Abstract
As population densities in urban areas increase, the associated demand on transportation infrastructure continues to exacerbate impacts on surrounding communities. These demands create a number of socioeconomic burdens including housing price impacts when communities are regularly exposed to excessive noise levels. Although noise impacts are not as commonly recognized or assessed in comparison to other environmental issues such as air, ground, or water pollution, it has been well documented in the literature that a wide range of health issues exist when communities are exposed to noise from transportation infrastructure. From a research perspective, the correlation of these health issues to the presence of impactful noise is difficult to quantify, as noise is subjective and requires translation into varying degrees of annoyance to deem it as detrimental from both health and economic perspectives. This dissertation utilizes spatial hedonic price (HP) models to estimate individuals’ marginal willingness-to-pay (MWTP) to reside in noise-impacted areas. These MWTP values can then be used to both valuate economic impacts and as a noise annoyance level proxy to identify zones that are at-risk due to excessive transportation noise exposure.The first analysis in this dissertation reviews salient transportation noise-related papers that have been published since Navrud’s comprehensive 2002 transportation noise literature review. In a review of recent literature, this dissertation found that transportation noise research has evolved to include advanced Geographic Information System data, and leverages increasingly powerful processors and statistical analysis programs. In addition, although significant transportation noise research has been conducted in Europe following EU Environmental Noise Directive 2002/49/EC, a relatively minimal number of studies have been conducted in the United States — especially in Southern California, revealing a research gap that this dissertation helps to address.The second analysis investigates the impacts of aircraft operations around Los Angeles International Airport. Using a subset of 2010-2014 single-family home sales data from the Los Angeles County Office of the Assessor (LACOA), HP spatial autoregressive models with autoregressive disturbances (SARAR) were estimated. The study hypothesizes and confirms that a negative impact value would be observed for homes being located within noise-mapped zones around the airport, along with an improvement in estimation values compared to previous fixed spatial effects ordinary least squares techniques.The third analysis in this dissertation investigates two important topics. First, it hypothesizes negative home value impacts from nearby freight rail operations in the densely populated South Bay region of Los Angeles County. Noise from freight rail lines is analyzed using an HP SARAR model and confirm negative valuation impacts to homes located near these rail lines. Second, it hypothesizes that by using a subset of the master LACOA dataset above, varying levels of spatial homogeneity can be comparatively analyzed between two samples that use similar data and modeling techniques. Results indicate that when neighboring zones have distinct differences in jurisdiction, fixed spatial effect delineations remain statistically significant. However, when neighboring zones have similar jurisdictional or demographic characteristics, spatial model parameters are able to account for fixed delineations.
