The benchmark interest rate in the United States was last recorded at 4.50 percent. This dataset provides the latest reported value for - United States Fed Funds Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

CSI: Expected Inflation: Next 5 Yrs data was reported at 2.400 % in Jul 2018. This records a decrease from the previous number of 2.600 % for Jun 2018. CSI: Expected Inflation: Next 5 Yrs data is updated monthly, averaging 2.900 % from Feb 1979 (Median) to Jul 2018, with 382 observations. The data reached an all-time high of 9.700 % in Feb 1980 and a record low of 2.300 % in Dec 2016. CSI: Expected Inflation: Next 5 Yrs data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H030: Consumer Sentiment Index: Unemployment, Interest Rates, Prices and Government Expectations. The questions were: 'What about the outlook for prices over the next 5 to 10 years? Do you think prices will be higher, to go up, on the average, during the next 12 months?' and 'By about what percent per year do you expect prices to go up or down, on the average, during the next 5 to 10 years?'

The benchmark interest rate in Germany was last recorded at 4.50 percent. This dataset provides - Germany Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Household Saving Rate in the United States decreased to 4.50 percent in May from 4.90 percent in April of 2025. This dataset provides - United States Personal Savings Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

United States CSI: Expected Inflation: Next 5 Yrs: Same data was reported at 1.000 % in May 2018. This records a decrease from the previous number of 2.000 % for Apr 2018. United States CSI: Expected Inflation: Next 5 Yrs: Same data is updated monthly, averaging 3.000 % from Feb 1979 (Median) to May 2018, with 380 observations. The data reached an all-time high of 16.000 % in Sep 1981 and a record low of 0.000 % in Jan 1997. United States CSI: Expected Inflation: Next 5 Yrs: Same data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H030: Consumer Sentiment Index: Unemployment, Interest Rates, Prices and Government Expectations. The questions were: 'What about the outlook for prices over the next 5 to 10 years? Do you think prices will be higher, to go up, on the average, during the next 12 months?' and 'By about what percent per year do you expect prices to go up or down, on the average, during the next 5 to 10 years?'

United States CSI: Expected Inflation: Next Yr: Up by 3-4% data was reported at 28.000 % in May 2018. This records an increase from the previous number of 25.000 % for Apr 2018. United States CSI: Expected Inflation: Next Yr: Up by 3-4% data is updated monthly, averaging 22.000 % from Jan 1978 (Median) to May 2018, with 485 observations. The data reached an all-time high of 33.000 % in May 1997 and a record low of 3.000 % in Apr 1980. United States CSI: Expected Inflation: Next Yr: Up by 3-4% data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H030: Consumer Sentiment Index: Unemployment, Interest Rates, Prices and Government Expectations. The questions were: 'During the next 12 months, do you think that prices in general will go up, or go down, or stay where they are now?' and 'By what percent do you expect prices to go up, on the average, during the next 12 months?'The questions were: 'During the next 12 months, do you think that prices in general will go up, or go down, or stay where they are now?' and 'By what percent do you expect prices to go up, on the average, during the next 12 months?'

Personal Loans Market Outlook 2032

The global personal loans market size was USD 65.3 Billion in 2023 and is likely to reach USD 1300 Billion by 2032, expanding at a CAGR of 32.5% during 2024–2032. The market is driven by the surging demand for personal loans due to the financial liberty and awareness among consumers, globally.



Increasing consumer spending and the growing need for financial flexibility are expected to drive the personal loans market during the forecast period. Personal loans, with their ease of access and competitive interest rates, have become a popular financing option for individuals to meet various financial needs, including debt consolidation, home renovation, and emergency expenses. The rise of digital lending platforms and the simplification of loan application processes have significantly surged the demand for personal loans.

Growing advancements in financial technology are shaping the trends in the personal loans market. The integration of artificial intelligence and machine learning technologies into lending platforms has enhanced the loan approval process, offering features such as instant approval and personalized interest rates. Furthermore, the development of secure digital platforms has enabled remote loan application and disbursement, making personal loans accessible to a wider audience.



Rising financial literacy and consumer awareness are creating opportunities for the personal loans market. The increasing understanding of credit scores, interest rates, and loan terms among consumers has fueled the demand for personal loans. Moreover, the growing focus on financial planning and the need for emergency funds have underscored the importance of personal loans in financial management. With its numerous advantages and wide range of applications, the personal loans market is poised for significant growth in the coming years.

Impact of Artificial Intelligence (AI) in Personal Loans Market

The use of artificial intelligence is likely to boost the personal loans market. AI's <a href="https://dataintelo.com/report/advanced-and-predictive-analytics-market" sty

The benchmark interest rate in Poland was last recorded at 5.25 percent. This dataset provides - Poland Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Residential Mortgage Service Market Outlook

The global residential mortgage service market size was valued at approximately USD 2.5 trillion in 2023 and is projected to reach USD 4.3 trillion by 2032, growing at a robust CAGR of 5.8% during the forecast period. This anticipated growth is driven by several factors including increasing urbanization, rising disposable incomes, and ongoing technological advancements in financial services. The market is witnessing substantial growth due to the surge in demand for housing loans, as more individuals aspire to own homes, encouraged by favorable government policies and low-interest rates.

One of the primary factors propelling the growth of the residential mortgage service market is the increasing rate of urbanization. As more people move from rural to urban areas in search of better employment opportunities and living standards, the demand for residential properties skyrockets. This urban influx creates a significant need for mortgage services to facilitate the purchase of homes. Furthermore, the development of infrastructure in urban areas makes them more appealing to potential homeowners, thereby driving the market for residential mortgage services.

Another crucial growth driver is the rise in disposable incomes and the overall improvement in economic conditions across various regions. As individuals' financial situations become more stable, they are more likely to invest in long-term assets such as real estate. Moreover, the availability of diverse mortgage products tailored to meet the specific needs of different consumer segments further stimulates market growth. Financial institutions are constantly innovating to offer more flexible and appealing mortgage solutions, catering to both high-income and middle-income groups.

Technological advancements in the financial sector are also playing a pivotal role in the expansion of the residential mortgage service market. The incorporation of artificial intelligence (AI) and machine learning (ML) in mortgage services has streamlined the loan approval and underwriting processes, making them faster and more efficient. Digital platforms and mobile applications have made it easier for consumers to apply for mortgages and manage their loans, enhancing customer experience and satisfaction. This technological integration not only improves operational efficiency for lenders but also attracts a tech-savvy consumer base.

In terms of regional outlook, North America holds a significant share of the global residential mortgage service market, thanks to its well-developed financial sector and high demand for housing. Europe follows closely, with countries like Germany and the UK showing strong growth due to favorable economic conditions and government policies supporting home ownership. The Asia Pacific region is expected to witness the highest growth rate, driven by rapid urbanization and rising disposable incomes in countries like China and India. Latin America and the Middle East & Africa are also poised for growth, albeit at a slower pace, as they continue to develop their financial infrastructures.

Service Type Analysis

The residential mortgage service market can be segmented by service type into loan origination, underwriting, loan servicing, loan closing, and others. Loan origination covers the initial stage of the mortgage process, where potential borrowers apply for a mortgage. This segment is crucial as it sets the stage for the entire mortgage process, involving tasks such as pre-approval, credit checks, and property appraisals. The efficiency and effectiveness of the loan origination process can significantly impact customer satisfaction and the overall success of the mortgage provider. Technological advancements in this segment, such as automated underwriting systems, have enhanced the speed and accuracy of loan originations.

Underwriting, another critical segment, involves assessing the risk of lending to a borrower based on credit history, employment status, and financial health. The underwriting process determines whether the lender will approve the mortgage application and under what terms. This segment has seen significant innovation with the use of AI and big data analytics, which help in making more accurate risk assessments and reducing the time required for underwriting. For mortgage lenders, efficient underwriting processes are essential to minimize defaults and enhance profitability.

Loan servicing includes managing the ongoing loan payments, ensuring timely repayments, and handling customer service issues. This is a

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.

LAB: Up or Down - Which Way Will It Go?

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 unemployment rate in fiscal year 2204 rose to 3.9 percent. The unemployment rate of the United States which has been steadily decreasing since the 2008 financial crisis, spiked to 8.1 percent in 2020 due to the COVID-19 pandemic. The annual unemployment rate of the U.S. since 1990 can be found here. Falling unemployment The unemployment rate, or the part of the U.S. labor force that is without a job, fell again in 2022 after peaking at 8.1 percent in 2020 - a rate that has not been seen since the years following the 2008 financial crisis. The financial crash caused unemployment in the U.S. to soar from 4.6 percent in 2007 to 9.6 percent in 2010. Since 2010, the unemployment rate had been steadily falling, meaning that more and more people are finding work, whether that be through full-time employment or part-time employment. However, the affects of the COVID-19 pandemic created a spike in unemployment across the country. U.S. unemployment in comparison Compared to unemployment rates in the European Union, U.S. unemployment is relatively low. Greece was hit particularly hard by the 2008 financial crisis and faced a government debt crisis that sent the Greek economy into a tailspin. Due to this crisis, and the added impact of the pandemic, Greece still has the highest unemployment rate in the European Union.

The main stock market index of United States, the US500, rose to 6211 points on July 1, 2025, gaining 0.10% from the previous session. Over the past month, the index has climbed 4.64% and is up 12.75% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks this benchmark index from United States. United States Stock Market Index - values, historical data, forecasts and news - updated on July of 2025.

In 2022, Portugal overturned the sinking mortgage interest rate it had gone through during the coronavirus (COVID-19) pandemic. The country did not escape from the overall trend of falling mortgage interest rates observed in Europe during the COVID-19 crisis, which positioned national mortgage interest rates at **** percent in the fourth quarter of 2021. Interest rates as a weapon against inflation Even though interest rates are affected by economic growth, monetary policies, the bond market, the stability of lenders, and the overall conditions of the housing market, inflation currently leads the European Central Bank (ECB)’s decisions regarding them. As inflation had been low in Europe since the 2008 financial crisis, the ECB lowered interest rates in an attempt to promote economic growth. However, the economic difficulties brought up by the coronavirus pandemic and the Russian-Ukrainian war have fueled inflation. To counteract this rise, the ECB increased interest rates. Portugal’s abrupt rise in interest rates on new residential loans from **** percent in 2021 to **** percent in 2023 demonstrates the balanced and calculated act between the two financial indices. High interest rates and low mortgage lending Compared to other European nations, Portugal has a low gross residential mortgage lending. In the third and fourth quarters of 2022, mortgage lending decreased in the country due to rising interest rates and worsening economic conditions, but have increased dramatically until 2024. Despite being in a rising trajectory in terms of outstanding residential mortgage lending since the second quarter of 2021, 2023 registered decreasing figures caused by the same economic contingencies. 2024 shows a different trend, however.

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.

How do you know when a stock will go up or down? (WBS Stock 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

United States CSI: Expected Inflation: Next 5 Yrs: Standard Deviation data was reported at 2.500 % in May 2018. This stayed constant from the previous number of 2.500 % for Apr 2018. United States CSI: Expected Inflation: Next 5 Yrs: Standard Deviation data is updated monthly, averaging 3.200 % from Feb 1979 (Median) to May 2018, with 380 observations. The data reached an all-time high of 10.900 % in Feb 1980 and a record low of 2.200 % in Apr 1999. United States CSI: Expected Inflation: Next 5 Yrs: Standard Deviation data remains active status in CEIC and is reported by University of Michigan. The data is categorized under Global Database’s USA – Table US.H030: Consumer Sentiment Index: Unemployment, Interest Rates, Prices and Government Expectations. The questions were: 'What about the outlook for prices over the next 5 to 10 years? Do you think prices will be higher, to go up, on the average, during the next 12 months?' and 'By about what percent per year do you expect prices to go up or down, on the average, during the next 5 to 10 years?'

The benchmark interest rate in Chile was last recorded at 5 percent. This dataset provides - Chile Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Loan Services Market Outlook

The global loan services market size was valued at approximately USD 5.6 trillion in 2023 and is projected to reach around USD 10.2 trillion by 2032, exhibiting a compound annual growth rate (CAGR) of 6.5% during the forecast period. This substantial growth can be attributed to a variety of factors, including increasing demand for personal and business loans, technological advancements in service delivery, and an expanding global middle class. Furthermore, the post-pandemic economic recovery has driven a surge in loan applications, as individuals and businesses seek financial support for consumption, investment, and expansion.

One of the primary growth drivers in the loan services market is the rising demand for personal loans. With increasing consumer expenditure and the desire for higher living standards, personal loans have become a popular financial product. The ease of obtaining personal loans through online platforms, along with competitive interest rates, has made them accessible to a broader demographic. Moreover, the development of innovative financial products tailored to meet specific needs, such as loans for home renovation, travel, or medical emergencies, has further propelled the market growth.

Another significant factor contributing to market expansion is the proliferation of digital lending platforms. Fintech innovations have revolutionized the loan services market by providing seamless, quick, and efficient loan processing options. Automated underwriting, real-time credit scoring, and digital verification processes have reduced the time and cost associated with traditional loan applications. This shift towards digitalization has not only enhanced customer experience but has also enabled lenders to reach underbanked and underserved populations, thus expanding their customer base.

The growth of small and medium-sized enterprises (SMEs) globally has also played a crucial role in driving the loan services market. SMEs often require external financial support for working capital, expansion, and operational needs. With governments and financial institutions recognizing the importance of SMEs to economic development, there has been a concerted effort to provide easy access to credit. Initiatives such as government-backed loan guarantees and business development programs have encouraged more businesses to apply for loans, thereby boosting the market.

Online Loans have revolutionized the way consumers access credit, offering unparalleled convenience and speed. With the rise of digital platforms, borrowers can now apply for loans from the comfort of their homes, bypassing the lengthy paperwork and in-person meetings traditionally associated with loan applications. This shift has been particularly beneficial for tech-savvy individuals and those with busy lifestyles, as online loans provide quick approvals and disbursals. Furthermore, the competitive nature of the online lending market has led to more favorable interest rates and terms for borrowers. As technology continues to advance, the online loan sector is expected to grow, offering even more innovative solutions to meet the diverse needs of consumers.

Regionally, North America and Asia Pacific are expected to be the key growth markets for loan services. North America's robust financial infrastructure and high consumer spending power make it a lucrative market for personal and business loans. Meanwhile, the rapid economic growth in Asia Pacific, coupled with increasing urbanization and a growing middle class, is expected to drive the demand for various types of loans. Additionally, favorable government policies and initiatives aimed at improving financial inclusion in emerging economies within the region further support market growth.

Type Analysis

The loan services market is segmented into several types, including personal loans, home loans, auto loans, student loans, business loans, and others. Personal loans represent a significant portion of the market, driven by increasing consumer demand for credit to fund various personal expenditures. The flexibility of personal loans, which can be used for a wide range of purposes such as home renovations, weddings, and medical expenses, makes them highly attractive to consumers. Additionally, the competitive interest rates offered by banks and other financial institutions, along wi

Graph and download economic data for Homeownership Rate in the United States (RHORUSQ156N) from Q1 1965 to Q1 2025 about homeownership, housing, rate, 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.

How do you predict if a stock will go up or down? (TTD Stock Prediction)

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 United States real estate brokerage market, valued at $197.33 billion in 2025, is projected to experience steady growth, exhibiting a Compound Annual Growth Rate (CAGR) of 2.10% from 2025 to 2033. This growth is driven by several key factors. A robust housing market, fueled by increasing population and urbanization, continues to generate significant demand for brokerage services. Technological advancements, such as improved online platforms and data analytics, are streamlining operations and enhancing efficiency for both brokers and consumers. The rise of iBuyers and proptech companies, while posing some competition, also contribute to market expansion by creating innovative solutions and attracting a broader customer base. Furthermore, a shift toward specialized services, catering to niche markets like luxury properties or commercial real estate, is expected to contribute to market diversification and growth. The market is segmented into residential and non-residential sectors, with sales and rental services further dividing each segment. Major players such as Keller Williams, RE/MAX, Coldwell Banker, and Berkshire Hathaway Home Services maintain significant market shares, competing through brand recognition, extensive networks, and technological capabilities. However, certain restraints are present. Interest rate fluctuations and economic uncertainty can impact buyer confidence and consequently, transaction volume. Increasing regulatory scrutiny and compliance costs also add operational challenges for brokerage firms. Competition from independent agents and disruptive technologies demands continuous adaptation and innovation to maintain market competitiveness. The residential segment is expected to remain the largest, driven by consistent demand, while the non-residential sector may show slightly slower growth given fluctuations in commercial investment and development cycles. The sales segment will likely maintain its predominance, although the rental market is anticipated to see growth, reflecting evolving consumer preferences and rental market trends. The ongoing evolution of the market will likely see greater consolidation among larger firms and an increased focus on technological solutions, enhancing transparency, customer experience, and overall market efficiency. This comprehensive report provides an in-depth analysis of the United States real estate brokerage market, covering the period from 2019 to 2033. It leverages extensive market research and data analysis to offer valuable insights into market trends, growth drivers, challenges, and key players. The report is essential for investors, industry professionals, and anyone seeking a comprehensive understanding of this dynamic sector. The base year for this analysis is 2025, with estimations for 2025 and forecasts extending to 2033, utilizing historical data from 2019-2024. Search terms optimized for maximum visibility include: real estate brokerage, US real estate market, real estate trends, residential real estate, commercial real estate, real estate agents, real estate investment, real estate technology, M&A real estate, and real estate market analysis. Recent developments include: May 2024: Compass Inc., the leading residential real estate brokerage by sales volume in the United States, acquired Parks Real Estate, Tennessee's top residential real estate firm that boasts over 1,500 agents. Known for its strategic acquisitions and organic growth, Compass's collaboration with Parks Real Estate not only enriches its agent pool but also grants these agents access to Compass's cutting-edge technology and a vast national referral network., April 2024: Compass has finalized its acquisition of Latter & Blum, a prominent brokerage firm based in New Orleans. Latter & Blum, known for its strong foothold in Louisiana and other Gulf Coast metros, has now become a part of Compass. This strategic move not only solidifies Compass' presence in the region but also propels it to a significant market share, estimated at around 15% in New Orleans.. Key drivers for this market are: 4., Increasing Urbanization Driving the Market4.; Regulatory Environment Driving the market. Potential restraints include: 4., Increasing Urbanization Driving the Market4.; Regulatory Environment Driving the market. Notable trends are: Industrial Sector Leads Real Estate Absorption, Retail Tightens Vacancy Rates.