Analyzing Credit of Municipal Bonds

Two general types of municipal bonds (or “munis” as they are commonly called):

  • Tax-backed or general obligation bonds.
  • Revenue bonds.

These bond types require their own distinct approaches when performing credit analysis.

Tax backed bonds: factors to consider

  • The debt burden of the community.  This is commonly examined in terms of debt per capita, debt as a percent of real estate values, and/or debt as a percent of community personal income.

  • The community’s budget history (i.e. does the community have a history of budget deficits?).

  • Calculate the community’s tax base and its access to other sources of revenue.

  • Evaluate the community’s socioeconomic landscape.

Revenue bonds: factors to consider

  • Four C’s Analysis: With respect to cash flow analysis, revenue bonds are more similar to corporate bonds, than tax backed bonds are, so a four C’s approach is relevant when assessing the credit quality of a revenue bond issue.

  • Understand the revenue source backing the bond.

  • Understand the flow of funds for the bond. Bondholders commonly receive a net revenue pledge, which means that debt service payments come after operational expenses of the municipality’s project are covered.

  • Understand the charge rates, which is the manner in which the customers of the bond financed project are charged for using the service provided by the project.

  • Understand the legal priority of revenue claims.

  • Understand the conditions under which additional bonds can be issued.

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Data Science in Finance: 9-Book Bundle

Data Science in Finance Book Bundle

Master R and Python for financial data science with our comprehensive bundle of 9 ebooks.

What's Included:

  • Getting Started with R
  • R Programming for Data Science
  • Data Visualization with R
  • Financial Time Series Analysis with R
  • Quantitative Trading Strategies with R
  • Derivatives with R
  • Credit Risk Modelling With R
  • Python for Data Science
  • Machine Learning in Finance using Python

Each book comes with PDFs, detailed explanations, step-by-step instructions, data files, and complete downloadable R code for all examples.