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 6.77 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.

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

Bulgaria was at the top of this ranking of ** European countries sorted by the growth rate of their volume of loans to households in 2023. Loans to households in the European Union and the European Economic Area are expected to grow on average by over ***** percent in 2024. Meanwhile, the loans and advances market in Germany is expected to increase by *** percent in 2024. Overall, the total value of the household loans market in the EU as a whole is expected to keep growing during that timeline.

Mortgage Loan Service Market Outlook

The global mortgage loan service market size was valued at approximately $10.5 trillion in 2023 and is projected to reach around $18.2 trillion by 2032, growing at a CAGR of 6.1% during the forecast period. The growth of this market is driven by the increasing urbanization, rising disposable incomes, and favorable government policies aimed at promoting homeownership across various regions. Additionally, the proliferation of digital banking and fintech solutions has made mortgage services more accessible, further contributing to the market's expansion.

One of the primary growth factors for the mortgage loan service market is the significant rise in housing demand globally. As urban populations swell and economic conditions improve, more individuals and families are seeking to purchase homes, driving the need for mortgage loans. This trend is particularly evident in emerging markets, where urbanization is occurring at an unprecedented rate. Governments are also playing a crucial role by implementing policies and grants to make housing more affordable, thereby boosting mortgage adoption.

Technological advancements are another significant factor propelling the mortgage loan service market. The integration of AI, big data analytics, and blockchain technology has revolutionized the way mortgage services are delivered. These technologies streamline application processes, enhance risk assessment, and improve customer service, making it easier and faster for consumers to secure loans. Fintech companies, in particular, are leveraging these technologies to offer more competitive rates and personalized loan products, thereby attracting a broader customer base.

Furthermore, the increasing participation of non-banking financial institutions (NBFIs) and credit unions has diversified the mortgage loan service market. These entities often provide more flexible and innovative loan products compared to traditional banks, meeting the needs of a more varied clientele. NBFIs and credit unions also tend to have more lenient approval processes, making them an attractive option for individuals with non-traditional income sources or lower credit scores. This diversification is contributing significantly to the market's growth.

Mortgage Loans Software is playing an increasingly pivotal role in the evolution of the mortgage loan service market. As the industry embraces digital transformation, software solutions are being developed to streamline the entire mortgage process, from application to approval. These software platforms facilitate better data management, enhance customer experience, and improve operational efficiency for service providers. By automating routine tasks and providing real-time analytics, Mortgage Loans Software helps lenders make more informed decisions, reduce processing times, and minimize errors. This technological advancement is not only beneficial for lenders but also empowers borrowers by offering them greater transparency and control over their mortgage journey.

Regionally, North America continues to dominate the mortgage loan service market due to its well-established financial infrastructure and high homeownership rates. However, the Asia Pacific region is expected to register the fastest growth during the forecast period, driven by rapid urbanization, rising incomes, and government initiatives aimed at affordable housing. Countries like China and India are particularly noteworthy due to their large and growing middle-class populations.

Type Analysis

The mortgage loan service market is segmented by type into fixed-rate mortgages, adjustable-rate mortgages, interest-only mortgages, reverse mortgages, and others. Fixed-rate mortgages are the most popular type, offering borrowers the stability of a constant interest rate over the life of the loan. This makes them particularly attractive in times of low-interest rates, as borrowers can lock in favorable terms for the long term. The predictability of monthly payments also makes fixed-rate mortgages a preferred choice for many homeowners.

Adjustable-rate mortgages (ARMs) offer lower initial interest rates compared to fixed-rate mortgages, making them an attractive option for borrowers who anticipate an increase in their income or plan to sell their property before the rate adjusts. However, the fluctuating interest rates can pose a risk, especially in volatile economic conditions. Despite this, the flexibility

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.

Policy interest rates in the U.S. and Europe are forecasted to decrease gradually between 2024 and 2027, following exceptional increases triggered by soaring inflation between 2021 and 2023. The U.S. federal funds rate stood at **** percent at the end of 2023, the European Central Bank deposit rate at **** percent, and the Swiss National Bank policy rate at **** percent. With inflationary pressures stabilizing, policy interest rates are forecast to decrease in each observed region. The U.S. federal funds rate is expected to decrease to *** percent, the ECB refi rate to **** percent, the Bank of England bank rate to **** percent, and the Swiss National Bank policy rate to **** percent by 2025. An interesting aspect to note is the impact of these interest rate changes on various economic factors such as growth, employment, and inflation. The impact of central bank policy rates The U.S. federal funds effective rate, crucial in determining the interest rate paid by depository institutions, experienced drastic changes in response to the COVID-19 pandemic. The subsequent slight changes in the effective rate reflected the efforts to stimulate the economy and manage economic factors such as inflation. Such fluctuations in the federal funds rate have had a significant impact on the overall economy. The European Central Bank's decision to cut its fixed interest rate in June 2024 for the first time since 2016 marked a significant shift in attitude towards economic conditions. The reasons behind the fluctuations in the ECB's interest rate reflect its mandate to ensure price stability and manage inflation, shedding light on the complex interplay between interest rates and economic factors. Inflation and real interest rates The relationship between inflation and interest rates is critical in understanding the actions of central banks. Central banks' efforts to manage inflation through interest rate adjustments reveal the intricate balance between economic growth and inflation. Additionally, the concept of real interest rates, adjusted for inflation, provides valuable insights into the impact of inflation on the economy.

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.

Type of Mortgage Loan:Conventional Mortgage Loans: Backed by private investors and typically require a down payment of 20% or more.Jumbo Loans: Loans that exceed the conforming loan limits set by Fannie Mae and Freddie Mac.Government-insured Mortgage Loans: Backed by the Federal Housing Administration (FHA), Department of Veterans Affairs (VA), or U.S. Department of Agriculture (USDA).Others: Includes non-QM loans, reverse mortgages, and shared equity programs.Mortgage Loan Terms:30-year Mortgage: The most common term, offering low monthly payments but higher overall interest costs.20-year Mortgage: Offers a shorter repayment period and lower long-term interest costs.15-year Mortgage: The shortest term, providing lower interest rates and faster equity accumulation.Others: Includes adjustable-rate mortgages (ARMs) and balloons loans.Interest Rate:Fixed-rate Mortgage Loan: Offers a stable interest rate over the life of the loan.Adjustable-rate Mortgage Loan (ARM): Offers an initial interest rate that may vary after a certain period, potentially leading to higher or lower monthly payments.Provider:Primary Mortgage Lender: Originates and services mortgages directly to borrowers.Secondary Mortgage Lender: Purchases mortgages from originators and packages them into securities for sale to investors. Key drivers for this market are: Digital platforms and AI-driven credit assessments have simplified the application process, improving accessibility and borrower experience. Potential restraints include: Fluctuations in interest rates significantly impact borrowing costs, affecting loan demand and affordability. Notable trends are: The adoption of online portals and mobile apps is transforming the mortgage process with faster approvals and greater transparency.

Brand performance data collected from AI search platforms for the query "mortgage rate forecast 2025".

More than ************* mortgage loans are projected to be affected by the increasing mortgage interest rates in Canada by 2025. About *********** of these mortgages are projected to be up for renewal in 2024. These loans were taken out at a time when interest rates were much lower, meaning that homeowners will be affected by a notable increase in their monthly payments.

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.

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

Forecast: Household Expenditure on Mortgage Interest and Charges in the US 2022 - 2026 Discover more data with ReportLinker!

NB Forecast: Mortgage Rate data was reported at 4.480 % in Dec 2028. This records a decrease from the previous number of 4.510 % for Sep 2028. NB Forecast: Mortgage Rate data is updated quarterly, averaging 2.990 % from Jun 2015 (Median) to Dec 2028, with 55 observations. The data reached an all-time high of 5.700 % in Sep 2024 and a record low of 1.810 % in Dec 2021. NB Forecast: Mortgage Rate data remains active status in CEIC and is reported by Norges Bank. The data is categorized under Global Database’s Norway – Table NO.M006: Money Market and Key Policy Rates: Forecast: Norges Bank.

Due to interest rates decreasing in recent years, mortgages in the United Kingdom have become overall more affordable: In 2007, when mortgages were the least affordable, a home buyer spent on average **** percent of their income on mortgage interest and *** percent on capital repayment. In 2019, the year with the most affordable mortgages, mortgage interest accounted for *** percent and capital repayment was **** percent of their income. As interest rates increase in response to the rising inflation, mortgage affordability is expected to worsen. Though below the levels observed before 2007, the total mortgage repayment between 2022 and 2026 is expected to exceed ** percent of income.

Canada Conventional Mortgage: 5 Years: Weekly data was reported at 6.490 % pa in 07 May 2025. This stayed constant from the previous number of 6.490 % pa for 30 Apr 2025. Canada Conventional Mortgage: 5 Years: Weekly data is updated weekly, averaging 5.700 % pa from Jan 2000 (Median) to 07 May 2025, with 1323 observations. The data reached an all-time high of 8.750 % pa in 31 May 2000 and a record low of 4.640 % pa in 12 Jul 2017. Canada Conventional Mortgage: 5 Years: Weekly data remains active status in CEIC and is reported by Bank of Canada. The data is categorized under Global Database’s Canada – Table CA.M005: Conventional Mortgage Rate. [COVID-19-IMPACT]

Forecast: Bank Lending Interest Rate in Italy 2024 - 2028 Discover more data with ReportLinker!

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

The U.S. mortgage market has declined notably since 2020 and 2021, mostly due to the effect of higher borrowing costs on refinance mortgages. The value of refinancing mortgage originations, amounted to 190 billion U.S. dollars in the fourth quarter of 2024, down from a peak of 851 billion U.S. dollars in the fourth quarter of 2020. The value of mortgage loans for the purchase of a property recorded milder fluctuations, with a value of 304 billion U.S. dollars in the fourth quarter of 2024. According to the forecast, mortgage lending is expected to slightly increase until the end of 2026. The cost of mortgage borrowing in the U.S. Mortgage interest rates in the U.S. rose dramatically in 2022, peaking in the final quarter of 2024. In 2020, a homebuyer could lock in a 30-year fixed interest rate of under three percent, whereas in 2024, the average rate for the same mortgage type exceeded 6.6 percent. This has led to a decline in homebuyer sentiment, and an increasing share of the population pessimistic about buying a home in the current market. The effect of a slower housing market on property prices and rents According to the S&P/Case Shiller U.S. National Home Price Index, housing prices experienced a slight correction in early 2023, as property transactions declined. Nevertheless, the index continued to grow in the following months. On the other hand, residential rents have increased steadily since 2000.