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.

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.

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.

Home Loans in the United Kingdom increased to 2054 GBP Million in May from -776 GBP Million in April of 2025. This dataset provides - United Kingdom Mortgage Lending- actual values, historical data, forecast, chart, statistics, economic calendar and news.

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.

We investigate whether homeowners respond strategically to news of mortgage modification programs. We exploit plausibly exogenous variation in modification policy induced by settlement of U.S. state government lawsuits against Countrywide Financial Corporation, which agreed to offer modifications to seriously delinquent borrowers. Using a difference-in-difference framework, we find that Countrywide's monthly delinquency rate increased more than 0.54 percentage points—a ten percent relative increase—immediately after the settlement's announcement. The estimated increase in default rates is largest among borrowers least likely to default otherwise. These results suggest that strategic behavior should be an important consideration in designing mortgage modification programs. (JEL D10, G21, G33, K00)

This poll, the first of two fielded April 2012, is a part of a continuing series of monthly surveys that solicits public opinion on a range of political and social issues. Respondents were asked how well Barack Obama was handling the presidency, terrorism, the economy, the war in Afghanistan, the housing market, and the issue of gasoline prices. Opinions were collected on whether respondents thought the country was headed in the right direction, the most important problem facing the nation, whether Congress was performing their job well, and the national economy. Respondents were also queried on their opinions of Barack Obama and Mitt Romney, as well as whether either of the two presidential candidates would be able to bring real change to Washington, whether they would be able to make the right decisions on various issues, and whether they would be an effective military leader. Additional topics included economic concerns, the suspension of Rick Santorum's presidential campaign, women's health issues, the future of the next generation of Americans, gasoline prices, the home mortgage crisis, federal income tax policies and the capital gains tax policy, the John Edwards trial, and the college education of the respondent's child. Finally, respondents were asked whether they voted in the 2008 presidential election and who they voted for, whether they supported the Tea Party movement, whether they usually vote Democratic or Republican, whether they planned to vote in a 2012 primary or caucus, how much attention they have paid to the 2012 presidential campaign, and whether they were registered to vote. Demographic information includes sex, age, race, social class, marital status, household makeup, education level, household income, employment status, religious preference, type of residential area (e.g., urban or rural), political party affiliation, political philosophy, and whether respondents thought of themselves as born-again Christians.

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

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 hosted feature layer has been published in RI State Plane Feet NAD 83.The RI Neighborhood Stabilization Program (NSP) Mapping analysis was performed to assist the Office of Housing and Community Development in identifying target areas with both a Foreclosure Rate (Block Group Level) >=6.5% and a Subprime Loan percentage rate >= 1.4% (Zip Code Level). Based on these criteria the following communities were identified as containing such target areas: Central Falls, Cranston, Cumberland, East Providence, Johnston, North Providence, Pawtucket, Providence, Warwick, West Warwick, and Woonsocket. Federal funding, under the Housing and Economic Recovery Act of 2008 (HERA), Neighborhood Stabilization Program (NSP), totaling $19.6 will be expended in these NSP Target Areas to assist in the rehabilitation and redevelopment of abandoned and foreclosed homes, stabilizing communities.The State of Rhode Island distributes funds allocated, giving priority emphasis and consideration to those areas with the greatest need, including those areas with - 1) Highest percentage of home foreclosures; 2) Highest percentage of homes financed by subprime mortgage loans; and 3) Anticipated increases in rate of foreclosure. The RI Office of Housing and Community Development, with the assistance of Rhode Island Housing, utilized the following sources to meet the above requirements. 1) U.S. Department of Housing & Urban Development (HUD) developed foreclosure data to assist grantees in identification of Target Areas. The State utilized HUD's predictive foreclosure rates to identify those areas which are likely to face a significant rise in the rate of home foreclosures. HUD's methodology factored in Home Mortgage Disclosure Act, income, unemployment, and other information in its calculation. The results were analyzed and revealed a high level of consistency with other needs data available. 2) The State obtained subprime mortgage loan information from the Federal Reserve Bank of Boston. Though the data does not include all mortgages, and was only available at the zip code level rather than Census Tract, findings were generally consistent with other need categories. This data was joined to the Foreclosure dataset in order to select areas with both a Foreclosure Rate >=6.5% and a Subprime Loan Rate >=1.4%. 3) The State also obtained, from the Warren Group, actual local foreclosure transaction records. The Warren Group is a source for real estate and banking news and transaction data throughout New England. This entity has analyzed local deed records in assembling information presented. The data set was normalized due to potential limitations. An analysis revealed a high level of consistency with HUD-predictive foreclosure rates.

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.

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.

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.

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

Evaluate Canada’s best mortgage rates in one place. RATESDOTCA’s Rate Matrix lets you compare pricing for all key mortgage types and terms. Rates are based on an average mortgage of $300,000

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'...

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 Approvals in the United Kingdom increased to 63.03 Thousand in May from 60.66 Thousand in April of 2025. This dataset provides the latest reported value for - United Kingdom Mortgage Approvals - 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 30-Year Fixed Rate Veterans Affairs Mortgage Index (OBMMIVA30YF) from 2017-01-03 to 2025-07-10 about veterans, 30-year, fixed, mortgage, rate, indexes, and USA.