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 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.

Fixed 30-year mortgage rates in the United States averaged 6.67 percent in the week ending August 8 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.

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.

Interactive chart displaying a two-year forecast for the Bank of Canada Target Rate and the Government of Canada 5-year Bond yield, including high, likely, and low rate scenarios.

Prices for Canada 5Y including live quotes, historical charts and news. Canada 5Y was last updated by Trading Economics this August 17 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 2025-08-06 about mortgage-backed, agency, Treasury, securities, banks, depository institutions, and USA.

The table below showcases the 10th, 25th, 50th, 75th, and 90th percentiles of mortgage rates for each zip code in Coffeen, Illinois. It's important to understand that mortgage rates can vary greatly and can change yearly.

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.

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.

Sweden Mortgage Bond Yield: Riksbank: Maximum: 5 Years data was reported at 0.800 % pa in Nov 2018. This records a decrease from the previous number of 0.855 % pa for Oct 2018. Sweden Mortgage Bond Yield: Riksbank: Maximum: 5 Years data is updated monthly, averaging 5.058 % pa from Jun 1986 (Median) to Nov 2018, with 390 observations. The data reached an all-time high of 14.980 % pa in Feb 1990 and a record low of 0.255 % pa in Aug 2018. Sweden Mortgage Bond Yield: Riksbank: Maximum: 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.

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: 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

Graph and download economic data for Credit Unions; Corporate and Foreign Bonds, Excluding Mortgage-Backed Securities and Other Asset-Backed Bonds; Asset, Transactions (BOGZ1FA473063095Q) from Q4 1946 to Q1 2025 about credit unions, asset-backed, mortgage-backed, foreign, transactions, bonds, securities, assets, depository institutions, and USA.

The basic information on mortgage loan products is data that allows you to check the properties, dividend payment information, and credit rating status of each mortgage-backed security (MBS). The data consists of the following three operations. ① Search for basic product information: Function to check product name, issuance date, interest rate classification, coupon rate, maturity date, bond type, etc. based on the issuance number and product number. ② Search for dividend and interest payment information: Function to search for payment frequency, payment date, principal payment amount, interest payment amount, and total payment amount based on the issuance number and product number. ③ Search for credit rating information: Function to search for rating agencies, credit ratings, and rating evaluation dates based on the base date, issuance number, and product number.

The mortgage interest rate in Germany decreased notably between 2013 and 2022, falling below *** 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 *** percent in the fourth 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 nearly *** 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 2024, house prices increased by nearly ** percent.

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.

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 AD&Co US Mortgage High Yield Index: Tier 3 (CRTINDEXTIER3) from Jun 2015 to Jul 2025 about tier-3, CAS, crt, STACR, mortgage, yield, interest rate, interest, rate, indexes, and USA.

In June 2025, the yield on a 10-year U.S. Treasury note was **** percent, forecasted to decrease to reach **** percent by February 2026. 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.