30 Year Mortgage Rate in the United States decreased to 6.85 percent in June 5 from 6.89 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

Fixed 30-year mortgage rates in the United States averaged 6.92 percent in the week ending May 30 of 2025. This dataset provides the latest reported value for - United States MBA 30-Yr Mortgage Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

DESCRIPTION

Create a model that predicts whether or not a loan will be default using the historical data.

Problem Statement:

For companies like Lending Club correctly predicting whether or not a loan will be a default is very important. In this project, using the historical data from 2007 to 2015, you have to build a deep learning model to predict the chance of default for future loans. As you will see later this dataset is highly imbalanced and includes a lot of features that make this problem more challenging.

Domain: Finance

Analysis to be done: Perform data preprocessing and build a deep learning prediction model.

Content:

Dataset columns and definition:

credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise.

purpose: The purpose of the loan (takes values "credit_card", "debt_consolidation", "educational", "major_purchase", "small_business", and "all_other").

int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates.

installment: The monthly installments owed by the borrower if the loan is funded.

log.annual.inc: The natural log of the self-reported annual income of the borrower.

dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income).

fico: The FICO credit score of the borrower.

days.with.cr.line: The number of days the borrower has had a credit line.

revol.bal: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle).

revol.util: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available).

inq.last.6mths: The borrower's number of inquiries by creditors in the last 6 months.

delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years.

pub.rec: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments).

Steps to perform:

Perform exploratory data analysis and feature engineering and then apply feature engineering. Follow up with a deep learning model to predict whether or not the loan will be default using the historical data.

Tasks:

1. Feature Transformation

Transform categorical values into numerical values (discrete)

1. Exploratory data analysis of different factors of the dataset.

2. Additional Feature Engineering

You will check the correlation between features and will drop those features which have a strong correlation

This will help reduce the number of features and will leave you with the most relevant features

1. Modeling

After applying EDA and feature engineering, you are now ready to build the predictive models

In this part, you will create a deep learning model using Keras with Tensorflow backend

Mortgage Application in the United States decreased by 3.90 percent in the week ending May 30 of 2025 over the previous week. This dataset provides - United States MBA Mortgage Applications - actual values, historical data, forecast, chart, statistics, economic calendar and news.

United States Mortgage Debt Outstanding: Effective Interest Rate data was reported at 3.799 % in Mar 2020. This records a decrease from the previous number of 3.872 % for Dec 2019. United States Mortgage Debt Outstanding: Effective Interest Rate data is updated quarterly, averaging 7.677 % from Mar 1977 (Median) to Mar 2020, with 173 observations. The data reached an all-time high of 11.449 % in Mar 1985 and a record low of 3.750 % in Dec 2017. United States Mortgage Debt Outstanding: Effective Interest Rate data remains active status in CEIC and is reported by Bureau of Economic Analysis. The data is categorized under Global Database’s United States – Table US.KB025: Mortgage Interest Paid. [COVID-19-IMPACT]

This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...).

15 Year Mortgage Rate in the United States decreased to 5.99 percent in June 5 from 6.03 percent in the previous week. This dataset includes a chart with historical data for the United States 15 Year Mortgage Rate.

Lower Limit of First Home Mortgage Rate: above LPR: Beijing data was reported at -0.450 % Point in 17 May 2025. This stayed constant from the previous number of -0.450 % Point for 16 May 2025. Lower Limit of First Home Mortgage Rate: above LPR: Beijing data is updated daily, averaging 0.550 % Point from Oct 2019 (Median) to 17 May 2025, with 2049 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 17 May 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.

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...

Analysis of ‘Annual Market Information Indices’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from http://data.europa.eu/88u/dataset/https-data-usmart-io-org-ae1d5c14-c392-4c3f-9705-537427eeb413-dataset-viewdiscovery-datasetguid-c410c7a0-14c3-442b-b75f-4c230ec59406 on 13 January 2022.

--- Dataset description provided by original source is as follows ---

House price index is based on average new house price value at loan approval stage and therefore has not been adjusted for changes in the mix of houses and apartments sold. Interest rates is based on building societies mortgage loans, published by Central Statistics Office up to 2007.
From 2008 interest rates is average rate of all 'mortgage lenders' reporting to the Central Bank.
From 2014 it is based on the floating rate for new customers as published by the Central Bank (Retail interest rates - Table B2.1). The reason for the drop between 2013 and 2014 is due to the difference in methodology - the 2014 data is the weighted average rate on new loan agreements. Further information can be found here:
http://www.centralbank.ie/polstats/stats/cmab/Documents/Retail_Interest_Rate_Statistics_Explanatory_Notes.pdf
Earnings is based on the average weekly earnings of adult workers in manufacturing industries, published by the Central Statistics Office. This series has been updated since 1996 using a new methodology and therefore it is not directly comparable with those for earlier years. House Construction Cost Index is based on the 1st day of the third month of each quarter.
Consumer Price index is based on the Consumer Price Index, published by the Central Statistics Office.
The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change.

--- Original source retains full ownership of the source dataset ---

Current Deposit & Loan Rates - These rates are compiled from information reported by the commercial banks to the Economic Information and Publications Department. The rates of interest being offered on time deposits relate to amounts J$100,000 and over. The savings rate represents an average range of rates offered on all categories of savings deposits. The average lending rate is a simple average of the range of interest rates offered on demand loans only.

Domestic Interest Rates (Commercial Banks Weighted Deposit Rates) - Compiled from monthly reports submitted by the commercial banks. These rates are based on actual volumes of all local currency deposits and loans extended at non zero rates of interest.

Domestic Interest Rates (Commercial Banks Weighted Time Deposit Rates) - Compiled from monthly reports submitted by the commercial banks. These rates are based on actual volumes of all local currency deposits and loans extended at non zero rates of interest.

Domestic Interest Rates (Commercial Banks Weighted Loan Rates) - Compiled from monthly reports submitted by the commercial banks. These rates are based on actual volumes of all local currency deposits and loans extended at non zero rates of interest.

Foreign Currency Interest Rates (Commercial Banks Weighted Time Deposit Rates) - Compiled from monthly reports submitted by the commercial banks. These rates are based on actual volumes of all foreign currency deposits and loans extended at non zero rates of interest.

Foreign Currency Interest Rates (Commercial Banks Weighted Loan Rates) - Compiled from monthly reports submitted by the commercial banks. These rates are based on actual volumes of all foreign currency deposits and loans extended at non zero rates of interest.

Comparative Bank Rates & Treasury Bill Rates - The average discount rate on three-month Treasury Bills or six month Treasury Bills in the case of Jamaica. The average discount rates for respective countries are sourced from the International Financial Statistics, an International Monetary Fund publication.

Private Money Markets Interest Rates

BOJ Interest Rates On Lending Facilities For DTI's - These interest rates fall under the Enhanced Liquidity Management Framework (ELMF), which was implemented by the Bank in 2013, for DTI.

Source: http://boj.org.jm/statistics/econdata/stats_list.php?type=5

Context

Have you ever wondered how lenders use various factors such as credit score, annual income, the loan amount approved, tenure, debt-to-income ratio etc. and select your interest rates?

The process, defined as ‘risk-based pricing’, uses a sophisticated algorithm that leverages different determining factors of a loan applicant. Selection of significant factors will help develop a prediction algorithm which can estimate loan interest rates based on clients’ information. On one hand, knowing the factors will help consumers and borrowers to increase their credit worthiness and place themselves in a better position to negotiate for getting a lower interest rate. On the other hand, this will help lending companies to get an immediate fixed interest rate estimation based on clients information. Here, your goal is to use a training dataset to predict the loan rate category (1 / 2 / 3) that will be assigned to each loan in our test set.

You can use any combination of the features in the dataset to make your loan rate category predictions. Some features will be easier to use than others.

Acknowledgements

https://datahack.analyticsvidhya.com/contest/janatahack-machine-learning-for-banking/True/#ProblemStatement

The benchmark interest rate in Norway was last recorded at 4.50 percent. This dataset provides the latest reported value for - Norway Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Annual Market Information Indices. Published by Department of Housing, Local Government, and Heritage. Available under the license Creative Commons Attribution Share-Alike 4.0 (CC-BY-SA-4.0).House price index is based on average new house price value at loan approval stage and therefore has not been adjusted for changes in the mix of houses and apartments sold. Interest rates is based on building societies mortgage loans, published by Central Statistics Office up to 2007.
From 2008 interest rates is average rate of all 'mortgage lenders' reporting to the Central Bank.
From 2014 it is based on the floating rate for new customers as published by the Central Bank (Retail interest rates - Table B2.1). The reason for the drop between 2013 and 2014 is due to the difference in methodology - the 2014 data is the weighted average rate on new loan agreements. Further information can be found here:
http://www.centralbank.ie/polstats/stats/cmab/Documents/Retail_Interest_Rate_Statistics_Explanatory_Notes.pdf
Earnings is based on the average weekly earnings of adult workers in manufacturing industries, published by the Central Statistics Office. This series has been updated since 1996 using a new methodology and therefore it is not directly comparable with those for earlier years. House Construction Cost Index is based on the 1st day of the third month of each quarter.
Consumer Price index is based on the Consumer Price Index, published by the Central Statistics Office.
The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change. ...

House price index is based on average new house price value at loan approval stage and therefore has not been adjusted for changes in the mix of houses and apartments sold. Interest rates is based on building societies mortgage loans, published by Central Statistics Office up to 2007. From 2008 interest rates is average rate of all 'mortgage lenders' reporting to the Central Bank. From 2014 it is based on the floating rate for new customers as published by the Central Bank (Retail interest rates - Table B2.1). The reason for the drop between 2013 and 2014 is due to the difference in methodology - the 2014 data is the weighted average rate on new loan agreements. Further information can be found here: http://www.centralbank.ie/polstats/stats/cmab/Documents/Retail_Interest_Rate_Statistics_Explanatory_Notes.pdf Earnings is based on the average weekly earnings of adult workers in manufacturing industries, published by the Central Statistics Office. This series has been updated since 1996 using a new methodology and therefore it is not directly comparable with those for earlier years. House Construction Cost Index is based on the 1st day of the third month of each quarter. Consumer Price index is based on the Consumer Price Index, published by the Central Statistics Office. The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change.

The benchmark interest rate in China was last recorded at 3 percent. This dataset provides the latest reported value for - China Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Bayesian inference with a large dataset is computationally intensive, as Markov chain Monte Carlo simulation requires a complete scan of the dataset for each proposed parameter update. To reduce the number of data points evaluated at each iteration of posterior simulation, we develop a double marginalized subsampling method, which is applicable to a wide array of microeconometric models including Tobit, Probit, regressions with non-Gaussian errors, heteroscedasticity and stochastic volatility, hierarchical longitudinal models, time-varying-parameter regressions, Gaussian mixtures, etc. We also provide an extension to double pseudo-marginalized subsampling, which has more applications beyond conditionally conjugate models. With rank-one update of the cumulative statistics, both methods target the exact posterior distribution, from which a parameter draw can be obtained with every single observation. Simulation studies demonstrate the statistical and computational efficiency of the marginalized sampler. The methods are also applied to a real-world massive dataset on the incidentally truncated mortgage rates.

The benchmark interest rate in Sweden was last recorded at 2.25 percent. This dataset provides the latest reported value for - Sweden Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The benchmark interest rate in Mexico was last recorded at 8.50 percent. This dataset provides - Mexico Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset provides values for MORTGAGE RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.