Retail Interest Rates - Mortgage Rates. Published by Central Bank of Ireland. Available under the license Creative Commons Attribution 4.0 (CC-BY-4.0).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....

Long term dataset showing the 30 year fixed rate mortgage average in the United States since 1971.

Fixed 30-year mortgage rates in the United States averaged 6.71 percent in the week ending March 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 values for 30 YEAR MORTGAGE RATE reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

The benchmark interest rate in Sweden was last recorded at 2.25 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.

Graph and download economic data for 15-Year Fixed Rate Mortgage Average in the United States (MORTGAGE15US) from 1991-08-30 to 2025-03-20 about 15-year, fixed, mortgage, interest rate, interest, rate, and USA.

Market DataResidential Mortgage Debt Outstanding—Enterprise Share, 1990 – 2010Total mortgages held or securitized by Fannie Mae and Freddie Mac as a Percentage of Residential Mortgage Debt Outstanding, 1990 – 2010. Note: Currently, FHFA does not have any plans to update this dataset through more recent periods.Single-Family Mortgages Originated and Outstanding, 1990 – 2011 Q2Statistics for conventional and government-insured or -guaranteed loans and, within each of those sectors, for fixed-rate and adjustable-rate mortgages. Conventional loans are also divided into jumbo and non-jumbo loans. Note: Currently, FHFA does not have any plans to update this dataset through more recent periods.​ Treasury and Federal Reserve Purchase Programs for GSE and Mortgage-Related Securities Data on activities by the Department of the Treasury and the Federal Reserve System to support mortgage markets through purchases of securities issued by Fannie Mae, Freddie Mac, and the Federal Home Loan Banks and by Ginnie Mae, a federal agency that guarantees securities backed by mortgages insured or guaranteed by the Federal Housing Administration, the Department of Veterans Affairs, and other federal agencies. More details are available on the Treasury and Federal Reserve Purchase Programs for GSE and Mortgage-Related Securities page. ​Note: Currently, FHFA does not have any plans to update this dataset through more recent periods.

This dataset provides a comprehensive monthly breakdown of mortgage loan data spanning from 1986 to 2019. Key metrics included are the contract interest rate, initial fees and charges, effective interest rate, term to maturity, mortgage loan amount, purchase price, and the loan-to-price ratio. The data offers valuable insights for analyzing trends in the mortgage market over more than three decades, making it a crucial resource for economists, financial analysts, and researchers interested in the evolution of housing finance. Additionally, the dataset can be used for predictive modeling and comparative market analysis.

Lower Limit of First Home Mortgage Rate: above LPR: Beijing data was reported at -0.450 % Point in 25 Mar 2025. This stayed constant from the previous number of -0.450 % Point for 24 Mar 2025. Lower Limit of First Home Mortgage Rate: above LPR: Beijing data is updated daily, averaging 0.550 % Point from Oct 2019 (Median) to 25 Mar 2025, with 1996 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 25 Mar 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.

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.

Mortgage Rate in Sweden decreased to 3.23 percent in January from 3.31 percent in December of 2024. This dataset includes a chart with historical data for Sweden Average Interest Rate on New Agreements for Mortgages to Households.

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

Chile Mortgage Rate: Mortgage Loan: Endorsable: Term: 1 to 8 Years data was reported at 2.949 % pa in Jan 2018. This records an increase from the previous number of 2.882 % pa for Dec 2017. Chile Mortgage Rate: Mortgage Loan: Endorsable: Term: 1 to 8 Years data is updated monthly, averaging 4.572 % pa from Dec 2000 (Median) to Jan 2018, with 206 observations. The data reached an all-time high of 8.844 % pa in Dec 2000 and a record low of 2.683 % pa in May 2017. Chile Mortgage Rate: Mortgage Loan: Endorsable: Term: 1 to 8 Years data remains active status in CEIC and is reported by Financial Market Commission. The data is categorized under Global Database’s Chile – Table CL.M008: Mortgage Rate.

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.

The International Bank for Reconstruction and Development (IBRD) loans are public and publicly guaranteed debt extended by the World Bank Group. IBRD loans are made to, or guaranteed by, countries that are members of IBRD. IBRD may also make loans to IFC. IBRD lends at market rates. Data are in U.S. dollars calculated using historical rates. This dataset contains the latest available snapshot of the Statement of Loans. The World Bank complies with all sanctions applicable to World Bank transactions.

This table contains 38 series, with data starting from 1957 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 item: Canada), Rates (38 items: Bank rate; Chartered bank administered interest rates - prime business; Chartered bank - consumer loan rate; Forward premium or discount (-), United States dollars in Canada: 1 month; ...).

The benchmark interest rate in China was last recorded at 3.10 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.

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.

Lower Limit of First Home Mortgage Rate: above LPR: Hubei: Shiyan data was reported at -0.200 % Point in 31 Dec 2022. This stayed constant from the previous number of -0.200 % Point for 30 Dec 2022. Lower Limit of First Home Mortgage Rate: above LPR: Hubei: Shiyan data is updated daily, averaging 0.000 % Point from Oct 2019 (Median) to 31 Dec 2022, with 1181 observations. The data reached an all-time high of 0.000 % Point in 14 May 2022 and a record low of -0.200 % Point in 31 Dec 2022. Lower Limit of First Home Mortgage Rate: above LPR: Hubei: Shiyan 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.

This table contains 71 series, with data starting from 1934 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Rates (71 items: Bank rate; last Tuesday or last Thursday; Bank rate; Chartered bank administered interest rates - prime business; Chartered bank - consumer loan rate ...).