What's New

You asked and we listened. In response to customer feedback, Equifax and Moody's Analytics have added loan origination vintage and risk score dimensions, as well as monthly updates of credit data.

Contact Client Services at 1-866-275-3266 to ensure you are getting the maximum benefit from these amazing features.

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CreditForecast.com can be used by lenders to benchmark their portfolio's performance against that of the entire credit market. This can be done across product lines and detailed levels of geography and for both the growth in credit and credit risk. Historically, lender benchmarking has been done solely vis-à-vis specific competitors. CreditForecast.com allows for benchmarking against the entire market, which will allow lenders to identify risks and opportunities that cannot be determined by benchmarking against a self-identified set of peers.

CreditForecast.com can be used by lenders to improve the predictive ability of their current customer-level scoring. Lenders have historically used credit scoring models based only on customers' past credit history. Since economic conditions change quickly and are often different than those that prevailed historically, credit decisions based on credit scores developed using historical information do not fully account for all the information available to lenders. CreditForecast.com provides lenders with a method to incorporate forward-looking economic information into credit decisions.

CreditForecast.com can be used to improve the accuracy of P&L forecasting. Most P&L forecasting done by lenders at a portfolio level does not take into account the changing economic environment. These models are often based on historical roll rates that remain constant regardless of economic conditions. Roll rates are highly dependent, however, on the health of the job market and broader economy. P&L models in which roll rates are a function of economic and credit variables available from CreditForecast.com thus result in more accurate forecasting.

CreditForecast.com can be used to evaluate the sensitivity of lenders' portfolios to shifting economic conditions. Just how important are falling unemployment or rising interest rates to loan growth and credit quality? Do they vary significantly across product lines and regions of the country? Lenders can determine whether, for example, a strongly expanding economy experiencing lower unemployment, but higher inflation and interest rates will have a negative impact on the credit quality of their mortgage versus credit card portfolios.

CreditForecast.com can be used by regulators to assess the performance of financial institutions. Certain lenders may be at heightened risk of suffering credit losses, given their regional and product line exposure. This risk can be measured using CreditForecast.com. Mortgage lenders in California, for example, may be at greater risk in a rising rate environment given surging activity and housing values in recent years. Lenders can also use CreditForecast.com to demonstrate to regulators their ability to measure and manage risk.

CreditForecast.com can be used by investors evaluating opportunities and risks in the rapidly growing asset- and mortgage-backed securities markets. Do interest rate spreads on ABS and MBS pools adequately reflect current and prospective economic and credit conditions? Are interest rate spreads appropriately discounting regional economic and credit differences across pools? Equity investors can also use CreditForecast.com to evaluate earnings growth prospects and stock price valuation of lending institutions.

CreditForecast.com can be used to determine the size of potential markets a lender is interested in entering. Users can identify areas for expansion which fit their target customers’ economic and credit profiles. This can be achieved by comparing trends and forecasts in existing locations to potential markets. Information broken down to the MSA and rest-of-state level provides users with great flexibility in analyzing different geographies.

Product Categories

To ensure detailed coverage of all major types of consumer credit, CreditForecast.com provides credit data for seven mutually exclusive product categories and 16 subcategories. Users can license all seven product categories for the full picture of U.S. consumer credit, or they can license just the categories that are most important to their business.

• Auto Bank - Auto trades opened through a bank or credit union.
• Auto Finance - Auto trades opened through a dealer or auto finance company.
• Total Auto - Auto trades opened through a dealer, auto finance company, bank or credit union.

• Bankcard - Unsecured or secured credit cards issued by a bank, national card company or credit union; includes revolving and open-type accounts.

• Consumer Finance - Loan or line of credit opened up with a sales financing company or a personal finance company. These are typically unsecured finance trades.
• Retail - Retail trades opened with a clothing company, department or variety store, mail order catalog (including the internet), grocery store, home furnishing store, jewelry or camera store, building or hardware store, oil company, sporting goods store, farm or gardening supply store, other retailer, or charge card/revolving trade with an auto company.

• First Mortgage - Mortgage trades with a mortgage or real estate company, bank, credit union or finance company, exclusive of Home Equity Revolving and Home Equity Installment.
• Agency First Mortgage – First Mortgage loans held or issued in securies by Freddie Mac or Fannie Mae, or guaranteed by the Federal Housing Administration (FHA) or U.S. Department of Veterans Affairs (VA).
• Non-Agency First Mortgage – All other First Mortgage loans not owned by Freddie Mac or Fannie Mae, or guaranteed by the Federal Housing Administration (FHA) or U.S. Department of Veterans Affairs (VA).
• Total Mortgage - First Mortgage trades plus Home Equity total trades.

• Home Equity Installment - Installment trade with a mortgage or real estate company, bank, credit union or finance company identified as home equity; exclusive of First Mortgage and Home Equity Revolving.
• Home Equity Revolving - Revolving trade with a mortgage or real estate company, bank, credit union or finance company identified as revolving, exclusive of Home Equity Installment.
• Home Equity Total - Installment or revolving trade with a mortgage or real estate company, bank, credit union or finance company identified as home equity.

• Student Loan - From a bank, credit union or finance company, or the government.

• Total(All) - Top-line summary of all the categories.
• Other - Primarily installment loans not otherwise classified (that is, not auto, first mortgage, home equity, or student loan)

Forecasting Methodology and Alternative Scenarios

Economic forecasts are based on the work of over 60 economists, nearly two-thirds of whom hold an advanced degree, who monitor and forecast the U.S. economy at the global, macro, regional and industry level. 

Forecasting is done for origination risk score bands, geographic areas, and vintanges, with the total forecast being additive. The forecast database includes a baseline most probable forecast and four alternative scenarios. The alternative scenarios vary according to where we are in the business cycle. The forecasts are updated with new history and forecasts quarterly. The economic and demographic forecasts are produced using Moody’s Analytics’s detailed econometric model system. View full details on all Moody's Analytics Macroeconomic Outlook Alternative Scenarios (must be logged in to view).

The credit forecasts are produced with a model developed using a pooled time series, cross sectional specification. Each equation is estimated for all metro areas using the available historic data. Projections of the credit, economic and demographic variables are available with a monthly periodicity and a five-year forecast horizon.

Forecasting Methodology and Alternative Scenarios
The forecasting models for CreditForecast.com consist of structural vintage-level models of aggregate consumer credit behavior.  Specifically, the models predict loan balances, performance, utilization and average credit scores across each of the lines of business.  Models are developed on aggregated cohorts of loans broken down by lines of business, vintage and geography.  CreditForecast.com generates time-series forecasts for all performance series across all lines of business within 250 geographies and four consumer origination credit score groups over the next five years. 
               
Explanatory variables for forecasting loan performance may be defined by three broad categories: vintage age or maturation, economic environment at time of origination, ongoing or time varying economic environment. Forecasting accuracy is the main criteria used to specify and accept models though attention is also paid to econometric stability and theoretical consistency of models.

Vintage Modeling Overview
The general approach to modeling the performance of individual loan cohorts (defined as the overlap of geography, vintage, original credit score and product category) considers three main drivers, Lifecycle, Vintage Quality, and Prevailing Conditions. These underlying drives impact each of the key components of the way retail credit is modeled.

• Lifecycle Component: performance curve for pools of loans as they age (example: credit cards tend to hit peak delinquency rates between the 6^th and 12^th month after issuance).
• Quality at Origination/ Vintage Quality: loans issued during different environments vary in quality depending on economic conditions at the time of origination.
• Prevailing Conditions Faced by Existing Account Holders: the effect of the business cycle (economic conditions) throughout the lifetime of a pool of retail loans.

* Cohort: overlap of geography, vintage, score and product category.

Forecasting New Vintages
One of the advantages of the new version of CreditForecast.com is that it is vintage based. Not only does this allow users to compare historical performance across origination time periods, but future vintages are forecasted as well in order to generate forecasts for any of the time series.

For example, consider the forecast of the 60 DPD rate in March 2013. % 60 DPD is defined as $ Balance of loans 60 DPD in March 2013 / $ outstanding balance at the start of March 2013. While the numerator may come mainly from loans already in existence as of March 2012, the denominator will be made up of loans that already exist as well as new originations through March 2013. Therefore both performance of the existing book and future originations need to forecasted in order to develop a complete picture.

Forecasting Changes in Volume
Economic factors were chosen to capture the default and prepayment incentives facing borrowers in aggregate.  Balances should decrease quickly when rates are low and borrowers can refinance.  Similarly, balances may be hypothesized to decrease when house prices are falling (a proxy for general economic health) and more loans are subject to default.  Seasonality plays a role in the time of year of the reporting period — more mortgages are paid off in the summer, for example, as borrowers move.  Time of year of origination also appears to have a permanent effect on loan performance as loans that are originated off-cycle tend to perform worse than those that are originated on-cycle.

Having estimated the period to period changes in volumes, a time series can then be computed for outstanding originations by computing balances for the runoff portfolio as well as for future loan originations. Change in balance equations are estimated both in terms of dollars and number of accounts.

Credit Performance Measures
All of the performance measures for delinquency and termination status are converted to conditional percentages by dividing by either the outstanding balance or number of accounts in each time period.  For example, the 30 day delinquency rate is computed as:
                30 DPD Rate (Number) = # 30 DPD accounts / # active accounts
                30 DPD Rate (Balance) = $ 30 DPD accounts balance / $ active accounts balance

Vintage level economic variables are measured at the start of each vintage period and remain constant through the life of each vintage. 

Other Credit Measures

A number of summary measures beyond balances and performance are included in the CreditForecast.com service, including aggregate payment and high credit amounts. These measures are converted to a per account basis by dividing by the number of active accounts in each geography-vintage-origination credit score cell and then modeling these per account values.

As the historical time series show little variation on a per account basis, models for payment, high credit and per account balance are estimated as a function of geography, origination credit score and economic factors.

Derived Series
Performance rates are modeled as these are stationary and directly tied to economic variables.  For example, the unemployment rate is highly correlated with delinquency rates but less correlated with the actual number of delinquent loans, as the latter fail to account for the size of the outstanding pool or denominator. 

Number of trades and dollar balances are computed for each performance category by multiplying the forecasted performance rates from each model by the forecasted balance and number of accounts from the relevant model.  Utilization rates are computed by dividing the high credit dollar balance by the dollar balance outstanding in each cohort.

Data: Historical and Forecast Databases

Historical Data
The historical credit data are made available from the Equifax National Consumer Database, which contains information collected from over 12,000 contributors who furnish data on tradelines, collections, public records and demographics across a broad range of industries, with updates on a daily basis. 

Within a month, the database experiences on the order of 1.7 billion tradeline updates, 120 million collection updates, 350,000 public record updates, 185,000 bankruptcy updates and 12 million address changes. Information being added to the database goes through a rigorous quality assurance process, including automated and manual reviews, to ensure the highest accuracy possible.

The credit data are sourced from the full Equifax credit database and include consumers with >=1 active trade. This results in approximately 226 million consumer records in a given month.  Selection always occurs at the end of the month, so that the results are not affected by any in-month reporting fluctuations. Monthly historical files starting with June 2005 were used.  Therefore, full “open-to-close” vintage data are available starting with July 2005, while information on trades in older vintages – when available from the consumer credit files – is also included.

Equifax developed special credit trend attributes that aggregate the consumer file into attributes specifically intended for monthly trending.   These attributes consider only trades with activity in the last three months and only look at current delinquency statuses in order to focus on the true state of credit (balances, high credit, delinquencies, etc.) in a given month.  These attributes were calculated for each consumer file and then aggregated to the ZIP code, county, MSA and state levels.  This will allow CreditForecast.com to quickly adapt to any future changes in county or MSA definitions.

Origination and current risk score level classification are done at the consumer level, based on ERS 3.0 – Odds Scale (score range 280-850).  There are four score bands, allowing the user flexibility in identifying consumer risk profiles of interest.  Forecasting is done separately for each of the score bands; the total forecast is additive. 

Comparison With Other Data Sources
The historical CreditForecast.com data have a number of significant advantages over publicly available sources of credit data.  These other sources include data on consumer loan delinquencies from the American Bankers Association, mortgage loan delinquency and foreclosures available from the Mortgage Bankers Association, and consumer and mortgage delinquency and charge-off data available from the Federal Reserve Board and FDIC.

The most significant advantage of the CreditForecast.com data is that they are available for the nation, states, and metropolitan areas.  The ABA and MBA data is available only for the nation and states, and the Federal Reserve Board and FDIC data is only available for the nation.

The CreditForecast.com data are also timelier than the other sources of credit data.  They are made available within four weeks of the end of the month, compared to two to three months after the end of the quarter for the other sources.

The CreditForecast.com data is also based on where the borrower resides.  The ABA data, by contrast, are based on the branch or operating location of the lending institutions participating in the survey.  This may or may not coincide with where the borrower lives.  This is of particular importance for the ABA’s data on bankcards, given that many of the cards are originated by lenders with operations in New York, Delaware and South Dakota to borrowers that live all over the country.  The MBA does ask lenders to report by the state where the property is located.

Since the CreditForecast.com data are based on borrower information, they are not affected by changes affecting lenders.  The ABA and MBA data can be significantly affected by any change in the lenders included in their surveys.  For example, the MBA has previously expanded the number of subprime mortgage loans in its survey, resulting in a substantial increase in measured credit problems.  The Federal Reserve and FDIC data, which are based in part on call reports provided by lending institutions, can also be affected by changes in the status of lenders.  Historically, the acquisition by a large commercial bank of a retailers’ credit card portfolio and the resolution of failing lending institutions have affected measured credit quality.    

Forecast Data
Projections of the credit, economic and demographic variables are available with a quarterly periodicity and a five-year forecast horizon. 

Forecasting is done for the origination and current risk score bands separately; the total forecast is additive.  The forecast database includes a baseline, most probable, forecast and four alternative scenarios.  The alternative scenarios vary according to where we are in the business cycle.  The forecasts are updated with new history and forecasts four times per year. 

The economic and demographic forecasts are produced using the detailed econometric model system of Moody’s Analytics.  A description of this system is available upon request.  The credit forecasts are produced with a model developed using a recursive pooled time series, cross-sectional specification.  Each equation is estimated for all metro areas using the available historical data.  The equations are log-linear in form, meaning the terms (unless otherwise indicated) are specified as the natural logarithm and the coefficients can be interpreted as elasticities.  All the equations contain metro area-specific constant terms to capture the metro area-specific fixed effects.  Finally, all of the equations contain an autoregressive term to capture the inertia in the series.

Consumer Credit Data Series
For each of the categories, historical data and forecasts are available at the national, Census region, Census division, state, and Metropolitan Statistical Area (MSA) levels for the following variables:

• Total Trades
• Total Trades with >$0 Balance
• Total Balances
• High Credit/Loan Amount
• Utilization/Balance Outstanding
• Payment Amount
• Delinquency counts, dollars and rates (percent of counts and percent of dollars)
      Total, Current, 30 DPD, 60 DPD, 90 DPD, 120+ DPD and Collections, Foreclosure     Started, Other Status
• Final Disposition counts and dollars
      Default (Closed Negative), Bankruptcy, Closed Positive

Historical and forecast data are available for several consumer credit risk and bankruptcy scores.  Mean Credit Scores for the nation, regions, divisions, states, and MSAs are provided. 

• U.S.
• 4 Census Regions
• 9 Census Divisions
• 50 States
• 200 standard MSAs and 50, non-urban, rest-of-state areas

Display as: Map Alphabetical Region Division State

• Employment, unemployment
• Population, households
• Income (median household, personal, disposable personal)
• Personal savings rate, real net worth
• Debt service ratio, consumer credit debt outstanding (revolving, non-revolving), household financial obligations ratio (homeowners, renters)
• Bankruptcies by chapter (7 and 13)
• CPI (overall, new/used vehicles, trucks, gasoline, housing, etc.)
• Sales (car, truck, house, retail, etc.)
• Home prices (median, FHFA), housing affordability
• Interest rates (federal funds, prime rate, credit card, commercial bank new car loan, etc.)
• GDP
• Vehicle price (Manheim Index)
• Population by age (large groupings)

• Homeownership rate by race
• Homeownership rate by age
• Total international migration
• Population breakouts by age (finer groupings), Hispanic status, gender, race, and cross products

Blended Variables
CreditForecast.com includes unique blended series created by merging economic data with credit data.  These blended series are available for historic data and forecasts, and for all geographies.

• Loan-to-Value Ratio (First Mortgage, Total Mortgage)
• Equity per Household (Total Mortgage)
• Equity Extraction to Income Ratio (Total Mortgage)
• Debt Service Burden (Total)

The Loan to Value Ratio (LTV) is the measurement of aggregate mortgage balances outstanding to the value of the housing stock.  Homeowners’ Equity per Household is the difference between the home value and mortgage debt for an average household.  These series provide information on homeowners’ real estate wealth, the equity cushion against a housing downturn, and the size of real estate debt. 

The Equity Extraction to Income Ratio measures the extraction of equity on existing homes as a percentage of disposable income.  This series is calculated using a similar methodology developed by former Chairman Alan Greenspan and Fed economist James Kennedy.  Equity extraction is the amount homeowners are withdrawing from their housing wealth, potentially to be used for home improvements, consumption and debt restructuring.

The Debt Service Burden is the minimum amount households must pay to service their debt as a share of their disposable income.  The regional series is constructed using a methodology similar to that used by the Federal Reserve in constructing national estimates and is benchmarked to be consistent with that series nationally. The debt service ratio is an accurate measure for households’ financial burden, although it is sensitive to changes in homeownership rates. 

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