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.

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

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.

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.

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.

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.

Graph and download economic data for Delinquency Rate on Single-Family Residential Mortgages, Booked in Domestic Offices, All Commercial Banks (DRSFRMACBS) from Q1 1991 to Q1 2025 about domestic offices, delinquencies, 1-unit structures, mortgage, family, residential, commercial, domestic, banks, depository institutions, rate, 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.

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.

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.

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.

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

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

Rates have been trending downward in Canada for the last five years. The ebbs and flows are caused by changes in Canada’s bond yields (driven by Canadians economic developments and international rate movements, particularly U.S. rate fluctuations) and the overnight rate (which is set by the Bank of Canada). As of August 2022, there has been a 225 bps increase in the prime rate, since beginning of year 2022, from 2.45% to 4.70% as of Aug 24th 2022. The following are the historical conventional mortgage rates offered by the 6 major chartered banks in Canada in the past 20 years.

The U.S. bank prime loan rate has undergone significant fluctuations over the past three decades, reflecting broader economic trends and monetary policy decisions. From a high of **** percent in 1990, the rate has seen periods of decline, stability, and recent increases. As of May 2025, the prime rate stood at *** percent, marking a notable rise from the historic lows seen in the early 2020s. Federal Reserve's impact on lending rates The prime rate's trajectory closely mirrors changes in the federal funds rate, which serves as a key benchmark for the U.S. financial system. In 2023, the Federal Reserve implemented a series of rate hikes, pushing the federal funds target range to 5.25-5.5 percent by year-end. This aggressive monetary tightening was aimed at combating rising inflation, and its effects rippled through various lending rates, including the prime rate. Long-term investment outlook While short-term rates have risen, long-term investment yields have also seen changes. The 10-year U.S. Treasury bond, a benchmark for long-term interest rates, showed an average market yield of **** percent in the second quarter of 2024, adjusted for constant maturity and inflation. This figure represents a recovery from negative real returns seen in 2021, reflecting shifting expectations for economic growth and inflation. The evolving yield environment has implications for both borrowers and investors, influencing decisions across the financial landscape.

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 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 yield on Canada 5 Year Bond Yield rose to 3.03% on July 11, 2025, marking a 0.07 percentage point increase from the previous session. Over the past month, the yield has edged up by 0.10 points, though it remains 0.36 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. This dataset includes a chart with historical data for Canada 5Y.

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

The mortgage interest rate in Germany decreased notably between 2013 and 2022, falling below 1.5 percent. This was part of an overall trend of falling mortgage interest rates in Europe. The mortgage interest rate in Germany has since increased to 3.9 percent in the second quarter of 2024. The German mortgage market In Europe, Germany is the second-largest mortgage market, with a total value of mortgages outstanding amounting to over 1.8 trillion euros. Mortgage loans are one of the oldest bank products. Among the factors that influence mortgage interest rates are inflation, economic growth, monetary policies, the bond market, the stability of lenders, and the overall conditions of the housing market. Mortgage loans The higher cost of borrowing has a significant effect on the market: While the interest rates were at their lowest, mortgage lending was on the rise. In 2023, when the rates reached a 10-year-high, the quarterly gross mortgage lending fell to the lowest value since 2014. Meanwhile, house prices have also increased substantially in recent years. According to the House Price Index in Germany, between 2015 and 2022, house prices increased by over 60 percent.