The 10-year treasury constant maturity rate in the U.S. is forecast to increase by *** percentage points by 2027, while the 30-year fixed mortgage rate is expected to fall by *** percentage points. From *** percent in 2024, the average 30-year mortgage rate is projected to reach *** percent in 2027.

Sweden Mortgage Bond Yield: Riksbank: Maximum: 2 Years data was reported at -0.080 % pa in Oct 2018. This records an increase from the previous number of -0.125 % pa for Sep 2018. Sweden Mortgage Bond Yield: Riksbank: Maximum: 2 Years data is updated monthly, averaging 3.463 % pa from Jan 1994 (Median) to Oct 2018, with 298 observations. The data reached an all-time high of 12.390 % pa in Aug 1994 and a record low of -0.400 % pa in Aug 2018. Sweden Mortgage Bond Yield: Riksbank: Maximum: 2 Years data remains active status in CEIC and is reported by The Riksbank. The data is categorized under Global Database’s Sweden – Table SE.M013: Mortgage Bond Yield.

Graph and download economic data for Treasury and Agency Securities: Non-MBS, Domestically Chartered Commercial Banks (H8B1302NDMCQG) from Q4 2009 to Q2 2025 about non-mortgage-backed, charter, agency, securities, Treasury, domestic, banks, depository institutions, and USA.

Sweden Mortgage Bond Yield: Riksbank: Average: 5 Years data was reported at 0.745 % pa in Nov 2018. This records a decrease from the previous number of 0.799 % pa for Oct 2018. Sweden Mortgage Bond Yield: Riksbank: Average: 5 Years data is updated monthly, averaging 4.894 % pa from Jun 1986 (Median) to Nov 2018, with 390 observations. The data reached an all-time high of 14.855 % pa in Mar 1990 and a record low of 0.197 % pa in Aug 2018. Sweden Mortgage Bond Yield: Riksbank: Average: 5 Years data remains active status in CEIC and is reported by The Riksbank. The data is categorized under Global Database’s Sweden – Table SE.M013: Mortgage Bond Yield.

Prices for Canada 5Y including live quotes, historical charts and news. Canada 5Y was last updated by Trading Economics this July 13 of 2025.

Switzerland Bond Yield: Domestic Borrower: 8 Years: Mortgage Bond Institutions data was reported at 0.327 % pa in Oct 2018. This records a decrease from the previous number of 0.338 % pa for Sep 2018. Switzerland Bond Yield: Domestic Borrower: 8 Years: Mortgage Bond Institutions data is updated monthly, averaging 2.767 % pa from Jan 1991 (Median) to Oct 2018, with 334 observations. The data reached an all-time high of 7.253 % pa in Aug 1992 and a record low of -0.250 % pa in Jun 2016. Switzerland Bond Yield: Domestic Borrower: 8 Years: Mortgage Bond Institutions data remains active status in CEIC and is reported by Swiss National Bank. The data is categorized under Global Database’s Switzerland – Table CH.M007: Government Bond Yield: by Borrower Type.

Graph and download economic data for Treasury and Agency Securities: Mortgage-Backed Securities (MBS), All Commercial Banks (TMBACBW027NBOG) from 2009-07-01 to 2025-07-02 about mortgage-backed, agency, securities, Treasury, banks, depository institutions, and USA.

Among the factors that influence mortgage interest rates are inflation, economic growth, monetary policies, the bond market, lenders' stability, and the housing market's overall conditions. The mortgage interest rate in Romania fluctuated during the period under observation, with an upward trend from the second quarter of 2017 onwards. The first quarter of 2023 reached the highest value recorded — **** percent; by the fourth quarter of 2024, it dropped to **** percent.

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.

The year 2021 saw the peak in issuance of residential mortgage backed securities (MBS), at *** trillion U.S. dollars. Since then, MBS issuance has slowed, reaching *** trillion U.S. dollars in 2023. What are mortgage backed securities? A mortgage backed security is a financial instrument in which mortgages are bundled together and sold to investors. The idea is that the risk of these individual mortgages is pooled when they are packaged together. This is a sound investment policy, unless the foreclosure rate increases significantly in a short amount of time. Mortgage risk Since mortgages are loans backed by an asset, the house, the risk is often considered relatively low. However, the loan maturities are very long, sometimes decades, meaning lenders must factor in the risk of a shift in the economic climate. As such, interest rates on longer mortgages tend to be higher than on shorter loans. The ten-year treasury yield influences these rates, since it is a long-term rate that most investors accept as risk-free. Additionally, a decline in the value of homeowner equity could lead to a situation where the debtor is “underwater” and owes more than the home is worth.

Sweden Mortgage Bond Yield: Riksbank: Minimum: 5 Years data was reported at 0.730 % pa in Oct 2018. This records an increase from the previous number of 0.605 % pa for Sep 2018. Sweden Mortgage Bond Yield: Riksbank: Minimum: 5 Years data is updated monthly, averaging 4.765 % pa from Jun 1986 (Median) to Oct 2018, with 389 observations. The data reached an all-time high of 14.720 % pa in Mar 1990 and a record low of 0.165 % pa in Jul 2018. Sweden Mortgage Bond Yield: Riksbank: Minimum: 5 Years data remains active status in CEIC and is reported by The Riksbank. The data is categorized under Global Database’s Sweden – Table SE.M013: Mortgage Bond Yield.

In December 2024, the yield on a 10-year U.S. Treasury note was **** percent, forecasted to decrease to reach **** percent by August 2025. Treasury securities are debt instruments used by the government to finance the national debt. Who owns treasury notes? Because the U.S. treasury notes are generally assumed to be a risk-free investment, they are often used by large financial institutions as collateral. Because of this, billions of dollars in treasury securities are traded daily. Other countries also hold U.S. treasury securities, as do U.S. households. Investors and institutions accept the relatively low interest rate because the U.S. Treasury guarantees the investment. Looking into the future Because these notes are so commonly traded, their interest rate also serves as a signal about the market’s expectations of future growth. When markets expect the economy to grow, forecasts for treasury notes will reflect that in a higher interest rate. In fact, one harbinger of recession is an inverted yield curve, when the return on 3-month treasury bills is higher than the ten-year rate. While this does not always lead to a recession, it certainly signals pessimism from financial 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.

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

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

United States - Treasury and Agency Securities: Mortgage-Backed Securities (MBS), All Commercial Banks was -0.40000 % Chg. at Annual Rate in January of 2025, according to the United States Federal Reserve. Historically, United States - Treasury and Agency Securities: Mortgage-Backed Securities (MBS), All Commercial Banks reached a record high of 27.30000 in October of 2020 and a record low of -10.30000 in October of 2022. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Treasury and Agency Securities: Mortgage-Backed Securities (MBS), All Commercial Banks - last updated from the United States Federal Reserve on July of 2025.

In the last five years, the mortgage lending sector has seen negative growth. During this period, industry turnover fell by an average of 3.8% per year, meaning that it is expected to amount to 6.5 billion euros in 2024. This nevertheless corresponds to an increase of 3.1% compared to the previous year. As in all sectors dedicated to the provision of financial services, industry turnover, which in this sector is made up of interest and commission income, was negatively impacted by the low level of interest rates. However, the mortgage banks were able to hold their own comparatively well on the market thanks to their favourable refinancing options. Thanks to their comparatively low default risk, Pfandbriefe have become increasingly popular with institutional investors such as insurers in recent years.Industry sales in 2024 will be influenced by the recent increases in the key interest rate by the European Central Bank (ECB). The sector can also build on the high demand for real estate in Germany, which is primarily based on ongoing urbanisation and positive economic growth. The ECB resumed its bond-buying programme in 2020 and expanded it during the coronavirus crisis, allowing real estate banks to refinance themselves at favourable conditions. At the same time, the price of Pfandbriefe has risen thanks to the increased demand for them, which has had a positive impact on this sector. Competition in the market for property loans will remain strong in 2024, meaning that price competition is likely to intensify in the current year.IBISWorld expects industry turnover to increase by an average of 3.4% annually over the next five years, so that it is likely to amount to 7.7 billion euros in 2029. Interest income in particular is expected to increase due to rising interest rates on the capital markets. However, commission income is likely to fall over the next five years as price competition continues to intensify. The search for ways to increase efficiency is likely to lead to an increased reduction in the number of employees.

The Global Bond Market is Segmented by Type (Treasury Bonds, Municipal Bonds, Corporate Bonds, High-Yield Bonds, Mortgage-Backed Securities, and More), by Issuer (Public Sector Issuers, Private Sector Issuers), by Sectors (Energy and Utilities, Technology, Media and Telecom, Healthcare, Consumers, Industrial, Real Estate and More), and Region. The Market Forecasts are Provided in Terms of Value (USD).

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.

ENJ Entergy New Orleans LLC First Mortgage Bonds 5.0% Series due December 1 2052

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 statistic shows the yield on ten-year government bonds in the Netherlands from 2011 to 2023 with a forecast for 2024 and 2025. In 2023, the long-term interest rate was at *** percent. A ten-year government bond, or treasury note, is a debt obligation issued by a government which matures in ten years. They are considered to be a low-risk investment as they are backed by the government and their ability to raise taxes to cover its obligations. Investors track them, however, for several reasons. First, these bonds are the benchmark that guides other financial interest rates, such as fixed mortgage rates. Second, their yield will tell how investors feel about the economy. The higher the yield on a ten-year government bond, the better the economic outlook.