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• 101. [Article] Coordinated Population Forecast for Malheur County, its Urban Growth Boundaries (UGB), and Area Outside UGBs 2016-2066

  During the 2000s, Malheur County, as a whole, experienced population decline (Figure 1); however three of its sub-areas recorded a slight population increase. Adrian grew at an average annual rate of nearly ...

  Citation × Citation Citation Abstract Copy Title: Coordinated Population Forecast for Malheur County, its Urban Growth Boundaries (UGB), and Area Outside UGBs 2016-2066 Author: Portland State University. Population Research Center, Ruan, Xiaomin, Proehl, Risa, Jurjevich, Jason R., Rancik, Kevin, Kessi, Janai, Tetrick, David, Michel, Julia Year: 2016 During the 2000s, Malheur County, as a whole, experienced population decline (Figure 1); however three of its sub-areas recorded a slight population increase. Adrian grew at an average annual rate of nearly two percent, while Ontario and the area outside UGBs saw more modest growth rates. Even so the population loss recorded by Vale, Nyssa, and Jordan Valley totaled nearly 600, leading the countywide population to decrease. Malheur County’s population decline in the 2000s was the combined result of a diminishing natural increase and periods of substantial net out-migration (Figure 12). The larger number of births relative to deaths has led to a natural increase (more births than deaths) in every year from 2000 to 2015. Net out-migration slowed toward the end of the last decade (2000-2010) combining with a relatively steady natural increase for moderate population increase in four out of the five years since 2010. Malheur County’s total population is forecast to grow by a little more than 400 persons (1.3 percent) from 2016 to 2066, which translates into a total countywide population of nearly 32,000 in 2066 (Figure 1). Population growth is forecast to be modest, becoming increasingly so as time progresses through the forecast period. Forecasting modest population growth is driven by both an aging population—contributing to a steady increase in deaths over the entire forecast period—as well as diminishing net out-migration. Title: Coordinated Population Forecast for Malheur County, its Urban Growth Boundaries (UGB), and Area Outside UGBs 2016-2066 Author: Portland State University. Population Research Center, Ruan, Xiaomin, Proehl, Risa, Jurjevich, Jason R., Rancik, Kevin, Kessi, Janai, Tetrick, David, Michel, Julia Year: 2016 During the 2000s, Malheur County, as a whole, experienced population decline (Figure 1); however three of its sub-areas recorded a slight population increase. Adrian grew at an average annual rate of nearly two percent, while Ontario and the area outside UGBs saw more modest growth rates. Even so the population loss recorded by Vale, Nyssa, and Jordan Valley totaled nearly 600, leading the countywide population to decrease. Malheur County’s population decline in the 2000s was the combined result of a diminishing natural increase and periods of substantial net out-migration (Figure 12). The larger number of births relative to deaths has led to a natural increase (more births than deaths) in every year from 2000 to 2015. Net out-migration slowed toward the end of the last decade (2000-2010) combining with a relatively steady natural increase for moderate population increase in four out of the five years since 2010. Malheur County’s total population is forecast to grow by a little more than 400 persons (1.3 percent) from 2016 to 2066, which translates into a total countywide population of nearly 32,000 in 2066 (Figure 1). Population growth is forecast to be modest, becoming increasingly so as time progresses through the forecast period. Forecasting modest population growth is driven by both an aging population—contributing to a steady increase in deaths over the entire forecast period—as well as diminishing net out-migration. Close

• 102. [Article] Coordinated Population Forecast for Grant County, its Urban Growth Boundaries (UGB), and Area Outside UGBs 2016-2066

  Grant County’s total population has declined since 2000, losing an average of about 50 persons per year between 2000 and 2010 (Figure 1). With the exception of minimal population increase among three small ...

  Citation × Citation Citation Abstract Copy Title: Coordinated Population Forecast for Grant County, its Urban Growth Boundaries (UGB), and Area Outside UGBs 2016-2066 Author: Portland State University. Population Research Center, Ruan, Xiaomin, Proehl, Risa, Jurjevich, Jason R., Rancik, Kevin, Kessi, Janai, Tetrick, David, Michel, Julia Year: 2016 Grant County’s total population has declined since 2000, losing an average of about 50 persons per year between 2000 and 2010 (Figure 1). With the exception of minimal population increase among three small UGBs, every sub-area recorded population loss during the 2000s. Prairie City and the area outside UGBs posted the largest losses, losing on average about 17 and 19 persons per year, respectively. Grant County’s population decline in the 2000s was the result of a consistent natural decrease as well as relatively steady net out-migration (Figure 12). The smaller number of births relative to deaths led to a natural decrease (more deaths than births) in nearly every year from 2000 to 2015. While net out-migration was common during the last decade (2000-2010), in recent years (2010-2015) net in-migration has occurred. Even so, natural decrease has persisted and generally exceeded net in-migration, leading to continued population decline. Grant County is expected to experience population decline over the 50-year forecast period. This population loss will likely occur at a relatively uniform rate among the county’s sub-areas, with losses occurring at an increasing rate as time progresses. For Grant County as a whole, increasing natural decrease and relatively steady net in-migration are expected to lead to population decline. An aging population is expected to not only lead to an increase in deaths, but a smaller proportion of women in their childbearing years will likely result in a long-term decline in births. Net in-migration is expected to persist throughout the entire forecast period, but will not fully offset the natural decrease, leading to population decline. Title: Coordinated Population Forecast for Grant County, its Urban Growth Boundaries (UGB), and Area Outside UGBs 2016-2066 Author: Portland State University. Population Research Center, Ruan, Xiaomin, Proehl, Risa, Jurjevich, Jason R., Rancik, Kevin, Kessi, Janai, Tetrick, David, Michel, Julia Year: 2016 Grant County’s total population has declined since 2000, losing an average of about 50 persons per year between 2000 and 2010 (Figure 1). With the exception of minimal population increase among three small UGBs, every sub-area recorded population loss during the 2000s. Prairie City and the area outside UGBs posted the largest losses, losing on average about 17 and 19 persons per year, respectively. Grant County’s population decline in the 2000s was the result of a consistent natural decrease as well as relatively steady net out-migration (Figure 12). The smaller number of births relative to deaths led to a natural decrease (more deaths than births) in nearly every year from 2000 to 2015. While net out-migration was common during the last decade (2000-2010), in recent years (2010-2015) net in-migration has occurred. Even so, natural decrease has persisted and generally exceeded net in-migration, leading to continued population decline. Grant County is expected to experience population decline over the 50-year forecast period. This population loss will likely occur at a relatively uniform rate among the county’s sub-areas, with losses occurring at an increasing rate as time progresses. For Grant County as a whole, increasing natural decrease and relatively steady net in-migration are expected to lead to population decline. An aging population is expected to not only lead to an increase in deaths, but a smaller proportion of women in their childbearing years will likely result in a long-term decline in births. Net in-migration is expected to persist throughout the entire forecast period, but will not fully offset the natural decrease, leading to population decline. Close

• 103. [Article] Methods and Data for Developing Coordinated Population Forecasts

  This document is to accompany the coordinated population forecasts and their corresponding reports prepared by the Population Research Center (PRC). Developing long-term coordinated population forecasts ...

  Citation × Citation Citation Abstract Copy Title: Methods and Data for Developing Coordinated Population Forecasts Author: Portland State University. Population Research Center Year: 2015 This document is to accompany the coordinated population forecasts and their corresponding reports prepared by the Population Research Center (PRC). Developing long-term coordinated population forecasts for a county and its sub-areas (UGBs and areas outside UGBs are referred to sub-areas in this document), requires these main stages: 1) compiling and evaluating historical and recent data to ascertain demographic characteristics and trends in the study area and to obtain a population base from which the forecasts may be launched; 2) making assumptions about the future and adjusting the data or rates in the forecasting models (calibrating the models) to incorporate predicted rates or trends; and 3) reconciling, or adjusting the forecasts so the sum of the sub-area forecasts is consistent with the forecast for the county total. We first develop population projections and then we make adjustments to these projections in order to produce population forecasts. Population projections are developed by extending historical and current demographic and housing trends into the future. Forecasting population requires that assumptions be made about future population change, and adjusting the projection models to account for circumstances that perhaps skewed past trends or may affect future change. Such circumstances in the past could be a building moratorium or the opening of a new group quarters facility. Events affecting future change would be, for example, planned future housing development that is higher than usual, a foreseen change in an area’s physical ability to accommodate growth (buildable land available is approaching capacity or improvements to infrastructure are underway), anticipated changes in the economy (the location of a new employer or the upswing or downturn of the economy in general), or an expected change in the local population and household composition (age, ethnicity, and average household size). Title: Methods and Data for Developing Coordinated Population Forecasts Author: Portland State University. Population Research Center Year: 2015 This document is to accompany the coordinated population forecasts and their corresponding reports prepared by the Population Research Center (PRC). Developing long-term coordinated population forecasts for a county and its sub-areas (UGBs and areas outside UGBs are referred to sub-areas in this document), requires these main stages: 1) compiling and evaluating historical and recent data to ascertain demographic characteristics and trends in the study area and to obtain a population base from which the forecasts may be launched; 2) making assumptions about the future and adjusting the data or rates in the forecasting models (calibrating the models) to incorporate predicted rates or trends; and 3) reconciling, or adjusting the forecasts so the sum of the sub-area forecasts is consistent with the forecast for the county total. We first develop population projections and then we make adjustments to these projections in order to produce population forecasts. Population projections are developed by extending historical and current demographic and housing trends into the future. Forecasting population requires that assumptions be made about future population change, and adjusting the projection models to account for circumstances that perhaps skewed past trends or may affect future change. Such circumstances in the past could be a building moratorium or the opening of a new group quarters facility. Events affecting future change would be, for example, planned future housing development that is higher than usual, a foreseen change in an area’s physical ability to accommodate growth (buildable land available is approaching capacity or improvements to infrastructure are underway), anticipated changes in the economy (the location of a new employer or the upswing or downturn of the economy in general), or an expected change in the local population and household composition (age, ethnicity, and average household size). Close

• 104. [Article] The Importance of Housing, Accessibility, and Transport Characteristic Ratings on Stated Neighborhood Preference

  Travel demand models commonly lack the ability to understand how changing residential preferences influence future housing, land use, and transportation policies. As communities struggle to address social ...

  Citation × Citation Citation Abstract Copy Title: The Importance of Housing, Accessibility, and Transport Characteristic Ratings on Stated Neighborhood Preference Author: Currans, Kristina Marie Year: 2016 Travel demand models commonly lack the ability to understand how changing residential preferences influence future housing, land use, and transportation policies. As communities struggle to address social challenges related to increased economic uncertainty, transportation and land use planning have become increasingly centered on assumptions concerning the market for residential environments and travel choices. In response, an added importance has been placed on the development of toolkits capable of providing robust and flexible models to aid in understanding how differing assumptions contribute to a set of planning scenarios and how future residential location decisions may be made. In this study, we aim to examine the ability for current land use and transportation models to adequately account for the mechanism that drive households to prefer different neighborhoods based on typical transportation modeling methods. In particular, we conduct an original stated preference survey to identify the characteristics and preferences of individuals and households that influence unconstrained residential neighborhood preferences. Our objectives are to (a) determine the key drivers of residential neighborhood preferences, (b) test the contribution of sociodemographic and economic household characteristics commonly used in regional modeling tools to allocate households regionally across neighborhoods (e.g. household size, income, age, tenure), and (c) identify the preference profiles that can improve the sensitivity of our regional and statewide transportation-land use models to residential neighborhood preferences. The presentation for this seminar was done jointly with Steven Gehrke. It can be accessed at http://archives.pdx.edu/ds/psu/18234. Title: The Importance of Housing, Accessibility, and Transport Characteristic Ratings on Stated Neighborhood Preference Author: Currans, Kristina Marie Year: 2016 Travel demand models commonly lack the ability to understand how changing residential preferences influence future housing, land use, and transportation policies. As communities struggle to address social challenges related to increased economic uncertainty, transportation and land use planning have become increasingly centered on assumptions concerning the market for residential environments and travel choices. In response, an added importance has been placed on the development of toolkits capable of providing robust and flexible models to aid in understanding how differing assumptions contribute to a set of planning scenarios and how future residential location decisions may be made. In this study, we aim to examine the ability for current land use and transportation models to adequately account for the mechanism that drive households to prefer different neighborhoods based on typical transportation modeling methods. In particular, we conduct an original stated preference survey to identify the characteristics and preferences of individuals and households that influence unconstrained residential neighborhood preferences. Our objectives are to (a) determine the key drivers of residential neighborhood preferences, (b) test the contribution of sociodemographic and economic household characteristics commonly used in regional modeling tools to allocate households regionally across neighborhoods (e.g. household size, income, age, tenure), and (c) identify the preference profiles that can improve the sensitivity of our regional and statewide transportation-land use models to residential neighborhood preferences. The presentation for this seminar was done jointly with Steven Gehrke. It can be accessed at http://archives.pdx.edu/ds/psu/18234. Close

• 105. [Article] Configuring the Urban Smart Grid: Transitions, Experimentation, and Governance

  In the face of challenges of energy security, decarbonization, resilience, and the replacement of aging infrastructure systems, federal, state, and local actors are facilitating the development of smart ...

  Citation × Citation Citation Abstract Copy Title: Configuring the Urban Smart Grid: Transitions, Experimentation, and Governance Author: Levenda, Anthony Michael Year: 2016 In the face of challenges of energy security, decarbonization, resilience, and the replacement of aging infrastructure systems, federal, state, and local actors are facilitating the development of smart electricity networks to transition towards a more sustainable electricity system. In the United States, development of "smart grids" is being pursued as a national policy mandate and goal, promising that the deployment of smart grid technologies -- referring in general to digital information and communication technologies that sense, monitor, control and manage the electric grid -- will make electricity systems more environmentally sustainable and reliable, and at the same time, provide opportunities for growth and innovation. This dissertation examines and analyzes three interconnected issues relating to these sociotechnical changes in electricity infrastructure: the material and discursive construction of the smart grid, urban smart grid experimentation, and the mobility of smart grid models and knowledge. A conceptual framework is proposed for investigating sociotechnical transitions that accounts for dimensions of power and politics that are commonly overlooked in conventional analysis, and highlights how governance regimes shape and are shaped by sociotechnical change. Utilizing Foucauldian discourse analysis and relational comparative case study methodology, this dissertation analyzes the development of the smart grid as a governmental program highlighting its rationalities, techniques, and imagined subjects. The findings of these analyses suggest that the transition to a smarter grid involves much more than top-down policy mandates; significant urban experimentation is involved, as well as inter-city learning that is shaped by local political economy and broader political rationalities. This dissertation also argues for a synthesis between policy mobilities and sociotechnical transitions theory, highlighting through case studies how urban smart grid experiments are influenced by experiences and knowledge generated from "vanguard" cities. The conclusion of this dissertation is that the creation of the smart grid is far from a purely technical infrastructural intervention, and instead, requires significant changes in the everyday social practices and conduct of energy consumers, while also reconfiguring the city, engaging in a material politics in order to govern energy transitions. Title: Configuring the Urban Smart Grid: Transitions, Experimentation, and Governance Author: Levenda, Anthony Michael Year: 2016 In the face of challenges of energy security, decarbonization, resilience, and the replacement of aging infrastructure systems, federal, state, and local actors are facilitating the development of smart electricity networks to transition towards a more sustainable electricity system. In the United States, development of "smart grids" is being pursued as a national policy mandate and goal, promising that the deployment of smart grid technologies -- referring in general to digital information and communication technologies that sense, monitor, control and manage the electric grid -- will make electricity systems more environmentally sustainable and reliable, and at the same time, provide opportunities for growth and innovation. This dissertation examines and analyzes three interconnected issues relating to these sociotechnical changes in electricity infrastructure: the material and discursive construction of the smart grid, urban smart grid experimentation, and the mobility of smart grid models and knowledge. A conceptual framework is proposed for investigating sociotechnical transitions that accounts for dimensions of power and politics that are commonly overlooked in conventional analysis, and highlights how governance regimes shape and are shaped by sociotechnical change. Utilizing Foucauldian discourse analysis and relational comparative case study methodology, this dissertation analyzes the development of the smart grid as a governmental program highlighting its rationalities, techniques, and imagined subjects. The findings of these analyses suggest that the transition to a smarter grid involves much more than top-down policy mandates; significant urban experimentation is involved, as well as inter-city learning that is shaped by local political economy and broader political rationalities. This dissertation also argues for a synthesis between policy mobilities and sociotechnical transitions theory, highlighting through case studies how urban smart grid experiments are influenced by experiences and knowledge generated from "vanguard" cities. The conclusion of this dissertation is that the creation of the smart grid is far from a purely technical infrastructural intervention, and instead, requires significant changes in the everyday social practices and conduct of energy consumers, while also reconfiguring the city, engaging in a material politics in order to govern energy transitions. Close

• 106. [Article] Assessing Transit Fare Equity in Utah Using a Geographic Information System

  The goal of this study is to develop and apply a new method for assessing social equity impacts of distance-based public transit fares. Shifting to a distance-based fare structure can disproportionately ...

  Citation × Citation Citation Abstract Copy Title: Assessing Transit Fare Equity in Utah Using a Geographic Information System Author: Farber, Steven, Batholomew, Keith, Li, Xiao, Paez, Antonio, Nurul Habib, Khandker M. Year: 2014 The goal of this study is to develop and apply a new method for assessing social equity impacts of distance-based public transit fares. Shifting to a distance-based fare structure can disproportionately favor or penalize different subgroups of a population based on variations in settlement patterns, travel needs, and most importantly, transit use. According to federal law, such disparities must be evaluated by the transit agency, but the area-based techniques identified by the Federal Transit Authority for assessing discrimination fail to account for disparities in distances travelled by transit users. This means that transit agencies currently lack guidelines for assessing the social equity impacts of replacing flat fare with distance-based fare structures. Our solution is to incorporate a joint ordinal-continuous model of trip generation and distance travelled into a GIS Decision Support System. The system enables a transit planner to visualize and compare estimated distances travelled and transit-cost maps for different population profiles and fare structures. We apply the method to a case study in the Wasatch Front, Utah, where the Utah Transit Authority is exploring a switch to a distance-based fare structure. The analysis reveals that overall distance-based fares benefit low-income, elderly, and non-white populations. However, the effect is geographically uneven, and may be negative for members of these groups living on the urban fringe. The research discusses a potential conflict between policies designed to promote equity and those intended to increase discretionary ridership. For example, increasing fares for longdistance discretionary riders travelling from suburban light-rail stations to the central business district may have a negative impact on ridership, despite being economically more efficient through cost-recapturing. At the same time, reducing fares for short-distance trips that are currently too expensive compared to private automobile travel may attract new riders. A full cost-benefit analysis sensitive to heterogeneous price elasticity is recommended for future research. It is also noted that the research findings are dependent on the current spatial distributions of population segments and travel patterns. The equity benefits of distance-based fares are primarily derived from residential clustering of low-income and minority groups in more compact urban areas near the center of the city. Demographic trends, such as inner-city gentrification and suburban aging-in-place, may impose increased travel demands on low income and elderly riders, nullifying future equity gains made by a transition to distance-based fares. Title: Assessing Transit Fare Equity in Utah Using a Geographic Information System Author: Farber, Steven, Batholomew, Keith, Li, Xiao, Paez, Antonio, Nurul Habib, Khandker M. Year: 2014 The goal of this study is to develop and apply a new method for assessing social equity impacts of distance-based public transit fares. Shifting to a distance-based fare structure can disproportionately favor or penalize different subgroups of a population based on variations in settlement patterns, travel needs, and most importantly, transit use. According to federal law, such disparities must be evaluated by the transit agency, but the area-based techniques identified by the Federal Transit Authority for assessing discrimination fail to account for disparities in distances travelled by transit users. This means that transit agencies currently lack guidelines for assessing the social equity impacts of replacing flat fare with distance-based fare structures. Our solution is to incorporate a joint ordinal-continuous model of trip generation and distance travelled into a GIS Decision Support System. The system enables a transit planner to visualize and compare estimated distances travelled and transit-cost maps for different population profiles and fare structures. We apply the method to a case study in the Wasatch Front, Utah, where the Utah Transit Authority is exploring a switch to a distance-based fare structure. The analysis reveals that overall distance-based fares benefit low-income, elderly, and non-white populations. However, the effect is geographically uneven, and may be negative for members of these groups living on the urban fringe. The research discusses a potential conflict between policies designed to promote equity and those intended to increase discretionary ridership. For example, increasing fares for longdistance discretionary riders travelling from suburban light-rail stations to the central business district may have a negative impact on ridership, despite being economically more efficient through cost-recapturing. At the same time, reducing fares for short-distance trips that are currently too expensive compared to private automobile travel may attract new riders. A full cost-benefit analysis sensitive to heterogeneous price elasticity is recommended for future research. It is also noted that the research findings are dependent on the current spatial distributions of population segments and travel patterns. The equity benefits of distance-based fares are primarily derived from residential clustering of low-income and minority groups in more compact urban areas near the center of the city. Demographic trends, such as inner-city gentrification and suburban aging-in-place, may impose increased travel demands on low income and elderly riders, nullifying future equity gains made by a transition to distance-based fares. Close

• 107. [Article] Affordable Housing Needs Study for the Portland Metropolitan Area Draft Final Report

  The purpose of this report is to respond to the recommendations of the Regional Housing Choice Task Force by providing information to guide housing choice policy for the Metro Council. In particular, the ...

  Citation × Citation Citation Abstract Copy Title: Affordable Housing Needs Study for the Portland Metropolitan Area Draft Final Report Author: Hough, George C., Jr., Martin, Sheila A., Mildner, Gerard C., Proehl, Risa S. Year: 2007 The purpose of this report is to respond to the recommendations of the Regional Housing Choice Task Force by providing information to guide housing choice policy for the Metro Council. In particular, the objectives of this project were to: estimate current and future affordable housing need for the Metro region; describe the distribution of households by income, age, and size across the metro region; describe the tenure of these households and the type of housing they will choose; identify and describe those household types that are most likely to struggle to meet the cost of housing based on their income; and make recommendations for improving analysis of affordable housing need in the future. Our approach to this task was to use output from the Metroscope model, using the base case scenario, to forecast the housing consumption decisions of households from 2005 to 2035. We chose the Metroscope model after also considering the State of Oregon?s Housing/Land Needs model. We concluded from examining the assumptions and abilities of each model that Metroscope is better able to offer the Metro Council the insight into the housing market required to inform housing choice policy. The Metroscope model incorporates housing supply and demand for the entire four-county metropolitan region (Multnomah, Washington, Clackamas, and Clark counties). The region comprises a single housing market; residents travel throughout the region to work, shop, and socialize. Thus, it makes little sense to examine any one county in isolation. While this report does not include the results for Clark County, its impact on demand and supply of housing in the rest of the region is taken into account in the Metroscope model and is reflected in the results presented here. Given the assumptions of the Metroscope model (described in Section 2), we address several questions, including: Where will household growth occur? What kinds of households will grow? What kinds of housing will these households live in? What percentage of their income will they pay for housing? What demographic groups are most cost-burdened and where do those households reside? This report offers a summary of the findings regarding each of these questions. Title: Affordable Housing Needs Study for the Portland Metropolitan Area Draft Final Report Author: Hough, George C., Jr., Martin, Sheila A., Mildner, Gerard C., Proehl, Risa S. Year: 2007 The purpose of this report is to respond to the recommendations of the Regional Housing Choice Task Force by providing information to guide housing choice policy for the Metro Council. In particular, the objectives of this project were to: estimate current and future affordable housing need for the Metro region; describe the distribution of households by income, age, and size across the metro region; describe the tenure of these households and the type of housing they will choose; identify and describe those household types that are most likely to struggle to meet the cost of housing based on their income; and make recommendations for improving analysis of affordable housing need in the future. Our approach to this task was to use output from the Metroscope model, using the base case scenario, to forecast the housing consumption decisions of households from 2005 to 2035. We chose the Metroscope model after also considering the State of Oregon?s Housing/Land Needs model. We concluded from examining the assumptions and abilities of each model that Metroscope is better able to offer the Metro Council the insight into the housing market required to inform housing choice policy. The Metroscope model incorporates housing supply and demand for the entire four-county metropolitan region (Multnomah, Washington, Clackamas, and Clark counties). The region comprises a single housing market; residents travel throughout the region to work, shop, and socialize. Thus, it makes little sense to examine any one county in isolation. While this report does not include the results for Clark County, its impact on demand and supply of housing in the rest of the region is taken into account in the Metroscope model and is reflected in the results presented here. Given the assumptions of the Metroscope model (described in Section 2), we address several questions, including: Where will household growth occur? What kinds of households will grow? What kinds of housing will these households live in? What percentage of their income will they pay for housing? What demographic groups are most cost-burdened and where do those households reside? This report offers a summary of the findings regarding each of these questions. Close

• 108. [Article] Do TODs Make a Difference?

  In this report, we present research that measures the outcomes of TOD areas in relation to their metropolitan area controls with respect to (1) jobs by sector; (2) housing choice for household types based ...

  Citation × Citation Citation Abstract Copy Title: Do TODs Make a Difference? Author: Nelson, Arthur C., Miller, Matt, Eskic, Dejan, Kim, Keuntae, Ewing, Reid, Liu, Jenny H., Berggren, Matt, Mumuni, Zakari Year: 2015 In this report, we present research that measures the outcomes of TOD areas in relation to their metropolitan area controls with respect to (1) jobs by sector; (2) housing choice for household types based on key demographic characteristics; (3) housing affordability based on transportation costs; and (4) job-worker balance as a measure of accessibility. Prior literature has not systematically evaluated TOD outcomes in these respects with respect to light rail transit (LRT), commuter rail transit (CRT), bus rapid transit (BRT), and streetcar transit (SCT) systems. Our analysis helps close some of these gaps. We apply our analysis to 23 fixed-guideway transit systems operating in 17 metropolitan areas in the South and West that have one or more of those systems. We find: (1) Most TOD areas gained jobs in the office, knowledge, education, health care and entertainment sectors, adding more than $100 billion in wages capitalized over time; (2) In assessing economic resilience associated with LRT systems, jobs continued to shift away from TOD areas before the Great Recession, the pace slowed during the recession but reversed during recovery, leading us to speculate that LRT TOD areas may have transformed metropolitan economies served by LRT systems; (3) Rents for offices, retail stores and apartments were higher when closer to SCT systems, had mixed results with respect LRT systems, but were mostly lower with respect to CRT systems (our BRT sample was too small to evaluate); (4) SCT systems performed best in terms of increasing their TOD-area shares of metropolitan population, households and householders by age, housing units, and renters with BRT systems performing less well while LRT and CRT systems experienced a much smaller shift in the share of growth; (5) Household transportation costs as a share of budgets increase with respect to distance from LRT transit stations to seven miles, suggesting the proximity to LRT stations reduces total household transportation costs; (6) Emerging trends that may favor higher-wage jobs locating in TOD areas over time than lower- or middle-wage jobs, perhaps because TOD areas attract more investment which requires more productive, higher-paid labor to justify the investment; and (7) The share of workers who commute 10 minutes or less to work increases nearly one-half of 1 percent for each half-mile their resident block group is to an LRT transit station, capping at a gain of 1.3 percent, which is not a trivial gain. Our report summarizes case studies of 23 transit systems and three journal articles based on our research. Title: Do TODs Make a Difference? Author: Nelson, Arthur C., Miller, Matt, Eskic, Dejan, Kim, Keuntae, Ewing, Reid, Liu, Jenny H., Berggren, Matt, Mumuni, Zakari Year: 2015 In this report, we present research that measures the outcomes of TOD areas in relation to their metropolitan area controls with respect to (1) jobs by sector; (2) housing choice for household types based on key demographic characteristics; (3) housing affordability based on transportation costs; and (4) job-worker balance as a measure of accessibility. Prior literature has not systematically evaluated TOD outcomes in these respects with respect to light rail transit (LRT), commuter rail transit (CRT), bus rapid transit (BRT), and streetcar transit (SCT) systems. Our analysis helps close some of these gaps. We apply our analysis to 23 fixed-guideway transit systems operating in 17 metropolitan areas in the South and West that have one or more of those systems. We find: (1) Most TOD areas gained jobs in the office, knowledge, education, health care and entertainment sectors, adding more than $100 billion in wages capitalized over time; (2) In assessing economic resilience associated with LRT systems, jobs continued to shift away from TOD areas before the Great Recession, the pace slowed during the recession but reversed during recovery, leading us to speculate that LRT TOD areas may have transformed metropolitan economies served by LRT systems; (3) Rents for offices, retail stores and apartments were higher when closer to SCT systems, had mixed results with respect LRT systems, but were mostly lower with respect to CRT systems (our BRT sample was too small to evaluate); (4) SCT systems performed best in terms of increasing their TOD-area shares of metropolitan population, households and householders by age, housing units, and renters with BRT systems performing less well while LRT and CRT systems experienced a much smaller shift in the share of growth; (5) Household transportation costs as a share of budgets increase with respect to distance from LRT transit stations to seven miles, suggesting the proximity to LRT stations reduces total household transportation costs; (6) Emerging trends that may favor higher-wage jobs locating in TOD areas over time than lower- or middle-wage jobs, perhaps because TOD areas attract more investment which requires more productive, higher-paid labor to justify the investment; and (7) The share of workers who commute 10 minutes or less to work increases nearly one-half of 1 percent for each half-mile their resident block group is to an LRT transit station, capping at a gain of 1.3 percent, which is not a trivial gain. Our report summarizes case studies of 23 transit systems and three journal articles based on our research. Close

• 109. [Article] Coordinated Population Forecast for Wallowa County, its Urban Growth Boundaries (UGB), and Area Outside UGBs 2016-2066

  Wallowa County’s total population has declined since 2000 at an average annual rate of about threetenths of one percent between 2000 and 2010 (Figure 1); however, Enterprise and Joseph experienced population ...

  Citation × Citation Citation Abstract Copy Title: Coordinated Population Forecast for Wallowa County, its Urban Growth Boundaries (UGB), and Area Outside UGBs 2016-2066 Author: Portland State University. Population Research Center, Ruan, Xiaomin, Proehl, Risa, Jurjevich, Jason R., Rancik, Kevin, Kessi, Janai, Tetrick, David, Michel, Julia Year: 2016 Wallowa County’s total population has declined since 2000 at an average annual rate of about threetenths of one percent between 2000 and 2010 (Figure 1); however, Enterprise and Joseph experienced population growth during the 2000s. In spite of population increases in the two most populous UGBs, the area outside UGBs declined by nearly one percent per year between 2000 and 2010, losing nearly 200 persons and effectively driving countywide population decline. Even so, Wallowa County’s population has slightly increased in recent years, growing by roughly 100 people between 2010 and 2015 (Figure 2). Wallowa County’s population decline in the 2000s was the result of a natural decrease and periods of net out-migration. The larger number of deaths relative to births has led to a natural decrease (more deaths than births) in every year from 2000 to 2015 (Figure 12). While net migration fluctuated dramatically during the last decade, net in-migration has been consistent during recent years, contributing to a population increase. Total population in Wallowa County as a whole will likely increase in the near-term (2015 to 2035), driven by population growth in Enterprise, Joseph, and the area outside UGBs (Figure 1). However, in the remaining 31-year period, the county is forecast to see population decline. A rise in the magnitude of natural decrease and slow growth of net in-migration are expected to cause population growth to reach its peak in 2025, turning to population decline through the remainder of the forecast period. An aging population is expected to not only lead to an increase in deaths, but a smaller proportion of women in their childbearing years will likely result in a long-term decline in births. Net migration is expected to remain relatively steady throughout the forecast period, but will not offset natural decrease after 2025. Title: Coordinated Population Forecast for Wallowa County, its Urban Growth Boundaries (UGB), and Area Outside UGBs 2016-2066 Author: Portland State University. Population Research Center, Ruan, Xiaomin, Proehl, Risa, Jurjevich, Jason R., Rancik, Kevin, Kessi, Janai, Tetrick, David, Michel, Julia Year: 2016 Wallowa County’s total population has declined since 2000 at an average annual rate of about threetenths of one percent between 2000 and 2010 (Figure 1); however, Enterprise and Joseph experienced population growth during the 2000s. In spite of population increases in the two most populous UGBs, the area outside UGBs declined by nearly one percent per year between 2000 and 2010, losing nearly 200 persons and effectively driving countywide population decline. Even so, Wallowa County’s population has slightly increased in recent years, growing by roughly 100 people between 2010 and 2015 (Figure 2). Wallowa County’s population decline in the 2000s was the result of a natural decrease and periods of net out-migration. The larger number of deaths relative to births has led to a natural decrease (more deaths than births) in every year from 2000 to 2015 (Figure 12). While net migration fluctuated dramatically during the last decade, net in-migration has been consistent during recent years, contributing to a population increase. Total population in Wallowa County as a whole will likely increase in the near-term (2015 to 2035), driven by population growth in Enterprise, Joseph, and the area outside UGBs (Figure 1). However, in the remaining 31-year period, the county is forecast to see population decline. A rise in the magnitude of natural decrease and slow growth of net in-migration are expected to cause population growth to reach its peak in 2025, turning to population decline through the remainder of the forecast period. An aging population is expected to not only lead to an increase in deaths, but a smaller proportion of women in their childbearing years will likely result in a long-term decline in births. Net migration is expected to remain relatively steady throughout the forecast period, but will not offset natural decrease after 2025. Close

• 110. [Article] Coordinated Population Forecast for Umatilla County, its Urban Growth Boundaries (UGB), and Area Outside UGBs 2016-2066

  Umatilla County’s total population has grown steadily since 2000, with average annual growth rates near one percent between 2000 and 2010 (Figure 1); however, some of its sub-areas experienced more rapid ...

  Citation × Citation Citation Abstract Copy Title: Coordinated Population Forecast for Umatilla County, its Urban Growth Boundaries (UGB), and Area Outside UGBs 2016-2066 Author: Portland State University. Population Research Center, Ruan, Xiaomin, Proehl, Risa, Jurjevich, Jason R., Rancik, Kevin, Kessi, Janai, Tetrick, David, Michel, Julia Year: 2016 Umatilla County’s total population has grown steadily since 2000, with average annual growth rates near one percent between 2000 and 2010 (Figure 1); however, some of its sub-areas experienced more rapid population growth during the 2000s. Hermiston, the most populous UGB, and Umatilla UGB posted the highest average annual growth rates at 2.1 and 2.8 percent, respectively, during the 2000 to 2010 period. Umatilla County’s positive population growth in the 2000s was the result of a steady natural increase and periods of substantial net in-migration. A larger number of births relative to deaths led to a natural increase (more births than deaths) in every year from 2000 to 2015 (Figure 12). While net in-migration fluctuated dramatically during the early and middle years of the last decade, the number of in-migrants has been slightly more stable during recent years, contributing to a population increase. Even so the natural increase continues to account for most of the population growth. Total population in Umatilla County as a whole as well as within its sub-areas will likely grow at a slightly faster pace in the near-term (2016 to 2035) compared to the long-term (2035-2066) (Figure 1). The tapering of growth rates is driven by an aging population—a demographic trend which is expected to contribute to an increase in deaths. Even so, natural increase is expected to persist, combining with steady in-migration for continued strong population growth. Umatilla County’s total population is forecast to increase by nearly 13,300 over the next 19 years (2016- 2035) and by close to 36,800 over the entire 50-year forecast period (2016-2066). All sub-areas are expected to experience population growth during the forecast period. Title: Coordinated Population Forecast for Umatilla County, its Urban Growth Boundaries (UGB), and Area Outside UGBs 2016-2066 Author: Portland State University. Population Research Center, Ruan, Xiaomin, Proehl, Risa, Jurjevich, Jason R., Rancik, Kevin, Kessi, Janai, Tetrick, David, Michel, Julia Year: 2016 Umatilla County’s total population has grown steadily since 2000, with average annual growth rates near one percent between 2000 and 2010 (Figure 1); however, some of its sub-areas experienced more rapid population growth during the 2000s. Hermiston, the most populous UGB, and Umatilla UGB posted the highest average annual growth rates at 2.1 and 2.8 percent, respectively, during the 2000 to 2010 period. Umatilla County’s positive population growth in the 2000s was the result of a steady natural increase and periods of substantial net in-migration. A larger number of births relative to deaths led to a natural increase (more births than deaths) in every year from 2000 to 2015 (Figure 12). While net in-migration fluctuated dramatically during the early and middle years of the last decade, the number of in-migrants has been slightly more stable during recent years, contributing to a population increase. Even so the natural increase continues to account for most of the population growth. Total population in Umatilla County as a whole as well as within its sub-areas will likely grow at a slightly faster pace in the near-term (2016 to 2035) compared to the long-term (2035-2066) (Figure 1). The tapering of growth rates is driven by an aging population—a demographic trend which is expected to contribute to an increase in deaths. Even so, natural increase is expected to persist, combining with steady in-migration for continued strong population growth. Umatilla County’s total population is forecast to increase by nearly 13,300 over the next 19 years (2016- 2035) and by close to 36,800 over the entire 50-year forecast period (2016-2066). All sub-areas are expected to experience population growth during the forecast period. Close