Mortgage debtors between 30 and 50 years had debts of the highest average value in Sweden in 2022, amounting to almost 3.18 million Swedish kronor. The corresponding figure for the age group of 18 to 30 years old was roughly 2.18 million Swedish kronor. The average debt value of new mortgage borrowers in Sweden has gradually increased since 2012. In Sweden, about half of the households live in an owner-occupied home with a mortgage, making it one of the biggest mortgage markets in Europe. The country has the fifth-highest value of mortgages outstanding but ranks lower in terms of gross residential mortgage lending.

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.

Assets and debts held by family units and by age groups, total amounts.

As of Q4 2024, Americans aged 50 to 61 years had the highest average student loan debt balance among all age groups, averaging 46,790.32 U.S. dollars of student debt per borrower. In comparison, Americans who were 24 years and younger had an average student debt balance of 14,161.76 U.S. dollars.

The generation X was the group of people with the highest average credit card balance in the United States in 2023. That year, the average credit card debt of the generation Z amounted to approximately 3,260 U.S. dollars. People in the silent generation had a credit card balance of roughly 3,410 U.S. dollars.

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.

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...

The total average debt of Baby Boomers in the United States amounted to nearly 94,880 U.S. dollars in 2023. Debt balances, however, varied greatly according to the generation. The Generation X held the highest debt on average (157,560 U.S. dollars), while generation Z held the lowest average debt (nearly 29,820 U.S. dollars).

Key information about Canada Household Debt

• Canada Household Debt reached 2,110.9 USD bn in Dec 2024, compared with the reported number of 2,161.8 USD bn in the previous month
• Canada Household Debt: USD mn data is updated monthly, available from Jan 1969 to Dec 2024
• The data reached an all-time high of 2,225.1 USD bn in Sep 2024 and a record low of 22.2 USD bn in Jan 1969

CEIC converts monthly Household Debt into USD. Statistics Canada provides Household Debt in local currency. The Federal Reserve Board period end market exchange rate is used for currency conversions. Loans are used due to the lack of Flow of Funds statistics. Household Debt prior to January 1990 is sourced from the Bank of Canada.


Further information about Canada Household Debt

• In the latest reports, Canada Household Debt accounted for 99.2 % of the country's Nominal GDP in Sep 2024
• Money Supply M2 in Canada increased 1,873.3 USD bn YoY in Nov 2024
• Canada Foreign Exchange Reserves was measured at 90.2 USD bn in Jan 2025
• The Foreign Exchange Reserves equaled 2.0 Months of Import in Dec 2024
• Canada Domestic Credit reached 4,647.3 USD bn in Nov 2024, representing an increased of 3.9 % YoY

Key information about Australia Household Debt: % of GDP

• Australia household debt accounted for 116.6 % of the country's Nominal GDP in Mar 2023, compared with the ratio of 118.0 % in the previous quarter.
• Australia household debt to GDP ratio is updated quarterly, available from Jun 1988 to Mar 2023.
• The data reached an all-time high of 129.4 % in Sep 2016 and a record low of 44.2 % in Sep 1988.

CEIC calculates quarterly Household Debt as % of Nominal GDP from quarterly Household Debt and quarterly Nominal GDP. The Australian Bureau of Statistics provides Household Debt in local currency and Nominal GDP in local currency.


Related information about Australia Household Debt: % of GDP

• In the latest reports, Australia Household Debt reached 1,957.6 USD bn in Mar 2023.
• Money Supply M2 in Australia increased 4.1 % YoY in Jun 2023.
• Australia Foreign Exchange Reserves was measured at 37.1 USD bn in Jun 2023.
• The Foreign Exchange Reserves equaled 1.5 Months of Import in May 2023.
• Australia Domestic Credit reached 3,627.0 USD bn in May 2023, representing an increased of 1.9 % YoY.
• The country's Non Performing Loans Ratio stood at 0.8 % in Mar 2023, compared with the ratio of 0.8 % in the previous quarter.

As of November 2024, the average remaining home loan balance was the highest in New South Wales, with an average outstanding balance of around 362,935 Australian dollars. In comparison, the average outstanding mortgage balance in Western Australia came to approximately 268,150 Australian dollars.

Statistics on student debt, including the average debt at graduation, the percentage of graduates who owed large debt at graduation and the percentage of graduates with debt who had paid it off at the time of the interview, are presented by the province of study and the level of study. Estimates are available at five-year intervals.

Graph and download economic data for Large Bank Consumer Mortgage Originations: Original Loan-to-Value (LTV): 50th Percentile (RCMFLOLTVPCT50) from Q3 2012 to Q4 2024 about FR Y-14M, origination, large, percentile, mortgage, loans, consumer, banks, depository institutions, and USA.

UK adults aged 35 to 44 were most likely to have a mortgage loan in 2022, with more than half of the respondents in a nationally representative survey sharing that they held one in their own name or joint names. The average for the country stood at 28 percent at that time. Among older generations, the percentage of mortgage holders declined, as these were more likely to have already paid off their mortgage.

Korea Average: AH: 30~39: Secured Loan data was reported at 43,550.000 KRW th in 2017. This records an increase from the previous number of 38,340.000 KRW th for 2016. Korea Average: AH: 30~39: Secured Loan data is updated yearly, averaging 31,510.000 KRW th from Mar 2010 (Median) to 2017, with 8 observations. The data reached an all-time high of 43,550.000 KRW th in 2017 and a record low of 23,740.000 KRW th in 2010. Korea Average: AH: 30~39: Secured Loan data remains active status in CEIC and is reported by Statistics Korea. The data is categorized under Global Database’s Korea – Table KR.H078: SHFLC: Household Assets, Liabilities & Income By Age Groups of Households Head (10Age).

Graph and download economic data for Delinquency Rate on Single-Family Residential Mortgages, Booked in Domestic Offices, All Commercial Banks (DRSFRMACBS) from Q1 1991 to Q1 2025 about domestic offices, delinquencies, 1-unit structures, mortgage, family, residential, commercial, domestic, banks, depository institutions, rate, and USA.

The Public Use Database (PUDB) is released annually to meet FHFA’s requirement under 12 U.S.C. 4543 and 4546(d) to publicly disclose data about the Enterprises’ single-family and multifamily mortgage acquisitions. The datasets supply mortgage lenders, planners, researchers, policymakers, and housing advocates with information concerning the flow of mortgage credit in America’s neighborhoods. Beginning with data for mortgages acquired in 2018, FHFA has ordered that the PUDB be expanded to include additional data that is the same as the data definitions used by the regulations implementing the Home Mortgage Disclosure Act, as required by 12 U.S.C. 4543(a)(2) and 4546(d)(1).The PUDB single-family datasets include loan-level records that include data elements on the income, race, and sex of each borrower as well as the census tract location of the property, loan-to-value (LTV) ratio, age of mortgage note, and affordability of the mortgage. New for 2018 are the inclusion of the borrower’s debt-to-income (DTI) ratio and detailed LTV ratio data at the census tract level. The PUDB multifamily property-level datasets include information on the unpaid principal balance and type of seller/servicer from which the Enterprise acquired the mortgage. New for 2018 is the inclusion of property size data at the census tract level. The multifamily unit-class files also include information on the number and affordability of the units in the property. Both the single-family and multifamily datasets include indicators of whether the purchases are from “underserved” census tracts, as defined in terms of median income and minority percentage of population.Prior to 2010 the single-family PUDB consisted of three files: Census Tract, National A, and National B files. With the 2010 PUDB a fourth file, National C, was added to provide information on high-cost mortgages acquired by the Enterprises. The single-family Census Tract file includes information on the location of the property based on the 2010 Census for acquisition years 2012 through 2021, and the 2020 Census beginning with the 2022 acquisition year. The National files contain other information but lack detailed geographic information in order to protect Enterprise proprietary data. The multifamily datasets also consist of a Census Tract file, and a National file without detailed geographic information.Several dashboards are available to analyze the data:Enterprise Multifamily Public Use Database DashboardThe Enterprise Multifamily Public Use Database (PUDB) Dashboard provides users an interactive way to generate and visualize Enterprise PUDB data of multifamily mortgage acquisitions by Fannie Mae and Freddie Mac. It shows characteristics about multifamily loans, properties and units at the national level, and characteristics about multifamily loans and properties at the state level. It includes key statistics, time series charts, and state maps of multifamily housing characteristics such as median loan amount, number of properties, average number of units per property, and unit affordability. The underlying aggregate statistics presented in the dashboard come from three multifamily data files in the Enterprise PUDB, updated annually since 2008, including two property-level datasets and a data file on the size and affordability of individual units.Enterprise Multifamily Public Use DashboardPress Release - FHFA Releases Data Visualization Dashboard for Enterprises’ Multifamily Mortgage AcquisitionsMortgage Loan and Natural Disaster DashboardFHFA published an interactive Mortgage Loan and Natural Disaster Dashboard that combines FHFA’s PUDB reports on single-family and multifamily acquisitions for the regulated entities, FEMA’s National Risk Index (NRI), and FHFA’s Duty to Serve 2023 High-Needs rural areas. Desired geographies can be exported to .pdf and Excel from the Public Use Database and National Risk Index Dashboard.Mortgage Loan and Natural Disaster DashboardMortgage Loan and Natural Disaster Dashboard FAQs

Debt financing (mortgage, line of credit, term loan, credit card) terms and conditions, average interest rates and average length of term for small and medium enterprises in 2020 by region, CMA level, North American Industry Classification System (NAICS), demographics, age of business, employment size, rate of growth, etc.

The data source is the "Credit Balance Monthly Report Data" submitted by each member institution of the Joint Credit Information Center. The individual's total monthly new credit loan amount and average interest rate are calculated under the segmentation of age groups and gender. If the loan is unsecured, it is defined as a personal credit loan. It does not include overdue, collection, and bad debt credit accounts.

The source of information is the "Credit Balance Monthly Report Data" submitted by each member institution of the Credit Information Center. It aggregates the amount of all individual credit loans and calculates the average interest rate by age group and gender. If the loan is unsecured, it is defined as a personal credit loan. It does not include overdue, collection, and bad debt credit accounts.