Graph and download economic data for AD&Co US Mortgage High Yield Index: Tier 0 (CRTINDEXTIER0) from Jun 2015 to Oct 2025 about tier-0, CAS, crt, STACR, mortgage, yield, interest rate, interest, rate, indexes, and USA.

Graph and download economic data for Contract Rate on 30-Year, Fixed-Rate Conventional Home Mortgage Commitments (DISCONTINUED) (MORTG) from Apr 1971 to Sep 2016 about conventional, 30-year, mortgage, interest rate, interest, rate, and USA.

CLTV Range 0% to 30 30.01% to 60 60.01% to 70 70.01% to 75 75.01% to 80 80.01% to 85 85.01% to 90 90.01% to 95 95.01% to 97 97.01% to 100 100.01% and up Credit Score Range 0% to 30 30.01% to 60 60.01% to 70 70.01% to 75 75.01% to 80 80.01% to 85 85.01% to 90 90.01% to 95 95.01% to 97 97.01% to 100 100.01% and up Total 639 and lower 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 640 to < 660 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 1% 660 to < 680 0% 0% 0% 0% 1% 0% 0% 1% 0% 0% 0% 3% 680 to < 700 0% 0% 0% 0% 1% 0% 1% 1% 0% 0% 0% 4% 700 to < 720 0% 1% 0% 0% 1% 0% 1% 2% 1% 0% 0% 7% 720 to < 740 0% 1% 1% 1% 2% 0% 1% 3% 1% 0% 0% 10% 740 to < 760 0% 1% 1% 1% 3% 1% 2% 3% 1% 0% 0% 14% 760 to < 780 0% 2% 2% 2% 5% 1% 2% 4% 1% 0% 0% 19% 780 and greater 1% 7% 4% 4% 11% 2% 4% 6% 1% 0% 0% 41% Total 2% 13% 9% 9% 25% 4% 12% 20% 5% 0% 0% 100.0%

It can be seen that the mortgage interest rate in Poland increased overall during the period under observation, reaching a value of *** percent as of the fourth quarter of 2024. Demand for mortgage loans in Poland      Despite the tightening of credit policy by banks, the demand for mortgage loans is not decreasing. The residential market has also seen increases both in sales and in the construction of new premises. The increase in salaries combined with the decrease in the mortgage loan cost results in Poles having no problems buying apartments despite high prices. Higher wages also affect their creditworthiness, which is essential when applying for a mortgage. The value of housing loans amounted to a record ***** billion zloty in 2019. Despite a decrease in 2017, the value of debt in 2019 increased by *** percent compared to the previous year. The increase in wealth has also been reflected in the average value of mortgages. In 2021, Bank Millennium granted the largest number of mortgages to Poles, although Bank ****** was the leader in terms of value.   Demand for housing in Poland      Despite a growing number of flats, the prices are not falling, but on the contrary, they are continually rising. An increase in prices was recorded in every major city. The annual rise in prices in many cities went up between ** and ** percent. The most significant price increase on the primary market was recorded in ******, while on the secondary market, Wroclaw prevailed. Nevertheless, Poles pay the most for a flat in the Polish capital Warsaw. In December 2024, the price per square meter of an apartment on the secondary market exceeded **** thousand zloty, while the price per square meter on the primary market was close to **** thousand zloty. However, the coronavirus (COVID-19) outbreak in Poland in March 2020 affected the investment plans in the real estate market. Both individual customers and developers recorded a significant decline in the number of construction projects commenced during this period.

Snapshot img { margin: 10px !important; } About Housing Mortgage Mortgage is a debt instrument that the borrower is obliged to pay back with a fixed set of payments and is secured by the collateral of a specified real estate property. Mortgages enable individuals and businesses to make large real estate purchases without paying the entire value of the purchase in one go. Borrowers repay the loan along with interest over a period of many years until they eventually own the property free and clear. However, if borrowers stop paying the mortgage, the lender can foreclose and may evict the property’s owner and sell it, using the income from the sale to clear the mortgage debt. In a fixed-rate mortgage system, borrowers pay the same interest rate for the life of the loan. Most fixed-rate mortgages have a 15 or 30-year term. There is no influence on borrowers’ payment if market interest rates rise. However, if market interest rates decline significantly, borrowers may be able to secure that lower rate by means of refinancing the mortgage. TechNavio's analysts forecast the Housing Mortgage market in the US to grow at a CAGR of 1.75 percent over the period 2013-2018.Covered in this Report This report covers the present scenario and the growth prospects of the Housing Mortgage market in the US for the period 2014-2018. To calculate the market size, the report considers the loan volume of primary housing mortgage banks, credit unions, and financial institutions. It takes into consideration the various product segments such as Home Purchase, Home Improvement, and Refinancing. The report mentions the role played by Federal Government by the way of government-sponsored enterprises operating in the system. TechNavio's report, the Housing Mortgage Market in the US 2014-2018, has been prepared based on an in-depth market analysis with inputs from industry experts. The report covers the US; it also covers the landscape of the Housing Mortgage market in the US and its growth prospects in the coming years. The report also includes a discussion of the key vendors operating in this market.Key Regions • USKey Vendors • Bank of America • Citigroup • JPMorgan Chase • U.S. Bancorp • Wells FargoOther Prominent Vendors • Ally Financial • Capital One Financial • Fifth Third Bancorp • Flagstar Bank, FSB • SunTrust Banks • Quicken Loans • Regions FinancialMarket Driver • Improved Demand for Home Loans • For a full, detailed list, view our reportMarket Challenge • Shrinking Lending Capacity • For a full, detailed list, view our reportMarket Trend • Less Incidence of Foreclosures • For a full, detailed list, view our reportKey Questions Answered in this Report • What will the market size be in 2018 and what will the growth rate be? • What are the key market trends? • What is driving this market? • What are the challenges to market growth? • Who are the key vendors in this market space? • What are the market opportunities and threats faced by the key vendors? • What are the strengths and weaknesses of the key vendors?

Lower Limit of First Home Mortgage Rate: above LPR: Beijing data was reported at -0.450 % Point in 02 Dec 2025. This stayed constant from the previous number of -0.450 % Point for 01 Dec 2025. Lower Limit of First Home Mortgage Rate: above LPR: Beijing data is updated daily, averaging 0.550 % Point from Oct 2019 (Median) to 02 Dec 2025, with 2248 observations. The data reached an all-time high of 0.550 % Point in 25 Jun 2024 and a record low of -0.450 % Point in 02 Dec 2025. Lower Limit of First Home Mortgage Rate: above LPR: Beijing data remains active status in CEIC and is reported by The People's Bank of China. The data is categorized under China Premium Database’s Money Market, Interest Rate, Yield and Exchange Rate – Table CN.MA: Lower Limit of First Home Mortgage Rate: Prefecture Level City. After adjustment on December 15, 2023: the lower limits of the first and second sets of interest rate policies in the six districts of the city are respectively no less than the market quoted interest rate for loans of the corresponding period plus 10 basis points, and no less than the market quoted interest rate for loans of the corresponding period plus 60 basis points; The lower limits of the first and second sets of interest rate policies in the six non-urban districts are not lower than the market quoted interest rate for loans of the corresponding period, and not lower than the market quoted interest rate for loans of the corresponding period plus 55 basis points.

Description

This data set represents thousands of loans made through the Lending Club platform, which is a platform that allows individuals to lend to other individuals. Of course, not all loans are created equal. Someone who is a essentially a sure bet to pay back a loan will have an easier time getting a loan with a low interest rate than someone who appears to be riskier. And for people who are very risky? They may not even get a loan offer, or they may not have accepted the loan offer due to a high interest rate. It is important to keep that last part in mind, since this data set only represents loans actually made, i.e. do not mistake this data for loan applications!

Format

A data frame with 10,000 observations on the following 55 variables.

emp_title

Job title.

emp_length

Number of years in the job, rounded down. If longer than 10 years, then this is represented by the value 10.

state

Two-letter state code.

homeownership

The ownership status of the applicant's residence.

annual_income

Annual income.

verified_income

Type of verification of the applicant's income.

debt_to_income

Debt-to-income ratio.

annual_income_joint

If this is a joint application, then the annual income of the two parties applying.

verification_income_joint

Type of verification of the joint income.

debt_to_income_joint

Debt-to-income ratio for the two parties.

delinq_2y

Delinquencies on lines of credit in the last 2 years.

months_since_last_delinq

Months since the last delinquency.

earliest_credit_line

Year of the applicant's earliest line of credit

inquiries_last_12m

Inquiries into the applicant's credit during the last 12 months.

total_credit_lines

Total number of credit lines in this applicant's credit history.

open_credit_lines

Number of currently open lines of credit.

total_credit_limit

Total available credit, e.g. if only credit cards, then the total of all the credit limits. This excludes a mortgage.

total_credit_utilized

Total credit balance, excluding a mortgage.

num_collections_last_12m

Number of collections in the last 12 months. This excludes medical collections.

num_historical_failed_to_pay

The number of derogatory public records, which roughly means the number of times the applicant failed to pay.

months_since_90d_late

Months since the last time the applicant was 90 days late on a payment.

current_accounts_delinq

Number of accounts where the applicant is currently delinquent.

total_collection_amount_ever

The total amount that the applicant has had against them in collections.

current_installment_accounts

Number of installment accounts, which are (roughly) accounts with a fixed payment amount and period. A typical example might be a 36-month car loan.

accounts_opened_24m

Number of new lines of credit opened in the last 24 months.

months_since_last_credit_inquiry

Number of months since the last credit inquiry on this applicant.

num_satisfactory_accounts

Number of satisfactory accounts.

num_accounts_120d_past_due

Number of current accounts that are 120 days past due.

num_accounts_30d_past_due

Number of current accounts that are 30 days past due.

num_active_debit_accounts

Number of currently active bank cards.

total_debit_limit

Total of all bank card limits.

num_total_cc_accounts

Total number of credit card accounts in the applicant's history.

num_open_cc_accounts

Total number of currently open credit card accounts.

num_cc_carrying_balance

Number of credit cards that are carrying a balance.

num_mort_accounts

Number of mortgage accounts.

account_never_delinq_percent

Percent of all lines of credit where the applicant was never delinquent.

tax_liens

a numeric vector

public_record_bankrupt

Number of bankruptcies listed in the public record for this applicant.

loan_purpose

The category for the purpose of the loan.

application_type

The type of application: either individual or joint.

loan_amount

The amount of the loan the applicant received.

term

The number of months of the loan the applicant received.

interest_rate

Interest rate of the loan the applicant received.

installment

Monthly payment for the loan the applicant received.

grade

Grade associated with the loan.

sub_grade

Detailed grade associated with the loan.

issue_month

Month the loan was issued.

loan_status

Status of the loan.

initial_listing_status

Initial listing status of the loan. (I think this has to do with whether the lender provided the entire loan or if the loan is across multiple lenders.)

disbursement_method

Dispersement method of the loan.

balance

Current...

Car loan interest rates in the United States decreased since mid-2024. Thus, the period of rapidly rising interest rates, when they increased from 3.85 percent in December 2021 to 7.92 percent in June 2024, has come to an end. The Federal Reserve interest rate is one of the main causes of the interest rates of loans rising or falling. If inflation stays under control, the Federal Reserve will start cutting the interest rates, which would have the effect of the cost of car loans falling too. How many cars have financing in the United States? Car financing exists because not everyone who wants or needs a car can purchase it outright. A financial institution will then lend the money to the customer for purchasing the car, which must then be repaid with interest. Most new vehicles in the United States in 2024 were purchased using car loans. It is not as common to use car loans for purchasing used vehicles as for new ones, although over a third of used vehicles were purchased using loans. The car industry in the United States The car financing business is huge in the United States, due to the high sales of both new and used vehicles in the country. A lot of the United States is very car-centric, which means that, outside large cities, it can often be difficult to do their daily commutes through other transportation methods. In fact, only a small percentage of U.S. workers used public transport to go to work. That is one of the factors that has helped establish the importance of the automotive sector in North America. Nevertheless, there are still countries in Asia-Pacific, Africa, the Middle East, and Europe with higher car-ownership rates than the United States.

Jumbo 30-Year Fixed Mortgage Rates

Zillow Home Value Forecast and Cash Buyer Data

By Zillow Data [source]

About this dataset

This dataset tracks the average jumbo mortgage rate quoted on Zillow Mortgages for a 30-year, fixed-rate, jumbo mortgage in one-hour increments during business hours. It provides insight into changes in the housing market and helps consumers make wiser decisions with their investments. In addition to tracking monthly mortgage rates, our dataset also covers consumer's home types and housing stock, cash buyer data, Zillow Home Value Forecast (ZHVF), negative equity metrics, affordability forecasts for both mortgages and rents as well as historic data including historical ZHVI and household income. With this unique blend of financial and real estate information, users are empowered to make more informed decisions about their investments. The data is updated weekly with the most recent statistics available so that users always have access to up-to-date information

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

How to Use This Dataset:

• To start exploring this dataset, identify what type of home you are interested in by selecting one of the four categories: “all homes” (Zillow defines all homes as single family, condominiums and coops with a county record); multifamily 5+; duplex/triplex; or condos/coops.
• Understand additional data products that are included such as Zillow Home Value Forecast (ZHVF), Cash Buyers % share, affordability metrics like mortgage affordability or rental affordability and historical ZHVI values along with its median value for particular households or geographies which needs deeper insights into other endogenous variables such detailed information like how many bedrooms a house has etc.

• Choose your geographic region on which you would want to collect more information– regions could include city breakdowns from nationwide level down till specific metropolitan etc . Also use special crosswalks available if needed between federally defined metrics for counties / metro areas combined with Zillow's own ones for greater accuracy when analysing external facors effect on data . To download all datasets at once - click here. .

• Gather more relevant external factors for analysis such as home values forecasts using our published methodology post given url , further to mention TransUnion credit bureau related debt amounts also consider median household incomes vis Bureaus of Labor Cost Indexes ; All these give us greater dimensional insights into market dynamics affecting any particular region finally culminating into deeper research findings when taken together . The reasons behind any fluctions observed can be properly derived as a result .

          Finally make sure that proper attribution is alwys done following mentioned Terms Of Use while downloading since 'All Data Accessed And Downloaded From This Page Is Free For Public Use By Consumers , Media
  

Research Ideas

• Using the Mortgage Rate Data to devise strategies to help persons purchasing jumbo mortgages determine the best time and rates to acquire a loan.
• Analyzing trends in the market by investigating changes in affordability over time by studying rent and mortgage affordability, price-to-income ratios, and historical ZHVIs with cash buyers.
• Comparing different areas of housing markets over diverse geographies using data on all homes, condos/co-ops, multifamily dwellings 5+ units, duplexes/triplexes across various counties or metro areas

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: MortgageRateJumboFixed.csv | Column name | Description | |:---------------------------|:---------------------------------------------------------------------------------------------------------------| | Date | The date of the mortgage rate. (Date) | | TimePeriod | The time period of the ...

Mortgage interest rates in Spain soared in 2022, after falling below *** percent at the end of 2021. In the first quarter of 2025, the average weighted interest rate stood at **** percent. That was lower than the rate in the same period the previous year. Despite the increase, Spain had a considerably lower mortgage interest rate than many other European countries. The aftermath of the property bubble Before the bursting of the real estate bubble, the housing market experienced a period of intense activity. A context marked by economic growth, high employment rate, low interest rates, skyrocketing house prices and land speculation, among others, encourage massive lending for the acquisition of property; in 2005 alone, more than *** million home mortgages were granted in Spain. When the bubble burst and the financial crisis hit the country, residential real estate transactions plummeted and households’ non-performing loans jumped to nearly ** billion euros as countless families were not able to cope with their debts. Over a decade after the onset of the crisis, and despite falling mortgage rates, the volume of mortgage loans keeps decreasing every year. A homeowner country Traditionally, Spain has been a country of homeowners; in 2021, the homeownership rate was roughly ** percent. While nearly half of Spanish households own their property with no outstanding payment, the percentage of households that have loan or mortgage pending has been decreasing in recent years. Despite ownership remaining as the preferred tenure option, cultural changes, job insecurity and mounting house prices are prompting Spaniards to opt more and more to become tenants instead of owners, as shown in the changing dynamics of the Spanish residential rental market.

This table contains 102 series, with data starting from 2013, and some select series starting from 2016. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada), Components (51 items: Total, funds advanced, residential mortgages, insured; Variable rate, insured; Fixed rate, insured, less than 1 year; Fixed rate, insured, from 1 to less than 3 years; ...), and Unit of measure (2 items: Dollars; Interest rate). For additional clarification on the component dimension, please visit the OSFI website for the Report on New and Existing Lending.

The global home loan market is experiencing robust growth, projected to maintain a Compound Annual Growth Rate (CAGR) exceeding 7% from 2025 to 2033. This expansion is driven by several key factors. Firstly, a consistently increasing global population, coupled with urbanization trends, fuels a persistent demand for housing. Secondly, favorable government policies in many regions, including subsidized interest rates and tax incentives for homebuyers, stimulate market activity. Furthermore, the rising disposable incomes in several developing economies are empowering more individuals to access home loans, contributing to market expansion. Innovative financial products, such as online loan applications and flexible repayment options offered by both traditional banks and fintech companies, are further accelerating market growth. Competition among providers, including banks, housing finance companies, and other financial institutions, is also driving innovation and affordability. However, the market faces certain restraints. Fluctuations in interest rates represent a significant challenge, impacting borrowing costs and consequently consumer demand. Economic downturns and periods of high inflation can also dampen market sentiment and reduce borrowing activity. Regulatory changes and stringent lending criteria in certain jurisdictions might restrict access to credit for some potential borrowers. Geopolitical instability and regional economic disparities also influence market growth, with some regions experiencing faster growth than others. The segmentation of the market by provider (banks dominating, followed by housing finance companies and others), interest rate type (fixed vs. floating), and loan tenure (with longer-term loans exhibiting higher demand) reveals opportunities for targeted marketing and product development. The leading companies, including Bank of America, Goldman Sachs (Marcus), and several international and regional players, are leveraging these trends to expand their market share. The geographical distribution of the market, with significant regional variations reflecting varying economic conditions and housing markets, presents diverse investment and growth opportunities. Recent developments include: September 2022: Citigroup Inc said it has slightly trimmed its mortgage workforce, due to an internal streamlining of functions.Less than 100 positions were affected.September 2022: Bank of America is launching a new mortgage product that would allow first-time homebuyers to purchase a home with no down payment, no mortgage insurance and zero closing costs.It will not require a minimum credit score and will instead consider other factors for eligibility.. Key drivers for this market are: Real Estate Market Trends, Government Policies. Potential restraints include: Real Estate Market Trends, Government Policies. Notable trends are: Turkey has the Highest Mortgage Interest Rate.

🏦 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...

Graph and download economic data for 5/1-Year Adjustable Rate Mortgage Average in the United States (DISCONTINUED) (MORTGAGE5US) from 2005-01-06 to 2022-11-10 about adjusted, mortgage, 5-year, interest rate, interest, rate, and USA.

Mortgage rates in the Netherlands increased sharply in 2022 and 2023, after declining gradually between 2008 and 2021. In December 2021, the average interest rate for new mortgage loans stood at **** percent, and by the end of 2023, it had risen to **** percent. In May 2025, mortgage rates decreased slightly, falling to **** percent on average. Mortgages with a 10-year fixed rate were the most affordable, at **** percent. Are mortgage rates in the Netherlands different from those in other European countries? When comparing this ranking to data that covers multiple European countries, the Netherlands’ mortgage rate was similar to the rates found in Spain, the United Kingdom, and Sweden. It was, however, a lot lower than the rates in Eastern Europe. Hungary and Romania, for example, had some of the highest mortgage rates. For more information on the European mortgage market and how much the countries differ from each other, please visit this dedicated research page. How big is the mortgage market in the Netherlands? The Netherlands has overall seen an increase in the number of mortgage loans sold and is regarded as one of the countries with the highest mortgage debt in Europe. The reason behind this is that Dutch homeowners were able to for many years to deduct interest paid from pre-tax income (a system known in the Netherlands as hypotheekrenteaftrek). Total mortgage debt of Dutch households has been increasing year-on-year since 2013.

Mortgage credit interest rate, percent in Montenegro, September, 2025 The most recent value is 4.92 percent as of September 2025, no change compared to the previous value of 4.92 percent. Historically, the average for Montenegro from September 2011 to September 2025 is 6.04 percent. The minimum of 4.24 percent was recorded in August 2021, while the maximum of 9.91 percent was reached in August 2014. | TheGlobalEconomy.com

This comprehensive dataset encapsulates a wide array of information regarding home mortgage activities in Utah from 2018 to 2022. It includes detailed data points such as loan types, purposes, amounts, and applicant demographics. Key metrics like loan-to-value ratios, interest rates, and applicant credit scores offer deep insights into the housing loan market. Additionally, it covers varied loan characteristics, property values, and applicant details, reflecting the dynamics of Utah's mortgage landscape. This rich dataset is invaluable for analyzing trends, understanding market behaviors, and examining the impact of financial policies in Utah's real estate sector.

Lending Club offers peer-to-peer (P2P) loans through a technological platform for various personal finance purposes and is today one of the companies that dominate the US P2P lending market. The original dataset is publicly available on Kaggle and corresponds to all the loans issued by Lending Club between 2007 and 2018. The present version of the dataset is for constructing a granting model, that is, a model designed to make decisions on whether to grant a loan based on information available at the time of the loan application. Consequently, our dataset only has a selection of variables from the original one, which are the variables known at the moment the loan request is made. Furthermore, the target variable of a granting model represents the final status of the loan, that are "default" or "fully paid". Thus, we filtered out from the original dataset all the loans in transitory states. Our dataset comprises 1,347,681 records or obligations (approximately 60% of the original) and it was also cleaned for completeness and consistency (less than 1% of our dataset was filtered out).

TARGET VARIABLE

The dataset includes a target variable based on the final resolution of the credit: the default category corresponds to the event charged off and the non-default category to the event fully paid. It does not consider other values in the loan status variable since this variable represents the state of the loan at the end of the considered time window. Thus, there are no loans in transitory states. The original dataset includes the target variable “loan status”, which contains several categories ('Fully Paid', 'Current', 'Charged Off', 'In Grace Period', 'Late (31-120 days)', 'Late (16-30 days)', 'Default'). However, in our dataset, we just consider loans that are either “Fully Paid” or “Default” and transform this variable into a binary variable called “Default”, with a 0 for fully paid loans and a 1 for defaulted loans.

EXPLANATORY VARIABLES

The explanatory variables that we use correspond only to the information available at the time of the application. Variables such as the interest rate, grade, or subgrade are generated by the company as a result of a credit risk assessment process, so they were filtered out from the dataset as they must not be considered in risk models to predict the default in granting of credit.

FULL LIST OF VARIABLES

Loan identification variables:

id: Loan id (unique identifier).

issue_d: Month and year in which the loan was approved.

Quantitative variables:

revenue: Borrower's self-declared annual income during registration.

dti_n: Indebtedness ratio for obligations excluding mortgage. Monthly information. This ratio has been calculated considering the indebtedness of the whole group of applicants. It is estimated as the ratio calculated using the co-borrowers’ total payments on the total debt obligations divided by the co-borrowers’ combined monthly income.

loan_amnt: Amount of credit requested by the borrower.

fico_n: Defined between 300 and 850, reported by Fair Isaac Corporation as a risk measure based on historical credit information reported at the time of application. This value has been calculated as the average of the variables “fico_range_low” and “fico_range_high” in the original dataset.

experience_c: Binary variable that indicates whether the borrower is new to the entity. This variable is constructed from the credit date of the previous obligation in LC and the credit date of the current obligation; if the difference between dates is positive, it is not considered as a new experience with LC.

Categorical variables:

emp_length: Categorical variable with the employment length of the borrower (includes the no information category)

purpose: Credit purpose category for the loan request.

home_ownership_n: Homeownership status provided by the borrower in the registration process. Categories defined by LC: “mortgage”, “rent”, “own”, “other”, “any”, “none”. We merged the categories “other”, “any” and “none” as “other”.

addr_state: Borrower's residence state from the USA.

zip_code: Zip code of the borrower's residence.

Textual variables

title: Title of the credit request description provided by the borrower.

desc: Description of the credit request provided by the borrower.

We cleaned the textual variables. First, we removed all those descriptions that contained the default description provided by Lending Club on its web form (“Tell your story. What is your loan for?”). Moreover, we removed the prefix “Borrower added on DD/MM/YYYY >” from the descriptions to avoid any temporal background on them. Finally, as these descriptions came from a web form, we substituted all the HTML elements by their character (e.g. “&” was substituted by “&”, “<” was substituted by “<”, etc.).

RELATED WORKS

This dataset has been used in the following academic articles:

Sanz-Guerrero, M. Arroyo, J. (2024). Credit Risk Meets Large Language Models: Building a Risk Indicator from Loan Descriptions in P2P Lending. arXiv preprint arXiv:2401.16458. https://doi.org/10.48550/arXiv.2401.16458

Ariza-Garzón, M.J., Arroyo, J., Caparrini, A., Segovia-Vargas, M.J. (2020). Explainability of a machine learning granting scoring model in peer-to-peer lending. IEEE Access 8, 64873 - 64890. https://doi.org/10.1109/ACCESS.2020.2984412

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

LoanStatNew,Description

addr_state,The state provided by the borrower in the loan application

annual_inc,The self-reported annual income provided by the borrower during registration.

annual_inc_joint,The combined self-reported annual income provided by the co-borrowers during registration

application_type,Indicates whether the loan is an individual application or a joint application with two co-borrowers

collection_recovery_fee,post charge off collection fee

collections_12_mths_ex_med,Number of collections in 12 months excluding medical collections

delinq_2yrs,The number of 30+ days past-due incidences of delinquency in the borrower's credit file for the past 2 years

desc,Loan description provided by the borrower

dti",A ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, excluding mortgage and the requested loan, divided by the borrower’s self-reported monthly income."

dti_joint, "A ratio calculated using the co-borrowers' total monthly payments on the total debt obligations, excluding mortgages and the requested loan,divided by the co-borrowers' combined self-reported monthly income"

earliest_cr_line,The month the borrower's earliest reported credit line was opened

emp_length,Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years.

emp_title,The job title supplied by the Borrower when applying for the loan.

funded_amnt,The total amount committed to that loan at that point in time.

funded_amnt_inv,The total amount committed by investors for that loan at that point in time.

grade,XYZ corp. assigned loan grade

home_ownership,"The home ownership status provided by the borrower during registration. Our values are: RENT, OWN, MORTGAGE, OTHER."

id,A unique assigned ID for the loan listing.

initial_list_status,"The initial listing status of the loan. Possible values are – W, F"

inq_last_6mths,The number of inquiries in past 6 months (excluding auto and mortgage inquiries)

installment,The monthly payment owed by the borrower if the loan originates.

int_rate,Interest Rate on the loan

issue_d,The month which the loan was funded

last_credit_pull_d,The most recent month XYZ corp. pulled credit for this loan

last_pymnt_amnt,Last total payment amount received

last_pymnt_d,Last month payment was received

loan_amnt,"The listed amount of the loan applied for by the borrower. If at some point in time, the credit department reduces

the loan amount, then it will be reflected in this value."

loan_status,Current status of the loan

member_id,A unique Id for the borrower member.

mths_since_last_delinq,The number of months since the borrower's last delinquency.

mths_since_last_major_derog,Months since most recent 90-day or worse rating

mths_since_last_record,The number of months since the last public record.

next_pymnt_d,Next scheduled payment date

open_acc,The number of open credit lines in the borrower's credit file.

out_prncp,Remaining outstanding principal for total amount funded

out_prncp_inv,Remaining outstanding principal for portion of total amount funded by investors

policy_code,"publicly available policy_code=1 new products not publicly available policy_code=2"

pub_rec,Number of derogatory public records

purpose,A category provided by the borrower for the loan request.

pymnt_plan,Indicates if a payment plan has been put in place for the loan

recoveries,post charge off gross recovery

revol_bal,Total credit revolving balance

revol_util,"Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit."

sub_grade,XYZ assigned assigned loan subgrade

term,The number of payments on the loan. Values are in months and can be either 36 or 60.

title,The loan title provided by the borrower

total_acc,The total number of credit lines currently in the borrower's credit file

total_pymnt,Payments received to date for total amount funded

total_pymnt_inv,Payments received to date for portion of total amount funded by investors

total_rec_int,Interest received to date

total_rec_late_fee,Late fees received to date

total_rec_prncp,Principal received to date

verified_status_joint,"Indicates if the co-borrowers' joint income was verified by XYZ corp., not verified, or if the income source was verified"

zip_code,The first 3 numbers of the zip code provided by the borrower in the loan application.

open_acc_6m,Number of open trades in last 6 months

open_il_6m,Number of currently active installment trades

open_il_12m,Number of installment accounts opened in past 12 months

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