30 Year Mortgage Rate in the United States increased to 6.72 percent in July 10 from 6.67 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 6.77 percent in the week ending July 4 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.

Mortgage News Daily is a leading news and analysis provider of U.S. mortgage markets and publish Mortgage News Daily rate index which is published daily.

Table of data representing

Graph and download economic data for 30-Year Fixed Rate Veterans Affairs Mortgage Index (OBMMIVA30YF) from 2017-01-03 to 2025-07-11 about veterans, 30-year, fixed, mortgage, rate, indexes, and USA.

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 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

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.

In the United States, interest rates for all mortgage types started to increase in 2021. This was due to the Federal Reserve introducing a series of hikes in the federal funds rate to contain the rising inflation. In the fourth quarter of 2024, the 30-year fixed rate rose slightly, to **** percent. Despite the increase, the rate remained below the peak of **** percent in the same quarter a year ago. Why have U.S. home sales decreased? Cheaper mortgages normally encourage consumers to buy homes, while higher borrowing costs have the opposite effect. As interest rates increased in 2022, the number of existing homes sold plummeted. Soaring house prices over the past 10 years have further affected housing affordability. Between 2013 and 2023, the median price of an existing single-family home risen by about ** percent. On the other hand, the median weekly earnings have risen much slower. Comparing mortgage terms and rates Between 2008 and 2023, the average rate on a 15-year fixed-rate mortgage in the United States stood between **** and **** percent. Over the same period, a 30-year mortgage term averaged a fixed-rate of between **** and **** percent. Rates on 15-year loan terms are lower to encourage a quicker repayment, which helps to improve a homeowner’s equity.

Lower Limit of First Home Mortgage Rate: Base Rate Discount: Jiangxi: Jingdezhen data was reported at 70.000 % in 07 Oct 2019. This stayed constant from the previous number of 70.000 % for 06 Oct 2019. Lower Limit of First Home Mortgage Rate: Base Rate Discount: Jiangxi: Jingdezhen data is updated daily, averaging 70.000 % from Jan 2019 (Median) to 07 Oct 2019, with 280 observations. The data reached an all-time high of 70.000 % in 07 Oct 2019 and a record low of 70.000 % in 07 Oct 2019. Lower Limit of First Home Mortgage Rate: Base Rate Discount: Jiangxi: Jingdezhen 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.

Lower Limit of First Home Mortgage Rate: Base Rate Discount: Fujian: Fuzhou data was reported at 70.000 % in 07 Oct 2019. This stayed constant from the previous number of 70.000 % for 06 Oct 2019. Lower Limit of First Home Mortgage Rate: Base Rate Discount: Fujian: Fuzhou data is updated daily, averaging 70.000 % from Jan 2019 (Median) to 07 Oct 2019, with 280 observations. The data reached an all-time high of 70.000 % in 07 Oct 2019 and a record low of 70.000 % in 07 Oct 2019. Lower Limit of First Home Mortgage Rate: Base Rate Discount: Fujian: Fuzhou 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.

Graph and download economic data for 30-Year Fixed Rate Jumbo Mortgage Index (OBMMIJUMBO30YF) from 2017-01-03 to 2025-07-11 about jumbo, 30-year, fixed, mortgage, rate, indexes, and USA.

Lower Limit of First Home Mortgage Rate: Base Rate Discount: Hubei: Shiyan data was reported at 70.000 % in 07 Oct 2019. This stayed constant from the previous number of 70.000 % for 06 Oct 2019. Lower Limit of First Home Mortgage Rate: Base Rate Discount: Hubei: Shiyan data is updated daily, averaging 70.000 % from Jan 2019 (Median) to 07 Oct 2019, with 280 observations. The data reached an all-time high of 70.000 % in 07 Oct 2019 and a record low of 70.000 % in 07 Oct 2019. Lower Limit of First Home Mortgage Rate: Base Rate Discount: Hubei: Shiyan 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.

Graph and download economic data for 30-Year Fixed Rate FHA Mortgage Index (OBMMIFHA30YF) from 2017-01-03 to 2025-07-14 about FHA, 30-year, fixed, mortgage, rate, indexes, and USA.

CN: Lower Limit of First Home Mortgage Rate: Base Rate Discount: Hubei: Suizhou data was reported at 70.000 % in 07 Oct 2019. This stayed constant from the previous number of 70.000 % for 06 Oct 2019. CN: Lower Limit of First Home Mortgage Rate: Base Rate Discount: Hubei: Suizhou data is updated daily, averaging 70.000 % from Jan 2019 (Median) to 07 Oct 2019, with 280 observations. The data reached an all-time high of 70.000 % in 07 Oct 2019 and a record low of 70.000 % in 07 Oct 2019. CN: Lower Limit of First Home Mortgage Rate: Base Rate Discount: Hubei: Suizhou 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.

Graph and download economic data for 30-Year Fixed Rate USDA Mortgage Index (OBMMIUSDA30YF) from 2017-01-03 to 2025-07-14 about USDA, 30-year, fixed, mortgage, rate, indexes, and USA.

Lower Limit of First Home Mortgage Rate: Base Rate Discount: Hunan: Chenzhou data was reported at 70.000 % in 07 Oct 2019. This stayed constant from the previous number of 70.000 % for 06 Oct 2019. Lower Limit of First Home Mortgage Rate: Base Rate Discount: Hunan: Chenzhou data is updated daily, averaging 70.000 % from Jan 2019 (Median) to 07 Oct 2019, with 280 observations. The data reached an all-time high of 70.000 % in 07 Oct 2019 and a record low of 70.000 % in 07 Oct 2019. Lower Limit of First Home Mortgage Rate: Base Rate Discount: Hunan: Chenzhou 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.

Graph and download economic data for 15-Year Fixed Rate Mortgage Average in the United States (MORTGAGE15US) from 1991-08-30 to 2025-07-10 about 15-year, fixed, mortgage, interest rate, interest, rate, and USA.

Lower Limit of First Home Mortgage Rate: Base Rate Discount: Hunan: Changde data was reported at 70.000 % in 07 Oct 2019. This stayed constant from the previous number of 70.000 % for 06 Oct 2019. Lower Limit of First Home Mortgage Rate: Base Rate Discount: Hunan: Changde data is updated daily, averaging 70.000 % from Jan 2019 (Median) to 07 Oct 2019, with 280 observations. The data reached an all-time high of 70.000 % in 07 Oct 2019 and a record low of 70.000 % in 07 Oct 2019. Lower Limit of First Home Mortgage Rate: Base Rate Discount: Hunan: Changde 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.

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