On November 17, 2022, Freddie Mac changed the methodology of the Primary Mortgage Market Survey® (PMMS®). The weekly mortgage rate is now based on applications submitted to Freddie Mac from lenders across the country. For more information regarding Freddie Mac’s enhancement, see their research note (https://www.freddiemac.com/research/insight/20221103-freddie-macs-newly-enhanced-mortgage-rate-survey).

Data are provided “as is” by Freddie Mac®, with no warranties of any kind, express or implied, including but not limited to warranties of accuracy or implied warranties of merchantability or fitness for a particular purpose. Use of the data is at the user’s sole risk. In no event will Freddie Mac be liable for any damages arising out of or related to the data, including but not limited to direct, indirect, incidental, special, consequential, or punitive damages, whether under a contract, tort, or any other theory of liability, even if Freddie Mac is aware of the possibility of such damages.

Copyright, 2016, Freddie Mac. Reprinted with permission.

The 30-year fixed rate mortgage is the most-common type of loan for home purchases in the United States. The data for this report is sourced from Freddie Mac's Primary Mortgage Market Survey. The values presented in this report are annual figures, derived from equally weighted monthly averages.

30 Year Mortgage Rate in the United States increased to 6.72 percent in July 10 from 6.67 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

The HUD monthly interest rate survey provides information on interest rates, loan terms, and house prices. The survey is conducted by property type, loan type, and lender type. How the survey is conducted The survey provides information on all properties, new properties, and previously occupied properties.The survey provides information on fixed-rate and adjustable-rate loans.The survey provides information on lenders such as savings associations, mortgage companies, commercial banks, and savings banks.What the survey includes The survey provides information on interest rates, loan terms, and house prices.The survey provides information on property type, loan type, and lender type.Update on the Discontinuation of FHFA's Monthly Interest Rate Survey (MIRS)On May 29, 2019, FHFA published its final Monthly Interest Rate Survey (MIRS), due to dwindling participation by financial institutions. MIRS had provided information on a monthly basis on interest rates, loan terms, and house prices by property type (all, new, previously occupied); by loan type (fixed- or adjustable-rate), and by lender type (savings associations, mortgage companies, commercial banks and savings banks); as well as information on 15-year and 30-year, fixed-rate loans. Additionally, MIRS provided quarterly information on conventional loans by major metropolitan area and by Federal Home Loan Bank district, and was used to compile FHFA’s monthly adjustable-rate mortgage index entitled the “National Average Contract Mortgage Rate for the Purchase of Previously Occupied Homes by Combined Lenders,” also known as the ARM Index.As some banks use the ARM Index as the basis for adjusting the interest rates on adjustable-rate mortgages, FHFA created and designated as the replacement for the ARM Index a version of Freddie Mac’s 30-year Primary Mortgage Market Survey® (PMMS®) that adjusts for differences between the two. This new index is called “MIRS Transition Index” and will be published on fhfa.gov on the final Thursday of every month. June 2019 was the first MIRS Transition index value to be published. The MIRS Transition index is intended to be used in lieu of the discontinued index for currently outstanding loans, and not as a reference rate on newly-originated adjustable-rate mortgages. The MIRS Transition Index was briefly referred to as PMMS+. It is not a replacement for PMMS.

Hong Kong HK: Residential Mortgage: New Loans Approved: Primary Market data was reported at 19.738 % in Sep 2018. This records a decrease from the previous number of 19.795 % for Aug 2018. Hong Kong HK: Residential Mortgage: New Loans Approved: Primary Market data is updated monthly, averaging 20.444 % from Dec 2000 (Median) to Sep 2018, with 214 observations. The data reached an all-time high of 60.719 % in Sep 2002 and a record low of 6.008 % in Feb 2010. Hong Kong HK: Residential Mortgage: New Loans Approved: Primary Market data remains active status in CEIC and is reported by Hong Kong Monetary Authority. The data is categorized under Global Database’s Hong Kong SAR – Table HK.KB008: Residential Property Loans: Residential Mortgage Survey: Ratios.

Analysis of ‘ Zillow Housing Aspirations Report’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/zillow-housing-aspirations-reporte on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Additional Data Products

Product: Zillow Housing Aspirations Report

Date: April 2017

Definitions

Home Types and Housing Stock

• All Homes: Zillow defines all homes as single-family, condominium and co-operative homes with a county record. Unless specified, all series cover this segment of the housing stock.
• Condo/Co-op: Condominium and co-operative homes.
• Multifamily 5+ units: Units in buildings with 5 or more housing units, that are not a condominiums or co-ops.
• Duplex/Triplex: Housing units in buildings with 2 or 3 housing units.

Additional Data Products

• Zillow Home Value Forecast (ZHVF): The ZHVF is the one-year forecast of the ZHVI. Our forecast methodology is methodology post.
• Zillow creates our negative equity data using our own data in conjunction with data received through our partnership with TransUnion, a leading credit bureau. We match estimated home values against actual outstanding home-related debt amounts provided by TransUnion. To read more about how we calculate our negative equity metrics, please see our here.
• Cash Buyers: The share of homes in a given area purchased without financing/in cash. To read about how we calculate our cash buyer data, please see our research brief.
• Mortgage Affordability, Rental Affordability, Price-to-Income Ratio, Historical ZHVI, Historical ZHVI and Houshold Income are calculated as a part of Zillow’s quarterly Affordability Indices. To calculate mortgage affordability, we first calculate the mortgage payment for the median-valued home in a metropolitan area by using the metro-level Zillow Home Value Index for a given quarter and the 30-year fixed mortgage interest rate during that time period, provided by the Freddie Mac Primary Mortgage Market Survey (based on a 20 percent down payment). Then, we consider what portion of the monthly median household income (U.S. Census) goes toward this monthly mortgage payment. Median household income is available with a lag. For quarters where median income is not available from the U.S. Census Bureau, we calculate future quarters of median household income by estimating it using the Bureau of Labor Statistics’ Employment Cost Index. The affordability forecast is calculated similarly to the current affordability index but uses the one year Zillow Home Value Forecast instead of the current Zillow Home Value Index and a specified interest rate in lieu of PMMS. It also assumes a 20 percent down payment. We calculate rent affordability similarly to mortgage affordability; however we use the Zillow Rent Index, which tracks the monthly median rent in particular geographical regions, to capture rental prices. Rents are chained back in time by using U.S. Census Bureau American Community Survey data from 2006 to the start of the Zillow Rent Index, and Decennial Census for all other years.
• The mortgage rate series is the average mortgage rate quoted on Zillow Mortgages for a 30-year, fixed-rate mortgage in 15-minute increments during business hours, 6:00 AM to 5:00 PM Pacific. It does not include quotes for jumbo loans, FHA loans, VA loans, loans with mortgage insurance or quotes to consumers with credit scores below 720. Federal holidays are excluded. The jumbo mortgage rate series is the average jumbo mortgage rate quoted on Zillow Mortgages for a 30-year, fixed-rate, jumbo mortgage in one-hour increments during business hours, 6:00 AM to 5:00 PM Pacific Time. It does not include quotes to consumers with credit scores below 720. Traditional federal holidays and hours with insufficient sample sizes are excluded.

About Zillow Data (and Terms of Use Information)

• Zillow is in the process of transitioning some data sources with the goal of producing published data that is more comprehensive, reliable, accurate and timely. As this new data is incorporated, the publication of select metrics may be delayed or temporarily suspended. We look forward to resuming our usual publication schedule for all of our established datasets as soon as possible, and we apologize for any inconvenience. Thank you for your patience and understanding.
• All data accessed and downloaded from this page is free for public use by consumers, media, analysts, academics etc., consistent with our published Terms of Use. Proper and clear attribution of all data to Zillow is required.
• For other data requests or inquiries for Zillow Real Estate Research, contact us here.
• All files are time series unless noted otherwise.
• To download all Zillow metrics for specific levels of geography, click here.
• To download a crosswalk between Zillow regions and federally defined regions for counties and metro areas, click here.
• Unless otherwise noted, all series cover single-family residences, condominiums and co-op homes only.

Source: https://www.zillow.com/research/data/

This dataset was created by Zillow Data and contains around 200 samples along with Unnamed: 1, Unnamed: 0, technical information and other features such as: - Unnamed: 1 - Unnamed: 0 - and more.

How to use this dataset

• Analyze Unnamed: 1 in relation to Unnamed: 0
• Study the influence of Unnamed: 1 on Unnamed: 0
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Acknowledgements

If you use this dataset in your research, please credit Zillow Data

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--- Original source retains full ownership of the source dataset ---

Hong Kong HK: Residential Mortgage: Value: NL: Primary Market data was reported at 7,987.000 HKD mn in Oct 2018. This records an increase from the previous number of 6,233.000 HKD mn for Sep 2018. Hong Kong HK: Residential Mortgage: Value: NL: Primary Market data is updated monthly, averaging 3,803.000 HKD mn from Dec 2000 (Median) to Oct 2018, with 215 observations. The data reached an all-time high of 10,163.000 HKD mn in Jun 2017 and a record low of 846.000 HKD mn in Aug 2008. Hong Kong HK: Residential Mortgage: Value: NL: Primary Market data remains active status in CEIC and is reported by Hong Kong Monetary Authority. The data is categorized under Global Database’s Hong Kong SAR – Table HK.KB006: Residential Property Loans: Residential Mortgage Survey: Value.

Hong Kong HK: Residential Mortgage: Case: NL: Primary Market data was reported at 1,270.000 Unit in Sep 2018. This records a decrease from the previous number of 1,925.000 Unit for Aug 2018. Hong Kong HK: Residential Mortgage: Case: NL: Primary Market data is updated monthly, averaging 1,117.000 Unit from Jan 2003 (Median) to Sep 2018, with 189 observations. The data reached an all-time high of 3,503.000 Unit in Jul 2003 and a record low of 241.000 Unit in Aug 2008. Hong Kong HK: Residential Mortgage: Case: NL: Primary Market data remains active status in CEIC and is reported by Hong Kong Monetary Authority. The data is categorized under Global Database’s Hong Kong SAR – Table HK.KB007: Residential Property Loans: Residential Mortgage Survey: Cases.

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 Q1 2025 about domestic offices, delinquencies, 1-unit structures, mortgage, family, residential, commercial, domestic, banks, depository institutions, rate, and USA.

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.

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.

Following a period of stagnation over most of the 2010s, the number of owner occupied housing units in the United States started to grow in 2017. In 2023, there were over 86 million owner-occupied homes. Owner-occupied housing is where the person who owns a property – either outright or through a mortgage – also resides in the property. Excluded are therefore rental properties, employer-provided housing and social housing. Homeownership sentiment in the U.S. Though homeownership is still a cornerstone of the American dream, an increasing share of people see themselves as lifelong renters. Millennials have been notoriously late to enter the housing market, with one in four reporting that they would probably continue to always rent in the future, a 2022 survey found. In 2017, just five years before that, this share stood at about 13 percent. How many renter households are there? Renter households are roughly half as few as owner-occupied households in the U.S. In 2023, the number of renter occupied housing units amounted to almost 45 million. Climbing on the property ladder for renters is not always easy, as it requires prospective homebuyers to save up for a down payment and qualify for a mortgage. In many metros, the median household income is insufficient to qualify for the median-priced home.

UK Student Loan Market size was valued at USD 3009.63 Billion in 2024 and is projected to reach USD 5394.76 Billion by 2032, growing at a CAGR of 7.56% from 2026 to 2032.Key Market DriversRising Higher Education Enrollment and Tuition Costs: The consistent growth in UK higher education participation rates combined with increasing tuition fees has significantly expanded the demand for student loans. This fundamental driver reflects both demographic trends and the continued perceived value of university education despite rising costs. UCAS data showed that 560,030 students were accepted into UK universities for the 2022/23 academic year, representing a 3.8% increase since 2019/20. The Student Loans Company reported that the average loan balance for borrowers who began repayment in 2022 was 45,060, a 17% increase from 2019 levels.International Student Growth and Specialized Financing: The UK has experienced substantial growth in international student numbers, creating expanded opportunities for private student lending as these students typically cannot access the same government-backed loans as domestic students. UCAS data showed international student acceptances increased by 12.3% between 2020 and 2023, with 70,055 non-UK students accepted in the 2022/23 academic year. Private student loan providers reported a 37% increase in lending to international students between 2020 and 2022, according to a Financial Conduct Authority market review.

Autoscraping's Zillow USA Real Estate Data is a comprehensive and meticulously curated dataset that covers over 10 million property listings across the United States. This data product is designed to meet the needs of professionals across various sectors, including real estate investment, market analysis, urban planning, and academic research. Our dataset is unique in its depth, accuracy, and timeliness, ensuring that users have access to the most relevant and actionable information available.

What Makes Our Data Unique? The uniqueness of our data lies in its extensive coverage and the precision of the information provided. Each property listing is enriched with detailed attributes, including but not limited to, full addresses, asking prices, property types, number of bedrooms and bathrooms, lot size, and Zillow’s proprietary value and rent estimates. This level of detail allows users to perform in-depth analyses, make informed decisions, and gain a competitive edge in their respective fields.

Furthermore, our data is continually updated to reflect the latest market conditions, ensuring that users always have access to current and accurate information. We prioritize data quality, and each entry is carefully validated to maintain a high standard of accuracy, making this dataset one of the most reliable on the market.

Data Sourcing: The data is sourced directly from Zillow, one of the most trusted names in the real estate industry. By leveraging Zillow’s extensive real estate database, Autoscraping ensures that users receive data that is not only comprehensive but also highly reliable. Our proprietary scraping technology ensures that data is extracted efficiently and without errors, preserving the integrity and accuracy of the original source. Additionally, we implement strict data processing and validation protocols to filter out any inconsistencies or outdated information, further enhancing the quality of the dataset.

Primary Use-Cases and Vertical Applications: Autoscraping's Zillow USA Real Estate Data is versatile and can be applied across a variety of use cases and industries:

Real Estate Investment: Investors can use this data to identify lucrative opportunities, analyze market trends, and compare property values across different regions. The detailed pricing and valuation data allow for comprehensive due diligence and risk assessment.

Market Analysis: Market researchers can leverage this dataset to track real estate trends, evaluate the performance of different property types, and assess the impact of economic factors on property values. The dataset’s nationwide coverage makes it ideal for both local and national market studies.

Urban Planning and Development: Urban planners and developers can use the data to identify growth areas, plan new developments, and assess the demand for different property types in various regions. The detailed location data is particularly valuable for site selection and zoning analysis.

Academic Research: Universities and research institutions can utilize this data for studies on housing markets, urbanization, and socioeconomic trends. The comprehensive nature of the dataset allows for a wide range of academic applications.

Integration with Our Broader Data Offering: Autoscraping's Zillow USA Real Estate Data is part of our broader data portfolio, which includes various datasets focused on real estate, market trends, and consumer behavior. This dataset can be seamlessly integrated with our other offerings to provide a more holistic view of the market. For example, combining this data with our consumer demographic datasets can offer insights into the relationship between property values and demographic trends.

By choosing Autoscraping's data products, you gain access to a suite of complementary datasets that can be tailored to meet your specific needs. Whether you’re looking to gain a comprehensive understanding of the real estate market, identify new investment opportunities, or conduct advanced research, our data offerings are designed to provide you with the insights you need.

What is this data?The Local Housing Profiles are a curated set of data on the housing market. The Chicago Metropolitan Agency for Planning (CMAP) provides these profiles for each of the 7 counties, 284 municipalities, and Chicago community area (CCA) in northeastern Illinois.How can this data be used? Are there any use cases?The Local Housing Profiles can be used by residents, practitioners, planners, and policymakers to understand the latest data on a community’s housing demand, supply, and affordability relative to regional trends.Who created this data? How and when?Developed in partnership with the Institute for Housing Studies at DePaul University (IHS), these reports include data from a number of sources, including socioeconomic, demographic, and housing unit data from the American Community Survey (ACS), and key housing market indicators generated from parcel-level administrative data and collected by the IHS via its Data Clearinghouse.Additional information on field names, data sources, and other metadata can be found in the Data Dictionary. More comprehensive background on the data tables summarized in the profiles can be found in the Technical Documentation.Where can I find the latest data? How frequently is it updated?The primary source is data from the U.S. Census Bureau’s 2023 American Community Survey program. It is expected that this product will be updated annually. However, as this item was developed in partnership with the IHS at DePaul University, please reach out the Data Specialist if you need additional information about plans for future updates.Questions?Are you looking for the PDF versions? Find and download the print-friendly Local Housing Data Profiles from the agency website.

DisclaimerBefore using this layer, please review the 2018 Rochester Citywide Housing Market Study for the full background and context that is required for interpreting and portraying this data. Please click here to access the study. Please also note that the housing market typologies were based on analysis of property data from 2008 to 2018, and is a snapshot of market conditions within that time frame. For an accurate depiction of current housing market typologies, this analysis would need to be redone with the latest available data.About the DataThis is a polygon feature layer containing the boundaries of all census blockgroups in the city of Rochester. Beyond the unique identifier fields including GEOID, the only other field is the housing market typology for that blockgroup.Information from the 2018 Housing Market Study- Housing Market TypologiesThe City of Rochester commissioned a Citywide Housing Market Study in 2018 as a technical study to inform development of the City's new Comprehensive Plan, Rochester 2034, and retained czb, LLC – a firm with national expertise based in Alexandria, VA – to perform the analysis.Any understanding of Rochester’s housing market – and any attempt to develop strategies to influence the market in ways likely to achieve community goals – must begin with recognition that market conditions in the city are highly uneven. On some blocks, competition for real estate is strong and expressed by pricing and investment levels that are above city averages. On other blocks, private demand is much lower and expressed by above average levels of disinvestment and physical distress. Still other blocks are in the middle – both in terms of condition of housing and prevailing prices. These block-by-block differences are obvious to most residents and shape their options, preferences, and actions as property owners and renters. Importantly, these differences shape the opportunities and challenges that exist in each neighborhood, the types of policy and investment tools to utilize in response to specific needs, and the level and range of available resources, both public and private, to meet those needs. The City of Rochester has long recognized that a one-size-fits-all approach to housing and neighborhood strategy is inadequate in such a diverse market environment and that is no less true today. To concisely describe distinct market conditions and trends across the city in this study, a Housing Market Typology was developed using a wide range of indicators to gauge market health and investment behaviors. This section of the Citywide Housing Market Study introduces the typology and its components. In later sections, the typology is used as a tool for describing and understanding demographic and economic patterns within the city, the implications of existing market patterns on strategy development, and how existing or potential policy and investment tools relate to market conditions.Overview of Housing Market Typology PurposeThe Housing Market Typology in this study is a tool for understanding recent market conditions and variations within Rochester and informing housing and neighborhood strategy development. As with any typology, it is meant to simplify complex information into a limited number of meaningful categories to guide action. Local context and knowledge remain critical to understanding market conditions and should always be used alongside the typology to maximize its usefulness.Geographic Unit of Analysis The Block Group – a geographic unit determined by the U.S. Census Bureau – is the unit of analysis for this typology, which utilizes parcel-level data. There are over 200 Block Groups in Rochester, most of which cover a small cluster of city blocks and are home to between 600 and 3,000 residents. For this tool, the Block Group provides geographies large enough to have sufficient data to analyze and small enough to reveal market variations within small areas.Four Components for CalculationAnalysis of multiple datasets led to the identification of four typology components that were most helpful in drawing out market variations within the city:• Terms of Sale• Market Strength• Bank Foreclosures• Property DistressThose components are described one-by-one on in the full study document (LINK), with detailed methodological descriptions provided in the Appendix.A Spectrum of Demand The four components were folded together to create the Housing Market Typology. The seven categories of the typology describe a spectrum of housing demand – with lower scores indicating higher levels of demand, and higher scores indicating weaker levels of demand. Typology 1 are areas with the highest demand and strongest market, while typology 3 are the weakest markets. For more information please visit: https://www.cityofrochester.gov/HousingMarketStudy2018/Dictionary: STATEFP10: The two-digit Federal Information Processing Standards (FIPS) code assigned to each US state in the 2010 census. New York State is 36. COUNTYFP10: The three-digit Federal Information Processing Standards (FIPS) code assigned to each US county in the 2010 census. Monroe County is 055. TRACTCE10: The six-digit number assigned to each census tract in a US county in the 2010 census. BLKGRPCE10: The single-digit number assigned to each block group within a census tract. The number does not indicate ranking or quality, simply the label used to organize the data. GEOID10: A unique geographic identifier based on 2010 Census geography, typically as a concatenation of State FIPS code, County FIPS code, Census tract code, and Block group number. NAMELSAD10: Stands for Name, Legal/Statistical Area Description 2010. A human-readable field for BLKGRPCE10 (Block Groups). MTFCC10: Stands for MAF/TIGER Feature Class Code 2010. For this dataset, G5030 represents the Census Block Group. BLKGRP: The GEOID that identifies a specific block group in each census tract. TYPOLOGYFi: The point system for Block Groups. Lower scores indicate higher levels of demand – including housing values and value appreciation that are above the Rochester average and vulnerabilities to distress that are below average. Higher scores indicate lower levels of demand – including housing values and value appreciation that are below the Rochester average and above presence of distressed or vulnerable properties. Points range from 1.0 to 3.0. For more information on how the points are calculated, view page 16 on the Rochester Citywide Housing Study 2018. Shape_Leng: The built-in geometry field that holds the length of the shape. Shape_Area: The built-in geometry field that holds the area of the shape. Shape_Length: The built-in geometry field that holds the length of the shape. Source: This data comes from the City of Rochester Department of Neighborhood and Business Development.

Mortgage brokers’ revenue is anticipated to climb at a compound annual rate of 4.5% over the five years through 2024-25 to £2.3 billion, including estimated growth of . Rising residential property transactions stimulated by government initiatives and rising house prices have driven industry growth. However, mortgage brokers have faced numerous obstacles, including downward pricing pressures from upstream lenders and a sharp downturn in the housing market as rising mortgage rates ramped up the cost of borrowing. After a standstill in residential real estate activity in the immediate aftermath of the COVID-19 outbreak, ultra-low base rates, the release of pent-up demand, the introduction of tax incentives and buyers reassessing their living situation fuelled a V-shaped recovery in the housing market. This meant new mortgage approvals for house purchases boomed going into 2021-22, ramping up demand for brokerage services. 2022-23 was a year rife with economic headwinds, from rising interest rates to fears of a looming recession. Yet, the housing market stood its ground, with brokers continuing to benefit from rising prices. Elevated mortgage rates eventually hit demand for houses in the first half of 2023, contributing to lacklustre house price growth in 2023-24, hurting revenue, despite a modest recovery in the second half of the year as mortgage rates came down. In 2024-25, lower mortgage rates and an improving economic outlook support house prices, driving revenue growth. Mortgage brokers’ revenue is anticipated to swell at a compound annual rate of 5.3% over the five years through 2029-30 to £2.9 billion. Competition from direct lending will ramp up. Yet, growth opportunities remain. The emergence of niche mortgage products, like those targeting retired individuals and contractors, as well as green mortgages, will support revenue growth in the coming years. AI is also set to transform the industry, improving cost efficiencies by automating tasks like document verification, risk assessment and customer profiling.

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.

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.

Colombia IOS: New Methodology: Principal Problems of the Industry: Loan Accessibility data was reported at 2.200 % in Jan 2023. This records an increase from the previous number of 1.410 % for Sep 2022. Colombia IOS: New Methodology: Principal Problems of the Industry: Loan Accessibility data is updated monthly, averaging 3.550 % from May 2011 (Median) to Jan 2023, with 136 observations. The data reached an all-time high of 7.900 % in Jan 2013 and a record low of 0.000 % in Dec 2021. Colombia IOS: New Methodology: Principal Problems of the Industry: Loan Accessibility data remains active status in CEIC and is reported by National Association of Businessmen of Colombia. The data is categorized under Global Database’s Colombia – Table CO.S012: Industrial Opinion Survey: Principal Problems of the Industry: New Methodology.