| dc.description.abstract | Local governments have many different options to secure financing for infrastructure projects, including using Tax-increment financing (TIF) bonds. The key feature of this type of bond is the repayment source: defined as an increment in tax revenue attributed to higher property valuation within the TIF project-impact zone. While the TIF concept brings numerous benefits, such as transferring risks associated with local economic growth, it is also associated with several key challenges. One of the most significant challenges is its complexity. For example, it is often quite difficult to understand how TIF projects affect the surrounding properties, their market valuation, and, consequently, the credit risk of the underlying TIF bond. As a result, potential investors shy away from such assets, which, in turn, translates into increased premiums. This dissertation presents a framework that aims to help the investors, financial advisors, and other key market players better understand the credit risk associated with TIF bonds. The framework focuses on monitoring the performance of the underlying projects as well as other important endogenous and exogenous factors, with the goal of reconstituting the asset credit risk profile and detecting early warning signals of changing conditions. The key benefit of the proposed framework is its capacity to use publicly available, time series data. The case study, illustrating the framework, is based on the “Uptown Development Authority” Tax Increment Reinvestment Zone (TIRZ) bond, which is a type of TIF, structured to support redevelopment projects in Houston, Texas. | en |
