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.

United States Mortgage Fixed Rate: Mth Avg: 15 Year data was reported at 4.250 % pa in Oct 2018. This records an increase from the previous number of 4.080 % pa for Sep 2018. United States Mortgage Fixed Rate: Mth Avg: 15 Year data is updated monthly, averaging 5.680 % pa from Sep 1991 (Median) to Oct 2018, with 326 observations. The data reached an all-time high of 8.800 % pa in Jan 1995 and a record low of 2.660 % pa in Apr 2013. United States Mortgage Fixed Rate: Mth Avg: 15 Year data remains active status in CEIC and is reported by Federal Home Loan Mortgage Corporation, Freddie Mac. The data is categorized under Global Database’s United States – Table US.M012: Mortgage Interest Rate.

The average mortgage interest rate decreased in nearly every country in Europe between 2012 and 2021, followed by an increase in response to inflation. In the first quarter of 2025, Poland, Hungary, and Romania topped the ranking as the countries with the highest mortgage interest rates in Europe. Conversely, Finland, Belgium, and Spain displayed the lowest interest rates. The UK, which is the country with the largest value of mortgages outstanding, had an interest rate of **** percent.

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 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 dataset provides insights into the global housing market, covering various economic factors from 2015 to 2024. It includes details about property prices, rental yields, interest rates, and household income across multiple countries. This dataset is ideal for real estate analysis, financial forecasting, and market trend visualization.

📑 Column Descriptions

United States Mortgage Fixed Rate: Wk Ending: 30 Year data was reported at 4.540 % pa in 26 Jul 2018. This records an increase from the previous number of 4.520 % pa for 19 Jul 2018. United States Mortgage Fixed Rate: Wk Ending: 30 Year data is updated weekly, averaging 4.550 % pa from Jan 2004 (Median) to 26 Jul 2018, with 760 observations. The data reached an all-time high of 6.800 % pa in 20 Jul 2006 and a record low of 3.310 % pa in 22 Nov 2012. United States Mortgage Fixed Rate: Wk Ending: 30 Year data remains active status in CEIC and is reported by Federal Home Loan Mortgage Corporation, Freddie Mac. The data is categorized under Global Database’s USA – Table US.M012: Mortgage Interest Rate.

United States Mortgage Adjustable Rate: Mth Avg: 5 Year data was reported at 4.110 % pa in Nov 2018. This records an increase from the previous number of 4.080 % pa for Oct 2018. United States Mortgage Adjustable Rate: Mth Avg: 5 Year data is updated monthly, averaging 3.540 % pa from Jan 2005 (Median) to Nov 2018, with 167 observations. The data reached an all-time high of 6.360 % pa in Jul 2006 and a record low of 2.610 % pa in May 2013. United States Mortgage Adjustable Rate: Mth Avg: 5 Year data remains active status in CEIC and is reported by Federal Home Loan Mortgage Corporation, Freddie Mac. The data is categorized under Global Database’s United States – Table US.M012: Mortgage Interest Rate.

United States Mortgage Fixed Rate: Mth Avg: 30 Year data was reported at 4.870 % pa in Nov 2018. This records an increase from the previous number of 4.830 % pa for Oct 2018. United States Mortgage Fixed Rate: Mth Avg: 30 Year data is updated monthly, averaging 7.635 % pa from Apr 1971 (Median) to Nov 2018, with 572 observations. The data reached an all-time high of 18.450 % pa in Oct 1981 and a record low of 3.350 % pa in Dec 2012. United States Mortgage Fixed Rate: Mth Avg: 30 Year data remains active status in CEIC and is reported by Federal Home Loan Mortgage Corporation, Freddie Mac. The data is categorized under Global Database’s United States – Table US.M012: Mortgage Interest Rate.

United States Mortgage Adjustable Rate: Wk Ending: 5 Year: Margin data was reported at 2.770 % pa in 29 Nov 2018. This records an increase from the previous number of 2.760 % pa for 22 Nov 2018. United States Mortgage Adjustable Rate: Wk Ending: 5 Year: Margin data is updated weekly, averaging 2.740 % pa from Jan 2005 (Median) to 29 Nov 2018, with 726 observations. The data reached an all-time high of 2.800 % pa in 04 May 2006 and a record low of 0.740 % pa in 19 Apr 2012. United States Mortgage Adjustable Rate: Wk Ending: 5 Year: Margin data remains active status in CEIC and is reported by Federal Home Loan Mortgage Corporation, Freddie Mac. The data is categorized under Global Database’s United States – Table US.M012: Mortgage Interest Rate.

Graph and download economic data for 15-Year Fixed Rate Mortgage Average in the United States (MORTGAGE15US) from 1991-08-30 to 2025-11-26 about 15-year, mortgage, fixed, interest rate, interest, rate, and USA.

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 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...

This dataset contains an history of nearly all of the real estate transactions concerning a single house/apartment in France from 2014 to today. Some variables likely to have an impact on the price of real estate are also provided as time series: the households income levels per city, the average debt level of french peoples, the average amount of savings of french people, the interest rates of loans, the price of the rent per city, the number of housings and number of vacant housings per city.

This dataset is provided under a permissive licence, and is free to use for commercial applications. It has a vocation of helping research concerning the dynamics of real estate prices.

The dataset consists in extraction from several openly available datasets put together in a practical format: The DVF+ database of real estate transactions, the IRCOM dataset of household incomes and income taxes, average interest rates of real estate loans from the banque de france website, the LOVAC dataset of number of vacant and occupied housings per city,~~ the OECD dataset of financial assets per capita~~, the "carte des loyers" dataset of 2018 and 2022 which list the average price of the rent per square meter, the Indice de Référence des Loyers (IRL) time series which is an index defining the maximum rent increase that can be applied to an already rented housing and is calculated every 3 months as the inflation adjusted buying power of 100€ in 1998, the TEC00104 eurostat dataset of debt levels.

Mortgage Rate in Sweden decreased to 2.80 percent in September from 2.84 percent in August of 2025. This dataset includes a chart with historical data for Sweden Average Interest Rate on New Agreements for Mortgages to Households.

United States WAS: Total Points: FHA 203 data was reported at 0.730 % in 20 Jul 2018. This records an increase from the previous number of 0.690 % for 13 Jul 2018. United States WAS: Total Points: FHA 203 data is updated weekly, averaging 1.110 % from Jan 1990 (Median) to 20 Jul 2018, with 1490 observations. The data reached an all-time high of 4.210 % in 06 Nov 1992 and a record low of -0.460 % in 30 May 2014. United States WAS: Total Points: FHA 203 data remains active status in CEIC and is reported by Mortgage Bankers Association. The data is categorized under Global Database’s USA – Table US.M013: Weekly Applications Survey: Mortgage Interest Rate.

The benchmark interest rate in China was last recorded at 3 percent. This dataset provides the latest reported value for - China Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

United States WAS: Total Points: 5-Year ARM data was reported at 0.320 % in 27 Jul 2018. This records an increase from the previous number of 0.290 % for 20 Jul 2018. United States WAS: Total Points: 5-Year ARM data is updated weekly, averaging 0.370 % from Jan 2011 (Median) to 27 Jul 2018, with 394 observations. The data reached an all-time high of 0.680 % in 16 Mar 2018 and a record low of 0.050 % in 30 May 2014. United States WAS: Total Points: 5-Year ARM data remains active status in CEIC and is reported by Mortgage Bankers Association. The data is categorized under Global Database’s USA – Table US.M013: Weekly Applications Survey: Mortgage Interest Rate.

Research in modelling housing market dynamics using agent-based models (ABMs) has grown due to the rise of accessible individual-level data. This research involves forecasting house prices, analysing urban regeneration, and the impact of economic shocks. There is a trend towards using machine learning (ML) algorithms to enhance ABM decision-making frameworks. This study investigates exogenous shocks to the UK housing market and integrates reinforcement learning (RL) to adapt housing market dynamics in an ABM. Results show agents can learn real-time trends and make decisions to manage shocks, achieving goals like adjusting the median house price without pre-determined rules. This model is transferable to other housing markets with similar complexities. The RL agent adjusts mortgage interest rates based on market conditions. Importantly, our model shows how a central bank agent learned conservative behaviours in sensitive scenarios, aligning with a 2009 study, demonstrating emergent behavioural patterns.