This dissertation research comprises two essays in informational finance in real estate. Finance has a rich and varied literature dealing with the topic of information and its effects for both markets and market participants. Real estate sub-markets offer a unique and productive venue for the examination of these effects. In the essays which comprise this dissertation, we investigate two distinct incidents of real estate markets and apply novel perspectives and paradigms to evaluate and explain them.

Essay 1 considers the phenomenon of gentrification from a market microstructure perspective. Specifically, we use models developed in the study of information cascades and herding to describe agent behavior and pricing dynamics in urban real estate markets. Our results identify pricing discrepancies that initiate the gentrification process, and characteristics of agents most likely to recognize and capture their value. They further support the hypothesis that gentrification is driven by the arrival of market participants who possess an informational endowment that differs significantly from current residents and who then instigate a herding response among their peers, which drive rental prices up in the gentrifying neighborhood.

Essay 2 addresses the nearly five-decade long discussion on vertical inequity in property taxation. In considering the state of the discourse, we note that while practitioners and academics differ on which is the most effective measure, they concur that no single measure can objectively be identified as superior. We apply novel methods from Information Theory and Bayesian Statistics to evaluate and better understand the most widely used measures of vertical inequity in property taxation. Our results provide an objective metric which can be used to compare measures of vertical inequity, frame the debate on measures of vertical inequity and, to potentially assist researchers and practitioners to identify more narrowly focused solutions.