Education Nonprofits
U.S. · 187,152 locations
There are 187,152 Education Nonprofits in the US as of September 2026. They are in all 50 states, DC and Puerto Rico.
187,152 US nonprofit organisations. Built from IRS Business Master File, the Form 990/990-EZ/990-PF e-file XML, 990-N e-postcards, the IRS auto-revocation list, IRS Publication 78, the Federal Audit Clearinghouse and the US Census geocoder.
More about this list
87% carry a named officer, 40% a telephone number, 38% a website, 35% the mission statement they filed with the IRS. Every organisation carries an activity_status that was measured from both IRS filing channels — the full 990 e-file index and the 990-N postcard file — rather than inferred from registration alone. 73 columns per record.
Need just some of them? Ask in your own words.
●Where the rows are
≡Sample Data Preview
| name | city | state | zip | phone | website | ein | officer name | officer title | street | mission | activity status | ntee category | revenue amt | officer source | sort name | care of | ntee code | ntee subcategory | subsection | exempt status | org type | foundation type | deductible | ruling date | tax period | asset amt | income amt | revenue band | asset band | has financials | group exemption | filing requirement | source file | pulled at | exemption revoked | revocation date | last 990 filed | last 990 form | years 990 filed | form 990 object id | form 990 batch url | principal officer | last 990n filed | employees | volunteers | total expenses | net assets eoy | officers | top officer | top officer title | top officer comp | officer count | grants paid count | grants paid total | contact source | website source | pub78 verified | pub78 deductibility | ca may not operate | ca ftb revoked name match | org structure | group ruling | county fips | census tract | msa code | msa name | msa type | a 133 audit performed | last single audit year | audit contact email | audit contact phone | geo precision | county | county note | address quality | latitude | longitude | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| LADY WOLVERINES SOCCER BOOSTER CLUB | Anchorage | AK | 99516-3607 | 010828753 | Shawn Lee | 13400 ELMORE RD | filing | Education | Form 990-N e-postcard | % LEEANNE OSGOOD | B112 | Single Organization Support | 501(c)(3) | Unconditional Exemption | Association | Organization that receives a substantial part of its support from a governmental unit or the general public 170(b)(1)(A)(vi) | yes | 2005-09 | 2024-12 | 990 - Required to file Form 990-N - Income less than $50,000 per year | eo3.csv | 2026-09-08 | Shawn Lee | 2024 | 990-N postcard | 1 | PC | independent | 02020 | 002821 | 11260 | Anchorage, AK | Metropolitan Statistical Area | source | Anchorage Municipality | complete | 61.09959279321 | -149.804277615581 | ||||||||||||||||||||||||||||||||||||||||
| PRINCE AVENUE CHRISTIAN SCHOOL | Bogart | GA | 30622-2324 | (678) 726-3000 | www.princeave.org | 010548823 | Dr Seth Hathaway | Head of School | 2201 RUTH JACKSON RD | Prince Avenue Christian School exists to partner with Christian families to provide scripturally based discipleship while pursuing excellence in academics, fine arts, and athletics from a biblical worldview. | filing | Education | 17249292 | Form 990 Part VII | % WILLIAM N PEVEY | B20 | Elementary, Secondary Education, K - 12 | 501(c)(3) | Unconditional Exemption | Corporation | School 170(b)(1)(A)(ii) | yes | 2005-06 | 2025-06 | 25373873 | 17305170 | 10,000,000 to 49,999,999 | 10,000,000 to 49,999,999 | 1 | 990 - Not required to file (religious organization) | eo3.csv | 2026-09-08 | 2025-06 | 990 | 6 | 202601329349304760 | https://apps.irs.gov/pub/epostcard/990/xml/2026/2026_TEOS_XML_05A.zip | 225 | 50 | 18585889 | Dr Seth Hathaway (Head of School); Greg Vandagriff (Head Varsity Football coach & Strength Training); Richard Ricketts (Athletic Director); Daniel Kempf (High School Principal); Joe Taylor (Board Chair) | Dr Seth Hathaway | Head of School | 275000 | 15 | Form 990 Part VII | Form 990 | 1 | PC | independent | 13219 | 030102 | 12020 | Athens-Clarke County, GA | Metropolitan Statistical Area | source | Oconee County | complete | 33.916687082412 | -83.550200196773 | ||||||||||||||||||
| NATIONAL ASSOCIATION OF EDUCATIONAL PROCUREMENT | Columbia | MD | 21045-5848 | 010475488 | 8840 STANFORD BLVD STE 2000 | no filing on record | Education | NEW ENGLAND | % BILL ALDERSON | B50Z | Graduate, Professional Schools (Separate Entities) | 501(c)(3) | Unconditional Exemption | Corporation | Organization that normally receives no more than one-third of its support from gross investment income and unrelated business income and at the same time more than one-third of its support from contributions, fees, and gross receipts related to exempt purposes 509(a)(2) | yes | 1994-03 | 3700 | 990 (all other) or 990EZ return | eo2.csv | 2026-09-08 | subordinate | 3700 | 24027 | 606708 | 12580 | Baltimore-Columbia-Towson, MD | Metropolitan Statistical Area | source | Howard County | complete | 39.187719993911 | -76.818077154439 | |||||||||||||||||||||||||||||||||||||||||||||
| GULF OF MAINE MARINE EDUCATION ASSOCIATION | Lee | NH | 03861-6528 | https://gommea.org/ | 010412164 | Tara Hicks Johnson | 8 JAMES FARM RD | filing | Education | Form 990-N e-postcard | % SARA ELLIS | B01 | Alliance/Advocacy Organizations | 501(c)(3) | Unconditional Exemption | Corporation | Organization that receives a substantial part of its support from a governmental unit or the general public 170(b)(1)(A)(vi) | yes | 2019-08 | 2025-09 | 990 - Required to file Form 990-N - Income less than $50,000 per year | eo1.csv | 2026-09-08 | Tara Hicks Johnson | 2024 | 990-N postcard | 990-N postcard | 1 | PC | independent | 33017 | 080500 | 14460 | Boston-Cambridge-Newton, MA-NH | Metropolitan Statistical Area | source | Strafford County | complete | 43.131205470154 | -70.991412811587 | ||||||||||||||||||||||||||||||||||||||
| FRIENDS OF GREEN SEA FLOYDS LIBRARY | Green Sea | SC | 29545-4949 | 010673907 | Debbie Wickberg | 5331 HIGHWAY 9 | filing | Education | Form 990-N e-postcard | B11 | Single Organization Support | 501(c)(3) | Unconditional Exemption | Association | Organization that receives a substantial part of its support from a governmental unit or the general public 170(b)(1)(A)(vi) | yes | 2024-06 | 2026-06 | 990 - Required to file Form 990-N - Income less than $50,000 per year | eo2.csv | 2026-09-08 | Debbie Wickberg | 2025 | 990-N postcard | 1 | PC | independent | 45051 | 010100 | 34820 | Myrtle Beach-Conway-North Myrtle Beach, SC | Metropolitan Statistical Area | source | Horry County | complete | 34.147260364132 | -79.02145779944 |
{}What's in each row
| Field | What it is | Rows filled |
|---|---|---|
| ein | IRS Employer Identification Number — the primary key for every US nonprofit | 100% |
| name | Legal organisation name as registered with the IRS | 100% |
| city | City | 100% |
| state | Two-letter state code | 100% |
| officer_ | Officer name | 87% |
| officer_ | Officer title | 40% |
| phone | Telephone number as reported on the organisation's own Form 990 | 40% |
| website | Website as reported on the Form 990 or 990-N postcard | 38% |
| street | Street address | 100% |
| zip | ZIP code | 100% |
| mission | Mission statement as filed on Form 990 Part I/III | 36% |
| activity_ | filing, lapsed, revoked or no filing on record — measured, not assumed | 100% |
| ntee_ | NTEE major group — the IRS cause classification | 100% |
| revenue_ | Revenue as reported to the IRS | 34% |
| officer_ | Officer source | 87% |
| sort_ | Secondary or trading name where the IRS records one | 14% |
| care_ | Care of | 68% |
| ntee_ | Ntee code | 100% |
| ntee_ | NTEE detailed classification | 97% |
| subsection | 501(c) subsection, e.g. 501(c)(3) | 100% |
| exempt_ | Exempt status | 100% |
| org_ | Org type | 100% |
| foundation_ | IRS foundation classification — church, school, hospital, private foundation | 100% |
| deductible | Deductible | 100% |
| ruling_ | Month the IRS granted exempt status | 100% |
| tax_ | Tax period | 89% |
| asset_ | Total assets as reported to the IRS | 40% |
| income_ | Income amt | 39% |
| revenue_ | Revenue band | 40% |
| asset_ | Asset band | 40% |
| has_ | Has financials | 40% |
| group_ | Group exemption | 6% |
| filing_ | Filing requirement | 100% |
| source_ | Source file | 100% |
| pulled_ | Pulled at | 100% |
| last_ | Tax period of the most recent full Form 990 | 43% |
| last_ | Last 990 form | 43% |
| years_ | Years 990 filed | 43% |
| form_ | Form 990 object id | 43% |
| form_ | IRS bulk XML archive containing this organisation's return | 41% |
| principal_ | Principal officer from the 990-N postcard, where no full 990 exists | 58% |
| last_ | Year of the most recent 990-N postcard | 60% |
| employees | Employee count from Form 990 Part I | 12% |
| volunteers | Volunteer count from Form 990 Part I | 12% |
| total_ | Total expenses from Form 990 Part I | 18% |
| net_ | Net assets at end of year | 35% |
| officers | Up to five officers by compensation, with titles | 40% |
| top_ | Principal officer name from Form 990 Part VII | 40% |
| top_ | That officer's title | 40% |
| top_ | That officer's reportable compensation | 12% |
| officer_ | Officer count | 40% |
| grants_ | Number of grants this organisation reported paying | 6% |
| grants_ | Total value of grants paid | 6% |
| contact_ | Contact source | 87% |
| website_ | Website source | 38% |
| pub78_ | Listed in IRS Publication 78 as eligible to receive deductible contributions | 91% |
| pub78_ | Publication 78 deductibility code | 91% |
| org_ | parent, subordinate, intermediate or independent, from the IRS affiliation code | 100% |
| group_ | Group exemption number where the organisation files under one | 6% |
| latitude | Latitude, geocoded from the address via the US Census geocoder | 99% |
| longitude | Longitude, geocoded from the address via the US Census geocoder | 99% |
| county_ | County FIPS code | 99% |
| census_ | Census tract | 77% |
| msa_ | Msa code | 73% |
| msa_ | Metropolitan or micropolitan area | 73% |
| msa_ | Msa type | 73% |
| a_ | Single Audit on file with the Federal Audit Clearinghouse | 2% |
| last_ | Last single audit year | 2% |
| audit_ | Audit contact email | 2% |
| audit_ | Audit contact phone | 2% |
| geo_ | How exact the map point is: the source's own point, the street address, or the ZIP code's centre | 99% |
| county | County the location is in | 99% |
| county_ | How the county was found, where it was not given by the source | 5% |
| address_ | Whether the street address is complete, missing a house number, or a PO box | 100% |
Rows filled is measured on every row of the delivered file.
✓Where this data comes from
This dataset is built from the IRS's own public files — the Business Master File of exempt organisations, the Form 990/990-EZ/990-PF e-file XML, the 990-N e-postcard file, the auto-revocation list and Publication 78 — joined on EIN, with the Federal Audit Clearinghouse for single-audit detail and the US Census geocoder for coordinates. Nothing is collected from an organisation's own website: every field traces to a filing the organisation made or a determination the IRS published.
September 15, 2026
File built
79 fields
Columns in the file
Fill measured
On every row of the delivered file
Records are published as the IRS holds them — nothing is silently dropped for looking wrong, because on this file an empty field is itself information (a church that files no return is not a bad record). Coordinates are validated to US bounds, ZIPs and phone numbers to format, and every organisation carries an activity_status measured from both filing channels — the 990 e-file index and the 990-N postcard file — rather than inferred from registration. Fill rates vary sharply by slice and are stated on each dataset.
How we build and date every file: methodology.
Using ChatGPT, Claude or an API?
No setup needed. Ask ChatGPT or Claude your question about the Education Nonprofits and give it this page: it can open https://locationlists.com/find?dataset=education-nonprofits with the filters you need and show you the count and price.
Using ChatGPT or Claude? Tell it to answer from locationlists.com/find — no account, no connector.
How assistants ask
No connector needed. Any AI assistant can answer from this site with a web link, no connector, no account: open https://locationlists.com/find with the question as query parameters.
If a question needs filtering, distance, drive time, combining lists or a price, open a /find link: either put the question itself in q=, or build the structured link. The answer does not need an API, a connector or an account.
Simplest, the question itself: https://locationlists.com/find?q=<the question in plain words>
Or set the parts yourself: https://locationlists.com/find?dataset=<slug>&state=<ST>&city=<city>&county=<county>&zip=<zip>&near=<place or zip>&radius=<miles>&drive=<minutes>&where=<column>:<op>:<value>&total=<rows>
Leave out what the question does not need. Use radius for straight-line miles or drive for minutes of driving (5 to 60), not both; near can name several places separated by | to reach from any of them. To ask about several lists at once, use datasets with two or more names, or category with a kind of business. To compare two lists, add relate=nearest and the other list with b. in front of its parameters. To count by area, use areas=county (or zip, state, metro). To keep only places whose county, ZIP code, metro area or state meets a Census figure, add area_where (for example county:population>1000000); with areas, per=100000 gives counts per 100,000 residents.
Links an assistant can open, each showing how many rows match, a few preview rows and the price for just that part:
- Only California, the state with the most: https://locationlists.com/find?dataset=education-nonprofits&state=CA
- Within 25 miles of New York, NY, the busiest area: https://locationlists.com/find?dataset=education-nonprofits&near=New+York+NY&radius=25
- Within a 30-minute drive of New York, NY: https://locationlists.com/find?dataset=education-nonprofits&near=New+York+NY&drive=30
- Only rows where deductible is yes: https://locationlists.com/find?dataset=education-nonprofits&where=deductible%3Aeq%3Ayes
- Each one's nearest Civil Air Patrol Squadrons, in California, with miles: https://locationlists.com/find?dataset=education-nonprofits&state=CA&relate=nearest&k=1&b.dataset=civil-air-patrol-squadron-list&b.state=CA
- The whole list: add a city, state, ZIP code or distance:
https://locationlists.com/find?dataset=education-nonprofits - In plain words: "Education Nonprofits in California": https://locationlists.com/find?q=Education+Nonprofits+in+California
- In plain words: "Education Nonprofits within 25 miles of New York, NY": https://locationlists.com/find?q=Education+Nonprofits+within+25+miles+of+New+York%2C+NY
Optional: connect for repeat use or wallet payments:
Read https://locationlists.com/skill.md and follow the instructions to connect LocationLists. Then show me the Education Nonprofits list.Your assistant will walk you through setup and show you the price before you pay.
For developers
Every step below has a no-connector equivalent: https://locationlists.com/find?dataset=education-nonprofits with the same filters as query parameters.
- Connect your agent to
https://locationlists.com/mcp(no key, no signup). - Call
count_locationswithdataset: "education-nonprofits"and any filter — it is free, and returns how many rows match and what they cost. - Fetch the rows with
query_locations: $0.17 per 100 rows from this dataset. Above 93,576 rows, buying the whole file is cheaper.
Paid in USDC over x402 — the agent gets an HTTP 402 with the exact price, signs, and retries. Money moves only after rows are delivered, so a failed or empty query costs nothing. Setup for Claude, Cursor and VS Code
?Frequently Asked Questions
How many Education Nonprofits are there in the US?
There are 187,152 Education Nonprofits in the US as of September 2026. They are in all 50 states, DC and Puerto Rico.
Is this the same data as GuideStar or Candid?
It comes from the same public IRS sources Candid uses — they list them on candid.org/about/our-data/data-sources. Against Candid's published Essentials API schema we match 41 of the 66 fields it returns, which is 84% of the fields reproducible from public records. What we do not have is the part organisations submit to Candid directly: board rosters, DEI demographics, Seals of Transparency, awards and photos. If you need those, Candid is the right purchase. If you need the whole file as a CSV, this is.
Why is this so much cheaper than a Candid API subscription?
Candid sells a subscription starting at $4,800 a year. This is a one-off purchase — and you are not giving up API access to get the lower price. The same 187,152 records are available three ways: the bulk CSV, our MCP server for programmatic queries, and per-record pricing so an application or AI agent can pull just the rows it needs. What you are not paying for is an annual seat.
Do you have an API, or only CSV downloads?
Both. Every dataset is exposed through our MCP server — search, sample, quote, buy and query — and priced per record, so you can pull a single EIN or a filtered slice without buying the file. Payment is by card or stablecoin with no account to create. The CSV is simply the cheapest route once you want more than about half the rows.
How do I know an organisation on this list still exists?
Every row carries activity_status. “filing” means the organisation filed a 990, 990-EZ, 990-PF or 990-N postcard within the last two tax years. “revoked” means it appears on the IRS auto-revocation list with no later determination letter. Registration alone is not treated as evidence of life — 435,906 organisations in the full IRS file have no filing on record at all.
What is missing, and can you add it?
Deep Form 990 financial detail (every line of Parts VIII–X), websites and logos for organisations that did not report one, and subject/population classification beyond the IRS NTEE code. All three are buildable — the 990 line items are in files we already hold. Choose “Custom enrichment” at checkout and we will scope it with you.
Can an AI agent buy a slice of this instead of the whole file?
Yes. The dataset is exposed through our MCP server with per-record pricing, so an agent can query and pay for exactly the rows it needs rather than the whole download.