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.

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

Switzerland Mortgage Rate: Fixed: by Maturity: 10 Years data was reported at 1.779 % pa in Sep 2018. This records an increase from the previous number of 1.697 % pa for Aug 2018. Switzerland Mortgage Rate: Fixed: by Maturity: 10 Years data is updated monthly, averaging 2.290 % pa from Jan 2008 (Median) to Sep 2018, with 129 observations. The data reached an all-time high of 4.700 % pa in Jun 2008 and a record low of 1.520 % pa in Sep 2016. Switzerland Mortgage Rate: Fixed: by Maturity: 10 Years data remains active status in CEIC and is reported by Swiss National Bank. The data is categorized under Global Database’s Switzerland – Table CH.M005: Mortgage Rates.

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.

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.

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.

Switzerland Mortgage Rate: Fixed: by Maturity: 5 Years data was reported at 1.252 % pa in Sep 2018. This records an increase from the previous number of 1.201 % pa for Aug 2018. Switzerland Mortgage Rate: Fixed: by Maturity: 5 Years data is updated monthly, averaging 1.580 % pa from Jan 2008 (Median) to Sep 2018, with 129 observations. The data reached an all-time high of 4.500 % pa in Jun 2008 and a record low of 1.170 % pa in May 2017. Switzerland Mortgage Rate: Fixed: by Maturity: 5 Years data remains active status in CEIC and is reported by Swiss National Bank. The data is categorized under Global Database’s Switzerland – Table CH.M005: Mortgage Rates.

Context

Case study ABC is a multinational corporation and finance company which provides is into personal loan business. This company provides personal loans to individuals based on their credit policy. In 2015, the company has seen an increases in the loss rate. Which means that the number of people defaulting on the loan is increasing. The company believes that targeting the customers who are more risky at early stage of defaulting would help in reducing the loss rate.

• See definition in About this file below

Interesting Questions

Level 1: Specific data Questions:

1. What is the account roll forward rate for the months of Mar , Apr, May and June for TB0?

2. What is the Princple outstanding balance roll forward rate for the same months as above for TB0?

3. What is the average attempt intensity, RPC rate and PTP rate on accounts for the months of mar, Apr and May

Level 2: Open Ended questions

1. What QCs will you do on this data to ensure the data is accurate and useable?

 Show the results of the QCs as well

1. Is there any correlation between the princple balance remaining and the term completed  Term Completed: The months on books / tenure

2. Identify the agents who are showing the best and worst performance.  Please provide the metrics that you used to rate these agents  Please prepare one paragraph explaining the approach and results

Level 3: Analytics question

1. Profile/Classify the accounts the accounts based on riskiness to roll forward  You can either use simple data manipulation to understand the which group of accounts have the highest risk or  Build any regression model / decision tree to identify the riskiness

Level 4: Strategy Question:

1. Based on the analysis that you have done till now, could you please come up with a strategy that you would like to implement to improve the collection efficiency (i.e., reduce roll forward rate). Please note that the aim of the company is to reduce the principle outstanding balance that has rolled forward.

Data Available

Loan Details: Contains information regarding the loan

Loan Status Mar to May: The status of the loan at the beginning of the month from Mar to May. Only TB0 Accounts have been given for the purpose of this case study

Loan Status Apr to June: The status of the loan at the beginning of the month from Apr to June. Only the status of the loans that have been extracted for TB0 have been obtained

Historical 6 months details: Various metrics have been calculated for the past 6 months for each of the TB0 accounts that are appearing in March to April

Variable Names:

Paidcure: The number of times in the past 6 months an account has paid full amount due so that he is no longer delinquenty

Paiduncure: The number of times in the past 6 months an account has paid a partial amount of the total due amount so that account is still delinquent

Unpaid: The number of times in the past 6 months an account has not paid any amount of the total due amount so that account is still delinquent

Rollb: The number of times in past 6 months that an account has rolled backwards. i.e If account is in TB2 in current month and moved to TB1 or TB0 or Regular in the following month

Rollf: The number of times in past 6 months that an account has rolled forwards. i.e If account is in TB2 in current month and moved to TB3 in the following month

Num6mondel: The number of times an account is delinquent in past 6 months

Num3mondel: The number of times an account is delinquent in past 3 months

num6mosdel_2plus: The number of times an account is in bucket TB2, TB3, TB4, TB5 or TB6 in past 6 months

num3mosdel_2plus The number of times an account is in bucket TB2, TB3, TB4, TB5 or TB6 in past 3 months

max6del: The maximum bucket in the account was present in the past 6 months. For example if the maximum bucket is TB4 in past 6 months, then the value is 4

max3del: The maximum bucket in the account was present in the past 3 months. For example if the maximum bucket is TB5 in past 6 months, then the value is 5

Call Details:

Contains a summary of calls made to the TB0 accounts for Mar-May data

LoanID : The agent who called the customer who is delinquent

Total_contacts: The total number of times an agent has tried to call the account

Right Party Contact: The number of times an agent was able to reach the borrower

Promise to pay: The number of times the customer promised to pay when the call was made

Loan Id mapping: The call details and rest of the data available have two different Ids captured. This mapping file maps the loan id between the tables so that there can be a unique identifier for each consumer

Notes

Assumptions - Loan_id: unique ID associated to every loan - Month - Month of the same year - Bucket - Categorizing accounts at various level of default or non default

Acknowledgement

Febina Abdulrahiman

Financial Access and Usage

Global Comparative Ratios Across 189 Jurisdictions

By International Monetary Fund [source]

About this dataset

This dataset provides an unprecedented opportunity to explore global financial access and usage trends from 2004-2016 from 189 of the world's reporting jurisdictions—which cover over 99 percent of the total adult population. With 152 time series and 47 indicator ratios, this Financial Access Survey gives insight into ways that access to and usage of financial services differ by households vs small/medium enterprises, life vs non-life insurance, deposits & microfinance institutions as well as credit unions & financial cooperatives. Utilizing this data, we can gain a better understanding of how policies or shifts in the global economy may influence or relate to access or utilization of services in certain regions while having comparable cross-economy comparisons. The IMF Monetary and Financial Statistics Manual Compilation Guide is utilized for all methodologies used in accumulating these datasets, while all data is available “as-is” with no guarantee provided either express or implied. Are you looking for ways to implement insightful macroeconomic analyses? Download FAS 2004–2016 now!

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For more datasets, click here.

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How to use the dataset

The Financial Access Survey provides global supply-side data on access to and usage of financial services by households and firms for 189 reporting jurisdictions, covering 99 percent of the world’s adult population. With a robust selection of time series in this dataset, users can make meaningful insights into trends over time or across countries concerning various indicators related to access and usage of financial services. To help users navigate this large dataset, the following guide explains how to use the data most effectively.

Understanding The Dataset Columns

The columns in the dataset provide information about each indicator such as country name, indicator name, code for that indicator, its attribute (i.e., rate/ratio), when data is available for that particular indicator. Once you have identified an interesting measure/indicator whether it be credit union density or life insurance penetration rate measure in a given country during a certain year period then you can look up those numbers from the rows provided in this dataset .

Understanding The Different Years Available & Comparing Numbers Over Time

It is useful for users to compare different indicators over time by looking at specific years within this dataset which will allow us to see if rates are increasing or decreasing worldwide patterns across these trends among different countries based on these various measures listed provided in this survey such as mortgage lending rate or ratio GDP per capita etc that have been collected . We can therefore make use of our knowledge off economic changes that have occurred over time within certain parts of world , no matter if they are longer term economic effects due increases certain capabilities within a geographical area or shorter term changes due taxation laws by governments etc driving some people either towards using or away from using certain kinds financial products .

#### Comparing Between Countries

This datasets allows us direct comparisons between different countries with regards how many people are currently making use particular types off finances services , we certainly be able analyse current international relationships between services providers as well customers where ever concerned about particular attributes mentioned above whether being deposit interest rates small business credits terms tenders so forth . Knowing more about related dynamics helps build better user experiences with providers who understand needs risks impacts generating larger customer bases globally which often beneficial both parties involved exchange relationship so not forget always keep cross border motif whenever eye process from afar !

Research Ideas

• Comparing the access to and usage of financial services in different countries to better inform research policy decisions.
• Analyzing trends in financial access and usage by jurisdiction over time, to identify areas needing improvement in order to promote financial inclusion and stability.
• Cross-referencing FAS data with macroeconomic indicators such as GDP information to measure the potential impact of changes in level of access on economic growth or other metrics specific to a country or region of interest

Acknowledgements

If you use this dataset in yo...

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.

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.

Indonesia Banking Survey: Loan Interest Rate: Whole Year Estimation: in USD: Investment data was reported at 6.566 % in Mar 2025. This records an increase from the previous number of 6.446 % for Dec 2024. Indonesia Banking Survey: Loan Interest Rate: Whole Year Estimation: in USD: Investment data is updated quarterly, averaging 6.330 % from Mar 2012 (Median) to Mar 2025, with 53 observations. The data reached an all-time high of 6.961 % in Sep 2023 and a record low of 4.454 % in Mar 2022. Indonesia Banking Survey: Loan Interest Rate: Whole Year Estimation: in USD: Investment data remains active status in CEIC and is reported by Bank Indonesia. The data is categorized under Indonesia Premium Database’s Business and Economic Survey – Table ID.SE003: Banking Survey: Interest Rate. [COVID-19-IMPACT]

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.

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...

Mortgage Application in the United States increased by 0.20 percent in the week ending November 21 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.

The inherent fragility of the agricultural industry significantly restricts the financing channels available to new agricultural operating entities. Access to credit loans emerges as a pivotal means to address capital shortages among farmers and enhance production inputs. Drawing on survey data from 17,745 new agricultural operating entities engaged in food production in Lu’an City, Anhui Province, and agricultural households documented in the China Household Finance Survey Database, this paper employs the Logit model and the Heckman selection model to empirically analyze the loan decision-making behavior of these entities from two perspectives: loan willingness and credit scale. The research reveals that several key variables exert a significant positive influence on the borrowing willingness of grain producers. Specifically, the planting area range, input range per hectare, the rate range of return on investment, membership in cooperatives, and operation as a family farm all notably enhance their willingness to seek loans. Conversely, the net income per hectare and the number of crop types cultivated significantly diminish their inclination to borrow. Additionally, male operators and those with higher educational backgrounds demonstrate a stronger willingness to obtain loans. Furthermore, the study indicates that the planting area and membership in cooperatives also positively correlate with the scale of loans secured by these agricultural operating entities. Therefore, from the perspective of food security, it is essential to cultivate food-producing new agricultural operating entities. This requires a focus on the counter-cyclical adjustment of financial support, increasing credit support during years of low investment returns. Additionally, it is necessary to develop multiple forms of moderate-scale operations, enhance policy support, and boost the production enthusiasm of food-producing new agricultural operating entities.

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.

Context

Have you ever wondered how lenders use various factors such as credit score, annual income, the loan amount approved, tenure, debt-to-income ratio etc. and select your interest rates?

The process, defined as ‘risk-based pricing’, uses a sophisticated algorithm that leverages different determining factors of a loan applicant. Selection of significant factors will help develop a prediction algorithm which can estimate loan interest rates based on clients’ information. On one hand, knowing the factors will help consumers and borrowers to increase their credit worthiness and place themselves in a better position to negotiate for getting a lower interest rate. On the other hand, this will help lending companies to get an immediate fixed interest rate estimation based on clients information. Here, your goal is to use a training dataset to predict the loan rate category (1 / 2 / 3) that will be assigned to each loan in our test set.

You can use any combination of the features in the dataset to make your loan rate category predictions. Some features will be easier to use than others.

Acknowledgements

https://datahack.analyticsvidhya.com/contest/janatahack-machine-learning-for-banking/True/#ProblemStatement