30 Year Mortgage Rate in the United States decreased to 6.81 percent in June 19 from 6.84 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

Interactive historical chart showing the 30 year fixed rate mortgage average in the United States since 1971.

Fixed 30-year mortgage rates in the United States averaged 6.84 percent in the week ending June 13 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.

Graph and download economic data for 30-Year Fixed Rate FHA Mortgage Index (OBMMIFHA30YF) from 2017-01-03 to 2025-06-20 about FHA, 30-year, fixed, mortgage, rate, indexes, and USA.

Graph and download economic data for 30-Year Fixed Rate Veterans Affairs Mortgage Index (OBMMIVA30YF) from 2017-01-03 to 2025-06-20 about veterans, 30-year, fixed, mortgage, rate, indexes, and USA.

Following the drastic increase directly after the COVID-19 pandemic, the delinquency rate started to gradually decline, falling below *** percent in the second quarter of 2023. In the second half of 2023, the delinquency rate picked up, but remained stable throughout 2024. In the first quarter of 2025, **** percent of mortgage loans were delinquent. That was significantly lower than the **** percent during the onset of the COVID-19 pandemic in 2020 or the peak of *** percent during the subprime mortgage crisis of 2007-2010. What does the mortgage delinquency rate tell us? The mortgage delinquency rate is the share of the total number of mortgaged home loans in the U.S. where payment is overdue by 30 days or more. Many borrowers eventually manage to service their loan, though, as indicated by the markedly lower foreclosure rates. Total home mortgage debt in the U.S. stood at almost ** trillion U.S. dollars in 2024. Not all mortgage loans are made equal ‘Subprime’ loans, being targeted at high-risk borrowers and generally coupled with higher interest rates to compensate for the risk. These loans have far higher delinquency rates than conventional loans. Defaulting on such loans was one of the triggers for the 2007-2010 financial crisis, with subprime delinquency rates reaching almost ** percent around this time. These higher delinquency rates translate into higher foreclosure rates, which peaked at just under ** percent of all subprime mortgages in 2011.

Share of first lien accounts 30 or more days past due. All past due active mortgages are included in the days past due calculations, including foreclosures. Borrowers who qualify for forbearance and stop making payments are also recorded as past due for all past due rate calculations. Days past due rates are presented using the number of accounts (accounts based). For more detail see: methodology (https://www.philadelphiafed.org/-/media/frbp/assets/surveys-and-data/y14/y-14-data-methodology).

Graph and download economic data for Large Bank Consumer Mortgage Balances: 30 or More Days Past Due: Including Foreclosures Rates: Accounts Based (RCMFLBACTDPDPCT30P) from Q3 2012 to Q4 2024 about 30 days +, accounts, FR Y-14M, large, balance, mortgage, consumer, banks, depository institutions, rate, and USA.

Credit card delinquency reached its highest level since 2019 in the first quarter of 2024, whereas mortgage delinquency declined to its lowest level. This is according to consumer data supplied by large banks that have to report such figures when handling over 100 billion U.S. dollars worth of assets. 3.56 percent of credit card balances were 30 days late - the highest percentage since tracking began in 2012. First-lien mortgage origination remained historically low, likely due to high interest rates and housing prices. Note the graphic shown here is different from another source on credit card delinquency rates in the U.S., as those figures are aggregates.

Federal Housing Administration (FHA) loans had the highest delinquency rate in the United States in 2024. As of the second quarter of the year, 10.6 percent of one-to-four family housing mortgage loans were 30 days or more delinquent. This percentage was lower for conventional loans and Veterans Administration loans. Despite a slight increase, the delinquency rate for all mortgages was one of the lowest on record.

Peru Lending Rate: Commercial Banks: Domestic Currency: Market Average: Last 30 Days data was reported at 20.126 % pa in Oct 2018. This records a decrease from the previous number of 20.639 % pa for Sep 2018. Peru Lending Rate: Commercial Banks: Domestic Currency: Market Average: Last 30 Days data is updated monthly, averaging 21.480 % pa from Jan 2002 (Median) to Oct 2018, with 202 observations. The data reached an all-time high of 28.265 % pa in Mar 2008 and a record low of 17.340 % pa in Apr 2002. Peru Lending Rate: Commercial Banks: Domestic Currency: Market Average: Last 30 Days data remains active status in CEIC and is reported by Central Reserve Bank of Peru. The data is categorized under Global Database’s Peru – Table PE.M007: Lending Rate: Commercial Banks.

Graph and download economic data for 30-Year Fixed Rate Jumbo Mortgage Index (OBMMIJUMBO30YF) from 2017-01-03 to 2025-06-23 about jumbo, 30-year, fixed, mortgage, rate, indexes, and USA.

Mortgage Rate in the United Kingdom decreased to 7.09 percent in May from 7.19 percent in April of 2025. This dataset provides - United Kingdom BBA Mortgage Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Delinquency rates rose across all U.S. states in 2022, with Mississippi ranking as the state with the highest share of mortgage loans which were between 30 and 89 days past due. As of December 2022, the average delinquency rate in the country was 1.4 percent, while in Mississippi, it stood at three percent. Wisconsin, Washington, and Oregon had the lowest delinquency rates during that period.

Graph and download economic data for 15-Year Fixed Rate Mortgage Average in the United States (MORTGAGE15US) from 1991-08-30 to 2025-06-18 about 15-year, fixed, mortgage, interest rate, interest, rate, and USA.

Kazakhstan Interbank Deposit Rate: Weighted Average: Less than 30 Days: USD data was reported at 2.130 % pa in Oct 2018. This records an increase from the previous number of 1.970 % pa for Sep 2018. Kazakhstan Interbank Deposit Rate: Weighted Average: Less than 30 Days: USD data is updated monthly, averaging 0.730 % pa from Dec 2001 (Median) to Oct 2018, with 203 observations. The data reached an all-time high of 5.420 % pa in Jun 2007 and a record low of 0.040 % pa in Nov 2015. Kazakhstan Interbank Deposit Rate: Weighted Average: Less than 30 Days: USD data remains active status in CEIC and is reported by The National Bank of the Republic of Kazakhstan. The data is categorized under Global Database’s Kazakhstan – Table KZ.M003: Interbank Deposit and Lending Rate: Weighted Average.

Graph and download economic data for 30-Year Fixed Rate USDA Mortgage Index (OBMMIUSDA30YF) from 2017-01-03 to 2025-06-20 about USDA, 30-year, fixed, mortgage, rate, indexes, and USA.

As of March 2025, the 30-day delinquency rate for commercial mortgage-backed securities (CMBS) varied per property type. The share of late payments for office CMBS was the highest at over **** percent, about ***** percentage points higher than the average for all asset classes. A 30-day delinquency refers to payments that are one month late, regardless of how many days the month has. Commercial mortgage-backed securities are fixed-income investment products which are backed by mortgages on commercial property.

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

Kazakhstan Interbank Lending Rate: Weighted Average: Less than 30 Days: RUB data was reported at 6.550 % pa in May 2018. This records a decrease from the previous number of 6.640 % pa for Apr 2018. Kazakhstan Interbank Lending Rate: Weighted Average: Less than 30 Days: RUB data is updated monthly, averaging 6.040 % pa from Dec 2001 (Median) to May 2018, with 113 observations. The data reached an all-time high of 16.230 % pa in Jan 2015 and a record low of 0.320 % pa in Aug 2010. Kazakhstan Interbank Lending Rate: Weighted Average: Less than 30 Days: RUB data remains active status in CEIC and is reported by The National Bank of the Republic of Kazakhstan. The data is categorized under Global Database’s Kazakhstan – Table KZ.M003: Interbank Deposit and Lending Rate: Weighted Average.