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 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.

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 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.

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

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.

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.

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.

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.

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

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.

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

30 Year Mortgage Rate in the United States increased to 6.72 percent in July 10 from 6.67 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

Mortgage rates increased at a record pace in 2022, with the 10-year fixed mortgage rate doubling between March 2022 and December 2022. With inflation increasing, the Bank of England introduced several bank rate hikes, resulting in higher mortgage rates. In May 2025, the average 10-year fixed rate interest rate reached **** percent. As borrowing costs get higher, demand for housing is expected to decrease, leading to declining market sentiment and slower house price growth. How have the mortgage hikes affected the market? After surging in 2021, the number of residential properties sold declined in 2023, reaching just above *** million. Despite the number of transactions falling, this figure was higher than the period before the COVID-19 pandemic. The falling transaction volume also impacted mortgage borrowing. Between the first quarter of 2023 and the first quarter of 2024, the value of new mortgage loans fell year-on-year for five straight quarters in a row. How are higher mortgages affecting homebuyers? Homeowners with a mortgage loan usually lock in a fixed rate deal for two to ten years, meaning that after this period runs out, they need to renegotiate the terms of the loan. Many of the mortgages outstanding were taken out during the period of record-low mortgage rates and have since faced notable increases in their monthly repayment. About **** million homeowners are projected to see their deal expire by the end of 2026. About *** million of these loans are projected to experience a monthly payment increase of up to *** British pounds by 2026.

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]

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.

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

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.

The benchmark interest rate in Sweden was last recorded at 2 percent. This dataset provides the latest reported value for - Sweden Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The global mortgage loan service market is experiencing robust growth, driven by factors such as increasing urbanization, rising disposable incomes, and favorable government policies promoting homeownership. The market, valued at approximately $2 trillion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 6% from 2025 to 2033. This expansion is fueled by a burgeoning demand for both residential and commercial mortgages, particularly in emerging economies with rapidly expanding middle classes. The residential segment currently dominates the market share, accounting for approximately 70%, with individual borrowers representing the largest application segment. However, the commercial estate and enterprise segments are witnessing significant growth, driven by increased corporate investments and infrastructural development. Key players like Rocket Mortgage, United Shore Financial Services, and Quicken Loans are leveraging technological advancements such as online platforms and AI-powered loan processing to enhance efficiency and customer experience, shaping the competitive landscape. The growth trajectory is expected to be influenced by fluctuating interest rates, macroeconomic conditions, and evolving regulatory frameworks. Nevertheless, the long-term outlook remains positive, underpinned by the fundamental drivers mentioned above. Technological advancements, particularly in fintech, are reshaping the mortgage loan service landscape. The rise of digital platforms, streamlined application processes, and enhanced data analytics are significantly improving accessibility and speed of loan approvals. This efficiency boost is leading to increased competition, encouraging lenders to offer more competitive interest rates and flexible repayment options to attract borrowers. Furthermore, the increasing adoption of alternative credit scoring models is broadening access to mortgage loans for previously underserved populations. Regional variations in market growth are expected, with North America and Asia-Pacific representing the largest markets. However, emerging economies in regions like South America and Africa hold significant potential for future growth, given the increasing demand for housing and infrastructural development within these markets. Geographic expansion and strategic partnerships remain key strategies for players aiming for market dominance within this evolving sector.