The schools that create the most student debt

The top 10 schools for student loan debt in the United States

By Andy Kriebel [source]

About this dataset

This dataset contains information on the amount of student loan debt originated by schools in the United States for the 2020-2021 academic year. The data includes the school name, city, state, zip code, school type, loan type, number of recipients, number of loans originated, amount of money loaned, and number of disbursements

How to use the dataset

There are a few things to keep in mind when using this dataset:

• The data is for the 2020-2021 academic year.
• The data is for student loan debt originated by schools in the United States.
• The data is sorted by school.
• The columns of interest are: School, City, State, Zip Code, School Type, Loan Type, Recipients, # of Loans Originated, $ of Loans Originated, # of Disbursements, and $ of Disbursements

Research Ideas

• The dataset can be used to calculate the amount of loan debt originated by each type of school.
• The dataset can be used to calculate the amount of loan debt originated by each state.
• The dataset can be used to help students estimate their future student loan debt

Acknowledgements

The data for this visualization comes from the Common Origination and Disbursement (COD) System through the Department of Education

Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Student Loan Debt by School 2020-2021.csv | Column name | Description | |:--------------------------|:-------------------------------------------------| | School | The name of the school. (String) | | City | The city where the school is located. (String) | | State | The state where the school is located. (String) | | Zip Code | The zip code of the school. (String) | | School Type | The type of school. (String) | | Loan Type | The type of loan. (String) | | Recipients | The number of recipients of the loan. (Integer) | | # of Loans Originated | The number of loans originated. (Integer) | | $ of Loans Originated | The amount of money originated in loans. (Float) | | # of Disbursements | The number of disbursements. (Integer) | | $ of Disbursements | The amount of money disbursed. (Float) |

Acknowledgements

If you use this dataset in your research, please credit Andy Kriebel.

Debt Balance Student Loans in the United States increased to 1.65 Trillion USD in the third quarter of 2025 from 1.64 Trillion USD in the second quarter of 2025. This dataset includes a chart with historical data for the United States Debt Balance Student Loans.

Statistics on student debt, including the average debt at graduation, the percentage of graduates who owed large debt at graduation and the percentage of graduates with debt who had paid it off at the time of the interview, are presented by the province of study and the level of study. Estimates are available at five-year intervals.

College Scorecard

The College Scorecard dataset is provided by the U.S. Department of Education and contains information on nearly every college and university in the United States. The dataset includes data on student loan repayment rates, graduation rates, affordability, earnings after graduation, and more. The goal of this dataset is to help students make informed decisions about their college choice by providing them with clear and concise information about each school's performance

How to use the dataset

This dataset can help understand the cost of attending college in the United States, as well as the average debt load for students. It can also be used to compare different schools in terms of their graduation rates and repayment rates

Columns

• UNITID: Unit ID for institution
• INSTNM: Institution name
• CITY: City
• STABBR: State
• ZIP: Zip code
• OPEID: OPE ID for institution
• OPEID6: OPE ID for institution (6-digit)
• ACCREDAGENCY: Accrediting Agency
• INSTURL: Institution URL
• NPCURL: Net Price Calculator URL
• SCH_DEG: Highest degree awarded
• HCM2: Carnegie Classification 2010:** Basic
• MAIN: Carnegie Classification 2010:** Main
• NUMBRANCH: Number of branch campuses
• PREDDEG: Predominant degree awarded
• HIGHDEG: Highest degree awarded
• CONTROL: Control of institution
• ST_FIPS: State FIPS code
• REGION: Region
• LOCALE: Locale code
• LOCALE2: Locale code (multiple categories per state)
• CCBASIC: Carnegie Classification 2010:** Basic
• CCMAIN: Carnegie Classification 2010:** Main
• CCUGPROF: Carnegie Classification 2010:** Undergraduate Profile
• CCSIZSET: Carnegie Classification 2010:** Size and Setting
• HBCU: Historically Black College or University
• PBI: Predominantly Black Institution
• ANNHI: Tribal College or University
• TRIBAL: Tribal College or University (Public)
• AANAPII: Asian American and Native American Pacific Islander-Serving Institution
• HSIP: Hispanic-Serving Institution (HSI)
• NANTI: Native American-Serving Nontribal Institution
• MENONLY: Men only
• WOMENONLY: Women only
• RELAFFIL: Religious affiliation
• DISTANCEONLY: Distance-only
• CURROPER: Currently operating
• VETERAN: Veteran-supportive
• LIMDEP: Limited-degree-granting
• HIGHDEG_GRANTED: Highest degree granted
• PS: Predominantly two-year public
• UGRD_ENRL_TOTAL: Undergraduate total enrollment
• GRAD_ENRL_TOTAL: Graduate total enrollment
• UGRD_ENRL_ORIG_YR2_RT: Undergraduate, first-time, first-year retention rate (%)

Acknowledgements

This data was originally collected by the US Department of Education and made available on their website. Thank you to the US Department of Education for making this data available!

Over 44.7 million Americans carry student loan debt, with the total amount valued at approximately $1.31 trillion (Quarterly Report, 2019). Ergo, consumer spending, a factor of GDP, is stifled and negatively impacts the economy (Frizell, 2014, p. 22). This study examined the relationship between student loan debt and the probability of a recession in the near future, as well as the effects of proposed student loan forgiveness policies through the use of a created model. The Federal Reserve Bank of St. Louis’s website (FRED) was used to extract data regarding total GDP per quarter and student loan debt per quarter ("Federal Reserve Economic Data," 2019). Through the combination of the student loan debt per quarter and total GDP per quarter datasets, the percentage of total GDP composed of student loan debt per quarter was calculated and fitted to a logistic curve. Future quarterly values for total GDP and the percentage of total GDP composed by student loan debt per quarter were found through Long Short Term Models and Euler’s Method, respectively. Through the creation of a probability of recession index, the probability of recession per quarter was compared to the percentage of total GDP composed by student loan debt per quarter to construct an exponential regression model. Utilizing a primarily quantitative method of analysis, the percentage of total GDP composed by student loan debt per quarter was found to be strongly associated[p < 1.26696* 10-8]with the probability of recession per quarter(p(R)), with the p(R) tending to peak as the percentage of total GDP composed of student loan debt per quarter strayed away from the carrying capacity of the logistic curve. Inputting the student loan debt forgiveness policies of potential congressional bills proposed by lawmakers found that eliminating 49.7 % and 36.7% of student loan debt would reduce the recession probabilities to be 1.73545*10-29% and 9.74474*10-25%, respectively.

This datasets contains information about NYCHA residents’ use of: a) NYC Financial Empowerment Centers: a program that provides free, one-on-one professional financial counseling and coaching to all NYC residents. Each row in the dataset represents the number of NYCHA residents on a Borough-level who utilized this service; b) EmpoweredNYC: is an initiative to assist New Yorkers with disabilities and their families to better manage their finances and become more financially stable. Each row in the dataset represents the number of NYCHA residents on a Borough-level who utilized this service; c) Student Loan Debt clinic: is an initiative to help New Yorkers understand their student loans and how to repay them. Each row in the dataset represents the number of NYCHA residents on a Borough-level who utilized this service; and d) Ready to Rent: a program providing free one-on-one financial counseling to New Yorkers seeking to apply for affordable housing units through HPD’s Housing Connect lottery. Each row in the dataset represents the number of NYCHA residents on a Borough-level who utilized this service. The dataset is part of the annual report compiled by the Mayor’s Office of Operations as mandated by the Local Law 163 of 2016 on different services provided to NYCHA residents. See other datasets in this report by searching the keyword “Services available to NYCHA Residents - Local Law 163 (2016)” on the Open Data Portal.

Data on the average amount of OSAP debt owed by students. The data is specific to those who attended programs with typical durations. Data is for: * 4-year undergraduate university students * 2-year college diploma students * 1-year private career college students The data fields are: * academic year of completion * postsecondary sector (university, publicly-assisted college, or private career college) * program duration (1 year, 2 years or 4 years) * average repayable debt after loan forgiveness applied through the Ontario Student Opportunity Grant Debt is in nominal dollars with no adjustment for inflation. *[OSAP]: Ontario Student Assistance Program

Presents statistics on the status of student loans borrowers and the change in debt in the financial year. The borrowers are mainly Scotland domiciles who studied anywhere in the UK together with a small number of EU students who studied in Scotland. Source agency: Student Loans Company Designation: National Statistics Language: English Alternative title: Student Loans for Higher Education in Scotland

🇸🇪 스웨덴

Statistics on postsecondary graduates who owed money for their education to government-sponsored student loans at graduation, including the average debt at graduation, the percentage of graduates who owed large debt at graduation and the percentage of debt paid off at the time of the interview, are presented by the province of study and the level of study. Estimates are available at five-year intervals.

🏦 Synthetic Loan Approval Dataset

A Realistic, High-Quality Dataset for Credit Risk Modelling

🎯 Why This Dataset?

Most loan datasets on Kaggle have unrealistic patterns where:

1. ❌ Credit scores don't matter
2. ❌ Approval logic is backwards
3. ❌ Models learn nonsense patterns

Unlike most loan datasets available online, this one is built on real banking criteria from US and Canadian financial institutions. Drawing from 3 years of hands-on finance industry experience, the dataset incorporates realistic correlations and business logic that reflect how actual lending decisions are made. This makes it perfect for data scientists looking to build portfolio projects that showcase not just coding ability, but genuine understanding of credit risk modelling.

📊 Dataset Overview

🔑 Key Features

Identifier:

-Customer ID (unique identifier for each application)

Demographics:

-Age, Occupation Status, Years Employed

Financial Profile:

-Annual Income, Credit Score, Credit History Length -Savings/Assets, Current Debt

Credit Behaviour:

-Defaults on File, Delinquencies, Derogatory Marks

Loan Request:

-Product Type, Loan Intent, Loan Amount, Interest Rate

Calculated Ratios:

-Debt-to-Income, Loan-to-Income, Payment-to-Income

💡 What Makes This Dataset Special?

1️⃣ Real-World Approval Logic The dataset implements actual banking criteria: - DTI ratio > 50% = automatic rejection - Defaults on file = instant reject - Credit score bands match real lending thresholds - Employment verification for loans ≥$20K

2️⃣ Realistic Correlations - Higher income → Better credit scores - Older applicants → Longer credit history - Students → Lower income, special treatment for small loans - Loan intent affects approval (Education best, Debt Consolidation worst)

3️⃣ Product-Specific Rules - Credit Cards: More lenient, higher limits - Personal Loans: Standard criteria, up to $100K - Line of Credit: Capped at $50K, manual review for high amounts

4️⃣ Edge Cases Included - Young applicants (age 18) building first credit - Students with thin credit files - Self-employed with variable income - High debt-to-income ratios - Multiple delinquencies

🎓 Perfect For - Machine Learning Practice: Binary classification with real patterns - Credit Risk Modelling: Learn actual lending criteria - Portfolio Projects: Build impressive, explainable models - Feature Engineering: Rich dataset with meaningful relationships - Business Analytics: Understand financial decision-making

📈 Quick Stats

Approval Rates by Product - Credit Card: 60.4% more lenient) - Personal Loan: 46.9 (standard) - Line of Credit: 52.6% (moderate)

Loan Intent (Best → Worst Approval Odds) 1. Education (63% approved) 2. Personal (58% approved) 3. Medical/Home (52% approved) 4. Business (48% approved) 5. Debt Consolidation (40% approved)

Credit Score Distribution - Mean: 644 - Range: 300-850 - Realistic bell curve around 600-700

Income Distribution - Mean: $50,063 - Median: $41,608 - Range: $15K - $250K

🎯 Expected Model Performance

With proper feature engineering and tuning: - Accuracy: 75-85% - ROC-AUC: 0.80-0.90 - F1-Score: 0.75-0.85

Important: Feature importance should show: 1. Credit Score (most important) 2. Debt-to-Income Ratio 3. Delinquencies 4. Loan Amount 5. Income

If your model shows different patterns, something's wrong!

🏆 Use Cases & Projects

Beginner - Binary classification with XGBoost/Random Forest - EDA and visualization practice - Feature importance analysis

Intermediate - Custom threshold optimization (profit maximization) - Cost-sensitive learning (false positive vs false negative) - Ensemble methods and stacking

Advanced - Explainable AI (SHAP, LIME) - Fairness analysis across demographics - Production-ready API with FastAPI/Flask - Streamlit deployment with business rules

⚠️ Important Notes

This is SYNTHETIC Data - Generated based on real banking criteria - No real customer data was used - Safe for public sharing and portfolio use

Limitations - Simplified approval logic (real banks use 100+ factors) - No temporal component (no time series) - Single country/currency assumed (USD) - No external factors (economy, market conditions)

Educational Purpose This dataset is designed for: - Learning credit risk modeling - Portfolio projects - ML practice - Understanding lending criteria

NOT for: - Actual lending decisions - Financial advice - Production use without validation

🤝 Contributing

Found an issue? Have suggestions? - Open an issue on GitHub - Suggest i...

This datasets contains information about NYCHA residents’ use of:

a) NYC Financial Empowerment Centers: a program that provides free, one-on-one professional financial counseling and coaching to all NYC residents. Each row in the dataset represents the number of NYCHA residents on a Borough-level who utilized this service;

b) EmpoweredNYC: is an initiative to assist New Yorkers with disabilities and their families to better manage their finances and become more financially stable. Each row in the dataset represents the number of NYCHA residents on a Borough-level who utilized this service;

c) Student Loan Debt clinic: is an initiative to help New Yorkers understand their student loans and how to repay them. Each row in the dataset represents the number of NYCHA residents on a Borough-level who utilized this service; and

d) Ready to Rent: a program providing free one-on-one financial counseling to New Yorkers seeking to apply for affordable housing units through HPD’s Housing Connect lottery. Each row in the dataset represents the number of NYCHA residents on a Borough-level who utilized this service.

The dataset is part of the annual report compiled by the Mayor’s Office of Operations as mandated by the Local Law 163 of 2016 on different services provided to NYCHA residents. See other datasets in this report by searching the keyword “Services available to NYCHA Residents - Local Law 163 (2016)” on the Open Data Portal.

🇸🇪 스웨덴

Funds-From-Operation-To-Total-Debt Time Series for KeyCorp. KeyCorp operates as the holding company for KeyBank National Association that provides various retail and commercial banking products and services in the United States. It operates in two segments, Consumer Bank and Commercial Bank. The company offers various deposits and investment products; commercial leasing, investment management, consumer finance; personal finance and financial wellness, lending, student loan refinancing, mortgage and home equity, credit card, treasury, and business advisory; and wealth management and investment services for institutional, non-profit, and high-net-worth clients. It also provides lending, cash management, equipment financing, and commercial mortgage loans; and capital market products and services, such as syndicated finance, debt and equity underwriting, fixed income and equity sales and trading, derivatives, foreign exchange, mergers and acquisition, other advisory, and public finance to large corporate and institutional clients. In addition, the company offers personal and institutional trust custody services, personal financial and planning services, access to mutual funds, treasury services, and international banking services. Further, it provides community development financing, securities underwriting, brokerage, and investment banking services, as well as merchant services. The company was founded in 1849 and is headquartered in Cleveland, Ohio.

Student Loans by Population Register County, Debt Calculation Date, Gender and Dimensions: Number of persons, Debt

This dataset contains the customer's data from a loan company known as Prosper. This dataset comprises of 113,937 loans with 81 variables on each loan, including loan amount, borrower rate (or interest rate), current loan status, borrower income, and many others.

Definition of Variables:

ListingKey: Unique key for each listing, same value as the 'key' used in the listing object in the API. ListingNumber: The number that uniquely identifies the listing to the public as displayed on the website. ListingCreationDate: The date the listing was created. CreditGrade: The Credit rating that was assigned at the time the listing went live. Applicable for listings pre-2009 period and will only be populated for those listings. Term: The length of the loan expressed in months. LoanStatus: The current status of the loan: Cancelled, Chargedoff, Completed, Current, Defaulted, FinalPaymentInProgress, PastDue. The PastDue status will be accompanied by a delinquency bucket. ClosedDate: Closed date is applicable for Cancelled, Completed, Chargedoff and Defaulted loan statuses. BorrowerAPR: The Borrower's Annual Percentage Rate (APR) for the loan. BorrowerRate: The Borrower's interest rate for this loan. LenderYield: The Lender yield on the loan. Lender yield is equal to the interest rate on the loan less the servicing fee. EstimatedEffectiveYield: Effective yield is equal to the borrower interest rate (i) minus the servicing fee rate, (ii) minus estimated uncollected interest on charge-offs, (iii) plus estimated collected late fees. Applicable for loans originated after July 2009. EstimatedLoss: Estimated loss is the estimated principal loss on charge-offs. Applicable for loans originated after July 2009. EstimatedReturn: The estimated return assigned to the listing at the time it was created. Estimated return is the difference between the Estimated Effective Yield and the Estimated Loss Rate. Applicable for loans originated after July 2009. ProsperRating (numeric): The Prosper Rating assigned at the time the listing was created: 0 - N/A, 1 - HR, 2 - E, 3 - D, 4 - C, 5 - B, 6 - A, 7 - AA. Applicable for loans originated after July 2009. ProsperRating (Alpha): The Prosper Rating assigned at the time the listing was created between AA - HR. Applicable for loans originated after July 2009. ProsperScore: A custom risk score built using historical Prosper data. The score ranges from 1-10, with 10 being the best, or lowest risk score. Applicable for loans originated after July 2009. ListingCategory: The category of the listing that the borrower selected when posting their listing: 0 - Not Available, 1 - Debt Consolidation, 2 - Home Improvement, 3 - Business, 4 - Personal Loan, 5 - Student Use, 6 - Auto, 7- Other, 8 - Baby&Adoption, 9 - Boat, 10 - Cosmetic Procedure, 11 - Engagement Ring, 12 - Green Loans, 13 - Household Expenses, 14 - Large Purchases, 15 - Medical/Dental, 16 - Motorcycle, 17 - RV, 18 - Taxes, 19 - Vacation, 20 - Wedding Loans BorrowerState: The two letter abbreviation of the state of the address of the borrower at the time the Listing was created. Occupation: The Occupation selected by the Borrower at the time they created the listing. EmploymentStatus: The employment status of the borrower at the time they posted the listing. EmploymentStatusDuration: The length in months of the employment status at the time the listing was created. IsBorrowerHomeowner: A Borrower will be classified as a homowner if they have a mortgage on their credit profile or provide documentation confirming they are a homeowner. CurrentlyInGroup: Specifies whether or not the Borrower was in a group at the time the listing was created. GroupKey: The Key of the group in which the Borrower is a member of. Value will be null if the borrower does not have a group affiliation. DateCreditPulled: The date the credit profile was pulled. CreditScoreRangeLower: The lower value representing the range of the borrower's credit score as provided by a consumer credit rating agency. CreditScoreRangeUpper: The upper value representing the range of the borrower's credit score as provided by a consumer credit rating agency. FirstRecordedCreditLine: The date the first credit line was opened. CurrentCreditLines: Number of current credit lines at the time the credit profile was pulled. OpenCreditLines: Number of open credit lines at the time the credit profile was pulled. TotalCreditLinespast7years: Number of credit lines in the past seven years at the time the credit profile was pulled. OpenRevolvingAccounts: Number of open revolving accounts at the time the credit profile was pulled. OpenRevolvingMonthlyPayment: Monthly payment on revolving accounts at the time the credit profile was pulled. InquiriesLast6Months: Number of inquiries in the past six months at the time the cre...

Student loans by Population registration county/municipal, Debt calculation date, Gender and Measures: Number of persons, Debt

Funds-From-Operation-To-Total-Debt Time Series for Webster Financial Corporation. Webster Financial Corporation operates as the bank holding company for Webster Bank, National Association that provides various financial products and services to businesses, individuals, and families in the United States. It operates through three segments: Commercial Banking, Healthcare Financial Services, and Consumer Banking. It offers checking, savings, and money market accounts; individual retirement account retirement savings; certificates of deposit; mortgages; home equity loans and lines of credit; business and commercial lines of credit; overdrafts; and term, commercial, student, SBA, and personal loans. The company also provides commercial real estate financing, equipment and lender finance, asset-based and community lending, and public finance solutions; financial planning, life and long-term insurance, personal retirement, and portfolio management solutions; employee retirement plans; credit cards; payroll services; automated clearing house payables and wires; bill pay, remote deposit capture, merchant, and lockbox services; treasury management and investment services; private banking services; capital markets and finance solutions; employee benefits solutions, including administrators of HSAs, emergency savings accounts, and flexible spending accounts administration services; wealth management services; and online and mobile banking services. Webster Financial Corporation was founded in 1870 and is headquartered in Stamford, Connecticut.

EKOS Research Associates and the Canada Millennium Scholarship Foundation conducted a monthly national study of the finances of post-secondary students from September 2001 until May 2002. The study was designed to capture the expenses and income of students on a monthly basis, in order to profile the financial circumstances of Canadian post-secondary students and the adequacy of available funding. The Web based Students Financial Survey provided accurate, quantifiable results for the first time on such issues as the incidence and level of assistance, the level of debt from outstanding bank loans, personal lines of credit, and credit cards. The study also yielded up-to-date information on student assets (such as automobiles, computers, and electronics), student earnings, time usage, and types of expenses incurred. The survey featured a panel of 1,524 post-secondary students from across the country, who participated in a very brief monthly survey, either via Internet or telephone. Students were required to complete a longer baseline wave of the survey in order to participate in the study. The baseline survey asked a number of questions concerning summer income and existing debt, including credit card debt. This dataset was received from the Canada Millennium Scholarship Foundation as is. Issues with value labels and missing values were discovered and corrected as best as possible with the documentation received. The variable gasst: Do you receive any government assistance? was not corrected due to lack of documentation about this variable. Some caution should be used with this dataset. This dataset was freely received from, the Canadian Millenium Scholarship Foundation. Some work was required for the variable and value labels, and missing values. They were correct as best as possible with the documentation received. Caution should be used with this dataset as some variables are lacking information.

The Canadian College Student Survey Consortium (the Consortium, CCSSC) includes the Association of Canadian Community Colleges (ACCC), individual participating colleges and the Canada Millennium Scholarship Foundation (CMSF). Established in late 2001, the Consortium conducted its first survey of college students in the spring of 2002. In 2003, it conducted a second survey, involving 27 colleges and approximately 9,900 students. This report summarizes the findings of the second annual survey. The survey collects data on college students' income, expenditures and use of time. The survey is unique in that it provides national-level information on the challenges Canadian college students face in terms of financial and access issues. Approximately 9,900 students completed the survey. Of which most students who responded to the survey are enrolled full-time in programs that take two years or longer to complete. Students' financial situations and time use vary greatly by program type as well as region. Many of the differences arise because of students' personal characteristics are correlated with the program they are enrolled in. The fact that some programs are more predominant in certain regions adds another dimension to this variation. This dataset was freely received from the Canada Millennium Scholarship Foundation. Some work was required for the variable and value labels, and missing values. They were corrected as best as possible with the documentation received. Caution should be used with this dataset as some variables are lacking information.