Transit-Oriented Development in the United States: Experiences, Challenges, and Prospects (2004)

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PART 3 THE IMPACTS OF TOD TOD is in a position to produce a wealth of benefits, although impacts vary considerably, and some disagreement is found in the literature. Still, evidence continues to accumulate showing that, under the right conditions, TOD can produce real and meaningful benefits, especially with regard to ridership increases and improved economic conditions in neighborhoods surrounding stations. Chapter 7 reviews evidence on the breadth of benefits attributed to TOD, drawing from the literature and secondary sources. The views of various local stakeholders regarding TOD’s potential benefits are also presented. Chapter 8 zeros in on TOD’s ridership impacts, reviewing experiences to date and presenting original research on how development around rail stops gets translated into additional passengers in the San Francisco Bay Area and Arlington County, Virginia. Chapter 9 looks at the benefits of TOD from a private-sector perspective in terms of land- value and real-estate market impacts. Experiences show that various factors, some within the sphere of public-sector influence and others outside it, have a strong bearing on whether development near transit gets translated into price premiums.

119 Chapter 7 Benefits of TOD TOD’s Range of Benefits TOD has attracted the interest of politicians, environmentalists, real-estate developers, and other groups in recent times because it yields benefits. TOD, as one of the more visible forms of smart growth, is increasingly viewed as an antidote to traffic congestion, the isolation and detachedness felt in many suburban communities, affordable- housing shortages, and inner-city decline and disinvestment. TOD, proponents maintain, can contribute toward creating a sustainable built form, functioning as a counter-magnet to automobile-induced sprawl. Under the right conditions, TOD can be a boon to local communities, especially when coupled with proactive public assistance. TOD can spur the redevelopment of declining neighborhoods (e.g., downtown Long Beach, California, and Arlington Heights, Illinois), spawn new suburban villages (e.g., Pleasant Hill, California, and Orenco, Oregon), breathe life into older suburban downtowns (e.g., Bethesda, Maryland, and Plano, Texas), and speed up the transition of places suffering from slow commercial encroachment (e.g., Ballston, Virginia, and Rutherford, New Jersey). Even larger aspirations have been attached to TOD, such as its potential for building human capital by increasing day-to-day social interaction and strengthening the bond between residents and their community. Quality of life is often used as an umbrella term for some of the less tangible benefits of TOD. Living in a neighborhood that allows one to drive less and walk, bike, and use public transit more, some feel, reduces stress, enables one to meet neighbors more often and spend more time with the family, increases physical activity, and offers a safer living environment (i.e., it increases the quality of life). By one account, “when people say ‘livability,’ they mean clean air and water, safe streets, positive race relations, affordable homes, quality public schools, greenery and open space, uncongested roads, and low taxes.”1 Finding pathways to such lofty goals and reconciling conflicts (e.g., between quality public schools and low taxes) is no easy task; nonetheless, TOD is increasingly being looked on as a promising approach to providing a more livable and sustainable future. The literature is replete with platitudes that have been heaped on the TOD concept; however, relatively few serious studies have been carried out that assign benefits to TOD in any quantitative or monetary sense. For the most part, anecdotes and story lines are relied on instead. Two benefits for which quantitative impacts have been measured—ridership increases and property value gains—receive special treatment as their own chapters in this report (Chapters 8 and 9, respectively). Methodologically, the challenge in gauging the payoff of TOD is attribution—how much of a change in

traffic congestion, property values, or open-space consumption is due to TOD versus all the other (confounding) factors that could account for the change. Presently, the state of knowledge on the benefits that can be assuredly attributed to TOD is fairly limited. Table 7.1 organizes TOD’s purported benefits into several categories, providing the structure for much of the discussion in this chapter. Some benefits are public in nature, accruing to society at large.2 Others are largely private, conferred on selective individuals, businesses, or property owners. Some benefits, such as increased affordable- housing opportunities, accrue to both the public and private spheres to some degree. Moreover, quite a few of the benefits attributed to TOD are associated with any form of compact, mixed-use development (e.g., neotraditional neighborhoods), not just TOD. Benefits like reduced road expenditures, preservation of open space, and lower parking costs are generic to any program that reduces sprawl and automobile usage (and more specifically VMT). Table 7.1 also divides benefits into primary and secondary categories. Primary benefits are those that represent a direct cause and effect between TOD and impacts. Secondary benefits spin off largely from primary ones and thus are 120 Note: Values in parentheses represent primary benefits and/or secondary benefits that are the source(s) of the secondary/collateral benefit listed. Primary Recipient of Benefit: Class of Benefit: Public Sector Private Sector 1. Increase ridership and farebox revenues 5. Increase land values, rents, and real-estate performance 2. Provide joint development opportunities 3. Revitalize neighborhoods Primary 4. Economic development 6. Increase affordable- housing opportunities A. Less traffic congestion and VMT-related costs, like pollution and fuel consumption (1) G. Increase retail sales (1, 2) B. Increase property- and sales- tax revenues (5) H. Increase access to labor pools (A, 6) C. Reduce sprawl/conserve open space (1, 3, 6) I. Reduced parking costs (C, 2) D. Reduce road expenditures and other infrastructure outlays (1) E. Reduce crime (3, 4) Secondary/Collateral F. Increased social capital and public involvement (3, 4) J. Increased physical activity (C, E, F) Table 7.1. Classes and Recipients of TOD Benefits

collateral. Many secondary benefits are financial in nature, representing “accounting transfers” (i.e., shifts from the bank accounts of one group to the bank accounts of another). Another important distinction to make regarding benefits is whether they are redistributive or generative. Redistributive impacts involve transfers and accordingly are mainly financial and pecuniary. Higher sales-tax receipts from increased retail-sales activities in a TOD community are offset by lower tax receipts from the loss of retail sales (to the TOD) in another community with an automobile-oriented shopping center. Generative impacts represent net efficiency gains that stem from improved resource allocations and accordingly are economic (versus financial) in nature. Any reduced traffic congestion and thus travel time savings afforded by TOD is an unmistakable economic benefit. Time has scarcity value, thus motorists and others who save time as a result of mode shifts spurred by TOD are able to use their time more productively, whether at work or with friends and family. Of course, attributing travel time savings to TOD is exceedingly difficult without an incredibly rich and extensive time-series database. Factors like induced travel demand (whereby short-term gains in average travel speeds are eventually eroded as motorists switch routes, modes, and when they travel) can further complicate the analysis. A 1998 study, Economic Impact Analysis of Transit Investments, concluded that transit’s impacts on cities and regions are largely redistributive, with few, if any, generative effects.3 Building a rail system, for instance, might shift growth from highway corridors to rail stations; however, total numbers of households and employment in a region will not be affected (whether the rail system is built or not).4 While transit construction might fail to lure new companies and big-dollar investments to a region that would not otherwise occur, not building transit, not linking it with land use, and allowing traffic congestion and quality of life to slip are likely to be “de-generative.” This was brought to light in Atlanta when several large employers threatened to leave the region because of worsening traffic congestion. This proved to be a wake-up call, prompting the governor of the state to appoint a powerful oversight agency, GRTA, whose principle charge is to ensure that land use and transportation are closely coordinated every step of the way. GRTA uses its financial authority (i.e., control of state transportation grants) to enforce its agenda. Mega-scale mixed-use developments near rail stops, such as the Atlantic Steel Project and Lindbergh Station in Atlanta, are taking shape in large part because automobile-dependent sprawl is no longer viewed as economically sustainable. One other point needs to be made about TOD benefits. One cannot simply sum the items listed in Table 7.1 as the totality of benefits because there is a fair degree of overlap among them. To do so would be double-counting. Touting the multiplicity of benefits attributed to TOD without acknowledging such double-counting can discredit TOD by giving nay-sayers an easy target for launching their critiques. It is fair to say that many of transit’s benefits are co-dependent and mutually reinforcing, with a fair amount of overlap between them. 121

Primary Benefits This section reviews the primary benefits associated with TOD from both a public- and a private-sector perspective. Public Sector Below, the four primary public-sector benefits—ridership increases, joint development opportunities, neighborhood revitalization, and economic development—are reviewed. (1) Ridership Increases. On the public side of the ledger, one of the primary benefits of TOD is higher ridership. What have been referred to as the “4 D’s”—density, diversity, design, and distance to transit—have a strong bearing on travel behavior in general and rates of transit ridership in particular. TODs, of course, score high on all four Ds: density—a doubling of density is associated with nearly a 60% increase in transit boardings according to one study;5 diversity—transit ridership rates at mixed-use suburban employment centers are on average 5% to 10% higher than they are at single-use employment centers (i.e., offices only);6 design—grid-like street patterns and pedestrian-friendly designs have been associated with transit-usage levels that are as much as 20% higher than usage levels at typical suburban subdivision designs;7 and distance to transit— in the Bay Area, those living near transit are generally five times as likely to commute via transit as other residents, and in the Washington (D.C.) Metropolitan Area and Toronto the likelihood increases to seven to eight times as high.8 Surveys over the past 15 years underscore the ridership payoff of TODs: • At the Randolph Towers near Arlington County’s Ballston Station, 69% of residents commuted to work via transit, compared with a regionwide transit mode share of just 9%;9 • Near the Pleasant Hill BART station, 55% of those living in Wayside Plaza and 37% of those living in Park Regency regularly commuted via BART versus a citywide average of 16%;10 and • Nearly 80% of residents who moved to the Orenco TOD in Hillsboro, Oregon, reported in a survey that their transit usage had increased since moving into their new residences.11 Virtually all other public benefits related to TOD stem from its ridership bonus. The ridership impact of TOD is considered so important that a separate chapter is devoted to the topic in this report. Chapter 8 presents original research probing the link between TOD and rail patronage in the Bay Area and in Arlington County, Virginia. As discussed in the chapter, high ridership is in large measure a result of “self-selection”—those who wish to commute via transit make being near a rail station a key factor in their residential location choice. Increased ridership represents a net economic benefit to the degree that it translates into the conservation of resources with scarcity value, such as less fuel consumption, and reduced negative externalities, such as less pollution (air, noise, and “time”). A 122

financial benefit that is pecuniary in nature is higher farebox revenues to transit agencies. (It’s a transfer benefit in the sense that money goes from the pockets of consumers, or transit riders, to the pockets of producers, or transit agencies; the generative, or economic, benefit of increased revenues is found in the ridership shifts and consequent congestion relief discussed below, not in the financial transfers.) (2) Joint Development Opportunities. TOD provides a financial benefit to transit operators who are able to capitalize on the ability to generate revenue (e.g., through air rights or ground leases) or reduce cost outlays (e.g., through sharing the costs of parking lots) from private development at or near a station. As discussed in Chapter 2, there are more than 100 instances of transit joint development currently underway in the United States. They are found mainly among rail properties in big cities, but some smaller bus agencies have managed to co-develop (and shed costs for) multimodal transfer facilities with private commercial projects as well. Today, WMATA, serving the Washington (D.C.) Metropolitan Area, collects around $6 million annually in joint development revenues, a figure the agency hopes to triple over the next decade. At the Bethesda Station alone, the agency receives $1.6 million in ground-lease revenues from the Bethesda Place mixed-use project. A statistical analysis of joint development projects in the Washington (D.C.) Metropolitan Area and Atlanta found that an even greater benefit was the increased patronage, and thus farebox revenue, that it spurred. Interdependencies between office development and ridership were found—jointly developed office space atop or near a rail stop spurred ridership, and ridership in turn spurred office development. (3) Revitalize Neighborhoods. TOD can be a catalyst to inner-city redevelopment, breathing new life and economic vitality into once- dormant neighborhoods. Ballston in Arlington County, Virginia, is a textbook example of this, as discussed in Chapter 12. In the 1970s, before Metrorail arrived, Ballston was a neighborhood in transition, with an odd mix of low- density apartments, fast-food outlets, automobile-repair shops, and other marginal land uses. Fortuitous circumstances, like the extension of the Orange Line to Vienna (which freed up land previously used for parking), coupled with proactive planning on the County’s part (e.g., density bonuses and targeted infrastructure enhancements), triggered the transformation of Ballston into a vibrant mixed-use center. Today, it is one of Northern Virginia’s most prestigious addresses for offices, restaurants, and hotels. The extension of Boston’s Red Line subway from Cambridge to Somerville sparked a similar transformation of Davis Square, a once-thriving commercial district that gradually declined during the post–World War II era. Streetscape improvements and storefront upgrading, funded through 123

Community Development Block Grants, accompanied the subway extension. Soon after the subway was opened, two new office buildings with a total of 170,000 square feet were added to Davis Square. Today, both are fully leased. Capitalizing on the potential community benefits conferred by TOD can be an uphill struggle in many inner-city areas. Research shows that even in good economic times, the mere presence of transit cannot, by itself, catalyze a miraculous transformation of depressed inner-city neighborhoods.12 A delphi panel study of professionals involved with TOD underscored the particular difficulties of bringing projects to fruition in inner-city settings. The panel agreed that difficult-to-surmount barriers include high financial risks, negative images, fear for safety, class and racial prejudices, and sometimes concern among residents that their neighborhoods will be gentrified.13 (4) Economic Development. Closely related to neighborhood revitalization is the ability of TOD to attract new investments and businesses to marginal or declining neighborhoods, thereby creating new and better- paying jobs. New employment, of course, has a multiplier effect, spinning off other local jobs. Union Station in Washington, D.C., a bustling facility for 50,000 daily train and bus riders, has sparked an urban renaissance. Retail sales have increased at an annual rate of 5%, and, according to one analysis, between 1,200 and 1,500 new jobs have been created at the station itself.14 The Fruitvale transit village in Oakland has sparked an economic renaissance in the once-declining neighborhood; however, it is unlikely that this would have occurred were it not for heavy subsidies, drawn from 20 separate funding sources, that have gone into the neighborhood. Several million dollars in grants went to façade improvements and building renovation for more than 100 properties along International Boulevard, Fruitvale’s main street. Before the program, vacancies had been as high as 40% in the area; now they are less than 1 percent.15 So far, the Fruitvale transit village has been credited with adding several hundred new jobs to the area, a figure that is expected to grow when the project reaches build out over the next few years. Private Sector Two primary benefits of TOD that accrue principally to private interests are increased land values and rents and increased affordable-housing opportunities. (5) Higher Land Values and Rents. Those owning properties and businesses near transit stations can reap financial gains from rising land prices and rent. This is presumably a pecuniary impact in that relative gains around transit stations are matched by relative losses for properties and businesses that lie away from stations. As reviewed in Chapter 9, some evidence suggests that parcels near rail stations that are part of a TOD or joint development project enjoy even higher premiums due to factors such as better 124

circulation and architectural integration. Land-value impacts vary considerably by setting and circumstances; however, in buoyant real-estate markets, such as the case of light-rail-served Santa Clara County in the late 1990s, premiums in the range of 25% to 100% are not unheard of. (6) More Affordable-Housing Opportunities. Many American cities with rail transit systems, San Francisco, Washington, D.C., Los Angeles, Chicago, and New York, to name a few, face an affordable- housing crisis. In San Francisco and Los Angeles, for example, only one out of four households can afford a median-priced owner-occupied home.16 TOD provides an opportunity to increase the stock of affordable units mainly because of its “location efficiencies.” Studies show that those living in TODs need to own and use fewer automobiles. This frees up income for housing purchases. Reduced parking also lowers the cost of housing. Researchers found that in San Francisco the average increase in the price of a housing unit with a parking space compared with a unit without parking is $39,000 to $46,000.17 Such numbers lend support to the LEM program, which is based on the very principle of households being able to trade off lower transportation costs for higher housing payments. TODs also help rental markets. The poorest 20% of American families spend 40% of their take-home pay on transportation. By reducing driving costs by $3,000 to $5,000 per year, TODs make it easier for low- income renters to afford the higher rents found in many rail-served cities. Secondary Benefits This section reviews secondary benefits that spin off of the primary ones reviewed in the previous section. The notation in each subheading links each secondary benefit to one or more primary ones—“reduce sprawl/conserve open space (1, 3, 6),” for instance, denotes that the secondary benefit of less sprawl and open-space conservation stems from the primary benefits of increased ridership (1), neighborhood revitalization (3), and affordable housing production (6). The numbers correspond to those shown in Table 7.1 for the listed primary benefit. In some instances, so- called secondary benefits are largely products of other secondary benefits, for instance, the private secondary benefit of “reduced parking costs (C, 2)” is partly a product of the public secondary benefit of reduced sprawl (C). Public Sector (A) Less Traffic Congestion and Other VMT-Related Costs (1). A primary second-order benefit of TOD, or so backers claim, is relief of traffic congestion and other “ills” of single-occupant automobile travel like high fuel consumption and air pollution. (This is an outcome of the primary impact of increased ridership, enumerated as the first public benefit in Table 7.1.) Reduced traffic congestion is clearly a generative benefit. In the chain of TOD increasing ridership that in turns relieves traffic congestion, travel-time savings are 125

the hoped-for “outcome” of the TOD “output.” The Texas Transportation Institute estimates that traffic congestion costs the nation $68 billion in time delay and extra fuel consumed per year, wasting 3.6 billion hours and 5.7 billion gallons of fuel.18 The increasing unpredictability of traffic congestion (e.g., not knowing when and where one will get stuck in traffic) likely adds deadweight economic loss through disruptive effects (e.g., having to cancel meetings at the last minute). Is TOD an effective palliative to traffic jams? There is no direct causal evidence that can be found in the literature; however, research has made a link between TOD and VMT reduction. In as much as VMT declines occur in peak hours, it follows that TOD reduces congestion levels to some degree. A study of residents living in TOD-like neighborhoods in the San Francisco Bay Area found that they averaged around half the VMT per year as residents of suburban subdivisions, controlling for factors like median household incomes.19 Drawing from its own literature review, the recent California TOD study maintains that TOD can “lower annual rates of driving by 20 to 40 percent for those living, working, and/or shopping near major transit stations.”20 Part of the environmental benefit of TOD comes not just from reducing VMT but also from substituting walk-and-ride and bike-and-ride access/egress for park-and-ride. From an air quality standpoint, transit riding does little good if most people use their automobiles to reach stations. For a 3-mile automobile trip, the typical distance driven to access a suburban park-and-ride lot in the United States, 84% of hydrocarbon emissions and 54% of nitrogen oxide emissions are due to cold starts (inefficient cold engines and catalytic converters during the first few minutes of driving) and hot evaporative soaks.21 That is, a sizeable share of tailpipe emissions of the two main precursors to the formation of photochemical smog occur from turning the automobile engine on and driving a mile and turning it off. Drive-alone access trips to rail stations, regardless of how short they are, emit levels of pollutants that are not too much below those of the typical 10-mile solo commute. Thus, relying on an automobile to access a metropolitan rail service can reduce the air quality benefits of patronizing transit. Accounting for the impacts of TODs in reducing VMT and promoting walk-and-ride access, the recent California study claims that “TODs can help households reduce rates of greenhouse gas emissions by 2.5 to 3.7 tons per year.”22 Because of its location, design, and density, the Uptown District TOD in San Diego was estimated to have 20% less emissions per household compared with households in nearby developments. (B) Increase Property- and Sales-Tax Revenues (5). A secondary by- product of rising land prices and rents from TOD is increases in property- and sales-tax revenues to host communities. From a regional 126

perspective, however, this is a financial transfer, for it means less property- and sales-tax revenues in (presumably more automobile- oriented) communities that would have housed these uses if the TOD did not exist. Still, property-tax income is an indirect form of value capture whereby governments share in some of the added value created by infrastructure investments like rail systems. By reducing the windfall that land speculators might enjoy, property-tax transfers score high on equity as well as efficiency grounds. And to the degree that TODs boost land-value premiums above those associated with being near transit, they yield even greater value-capture returns to jurisdictions with the political foresight and wherewithal to promote transit-supportive growth. As a case in point, take the Pentagon City Fashion Center in Arlington County, Virginia. Surveys show that around half of the shoppers and customers going to the Fashion Center arrive by Metrorail. Many are federal workers who come from Washington’s Federal Triangle area, a 5- to 10-minute train ride away. Every time they make a purchase, they produce sales-tax revenues for Arlington County, which by conservative estimates are several million dollars annually. Overall, Arlington County’s Rosslyn-Ballston TOD corridor has been credited with generating 32.8% of the County’s real-estate tax revenue, even though it makes up just 7.6% of the County’s land area.23 While this added value is mainly redistributive, one could argue that some of it is generative since the County financially participates in the land- value premiums enjoyed by rail- served properties, resulting from accessibility improvements. Evidence on the tax benefits of TOD is also found in California. More than 60% of customers going to the San Francisco Center and Horton Plaza in San Diego (both regional retail centers near downtown rail stops) take transit.24 Without rail transit connections, a substantial share of these retail sales transactions would occur at automobile-served suburban shopping malls. The 55-acre La Mesa Village Plaza TOD in San Diego is estimated to have generated over $3.2 million in additional tax revenues over the past decade as a result of stepped-up retail activities. It should be noted that subsidies—in the form of redevelopment financing, discounted land costs, and site remediation grants—were needed to produce these tax gains. (C) Reduce Sprawl/Conserve Open Space (1, 3, 6). By encouraging infill and accommodating small-lot projects, TODs can reduce pressures to convert farmland and open spaces into tract housing and other land- hungry suburban development. The seeds of greater Portland’s ambitious TOD initiatives lie in state-mandated Urban Growth Boundaries (UGBs) whose principle purpose is to preserve open space and farmland (see Chapter 17). TCRP Report 74: Costs of Sprawl—2000 concluded that contiguous, compact development could save the United States nearly 2.5 million acres of land—much of it agricultural and 127

environmentally sensitive—over the next 25 years.25 Sprawl-like development uses 10 to 40% more land than compact development.26 By one estimate, switching to higher- density development patterns could save as much as 350,000 acres of farmland by 2040 in 11 counties of California’s Central Valley agricultural belt.27 Besides saving land and money, reducing sprawl through TOD can produce other environmental benefits. One is improved water quality through reducing the amount of impermeable surface runoff. Another is preserving biodiversity by reducing the fragmentation of natural habitat and grazing grounds. (D)Reduce Road Expenditures and Other Infrastructure Outlays (1). Among the highest costs associated with low-density, automobile- supported patterns of growth are outlays for roads, sewer- and water- line extensions, and other infrastructure expansions. TCRP Report 74 suggests that developments like TOD can reduce fiscal outlays for water, sewage, and roads by as much as 25%.28 Overall, a savings of 188,300 lane-miles of local roads (valued at $110 billion) and some $12 billion in reduced water- and sewer-line extensions could be achieved by redirecting growth to compact centers over the 2000 to 2025 period. While some of these savings would be offset by additional outlays for regional transit systems and higher service costs in other sectors (e.g., for fire protection as a result of more buildings in dense settings), on balance a stepped-up transit investment and TOD program that effectively curbed sprawl would likely save the United States over $10 billion annually in public infrastructure expenditures. (E) Reduce Crime and Increase Safety (3, 4). By creating active places that are busy throughout the day and evening, providing “eyes on the street,” TODs increase safety for pedestrians, transit users, and the community at-large. Mixed-use, compact, and pedestrian-friendly places near transit nodes are very much in keeping with Jane Jacobs’s prescription for livable, vibrant, uplifting, and safe-feeling cities as poignantly described in her book, The Life and Death of Great American Cities.29 TOD can also create “defensible spaces” that instill a sense of safety and well-being, particularly for families with kids, through a tacit form of neighborhood policing. A review of transit stations in Tucson, Corpus Christi, and New York City found that street life in combination with lighting improvements, addition of retail kiosks, street art, and a police presence were associated with declines in both perceived and actual crime rates.30 Another way TODs can increase safety is by providing less hazardous settings for pedestrians and cyclists. One study estimated that accidents involving pedestrians cost the state of California $4 billion in lost productivity and medical expenses in 1999.31 The various streetscape, traffic-calming, and integrated- pathway networks that accompany many TODs can reduce accidents by slowing down moving cars and 128

shielding pedestrians and cyclists from harm’s way. Countries with world-class transit services and transit-supportive land-use patterns, like Germany and the Netherlands, have witnessed dramatic reductions in pedestrian and bicycle accidents through such design treatments.32 (F) Increased Social Capital and Public Involvement (3, 4). Robert Putman, in his highly acclaimed book, Bowling Alone, makes the point that less automobile-dependent settings, like TODs, spur volunteerism, social interaction, and community engagement.33 Because they regularly come into face-to-face contact, “chat across the fence,” and get to know their neighbors and neighborhoods, Putman contends that those living in TOD-like places get involved in community affairs (expressed by higher levels of participation in neighborhood clean- up drives, PTA meetings, voting, and the like). He estimates that for every 10% decrease in driving time there is a 10% increase in civic participation. Some critics cringe at such physical- determinist talk; however, the flip side of the coin is research showing that living in automobile-dependent sprawling suburbs is associated with commuting stress and higher rates of absenteeism.34 The award-winning mixed-use TOD built at the Orenco light-rail station in Hillsboro, Oregon, features a wide range of housing options, from multifamily rowhomes to small-lot, detached single-family units (see Photo 7.1). The Orenco project was designed to encourage walking, both within the community and for access to light-rail transit. To create a pedestrian scale, $500,000 in federal clean air funds were “flexed” to finance the project’s main promenade. Orenco’s interconnected street system shortens walking distances, and tree- lined roads, combined with on-street parking, have created a comfortable sidewalk environment. Surveys show that the primary reason people have bought new homes in Orenco has been “community design and amenities.”35 Orenco’s human-scale “community feel” has no doubt increased social capital by strengthening the bond between residents and their neighborhoods. The diverse stock of housing has also given consumers a wide array of choices in how to spend their disposable income for the two “big-ticket items”: housing and transportation. Private Sector (G)Increase Retail Sales (1, 2). By concentrating walk-on and walk-off traffic around rail stops, TODs are thought to increase shopping activities at nearby retail outlets. Those passing by when exiting transit stations after work, for example, might be inclined to pick up small items at nearby stores. Increased retail sales, however, are a pure financial transfer—from the pockets of consumers and merchants of automobile-oriented shops to the pockets of those doing business near transit stops. Chicago’s Union Station, the second busiest railroad station in the United States, is home to several hundred locally owned and operated businesses. In the mid-1990s, the station’s food retailers were 129

generating more than $12.5 million in sales annually, which is about $600 per square foot of rentable space.36 This sales figure ranks the station as one of the top food retail locations in the country. (H)Increased Access to Labor Pools (A, 6). Placing more workers within easy reach of jobs via transit can increase the pool of labor and specialized skills from which employers can draw, providing transit-accessible businesses a competitive advantage. Recent research demonstrates that higher levels of accessibility during commute hours are associated with higher labor productivity.37 This held not only nationwide, but also within the fairly intensively transit-served San Francisco Bay Area. Bay Area communities with high levels of transit accessibility (which TOD contributes to) were found to have higher levels of economic output per worker when controlled for factors like population size and employment densities. The flip side of poor access to labor is economic losses. The San Francisco Bay Area Economic Forum estimates that local businesses lose some $2 billion annually in lost productivity because of employees sitting in traffic jams.38 (I) Reduced Parking Costs (C, 2). Businesses and homeowners located 130 Photo 7.1. Variety of Housing Products and Communal Spaces at Orenco Station, Hillsboro, Oregon. Various amenities and streetscape improvements have drawn many homebuyers to the rail-served community and promoted social interaction, something that many new suburban communities lack.

near transit stops are able to economize on parking, partly because larger shares of trip ends are by transit and also because of shared-parking possibilities. The Commons mixed- use TOD in downtown Denver, for example, has below-standard parking (2 spaces per 1,000 square feet of commercial space compared to a norm of 2.5 to 3 spaces). Shared parking has further lowered supplies. At around $25,000 per space for underground parking, reduced parking afforded by TOD saved the developer several million dollars. Part of these savings, presumably, is passed on to consumers (especially when there is a “buyer’s,” or price- elastic, real-estate market). (J) Increased Physical Activity (C, E, F). America currently faces a serious obesity problem in part because so many teenagers and adults live a sedentary lifestyle. The U.S. Surgeon General recommends accumulating 30 minutes of moderate physical activity per day. However, 74% of U.S. adults do not get enough physical activity to meet public health recommendations, and about one in four U.S. adults remains completely inactive during their leisure time.39 Public health officials contend that walking has been engineered out of everyday life because of automobile-dependent landscapes. As walking-friendly environments, TODs can play a role in increasing physical activity. A recent national study found that those living in more compact settings were 10% less likely to be obese than those living in low-density neighborhoods, all else being equal.40 Another study, based on travel-diary data from the San Francisco Bay Area, found that mixed land-use patterns, like those found at most TODs, significantly increased the odds of walking for non-work trips of 2 miles or less, controlling for factors like rainfall and slope that might deter foot travel.41 Debates Not everyone sees TOD in a positive light. A spirited debate has surfaced about the pros and cons of TOD, with environmentalists and transit advocates praising TOD and skeptics criticizing it. Portland’s experiences are often cited to underscore TOD’s beneficial side. Chapter 17 discusses Portland’s many TOD successes. Portland’s MAX light- rail system opened in 1986, and by 2000 more than $2.4 billion in development had occurred within walking distance of the Eastside and Westside stations.42 Job access has been materially enhanced by MAX—the Westside line today serves 24,000 high-tech jobs, providing mobility to what is increasingly a vital part of the region’s economy. More than 1,800 multifamily housing units have been built on infill sites along light-rail and streetcar lines. Numerous accounts and studies have chronicled the rising land values and rents in neighborhoods served by Portland’s light rail system.43 Not all interpretations of Portland’s experiences are so generous. In a critique of the idea that transit’s benefits get translated into higher land values that can be recaptured, one Portland observer commented: Instead of value capture, Portland is having to subsidize transit oriented 131

development at light rail stations by means of property-tax abatements, zoning bonuses, and permit expediting. Transit oriented development was not occurring naturally in Portland and subsidy is being used to jumpstart it. The major obstacle is that land prices are not high enough to justify the densities and structured parking that are desired by transit oriented development planners. However, rationalizing the subsidy is difficult. TOD is supposed to yield benefits, not costs. Assurances about reducing urban sprawl, increasing use of alternative modes, and reducing pollution are not substantiated. In Portland, there appears to be a continuing need for subsidy.44 Even at the level of a specific light-rail station, opinions differ markedly regarding net impacts. Take the much- vaunted Orenco Station, discussed earlier in this chapter. On the rosy side are surveys showing that nearly 80% of residents living near the Orenco Station said that they ride transit more since moving to their new residence.45 Another researcher estimated that 22% of Orenco commuters regularly use public transit, higher than the 5% average for the region.46 The Orenco TOD’s popularity is underscored by the fact that, according to one observer, homes are selling 60% faster than comparable units in non-TOD projects.47 As a further testament to its success, TOD boosters point out that Orenco was voted America’s Best Planned Community by the National Association of Home Builders in 1999. In striking contrast, a critical perspective on Orenco is offered by analysts from the Cascade Policy Institute: Most of (Orenco’s) earliest construction took place adjacent to Cornell Road, while the land immediately surrounding the rail stop remained vacant . . . In terms of transit use, Orenco Station has largely proven to be a disappointment. Most people who take the train . . . arrive there by car. Three large employers . . . provide free shuttles for their employees to get to and from the light-rail station. This inflates light rail ridership, but adds to local traffic—shuttles circulate for hours, often times empty—thereby diminishing the alleged environmental benefits of rail.48 Based on a separate survey of Orenco’s residents, another critic claims that “Three-quarters . . . always drive; and only one out of six use transit (including bus) more than twice a week.”49 She further notes that “Orenco Station fails the housing affordability test, with housing going around 30% higher than the county average.”50 Another critic challenges the very premise that TOD relieves traffic congestion. In a paper written for the Heritage Foundation, Wendell Cox wrote Transit-oriented development increases congestion. The overwhelming majority of travel to proposed transit-oriented developments will be by automobile. This will strain road space, slowing traffic and increasing pollution as a consequence.51 This last comment speaks to the protracted nature of TOD’s impacts. By attracting park-and-riders, passenger drop-off traffic, pedestrians, and others to a concentrated area, transit stations are 132

often surrounded by congested intersections. Also, in the near term, TODs unquestionably add more traffic to nearby city streets. Over the longer run, however, one expects less overall traffic congestion as TODs mature and win over more customers, and VMT is certainly less with growth around transit stops than without it. In modeling transportation and land-use scenarios for metropolitan Sacramento, California, using state-of- the-practice simulation approaches, researchers found that the addition of TOD to transit scenarios reduced VMT by up to 9% compared with baseline conditions.52 This translated to an economic benefit of 15 cents per trip, with benefits accruing to all income groups. The researchers further found that TOD helped reduce the regressive income effects of higher road pricing as part of a balanced transportation strategy. Suffice it to say, many different “spins” have been placed on the impacts of TOD. For this very reason, TOD was called “a much-hyped concept” in a recent national publication, “with a predictable amount of misinformation and misrepresentation within the policy and development worlds.”53 Conflicting interpretations and research findings stem in part from methodological differences and vagaries, but they also reflect the ideological leanings of analysts. Polarized research findings make it difficult to inform policy-makers about the benefits of TOD. Invariably, decisions regarding TOD get driven more by political and ideological considerations than by objective research. Perceptions of Benefits Notwithstanding what the literature and research say (or don’t say) about TOD’s benefits and disadvantages, many public- agency professionals involved with TOD at some level have formed their own opinions. The national survey of stakeholder groups asked respondents to rate, on a scale of 1 (lowest) to 7 (highest), the importance of TOD in achieving various benefits. Figure 7.1 shows that the highest marks generally went to TOD’s prospects for boosting ridership. TOD also generally scored well on its ability to improve neighborhood and housing conditions. TOD’s contributions to livability and holding sprawl in check were rated most highly by respondents from large east- coast rail cities. There generally appeared to be the least amount of confidence that TOD could do much to relieve traffic congestion. Respondents from MPOs were particularly skeptical of TOD’s congestion-relieving benefits. Overall, there was the highest confidence in TOD’s ability to improve local conditions like neighborhood quality and housing affordability, and less faith in its role in stemming acute regionwide problems like sprawl and traffic congestion. The 90 survey respondents from transit agencies were further asked to rate the impacts of joint development projects based on their own community’s experiences. Figure 7.2 presents the results. Transit-agency respondents felt joint development was most effective at spurring redevelopment and creating better-designed (e.g., architecturally integrated) projects. They assigned moderate credit to joint development’s abilities to increase public-sector revenues and transit ridership. They were least confident that it raised property values or contributed significantly to smart-growth agendas. 133

Conclusion The potential benefits of TOD are wide- ranging, spanning across social, environmental, and fiscal concerns. Focusing growth around transit stations capitalizes on expensive public investments in transit by producing local and regional benefits. TOD, proponents say, can be an effective tool in curbing sprawl, reducing traffic congestion, and expanding housing choices. The most direct benefit of TOD is increased ridership and the associated revenue gains. Research shows that residents living near stations are five to six times more likely to commute via transit than are other residents in a region. Other primary benefits include the revitalization of declining neighborhoods, financial gains for joint development opportunities, increases in the supply of affordable housing, and profits to those who own land and businesses near transit stops. Among TOD’s secondary benefits are congestion relief, land conservation, reduced outlays for roads, and improved safety for pedestrians and cyclists. Many of these benefits feed off of each other, and quite a few are redistributive in nature—gains 134 2 3 4 5 6 Relieve Traffic Congestion Reduce Sprawl Increase Political Support for Transit Increase Housing Choices Improve Neighborhood Quality Increase Ridership Mean Rating (1=minimal; 4=moderate; 7=significant) State DOTs MPOs Redevelopment Agencies Local Governments Transit Agencies Figure 7.1. Rating of Impact of TOD in Achieving Benefits Based on Experiences in Stakeholder’s Community.

by some are matched by losses experienced by others. Impacts of TOD no doubt vary by time and circumstances. In a boom economy, when highways are jam-packed, the benefits of living, working, and running a business near a grade-separated, high- performance transit line are likely much greater than during an economic downturn. TOD is also likely to be more highly valued in big congested cities than in small uncongested ones. It is because of such variation that our knowledge of benefits remains partial. Such variation has also given rise to harsh debates and conflicting signals on TOD benefits, especially in “best case” settings like Portland, Oregon. Those working for transit agencies and local, regional, and state governments generally give TOD a moderate rating in terms of its ability to produce benefits. TOD gets high marks for contributing to neighborhood and housing conditions. Its greatest benefit, according to national survey respondents, is in increasing ridership. It is to the potential ridership benefits of TOD that we now turn. Notes 1 Project for Public Spaces, Inc., TCRP Report 22: The Role of Transit in Creating Livable Metropolitan Communities (Washington, D.C.: Transportation Research Board, National Research Council, 1997), 5–6. 2 The economist definition of “public” is non- exclusivity, non-rivalness, and often natural monopoly properties (like economies of scale). 3 Cambridge Systematics, Inc., R. Cervero, and D. Aschauer, TCRP Report 35: Economic Impact Analysis of Transit Investments: Guidebook for Practitioners (Washington, D.C.: Transportation Research Board, National Research Council, 1998). 4 Transit investments certainly increase employment in the form of construction jobs 135 9.8% 4.9% 11.8% 23.1% 2.6% 12.5% 56.1% 56.1% 41.4% 30.7% 50.0% 37.5% 34.1% 39.0% 44.1% 46.2% 47.7% 50.0% 0% 10% 20% 30% 40% 50% 60% 70% 80% Increases Property Values Promotes Smart Growth Increases Ridership Increases Public- Sector Revenues Improves Urban Design Spurs Redevelopment Percent Rating Significant Moderate Minimal Figure 7.2. Rating of Joint Development Impacts by Transit-Agency Respondents.

and other project-related activities; however, these are redistributive not generative in nature. That is, they represent financial impacts in the form of shifting money from national taxpayers to local (or imported) construction firms courtesy of federal, state, and local grants, in addition to other financial sources. 5 Parsons Brinckerhoff Quade & Douglass, Inc., R. Cervero, Howard/Stein-Hudson Associates, and J. Zupan, “Regional Transit Corridors: The Land Use Connection,” TCRP Project H-1 (Washington, D.C.: Transportation Research Board, National Research Council, 1995). 6 R. Cervero, “Mixed Land Uses and Commuting: Evidence from the American Housing Survey,” Transportation Research A, Vol. 30, No. 5 (1996): 361–377. 7 R. Cervero, “Built Environments and Mode Choice: Toward a Normative Framework,” Transportation Research D, Vol. 7 (2002): 265–284. 8 R. Cervero, “Transit-Based Housing in California: Evidence on Ridership Impacts,” Transport Policy, Vol. 3 (1994): 174–183; JHK and Associates, Development-Related Survey I (Washington, D.C.: Washington Metropolitan Area Transit Authority, 1987); JHK and Associates, Development-Related Survey II (Washington, D.C.: Washington Metropolitan Area Transit Authority, 1989); M. Stringham, “Travel Behavior Associated with Land Uses Adjacent to Rapid Transit Stations,” ITE Journal, Vol. 52, No. 1 (1982): 18–22. 9 JHK and Associates, 1987, op. cit. 10 R. Cervero, Ridership Impacts of Transit- Focused Development in California, Monograph 45 (Berkeley: Institute of Urban and Regional Development, University of California, 1993). 11 Portland TriMet Transit Agency, “Transit- Oriented Development Research Associated with Westside MAX Opening” (Portland, Oregon: 1999). 12 M. Boarnet and R. Crane, “Public Finance and Transit-Oriented Planning: New Evidence from Southern California,” Journal of Planning Education and Research, Vol. 17 (1998): 206–219; A. Loukaitous- Sideris and R. Bannerjee, “Blue Line Blues: Why the Vision of Transit Village May Not Materialize Despite Impressive Growth in Transit Ridership,” Journal of Urban Design, Vol. 5, No. 2 (2000): 101–125. 13 A. Loukaitous-Sideris, “Transit-Oriented Development in the Inner City: A Delphi Survey,” Journal of Public Transportation, Vol. 3, No. 2 (2000): 75–98. 14 Project for Public Spaces, Inc., 1997, op. cit. 15 T. Parker, G. Arrington, M. McKeever, and J. Smith-Heimer, Statewide Transit-Oriented Development Study: Factors for Success in California (Sacramento: California, Department of Transportation, 2002), 94–95. 16 California Building Industry Association, Where Will They Live? (Sacramento: 2001). 17 Calthorpe Associates, Wasatch Front Transit Oriented Development Guidelines (Salt Lake City, Utah: Envision Utah, 2002). 18 G. Sciara, “Traffic Congestion: Issues and Options” (conference summary, Conference on Traffic Congestion, Washington, D.C., June 26–27, 2003). 19 J. Holtzclaw, “Using Residential Patterns and Transit to Decrease Auto Dependence and Costs” (San Francisco: Natural Resources Defense Council, 1999). http:// www.smartgrowth.org/library/cheers.html. 20 T. Parker et al., 2002, op. cit., p. 6. 21 R. Cervero, BART @ 20: Land Use and Development Impacts, Monograph 49 (Berkeley: Institute of Urban and Regional Development, University of California, 1995); Barry and Associates, Air Quality in California (Sacramento: California Air Resources Board, 1999). 22 Parker et al., 2002, op. cit., p. 43. 23 Arlington County Department of Community Planning, Housing and Development, Development in the Metro Corridors 2000 (Arlington County, Virginia, 2002). 24 Cervero, 1993, op. cit.; Air Resources Board, The Land Use-Air Quality Linkage (Sacramento: California Environmental Protection Agency, 1994). 136

25 R. Burchell, G. Lowenstein, W. Dolphin, C. Galley, A. Downs, S. Seskin, K. Still, and T. Moore, TCRP Report 74: Costs of Sprawl—2000 (Washington, D.C.: Transportation Research Board, National Research Council, 2002). 26 J. Landis, “Imagining Land Use Futures: Applying the California Urban Futures Model,” Journal of the American Planning Association, Vol. 61, No. 4 (1995): 438–457. 27 B. Muller and T. Bradshaw, Central Valley Alternative Growth Futures: Options for Preserving California’s Agricultural Capacity (University of California at Berkeley: Institute of Urban and Regional Development Working Paper, 1995). 28 Burchell et al., 2002, op. cit. 29 J. Jacobs, The Death and Life of Great American Cities (New York: Vintage Books, 1961). 30 Project for Public Spaces, Inc., 1997, op. cit., pp. 65–83. 31 Surface Transportation Policy Project, Dangerous by Design: Pedestrian Safety in California (San Francisco: 1999). http://www.transact.org/Ca/design/toc.htm. 32 J. Pucher and L. Dijkstra, “Making Walking and Cycling Safer: Lessons from Europe,” Transportation Quarterly, Vol. 54 (2000): 25–50. 33 R. Putman, Bowling Alone: The Collapse and Revival of American Community (New York: Simon & Schuster, 2000). 34 R. Navaco, R. Stokols, and L. Milanesi, “Subjective and Objective Dimensions of Travel Impedance as Determinants of Commuting Stress,” American Journal of Community Psychology, Vol. 18 (1990): 231–257. 35 L. Weigand, “Orenco Station,” Livable Oregon Case Study, brochure (June 1999). 36 Project for Public Spaces, Inc., 1997, op. cit., p. 62. 37 R. Cervero, “Efficient Urbanisation: Economic Performance and the Shape of the Metropolis,” Urban Studies, Vol. 38, No. 10 (2001): 1651–1672. 38 Local Government Commission, Building Livable Communities: A Policy Maker’s Guide to Infill Development (Sacramento: 1995). 39 U.S. Department of Health and Human Services, Center for Disease Control and Prevention, National Center for Chronic Disease Prevention and Health Promotion, Activity DoNaP, Promoting Physical Activity—A Guide for Community Action (Champaign, Illinois: Human Kinetics, 1999). 40 R. Ewing, T. Schmid, R. Killingsworth, A. Zlot, S. Raudenbush, “Relationships Between Urban Sprawl and Physical Activity, Obesity, and Morbidity,” American Journal of Health Promotion, Vol. 18, No. 1 (2003): 47–57. 41 R. Cervero and M. Duncan, “Walking, Bicycling, and Urban Landscapes: Evidence from the San Francisco Bay Area,” American Journal of Public Health, Vol. 93, No. 9 (2003): 1478–1483. 42 G. B. Arrington, “The End of the Suburbs?” Community Building Sourcebook (Portland, Oregon: 1999). 43 See http://www.metrokc.gov/kcdot/alts/tod/ portland.htm. 44 K. Duecker, “A Critique of the Urban Transportation Planning Process—The Performance of Portland’s 2000 Regional Transportation Plan,” Transportation Quarterly, Vol. 56, No. 2 (2002): 20–21. 45 G. B. Arrington, Reinventing the American Dream of a Livable Community: Light Rail and Smart Growth in Portland (paper presented at the 8th Joint Conference on Light Rail Transit Investment for the Future, Transportation Research Board, Washington, D.C., 2000). 46 B. Podobnik, “Portland Neighborhood Survey: Report on Findings from Zone 2, Orenco Station,” unpublished (Portland, Oregon: Lewis and Clark University, January 2002). 47 Transit Alliance, On the Move, newsletter (February 2000). 48 M. Barton and J. Charles, The Mythical World of Transit-Oriented Development: Light Rail and the Orenco Neighborhood, Hillsboro, Oregon (Portland, Oregon: Cascade Policy Institute, 2003). 137

49 C. Bae, “Orenco Station, Portland, Oregon: A Successful Transit Oriented Development Experiment?” Transportation Quarterly, Vol. 56, No. 3 (2002): 9–18. 50 Ibid., p. 12. 51 T. Still, “Transit-Oriented Development: Reshaping America’s Metropolitan Landscape,” On Common Ground (Winter 2002): 47. 52 R. Johnston, C. Rodier, M. Choy, and J. Abraham, Air Quality Impact of Regional Land Use Policies: Final Report for the Environmental Protection Agency (Davis, California: Department of Environmental Sciences and Policy, University of California, Davis, February 2000). 53 D. Costello, R. Mendelsohn, A. Canby, and J. Bender, The Returning City: Historic Presentation and Transit in the Age of Civic Revival (Washington, D.C.: Federal Transit Administration, National Trust for Historic Preservation, 2003), 10. 138

139 Chapter 8 Evidence on Ridership Impacts TOD and Ridership If there is any single benefit of TOD that all sides agree is beneficial to society as a whole, it is increased ridership. TOD is poised to relieve traffic congestion, improve air quality, cut down on tailpipe emissions, and increase pedestrian safety in transit-served neighborhoods by coaxing travelers out of their automobiles and into trains and buses. However, congestion relief and environmental benefits accrue to an appreciable degree only if TODs result in people making the switch from driving alone to using transit. While some critics charge that rail transit investments generally lure bus riders to rail, experiences show that TOD can attract significant shares of former motorists. A California study found that among those who drove to work when they lived away from transit, 52.3% switched to transit commuting on moving within a 1⁄2-mile walking distance of a rail station.1 On balance, research to date shows that TOD yields an appreciable ridership bonus: well- designed, concentrated, mixed-use development around transit nodes can boost patronage as much as five to six times higher than comparable development away from transit. While the chief environmental benefit of TOD comes from coaxing motorists over to mass transit, a secondary benefit is more walking and bicycle trips to and from transit. Larger shares of rail trips accessed by walk-and-ride and bike-and- ride can reduce the need for parking, improve air quality, and promote physical activity. All transit trips involve some degree of walking; however, recent research makes clear that attending to the mobility and design needs of those who exclusively walk to and from stations is especially important.2 Another important ridership dimension of TODs is their mixed-use attributes. Some destinations, like offices and residences, produce trips during peak hours when trains and buses are often full. Others, like entertainment complexes, restaurants, and retail shops, generate trips mainly during off-peak hours, helping to squeeze efficiencies into the deployment of costly rail services. When mixed-use TODs are aligned along linear corridors—like “pearls on a necklace”—trip origins and destinations are evenly spread out, producing efficient bi-directional flows. This has been the case in world-class transit metropolises like Stockholm, Copenhagen, and Curitiba, Brazil, where mixed-use TODs have given rise to 55%–45% directional splits.3 This is in contrast to many U.S. settings, where peak-period trains and buses are filled to the brim in one direction but nearly empty in the other. Mixed and balanced land uses ensure mixed and balanced traffic flows. Why is it important to know about the ridership impacts of TOD? The main

reason is that evidence can be useful in informing public policy. One application is the setting of credits and waivers against transportation impact fees. Los Angeles, Orlando, and Santa Clara County (CA) currently employ sliding- scale programs, adjusting impact fees downward for TODs. The Santa Clara County Congestion Management Agency recommends a 9% reduction in estimated trip generation levels when setting impact fees for new housing projects that lie within 2,000 feet of a light-rail or commuter-rail station. Research can also help inform policy initiatives like LEM programs by shedding light on the commuting cost savings of transit-based housing. It can also be of value to long- range modeling whose outputs weigh heavily on how scarce transportation dollars are allocated in Transportation Improvement Programs (TIPs). The recent scenario testing in Sacramento, California, using an integrated land-use and transportation model, for example, showed that rail investments combined with TOD and road pricing was more cost-effective and environmentally benign than a beltway scenario.4 The region’s TIP followed suit by giving high priority to several major transit projects. Reviewing the Evidence Research to date has measured ridership impacts of residences, offices, and retail shops that are within walking distance of transit stations, normally defined as 1⁄4 to 1⁄2 mile away. Below, key findings based on U.S. experiences are summarized. Residences Most of the evidence on the ridership impacts of TOD is for residential land uses. Past studies have mostly compared transit modal shares between those living within a walkable distance of a station and those who live farther away. Among the research findings to date are the following: • Surveys from 1992 and 1993 of Bay Area workers living near BART found that, on average, 32% commuted by rail; this is more than six times the regional average of just 5%. Automobile availability and parking prices had a huge bearing on ridership rates. Station-area residents from households with no automobiles were 14 times more likely to rail commute than those from three- automobile households. And 42% of station-area residents who paid for parking commuted by rail compared with just 4.5% who received free parking.5 Further, if a commute was to downtown San Francisco and a station-area resident from a one- automobile household had to pay for parking, there was an 82% likelihood he or she would take transit; if, on the other hand, the person commuted to a non–San Francisco destination and could park for free, the probability plummeted to just 4%. Recent research updating this study similarly found that the probability of workers who live near California rail stops taking transit to work varied dramatically according not only to parking policies at the workplace but also whether they were able to flex their work schedules (see Figure 8.1). • The highest transit capture rates among those living near rail stops have been recorded for the Washington (D.C.) Metropolitan 140

Area.6 Surveys from the late 1980s show the share of work trips taken by rail ranging from 18% to 63%, with the highest rates among residents heading to jobs in the District of Columbia. More recent surveys of those living along the 4-mile long, 1⁄2-mile-wide Rosslyn- Ballston corridor reveal that 39% use transit to get to work and 10% walk or bike; these rates are three times higher than the average for Arlington County as a whole.7 Also, 64% of rail patrons who live along the corridor walk to stations. Moreover, because of the mixed-use nature of TODs along the Rosslyn-Ballston corridor, counts of station entries and exits are fairly similar during peak hours—that is, stations handle a balance of trip origins and destinations, making efficient use of available capacity. (See Chapter 12 for further discussions on TOD ridership impacts in Arlington County.) • A study of Santa Clara County’s light-rail corridor found TOD residents patronized transit as their predominant commute mode more than five times as often as residents countywide.8 • At the Center Commons mixed- income TOD in Portland, transit mode share increased nearly 50% for work trips (from 31% before moving into the project to 46% after) and by 60% for non-work trips (from 20% to 32%).9 141 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1 1.2 1.3 1.4 1.5 Travel Time Ratio = (Travel Time Highway/Travel Time Transit) Pr ob ab ili ty o f C ho os in g Tr an si t Flextime, Paid Parking Flextime, Free Parking No Flextime, Paid Parking No Flextime, Free Parking Figure 8.1. Sensitivity of Rail Commuting to Parking Prices, Availability of Flextime Work Schedules, and Travel Time Ratios via Highway versus Transit, Based on Model for Predicting the Likelihood of California Station-Area Residents Commuting by Rail Transit in 2003. Source: H. Lund, R. Cervero, and R. Willson, Travel Characteristics of Transit-Focused Development in California (Oakland, California: Bay Area Rapid Transit District and California Department of Transportation, 2004).

Offices Many offices enjoy high rates of transit ridership by virtue of the fact that they are located downtown where levels of transit accessibility are the highest. The availability of free parking at most non- downtown workplaces erodes transit ridership. Evidence on the ridership rates of offices near rail stops (summarized below) comes mainly from California and the Washington (D.C.) Metropolitan Area. • Surveys of rail commuting in the Metropolitan Washington (D.C.) Area found that nearly 50% of those working in offices within 1,000 feet of downtown Metrorail stations rail commuted; in the case of offices that were comparable distances from the more suburban Crystal City and Silver Spring stations, the shares were 16% to 19%.10 Place of residence was a particularly important explainer of whether office workers patronized transit. In the case of the Silver Spring Metro Center, a 150,000-square-foot office tower 200 feet from the Metrorail portal, 52% of workers who lived in Washington, D.C., rail commuted; among those living in surrounding Montgomery County, Metrorail was used by just 10%.11 • At one of San Diego’s most prominent joint development projects, the Metropolitan Transit System (MTS)/James R. Mills Building, surveys show that 18% of building users arrive by transit.12 • Surveys of those working in offices near BART found that workers were 2.5 times more likely to get to work by rail than other Bay Area commuters.13 Living near transit made a difference. On average, 19.3% of those who lived in a city served by BART and who worked near BART commuted by rail compared with 12.8% of those who worked in a similar setting but did not live in a BART-served city. Retail Retail shops and consumer services can be particularly attractive additions to TODs because they often generate off- peak and weekend trips. Thus, they help to fill trains and buses during periods of underutilized capacity. As all-day, all- week trip generators, they improve the cost-effectiveness of expensive rail investments. At least three studies have documented ridership rates among those shopping at retail stores near rail stations. Findings include the following: • For retail centers near Washington (D.C.) Metrorail stations, location and time of day of trips were the most important determinants of mode choice: well over 50% of shopping trips made to large downtown retail stores or made to other close-by malls at midday were made by Metrorail.14 • A 1993 survey found that over 60% of customers surveyed at downtown San Diego’s Horton Plaza, two blocks from the Trolley line, arrived by transit or on foot.15 • Experiences in the San Francisco Bay Area reveal that location of retail centers has a strong bearing on rail capture rates.16 Surveys from 1993 show that 33.8% of patrons at 142

the San Francisco Centre in downtown San Francisco, which has a direct portal connection to BART, arrived by transit. For two suburban malls also within an easy walk to BART, the shares were below 20%. Proximity and Built Environments Also Matter Research also shows that proximity to transit matters a lot. Table 8.1, based on 1987 experiences in the Washington (D.C.) Metropolitan Area, reveals that commuting by transit erodes rapidly with distance from rail stations. For instance, 63% of residents of The Consulate apartment complex, 300 feet from the Van Ness-UDC Station, commuted via Metrorail; at the Connecticut Heights project, 3,800 feet away from the same station, 24% rode Metrorail to work.17 In the Washington (D.C.) Metropolitan Area, the share of trips by transit fell by around 0.65% for every 100-foot increase in the distance of a residential site from a Metrorail station portal. In California, the ridership gradient is even steeper. Surveys of residents of 27 housing projects near rail stops in the Bay Area, San Diego, and Sacramento showed that ridership fell by 0.85% for every 100-foot increase in walking distance.18 In addition to relative proximity to a station, built-environment characteristics of TODs also influence transit ridership. The study of 27 transit-based housing projects in California found density to be the most important land-use predictor of ridership rates.19 Findings were similar for offices: on average, every addition of 100 employees per acre was associated with a 2.2% increase in rail commuting. The California surveys of residences and offices within 1⁄2 mile of stations found land-use mixes and the quality of the walking environment had relatively little impact on transit usage after controlling for density: “It could be that within a quarter to a half mile radius of a station, features of the built environment (ignoring issues of safety and urban 143 Metrorail Station Housing Project Distance to Station (ft) Percent of Commute Trips by: Rail Auto Other River Place North 1,000 45.3 41.5 13.3 River Place South 1,500 40.0 60.0 0.0 Rosslyn (VA) Prospect House 2,200 18.2 81.9 0.0 Crystal Square Apts. 500 36.3 48.8 14.9Crystal City (VA) Crystal Plaza Apts. 1,000 44.0 45.0 11.0 The Consulate 300 63.0 32.6 4.4Van Ness-UDC (DC) Connecticut Heights 3,800 24.0 56.0 20.0 Twin Towers 900 36.4 52.3 11.4Silver Spring (MD) Georgian Towers 1,400 34.7 43.1 22.2 Note: “Other” consists of bus, walking, cycling, and other travel modes. Source: JHK and Associates, Development-Related Survey I (Washington, D.C.: Washington Metropolitan Area Transit Authority, 1987). Table 8.1. Modal Splits for Residential Projects Near Metrorail Stations, Washington (D.C.) Metropolitan Area, 1987

blight) matter little—as long as places are near a station, the physical characteristics of the immediate neighborhood are inconsequential.”20 In their comprehensive review of empirical studies on travel and built environments, Reid Ewing and Robert Cervero concluded: “transit use depends primarily on local densities and secondarily on the degree of land use mixing.”21 Still, several studies show that the influences of mixed uses and urban design on transit ridership are not inconsequential, although these studies were conducted across all land-use settings, not just TOD. A study of six large suburban employment centers found that the existence of a retail component in an office building increases transit commute shares by 3%.22 Additionally, using data on over 15,000 households from the 1985 American Housing Survey, another study found that the presence of retail shops within 300 feet of one’s residence increased the probability of transit commuting by 3% (on average) ostensibly because transit users could pick up convenience items when heading home after work.23 Recent research using data from rail-served Montgomery County, Maryland, reached a similar conclusion: mixed uses at origins and destinations induce rail travel for all trip purposes, with elasticities between transit usage and land-use diversity ranging from 0.45 to 0.62.24 Self-Selection and Rail Commuting Ridership gains tied to TOD are significantly a product of self-selection. Those with a lifestyle predisposition for transit-oriented living conscientiously sort themselves into apartments, townhomes, and single-family units within an easy walk of a transit node. That is, being near transit and being able to regularly get around via trains and buses weighs heavily in residential location choice. High ridership rates are simply a manifestation of this lifestyle preference. A study of Santa Clara County’s Guadalupe light-rail corridor, for example, found TOD residents got to work via transit five times as often as the typical employed resident of the county.25 Self-selection was evident in that 42% of respondents stated that being close to transit was a big factor in the choice of a home or apartment. As further evidence of self-selection, a 1993 survey of San Francisco Bay Area residents living near rail transit found that 56.2% got to work by trains or buses at their previous Bay Area residence that was far away from a rail stop.26 That study concluded that many TOD residents have a proclivity to patronize transit, whether to avoid the stress of commuting, for reasons of personal taste, or to make more productive use of time spent getting to work. A recent study explicitly examined residential self-selection as a primary determinant of ridership rates among TOD residents.27 Using data on travel diaries and locations of residences and workplaces from the 2000 Bay Area Travel Survey, a nested logit model was estimated. The selection of rail transit for commuting was nested within the choice of whether to reside within 1⁄2 mile of a rail station. Factors used to explain whether someone lived near transit included workplace location, job accessibility via highway and transit networks, and household and personal characteristics (e.g., type of household, 144

type of occupation, and automobile ownership levels). Using records for more than 11,000 individuals, it was found that 19.6% of those living within 1⁄2 mile of a rail stop got to work by rail transit; among those living beyond the 1⁄2-mile radius, the share was 8.6%. For the residential-location component of the nested choice model, whether one worked within 1⁄4 mile of a rail station was the most significant predictor of whether one lived near transit. In addition to residential location, automobile-ownership levels were found to have a strong bearing on whether workers commuted by rail. All three factors—residential location, automobile-ownership levels, and rail commuting—were found to be closely interdependent. Using conditional probabilities, the study suggested that upwards of 40% of the ridership bonus associated with TOD is a product of residential location (i.e., self-selection). From the nested logit results of the Bay Area study, a sensitivity test was conducted to show how probabilities of rail commuting varied as a function of three policy variables: residential location (within 1⁄2 mile of a station or beyond); workplace location (within 1⁄4 mile of a station or beyond); and household automobile-ownership levels (0, 1, 2, 3+). The resulting sensitivity plot, Figure 8.2, shows probabilities of rail commuting are very high among all groups when the worker lives in a household with no automobiles. Adding one automobile results in probabilities plummeting; they fall most precipitously for those residing and working away from stations. For residents of transit-based housing, probabilities fall more gradually with automobile-ownership levels. For those living away from transit, the likelihood of rail commuting is not much different in two-automobile and three-or-more- automobile households. And for those 145 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 0 1 2 3+ Number of Automobiles in Household P ro ba bi lit y Co m m u te by R ai l Reside Near/Work Near Rail Reside Away/ Work Away from Rail Reside Near/ Work Away from Rail Reside Away/ Work Near Rail Figure 8.2. Sensitivity Plots of Rail-Commute Probabilities by Number of Automobiles in Household for Those Living and Working Near and Away from Stations. Note: Reside Near = 1⁄2 mile or less; Work Near = 1⁄2 mile or less. Source: R. Cervero and M. Duncan, Residential Self Selection and Rail Commuting: A Nested Logit Analysis, Working Paper 604 (Berkeley: University of California Transportation Center, 2002).

living and working away from a rail stop, the odds of commuting by a non-rail mode is about the same for a one- and a three-or-more-automobile household— less than 1 in 10. Figure 8.2 also reveals that working near transit interacts with automobile- ownership levels to produce different probabilities among station-area dwellers and their counterparts. Working near transit and having no automobiles means there is a very high likelihood, well over 80%, of rail commuting for both groups. Adding an automobile to the household results in the probability dropping far more sharply for non- station-area residents, however, to below the probability (0.28) for station-area residents who work beyond 1⁄4 mile of the station. This suggests that an appreciable share of station-area dwellers who rail commute do so out of choice rather than necessity, further hinting that self-selection has taken place. Adding a second automobile to a station-area household, however, lowers the probability of rail-commuting sharply, below that of a non-station-area worker from a two-automobile household whose job site is near a rail stop. This indicates that the transit- ridership benefits of transit-based housing comes from those with relatively few (i.e., under two) automobiles in the household. In terms of public policy, this argues for flexing parking standards for housing projects near rail stations. More recent research has confirmed that those living in compact, transit- accessible locations tend to own fewer automobiles and log fewer vehicle miles of travel per year. As part of an evaluation of the LEM concept, John Holtzclaw and a team of collaborators recently studied travel behavior and automobile-ownership levels as functions of land-use and transit- accessibility characteristics of neighborhoods in three regions with LEM programs: Chicago, Los Angeles, and San Francisco. A doubling of residential density was found to reduce household automobile ownership and VMT per capita in the 32% to 43% range. The influence of transit accessibility on automobile ownership was less than that of density, but it was still appreciable.28 Self-selection in no way diminishes the importance of planning for and building transit-oriented residences. If the marketplace was perfectly functioning, then a case might be made for governments to get out of the way so that producers and consumers could sort themselves into station areas unfettered. However, marketplaces are not perfect; factors such as NIMBY resistance to new construction, exclusionary zoning, imperfect information, or negative externalities affect them. Accordingly, findings of self-selection underscore the importance of breaking down barriers to residential mobility and introducing market-responsive zoning in and around transit nodes—zoning that acknowledges that those living near transit tend to be in smaller households with fewer automobiles. Flexible parking standards and LEMs would further encourage self- selection of TODs. Transit Joint Development and Ridership Some evidence suggests that joint development projects, such as air-rights development on transit-agency property, 146

yield among the highest ridership dividends of any form of TOD. In a 1983 study of nine transit joint development projects in the United States, Keefer found that every 1,000 square feet of new commercial floor space near a rail station generated an additional six transit trips per day, yielding an additional $11.4 million (in 1982 dollars) in annual farebox receipts.29 Case studies from the early 1980s estimated that fully realized joint development at rail stations with buoyant real-estate markets could increase ridership by 10% to 25%.30 An empirical investigation of joint development projects in the Washington (D.C.) Metropolitan Area and Atlanta found more modest impacts, although interdependencies between office development and ridership were found statistically. Jointly developed office space on top of or near a rail stop spurred ridership, and ridership in turn spurred office development.31 Statistically, a 10% increase in a rail station’s share of regional office growth was associated with around a 1% increase in that station’s share of systemwide ridership. High rates of transit usage have also been found among patrons of joint development projects in San Diego and Miami.32 The ridership boost offered by joint development projects could be due to design factors, such as architectural integration of transit stations and adjoining buildings, improved pedestrian circulation, and transit’s visible presence. TOD-Ridership Case Study: San Francisco Bay Area As revealed by discussions so far in this chapter, the ridership impacts of development around transit have been studied more in the San Francisco Bay Area than anywhere. Surveys of residents, office workers, and shoppers in the early 1990s showed that being near transit significantly boosted ridership levels, as documented in the 1993 monograph, Ridership Impacts of Transit-Focused Development in California. This study was recently updated based on travel-diary surveys conducted in May 2003; the recent surveys found that TOD’s ridership bonus has held steady.33 To further probe the connection between land development and transit usage in the Bay Area, research was carried out, as part of the TCRP Project H-27 study, using recently released data from Census 2000 on journey-to-work travel and neighborhood attributes. Using the census data and Geographic Information System (GIS) tools, an aggregate analysis of proximity to transit and modal splits was conducted using each of the 129 rail stations in the San Francisco Bay Area as a data observation.34 The Bay Area features three types of rail services—heavy rail (BART), commuter rail (Caltrain and Altamont Commuter Express), and light rail (VTA)—thus the breadth of rail offerings enriched the analysis. (Map 8.1 shows the extent of regional rail services in the urbanized portions of the Bay Area.) The analysis that follows uses commuting, socio-demographics, and neighborhood characteristics of households within 1 mile of each of the 129 Bay Area rail stations to probe how station-area land-use characteristics influence transit commute modal splits. GIS tools allowed census-tract-level data to be interpolated for 1-mile rings 147

around rail stations. On average, the share of motorized commute trips made by transit among those residing within 1 mile of the 129 Bay Area rail stations in the year 2000 was 12.6%. This compares to a regionwide transit modal split of 9.7%, based on Census 2000.35 Ridership and the 3Ds: Density, Diversity, and Design Simple bivariate regression plots reveal that among those living within a mile of a Bay Area rail stop, the “3 Ds” of the built environment—density, diversity, and design—matter greatly.36 For the 129 Bay Area rail stations that were studied, a strong positive relationship was exhibited between shares of commutes by transit among station-area residents and each of the “3Ds”— specifically, residential densities, numbers of retail and service jobs, and city block patterns. Figure 8.3 summarizes the results of simple bivariate regression equations that estimate shares of motorized commutes by transit as a function of each of the “D” dimensions.37 In general, Year 2000 transit commute shares among those residing within a mile of a station rose with residential densities, with the relationship exhibiting a slight logarithmic bend. From the equation, the likelihood that a Bay Area station-area resident rail commuted was 24.3% at densities of 10 units per gross acre. Doubling densities to 20 units per acre increased the likelihood to 43.4% and quadrupling them to 40 units per acre catapulted the probability to 66.6%. The second “D” in Figure 8.3 relates to diversity, or land-use mix. The index used here is the number of retail and service jobs per gross acre within a mile radius of a station. From the perspective of modeling modal shares among residents, the addition of retail and service activities represents a diversification of land uses. Virtually all TODs, even if they are predominantly residential in nature, include retail and service uses. As noted earlier, several studies suggest that the presence of shops, eateries, and other services in a station area can boost transit patronage by several percentage points since riders can easily pick up convenience items when en route to home in the evening, just as they often do by automobile.38 The regression equation shown in Figure 8.3 shows that transit modal shares rise with numbers of retail and service jobs up to a point; at 80 or more jobs per 148 Map 8.1. Rail Transit Coverage in the San Francisco Bay Area.

149 Dwelling Units per Gross Acre 403020100 Pr op . M ot or ize d Co m m ut es b y Tr an sit .6 .5 .4 .3 .2 .1 0.0 Retail & Service Jobs per Gross Acre 120100806040200 Pr op . M ot or ize d Co m m ut es b y Tr an sit .6 .5 .4 .3 .2 .1 0.0 No. City Blocks per Acre .4.3.2.10.0 Pr op . M ot or ize d C om m ut es b y Tr an sit .6 .5 .4 .3 .2 .1 0.0 DENSITY DESIGN DIVERSITY BIVARIATE REGRESSIONS DENSITY: Prop. Commutes by Transit = .0015 + .0266(Housing Density) – .00025 (Housing Density)2 R2 = .738 DIVERSITY: Prop. Commutes by Transit = .0510 + .0121 (Retail & Service Jobs) – .000071 (Retail & Service Jobs)2 R2 = .566 DESIGN: Prop. Commutes by Transit = .0830 – .844 (No. City Blocks per Acre) + 6.130 (No. City Blocks per Acre)2 R2 = .817 Note: N = 129 for all equations. All predictor variables are significant at the .01 probability level. Figure 8.3. Transit Commute Modal Splits and the “3Ds” of TODs (Influence of Density, Diversity, and Design on Proportion of Commutes by Transit for Bay Area Station- Area Residents, 2000).

acre, transit modal splits trend downward, possibly representing the fact that these are different residential markets given that residences generally represent a small share of land uses at such high employment densities. From the equation, the likelihood of a station- area resident rail-commuting was 11% with five retail/service jobs per gross acre. Raising this to 20 jobs per acre boosts the transit commute modal share to 26.5%, and increasing it to 60 jobs per acre shoots the share up to 52.1%. The third “D” in Figure 8.3 gauges the design features of neighborhoods around Bay Area transit stations. Specifically, it measures the average number of city blocks per acre within a 1-mile radius of stations. It gets at the general scale, land platting, and street connectivity of station areas. The larger the number, the more blocks per acre and correspondingly, the more walkable a neighborhood generally is. The average number of blocks per acre ranged from a low of .028 per acre in BART-served Orinda, an affluent suburb in Contra Costa County, to a high of .353 per acre for areas around the Embarcadero BART station in downtown San Francisco. (Stated another way, the average block size in Orinda was 35.7 acres compared with 2.8 acres around the Embarcadero Station.) Among any single built-environment variable, average block size (expressed in quadratic form) was the strongest predictor of transit modal shares, indicated by the R-squared statistic of 0.817. The equation predicts that at an average city block size of 6 acres (for the 1-mile radius around a station), the likelihood that residents rail-commuted was 11.2%; shrinking the average block size to 3 acres increased the probability of taking transit to work to 48.2%. Weighing Factors in Combination: Multiple Regression Results While revealing, a limitation of the simple plots and equations discussed above is that built environment factors are correlated—dense settings, for example, also tend to be the most land- use diverse. Moreover, other factors that might be associated with built- environment variables, like parking supplies and median household income levels, could also be significant predictors. Failure to account for these other relevant variables can bias the statistical results. In this spirit, a multiple regression equation was estimated that predicts the influences of the three built-environment variables in combination with other “control” variables. Table 8.2 presents the best-fitting multiple regression results. Including characteristics of stations and neighborhoods resulted in the removal of some of the built-environment variables presented in Figure 8.3 due to multi-collinearity. Still, the results are revealing. Notably, residential densities within a mile of a station still matter when it comes to transit commuting among station-area residents. Controlling for other factors, every 10 additional units per gross acre (which on a net residential acre basis generally corresponds to 3 to 4 additional units) is associated with a 3.7% increase in transit commute modal shares. Of particular note, however, is the fact that density and design positively interact with each other. That is, higher residential densities combined with small city blocks boost transit commute shares up even higher. For example, accounting for interaction effects, a 150

doubling of mean residential densities from 10 to 20 dwelling units per gross acre leads to a rise in transit commute mode share from 20.4% to 24.1% for a typical Bay Area station setting with an average block size of 6 acres; the commute mode share rises to 27.6% if higher residential densities are combined with a smaller average block size of 4 acres.39 Other variables in Table 8.2 also reveal something about ridership rates among station-area residents. Enhancing job access over the transit network increases the share of work trips by transit; predictably, doing so over the highway network has the opposite effect.40 Park- and-ride supplies further increase the odds of rail commuting, even among those living within a mile of a station. 151 Coefficient T-Statistic Probability Built-Environment Variables Residential Density: Housing Units per Gross Acre .0037 3.226 .002 Density*Design Interaction: (Housing Units per Gross Acre; * No. City Blocks per Acre) .0351 3.659 .000 Transportation Variables Transit Job Accessibility: No. of Jobs (in 100,000s) Accessible over Transit Network During Peak Hours .0857 10.972 .000 Highway Job Accessibility: No. of Jobs (in 100,000s) Accessible over Highway Network During Peak Hours –.0035 –4.689 .002 Parking Supply: No. of spaces (in 1,000s) at station .0234 3.613 .000 Household Variables Automobile Ownership: Mean No. of Vehicles per Household –.0851 –4.689 .000 Income: Mean Household Income (in $10,000s) .0359 2.085 .039 Constant .1880 5.096 .000 Summary Statistics N = 129 R2 = .928 F-ratio (F) = 224.1 (probability = .000) Table 8.2. Multiple Regression Results for Predicting Share of Year 2000 Motorized Commute Trips by Transit as Functions of Built Environment, Transportation, and Household Variables (for 129 San Francisco Bay Area Stations and 1-Mile Rings, Ordinary Least Squares Estimation)

While density exerts a stronger influence on transit modal splits than do parking supplies, it is notable that even among those living within walking distance of a station, availability of parking is still an inducement to transit riding. The final set of control variables in Table 8.2 captures socio-demographic attributes of station areas. All else being equal, the share of motorized commutes by transit falls as average automobile-ownership levels rise in station areas. This is to be expected. Perhaps more surprising is the positive association of household income with transit modal splits. Given that Bay Area rail systems converge on central business districts that contain large shares of the region’s professional office sector, the positive influence of income is not unexpected. This relationship probably reflects self-selection: office workers with downtown jobs and comparatively high incomes are more likely to reside near rail stops for purposes of economizing on commute trips. Overall, the model shown in Table 8.2 was a very good predictor, explaining over 90% of the variation in modal shares of transit commutes among neighborhoods surrounding the 129 Bay Area rail stations. The results suggest that building housing around rail stops is positively associated with transit commuting; doing so at higher densities bumps up transit’s market share even more. Combining higher densities with a more walkable scale design of city streets and block patterns draws even larger shares of employed residents to transit. In combination, these results underscore the importance of creating and redeveloping neighborhoods around rail stops that are transit-supportive in their designs. TOD-Ridership Case Study: Arlington County, Virginia No place in the United States has witnessed more high-rise, mixed-use development along a rail corridor over the past three decades than Arlington County, Virginia. Accordingly, there is no better place to examine the ridership bonus associated with TOD. As discussed in Chapter 12, Arlington County’s two major rail corridors— Rosslyn-Ballston and Jefferson Davis— have witnessed an explosive growth in building activity since 1970, when Metrorail planning got underway: 24.4 million square feet of office space, 3.8 million square feet of retail space, some 24,000 mixed-income dwelling units, and over 6,300 hotel rooms.41 These additions were hardly the results of good fortune or happenstance. Rather, the transformation of once-rural Arlington County into a showcase of compact, mixed-use TOD has been the product of ambitious, laser-focused station-area planning and investment. For purposes of examining the relationship between building activities and rail ridership in Arlington County, a cross-sectional/time-series database was built using annualized counts of development activities for the 1985-to-2002 period. Data were compiled only for seven station areas— Ballston, Clarendon, Court House, Crystal City, Pentagon City, Rosslyn, and Virginia Square—where building activities had occurred. (See Map 8.2 on Arlington County’s Metrorail stations.) In combination, 18 time points of data for seven stations provided a pooled database of 126 observations. Building-activity data were obtained from the Arlington 152

County Department of Community Planning, Housing, and Development; summary information can be found in the report titled Development in the Metro Corridors—2000.42 Supplemental data on Washington Metrorail service levels were obtained from the regional transit agency, WMATA, and additional information such as mean regional gasoline prices (for each time point) were obtained from various secondary sources.43 For the 1985-to-2002 period, the average count of daily station entries and exits was 7,840 for the Arlington County stations that were studied. The mean amount of development activity within the seven station areas was 3,920 dwelling units and 4.2 million square feet of office and retail space. Station Counts and Development Activity As expected, there was a fairly strong association between the number of boardings and alightings at Metrorail stations and the amount of development that existed. Figure 8.4 shows that ridership gains closely tracked increases in the number of dwelling units and the amount of commercial square footage in the seven station areas over the 1985-to- 2002 period. From the simple linear regression equations, every additional dwelling unit added slightly more than one additional boarding and exit. Given that residents usually enter and leave a station during the same day, this corresponds to roughly one daily Metrorail trip for every two housing units added—still a respectable number. 153 Map 8.2. Washington Metrorail Rail Stations in Arlington County. The station areas of the seven Metrorail stations with significant development activity since 1970 are shaded.

Metrorail ridership was equally responsive to office and retail construction. The bivariate equation suggests that each additional 1,000 square feet of commercial floor space was associated with an additional station boarding or exit. (Again, to the degree that employees or customers entered and left the same station, this corresponds to one additional Metrorail journey per 2,000 additional square feet of commercial floor space.) In very general terms, these relationships correspond to an elasticity of around 0.5; that is, a doubling of building activity was associated with a 50% increase in Metrorail ridership. 154 No. of Housing Units in Station Area 12,00010,0008,0006,0004,0002,0000 Av er ag e Da ily S ta tio n Bo ar di ng s an d Ex its 18,000 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2,000 0 Square Feet of Office & Retail Space 12,000,00010,000,0008,000,0006,000,0004,000,0002,000,0000 Av er ag e Da ily S ta tio n Bo ar di ng s an d Ex its 18,000 16,000 14,000 12,000 10,000 8,000 6,000 4,000 2,000 BIVARIATE REGRESSIONS HOUSING UNITS: Station Boardings & Exits = 3204.9 + 1.173 (Housing Units) R2 = .680 OFFICE & RETAIL DEVELOPMENT: Station Boardings & Exits = 3603.6 + 1.018 (Square Feet of Office & Retail Space, in 1,000s) R2 = .723 Note: N = 126 for both equations. All predictor variables are significant at the .01 probability level. Figure 8.4. Station Boardings and Exits as Functions of Development Activity in Arlington County, Virginia, 1985–2002.

Arlington County Ridership Model A limited set of variables was available to estimate the Arlington County ridership model because annual data, such as the data obtained for building activities, are rarely compiled for other potential predictor variables (e.g., census data are available only once every 10 years). Fortunately, annual data were available from WMATA on rail service levels, as represented by the amount of passenger space in rail cars (assuming four passengers per square meter of floor space) passing through stations per day. Given that transit ridership is highly sensitive to transit service levels (with elasticities typically in the range of 0.7 to 0.8), the availability of this variable on an annual basis for all stations, along with boardings and exits, enabled a streamlined model to be estimated.44 Other annualized data that were candidates for entry into the model included mean regional gasoline, station parking supplies, and dummy variables for time points (to control for secular trends) and station areas (to control for idiosyncratic characteristics of particular stations not captured by other variables in the equation). In estimating a model of ridership as a function of service levels and other explainers, ordinary least squares estimation can produce biased results. This is because of the endogeneity, or interrelatedness, of transit supply and demand. Over time, service levels influence ridership, and, assuming that transit planners are doing their job, ridership influences how much service is delivered. To account for this simultaneous relationship, instrumental variables, representing exogenous influences, were used to estimate values of the predictor variable “rail service frequency.” The second stage of estimation involves using these predicted values, along with other variables, to explain Metrorail boardings and alightings. This technique is often referred to as two-stage least squares estimation.45 Table 8.3 presents the best-fitting multiple regression results. As expected, Metrorail boardings and alightings rose with service intensities over the 1985-to- 2002 period. Office and retail building activities were even more influential. Because of the close association of commercial and residential construction, both variables could not enter the equation at the same time; office-retail development was the strongest predictor of the two; therefore, it was used in the model. Residential development did enter the model; however, it entered as an interactive term with the service frequency variable. That is, Metrorail station boardings and alightings increased through the combined influences of increases in residential construction and service levels. Because of the multicollinearity of factors like development and service levels, including this interactive term enabled the model to be expanded without contaminating the results. The model reveals the following relationships. Holding all else constant: • Every 1,000 additional passenger spaces passing through a station per day attracted, on average, 210 additional passengers; • Every 100,000 square feet of additional office and retail floor space increased average daily 155

boardings and alightings by nearly 50; and • Every 100 additional residential units, when combined with 100 additional railcar passenger spaces per day, led to more than 50 additional Metrorail boardings and alightings per day. For a streamlined equation, the model had fairly good predictive powers, explaining over three-quarters of the variation in Metrorail boardings and exits across the seven Arlington County stations between 1985 and 2002. Clearly, a fairly robust and well- functioning relationship exists between building activities, service levels, and ridership in Arlington County. Along with the strong general influence of real-estate development on patronage counts (as discussed in Chapter 12), Arlington County’s balance of housing and employment growth along Metrorail corridors has given rise to balanced flows. Also, the extensive pedestrian and landscaping improvements made to station areas have encouraged many passengers to walk-and-ride. Conclusions A considerable body of research shows that under the right conditions, TODs can increase transit ridership and its associated environmental benefits. This 156 Coefficient T-Statistic Probability Transit-Service-Level Variable Rail Service Frequency: No. of Passenger Seats Passing Through Metrorail Station per Day** .2096 1.190 .236 Building-Activity Variable Office-Retail Development: Square Footage of Office and Commercial Floor Space (in 1,000s) in Station Area .4740 2.186 .031 Residential Development-Service Frequency Interaction: Dwelling Units, in 1,000s * Rail Service Frequency .0055 2.124 .036 Constant 1239.3 0.748 .456 Summary Statistics N = 126 R2 = .772 F = 137.3 (probability = .000) ** Instrument variables used to estimate predicted value were mean regional gasoline price ($); office-retail development; time-series dummy (1985=1, 1986=2, etc.); and station-area (0–1) dummy variables for Ballston, Clarendon, Court House, Crystal City, Pentagon City, and Rosslyn Stations. Table 8.3. Multiple Regression Results for Predicting Metrorail Station Boardings and Exits as Functions of Transit Service Levels and Building Activities (for Seven Arlington County Metrorail Stations, 1985 to 2002, Two-Stage Least Squares Estimation)

is partly a product of self-selection: those with a lifestyle preference for transit-oriented living move into TOD neighborhoods and act on their preference. Higher transit ridership is also a product of the compact, mixed- use, and walking-friendly attributes of many TODs. From a public policy perspective, evidence on TOD’s ridership bonus gives credence to programs, like sliding-scale impact fees, that reward dense, mixed-use projects, and flexible parking standards that reflect the below-average automobile ownership rates among TOD residents. Research shows that those living in TODs usually patronize transit five to six times as often as the typical resident of a region. There is some evidence that size and connectivity of a rail system has some bearing on the ridership impacts of TOD. The highest recorded rail capture rates are found in the Washington (D.C.) Metropolitan Area, which could be because Metrorail has the most extensive network of any recent-generation rail system in America, providing good accessibility to many parts of the region. Transit capture rates of those working and shopping in TODs tended to be lower than those of residents partly because self-selection is not as prevalent. Still, capture rates can be appreciable for non-residents of TODs, as high as nearly 50% in the case of those working in offices near central-city stations. Joint development projects sometimes can boost transit’s modal shares even higher, mostly likely because of conducive design factors like good pedestrian connectivity between rail stations and adjoining buildings. Research conducted using recent data on transit usage and land-use characteristics of stations in the San Francisco Bay Area and Arlington County lends further support to past studies. For the Bay Area, transit commute shares increase with density, land-use diversity, and pedestrian-oriented design of neighborhoods around rail stops. Significant interaction effects were found between residential density and city block size. In Arlington County, office-retail development was the most powerful predictor of ridership at seven Metrorail stations. Housing construction interacted with transit service levels to give ridership a further boost. Given the preponderance of evidence, the ridership benefits of TOD are unassailable. Society at-large reaps the dividends of people traveling in efficient and sustainable modes like public transit. Whether private interests similarly benefit from TOD, as reflected by real- estate market conditions, is the topic of the next chapter. Notes 1 R. Cervero, Ridership Impacts of Transit- Focused Development in California, Monograph 45 (Berkeley: Institute of Urban and Regional Development, University of California, 1993). 2 R. Cervero, “Walk-and-Ride: Factors Influencing Pedestrian Access to Transit,” Journal of Public Transportation, Vol. 3, No. 4 (2001): 1–23. 3 R. Cervero, The Transit Metropolis: A Global Inquiry (Washington, D.C.: Island Press, 1998). 4 J. Hunt, R. Johnston, J. Abraham, C. Rodier, G. Garry, S. Putman, and T. de la Barra, “Comparisons from Sacramento Model Test Bed,” Transportation Research Record: Journal of the Transportation Research Board, No. 1780 (2001): 53–63. 157

5 R. Cervero, “Transit-Based Housing in California: Evidence on Ridership Impacts,” Transport Policy, Vol. 1, No. 3 (1994A): 174–183. 6 JHK and Associates, Development-Related Survey I (Washington, D.C.: Washington Metropolitan Area Transit Authority, 1987); JHK and Associates, Development-Related Survey II (Washington, D.C.: Washington Metropolitan Area Transit Authority, 1989). 7 Arlington County Department of Community Planning, Housing and Development, Arlington County Profile 2003 (March 2003). 8 Gerston & Associates, Transit-Based Housing (San Jose, Santa Clara County Transportation Agency and the Santa Clara Valley Manufacturing Group, 1995). 9 C. Switzer, The Center Commons Transit Oriented Development: A Case Study, unpublished student report prepared for MURP degree (Portland, Oregon: Master of Urban and Regional Planning Program (MURP), Portland State University, Fall 2002). 10 JHK and Associates, 1987, op. cit. 11 JHK and Associates, 1989, op. cit. 12 J. Martin, MTS Joint Development Site Transit Surveys (San Diego: San Diego Association of Governments, September 1996). 13 R. Cervero, “Rail-Oriented Office Development in California: How Successful?” Transportation Quarterly, Vol. 48, No. 1 (1994B): 33–44. 14 JHK and Associates, 1989, op. cit. 15 N. Bragado, “Transit Joint Development in San Diego: Policies and Practices,” Transportation Research Record: Journal of the Transportation Research Board, No. 1669 (1999): 22–29. 16 Cervero, 1993, op. cit. 17 JHK and Associates, 1987, op. cit. 18 Cervero, 1993, op. cit. 19 Ibid. 20 Cervero, 1994A, op. cit., p. 181. 21 R. Ewing and R. Cervero, “Travel and the Built Environment: A Synthesis (with Discussion),” Transportation Research Record: Journal of the Transportation Research Board, No. 1780 (2001): 92. 22 R. Cervero, “Land Uses and Travel at Suburban Activity Centers,” Transportation Quarterly, Vol. 45 (1991): 479–491. 23 R. Cervero, “Mixed Land-Uses and Commuting: Evidence from the American Housing Survey,” Transportation Research A, Vol. 30, No. 5 (1996): 361–377. 24 R. Cervero, “Built Environments and Mode Choice: Toward a Normative Framework,” Transportation Research D, Vol. 7 (2002): 265–284. Elasticities gauge the percent change in the probability of rail commuting given a 1% increase in the land-use diversity index. 25 Gerston Associates, 1995, op. cit.; G. Richards, “Housing, High-Tech Offices Spring Up Along New Light-Rail Line,” San Jose Mercury News, December 15, 1999, p. B1. 26 Cervero, 1994A, op. cit. 27 R. Cervero and M. Duncan, Residential Self- Selection and Rail Commuting: A Nested Logit Analysis, Working Paper 604, (Berkeley: University of California Transportation Center, 2002). 28 J. Holtzclaw, H. Dittmar, D. Goldstein, P. Haas, “Location Efficiency: Neighborhood and Socio-Economic Characteristics Determine Auto Ownership and Use-Studies in Chicago, Los Angeles, and San Francisco,” Transportation Planning and Technology, Vol. 25, (2002): 1–27. 29 L. Keefer, A Review of Nine UMTA-Assisted Joint Development Projects (Washington, D.C.: U.S. Department of Transportation, Urban Mass Transportation Administration, 1983). 30 S. Cooke, “Joint Development,” Urban Land, Vol. 43, No. 7 (1984): 16–20. 31 R. Cervero, “Rail Transit and Joint Development: Land Market Impacts in Washington, D.C. and Atlanta,” Journal of the American Planning Association, Vol. 60, No. 1 (1994C): 83–94. 32 R. Cervero, P. Hall, and J. Landis, Transit Joint Development in the United States, 158

Monograph 42 (Berkeley: University of California, Institute of Urban and Regional Development, 1992). 33 H. Lund, R. Cervero, and R. Willson, Travel Characteristics of Transit-Focused Development in California (Oakland, California: Bay Area Rapid Transit District and California Department of Transportation, 2004). 34 Other rail stops exist in the city of San Francisco, including streetcar, cable car, and light-rail MUNI stops; however, these were omitted because they are unrepresentative of rail stops for the region as a whole. Many of these stops consist of on-street medians served by trains operating in mixed traffic. Also, Amtrak and Capitol Corridor Express trains serve the Bay Area, operating on mixed-freight corridors; however, station data were also omitted for these intercity services because they were not considered to be representative of transit operating conditions for the region as a whole. 35 U.S. Census Bureau, 2000 Census Transportation Planning Package, San Francisco-Oakland-San Jose Metropolitan Statistical Area (Washington, D.C.: 2001). 36 For review of the “3 Ds” principle, see R. Cervero and K. Kockelman, “Travel Demand and the 3D’s: Density, Diversity, and Design,” Transportation Research D, Vol. 2, No. 3 (1997): 119–129; and Cervero, 2002, op. cit. 37 Proportions of Year 2000 commutes made by residents residing within a 1-mile radius of stations are for trips made by motorized nodes only (i.e., exclusive of walking, bicycling, and work-at-home options). 38 Cervero, 1996, op. cit.; R. Cervero and C. Radisch, “Travel Choices in Pedestrian Versus Automobile-Oriented Neighborhoods,” Transport Policy, Vol. 3, No. 3 (1996): 127–141. 39 In this scenario, mean values are used for all other variables in the regression equation in Table 8.2 as follows: parking supply = 350 spaces, mean household income = $76,000, highway accessibility = 895,200, transit accessibility = 88,900, and mean vehicles per household = 1.6. 40 Isochronic measures of accessibility were estimated by accumulating census-tract job totals within 30-minute centroid-to-centroid travel distance ranges using peak-period network travel times obtained from the MTC, the regional planning organization for the nine-county San Francisco-Oakland-San Jose Consolidated Metropolitan Area. 41 Arlington County Department of Community Planning, Housing and Development, Development in the Metro Corridors— 2000 (July 2002). 42 Ibid. 43 Mean gasoline prices, for instance, were obtained from the Metropolitan Washington Council of Governments’s gasoline-price database, which is used to estimate long- range regional transportation models. 44 For a summary of empirical evidence on transit service elasticities, see M. Wachs, “Consumer Attitudes Towards Urban Transit Services: An Integrated Review,” Journal of the American Institute of Planners, Vol. 42, No. 1 (1976): 90–102. 45 For further discussions on two-stage least squares estimation, see R. Pindyck and D. Rubinfeld, Econometric Models and Economic Forecasts (New York: McGraw-Hill, 1997). 159

161 Chapter 9 Real-Estate Market Impacts of TOD TOD and Real-Estate Markets If transit investments create benefits, real-estate markets tell us. As long as there is a finite supply of parcels around stations, those wanting to live, work, or do business near transit will bid up land prices. The benefits of being well connected to the rest of the region (i.e., being accessible) get capitalized into the market value of land. As the cliché goes, rail-served properties enjoy good “location, location, location”: residents can more easily reach jobs and shops; more potential shoppers pass by retail outlets; and for employers, the laborshed of workers is enlarged. For some, stress reduction is perhaps also part of the attraction of being near transit. A developer of transit-based housing in St. Louis remarked: “The MetroLink station adds value to the project as part of the ‘no hassle’ lifestyle we are selling.”1 Because the benefit conferred by being near transit is improved accessibility, looking at the land-value premiums is a good way to gauge the benefits of TOD. While research findings are varied, most of the evidence suggests that being near transit enhances property values and rents. At the Orenco Station in Hillsboro, Oregon, absorption of housing averaged eight units per month in 2001, and prices were running 20% to 30% above the area’s average, according to brokers with Costa Pacific Homes, one of Orenco’s homebuilders.2 Near the Mockingbird light-rail station in Dallas, office and retail space today rent for $40 per square foot, some 40% above market rates. Even higher premiums have been recorded for office and retail space near Washington Metrorail stations in Arlington, Virginia, and Bethesda, Maryland.3 Rising land values have occurred not only in rail-served edge cities but also transitional inner-city neighborhoods. In the District of Columbia, land prices near the U Street and 14th Street Metrorail Station, in a predominantly minority neighborhood known for its jazz clubs and night-time entertainment, have nearly doubled in the past 3 years. The idea that transit confers benefits to local real-estate markets is hardly new. After all, some of the toniest neighborhoods developed at the turn of the 20th century—Shaker Heights in Cleveland, Chestnut Hill in Boston, Roland Park in Baltimore, and Riverside near Chicago—were served by streetcar lines. While the fortunes of neighborhoods skirted by rail corridors suffered during the ascendancy of automobiles and freeways during the middle and latter parts of the century, in the 21st century, the tables once again appear to be turning. In Dallas, San Jose, Portland, Northern Virginia, Northeast New Jersey, and other rail-served settings, residential properties within an easy walk of light-rail stops are once again hot commodities. Many are fully leased and quite a few command top- dollar rents.

Evidence on Market Performance Most studies on the land-value benefits of transit have evaluated the influence of proximity to or distance from stations, not whether a parcel of land is in a TOD. Research findings on the effects of proximity to transit on land values are not very consistent in part because impacts vary depending on severity of traffic congestion, local real-estate market conditions, swings in business cycles, and other factors. Some of these issues are addressed further in this chapter. Below, empirical evidence on the land- value and market-performance impacts of transit systems is reviewed, first for residential housing and then for commercial properties. Relatively little research has been conducted on the land-value impacts of transit on other uses, like industrial activities; however, this should not be a concern since such uses are not particularly prominent in TODs. Residential Properties Most, although not all, studies of transit’s impacts on residential properties have recorded premiums or net benefits. Studies over the past two decades show average housing value premiums associated with being near a station (usually expressed as being within 1⁄4 to 1⁄2 mile of a station) are 6.4% in Philadelphia, 6.7% in Boston, 10.6% in Portland, 17% in San Diego, 20% in Chicago, 24% in Dallas, and 45% in Santa Clara County.4 The type of transit technology has some bearing on land-value premiums. A study of experiences in the San Francisco Bay Area found that heavy- rail systems conferred the highest capitalization benefits to single-family housing because of faster speeds, more frequent services, and wider spatial coverage than light-rail and commuter- rail systems.5 The study found that for every meter closer a single-family home was to a BART station, its sales price increased by $2.29, all else being equal. Alameda County homes several blocks from BART stations sold, on average, for 39% more than otherwise comparable ones 20 miles from the nearest station. In the case of light-rail systems, however, capitalization benefits (i.e., value-added) were far smaller, and, in some instances, single-family homes within 900 feet of a station actually sold for less because of transit’s “nuisance effect.” A study of Atlanta’s MARTA system suggested impacts also varied by type of neighborhood: transit accessibility increased home prices in Atlanta’s lower-income census tracts but decreased values in upper-income areas.6 It is not hard to find conflicting signals on transit’s residential property impacts. A study of Portland’s MAX light-rail system found positive land-value effects only within a 500-meter walking distance of stations.7 A different study of both light-rail-served Portland and heavy-rail-served San Francisco Bay Area suburbs found residential property values were lower within a few blocks of rail stops than five or six blocks away.8 A study of single-family sales prices found no disamenity effect when homes were within 300 meters of BART stations.9 The same study, however, found a huge effect for commuter-rail services: in 1990, homes within 300 meters of the Caltrain stations 162

sold at an average discount of $51,000. It seems plausible that whereas disamenity effects exist from being “too close” to rail transit in suburban settings, in fairly dense, mixed-use environments (with Manhattan as an extreme), ambient noise levels are so high and streets are so busy that there are no perceived nuisances from living within a block or so of a rail stop. The alignment also comes into play: because of noise levels, elevated structures depress residential values the most, whereas the effects of below- ground systems are often negligible. Commercial Properties Evidence on land-value benefits exists for office and commercial-retail parcels near heavy-rail systems in the Washington (D.C.) Metropolitan Area, the San Francisco Bay Area, and greater Atlanta.10 Comparable or even larger premiums have been found for commercial properties near light-rail stations in Santa Clara County, California, and suburban Dallas.11 Even bus malls, experience shows, confer substantial benefits on commercial properties. Office rents along Denver’s downtown transit mall, for example, were 8% to 16% higher than comparable space off the mall in late 2002. Sixty- percent premiums were found for retail shops on the mall relative to the typical downtown retail outlets.12 Most evidence on commercial property comes from heavy-rail systems, and, as in the case of residential properties, it is not altogether consistent. An early study of BART found no evidence that rail’s presence increased commercial property rents around a suburban station and two inner-city stops.13 The absence of appreciable gains could have been due to the fact that, at the time, BART was too new for meaningful accessibility benefits to have accrued, along with the fact that few zoning changes had been introduced. A study in Washington, D.C., found evidence of benefits to commercial properties in anticipation of heavy-rail services: property values fell by 7% for every 10% increase in distance from a Metrorail station, up to a radius of 2,500 feet.14 No follow-up work was conducted to see if value gains held over time, although numerous subsequent case studies suggest that Metrorail has materially benefited nearby commercial properties.15 Two studies of MARTA heavy-rail service reached opposite conclusions on impacts to commercial properties. One found that offices within 1 mile of highway interchanges commanded office rent premiums; however, those within a mile of MARTA stations typically leased for less than comparable space farther away.16 Another concluded that commercial properties were “influenced positively by both access to rail stations and policies that encourage more intensive development around those stations.”17 Although theory suggests light-rail systems confer smaller benefits to commercial properties, some researchers have reported otherwise. A study of the DART system compared differences in land values of “comparable” retail and office properties near and not near light- rail stations.18 The average percent change in land values from 1994 to 1998 for retail and office properties near DART stops was 37% and 14%, respectively; for “control” parcels, the average changes were 7.1% and 3.7%, respectively. For retail uses, this study 163

suggested a value-added premium of 30%. Anecdotally, the authors noted that North Park, the only regional mall served by DART, generally outperformed other malls in the Metroplex area, remaining 100% occupied during the 1994-to-1998 period while rents increased 20%. A follow-up study found office properties increased in value 53% faster than control sites from 1997 to 2001; however, no premiums were recorded for retail properties over this period.19 Several California studies of light rail’s impacts on commercial properties have been more rigorous in their research designs; however, findings were generally inconclusive. A study of Santa Clara County’s light-rail system found that properties within 1⁄2 mile of stations commanded premiums, although those that were 1⁄4 to 1⁄2 mile away were worth even more.20 Compared with other properties in the county, the estimated monthly lease premium within 1⁄4 mile of a station was 3.3 cents per square foot, and for properties 1⁄4 to 1⁄2 mile away, it was 6.4 cents per square foot. Sales premiums of $8.73 and $4.87 per square foot, respectively, were found, though models of sales values had poorer statistical fits. TODs and Land-Value Premiums The studies cited above looked at the effect of proximity to transit stations on land values and rents as opposed to the affects of TOD per se. Few studies have looked specifically at differences in rents and land values between projects that are in TODs and those that are not. Studies that have looked at differences have often used matched-pair comparisons. In general, experiences show that mixed- use projects in walking-friendly settings served intensively by transit produce healthy real-estate results. A study of experiences in the San Francisco Bay Area in the mid-1990s found that multifamily units within TODs commanded higher rents than otherwise comparable projects not within TODs. Besides being near transit, these multifamily projects also had fairly high densities (over 50 units per net acre) and featured convenience retail shops and various pedestrian amenities, thus taking on the attributes of a compact, mixed-use TOD. In 1994, rents for one-bedroom units near the Pleasant Hill BART station were $1.20 per square foot compared with an average of $1.09 for similar projects (in terms of size, age, and amenities) that were in the same geographic submarket but away from BART. Two-bedroom units near the Pleasant Hill Station leased for $1.09 per square foot compared with $0.94 per square foot for comparable units away from BART. On average, rents for one- and two-bedroom units in TOD apartments in the East Bay were 10% to 15% higher than non-TOD units in the same municipality that were otherwise comparable. At Dallas’s Mockingbird Station, TOD residential rents were going for $1.60 per square foot per month in mid-2003; other comparable nearby properties not served by transit were getting $1.30, or 20% less. In Englewood, Colorado, apartments rented at CityCenter—a transit-oriented village with civic uses, a cultural and performance center, and retail—are more than twice as expensive as comparable units elsewhere in the city. CityCenter’s Class A office space is also leasing at a premium: gross annual 164

lease rates of $21 to $25 per square foot in mid-2002 compared with $13.50 to $17 per square foot for Class A space elsewhere in the city.21 Moreover, CityCenter’s office occupancy rate is close to 100%, compared to 90% for the Denver metropolitan area. The project’s retail sector is also out- performing its competitors: annual rents for stores averaged $18 to $20 per square foot in 2002 versus $8 to $14 per square foot for the city of Englewood. About 90% of CityCenter’s retail space was leased and occupied in mid-2002 compared with a citywide average of 80%. Another good example of TOD’s added value in the Denver region is 16 Market Square in Denver’s central business district (CBD). The project lies next to the Market Street Station, Denver’s “100% transit location,” where all of the city’s downtown-bound bus lines converge. In late 2002, 16 Market Square—with ground-floor retail and five stories of renovated office space—enjoyed a 60% premium over comparable downtown office space. Also, its commercial space was 100% leased; no other commercial building in downtown Denver can lay such a claim. What these experiences tell us is that while proximity to good-quality transit is an important trait of TOD, this is not the only factor that adds value. When combined with higher-than-typical densities, consumer retail and services, and pedestrian amenities, proximity to transit can confer land-value benefits that are well above those of competitive markets. TOD’s synergy of proximity, density, mixed uses, and walking- friendliness, under the right conditions, gets expressed through geometric gains in property values and overall real-estate market performance. Joint Development and Land-Value Premiums What about the joint development projects? Do projects physically linked to transit stations, like air-rights towers or passageway connections, out-perform other markets? A comprehensive study of transit joint development projects in the Washington (D.C.) Metropolitan Area and Atlanta suggested that they do.22 The study of five rail stations in Washington, D.C., and Atlanta over the 1978-to-1989 period found jointly developed projects were better performers: in addition to average rent premiums of 7% to 9%, physically integrated projects tended to enjoy lower vacancy rates and faster absorption of new leasable space. On average, joint development projects added more than $3 per gross square foot to annual office rents over the 1978-to-1989 period. Moreover, Atlanta’s and Washington’s joint development projects, the study found, were generally “better” projects (i.e., they were architecturally integrated, they enjoyed better on-site circulation [of both people and automobiles], and they made more efficient use of space through resource-sharing such as shared parking). In addition, the research showed that average office rents of transit joint development projects rose with increases in systemwide ridership. Other matched-pair studies of joint development in the Washington (D.C.) Metropolitan Area have reported comparable rent premiums of up to 10%.23 A matched-pair comparison between projects near rail stations and freeway 165

interchanges further substantiated these research findings.24 Office projects in Atlanta’s and Washington’s TODs showed modest rent premiums over their freeway-oriented counterparts. Premiums were attributed, in part, to rail-served neighborhoods being more pedestrian-friendly and having more net leasable space (due mainly to lower parking requirements). Whether adjacent commercial properties are physically integrated with rail stations, such as through air-rights development or direct passageway connections, was also found to have a bearing on market performance. Evidence likewise shows that renovation of stations improves the market performance of retail both within and close to stations. A recent study of older neighborhoods and business districts in the Northeast found rail- station rehabilitation was positively associated with increases in retail rents and surrounding commercial property values, with benefits increasing with city size and urban densities.25 The Importance of Business Cycles, System Maturation, and Timing More studies on the link between proximity to transit and land values have been carried out in the San Francisco Bay Area than anywhere else. A study led by John Landis of Bay Area real- estate market conditions in the early 1990s found that for every meter that a BART-served Alameda County home was closer to a BART station, its 1991 sales price rose by $2.39, all else being equal.26 However, no premium was found in the city of San Jose, and, in fact, the study suggested that there was a disbenefit associated with being near light rail: “Transit in San Jose actually takes away value from homes that are located within reach of its stations.”27 Statistically, homes within 300 meters (a little less than 1⁄5 mile) of a light-rail station sold for $31,424 (in 1990 currency) less than homes more than 300 meters away, all else being equal. The Landis study from the early 1990s stands in marked contrast to several recent studies that have recorded positive and appreciable premiums associated with being near light rail in both the city of San Jose and Santa Clara County as a whole.28 A study by Robert Cervero and Michael Duncan examined relationships in 1999, when Santa Clara County’s economy was on a roll, using land-sales data from the county assessor’s office to study the effects of proximity on single-family homes, rental properties, and condominiums. Hedonic price models, based on multiple regression estimation, were used to net out the effects of proximity to transit from other factors that influence land values.29 This study found that in 1999 substantial benefits accrued to residential parcels within a 1⁄4-mile distance of a rail station, whether it was light rail or commuter rail (see Figure 9.1). Large apartments that were within a 1⁄4-mile distance of light-rail stops, for example, commanded a premium of around $9 per square foot. Compared with parcels that were within 4 miles of a light-rail station, this translated into an overall land-value premium of 28%. What explains the huge difference in recorded land-value impacts between 1991 and 1999? There are four likely reasons: condition of the regional economy; levels of traffic congestion; system maturation and extensiveness; and institutional commitments to TOD. The 166

point on the business cycle when land- value impacts are measured probably has a lot to do with how much of a premium is recorded, if any. In 1990, the year for which the Landis study measured no impact, the Bay Area was in the trough of a deep recession; therefore, little value was associated with being near transit. In fact, so many people were out of work that traffic congestion had almost disappeared (one of the few benefits of economic downturns). By the late 1990s, when Cervero and Duncan gauged impacts, the Bay Area’s economy and real-estate market were red hot on the heels of the dot-com boom. Traffic congestion was as bad as ever, revealed by public opinion polls that identified gridlock as the number-one local problem in the minds of Bay Area residents. In 1999, in fact, the Bay Area was ranked as the nation’s second most congested region by the Texas Transportation Institute, and Santa Clara County was the most congested of the region’s nine counties.30 Under these conditions, being near transit was a bonus. While the macro-economy might have been an overriding factor influencing the degree to which land-value premiums existed, another plausible explanation is system maturation. In 1991, Santa Clara County’s light-rail system was in its infancy, providing service over 21 track miles; by the late 1990s, it was firmly entrenched in the local transportation scene, covering 167 $4 .10 $25. 40 $9 .20 $4 .16 0 5 10 15 20 25 30 < 1/4 mile of LRT < 1/4 mile of Caltrain & Business District (BD) < 1/4 mile LRT < 1/4 mile Caltrain A dd iti on al L an d Va lu e/ Sq . F t. ($, 19 99 ) (24%) (103%) (28%) (17%) COMMERCIAL PARCELS RESIDENTIAL PARCELS Figure 9.1. Commercial and Residential Land-Value Premiums in Santa Clara County, 1999. Sources: R. Cervero and M. Duncan, “Benefits of Proximity to Rail on Housing Markets: Experiences in Santa Clara County,” Journal of Public Transportation, Vol. 5, No. 1 (2002): 1–18; and R. Cervero and M. Duncan, “Transit’s Value Added,” Urban Land, Vol. 61, No. 2 (2002): 77–84.

nearly 30 track miles and offering more frequent services. Ten years into service, the light-rail system was beginning to take on more of the characteristics of a network as opposed to a single line. It must be remembered that transit has to compete with the private automobile, which operates on extensive hierarchical networks of local roads, collectors, highways, and freeways. Such networks provide high levels of connectivity, or accessibility. And, of course, it is enhanced accessibility that drives up property values around rail stations. Only when transit begins to mimic the network attributes of its chief competitor, the automobile-highway system, will accessibility improvements be significant enough to register through real-estate transactions. This was not the case in 1991 when the Landis study was conducted, but it was far more the case in 1999 when the Cervero and Duncan study looked at conditions. Another explanation could be better institutional support. In the early 1990s, VTA had no in-house program aimed at promoting TOD and joint development. By the late 1990s, the agency was very active in both areas, having hired a full- time staff member who worked closely with developers, industry, and public agencies in building a coalition to advance TOD. These efforts paid off, for few areas of the United States matched the amount of development that took place around light-rail transit during the boom years of the late 1990s in Santa Clara County. Between 1997 and 1999, some 4,500 housing units and 9 million square feet of commercial- office floor space were added within walking distance of the only recently opened 8-mile Tasman West corridor. Exorbitant housing prices at the time— in 2000, the median single-family home in the Silicon Valley cost $617,000, an 87% jump from 5 years earlier—created a ready-made market for small, more affordable units near light-rail stops.31 Among the instruments successfully introduced by local governments to leverage TOD were tax-exempt financing, public assistance with land assembly, and overlay zones that permitted higher densities than the norm. Of course, the various prerequisites to land-value premiums reviewed in this section are co-related—traffic congestion spurred more rail services and TOD institutional support. In 1991, the year in which Landis measured impacts, these conditions did not exist. The degree to which TOD yields benefits, it would appear, has a lot to do with timing and at what point along the business cycle studies are carried out. Moreover, benefits are also not automatic. They require proactive measures on the part of local governments to create TODs that allow the value-added opportunities of rail investments to be more completely fulfilled. Leveraging Transit’s Added Value Through Proactive Planning: The San Diego Experience This last point (i.e., the importance of proactive government support for TOD toward reaping land-value benefits) is underscored by experiences in San Diego. When it opened in 1981, the 16-mile San Diego Trolley system— with service from downtown San Diego to the Mexican border at Tijuana—was a huge ridership success. Within 2 years of its opening, trains were so full that the 168

169 system was recovering 95% of its operating costs, an unprecedented achievement in the U.S. light-rail transit industry. (Map 9.1 shows San Diego’s existing and planned rail transit network.) In terms of land-use changes and TOD, however, the “Tijuana Trolley” (i.e., the southern Blue Line [or South Line] on Map 9.1) has hardly been a success. No notable developments have occurred along the Southern Blue Line over the past two decades, nor should have they been expected. For this first leg of the Trolley system, funded solely with local monies, the overriding objective was right-of-way and construction cost minimization. The South Line operates on disused freight track that abuts sagebrush and an odd mix of warehouses, factories, a military complex, and various automobile- oriented uses. Moreover, the South County area was not “where the action was.” Employment has barely increased in this part of San Diego County since 1980. Accordingly, transit was not poised to induce appreciable land-use changes. Experiences show that transit investments do not create new regional growth but rather redistribute growth that would have occurred regardless.32 Later extensions north of downtown, notably along the Mission Valley corridor, were an entirely different story (see Photo 9.1). North County was abuzz with real-estate construction when the Mission Valley rail extension and Coaster commuter-rail line broke ground in the mid-1990s. Thus, unlike with the Tijuana Trolley, transit was poised to channel land-use changes in these two areas. The Mission Valley extension, moreover, represented a change in the thinking of the region’s transit decision- makers. Rather than trying to minimize cost, the mindset became one of maximizing development potential. As discussed in Chapter 19, this was part of a larger smart-growth agenda that sought to put the region on a more sustainable pathway. The Mission Valley light-rail line became the region’s model for transit-oriented growth. The line crosses the San Diego River three times in order to site development on the flat valley floor and preserve the sensitive hillsides that define the valley. Helping to lead the way was the city of San Diego’s progressive TOD ordinance that incentivizes compact, infill development near Trolley stops (see Chapter 4). These efforts paid off. Between 1982 (when the Trolley extension was first proposed) and 1995, the Mission Valley saw the addition of 7,000 new housing units, 2,375 new hotel rooms, 1.6 million square feet of retail space, and some 6 million square feet of office inventory.33 Since 1995, these figures have trended steadily upward. The impact of this “about-face” in policy is clearly reflected by differences in land-value impacts. A hedonic price model was estimated for each of San Diego’s transit lines using real-estate sales transaction data from Metroscan, a proprietary database available from First American Real Estate Solutions. For commercial properties (including offices, retail, restaurants, and hotels), data were acquired for calendar years 1999, 2000, and 2001. Models were also estimated for residential parcels based on Metroscan data from the year 2000. Combining sales transaction data with information on site (e.g., building size and quality), transportation (e.g., highway

170 Map 9.1. San Diego Rail Systems: Existing and Planned Light-Rail “Trolley” Extensions (Blue and Orange Lines) and Coaster Commuter-Rail Line. Source: San Diego Metropolitan Transit Development Board.

171 Photo 9.1. Contrasting Land-Use Outcomes Along San Diego’s Trolley Corridor. The top photo shows an inhospitable setting for land-use changes along the former freight corridor where the South Line operates between downtown San Diego and the Mexican border. The bottom photo shows the substantial amount of moderately dense housing recently built along the Mission Valley light-rail corridor, due in part to proactive planning by the city of San Diego.

travel times), and neighborhood characteristics of each parcel, hedonic price models enabled the added or discounted value from being near transit stops, to be netted out.34 Figure 9.2 shows the recorded land-value premiums or discounts for commercial properties broken down by rail line, including the Coaster commuter-rail service that connects downtown San Diego to the northern part of the county. Premiums represent percentage differences attributable to being near transit for “typical” commercial properties within 1⁄2 mile of a Trolley or Coaster stop, holding all other factors constant. “Typical” means the average characteristics of commercial property in the database (e.g., the average commercial structure was an office building of 6,600 square feet in size in a neighborhood with seven workers per acre. Figure 9.2 reveals that offices, retail establishments, restaurants, and other commercial facilities near Mission Valley Trolley stops and the downtown Coaster station enjoyed huge premiums, in the 30%-to-40% range. Both settings have benefited from proactive TOD planning, including targeted public infrastructure improvements (e.g., sidewalk upgrades and public landscaping), overlay zones to encourage mixed uses, and streamlining of building reviews. In contrast, there was a disbenefit, or land-value discount, associated with parcels near Trolley stops on the South Line. Where the commercial real-estate market was strong and proactive planning took place, premiums were appreciable. Where the market was soft and little effort was made to promote TOD, premiums were nonexistent, and some discounts occurred. For the housing sector, premiums were recorded for multifamily units and condominiums across all Trolley lines. 172 38.5% -4.2% 1.9% 30.4% -0.5% -3.9% –10% – 5% 0% 5% 10% 15% 20% 25% 30% 35% 40% Trolley: South (Blue) Line Trolley: East (Orange) Line Trolley: North Line Trolley: Downtown Coaster: Non-Downtown Coaster: Downtown Figure 9.2. Commercial Land-Value Premiums or Discounts in San Diego County, by Rail Line. Source: R. Cervero and M. Duncan, Land Value Impacts of Rail Transit Services in San Diego County, report prepared for the National Association of Realtors and the Urban Land Institute (Washington, D.C.: June 2002).

Differences were minimal. In the case of the Coaster commuter-rail line, however, premiums were huge for condominiums (46.1%) and single-family homes (17%). Apparently, owning a condominium or detached home within an easy walk of commuter rail is highly valued among the many professional workers with downtown jobs who live in the North County. Given that Interstate-5 north of downtown San Diego is the region’s most congested freeway, many home- owners appear willing to pay a premium—$85,000 for the typical condominium—to be within easy access of a Coaster station. Experiences from San Diego County reveal that rail transit is capable of producing appreciable land-value benefits, although this is not automatic and relationships vary by type of land use and corridor. Subregional market characteristics have a bearing on outcomes. In the buoyant North County area, for-sale residential units reap large premiums, and in the healthy Mission Valley corridor and newly refurbished waterfront of downtown, commercial markets seem to flourish in transit’s presence. In the soft real-estate market of the South County along the Tijuana Trolley corridor, the opposite holds true. Transit’s Added Value and Public Policies Some of the land-value premiums associated with being near transit could be due to supportive public policies that are targeted at TODs. At The Commons, in Denver, planned use development (PUD) zoning was a factor in the master- developer’s ability to sell portions of the property to individual developers at a premium. In a statistical sense, it is difficult to separate out the importance of being close to transit stops from public-policy incentives, like zoning bonuses, in explaining land-value increases. In many instances, they are likely to be codependent: zoning incentives are necessary if proximity to transit is to yield dividends, and proximity to transit is necessary if density bonuses and other zoning “perks” are to pay off. Notwithstanding the statistical challenges, several studies have sought to gauge the importance of public policies and strategic planning in leveraging the accessibility benefits conferred by transit investments. Using data from Washington County, Oregon, (served by Portland’s Westside light-rail line), research found that announcements on the planned siting of light-rail stations and the use of zoning tools (e.g., overlays and interim restrictions) to promote TOD induced land-value increases even before the system began operating.35 A study of TOD planning in Atlanta also found that policies aimed at encouraging more intensive development around stations, including parking waivers and minimum FAR requirements, interacted with proximity to stations to yield rent premiums.36 Perhaps the most important public- policy implication of transit’s potential to add value is in the financial arena. The existence of land-value premiums provides a potential source of revenue for transit agencies to tap into to help defray capital costs. Value capture makes sense in theory, but it is often difficult to implement in practice. Since the public sector invests taxpayer monies in rail systems, recapturing some of the value-added, one can argue, is equitable 173

from a societal point of view. Why let a fortunate group of landowners who happen to own property where stations are sited reap huge windfalls, especially when money is so desperately needed to retire capital bonds for expensive rail systems? Besides being equitable, public co-participation in land-value gains can also reduce the kind of land speculation that can drive real-estate prices so high that housing becomes unaffordable, an outcome that subverts the purpose of many TODs. Recapturing value is particularly important to jump-starting TODs. This is especially true in distressed inner-city settings where a lot of upfront improvements and amenities are often needed to entice private investment. The responsibility often falls on cash- strapped municipalities to take the lead in attracting private capital to rail station areas by “sprucing up” the neighborhood through generous landscaping and sidewalk improvements and, in riskier settings, underwriting land-acquisition costs. All of this takes money, often lots of it. Thus, value capture provides a source of funds not only to help pay off the debt on transit investments but also to cover the cost of upfront ancillary improvements that can help jump-start a TOD. In America, value capture occurs indirectly through higher property-tax receipts. However, these are largely transfer effects since gains in values of properties near rail stops (due to relative improvements in accessibility) are, theoretically at least, offset by losses in property values for sites farther away (due to relative decreases in accessibility). Even if there are net gains in property value income, these monies end up in the general treasury and rarely get channeled back into transit projects, much less TODs. Only through tax income dedicated to transit agencies are tax receipts from land-value gains a bona fide form of value capture. A more direct means of recapturing value is through joint development, such as air-rights leasing, ground leasing of adjacent agency-owned parcels, or station connection fees. Hong Kong’s rail system covers all of its costs, including interest, from rents produced by land developments around stations and fare receipts. To date, U.S. transit properties have been far more timid in recapturing value, although a few are beginning to move aggressively in this direction. Presently, WMATA, serving the nation’s capital and the surrounding area, “recaptures” around $6 million annually in value-added through various lease and interface fee arrangements, a number that is expected to grow markedly in coming years as very large joint development projects, like White Flint, take form. At Chicago’s Union Station, value capture occurs through rent surcharges (see Photo 9.2). Chicago’s RTA receives as much as 24% of gross sales receipts when sales volumes reach certain thresholds. This rent is in addition to common-area charges that cover maintenance expenses. One of the most direct means of recapturing value is through benefit assessments. Los Angeles’s MTA obtained 9% of the funds used to pay for the $1.5-billion Red Line subway through special assessments levied against owners of commercial properties in and around subway stations. MTA’s 174

benefit-assessment program, scheduled to sunset in 2008, was made possible through statutory legislation that granted the agency special access to beneficiary forms of financing. In most cases, a benefit-assessment district can only be formed if the majority of property- owners within the district agree to levy themselves to fund the improvement. While land-owners are often willing to do this to pay for improvements, like sidewalks, that directly abut their properties, getting them to agree to chip in to help finance rail systems or TODs is more difficult. Convincing property- owners that transit adds value to their land-holdings is further made difficult by the fact that empirical evidence is inconsistent, even in Los Angeles. A recent study used hedonic-price modeling, similar to what was discussed above for Santa Clara County and San Diego, to net out the effects of proximity to rail lines (heavy rail, light rail, and commuter rail) as well as BRT (MetroRapid) services in Los Angeles County.37 Appreciable land-value premiums (6.1%) were found around 175 Photo 9.2. Chicago’s Union Station. The top photo shows the exterior of the refurbished historic train station. The bottom photo shows an active restaurant and retail activities within the structure.

Red Line subway stations for multifamily housing units; however, land-value discounts, or disbenefits, were measured around Red Line stations for commercial-office properties and condominiums. Premiums were found for these uses along some, but not all, Metrolink commuter-rail, light-rail, and even BRT stops. A confounding factor that might have depressed land values for commercial parcels near some Red Line stations is that many of these stations lie in redevelopment districts. Being in a distressed inner-city setting could have suppressed real-estate values near some subway stations, regardless of transit’s presence. Nonetheless, the lack of a consistent pattern of land-value premiums makes it difficult to implement benefit-assessment financing in practice. The rational nexus doctrine that courts apply in weighing whether benefits have been conferred by public infrastructure sets a high standard that transit investments cannot always meet. Lastly, value capture can also occur through land acquisition and banking aimed at securing profits through long- term leases or even fee-simple sales (i.e., real-estate development on the part of the transit-service provider). This is how the first generation of U.S. streetcar lines from a century ago were built and continues today to be how the majority of suburban rail lines in large Japanese cities are funded.38 The reduction in federal contributions to new rail starts (from 80% to 50%) and increased competition for the shrinking pot have prompted more and more localities to think in entrepreneurial terms. In contributing some $28 million toward the $125-million price tag for the light- rail extension to Portland’s International Airport, Bechtel Enterprises, in partnership with Trammell Crow, is hoping to recoup its cost and then some by developing a 120-acre mixed-use TOD at the Cascade Station. The Pasadena Construction Authority, franchised to build the recently opened Gold Line to Pasadena, hopes to recapture around $30 million of the capital cost of this extension by developing excess property obtained during right-of-way acquisition. Summary and Conclusion The weight of evidence to date shows that development near transit stops enjoys land-value premiums and generally out-performs competitive markets. This generally holds for residential housing (especially condominiums and rental units) as well as office, retail, and other commercial facilities. However, the payoffs are not automatic, and quite often a number of preconditions must be in place. One precondition is an upswing in the economy, with plentiful demand for real estate. Another is that traffic congestion is getting worse. Only then will there be market pressures to bid up land prices and a clear benefit to having good rail access: it provides an alternative to fighting highway traffic. Also important are public policies, such as zoning bonuses, which further leverage the TOD and system expansion that produces the spillover benefits of a highly integrated network. Moreover, if significant premiums are to accrue, it is important that transit be in a neighborhood free from signs of stagnation or distress that has a reasonably healthy real-estate market. In San Diego, premiums were recorded for commercial properties in the Mission Valley corridor, an area that has 176

generally enjoyed sustained growth over the past decade. Pro-development policies introduced by local governments, like overlay zoning to encourage mixed land uses and targeted infrastructure investments, bolstered commercial property values in the Mission Valley corridor. This stands in marked contrast to the South Line where little effort has been made to leverage TOD, in large part because of stagnant growth, and, predictably, no meaningful land-use changes have occurred. Insights into the property value impacts of TODs carry policy significance. For one, public entities are in a position to recapture some of the value added through benefit assessments, land acquisitions and re-sales, and ground/air-rights leases. Some areas, such as the Washington (D.C.) Metropolitan Area, Los Angeles, and Portland, have been particularly aggressive in recapturing some of the value created by transit investments; however, legal and institutional concerns continue to impede progress in this area. Because TODs take time to evolve, experiences suggest that land-value benefits take time to accrue as well. This was underscored by experiences in Santa Clara County, where in the transit system’s infancy, no measurable land- value premiums were found, but by the system’s 10th anniversary, when the real-estate market had revved up, benefits were appreciable. Savvy developers increasingly understand the long-term nature of profiting from TOD. In the words of one active TOD developer in the Denver region: “we’re not here to ‘flip’ properties in the search for quick profits; with TOD and infill in general, we’re in it for the long haul.” More and more, developers are using long-term pro forma when evaluating the potential payoff of TOD. Like any long- term investment, asset management is essential to reaping handsome profits. For this, the public sector needs to do its part to ensure that transit-served neighborhoods are, and will continue to be, viable places. Through effective partnerships with transit agencies, local government, and others—and under the right conditions—all parties are in a position to reap the financial gains conferred by well-planned and well- managed TOD. Notes 1 P. Downs, “Magnetic MetroLink,” Stlcommercemagazine, online newsletter (February 2001). http://www. stlcommercemagazine.com. 2 Urban Land Institute, Development Around Transit: Enhancing Real Estate, Increasing Ridership, and Improving Communities, draft manuscript (forthcoming). 3 R. Cervero, “Rail Transit and Joint Development: Land Market Impacts in Washington, D.C. and Atlanta,” Journal of the American Planning Association, Vol. 60, No. 1 (1994): 83–94. 4 T. Parker, G. Arrington, M. McKeever, and J. Smith-Heimer, Statewide Transit-Oriented Development Study: Factors for Success in California (Sacramento: California Department of Transportation, 2002); R. Armstrong, “Impacts of Commuter Rail Service as Reflected in Single-Family Residential Property Values,” Transportation Research Record, No. 1466 (1994): 88–98; M. Al-Mosaind, K. Dueker, and J. Strathman, “Light-Rail Transit Stations and Property Values: A Hedonic Price Approach,” Transportation Research Record, No. 1400 (1993): 90–94; R. Cervero and M. Duncan, “Benefits of Proximity to Rail on Housing Markets: Experiences in Santa Clara County,” Journal of Public Transportation, 177

Vol. 5, No. 1 (2002A): 1–18; R. Cervero and M. Duncan, Land Value Impacts of Rail Transit Services in San Diego County, report prepared for the National Association of Realtors and the Urban Land Institute (Washington, D.C.: June 2002B); A. Gruen, The Effect of CTA and Metra Stations on Residential Property Values: Transit Stations Influence Residential Property Values, Chicago, report to the Regional Transportation Authority (June 1997); B. Weinstein and T. Clower, The Initial Economic Impacts of the DART LRT System (Denton, Texas: University of North Texas, Center for Economic Development and Research, 1999). 5 J. Landis, S. Guathakurta, and M. Zhang, Capitalization of Transportation Investments into Single-Family Home Prices, Working Paper 619 (Berkeley: Institute of Urban and Regional Development, University of California, 1994). 6 A. Nelson, “Effects of Elevated Heavy-Rail Transit Stations on House Prices with Respect to Neighborhood Income,” Transportation Research Record, No. 1359 (1992): 127–132. 7 Al-Moisand et al., 1993, op. cit. 8 S. Lewis-Workman and D. Brod, “Measuring the Neighborhood Benefits of Rail Transit Accessibility,” Transportation Research Record, No. 1576 (1997): 147–153. 9 Landis et al., 1994, op. cit. 10 D. Damm, S. Lerman, E. Lerner-Lam, and J. Young, “Response of Urban Real Estate Values in Anticipation of the Washington Metro,” Journal of Transport Economics and Policy, Vol. 14, No. 3 (1980): 20–30; R. Cervero and J. Landis, “Assessing Impacts of Urban Rail Transit on Local Real Estate Markets Using Quasi-Experimental Comparisons,” Transportation Research A, Vol. 27, No 1 (1993): 13–22; C. Bollinger, K. Ihlanfeldt, and D. Bowes, “Spatial Variation in Office Rents Within the Atlanta Region,” Urban Studies, Vol. 35, No. 7 (1998): 1097–1117. 11 Cervero and Duncan, 2002A, op. cit.; Weinstein and Clower, 1999, op. cit.; B. Weinstein, DART Light Rail’s Effect on Taxable Property Valuations and Transit- Oriented Development (Denton, Texas: University of North Texas, Center for Economic Development and Research, January 2003). 12 Fredrick Ross Company, View: Commercial Real Estate Quarterly, Vol. 8, No. 1 (January 2003). 13 C. Falcke, Study of BART’s Effects on Property Prices and Rents, BART Impact Study (Washington, D.C.: Urban Mass Transportation Administration, U.S. Department of Transportation, 1978). 14 Damm et al., 1980, op. cit. 15 R. Dunphy, “Transit-Oriented Development: Making a Difference?” Urban Land, Vol. 54, No. 7 (1995): 32–36, 48; M. Bernick and R. Cervero, Transit Villages in the 21st Century (New York: McGraw-Hill, 1996); A. McNeal and R. Doggett, “Metro Makes Its Mark,” Urban Land, Vol. 58, No. 9 (1999): 78–81, 118. 16 Bollinger et al., 1998, op. cit. 17 A. Nelson, “Transit Stations and Commercial Property Values: A Case Study with Policy and Land-Use Implications,” Journal of Public Transportation, Vol. 2, No. 3 (1999): 77–93. 18 Weinstein and Clower, 1999, op. cit. 19 Weinstein, 2003, op. cit. 20 R. Weinberger, “Commercial Property Values and Proximity to Light Rail: Calculating Benefits with a Hedonic Price Model” (paper presented at the 79th Annual Meeting of the Transportation Research Board, Washington, D.C, 2000). 21 C. Lockwood, “Raising the Bar,” Urban Land, Vol. 62, No. 2 (2003): 70–77. 22 Cervero, 1994, op. cit. 23 S. Cook, “Joint Development,” Urban Land, Vol. 43, No. 7 (1984): 16–20. 24 Cervero and Landis, 1993, op. cit. 25 The Great American Station Foundation, Economic Impact of Station Revitalization, (Las Vegas, New Mexico: 2001). 26 J. Landis, S. Guathakurta, W. Huang, and M. Zhang, Rail Transit Investments, Real 178

Estate Values, and Land Use Change: A Comparative Analysis of Five California Rail Systems, Monograph 48 (Berkeley, Institute of Urban and Regional Development, University of California, 1995). 27 Ibid., p. 40. 28 R. Weinberger, “Light Rail Proximity: Benefit or Detriment in the Case of Santa Clara County, California?” Transportation Research Record: Journal of the Transportation Research Board, No. 1747 (2001): 104–113; Cervero and Duncan, 2002A, op. cit. 29 Hedonic price theory assumes that many goods are actually a combination of different attributes and that the overall transaction price can thus be decomposed into the component (or “hedonic”) prices of each attribute. For more on this technique, see: S. Rosen, “Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition,” Journal of Political Economics, Vol. 82 (1974): 34–55, and T. Batrik, “Measuring the Benefits of Amenity Improvements on Hedonic Models,” Land Economics, Vol. 64, No. 2 (1988): 172–183. 30 T. Lomax and D. Shrank, 2000 Urban Mobility Report (College Station, Texas: Texas Transportation Institute, Texas A&M University, 2000). 31 Association of Bay Area Governments, Silicon Valley Projections 2000 (Oakland, California: 2001). 32 Cambridge Systematics, Inc., R. Cervero, and D. Aschauer, TCRP Report 35: Economic Impact Analysis of Transit Investments: Guidebook for Practitioners (Washington, D.C.: Transportation Research Board, National Research Council, 1998). 33 W. Lorenz, Designing Light Rail Transit Compatible with Urban Form (San Diego: San Diego Metropolitan Transit Development Board, 1996). 34 For more information about these analyses, see Cervero and Duncan, 2002B, op. cit. 35 G. Knaap, C. Ding, and L. Hopkins, “Do Plans Matter? The Effects of Light Rail Plans on Land Values in Station Areas,” Journal of Planning Education and Research, Vol. 21 (2001): 32–39. 36 Nelson, 1999, op. cit. 37 R. Cervero and M. Duncan, Land Value Impacts of Rail Transit Services in Los Angeles County, report prepared for the National Association of Realtors and the Urban Land Institute (Washington, D.C.: June 2002C). 38 R. Cervero, The Transit Metropolis: A Global Inquiry (Washington, D.C.: Island Press, 1998). 179