Fixed 30-year mortgage rates in the United States averaged 6.88 percent in the week ending June 20 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.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Mortgage Rates: Hot Economic Conjecture Puts the Squeeze on Homebuyers

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

What are 30 year mortgage rates?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Graph and download economic data for 30-Year Fixed Rate Jumbo Mortgage Index (OBMMIJUMBO30YF) from 2017-01-03 to 2025-06-27 about jumbo, 30-year, fixed, mortgage, rate, indexes, and USA.

The Federal National Mortgage Association, commonly known as Fannie Mae, was created by the U.S. congress in 1938, in order to maintain liquidity and stability in the domestic mortgage market. The company is a government-sponsored enterprise (GSE), meaning that while it was a publicly traded company for most of its history, it was still supported by the federal government. While there is no legally binding guarantee of shares in GSEs or their securities, it is generally acknowledged that the U.S. government is highly unlikely to let these enterprises fail. Due to these implicit guarantees, GSEs are able to access financing at a reduced cost of interest. Fannie Mae's main activity is the purchasing of mortgage loans from their originators (banks, mortgage brokers etc.) and packaging them into mortgage-backed securities (MBS) in order to ease the access of U.S. homebuyers to housing credit. The early 2000s U.S. mortgage finance boom During the early 2000s, Fannie Mae was swept up in the U.S. housing boom which eventually led to the financial crisis of 2007-2008. The association's stated goal of increasing access of lower income families to housing finance coalesced with the interests of private mortgage lenders and Wall Street investment banks, who had become heavily reliant on the housing market to drive profits. Private lenders had begun to offer riskier mortgage loans in the early 2000s due to low interest rates in the wake of the "Dot Com" crash and their need to maintain profits through increasing the volume of loans on their books. The securitized products created by these private lenders did not maintain the standards which had traditionally been upheld by GSEs. Due to their market share being eaten into by private firms, however, the GSEs involved in the mortgage markets began to also lower their standards, resulting in a 'race to the bottom'. The fall of Fannie Mae The lowering of lending standards was a key factor in creating the housing bubble, as mortgages were now being offered to borrowers with little or no ability to repay the loans. Combined with fraudulent practices from credit ratings agencies, who rated the junk securities created from these mortgage loans as being of the highest standard, this led directly to the financial panic that erupted on Wall Street beginning in 2007. As the U.S. economy slowed down in 2006, mortgage delinquency rates began to spike. Fannie Mae's losses in the mortgage security market in 2006 and 2007, along with the losses of the related GSE 'Freddie Mac', had caused its share value to plummet, stoking fears that it may collapse. On September 7th 2008, Fannie Mae was taken into government conservatorship along with Freddie Mac, with their stocks being delisted from stock exchanges in 2010. This act was seen as an unprecedented direct intervention into the economy by the U.S. government, and a symbol of how far the U.S. housing market had fallen.

Graph and download economic data for 30-Year Fixed Rate FHA Mortgage Index (OBMMIFHA30YF) from 2017-01-03 to 2025-06-26 about FHA, 30-year, fixed, mortgage, rate, indexes, and USA.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Mortgage Rates Soar, Making Homeownership Out of Reach for Many

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Graph and download economic data for 30-Year Fixed Rate Veterans Affairs Mortgage Index (OBMMIVA30YF) from 2017-01-03 to 2025-06-27 about veterans, 30-year, fixed, mortgage, rate, indexes, and USA.

Optimal Blue is a leading provider of mortgage rates in the U.S. markets. Their most popular offering is the Optimal Blue Mortgage Market Indices (OBMMI).

Finland Lending Rate: Stock: Households data was reported at 1.483 % pa in Sep 2018. This records a decrease from the previous number of 1.484 % pa for Aug 2018. Finland Lending Rate: Stock: Households data is updated monthly, averaging 2.601 % pa from Jan 2003 (Median) to Sep 2018, with 189 observations. The data reached an all-time high of 5.893 % pa in Oct 2008 and a record low of 1.483 % pa in Sep 2018. Finland Lending Rate: Stock: Households data remains active status in CEIC and is reported by Bank of Finland. The data is categorized under Global Database’s Finland – Table FI.M006: Lending Rates.

Provides basic information on mortgage loans, information on dividend interest payment, and credit rating

Finland Lending Rate: Stock: Households: Housing Loans data was reported at 3.313 % pa in Mar 2025. This records a decrease from the previous number of 3.405 % pa for Feb 2025. Finland Lending Rate: Stock: Households: Housing Loans data is updated monthly, averaging 2.006 % pa from Jan 2003 (Median) to Mar 2025, with 267 observations. The data reached an all-time high of 5.460 % pa in Oct 2008 and a record low of 0.786 % pa in Dec 2021. Finland Lending Rate: Stock: Households: Housing Loans data remains active status in CEIC and is reported by Bank of Finland. The data is categorized under Global Database’s Finland – Table FI.M004: Lending Rates.

Bankrate is the market leading online aggregator and publisher of market rates, including mortgage rates, in the U.S. markets.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

AGNC: Will Mortgage REIT Thrive in a Rising Rate Environment?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Blackstone Mortgage reported 332.06M in Interest Income for its fiscal quarter ending in March of 2025. Data for Blackstone Mortgage | BXMT - Interest Income including historical, tables and charts were last updated by Trading Economics this last July in 2025.

Blackstone Mortgage reported 80.44M in Interest Expense on Debt for its fiscal quarter ending in March of 2025. Data for Blackstone Mortgage | BXMT - Interest Expense On Debt including historical, tables and charts were last updated by Trading Economics this last June in 2025.

Finland Lending Rate: Stock: Households: Housing Loans: Over 5 Years data was reported at 0.976 % pa in Sep 2018. This records a decrease from the previous number of 0.979 % pa for Aug 2018. Finland Lending Rate: Stock: Households: Housing Loans: Over 5 Years data is updated monthly, averaging 2.209 % pa from Jan 2003 (Median) to Sep 2018, with 189 observations. The data reached an all-time high of 5.458 % pa in Oct 2008 and a record low of 0.976 % pa in Sep 2018. Finland Lending Rate: Stock: Households: Housing Loans: Over 5 Years data remains active status in CEIC and is reported by Bank of Finland. The data is categorized under Global Database’s Finland – Table FI.M006: Lending Rates.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Will Invesco Mortgage (IVR-C) Pay Off Despite Low Interest Rates?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

In 2023, the average mortgage rates in European countries varied from 2.6 percent in Bulgaria to over eight percent in Hungary. The mortgage rate for a home purchase is decided depending on the individual situation of the homebuyer, their credit history, and income, but they also follow macro determinants including the base lending rate, inflation, economic growth, and the health of the housing market. Starts, completions and prices The supply of new housing varies in different countries in Europe. In 2023, the number of new housing units completed per 1,000 citizens was between 0.8 and seven, with this number varying greatly in different countries. Ireland and Poland were among the countries with most completed housing units. When it comes to housing starts, Ireland tops the ranking. The average transaction price of a new dwelling in 2023 ranged anywhere from roughly 1,300 euros per square meter to under 5,000 euros per square meter. Housing stock As the most populous country in Europe, Germany has the largest housing stock. Comparing the number of housing units per 1,000 citizens is an easy way to identify housing shortages. In Greece and the UK, for example, the number of dwellings per 1,000 citizens measured less than 400, compared to Bulgaria and Spain, where it was around 600.

The number of units added to the housing stock between 2010 and 2023 was lower than the number of households formed in the United States during that period. However, most of that shortage was formed between 2011 and 2016. After that, the number of new homes added surpasssed household formation in a couple of years, such as 2018 and 2022.