S&P Global Ratings uses the U.S. Local Governments General Obligation Credit Scoring Model to generate a standardized credit analysis to assist in assigning and surveilling U.S. local government general obligation (GO) ratings based on the applicable criteria methodology.

Purpose Of The Model

The U.S. Local Governments General Obligation Credit Scoring Model applies the "U.S. Local Governments General Obligation Ratings: Methodology And Assumptions," published Sept. 12, 2013, criteria methodology. By standardizing the calculations and inputs used in our analysis, the model provides for the consistent application of the U.S. local governments GO criteria. S&P Global Ratings' U.S. local governments GO criteria explain the methodology and assumptions for assigning issuer credit ratings (ICRs) and issue ratings based on GO pledges by U.S. local governments (excluding special districts).

The model is used to perform credit analysis for new issuance and surveillance of GO ratings for U.S. local governments whenever a GO analysis of U.S. local governments is applied, which includes credit assessments or as an input to other criteria that utilize GO ratings, such as our appropriation-backed debt criteria. The model is also used to generate Institutional Framework (IF) and Metropolitan Statistical Area (MSA) scores, as defined by the criteria, which are inputs to our GO rating analysis.

Summary Description Of The Model

The criteria provide a rating methodology for U.S. local governments based on both a quantitative and qualitative assessment and the scoring of seven key factors:

• Institutional framework;
• Economy;
• Management;
• Budgetary flexibility;
• Budgetary performance;
• Liquidity; and
• Debt and contingent liabilities.

Each factor is scored on a 1 (best) to 5 (worst) scale. The model calculates six of the factor scores (excluding the IF score) by first assigning an initial score, which is determined quantitatively by one or two key ratios or data points. The model calculates the final factor scores by adjusting the initial scores up or down based on which qualitative adjustments are applied. Some qualitative adjustments are applied formulaically by the model (when based solely on data), while the majority are manual inputs to the model. IF scores also use a 1-to-5 scale, but, instead of having an initial score and qualitative adjustments, they comprise an average of four component scores for the following categories: predictability, revenue and expenditure balance, transparency and accountability, and system support. These four categories are assessed by analysts for each local government type within each state using a separate IF component of the model. The resulting final IF scores become inputs to the model for individual credit analysis; the model selects the IF score based upon the local government's state and type, as identified by the analyst.

The model calculates an indicative rating from the weighted average of the seven factors detailed above. The model applies the factor weights defined in the criteria. The economy score receives a 30% weight, and the management score receives 20%. The financial-related scores--liquidity, budgetary performance, and budgetary flexibility--each account for 10%. The IF score also receives a 10% weight, as does the debt and contingent liabilities score. Based on table 1 in the criteria, the model maps the factor scores' weighted average to an indicative rating.

The model applies overriding factors, when applicable, based on the analyst's manual inputs or on certain factor scores or data values as defined in the criteria. Overrides either notch (up or down) the indicative rating or cap the suggested GO rating. If a rating cap is applied within the model, it sets a maximum level for the suggested GO rating, but the analyst can also recommend any rating below the cap based on the overriding condition's severity. Examples of overriding conditions include a weak liquidity or management score, sustained large positive fund balances, and structural imbalance.

The indicative rating, net of any overrides that notch it up or down, can also be adjusted up or down by one notch of flexibility, which is input into the model when recommended by the analyst based upon peer comparisons and credit trends. However, as per the criteria, the model limits the suggested GO rating to be equal to or lower than a rating cap.

The suggested GO rating outcome is calculated by the model based on the indicative rating (as calculated using the seven final factor scores), override conditions (including when the analyst recommends a rating lower than a rating cap), and the one-notch flexibility, if applied by the analyst.

Assumptions Underlying The Model

The methodology applied within the model does not incorporate assumptions beyond the criteria.

Inputs To The Model

Model inputs include raw data, analytic data adjustments, qualitative adjustments and overrides, the most recently reported fiscal year, Financial Management Assessment (FMA) scores, MSA scores, and IF scores. In the course of monitoring ratings, we may apply certain assumptions to model inputs when using the model, but such assumptions are not typically applied for rating actions.

Raw data inputs include economic (both local-level, such as market and assessed value, and county-level, such as the unemployment rate and population), financial, and debt-related data points. Financial data are primarily derived from annual audit reports, but also include budgets, unaudited estimates and projections, and other financial information received from the obligor (e.g. general fund revenues, expenditures, and available fund balance). Debt-related data include a local government's direct debt burden, the percent of debt amortizing within 10 years, pension, and other postretirement employee benefits obligation data, among other measures.

Rules for selecting raw data for input into the model are a component of the model. The general rules include determining which year of data to select, the best statement type to select, the most recently updated data, and the most preferred source of data.

Analytic data adjustments are made to better align the raw data with our view of the ongoing operational reality of a particular entity, and to improve the financial results' comparability across entities. Data adjustments may also be employed to portray what we view as a more accurate depiction of recurring activity. The article "S&P Public Finance Local GO Criteria: How We Adjust Data For Analytic Consistency," published Sept. 12, 2013, provides further detail and examples of analytic data adjustments.

Qualitative adjustments, when applied, move initial factor scores up or down to determine the final factor scores calculated by the model. Each qualitative adjustment defined in the criteria is assessed and indicated within the model whether it applies or not; some are calculated by the model based upon quantitative metrics, while the rest are manual inputs based upon qualitative analytic judgment. In the few cases where qualitative adjustments can have multiple levels of impact, the level of impact is also an input to the model.

Similar to qualitative adjustments, some overriding factors are based upon a qualitative assessment of credit characteristics. In these cases, whether a qualitative override is applied or not serves as an input to the model.

The criteria indicate that most of the ratios included in the criteria are based upon data from the "most recently reported fiscal year," which could mean an audit report or unaudited financial data for a fiscal year. Within the model, we select the appropriate year of data to use for ratios based upon our view of the most recent data available for the local governments.

Our FMA assesses a local government's financial management policies and practices and can take values of "strong," "good," "standard," or "vulnerable" based upon a weighted average of the seven component scores. Inputs to the model include the seven FMA component scores, from which the model calculates the FMA score. See the criteria article "Financial Management Assessment," published June 27, 2006, for complete details on the FMA analysis.

We analyze MSAs using a component of the model to generate MSA scores, which we use to determine whether local governments receive the broad and diversified economy qualitative adjustment on the economy factor. If the MSA score is "moderate," applying the broad and diverse adjustment to the initial economic score may be warranted if we determine the local government benefits significantly from participation within its respective MSA. In that case, whether the broad and diversified economy qualitative adjustment is applicable is an input to the model. The local GO criteria article describes the MSA scoring in detail, which is determined and published separately from credit rating analyses.

IF scores are defined within the U.S. local governments GO criteria, upon which we have based our assessment for all the local government types in each state and assigned scores. Those scores serve as the IF factor score inputs to the model in our credit analysis based upon the local government's type; they are determined within a component of the model that is separate from credit rating analyses.

Data Used In Model Development And Calibration

The data used within the model come from various sources, including audits, budgets, disclosure documents, the obligor, state sources, websites, and economic data providers.

The criteria are calibrated to provide rating results consistent with the extraordinarily historically low levels of local government defaults. See the "Local Governments Rating Calibrations" section within the criteria for complete details.

The Limits And Uncertainties Of The Model

The U.S. Local Governments General Obligation Credit Scoring model is only used for GO analysis of U.S. local governments (excluding special districts). It does not provide inputs to any other analyses, except where the GO rating on local governments is specified as an input to other criteria (e.g., appropriation debt).

Material Changes To The Model

Effective April 2021, we made material changes to the model to resolve a model error.

The first change corrected a calculation error in the MSA calculator that had resulted in the miscalculation of two MSA assessments in the model. The correction of this error resulted in one change to a credit rating. For more information regarding the rating action associated with the correction of this model error please see "Error Identified In U.S. Local Government General Obligation Model, Review Indicates One Rating Affected", published Feb. 3, 2021.

Other changes were made to correct the model implementation of the border values in two adjustment calculations, one in the economic score and another in the budgetary flexibility score, to make them consistent with the criteria. These model changes did not result in changes to credit ratings.

Related Criteria And Research

• U.S. Local Governments General Obligation Ratings: Methodology And Assumptions, Sept. 12, 2013
• Financial Management Assessment, June 27, 2006