Editor’s note

Greg Garrett wrote this paper on Smart Growth and presented it at the Eastern Economic Association Conference in February, 2001. This paper is very strong as it follows very carefully the format of a typical research paper. This paper does not have the required cover page. This cover page should have your name and a “catchy” professional title.

The Issue

Urban Sprawl and growth management have been highly debated topics among city planners, developers, and economists. The most recent forum in this debate has been the establishment of “smart growth” policies. Acting as a new term for growth management can this also be a new formula for controlling urban sprawl? The purpose of this paper is to find out exactly what characterizes this latest form of growth management, what existing research shows, and to quantitatively evaluate growth management in Montgomery County, Maryland where such a plan has been in place for the past 20 years. This paper will address how growth management or smart growth effects housing prices, vehicle registration, population density and per capita income. The pro-growth and growth management positions have done well to point out excellent reasons for their respective positions. While other studies have considered a similar approach, this study will not take a position that promotes either the pro-growth or pro-growth management position, which is something that is lacking from the pool of available research.

Review of Literature

Past research on growth management has indicated that such policies have the potential to create resource efficient, affordable housing communities (Danielsen, Lang, & Fulton 1999) however, critics argue they can also create congested, expensive cities (O’Toole 2000). The contrast in these opinions leave little room for an individual newly exposed to this debate to formulate a holistic opinion of their own. As a result, a critique of both sides, which offers a holistic analysis, should provide a firm foundation for this research. This will begin by stating that the underlying question in the urban sprawl vs. growth management debate is whether urban sprawl is an outcome of government planning and if creating a community which operates more like a free market will curb this problem (Danielson et. al. 1999; O’Toole 2000; Carliner1999; Easterbrook 1999).

In order to address this question, one must first know what urban sprawl is, the problem is that no clear definition has been given (Bogart 1998). Characteristics of urban sprawl are “leapfrog developments” where “high density development and vacant land are interspersed as you move away from the Central Business District (CBD) (1998). What this means is that as you move away from the urban center, which is typically the downtown area, there are pockets of development with large spaces of undeveloped land between them. According to Randall Holcombe (1995) “strip or ribbon development, or low-density single-dimensional development” additionally characterizes urban sprawl. This form of sprawl is seen when high levels of commercial development occur around arterial roadways and large interstates followed by a large amount of single-family developmenst outside of the commercial area. Danielson et. al. (1999) continue to develop the characterization by stating that “very low built densities, unlimited outward expansion, and segregated land uses” characterize sprawl. Some of the common arguments against this behavior are that it is unaesthetic, inefficient, causes traffic congestion, and is damaging to the environment (Holcombe 1995). On the other hand, common support for urban sprawl is that it reduces congestion, represents market choice, creates viable economies, and is necessary to support a growing population (O’Toole 2000; Easterbrook 1999).

Growth management is a term that represents a vast amount of similar but different ideas on how to deal with what some consider the problem of urban sprawl. Growth management can be considered to be policies that are put into place, typically in the form of growth boundaries, to restrict or direct development into a certain area (Holcombe 1995). A currently popular form of growth management is smart growth. Used in cities this term has taken on a political value which has allowed many cities to obtain greater recognition for their growth management policies. Montgomery County is a prime example of this as their growth management policies, which had been in place since the early 1980’s, took on the title of smart growth in 1993 (Carter 2000). Much like urban sprawl, smart growth is without a concise definition (O’Neill 1999). Danielson et. al. (1999) pose their definition of smart growth as “a type of high-density development, one in which land uses are mixed in such a way that people benefit from greater built densities.” Christopher Leinberger (1998) states “Smart growth is defined as concentrated development which is pedestrian in character and connected to the region by multi-modal transportation options, not just the automobile.” Finally the Urban Land Institute offers a list of common characteristics of smart growth:

· Development is economically viable and preserves open space and natural resources.

· Land use planning is comprehensive, integrated, and regional.

· Public, private, and nonprofit sectors collaborate on growth and development issues to achieve mutually beneficial outcomes.

· Certainty and predictability are inherent to the development process.

· Infrastructure is maintained and enhanced to serve existing and new residents.

· Redevelopment of infill housing, brownfield sites, and obsolete buildings is actively pursued.

· Urban centers and neighborhoods are integral components of a healthy regional economy.

· Compact suburban development is integrated into existing commercial areas, new town centers, and/or near existing or planned transportation facilities.

· Development on the urban fringe integrates a mix of land uses, preserves open space, is fiscally responsible, and provides transportation options. (O’Neille 1999).

Common support for smart growth is that it is sustainable growth, environmentally friendly, encourages the use and efficiency of public transportation, reduces congestion and promotes a sense of community (Leinberger 1998; O’Neille 1999). Arguments against these policies are that it increases congestion, pollution and housing prices, is not representative of market decisions, and is an effort of suburban communities to prevent others from getting in on the “good life” (O’Toole 2000; Carliner 1999; Eastwood 1999; Staley & Mildner 1999).

One reason for government planning, especially in the form of growth management policies, is to reduce the negative externalities that exist in and around where people work. Obvious externalities come to mind when we think of industrial and commercial land uses: pollution, congestion, noise, vibration, odor and parking are most of them. Low-income and high-density housing, which are commonly a part of government planning, tend to have certain externalities such as obstruction of view, housing deterioration, and also poor or insufficient parking. For many of these reasons we see individuals trying to move to where they can avoid or reduce such externalities. One alternative is for government planning to enact more policies to reduce these externalities. Bogart (1998) points out one problem that may occur from this is, “[that] the policy has no direct impact on the overall level of damage done and thus might only be ‘moving the problem around’.” If we consider that these policies are restricting the production of a good, be it housing or available office space for example, the reduction of this good could result in a deadweight loss for the population (1998). However, if the costs as a result of the externalities are high compared to the lost consumption opportunities, then the restricting policy could still be a Pareto improving situation. According to Bogart, ultimately it comes down to a situation where if the costs of obtaining the good are not very high, then the deadweight loss is correspondingly low, and it can make sense for any given suburb to restrict entry of capital-intensive production (1998).

Whether or not growth management policies are a good idea few cities have actually developed without them. However when cities attempt to put in place such practices numerous problems have occurred. Sometimes issues over political control, hidden agendas and class segregation come into play (O’Toole 2000; Easterbrook 1999; Holcombe 1995). But why and how do such hindrances enter into the arena of a seemingly practical idea? Randall Holcombe (1995) suggests that one problem is that growth management policies offer voters, who have little or no investment in the land being controlled, the power to make decisions on how that land will be used. As a result they can act in their own best interest without incurring the costs of their decisions. Herein lies a problem because a situation is created where a government failure is present. On one hand growth management policy allows land use decisions to occur without any potential for loss to the decision makers: the voters do not own the land but impose their collective will upon the landowner. As a result a decision may be made that does not see a full economic gain. However, on the other hand, this intervention may simply be an attempt to fix a market failure in another area. In the end the problem has just been moved around and the policy failed to truly solve the situation, which is what Bogart illustrated earlier.

Ultimately, the growth management policies that seek to create efficiency and maximum land use can end up doing just the opposite. Holcombe adds an interesting thought when he points out that urban sprawl may be avoided without government planning. Holcombe highlights Houston, Texas where during its initial development there were no zoning laws. He points out that the free market decisions that occurred as this city developed created a denser area with separations between residential, industrial, and retail development (1995). Here the externalities faced by developers and residents were solved without the interference of government policy. Holcombe concludes by saying that the presence of an “invisible hand” will resolve most development issues and that government intervention is, in fact, not necessary (1995).

Another argument against growth management is that its policies, particularly those concerning housing density, do not represent what the market is demanding (Carliner 1999; Easterbrook 1999). These authors are quick to point out that people do not want to live in high-density housing, but instead live in their own house on a lot that provides a front and back yard (Carliner 1999). Michael Carliner (1999) points out using a 1998 Census Bureau statistic “[that] Among new homes sold in 1997, only one-third were on lots of less than 7,000 square feet.” These authors conclude that growth management policies are an attempt of individuals who have found their niche in the suburbs to keep others from getting in on what they got.

Arguing for smart growth Christopher Leinberger (1999) says that most of the initiatives that make smart growth work are market based. Putting in place amenities that allow smart growth to overcome such obstacles as land assemblage, zoning, poor schools, and crime does this (1999). Leinberger feels that the establishment of smart growth developments creates another amenity for consumers. “That amenity is urbanity, the ability to have nearly everything a buyer could want within walking distance or a short car drive away” (1999). Leinberger argues it is this very amenity that has kept cities, in general, around for so long. The author illustrates a unique concept for why smart growth is a market-based growth management policy. He says that for smart growth to be successful it should be developed in two phases (1999). The first phase, lasting about five to seven years targets the upper middle and middle-income empty nesters, retirement, and young adult segments since they do not require public schooling. The second phase will target families since the public schools will have improved or substitutions will have immerged (1999). Leinberger goes on to talk about the necessity for communication amongst all involved parties to make smart growth even more market based, but falls short in providing any developed examples of where this has been the case.

If it has not become apparent, one of the major focuses of growth management is housing density. One of the arguments that support growth management and its urban density policies has been the vast amount of voter approval for such policies. (Danielsen et. al. 1999). During the 1998-midterm elections “voters approved over 160 state and local ballot measures intended to limit urban sprawl” (1999). However it is a non-market based policy that the authors suggest in order to create higher density housing that is competitive and does not derail a local economy. “Land uses, design practices, and financial incentives that improve the costs of and marketability of more densely built housing are key to balancing the competing pressures inherent in smart growth” (1999). One of these pressures, pointed out earlier is the evidence of increased housing prices. Danielson et. al. agree to the validity of this problem and remark that in order for a city to effectively curb sprawl and increase housing density, they must not restrict growth in built up areas (1999). The authors go on to say that higher density must be taken within the context of the area being developed. In a suburban area the amount of density could be much less than that of an urban infill area. They also recognize that density should be taken in terms of areas that are going to be designated as mix use and residential (1999). Other considerations must be taken into effect if smart growth is going to find ways to compete in the market. The authors mention that smart growth planners must pay closer attention to the architecture of the housing. Since housing is more dense the appearance of housing can impose more on neighbors. Ultimately a smart growth area must offer a total package that is better than that of the current suburban trends in order for this type of development to be competitive (1999). “Americans appear to hate two things: density and sprawl. Smart growth’s fate may depend on which they ultimately hate more” (1999).

Evaluating a Growth Management Policy

Most methods of evaluating an economic program are done through keeping track of job growth and the cost of creating those jobs (Bartik 1994; Courant 1994; & Foley 1992). Paul Foley (1992) notes that one of the key problems faced in evaluating economic policies is that many of them become too sophisticated and incomprehensible. It is suggested by Foley that when an evaluation is done it is aware of the following pitfalls seen in past works:

1. unclear or vague objectives for policy;

2. objectives poorly quantified or only defined in a qualitative manner;

3. difficulties in determining full exchequer(1) or resource-cost implications;

4. difficulty measuring secondary effects;

5. reliance on the accuracy, perception and honesty of those involved with an initiative, particularly with respect to sensitive issues. (1992)

Another factor to be aware of is deadweight spending, or the extent to which projects would have gone ahead without the introduction of the policy (1992). The other approach to this is to make sure that the evaluation does take into account “additionality”, which is “the amount of output from a policy as compared with what would have occurred without government intervention” (1992). Foley offers four methods for evaluating an economic policy. The first of which is called Internal Review: administrative effectiveness. This type of evaluation is primarily concerned with measuring the performance in delivering outputs, rather than their eventual economic effects or efficiency. The problem with this approach is that it offers no indication of the economic impact of the evaluated policy. Next is External Review: financial efficiency. External Review develops measures of performance that consider how policy outputs relate to resource inputs. “This approach tends to place too great an emphasis on economy in the use of public resources and efficiency in the provision of public services, rather than their effectiveness in generating desired economic output” (Foley 1992). The third type of evaluation is Understanding and Explanation. Much like its name suggests, Understanding and Explanation evaluation is concerned with obtaining a deeper understanding of how a policy works. “This type of evaluation should go beyond simple ‘before-and-after’ snapshot comparisons and look at longitudinal analysis in the processes of economic growth and change and the forces and mechanisms which induce it” (1992).

Social accounting is another method of economic evaluation offered by Foley that effectively considers the five factors illustrated earlier as well as offers sufficient flexibility for comparison. As illustrated by Foley’s (1992) evaluation, “Social accounting stresses two main factors. First an investigation of the distribution and consequences of policy impact. Secondly, an examination of the wider impact of secondary factors arising from initiatives.” Foley goes on to point out that the best method to perform this evaluation is through interviews with the involved parties. This point is defended by evidence offered from past studies that attempted evaluation through comparison studies, either with other areas or by comparing the area to itself with and without the evaluation (1992). The ultimate problem with these forms of evaluation is that no two areas are exactly alike, and the conditions in the same city are not alike at different times as well (1992).

Theory

In order to successfully study the effects of growth management it will be necessary to narrow and define the term for its use in this study. As a result, the study will consider growth management to be high-density, mixed-use development where a town center has been established; furthermore, growth management will include programs that aim to maximize the city’s use of all resources while reducing waste. Danielson et. al. (1999) evaluated high-density housing at 6 to 7 housing units per acre and low-density as 3 to 4 units per acre: this study used these measurements when selecting the area to be studied. While other characteristics of growth management such as improved sense of place and better transit conditions for pedestrians have been promoted as part of the growth management definition, they are more subjective and not easily measured in a quantitative evaluation. As a result those characteristics are not included in this study, but are recognized as a part of many growth management plans.

Any city that implemented a growth management plan, which included in its goals the promotion of increased housing density, mixed use development, and more efficient use of resources and waste disposal was considered for the population of this study. Also considered was the length of time that the smart growth policy was in place. In order to run an accurate analysis the program would have to be in effect long enough to show an effect on variables. Of the population, the best fitting area was Montgomery County, Maryland. This area put into effect its growth management policies in the beginning of the 1980’s, and has measured the change in single-family housing prices since 1982, this will allow for a 17-year period of recorded data.

For the study the dependent variable is housing prices. The dependent variable was chosen on the basis that it is illustrated by various authors as most commonly affected by growth management (Danielson et. al. 1999, O’Neille 1999). The independent variables are population density, per capita income and motor vehicle registration. All variables, where applicable, are calculated in real terms. Additionally the national mortgage rate was found in order to consider the possibility of housing prices in terms of a variable known to affect it. Variables where chosen on the basis that they will have an effect on the HOUSING PRICES, and that they have shown to do so in past studies. Easterbrook (1999) and Carliner (1999) both pointed out in their response to Danielson et. al’s. (1999) report on “Retracting Suburbia: Smart Growth and the Future of Housing” that increased POPULATION DENSITY would effect the cost of housing significantly. It is hypothesized that POPULATION DENSITY will show a positive relation with HOUSING PRICES. PER CAPITA INCOME is a representation of the means an individual has to afford housing and it is hypothesized that PER CAPITA INCOME will have a positive effect on HOUSING PRICES. The use of MOTOR VEHICLE REGISTRATION was incorporated into the study since increased use of public transportation, decreased travel times and alternative means of travel are advocated by growth management policies. This variable will be a good indicator of whether or not the policy in Montgomery is actually achieving one of its intended goals. It is hypothesized that MOTOR VEHICLE REGISTRATION will have a positive effect on HOUSING PRICES. An OLS regression analysis will be used to evaluate the relationship among these variables.

Data was gathered through a number of sources. The most commonly used was the Maryland County Planning Department where the data on POPULATION, AREA, MOTOR VEHICLE REGISTRATIONS and MEDIAN SINGLE FAMILY HOUSING PRICES were gathered. PER CAPITA INCOME was gathered from the Bureau of Economic Analysis. Information on the CPI was gathered from the Bureau of Labor Statistics and was used to convert any data from nominal to real terms. NATIONAL MORTGAGE RATE DATA was acquired from the U.S. Department of Housing and Urban Development (HUD).

Data

A regression analysis of the variables provided the following equation:

Discussion

Montgomery County, Maryland has taken the position that they want to live without urban sprawl. As a result they have put into place government policies that aim to reduce this event. Through various actions included in their Wedges and Corridors program Montgomery County has worked to ensure that the externalities involved with urban sprawl are kept to a minimum. The Wedges and Corridor plan acts to ensure that development occurs in transit corridors between open space low-density wedges. William Hussmann (1998) illustrates a guiding principle of this program when he says, “In Montgomery County, our major centers ‘grow up’ rather than ‘grow out’.” The idea of the plan is to promote high-density development around the transit nodes in the county, labeled corridors. The corridors found in each city are connected by the high-speed metro rail system which in turn connects it to the other major cities in the county and to Washington D.C. This plan offers a feasible alternative to driving and provides a means for business to send and receive goods. The plan encourages major employers to build around the corridors and the county enacted the Moderate Price Dwelling Unit (MPDU) ordinance in the corridors to ensure that employees would live in this area as well. “The MPDU law provides that within any subdivision of more than 50 units, at least 12.5% to 15% must be sold at moderate cost. As an incentive, builders are allowed a density bonus of up to 22% in the total number of units allowed” (Hassmann 1998). Additionally the corridor development is planned so that each corridor within the city is spaced four miles apart. This allows for the resident to find a quick change of scene from the common glass and concrete in the corridors while at the same time preserving green space. “To complete the picture of maintaining growth in the corridors and green space in the Wedge, a ring or residential communities consisting of a variety of housing types, local shopping, recreational and educational facilities, surround the downtown” (1998). The overall economic output achieved by this policy is that it works to reduce the externalities of high-density development while avoiding the occurrence of urban sprawl.

So how does Montgomery County’s growth management policies hold up to five standards of measure endorsed by Paul Foley? The first of these standards is whether or not the objectives of the policy are unclear or vague. Montgomery County passes easily here as it thoroughly describes exactly how it plans to lay out the development of the county, in what areas it will focus development and how it will encourage residents and business to settle in these areas. The second standard is that the objectives are not poorly quantified and are not only defined in a qualitative manner. Again the county passes here. Between determining the amount, in miles, of green space that must remain between the corridors, and delegating a number that constitutes high-density development for their MPDU program Montgomery County has partially met this goal. The completing factor for this goal lies in the county’s thorough maintenance of demographic data which they use to measure their progress. The third and fourth standards are where it becomes more difficult to determine. The third standard is if the county fully measured resource-cost implication and the fourth is measuring secondary effects. In the case of resource-cost implications it is hard to make a good evaluation of the policies since they have been in effect for so long. Data that would offer a sign of how this progress is doing is unavailable. As for measuring secondary effects the evidence of constantly increasing motor vehicle registration tends to offer that this area has not been accounted for. It is one of the main goals of the Wedges and Corridors program to encourage the use of alternative transportation. Obviously this is not being fully realized if the amount of vehicles increases steadily each year. As for the fifth standard, which is how accurate and honest the policymakers where in creating and implementing the growth management policy, this standard too cannot be determined. However in the case of ends justifying means, the following results would tend to show that the county was successful in this area.

From the OLS regression it was found that all independent variable were significant except for MORTGAGE RATE. It may be possible that the MORTGAGE RATE data was a bad point because it was a national measure and local MORTGAGE RATES may have varied. The interesting trend found in the data was the reduction in the HOUSING PRICES starting in 1997.

POPULATION DENSITY increased steadily over the 17-year time period but seemed to not have a noticeably positive effect on HOUSING PRICES. INCOME also rose steadily until 1989 when after that it has fluctuated between $25,000 and $27,000. However, there does not appear to be a strong positive influence on HOUSING PRICES, as it tended to consistently rise until 1997.

Last was MOTOR VEHICLE REGISTRATION which did show an increase over this time, but this variable was not utilized so much for it effect on HOUSING PRICES but rather as a determinant of the economic impact the policies are having. The increase in MOTOR VEHICLE REGISTRATION does show that government policy has not had a strong effect on this trend.

This is interesting because the research illustrated that a decrease in vehicle usage should occur after the implementation of a growth management plan, particularly that in Montgomery County. Even with these varying trends in the independent variable, the decrease in HOUSING PRICES is a remarkable trend. The first major decrease in 1997 may be due in large to the annexation of additional land in that same year. But the appearance of this trend both before and after the land annexation is worthy of noting. It may be possible that the MPDU program is a success in Montgomery County. If so, Montgomery County could pose as an example of how government policy can intervene to reduce externalities while also reducing deadweight loss. Over the time period evaluated we find that all independent variable have increased at a relatively constant rate, as well HOUSING PRICES have increased at a constant rate.

Conclusion

From this study we have found that it is possible for government policy to intervene in a manner that will reduce the presence of externalities in a city without shifting them to another area or substituting them with different externalities. Montgomery County provides an example of a city where expansion is present but policy was created to effectively manage it to the city’s desire without significant externalities. What has occurred in this area provides hope for future cities that face similar situations. However, it is important to keep in mind what Paul Foley mentioned, that no two cities are alike and this exact policy will not necessarily prove successful elsewhere. The fact that Montgomery County had high population density and a great deal of infrastructure to move that population should be considered. Additionally, this study was not able to discern whether the reduction in housing cost was unique to Montgomery County or was a trend in and around the county. Further research did not provide strong enough data to say for sure if this was the situation. Nonetheless the debate will continue between the pro-growth and pro-growth management parties and this research will hopefully shine some light into the arena. The presence of increasing motor vehicle registration in the county leaves an open end in this research, the reason and impact of this deserves consideration in future research. Keeping in mind that growth management relies on the want of the community to not grow in a manner liken to that of urban sprawl, it is apparent that Montgomery County actively made this choice and have successfully found a manner that will avoid doing so.
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