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.

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.

Very Important

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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

Expenditure on rent by renters and mortgages by mortgage holders, by region and age from the Living Costs and Food Survey for the financial year ending 2022. Data is presented as a proportion of total expenditure and a proportion of disposable income.

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...

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 ...

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.

Chile RB: CM: CC: Mortgage for Housing: Percentage of Total Income of Persons Representing the Dividend data was reported at 0.000 NA in Mar 2019. This records an increase from the previous number of -9.100 NA for Dec 2018. Chile RB: CM: CC: Mortgage for Housing: Percentage of Total Income of Persons Representing the Dividend data is updated quarterly, averaging 0.000 NA from Mar 2005 (Median) to Mar 2019, with 57 observations. The data reached an all-time high of 21.400 NA in Mar 2011 and a record low of -23.100 NA in Mar 2012. Chile RB: CM: CC: Mortgage for Housing: Percentage of Total Income of Persons Representing the Dividend data remains active status in CEIC and is reported by Central Bank of Chile. The data is categorized under Global Database’s Chile – Table CL.KB019: Bank Loan Survey.

Graph and download economic data for Consumer Unit Characteristics: Percent Homeowner with Mortgage by Income Before Taxes: Total Complete Income Reporters (CXU980230LB02A2M) from 1984 to 2003 about consumer unit, homeownership, mortgage, tax, percent, income, and USA.

The industry is composed of non-depository institutions that conduct primary and secondary market lending. Operators in this industry include government agencies in addition to non-agency issuers of mortgage-related securities. Through 2025, rising per capita disposable income and low levels of unemployment helped fuel the increase in primary and secondary market sales of collateralized debt. Nonetheless, due to the sharp contraction in economic activity at the onset of the period, revenue gains were limited, but climbed in the latter part of the period as the economy has normalized. Interest rates climbed significantly to tackle significant inflationary pressures, which increased borrowing costs, hindering loan volumes but increasing interest income for each loan. However, the Fed cut interest rates in 2024 and is anticipated to cut rates in the latter part of the current year, reducing borrowing costs and providing a boost to loan volumes. Overall, these trends, along with volatility in the real estate market, have caused revenue to slump at a CAGR of 1.3% to $488.9 billion over the past five years, including an expected decline of 0.1% in 2025 alone. The high interest rate environment has hindered real estate loan demand but increased interest income, boosting profit to 15.6% of revenue in the current year. Higher access to credit and higher disposable income have fueled primary market lending over much of the period, increasing the variety and volume of loans to be securitized and sold in secondary markets. An additional boon for institutions has been an increase in interest rates, which raised interest income as the spread between short- and long-term interest rates increased. These macroeconomic factors, combined with changing risk appetite and regulation in the secondary markets, have resurrected collateralized debt trading since the middle of the period. Although institutions are poised to benefit from strong economic growth, inflationary pressures easing and the decline in the 30-year conventional mortgage rate, the rate of homeownership is still expected to fall but at a slower pace compared to the current period. Shaky demand from commercial banking and uncertainty surrounding inflationary pressures will influence institutions' decisions on whether or not to sell mortgage-backed securities and commercial loans to secondary markets. These trends are expected to cause revenue to decline at a CAGR of 1.0% to $465.4 billion over the five years to 2030.

Total mortgage purchase applications by year from 2018-2024 showing the number of home purchase applications submitted by U.S. home buyers across all loan types

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show comparison of housing ownership costs and rental costs to income by Super District in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

Total area within the tract (in acres)

SqMi

Total area within the tract (in square miles)

County

County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

CountyName

County Name

HUM_SMOCAPI_e

# Housing units with a mortgage, costs as a percentage of income computed, 2017

HUM_SMOCAPI_m

# Housing units with a mortgage, costs as a percentage of income computed, 2017 (MOE)

MSMOCAPI30PctPlus_e

# Housing units with a mortgage, costs 30.0 percent of income or more, 2017

MSMOCAPI30PctPlus_m

# Housing units with a mortgage, costs 30.0 percent of income or more, 2017 (MOE)

pMSMOCAPI30PctPlus_e

% Housing units with a mortgage, costs 30.0 percent of income or more, 2017

pMSMOCAPI30PctPlus_m

% Housing units with a mortgage, costs 30.0 percent of income or more, 2017 (MOE)

HUNM_SMOCAPI_e

# Housing units without a mortgage, costs as a percentage of income computed, 2017

HUNM_SMOCAPI_m

# Housing units without a mortgage, costs as a percentage of income computed, 2017 (MOE)

NMSMOCAPI30PctPlus_e

# Housing units without a mortgage, costs 30.0 percent of income or more, 2017

NMSMOCAPI30PctPlus_m

# Housing units without a mortgage, costs 30.0 percent of income or more, 2017 (MOE)

pNMSMOCAPI30PctPlus_e

% Housing units without a mortgage, costs 30.0 percent of income or more, 2017

pNMSMOCAPI30PctPlus_m

% Housing units without a mortgage, costs 30.0 percent of income or more, 2017 (MOE)

OccGRAPI_e

# Occupied units for which rent as a percentage of income can be computed, 2017

OccGRAPI_m

# Occupied units for which rent as a percentage of income can be computed, 2017 (MOE)

GRAPI30PctPlus_e

# Gross rent 30.0 percent of income or greater, 2017

GRAPI30PctPlus_m

# Gross rent 30.0 percent of income or greater, 2017 (MOE)

pGRAPI30PctPlus_e

% Gross rent 30.0 percent of income or greater, 2017

pGRAPI30PctPlus_m

% Gross rent 30.0 percent of income or greater, 2017 (MOE)

HousingCost30PctPlus_e

# All occupied units for which costs exceed 30 percent of income, 2017

HousingCost30PctPlus_m

# All occupied units for which costs exceed 30 percent of income, 2017 (MOE)

PayingForHousing_e

# Total households paying for housing (rent or owner costs), 2017

PayingForHousing_m

# Total households paying for housing (rent or owner costs), 2017 (MOE)

pHousingCost30PctPlus_e

% Occupied units for which costs exceed 30 percent of income, 2017

pHousingCost30PctPlus_m

% Occupied units for which costs exceed 30 percent of income, 2017 (MOE)

last_edited_date

Last date the feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

Annual mortgage application counts from 2018-2024 showing total applications submitted by U.S. home buyers across all loan types including conventional, FHA, VA, and USDA mortgages

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/).

The American Community Survey (ACS) is a nationwide survey conducted by the U.S. Census Bureau that is designed to provide communities a fresh look at how they are changing. It is a critical element in the Census Bureau's reengineered decennial census program, incorporating the detailed socioeconomic and housing questions that were previously asked on the decennial census long form into the ACS questionnaire. The ACS now collects and produces this detailed population and housing information every year instead of every ten years. Data are collected on an on-going basis throughout the year and are released each year for large geographic areas, those with 65,000 persons or more. However, sample sizes are not large enough for annual releases that cover smaller areas, those with less than 65,000 persons. Data that are suitable for areas with 20,000 to 65,000 persons are accumulated over three years and termed a three-year period estimate, the first of which was for the 2005-2007 period. Data that are suitable for areas with less than 20,000 persons are accumulated over five years and termed a five-year period estimate, the first of which was for the 2005-2009 period. The data in this series of RGIS Clearinghouse tables are for all New Mexico counties and are based on the 2005-2009 ACS Five-Year Period Estimates collected between January 2005 and December 2009. These data tables are a summary of all major housing topics published through the ACS, providing information about the condition of housing, and illuminating various financial characteristics of the housing stock. Major topics include housing occupancy, year structure built, rooms and bedrooms, housing tenure (owners and renters), year householder moved into unit, vehicles available, type of house heating fuel, units without complete plumbing and kitchen facilities or without telephone service, occupants per room, home value, mortgage status, monthly owner costs, owner costs as a percentage of household income, gross rent, and gross rent as a percentage of household income. Percentages are shown along with numeric estimates for most data items. Because the data are based on a sample the Census Bureau also provides information about the magnitude of sampling error. Consequently, the estimated margin of error (MOE) is shown next to each data item. Each housing topic is covered in a separate file in both Excel and CSV formats. These files, along with file-specific descriptions (in Word and text formats) are available in a single zip file.

Portugal, Canada, and the United States were the countries with the highest house price to income ratio in 2024. In all three countries, the index exceeded 130 index points, while the average for all OECD countries stood at 116.2 index points. The index measures the development of housing affordability and is calculated by dividing nominal house price by nominal disposable income per head, with 2015 set as a base year when the index amounted to 100. An index value of 120, for example, would mean that house price growth has outpaced income growth by 20 percent since 2015. How have house prices worldwide changed since the COVID-19 pandemic? House prices started to rise gradually after the global financial crisis (2007–2008), but this trend accelerated with the pandemic. The countries with advanced economies, which usually have mature housing markets, experienced stronger growth than countries with emerging economies. Real house price growth (accounting for inflation) peaked in 2022 and has since lost some of the gain. Although, many countries experienced a decline in house prices, the global house price index shows that property prices in 2023 were still substantially higher than before COVID-19. Renting vs. buying In the past, house prices have grown faster than rents. However, the home affordability has been declining notably, with a direct impact on rental prices. As people struggle to buy a property of their own, they often turn to rental accommodation. This has resulted in a growing demand for rental apartments and soaring rental prices.

Households in the lower income quantiles in England in 2024 were more likely to own a household outright than to be currently buying with a mortgage. As the weekly gross income of a household goes up, so does the likelihood that it occupies a home purchased with a mortgage. Of households in the first quantile (lowest income), *** percent were buying with a mortgage, compared to **** percent in the fifth quantile (highest income).

In the third quarter of 2024, household debt in the United States amounted to over 71.66 percent of its GDP. It can be generally observed that U.S. households are more indebted by the end of the year than in any other quarter. The debt of households peaked in the last quarter of 2020, reaching the highest value since 2013. Debt to GDP ratio As it can be observed here, the household debt to GDP ratio decreased overall in the recent years. The steady growth of the gross domestic product in the United States could be a factor explaining this tendency. If the volume of debt grows at a slower pace than the GDP, the debt to GDP ratio would decrease. In addition to that, the overall value of mortgage debt in the U.S., which is the most significant component of the household debt, decreased from 2012 to the third quarter of 2014, but it has rebounded since then. Public debt in the U.S. Public debt in the United States, which is the amount of money borrowed by the government to finance budget deficits, has been increasing almost every single year. Not only that, but according to that forecast it is also expected to keep increasing during the coming years. The major holders of American government debt, as of December 2023, were Federal Reserve and government accounts and foreign and international holders. The ratio of national debt to GDP of the United States was higher than that of other major economies, but lower than that of Japan. Some of the lowest debt to GDP ratios were observed in Hong Kong SAR, Kuwait, and Turkmenistan.

Data for Indicator 11.1.2 comes from Census Bureau's American Community Survey (ACS) estimates. The Census Bureau defined households with Selected Monthly Owner Cost as A Percentage of Income (SMOCAPI) or Gross Rent as A Percentage of Income (GRAPI) that is over 35% of household income (excluding units where SMOCAPI and GRAPI cannot be computed). SMOCAPI only includes the count of households where the owner is still paying a mortgage. SMOCAPI does not include households where the owner has paid off the mortgage.