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 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

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.

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

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.

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.

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]

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

Mortgage interest rates worldwide varied greatly in 2024, from less than **** percent in many European countries, to as high as ** percent in Turkey. The average mortgage rate in a country depends on the central bank's base lending rate and macroeconomic indicators such as inflation and forecast economic growth. Since 2022, inflationary pressures have led to rapid increase in mortgage interest rates. Which are the leading mortgage markets? An easy way to estimate the importance of the mortgage sector in each country is by comparing household debt depth, or the ratio of the debt held by households compared to the county's GDP. In 2023, Switzerland, Australia, and Canada had some of the highest household debt to GDP ratios worldwide. While this indicator shows the size of the sector relative to the country’s economy, the value of mortgages outstanding allows to compare the market size in different countries. In Europe, for instance, the United Kingdom, Germany, and France were the largest mortgage markets by outstanding mortgage lending. Mortgage lending trends in the U.S. In the United States, new mortgage lending soared in 2021. This was largely due to the growth of new refinance loans that allow homeowners to renegotiate their mortgage terms and replace their existing loan with a more favorable one. Following the rise in interest rates, the mortgage market cooled, and refinance loans declined.

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

Mortgage Interest Rate: Flexible data was reported at 6.800 % pa in 18 May 2025. This stayed constant from the previous number of 6.800 % pa for 17 May 2025. Mortgage Interest Rate: Flexible data is updated daily, averaging 8.600 % pa from Feb 2023 (Median) to 18 May 2025, with 837 observations. The data reached an all-time high of 8.750 % pa in 31 Jul 2024 and a record low of 6.800 % pa in 18 May 2025. Mortgage Interest Rate: Flexible data remains active status in CEIC and is reported by ANZ Bank New Zealand. The data is categorized under High Frequency Database’s Lending Rates – Table NZ.DL001: Mortgage Interest Rate.

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.

The US mortgage lending market, a cornerstone of the American economy, is experiencing robust growth, projected to maintain a Compound Annual Growth Rate (CAGR) exceeding 5% from 2025 to 2033. This expansion is fueled by several key factors. Firstly, a consistently increasing population and household formations drive demand for housing, consequently boosting mortgage loan originations. Secondly, historically low interest rates in recent years have stimulated borrowing, making homeownership more accessible. Furthermore, government initiatives aimed at supporting homeownership, along with increasing disposable incomes in certain segments of the population, contribute to the market's positive trajectory. The market is segmented by loan type (fixed-rate mortgages and home equity lines of credit), service providers (commercial banks, financial institutions, credit unions, and other lenders), and application mode (online and offline). Competition is intense among major players like Bank of America, Chase Bank, and US Bank, with smaller institutions and credit unions vying for market share. While the overall trend is positive, potential headwinds include fluctuations in interest rates, economic downturns impacting consumer confidence, and stringent regulatory environments which can impact lending practices. The geographical distribution of the US mortgage lending market reflects regional economic variations. While the United States dominates North America's market share, growth potential exists across various international markets. European and Asian markets, though characterized by distinct regulatory landscapes and consumer behaviors, present opportunities for expansion. The market's future trajectory will depend on several interconnected factors, including macroeconomic conditions, demographic shifts, and technological advancements influencing the mortgage lending process. The continued adoption of digital technologies is expected to streamline lending processes and expand access, impacting the future of the market significantly. Strategic partnerships and acquisitions are also anticipated, further consolidating the market landscape and driving innovation. Recent developments include: August 2023: Spring EQ, a provider of home equity financing solutions, has entered into a definitive agreement to be acquired by an affiliate of Cerberus Capital Management, L.P., a global leader in alternative investing. The main aim of the partnership is to support Spring EQ's mission to deliver offerings and expand its leadership in the home equity financing market., June 2023: VIU by HUB, a digital insurance brokerage platform subsidiary of Hub International Limited, has entered into a new partnership with Unison, a home equity-sharing company. The collaboration will allow homeowners to compare insurance coverage quotes from various carriers and receive expert advice throughout the process.. Key drivers for this market are: Home Renovation Trends are Driving the Market. Potential restraints include: Home Renovation Trends are Driving the Market. Notable trends are: Home Equity Lending Market is Being Stimulated By Rising Home Prices.

The USA home loan market is experiencing robust growth, projected to maintain a Compound Annual Growth Rate (CAGR) of 18% from 2025 to 2033. While the exact market size for 2025 is not provided, considering a typical large market size and the substantial growth rate, a reasonable estimate would place the market value at approximately $2 trillion in 2025. This significant expansion is driven by several key factors, including a rising population, increasing urbanization, favorable government policies promoting homeownership, and historically low-interest rates (though this last factor is less significant in recent years). The market is witnessing a shift towards digital platforms and online mortgage applications, streamlining the process for borrowers and increasing competition amongst lenders. However, challenges remain, such as fluctuating interest rates, potential economic downturns impacting affordability, and stringent lending regulations designed to protect borrowers. The competitive landscape is dominated by major players like Rocket Mortgage, LoanDepot, Wells Fargo, and Bank of America, along with regional and independent mortgage lenders. These companies are constantly innovating to cater to evolving customer preferences, offering personalized services, and leveraging data analytics for improved risk assessment. The market segmentation is likely diverse, encompassing various loan types (e.g., fixed-rate, adjustable-rate, FHA, VA loans), loan amounts, and borrower demographics. Future growth will depend on macroeconomic factors, including inflation, employment rates, and overall consumer confidence. Continued technological advancements and regulatory changes will significantly influence the market trajectory throughout the forecast period. Key drivers for this market are: Increase in digitization in mortgage lending market, Increase in innovations in software designs to speed up the mortgage-application process. Potential restraints include: Increase in digitization in mortgage lending market, Increase in innovations in software designs to speed up the mortgage-application process. Notable trends are: Growth in Nonbank Lenders is Expected to Drive the Market.

The benchmark interest rate in China was last recorded at 3 percent. This dataset provides the latest reported value for - China Interest 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.

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