College Scorecard Scraper: 30 Data Fields per Record (2026)

Get comprehensive data on over 6,000 US colleges and universities, including admissions rates, tuition costs, and median earnings 6 years post-enrollment. You can search by school name, state, and ownership type, or look up institutions using their 8-digit OPE ID. This Actor is a direct interface to the Department of Education College Scorecard API, providing 30 fields per record. This is a good choice for researchers, analysts, and consultants needing structured higher education data but not for anyone requiring historical data beyond the annually updated figures.

Try it: open College Scorecard Scraper on Apify, sign in on the free plan and run the prefilled example.

Can you try College Scorecard Scraper before paying?

Yes. Apify's free plan includes $5.00 of prepaid usage every month and asks for no credit card. At $0.005 per result, that covers up to 1,000 results of College Scorecard Scraper a month, before run-start charges and platform usage.

The example request further down caps maxItems at 100, so a first run returns at most 100 results and costs at most $0.50 in result charges. That is enough to see the real shape of the data before deciding anything.

College Scorecard Scraper was last updated on 2026-06-02. It is one of 1,729 Actors CrawlerBros publishes on Apify, which together have 746,619 lifetime public runs and an average rating of 4.63 out of 5 across 416 reviews.

What does it cost to run College Scorecard Scraper?

Each result costs $0.005 on Apify's free plan, which is $5.00 per 1,000 results. Starting a run is charged separately at $0.005 per GB of Actor memory. Apify also bills the platform usage each run consumes, at the rates of your Apify plan, on top of these charges.

The maxItems control, which sets the maximum number of records to return, directly influences the number of results written to the dataset. Each result costs $0.005. To evaluate if the Actor meets your needs before committing significant spend, execute a run with a small maxItems value. For instance, the example input's default maxItems of 100 will cost at most $0.50 in result charges.

How do you run College Scorecard Scraper from the API?

The schema marks 1 of its 7 controls as required: mode. Nothing in the payload below is illustrative. Those are the schema's prefilled defaults for College Scorecard Scraper, so the request works once your token is in place.

Call the synchronous endpoint to start a run and receive dataset items in one request:

curl -X POST "https://api.apify.com/v2/acts/crawlerbros~college-scorecard-scraper/run-sync-get-dataset-items?token=$APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"mode":"search","query":"MIT","maxItems":100}'

The same run from Python, using the official client:

from apify_client import ApifyClient

client = ApifyClient("<YOUR_APIFY_TOKEN>")

run_input = {
  "mode": "search",
  "query": "MIT",
  "maxItems": 100
}

run = client.actor("crawlerbros~college-scorecard-scraper").call(run_input=run_input)

for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

And from Node.js:

import { ApifyClient } from 'apify-client'

const client = new ApifyClient({ token: '<YOUR_APIFY_TOKEN>' })

const input = {
  "mode": "search",
  "query": "MIT",
  "maxItems": 100
}

const run = await client.actor('crawlerbros~college-scorecard-scraper').call(input)
const { items } = await client.dataset(run.defaultDatasetId).listItems()
console.log(items)

Because the call is synchronous, your client waits for the whole run. Keep it for exploration. For scheduled work, start the run without waiting and collect the dataset afterwards, so network trouble costs you a retry rather than the results.

Which College Scorecard Scraper inputs matter, and which can you skip?

This Actor exposes 7 controls, with mode being the only required one. For a broad initial exploration, set mode to "search" and populate the query control with a school name. For more refined results, use the state or ownershipType filters. The opeids array is specific to "getByOpeid" mode and should be left blank for typical searches.

• mode (string): What to fetch from the College Scorecard API. Default: "search".
• apiKey (string): Free API key from api.data.gov. Leave blank to use the demo key (DEMO_KEY) which allows 40 requests/hour per IP without registration. Register for free at https://api.data.gov/signup/ for 1,000 requests/hour.
• query (string): School name to search for (mode=search). Partial names work, e.g. 'MIT', 'Harvard', 'State University'.
• state (string): Filter results to a specific US state (mode=search).
• ownershipType (string): Filter by school ownership type (mode=search).
• opeids (array): List of OPE ID strings to look up (mode=getByOpeid). OPE IDs are 8-digit identifiers, e.g. '00231500' for MIT.
• maxItems (integer): Maximum number of records to return. Default: 100.

Fixed-choice controls: mode accepts search (Search schools by name, state, or type), getByOpeid (Get specific school(s) by OPE ID); state accepts 57 values, including "" (All states), AL (Alabama), AK (Alaska), AZ (Arizona); ownershipType accepts "" (All types), 1 (Public), 2 (Private non-profit), 3 (Private for-profit).

What does College Scorecard Scraper return?

Each record provides 30 output fields, detailing an institution's admissions, costs, and student outcomes. You will find data such as admissionsRate, tuitionInState, completionRate, and medianEarnings. The records do not contain granular course catalogs, faculty contact information, or real-time enrollment figures, as the underlying data is updated annually.

• unitId: IPEDS Unit ID
• opeId: OPE ID (8-digit)
• name: Institution name
• city: City
• state: State abbreviation
• zip: ZIP code
• url: School website URL
• alias: Common abbreviation or alias
• ownershipType: Public / Private non-profit / Private for-profit
• carnegieClassification: Carnegie Basic Classification label
• predominantDegreeAwarded: Certificate, Associate, Bachelor's, or Graduate
• locale: City/Suburb/Town/Rural size classification
• admissionsRate: Admission rate (0-1)
• satReadingMedian: SAT Evidence-Based Reading median score
• satMathMedian: SAT Math median score
• satCumulativeAverage: SAT overall average
• actCumulativeMedian: ACT Composite median score
• tuitionInState: In-state tuition (USD)
• tuitionOutOfState: Out-of-state tuition (USD)
• costAttendanceAcademicYear: Total cost of attendance for academic year (USD)
• studentSize: Undergraduate enrollment
• percentUndergraduatesWithPellGrant: Share of undergrads receiving Pell Grants
• federalLoanRate: First-generation student share
• retentionRate: Full-time retention rate (4-year institutions)
• completionRate: 150% time completion rate
• medianEarnings: Median earnings 6 years after entry (USD)
• avgFacultyDegree: Faculty count
• recordType: Always school
• sourceUrl: API endpoint used
• scrapedAt: ISO 8601 timestamp

These are the documented fields. Optional ones can be empty on a given record, so measure how often each field your deliverable depends on is populated across a real sample before automating the handoff.

How do you build the workflow end to end?

Open College Scorecard Scraper and work through these in order. Each step ends with something to check, so a bad configuration surfaces on a small run rather than a scheduled one.

1. Set mode to "search" and enter a query like "MIT", leaving maxItems at its default of 100.
2. Examine the returned records to ensure fields like admissionsRate, tuitionInState, and medianEarnings are populated as expected.
3. If the initial data is satisfactory, use the state or ownershipType filters to narrow your search to specific demographics or regions.
4. To retrieve data for known institutions, switch mode to "getByOpeid" and input a list of 8-digit OPE IDs into the opeids array.
5. Verify that each OPE ID lookup returns a corresponding school record with consistent data across its fields.
6. For larger data extraction needs, increase maxItems beyond 100, up to 10000. Consider supplying an apiKey for increased request limits.

How do you apply it? Three worked playbooks

These are College Scorecard Scraper's own documented use cases, each worked through as an operating pattern rather than a description.

Use case 1: College research

Outcome: Compare institutions by cost, admissions selectivity, and outcomes

Configure: Set mode to "search", specify a query such as "University of California", and select a state like "CA".

Working method: Start by searching for a well-known institution or a university system to understand the data structure. Then, refine your search using specific state and ownershipType filters. Compare the admissionsRate, tuitionInState, and medianEarnings fields across the filtered results to identify patterns.

Deliverable: A spreadsheet comparing admissions rates, tuition costs, and median earnings for a curated list of institutions, organized by specified filters.

Stop condition: Crucial fields such as admissionsRate, tuitionInState, or medianEarnings consistently return null or zero values for institutions where this data should be present.

Use case 2: Education analytics

Outcome: Analyze trends across institutional types, states, and regions

Configure: Set mode to "search", leave query blank to search broadly, and iterate through different state values or ownershipType filters (e.g., "1" for Public).

Working method: Begin by collecting data for all public institutions in a single state. Then, expand your collection to cover different ownership types and multiple states or regions. Aggregate data on admissionsRate, completionRate, and percentUndergraduatesWithPellGrant to identify macro trends by region or institutional category.

Deliverable: An analytical report or dashboard illustrating trends in admissions, completion rates, and Pell Grant recipients across various states and institutional ownership models.

Stop condition: A significant portion of institutions within a specific filter combination show inconsistent or missing data for key analytical fields like percentUndergraduatesWithPellGrant.

Use case 3: Career outcome studies

Outcome: Compare median earnings by school, state, or degree type

Configure: Set mode to "search", use a broad query or state, and optionally filter by ownershipType.

Working method: Focus initial runs on a specific geographic area or type of institution to observe medianEarnings data. Identify any patterns or outliers. Gradually expand the scope to compare earnings outcomes across different states or by filtering for specific school names to build a comprehensive picture.

Deliverable: A dataset or report detailing median earnings 6 years after entry, categorized by institution, state, and potentially ownership type, to highlight career outcome differences.

Stop condition: The medianEarnings field consistently returns values that are zero, null, or otherwise nonsensical for a range of institutions relevant to the study.

What breaks, and how do you design around it?

The free DEMO_KEY for the College Scorecard API has a limit of 1,000 requests per hour. If your workflow requires higher volume, registering for a free key at api.data.gov/signup/ increases this limit. When searching for institutions across multiple states, you will need to perform separate runs for each state and then merge the resulting datasets, as the state filter accepts only one value at a time.

When should you not use College Scorecard Scraper?

This Actor may not be suitable if your project demands real-time data or highly detailed academic program specifics beyond what the College Scorecard API offers. For example, if you require daily updates on tuition fluctuations or specific course availability within departments, the annual update cycle of this data source makes it inadequate. Similarly, if your work depends on granular rankings and specialized program information often found in proprietary datasets, consider using a dedicated scraper like the BestColleges Scraper or US News Rankings Scraper. These alternatives are designed to extract richer, more frequently updated content from specific academic review sites. If your primary goal is to retrieve housing market data, the Statistics Canada Housing Market Data Scraper would be a more appropriate choice.

What should you check before trusting the output?

• Verify that admissionsRate values are between 0 and 1; a value outside this range may indicate a data issue.
• Check that medianEarnings and tuitionInState fields contain plausible non-zero monetary values for relevant institutions.
• Confirm that the ownershipType field accurately categorizes a sample of institutions as public, private non-profit, or private for-profit.
• For searches using query and state, ensure that the returned institution names and states align with your input criteria.
• Look for consistency in carnegieClassification and locale descriptors across similar institutions.
• If using opeids, confirm that the returned opeId for each record matches the input OPE ID.

None of this proves a record is correct. It gives a scheduled College Scorecard Scraper run defined points where it should stop instead of quietly passing bad data downstream.

Frequently asked questions

How many US colleges and universities are covered?

The College Scorecard API includes data for over 6,000 US colleges and universities that participate in federal financial aid programs. This provides a comprehensive overview of higher education institutions nationwide.

Is an API key required to use this Actor?

No API key is required to start. The Actor defaults to using a free DEMO_KEY, which permits up to 1,000 requests per hour. For higher usage volumes, you can register for a free API key at api.data.gov/signup/.

What is an OPE ID and how is it used?

An OPE ID, or Office of Postsecondary Education ID, is a unique 8-digit identifier for each institution. These IDs are essential for accurately looking up specific schools when using the getByOpeid mode in the Actor.

How current is the data provided by this Actor?

The College Scorecard API, which powers this Actor, is updated annually by the Department of Education. Therefore, the data reflects the most recent academic year available, not real-time changes or historical trends.

What are the costs associated with running this Actor?

On Apify's free plan, each result from this Actor costs $0.005, which equates to $5.00 per 1,000 results. A run-start fee is charged every time a run begins, and additional platform usage is billed separately. Apify's free plan includes $5.00 of monthly usage, covering up to 1,000 results before run-start charges.

Where to go next

When you are ready to run it, open College Scorecard Scraper on Apify; the free plan covers up to 1,000 results a month.

Start with the College Scorecard Scraper Actor page for the current input schema, pricing tier, and run history.

Other Actors we maintain for related data:

• BestColleges Scraper: Scrape ranked US college programs and school profiles from BestColleges (bestcolleges.com).
• US News Rankings Scraper: Scrape US News & World Report rankings - universities, hospitals, high schools, and graduate schools.
• Statistics Canada Housing Market Data Scraper: Scrape official Statistics Canada housing data: new housing price index, CMHC housing starts/completions, average rents, vacancy rates, condo price index, building permits, mortgage rate, and unsold new-home inventory - by city/CMA.

Resources

• Actor documentation, input schema, and pricing: verified against the published Actor on 2026-10-06.

• Actor last updated by its maintainers on 2026-06-02.

• Run outcome figures cover the 30 day public window ending 2026-10-06.

• College Scorecard Scraper on Apify