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Interactive chart showing the daily 10 year treasury yield back to 1962. The 10 year treasury is the benchmark used to decide mortgage rates across the U.S. and is the most liquid and widely traded bond in the world.
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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.
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.
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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.
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Graph and download economic data for Treasury and Agency Securities: Mortgage-Backed Securities (MBS), All Commercial Banks (TMBACBW027NBOG) from 2009-07-01 to 2025-06-18 about mortgage-backed, agency, securities, Treasury, banks, depository institutions, and USA.
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Prices for Canada 5Y including live quotes, historical charts and news. Canada 5Y was last updated by Trading Economics this July 1 of 2025.
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Sweden Mortgage Bond Yield: Riksbank: Average: 2 Years data was reported at -0.105 % pa in Oct 2018. This records an increase from the previous number of -0.267 % pa for Sep 2018. Sweden Mortgage Bond Yield: Riksbank: Average: 2 Years data is updated monthly, averaging 3.308 % pa from Jan 1994 (Median) to Oct 2018, with 298 observations. The data reached an all-time high of 11.425 % pa in Aug 1994 and a record low of -0.411 % pa in Jun 2018. Sweden Mortgage Bond Yield: Riksbank: Average: 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.
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Fixed 30-year mortgage rates in the United States averaged 6.88 percent in the week ending June 20 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.
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 — 7.85 percent; by the fourth quarter of 2024, it dropped to 6.01 percent.
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Graph and download economic data for AD&Co US Mortgage High Yield Index: Tier 3 (CRTINDEXTIER3) from Jun 2015 to May 2025 about tier-3, CAS, crt, STACR, mortgage, yield, interest rate, interest, rate, indexes, and USA.
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.
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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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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
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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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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
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The Bond Market report segments the industry into 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 (Government Backed Entities, Financial Corporations, and more), and Geography (North America, Europe, Asia Pacific, South America, Middle East).
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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.
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)
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)
Stock price prediction
Portfolio optimization
Algorithmic trading
Market sentiment analysis
Risk management
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
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
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Graph and download economic data for AD&Co US Mortgage High Yield Index: Tier 1 (CRTINDEXTIER1) from Jun 2015 to May 2025 about CAS, crt, STACR, Tier-1, mortgage, yield, interest rate, interest, rate, indexes, and USA.
Ten-year government bonds in the Netherlands had a yield of 2.8 percent in 2023, compared to 1.47 percent in 2022. 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.
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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 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.
An index that can be used to gauge broad financial conditions and assess how these conditions are related to future economic growth. The index is broadly consistent with how the FRB/US model generally relates key financial variables to economic activity. The index aggregates changes in seven financial variables: the federal funds rate, the 10-year Treasury yield, the 30-year fixed mortgage rate, the triple-B corporate bond yield, the Dow Jones total stock market index, the Zillow house price index, and the nominal broad dollar index using weights implied by the FRB/US model and other models in use at the Federal Reserve Board. These models relate households' spending and businesses' investment decisions to changes in short- and long-term interest rates, house and equity prices, and the exchange value of the dollar, among other factors. These financial variables are weighted using impulse response coefficients (dynamic multipliers) that quantify the cumulative effects of unanticipated permanent changes in each financial variable on real gross domestic product (GDP) growth over the subsequent year. The resulting index is named Financial Conditions Impulse on Growth (FCI-G). One appealing feature of the FCI-G is that its movements can be used to measure whether financial conditions have tightened or loosened, to summarize how changes in financial conditions are associated with real GDP growth over the following year, or both.
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Interactive chart showing the daily 10 year treasury yield back to 1962. The 10 year treasury is the benchmark used to decide mortgage rates across the U.S. and is the most liquid and widely traded bond in the world.