House Price Index YoY in the United States decreased to 2.80 percent in May from 3.20 percent in April of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index YoY.

The FHFA House Price Index (FHFA HPI®) is the nation’s only collection of public, freely available house price indexes that measure changes in single-family home values based on data from all 50 states and over 400 American cities that extend back to the mid-1970s. The FHFA HPI incorporates tens of millions of home sales and offers insights about house price fluctuations at the national, census division, state, metro area, county, ZIP code, and census tract levels. FHFA uses a fully transparent methodology based upon a weighted, repeat-sales statistical technique to analyze house price transaction data. ​ What does the FHFA HPI represent? The FHFA HPI is a broad measure of the movement of single-family house prices. The FHFA HPI is a weighted, repeat-sales index, meaning that it measures average price changes in repeat sales or refinancings on the same properties. This information is obtained by reviewing repeat mortgage transactions on single-family properties whose mortgages have been purchased or securitized by Fannie Mae or Freddie Mac since January 1975. The FHFA HPI serves as a timely, accurate indicator of house price trends at various geographic levels. Because of the breadth of the sample, it provides more information than is available in other house price indexes. It also provides housing economists with an improved analytical tool that is useful for estimating changes in the rates of mortgage defaults, prepayments and housing affordability in specific geographic areas. U.S. Federal Housing Finance Agency, All-Transactions House Price Index for Connecticut [CTSTHPI], retrieved from FRED, Federal Reserve Bank of St. Louis; https://fred.stlouisfed.org/series/CTSTHPI, August 2, 2023.

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
• More datasets

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 ---

Graph and download economic data for Median Sales Price of Houses Sold for the United States (MSPUS) from Q1 1963 to Q2 2025 about sales, median, housing, and USA.

In 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.

30 Year Mortgage Rate in the United States decreased to 6.72 percent in July 31 from 6.74 percent in the previous week. This dataset includes a chart with historical data for the United States 30 Year Mortgage Rate.

Fixed 30-year mortgage rates in the United States averaged 6.83 percent in the week ending July 25 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.

In 2022, house price growth in the UK slowed, after a period of decade-long increase. Nevertheless, in March 2025, prices reached a new peak, with the average home costing ******* British pounds. This figure refers to all property types, including detached, semi-detached, terraced houses, and flats and maisonettes. Compared to other European countries, the UK had some of the highest house prices. How have UK house prices increased over the last 10 years? Property prices have risen dramatically over the past decade. According to the UK house price index, the average house price has grown by over ** percent since 2015. This price development has led to the gap between the cost of buying and renting a property to close. In 2023, buying a three-bedroom house in the UK was no longer more affordable than renting one. Consequently, Brits have become more likely to rent longer and push off making a house purchase until they have saved up enough for a down payment and achieved the financial stability required to make the step. What caused the recent fluctuations in house prices? House prices are affected by multiple factors, such as mortgage rates, supply, and demand on the market. For nearly a decade, the UK experienced uninterrupted house price growth as a result of strong demand and a chronic undersupply. Homebuyers who purchased a property at the peak of the housing boom in July 2022 paid ** percent more compared to what they would have paid a year before. Additionally, 2022 saw the most dramatic increase in mortgage rates in recent history. Between December 2021 and December 2022, the **-year fixed mortgage rate doubled, adding further strain to prospective homebuyers. As a result, the market cooled, leading to a correction in pricing.

Price to Rent Ratio in the United States increased to 134.20 in the fourth quarter of 2024 from 133.60 in the third quarter of 2024. This dataset includes a chart with historical data for the United States Price to Rent Ratio.

Key information about House Prices Growth

• Mexico house prices grew 8.8% YoY in Dec 2024, following an increase of 9.2% YoY in the previous quarter.
• YoY growth data is updated quarterly, available from Mar 2006 to Dec 2024, with an average growth rate of 7.4%.
• House price data reached an all-time high of 11.7% in Mar 2023 and a record low of 2.2% in Jun 2010.

Existing Home Sales in the United States decreased to 3930 Thousand in June from 4040 Thousand in May of 2025. This dataset provides the latest reported value for - United States Existing Home Sales - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

What are 30 year mortgage rates?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Abstract copyright UK Data Service and data collection copyright owner.A series of surveys were carried out to provide factual and detailed information on the performance of 6 local authorities in council house allocation, improvement grants, council mortgages and council house sales. The information was intended to support inter-authority comparisons, and to check on variability of policy and practice. The emphasis was on the extent to which housing need was being met and housing opportunities created. Main Topics: Attitudinal/Behavioural Questions (SN: 205) This dataset records information collected from the West Bromwich Waiting List. Type of list, length of application, applicant's marital and family situation, whether baby expected at application data, 'points' (total and detailed breakdown, e.g. size of family points, shared accommodation points). Period of residence/employment in West Bromwich County Borough, tenure, household size and type, bedrooms for applicant's family, use of separate living room, whether family separated by accommodation (length of time), other persons in dwelling, amenities, any personal disabilities, cleanliness. Type of dwelling recommended/allocated, number of bedrooms needed, area, offers made, rent/floor area allocated, rateable value allowed, age/grade choice and allocation, category of tenant, origin of letting, present location, location allocated, comparison of density of occupation (present and previous). Background Variables (SN: 205) Age, sex, ethnic origin, household status, place of residence, number of children less than/over 16 years of age, number under 5 years of age. Attitudinal/Behavioural Questions (SN: 263, 268, 271, 274, 277 and 280) Type of list, type of house, tenure, number of bedrooms, whether living room shared, other persons in house, standard of decorations. Type of house wanted, reasons for application, offers made, rent record. Expectant mother at application, medical claims 'points'. Required: type of dwelling, number of bedrooms, garage or car space. Location, age and grade of house (chosen and allocated). Present, chosen and allocated density of occupation. Floor space allocated. Background Variables (SN: 263, 268, 271, 274, 277 and 280) Age, marital status, place of birth, children 16 and under/5 and under, household size and type, length of residence at present address and in UK. Attitudinal/Behavioural Questions (SN: 264) Length of residence, whether on council waiting list, owner occupier, whether other property owned, present rent, rent willing to pay, general condition of property, cleanliness, rent record, medical problems, offers made, type of dwelling allocated, rent allocated, rateable value allocated, category of tenant, origin of letting, present, chosen and allocated location, age and grade of house, density of occupation allocated. Background Variables (SN: 264) Age, children 15 and under/5 and under, household type and size, number in employment, total income, car ownership. Attitudinal/Behavioural Questions (SN: 265) Size and age of house, mortgage intention, market price, sale price, % discount, market price above construction cost, length of tenancy, reasons for withdrawal, rent record, previous tenure, family size on application, whether still at same address, density of occupation, grade of estate, car parking facilities. Background Variables (SN: 265) Age, children 15 and 5 and under, household type. Attitudinal/Behavioural Questions (SN: 266) Term of loan sought, reference satisfactory, income satisfactory, price, loan sought, valuation, advance approved, balance of annual repayments, valuation as % price, loan granted as % price, loan approved as % valuation, loan approved as % price, time taken for approval, whether applicant is tenant, whether part of house would be let in future, freehold or leasehold, rateable value, notices to repair outstanding, type of property, number of bedrooms, garden, garage, hot water system, age of buildings, annual basic earnings, overtime, total earnings, total household income, annual repayment as % applicant's annual earnings, annual repayments as % household annual earnings, mortgage held. Background Variables (SN: 266) Age, place of birth, family size, social class. Variables (SN: 267, 270, 273, 276 and 279) Type of grant, nature of work, cost approved, maximum grant, age of property, tenure, mortgage, cost of improvement, cost of repairs as % approved costs, grant as % total costs, total cost of work, grant approved, date of application, time taken from application to approval, time taken from approval to completion, time taken from application to completion, area, house type. Attitudinal/Behavioural Questions (SN: 269, 272, 275, 278 and 281) Period of loan sought, income status, period of loan granted, category of tenant, price, loan applied for, valuation, advance given, balance, annual repayments, valuation as % price, loan granted as % loan sought, loan as % price, loan as % valuation, time taken from application to approval. Length of tenancy, rate of interest, earnings, overtime, other earnings, total applicant's earnings, total household income, previous rent, repayments as % previous rent. Whether applicant is tenant, whether part of house would be let in future, freehold or leasehold, rateable value, repairs required, type of house, garden, garage, hot water system, central heating, number of bedrooms, age of property, mortgage, area, grade of estate, previous tenure, density of occupation. Background Variables (SN: 269, 272, 275, 278 and 281) Age, social class, children 16 and under/5 and under, household type and size.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Mortgage Rates: Hot Economic Conjecture Puts the Squeeze on Homebuyers

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

House Price Index MoM in the United States decreased by 0.20 percent in May from -0.30 percent in April of 2025. This dataset includes a chart with historical data for the United States FHFA House Price Index MoM.

This table contains data described by the following dimensions (Not all combinations are available): Geography (247 items: Carbonear; Newfoundland and Labrador; Corner Brook; Newfoundland and Labrador; Grand Falls-Windsor; Newfoundland and Labrador; Gander; Newfoundland and Labrador ...), Type of structure (4 items: Apartment structures of three units and over; Apartment structures of six units and over; Row and apartment structures of three units and over; Row structures of three units and over ...), Type of unit (4 items: Two bedroom units; Three bedroom units; One bedroom units; Bachelor units ...).

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.

Prior to 1974 the data was based on surveys of existing house sales in Dublin carried out by the Valuation Office on behalf of the D. O. E. Since 1974 the data has been based on information supplied by all lending agencies on the average price of mortgage financed existing house transactions. Average house prices are derived from data supplied by the mortgage lending agencies on loans approved by them rather than loans paid. In comparing house prices figures from one period to another, account should be taken of the fact that changes in the mix of houses (incl apartments) will affect the average figures. Data for 1969/1970 is not available for Cork, Limerick, Galway, Waterford and Other areas 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. National and Other Areas figure changed for 2015 on 27/6/15 as revised data received from Local Authorities Prices includes houses and apartments measured in €

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Mortgage Rates Soar, Making Homeownership Out of Reach for Many

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Abstract copyright UK Data Service and data collection copyright owner.A series of surveys were carried out to provide factual and detailed information on the performance of 6 local authorities in council house allocation, improvement grants, council mortgages and council house sales. The information was intended to support inter-authority comparisons, and to check on variability of policy and practice. The emphasis was on the extent to which housing need was being met and housing opportunities created. Main Topics: Attitudinal/Behavioural Questions (SN: 205) This dataset records information collected from the West Bromwich Waiting List. Type of list, length of application, applicant's marital and family situation, whether baby expected at application data, 'points' (total and detailed breakdown, e.g. size of family points, shared accommodation points). Period of residence/employment in West Bromwich County Borough, tenure, household size and type, bedrooms for applicant's family, use of separate living room, whether family separated by accommodation (length of time), other persons in dwelling, amenities, any personal disabilities, cleanliness. Type of dwelling recommended/allocated, number of bedrooms needed, area, offers made, rent/floor area allocated, rateable value allowed, age/grade choice and allocation, category of tenant, origin of letting, present location, location allocated, comparison of density of occupation (present and previous). Background Variables (SN: 205) Age, sex, ethnic origin, household status, place of residence, number of children less than/over 16 years of age, number under 5 years of age. Attitudinal/Behavioural Questions (SN: 263, 268, 271, 274, 277 and 280) Type of list, type of house, tenure, number of bedrooms, whether living room shared, other persons in house, standard of decorations. Type of house wanted, reasons for application, offers made, rent record. Expectant mother at application, medical claims 'points'. Required: type of dwelling, number of bedrooms, garage or car space. Location, age and grade of house (chosen and allocated). Present, chosen and allocated density of occupation. Floor space allocated. Background Variables (SN: 263, 268, 271, 274, 277 and 280) Age, marital status, place of birth, children 16 and under/5 and under, household size and type, length of residence at present address and in UK. Attitudinal/Behavioural Questions (SN: 264) Length of residence, whether on council waiting list, owner occupier, whether other property owned, present rent, rent willing to pay, general condition of property, cleanliness, rent record, medical problems, offers made, type of dwelling allocated, rent allocated, rateable value allocated, category of tenant, origin of letting, present, chosen and allocated location, age and grade of house, density of occupation allocated. Background Variables (SN: 264) Age, children 15 and under/5 and under, household type and size, number in employment, total income, car ownership. Attitudinal/Behavioural Questions (SN: 265) Size and age of house, mortgage intention, market price, sale price, % discount, market price above construction cost, length of tenancy, reasons for withdrawal, rent record, previous tenure, family size on application, whether still at same address, density of occupation, grade of estate, car parking facilities. Background Variables (SN: 265) Age, children 15 and 5 and under, household type. Attitudinal/Behavioural Questions (SN: 266) Term of loan sought, reference satisfactory, income satisfactory, price, loan sought, valuation, advance approved, balance of annual repayments, valuation as % price, loan granted as % price, loan approved as % valuation, loan approved as % price, time taken for approval, whether applicant is tenant, whether part of house would be let in future, freehold or leasehold, rateable value, notices to repair outstanding, type of property, number of bedrooms, garden, garage, hot water system, age of buildings, annual basic earnings, overtime, total earnings, total household income, annual repayment as % applicant's annual earnings, annual repayments as % household annual earnings, mortgage held. Background Variables (SN: 266) Age, place of birth, family size, social class. Variables (SN: 267, 270, 273, 276 and 279) Type of grant, nature of work, cost approved, maximum grant, age of property, tenure, mortgage, cost of improvement, cost of repairs as % approved costs, grant as % total costs, total cost of work, grant approved, date of application, time taken from application to approval, time taken from approval to completion, time taken from application to completion, area, house type. Attitudinal/Behavioural Questions (SN: 269, 272, 275, 278 and 281) Period of loan sought, income status, period of loan granted, category of tenant, price, loan applied for, valuation, advance given, balance, annual repayments, valuation as % price, loan granted as % loan sought, loan as % price, loan as % valuation, time taken from application to approval. Length of tenancy, rate of interest, earnings, overtime, other earnings, total applicant's earnings, total household income, previous rent, repayments as % previous rent. Whether applicant is tenant, whether part of house would be let in future, freehold or leasehold, rateable value, repairs required, type of house, garden, garage, hot water system, central heating, number of bedrooms, age of property, mortgage, area, grade of estate, previous tenure, density of occupation. Background Variables (SN: 269, 272, 275, 278 and 281) Age, social class, children 16 and under/5 and under, household type and size.