Housing affordability in the UK has worsened notably since 2020, with the share of income spent on mortgage payments rising for first-time and repeat buyers. In 2024, homebuyers spent, on average, 20.5 percent of their income on mortgage payments, up from 16.2 percent in 2020. First-time buyers spent a notably higher percentage than repeat buyers. One of the main factors for the declining affordability is the rising housing costs. House prices have increased rapidly since the COVID-19 pandemic. Mortgage rates have also soared since, leading to notably higher monthly payments.

Graph and download economic data for Mortgage Debt Service Payments as a Percent of Disposable Personal Income (MDSP) from Q1 1980 to Q2 2025 about disposable, payments, mortgage, personal income, debt, percent, personal, income, services, and USA.

As at February 2025, couples aged 25 to 34 years old in Sydney, Australia spent an average of around **** percent of their household income on mortgage repayments for an entry-priced house. In comparison, couples in the same age bracket in Darwin were spending around **** percent of their household income on mortgage repayments for a house.

In 2023, Italians paid a lower percentage of their monthly income towards mortgage payment compared to the year before. On average, mortgage installments amounted to **** percent of the monthly household income in 2023, down from **** percent the previous year.

Mortgage repayments as a percentage of monthly equivalised disposable household income, throughout the house price and income distribution.

The average mortgage payment in the large and mid-sized cities in Canada ranged between 1,300 Canadian dollars and 2,600 Canadian dollars. In the fourth quarter of the year, Vancouver topped the ranking, with homebuyers paying, on average, ***** Canadian dollars monthly. In Toronto, the average monthly scheduled mortgage payment was ***** Canadian dollars. Canada’s housing market House prices in Canada vary widely across the country. In 2023, the average sales price of detached single-family homes in Vancouver was nearly three times as expensive as the national average. Vancouver is undoubtedly considered the least affordable housing market: In 2023, the cost of buying a home with a **-year mortgage in Canada was approximately ** percent of the median household income, whereas in Vancouver, it was nearly *** percent. Development of house prices The development of house prices depends on multiple factors, such as availability on the market and demand. Since 2005, house prices in Canada have been continuously growing. According to the MSL composite house price index, 2021 measured the highest house price increase.

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

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

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 ...

When comparing the mortgage or rental costs incurred by owners with mortgage, private renters and social renters in England, private renters pay a considerably larger share of their income than the other two groups. While owner occupiers with mortgages paid approximately **** percent of their income on mortgage in 2024, private renters paid ** percent, or more than *********. In terms of average monthly costs, renting a three-bedroom house is more expensive than buying.

This statistic shows the share of income spent on mortgage payments in selected metro areas in the Unites States in 2018. In 2018, Los Angeles, California was the third least affordable metro area because **** percent of the median household income was spent on median mortgage payments. For comparison, this is higher than the **** percent homeowners spent, on average, on mortgage payments in the United States in 2018.

DESCRIPTION

A banking institution requires actionable insights into mortgage-backed securities, geographic business investment, and real estate analysis. The mortgage bank would like to identify potential monthly mortgage expenses for each region based on monthly family income and rental of the real estate. A statistical model needs to be created to predict the potential demand in dollars amount of loan for each of the region in the USA. Also, there is a need to create a dashboard which would refresh periodically post data retrieval from the agencies. The dashboard must demonstrate relationships and trends for the key metrics as follows: number of loans, average rental income, monthly mortgage and owner’s cost, family income vs mortgage cost comparison across different regions. The metrics described here do not limit the dashboard to these few. Dataset Description

Variables

Description Second mortgage Households with a second mortgage statistics Home equity Households with a home equity loan statistics Debt Households with any type of debt statistics Mortgage Costs Statistics regarding mortgage payments, home equity loans, utilities, and property taxes Home Owner Costs Sum of utilities, and property taxes statistics Gross Rent Contract rent plus the estimated average monthly cost of utility features High school Graduation High school graduation statistics Population Demographics Population demographics statistics Age Demographics Age demographic statistics Household Income Total income of people residing in the household Family Income Total income of people related to the householder Project Task: Week 1

Data Import and Preparation:

Import data.

Figure out the primary key and look for the requirement of indexing.

Gauge the fill rate of the variables and devise plans for missing value treatment. Please explain explicitly the reason for the treatment chosen for each variable.

Exploratory Data Analysis (EDA):

Perform debt analysis. You may take the following steps:

Explore the top 2,500 locations where the percentage of households with a second mortgage is the highest and percent ownership is above 10 percent. Visualize using geo-map. You may keep the upper limit for the percent of households with a second mortgage to 50 percent

Use the following bad debt equation:

Bad Debt = P (Second Mortgage ∩ Home Equity Loan) Bad Debt = second_mortgage + home_equity - home_equity_second_mortgage Create pie charts to show overall debt and bad debt

Create Box and whisker plot and analyze the distribution for 2nd mortgage, home equity, good debt, and bad debt for different cities

Create a collated income distribution chart for family income, house hold income, and remaining income

Perform EDA and come out with insights into population density and age. You may have to derive new fields (make sure to weight averages for accurate measurements):

Use pop and ALand variables to create a new field called population density

Use male_age_median, female_age_median, male_pop, and female_pop to create a new field called median age

Visualize the findings using appropriate chart type

Create bins for population into a new variable by selecting appropriate class interval so that the number of categories don’t exceed 5 for the ease of analysis.

Analyze the married, separated, and divorced population for these population brackets

Visualize using appropriate chart type

Please detail your observations for rent as a percentage of income at an overall level, and for different states.

Perform correlation analysis for all the relevant variables by creating a heatmap. Describe your findings.

Project Task: Week 2

Data Pre-processing:

The economic multivariate data has a significant number of measured variables. The goal is to find where the measured variables depend on a number of smaller unobserved common factors or latent variables.

Each variable is assumed to be dependent upon a linear combination of the common factors, and the coefficients are known as loadings. Each measured variable also includes a component due to independent random variability, known as “specific variance” because it is specific to one variable. Obtain the common factors and then plot the loadings. Use factor analysis to find latent variables in our dataset and gain insight into the linear relationships in the data.

  Following are the list of latent variables:

Highschool graduation rates

Median population age

Second mortgage statistics

Percent own

Bad debt expense

Data Modeling :

Build a linear Regression model to predict the total monthly expenditure for home mortgages loan.

  Please refer deplotment_RE.xlsx. Column hc_mortgage_mean is predicted variable. This is the mean monthly mortgage and owner costs of specified geographical location.

  Note: Exclude loans from prediction model which have NaN (Not a Numb...

This report analyses mortgage affordability in Australia, which is measured as the proportion of a household's monthly income that remains after making their mortgage repayment. This is calculated based on the average monthly repayments for a standard 25-year mortgage on the mean house price less a deposit in Australia. An increase in the percentage indicates an improvement in the affordability of the average mortgage. The data for this report is sourced from the Australian Bureau of Statistics and is measured as a percentage of average household income.

Shelter cost by tenure including presence of mortgage payments and subsidized housing for Canada, provinces and territories, census metropolitan areas and census agglomerations. Includes shelter-cost-to-income ratio, household total income groups and household type including census family structure, off reserve.

Average household income, debt-to-income ratios, and area median income comparisons for Gen Z home buyers.

The Housing Affordability Index, calculated by the Runstad Center for Real Estate Studies, measures the ability of a middle-income family to carry the mortgage payments on a median-price home. When the index is 100 there is a balance between the family’s ability to pay and the cost. Higher indexes indicate housing is more affordable.

For example, an index of 126 means that a median-income family has 26 percent more income than the bare minimum required to qualify for a mortgage on a median-price home. An index of 80 means that a median-income family has less income than the minimum required.

Graph and download economic data for Household Debt Service Payments as a Percent of Disposable Personal Income (TDSP) from Q1 1980 to Q2 2025 about disposable, payments, personal income, debt, percent, households, personal, income, services, and USA.

Rental Affordability Analysis Based on Median Income

Trends in Tier-Based Affordability Across the U.S

By Zillow Data [source]

About this dataset

This dataset contains rental affordability data for different regions in the US, giving valuable insights into regional rental markets. Renters can use this information to identify where their budget will go the farthest. The cities are organized by rent tier in order to analyze affordability trends within and between different housing stock types. Within each region, the data includes median household income, Zillow Rent Index (ZRI), and percent of income spent on rent.

The Zillow Home Value Forecast (ZHVF) is used to calculate future combined mortgage pay/rent payments in each region using current median home prices, actual outstanding debt amounts and 30-year fixed mortgage interest rates reported through partnership with TransUnion credit bureau. Zillow also provides a breakdown of cash vs financing purchases for buyers looking for an investment or cash option solution.

This dataset provides an effective tool for consumers who want to better understand how their budget fits into diverse rental markets across the US; from condominiums and co-ops, multifamily residences with five or more units, duplexes and triplexes - every renter can determine how their housing budget should be adjusted as they consider multiple living possibilities throughout the country based on real-time price data!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Introduction

Getting Started

• First, you'll need to download the TieredAffordability_Rental.csv dataset from this Kaggle page onto your computer or device.

• After downloading the data set onto your device, open it with any CSV viewing software of your choice (ex: Excel). It will include columns for RegionName**RegionName** , homes type/housing stock (All Homes or Condo/Co-op) SizeRank , Rent tier tier , Date date , median household income income , Zillow Rent Index zri and PercentIncomeSpentOnRent percentage (what portion of monthly median house-hold goes toward monthly mortgage payment) .

• To begin analyzing rental prices across different regions using this dataset, look first at column four: SizeRank; which ranks each region based on size - smallest regions listed first and largest at last - so that you can compare a similar range of Regions when looking at affordability by home sizes larger than one unit multiplex dwellings.*Duples/Triplex*. Once there is an understanding of how all homes compare overall now it is time to consider home types Multifamily 5+ units according to rent tiers tier .

• Next, choose one or more region(s) for comparison based on their rank in SizeRank column –so that all information gathered about them reflects what portionof households fall into certain categories ; eg; All Homes / Small Home /Large Home / MultiPlex Dwelling and what tier does each size rank falls into eg.: Affordable/Slightly Expensive/ Moderately Expensive etc.. This will enable further abstraction from other elements like date vs inflation rate per month or periodical intervals set herein by Rate segmentation i e dates givenin ‘Date’Columns – making the task easier and more direct while analyzing renatalAffordibility Analysis Based On Median Income zri 00 zwi & PCISOR 00 PCIRO

Research Ideas

• Use the PercentIncomeSpentOnRent column to compare rental affordability between regions within a particular tier and determine optimal rent tiers for relocating families.
• Analyze how market conditions are affecting rental affordability over time by using the income, zri, and PercentageIncomeSpentOnRent columns.
• Identify trends in housing prices for different tiers over the years by comparing SizeRank data with Zillow Home Value Forecast (ZHVF) numbers across different regions in order to identify locations that may be headed up or down in terms of home values (and therefore rent levels)

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: TieredAffordability_Rental.csv | Column name | Description | |:-----------------------------|:-------------------------------------------------------------| | RegionName | The name of the region. (String) ...

Description: This metric examines how affordable the average First Time Buyer (FTB) priced property would be for a couple earning the average FTB disposable income by NUTS 2 Region, NUTS 3 Region and County, for each year between 2016 and 2021, with this ratio expressed as a percentage (i.e. average monthly mortgage repayment due on the average FTB priced property as a percentage of the average monthly disposable income of an FTB couple). This percentage should be compared relative to the standard affordability mark of 30% (i.e. housing costs should be below 30% of a household’s disposable income). For example, in the attached excel file, the data shows that the Border recorded an Average Mortgage Repayment to Disposable Income Ratio for First Time Buyers of 17.1% in 2021, which was below the standard affordability mark of 30%. This implies that a FTB couple from the Border – on average disposable income levels in the Border and adjusted to reflect incomes of people aged 40 or below in the Border – would only typically have to pay 17.1% of their joint monthly disposable income on their mortgage instalments on the average priced FTB property in the Border. In contrast, the corresponding ratio for Dublin and the Mid-East is 35% and 31.5%, which are both above the standard affordability mark and show that housing for FTBs – on average – is relatively unaffordable in these areas.Basic Calculations = (Average mortgage repayment on average FTB priced property / Average disposable income of a couple under the age of 40).For full detail on the methodology for the development of this ratio please see the RDM FAQ section.This ratio has been developed by the Regional Economist at the three Regional Assemblies and is primarily based on the CSO County Income and Regional GDP as well as the CSO Regional Property Price Index.Geography available in RDM: State, Regional Assembly and Strategic Planning Area (SPA), County (26).Source: Regional AssembliesWeblink: n/aDate of last source data update: April 2023Update Schedule: Annual

Average household income, debt-to-income ratios, and area median income comparisons for Early Millennial home buyers.

This statistic shows the most costly housing markets in the United States in the first quarter of 2018, by income spent on mortgage. The average mortgage payment in San Diego, California amounted to **** percent of the average income in that period.

Affordability ratios calculated by dividing house prices by gross annual residence-based earnings. Based on the median and lower quartiles of both house prices and earnings in England and Wales.