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.

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.

United States - Mortgage Debt Service Payments as a Percent of Disposable Personal Income was 5.89% in April of 2025, according to the United States Federal Reserve. Historically, United States - Mortgage Debt Service Payments as a Percent of Disposable Personal Income reached a record high of 8.95 in October of 2007 and a record low of 4.37 in January of 1980. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Mortgage Debt Service Payments as a Percent of Disposable Personal Income - last updated from the United States Federal Reserve on December of 2025.

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.

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.

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.

The mortgage debt service ratio in the United States remained fairly stable in 2024, after recovering from a dip in 2020 and 2021. The ratio measures the mortgage debt service payments as a percentage of disposable personal income during a specific quarter and shows the financial burden placed on households by mortgage borrowing. In the fourth quarter of 2024, the total required mortgage payments amounted to approximately **** percent of disposable personal income. This was substantially lower than the spike recorded during the subprime mortgage crisis.

View quarterly updates and historical trends for US Household Mortgage Debt Service Payments as a Percent of Disposable Personal Income. from United State…

Around ** percent of people who have mortgage pay 10 to 14 percent of the net income of their household. A share of ** percent of people who plan to get a mortgage stated that their monthly payment will be more than 40 percent of the household's net income.

This data layer depicts, by census tract, mortgage payments as a percentage of household income in the past 12 months for the San Francisco Bay Region. The source data, from the United States Census Bureau, has been reprocessed by the Metropolitan Transportation Commission.

To produce this feature set, the Metropolitan Transportation Commission downloaded American Community Survey (ACS) table B25091 to create a feature set representing housing unit mortgage payments as a percentage of household income by the following categories: ● Mortgage less than 30% of household income ● Mortgage is 30.0% to 49.9% of household income ● Mortgage is greater than or equal to 50% of household income

The resulting attribute table had all margin of error fields deleted, housing units without a mortgage fields deleted, percentage fields added, county code field added, jurisdiction name added, and the source field names were changed.

The source table used to develop this feature service is from the United States Census Bureau, 2015-2019 American Community Survey 5-Year Estimates and can be downloaded from https://data.census.gov/cedsci/table?q=B25091%3A%20MORTGAGE%20STATUS%20BY%20SELECTED%20MONTHLY%20OWNER%20COSTS%20AS%20A%20PERCENTAGE%20OF%20HOUSEHOLD%20INCOME%20IN%20THE%20PAST%2012%20MONTHS&g=0400000US06%241500000&tid=ACSDT5Y2019.B25091

Household Debt Service Ratio (DSR) (https://fred.stlouisfed.org/series/TDSP) is the ratio of total required household debt payments to total disposable income.

The DSR is divided into two parts. The Mortgage DSR (MDSP) (https://fred.stlouisfed.org/series/MDSP) is total quarterly required mortgage payments divided by total quarterly disposable personal income. The Consumer DSR (CDSP) (https://fred.stlouisfed.org/series/CDSP) is total quarterly scheduled consumer debt payments divided by total quarterly disposable personal income. The Mortgage DSR and the Consumer DSR sum to the DSR.

For questions on the data, please contact the data source (https://www.federalreserve.gov/apps/ContactUs/feedback.aspx?refurl=/releases/housedebt/%). For questions on FRED functionality, please contact us here (https://fred.stlouisfed.org/contactus/).

In 2023, the average monthly home loan repayments of working households with housing loan debt in Japan accounted for **** percent of their disposable income. The share of mortgage repayments to disposable income per month increased by *** percentage points.

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Overview:

Explore the dataset and potentially gain valuable insight into your data science project through interesting features. The dataset was developed for a portfolio optimization graduate project I was working on. The goal was to the monetize risk of company deleveraging by associated with changes in economic data. Applications of the dataset may include. To see the data in action visit my analytics page. Analytics Page & Dashboard and to access all 295,000+ records click here.

• Mortgage-Backed Securities
• Geographic Business Investment
• Real Estate Analysis

For any questions, you may reach us at research_development@goldenoakresearch.com. For immediate assistance, you may reach me on at 585-626-2965. Please Note: the number is my personal number and email is preferred

Statistical Themes:

Note: in total there are 75 fields the following are just themes the fields fall under Home Owner Costs: Sum of utilities, property taxes.

• Second Mortgage: Households with a second mortgage statistics.
• Home Equity Loan: 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, property taxes statistics
• Gross Rent: Contract rent plus the estimated average monthly cost of utility features
• Gross Rent as Percent of Income Gross rent as the percent of income very interesting
• High school Graduation: High school graduation statistics.
• Population Demographics: Population demographic 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.

Sources, if you wish to get the data your self :)

2012-2016 ACS 5-Year Documentation was provided by the U.S. Census Reports. Retrieved May 2, 2018, from

• Census estimate, error, and location data
• Census location information

Access All 325,258 Location of Our Most Complete Database Ever:

Providing you the potential to monetize risk and optimize your investment portfolio through quality economic features at unbeatable price. Access all 295,000+ records on an incredibly small scale, see links below for more details:

• Full Dataset: View Full Dataset
• Real Estate Research: View Research

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 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.

This table is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

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 is part of a series of tables that present a portrait of Canada based on the various census topics. The tables range in complexity and levels of geography. Content varies from a simple overview of the country to complex cross-tabulations; the tables may also cover several censuses.

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.