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Feasibility Study on Mixed-Use Transit-Joint Development in Growing Urban Areas, Using Meaningful Urban-Form and Nonurban-Form Variables

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Title:
Feasibility Study on Mixed-Use Transit-Joint Development in Growing Urban Areas, Using Meaningful Urban-Form and Nonurban-Form Variables
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ZHANG, SHAO-MING ( Author, Primary )
Copyright Date:
2008

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Subjects / Keywords:
Censuses ( jstor )
Entropy ( jstor )
Housing ( jstor )
Housing units ( jstor )
Land use ( jstor )
Metropolitan areas ( jstor )
Real estate ( jstor )
Regression analysis ( jstor )
Ridership ( jstor )
Transportation ( jstor )

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University of Florida
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University of Florida
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Copyright Shao-Ming Zhang. Permission granted to University of Florida to digitize and display this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
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8/31/2010
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658201976 ( OCLC )

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FEASIBILITY STUDY ON MIXED-USE TRANSIT-JOINT DEVELOPMENT IN GROWING URBAN AREAS, USING MEANINGFUL URBAN-FORM AND NONURBAN-FORM VARIABLES By SHAO-MING ZHANG A THESIS PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF ARTS IN URBAN AND REGIONAL PLANNING UNIVERSITY OF FLORIDA 2005

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Copyright 2005 by Shao-Ming Zhang

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This document is dedicated to my parents.

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ACKNOWLEDGMENTS I would first like to thank my family and friends for their constant encouragement and support. Next, I would like to thank my advisor, Dr. Ruth Lorraine Steiner. Her enthusiasm for solving urban problems with a multi-disciplinary perspective and her profound knowledge of transit-oriented development has greatly influenced me. I would also like to thank my graduate committee, Dr. James Nicholas and Dr. Paul Zwick, for their help and guidance. iv

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TABLE OF CONTENTS page ACKNOWLEDGMENTS.................................................................................................iv LIST OF TABLES............................................................................................................vii LIST OF FIGURES.........................................................................................................viii ABSTRACT.......................................................................................................................xi CHAPTER 1 INTRODUCTION........................................................................................................1 Background and Motivation for This Research............................................................1 Problem Statement........................................................................................................8 Research Question........................................................................................................9 Organization of the Thesis..........................................................................................10 2 HOW DO PEOPLE DEFINE TOD AND TJD?.........................................................11 Defining TOD.............................................................................................................11 History of TOD....................................................................................................11 Various Definitions of TOD................................................................................12 Peter Calthorpe’s “TOD Idealism”......................................................................14 “From Rhetoric to Reality”.................................................................................22 Experiencing TJD.......................................................................................................25 Defining TJD.......................................................................................................25 Effects of Rail TJDs on House Prices.................................................................27 Financial Initiatives for TJD................................................................................28 Conclusions.................................................................................................................29 Choosing Rail TJD for the Study........................................................................29 Defining the Study Area......................................................................................30 Choosing Two Regression Models for the Study................................................30 Introducing the Household Income Level as a Variable into the Regression Analysis............................................................................................................31 3 HOW DO PEOPLE MEASURE THE LEVEL OF LAND-USE MIX?....................32 Frank & Pivo’s Entropy Index....................................................................................32 v

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Cervero & Kockelman’s Dissimilarity Index.............................................................33 Conclusions.................................................................................................................33 4 RESEARCH METHODOLOGY...............................................................................35 Key Research Questions and Overview of Research Approach.................................35 Conceptual Model.......................................................................................................36 Data Source.................................................................................................................38 Data Analysis..............................................................................................................38 Urban-Form Variables.........................................................................................38 Density.........................................................................................................38 Level of Land-Use Mix................................................................................38 Non-Urban-Form Variables.................................................................................39 Property Information...........................................................................................41 5 DESCRIPTION OF CASE STUDY AREA...............................................................43 Describing the Case Study Area—Atlanta metropolitan Area...................................43 Atlanta Overview.................................................................................................43 Socio-Economic Characteristics of the Region...................................................46 MARTA and TJDs in the Region........................................................................52 6 RESULTS AND DISCUSSIONS...............................................................................60 Results.........................................................................................................................60 Discussions.................................................................................................................63 7 CONCLUSIONS AND AREAS FOR FURTHER RESEARCH...............................69 Conclusions.................................................................................................................69 Areas for Further Research.........................................................................................71 APPENDIX A CALCULATING THE LEVEL OF LAND-USE MIX..............................................73 B APPLYING THE VALUES FROM THE US CENSUS DATA TO THE PARCEL DATA.........................................................................................................78 LIST OF REFERENCES...................................................................................................81 BIOGRAPHICAL SKETCH.............................................................................................84 vi

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LIST OF TABLES Table page 6-1 Variables used in the first regression model............................................................60 6-2 “Taxable value of the property” as dependent variable...........................................61 6-3 Variables used in the second regression model........................................................62 6-4 Coefficients of the independent variables with the “number of workers using transit for traveling” as its dependent variable.........................................................63 6-5 Percentage of population in each age category (Source: Census 2000 Supplementary Survey)............................................................................................66 6-6 Percentage of different types of household (Source: Census 2000 Supplementary Survey).....................................................................................................................66 6-7 Percentage of different types of housing units (Source: Census 2000 Supplementary Survey)............................................................................................67 vii

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LIST OF FIGURES Figure page 2-1 Calthorpe’s diagram of TOD.................................................................................15 2-2 TODs with different core commercial areas sizes and locations...........................16 2-3 Different housing types with various residential densities....................................17 2-4 Different housing types for TOD ..........................................................................17 2-5 Various combinations of housing types with the same average residential density (Source: Calthorpe, 1993).........................................................................17 2-6 Diagram of second areas in TOD ..........................................................................18 2-7 Minimum amount of public, core commercial and residential uses in TODs ......18 2-8 Diagram of TODs’ relationship to transit and circulation ....................................19 2-9 Diagram of urban TOD .........................................................................................19 2-10 Diagram of neighborhood TOD ............................................................................20 2-11 TODs in three types of locations ..........................................................................20 2-12 Example of TOD design in the redevelopable sites ..............................................21 2-13 Example of TOD design in the infill sites ............................................................21 2-14 Example of TOD design in the new growth areas ................................................21 2-15 Design features of TODs.......................................................................................21 4-1 Conceptual Model One..........................................................................................37 4-2 Conceptual Model Two..........................................................................................37 4-3 A map showing the parcel data overlaying with the US Census data...................41 5-1 The location of the Atlanta metropolitan area in the United States.......................44 viii

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5-2 Transportation access to the Atlanta metropolitan area.........................................45 5-3 Exciting nightlife, Atlanta, Georgia.......................................................................46 5-4 Evening falls over Atlanta, Georgia.......................................................................46 5-5 Age distribution of people in the Atlanta metropolitan area..................................47 5-6 Types of households in the Atlanta metropolitan area...........................................48 5-7 Geographic mobility of residents of the Atlanta metropolitan area.......................49 5-8 Poverty rates...........................................................................................................50 5-9 Types of Housing Units.........................................................................................51 5-10 Occupants with housing cost burden.....................................................................51 5-11 Time-lapse photo showing congestion and brake lights on freeway heading downtown...............................................................................................................53 5-12 Rail map of MARTA.............................................................................................54 5-13 MARTA train moving in front of the Atlanta Skyline..........................................55 5-14 Rail operator in front of his train...........................................................................55 5-15 Site of Lindberg City Center..................................................................................57 5-16 Master plan of Lindberg City Center.....................................................................58 5-17 How retail shops, restaurants, offices, and multifamily residential space can be connected through the Main Street in Lindberg City Center.................................58 6-1 Difference between the expected cumulative probability and the observed cumulative probability in the first regression model for the case study................68 A-1 Selecting parcels in each buffer.............................................................................73 A-2 Map showing overlapped buffers...........................................................................74 A-3 Square footages of buildings on selected parcels..................................................75 A-4 Statistical tools used to get total square footage of the office space inside every buffer......................................................................................................................75 A-5 Applying the entropy index values to the parcels inside each buffer....................76 A-6 Joining the tables for the level of land use mix.....................................................76 ix

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A-7 Area around Five Points Station with all the GIS layers.......................................77 B-1 One census block group being selected.................................................................78 B-2 Selecting parcels with their centers in the selected census block..........................79 B-3 Census block group ID number being applied to the selected parcels inside the census block group.................................................................................................79 B-4 Joining the tables for the nonurban-form variables...............................................80 x

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Abstract of Thesis Pres ented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Master of Arts in Urban and Regional Planning FEASIBILITY STUDY ON MIXED-USE TRANSIT-JOINT DEVELOPMENT IN THE GROWING URBAN AREAS, USING MEANINGFUL URBAN-FORM AND NON-URBAN-FORM VARIABLES Shao-Ming Zhang August 2005 Chair: Ruth Steiner Major Department: Urban and Regional Planning Transit-Oriented Development (TOD), known as a form of walkable, mixed-used, location-efficient development with convenient transit service, has been a buzzword in the fields of urban planning, architecture and real estate development for over a decade in the U.S. Although separately confronted with their individual financial barriers, public-private partnerships between the transit agencies and the private developers are emerging as a promising way to implement TODs. Often seen in the Mixed-Used Transit-Joint Development (TJD), a subset of TOD, the public-private partnerships can be further categorized based on their financial agreements, such as ground lease, air-rights lease, operational cost sharing, construction coast sharing, and station connection-fee programs. In order to create partnerships that allow the financial benefits to translate into profitability for both the private developer and the transit agencies, the financial benefits that TJDs’ major characteristics, including high-density and mixed land-use, would bring to the project need to be understood first. xi

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In this research, two multiple regression model were constructed to examine the effects of TJD projects’ major characteristics on real estate values and the transit ridership, controlling for proximity to transit. When searching the meaningful variables for the regression analysis, the author improves the current model in measuring the level of land-use mix at the parcel level based on Frank & Pivo’s Entropy Index model. The results from the case study in the Atlanta Metropolitan area challenge the hypothesis that higher density and greater level of land-use mix would increase the financial return from the TJD projects. xii

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CHAPTER 1 INTRODUCTION Background and Motivation for This Research The 2000 U.S. Census revealed that the country is growing much faster than expected. If this surprising trend continues, by 2050 the United States will have more than half a billion people –double its current size (Half a billion 2002). The rapid population growth, urban sprawl, suburbanization and traffic congestions cost people more and more time on the road. After housing, transportation is now the second largest expense for American families (Candy 2003). Inefficient transportation in the urban areas of the United States has been effectively restricting many American families’ access to the resources they need to increase their wealth. In many areas of the United States, traffic woes have generated a cohort of individuals who are drawn to the idea of living near public transit and enjoying a less stressful commute to work (Transit Cooperative Research Program (TRCP) 2002). While the American love affair with the automobile is not yet over, concerns about the negative effects of automobiles –on the physical environment, on public health, on the quality of life, and on the relationships among people in the community –have led to policies and projects that promote more public transit use, including heavy and light rail, bicycle paths and public walkways. A greater federal investment in non-highway-related projects, such as public transit, intercity rail, bicycling, walking, and inter-modal connections, has also been reported (STPP 2003). The renewed interest in transit use and transit investment, together with the resurgence of investment in America’s downtown areas, calls for a diversification of 1

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2 real estate projects and a type of development know as transit-oriented development (TOD), a form of pedestrian-friendly, mixed-used, and location-efficient development with convenient transit service (Belzer and Autler 2002). The Intermodal Surface Transportation Efficiency Act (ISTEA) in 1991 and the Transportation Equity Act for the 21st Century (TEA ) in 1998 laid the groundwork for building safe, environmentally–sound, transit-oriented communities. Several federal initiatives, such as a more permissive interpretation of the federal common-grant rules for new mixed-used transit-joint development (TJD) and the location efficient mortgage (LEM) program, underwritten by Fannie Mae, have also sought to leverage TOD. The shrinking household size and the increasing share of foreign immigrants in the United States have contributed to the rising popularity of TOD. Growing numbers of singles and single-parent families, childless couples, and “empty-nesters” seeking to downsize their living quarters, along with influxes of foreign immigrants coming from countries with a heritage of transit –oriented living, have created ready-made consumer markets for TODs (Calthorpe 1993; Transit Cooperative Research Program (TRCP) 2002). Furthermore, a receptive policy environment, together with changing demographic characteristics in favor of TOD, have heightened public interest in TOD as a means of redressing a number of urban problems, such as traffic congestion, affordable housing shortages, air pollution, and incessant urban sprawl (Transit Cooperative Research Program (TRCP) 2002). A review of international trends also uncovers latent factors that challenge the conventional strategy of building more highways, and which may further catalyze the United States’ transition from an auto-dominated country to a more transit-dependent society. In China and India, together home to 2.3 billion people, motor vehicle use is

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3 skyrocketing. Car sales in China increased seventy three percent in 2003 alone, and by 2030 China is projected to have more motor vehicles than the U.S. (INFORM Reports 2004). As a consequence of such enormous transportation growth, China and India are vying with the United States for rapidly dwindling oil reserves. The world’s second and sixth largest oil-consuming nations respectively, they are both expected to see annual growth rates of about four percent in oil consumption— the fastest rates in the world —over the next two decades (INFORM Reports 2004). Most energy supply experts conclude that oil supplies are likely to peak within the next ten to thirty years. However, none of these estimations factor in the unexpected increases from new oil-based transportation systems in China and India. The foreseeable shortfall of oil supply in the near future, and the resultant significant rise in gasoline prices, would likely change people’s view of public transit as an alternative transportation mode in the United States, a shift that could create a vast market worldwide for TODs in the long run. Despite the nuances in defining TOD by different transit authorities, successful pilot cases in Portland, Oregon, San Francisco, California, Dallas, Texas, Denver, Colorado, and Atlanta, Georgia, have illustrated various implementations of TOD principles throughout the country. Noticeably, financial support from the government at various levels has played a very important role in guaranteeing the actual completions of these projects. However, the nation’s switch from highway-dominated development to transit-oriented development comes at a time when financial resources at every level — federal, state, and local — are shrinking. Looming deficits and unanticipated expenditures brought on by the September 11th attacks threaten the financial stability of most domestic programs. In spite of the appealing aspects of TOD, critics raise doubts

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4 about the financial feasibility of putting the “idealistic” theory into practice, further jeopardizing the possibility of widespread implementation of TOD principles. To date, record has shown some failed TOD cases. Laguna West, for instance, was originally touted as a TOD prototype for the suburbs of Sacramento, but a downturn in the real estate market led to eventual bankruptcy (Transit Cooperative Research Program (TRCP) 2002). Anticipated TODs in some parts of the United States have failed to break ground because of unrealistic market expectations (Transit Cooperative Research Program (TRCP) 2002). Harsher critiques concerning the financial barriers to TOD implementation even reprobate the idea of using transit systems in most parts of the country. Andrew Jakes once said in his article (“Transit success? It’s the real estate, stupid”): “The San Francisco cable car, the Las Vegas bus system and the existing Seattle monorail transit systems make money. But the overwhelming majority of U.S. transit systems, particularly newly installed West Coast systems are big financial losers” (Jakes 1998). On one side, concerns about financial loss from “risky” TODs discourage governments and public transit authorities from providing more funding for such development. Locational liability of the areas covered by existing transit system, delay of station-area development, non-supportive government policies including exclusionary zoning and suburban-like building codes, difficulties in cross-jurisdictional cooperation, insufficient consideration of the real estate market, and lack of market feasibility studies, all contribute to hesitant public involvement in spurring TODs (Porter 1997). On the other side, private developers, another major financial source for transit-oriented developments, make decisions based primarily on the real estate market rather than the

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5 presence of transit. Even if there appears to be a burgeoning demand for transit-oriented development, private developers, lenders, and investment groups must be sufficiently convinced that this demand is real and sustainable before they risk capital creating a TOD (Transit Cooperative Research Program (TRCP) 2002). Although separately confronted with their individual financial barriers, public-private partnerships are emerging as a promising way of implementing TODs. The public sector has the power to resolve land-assembly problems, ensure that the site is development-ready, ease the entitlement process, and even contribute land and infrastructure costs, all of which bring developers not only financial resources, but also the confidence of being grounded in the reality of the local market (Dunphy, Myerson, Pawlukiewicz 2003). Together with the real estate savvy, contacts with end users, and understanding of financial resources contributed by private developers, these assets can help engender publicprivate partnerships that provide opportunities to set mutual expectations and to share risks, costs, and rewards (Dunphy, Myerson, Pawlukiewicz 2003). Financial outcomes could be further enhanced through tax increment financing, and government and foundation grant funding. The formation of public-private partnerships is described in the literature as the best solution to the problem of successfully implementing TODs (Transit Cooperative Research Program (TRCP) 2004). Mixed-used transit-joint development (TJD), defined in the recent literature as a subset of TOD, is a form that is project-specific and takes place either on or adjacent to transit-agency land, and represents the most frequently discussed scenario for public-private partnership in TOD projects (Transit Cooperative Research Program (TRCP) 2004). In spite of the varying definitions of both TOD and TJD by different institutions,

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6 the distinction between the two closely related terms is so ambiguous that quite often transit agencies (and other stakeholder groups) use the terms interchangeably (Transit Cooperative Research Program (TRCP) 2004). In general, their differences are in regards to scale: TOD usually encompasses multiple city blocks, representing a neighborhood in size and character, while TJD tends to be project-specific, often occurring within a city block and tied to a specific real estate development (Transit Cooperative Research Program (TRCP) 2002). Public transit agencies have been given the flexibility of operating TJD projects by FTA’s Circular 9300.1: “FTA encourages incidental uses of real property that can raise additional revenues for the transit system or, at a reasonable cost, enhance system ridership. FTA approval is required for these incidental uses of real property which must be compatible with the original purposes of the grant” (FTA 1997). This provision has been interpreted as empowering transit agencies to sell land holdings financed by federal grants without having to return proceeds as long as the funds are used to shape communities being served by transit (Federal Register Notice 1997). In addition to the pioneering TJD practice of the Washington metropolitan Area Transit Authority (WMATA) and San Francisco’s Bay Area Rapid Transit District (BART), various other transit agencies in the United States have also been implementing TJD projects. Often occurring on a transit agency’s property, these TJDs can be further categorized based on their lease types and financial agreements, including ground lease, air-rights lease, operational cost sharing, construction coast sharing, station connection-fee programs, or other initiatives that promote real-estate development at or near transit stations to the mutual benefit of public and private interests (Transit Cooperative

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7 Research Program (TRCP) 2004). Whether in the form of ground or air rights leasing, station connection fees, or the sharing of capital-construction costs, experience shows that successful TJDs typically involve carefully crafted collaborations among many individuals, organizations, and institutions with vested interests in the outcomes, including developers, lenders, transit agencies, local and regional planning organizations, and public interest groups (Knight and Trygg 1977; Porter 1997). An early review of TJD identified two of the main obstacles from the point of the view of elected officials: doubts about the profitability of TJD, and shortages of qualified staff members who can package TJD deals and produce financial pro forma (Keefer 1984). Involvement of different interest groups and various lease types and financial agreements makes it even more difficult for the related agencies to prepare financial pro forma, which is used to prove the profitability and the feasibility of a TJD. A necessary feasibility study on such development would provide practitioners both in public institutions and private sectors a more sound foundation for further cooperation in TOD and TJD projects. In addition to serving this purpose, this research is also the author’s attempt to integrate knowledge from different disciplines for solutions to the problems of communities. Creative and feasible solutions are more likely to arise from comprehensive knowledge and philosophical thinking in various realms including politics, economics, ecology, sociology, technology, and aesthetics. All of these concerns should be viewed holistically for a successful project. In reality architects, planners, landscape architects, traffic engineers, civil engineers, biologists, developers, environmentalists, bankers, and even neighborhood groups too often seek to optimize

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8 only a specific aspect of a project, without looking beyond their individual issues, which are in fact interconnected (Calthorpe 1993). Problem Statement A growing body of literature shows the financial benefits of TJDs. The challenge that remains is how to create partnerships that allow those benefits to translate into profitability for both the private sector (the developer), and the public sector (the transit agencies) that are often the landowners of such developments. There are several keys to the negotiation of a successful private-public partnership for TJD projects: Quantification of the increases in property values, resulting from both the transit connection and TJD arrangements. Calculation of the financial benefits from increased ridership associated with TJDs. Consideration of the increased costs of such projects, because of their high-density and mixed-used (Dunphy, Myerson, Pawlukiewicz 2003). Theory maintains that the savings in travel-time and enhanced accessibility conferred by TJD should get capitalized into higher land values and market rents. Numerous studies have also demonstrated that all else being equal, being adjacent to rail stations raises property values, though to varying degrees (Transit Cooperative Research Program (TRCP) 2002). On the other hand, the effects of TJDs themselves and their major characteristics (particularly high-density and mixed land-use) on real estate values, controlling for proximity to transit, has been examined only sparingly (Transit Cooperative Research Program (TRCP) 2002). To understand the risks and profits of a development, real estate market analysis is often used to identify the demand and supply, usually for a particular property type (e.g., apartment buildings, or offices). Involving more than the sum of the parts of different property types, mixed-use development is complex for both developers

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9 and public agencies. Compounded with the difficulties of measuring the complicated impacts of a transit system on developments, quantifying the increase in property values for TJD arrangements under particular market conditions using a universally applicable equation is almost an impossible task. Normally multiple regression models that partial out the unique effect of proximity to transit, as well as the presence of TJD characteristics, are used in explaining increases in property values, thus approximating the reality. In order to quantify the increase in transit ridership, the impacts of TJD on people’s transportation modal choice also has to be studied. The results of such a study would provide calculated financial benefits from increased ridership within a certain market frame, which could be an important factor in the decisions of transit agencies. Because the synergy among different uses and functions required for TJD is extremely difficult to achieve, TJDs almost always involve more complexity and higher costs than other forms of infill development (Belzer and Autler 2002). Therefore, balancing the financial benefits and increased costs of TJD projects presents another important concern in creating successful partnerships. Research Question This paper answers the following general question: What financial benefits would TJDs’ major characteristics, specifically high-density and mixed land-use, bring to a project? This broad question is answered by considering the following related questions: To what extent will high-density and mixed land-use increase the property values of TJDs? Will an increase in the density and the level of land-use mix of TJDs bring a higher transit ridership?

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10 What is the impact of other factors, including the household income level of the neighborhood around TJDs, on the financial return? These questions are addressed through two regression analyses. One multiple regression model was used to examine the effects of TJD projects’ major characteristics, high-density and mixed-use, on real estate values, controlling for proximity to transit. Household income level was also included in this regression model to understand its role in the feasibility of TJD projects. Another multiple regression model was developed to understand the relationship between transit ridership and TJD arrangements under certain socio-economic situations. Organization of the Thesis This thesis is organized into seven chapters including this introduction. The first three chapters present the background of the study and review previous work on the topic. Chapter 2 reviews the literature on the definitions and theories of TOD and TJD. Chapter 3 reviews previous work on measuring the level of land-use mix. In Chapter 4 and 5, the framework for this research is outlined. Chapter 4 defines the research methodology and Chapter 5 describes the case study area. The results of the research are presented and discussed in Chapter 6. Chapter 7 is a summary of the research and a discussion of areas for further research.

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CHAPTER 2 HOW DO PEOPLE DEFINE TOD AND TJD? Although varying in scope and specificity, most TOD and TJD definitions share several common elements, including high-density and mixed land-use. In this chapter, different definitions and theories of TOD and TJD are reviewed. Defining TOD Before the definitions and the theories of TJD, a subset of TOD, are discussed, the history of TOD and various definitions of TOD must first be introduced. Based on Calthorpe’s framework of TOD theory, Belzer and Autler define TOD with both adaptiveness and flexibility. History of TOD By putting TOD in its historical context, Belzer and Autler depict evolving definitions and objectives of TOD through each historical TOD phase, namely, Development-Oriented Transit in the early twentieth century, Auto-Oriented Transit during the post-war years, today’s Transit-Related Development, and tomorrow’s Transit-Oriented Development (Belzer and Autler 2002). By the early 1900s, electric streetcar systems had emerged in many cities like New York and San Francisco, contributing to the growth of America’s suburbs (Middleton 1967). With typical features such as a transit depot and public space in the center of the neighborhood, small cottage-type houses, and a street pattern and scale that allowed convenient walking distances to transit, the early street suburbs were successful examples of pedestrian access to transit service that connects to downtown jobs and neighborhood 11

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12 services (Cervero 1993). Although some may date transit-oriented development projects in the United States back to the late nineteenth and early twentieth centuries, Belzer and Autler described those railroad and streetcar suburbs more aptly as “development-oriented transit” than “transit-oriented development” because private developers built transit serving and adding value to their development rather than vice-versa. Starting in the 1930s, American’s love affair with the automobile and highways, however, broke the interdependence of housing, jobs and transit inherent in early streetcar suburbs (Belzer and Autler 2002). Precipitous declines in transit use and abandonment of many rail systems characterized the post-war auto-oriented society in the United States. With the exception of some commuter suburbs around older cities, most transit became a last resort rather than a reliable transportation option tied to development (Belzer and Autler 2002). Many projects today are named transit-related development merely to acknowledge the physical connection between transit and development. “Transit Adjacent Development” (TAD) has emerged in much of the written literature to characterize such development that lacks any functional connectivity to transit, whether in terms of land-use composition, means of station access, or site design (Parsons, Brinckerhoff, Quade and Douglas, Inc. 2001). Various Definitions of TOD Besides Belzer and Autler ’s bona fide TOD as tomorrow’s new paradigm of development, various terms have surfaced to convey the idea of TOD, like “transit villages,” “transit-supportive development,” and “transit-friendly design” (Transit Cooperative Research Program (TRCP) 2002). A sample of TOD definitions can be found in the literature:

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13 Development within a specified geographical area around a transit station with a variety of land uses and a multiplicity of landowners. (Salvesen 1996, p.37) A mixed-use community that encourages people to live near transit services and to decrease their dependence on driving. (Still 2002, p.44) A compact, mixed-use community, centered around a transit station that, by design, invites residents, workers, and shoppers to drive their cars less and ride mass transit more. The transit village extends roughly a quarter mile from a transit station, a distance that can be covered in about 5 minutes by foot. The centerpiece of the transit village is the transit station itself and the civic and public spaces that surround it. The transit station is what connects village residents to the rest of the regionThe surrounding public space serves the important function of being a community gathering spot, a site for special events, and a place for celebrations—a modern-day version of the Greek agora. (Bernick and Cervero 1996, p. 5) A mix of residential, retail and office uses and a supporting network of roads, bicycle and pedestrian ways focused on a major transit stop designed to support a high level of transit use. The key features of TOD include (a) a mixed-use center at the transit stop, oriented principally to transit riders and pedestrian and bicycle travel from the surrounding area; (b) high density of residential development proximate to the transit stop sufficient to support transit operations and neighborhood commercial uses within the TOD; and (c) a network of roads, and bicycle and pedestrian paths to support high levels of pedestrian access within the TOD and high levels of transit use. (Oregon 1995) Although varying in scope and specificity, most TOD definitions share several common elements, such as mixed land use and functional connection to the transit system. Traits that are less universal in the definitions of TOD relate to the notions of “New Urbanism” and “Smart Growth,” including compactness and pedestrian-friendly environment. Admitting the existence of different definitions, interpretations, and implementations of TOD, some observers have sought to categorize TOD projects. White and McDaniel (1999) have identified six forms of TOD projects based on their physical design characteristics: (1) Single-Use Corridors: concentrations of single transit-intensive uses in transit corridors; (2) Mixed-Use Corridors: concentrations of a variety of land uses on a single parcel or group of parcels within a transit corridor; (3) Neo-Traditional Development: development that primarily focuses on design features that

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14 reproduce traditional town or village settings with small lots, narrow streets, detached parking behind houses, reduced setbacks, and front porches; (4) Transit-Oriented Development: compact, mixed-used development concentrated near transit stops; (5) Hamlet or Village Concept: single-family homes clustered around a central green area or commons; and (6) Purlieu: A development of approximately 150 acres and 7,000 residents, with comprehensive and detailed design regulations. Lacking quantitative interpretations of different TOD implementations, practitioners generally find it difficult to analyze and evaluate ambiguously defined TOD projects. One person’s TOD can be another person’s TAD. For developers, architects, designers and planners, quantifiable guidelines and references are necessary in order to rigorously implement TOD projects. Peter Calthorpe’s “TOD Idealism” Calthorpe frames modern design theory of TOD with its practical implications in The Next American Metropolis: Ecology, Community, and the American Dream, by providing both quantifiable guidelines and quality urban design projects as examples of TOD. Based on a well-defined TOD concept (Figure 2-1),1 Calthorpe further delineated TOD as a mixed-use (residential, retail, office, open space, and public uses) community within an average 2,000-foot walking distance of a transit stop and core commercial area. Calthorpe’s principles of implementing TOD successfully link his definitions to his guidelines of urban design: To organize growth on a regional level to be compact and transit-supportive; 1 TOD concept is simple: moderate and high-density housing, along with complementary public uses, jobs, retail and services, are concentrated in mixed-use developments at strategic points along the regional transit system.

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15 Figure 2-1: Calthorpe’s diagram of TOD (Source: Calthorpe 1993) To place commercial, housing, jobs, parks, and civic uses within walking distance of transit stops; To create pedestrian-friendly street networks that connect directly to local destinations; To provide a mix of housing types, densities, and costs; To preserve sensitive habitat, riparian zones, and high quality open space; To make public spaces the focus of building orientation and neighborhood activity; and To encourage infill and redevelopment alone transit corridors within existing neighborhoods (Calthorpe 1993). Commercial areas, residential areas, public spaces, and secondary areas are the four major required components of Calthorpe’s TOD design. A mixed-used core commercial area located adjacent to a transit stop is required to provide at a minimum convenience retail and local offices at a minimum, while larger core areas could have more flexible combinations including major supermarkets, restaurants, service commercial, entertainment uses, comparison retail, second-floor residential, and employment-intensive office and light industrial uses. Although noting the variances in the size and

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16 location of TOD projects, Calthorpe strictly defines the ratio of the commercial core areas to the total area of the project. Whether this area functions as community center, neighborhood center, or convenience center, at least ten percent of the total TOD site area and a minimum of 10,000 sq-ft of retail space adjacent to the transit stop are designated as the commercial core area according to Calthorpe’ s design guidelines (Figure 2-2). Figure 2-2: TODs with different core commercial areas sizes and locations (Source: Calthorpe 1993) Housing within a convenient walking distance from core commercial areas and transit stops is designed to meet with the residential density requirement of eighteen dwelling units per acre, averaged by densities of a mix of housing types. By listing a matrix of housing types (Figure 2-3) including small lot single-family, townhouses, duplexes, apartments and condominiums (Figure 2-4), Calthorpe points out the flexibility in housing design and combinations that can satisfy the density requirement of TODs (Figure 2-5).

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17 Figure 2-3: Different housing types with various residential densities (Source: Calthorpe 1993) Figure 2-4: Different housing types for TOD (Source: Calthorpe 1993) Figure 2-5: Various combinations of housing types with the same average residential density (Source: Calthorpe, 1993) Parks, plazas, greens, public buildings, and public services can all be used to serve residents and workers in TODs and neighboring areas as the “Public Uses” component. A “Secondary Area,” according to Calthorpe’ s TOD theory, is the area adjacent to TODs, which may include areas across an arterial and no further than one mile from the core commercial area. Although it may have lower density land uses, the “Secondary Area” is expected to provide multiple direct street and bicycle connections to the transit stop and core commercial area, with a minimum of arterial crossings (Calthorpe 1993) (Figure 2-6).

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18 Figure 2-6: Diagram of second areas in TOD (Source: Calthorpe 1993) To Calthorpe, all TODs must be mixed-use and contain a minimum amount of public, core commercial and residential uses (Figure 2-7). Vertical mixed-use buildings are encouraged, as a bonus to the basic horizontal mixed-use requirement. Figure 2-7: Minimum amount of public, core commercial and residential uses in TODs (Source: Calthorpe 1993) Besides the stated guidelines of density and mixed-use, the relationship between the development and transit is also regulated quantifiably in that the site must be located on an existing or planned trunk transit line or on a feeder bus route within ten minutes transit travel time from a stop on the trunk line (Figure 2-8).

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19 Figure 2-8: Diagram of TODs’ relationship to transit and circulation (Source: Calthorpe 1993) Within these strict definitions and guidelines, Calthorpe provide various alternatives and some flexibility based on the location of a development. According to regional conditions, Calthorpe’s TODs fall into two categories: Urban TOD: located directly on the trunk line transit network: at light rail, heavy rail, or express bus stops. They are suggested to be developed with high commercial intensities, job clusters, and moderate to high residential densities (Figure 2-9). Figure 2-9: Diagram of urban TOD (Source: Calthorpe 1993) Neighborhood TOD: located on a local or feeder bus line within ten minutes transit travel time (no more than three miles) from a trunk line transit stop. They are suggested to have a moderate density residential, service, retail, entertainment, civic, and recreational use.

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20 Figure 2-10: Diagram of neighborhood TOD (Source: Calthorpe 1993) According to the nature and different problems associated with the locations, Calthorpe provided three scenarios for TODs (Figure 2-11): Development in redevelopable sites that could be revitalized with new more intensive uses and transit service, Development in infill sites that are vacant parcels surrounded by existing urban development, Development in new growth areas that are larger, undeveloped properties typically on the city’s periphery (Calthorpe 1993). Figure 2-11: TODs in three types of locations (Source: Calthorpe 1993) Besides descriptions in words, examples of designs for each scenarios and architectural design guidelines provided by Calthorpe greatly enhanced our understanding towards such development (Figure 2-12, Figure 2-13, Figure 2-14, Figure 2-15):

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21 Figure 2-12: Example of TOD design in the redevelopable sites (Source: Calthorpe 1993) Figure 2-13: Example of TOD design in the infill sites (Source: Calthorpe 1993) Figure 2-14: Example of TOD design in the new growth areas (Source: Calthorpe 1993) Figure 2-15: Design features of TODs (Source: Calthorpe 1993)

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22 Together, Calthorpe’s written works and design practice provide a positive example of quantifiable interpretation and detailed guidelines of TOD, backed up with systematic analysis on different levels. However, the gulf between Calthorpe’s appealing theoretical models and the widespread practice of TOD under various and changing situations remains imposing. Laguna West, a master-planned community in southwest Sacramento County, is an example of Calthorpe’s application of TOD. It was originally advertised as a TOD prototype for the suburbs of Sacramento, but a downturn in the real estate market led to eventual bankruptcy (Transit Cooperative Research Program (TRCP) 2002). The ever-changing nature of the real estate market and the diversity of locational constraints demand a more adaptive definition of TOD and newer quantitative approaches to gauge the determinants of successful TOD projects. “From Rhetoric to Reality” By describing TOD as a continuum process under various circumstances with different objectives, Belzer and Autler’s TOD definition achieves a new theoretical understanding incorporating both adaptiveness and flexibility. It provides practitioners with the freedom to devise their own quantifiable guidelines, tied to both the desired functional outcomes of a project and localized situations within a certain timeframe out of a continuous timeline. Belzer and Autler wisely avoid both the pitfalls of rigidness and ambiguity by discussing TOD within a thoughtful framework: 1. A focus on the desired functional outcomes of TOD, not just physical characteristics. Although appropriate physical qualities (e.g. density, distance, and urban form) are essential for making TOD work, an exclusive focus on these characteristics can obscure the main goal of transit-oriented development, which is not to create a particular physical form but to create places that function differently from traditional development. TOD projects should capitalize on the synergy that results from a functional integration of land use and transit, such as reduced auto dependency, which in turn leads to other benefits. Physical characteristics are a means of achieving those desired ends, not ends in and of themselves.

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23 2. Acknowledgement of a continuum of success. The degree to which a TOD project achieves desired functional outcomes can vary depending on the quality of the project and the characteristics of the place. This provides criteria that can be used as performance measures to assess how well projects fulfill certain goals. A high-density development within one quarter mile of a transit station may fail to take advantage of the full range of synergies made possible by TOD, even if it is better in some ways (e.g. mode split3) than more conventional development. Focusing on functional outcomes allows such a project to be labeled a partial success rather than wholly labeling it TOD on the basis of physical characteristics. 3. Adaptation to different locations and situations. Transit systems and locations vary greatly in their characteristics and their suitability for TOD. We should not expect the same results from a project in the core of a metropolitan area and one in the distant suburbs, just as we cannot necessarily hope for the same outcome in Dallas as in Chicago. Focusing on quantifiable functional outcomes accounts for both different degrees of success and the uniqueness of individual places. Just as a project can be judged as more or less successful TOD, so two projects with the same functional outcomes in very different places can be assessed within the context of those places. (Belzer and Autler 2002, p. 12) Partly because of the gap between the complexity of the interrelated objects in the open system and the quantitative process approximating the phenomenon in an assumed isolated system within a certain timeframe, it is as paradoxical to believe Socratic Wisdom as to understand that strictly-defined quantitative guidelines are sometimes harder to be implemented than flexible guidelines providing a right perspective and a proper way of thinking towards a suitable solution for various situations. Coming back to the original motivation behind TOD efforts, which is to improve our quality of life through the powerful combination of transit stations and development, Belzer and Autler ’s pioneering way of interpreting and evaluating TOD transforms a rhetorical “must be” into a realistic “could be.” Informed by earlier emphasis on the specific components and elements of TOD rather than the holistic scrutiny of their functional outcomes in the complex reality, definitions of transit-oriented development often focus on built form. For example, Bernick and Cervero (1996) emphasize the role of the “three Ds” (density, diversity, and design) in the success of TOD (Belzer and Autler 2002). Experience

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24 proves that proper built form alone, although a necessary element, is not sufficient for achieving all the benefits of TOD. For example, units per acre, a measure of physical form, gives very little information about the way a place functions: a high-density area can easily be less pedestrian-friendly than a low-density one (Belzer and Autler, 2002). Because most definitions and guidelines of TOD, such as those developed by Calthorpe, focus on built form, many projects that are billed as successful TODs don’t function very well in reality (Belzer and Autler 2002). Self-limited by the “must-be”s in their definitions, these projects may have overcome an important barrier by creating dense mixed-use development next to a transit station, but they fall short when measured by performance rather than physical characteristics. In contrast, a focus on outcomes allows a better benchmark of success and a better measure of the tradeoffs that most projects must make (Belzer and Autler 2002). Belzer and Autler present a definition in the form of six interrelated performance criteria that can be used to evaluate project function and outcomes: Location efficiency, which converts driving from a necessity into an option, Value recapture for the individuals and households, Livability, including improved air quality, increased mobility choices, decreased congestion, improved access to retail, services, recreational, and cultural opportunities, etc, Financial return for both private developers and transit agencies, Choice of a diversity of housing types, retail types, and transportation, Efficient regional land-use patterns resulting in less loss of farmland and open space, more suitable regional and sub-regional balance between jobs and housing, shorter commutes, and less traffic and air pollution, etc. This new definition provides not only the space for people to ponder the reasons for those “must-be”s in previous definitions and under different circumstances, but also the

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25 flexibility of implementing the project through consecutive stages and evolving phases in an ever-changing process. For example, Belzer and Autler discuss financial return by providing both reasons for mixed-use and flexibility in thinking of the ways to finance and implement such projects, instead of saying simply that transit-oriented development must be mixed-use and location-efficient: Assuming that each use within the program yields an acceptable rate of return, a mixed-use strategy can be more advantageous for the developer than a single-use project because it allows for greater flexibility in responding to various market cycles, protects against market volatility, and holds value over time. In addition, it may be easier to finance smaller increments of different development products than one large single use because the project risk is spread among a wider variety of lenders and equity investors. While TOD projects may require more complex financing strategies, the potential exists for increased return, particularly if projects are designed to take advantage of the benefits provided by location efficiency. (Belzer and Autler, 2002, Page 13) Experiencing TJD Defining TJD Although the distinction between TOD and TJD is not always clear, mixed-used transit-joint development (TJD) is treated as a subset of TOD. Most TJDs in the United States are commercial in nature. Rail agencies tend to focus on large-scale TJD projects, most commonly commercial office-retail developments and mixed residential-retail developments, while the joint development projects of bus operators are more likely to be single or specialized uses like sports facilities, entertainment centers, or daycare facilities (Transit Cooperative Research Program (TRCP) 2004). TJD is distinguished from other forms of TOD mainly by being tied to a specific real-estate project, venture, or brokered deal and involving the direct participation of a public entity, often a transit agency, in revenue streams and sometimes ownership (Transit Cooperative Research Program (TRCP) 2004). Existing TJD projects often

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26 occur on a transit agency’s property or in its air rights, although they can also be built on nearby private land if an improvement is physically or functionally integrated with a transit facility (Transit Cooperative Research Program (TRCP) 2004). Various initiatives, including air-rights development, ground-lease arrangements and station connection-fee programs, are used to promote TJD projects for the mutual benefit of public and private interests (Transit Cooperative Research Program (TRCP) 2004). The Washington metropolitan Area Transit Authority (WMATA) is a pioneer in the practice of TJD. WMATA defines joint development as: “a creative program through which property interests owned and/or controlled by WMATA are marketed to office, retail/commercial, recreational/entertainment, and residential developers with the objective of developing transit-oriented development projects” (Transit Cooperative Research Program (TRCP) 2004). Between 1970 and 2002, WMATA formally entered into thirty-eight joint development projects in the District of Columbia and the bi-state area. The sum value of these ventures has exceeded $2.5 billion (Transit Cooperative Research Program (TRCP) 2004). Collectively, these projects, including everything from air-rights leases and land rents to station connection fees, yield the agency some $6 million in annual revenues (Transit Cooperative Research Program (TRCP) 2004). San Francisco’s Bay Area Rapid Transit (BART) District, also active in joint development, entered into eight joint development agreements between 1984 and 2003 (Transit Cooperative Research Program (TRCP) 2004). Although rail systems have no monopoly on TOD and TJD, most cases of TJD projects are initiated by rail agencies. Some successful TJD projects in the world over the past decade have occurred in and around bus-way stations — most notably in Ottawa,

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27 Canada, and Curitiba, Brazil (Cervero 1998; Parsons, Brinckerhoff, Quade and Douglas Inc. et al. 1995). Bus-based TODs are currently manifesting in San Diego’s mixed-use Uptown District, and are also being aggressively planned in North Carolina for Charlotte’s northeast and north side BRT corridors (Transit Cooperative Research Program (TRCP) 2002). Because of rail agencies’ greater institutional capacities and planning resources, they are however more likely to negotiate monetary contributions with private developers than are bus agencies (Transit Cooperative Research Program (TRCP) 2004). Effects of Rail TJDs on House Prices Existing studies have demonstrated that adjacency to rail stops has a much more significant impact on property values than adjacency to bus stops (Transit Cooperative Research Program (TRCP) 2002). Adjacency to rail transit stations no doubt improves access of neighborhood residents to commercial activity centers. At the same time, however, rail transit stations also bring noise, traffic, and other nuisances to neighborhoods. Some researchers speculate that transit stations may have a positive effect on the values of homes in lower-income neighborhoods because the benefits of rail transit station accessibility more than offset any nuisances, and that transit stations may have a negative effect on values of homes in high-income neighborhoods because the nuisance of transit stations more than offsets the benefits. Based on Nelson’s study in Atlanta, Georgia, it appears that elevated transit stations have positive price effects on homes in lower income neighborhoods and negative price effects on homes in higher income neighborhoods within a region approximately 2.7 mi east to west by 1.7 mi north to south in DeKalb County (Nelson 1992).

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28 Financial Initiatives for TJD Transit agencies in fast-growing areas like greater Washington D.C., Atlanta, Dallas, San Diego, and the San Francisco Bay Area have been aggressive in pursuing TJD projects. Experience shows that, if they are to be successful, TJD projects must be proactively championed by the public sector (Transit Cooperative Research Program (TRCP) 2004). For most TJD projects, transit agencies use a variety of tools to spread risks and rewards, the most common type of which is leasing of ground space (Transit Cooperative Research Program (TRCP) 2004). There are also other financial tools for available to partnerships, such as air-rights leases, sharing of operation costs, sharing of construction costs and station connection fees. There are notable examples of air-rights leases (mostly office space) above rail stations: Bethesda Station mixed-use project, Ballston in Arlington County, Great American Plaza in San Diego, Union Station in Los Angeles, Datran Center at the South Dadeland Station in Miami, and Resurgens Plaza at Atlanta’s Lenox Square metropolitan Atlanta Rapid Transit Authority (MARTA) Station (Transit Cooperative Research Program (TRCP) 2004). Some TJD projects involve the sharing of operation costs between transit agencies and the private sectors (e.g., ventilation systems, utilities, and parking facilities). “WMATA’s Farragut West Station, for example, taps into the International Square office and retail project’s heating and air conditioning system. At the Bethesda Station, heat generated by the transit system is being recycled into an integrated mixed-use office-retail-housing project” (Transit Cooperative Research Program (TRCP) 2004). Some TJD projects involve the sharing of construction costs (e.g., building foundations, parking facilities, and construction staging areas). “Developer-financed bus bays and drop-off

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29 spaces at the Van Ness and Bethesda Stations, for example, saved WMATA an estimated $2.1 million (1982 dollars) in construction costs. Still, rail agencies have been far more aggressive in seeking out cost-sharing deals, especially east coast transit agencies like WMATA and New York City’s metropolitan Transportation Authority” (Transit Cooperative Research Program (TRCP) 2004). Station connection fees, another common form of financial initiative, likewise tend to fall within the province of rail agencies (Transit Cooperative Research Program (TRCP) 2004). They are especially popular with retail developers since they can deliver potential shoppers to the ground floors of connecting buildings. In the case of the Friendship Heights Station, a major retailer paid WMATA a one-time fee of $300,000 (1982 currency) for the right to connect to the station rotunda and also paid for the design and construction of the tunnel. They were followed by two other retail developers who paid tie-in fees of $737,000 and $775,000, respectively, plus annual rents, for their own connections to Friendship Heights (Transit Cooperative Research Program (TRCP) 2004). Conclusions Findings from the literature review about how people define TJD informed the choices of study area and related variables for the regression analysis in this research: Choosing Rail TJD for the Study Because of rail agencies’ greater institutional capacities and planning resources, most TJD cases available for scrutiny are initiated by rail agencies (Transit Cooperative Research Program (TRCP) 2004). Another reason for choosing properties around rail systems for this study is that existing studies have demonstrated that adjacency to rail

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30 stops has a much more significant impact on property values than adjacency to bus stops (Transit Cooperative Research Program (TRCP) 2002). Defining the Study Area Calthorpe delineated TOD as a mixed-use community within an average 2,000-foot walking distance of a transit stop and core commercial area. As a result, parcels within a 2,000-foot distance from rail stations were selected for this research. Choosing Two Regression Models for the Study The two main analytical approaches that were used to measure transit’s impacts on real estate values are: (1) hedonic price models— normally multiple regression models that partial out the unique effect of proximity in transit, and the presence of TJD characteristics, in explaining property values; and (2) matched-pair comparisons—comparisons of effective contract rents and per-square-foot land values between station areas and control sites (Transit Cooperative Research Program (TRCP) 2002). In this research, one multiple regression model was set up to understand the effects of TJD arrangements on the property value. Most existing feasibility analyses focus merely on studying the financial return from the increase in real estate values of TJD properties. As a result, projects were predicated on a purely financial rationale rather than a broad vision of how transit could work in tandem with surrounding development (Belzer and Autler 2002). Increasing evidence, however, shows that TJD can yield more benefits than merely increased land value, including growing transit ridership, an important factor for transit agencies to consider when exploring partnerships with private developers.

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31 Therefore, both the increase in real estate values of TJD properties and the ridership increase produced by TJD projects were calculated in the study using two multiple regression models. Introducing the Household Income Level as a Variable into the Regression Analysis Belzer and Autler point out that the residential projects fail to include units targeted at a mix of income groups or household sizes, but focus solely on one particular market segment, be it subsidized dwellings targeted at lower income households or luxury units for young singles and empty nesters (Belzer and Autler 2002). As a result, the different roles played by household income level also composed an important component in this feasibility study. Nelson’s study in Atlanta, Georgia, further shows that elevated transit stations have positive price effects on homes in lower income neighborhoods and negative price effects on homes in higher income neighborhoods within a region approximately 2.7 mi east to west by 1.7 mi north to south in DeKalb County (Nelson 1992). Therefore, the household income level and the housing information of the neighborhoods surrounding rail transit stations were introduced into the model as its non-urban-form variables.

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CHAPTER 3 HOW DO PEOPLE MEASURE THE LEVEL OF LAND-USE MIX? Multiple regression models that partial out the unique effect of proximity in transit and the presence of TJD characteristics were used to measure transit’s impacts on real estate values in explaining property values. Mixed land-use is a very important characteristic of TJD projects according to most TOD and TJD theories. Calthorpe defines TOD as a mixed-use (residential, retail, office, open space, and public uses) community within an average 2,000-foot walking distance of a transit stop and core commercial area (Calthorpe 1993). In order to understand the relationship between mixed land-use and the financial return from TJDs including the increases in the property values and in the transit ridership, the level of land-use mix of a TJD community needs to be measured quantitatively. The two most common ways of measuring level of land-use mix (land use heterogeneity) are Frank & Pivo’ s Entropy Index and Cervero & Kockelman’ s Dissimilarity Index. Frank & Pivo’s Entropy Index Frank and Pivo developed an entropy index to describe the evenness of the distribution of built square footage among seven land-use categories. According to their paper in “Transportation Research Record 1466,” the entropy index was based on the following equation (Frank and Pivo 1994): Level of land-use mix (entropy value) = [single family log (single family)] + [multifamily log (multifamily)] +[retail and service log (retail and service)] + [office log (office)] 32

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33 +[entertainment log (entertainment)] + [institutional log (institutional)] + [industrial/manufacturing log (industrial/manufacturing)] Cervero & Kockelman’s Dissimilarity Index The land-use mix index developed by Cervero and Kockelman may also be called a "dissimilarity index" since it is based on "points" awarded to each actively developed hectare based on the dissimilarity of its land use from those of the eight adjacent hectares. The average of these point accumulations across all active hectares in a tract is the dissimilarity or mix index for the tract (Kockelman 1997): Conclusions The Dissimilarity Index, developed by Cervero and Kockelman using the data consist of dominant land uses assigned to 1-hectare squares of land, has its limitation in measuring land use heterogeneity below the scale of a hectare. In order to understand the relationship between the level of land-use mix and increases in property values at the parcel level, Frank & Pivo’ s Entropy Index, was deemed more suitable for this research. In Frank and Pivo’s paper, their equation resulted in the development of a normalized value between a minimum of 0 and 0.845 (Frank and Pivo 1994). However, proportions of each land use among all the land uses are always smaller than 1, which may result in a negative value for the entropy value according to calculation of Frank and Pivo’ s Entropy Index. Therefore, an improved calculation of the level of land-use mix is developed based on Frank and Pivo’s Entropy Index for this research:

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34 Level of land-use mix = -A / LN (N) Where: A= {b(n)/a * LN [b(n)/a]} With b(n)/a = proportion of building floor area of each land use among total square feet of all the land uses present in a buffer (when building floor area of one specific land use equals to 0, value of 0.01 will be given for its calculation); N = Number of land use categories used in the research.

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CHAPTER 4 RESEARCH METHODOLOGY In this chapter, the key research questions are reviewed and the methodology used to conduct this research is described. The data quality of each variable for the regression analysis is also discussed in this chapter. The findings of the literature on the different definitions of TJD as well as various measurements of the level of land-use mix both refined the approach of this research. Based on the conceptual model, US Census 2000 data and parcel data were examined for the case study. Key Research Questions and Overview of Research Approach This research tried to quantify the financial benefits brought from TJDs’ major characteristics, specifically high-density and mixed land-use. Specific questions included: To what extent will high-density and mixed land-use increase the property values of TJDs? Will an increase in the density and the level of land-use mix of TJDs bring a higher transit ridership? What is the impact of other factors, including the household income level of the neighborhood around TJDs, on the financial return from TJDs? These questions were addressed through two regression analysis. One multiple regression model was used to examine the effects of TJD projects’ major characteristics, high-density and mixed-use, on real estate values, controlling for proximity to transit. In addition, household income level was also included in this regression model to understand its role on the feasibility of TJD projects. 35

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36 Another multiple regression model was developed to understand the relationship between transit ridership and the TJD arrangements under certain socio-economic situations. A case study of the parcels within a 2,000-foot distance from MARTA’s rail stations in the Atlanta metropolitan area utilized meaningful urban-form and non-urban-form variables for the regression analysis. Conceptual Model In this research, “Population Density,” “Average Floor Area Ratio,” “Level of Land-Use Mix,” “Median Household Income,” “Per Capita Income,” “Total Housing Units inside the Census Block Group,” “Median Number of Rooms per Housing Unit,” “Number of the Workers Using Transit for Traveling,” and “Average Travel Time to Work” were calculated from the census data at the block-group scale. “Square Footage of the Property,” “Acreage” and “The Year When the Building was built” were obtained from the parcel data. All these independent variables were used to develop the first multiple regression model to understand the impacts of TJD’s characteristics on the property value under certain socio-economic conditions (Figure 4-1). Similarly, “Population Density,” “Average Floor Area Ratio,” “Level of Land-Use Mix,” “Median Household Income,” “Per Capita Income,” “Total Housing Units inside the Census Block Group,” “Median Number of Rooms per Housing Unit,” “Average Travel Time to Work,” “Square Footage of the Property,” “Acreage” and “The Year When the Building was built” were used as independent variables to understand their relationship with “Number of the Workers Using Transit for Traveling” (Figure 4-2).

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37 Property Value Level of Land-Use Mix Average Travel Time to Work Number of workers using transit for traveling Property information Total housing units Median number of rooms per housing unit Density Income Square Footage Year Built Acreage Average Floor Area Ratio Population Density Per Capita Median Household Income Income Figure 4-1: Conceptual Model One Number of Workers Using Transit for Traveling Level of Mixed Land Use Income Total housing units Median number of rooms per housing unit Property information Average Travel Time to Work Density Median Household Income Square Footage Average Floor Area Ratio Population Density Per Capita Acreage Year Built Income Figure 4-2: Conceptual Model Two

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38 Data Source Data for each variable were obtained or calculated from the census 2000 data at the block-group level and the parcel data. Data Analysis Urban-Form Variables Land use, density and design are all crucial to the success of TJD projects. Land use and density of projects are more often used to identify TOD and TJD projects, as it is almost impossible to quantify various community designs. Density and level of land-use mix were therefore introduced as the urban-form variables in this research. Density Density can be defined in at least two ways: how dense the built areas are clustered within an area, and how the population who use the space is clustered within an area. Therefore both “average floor area ratio” calculated from parcel data and “population density” calculated from US Census data were introduced to the regression analysis. Level of Land-Use Mix An improved calculation of the level of land-use mix was developed based on Frank and Pivo’s Entropy Index for this research: Level of land-use mix = -A / LN (N) Where: A= {b(n)/a * LN [b(n)/a]} With b(n)/a = proportion of building floor area of each land use among total square feet of all the land uses present in a buffer (when building floor area of one specific land use equals to 0, value of 0.01 will be given for its calculation); N = Number of land use categories used in the research.

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39 When calculating the level of land-use mix of the parcels inside each 2000 feet buffer around transit stations using the Entropy Index, various land uses of the parcels were grouped into 11 categories for this research: “Office,” “Institutional,” “Recreational,” “Single Family,” “Multifamily,” “Parking/TCU,” “Commercial,” “Park/ Open Space,” “Industrial,” “Vacant,” “Unknown/ Other.” While many of the buffers were isolated from each other, some were overlapped with each other, producing many parcels selected within more than 2 buffers. In order to apply one level of land-use mix value to each parcel, the maximum entropy index value among all the values calculated from the related buffers was applied to the parcels contained by more than 2 buffers. Arc GIS 9.0 was used to calculate the level of land-use mix for this research (See Appendix A). Non-Urban-Form Variables Previous study suggests that other factors than the urban-form variables affect the financial return from TJDs. Based on Nelson’s study in Atlanta, Georgia, it appears that elevated transit stations have positive price effects on homes in lower income neighborhoods and negative price effects on homes in higher income neighborhoods within a region approximately 2.7 mi east to west by 1.7 mi north to south in DeKalb County (Nelson 1992). The income level and housing information of the neighborhoods surrounding transit stations were therefore introduced into the model as its non-urban-form variables. Information on “Median Household Income,” “Per Capita Income” obtained from US Census data were used for regression analysis as income-related variables. Information on “Total Housing Units” and “Median Number of Rooms per Housing Unit” were introduced into the regression analysis as housing-related variables.

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40 To MARTA, increases in ridership mean increases in its operating revenue, which is an important component in MARTA’s decision-making for TJD projects in Atlanta. Information on “Number of Workers Using Transit for Traveling” was obtained from US Census data. The “Average Travel Time to Work” was calculated from the information provided by the US Census data, using the following equation: Total travel time to work = ([P031003]*2.5 + [P031004]*7.5 + [P031005] *12.5 + [P031006] *17.5 + [P031007] *22.5 + [P031008] *27.5 + [P031009] *32.5 + [P031010] *37.5 + [P031011] *42.5 + [P031012] *52 + [P031013] *74.5 + [P031014] *90) / [P031002] Where: P031002 = Number of workers traveling to work; P031003 = Number of workers traveling to work for less than 5 minutes; P031004 = Number of workers traveling to work for 5 to 9 minutes; P031005 = Number of workers traveling to work for 10 to 14 minutes; P031006 = Number of workers traveling to work for 15 to 19 minutes; P031007 = Number of workers traveling to work for 20 to 24 minutes; P031008 = Number of workers traveling to work for 25 to 29 minutes; P031009 = Number of workers traveling to work for 30 to 34 minutes; P031010 = Number of workers traveling to work for 35 to 39 minutes; P031011 = Number of workers traveling to work for 40 to 44 minutes; P031012 = Number of workers traveling to work for 45 to 59 minutes; P031013 = Number of workers traveling to work for 60 to 89 minutes; P031014 = Number of workers traveling to work for 90 or more minutes.

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41 Most of the data for calculating these non-urban-form variables was obtained from the US Census data. However, the parcel data containing all the property information was at a much smaller scale than the US Census data (Figure 4-3). In order to overlay the different datasets at these different scales, Arc GIS 9.0 was used for the analysis (See Appendix B). Figure 4-3: A map showing the parcel data overlaying with the US Census data Property Information Several variables providing property information were also introduced into the regression analysis. Information of the acreage of the parcel, square footage of the

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42 buildings, and the year when the buildings were built are obtained from the parcel data. Taxable values of the property were used as the property values of parcels.

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CHAPTER 5 DESCRIPTION OF CASE STUDY AREA Previous researches on TJDs have done many case studies in the San Francisco Bay area, southern California, Washington, D.C. and New York City (Bernick & Cervero, 1996). The Atlanta metropolitan area in Georgia is one of the fastest growing areas in the southeastern United States. The metropolitan Atlanta Rapid Transit Authority (MARTA), formed as a joint public instrumentality of the City of Atlanta and the counties of Fulton, DeKalb, Cobb, Clayton, and Gwinnett, operates a bus and rapid rail transportation system in the region. As one of the TJD projects around MARTA’s rail stations, Lindbergh City Center has been the subject of several earlier studies (Dumbaugh 2004). A region-wide feasibility study on the TJD projects around MARTA’s rail stations, however, has not been done before. In this research, a case study in the Atlanta metropolitan area was used to understand the financial benefits of TJDs’ major characteristics, specifically high-density and mixed-land use, in the setting of the southeastern United States. Describing the Case Study Area—Atlanta metropolitan Area Atlanta Overview With a current population of more than 369,000, Atlanta, the capital of the state of Georgia was first named Marthasville in honor of the then-governor's daughter, nicknamed Terminus for its rail location, and then changed soon after to Atlanta, the feminine of Atlantic. Covering a much bigger area than the City of Atlanta, the Atlanta 43

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44 metropolitan area has a population of more than four million, which makes it one of the largest metropolitan areas in the southeastern United States (Figure 5-1). Figure 5-1: The location of the Atlanta metropolitan area in the United States (Source: http:// www.MapQuest.com ) Founded in 1837 as the end of the Western & Atlantic railroad line, the City of Atlanta today remains a transportation hub, both domestically and internationally (Figure 5-2). Hartsfield Atlanta International Airport is one of the nation's busiest in daily passenger flights. Direct flights to Europe, South America, and Asia have made the Atlanta metropolitan area easily accessible to the more than 1,000 international businesses and more than 50 countries that have representation in the city through consulates, trade offices, and chambers of commerce (Conceirge.com). Having emerged as a banking center, the city embraces the world headquarters of Fortune 500 companies such as CNN, Coca-Cola, Delta Airlines, Holiday Inn Worldwide, and United Parcel Service.

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45 Figure 5-2: Transportation access to the Atlanta metropolitan area (Source: http:// www.MapQuest.com ) Atlanta's character is evolving as immigrants continue to arrive from diverse ethnic groups. Irish immigrants had a major role in the city's early history, along with Germans and Austrians (Concierge.com). In the past two decades Atlanta has seen significant growth in its Asian and Latin-American communities. Restaurants, shops, and institutions with Asian and Latino flavors have become part of the city's culture. For more than four decades Atlanta has been associated with the civil rights movement. Among the many accomplishments of which Atlanta's African-American community is proud is the Nobel Peace Prize that Martin Luther King Jr. won in 1964. Andrew Young, the first black congressman from the South since Reconstruction, was elected as Atlanta’s major in 1981, after serving as ambassador to the United Nations during President Jimmy Carter's administration. In contrast with the stereotyped romantic versions of the south as moving at a leisurely pace, the hectic building and rebuilding that characterized the period after the Civil War continues unabated (Concierge.com). Still viewed by die-hard Southerners as

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46 the heart of the Old Confederacy, Atlanta has presented itself as a fast-paced modern city proud of its heritage (Concierge.com). In the past two decades, Atlanta has experienced unprecedented growth. Since the late 1970s dozens of dazzling skyscrapers (Figure 5-3, Figure 5-4) designed by such luminaries as Philip Johnson, I. M. Pei, and Marcel Breuer have reshaped the city's profile (Concierge.com). Figure 5-3: Exciting nightlife, Atlanta, Georgia (Source: Concierge.com) Figure 5-4: Evening falls over Atlanta, Georgia (Source: Concierge.com) Socio-Economic Characteristics of the Region According to US Census data, the Atlanta metropolitan area in the year of 2000 had a household population of 4.0 million, fifty-one percent of whom were females and forty

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47 nigh percent of whom were males. The median age was 32.9 years. Twenty-seven percent of the population was under eighteen years and seven percent were sixty-five years and older (U.S. Census Bureau) (Figure 5-5). Figure 5-5: Age distribution of people in the Atlanta metropolitan area (Source: Census 2000 Supplementary Survey) In the Atlanta metropolitan area, for people reporting one race alone, sixty-four percent were White; twenty-nine percent were Black or African American; less than half a percent were American Indian and Alaska Native; three percent were Asians; less than half a percent were Native Hawaiian or Other Pacific Islander, and three percent were Some other race. One percent reported Two or more races (U.S. Census Bureau). In the City of Atlanta itself, however, the breakdown is dramatically different. For people reporting one race alone, sixty-five percent were Black or African American in the City of Atlanta (U.S. Census Bureau).

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48 In 2000, there were 1.5 million households in the Atlanta metropolitan area. The average household size was 2.69 people. Families made up sixty seven percent of the households, including both married-couple families (forty-eight percent) and other families (nineteen percent) (Figure 5-6). Non-family households made up thirty-three percent of all households in the Atlanta metropolitan area. Most of the non-family households were people living alone, but some were comprised of people living in households in which no one was related to the householder (U.S. Census Bureau). Figure 5-6: Types of households in the Atlanta metropolitan area (Source: Census 2000 Supplementary Survey) In 2000, eighty-one percent of the people at least one year old living in the Atlanta metropolitan area were staying in the same residence one year earlier; nine percent had moved during the past year from another residence in the same county, five percent from another county in the same state, four percent from another state, and one percent from abroad (U.S. Census Bureau) (Figure 5-7).

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49 Figure 5-7: Geographic mobility of residents of the Atlanta metropolitan area (Source: Census 2000 Supplementary Survey) In the year of 2000, eighty four percent of people twenty-five years and older had at least graduated from high school and thirty-two percent had a bachelor's degree or higher. Among people sixteen to nineteen years old, fifteen percent were dropouts; they were not enrolled in school and had not graduated from high school (U.S. Census Bureau). In the year 2000, for the employed population sixteen years and older, the leading industries in the Atlanta metropolitan area were Educational, health, and social services, fourteen percent, and Retail trade, twelve percent (U.S. Census Bureau). Seventy-nine percent of the workers in the Atlanta metropolitan area drove to work alone in the year of 2000, twelve percent carpooled, four percent took public transportation, and two percent used other means. The remaining four percent worked at home. It took those who commuted to work an average of thirty minutes to get to work (U.S. Census Bureau).

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50 In 2000, the median income of households in the Atlanta metropolitan area was $50,237. Ten percent of people were in poverty. Thirteen percent of related children under eighteen were below the poverty level, compared with ten percent of people sixty-five years old and over. Eight percent of all families and twenty percent of families with a female householder and no husband present had incomes below the poverty level (U.S. Census Bureau) (Figure 5-8). Figure 5-8: Poverty rates (Source: Census 2000 Supplementary Survey) In 2000, the Atlanta metropolitan area had a total of 1.6 million housing units, 5.8 percent of which were vacant. Of the total housing units, sixty-six percent were in single-unit structures, twenty-nine percent were in multi-unit structures, and four percent were mobile homes(U.S. Census Bureau) (Figure 5-9). Thirty-four percent of the housing units were built since 1990 (U.S. Census Bureau).

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51 Figure 5-9: Types of Housing Units (Source: Census 2000 Supplementary Survey) In the year of 2000, the Atlanta metropolitan area had 1.5 million occupied housing units 956,000 owner occupied and 542,000 renter occupied. Three percent of the households did not have telephone service and seven percent of the households did not have access to a car, truck, or van for private use (U.S. Census Bureau). Twenty-six percent of owners with mortgages, nine percent of owners without mortgages, and forty-three percent of renters in the Atlanta metropolitan area spent thirty percent or more of household income on housing (U.S. Census Bureau) (Figure 5-10). Figure 5-10: Occupants with housing cost burden (Source: Census 2000 Supplementary Survey)

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52 MARTA and TJDs in the Region In 1996, the U.S. Environmental Protection Agency (EPA), as executor of the Clean Air Act (CAA) amendments of 1990, declared the Atlanta metropolitan region to have entered a “conformity lapse,” which meant planners at the Atlanta Regional Commission (ARC) had to come up with a long-term transportation plan to bring Atlanta into conformity with CAA standards by 1998 (Feigon, Hoyt, and Ohland 2004). At the root of Atlanta’s multiple problems, including traffic congestions and air pollution, were at least two decades of unplanned growth and road building. The Atlanta metropolitan area sprawls over several counties in the region, including the counties of Fulton, DeKalb, Cobb, Clayton, and Gwinnett. Commentators despaired of Atlanta’s dozens of balkanized municipalities ever being able to work out a passable transportation improvement plan under the guidance of the beleaguered ARC (Feigon, Hoyt, and Ohland 2004). In the absence of an effective regional planning body, MARTA emerged as a key player in the campaign to relieve the city’s traffic congestion. MARTA was formed as a joint public instrumentality of the City of Atlanta and the counties of Fulton, DeKalb, Cobb, Clayton, and Gwinnett by action of the General Assembly of the State of Georgia (the MARTA Act) to design and implement a rapid transit system for the Atlanta metropolitan area (MARTA). MARTA now operates both bus and rapid rail transportation system in the region. Making transit more attractive to Atlanta residents has become an important priority in the region’s effort to combat both congestion and poor air quality (Feigon, Hoyt, and Ohland 2004) (Figure 5-11).

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53 Figure 5-11: Time-lapse photo showing congestion and brake lights on freeway heading downtown (Source: MARTA) MARTA’s rail system consists of 47.6 miles of operational double track and thirty-eight fully-functioning stations (MARTA). The rail system has lines running both east-west and north-south. The main lines intersect at the Five Points Station, located in Atlanta’s Downtown Business District (Figure 5-12).

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54 Figure 5-12: Rail map of MARTA (Source: MARTA) The fixed rail system, which consists of steel-wheel trains, operates at speeds up to seventy miles per hour on steel rails using an electrified “third rail” as the power source (Figure 5-13). The rail transit system consist of approximately 338 air-conditioned vehicles, which operate in combinations of two to eight vehicle trains (Figure 5-14).

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55 Figure 5-13: MARTA train moving in front of the Atlanta Skyline (Source: MARTA) Figure 5-14: Rail operator in front of his train (Source: MARTA)

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56 MARTA operates under severe financial constraints, as it is the only large metropolitan transit system in the nation that receives no money from state government (Feigon, Hoyt, and Ohland 2004). Any increase in ridership means an increase in its operating revenue. As young professionals began returning to live in the city core in the 1990’s, MARTA recognized that there was a market for TOD around its rail stations (Feigon, Hoyt, and Ohland 2004). MARTA has completed several transit-joint development projects, leasing the air-rights over several stations. Following the downturn of the real estate market in the 1980s’, MARTA’ s various parking lots and unused parcels became tremendously valuable with the return of bullish Atlanta real estate market in the 1990s’(Feigon, Hoyt, and Ohland 2004). Despite the predominance of rail technologies, they do not have a monopoly on TOD and TJD. MARTA also operates 158 bus routes in the region besides its rail system. Nevertheless, most TJD activities have been observed around its rail stations. Atlanta’s Lindberg Station, in particular has become one of the most well-known TOD and TJD projects in the nation. The theory of TJD were able to be put into practice at Lindbergh City Center due largely to FTA’ s joint development policy ruling that enables land purchased using federal funds to be leased to the private sector as long as the resulting development is transit supportive. Using a competitive-bid process, MARTA selected a master developer, Carter and Associates, in 1997 to implement a large-scale mixed-use project at a forty-seven-acre site surrounding Lindbergh Station. Located along Piedmont Avenue between Atlanta’s rapidly growing Midtown and Buckhead districts, and with great

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57 access to the region’s downtown and Perimeter Center employment hubs, the site seemed ideal for the project (Figure 5-15). Figure 5-15: Site of Lindberg City Center (Source: http://www.carterusa.com/) MARTA’s final plan for the site includes about 2.5 million square feet of commercial and office space, 2.2 million of which was reserved for BellSouth, 300,000 square feet of retail, roughly 1,300 residential units, as well as a 160-room hotel (Dumbaugh 2004) (Figure 5-16). A pedestrian-friendly Main Street, featuring retail shops, restaurants and offices, will bridge over the rail station into a multifamily residential –office district (Figure 5-17).

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58 Figure 5-16: Master plan of Lindberg City Center (Source: http://www.carterusa.com/) Figure 5-17: How retail shops, restaurants, offices, and multifamily residential space can be connected through the Main Street in Lindberg City Center (Source: http://www.carterusa.com/ )

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59 BellSouth, one of Atlanta’s largest companies, will be the project’s anchor tenant: BellSouth’s move to the Lindbergh site reflects a corporate decision to relocate scattered suburban offices to a central-city transit node in response to growing employee frustration over traffic congestion and a perception that quality of life was eroding. The consolidation of its offices into three new centers will mean that eighty percent of company employees in metropolitan Atlanta will work near a MARTA station, compared with thirty percent today. BellSouth hopes to lure its employees to MARTA by providing free or discounted transit passes and free private parking at outlying MARTA stations. (Transit Cooperative Research Program (TRCP) 2004, page 50)

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CHAPTER 6 RESULTS AND DISCUSSIONS Results In this research, two separate linear regression models were used in order to understand the roles the urban-form and the non-urban-form variables plays in deciding the feasibility of TJD projects. Those urban-form and non-urban-form variables, together with related property information (Table 6-1), were used as independent variables in the first multiple regression model to understand the impacts of TJD arrangements on the property value (taxable value of the property) under certain socio-economic conditions. Table 6-1: Variables used in the first regression model Mean Std. Deviation N Taxable Value 421035.565273 3382489.5618903 4688 Acreage .348795 .9923584 4688 Average Travel Time 31.115570 8.3806150 4688 Square Footage 10310.802901 50654.2334957 4688 Total Housing Units 496.231655 328.8233493 4688 Median Num of Rooms per Housing Units 4.538289 1.3922763 4688 Level of Land-Use Mix .590935 .1351577 4688 Population Density 7373.928775 7597.4111458 4688 Average Floor Area Ratio (FAR) 22088.698166 58819.4452637 4688 Median HH Income 30117.067193 25713.4279892 4688 Per Capita Income 18677.457765 17288.5931482 4688 Year Built 1940.415316 46.1487802 4688 Number of workers using transit for traveling 77.512799 47.6354418 4688 In SPSS 12.0, step-wise regression method was used to eradicate the multi-co linearity of the model. Four independent variables were selected out as the variables 60

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61 playing a significant role in deciding the property value (taxable value of the property) of the parcels in the study areas in the Atlanta metropolitan area: “square footage of the buildings on the property,” “acreage of the parcel,” “population density,” “total housing units in the census group block to which specific parcels belong.” A model with an adjusted R-Square value of 0.394 was constructed. Contrary to the hypothesis of most TOD theories, the level of land-use mix and the average floor area ratio, two of the most commonly used identifying variables, were excluded from the model due to their insignificant impact on property value. Population density, another identifier of density, played a significant role in deciding the property value in this model, although its coefficient calculated from the linear regression model was negative, and thus different than expected (Table 6-2). Table 6-2: “Taxable value of the property” as dependent variable Independent Variables Unstandardized Coefficients (Constant) 76509.977 Square Footage 46.431 Acreage -506030.460 Population Density -14.868 Total Housing Units 306.133 The second multiple regression model was used to understand the impacts of the urban-form and non-urban-form variables, and related property information on the transit ridership within the study region including TJD projects and potential TJD sites in the Atlanta metropolitan area (Table 6-3).

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62 Table 6-3: Variables used in the second regression model Mean Std. Deviation N Number of workers using transit for traveling 77.512799 47.6354418 4688 ACREAGE .348795 .9923584 4688 Average Travel Time 31.115570 8.3806150 4688 Square Footage 10310.802901 50654.2334957 4688 Total Housing Units 496.231655 328.8233493 4688 Median Num of Rooms per Housing Units 4.538289 1.3922763 4688 Taxable Value 421035.565273 3382489.5618903 4688 Level of Land-Use Mix .590935 .1351577 4688 Population Density 7373.928775 7597.4111458 4688 FAR 22088.698166 58819.4452637 4688 Median HH Income 30117.067193 25713.4279892 4688 Per Capita Income 18677.457765 17288.5931482 4688 Year Built 1940.415316 46.1487802 4688 In SPSS 12.0, step-wise regression method was used to eradicate the multi-co linearity of the model. Ten independent variables were selected out as the variables playing an important role in deciding the transit ridership in the study areas in the Atlanta metropolitan area: “average travel time,” “median number of rooms per housing unit,” “total housing units in the census group block specific parcels belong to,” “per capita income,” “level of land-use mix,” “population density,” “average floor area ratio,” “property value (taxable value of the property) of the parcels,” “acreage of the parcel,” and “square footage of the buildings on the parcel.” “Property value (taxable value of the property) of the parcels” was then removed because of multi-co linearity. And a model with an adjusted R-Square value of 0.519 was constructed. Although the variables indicating the density (“population density” and “average area floor ratio”) and the level of land-use mix played significant roles in deciding the

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63 number of workers using transit for traveling in the linear regression model, level of land-use mix was negatively related to the “number of workers using transit for traveling,” partly rejecting some of the TOD theories that increasing the level of land-use mix would result in an increase of number of workers using transit for traveling (Table 6-4). A higher population density in this regression model resulted in an increase of number of workers using transit for traveling, which supports most of the hypotheses for TOD/ TJD research and the TOD/TJD theories. Table 6-4: Coefficients of the independent variables with the “number of workers using transit for traveling” as its dependent variable Independent Variables Unstandardized Coefficients: B (Constant) 38.563 Average Travel Time 1.860 Median Num of Rooms per Housing Units -1.810 Total Housing Units .071 Per Capita Income 1999 -.001 Level of land-use Mixed -49.632 Population Density .001 FAR -5.710E-05 Acreage -2.328 Square Footage 4.760E-05 Discussions This research was carried out to understand the financial benefits brought by TJDs’ major characteristics, specifically high-density and mixed land-use. Financial benefits, including increases in property values and in transit ridership, are often assumed for TJDs with a higher density and a greater level of land-use mix in previous studies (Belzer and Autler 2002).

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64 The results of this research, however partly reject the hypothesis that a higher density and a greater level of land-use mix bring a higher financial return, particularly in the case of the TJD projects in the Atlanta metropolitan area, Georgia, a growing urban area in the United States. On one hand, “population density” played a significant role in determining the property value of the parcels in the study region, while the “level of land-use mix” played an insignificant role in determining property value. On the other hand, two indicators of density, “population density” and “average area floor ratio,” also had significant impacts on the number of workers using transit for traveling in the study region. An increase in the level of land-use mix, however, resulted in a decrease in the number of workers using transit for traveling, an important factor in the success of TJD projects. Contrary to Nelson’s results on the relationship between the income level and the property value of the parcels around the transit stations in DeKalb County, Georgia (Nelson 1992), variables indicating income level did not play a significant role in determining property value in the regression analysis for this research. Based on the results of the case study in the Atlanta metropolitan area, a higher level of land-use mix is unlikely to make TJD projects more financially successful. It is also difficult to reach the conclusion that higher densities would bring a higher financial return to the TJD projects in the study region, because a higher population density results in a decrease in the property value and an increase in the numbers of workers using transit for traveling at the same time. Furthermore, the numbers of workers using transit for traveling is just one component of the transit ridership, for people may take transit with other purposes than merely going to work. Previous study also indicate that population density may be a proxy to other locational factors (Steiner 1997). In another

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65 word, changes in property value associated with changes in population density may be caused by locational differences among the transit stations in the study region. But before drawing the conclusion that increases in the level of land-use mix and density will not necessarily make TJD projects more feasible in the Atlanta metropolitan area, Georgia — which is contrary to the related theories — several factors must be considered. First, mixed land use in the TOD theory stands for well-connected mixed land use with quality urban design (Calthorpe 1993). Parcels with various land uses and densities in the study region may just be juxtaposed without a considerate design for TJD purposes. It is accepted that land use, density and designs are all crucial to the success of TJD projects. Difficulties in quantifying various unique designs of TJD projects has resulted in a lack of discussion of design elements and their interrelation with land use and density in most quantitative TJD and TOD researches. Emphasis on land use and density may have obscured the potentially prominent role of actual design in deciding the success and feasibility of TJD projects. Second, TJD or TOD is not the panacea for all urban problems in the United States. Employment and other economic factors in many senses play a much more important role in shaping urban development than transit systems. Although the Atlanta metropolitan area, with seven percent of its households in the year of 2000 having no access to a car, truck, or van for private use according to the US census data, has a market for TJD projects, decades of unplanned urban sprawl have created an urban pattern that makes the change towards mixed-used development expensive and difficult.

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66 Third, by comparing several socio-economic characteristics of the study region with other parts of the United States, some unique factors of the study region may make the implementation of TJDs in the Atlanta metropolitan area more difficult than in some other parts of the United States: The Atlanta metropolitan area has a smaller age cohort of over sixty-five years old, who are possibly more likely to be the transit riders (Table 6-5). Table 6-5: Percentage of population in each age category (Source: Census 2000 Supplementary Survey) Age Category Atlanta metropolitan area The United States San Francisco County, California Washington, D.C. 65 and over 7% 12% 13% 12% 45 to 64 21% 22% 22% 23% 25 to 44 36% 30% 41% 34% 18 to 24 9% 9% 9% 10% Under 18 27% 26% 15% 21% Both San Francisco County and Washington, D.C., where most successful TJDs have been reported, have lower percentages of married-couple families and a higher percentages of people living alone than the Atlanta metropolitan area (Table 6-6). Table 6-6: Percentage of different types of household (Source: Census 2000 Supplementary Survey) Type of household Atlanta metropolitan area The United States San Francisco County, California Washington, D.C. Married-couple families 48% 51% 31% 21% Other families 19% 17% 14% 23% People living alone 25% 26% 38% 47% Other non-family households 7% 6% 17% 9%

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67 Both San Francisco County and Washington, D.C., where most successful TJDs have been reported, have lower percentages of single-unit structures and a higher percentages of multi-unit structures than the Atlanta metropolitan area (Table 6-7). Table 6-7: Percentage of different types of housing units (Source: Census 2000 Supplementary Survey) Type of housing unit Atlanta metropolitan area The United States San Francisco County, California Washington, D.C. Single-unit structures 66% 66% 33% 39% In multi-unit structures 29% 27% 67% 60% Mobile homes 4% 8% 0% 0% Fourth, a linear relationship between the dependent and independent variables is assumed in the regression models. In reality, the relationships among them are much more complicated than the model. Besides, many other factors like the interactive items among the variables are not considered in the model either. All of these factors together create a gap between the regression models and reality (Figure 6-1).

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68 0.00.20.40.60.81.0Observed Cum Prob 0.00.20.40.60.81.0Expected Cum Prob Figure 6-1: Difference between the expected cumulative probability and the observed cumulative probability in the first regression model for the case study Finally, the new formula used to calculate the level of land-use mix in this research still leaves room for further discussions and improvements: Some researchers calculated “N” in the equation as the number of different land uses that are not vacant. In order to calculate the level of land-use mix for all the parcels inside all the buffers in the study region, a same “N” value (the maximum number of different land uses) is applied to the calculation. There are some values for the parcels inside some of the buffers are zero, for some groups of parcels around certain stations do not have all the categories of the land uses. Zero then is treated as 0.01 to let it working for LN function. Some but insignificant differences are created from doing this. The equation uses the square footage of the buildings on the parcels instead of the acreage of the parcels for the calculation. However, there are some land uses that are functional even if there is no building on them, including the parking space, open space and parks, which actually are important components according to Calthorpe’s theory of TOD (Calthorpe 1993). The vacant lands designated for some specific land use are not included in the calculation of the level of land-use mix. But in a rapidly changing and growing urban area like the Atlanta metropolitan area, those lands may have some hidden impacts on the overall performance of the TJD projects.

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CHAPTER 7 CONCLUSIONS AND AREAS FOR FURTHER RESEARCH Conclusions Advocates of TJD assume that increases in the density and level of land-use mix will bring greater financial return to both private developers and transit agencies. This research was carried out to understand the financial benefits brought from TJDs’ major characteristics, specifically high-density and mixed land-use. Findings in the literature review identified the increases of the property values and in the transit ridership as two of the major financial benefits of TJDs. A case study in the Atlanta metropolitan area selected parcels inside a 2,000 foot buffer around MARTA’s transit stations for regression analyses. Two regression models were constructed for the study. One multiple regression model was used to examine the effects of TJD projects’ major characteristics, specifically high-density and mixed-use, on real estate values, controlling for proximity to transit. Another multiple regression model was developed to understand the relationship between transit ridership and the TJD arrangements under certain socio-economic situations. Using meaningful urban-form and non-urban-form variables, these two regression models were used to address several questions: To what extent will high-density and mixed land-use increase the property values of TJDs? Will an increase in the density and the level of land-use mix of TJDs bring a higher transit ridership? 69

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70 What is the impact of other factors, including the household income level of the neighborhood around TJDs, on the financial return from TJDs? However, the results of this research partly reject the hypothesis that a higher density and a greater level of land-use mix bring a higher financial return to TJD projects, specifically in the Atlanta metropolitan area, Georgia, a growing urban area in the United States. Based on the results of the case study, the level of land-use mix played an insignificant role in determining the property value. Furthermore, an increase in the level of land-use mix resulted in a decrease in the number of workers using transit for traveling. It is also difficult to reach the conclusion that a higher density would bring a higher financial return to the TJD projects in the study region, because a higher population density results in a decrease in the property value and an increase in the numbers of workers using transit for traveling at the same time. Different from Nelson’s results on the relationship between the income and the property level of the parcels around the transit stations in DeKalb County, Georgia (Nelson 1992), variables indicating the income level do not play a significant role in deciding the property value in the regression analysis for this research. Before any conclusion that increases in the level of land-use mix and density will not necessarily make TJD projects more feasible in the Atlanta metropolitan area can be drawn, several factors were discussed: Mixed land use in the TOD theory stands for well-connected mixed land use with quality urban design (Calthorpe 1993). Parcels with various land uses and densities in the study region may just be juxtaposed without a considerate design for TJD purposes. TJD or TOD is not the panacea for all the urban problems of the United States. Employment and other economic factors in many senses play a much more important role in shaping urban development than transit systems. Decades of unplanned urban sprawl have created an urban pattern that makes the change towards mixed-used development costly.

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71 There may be several unique social-economic characteristics of the study region making the implementation of TJDs in the Atlanta metropolitan area more difficult than in some other parts of the United States. A linear relationship between the dependent and independent variables is assumed in the regression models. In reality, the relationships among them are much more complicated than the model. The formula used to calculate the level of land-use mix in the research still has several points for further discussions and improvements. At last, it is also noticeable that reasons for pursuing TJD projects with growing concerns over environmental, economical, and social sustainability goes far beyond the calculation of the financial returns from TJD projects. As a way of smart growth, TJD projects offer the hopes, if only to a modest degree, of relieving traffic congestions, air pollution, energy depletion, and social disintegration of the neighborhoods. Areas for Further Research Land use, density and designs are all crucial to the success of TJD projects. However, it is often mistakenly assumed that mixed land use and high-density alone enable higher financial return from TOD and TJD projects. While greater financial return is probably more likely if projects are designed to take advantage of the benefits provided by these other two characteristics, mixed-use and high density are not determinative in and of themselves. Land use and density of projects are more often used to identify TOD and TJD projects rather than design, but experience shows that actual design of projects may have played a major role in deciding their success. Identified as America’s prototypical TJD project because of its mixed land use and high density, the Lindbergh Station project raised several questions about the roles of density and mixed land use in a real TJD project:

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72 Research shows that too much parking has a deleterious effect on transit ridership, aggravates traffic congestion, and drives up the cost of projects. Too much density is just as bad. Construction parking at Lindbergh cost about $10,000 per space, driving up project costs. One of the results is that Lindbergh’s apartments and condos will be priced above what many Atlanta residents can afford. As long as parking is convenient, and taking transit and walking around Atlanta is not, Lindbergh residents with the means to choose are not likely to choose transit. Too much density exacerbates the problem, because density combined with the transit access and increased mobility causes the value of the property to inflate, which creates pressure to create monocultures of offices or retail. At $40 a square foot, for example, space may be too expensive for any use other than high-end offices or a concentration of commercial activity that needs to draw patrons from a vast market area that is not serviceable by transit. (Feigon, Hoyt, and Ohland 2004) On the contrary, some bedroom communities around the San Francisco Bay area exemplify successful transit-oriented developments without mixed land use. And homogenous developments of student apartments thrive along the bus routes in the university town of Gainesville. The roles that land uses, density and designs, together with the interactive effects among them, are playing still need further research towards a better and comprehensive understanding.

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APPENDIX A CALCULATING THE LEVEL OF LAND-USE MIX In order to understand and calculate the level of land-use mix using the new equation, several steps were carried out in Arc GIS 9.0 as follows: 1. Based on the land use codes provided in the parcel data, various land uses are grouped into 11 categories: “Office,” “Institutional,” “Recreational,” “Single Family,” “Multifamily,” “Parking/TCU,” “Commercial,” “Park/ Open Space,” “Industrial,” “Vacant,” “Unknown/ Other.” 2. Individual layers containing the parcels inside each 2000 feet buffer around every transit stations are created to calculate the level of land-use mix for each buffer, which will be applied to the parcels inside each buffer (Figure A -1). Figure A-1: Selecting parcels in each buffer 73

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74 While many of the buffers are isolated from each other, some are overlapped with each other, producing many parcels selected within more than 2 buffers (Figure A-2). In order to apply one level of land-use mix value to each parcel, the maximum entropy index value among all the values calculated from the related buffers is applied to the Figure A-2: Map showing overlapped buff parcels contained by more than 2 buffers. land use category are selected. Square footag ers 3. Within every buffer, parcels with each es of the buildings on the selected parcels are then obtained for the calculations (Figure A-3).

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75 Figure A-3: Square footages of buildings on selected parcels 4. Individual tables are created for each buffer to calculate the level of land-use mix for each buffer. Statistic tools provided by Arc GIS 9.0 are used to get total square footage of all the parcels inside every buffer with each land use category (Figure A-4). Figure A-4: Statistical tools used to get total square footage of the office space inside every buffer 5. Entropy index values (level of land-use mix), calculated using the new equation, are applied to the parcels inside each buffers (Figure A-5).

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76 Figure A-5: Applying the entropy index values to the parcels inside each buffer 6. Tables containing the information of each parcel’s level of land-use mix are joined to the database containing the information of all the other urban-from variables and non-urban-form variables (Figure A-6). Figure A-6: Joining the tables for the level of land use mix 7. A dataset with the information of all the urban-form and non-urban-form variables for the research is created (Figure A-7).

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77 Figure A-7: Area around Five Points Station with all the GIS layers

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APPENDIX B APPLYING THE VALUES FROM THE US CENSUS DATA TO THE PARCEL DATA Several steps are carried out in Arc GIS 9.0 to give the values of the all the non-urban-form variables obtained from the US Census data to the individual parcels inside every census block groups: 1. Select one census block group (Figure B-1); Figure B-1: One census block group being selected 2. Select the parcels with the study region that have their centers in the selected census block group at the same time (Figure B-2); 78

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79 Figure B-2: Selecting parcels with their centers in the selected census block 3. Find the census block group ID in its attribute table; 4. Apply the census block group ID number to the selected parcels inside the census block group (Figure B-3); Figure B-3: Census block group ID number being applied to the selected parcels inside the census block group

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80 5. After each parcel has been given its census block group ID number, the parcel data is joined with the database containing the information of all the non-urban-form variables for this research (Figure B-4); Figure B-4: Joining the tables for the nonurban-form variables 6. A new file at the parcel level then is created with the information of all the non-urban-form variables for this research provided by overlaying files at the census block group level.

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LIST OF REFERENCES Belzer, D. and Autler, G. 2002. A Discussion Paper Prepared for the Brookings Institution Center on Urban and metropolitan Policy and The Great American Station Foundation. Washington, DC. Bernick, M. and Cervero, R. 1996. Transit Villages for the 21st Century. New York: McGraw-Hill. Calthorpe, P. 1993. The Next American Metropolis: Ecology, Community, and the American Dream. New York: Princeton Architectural Press. Candy, A. 2003. Affordable Housing and Transportation: Creating New Linkages Benefiting Low-Income Families. Housing Facts & Findings, Vol. 5 No. 2: FannieMae Foundation. Online reference. April, 2005. < http://www.fanniemaefoundation.org/programs/hff/v5i2-affordable.shtml > Cervero, R. 1993. Transit-Supportive Development in the U.S. Washington, DC: Federal Transit Administration. Concierge.com. April, 2005. < http://www.concierge.com/destination/atlanta/overview > Dumbaugh, E. 2004. Overcoming Financial and Institutional Barriers to TOD: Lindbergh Station Case Study. Journal of Public Transportation, Vol. 7, No. 3, Pages 43-68. Dunphy, R. Myerson, D. and Pawlukiewicz, M. 2003. Ten Principle for Successful Development Around Transit. Washington, DC: The Urban Land Institute. Federal Register Notice, 1997. April, 2005. < http://www.whitehouse.gov/omb/fedreg/1997.html >. Feigon, S. Hoyt, D. and Ohland, G. 2004. The Atlanta Case Study: Lindbergh City Center in The New Transit Town: Best Practices in Transit-Oriented Development, Edited by Dittmar H. and Ohland G. 2004. Washington, D.C.: Island Press. Frank, L. and Pivo, G. 1994. Impacts of Mixed Use and Density on Utilization of Three Modes of Travel: Single-Occupant Vehicle, Transit, and Walking. Transportation Research Record # 1466. Washington, D.C.: Transportation Research Board. 81

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82 Federal Transit Authority (FTA) 1997. FTA’ s Circular 9300. April, 2005. < http://www.fta.dot.gov/library/policy/9300.1A/toc.htm >. Half a Billion Americans? The Economist. April, 2005. . INFORM Reports 2004. INFORM. April, 2005. < http://www.informinc.org/index.php > Jakes, A. 1998. Transit Success? It’s The Real Estate, Stupid. The Seattle Daily Journal of Commerce. Online reference. April, 2005. < http://www.djc.com> Keefer, L. 1984. Profit Implications of Joint Development: Three Institutional Approaches. Washington, D.C.: U.S. Department of Transportation, Urban Mass Transportation Administration. Knight, R. and Trygg, L. 1977. Land Use Impacts of Rapid Transit: Implications of Recent Experiences. Washington, D.C.: U.S. Department of Transportation, DOT-TPI-10-77-29. Kockelman, K. 1997. The Effects of Location Elements on Home Purchase Prices and Rents: Evidence from the San Francisco Bay Area. Transportation Research Record # 1606. Washington, D.C.: Transportation Research Board. Metropolitan Atlanta Rapid Transit Authority (MARTA). 2005. April, 2005. < http://www.itsmarta.com > Middleton, W. 1967. The Time of the Trolley. Milwaukee: Kalmback Publishing. Nelson, A. 1992. Effects of Elevated Heavy-Rail Transit Stations on House Prices with Respect to Neighborhood Income. Transportation Research Record # 1359. Washington, DC: Transportation Research Board. Oregon Revised Statutes, Section 307-600-1. 1995. April, 2005. < http://www.leg.state.or.us/95reg/measures/hb3100.dir/hb3133.en.html >. Parsons, Brinckerhoff, Quade and Douglas, Inc. 2001. Transit Oriented Development in America: A Working Paper. Sacramento: California Department of Transportation Statewide TOD Study, draft report. Porter, D. 1997. Transit Focused Development: A Synthesis of Practice. Washington, DC: Transportation Research Board, National Research Council. Salvesen, D. 1996. Promoting Transit-Oriented Development. Urban Land, July. April, 2005. < http://www.uli.org/AM/Template.cfm?section=July4&template=/MembersOnly.cfm&ContentID=3387 >.

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83 Steiner, R. 1997. Traditional Neighborhood Shopping Districts: Patterns of Use and Modes of Access. Ph.D. dissertation. University of California, Berkeley. Still, T. 2002. Transit-Oriented Development: Reshaping America’s metropolitan Landscape. On Common Ground, Winter, Pages 44-47. Surface Transportation Policy Project (STPP) 2003. Stay The Course: How To Make TEA-21 Even Better, Surface Transportation Policy Project Report. Washington, DC. Transit Cooperative Research Program (TRCP) 2002. Transit-Oriented Development and Joint Development in the United States: A Literature Review. Washington DC: Transit Cooperative Research Program (TRCP), Research Results Digest, Number 52. Transit Cooperative Research Program (TRCP) 2004. Transit-Oriented Development in the United States: Experiences, Challenges, and Prospects. Washington DC: Transit Cooperative Research Program (TRCP), Report 102. U.S. Census Bureau. April, 2005. < http://www.census.gov/acs/www/Products/Profiles/Single/2000/ACS/GA.htm >

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BIOGRAPHICAL SKETCH Shao-Ming Zhang has been a master’s student of urban planning at the University of Florida since fall 2002. He was born in Hangzhou, China, and studied architecture at Zhejiang University. During his study in China, he interned at the East China Architecture Design Research Institute in Shanghai. After graduation he came to the United States to study for his first master’s degree in urban pla nning with a graduate a ssistantship from the Department of Urban and Regional Planning. Focusing on the study of urban design, he also finished all the courses required for an Interdisciplinary Concentration in Geographic Information System (ICGIS) at the University of Florida. Besides, he also got enrolled in the Department of Civil and Coastal Engineering at the University of Florida for his second master’s degree. He had his first internship in the United States at Regional Transit System (RTS) in the City of Gainesville, Florida. After working as an urban planner under architect and planner Dennis A. Smeltz in 2004, he started his career in Orlando, Florida, as an urban designer in the summer of 2005. 84