Mortgage Application in the United States decreased by 0.50 percent in the week ending August 22 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.

MBA Mortgage Market Index in the United States decreased to 275.80 points in August 22 from 277.10 points in the previous week. This dataset includes a chart with historical data for the United States MBA Mortgage Market Index.

The U.S. mortgage market has declined notably since 2020 and 2021, mostly due to the effect of higher borrowing costs on refinance mortgages. The value of refinancing mortgage originations, amounted to 190 billion U.S. dollars in the fourth quarter of 2024, down from a peak of 851 billion U.S. dollars in the fourth quarter of 2020. The value of mortgage loans for the purchase of a property recorded milder fluctuations, with a value of 304 billion U.S. dollars in the fourth quarter of 2024. According to the forecast, mortgage lending is expected to slightly increase until the end of 2026. The cost of mortgage borrowing in the U.S. Mortgage interest rates in the U.S. rose dramatically in 2022, peaking in the final quarter of 2024. In 2020, a homebuyer could lock in a 30-year fixed interest rate of under three percent, whereas in 2024, the average rate for the same mortgage type exceeded 6.6 percent. This has led to a decline in homebuyer sentiment, and an increasing share of the population pessimistic about buying a home in the current market. The effect of a slower housing market on property prices and rents According to the S&P/Case Shiller U.S. National Home Price Index, housing prices experienced a slight correction in early 2023, as property transactions declined. Nevertheless, the index continued to grow in the following months. On the other hand, residential rents have increased steadily since 2000.

The value of residential mortgage lending picked up across three of the 16 ranked European countries in 2024. In the UK, new lending in the fourth quarter of the year amounted to **** billion euros, up from ***** billion euros in the same quarter in 2023. This trend was also observed across the rest of the large mortgage markets in Europe — Germany, France, and the Netherlands. One of the main reasons for the recovery was the easing of the mortgage interest rates.

Following the COVID-19 pandemic in 2020, the number of residential mortgage approvals in the UK plummeted. As the measures eased, the market rebounded, peaking at ******* mortgage approvals in November 2020. In 2022 and 2023, mortgage lending declined again as a response to the rising mortgage interest rates and the cooling of the housing market. In May 2025, the number of mortgage approvals exceeded *******—up from about ****** in the same month a year ago. The increase indicated a rise in mortgage demand and an improvement in consumer sentiment.

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using Home Mortgage Disclosure Act (HMDA) data to show mortgage loan applications, originations, denials, and amounts, for 2015, by census tract in the Atlanta Region.

Attributes:

GEOID10 = 2010 Census tract identifier (combination of FIPS codes for state, county, and tract)

County = County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

Area_Name = 2010 Census tract number and county name

Total_Population_ACS_2016 = # Total population estimate, 2016 (American Community Survey)

Total_Population_ACS_MOE_2016 = # Total population estimate (Margin of Error), 2016 (American Community Survey)

Planning_Region = Planning region designation for ARC purposes

AcresLand = Land area within the tract (in acres)

AcresWater = Water area within the tract (in acres)

AcresTotal = Total area within the tract (in acres)

SqMi_Land = Land area within the tract (in square miles)

SqMi_Water = Water area within the tract (in square miles)

SqMi_Total = Total area within the tract (in square miles)

TRACTCE10 = Census tract Federal Information Processing Series (FIPS) code. Census tracts are identified by an up to four-digit integer number and may have an optional two-digit suffix; for example 1457.02 or 23. The census tract codes consist of six digits with an implied decimal between the fourth and fifth digit corresponding to the basic census tract number but with leading zeroes and trailing zeroes for census tracts without a suffix. The tract number examples above would have codes of 145702 and 002300, respectively.

CountyName = County Name

ALL_LOAN_APPS_2015 = All Loan Applications, 2015

ALL_LOAN_ORIG_2015 = All Loan Originations, 2015

ALL_LOAN_DENIALS_2015 = All Loan Denials, 2015

HOME_PURCH_LOAN_APPS_2015 = Home Purchase Loan Applications, 2015

HOME_PURCH_LOAN_ORIG_2015 = Home Purchase Loan Originations, 2015

HOME_PURCH_LOAN_DENIALS_2015 = Home Purchase Loan Denials, 2015

HOME_REFI_LOAN_APPS_2015 = Home Refinancing Loan Applications, 2015

HOME_REFI_LOAN_ORIG_2015 = Home Refinancing Loan Originations, 2015

HOME_REFI_LOAN_DENIALS_2015 = Home Refinancing Loan Denials, 2015

MED_AMT_LOAN_APLD_1K = Median Amount of Loan Applied for, $1k

MED_AMT_LOAN_ORIG_1K = Median Amount of Loan Originated, $1k

MED_AMT_LOAN_DEND_1K = Median Amount of Loan Denied, $1k

MED_AMT_HOME_PURCH_LOAN_APLD_1K = Median Amount of Home Purchase Loan Applied for, $1k

MED_AMT_HOME_PURCH_LOAN_ORIG_1K = Median Amount of Home Purchase Loan Originated, $1k

MED_AMT_HOME_PURCH_LOAN_DEND_1K = Median Amount of Home Purchase Loan Denied, $1k

MED_AMT_HOME_REFI_LOAN_APLD_1K = Median Amount of Home Refinancing Loan Applied for, $1k

MED_AMT_HOME_REFI_LOAN_ORIG_1K = Median Amount of Home Refinancing Loan Originated, $1k

MED_AMT_HOME_REFI_LOAN_DEND_1K = Median Amount of Home Refinancing Loan Denied, $1k

MED_INCOME_FOR_LOAN_APLD_1K = Median Applicant Income for Loan Applied for, $1k

MED_INCOME_FOR_LOAN_ORIG_1K = Median Applicant Income for Loan Originated, $1k

MED_INCOME_FOR_LOAN_DEND_1K = Median Applicant Income for Loan Denied, $1k

MED_INCOME_PURCH_LOAN_APLD_1K = Median Applicant Income for Home Purchase Loan Applied for, $1k

MED_INCOME_PURCH_LOAN_ORIG_1K = Median Applicant Income for Home Purchase Loan Originated, $1k

MED_INCOME_PURCH_LOAN_DEND_1K = Median Applicant Income for Home Purchase Loan Denied, $1k

MED_INCOME_REFI_LOAN_APLD_1K = Median Applicant Income for Home Refinancing Loan Applied for, $1k

MED_INCOME_REFI_LOAN_ORIG_1K = Median Applicant Income for Home Refinancing Loan Originated, $1k

MED_INCOME_REFI_LOAN_DEND_1K = Median Applicant Income for Home Refinancing Loan Denied, $1k

LOANS_ORIG_PER_SQ_MI_2015 = Loans Originated per Square Mile, 2015

HOME_PURCH_LOANS_ORIG_SQMI_2015 = Loans Originated for Home Purchase per Square Mile, 2015

HOME_REFI_LOANS_ORIG_SQMI_2015 = Loans Originated for Home Refinancing per Square Mile, 2015

PCT_LOAN_APPS_ORIG_2015 = % of Loan Applications Originated, 2015

PCT_HOME_PURCH_LN_APPS_ORG_2015 = % of Home Purchase Loan Applications Originated, 2015

PCT_HOME_REFI_LN_APPS_ORG_2015 = % of Home Refinancing Loan Applications Originated, 2015

Chg_Loans_Orig_2012_15 = Change in Loan Originations, 2012-2015

Chg_Purch_Loans_Orig_2012_15 = Change in Loan Originations for Home Purchase, 2012-2015

Chg_Refi_Loans_Orig_2012_15 = Change in Loan Originations for Home Refinancing, 2012-2015

Chg_Loans_Orig_SqMi_2012_15 = Change in Loans Originated per Square Mile, 2012-2015

Chg_PurLoans_Orig_SqMi_2012_15 = Change in Loans Originated for Home Purchase per Square Mile, 2012-2015

Chg_RefLoans_Orig_SqMi_2012_15 = Change in Loans Originated for Home Refinancing per Square Mile, 2012-2015

ChgPct_Loan_Apps_Orig_2012_15 = Change in % of Loan Applications Originated, 2012-2015

ChgPct_PurLoan_AppsOrig_2012_15 = Change in % of Home Purchase Loan Applications Originated, 2012-2015

ChgPct_RefLoan_AppsOrig_2012_15 = Change in % of Home Refinancing Loan Applications Originated, 2012-2015

Chg_Loans_Orig_2014_15 = Change in Loan Originations, 2014-2015

Chg_Purch_Loans_Orig_2014_15 = Change in Loan Originations for Home Purchase, 2014-2015

Chg_Refi_Loans_Orig_2014_15 = Change in Loan Originations for Home Refinancing, 2014-2015

Chg_Loans_Orig_SqMi_2014_15 = Change in Loans Originated per Square Mile, 2014-2015

Chg_PurLoans_Orig_SqMi_2014_15 = Change in Loans Originated for Home Purchase per Square Mile, 2014-2015

Chg_RefLoans_Orig_SqMi_2014_15 = Change in Loans Originated for Home Refinancing per Square Mile, 2014-2015

ChgPct_Loan_Apps_Orig_2014_15 = Change in % of Loan Applications Originated, 2014-2015

ChgPct_PurLoan_AppsOrig_2014_15 = Change in % of Home Purchase Loan Applications Originated, 2014-2015

ChgPct_RefLoan_AppsOrig_2014_15 = Change in % of Home Refinancing Loan Applications Originated, 2014-2015

last_edited_date = Last date the feature was edited by ARC

Source: Home Mortgage Disclosure Act (HMDA), Atlanta Regional Commission

Date: 2015

For additional information, please visit the Atlanta Regional Commission at www.atlantaregional.com.

Mortgage Approvals in the United Kingdom increased to 65.35 Thousand in July from 64.57 Thousand in June of 2025. This dataset provides the latest reported value for - United Kingdom Mortgage Approvals - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

In the United States, interest rates for all mortgage types started to increase in 2021. This was due to the Federal Reserve introducing a series of hikes in the federal funds rate to contain the rising inflation. In the fourth quarter of 2024, the 30-year fixed rate rose slightly, to **** percent. Despite the increase, the rate remained below the peak of **** percent in the same quarter a year ago. Why have U.S. home sales decreased? Cheaper mortgages normally encourage consumers to buy homes, while higher borrowing costs have the opposite effect. As interest rates increased in 2022, the number of existing homes sold plummeted. Soaring house prices over the past 10 years have further affected housing affordability. Between 2013 and 2023, the median price of an existing single-family home risen by about ** percent. On the other hand, the median weekly earnings have risen much slower. Comparing mortgage terms and rates Between 2008 and 2023, the average rate on a 15-year fixed-rate mortgage in the United States stood between **** and **** percent. Over the same period, a 30-year mortgage term averaged a fixed-rate of between **** and **** percent. Rates on 15-year loan terms are lower to encourage a quicker repayment, which helps to improve a homeowner’s equity.

Mortgage rates increased at a record pace in 2022, with the 10-year fixed mortgage rate doubling between March 2022 and December 2022. With inflation increasing, the Bank of England introduced several bank rate hikes, resulting in higher mortgage rates. In May 2025, the average 10-year fixed rate interest rate reached **** percent. As borrowing costs get higher, demand for housing is expected to decrease, leading to declining market sentiment and slower house price growth. How have the mortgage hikes affected the market? After surging in 2021, the number of residential properties sold declined in 2023, reaching just above *** million. Despite the number of transactions falling, this figure was higher than the period before the COVID-19 pandemic. The falling transaction volume also impacted mortgage borrowing. Between the first quarter of 2023 and the first quarter of 2024, the value of new mortgage loans fell year-on-year for five straight quarters in a row. How are higher mortgages affecting homebuyers? Homeowners with a mortgage loan usually lock in a fixed rate deal for two to ten years, meaning that after this period runs out, they need to renegotiate the terms of the loan. Many of the mortgages outstanding were taken out during the period of record-low mortgage rates and have since faced notable increases in their monthly repayment. About **** million homeowners are projected to see their deal expire by the end of 2026. About *** million of these loans are projected to experience a monthly payment increase of up to *** British pounds by 2026.

Graph and download economic data for Delinquency Rate on Single-Family Residential Mortgages, Booked in Domestic Offices, All Commercial Banks (DRSFRMACBS) from Q1 1991 to Q2 2025 about domestic offices, delinquencies, 1-unit structures, mortgage, family, residential, commercial, domestic, banks, depository institutions, rate, and USA.

Abstract copyright UK Data Service and data collection copyright owner.The Survey of Mortgage Lenders (SML) was launched on 1st April 1992 to succeed the 5% Sample Survey of Building Society Mortgage Completions (BSM) (See GN:33191). The aims were to improve the survey in three principal ways: a) to broaden the range of institutions surveyed to incorporate other mortgage lenders in addition to building societies and Abbey National. With the entry of the high street banks and then the centralised lenders into the mortgage market, information provided by the building societies no longer represented the whole market in the way it did when the BSM was set up in the 1960s. b) to extend its coverage to include further advances, remortgages and top-up loans in addition to first mortgages. c) to increase the level of detail on the questionnaire especially with respect to the characteristics of the mortgage loan. An important consideration for users of the data is that the SML figures allow continuity with the BSM survey results to be maintained for a reasonable period. Main Topics: Financial institution code, date mortgage completed, whether dwelling is wholly or partly occupied by borrower. Mortgage amount, type of advance, whether solely for purchase of property, period of mortgage, gross rate of interest, whether the interest charged is fixed or variable rate, whether interest payments are discounted or deferred, repayment method, source of mortgage business, purchase price and whether discounted in any way, location of dwelling, whether new, age of dwelling, type of dwelling, number of habitable rooms, number, sex and age of borrowers, basic income of main borrower, other income and total income on which mortgage is based, whether applicant previously owner occupier, previous tenure. The institutions are divided into four strata according to the size of their assets. All the largest were asked to complete questionnaires on a sample of 5 per cent of their new mortgage advances. Mortgages are included if their reference numbers end in specified digits chosen so that every twentieth mortgage is selected. Institutions in the next stratum are arranged in order of size of assets and alternate institutions chosen each of which are asked to complete questionnaires on 10 per cent of their mortgages. In the next stratum 20 per cent of the mortgages of every fourth institution are obtained. The smallest institutions are completely excluded.

Following the drastic increase directly after the COVID-19 pandemic, the delinquency rate started to gradually decline, falling below *** percent in the second quarter of 2023. In the second half of 2023, the delinquency rate picked up, but remained stable throughout 2024. In the first quarter of 2025, **** percent of mortgage loans were delinquent. That was significantly lower than the **** percent during the onset of the COVID-19 pandemic in 2020 or the peak of *** percent during the subprime mortgage crisis of 2007-2010. What does the mortgage delinquency rate tell us? The mortgage delinquency rate is the share of the total number of mortgaged home loans in the U.S. where payment is overdue by 30 days or more. Many borrowers eventually manage to service their loan, though, as indicated by the markedly lower foreclosure rates. Total home mortgage debt in the U.S. stood at almost ** trillion U.S. dollars in 2024. Not all mortgage loans are made equal ‘Subprime’ loans, being targeted at high-risk borrowers and generally coupled with higher interest rates to compensate for the risk. These loans have far higher delinquency rates than conventional loans. Defaulting on such loans was one of the triggers for the 2007-2010 financial crisis, with subprime delinquency rates reaching almost ** percent around this time. These higher delinquency rates translate into higher foreclosure rates, which peaked at just under ** percent of all subprime mortgages in 2011.

The Pre-1990 HMDA Aggregation Data were prepared annually during this period by the FFIEC on behalf of institutions reporting HMDA data. The Aggregation Data consists of home purchase and home improvement loans that a depository institution originated or purchased during each calendar year. The collected HMDA data were individually aggregated up to the tract level by the reporting depository institution and submitted accordingly to the FFIEC. Individual records are the summary of loan activity for the specified respondent for the indicated census tract except when the census tract numbers were either 888888 or 999999. The 888888 tract records are the sum of all loan activity by the reporter outside of the MSA being reported, but not appearing in any other MSA report. The 999999 tract records are the consolidated county summary data for loans made in untracted counties or counties with 1980 total population less than 30,000. The 1988 and 1989 Aggregation Data files include aggregated data from nondepository institutions, specifically mortgage banking subsidiaries of bank holding companies.

30 Year Mortgage Rate in the United States decreased to 6.56 percent in August 28 from 6.58 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

Mortgage interest rates worldwide varied greatly in June 2025, from less than ******percent in many European countries to as high as ***percent in Turkey. The average mortgage rate in a country depends on the central bank's base lending rate and macroeconomic indicators such as inflation and forecast economic growth. Since 2022, inflationary pressures have led to rapid increases in mortgage interest rates. Which are the leading mortgage markets? An easy way to estimate the importance of the mortgage sector in each country is by comparing household debt depth, or the ratio of the debt held by households compared to the county's GDP. In 2024, Switzerland, Australia, and Canada had some of the highest household debt to GDP ratios worldwide. While this indicator shows the size of the sector relative to the country’s economy, the value of mortgages outstanding allows to compare the market size in different countries. In Europe, for instance, the United Kingdom, Germany, and France were the largest mortgage markets by outstanding mortgage lending. Mortgage lending trends in the U.S. In the United States, new mortgage lending soared in 2021. This was largely due to the growth of new refinance loans that allow homeowners to renegotiate their mortgage terms and replace their existing loan with a more favorable one. Following the rise in interest rates, the mortgage market cooled, and refinance loans declined.

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

Intelligent Digital Mortgage Platform Market Outlook

The global intelligent digital mortgage platform market size was valued at USD 2.5 billion in 2023 and is forecasted to reach USD 8.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 14.6% during the forecast period. This robust growth can be attributed to the increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies in the financial sector, which is driving efficiency and accuracy in mortgage services.

One of the primary growth factors for the intelligent digital mortgage platform market is the rising demand for streamlined and automated mortgage processes. Traditional mortgage processes are often cumbersome, involving extensive paperwork and long approval times. The integration of AI and ML into mortgage platforms significantly reduces these inefficiencies by automating tasks such as document verification, risk assessment, and customer service. This automation not only speeds up the process but also minimizes human errors, thereby enhancing customer experience and satisfaction.

Another crucial driver is the increasing compliance and regulatory requirements in the financial sector. Financial institutions are increasingly turning to intelligent digital mortgage platforms to ensure compliance with evolving regulatory standards. These platforms offer advanced analytics and reporting capabilities, which help institutions to stay compliant with regulations such as the Dodd-Frank Act and the European Union's Mortgage Credit Directive. By leveraging these technologies, lenders can avoid hefty fines and penalties, thus safeguarding their reputation and financial stability.

The growing penetration of internet and smartphone usage also plays a significant role in the market's growth. With more consumers becoming tech-savvy, there is a heightened expectation for digital solutions that offer convenience, speed, and transparency. Intelligent digital mortgage platforms cater to these expectations by providing online application processes, real-time status updates, and digital communication channels. This shift towards digitalization is particularly pronounced among millennials and Gen Z, who are entering the housing market in increasing numbers and prefer digital interactions over traditional methods.

From a regional perspective, North America holds the largest market share due to the early adoption of advanced technologies and the presence of major market players. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. The rising middle-class population, increasing urbanization, and government initiatives to promote digital banking are some of the key factors driving the market in this region. Countries like China and India are investing heavily in digital infrastructure, which is expected to create lucrative opportunities for market growth.

Component Analysis

The intelligent digital mortgage platform market by component is segmented into software and services. The software segment encompasses various applications and tools integrated into the platforms to enhance their functionality. These software solutions include AI-based underwriting systems, automated document verification, and customer relationship management (CRM) systems. The increasing demand for these advanced software solutions is largely driven by their ability to automate and streamline mortgage processes, thereby reducing operational costs and improving efficiency.

Within the software segment, AI-based underwriting systems are gaining significant traction. These systems leverage machine learning algorithms to assess the creditworthiness of applicants, predict default risks, and make data-driven lending decisions. By automating the underwriting process, financial institutions can significantly reduce the time and effort required to evaluate loan applications, thereby speeding up the approval process and enhancing customer satisfaction. Additionally, these systems offer greater accuracy and consistency in decision-making, which helps in minimizing the risk of defaults and bad loans.

The services segment includes various support and maintenance services required to ensure the smooth functioning of intelligent digital mortgage platforms. These services encompass installation, integration, training, consulting, and ongoing technical support. As the adoption of these platforms increases, the demand for associated services is also expected to rise. Financial institutions rely on these services to maximize the val

Bulgaria Lending Rate: OA: HN: Other Loans: Mortgage: EUR: Up to 1 Year data was reported at 3.144 % pa in Apr 2021. This stayed constant from the previous number of 3.144 % pa for Mar 2021. Bulgaria Lending Rate: OA: HN: Other Loans: Mortgage: EUR: Up to 1 Year data is updated monthly, averaging 6.516 % pa from Jan 2007 (Median) to Apr 2021, with 140 observations. The data reached an all-time high of 14.382 % pa in Oct 2009 and a record low of 1.732 % pa in Feb 2011. Bulgaria Lending Rate: OA: HN: Other Loans: Mortgage: EUR: Up to 1 Year data remains active status in CEIC and is reported by Bulgarian National Bank. The data is categorized under Global Database’s Bulgaria – Table BG.M005: Banks Lending Rate: Outstanding Amounts.

Despite a short period of decrease after the burst of the U.S. housing bubble and the global financial crisis, the total amount of mortgage debt in the United States has been on the rise in recent years. In 2024, the mortgage debt amounted to 20.83 trillion U.S. dollars, up from 13.5 trillion U.S. dollars a decade ago. Which factors impact the amount of mortgage debt? One of the most important factors responsible for the growth of mortgage debt is the number of home sales: The more home transactions, the more mortgages are sold, adding to the volume of debt outstanding. Additionally, as house prices increase, so does the gross lending and debt outstanding. On the other hand, high numbers of housing unit foreclosures and mortgage debt restructuring and short-sales can reduce mortgage debt. Which property type has the largest share of the mortgage market? The total mortgage debt includes different property types, such as one-to-four family residential, multifamily residential, commercial, and farm, but the overwhelming share of debt can be attributed to mortgage debt one-to-four family residences.