Fixed 30-year mortgage rates in the United States averaged 6.77 percent in the week ending August 1 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 6.63 percent in August 7 from 6.72 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year 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.

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.

Discover the power of McGRAW’s comprehensive data solutions, the industry's largest and most complete property and ownership database in the nation. Additionally, the mortgage industry's most sought-after analytics solutions for loan quality, risk management, compliance, and collateral valuation. These data sets are built to empower businesses with reliable, accurate, and actionable insights across the mortgage, real estate, and title sectors. With access to over 150 million records and 200 attributes, our expansive data repository enables you to streamline decision-making, optimize marketing, and enhance customer targeting across industries. Take a look at the comprehensive data sets below:

Mortgage Data Our mortgage data encompasses loan origination, borrower profiles, mortgage terms, and payment statuses, providing a complete view of borrowers and mortgage landscapes. We deliver details on active and historical mortgages, including lender information, loan types, interest rates, and mortgage maturity. This empowers financial institutions and analysts to predict market trends, assess creditworthiness, and personalize customer outreach with accuracy.

Property Data McGRAW’s property data includes detailed attributes on residential and commercial properties, spanning property characteristics, square footage, zoning information, construction dates, and much more. Our data empowers real estate professionals, property appraisers, and investors to make well-informed decisions based on current and historical property details.

Title Data Our title data service provides a clear view of ownership history and title status, ensuring comprehensive information on property chain-of-title, lien positions, encumbrances, and transaction history. This invaluable data assists title companies, legal professionals, and financial institutions in validating title claims, mitigating risks, and reducing time-to-close.

Ownership Data McGRAW ownership data supplies in-depth insights into individual and corporate property ownership, offering information on property owners, purchase prices, and ownership duration. This dataset is crucial for due diligence, investment planning, and market analysis, giving businesses the competitive edge to identify opportunities and assess ownership patterns in the marketplace.

Unmatched Data Quality & Coverage Our data covers the full spectrum of residential and commercial properties in the United States, with attributes verified for accuracy and updated regularly. From state-of-the-art technology to rigorous data validation practices, McGRAW’s data quality stands out, providing the confidence that businesses need to make strategic decisions.

Why Choose McGRAW Data?

Extensive Reach: Over 150 million records provide unparalleled depth and breadth of data coverage across all 50 states.

Diverse Attributes: With 200 attributes across mortgage, property, title, and ownership data, businesses can customize data views for specific needs.

Actionable Insights: Our data analytics tools and customizable reports translate raw data into valuable insights, helping you stay ahead in the competitive landscape.

Leverage McGRAW’s data solutions to unlock a holistic view of the mortgage, property, title, and ownership landscapes. For real estate professionals, lenders, and investors seeking data-driven growth, McGRAW provides the tools to elevate decision-making, enhance operational efficiency, and drive business success in today’s data-centric market.

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.

This dataset contains a wealth of information from 52,000 loan applications, offering detailed insights into the factors that influence loan approval decisions. Collected from financial institutions, this data is highly valuable for credit risk analysis, financial modeling, and predictive analytics. The dataset is particularly useful for anyone interested in applying machine learning techniques to real-world financial decision-making scenarios.

Overview: This dataset provides information about various applicants and the loans they applied for, including their demographic details, income, loan terms, and approval status. By analyzing this data, one can gain an understanding of which factors are most critical for determining the likelihood of loan approval. The dataset can also help in evaluating credit risk and building robust credit scoring systems.

Dataset Columns: Applicant_ID: Unique identifier for each loan application. Gender: Gender of the applicant (Male/Female). Age: Age of the applicant. Marital_Status: Marital status of the applicant (Single/Married). Dependents: Number of dependents the applicant has. Education: Education level of the applicant (Graduate/Not Graduate). Employment_Status: Employment status of the applicant (Employed, Self-Employed, Unemployed). Occupation_Type: Type of occupation, which provides insights into the nature of the applicant’s job (Salaried, Business, Others). Residential_Status: Type of residence (Owned, Rented, Mortgage). City/Town: The city or town where the applicant resides. Annual_Income: The total annual income of the applicant, a key factor in loan eligibility. Monthly_Expenses: The monthly expenses of the applicant, indicating their financial obligations. Credit_Score: The applicant's credit score, reflecting their creditworthiness. Existing_Loans: Number of existing loans the applicant is servicing. Total_Existing_Loan_Amount: The total amount of all existing loans the applicant has. Outstanding_Debt: The remaining amount of debt yet to be paid by the applicant. Loan_History: The applicant’s previous loan history (Good/Bad), indicating their repayment reliability. Loan_Amount_Requested: The loan amount the applicant has applied for. Loan_Term: The term of the loan in months. Loan_Purpose: The purpose of the loan (e.g., Home, Car, Education, Personal, Business). Interest_Rate: The interest rate applied to the loan. Loan_Type: The type of loan (Secured/Unsecured). Co-Applicant: Indicates if there is a co-applicant for the loan (Yes/No). Bank_Account_History: Applicant’s banking history, showing past transactions and reliability. Transaction_Frequency: The frequency of financial transactions in the applicant’s bank account (Low/Medium/High). Default_Risk: The risk level of the applicant defaulting on the loan (Low/Medium/High). Loan_Approval_Status: Final decision on the loan application (Approved/Rejected).

Negative Equity in U.S. Housing Market

Measuring Home Values, Debt, and Credit Risk

By Zillow Data [source]

About this dataset

This dataset, Negative Equity in the US Housing Market, provides an in-depth look into the negative equity occurring across the United States during this single quarter. Included are metrics such as total amount of negative equity in millions of dollars, total number of homes in negative equity, percentage of homes with mortgages that are in negative equity and more. These data points provide helpful insights into both regional and national trends regarding the prevalence and rate of home mortgage delinquency stemming from a diminishment of value from peak levels.

Home types available for analysis include 'all homes', condos/co-ops, multifamily units containing five or more housing units as well as duplexes/triplexes. Additionally, Cash buyers rates for particular areas can also be determined by referencing this collection. Further metrics such as mortgage affordability rates and impacts on overall indebtedness are readily calculated using information related to Zillow's Home Value Index (ZHVI) forecast methodology and TransUnion data respectively.

Other variables featured within this dataset include characteristics like region type (i.e city, county ..etc), size rank based on population values , percentage change in ZHVI since peak levels as well as loan-to-value ratio greater than 200 across all regions constituted herein (NE). Moreover Zillow's own Secondary Mortgage Market Survey data is utilized to acquire average mortgage quote rates while correlative Census Bureau NCHS median household income figures represent typical assessable proportions between wages and debt obligations . So whether you're looking to assess effects along metro lines or detailed buffering through zip codes , this database should prove sufficient for insightful explorations! Nonetheless users must strictly adhere to all conditions encompassed within Terms Of Use commitments put forth by our lead provider before accessing any resources included herewith

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Research Ideas

• Analyzing regional and state trends in negative equity: Analyze geographic differences in the percentage of mortgages “underwater”, total amount of negative equity, number of homes at least 90 days late, and other key indicators to provide insight into the factors influencing negative equity across regions, states and cities.
• Tracking the recovery rate over time: Track short-term changes in numbers related to negative equity (e.g., region or area ZHVI Change from Peak) to monitor recovery rates over time as well as how different policy interventions are affecting homeownership levels in affected areas.
• Exploring best practices for promoting housing affordability: Compare affordability metrics (e.g., mortgage payments, price-to-income ratios) across different geographic locations over time to identify best practices for empowering homeowners and promoting stability within the housing market while reducing local inequality impacts related to availability of affordable housing options and access to credit markets like mortgages/loans etc

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: NESummary_2017Q1_Public.csv | Column name | Description | |:------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------| | RegionType | The type of region (e.g., city, county, metro etc.) (String) | | City | Name of the city (String) | | County | Name of the county (String) | | State | Name of the state (String) | | Metro ...

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 Federal Perkins Loan Cohort Default Rates is a data collection that is part of the Federal Perkins Loan program; the most recent Federal Perkins Loan Cohort Default Rates are available . Historical program data is available electronically since 2006 at . The data collection is conducted using a web-based entry system wherein postsecondary institutions must submit information electronically if they participate in the Federal Perkins Loan program. Key statistics produced from this data collection are the Federal Perkins Loan cohort default rates (previously known as the Orange Book).

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.

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

Start A New Notebook!

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

The benchmark interest rate in China was last recorded at 3 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.

Small Business Administration Loan Guarantee (SBA)

1.5 Million Loan Guarantees Across 7(a) and 504 Programs

By Noah Brod [source]

About this dataset

The Small Business Administration (SBA) Loan Guarantee Data provides a comprehensive look at loans that were approved by the SBA from January 1, 1990 to December 31, 1999. This dataset offers insight into roughly 1.5 million approved loans, including details such as the loan amount, interest rate, project county, and more.

This data was collected as part of an FOIA request and is updated quarterly for up-to-date information. It should be noted that the SBA is not a direct lender but rather a guarantor of the loan which is made by either a bank or non-bank lender.

The dataset includes detailed fields such as AsOfDate, Program Type, Gross Approval Amounts and Initial Interest Rates; Fanchise Codes and County Information; Delivery Method and Status Reports; Congressional Districts involved in financing these loans; Jobs Supported as part of each loan; Billing Information related to ChargeOff Dates and Amounts; SBADistrict Office associated with individual loan approvals ;and more!

This unique pool of data can offer invaluable insights into the mechanisms behind small business lending throughout the nineties in America – showing how much has changed since then but also how some trends remain consistent over time. The Small Business Administration Loan Guarantee Data can help shine light on important topics such as demographic disparities among borrowers or regional differences between approving offices - increasing our understanding of American business practices overall!

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Featured Notebooks

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Research Ideas

• Using NaicsCode, initialize a visual representation of the most popular types of businesses that receive SBA loan ensures to get a better sense of which industries are the biggest uses for this financing program.
• Calculating Loan Status data over a period of time to analyse trends in terms of loan defaults and how much money is disbursed vs charged off.
• Examining GrossApproval and SBAGuarterNeedApproval data to determine which zipcodes or states have received more funding from the SBA and apply this information in aid targeting certain areas as part of govermental stimulus packages during tough economic times

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: 7a_504_FOIA%20Data%20Dictionary.csv

File: FOIA%20-%207(a)(FY1991-FY1999).csv | Column name | Description | |:--------------------------|:-------------------------------------------------------------| | AsOfDate | Date the data was last updated. (Date) | | Program | Type of loan program. (String) | | BorrName | Name of the borrower. (String) | | BorrStreet | Street address of the borrower. (String) | | BorrCity | City of the borrower. (String) | | BorrState | State of the borrower. (String) | | BorrZip | Zip code of the borrower. (String) | | BankName | Name of the bank. (String) | | BankStreet | Street address of the bank. (String) | | BankCity | City of the bank. (String) | | BankState | State of the bank. (String) | | BankZip | Zip code of the bank. (String) | | GrossApproval | Total amount of the loan approved. (Number) | | SBAGuaranteedApproval | Amount of the loan guaranteed by the SBA. (Number) | | ApprovalDate | Date the loan was approved. (Date) | | ApprovalFiscalYear | Fiscal year the loan was approved. (Number) | | FirstDisbursementDate | Date the loan was disbursed. (Date) | | DeliveryMethod | Method of delivery for the loan. (String) | | subpgmdesc | Description of the loan program. (String) ...

StatBank dataset: DNRUDDK Title: Mortgage lending to Danish counterparties by values, data type, sector, type of interest rate, redemptions, currency and property category Period type: month Period format (time in data): yyyyMmm The oldest period: 2022M12 The most recent period: 2024M09

Abstract copyright UK Data Service and data collection copyright owner.The 5% Sample Survey of Building Society Mortgage Completions (BSM) has been in existence since 1965. The Archive holds data from 1974. Monthly returns, giving detailed information on a nominal 5% sample of all mortgage completions, have been submitted on a voluntary basis by most building societies to the Department of Environment who process the data on a quarterly basis. The survey results have served as the offical source of statistics on the owner-occupied housing market, providing a wealth of information on mortgage advances, dwelling prices and the characteristics of borrowers and properties. An increased share of the mortgage market being accounted for by other lenders and a widening range of mortgage products during the 1980s have necessitated change, leading to the BSM being succeeded by the Survey of Mortgage Lenders (SML) in 1992 (see GN: 33254). 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: Building Society code, date mortgage completed, whether dwelling is wholly or partly occupied by borrower. Mortgage amount, whether solely for purchase of property, period of mortgage, gross rate of interest, repayment method. Purchase price and whether discounted in any way. Location of dwelling, whether new, age of dwelling, type, number of habitable rooms, whether garage, rateable value. Number and sex of borrowers, age of main borrower, basic income, other income, total income, whether applicant previously owner occupier, previous tenure, whether main borrower nominated by LA under support lending scheme. Building Societies are divided into four strata according to the size of their assets. All the largest societies are 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. Societies in the next stratum are arranged in order of size of assets and alternate societies 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 society are obtained. The smallest societies are completely excluded.

France Mortgage Rate: Avg: Consumer: More than 1 Year data was reported at 1.510 % in Sep 2018. This records a decrease from the previous number of 1.530 % for Aug 2018. France Mortgage Rate: Avg: Consumer: More than 1 Year data is updated monthly, averaging 3.690 % from Jan 2003 (Median) to Sep 2018, with 189 observations. The data reached an all-time high of 5.190 % in Dec 2008 and a record low of 1.500 % in Jan 2017. France Mortgage Rate: Avg: Consumer: More than 1 Year data remains active status in CEIC and is reported by Bank of France. The data is categorized under Global Database’s France – Table FR.M009: Mortgage Rate.

New Zonage “A/B/C” applicable from 01/10/2014 (Ministerial Decree of 01 August 2014). The “A/B/C” zoning, created in 2003 at the time when Robien’s rental investment scheme was introduced, characterises the tension of the local real estate market, i.e. the adequacy of the demand for and the supply of available housing on a territory. It consists of five modalities ranging from the most tense (Abis) to the most relaxed (C).Franche-Comté is only affected by zones B2 and C. Several financial schemes use this zoning to determine the eligibility of territories for aid or to adjust their parameters (level of aid, ceiling of rents, etc.). These include the Intermediate Rental Investment Facility for Individuals (see Duflot Zoning), the Old Borloo, the Intermediate Rental Loan (PLI), the Zero Rate Loan (PTZ), the Social Accession Rental Loan (PSLA) and the Social Access Loan (PAS) to property, and the reduced rate VAT in the ANRU area.Some ANAH aid to social lenders is also linked to a ceiling on rent and the amount of resources of the tenant, which varies according to the zoning A/B/C. Following a consultation conducted by the Regional Prefect with the local authorities in the 4th quarter of 2013, the new zoning A/B/C was adopted by the Minister in charge of Housing on 1 August 2014. For Franche-Comté, 19 new municipalities were reclassified from C to B2, while no decommissioning was recorded. Its entry into force varies between 1 October 2014 and 1 February 2015 depending on the arrangements attached to it: as of 1 October 2014 for: — the zero-rate loan; — the guarantee scheme of the FGAS; — the reduced rate VAT scheme for intermediate rental accommodation (279-0a A of the CGI); — the aid scheme for intermediate rental investment for private individuals (199 novitiies of the General Tax Code (CGI); — promises of sales of public land, pursuant to Article R. 3211-15 of the General Code of Ownership of Public Persons; on 1 January 2015 for: — the benefit of aid from the National Housing Agency, the ‘old Borloo’ tax scheme; — the intermediate rental loan; — reduced VAT in ANRU area; — devices related to HLM promotion; — the assessment of resources for new intermediate dwellings held by HLML bodies in the context of their service of general economic interest; as of 1 February 2015 for: — approvals of social loans for leasing-accession. Data sources: order of the Minister of Housing dated 01 August 2014

Abstract copyright UK Data Service and data collection copyright owner.The 5% Sample Survey of Building Society Mortgage Completions (BSM) has been in existence since 1965. The Archive holds data from 1974. Monthly returns, giving detailed information on a nominal 5% sample of all mortgage completions, have been submitted on a voluntary basis by most building societies to the Department of Environment who process the data on a quarterly basis. The survey results have served as the offical source of statistics on the owner-occupied housing market, providing a wealth of information on mortgage advances, dwelling prices and the characteristics of borrowers and properties. An increased share of the mortgage market being accounted for by other lenders and a widening range of mortgage products during the 1980s have necessitated change, leading to the BSM being succeeded by the Survey of Mortgage Lenders (SML) in 1992 (see GN: 33254). 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: Building Society code, date mortgage completed, whether dwelling is wholly or partly occupied by borrower. Mortgage amount, whether solely for purchase of property, period of mortgage, gross rate of interest, repayment method. Purchase price and whether discounted in any way. Location of dwelling, whether new, age of dwelling, type, number of habitable rooms, whether garage, rateable value. Number and sex of borrowers, age of main borrower, basic income, other income, total income, whether applicant previously owner occupier, previous tenure, whether main borrower nominated by LA under support lending scheme. Building Societies are divided into four strata according to the size of their assets. All the largest societies are 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. Societies in the next stratum are arranged in order of size of assets and alternate societies 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 society are obtained. The smallest societies are completely excluded.