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A growing body of evidence from individual-level studies demonstrating that the Housing First approach is effective at keeping those experiencing homelessness in stable housing has led to the approach being championed by many leading experts, especially as a way to address chronic homelessness (O'Flaherty, 2019). This helps us understand the relationship between Housing First and an individual’s homelessness, but we know very little about the relationship between implementation of a Housing First approach and overall homelessness rates in a community. In a 2019 survey of homelessness research published by the Journal of Housing Economics, Brendan O’Flaherty wrote: “What has been missing in studies of Housing First are estimates of aggregate impact: does operating a Housing First program actually reduce the total amount of homelessness in a community?” Through this study, I sought to understand if Continuums of Care (CoC) that have adopted a Housing First approach by dedicating a higher proportion of their resources towards permanent housing units are associated with a lower proportion of people experiencing homelessness between the years 2009 and 2017 than CoCs dedicating a higher proportion of their resources towards emergency shelter and other short-term solutions. Additionally, I sought to understand how that relationship between the implementation of a Housing First approach and homelessness rates change as the values of median rent, unemployment, and other covariates typically associated with homelessness rates change. I hypothesized that CoCs adopting a Housing First approach, as defined in the context of this study, would experience lower homelessness rates. The hypothesis that homelessness rates would decrease as the Housing First index increases was supported by the results, but the relationship is more complex than hypothesized. The relationship between Housing First and homelessness rates was quadratic in nature and influenced by an interaction effect wit

This report aims to address questions on changing socio-economic and physical conditions in Frederick and how those conditions could formulated into a new neighborhood identification system. This report recommends that Geographic Information Systems (GIS) mapping, online surveys and photo sharing applications are some solutions for creating such a neighborhood identification system. While these planning technologies provide multiple ways or “layers” to define neighborhood boundaries, survey distribution is necessary to contextualize the quantitative results of the analysis.

This research primarily looks at trends in unsheltered homelessness and foreclosures in Maryland between 2005 and 2011 in order to determine what kind of impact the foreclosure crisis has had on homelessness. To complement these quantitative data, qualitative information was gathered through interviews and local Continuum of Care plans. The results of this investigation do not support any direct causal relationship between new foreclosures and homelessness; however, it is possible that foreclosures have pushed higher-income renters into the rental market. Through the combined impacts of the build-up of the housing bubble and the injection of these new higher-income renters, rental costs have continued their upward trend. In this way, it is possible that foreclosures have indirectly led to an increase in homelessness by pushing rental costs upward even after the housing bubble had burst. However, this research also highlighted many shortcomings associated with the homeless point-in-time count methodology that make it difficult to identify causal relationships such as this with any high level of certainty. Several recommendations are provided at the conclusion of this research in order to help alleviate homelessness, improve the available data, and conduct additional research to further our collective knowledge on the nature of homelessness and its causes.