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.

Context

The Federal Reserve sets interest rates to promote conditions that achieve the mandate set by the Congress — high employment, low and stable inflation, sustainable economic growth, and moderate long-term interest rates. Interest rates set by the Fed directly influence the cost of borrowing money. Lower interest rates encourage more people to obtain a mortgage for a new home or to borrow money for an automobile or for home improvement. Lower rates encourage businesses to borrow funds to invest in expansion such as purchasing new equipment, updating plants, or hiring more workers. Higher interest rates restrain such borrowing by consumers and businesses.

Content

This dataset includes data on the economic conditions in the United States on a monthly basis since 1954. The federal funds rate is the interest rate at which depository institutions trade federal funds (balances held at Federal Reserve Banks) with each other overnight. The rate that the borrowing institution pays to the lending institution is determined between the two banks; the weighted average rate for all of these types of negotiations is called the effective federal funds rate. The effective federal funds rate is determined by the market but is influenced by the Federal Reserve through open market operations to reach the federal funds rate target. The Federal Open Market Committee (FOMC) meets eight times a year to determine the federal funds target rate; the target rate transitioned to a target range with an upper and lower limit in December 2008. The real gross domestic product is calculated as the seasonally adjusted quarterly rate of change in the gross domestic product based on chained 2009 dollars. The unemployment rate represents the number of unemployed as a seasonally adjusted percentage of the labor force. The inflation rate reflects the monthly change in the Consumer Price Index of products excluding food and energy.

Acknowledgements

The interest rate data was published by the Federal Reserve Bank of St. Louis' economic data portal. The gross domestic product data was provided by the US Bureau of Economic Analysis; the unemployment and consumer price index data was provided by the US Bureau of Labor Statistics.

Inspiration

How does economic growth, unemployment, and inflation impact the Federal Reserve's interest rates decisions? How has the interest rate policy changed over time? Can you predict the Federal Reserve's next decision? Will the target range set in March 2017 be increased, decreased, or remain the same?

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 table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...).

Lower Limit of First Home Mortgage Rate: above LPR: Beijing data was reported at -0.450 % Point in 02 Dec 2025. This stayed constant from the previous number of -0.450 % Point for 01 Dec 2025. Lower Limit of First Home Mortgage Rate: above LPR: Beijing data is updated daily, averaging 0.550 % Point from Oct 2019 (Median) to 02 Dec 2025, with 2248 observations. The data reached an all-time high of 0.550 % Point in 25 Jun 2024 and a record low of -0.450 % Point in 02 Dec 2025. Lower Limit of First Home Mortgage Rate: above LPR: Beijing data remains active status in CEIC and is reported by The People's Bank of China. The data is categorized under China Premium Database’s Money Market, Interest Rate, Yield and Exchange Rate – Table CN.MA: Lower Limit of First Home Mortgage Rate: Prefecture Level City. After adjustment on December 15, 2023: the lower limits of the first and second sets of interest rate policies in the six districts of the city are respectively no less than the market quoted interest rate for loans of the corresponding period plus 10 basis points, and no less than the market quoted interest rate for loans of the corresponding period plus 60 basis points; The lower limits of the first and second sets of interest rate policies in the six non-urban districts are not lower than the market quoted interest rate for loans of the corresponding period, and not lower than the market quoted interest rate for loans of the corresponding period plus 55 basis points.

Lower Limit of First Home Mortgage Rate: above LPR: Fujian: Fuzhou data was reported at -0.200 % Point in 02 Apr 2024. This stayed constant from the previous number of -0.200 % Point for 01 Apr 2024. Lower Limit of First Home Mortgage Rate: above LPR: Fujian: Fuzhou data is updated daily, averaging 0.000 % Point from Oct 2019 (Median) to 02 Apr 2024, with 1639 observations. The data reached an all-time high of 0.000 % Point in 18 May 2022 and a record low of -0.200 % Point in 02 Apr 2024. Lower Limit of First Home Mortgage Rate: above LPR: Fujian: Fuzhou data remains active status in CEIC and is reported by The People's Bank of China. The data is categorized under China Premium Database’s Money Market, Interest Rate, Yield and Exchange Rate – Table CN.MA: Lower Limit of First Home Mortgage Rate: Prefecture Level City.

DESCRIPTION

Create a model that predicts whether or not a loan will be default using the historical data.

Problem Statement:

For companies like Lending Club correctly predicting whether or not a loan will be a default is very important. In this project, using the historical data from 2007 to 2015, you have to build a deep learning model to predict the chance of default for future loans. As you will see later this dataset is highly imbalanced and includes a lot of features that make this problem more challenging.

Domain: Finance

Analysis to be done: Perform data preprocessing and build a deep learning prediction model.

Content:

Dataset columns and definition:

credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise.

purpose: The purpose of the loan (takes values "credit_card", "debt_consolidation", "educational", "major_purchase", "small_business", and "all_other").

int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates.

installment: The monthly installments owed by the borrower if the loan is funded.

log.annual.inc: The natural log of the self-reported annual income of the borrower.

dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income).

fico: The FICO credit score of the borrower.

days.with.cr.line: The number of days the borrower has had a credit line.

revol.bal: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle).

revol.util: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available).

inq.last.6mths: The borrower's number of inquiries by creditors in the last 6 months.

delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years.

pub.rec: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments).

Steps to perform:

Perform exploratory data analysis and feature engineering and then apply feature engineering. Follow up with a deep learning model to predict whether or not the loan will be default using the historical data.

Tasks:

1. Feature Transformation

Transform categorical values into numerical values (discrete)

1. Exploratory data analysis of different factors of the dataset.

2. Additional Feature Engineering

You will check the correlation between features and will drop those features which have a strong correlation

This will help reduce the number of features and will leave you with the most relevant features

1. Modeling

After applying EDA and feature engineering, you are now ready to build the predictive models

In this part, you will create a deep learning model using Keras with Tensorflow backend

Lower Limit of First Home Mortgage Rate: Base Rate Discount: Fujian: Zhangzhou data was reported at 70.000 % in 07 Oct 2019. This stayed constant from the previous number of 70.000 % for 06 Oct 2019. Lower Limit of First Home Mortgage Rate: Base Rate Discount: Fujian: Zhangzhou data is updated daily, averaging 70.000 % from Jan 2019 (Median) to 07 Oct 2019, with 280 observations. The data reached an all-time high of 70.000 % in 07 Oct 2019 and a record low of 70.000 % in 07 Oct 2019. Lower Limit of First Home Mortgage Rate: Base Rate Discount: Fujian: Zhangzhou data remains active status in CEIC and is reported by The People's Bank of China. The data is categorized under China Premium Database’s Money Market, Interest Rate, Yield and Exchange Rate – Table CN.MA: Lower Limit of First Home Mortgage Rate: Prefecture Level City.

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.

Dataset underlying the Seniors First reverse mortgage comparison widget. Displays indicative rate types, features, and eligibility details for multiple Australian reverse-mortgage providers. Data is aggregated and refreshed periodically for consumer education.

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.

This dataset provides values for MORTGAGE RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

House price index is based on average new house price value at loan approval stage and therefore has not been adjusted for changes in the mix of houses and apartments sold.
Interest rates is based on building societies mortgage loans, published by Central Statistics Office up to 2007.
From 2008 interest rates is average rate of all 'mortgage lenders' reporting to the Central Bank.
From 2014 it is based on the floating rate for new customers as published by the Central Bank (Retail interest rates - Table B2.1). The reason for the drop between 2013 and
2014 is due to the difference in methodology - the 2014 data is the weighted average rate on new loan agreements. Further information can be found here:
http://www.centralbank.ie/polstats/stats/cmab/Documents/Retail_Interest_Rate_Statistics_Explanatory_Notes.pdf
Earnings is based on the average weekly earnings of adult workers in manufacturing industries, published by the Central Statistics Office. This series has been updated since 1996 using a new methodology and therefore it is not directly comparable with those for earlier years. House Construction Cost Index is based on the 1st day of the third month of each quarter.
Consumer Price index is based on the Consumer Price Index, published by the Central Statistics Office.
The most current data is published on these sheets. Previously published data may be subject to revision. Any change from the originally published data will be highlighted by a comment on the cell in question. These comments will be maintained for at least a year after the date of the value change.

Lower Limit of First Home Mortgage Rate: above LPR: Gansu: Pingliang data was reported at -0.300 % Point in 30 Sep 2023. This stayed constant from the previous number of -0.300 % Point for 29 Sep 2023. Lower Limit of First Home Mortgage Rate: above LPR: Gansu: Pingliang data is updated daily, averaging 0.000 % Point from Oct 2019 (Median) to 30 Sep 2023, with 1454 observations. The data reached an all-time high of 0.000 % Point in 18 May 2022 and a record low of -0.300 % Point in 30 Sep 2023. Lower Limit of First Home Mortgage Rate: above LPR: Gansu: Pingliang data remains active status in CEIC and is reported by The People's Bank of China. The data is categorized under China Premium Database’s Money Market, Interest Rate, Yield and Exchange Rate – Table CN.MA: Lower Limit of First Home Mortgage Rate: Prefecture Level City.

Mortgage Pricing Engine Market Outlook

According to our latest research, the global mortgage pricing engine market size reached USD 1.85 billion in 2024, reflecting robust demand for advanced pricing automation solutions across the mortgage sector. The market is projected to grow at a CAGR of 8.3% during the forecast period, reaching USD 3.64 billion by 2033. This growth is fueled by the increasing digital transformation initiatives in the banking and financial services sector, the rising complexity of mortgage products, and the need for real-time, accurate pricing to enhance both compliance and customer experience.

One of the primary factors driving the expansion of the mortgage pricing engine market is the accelerating pace of digitalization within the financial services industry. Lenders and financial institutions are increasingly adopting mortgage pricing engines to automate the traditionally manual and error-prone process of mortgage rate calculation. This shift not only streamlines operations but also ensures that pricing remains competitive and compliant with evolving regulations. The integration of advanced analytics and artificial intelligence within these engines enables lenders to analyze vast datasets quickly, offering personalized rates and improving the overall decision-making process. As consumer expectations for faster and more transparent mortgage approvals rise, the adoption of mortgage pricing engines is becoming indispensable for institutions aiming to maintain a competitive edge.

Another significant growth factor is the ever-evolving regulatory landscape, which necessitates the use of sophisticated technology to ensure compliance. Mortgage pricing engines are designed to automatically incorporate regulatory updates, helping lenders avoid costly penalties and reputational damage. This capability is particularly crucial in regions where regulatory requirements are frequently updated or vary significantly between jurisdictions. The ability to provide audit trails and ensure transparency in pricing calculations further enhances the appeal of these solutions. As regulators continue to emphasize consumer protection and fair lending practices, the demand for robust, compliant mortgage pricing engines is expected to surge.

Furthermore, the growing complexity and diversity of mortgage products have made manual pricing unsustainable for most lenders. With multiple loan types, fluctuating interest rates, and borrower-specific criteria, the need for dynamic and flexible pricing tools has never been greater. Mortgage pricing engines enable lenders to quickly adapt to market changes, optimize pricing strategies, and offer tailored solutions to different customer segments. This flexibility not only improves profitability but also enhances customer satisfaction by offering more relevant and competitive loan options. As the mortgage landscape continues to evolve, the role of pricing engines in supporting innovation and agility will only increase.

From a regional perspective, North America continues to dominate the mortgage pricing engine market, accounting for the largest share in 2024. This leadership is underpinned by the region's advanced financial infrastructure, high adoption of digital technologies, and stringent regulatory environment. Europe follows closely, driven by increasing digitalization in banking and growing demand for efficient mortgage origination solutions. Meanwhile, the Asia Pacific region is emerging as a high-growth market, supported by rapid urbanization, expanding middle class, and rising homeownership rates. Latin America and the Middle East & Africa are also witnessing steady adoption, although market penetration remains lower compared to developed regions. As financial institutions worldwide seek to modernize their lending processes, regional dynamics will continue to shape the evolution of the global mortgage pricing engine market.

Component Analysis

The mortgage pricing engine market is segmented by component into software and services, each playing a vital role in the overall ecosystem. The software segment is the primary revenue generator, accounting for a significant portion of the market in 2024. Mortgage pricing engine software provides the core functionality required for real-time rate calculation, compliance checks, and integration with other banking systems. The demand for advanced software solutions is being driven by the need for seamless, automated work

The benchmark interest rate in Canada was last recorded at 2.25 percent. This dataset provides - Canada Interest Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Lower Limit of First Home Mortgage Rate: above LPR: Fujian: Zhangzhou data was reported at -0.500 % Point in 28 May 2024. This stayed constant from the previous number of -0.500 % Point for 27 May 2024. Lower Limit of First Home Mortgage Rate: above LPR: Fujian: Zhangzhou data is updated daily, averaging 0.000 % Point from Oct 2019 (Median) to 28 May 2024, with 1695 observations. The data reached an all-time high of 0.000 % Point in 18 May 2022 and a record low of -0.500 % Point in 28 May 2024. Lower Limit of First Home Mortgage Rate: above LPR: Fujian: Zhangzhou data remains active status in CEIC and is reported by The People's Bank of China. The data is categorized under China Premium Database’s Money Market, Interest Rate, Yield and Exchange Rate – Table CN.MA: Lower Limit of First Home Mortgage Rate: Prefecture Level City.

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.