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.

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.

Table B.3.1 presents quarterly mortgage rate data specific to the Irish market. These data include all euro and non-euro denominated mortgage lending in the Republic of Ireland only. New business refers to new mortgage lending drawdowns during the quarter, broken down by type of interest rate product (i.e. fixed, tracker and SVR). The data also provide further breakdown of mortgages for principal dwelling house (PDH) and buy-to-let (BTL) properties. Renegotiations of existing loans are not included. .hidden { display: none }

Jumbo 30-Year Fixed Mortgage Rates

Zillow Home Value Forecast and Cash Buyer Data

By Zillow Data [source]

About this dataset

This dataset tracks the average jumbo mortgage rate quoted on Zillow Mortgages for a 30-year, fixed-rate, jumbo mortgage in one-hour increments during business hours. It provides insight into changes in the housing market and helps consumers make wiser decisions with their investments. In addition to tracking monthly mortgage rates, our dataset also covers consumer's home types and housing stock, cash buyer data, Zillow Home Value Forecast (ZHVF), negative equity metrics, affordability forecasts for both mortgages and rents as well as historic data including historical ZHVI and household income. With this unique blend of financial and real estate information, users are empowered to make more informed decisions about their investments. The data is updated weekly with the most recent statistics available so that users always have access to up-to-date information

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How to use the dataset

How to Use This Dataset:

• To start exploring this dataset, identify what type of home you are interested in by selecting one of the four categories: “all homes” (Zillow defines all homes as single family, condominiums and coops with a county record); multifamily 5+; duplex/triplex; or condos/coops.
• Understand additional data products that are included such as Zillow Home Value Forecast (ZHVF), Cash Buyers % share, affordability metrics like mortgage affordability or rental affordability and historical ZHVI values along with its median value for particular households or geographies which needs deeper insights into other endogenous variables such detailed information like how many bedrooms a house has etc.

• Choose your geographic region on which you would want to collect more information– regions could include city breakdowns from nationwide level down till specific metropolitan etc . Also use special crosswalks available if needed between federally defined metrics for counties / metro areas combined with Zillow's own ones for greater accuracy when analysing external facors effect on data . To download all datasets at once - click here. .

• Gather more relevant external factors for analysis such as home values forecasts using our published methodology post given url , further to mention TransUnion credit bureau related debt amounts also consider median household incomes vis Bureaus of Labor Cost Indexes ; All these give us greater dimensional insights into market dynamics affecting any particular region finally culminating into deeper research findings when taken together . The reasons behind any fluctions observed can be properly derived as a result .

          Finally make sure that proper attribution is alwys done following mentioned Terms Of Use while downloading since 'All Data Accessed And Downloaded From This Page Is Free For Public Use By Consumers , Media
  

Research Ideas

• Using the Mortgage Rate Data to devise strategies to help persons purchasing jumbo mortgages determine the best time and rates to acquire a loan.
• Analyzing trends in the market by investigating changes in affordability over time by studying rent and mortgage affordability, price-to-income ratios, and historical ZHVIs with cash buyers.
• Comparing different areas of housing markets over diverse geographies using data on all homes, condos/co-ops, multifamily dwellings 5+ units, duplexes/triplexes across various counties or metro areas

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: MortgageRateJumboFixed.csv | Column name | Description | |:---------------------------|:---------------------------------------------------------------------------------------------------------------| | Date | The date of the mortgage rate. (Date) | | TimePeriod | The time period of the ...

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 ...).

Source: From lending institutions and local authorities
The loan payments dataset stops in 2007.
The figures on fixed interest rate mortgages relate to mortgages which provide that the rate of interest may not be changed, or may only be changed at intervals of not less than one year.
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.

Source: From lending institutions and local authorities
The Loan payments dataset stops in 2007.
This data contains an unquantified element of refinancing of existing mortgages (e.g. involving the redemption of an existing mortgage and its replacement with a mortgage from a different lender).
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.

15 Year Mortgage Rate in the United States decreased to 5.51 percent in November 27 from 5.54 percent in the previous week. This dataset includes a chart with historical data for the United States 15 Year Mortgage 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.

Maryland Mortgage (Single Family Loans) FY 2011-2023

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1937611%2F10480239c86982edd70e7ae416a69af9%2FDesigner%20(4).jpeg?generation=1718944571454533&alt=media" alt="">

This dataset contains detailed information about single family loans provided by the Maryland Mortgage Program from FY 2011 to 2023. The Maryland Mortgage Program offers various forms of assistance, including Down Payment Assistance (DPA) and Partner Match programs, aimed at helping first-time homebuyers afford mortgages. This dataset includes total loan amounts, DPA loan amounts, and the number of mortgages financed across different counties in Maryland.

Columns Information

FY

• Description: Fiscal Year of the data.
• Data Type: Integer

County

• Description: Name of the county in Maryland.
• Data Type: String

Total Loan Amount

• Description: Total amount of loans provided in the county.
• Data Type: Numeric (Format: Currency, e.g., USD)

DPA Loan Amount

• Description: Total amount of Down Payment Assistance loans provided.
• Data Type: Numeric (Format: Currency, e.g., USD)

Mortgages Financed

• Description: Number of mortgages financed in the county.
• Data Type: Integer

DISCLAIMER: Some of the information may be related to the Department’s bond funded loan programs and should not be used for making investment decisions. For comprehensive financial information, please refer to the publicly accessible Electronic Municipal Market Access system (EMMA) and the Department’s website under Investor Information.

More information accessible here: Maryland DHCD Investor Information

Content

More details about each file are in the individual file descriptions.

Context

This is a dataset from the Federal Reserve hosted by the Federal Reserve Economic Database (FRED). FRED has a data platform found here and they update their information according to the frequency that the data updates. Explore the Federal Reserve using Kaggle and all of the data sources available through the Federal Reserve organization page!

• Update Frequency: This dataset is updated daily.

Acknowledgements

This dataset is maintained using FRED's API and Kaggle's API.

Cover photo by David Hellmann on Unsplash
Unsplash Images are distributed under a unique Unsplash License.

Description

This data set represents thousands of loans made through the Lending Club platform, which is a platform that allows individuals to lend to other individuals. Of course, not all loans are created equal. Someone who is a essentially a sure bet to pay back a loan will have an easier time getting a loan with a low interest rate than someone who appears to be riskier. And for people who are very risky? They may not even get a loan offer, or they may not have accepted the loan offer due to a high interest rate. It is important to keep that last part in mind, since this data set only represents loans actually made, i.e. do not mistake this data for loan applications!

Format

A data frame with 10,000 observations on the following 55 variables.

emp_title

Job title.

emp_length

Number of years in the job, rounded down. If longer than 10 years, then this is represented by the value 10.

state

Two-letter state code.

homeownership

The ownership status of the applicant's residence.

annual_income

Annual income.

verified_income

Type of verification of the applicant's income.

debt_to_income

Debt-to-income ratio.

annual_income_joint

If this is a joint application, then the annual income of the two parties applying.

verification_income_joint

Type of verification of the joint income.

debt_to_income_joint

Debt-to-income ratio for the two parties.

delinq_2y

Delinquencies on lines of credit in the last 2 years.

months_since_last_delinq

Months since the last delinquency.

earliest_credit_line

Year of the applicant's earliest line of credit

inquiries_last_12m

Inquiries into the applicant's credit during the last 12 months.

total_credit_lines

Total number of credit lines in this applicant's credit history.

open_credit_lines

Number of currently open lines of credit.

total_credit_limit

Total available credit, e.g. if only credit cards, then the total of all the credit limits. This excludes a mortgage.

total_credit_utilized

Total credit balance, excluding a mortgage.

num_collections_last_12m

Number of collections in the last 12 months. This excludes medical collections.

num_historical_failed_to_pay

The number of derogatory public records, which roughly means the number of times the applicant failed to pay.

months_since_90d_late

Months since the last time the applicant was 90 days late on a payment.

current_accounts_delinq

Number of accounts where the applicant is currently delinquent.

total_collection_amount_ever

The total amount that the applicant has had against them in collections.

current_installment_accounts

Number of installment accounts, which are (roughly) accounts with a fixed payment amount and period. A typical example might be a 36-month car loan.

accounts_opened_24m

Number of new lines of credit opened in the last 24 months.

months_since_last_credit_inquiry

Number of months since the last credit inquiry on this applicant.

num_satisfactory_accounts

Number of satisfactory accounts.

num_accounts_120d_past_due

Number of current accounts that are 120 days past due.

num_accounts_30d_past_due

Number of current accounts that are 30 days past due.

num_active_debit_accounts

Number of currently active bank cards.

total_debit_limit

Total of all bank card limits.

num_total_cc_accounts

Total number of credit card accounts in the applicant's history.

num_open_cc_accounts

Total number of currently open credit card accounts.

num_cc_carrying_balance

Number of credit cards that are carrying a balance.

num_mort_accounts

Number of mortgage accounts.

account_never_delinq_percent

Percent of all lines of credit where the applicant was never delinquent.

tax_liens

a numeric vector

public_record_bankrupt

Number of bankruptcies listed in the public record for this applicant.

loan_purpose

The category for the purpose of the loan.

application_type

The type of application: either individual or joint.

loan_amount

The amount of the loan the applicant received.

term

The number of months of the loan the applicant received.

interest_rate

Interest rate of the loan the applicant received.

installment

Monthly payment for the loan the applicant received.

grade

Grade associated with the loan.

sub_grade

Detailed grade associated with the loan.

issue_month

Month the loan was issued.

loan_status

Status of the loan.

initial_listing_status

Initial listing status of the loan. (I think this has to do with whether the lender provided the entire loan or if the loan is across multiple lenders.)

disbursement_method

Dispersement method of the loan.

balance

Current...

This dataset contains two sheets showing: The number of regulated mortgages outstanding as at end 2022 in the UK by region/country, broken down by interest rate type (for example fixed rate, Standard Variable Rate etc) The number regulated fixed rate mortgages outstanding as at end 2022 in the UK by region/country, broken down by the month in which the fixed rate ('incentive rate') ends The data was provided to the GLA by the FCA, and the source is FCA Mortgages Performance Product Sales Data (PSD007).

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 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 Maryland Mortgage Program provides help in the form of Down Payment Assistance, as well as a range of Partner Match programs from employers, developers and community organizations that can help you cover these down payment and closing costs. These programs may make it possible for first-time homebuyers to afford a mortgage when they would not be able to do so the conventional way. DISCLAIMER: Some of the information may be tied to the Department’s bond funded loan programs and should not be relied upon in making an investment decision. The Department provides comprehensive quarterly and annual financial information and operating data regarding its bonds and bond funded loan programs, all of which is posted on the publicly-accessible Electronic Municipal Market Access system website (commonly known as EMMA) that is maintained by the Municipal Securities Rulemaking Board, and on the Department’s website under Investor Information. More information accessible here: http://dhcd.maryland.gov/Investors/Pages/default.aspx

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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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...

The benchmark interest rate in Norway was last recorded at 4 percent. This dataset provides the latest reported value for - Norway Interest Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

Lending Club offers peer-to-peer (P2P) loans through a technological platform for various personal finance purposes and is today one of the companies that dominate the US P2P lending market. The original dataset is publicly available on Kaggle and corresponds to all the loans issued by Lending Club between 2007 and 2018. The present version of the dataset is for constructing a granting model, that is, a model designed to make decisions on whether to grant a loan based on information available at the time of the loan application. Consequently, our dataset only has a selection of variables from the original one, which are the variables known at the moment the loan request is made. Furthermore, the target variable of a granting model represents the final status of the loan, that are "default" or "fully paid". Thus, we filtered out from the original dataset all the loans in transitory states. Our dataset comprises 1,347,681 records or obligations (approximately 60% of the original) and it was also cleaned for completeness and consistency (less than 1% of our dataset was filtered out).

TARGET VARIABLE

The dataset includes a target variable based on the final resolution of the credit: the default category corresponds to the event charged off and the non-default category to the event fully paid. It does not consider other values in the loan status variable since this variable represents the state of the loan at the end of the considered time window. Thus, there are no loans in transitory states. The original dataset includes the target variable “loan status”, which contains several categories ('Fully Paid', 'Current', 'Charged Off', 'In Grace Period', 'Late (31-120 days)', 'Late (16-30 days)', 'Default'). However, in our dataset, we just consider loans that are either “Fully Paid” or “Default” and transform this variable into a binary variable called “Default”, with a 0 for fully paid loans and a 1 for defaulted loans.

EXPLANATORY VARIABLES

The explanatory variables that we use correspond only to the information available at the time of the application. Variables such as the interest rate, grade, or subgrade are generated by the company as a result of a credit risk assessment process, so they were filtered out from the dataset as they must not be considered in risk models to predict the default in granting of credit.

FULL LIST OF VARIABLES

Loan identification variables:

id: Loan id (unique identifier).

issue_d: Month and year in which the loan was approved.

Quantitative variables:

revenue: Borrower's self-declared annual income during registration.

dti_n: Indebtedness ratio for obligations excluding mortgage. Monthly information. This ratio has been calculated considering the indebtedness of the whole group of applicants. It is estimated as the ratio calculated using the co-borrowers’ total payments on the total debt obligations divided by the co-borrowers’ combined monthly income.

loan_amnt: Amount of credit requested by the borrower.

fico_n: Defined between 300 and 850, reported by Fair Isaac Corporation as a risk measure based on historical credit information reported at the time of application. This value has been calculated as the average of the variables “fico_range_low” and “fico_range_high” in the original dataset.

experience_c: Binary variable that indicates whether the borrower is new to the entity. This variable is constructed from the credit date of the previous obligation in LC and the credit date of the current obligation; if the difference between dates is positive, it is not considered as a new experience with LC.

Categorical variables:

emp_length: Categorical variable with the employment length of the borrower (includes the no information category)

purpose: Credit purpose category for the loan request.

home_ownership_n: Homeownership status provided by the borrower in the registration process. Categories defined by LC: “mortgage”, “rent”, “own”, “other”, “any”, “none”. We merged the categories “other”, “any” and “none” as “other”.

addr_state: Borrower's residence state from the USA.

zip_code: Zip code of the borrower's residence.

Textual variables

title: Title of the credit request description provided by the borrower.

desc: Description of the credit request provided by the borrower.

We cleaned the textual variables. First, we removed all those descriptions that contained the default description provided by Lending Club on its web form (“Tell your story. What is your loan for?”). Moreover, we removed the prefix “Borrower added on DD/MM/YYYY >” from the descriptions to avoid any temporal background on them. Finally, as these descriptions came from a web form, we substituted all the HTML elements by their character (e.g. “&” was substituted by “&”, “<” was substituted by “<”, etc.).

RELATED WORKS

This dataset has been used in the following academic articles:

Sanz-Guerrero, M. Arroyo, J. (2024). Credit Risk Meets Large Language Models: Building a Risk Indicator from Loan Descriptions in P2P Lending. arXiv preprint arXiv:2401.16458. https://doi.org/10.48550/arXiv.2401.16458

Ariza-Garzón, M.J., Arroyo, J., Caparrini, A., Segovia-Vargas, M.J. (2020). Explainability of a machine learning granting scoring model in peer-to-peer lending. IEEE Access 8, 64873 - 64890. https://doi.org/10.1109/ACCESS.2020.2984412