Abstract

Title: Cotality Loan-Level Market Analytics (LLMA)

Cotality Loan-Level Market Analytics (LLMA) for primary mortgages contains detailed loan data, including origination, events, performance, forbearance and inferred modification data. This dataset may not be linked or merged with any of the other datasets we have from Cotality.

Formerly known as CoreLogic Loan-Level Market Analytics (LLMA).

Methodology

Cotality sources the Loan-Level Market Analytics data directly from loan servicers. Cotality cleans and augments the contributed records with modeled data. The Data Dictionary indicates which fields are contributed and which are inferred.

The Loan-Level Market Analytics data is aimed at providing lenders, servicers, investors, and advisory firms with the insights they need to make trustworthy assessments and accurate decisions. Stanford Libraries has purchased the Loan-Level Market Analytics data for researchers interested in housing, economics, finance and other topics related to prime and subprime first lien data.

Cotality provided the data to Stanford Libraries as pipe-delimited text files, which we have uploaded to Data Farm (Redivis) for preview, extraction and analysis.

For more information about how the data was prepared for Redivis, please see Cotality 2024 GitLab.

Usage

Per the End User License Agreement, the LLMA Data cannot be commingled (i.e. merged, mixed or combined) with Tax and Deed Data that Stanford University has licensed from Cotality, or other data which includes the same or similar data elements or that can otherwise be used to identify individual persons or loan servicers.

The 2015 major release of Cotality Loan-Level Market Analytics (for primary mortgages) was intended to enhance the Cotality servicing consortium through data quality improvements and integrated analytics. See **Cotality_LLMA_ReleaseNotes.pdf **for more information about these changes.

For more information about included variables, please see Cotality_LLMA_Data_Dictionary.pdf.

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For more information about how the database was set up, please see LLMA_Download_Guide.pdf.

Bulk Data Access

Data access is required to view this section.

This dataset contains a wealth of information from 52,000 loan applications, offering detailed insights into the factors that influence loan approval decisions. Collected from financial institutions, this data is highly valuable for credit risk analysis, financial modeling, and predictive analytics. The dataset is particularly useful for anyone interested in applying machine learning techniques to real-world financial decision-making scenarios.

Overview: This dataset provides information about various applicants and the loans they applied for, including their demographic details, income, loan terms, and approval status. By analyzing this data, one can gain an understanding of which factors are most critical for determining the likelihood of loan approval. The dataset can also help in evaluating credit risk and building robust credit scoring systems.

Dataset Columns: Applicant_ID: Unique identifier for each loan application. Gender: Gender of the applicant (Male/Female). Age: Age of the applicant. Marital_Status: Marital status of the applicant (Single/Married). Dependents: Number of dependents the applicant has. Education: Education level of the applicant (Graduate/Not Graduate). Employment_Status: Employment status of the applicant (Employed, Self-Employed, Unemployed). Occupation_Type: Type of occupation, which provides insights into the nature of the applicant’s job (Salaried, Business, Others). Residential_Status: Type of residence (Owned, Rented, Mortgage). City/Town: The city or town where the applicant resides. Annual_Income: The total annual income of the applicant, a key factor in loan eligibility. Monthly_Expenses: The monthly expenses of the applicant, indicating their financial obligations. Credit_Score: The applicant's credit score, reflecting their creditworthiness. Existing_Loans: Number of existing loans the applicant is servicing. Total_Existing_Loan_Amount: The total amount of all existing loans the applicant has. Outstanding_Debt: The remaining amount of debt yet to be paid by the applicant. Loan_History: The applicant’s previous loan history (Good/Bad), indicating their repayment reliability. Loan_Amount_Requested: The loan amount the applicant has applied for. Loan_Term: The term of the loan in months. Loan_Purpose: The purpose of the loan (e.g., Home, Car, Education, Personal, Business). Interest_Rate: The interest rate applied to the loan. Loan_Type: The type of loan (Secured/Unsecured). Co-Applicant: Indicates if there is a co-applicant for the loan (Yes/No). Bank_Account_History: Applicant’s banking history, showing past transactions and reliability. Transaction_Frequency: The frequency of financial transactions in the applicant’s bank account (Low/Medium/High). Default_Risk: The risk level of the applicant defaulting on the loan (Low/Medium/High). Loan_Approval_Status: Final decision on the loan application (Approved/Rejected).

1. Data Source

This dataset is a synthetic version inspired by the original Credit Risk dataset on Kaggle and enriched with additional variables based on Financial Risk for Loan Approval data. SMOTENC was used to simulate new data points to enlarge the instances. The dataset is structured for both categorical and continuous features.

2. Metadata

The dataset contains 45,000 records and 14 variables, each described below:

3. Data Usage

The dataset can be used for multiple purposes:

• Exploratory Data Analysis (EDA): Analyze key features, distribution patterns, and relationships to understand credit risk factors.
• Classification: Build predictive models to classify the loan_status variable (approved/not approved) for potential applicants.
• Regression: Develop regression models to predict the credit_score variable based on individual and loan-related attributes.

Mind the data issue from the original data, such as the instance > 100-year-old as age.

This dataset provides a rich basis for understanding financial risk factors and simulating predictive modeling processes for loan approval and credit scoring.

Feel free to leave comments on the discussion. I'd appreciate your upvote if you find my dataset useful! 😀

The industry is composed of non-depository institutions that conduct primary and secondary market lending. Operators in this industry include government agencies in addition to non-agency issuers of mortgage-related securities. Through 2025, rising per capita disposable income and low levels of unemployment helped fuel the increase in primary and secondary market sales of collateralized debt. Nonetheless, due to the pandemic and the sharp contraction in economic activity in 2020, revenue gains were limited, but have climbed as the economy has normalized and interest rates shot up to tackle rampant inflation. However, in 2024 the Federal Reserve cut interest rates as inflationary pressures eased and is expected to be cut further in 2025. Overall, these trends, along with volatility in the real estate market, have caused revenue to slump at a CAGR of 1.5% to $485.0 billion over the past five years, including an expected decline of 1.1% in 2025 alone. The high interest rate environment has hindered real estate loan demand and caused industry profit to shrink to 11.6% of revenue in 2025. Higher access to credit and higher disposable income have fueled primary market lending over much of the past five years, increasing the variety and volume of loans to be securitized and sold in secondary markets. An additional boon for institutions has been an increase in interest rates in the latter part of the period, which raised interest income as the spread between short- and long-term interest rates increased. These macroeconomic factors, combined with changing risk appetite and regulation in the secondary markets, have resurrected collateralized debt trading since the middle of the period. Although the FED cut interest rates in 2024, this will reduce interest income for the industry but increase loan demand. Although institutions are poised to benefit from a strong economic recovery as inflationary pressures ease, relatively steady rates of homeownership, coupled with declines in the 30-year mortgage rate, are expected to damage the primary market through 2030. Shaky demand from commercial banking and uncertainty surrounding inflationary pressures will influence institutions' decisions on whether or not to sell mortgage-backed securities and commercial loans to secondary markets. These trends are expected to cause revenue to decline at a CAGR of 0.8% to $466.9 billion over the five years to 2030.

This dataset provides granular, event-level records of customer loan repayments, including payment amounts, statuses, timing, and outstanding balances. It enables robust risk modeling, customer segmentation, and repayment behavior analysis for lenders and financial institutions. The inclusion of customer and loan attributes supports advanced analytics and credit scoring applications.

Bank Customer Dataset for Personal Loan Prediction This dataset contains demographic, financial, and behavioral data of 5,000 bank customers collected during a marketing campaign aimed at offering personal loans. The primary objective is to predict whether a customer accepted the personal loan offer (personal_loan), making this a supervised binary classification problem.

The dataset includes 14 features such as age, income, credit card usage, education level, mortgage value, and account ownership information. It can be used for machine learning tasks such as classification modeling, feature selection, customer segmentation, and marketing analytics.

Primary sample comprises 274 loans funded in 2008 first semester, where 137 are defaulted and 137 non-defaulted. Test sample comprises all the 3,788 loans funded in 2011 third trimester, where 401 are defaulted and 3,387 are non-defaulted.*** significant at the 1% level** significant at 5% the level* significant at the 10% level.Logistic regression analysis for potentially insolvent status of P2P borrowers, showing B coefficients and significance levels.

The global auto loan origination software market size is estimated to reach USD 1,736.1 million by 2033, exhibiting a CAGR of 8.5% from 2025 to 2033. The rising demand for automated loan origination processes, increasing smartphone and internet penetration, and growing adoption of cloud-based services are key factors driving the market. The advent of digital lending platforms and the increasing adoption of AI-powered solutions are further contributing to the market growth. The on-premise segment held a larger market share in 2025 due to its high level of customization and security. However, the cloud-based segment is expected to witness significant growth over the forecast period owing to its cost-effectiveness, scalability, and flexibility. The increasing adoption of cloud computing services by banks and credit unions is expected to further boost the growth of the cloud-based segment. North America is expected to dominate the market throughout the forecast period due to the presence of well-established players and the early adoption of advanced technologies in the region.

This dataset provides detailed, step-level tracking of user journeys through online loan application funnels, including timestamps, device and location data, step statuses, and error information. It enables in-depth analysis of user behavior, identification of process bottlenecks, and optimization of digital lending conversion rates. Ideal for financial institutions seeking to improve their digital onboarding and loan origination processes.

The commercial loan software market is projected to reach $1,024.24 million by 2033, growing at a CAGR of 6.13% from 2025 to 2033. The growth of the market is primarily driven by the increasing need for automated and efficient loan processing systems. Commercial loan software streamlines the loan origination, risk management, portfolio management, and compliance management processes, enabling faster loan approvals and reduced operational costs. The market is segmented by deployment type, end-user, and loan type. On-premises deployment is the most popular deployment type, accounting for a significant market share due to its high level of security and control. However, the cloud-based deployment type is gaining traction due to its ease of use and scalability. Banks and credit unions are the major end-users of commercial loan software, followed by fintech companies and mortgage companies. The commercial real estate loans segment holds the largest market share, with business loans, equipment financing, and working capital loans also contributing to market growth. North America is the largest regional market, followed by Europe and Asia Pacific. The commercial loan software market is projected to grow from $1.3 billion in 2021 to $4.2 billion by 2028, at a CAGR of 16.2% during the forecast period. The market growth is attributed to the rising need for automation and efficiency in commercial lending processes, increasing adoption of cloud-based loan software, and growing demand for risk management and compliance solutions. Key drivers for this market are: Digital transformation in banking, Increased demand for automation; Growth of small business lending; Integration of AI analytics; Expansion in emerging markets. Potential restraints include: Increasing digitization in finance, Growing demand for automation; Regulatory compliance pressures; Rising competition in lending; Enhanced customer experience focus.

The Japan Mortgage/Loan Brokers report features an extensive regional analysis, identifying market penetration levels across major geographic areas. It highlights regional growth trends and opportunities, allowing businesses to tailor their market entry strategies and maximize growth in specific regions.

Credit Analysis Software Market Outlook

The global credit analysis software market size was valued at approximately USD 3.5 billion in 2023 and is forecasted to reach around USD 7.2 billion by 2032, growing at a CAGR of 8.2% during the forecast period. This growth is driven by the increasing need for efficient credit risk management solutions across various financial institutions. The burgeoning demand for automation in credit assessment procedures and the growing adoption of advanced analytics and artificial intelligence in credit analysis are key factors contributing to the market's expansion.

Several factors are propelling the growth of the credit analysis software market. Firstly, the increasing complexity of financial transactions and the growing volume of credit applications have necessitated the adoption of automated credit analysis systems. These systems enhance the accuracy and efficiency of credit risk assessment, thereby reducing the likelihood of defaults. Additionally, the integration of artificial intelligence, machine learning, and big data analytics in credit analysis software allows for more nuanced analysis, enabling financial institutions to make more informed lending decisions.

Moreover, regulatory requirements and compliance standards have become more stringent, compelling financial institutions to adopt robust credit analysis solutions. Regulations like Basel III and the Dodd-Frank Act mandate higher levels of transparency and risk management, which can be effectively achieved through advanced credit analysis software. This regulatory push is a significant growth driver for the market, as institutions strive to meet compliance standards while maintaining operational efficiency.

Another critical growth factor is the increasing digital transformation in the banking and financial sector. The shift towards digital banking and fintech innovations is fostering the adoption of credit analysis software. Digital platforms offer seamless integration with various financial products and services, enhancing the overall customer experience. As financial institutions continue to digitize their operations, the demand for sophisticated credit analysis tools is expected to rise, further driving market growth.

In this evolving landscape, the role of a Lending Analytics Solution becomes increasingly significant. Such solutions are designed to streamline the lending process by providing comprehensive insights into borrower behavior and creditworthiness. By leveraging data analytics, financial institutions can enhance their decision-making processes, reduce risks, and improve customer satisfaction. The integration of a Lending Analytics Solution can also lead to more personalized lending experiences, as it allows institutions to tailor their offerings based on detailed customer profiles and predictive analytics. This not only helps in mitigating risks but also in identifying new opportunities for growth and expansion in the lending market.

Regionally, North America holds a significant share in the credit analysis software market, driven by the strong presence of major financial institutions and technological advancements in the region. Europe follows closely, with a growing emphasis on regulatory compliance and risk management. Asia Pacific is anticipated to witness the highest growth rate, fueled by the rapid economic development, increasing digitalization, and the expanding banking sector in countries like China and India. Latin America and the Middle East & Africa are also expected to contribute to the market's growth, albeit at a slower pace, as they gradually adopt advanced credit analysis solutions.

Component Analysis

The credit analysis software market, when segmented by component, can be broadly categorized into software and services. The software segment encompasses a range of solutions, including on-premises and cloud-based platforms, designed to automate and enhance the credit analysis process. This segment is experiencing substantial growth due to the increasing reliance on digital tools for credit risk assessment and management. Advanced software solutions offer a plethora of features, such as real-time data analytics, automated report generation, and predictive modeling, which significantly improve the efficiency and accuracy of credit analysis.

Within the software segment, cloud-based solutions are gaining immense popularity due to their scalability, flexibility, and cos

The France Mortgage/Loan Brokers report features an extensive regional analysis, identifying market penetration levels across major geographic areas. It highlights regional growth trends and opportunities, allowing businesses to tailor their market entry strategies and maximize growth in specific regions.

The private student loan market is experiencing robust growth, driven by rising tuition fees and a growing awareness of alternative financing options beyond federal loans. While precise figures for market size and CAGR are not provided, leveraging industry reports and trends, we can estimate a 2025 market size of approximately $150 billion USD, with a projected Compound Annual Growth Rate (CAGR) of 8% between 2025 and 2033. This growth is fueled by several key factors: increasing undergraduate and graduate enrollment, the rising cost of education exceeding the capacity of federal loan programs, and the expansion of private lenders offering diverse loan products catering to various educational needs, including career training programs. The market segmentation shows strong demand across undergraduate, graduate, and career training loans, with students and parents as the primary borrowers. Leading companies such as Sallie Mae, SoFi, and Discover Bank are aggressively competing for market share through innovative loan products and technological advancements in online application processes and servicing. However, market growth is not without its challenges. Regulatory changes, fluctuating interest rates, and economic downturns can significantly impact borrowing and repayment rates. The increasing awareness of student loan debt and its consequences can also impact borrowing behavior and drive stricter lending criteria. Furthermore, regional variations in educational costs and financial literacy levels contribute to diverse market penetration across North America, Europe, and Asia-Pacific. Despite these restraints, the long-term outlook remains positive, with continued technological innovations and evolving financing solutions expected to fuel further market expansion. The focus on providing tailored financial solutions, improving transparency, and enhancing borrower experience is crucial for companies to thrive in this competitive landscape.

This dataset is published by the Atlanta Regional Commission Research & Analytics group to show Paycheck Protection Program Loans for Georgia. Source: US Department of Treasuryhttps://home.treasury.gov/policy-issues/cares-act/assistance-for-small-businesses/sba-paycheck-protection-program-loan-level-data

The Japanese mortgage and loan broker market, valued at 5.20 million in 2025, is projected to grow at a CAGR of 3.92% through 2033. This growth is driven by increasing homeownership rates, rising disposable incomes, and government incentives for homebuyers. The market is segmented by loan type, term, interest rate, and provider. Key players include Bank of Japan, Bank of China, Suruga Bank Ltd., and SMBC Trust Bank. Major trends shaping the market include the increasing popularity of online mortgage applications, the growing demand for jumbo loans, and the rising interest rates. However, the market is also facing challenges such as stringent regulations, rising competition, and the impact of the COVID-19 pandemic. Despite these challenges, the long-term outlook for the market remains positive, as Japan's housing market is expected to continue to grow in the coming years. Recent developments include: In March 2024, Leading Japanese online stocks broker Matsui Stocks Co., Ltd. established a partnership with global fintech firm Broadridge Financial Solutions, Inc. to boost its stock lending business via Broadridge's cloud-based SaaS post-trade processing technology., In July 2023, Mitsubishi UFJ Financial Group and Morgan Stanley expanded their 15-year-old partnership. At their joint brokerage operations, the Japanese and American institutions have decided to work together more closely on forex trading, as well as on researching and selling Japanese stocks to institutional investors.. Key drivers for this market are: Increase in demand for Financial Home Loan Solutions, Increased Accessibility to Loan Broker Services. Potential restraints include: Increase in demand for Financial Home Loan Solutions, Increased Accessibility to Loan Broker Services. Notable trends are: Consistent level of interest rate and Increasing Real Estate price affecting Japan's Mortgage/Loan Broker Market..

The global lending software market is experiencing robust growth, driven by the increasing adoption of digital lending solutions and the need for enhanced efficiency and automation in financial institutions. The market, estimated at $15 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated $45 billion by 2033. This expansion is fueled by several key factors, including the rising demand for streamlined loan origination processes, improved risk management capabilities, and the need for real-time data analytics to optimize lending decisions. The shift towards cloud-based solutions, offering scalability and cost-effectiveness, is a significant trend contributing to market growth. Furthermore, regulatory changes promoting financial inclusion and digital transformation are indirectly boosting the adoption of lending software across various segments. Competition in the market is intense, with established players like Fiserv, Finastra, and Ellie Mae alongside emerging fintech companies vying for market share. The market segmentation reveals significant opportunities across various loan types and applications. Retail lending and commercial lending dominate the market share, followed by residential mortgages and trade finance. Loan Origination Software (LOS) represents a substantial portion of the market due to its critical role in automating the loan application process. However, the demand for integrated solutions encompassing loan management, analytics, and servicing is increasing, leading to the rise of comprehensive platforms that cater to the entire loan lifecycle. Geographic distribution shows a strong presence in North America and Europe, reflecting higher levels of technological adoption and established financial infrastructure. However, growth potential in Asia-Pacific and other emerging markets is significant, driven by increased financial inclusion initiatives and rapid digitalization within the financial sector. Despite the positive outlook, challenges such as data security concerns, integration complexities, and the need for ongoing software maintenance could potentially restrain market growth.

The student loan debt collection market is experiencing significant growth, driven by the escalating burden of student loan debt globally. The increasing number of student loan borrowers defaulting on their payments fuels the demand for efficient and effective collection strategies. While precise market size figures are unavailable, considering a reasonable CAGR of 8% based on industry trends and the substantial volume of outstanding student loans, the market size in 2025 could be conservatively estimated at $15 billion USD. This growth is fueled by several key drivers, including technological advancements in debt collection (such as AI-powered analytics and automated communication tools), the increasing outsourcing of collection activities by educational institutions and government agencies, and a greater emphasis on regulatory compliance within the debt collection industry. The market is segmented by application (schools, banks, government, non-profits) and collection type (telephone, SMS, email, others). North America currently dominates the market due to the high level of student loan debt in the United States and Canada. However, growing student loan burdens in developing economies, particularly in Asia-Pacific, present lucrative opportunities for expansion. Despite significant growth potential, several restraints challenge the market. These include stringent regulations designed to protect borrowers from aggressive collection practices, increasing borrower awareness of their rights, and the ethical considerations surrounding debt collection in a sensitive area like student loans. The shift towards digital collection methods presents both opportunities and challenges, as institutions balance efficiency with protecting borrower data and maintaining ethical standards. Competition among collection agencies is fierce, requiring agencies to differentiate themselves through advanced technology, personalized communication strategies, and a commitment to ethical debt recovery. The future of the market hinges on the ability of collection agencies to adapt to evolving regulations, technological advancements, and borrower expectations while ensuring the ethical and responsible recovery of student loan debt.

The International Bank for Reconstruction and Development (IBRD) loans are public and publicly guaranteed debt extended by the World Bank Group. IBRD loans are made to, or guaranteed by, countries that are members of IBRD. IBRD may also make loans to IFC. IBRD lends at market rates. Data are in U.S. dollars calculated using historical rates. This dataset contains the latest available snapshot of the Statement of Loans. The World Bank complies with all sanctions applicable to World Bank transactions.

The European used car financing market is experiencing robust growth, driven by increasing demand for used vehicles, attractive financing options, and a shift towards flexible ownership models. The market, estimated at €XX million in 2025, is projected to grow at a Compound Annual Growth Rate (CAGR) of 7.89% from 2025 to 2033. Several factors contribute to this positive outlook. Firstly, the rising cost of new cars is pushing more consumers towards the used car market, creating higher demand for financing solutions. Secondly, banks, non-banking financial companies (NBFCs), and Original Equipment Manufacturers (OEMs) are aggressively competing to offer competitive financing packages, including flexible loan terms, lower interest rates, and innovative digital lending platforms. This increased competition benefits consumers, stimulating market growth. Furthermore, the increasing popularity of subscription services and leasing options for used cars is adding another dimension to the financing landscape, diversifying revenue streams and expanding market reach. The segmentation by car type reveals SUVs and MPVs as particularly strong growth areas, reflecting evolving consumer preferences towards larger vehicles. Geographical analysis reveals strong performance across major European markets, with the UK, Germany, and France leading the charge. However, certain challenges persist. Economic uncertainties and potential interest rate hikes could dampen consumer spending and impact the demand for used car financing. Furthermore, the used car market's vulnerability to fluctuating vehicle prices and changing consumer confidence levels poses a risk to consistent growth. To mitigate these risks, lenders are focusing on enhancing credit scoring models, improving risk assessment methodologies, and leveraging data analytics to optimize lending decisions and minimize defaults. The success of the market will hinge on the ability of financiers to adapt to evolving consumer needs, manage risk effectively, and maintain competitive financing options in a dynamic economic environment. The forecast suggests a continuously expanding market, with significant opportunities for established players and new entrants alike to capture market share. The consistent growth projections reaffirm the long-term potential and attractiveness of this segment within the broader European automotive sector. This in-depth report provides a comprehensive analysis of the Europe used car financing market, covering the period from 2019 to 2033. It delves into market size, growth drivers, challenges, and emerging trends, offering valuable insights for stakeholders across the automotive finance sector. With a base year of 2025 and an estimated market value exceeding [Insert Estimated Market Value in Million Units] for that year, this report is essential for understanding the dynamics of this rapidly evolving market. Keywords: used car financing Europe, European auto loans, used car finance market size, automotive financing trends, car loan market analysis. Recent developments include: October 2021: Auto1 FT, the leading financial partner of the automotive industry, announced the elimination of all manual input and paper processes in car financing. The company announced the integration of blockchain to assist car financing, both new and used, which is expected to reduce the paperwork efforts.. Key drivers for this market are: Increasing Adoption of 2-wheelers across the Globe. Potential restraints include: Rise in demand of Electric Vehicles. Notable trends are: Online Purchase Has Gained Traction in Used Car Segment.