The median credit score for conventional non-conforming mortgage applicants in the U.S. was the highest in 2023. The median credit score for these mortgages was 780 in the third quarter of the year. FHA loans, on the other hand, had the lowest median credit score, at 668.

Credit bureaus and rating agencies in the US have experienced notable growth in recent years due to heightened demand for information. The reliance on data analytics has driven increased interest in these services, which provide vital information on creditworthiness for both individuals and businesses. This has been particularly significant as businesses and individuals seek to make well-informed financial decisions. Despite challenges related to the pandemic, inflation and high interest rates, the industry has thrived and profit has soared, indicating its resilience and the critical nature of the services it offers in a data-driven economy. While long-term demand for information has buoyed the industry, providers’ trajectory has been influenced by broader economic conditions, notably equity market fluctuations. The industry weathered initial pandemic-related disruptions, which precipitated a sharp fall in stock prices and corporate profit. Nonetheless, rapid fiscal and monetary responses bolstered investor confidence and led to a robust rebound in equity markets, contributing to massive revenue growth in 2020 and 2021. Soaring interest rates in 2022 and 2023 boosted recessionary fears among investors, hindering demand for equities, reducing stock prices and thus contributing to a major drop in revenue in 2022. These effects have percolated into the real economy as consumer and business borrowing has slowed, constraining aggregate household debt and corporate debt. These effects have negatively impacted the industry in 2023 and 2024, though a rebound in the stock market has prevented a major collapse in revenue. Overall, revenue for credit bureaus and rating agencies in the US is anticipated to soar at a CAGR of 4.3% over the past five years, reaching $16.4 billion in 2024. This includes a 1.3% drop in revenue in that year. Looking ahead, credit bureaus and rating agencies will face a more tempered growth trajectory over the next five years. The broad adoption of online services and data analytics has led to market saturation, reducing opportunities for exponential revenue growth. Nonetheless, stable economic growth and business formation should sustain a steady demand for credit reporting and rating services. The predicted slower growth in equity prices will moderate financial institutions' borrowing capacity, which will also contribute to the slowdown in revenue growth. Overall, revenue for credit bureaus and rating agencies in the United States is forecast to inch upward at a CAGR of 1.1% over the next five years, reaching $17.4 billion in 2029.

Asian and White mortgage applicants had, on average, higher credit score than Black and Hispanic applicants. Overall, credit scores declined between the first quarter of 2021 and the second quarter of 2022. Asian mortgage applicants had a credit score of *** in the second quarter of 2022.

Our US Home Ownership Data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes various data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences. 1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc. 2. Demographics - Gender, Age Group, Marital Status, Language etc. 3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc 4. Persona - Consumer type, Communication preferences, Family type, etc 5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc. 6. Household - Number of Children, Number of Adults, IP Address, etc. 7. Behaviours - Brand Affinity, App Usage, Web Browsing etc. 8. Firmographics - Industry, Company, Occupation, Revenue, etc 9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc. 10. Auto - Car Make, Model, Type, Year, etc. 11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases: 360-Degree Customer View: Get a comprehensive image of customers by the means of internal and external data aggregation. Data Enrichment: Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity. Advertising & Marketing: Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

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.

In 2020, ** percent of all auto loans were expected to be non-prime originations, up *** percent from 2019. However, ** percent of all auto loans were non-prime originations at the start of the recession in 2007. Non-prime loans are similar to subprime loans in that both are loan products accessible for those with low credit scores, however the average credit score needed for a non-prime loan in 2020 was ** points higher than the average score needed for a subprime loan in 2008. Income documentation is required to obtain non-prime credit, whereas none was needed for a subprime loan in 2008.

This data set provides recent market information on corporate loan transactions (339 records based on 187 agreements) sourced from filings by US SEC Registrants in all non-financial industries for the First Quarter 2021 (Q1 2021). Of the 339 records there are 8 loan transactions with credit rating equal to or higher than A-, 55 records in the BBB letter category, 47 records in the BB letter category, 46 records in the B letter category, 11 records that are rated at less than B-, and 172 records that are not rated. These 339 corporate loan transactions span the 11 GICS Sectors as follows: Energy 12; Materials 38; Industrials 50; Consumer Discretionary 86; Consumer Staples 20; Healthcare 31; Financials (mainly REITS, excludes FIs) 50; Information Technology 14; Communication Services 8; Utilities 20; and Real Estate 10. The following categories of data are described as follows: 1) Filing Information (with 7 data fields) includes the Filing Company Name, Filing Date, Filing Form and Type of Agreement (credit agreement, amended and restated credit agreement, or an amendment to a credit agreement), and Filing Company’s Central Index Key (CIK), SIC Code and GICS Sector Name; 2) Borrower and Other Loan Participant Details (6 data fields) includes the Primary Borrower’s Name, the Primary Borrower’s country of incorporation, the name(s) of additional borrowers, the name(s) of guarantors, the relationship of the guarantor(s) to the primary borrower, the name(s) of the lead arranger(s)/lender(s); 3) Credit Rating Information (17 data fields) includes publicly available S&P credit ratings and Moody’s credit ratings, if available, as at the date of the credit agreement for Primary Borrower, Parent, Issue and the respective dates of the ratings; 4) Loan Characteristics (16 data fields) includes whether the loan is a revolver or term loan, senior secured or senior unsecured, or other asset class. Also includes data on the loan amount, currency, pricing notes, collateral type, and types of financial covenants; and 5) Loan Pricing Details (11 data fields) includes the type of reference interest rate (e.g., LIBOR), the lending margin (or the fixed rate), commitment fee, annual (facility) fee, and other types of fees, as well as notes and other information related to the pricing. This data/information would assist any company’s finance & treasury department in negotiating pricing with banks/lenders or a multinational corporation’s international tax department to price its intercompany loans for transfer pricing purposes. CUFTanalytics has a database of over 14,000 records from corporate loan transactions from January 2009 to present day.

The ICE BofA Option-Adjusted Spreads (OASs) are the calculated spreads between a computed OAS index of all bonds in a given rating category and a spot Treasury curve. An OAS index is constructed using each constituent bond's OAS, weighted by market capitalization. The Corporate Master OAS uses an index of bonds that are considered investment grade (those rated BBB or better). When the last calendar day of the month takes place on the weekend, weekend observations will occur as a result of month ending accrued interest adjustments.

This data represents the ICE BofA US Corporate Index value, which tracks the performance of US dollar denominated investment grade rated corporate debt publicly issued in the US domestic market. To qualify for inclusion in the index, securities must have an investment grade rating (based on an average of Moody's, S&P, and Fitch) and an investment grade rated country of risk (based on an average of Moody's, S&P, and Fitch foreign currency long term sovereign debt ratings). Each security must have greater than 1 year of remaining maturity, a fixed coupon schedule, and a minimum amount outstanding of $250 million. Original issue zero coupon bonds, "global" securities (debt issued simultaneously in the eurobond and US domestic bond markets), 144a securities and pay-in-kind securities, including toggle notes, qualify for inclusion in the Index. Callable perpetual securities qualify provided they are at least one year from the first call date. Fixed-to-floating rate securities also qualify provided they are callable within the fixed rate period and are at least one year from the last call prior to the date the bond transitions from a fixed to a floating rate security. DRD-eligible and defaulted securities are excluded from the Index.

ICE BofA Explains the Construction Methodology of this series as: Index constituents are capitalization-weighted based on their current amount outstanding. With the exception of U.S. mortgage pass-throughs and U.S. structured products (ABS, CMBS and CMOs), accrued interest is calculated assuming next-day settlement. Accrued interest for U.S. mortgage pass-through and U.S. structured products is calculated assuming same-day settlement. Cash flows from bond payments that are received during the month are retained in the index until the end of the month and then are removed as part of the rebalancing. Cash does not earn any reinvestment income while it is held in the Index. The Index is rebalanced on the last calendar day of the month, based on information available up to and including the third business day before the last business day of the month. Issues that meet the qualifying criteria are included in the Index for the following month. Issues that no longer meet the criteria during the course of the month remain in the Index until the next month-end rebalancing at which point they are removed from the Index.

When the last calendar day of the month takes place on the weekend, weekend observations will occur as a result of month ending accrued interest adjustments.

Certain indices and index data included in FRED are the property of ICE Data Indices, LLC (“ICE DATA”) and used under license. ICE® IS A REGISTERED TRADEMARK OF ICE DATA OR ITS AFFILIATES AND BOFA® IS A REGISTERED TRADEMARK OF BANK OF AMERICA CORPORATION LICENSED BY BANK OF AMERICA CORPORATION AND ITS AFFILIATES (“BOFA”) AND MAY NOT BE USED WITHOUT BOFA’S PRIOR WRITTEN APPROVAL. ICE DATA, ITS AFFILIATES AND THEIR RESPECTIVE THIRD PARTY SUPPLIERS DISCLAIM ANY AND ALL WARRANTIES AND REPRESENTATIONS, EXPRESS AND/OR IMPLIED, INCLUDING ANY WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE OR USE, INCLUDING WITH REGARD TO THE INDICES, INDEX DATA AND ANY DATA INCLUDED IN, RELATED TO, OR DERIVED THEREFROM. NEITHER ICE DATA, NOR ITS AFFILIATES OR THEIR RESPECTIVE THIRD PARTY PROVIDERS SHALL BE SUBJECT TO ANY DAMAGES OR LIABILITY WITH RESPECT TO THE ADEQUACY, ACCURACY, TIMELINESS OR COMPLETENESS OF THE INDICES OR THE INDEX DATA OR ANY COMPONENT THEREOF. THE INDICES AND INDEX DATA AND ALL COMPONENTS THEREOF ARE PROVIDED ON AN “AS IS” BASIS AND YOUR USE IS AT YOUR OWN RISK. ICE DATA, ITS AFFILIATES AND THEIR RESPECTIVE THIRD PARTY SUPPLIERS DO NOT SPONSOR, ENDORSE, OR RECOMMEND FRED, OR ANY OF ITS PRODUCTS OR SERVICES.

Copyright, 2023, ICE Data Indices. Reproduction of this data in any form is prohibited except with the prior written permission of ICE Data Indices.

The end of day Index values, Index returns, and Index statistics (“Top Level Data”) are being provided for your internal use only and you are not authorized or permitted to publish, distribute or otherwise furnish Top Level Data to any third-party without prior written approval of ICE Data. Neither ICE Data, its affiliates nor any of its third party suppliers shall have any liability for the accuracy or completeness of the Top Level Data furnished through FRED, or for delays, interruptions or omissions therein nor for any lost profits, direct, indirect, special or consequential damages. The Top Level Data is not investment advice and a reference to a particular investment or security, a credit rating or any observation concerning a security or investment provided in the Top Level Data is not a recommendation to buy, sell or hold such investment or security or make any other investment decisions. You shall not use any Indices as a reference index for the purpose of creating financial products (including but not limited to any exchange-traded fund or other passive index-tracking fund, or any other financial instrument whose objective or return is linked in any way to any Index) without prior written approval of ICE Data. ICE Data, their affiliates or their third party suppliers have exclusive proprietary rights in the Top Level Data and any information and software received in connection therewith. You shall not use or permit anyone to use the Top Level Data for any unlawful or unauthorized purpose. Access to the Top Level Data is subject to termination in the event that any agreement between FRED and ICE Data terminates for any reason. ICE Data may enforce its rights against you as the third-party beneficiary of the FRED Services Terms of Use, even though ICE Data is not a party to the FRED Services Terms of Use. The FRED Services Terms of Use, including but limited to the limitation of liability, indemnity and disclaimer provisions, shall extend to third party suppliers.

Our consumer data is gathered and aggregated via surveys, digital services, and public data sources. We use powerful profiling algorithms to collect and ingest only fresh and reliable data points.

Our comprehensive data enrichment solution includes a variety of data sets that can help you address gaps in your customer data, gain a deeper understanding of your customers, and power superior client experiences.

1. Geography - City, State, ZIP, County, CBSA, Census Tract, etc.
2. Demographics - Gender, Age Group, Marital Status, Language etc.
3. Financial - Income Range, Credit Rating Range, Credit Type, Net worth Range, etc
4. Persona - Consumer type, Communication preferences, Family type, etc
5. Interests - Content, Brands, Shopping, Hobbies, Lifestyle etc.
6. Household - Number of Children, Number of Adults, IP Address, etc.
7. Behaviours - Brand Affinity, App Usage, Web Browsing etc.
8. Firmographics - Industry, Company, Occupation, Revenue, etc
9. Retail Purchase - Store, Category, Brand, SKU, Quantity, Price etc.
10. Auto - Car Make, Model, Type, Year, etc.
11. Housing - Home type, Home value, Renter/Owner, Year Built etc.

Consumer Graph Schema & Reach: Our data reach represents the total number of counts available within various categories and comprises attributes such as country location, MAU, DAU & Monthly Location Pings:

Data Export Methodology: Since we collect data dynamically, we provide the most updated data and insights via a best-suited method on a suitable interval (daily/weekly/monthly).

Consumer Graph Use Cases:

360-Degree Customer View:Get a comprehensive image of customers by the means of internal and external data aggregation.

Data Enrichment:Leverage Online to offline consumer profiles to build holistic audience segments to improve campaign targeting using user data enrichment

Fraud Detection: Use multiple digital (web and mobile) identities to verify real users and detect anomalies or fraudulent activity.

Advertising & Marketing:Understand audience demographics, interests, lifestyle, hobbies, and behaviors to build targeted marketing campaigns.

Using Factori Consumer Data graph you can solve use cases like:

Acquisition Marketing Expand your reach to new users and customers using lookalike modeling with your first party audiences to extend to other potential consumers with similar traits and attributes.

Lookalike Modeling

Build lookalike audience segments using your first party audiences as a seed to extend your reach for running marketing campaigns to acquire new users or customers

And also, CRM Data Enrichment, Consumer Data Enrichment B2B Data Enrichment B2C Data Enrichment Customer Acquisition Audience Segmentation 360-Degree Customer View Consumer Profiling Consumer Behaviour Data

Here's the schema of Consumer Data: person_id first_name last_name age gender linkedin_url twitter_url facebook_url city state address zip zip4 country delivery_point_bar_code carrier_route walk_seuqence_code fips_state_code fips_country_code country_name latitude longtiude address_type metropolitan_statistical_area core_based+statistical_area census_tract census_block_group census_block primary_address pre_address streer post_address address_suffix address_secondline address_abrev census_median_home_value home_market_value property_build+year property_with_ac property_with_pool property_with_water property_with_sewer general_home_value property_fuel_type year month household_id Census_median_household_income household_size marital_status length+of_residence number_of_kids pre_school_kids single_parents working_women_in_house_hold homeowner children adults generations net_worth education_level occupation education_history credit_lines credit_card_user newly_issued_credit_card_user credit_range_new
credit_cards loan_to_value mortgage_loan2_amount mortgage_loan_type
mortgage_loan2_type mortgage_lender_code
mortgage_loan2_render_code
mortgage_lender mortgage_loan2_lender
mortgage_loan2_ratetype mortgage_rate
mortgage_loan2_rate donor investor interest buyer hobby personal_email work_email devices phone employee_title employee_department employee_job_function skills recent_job_change company_id company_name company_description technologies_used office_address office_city office_country office_state office_zip5 office_zip4 office_carrier_route office_latitude office_longitude office_cbsa_code
office_census_block_group
office_census_tract office_county_code
company_phone
company_credit_score
company_csa_code
company_dpbc
company_franchiseflag
company_facebookurl company_linkedinurl company_twitterurl
company_website company_fortune_rank
company_government_type company_headquarters_branch company_home_business
company_industry
company_num_pcs_used
company_num_employees
company_firm_individual company_msa company_msa_name
company_naics_code
company_naics_description
company_naics_code2 company_naics_description2
company_sic_code2
company_sic_code2_desc...

The Federal National Mortgage Association, commonly known as Fannie Mae, was created by the U.S. congress in 1938, in order to maintain liquidity and stability in the domestic mortgage market. The company is a government-sponsored enterprise (GSE), meaning that while it was a publicly traded company for most of its history, it was still supported by the federal government. While there is no legally binding guarantee of shares in GSEs or their securities, it is generally acknowledged that the U.S. government is highly unlikely to let these enterprises fail. Due to these implicit guarantees, GSEs are able to access financing at a reduced cost of interest. Fannie Mae's main activity is the purchasing of mortgage loans from their originators (banks, mortgage brokers etc.) and packaging them into mortgage-backed securities (MBS) in order to ease the access of U.S. homebuyers to housing credit. The early 2000s U.S. mortgage finance boom During the early 2000s, Fannie Mae was swept up in the U.S. housing boom which eventually led to the financial crisis of 2007-2008. The association's stated goal of increasing access of lower income families to housing finance coalesced with the interests of private mortgage lenders and Wall Street investment banks, who had become heavily reliant on the housing market to drive profits. Private lenders had begun to offer riskier mortgage loans in the early 2000s due to low interest rates in the wake of the "Dot Com" crash and their need to maintain profits through increasing the volume of loans on their books. The securitized products created by these private lenders did not maintain the standards which had traditionally been upheld by GSEs. Due to their market share being eaten into by private firms, however, the GSEs involved in the mortgage markets began to also lower their standards, resulting in a 'race to the bottom'. The fall of Fannie Mae The lowering of lending standards was a key factor in creating the housing bubble, as mortgages were now being offered to borrowers with little or no ability to repay the loans. Combined with fraudulent practices from credit ratings agencies, who rated the junk securities created from these mortgage loans as being of the highest standard, this led directly to the financial panic that erupted on Wall Street beginning in 2007. As the U.S. economy slowed down in 2006, mortgage delinquency rates began to spike. Fannie Mae's losses in the mortgage security market in 2006 and 2007, along with the losses of the related GSE 'Freddie Mac', had caused its share value to plummet, stoking fears that it may collapse. On September 7th 2008, Fannie Mae was taken into government conservatorship along with Freddie Mac, with their stocks being delisted from stock exchanges in 2010. This act was seen as an unprecedented direct intervention into the economy by the U.S. government, and a symbol of how far the U.S. housing market had fallen.

This data represents the effective yield of the ICE BofA US High Yield Index, which tracks the performance of US dollar denominated below investment grade rated corporate debt publicly issued in the US domestic market. To qualify for inclusion in the index, securities must have a below investment grade rating (based on an average of Moody's, S&P, and Fitch) and an investment grade rated country of risk (based on an average of Moody's, S&P, and Fitch foreign currency long term sovereign debt ratings). Each security must have greater than 1 year of remaining maturity, a fixed coupon schedule, and a minimum amount outstanding of $100 million. Original issue zero coupon bonds, "global" securities (debt issued simultaneously in the eurobond and US domestic bond markets), 144a securities and pay-in-kind securities, including toggle notes, qualify for inclusion in the Index. Callable perpetual securities qualify provided they are at least one year from the first call date. Fixed-to-floating rate securities also qualify provided they are callable within the fixed rate period and are at least one year from the last call prior to the date the bond transitions from a fixed to a floating rate security. DRD-eligible and defaulted securities are excluded from the Index.

ICE BofA Explains the Construction Methodology of this series as: Index constituents are capitalization-weighted based on their current amount outstanding. With the exception of U.S. mortgage pass-throughs and U.S. structured products (ABS, CMBS and CMOs), accrued interest is calculated assuming next-day settlement. Accrued interest for U.S. mortgage pass-through and U.S. structured products is calculated assuming same-day settlement. Cash flows from bond payments that are received during the month are retained in the index until the end of the month and then are removed as part of the rebalancing. Cash does not earn any reinvestment income while it is held in the Index. The Index is rebalanced on the last calendar day of the month, based on information available up to and including the third business day before the last business day of the month. Issues that meet the qualifying criteria are included in the Index for the following month. Issues that no longer meet the criteria during the course of the month remain in the Index until the next month-end rebalancing at which point they are removed from the Index.

When the last calendar day of the month takes place on the weekend, weekend observations will occur as a result of month ending accrued interest adjustments.

Certain indices and index data included in FRED are the property of ICE Data Indices, LLC (“ICE DATA”) and used under license. ICE® IS A REGISTERED TRADEMARK OF ICE DATA OR ITS AFFILIATES AND BOFA® IS A REGISTERED TRADEMARK OF BANK OF AMERICA CORPORATION LICENSED BY BANK OF AMERICA CORPORATION AND ITS AFFILIATES (“BOFA”) AND MAY NOT BE USED WITHOUT BOFA’S PRIOR WRITTEN APPROVAL. ICE DATA, ITS AFFILIATES AND THEIR RESPECTIVE THIRD PARTY SUPPLIERS DISCLAIM ANY AND ALL WARRANTIES AND REPRESENTATIONS, EXPRESS AND/OR IMPLIED, INCLUDING ANY WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE OR USE, INCLUDING WITH REGARD TO THE INDICES, INDEX DATA AND ANY DATA INCLUDED IN, RELATED TO, OR DERIVED THEREFROM. NEITHER ICE DATA, NOR ITS AFFILIATES OR THEIR RESPECTIVE THIRD PARTY PROVIDERS SHALL BE SUBJECT TO ANY DAMAGES OR LIABILITY WITH RESPECT TO THE ADEQUACY, ACCURACY, TIMELINESS OR COMPLETENESS OF THE INDICES OR THE INDEX DATA OR ANY COMPONENT THEREOF. THE INDICES AND INDEX DATA AND ALL COMPONENTS THEREOF ARE PROVIDED ON AN “AS IS” BASIS AND YOUR USE IS AT YOUR OWN RISK. ICE DATA, ITS AFFILIATES AND THEIR RESPECTIVE THIRD PARTY SUPPLIERS DO NOT SPONSOR, ENDORSE, OR RECOMMEND FRED, OR ANY OF ITS PRODUCTS OR SERVICES.

Copyright, 2023, ICE Data Indices. Reproduction of this data in any form is prohibited except with the prior written permission of ICE Data Indices.

The end of day Index values, Index returns, and Index statistics (“Top Level Data”) are being provided for your internal use only and you are not authorized or permitted to publish, distribute or otherwise furnish Top Level Data to any third-party without prior written approval of ICE Data. Neither ICE Data, its affiliates nor any of its third party suppliers shall have any liability for the accuracy or completeness of the Top Level Data furnished through FRED, or for delays, interruptions or omissions therein nor for any lost profits, direct, indirect, special or consequential damages. The Top Level Data is not investment advice and a reference to a particular investment or security, a credit rating or any observation concerning a security or investment provided in the Top Level Data is not a recommendation to buy, sell or hold such investment or security or make any other investment decisions. You shall not use any Indices as a reference index for the purpose of creating financial products (including but not limited to any exchange-traded fund or other passive index-tracking fund, or any other financial instrument whose objective or return is linked in any way to any Index) without prior written approval of ICE Data. ICE Data, their affiliates or their third party suppliers have exclusive proprietary rights in the Top Level Data and any information and software received in connection therewith. You shall not use or permit anyone to use the Top Level Data for any unlawful or unauthorized purpose. Access to the Top Level Data is subject to termination in the event that any agreement between FRED and ICE Data terminates for any reason. ICE Data may enforce its rights against you as the third-party beneficiary of the FRED Services Terms of Use, even though ICE Data is not a party to the FRED Services Terms of Use. The FRED Services Terms of Use, including but limited to the limitation of liability, indemnity and disclaimer provisions, shall extend to third party suppliers.

With Versium REACH Demographic Append you will have access to many different attributes for enriching your data.

Basic, Household and Financial, Lifestyle and Interests, Political and Donor.

Here is a list of what sorts of attributes are available for each output type listed above:

Basic: - Senior in Household - Young Adult in Household - Small Office or Home Office - Online Purchasing Indicator
- Language - Marital Status - Working Woman in Household - Single Parent - Online Education - Occupation - Gender - DOB (MM/YY) - Age Range - Religion - Ethnic Group - Presence of Children - Education Level - Number of Children

Household, Financial and Auto: - Household Income - Dwelling Type - Credit Card Holder Bank - Upscale Card Holder - Estimated Net Worth - Length of Residence - Credit Rating - Home Own or Rent - Home Value - Home Year Built - Number of Credit Lines - Auto Year - Auto Make - Auto Model - Home Purchase Date - Refinance Date - Refinance Amount - Loan to Value - Refinance Loan Type - Home Purchase Price - Mortgage Purchase Amount - Mortgage Purchase Loan Type - Mortgage Purchase Date - 2nd Most Recent Mortgage Amount - 2nd Most Recent Mortgage Loan Type - 2nd Most Recent Mortgage Date - 2nd Most Recent Mortgage Interest Rate Type - Refinance Rate Type - Mortgage Purchase Interest Rate Type - Home Pool

Lifestyle and Interests: - Mail Order Buyer - Pets - Magazines - Reading
- Current Affairs and Politics
- Dieting and Weight Loss - Travel - Music - Consumer Electronics - Arts
- Antiques - Home Improvement - Gardening - Cooking - Exercise
- Sports - Outdoors - Womens Apparel
- Mens Apparel - Investing - Health and Beauty - Decorating and Furnishing

Political and Donor: - Donor Environmental - Donor Animal Welfare - Donor Arts and Culture - Donor Childrens Causes - Donor Environmental or Wildlife - Donor Health - Donor International Aid - Donor Political - Donor Conservative Politics - Donor Liberal Politics - Donor Religious - Donor Veterans - Donor Unspecified - Donor Community - Party Affiliation

This data represents the semi-annual yield to worst of the ICE BofA US Corporate Index, which tracks the performance of US dollar denominated investment grade rated corporate debt publicly issued in the US domestic market. To qualify for inclusion in the index, securities must have an investment grade rating (based on an average of Moody's, S&P, and Fitch) and an investment grade rated country of risk (based on an average of Moody's, S&P, and Fitch foreign currency long term sovereign debt ratings). Each security must have greater than 1 year of remaining maturity, a fixed coupon schedule, and a minimum amount outstanding of $250 million. Original issue zero coupon bonds, "global" securities (debt issued simultaneously in the eurobond and US domestic bond markets), 144a securities and pay-in-kind securities, including toggle notes, qualify for inclusion in the Index. Callable perpetual securities qualify provided they are at least one year from the first call date. Fixed-to-floating rate securities also qualify provided they are callable within the fixed rate period and are at least one year from the last call prior to the date the bond transitions from a fixed to a floating rate security. DRD-eligible and defaulted securities are excluded from the Index.

ICE BofA Explains the Construction Methodology of this series as:

Index constituents are capitalization-weighted based on their current amount outstanding. With the exception of U.S. mortgage pass-throughs and U.S. structured products (ABS, CMBS and CMOs), accrued interest is calculated assuming next-day settlement. Accrued interest for U.S. mortgage pass-through and U.S. structured products is calculated assuming same-day settlement. Cash flows from bond payments that are received during the month are retained in the index until the end of the month and then are removed as part of the rebalancing. Cash does not earn any reinvestment income while it is held in the Index. The Index is rebalanced on the last calendar day of the month, based on information available up to and including the third business day before the last business day of the month. Issues that meet the qualifying criteria are included in the Index for the following month. Issues that no longer meet the criteria during the course of the month remain in the Index until the next month-end rebalancing at which point they are removed from the Index.

When the last calendar day of the month takes place on the weekend, weekend observations will occur as a result of month ending accrued interest adjustments.

Yield to worst is the lowest potential yield that a bond can generate without the issuer defaulting. The standard US convention for this series is to use semi-annual coupon payments, whereas the standard in the foreign markets is to use coupon payments with frequencies of annual, semi-annual, quarterly, and monthly.

Certain indices and index data included in FRED are the property of ICE Data Indices, LLC (“ICE DATA”) and used under license. ICE® IS A REGISTERED TRADEMARK OF ICE DATA OR ITS AFFILIATES AND BOFA® IS A REGISTERED TRADEMARK OF BANK OF AMERICA CORPORATION LICENSED BY BANK OF AMERICA CORPORATION AND ITS AFFILIATES (“BOFA”) AND MAY NOT BE USED WITHOUT BOFA’S PRIOR WRITTEN APPROVAL. ICE DATA, ITS AFFILIATES AND THEIR RESPECTIVE THIRD PARTY SUPPLIERS DISCLAIM ANY AND ALL WARRANTIES AND REPRESENTATIONS, EXPRESS AND/OR IMPLIED, INCLUDING ANY WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE OR USE, INCLUDING WITH REGARD TO THE INDICES, INDEX DATA AND ANY DATA INCLUDED IN, RELATED TO, OR DERIVED THEREFROM. NEITHER ICE DATA, NOR ITS AFFILIATES OR THEIR RESPECTIVE THIRD PARTY PROVIDERS SHALL BE SUBJECT TO ANY DAMAGES OR LIABILITY WITH RESPECT TO THE ADEQUACY, ACCURACY, TIMELINESS OR COMPLETENESS OF THE INDICES OR THE INDEX DATA OR ANY COMPONENT THEREOF. THE INDICES AND INDEX DATA AND ALL COMPONENTS THEREOF ARE PROVIDED ON AN “AS IS” BASIS AND YOUR USE IS AT YOUR OWN RISK. ICE DATA, ITS AFFILIATES AND THEIR RESPECTIVE THIRD PARTY SUPPLIERS DO NOT SPONSOR, ENDORSE, OR RECOMMEND FRED, OR ANY OF ITS PRODUCTS OR SERVICES.

Copyright, 2023, ICE Data Indices. Reproduction of this data in any form is prohibited except with the prior written permission of ICE Data Indices.

The end of day Index values, Index returns, and Index statistics (“Top Level Data”) are being provided for your internal use only and you are not authorized or permitted to publish, distribute or otherwise furnish Top Level Data to any third-party without prior written approval of ICE Data. Neither ICE Data, its affiliates nor any of its third party suppliers shall have any liability for the accuracy or completeness of the Top Level Data furnished through FRED, or for delays, interruptions or omissions therein nor for any lost profits, direct, indirect, special or consequential damages. The Top Level Data is not investment advice and a reference to a particular investment or security, a credit rating or any observation concerning a security or investment provided in the Top Level Data is not a recommendation to buy, sell or hold such investment or security or make any other investment decisions. You shall not use any Indices as a reference index for the purpose of creating financial products (including but not limited to any exchange-traded fund or other passive index-tracking fund, or any other financial instrument whose objective or return is linked in any way to any Index) without prior written approval of ICE Data. ICE Data, their affiliates or their third party suppliers have exclusive proprietary rights in the Top Level Data and any information and software received in connection therewith. You shall not use or permit anyone to use the Top Level Data for any unlawful or unauthorized purpose. Access to the Top Level Data is subject to termination in the event that any agreement between FRED and ICE Data terminates for any reason. ICE Data may enforce its rights against you as the third-party beneficiary of the FRED Services Terms of Use, even though ICE Data is not a party to the FRED Services Terms of Use. The FRED Services Terms of Use, including but limited to the limitation of liability, indemnity and disclaimer provisions, shall extend to third party suppliers.

A randomized encouragement design yields null average effects of a credit builder loan (CBL) on consumer credit scores. But machine learning algorithms indicate the nulls are due to stark, offsetting treatment effects depending on baseline installment credit activity. Delinquency on preexisting loan obligations drives the negative effects, suggesting that adding a CBL overextends some consumers and generates negative externalities on other lenders. More favorably for the market, CBL take-up generates positive selection on score improvements. Simple changes to CBL practice, particularly to provider screening and credit bureau reporting, could ameliorate the negative effects for consumers and the market.

Analysis of ‘ Zillow Housing Aspirations Report’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/zillow-housing-aspirations-reporte on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Additional Data Products

Product: Zillow Housing Aspirations Report

Date: April 2017

Definitions

Home Types and Housing Stock

• All Homes: Zillow defines all homes as single-family, condominium and co-operative homes with a county record. Unless specified, all series cover this segment of the housing stock.
• Condo/Co-op: Condominium and co-operative homes.
• Multifamily 5+ units: Units in buildings with 5 or more housing units, that are not a condominiums or co-ops.
• Duplex/Triplex: Housing units in buildings with 2 or 3 housing units.

Additional Data Products

• Zillow Home Value Forecast (ZHVF): The ZHVF is the one-year forecast of the ZHVI. Our forecast methodology is methodology post.
• Zillow creates our negative equity data using our own data in conjunction with data received through our partnership with TransUnion, a leading credit bureau. We match estimated home values against actual outstanding home-related debt amounts provided by TransUnion. To read more about how we calculate our negative equity metrics, please see our here.
• Cash Buyers: The share of homes in a given area purchased without financing/in cash. To read about how we calculate our cash buyer data, please see our research brief.
• Mortgage Affordability, Rental Affordability, Price-to-Income Ratio, Historical ZHVI, Historical ZHVI and Houshold Income are calculated as a part of Zillow’s quarterly Affordability Indices. To calculate mortgage affordability, we first calculate the mortgage payment for the median-valued home in a metropolitan area by using the metro-level Zillow Home Value Index for a given quarter and the 30-year fixed mortgage interest rate during that time period, provided by the Freddie Mac Primary Mortgage Market Survey (based on a 20 percent down payment). Then, we consider what portion of the monthly median household income (U.S. Census) goes toward this monthly mortgage payment. Median household income is available with a lag. For quarters where median income is not available from the U.S. Census Bureau, we calculate future quarters of median household income by estimating it using the Bureau of Labor Statistics’ Employment Cost Index. The affordability forecast is calculated similarly to the current affordability index but uses the one year Zillow Home Value Forecast instead of the current Zillow Home Value Index and a specified interest rate in lieu of PMMS. It also assumes a 20 percent down payment. We calculate rent affordability similarly to mortgage affordability; however we use the Zillow Rent Index, which tracks the monthly median rent in particular geographical regions, to capture rental prices. Rents are chained back in time by using U.S. Census Bureau American Community Survey data from 2006 to the start of the Zillow Rent Index, and Decennial Census for all other years.
• The mortgage rate series is the average mortgage rate quoted on Zillow Mortgages for a 30-year, fixed-rate mortgage in 15-minute increments during business hours, 6:00 AM to 5:00 PM Pacific. It does not include quotes for jumbo loans, FHA loans, VA loans, loans with mortgage insurance or quotes to consumers with credit scores below 720. Federal holidays are excluded. The jumbo mortgage rate series is the average jumbo mortgage rate quoted on Zillow Mortgages for a 30-year, fixed-rate, jumbo mortgage in one-hour increments during business hours, 6:00 AM to 5:00 PM Pacific Time. It does not include quotes to consumers with credit scores below 720. Traditional federal holidays and hours with insufficient sample sizes are excluded.

About Zillow Data (and Terms of Use Information)

• Zillow is in the process of transitioning some data sources with the goal of producing published data that is more comprehensive, reliable, accurate and timely. As this new data is incorporated, the publication of select metrics may be delayed or temporarily suspended. We look forward to resuming our usual publication schedule for all of our established datasets as soon as possible, and we apologize for any inconvenience. Thank you for your patience and understanding.
• All data accessed and downloaded from this page is free for public use by consumers, media, analysts, academics etc., consistent with our published Terms of Use. Proper and clear attribution of all data to Zillow is required.
• For other data requests or inquiries for Zillow Real Estate Research, contact us here.
• All files are time series unless noted otherwise.
• To download all Zillow metrics for specific levels of geography, click here.
• To download a crosswalk between Zillow regions and federally defined regions for counties and metro areas, click here.
• Unless otherwise noted, all series cover single-family residences, condominiums and co-op homes only.

Source: https://www.zillow.com/research/data/

This dataset was created by Zillow Data and contains around 200 samples along with Unnamed: 1, Unnamed: 0, technical information and other features such as: - Unnamed: 1 - Unnamed: 0 - and more.

How to use this dataset

• Analyze Unnamed: 1 in relation to Unnamed: 0
• Study the influence of Unnamed: 1 on Unnamed: 0
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Zillow Data

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--- Original source retains full ownership of the source dataset ---

This data represents the effective yield of the ICE BofA US Corporate Index, which tracks the performance of US dollar denominated investment grade rated corporate debt publicly issued in the US domestic market. To qualify for inclusion in the index, securities must have an investment grade rating (based on an average of Moody's, S&P, and Fitch) and an investment grade rated country of risk (based on an average of Moody's, S&P, and Fitch foreign currency long term sovereign debt ratings). Each security must have greater than 1 year of remaining maturity, a fixed coupon schedule, and a minimum amount outstanding of $250 million. Original issue zero coupon bonds, "global" securities (debt issued simultaneously in the eurobond and US domestic bond markets), 144a securities and pay-in-kind securities, including toggle notes, qualify for inclusion in the Index. Callable perpetual securities qualify provided they are at least one year from the first call date. Fixed-to-floating rate securities also qualify provided they are callable within the fixed rate period and are at least one year from the last call prior to the date the bond transitions from a fixed to a floating rate security. DRD-eligible and defaulted securities are excluded from the Index.

ICE BofA Explains the Construction Methodology of this series as: Index constituents are capitalization-weighted based on their current amount outstanding. With the exception of U.S. mortgage pass-throughs and U.S. structured products (ABS, CMBS and CMOs), accrued interest is calculated assuming next-day settlement. Accrued interest for U.S. mortgage pass-through and U.S. structured products is calculated assuming same-day settlement. Cash flows from bond payments that are received during the month are retained in the index until the end of the month and then are removed as part of the rebalancing. Cash does not earn any reinvestment income while it is held in the Index. The Index is rebalanced on the last calendar day of the month, based on information available up to and including the third business day before the last business day of the month. Issues that meet the qualifying criteria are included in the Index for the following month. Issues that no longer meet the criteria during the course of the month remain in the Index until the next month-end rebalancing at which point they are removed from the Index. When the last calendar day of the month takes place on the weekend, weekend observations will occur as a result of month ending accrued interest adjustments.

Certain indices and index data included in FRED are the property of ICE Data Indices, LLC (“ICE DATA”) and used under license. ICE® IS A REGISTERED TRADEMARK OF ICE DATA OR ITS AFFILIATES AND BOFA® IS A REGISTERED TRADEMARK OF BANK OF AMERICA CORPORATION LICENSED BY BANK OF AMERICA CORPORATION AND ITS AFFILIATES (“BOFA”) AND MAY NOT BE USED WITHOUT BOFA’S PRIOR WRITTEN APPROVAL. ICE DATA, ITS AFFILIATES AND THEIR RESPECTIVE THIRD PARTY SUPPLIERS DISCLAIM ANY AND ALL WARRANTIES AND REPRESENTATIONS, EXPRESS AND/OR IMPLIED, INCLUDING ANY WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE OR USE, INCLUDING WITH REGARD TO THE INDICES, INDEX DATA AND ANY DATA INCLUDED IN, RELATED TO, OR DERIVED THEREFROM. NEITHER ICE DATA, NOR ITS AFFILIATES OR THEIR RESPECTIVE THIRD PARTY PROVIDERS SHALL BE SUBJECT TO ANY DAMAGES OR LIABILITY WITH RESPECT TO THE ADEQUACY, ACCURACY, TIMELINESS OR COMPLETENESS OF THE INDICES OR THE INDEX DATA OR ANY COMPONENT THEREOF. THE INDICES AND INDEX DATA AND ALL COMPONENTS THEREOF ARE PROVIDED ON AN “AS IS” BASIS AND YOUR USE IS AT YOUR OWN RISK. ICE DATA, ITS AFFILIATES AND THEIR RESPECTIVE THIRD PARTY SUPPLIERS DO NOT SPONSOR, ENDORSE, OR RECOMMEND FRED, OR ANY OF ITS PRODUCTS OR SERVICES.

Copyright, 2023, ICE Data Indices. Reproduction of this data in any form is prohibited except with the prior written permission of ICE Data Indices.

The end of day Index values, Index returns, and Index statistics (“Top Level Data”) are being provided for your internal use only and you are not authorized or permitted to publish, distribute or otherwise furnish Top Level Data to any third-party without prior written approval of ICE Data. Neither ICE Data, its affiliates nor any of its third party suppliers shall have any liability for the accuracy or completeness of the Top Level Data furnished through FRED, or for delays, interruptions or omissions therein nor for any lost profits, direct, indirect, special or consequential damages. The Top Level Data is not investment advice and a reference to a particular investment or security, a credit rating or any observation concerning a security or investment provided in the Top Level Data is not a recommendation to buy, sell or hold such investment or security or make any other investment decisions. You shall not use any Indices as a reference index for the purpose of creating financial products (including but not limited to any exchange-traded fund or other passive index-tracking fund, or any other financial instrument whose objective or return is linked in any way to any Index) without prior written approval of ICE Data. ICE Data, their affiliates or their third party suppliers have exclusive proprietary rights in the Top Level Data and any information and software received in connection therewith. You shall not use or permit anyone to use the Top Level Data for any unlawful or unauthorized purpose. Access to the Top Level Data is subject to termination in the event that any agreement between FRED and ICE Data terminates for any reason. ICE Data may enforce its rights against you as the third-party beneficiary of the FRED Services Terms of Use, even though ICE Data is not a party to the FRED Services Terms of Use. The FRED Services Terms of Use, including but limited to the limitation of liability, indemnity and disclaimer provisions, shall extend to third party suppliers.

Credit repair service providers identify errors in credit reporting and dispute inaccurate information with the appropriate organizations to improve credit ratings. The industry's performance often behaves countercyclically to the overall economy. Despite this, revenue fell during COVID-19 as massive government aid pushed up savings. These savings kept consumers financially stable, so demand credit repair services declined in 2020. As economic restrictions were lifted, many households went on a spending spree and ruined their credit, so revenue for the industry rose in 2021. While interest rates have been volatile, they've risen over time as the Federal Reserve has increased borrowing costs to cool the economy. Higher interest rates make it harder for consumers to pay off debt, ruining their credit. This raises demand for the industry's services. Overall, revenue for credit repair service providers is expected to increase at a CAGR of 2.8% during the current period, reaching $6.6 billion in 2023. Revenue is anticipated to rise 2.5% in that year.The industry will grow modestly in the near future, but it will face some challenges. The outlook period will be marked by significant volatility, as determinants of revenue (e.g., consumer spending, interest rates, corporate profit) will shift significantly over this time frame. The Federal Reserve will continue to raise interest rates to bring the inflation rate down to 2.0%. Since the cost of borrowing will continue to increase, the industry will benefit. Economic growth will be strong, making individuals more credit-worthy and reducing demand for credit repair services. Individuals will be more able to repair their credit on their own as online resources get more comprehensive. Overall, revenue for credit repair service providers is forecast to cincrease at a CAGR of 1.0% during the outlook period, reaching $7.0 billion in 2028. Profit is expected to comprise 10.1% of revenue in that year.

As of 2023, the largest share of personal loan complaints reported to the Consumer Financial Protection Bureau (CFPB) in the United States concerned either fees or interest charged unexpectedly, or problems when making payments. Complaints about problems with credit reports or credit scores followed, accounting for ** percent of the personal loan complaints received by the Consumer Financial Protection Bureau that year.

Graph and download economic data for Bank Prime Loan Rate Changes: Historical Dates of Changes and Rates (PRIME) from 1955-08-04 to 2024-12-20 about prime, loans, interest rate, banks, interest, depository institutions, rate, and USA.

Personal Loans Market Outlook 2032

The global personal loans market size was USD 65.3 Billion in 2023 and is likely to reach USD 1300 Billion by 2032, expanding at a CAGR of 32.5% during 2024–2032. The market is driven by the surging demand for personal loans due to the financial liberty and awareness among consumers, globally.



Increasing consumer spending and the growing need for financial flexibility are expected to drive the personal loans market during the forecast period. Personal loans, with their ease of access and competitive interest rates, have become a popular financing option for individuals to meet various financial needs, including debt consolidation, home renovation, and emergency expenses. The rise of digital lending platforms and the simplification of loan application processes have significantly surged the demand for personal loans.

Growing advancements in financial technology are shaping the trends in the personal loans market. The integration of artificial intelligence and machine learning technologies into lending platforms has enhanced the loan approval process, offering features such as instant approval and personalized interest rates. Furthermore, the development of secure digital platforms has enabled remote loan application and disbursement, making personal loans accessible to a wider audience.



Rising financial literacy and consumer awareness are creating opportunities for the personal loans market. The increasing understanding of credit scores, interest rates, and loan terms among consumers has fueled the demand for personal loans. Moreover, the growing focus on financial planning and the need for emergency funds have underscored the importance of personal loans in financial management. With its numerous advantages and wide range of applications, the personal loans market is poised for significant growth in the coming years.

Impact of Artificial Intelligence (AI) in Personal Loans Market

The use of artificial intelligence is likely to boost the personal loans market. AI's <a href="https://dataintelo.com/report/advanced-and-predictive-analytics-market" sty