A collection of key statistics about home loans in Australia, including interest rates, loan sizes, refinancing trends, and borrowing activity based on the latest data from the ABS and RBA.

These data relate to new mortgage lending on residential property in Ireland on an annual basis. Data relates to those institutions [(banks and non-bank mortgage lenders)] who issue at least €50 million of new mortgage lending in a six-month period and are subsequently required to submit loan-level information to the Central Bank for the purposes of the macroprudential mortgage measures. The value and volume of new lending is provided, by borrower type, along with the distribution of lending by Loan-to-value and Loan-to-income ratio. Average characteristics are also provided. These data do not constitute official statistics. These data are published to support transparency and understanding of market developments.

The Federal Home Loan Bank (FHLB) system was created by the Federal Home Loan Bank Act of 1932 as a government sponsored enterprise to support mortgage lending and related community investment. It is composed of 11 FHLBanks, more than 6,500 member financial institutions, and the System's fiscal agent, the Office of Finance. Each FHLBank is a separate, government-chartered, member-owned corporation.

The India Home Loan Market is segmented By Customer Type (Salaried, Self-Employed), By Source (Bank and Housing Finance Companies), By Interest Rate (Fixed Rate and Floating Rate), and By Tenure (up to 5 Years, 6 - 10 Years, 11 - 24 Years, and 25 - 30 Years). The report offers market size and forecasts in value (USD) for all the above segments.

In financial year 2024, banks in India advanced over *** trillion Indian rupees in housing loans. This was an increase compared to the previous year. This reflected renewed homebuyer sentiment, as an increasing number of Indians were investing in buying residential property. Growth of home loans market Forty years ago, home loans were an alien concept. People would direct their provident fund savings and retirement benefits toward buying a home. However, three key institutions: HDFC, ICICI Ltd, and the State bank of India with their new lending concepts led to significant changes in the home loan market. Currently different commercial banks, NBFCs, and housing finance companies have flooded the mortgage market, and giving prospective home buyers from diverse strata of society with bargaining power and a chance at affording a home. Inflation and home loans   India is not untouched by global inflation. To address the problem, the Reserve Bank of India hiked the repo rate **** times since April 2022 to *** percent. Consequently, leading banks and housing finance companies raised their lending rates. For a prospective homebuyer, this meant a rise in tenure for home loans. In other words, equivalent monthly payments (EMIs)for homebuyers have lengthened and become more expensive. In financial year 2022, banks in India advanced around *** trillion Indian rupees in housing loans almost reaching pre-COVID levels.

As of March 2025, ICICI Bank provided the lowest interest rates for its home loans in India, with an average of **** percent. Bank of Maharashtra accounted for the highest interest rate with an average of **** percent.

The Global Home Loan Market Report is Segmented by Loan Purpose (Purchase, Home Improvement/Renovation, Others), Provider (Banks, Housing Finance Companies, Others), Interest Rates (Fixed Interest Rates, Floating Interest Rates), Loan Tenure (Less Than or Equal To 10 Years, 11 – 20 Years, and More), and Geography (North America, South America, and More). The Market Forecasts are Provided in Terms of Value (USD).

According to a survey conducted from April to May 2025, the largest share of home loan borrowers in Japan, around ** percent, had a borrowing rate of over *** to *** percent per annum. According to the same survey, over ** percent of home loan borrowers chose a variable rate housing loan.

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.

China Consumer Loan: Residential Housing Mortgage Loan data was reported at 25,750.000 RMB bn in 2018. This records an increase from the previous number of 21,860.500 RMB bn for 2017. China Consumer Loan: Residential Housing Mortgage Loan data is updated yearly, averaging 2,473.416 RMB bn from Dec 1997 (Median) to 2018, with 20 observations. The data reached an all-time high of 25,750.000 RMB bn in 2018 and a record low of 13.100 RMB bn in 1997. China Consumer Loan: Residential Housing Mortgage Loan 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 and Banking – Table CN.KB: Loan: Consumer Loan.

The Home Mortgage Disclosure Act (HMDA) database (Consumer Financial Protection Bureau, 2022) has compiled mortgage lending data since 1981, but the collection and dissemination methods have changed over time (Federal Financial Institutions Examination Council, 2018), creating barriers to conducting longitudinal analyses. This HMDA Longitudinal Dataset (HLD) organizes and standardizes information across different eras of HMDA data collection between 1981 and 2021, enabling such analysis. This collection contains two types of datasets: 1) HMDA aggregated data by census tract for each decade and 2) HMDA aggregated data by census tract for individual years. Items for analysis include borrower income values, mortgages by loan type (e.g., conventional, Federal Housing Administration (FHA), Veterans Affairs (VA), refinances), and mortgages by borrower race and gender.

HERA Section 1212k requires FHFA to prepare a Public Use Database containing information on their loan purchases at the Census Tract level.

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.

In 2024, the value of personal housing loans in China amounted to ************** yuan, representing a slight drop of *** percent compared to the previous year. The overall value of outstanding mortgages more than doubled between 2016 and 2021 before it plateaued afterwards. A key factor to the growth of the real estate market China's personal housing loan market emerged in the 1990s in tandem with the marketization of the country's real estate sector. Its subsequent expansion also mirrored the growth in the property industry. Thanks to the dramatic rise in home prices across China since the early 2000s, substantial capital has poured into the market through real estate development loans and personal housing credits. For almost two decades, many Chinese middle class citizens accumulated their personal wealth through the considerable appreciation of their properties, which they financed with the help of mortgages. Risks The persistently high level of outstanding personal mortgage is becoming increasingly concerning amidst China’s current economic and market situation. With the country’s economic slowdown and the oversupply in the property sector, the housing market is losing steam, resulting in elevated risks of bad debts to financial institutions. At the same time, the household debt in China is now staying above ** percent of the country’s GDP, undermining the ability to consume and invest in the Chinese population.

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.

Home Loans in Australia decreased to 53168.90 AUD Million in the first quarter of 2025 from 54510.80 AUD Million in the fourth quarter of 2024. This dataset provides - Australia Home Loans- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Mortgage Originations in the United States increased to 458.28 Billion USD in the second quarter of 2025 from 425.63 Billion USD in the first quarter of 2025. This dataset includes a chart with historical data for the United States Mortgage Originations.

Our US Home Ownership Data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes various data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences. 1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc. 2. Demographics - Gender, Age Group, Marital Status, Language etc. 3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc 4. Persona - Consumer type, Communication preferences, Family type, etc 5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc. 6. Household - Number of Children, Number of Adults, IP Address, etc. 7. Behaviours - Brand Affinity, App Usage, Web Browsing etc. 8. Firmographics - Industry, Company, Occupation, Revenue, etc 9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc. 10. Auto - Car Make, Model, Type, Year, etc. 11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases: 360-Degree Customer View: Get a comprehensive image of customers by the means of internal and external data aggregation. Data Enrichment: Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity. Advertising & Marketing: Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

1) Data Introduction • The Home Loan Approval dataset determines eligibility for a home loan based on customer details provided by Dream Housing Finance company.

2) Data Utilization (1) Home Loan Approval data has characteristics that: • The dataset includes various socioeconomic variables such as gender, marital status, education, number of dependents, income, loan amount, and credit history. (2) Home Loan Approval data can be used to: • Financial Industry: It is useful for banks and financial institutions to explore variables that determine whether they are lending or not and to develop credit rating models. • Research: It helps to build a database by securing data to evaluate credit risk.

Graph and download economic data for Mortgage Debt Outstanding by Type of Holder and Property: Federal and Related Agencies: Federal Home Loans Banks for One- to Four-Family Residences (DISCONTINUED) (MDOTHFRAFHLBTP1T4FR) from Q1 1949 to Q3 2019 about 1 to 4 unit structures, agency, mortgage, family, debt, residents, federal, and USA.