Guides
Using BLS and Census data to choose a coworking location before signing
Coworking space operations get sharper when BLS and Census data size a metro's professional base, count small firms, and time the lease before you sign.
What to take away
- Coworking space operations get a demand check from four federal series: OEWS wage tables, QCEW county counts, LAUS metro unemployment, and Census business patterns.
- OEWS tells you how many architects, accountants, and software developers earn enough in a metro to buy a desk.
- QCEW county series show whether target industries are adding or shedding jobs quarter to quarter.
- LAUS unemployment trends tell you when the local labor market is loose enough to push freelancers and remote workers into shared offices.
- Census business patterns count small firms near a specific address, which is the closest thing to a membership prospect list.
- A scored shortlist turns those four sources into one number per neighborhood, so the lease decision is not a hunch.
What BLS and Census actually measure that predicts membership demand
Coworking demand comes from people who need a desk but not a lease. The Bureau of Labor Statistics counts those people by occupation, industry, county, and metro. The Census Bureau counts the firms that employ them.
BLS publishes several series that matter for site selection. OEWS is an annual wage and employment estimate by occupation and area. QCEW is a quarterly count of employment and wages by county and industry. LAUS is a monthly unemployment rate for metros and counties.
Census business patterns is an annual count of establishments by industry, employment size, and geography. It reaches down to the ZIP code, which is the scale a coworking operator actually leases at.
These series answer different questions. OEWS asks who earns enough. QCEW asks whether the industry is growing. LAUS asks whether the labor market is tight. Business patterns asks how many small firms sit within walking distance.
None of them measures coworking demand directly. They measure the raw material: professional workers, small firms, and industry momentum. That is what a membership base is built from.
The geography overview from BLS is the map for finding each series by metro, county, or state, and it is the first page to bookmark when you compare markets. Overview of BLS Statistics by Geography
For a full walkthrough of how these data points fit into a lease decision, see site selection explained.
Pulling OEWS wage tables to size the professional base in a metro
OEWS tables list employment and mean wages for hundreds of occupations in every metro area. You do not need all of them. You need the ones that describe a coworking member.
The core occupations are management, business and financial operations, computer and mathematical, architecture and engineering, and legal roles. These are the workers most likely to rent a desk when their employer goes remote or when they go independent.
Start with the national table to learn the occupation codes and the wage structure. Occupational Employment and Wage Statistics (OEWS) Tables
Then pull the metro-level table for each candidate city. You want three numbers per occupation: total employment, mean annual wage, and the location quotient. The quotient tells you whether the metro has more of that occupation than the national average.
A metro with a high location quotient in computer and mathematical occupations has a deep tech professional base. A metro with a high quotient in legal occupations has a different member profile, and often a different price point.
Set your wage floor against the national OEWS mean, then check how far each candidate metro sits above it. San Jose, Seattle, and the New York metro carry the highest counts of high-earning professional occupations, while most Texas and Florida metros sit closer to the national figure.
Add the counts across your target occupations. That sum is your professional base estimate. It is not your membership number, but it is the pool you are fishing in.
Compare that pool to the number of coworking seats already open in the metro. A large pool with few seats is a gap. A large pool with many seats is a fight.
This is where choosing a location starts to look quantitative rather than intuitive.
Reading QCEW county employment and wage series for target industries
QCEW is the workhorse series for county-level market analysis. It reports employment and average weekly wages by industry for every county in the United States, updated quarterly.
The series matters because coworking demand is industry-specific. A county adding jobs in professional, scientific, and technical services is adding the exact workers who rent desks. A county adding jobs in warehousing is not.
Pull the county file for each candidate location. Focus on NAICS sector 54, professional services, plus information and finance, the sectors that rent the most desks.
Look at four quarters of data, not one. You want the direction of travel. A county with three straight quarters of job growth in sector 54 is a market with momentum.
The QCEW portal publishes these files by area and industry. Quarterly Census of Employment and Wages
Average weekly wages in those sectors tell you the spending power of the local professional base. A county where sector 54 wages are rising faster than the national average is a county where members can absorb a rate increase.
Watch for concentration risk. If one employer accounts for most of the professional employment in a county, your membership base depends on that employer's remote work policy.
Pair QCEW with the published markets to consider breakdown before you commit to a county.
Using LAUS unemployment trends to time a coworking space launch
LAUS gives you the monthly unemployment rate for every metro and county. It is the fastest-moving of the four series, which makes it the timing tool.
The relationship is not obvious. Very low unemployment means workers can change jobs easily and employers are hiring hard, which can mean fewer freelancers. Rising unemployment means layoffs, which pushes some workers into independent work and shared offices.
What you want is a metro where unemployment is moderate and stable, not at a historic low and not spiking. Stability means the local economy is not in crisis, and the freelance pipeline keeps refilling.
Track the 12-month trend, not the latest print. A metro whose rate has drifted up by half a percentage point over a year is adding independent workers. A metro whose rate has collapsed to a multi-decade low may be a tight hiring market with fewer solo operators.
The LAUS home page publishes metro and county rates, plus the historical series you need for the trend line. LAUS Home
Use the trend to set your launch quarter. Signing a lease into a rising-unemployment metro can mean a larger freelance pool but weaker corporate budgets. Signing into a falling-unemployment metro means the opposite.
For a longer discussion of how timing interacts with market choice, see the expansion and market guide.
Census business patterns for counting small firms near a site
Census business patterns counts establishments by industry, employment size class, and geography, down to the ZIP code. That granularity is what makes it a site tool rather than a market tool.
Your target is firms with one to nine employees and ten to nineteen employees. These are the businesses that buy coworking memberships for a founder, a remote team, or a satellite office.
Pull the ZIP code data for every address on your shortlist. Count firms in professional services, information, finance, and arts and entertainment. Those are the sectors that rent desks.
Compare ZIP codes across one metro before comparing metros. Cook County, Illinois holds thousands of one-to-nine-employee professional firms inside the Chicago Loop and a fraction of that in its western suburbs, so the same rent per square foot buys a very different prospect list.
Business patterns also gives you payroll size classes, which is a rough proxy for how much a firm can spend on workspace. A ZIP with many firms in the highest payroll class can support premium pricing.
The data is annual and lags by roughly a year, so treat it as a baseline, not a live feed. Combine it with QCEW for the recent direction.
If you are weighing several addresses, the locations worth comparing framework applies the same logic to safety, demand, and fit.
Building a location shortlist with a scoring table
A scored shortlist turns four data series into one comparable number per site. The method is simple enough to run in a spreadsheet in an afternoon.
- Define your target occupations and industries. Use OEWS occupation groups and NAICS sectors 54, 55, 51, and 52 as the default set.
- Pull the metro OEWS table and sum employment in target occupations. Record the location quotient for each.
- Pull four quarters of QCEW county data for the target sectors. Record the year-over-year employment change and average weekly wage.
- Pull the 12-month LAUS unemployment trend for the metro. Record the direction and the size of the move.
- Pull Census business patterns for each candidate ZIP. Count firms with one to nineteen employees in target sectors.
- Score each site from 1 to 5 on each metric, weight the metrics, and total the scores.
Weights should reflect your model. A solo-desk operator weights small firm counts heavily. A team-suite operator weights QCEW wage growth and OEWS location quotients.
| Metric | Source | Weight | Site A | Site B |
|---|---|---|---|---|
| Target occupation employment | OEWS metro table | 25% | 5 | 3 |
| Location quotient | OEWS metro table | 10% | 4 | 5 |
| QCEW sector job growth | QCEW county series | 25% | 3 | 4 |
| QCEW average weekly wage | QCEW county series | 10% | 4 | 3 |
| LAUS 12-month trend | LAUS metro series | 10% | 5 | 2 |
| Small firms within 1 mile | Census business patterns | 20% | 3 | 5 |
| Weighted total | 100% | 3.95 | 3.65 |
A weighted total is not a verdict. It is a way to see which site wins on which dimension, and whether the trade-offs match your operating model.
Run the same table for every address on the list. If two sites tie, break it on rent per square foot and on build-out cost, which in US jurisdictions turns on the locally adopted International Building Code and on the federal ADA standards enforced by the Department of Justice.
Where SBA counseling and market data meet site selection
Federal data tells you where the demand is. SBA resources tell you how to finance and operate the space once you pick it.
SBA district offices and Small Business Development Centers offer free counseling on lease review, business plan stress-testing, and loan packaging. Many centers will review a scored shortlist with you before you sign.
The SBA also publishes loan programs used by coworking operators, including 7(a) and 504 loans for real estate and build-out. Eligibility depends on your entity structure and the property, so confirm details with a lender.
BLS publishes a business leader portal that collects the data tools most relevant to site selection in one place. Business Leader
Use the SBA for capital and compliance questions, and the BLS and Census series for demand. The two together cover the decision from both ends.
Before signing, confirm the space meets ADA accessibility standards and the code edition your state or city has adopted. California, New York, and Illinois each amend the model code on their own schedule, so the build-out cost of the same floor plan differs by state.
Common questions
How far back should I pull QCEW data? At least four quarters, ideally eight. You want to see whether job growth in target sectors is a trend or a single quarter.
Can I use OEWS for a city that is not a metro area? OEWS publishes for metropolitan and nonmetropolitan areas. If your site is outside a metro, use the state or nonmetro estimate as a proxy.
Does LAUS predict coworking membership directly? No. It predicts the size of the freelance and independent worker pool, which is one input into membership demand.
How often is Census business patterns updated? Annually, with a lag of roughly a year. Pair it with QCEW for a more current picture of the same area.
What if two sites score the same? Break the tie on rent per square foot, the code edition your state has adopted, and the ADA compliance work the landlord will carry. The data rarely separates sites that are truly close.
Do I need paid data to do this? No. OEWS, QCEW, LAUS, and Census business patterns are all published free by federal agencies.
