Mortgage News Daily is a leading news and analysis provider of U.S. mortgage markets and publish Mortgage News Daily rate index which is published daily.

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.

In the United States, interest rates for all mortgage types started to increase in 2021. This was due to the Federal Reserve introducing a series of hikes in the federal funds rate to contain the rising inflation. In the fourth quarter of 2024, the 30-year fixed rate rose slightly, to **** percent. Despite the increase, the rate remained below the peak of **** percent in the same quarter a year ago. Why have U.S. home sales decreased? Cheaper mortgages normally encourage consumers to buy homes, while higher borrowing costs have the opposite effect. As interest rates increased in 2022, the number of existing homes sold plummeted. Soaring house prices over the past 10 years have further affected housing affordability. Between 2013 and 2023, the median price of an existing single-family home risen by about ** percent. On the other hand, the median weekly earnings have risen much slower. Comparing mortgage terms and rates Between 2008 and 2023, the average rate on a 15-year fixed-rate mortgage in the United States stood between **** and **** percent. Over the same period, a 30-year mortgage term averaged a fixed-rate of between **** and **** percent. Rates on 15-year loan terms are lower to encourage a quicker repayment, which helps to improve a homeowner’s equity.

MBA Mortgage Market Index in the United States increased to 281.60 points in July 4 from 257.50 points in the previous week. This dataset includes a chart with historical data for the United States MBA Mortgage Market Index.

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

Price Mortgage, a reputable financial services provider, offers a wealth of information on the mortgage industry. Through their digital platform, users can gain insight into the latest mortgage market trends, rates, and regulations. The company's website serves as a valuable resource for mortgage professionals, lenders, and borrowers alike, providing a comprehensive overview of the mortgage landscape.

With a focus on mortgage origination and servicing, Price Mortgage has established itself as a trusted authority in the industry. Their online presence combines expert analysis, market news, and tools to help users navigate the complex world of mortgages. Whether seeking to stay informed about market fluctuations or to explore options for refinancing or purchasing a new home, Price Mortgage's digital platform is an essential destination for anyone involved in the mortgage sector.

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

The 10 largest mortgage lenders in the United Kingdom accounted for approximately 81 percent of the total market, with the top three alone accounting for 41 percent in 2023. Lloyds Banking Group had the largest market share of gross mortgage lending, with nearly 36.8 billion British pounds in lending in 2023. HSBC, which is the largest UK bank by total assets, ranked fourth. Development of the mortgage market In 2023, the value of outstanding in mortgage lending to individuals amounted to 1.6 trillion British pounds. Although this figure has continuously increased in the past, the UK mortgage market declined dramatically in 2023, registering the lowest value of mortgage lending since 2015. In 2020, the COVID-19 pandemic caused the market to contract for the first time since 2012. The next two years saw mortgage lending soar due to pent-up demand, but as interest rates soared, the housing market cooled, leading to a decrease in new loans of about 100 billion British pounds. The end of low interest rates In 2021, mortgage rates saw some of their lowest levels since recording began by the Bank of England. For a long time, this was particularly good news for first-time homebuyers and those remortgaging their property. Nevertheless, due to the rising inflation, mortgage rates started to rise in the second half of the year, resulting in the 10-year rate doubling in 2022.

Graph and download economic data for 30-Year Fixed Rate Jumbo Mortgage Index (OBMMIJUMBO30YF) from 2017-01-03 to 2025-07-11 about jumbo, 30-year, fixed, mortgage, rate, indexes, and USA.

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

Mortgage Application in the United States increased by 9.40 percent in the week ending July 4 of 2025 over the previous week. This dataset provides - United States MBA Mortgage Applications - actual values, historical data, forecast, chart, statistics, economic calendar and news.

According to Cognitive Market Research, The Global Mortgage Insurance market size is USD XX million in 2024 and will expand at a compound annual growth rate (CAGR) of 6.20% from 2024 to 2031.

North America Mortgage Insurance held the major market of more than 40% of the global revenue and will grow at a compound annual growth rate (CAGR) of 4.4% from 2024 to 2031.
Europe Mortgage Insurance held the major market of more than 30% of the global revenue and will grow at a compound annual growth rate (CAGR) of 4.7% from 2024 to 2031.
Asia Pacific Mortgage Insurance held the market of around 23% of the global revenue and will grow at a compound annual growth rate (CAGR) of 8.2% from 2024 to 2031
South America Mortgage Insurance market of more than 5% of the global revenue and will grow at a compound annual growth rate (CAGR) of 5.6% from 2024 to 2031.
Middle East and Africa Mortgage Insurance held the major market of around 2% of the global revenue and will grow at a compound annual growth rate (CAGR) of 5.9% from 2024 to 2031.
The borrower-paid mortgage insurance segment is set to rise due to the growing consumer preference for seamless online experiences, accelerating the adoption of digital and direct channels and enhancing accessibility, transparency, and efficiency in the mortgage insurance market.
Expansion of the real estate sector, risk mitigation strategies by financial institutions, and regulatory compliance, ensuring lenders' protection against borrower defaults.

Various Strategies Adopted by Key Players to Provide Viable Market Output

The expanding real estate sector and the imperative for risk mitigation among financial institutions fuels the mortgage insurance market. With rising homeownership, mortgage insurance becomes pivotal, safeguarding lenders from borrower defaults. Key players employ diverse strategies, including technological advancements for efficient risk assessment, partnerships with financial entities, and product innovation. Enhanced customer-centric solutions, compliance with regulatory changes, and strategic alliances contribute to market growth, ensuring robust risk management and sustained industry competitiveness.

For instance, in September 2022, The National Association of Minority Mortgage Bankers of America and Enact Holdings, Inc., a major provider of private mortgage insurance via its insurance subsidiaries, announced two new programs to help borrowers achieve the dream of homeownership.

(Source: https://content.enactmi.com/2022-09/NAMMBA%20and%20Enact%20Mortgage%20Insurance%20Launch%20Innovative%20Programs%20to%20Aid%20Lenders%20and%20Borrowers.pdf )

Technological Innovations in Data Analytics to Propel Market Growth

Technological innovations in data analytics are revolutionizing the mortgage insurance market by providing advanced risk assessment tools. With sophisticated analytics, insurers can analyze vast datasets, assess borrower creditworthiness more accurately, and tailor insurance products accordingly. This innovation enhances underwriting processes, improves risk management strategies, and fosters more precise pricing models. As a result, the mortgage insurance industry benefits from increased efficiency, reduced risk exposure, and a more responsive approach to market dynamics, ensuring sustainable growth and stability.

For instance, in June 2021, Prima Solutions announced the avoidance of version 9.19 of its cloud-based medium for life and health, Prima L&H. This new version differs from traditional solutions by covering mortgage, health, and life insurance, all in the same system.

(Source: https://www.prima-solutions.com/en/news/prima-solutions-launches-mortgage-insurance-with-the-new-version-of-prima-lh/ )

Market Restraints of the Mortgage Insurance

Changes in Regulatory Frameworks to Restrict Market Growth

The mortgage insurance market experiences shifts due to changes in regulatory frameworks, impacting its dynamics. Evolving regulations, such as alterations in underwriting standards or capital requirements, influence the market's structure and operational practices. While regulatory changes aim to enhance financial stability, they can also impose constraints on insurers, limiting flexibility and potentially increasing compliance costs. These restraints may lead to adjustments in premium rates or coverage terms, affecting mortgage insurance providers'...

Mortgage Rate in the United Kingdom decreased to 6.98 percent in June from 7.09 percent in May of 2025. This dataset provides - United Kingdom BBA Mortgage Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Nahb Housing Market Index in the United States decreased to 32 points in June from 34 points in May of 2025. This dataset provides the latest reported value for - United States Nahb Housing Market Index - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Graph and download economic data for Housing Inventory: Median Days on Market in the United States (MEDDAYONMARUS) from Jul 2016 to Jun 2025 about median and USA.

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.

(UWMC) United Wholesale Mortgage: Navigating the Mortgage Market Maze

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

In 2023, mortgage interest rates in Canada increased for all types of mortgages. The interest rate for fixed mortgage interest rates for five years and more doubled, from 2.38 percent to 5.52 percent between December 2021 and December 2023. The higher borrowing costs led to the housing market contracting in 2022 and corrections of the property prices across the country.

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.

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.

Chimera Investment (CIM) - Navigating the Mortgage Market: A Forecast

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