Following the drastic increase directly after the COVID-19 pandemic, the delinquency rate started to gradually decline, falling below *** percent in the second quarter of 2023. In the second half of 2023, the delinquency rate picked up, but remained stable throughout 2024. In the first quarter of 2025, **** percent of mortgage loans were delinquent. That was significantly lower than the **** percent during the onset of the COVID-19 pandemic in 2020 or the peak of *** percent during the subprime mortgage crisis of 2007-2010. What does the mortgage delinquency rate tell us? The mortgage delinquency rate is the share of the total number of mortgaged home loans in the U.S. where payment is overdue by 30 days or more. Many borrowers eventually manage to service their loan, though, as indicated by the markedly lower foreclosure rates. Total home mortgage debt in the U.S. stood at almost ** trillion U.S. dollars in 2024. Not all mortgage loans are made equal ‘Subprime’ loans, being targeted at high-risk borrowers and generally coupled with higher interest rates to compensate for the risk. These loans have far higher delinquency rates than conventional loans. Defaulting on such loans was one of the triggers for the 2007-2010 financial crisis, with subprime delinquency rates reaching almost ** percent around this time. These higher delinquency rates translate into higher foreclosure rates, which peaked at just under ** percent of all subprime mortgages in 2011.

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 Global Financial Crisis of 2008-09 was a period of severe macroeconomic instability for the United States and the global economy more generally. The crisis was precipitated by the collapse of a number of financial institutions who were deeply involved in the U.S. mortgage market and associated credit markets. Beginning in the Summer of 2007, a number of banks began to report issues with increasing mortgage delinquencies and the problem of not being able to accurately price derivatives contracts which were based on bundles of these U.S. residential mortgages. By the end of 2008, U.S. financial institutions had begun to fail due to their exposure to the housing market, leading to one of the deepest recessions in the history of the United States and to extensive government bailouts of the financial sector.

Subprime and the collapse of the U.S. mortgage market

The early 2000s had seen explosive growth in the U.S. mortgage market, as credit became cheaper due to the Federal Reserve's decision to lower interest rates in the aftermath of the 2001 'Dot Com' Crash, as well as because of the increasing globalization of financial flows which directed funds into U.S. financial markets. Lower mortgage rates gave incentive to financial institutions to begin lending to riskier borrowers, using so-called 'subprime' loans. These were loans to borrowers with poor credit scores, who would not have met the requirements for a conventional mortgage loan. In order to hedge against the risk of these riskier loans, financial institutions began to use complex financial instruments known as derivatives, which bundled mortgage loans together and allowed the risk of default to be sold on to willing investors. This practice was supposed to remove the risk from these loans, by effectively allowing credit institutions to buy insurance against delinquencies. Due to the fraudulent practices of credit ratings agencies, however, the price of these contacts did not reflect the real risk of the loans involved. As the reality of the inability of the borrowers to repay began to kick in during 2007, the financial markets which traded these derivatives came under increasing stress and eventually led to a 'sudden stop' in trading and credit intermediation during 2008.

Market Panic and The Great Recession

As borrowers failed to make repayments, this had a knock-on effect among financial institutions who were highly leveraged with financial instruments based on the mortgage market. Lehman Brothers, one of the world's largest investment banks, failed on September 15th 2008, causing widespread panic in financial markets. Due to the fear of an unprecedented collapse in the financial sector which would have untold consequences for the wider economy, the U.S. government and central bank, The Fed, intervened the following day to bailout the United States' largest insurance company, AIG, and to backstop financial markets. The crisis prompted a deep recession, known colloquially as The Great Recession, drawing parallels between this period and The Great Depression. The collapse of credit intermediation in the economy lead to further issues in the real economy, as business were increasingly unable to pay back loans and were forced to lay off staff, driving unemployment to a high of almost 10 percent in 2010. While there has been criticism of the U.S. government's actions to bailout the financial institutions involved, the actions of the government and the Fed are seen by many as having prevented the crisis from spiraling into a depression of the magnitude of The Great Depression.

For the past decade, buying a home in the UK has been more affordable than renting one, when only considering the monthly costs. The renting versus buying gap fluctuated during the period and in 2016, it reached its highest value of 131 British pounds. In 2023, the monthly costs for a first-time buyer were 1,231 British pounds, compared to 1,258 British pounds for renters. Rental growth vs house price growth Housing costs in the UK have been on an uprise, with both renting and buying a home increasingly unreachable. Though the monthly costs of buying have consistently been lower in the past decade, house price growth has been much stronger than rental growth since the beginning of the pandemic. Additionally, buyers have been affected by the aggressive mortgage rate hikes, making acquiring their first home even less affordable. Barriers to homeownership Buying a home is not straightforward. For younger (18-40) potential first-time buyers, there are a number of barriers. Approximately one in three first-time buyers point out that raising a deposit was the main obstacle. Other reasons stopping buyers were not being able to take out a mortgage on their current income and poor credit ratings. Unsurprisingly, the highest share of people who buy a home with a mortgage was in the age group of 45 to 55-year-olds.

Following the FCA's Policy Statement on the changes to the responsible lending rules, it has published further data on the mortgage prisoner population. The FCA's Mortgage Market Study estimated that around 140,000 borrowers were unable to switch to a better deal even though they were up-to-date with their payments. To help fix this it changed its rules late last year to allow lenders to assess affordability based on a borrower’s track record of making mortgage payments.

The charts show analysis of the entire dataset which includes all borrowers in closed mortgage books and those who have mortgages owned by unregulated firms regardless of their eligibility to switch because of our new rules. The FCA conducted a data gathering exercise which collected details on all mortgage accounts owned by unregulated firms. This data was combined with details of mortgage accounts held in closed books of regulated firms.

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Data and Documentation section...Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section..Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau''s Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities and towns and estimates of housing units for states and counties..Explanation of Symbols:An ''**'' entry in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate..An ''-'' entry in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution..An ''-'' following a median estimate means the median falls in the lowest interval of an open-ended distribution..An ''+'' following a median estimate means the median falls in the upper interval of an open-ended distribution..An ''***'' entry in the margin of error column indicates that the median falls in the lowest interval or upper interval of an open-ended distribution. A statistical test is not appropriate..An ''*****'' entry in the margin of error column indicates that the estimate is controlled. A statistical test for sampling variability is not appropriate. .An ''N'' entry in the estimate and margin of error columns indicates that data for this geographic area cannot be displayed because the number of sample cases is too small..An ''(X)'' means that the estimate is not applicable or not available..Estimates of urban and rural population, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..While the 2013 American Community Survey (ACS) data generally reflect the February 2013 Office of Management and Budget (OMB) definitions of metropolitan and micropolitan statistical areas; in certain instances the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB definitions due to differences in the effective dates of the geographic entities..In data year 2013, there were a series of changes to data collection operations that could have affected some estimates. These changes include the addition of Internet as a mode of data collection, the end of the content portion of Failed Edit Follow-Up interviewing, and the loss of one monthly panel due to the Federal Government shut down in October 2013. For more information, see: User Notes.Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables..Source: U.S. Census Bureau, 2013 American Community Survey

Credit risk is the probability of a financial loss resulting from a borrower's failure to repay a loan. Essentially, credit risk refers to the risk that a lender may not receive the owed principal and interest, which results in an interruption of cash flows and increased costs for collection.

Dataset features

1. Debt (Задолженность): The total outstanding debt amount owed by the client.
2. Overdue Days (Просрочка, дни): The number of days a payment is overdue.
3. Initial Limit (Первоначальный лимит): The initial credit limit assigned to the client.
4. Birth Date (BIRTHDATE): The client's date of birth.
5. Sex (SEX): The client's gender (e.g., male, female).
6. Education (EDU): The client's level of education (e.g., high school, university).
7. Income (INCOME): The client's monthly or annual income.
8. Loan Term (TERM): The duration of the loan or credit agreement in months.
9. Credit History Rating (Рейтинг кредитной истории): A score or rating reflecting the client's credit history.
10. Living Area (LV_AREA): The geographical area or region where the client resides.
11. Settlement Name (LV_SETTLEMENTNAME): The name of the city, town, or settlement where the client lives.
12. Industry Name (INDUSTRYNAME): The industry or sector in which the client is employed.
13. Probability of Default (PDN): The estimated likelihood that the client will default on the loan.
14. Client ID (CLIENTID): A unique identifier assigned to the client.
15. Scoring Mark (SCORINGMARK): A credit score or risk assessment mark assigned to the client.
16. Underage Children Count (UNDERAGECHILDRENCOUNT): The number of underage children the client has.
17. Velcom Scoring (VELCOMSCORING): A specific scoring metric (possibly telecom-related) used in risk assessment.
18. Family Status (FAMILYSTATUS): The client's marital or family status (e.g., single, married, divorced).

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Poverty and social exclusion, social services, climate change, and the national economic situation and statistics.

Topics: 1. Poverty and social exclusion: own life satisfaction (scale); satisfaction with family life, health, job, and satisfaction with standard of living (scale); personal definition of poverty; incidence of poverty in the own country; estimated proportion of the poor in the total population; poor persons in the own residential area; estimated increase of poverty: in the residential area, in the own country, in the EU, and in the world; reasons for poverty in general; social and individual reasons for poverty; population group with the highest risk of poverty; things that are necessary to being able to afford to have a minimum acceptable standard of living (heating facility, adequate housing, a place to live with enough space and privacy, diversified meals, repairing or replacing a refrigerator or a washing machine, annual family holidays, medical care, dental care, access to banking services as well as to public transport, access to modern means of communication, to leisure and cultural activities, electricity, and running water); perceived deprivation through poverty in the own country regarding: access to decent housing, education, medical care, regular meals, bank services, modern means of communication, finding a job, starting up a business of one’s own, maintaining a network of friends and acquaintances; assessment of the financial situation of future generations and current generations compared to parent and grandparent generations; attitude towards poverty: necessity for the government to take action, too large income differences, national government should ensure the fair redistribution of wealth, higher taxes for the rich, economic growth reduces poverty automatically, poverty will always exist, income inequality is necessary for economic development; perceived tensions between population groups: rich and poor, management and workers, young and old, ethnic groups; general trust in people, in the national parliament, and the national government (scale); trust in institutions regarding poverty reduction: EU, national government, local authorities, NGOs, religious institutions, private companies, citizens; reasons for poverty in the own country: globalisation, low economic growth, pursuit of profit, global financial system, politics, immigration, inadequate national social protection system; primarily responsible body for poverty reduction; importance of the EU in the fight against poverty; prioritized policies of the national government to combat poverty; assessment of the effectiveness of public policies to reduce poverty; opinion on the amount of financial support for the poor; preference for governmental or private provision of jobs; attitude towards tuition fees; increase of taxes to support social spending; individual or governmental responsibility (welfare state) to ensure provision; attitude towards a minimum wage; optimism about the future; perceived own social exclusion; perceived difficulties to access to financial services: bank account, bank card, credit card, consumer loans, and mortgage; personal risk of over-indebtedness; attitude towards loans: interest free loans for the poor, stronger verification of borrowers by the credit institutions, easier access to start-up loans for the unemployed, free financial advice for the poor, possibility to open a basic bank account for everyone; affordable housing in the residential area; extent of homelessness in the residential area, and recent change; adequacy of the expenditures for the homeless by the national government, and the local authorities; assumed reasons for homelessness: unemployment, no affordable housing, destruction of the living space by a natural disaster, debt, illness, drug or alcohol addiction, family breakdown, loss of a close relative, mental health problems, lack of access to social services and support facilities, lack of identity papers, free choice of this life; probability to become homeless oneself; own support of homeless people: monetary donations to charities, volunteer work in a charity, help find access in emergency shelters and with job search, direct donations of clothes to homeless people, buying newspapers sold by homeless people, food donations; sufficient household income, or difficulties to make ends meet; ability to afford the heating costs, a week’s holiday once a year, and a meal with meat every second day; expected development of the financial situation of the household; assessment of the risk of potential difficulties in the next 12 months in paying: rent, mortgage, consumer loan rates, electricity bills, unexpected events, daily consumer goods; job security; difficulties in fulfilling family responsibilities because of the workload; difficulties in concentrating at work due to family commitments; necessary minimum monthly income for the own household; comparison of the monthly...

Rent prices per square meter in the largest Dutch cities have been on an upward trend after a slight decline in 2020. Amsterdam remained the most expensive city to live in, averaging a monthly rent of 27.6 euros per square meter for residential real estate in the private rental sector. Monthly rents in Utrecht were around six euros cheaper per square meter. Both cities were above the average rent price of residential property in the Netherlands overall, whereas Rotterdam and The Hague were slightly below that. Buying versus renting, what do the Dutch prefer? The Netherlands is one of Europe’s leading countries when it comes to homeownership, having funded this with a mortgage. In 2023, around 60 percent of people living in the Netherlands were homeowners with a mortgage. This is because Dutch homeowners were able to for many years to deduct interest paid from pre-tax income (a system known in the Netherlands as hypotheekrenteaftrek). This resulted in the Netherlands having one of the largest mortgage debts across the European continent. Total mortgage debt of Dutch households reached a value of approximately 803 billion euros in 2023. Is the Dutch housing market overheating? There are several indicators for the Netherlands that allow to investigate whether the housing market is overheating or not. House price indices corrected for inflation in the Netherlands suggest, for example, that prices have declined since 2022. The Netherlands’ house-price-to-rent-ratio, on the other hand, has exceeded the pre-crisis level in 2019. These figures, however, are believed to be significantly higher for cities like Amsterdam, as it was suggested for a long time that the prices of owner-occupied houses were increasing faster than rents in the private rental sector.

In Bulgaria, the Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania and Slovakia also 300 face-to-face interviews have been conducted.

Mortgage Backed Security Market Outlook

The global mortgage-backed security (MBS) market size was valued at approximately $2.1 trillion in 2023 and is projected to reach $3.5 trillion by 2032, growing at a CAGR of 5.5%. A key driver of this growth is the increasing demand for mortgage-backed securities due to their ability to provide liquidity and diversify investment portfolios. The growth is further stimulated by favorable government policies and increased homeownership rates, which collectively bolster the issuance of new MBS.

One of the primary growth factors for the MBS market is the low-interest-rate environment, which has persisted over recent years. This scenario has encouraged borrowing and refinancing activities, leading to a higher number of mortgages that can be securitized. Moreover, the stability and relatively lower risk associated with MBS compared to other investment vehicles make them an attractive option for institutional investors. Additionally, advancements in financial technology have streamlined the process of bundling and selling these securities, increasing market efficiency.

Another significant factor contributing to the expansion of the MBS market is the role of government-sponsored enterprises (GSEs) such as Fannie Mae, Freddie Mac, and Ginnie Mae. These GSEs guarantee a significant portion of the residential MBS, providing a safety net that minimizes risk for investors. The support from these entities ensures a continuous and reliable flow of investment into the housing sector, which in turn stimulates further securitization of mortgages. Moreover, government policies aimed at bolstering housing finance systems in emerging markets are expected to create additional opportunities for growth.

The diversification of mortgage products, including the rise in demand for commercial mortgage-backed securities (CMBS), is another driving force for the market. Commercial real estate has shown robust growth, and investors are increasingly looking towards CMBS as a way to gain exposure to this sector. The structured nature of these securities, offering tranches with varying risk and return profiles, allows investors to tailor their investment strategies according to their risk tolerance.

In the context of the MBS market, Lenders Mortgage Insurance (LMI) plays a crucial role in facilitating homeownership, especially for borrowers who are unable to provide a substantial down payment. LMI is a type of insurance that protects lenders against the risk of borrower default, allowing them to offer loans with lower down payment requirements. This insurance is particularly significant in markets where home prices are high, and saving for a large deposit is challenging for many potential homeowners. By mitigating the risk for lenders, LMI enables more individuals to enter the housing market, thereby supporting the overall growth of mortgage-backed securities. As a result, LMI not only aids in increasing homeownership rates but also contributes to the liquidity and stability of the housing finance system.

Type Analysis

The mortgage-backed security market is bifurcated into Residential MBS and Commercial MBS. Residential MBS (RMBS) dominate the market due to the larger volume of residential mortgages compared to commercial ones. RMBS are typically backed by residential loans, including home mortgages, and are considered less risky. They offer a steady income stream to investors through mortgage payments made by homeowners. The demand for RMBS is bolstered by the high rate of homeownership and the continuous flow of new mortgages.

On the other hand, Commercial MBS (CMBS) are seeing increased traction due to their attractive yields and the growth of the commercial real estate sector. CMBS are backed by loans on commercial properties such as office buildings, retail centers, and hotels. They offer investors exposure to the commercial property market, which is often less correlated with the residential real estate market, providing an additional layer of diversification. The complexity and higher risk associated with CMBS attract sophisticated investors looking for higher returns.

Within RMBS, the market is further segmented into agency RMBS and non-agency RMBS. Agency RMBS are guaranteed by GSEs, making them more secure and attractive to risk-averse investors. Non-agency RMBS, though not backed by GSEs, offer higher yields and are appealing to investors with a higher risk appetite. The interplay betw

Social impact of the crisis.
Topics: number of children under the age of 15 in own household; self-rated living standard of own household (scale); development of poverty in the last twelve months in: residential area, own country, European Union; estimated share of poor people in the own country (in percent); financial difficulties of the own household; changes in the last six months with regard to the affordability of: personal healthcare, childcare, long-term care; expected impact of economic and financial events on personal future pension; concern regarding the appropriateness of personal income in old age (scale); financial difficulties during the last year; expected development of the own financial situation in the next twelve months; assessment of the risk to not being able to: pay rent or mortgage on time, cope with unexpected expense of 1,000 €, repay consumer loans, pay daily consumer items; likelihood to be obliged to leave current accommodation within the next twelve months due to financial reasons; confidence to keep current job in the next twelve months; likelihood to find a new job within six months (scale).

Demography: age; sex; nationality; age at end of education; occupation; professional position; type of community; own a mobile phone and fixed (landline) phone; household composition and household size.

Additionally coded was: respondent ID; type of phone line; language of the interview; country; date of interview; time of the beginning of the interview; duration of the interview; region; weighting factor.

Social impact of the crisis.
Topics: development of poverty in the last twelve months in: residential area, own country, European Union; estimated share of poor people in the own country (in percent); financial difficulties of the own household; changes in the last six months with regard to the affordability of: personal healthcare, childcare, long-term care; expected impact of economic and financial events on personal future pension; concern regarding the appropriateness of personal income in old age (scale); financial difficulties during the last year; expected development of the own financial situation in the next twelve months; assessment of the risk to not being able to: pay rent or mortgage on time, cope with unexpected expense of 1,000 €, repay consumer loans, pay daily consumer items; likelihood to be obliged to leave current accommodation within the next twelve months due to financial reasons; confidence to keep current job in the next twelve months; likelihood to find a new job within six months (scale).

Demography: sex; age; age at end of education; occupation; professional position; type of community; household composition and household size; current living standard (scale).

Additionally coded was: interviewer ID; language of the interview; country; date of interview; time of the beginning of the interview; duration of the interview; type of phone line; call history; region; weighting factor.

Abstract

In 2007, the Government of Indonesia launched the “People’s Business Loan” (Kredit Usaha Rakyat, KUR) program as a flagship public program to enhance MSMEs’ access to finance. Since its inception, KUR has grown into one of the world’s largest public support programs for MSMEs. This survey includes a nationally representative sample of 1,402 KUR borrowers who received micro or small KUR loans between December 2015 and March 2020. The survey covers basic business information, business practices, workers, revenue, financial history prior to receiving KUR for the first time, and financial history after receiving KUR for the first time. In addition, firms were asked one of two of the following modules: experiences with the KUR program or impact of COVID-19 on the business. The data was collected by phone in January and February 2021, and weighted stratified sampling was used to ensure a representative sample and enable subgroup analysis.

Geographic coverage

Nationally representative survey of KUR borrowers

Analysis unit

Business

Universe

Businesses who received KUR loans between December 2015 and March 2020.

Kind of data

Sample survey data [ssd]

Sampling procedure

An administrative database (SIKP), which contains basic characteristics of all KUR borrowers since 2016, served as the sampling frame for the quantitative data collection. Weighted stratified random sampling was used to select the sample. Strata were based on four characteristics that may influence beneficiaries’ experiences with KUR and how KUR may change their business: gender of KUR recipient, size of KUR loan, financial institution that issued the KUR loan, and geographic region. Strata including less than 1% of KUR beneficiaries were oversampled in order to ensure that each subgroup of interest would have sufficient representation in the sample in order to draw precise estimates at the subgroup level.

Stratified sampling methodology was chosen because the team wanted to ensure that subgroup analysis was feasible across certain dimensions. Some of the subgroups of interest represent only a small portion of KUR borrowers, so a random sampling approach without using strata may not have provided a sufficient number of observations to draw any conclusions about some of these subgroups. Gender was included as a stratification variable to ensure that a gender-sensitive analysis was feasible. Female entrepreneurs in Indonesia face greater financing constraints than male entrepreneurs (World Bank 2023), so KUR may have particularly strong impacts for female entrepreneurs. Nevertheless, the market-based implementation of KUR may also limit the ability of KUR to reach female entrepreneurs, if it does not alleviate gendered constraints to accessing financing. Micro KUR loans and small KUR loans have different requirements and offer different sizes of subsidies to the KUR distributors. As such, it is critical to be able to analyze them separately. Because less than 10 percent of KUR loans are small KUR loans, stratification on this variable ensures that there is enough statistical power to draw conclusions about small KUR loans. One financial institution, BRI, issues the majority of KUR loans. Because KUR is implemented by different distributors and some aspects of implementation are left to the distributor’s discretion, it is important to understand whether the implementation of KUR looks different when issued by the dominant bank or when issued by other distributors. Finally, financing conditions and alternatives vary across geography. Because the environment may shift how important KUR is to MSMEs, it is important to be able to understand how trends vary across different regions. Some regions have less than 10 percent of KUR borrowers in them, so a simple random selection may not have produced enough observations in some regions to allow for analysis disaggregated by region.

Generally, strata including firms with KUR loans of more than 25 million and those outside of Jawa were over-sampled, while firms receiving loans of less than 25 million in Jawa were under-sampled to ensure the total sample size rested within budget and logistical constraints. Finally, an even number of firms were selected for the sample from each strata so that they can be split into halves, where one half would answer the modules in questionnaire A and the other half would answer modules in questionnaire B. This allows the design weights to remain constant for all variables in the survey and facilitates data analysis. The modules to be asked were randomly assigned and balanced across sampling strata to ensure all modules included nationally representative information. Due to the weighted sampling design, design weights are used in all descriptive analysis in this report, and once incorporating the design weights the analysis is representative of all KUR recipients since 2016.

The survey firm received a randomized order list of firms within each strata and were instructed to call respondents until reaching the quota per strata.

Sampling deviation

In practice, there were two extra interviews conducted, leading to a total number of interviews of 1,402 instead of the targeted 1,400 interviews. The design weights used in the analysis were adjusted to the actual number of interviews conducted in each strata.

Mode of data collection

Other [oth]

Response rate

Overall, 10,789 phone-calls were attempted. Of these calls, about 30 percent of the calls were not connected and classified as ‘voice mail’, 15 percent were notified that the number is inactive, and 13 percent were notified that the number is not registered. 28 percent of the overall phone-call attempts were connected, and 13 percent were successfully interviewed.

In 2024, there were approximately **** million housing units occupied by renters in the United States. This number has been gradually increasing since 2010 as part of a long-term upward swing since 1975. Meanwhile, the number of unoccupied rental housing units has followed a downward trend, suggesting a growing demand and supply failing to catch up. Why are rental homes in such high demand? This high demand for rental homes is related to the shortage of affordable housing. Climbing the property ladder for renters is not always easy, as it requires prospective homebuyers to save up for a down payment and qualify for a mortgage. In many metros, the median household income is insufficient to qualify for the median-priced home. How many owner occupied homes are there in the U.S.? In 2023, there were over ** million owner occupied homes. Owner occupied housing is when the person who owns a property – either outright or through a mortgage – also resides in the property. Excluded are therefore rental properties, employer-provided housing and social housing.

Snapshot img

Debt Settlement Market Size 2024-2028

The debt settlement market size is forecast to increase by USD 5.07 billion at a CAGR of 10.3% between 2023 and 2028.

The market is experiencing significant growth due to the increasing trend of consumers seeking relief from mounting credit card debts. One-time debt settlement has gained popularity as an effective solution for individuals looking to reduce their outstanding debt balances. However, the time-consuming nature of negotiations between debtors and creditors poses a challenge for market expansion. Despite this, the market's strategic landscape remains favorable for companies offering debt settlement services. Key drivers include the rising number of consumers struggling with debt, increasing awareness of debt settlement as a viable debt relief option, and the growing preference for affordable and flexible debt repayment plans.
Companies seeking to capitalize on market opportunities should focus on streamlining the negotiation process, leveraging technology to enhance customer experience, and building trust and transparency with clients. Effective operational planning and strategic partnerships with creditors can also help companies navigate the challenges of a competitive and complex market.

What will be the Size of the Debt Settlement Market during the forecast period?

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The market encompasses a range of companies offering financial wellness programs to help consumers manage and reduce their debt. These programs include medical Debt collection, consumer debt relief, and financial education resources. Online financial resources and debt management software are increasingly popular, providing consumers with affordable debt solutions and debt negotiation strategies. However, it's crucial for consumers to be aware of debt settlement scams and their settlement success rates. Debt consolidation loans and financial planning tools are also viable options for responsible debt management. Furthermore, financial literacy education and workshops are essential for consumers to understand debt reduction calculators and credit reporting errors.
Consumer financial protection agencies offer financial counseling services and financial planning advice to promote financial wellness strategies and responsible borrowing. Student loan forgiveness programs are also gaining traction in the market. Overall, the market for debt settlement and financial wellness solutions continues to evolve, with a focus on providing accessible and effective debt relief options for consumers.

How is this Debt Settlement Industry segmented?

The debt settlement industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Type

  Credit card debt
  Student loan debt
  Medical debt
  Auto loan debt
  Unsecured personal loan debt
  Others


End-user

  Individual
  Enterprise
  Government


Distribution Channel

  Online
  Offline
  Hybrid


Service Type

  Debt Settlement
  Debt Consolidation
  Debt Management Plans
  Credit Counseling


Provider Type

  For-profit Debt Settlement Companies
  Non-profit Credit Counseling Agencies
  Law Firms
  Financial Institutions


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  Middle East and Africa



  APAC

    China
    India
    Japan
    South Korea


  South America



  Rest of World (ROW)

By Type Insights

The credit card debt segment is estimated to witness significant growth during the forecast period.

The market experiences significant activity due to the escalating credit card debt among consumers. In India, for instance, the rising financial hardships faced by borrowers are evident in the increasing credit card defaults. The latest data indicates that credit card defaults in India reached 1.8% in June 2024, a notable increase from 1.7% six months prior and 1.6% in March 2023. This trend underscores the mounting financial pressures on consumers. The outstanding credit card debt in India mirrors this trend, with approximately USD3.25 billion in outstanding balances as of June 2024, a slight increase from the previous year.

Debt elimination and negotiation strategies, such as debt relief programs and debt consolidation, have become increasingly popular among consumers seeking financial relief. Credit reporting agencies play a crucial role in this process, as they maintain and report consumers' credit histories to lenders. Student loan debt, medical debt, tax debt, and payday loans are other significant contributors to the market. Consumers often turn to debt validation, credit repair, and financial coaching for guidance in managing their debts. Online platforms, mobile apps, and budgeting tools have become

Consumer and Corporate Debt Consolidation Market Outlook

The consumer and corporate debt consolidation market size is projected to grow from USD 2.1 trillion in 2023 to approximately USD 2.7 trillion by 2032, driven by an estimated Compound Annual Growth Rate (CAGR) of 2.9%. This growth is underpinned by factors such as increasing consumer debt levels, and a heightened awareness of financial management strategies. The growing trend among both individuals and businesses to consolidate multiple debts into a single loan has spurred significant interest and investment in this market. This is further accentuated by the increasing number of financial institutions offering tailored debt consolidation services, thus enhancing market dynamics.

One major growth factor in the consumer and corporate debt consolidation market is the rising levels of consumer debt worldwide. This encompasses credit card debts, personal loans, and other forms of consumer credit that have been steadily increasing, fueled by consumer spending and economic cycles. As individuals accumulate various debts, there's a growing need for effective financial management solutions to streamline payments and reduce interest burdens. Debt consolidation serves as an attractive option by amalgamating multiple debt obligations into a singular loan with more favorable terms. This is particularly appealing in developed regions where credit card usage is widespread, and individuals seek to manage their debt more efficiently.

The concept of Consumer Credit plays a pivotal role in the debt consolidation market. It refers to the credit extended to individuals for personal, family, or household purposes, and is a significant component of consumer debt. As consumer credit levels rise, individuals often find themselves juggling multiple credit obligations, including credit card balances, personal loans, and retail financing. This complexity can lead to financial strain, making debt consolidation an attractive option. By consolidating consumer credit into a single loan with potentially lower interest rates, individuals can simplify their financial landscape and focus on managing a single monthly payment. This not only aids in reducing the overall interest burden but also helps in improving credit scores over time, as individuals are better able to meet their financial commitments.

Corporate debt consolidation is also a substantial driver of market growth, particularly as businesses attempt to optimize their balance sheets and manage cash flows more effectively. The post-pandemic era has seen a number of businesses grappling with multiple lines of credit and loans, leading to increased interest in consolidation solutions. These strategies allow businesses to convert high-interest debt into lower-cost financing, thereby freeing up capital for operational needs and growth initiatives. Moreover, small and medium enterprises (SMEs) are increasingly seeking such financial interventions to stabilize their finances, thus contributing to market expansion.

Another key growth factor is the technological advancements in financial services which have facilitated easier access to debt consolidation services. The integration of digital platforms has transformed how debt consolidation services are offered, making them more accessible to a broader audience. Online platforms allow users to easily compare different loan options, understand the terms, and even apply for consolidation loans without the need for physical visits to financial institutions. This technological integration not only streamlines the process for consumers but also expands the reach of service providers, thus driving market penetration across diverse demographics.

Regionally, North America holds a significant share of the debt consolidation market, owing to the high levels of consumer debt and the presence of well-established financial institutions. However, Asia-Pacific is expected to witness the fastest growth during the forecast period, driven by the rising middle-class population and increasing consumer credit demands. The debt consolidation market in Europe is also showing promising trends, as more individuals and corporates seek to simplify their financial obligations in the face of economic uncertainties. Meanwhile, regions such as Latin America and the Middle East & Africa are increasingly adopting these financial strategies, albeit at a slower pace compared to more developed regions.

Type Analysis

The consumer and corporate debt consolidat

Abstract

In 1999, in response to the escalating AIDS epidemic in South Africa, the National Department of Health established a new initiative to design, implement and evaluate strategies for addressing HIV/AIDS within three pilot sites across the country. All three sites we re responsible for implementing a core re package of HIV- related services and support, including the provision of voluntary counselling and testing services and the training of health care workers in the implementation of National HIV/AIDS clinical care guidelines. However, in addition to this basic package, the pilot sites we re encouraged to test more innova t i ve and multi-sectoral approaches to HIV control, and it is in this context that the IMAGE (Intervention with micro-finance for AIDS and Gender Equity) Study was developed.

The IMAGE study 2001-2003 is a programme of intervention research based in Sekhukhuneland - a densely settled rural area of South Africa’s Limpopo Province. Collaborative partners include a microfinance NGO, the Small Enterprise Foundation (SEF), academic institutions from the South and North - the University of the Witwatersrand's Rural AIDS & Development Action Research Programme (RADAR) and the London School of Hygiene and Tropical Medicine - and national government (South African National Department of Health).

The study combines a poverty alleviation programme and participatory learning and action intervention. The intervention comprises participation in TCP and access to "Sisters for Life" training sessions. The Small Enterprise Foundation Credit Program, (TCP) is a poverty-targeted micro-finance programme operating in the Limpopo Province. The "Sisters for Life" programme, a two phase participatory learning and action curriculum developed in South Africa, and implemented with TCP clients during fortnightly centre meetings. The IMAGE study seeks to evaluate the impact of this work among clients, their households and their communities. The research provides an opportunity to explore the potential for d e velopmental programs to have a role in pre venting HIV infections and g e n d e r-based violence.The study is built around the prospective follow-up of three cohort pairs; IMAGE clients, young people living in the household of IMAGE clients and young people living in communities where the IMAGE programme was operating.

Geographic coverage

The study covers eight villages in the Sekhukhuneland region of South Africa's rural Limpopo Province.

Analysis unit

Units of analysis in the study include households and individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

All households in these villages were eligible for inclusion in the IMAGE Study Baseline survey. Households in settlement areas outside village boundaries we re not included in the sampling frame. 200 households were randomly selected in each of the eight study villages (total sample size 1600). A household in this study was defined as a group of people who are permanently resident on the same property (or dwelling) and who eat from the same pot of food when staying at home. Within selected households, all individuals aged 14-35 years at the date of interview were eligible for inclusion in the study. Individuals listed as permanent household members we re eligible for inclusion, including those household members staying staying away from the home. The total expected sample size, including adjustment for people who refuse and those who could not be traced, was 3000.

A sampling frame including all households within recognised boundaries of the eight study villages was generated from Participatory Wealth Ranking (PWR) data.

The Participatory Wealth Ranking (PWR) process assisted the project to identify households eligible for inclusion in IMAGE in both intervention and comparison villages. Community members were invited to attend an open meeting in the village. Those who attended were sub-divided by neighbourhood and these groups each drew a map of their village section (usually of 50-200 households), providing a list of all the households. Following this, groups of 4-6 village section residents who know the community well (known as reference groups) held a discussion on aspects of poverty in their section. Participants were asked to characterise households that are poor, those that are doing a bit better and those that are doing well. Households are then ranked in categories by these residents, from the poorest to the most well off households, according to the poverty definitions provided. Each house was ranked on three occasions by different groups of individuals, providing an aggregate "score". Consistent agreement between the three reference groups is usually achieved through this process. The groups must produce results that are consistent in identifying poor households for the results to be accepted. After this process, participants were asked to describe the characteristics of the households in each category. The number of categories was not set in advance, but at least four categories had to be delineated for the results to be deemed valid. Ranking information was recorded on pre-designed forms at all stages of the process. The discussions were then assessed by a trained SEF staff member, who assigns a cut-off score. Households below a given "cut-off" are characterised as poor enough to be eligible for loans.

C o h o rt Study I: The impact of IMAGE on loan re c i p i e n t s Eligible women in villages where IMAGE was operating joined the pro g r a m m e t h rough a self-selection process in response to house-to-house visits from SEF staff. All such loan recipients recruited in the first year of program operation were asked to join the study. Comparison women ("non-loan recipients") were recruited from randomly selected households that were eligible to be involved in IMAGE (on the basis of PWR), but from a village where IMAGE was not operating. Comparison women were current residents, matched to a loan recipient both on village type and age (in groups of 18-25, 26-35, 36-45, 46-55, over 55 yrs). Sample size Approximately 500 new loan recipients were recruited in Intervention villages. This represents programme penetration such that approximately 10% of households in intervention villages will be likely to have an IMAGE client. The recruitment period was approximately 12 months. Each loan recipient was matched to a single comparison woman. The total sample size was thus double the number of loan recipients recruited during the recruitment period, i.e. about 1000.

C o h o rt Study II: The impact of IMAGE on young-people living in the households of loan recipients All individuals of both sexes aged 14 - 35 years who are currently re s i d e n t members of households of loan recipients and non-loan recipients enrolled to Cohort Study I were eligible for inclusion to Cohort Study II. IMAGE clients falling in this age group were also recruited to IMAGE Cohort Study II. Sample size Demographic data suggested that there will be, on average, three individuals in this age group in every two households recruited to Cohort Study I. The total sample size was thus approximately 750 in each arm of the study (Total 1500).

C o h o rt Study III: The impact of IMAGE on communities Individuals of both sexes aged 14 - 35 years living in randomly selected households from the study villages were eligible for this study. Both young people currently staying in the home (the de facto population), and young people who are recorded as "permanent household members", but who are not currently staying in the home were eligible for Cohort Outcome Study III. Sampling and sample size

Mode of data collection

Face-to-face [f2f]

Research instrument

Cohort Study I: The impact of IMAGE on loan recipients Study procedures : Baseline:Each loan recipient and non-loan recipient was interviewed at this stage with a standardised questionnaire (Senior Female Questionnaire). Additionally, information on the household was gathered at this point in an interview either with the woman, or with the head of the household (using the Household Questionnaire). Study procedures : Follow up:Loan recipients and non-loan recipients were re-interviewed using an adapted version of the above questionnaires two years after enrolment of the loan recipient to IMAGE. Follow-up interviews were conducted with all women enrolled at baseline, including those who later dropped out of IMAGE. An effort was made to interview women who had moved out of their home during this two years of follow-up, but who were still traceable at that point.

Cohort Study II: The impact of IMAGE on young-people living in the households of loan recipients Study procedures : Baseline: The baseline state for Cohort Study II was as for Cohort Study I. Young people were interviewed within two months of loan recipient/non loan recipient interviews being conducted, where possible. Interviews were conducted using a standardised questionnaire (Young Person Questionnaire). Study recruits were also asked to provide a sample collected from inside the mouth (Oral Mucosal Transudate, or OMT), using a specially designed collection device to test for the presence of antibodies to HIV. Study procedures: Follow-up: Follow up interviews with young people were conducted two years after their enrolment in the study, utilising a modified version of the Young Person Questionniare. Repeat OMT samples were also collected at this time.

Cohort Study III: The impact of IMAGE on communities Study procedures : Baseline: A survey covering all villages was conducted during the three months prior to IMAGE being made available in the Intervention villages. Household Questionnaires and Young Person

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.

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