free leisure-products datasets.