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.

Graph and download economic data for 30-Year Fixed Rate FHA Mortgage Index (OBMMIFHA30YF) from 2017-01-03 to 2025-07-10 about FHA, 30-year, fixed, mortgage, rate, indexes, and USA.

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.

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.

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.

Table of data representing

15-Year Fixed Rate Mortgage Average in the United States was 5.80% in July of 2025, according to the United States Federal Reserve. Historically, 15-Year Fixed Rate Mortgage Average in the United States reached a record high of 8.89 in December of 1994 and a record low of 2.10 in July of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for 15-Year Fixed Rate Mortgage Average in the United States - last updated from the United States Federal Reserve on July of 2025.

This dataset provides values for 30 YEAR MORTGAGE RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

30-Year Fixed Rate Mortgage Average in the United States was 6.67% in July of 2025, according to the United States Federal Reserve. Historically, 30-Year Fixed Rate Mortgage Average in the United States reached a record high of 18.63 in October of 1981 and a record low of 2.65 in January of 2021. Trading Economics provides the current actual value, an historical data chart and related indicators for 30-Year Fixed Rate Mortgage Average in the United States - last updated from the United States Federal Reserve on July of 2025.

Graph and download economic data for 30-Year Fixed Rate Jumbo Mortgage Index (OBMMIJUMBO30YF) from 2017-01-03 to 2025-07-10 about jumbo, 30-year, fixed, mortgage, rate, indexes, and USA.

Mortgage rates increased at a record pace in 2022, with the 10-year fixed mortgage rate doubling between March 2022 and December 2022. With inflation increasing, the Bank of England introduced several bank rate hikes, resulting in higher mortgage rates. In May 2025, the average 10-year fixed rate interest rate reached **** percent. As borrowing costs get higher, demand for housing is expected to decrease, leading to declining market sentiment and slower house price growth. How have the mortgage hikes affected the market? After surging in 2021, the number of residential properties sold declined in 2023, reaching just above *** million. Despite the number of transactions falling, this figure was higher than the period before the COVID-19 pandemic. The falling transaction volume also impacted mortgage borrowing. Between the first quarter of 2023 and the first quarter of 2024, the value of new mortgage loans fell year-on-year for five straight quarters in a row. How are higher mortgages affecting homebuyers? Homeowners with a mortgage loan usually lock in a fixed rate deal for two to ten years, meaning that after this period runs out, they need to renegotiate the terms of the loan. Many of the mortgages outstanding were taken out during the period of record-low mortgage rates and have since faced notable increases in their monthly repayment. About **** million homeowners are projected to see their deal expire by the end of 2026. About *** million of these loans are projected to experience a monthly payment increase of up to *** British pounds by 2026.

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.

Mortgage Interest Rate: Flexible data was reported at 6.800 % pa in 18 May 2025. This stayed constant from the previous number of 6.800 % pa for 17 May 2025. Mortgage Interest Rate: Flexible data is updated daily, averaging 8.600 % pa from Feb 2023 (Median) to 18 May 2025, with 837 observations. The data reached an all-time high of 8.750 % pa in 31 Jul 2024 and a record low of 6.800 % pa in 18 May 2025. Mortgage Interest Rate: Flexible data remains active status in CEIC and is reported by ANZ Bank New Zealand. The data is categorized under High Frequency Database’s Lending Rates – Table NZ.DL001: Mortgage Interest Rate.

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.

Graph and download economic data for 30-Year Fixed Rate Veterans Affairs Mortgage Index (OBMMIVA30YF) from 2017-01-03 to 2025-07-10 about veterans, 30-year, fixed, mortgage, rate, indexes, and USA.

Lower Limit of First Home Mortgage Rate: above LPR: Inner Mongolia: Erdos data was reported at -0.200 % Point in 03 Jun 2024. This stayed constant from the previous number of -0.200 % Point for 02 Jun 2024. Lower Limit of First Home Mortgage Rate: above LPR: Inner Mongolia: Erdos data is updated daily, averaging 0.000 % Point from Oct 2019 (Median) to 03 Jun 2024, with 1701 observations. The data reached an all-time high of 0.000 % Point in 14 May 2022 and a record low of -0.200 % Point in 03 Jun 2024. Lower Limit of First Home Mortgage Rate: above LPR: Inner Mongolia: Erdos 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.

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