The aim of this dissertation is to advocate policies that can effectively address the challenge of unsheltered homelessness. Using a case study of Los Angeles homelessness, I evaluated policies aimed at easing homelessness using scenario analysis, employing system dynamic (SD) modeling. This study primarily focuses on the evaluation of the Housing First approach and the identification of more effective responses to homelessness. I use a linear regression model to identify key prevention policy levers, including, but not limited to, limits on eviction moratorium, rent stabilization, and affordable housing. Drawing on the information gathered from the regression, the SD model is able to capture the impact of the key factors on homelessness prevention. Combined with the housing sub-systems, the SD model can simulate the behavior of the homeless population under different policy arrangements. The evidence drawn from the statistical model as well as the SD model suggests that when long-term and short-term housing programs are compared, long-term housing programs, such as the permanent supportive housing (PSH) approach, better meet the needs of chronically and mentally ill homeless people in Los Angeles, though they are more costly than short-term housing approaches. In order to mitigate homelessness, the City and County of Los Angeles should: (1) Adopt a homelessness prevention plan that can constrain growth in the number of people evicted from rental units and the number of people discharged from jails and foster families. (2) Continue to employ the PSH approach as its primary means of combating homelessness. (3) Help recipients of long-term support become self-sufficient in order to reduce the cumulative financial burden created by the operation of long-term support programs for homeless people. The SD simulation model results suggest that, if Los Angeles tripled total funding for PSH programs, without adopting any short-term housing approaches, the unsheltered homeless population would likely fall below five hundred by 2030.
","abstract_html":"<p>The aim of this dissertation is to advocate policies that can effectively address the challenge of unsheltered homelessness. Using a case study of Los Angeles homelessness, I evaluated policies aimed at easing homelessness using scenario analysis, employing system dynamic (SD) modeling. This study primarily focuses on the evaluation of the Housing First approach and the identification of more effective responses to homelessness. I use a linear regression model to identify key prevention policy levers, including, but not limited to, limits on eviction moratorium, rent stabilization, and affordable housing. Drawing on the information gathered from the regression, the SD model is able to capture the impact of the key factors on homelessness prevention. Combined with the housing sub-systems, the SD model can simulate the behavior of the homeless population under different policy arrangements. The evidence drawn from the statistical model as well as the SD model suggests that when long-term and short-term housing programs are compared, long-term housing programs, such as the permanent supportive housing (PSH) approach, better meet the needs of chronically and mentally ill homeless people in Los Angeles, though they are more costly than short-term housing approaches. In order to mitigate homelessness, the City and County of Los Angeles should: (1) Adopt a homelessness prevention plan that can constrain growth in the number of people evicted from rental units and the number of people discharged from jails and foster families. (2) Continue to employ the PSH approach as its primary means of combating homelessness. (3) Help recipients of long-term support become self-sufficient in order to reduce the cumulative financial burden created by the operation of long-term support programs for homeless people. The SD simulation model results suggest that, if Los Angeles tripled total funding for PSH programs, without adopting any short-term housing approaches, the unsheltered homeless population would likely fall below five hundred by 2030.</p>","abstract_has_math":false,"creators":["Kowarsch, Dandan"],"institution":null,"degree_name":"Political Science, PhD","degree_level":"Open Access Dissertation","degree_discipline":"School of Social Science, Politics, and Evaluation","degree_department":null,"school":null,"contributors":["Yi Feng","Jacek Kugler"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-01-01T08:00:00Z","date_published":"2022-01-01T08:00:00Z","updated_at":"2026-07-24T01:40:15Z","subjects":["Housing First Policy","Permanent Supportive Housing","Rapid Re-housing","Simulation","System Dynamics Modeling","Unsheltered Homelessness","Political Science"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarship.claremont.edu/cgu_etd/452","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Yi Feng","Jacek Kugler"]},{"key":"dc:creator","label":"Author","values":["Kowarsch, Dandan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2023-04-19T07:00:00Z"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["School of Social Science, Politics, and Evaluation"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Open Access Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Political Science, PhD"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Housing First Policy","Permanent Supportive Housing","Rapid Re-housing","Simulation","System Dynamics Modeling","Unsheltered Homelessness","Political Science"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarship.claremont.edu/cgu_etd/452"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The aim of this dissertation is to advocate policies that can effectively address the challenge of unsheltered homelessness. Using a case study of Los Angeles homelessness, I evaluated policies aimed at easing homelessness using scenario analysis, employing system dynamic (SD) modeling. This study primarily focuses on the evaluation of the Housing First approach and the identification of more effective responses to homelessness. I use a linear regression model to identify key prevention policy levers, including, but not limited to, limits on eviction moratorium, rent stabilization, and affordable housing. Drawing on the information gathered from the regression, the SD model is able to capture the impact of the key factors on homelessness prevention. Combined with the housing sub-systems, the SD model can simulate the behavior of the homeless population under different policy arrangements. The evidence drawn from the statistical model as well as the SD model suggests that when long-term and short-term housing programs are compared, long-term housing programs, such as the permanent supportive housing (PSH) approach, better meet the needs of chronically and mentally ill homeless people in Los Angeles, though they are more costly than short-term housing approaches. In order to mitigate homelessness, the City and County of Los Angeles should: (1) Adopt a homelessness prevention plan that can constrain growth in the number of people evicted from rental units and the number of people discharged from jails and foster families. (2) Continue to employ the PSH approach as its primary means of combating homelessness. (3) Help recipients of long-term support become self-sufficient in order to reduce the cumulative financial burden created by the operation of long-term support programs for homeless people. The SD simulation model results suggest that, if Los Angeles tripled total funding for PSH programs, without adopting any short-term housing approaches, the unsheltered homeless population would likely fall below five hundred by 2030.
"]},{"key":"dc:title","label":"Title","values":["Evaluating Los Angeles Homelessness Policy Using System Dynamics Modeling"]}]}],"canonical_facts":{"dc:contributor":["Yi Feng","Jacek Kugler"],"dc:creator":["Kowarsch, Dandan"],"dc:date.available":["2023-04-19T07:00:00Z"],"dc:description.abstract":["The aim of this dissertation is to advocate policies that can effectively address the challenge of unsheltered homelessness. Using a case study of Los Angeles homelessness, I evaluated policies aimed at easing homelessness using scenario analysis, employing system dynamic (SD) modeling. This study primarily focuses on the evaluation of the Housing First approach and the identification of more effective responses to homelessness. I use a linear regression model to identify key prevention policy levers, including, but not limited to, limits on eviction moratorium, rent stabilization, and affordable housing. Drawing on the information gathered from the regression, the SD model is able to capture the impact of the key factors on homelessness prevention. Combined with the housing sub-systems, the SD model can simulate the behavior of the homeless population under different policy arrangements. The evidence drawn from the statistical model as well as the SD model suggests that when long-term and short-term housing programs are compared, long-term housing programs, such as the permanent supportive housing (PSH) approach, better meet the needs of chronically and mentally ill homeless people in Los Angeles, though they are more costly than short-term housing approaches. In order to mitigate homelessness, the City and County of Los Angeles should: (1) Adopt a homelessness prevention plan that can constrain growth in the number of people evicted from rental units and the number of people discharged from jails and foster families. (2) Continue to employ the PSH approach as its primary means of combating homelessness. (3) Help recipients of long-term support become self-sufficient in order to reduce the cumulative financial burden created by the operation of long-term support programs for homeless people. The SD simulation model results suggest that, if Los Angeles tripled total funding for PSH programs, without adopting any short-term housing approaches, the unsheltered homeless population would likely fall below five hundred by 2030.
"],"dc:identifier":["https://scholarship.claremont.edu/cgu_etd/452"],"dc:subject":["Housing First Policy","Permanent Supportive Housing","Rapid Re-housing","Simulation","System Dynamics Modeling","Unsheltered Homelessness","Political Science"],"dc:title":["Evaluating Los Angeles Homelessness Policy Using System Dynamics Modeling"],"thesis:degree_discipline":["School of Social Science, Politics, and Evaluation"],"thesis:degree_level":["Open Access Dissertation"],"thesis:degree_name":["Political Science, PhD"]},"updated_at":"2026-07-24T01:40:15Z"}