30 Year Mortgage Rate in the United States decreased to 6.23 percent in November 26 from 6.26 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

Fixed 30-year mortgage rates in the United States averaged 6.40 percent in the week ending November 21 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.

Mortgage rates surged at an unprecedented pace in 2022, with the average 10-year fixed rate doubling between March and December of that year. In response to mounting inflation, the Bank of England implemented a series of rate hikes, pushing borrowing costs steadily higher. By October 2025, the average 10-year fixed mortgage rate stood at **** percent. As financing becomes more expensive, housing demand has cooled, weighing on market sentiment and slowing house price growth. How have the mortgage hikes affected the market? After surging in 2021, the number of residential properties sold fell significantly in 2023, dipping to just above *** million transactions. This contraction in activity also dampened mortgage lending. Between the first quarter of 2023 and the first quarter of 2024, the value of new mortgage loans declined year-on-year for five consecutive quarters. Even as rates eased modestly in 2024 and housing activity picked up slightly, volumes remained well below the highs recorded in 2021. How are higher mortgages impacting homebuyers? For homeowners, the impact is being felt most acutely as fixed-rate deals expire. Mortgage terms in the UK typically range from two to ten years, and many borrowers who locked in historically low rates are now facing significantly higher repayments when refinancing. By the end of 2026, an estimated five million homeowners will see their mortgage deals expire. Roughly two million of these loans are projected to experience a monthly payment increase of up to *** British pounds by 2026, putting additional pressure on household budgets and constraining affordability across the market.

The benchmark interest rate in the United States was last recorded at 4 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.

The benchmark interest rate in Sweden was last recorded at 1.75 percent. This dataset provides the latest reported value for - Sweden Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This table contains 102 series, with data starting from 2013, and some select series starting from 2016. This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada), Components (51 items: Total, funds advanced, residential mortgages, insured; Variable rate, insured; Fixed rate, insured, less than 1 year; Fixed rate, insured, from 1 to less than 3 years; ...), and Unit of measure (2 items: Dollars; Interest rate). For additional clarification on the component dimension, please visit the OSFI website for the Report on New and Existing Lending.

The benchmark interest rate in the United Kingdom was last recorded at 4 percent. This dataset provides - United Kingdom Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Daily Global Trends - Insights on Popularity

Analyzing Crowd Behaviour and Buzz Worldwide

By Jeffrey Mvutu Mabilama [source]

About this dataset

This dataset provides a comprehensive look into 2020’s top trends worldwide, with information on the hottest topics and conversations happening all around the globe. With details such as trending type, country origin, dates of interest, URLs to find further information, keywords related to the trend and more - it's an invaluable insight into what's driving society today.

You can use this data in conjunction with other sources to get ideas for businesses or products tailored to popular desires or opinions. If you are interested in international business perspectives then this is also your go-to source; you can adjust how best to interact with people from certain countries upon learning what they hold important in terms of search engine activity.

It also gives key insights into buzz formation by monitoring trends over many countries over different periods of time then analysing whether events tend to last longer or if their effect is short-lived and how much impact it made in terms column ‘traffic’ – number of searches for an individual topic – for the duration of its period affecting higher positions and opinion polls. In addition, marketing / advertising professionals can anticipate what content is likely best received by audiences based off previous trends related images/snippets provided with each trend/topic as well as URL links tracking users who have shown interest.. This way they become better prepared when rolling out campaigns targeted at specific regions/areas taking cultural perspective into consideration rather than just raw numbers.

Last but not least it serves perfectly as great starting material when getting acquainted foreigners online (at least we know what conversation starters won't be awkward mentioned!) before deepening our empathetic understanding like terms used largely solely within cultures such as TV program titles… So…… question is: What will be next big thing? See for yourself.

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How to use the dataset

How to use this dataset for Insights on Popularity?

This Daily Global Trends 2020 dataset provides valuable information about trends around the world, including insights on their popularity. It can be used to identify popular topics and find ways to capitalize on them through marketing, business ideas and more. Below are some tips for how to use this data in order to gain insight into global trends and the level of popularity they have.

• For Business Ideas: Use the URL information provided in order to research each individual trend, analyzing both when it gained traction as well as when its popularity faded away (if at all). This will give insight into transforming a brief trend into a long-lived one or making use of an existing but brief surge in interest – think new apps related to a trending topic! Combining the geographic region listed with these timeframes gives even more granular insight that could be used for product localization or regional target marketing.

• To study Crowd Behaviour & Dynamics: Explore both country-wise and globally trending topics by looking at which countries similarly exhibit interest levels for said topics. Go further by understanding what drives people’s interest in particular subjects from different countries; here web scraping techniques can be employed using the URLs provided accompanied with basic text analysis techniques such as word clouds! This allows researchers/marketers get better feedback from customers from multiple regions, enabling smarter decisions based upon real behaviour rather than assumptions.

• For **Building Better Products & Selling Techniques: Utilize combine Category (Business, Social etc.), Country and Related keywords mentioned with traffic figures so that you can obtain granular information about what excites people across cultures i.e ‘Food’ is popular everywhere but certain variations depending upon geo-location may not sell due need catering towards local taste buds.-For example selling frozen food that requires little preparation via supermarket chains showing parallels between nutritional requirements vs expenses incurred while shopping will drive effective sales strategy using this data set . Further combining date information also helps make predictions based upon buyers behaviour over seasons i.e buying seedless watermelons during winter season would be futile .

• For Social & Small Talk opportunities - Incorporating recently descr...

BUSINESS PROBLEM-1 BACKGROUND: The Lending Club is a peer-to-peer lending site where members make loans to each other. The site makes anonymized data on loans and borrowers publicly available. BUSINESS PROBLEM: Using lending club loans data, the team would like to test below hypothesis on how different factors effecing each other (Hint: You may leverage hypothesis testing using statistical tests) a. Intrest rate is varied for different loan amounts (Less intrest charged for high loan amounts) b. Loan length is directly effecting intrest rate. c. Inrest rate varies for different purpose of loans d. There is relationship between FICO scores and Home Ownership. It means that, People with owning home will have high FICO scores. DATA AVAILABLE:  LoansData.csv The data have the following variables (with data type and explanation of meaning)  Amount.Requested - numeric. The amount (in dollars) requested in the loan application.  Amount.Funded.By.Investors - numeric. The amount (in dollars) loaned to the individual.  Interest.rate – character. The lending interest rate charged to the borrower.  Loan.length - character. The length of time (in months) of the loan.  Loan.Purpose – categorical variable. The purpose of the loan as stated by the applicant.  Debt.to.Income.Ratio – character. The % of consumer’s gross income going toward paying debts.  State - character. The abbreviation for the U.S. state of residence of the loan applicant.  Home.ownership - character. Indicates whether the applicant owns, rents, or has a mortgage.  Monthly.income - categorical. The monthly income of the applicant (in dollars).  FICO.range – categorical (expressed as a string label e.g. “650-655”). A range indicating the applicants FICO score.  Open.CREDIT.Lines - numeric. The number of open lines of credit at the time of application.  Revolving.CREDIT.Balance - numeric. The total amount outstanding all lines of credit.  Inquiries.in.the.Last.6.Months - numeric. Number of credit inquiries in the previous 6 months.  Employment.Length - character. Length of time employed at current job.

BUSINESS PROBLEM - 2 BACKGROUND: When an order is placed by a customer of a small manufacturing company, a price quote must be developed for that order. Because each order is unique, quotes must be established on an order-by-order basis by a pricing expert. The price quote process is laborintensive, as prices depend on many factors such as the part number, customer, geographic location, market, and order volume. The sales department manager is concerned that the pricing process is too complex, and that there might be too much variability in the quoted prices. An improvement team is tasked with studying and improving the pricing process. After interviewing experts to develop a better understanding of the current process, the team designed a study to determine if there is variability between pricing experts. That is, do different pricing experts provide different price quotes? Two randomly selected pricing experts, Mary and Barry, were asked to independently provide prices for twelve randomly selected orders. Each expert provided one price for each of the twelve orders. BUSINESS PROBLEM: We would like to assess if there is any difference in the average price quotes provided by Mary and Barry. DATA AVAILABLE:  Price_Quotes.csv The data set contains the order number, 1 through 12, and the price quotes by Mary and Barry for each order. Each row in the data set is the same order. Thus, Mary and Barry produced quotes for the same orders. BUSINESS PROBLEM-3: BACKGROUND: The New Life Residential Treatment Facility is a NGO that treatsteenagers who have shown signs of mental illness. It provides housing and supervision of teenagers who are making the transition from psychiatric hospitals back into the community. Because many of the teenagers were severely abused as children and have been involved with the juvenile justice system, behavioral problems are common at New Life. Employee pay is low and staff turnover (attrition) is high. A reengineering program wasinstituted at New Life with the goals of lowering behavioral problems of the kids and decreasing employee turnover rates. As a part of this effort, the following changes were made:  Employee shifts were shortened from 10 hours to 8 hours each day.  Employees were motivated to become more involved in patient treatments. This included encouraging staff to run varioustherapeutic treatment sessions and allowing staff to have more say in program changes.  The activities budget wasincreased.  A facility-wide performance evaluation system was putinto place that rewarded staff participation andinnovation.  Management and staff instituted a program designed to raise expectations about appropriate behavior from the kids. Thisincluded strict compliance with reporting of behavioral violations, insistence o...

This dataset was generated from a public earning's call (press release article). And used to generate examples of the way real humans would speak regarding the matters in the article, within real world scenarios. Here they are below:

Here are the linguistic variations for each of the queries in the dataset, based on the example article provided:

Here are five examples related to strong average loan growth in US Personal Banking (#5):

1. Mortgage Loans: An increase in demand for mortgage loans contributed to the strong average loan growth in US Personal Banking. Customers taking advantage of low interest rates led to a surge in mortgage applications and approvals.

2. Auto Loans: Robust consumer spending and increased car sales led to higher demand for auto loans, contributing to the strong loan growth in US Personal Banking. Customers seeking financing options for purchasing vehicles played a significant role in this growth.

3. Personal Loans: The availability of personal loans with favorable terms and competitive interest rates attracted borrowers, resulting in strong average loan growth in US Personal Banking. Customers availed personal loans for various purposes such as home improvements, debt consolidation, or financing other personal expenses.

4. Small Business Loans: US Personal Banking also witnessed strong loan growth due to increased lending to small businesses. As entrepreneurs and small business owners sought capital for expansion, equipment purchases, or working capital, the demand for small business loans rose, contributing to the growth.

5. Student Loans: The higher education sector continued to rely on student loans to finance tuition fees and related expenses. With the increasing cost of education, a rise in student loan applications and approvals contributed to the strong average loan growth in US Personal Banking.

General Queries Query: "What was the revenue for Personal Banking and Wealth Management (PBWM) in the last quarter?"

Variation 1: "What were the PBWM revenues in the previous quarter?" Variation 2: "Can you provide the revenue figure for PBWM in the last quarter?" Variation 3: "How much revenue did PBWM generate in the last quarter?" Variation 4: "What was the total revenue for PBWM in the most recent quarter?" Variation 5: "Could you tell me the revenue earned by PBWM in the last quarter?" Query: "What were the revenue figures for different divisions under US Personal Banking?"

Variation 1: "Can you provide the revenue breakdown for various divisions within US Personal Banking?" Variation 2: "What were the revenues generated by the different divisions in US Personal Banking?" Variation 3: "How did the revenue distribution look across different divisions in US Personal Banking?" Variation 4: "What were the individual revenue figures for each division within US Personal Banking?" Variation 5: "Could you give me a breakdown of the revenues for different divisions in US Personal Banking?" Query: "How did operating expenses change for PBWM?"

Variation 1: "What was the change in operating expenses for PBWM?" Variation 2: "Were there any fluctuations in the operating expenses of PBWM?" Variation 3: "How did the operating expenses for PBWM evolve over the specified period?" Variation 4: "Can you provide insights into the changes in operating expenses for PBWM?" Variation 5: "What was the percentage change in operating expenses for PBWM?" Query: "What factors contributed to the increase in PBWM's cost of credit?"

Variation 1: "What were the drivers behind the rise in PBWM's cost of credit?" Variation 2: "Which factors influenced the increase in PBWM's cost of credit?" Variation 3: "Can you identify the elements that led to the higher cost of credit for PBWM?" Variation 4: "What were the contributing factors to the cost of credit escalation in PBWM?" Variation 5: "What were the key reasons behind the growth in PBWM's cost of credit?" Query: "What led to the decrease in PBWM's net income?"

Variation 1: "What were the factors responsible for the decline in PBWM's net income?" Variation 2: "Can you identify the causes of the reduction in PBWM's net income?" Variation 3: "What influenced the decrease in net income for PBWM?" Variation 4: "Were there specific drivers that contributed to the decline in PBWM's net income?" Variation 5: "What were the primary reasons behind the decrease in PBWM's net income?" These linguistic variations provide different ways to ask the same questions, allowing for a more diverse and robust training dataset for the chatbot.

Here are the extracted entities from the provided article:

Account Line Entities:

Revenues Operating expenses Cost of credit Net income Business Line Entities:

Personal Banking and Wealth Management (PBWM) Branded Cards Retail Services Retail Banking Global Wealth Management Markets Banking Investment Banking Corporate Lending...

The benchmark interest rate in Japan was last recorded at 0.50 percent. This dataset provides - Japan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This paper presents the results of a financial intervention using loans, of which 50% is forgiven given a drop in crop prices below a certain threshold, in Ghana intended to investigate 1) the role of crop-price risk in reducing demand for credit among farmers and 2) how risk mitigation changes farmers' investment decisions. After baseline survey was taken and farmers were randomized into treatment and control, 20 meetings were set up in order to inform maize and garden egg farmers in the five villages of new loan product that would be distributed conditional on investment in their farms. 169 farmers attended the meetings, of which 91 were maize farmers and 78 were garden egg farmers. Two sets of meetings were in place, 10 that offered a standard loan product and 10 that offered a loan product that had a 50% forgiveness mechanism built in if average crop prices fell below a certain threshold (set at the 10th percentile for historical garden egg prices and the 7th percentile for maize prices). The average loan size for all loans was approximately 238 GHS, or 159 USD. Farmers were not informed of what type of meeting they would attend, and were unaware that there was a difference between the meetings prior to attending. Outcome measures were types of individuals that are likely to take up the loan under both the control and treatment condit ions, and the impact that the indemnified loan had on investment and profits versus the standard loan.

This table contains data described by the following dimensions (Not all combinations are available): Geography (13 items: Canada; Newfoundland and Labrador; Prince Edward Island; Atlantic provinces ...), Housing estimates (3 items: Housing starts; Housing under construction; Housing completions ...), Type of unit (6 items: Total units; Multiples; Single-detached; Semi-detached ...), Seasonal adjustment (2 items: Unadjusted; Seasonally adjusted at annual rates ...).

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.

The benchmark interest rate in Portugal was last recorded at 2.15 percent. This dataset provides - Portugal Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The benchmark interest rate in Pakistan was last recorded at 11 percent. This dataset provides - Pakistan Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

These National Statistics provide monthly estimates of the number of residential and non-residential property transactions in the UK and its constituent countries. National Statistics are accredited official statistics.

England and Northern Ireland statistics are based on information submitted to the HM Revenue and Customs (HMRC) Stamp Duty Land Tax (SDLT) database by taxpayers on SDLT returns.

Land and Buildings Transaction Tax (LBTT) replaced SDLT in Scotland from 1 April 2015 and this data is provided to HMRC by https://www.revenue.scot/">Revenue Scotland to continue the time series.

Land Transaction Tax (LTT) replaced SDLT in Wales from 1 April 2018. To continue the time series, the https://gov.wales/welsh-revenue-authority">Welsh Revenue Authority (WRA) have provided HMRC with a monthly data feed of LTT transactions since July 2021.

LTT figures for the latest month are estimated using a grossing factor based on data for the most recent and complete financial year. Until June 2021, LTT transactions for the latest month were estimated by HMRC based upon year on year growth in line with other UK nations.

LTT transactions up to the penultimate month are aligned with LTT statistics.

Go to Stamp Duty Land Tax guidance for the latest rates and information.

Go to Stamp Duty Land Tax rates from 1 December 2003 to 22 September 2022 and Stamp Duty: rates on land transfers before December 2003 for historic rates.

The latest release was published 09:30 28 November 2025 and was updated with provisional data from completed transactions during October 2025.

The next release will be published 09:30 09 January 2026 and will be updated with provisional data from completed transactions during November 2025.

https://webarchive.nationalarchives.gov.uk/ukgwa/20240320184933/https://www.gov.uk/government/statistics/monthly-property-transactions-completed-in-the-uk-with-value-40000-or-above">Archive versions of the Monthly property transactions completed in the UK with value of £40,000 or above are available via the UK Government Web Archive, from the National Archives.

[THIS DATASET HAS BEEN WITHDRAWN]. This dataset contains the linkages between designated features and their relevant critical loads or levels. The designated features come from the UK network of protected nature sites including: (i) Special Areas of Conservation (SAC) (ii) Special Protection Areas (SPA) (iii) Sites of Special Scientific Interest (SSSI) - England, Scotland, Wales (iv) Areas of Special Scientific Interest (ASSI) - Northern Ireland. Critical loads and levels are set under the auspices of the United Nations Economic Commission for Europe (UNECE) Convention on Long-Range Transboundary Air Pollution. For nutrient nitrogen critical loads are based on empirical evidence, mainly observations from experiments and targeted gradient studies. These empirical critical loads are assigned to habitat classes of the European Nature Information System (EUNIS) to enable consistency of habitat terminology and understanding across Europe. In order to assign the relevant critical load to Annex I features, A/SSSI habitat features, or habitats of Annex II/SPA features, habitat correspondence tables are used to determine the relationship between the EUNIS classes for which nitrogen critical loads are set and the interest features. Critical loads of acidity are based on soil and habitat types. They are set for six Broad Habitats; acid grassland, calcareous grassland, dwarf shrub heath, bogs, montane, unmanaged coniferous and broadleaved woodland. Critical Levels for air pollutants are not habitat specific and have been set to cover broad vegetation types (e.g. forest arable, semi-natural), often with critical values set for sensitive lichens and bryophytes. They have been derived from experiments and observation that show varied effects on vegetation including visible injury symptoms of exposure and species composition changes in semi-natural vegetation. Full details about this dataset can be found at https://doi.org/10.5285/c37cb567-6afc-4cf8-b0bc-8ec054346a6f

The benchmark interest rate In the Euro Area was last recorded at 2.15 percent. This dataset provides - Euro Area Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Net-Interest-Income Time Series for PlayAGS Inc. PlayAGS, Inc. designs and supplies gaming products and services for the gaming industry in the United States and internationally. It operates in three segments: Electronic Gaming Machines (EGM), Table Products, and Interactive Games (Interactive). The EGM segment offers a library of video and mechanical slot titles for the marketplace; EGM cabinets, including the Orion Starwall, Orion Curve Premium, Orion Rise, Big Red, Spectra UR43 Premium, Spectra SL49+ Premium, Spectra SL75+ with Premium and Core Content, Revel, Spectra UR49C, Spectra UR43, Orion Portrait, Orion Slant, Orion Curve, Orion Upright, ICON, and Spectra SL49+; and conversion kits that allow existing game titles to be converted to other game titles offered within that operating platform. This segment serves Class II Native American and Mexico gaming; and Class III Native American, commercial, and charitable jurisdictions. The Table Products segment provides table products, including live felt table games, side bets, progressives, card shufflers, signage, and other ancillary table game equipment; table technology related to blackjack, poker, baccarat, craps, and roulette; Dex S, a single deck card shuffler for poker tables; and the Pax S, a single-deck shuffler. This segment offers its products under the In Bet Gaming, Buster Blackjack, Double Draw Poker, and Criss Cross Poker. The Interactive segment provides a platform for business-to-business game aggregation used by real-money gaming; and business-to-consumer free-to-play social casino games through its mobile app, Lucky Play Casino. The company was formerly known as AP Gaming Holdco, Inc. and changed its name to PlayAGS, Inc. in December 2017. PlayAGS, Inc. was incorporated in 2005 and is headquartered in Las Vegas, Nevada.