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

The average amount of non-mortgage debt held by consumers in the United States has been falling steadily during the past years, amounting to ****** U.S. dollars in 2023. While respondents had ****** U.S. dollars of debt in 2018, that volume decreased to ****** U.S. dollars in 2019, which constituted the largest year-over-year decrease.What age groups are more indebted in the U.S.?The age group with the highest level of consumer debt in the U.S. was belonging to the Generation X with approximately ******* U.S. dollars of debt in 2022. The next generations with high consumer debt levels were baby boomers and millennials, whose debt levels were similar. In comparison, credit card debt is more equally distributed across all ages. There is an exception among people under 35 years old, who are significantly less burdened with credit card debt. However, most consumers expect to get rid of their debt in the short term. College expenses as a source of debtEducational expenses were not among the leading sources of debt among consumers in the U.S. in 2022. Instead, they made up about ** percent of the total. However, around ** percent of undergraduates from lower-income families had student loans, while over a fifth of undergraduates from higher-income families had student loans. Independently of how they cover these expenses, the confidence of students and parents about being able to pay these college costs was high in most cases.

Credit card debt in the United States has been growing at a fast pace between 2021 and 2025. In the fourth quarter of 2024, the overall amount of credit card debt reached its highest value throughout the timeline considered here. COVID-19 had a big impact on the indebtedness of Americans, as credit card debt decreased from *** billion U.S. dollars in the last quarter of 2019 to *** billion U.S. dollars in the first quarter of 2021. What portion of Americans use credit cards? A substantial portion of Americans had at least one credit card in 2025. That year, the penetration rate of credit cards in the United States was ** percent. This number increased by nearly seven percentage points since 2014. The primary factors behind the high utilization of credit cards in the United States are a prevalent culture of convenience, a wide range of reward schemes, and consumer preferences for postponed payments. Which companies dominate the credit card issuing market? In 2024, the leading credit card issuers in the U.S. by volume were JPMorgan Chase & Co. and American Express. Both firms recorded transactions worth over one trillion U.S. dollars that year. Citi and Capital One were the next banks in that ranking, with the transactions made with their credit cards amounting to over half a trillion U.S. dollars that year. Those industry giants, along with other prominent brand names in the industry such as Bank of America, Synchrony Financial, Wells Fargo, and others, dominate the credit card market. Due to their extensive customer base, appealing rewards, and competitive offerings, they have gained a significant market share, making them the preferred choice for consumers.

Graph and download economic data for Mortgage Debt Service Payments as a Percent of Disposable Personal Income (MDSP) from Q1 1980 to Q4 2024 about disposable, payments, mortgage, debt, personal income, percent, personal, services, income, and USA.

Monthly car loan payments in the United States were significantly higher than lease payments in 2024. People leasing a new vehicle paid on average 586 U.S. dollars per month, which is slightly lower than in 2023. Meanwhile, the average loan payment for a new car increased in the past two years, reaching 734 U.S. dollars in 2024.

Concept: Household debt service ratio – Expected household debt payments to disposable income ratio as a quarterly moving average, seasonally adjusted. Household debt – Ratio of total household debt held by financial institutions to disposable income accumulated over the past twelve months. Source: Central Bank of Brazil – Department of Economics 19882-household-debt 19882-household-debt

Graph and download economic data for Federal government current expenditures: Interest payments (A091RC1Q027SBEA) from Q1 1947 to Q1 2025 about payments, expenditures, federal, government, interest, GDP, and USA.

Debt service ratios, interest and obligated principal payments on debt, and related statistics for households, Canada.

In 2023, the average monthly repayments of loans for the purchase of houses and land of working households in Japan amounted to around 27.8 thousand Japanese yen. The average monthly loan repayments for purchases on credit increased to 77 thousand yen a month per household.

Concept: Household debt service ratio – Expected household debt payments to disposable income ratio as a quarterly moving average, seasonally adjusted. Household debt – Ratio of total household debt held by financial institutions to disposable income accumulated over the past twelve months. Source: Central Bank of Brazil – Department of Economics 19881-household-debt-service-ratio---seasonally-adjusted-data 19881-household-debt-service-ratio---seasonally-adjusted-data

Housing data for proportion of outstanding debt, schedule payment and financial obligations by credit type can aid with your research. This adapted Equifax data is by Canada and by average CMA size. A detailed table with each CMA is available. Both tables are broken into debt by: mortgages HELOCs lines of credit auto loans credit cards all other credit Note: Quarterly data ranges from 2019 Q1 to 2023. Source: Equifax with calculations from CMHC

Graph and download economic data for Personal interest payments (B069RC1) from Jan 1959 to May 2025 about payments, personal, interest, and USA.

The average monthly payment for new car financing in the United States was on average *** U.S. dollars higher than for used cars in the second quarter of 2024. Meanwhile, the down payment for new automobiles was several thousand U.S. dollars higher than for used ones. In the second quarter of 2024, the average amount financed for the purchase of a new car amounted to ****** U.S. dollars on average. The average maturity of new car loans at finance companies was ** months in early 2024.

Graph and download economic data for Consumer Loans: Credit Cards and Other Revolving Plans, All Commercial Banks (CCLACBW027SBOG) from 2000-06-28 to 2025-06-18 about revolving, credit cards, loans, consumer, banks, depository institutions, and USA.

Young people in the United Kingdom had on average a higher monthly debt from buy now, pay later services than older consumers. Consumers between 18 and 24 years old had the highest volume of debt from buy now, pay later (BNPL) in 2022. Meanwhile, the orders by BNPL users from 45 to 54 years of age amounted to 154 British pounds on average.

The grant element of a loan is the grant equivalent expressed as a percentage of the amount committed. It is used as a measure of the overall cost of borrowing. To obtain the average, the grant elements for all public and publicly guaranteed loans have been weighted by the amounts of the loans. The grant equivalent of a loan is its commitment (present) value, less the discounted present value of its contractual debt service; conventionally, future service payments are discounted at 10 percent. Commitments cover the total amount of loans for which contracts were signed in the year specified. Public debt is an external obligation of a public debtor, including the national government, a political subdivision (or an agency of either), and autonomous public bodies. Publicly guaranteed debt is an external obligation of a private debtor that is guaranteed for repayment by a public entity. Data for private nonguaranteed debt are not available.

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...

Graph and download economic data for Delinquency Rate on Credit Card Loans, All Commercial Banks (DRCCLACBS) from Q1 1991 to Q1 2025 about credit cards, delinquencies, commercial, loans, banks, depository institutions, rate, and USA.

In 2023, new car leases were the type of vehicle financing with the highest average monthly payment. Monthly car loan payments in the United States tend to increase each year, with the monthly payment for new car loans reaching 725 U.S. dollars in the third quarter of 2023. The average monthly payment for used car loans in the U.S. amounted to 533 U.S. dollars in the third quarter of 2023.

As of April 2023, federal student loan forgiveness programs forgave an average amount of around 21,347 U.S. dollars to eligible borrowers in the United States. In comparison, the average amount of student debt forgiven by the Public Service Loan Forgiveness program was 63,826 U.S. dollars per borrower. Public Service Loan Forgiveness grants federal loan forgiveness to borrowers who have been working for a qualifying public service employer full-time for 10 years and have made 120 monthly payments while working for that employer.