Determinants of restrictive suburban zoning: An empirical analysis

JOURNAL

OF URBAN

ECONOMICS

Determinants

21, 1-21 (1987)

of Restrictive Suburban An Empirical Analysis’

BARBARA

SHERMANROLLESTON,

Zoning:

PH.D.

Department of Economics, Baldwin- Wallace CoIlege, Berea, Ohio 44017 Received

November

5,1984;

revised

April

8,1985

I. INTRODUCTION In recent years, considerable research has focused on the role which local land use policies, particularly zoning, play in guiding metropolitan development and in producing and maintaining patterns of social and economic segmentation across urban areas. Both theoretical and empirical research on zoning suggest that zoning distorts urban property markets (Fischel [Xl), creates barriers to residential mobility (Shlay and Rossi [21]), impedes economic and social integration (Branfman et al. [5]), and generates fiscal inequities which deprive households of equal access to publicly provided goods and services (Logan [12,13]). From a policy perspective, much of the interest in and research on zoning has been directed toward documenting these allocative and distributional effects. Although the effects of zoning have been studied, little empirical evidence is available which identifies the factors encouraging restrictive zoning practices. Rather, the underlying objectives of suburban zoning policies are typically inferred from evidence regarding their effects. Reducing the serious social and economic effects of restrictive zoning, however, requires an informed understanding of the factors which generate these policies. This study develops a model which examines alternative hypotheses regarding the determinants of zoning. Both theory and observation suggest that three types of incentives are pertinent to suburban zoning decisions. From a legal and economic perspective, the rationale for zoning is to promote the “general welfare” by separating incompatible land uses. Thus, zoning minimizes the negative external effects which some land uses impose on others and serves as a market corrective which promotes an efficient pattern of land development. A number of economists argue, however, that while zoning is often couched in externality terms, it is often a reflection of other objectives which have little, if anything, to do with efficiency criteria ([8], Mills [14], Nelson [15], White [28]). ‘The author thanks Greg Pett for valuable comments on an earlier the Lincoln Institute of Land Policy for its financial support.

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of this paper

0094.1190/87 Copyright Q 1987 by Academic All rights

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SHERMAN

ROLLESTON

An alternative explanation is that land use is regulated to accomplish local fiscal objectives (Hamilton [9], [28]). Concern over the composition and size of the local tax base and over the level of public servicesdemanded by land users, it is argued, encourages communities to view local development patterns in terms of their fiscal implications. Furthermore, zoning may serve as a policy tool through which neighboring communities engage in fiscal competition in the battle for high revenue-generating land uses and low expenditure-demanding land users. The fiscal “motive” theory provides a particularly relevant explanation for contemporary zoning patterns in light of the rapid growth of the local public sector and the highly fragmented governmental structure of metropolitan areas. A number of empirical studies conclude, however, that fiscal zoning incentives are weak and at most, are secondary in importance to other local objectives (Babcock [4], [5], James and Windsor [lo], [17], Windsor [30]). These studies and others suggest that communities practice exclusionary rather than fiscal or externality zoning. Here, communities attempt to build “invisible walls” which exclude particular categories of land users whose entrance would disrupt the homogeneity of exclusive residential districts. Thus, zoning policies allow communities to practice subtle discrimination against low-income and minority groups. The hypotheses summarized above provide the framework for proposing an improved approach for estimating the determinants of restrictive suburban zoning. In light of the strong argument for fiscal zoning, a central goal of this study is to assessthe link between suburban fiscal environments and zoning policies. Other major objectives of this study are to measure the interjurisdictional determinants of restrictive zoning and to examine the relationship between residential and nonresidential zoning decisions in suburban communities. The next section describes this study’s empirical model for analyzing suburban zoning. A discussion of the study area, the sample, and the construction of the zoning variables follows in section III. Subsequent sections discuss the study’s research hypotheses, empirical results, and finally conclusions. II. THE EMPIRICAL MODEL The lack of conclusive evidence regarding zoning incentives within suburban communities stems from a number of weaknessesin the previous literature. Most serious among these is the focus on the effects rather than on the determinants of zoning. Empirical studies of externality zoning, for example, examine whether zoning and land-use characteristics affect residential property values (Cresine et al. [6], Lafferty and Frech [ll], Reuter [19], Stull [22]). Studies of the fiscal motive for zoning typically estimate the fiscal impact of alternative land uses permitted by local zoning ordinances

RESTRICTIVE SUBURBAN ZONING

3

[lo, 171. Similarly, a study of exclusionary zoning examines the effect of restrictive zoning on income and racial segmentation within metropolitan regions [5]. In each case, these studies incorrectly equate the estimated effects of zoning with its underlying intent, and thus are conceptually r&specified. By contrast, this study specities a causal model explicitly relating measures of local zoning restrictiveness to a set of variables which measure alternative zoning determinants. This model differs from others in a number of additional significant ways. For example, it measures the importance of interjurisdictional pressures on zoning restrictiveness, controls for interrelationships between residential and nonresidential zoning decisions, and develops a number of variables which measure alternative dimensions of the fiscal incentive for zoning. Similar to previous studies [9, 10, 17, 28, 301, this research focuses on zoning practices guiding the development of vacant land in suburban communities. Within such communities, decisions regarding the zoning of vacant land have several facets. First, communities must decide how to allocate the land between residential and nonresidential uses. Second, they must decide how restrictively to zone land within each major use category. It is likely that the first decision is largely predetermined by existing patterns of land development. Bedroom suburbs, for example, may choose to designate vacant land for additional residential use. This choice may reflect concerns over the negative externalities that nonresidential development is believed to impose on existing residential areas. Mixed-use communities; on the other hand, may be more likely to zone vacant land for additional nonresidential use, at least up to a certain threshold level. This decision may be based on the perceived fiscal benefits of commercial development-benefits that may outweigh any attendant negative extemalities, given the existence of prior nonresidential development. As modeled here, the land-use allocation decision is a binary choice, requiring the local government to choose between residential and nonresidential zoning designations. Zoning restrictiveness decisions, on the other hand, involve multiple choices among a large number of bulk and specific use designations. These varied decisions may be less tied to past history and more reflective of current community objectives. Proper specification of the zoning process, therefore, requires a model of the decision to allocate vacant land to residential and nonresidential uses and of the subsequent decisions to establish levels of zoning restrictiveness within each land-use category. Specifically, the model presented here describes the relationships between and among (1) the land-use allocation decision, (2) decisions regarding residential zoning restrictiveness, (3) decisions regarding nonresidential zoning restrictiveness, (4) variables which measure local determinants of zoning, and (5) variables which measure

4

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SHERMAN

ROLLESTON

nonlocal determinants of zoning reflecting community responses to perceived interjurisdictional spillovers. A two-step block recursive equation system is used to estimate this model of the suburban zoning process. In the first step, (l), the proportion of land allocated to residential use is estimated as a function of existing land-use and tax-base composition characteristics. Predicted values generated in this step are used as explanatory variables in a second set of equations, (2) and (3), which estimate the determinants of residential and nonresidential zoning restrictiveness, respectively. PCTRES = a, + BijBLANDj +B,BUSBASE, LOTINDEX,

+ BZjBLANDSQj + ur

(1)

= ~10+ Br PC$RESj + B, jBUSINDEXj + BsjRELFISCj + B,,CHGFISCj + B, jBUSBASE j + B,,EDENS,

BUSINDEX,

+ B,RELMIN,

+ B,RANGE,

+B,EMPACC,

+ B,,,jYEARSj + u2

= a, + Bi PCT^RESj + BzjLOTGDEXj

(2) + BsjRELFISCj

+ B,,CHGFISCj + BSjTVACj + B,,RELMIN, + B,RANGE,

-t-B,EMPACC, + BsjYEARSj + ug. (3)

Table 1 indicates the type of incentive that each explanatory variable is designed to measure and its hypothesized relationship to the appropriate dependent variable. Categorizing these variables as externality, fiscal, and exclusionary incentives serves as a framework within which to evaluate the underlying community characteristics which determine local zoning restrictiveness. A given set of local zoning restrictions may be used simultaneously to preserve property values (externality zoning), accomplish fiscal objectives (fiscal zoning), and restrict access of minorities (exclusionary zoning). Thus, the level of restrictiveness that we observe reflects the community’s consideration of local and nonlocal characteristics that generate externality, fiscal, and exclusionary zoning incentives. This model allows us to estimate the importance of community characteristics that contribute to suburban zoning restrictiveness. The definition and construction of the explanatory variables and the research hypotheses are discussed below, following a discussion of the sample, study area, and zoning variables.

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RESTRICTIVE SUBURBAN ZONING TABLE 1 Summary of Hypothesized Relationships between Explanatory and Dependent Variables Dependent variables PCTRES (representing the percentage of vacant land zoned for residential use) LOTINDEX (representing residential zoning restrictiveness)

BUSINDEX (representing nonresidential zoning restrictiveness)

Explantory variables (1) (2) (3) (1) (2) (3) (4) (5) (6) (7) (8) (9)

BLAND BLANDSQ BUXASE PCTRS BUSINDEX RELFISC CHGFISC BUSBASE EDENS RELMIN RANGE EMPACC

(lo) y%Rs (l) (2) (3) (4) (5) (6) (7) (8) (9)

pcTRs LOTINDEX RELFISC CHGFISC TVAC RELMIN RANGE EMPACC YEARS

Type of determinant Externality Externality Fiscal

Fiscal Fiscal Fiscal Externality Exclusionary Exclusionary Uncertain Uncertain

Hypothesized relationship

+ i + + + + +

Fiscal Fiscal Externality Exclusionary Exclusionary Uncertain Uncertain

III. THE RESEARCH SETTING AND THE MEASUREMENT OF ZONING Estimation of the model described above requires that a number of specific criteria be met with respect to the sample, the study area, and the data. First, the sample must include a large number of contiguous communities within a single metropolitan area. This requirement is essential to test the hypothesis that communities use zoning in response to both intra- and interjurisdictional pressures. In addition, the study area must contain a sizable amount of vacant developable land, and its constituent communities must face development pressures which make them attractive to potential residential and/or nonresidential land users. Finally, data which provide uniform and sufficiently detailed information on the zoning practices of the suburban communities within the selected study area are required. Municipal zoning data collected by the state of New Jersey facilitate a regionwide

6

BARBARA SHERMAN ROLLESTON

analysis of zoning within that state.2 These data provide a unique opportunity to conduct a comparative analysis of zoning across a large number of communities and to assessboth intra- and interjurisdictional factors which may influence local zoning patterns. The study area selected is a region in northeastern New Jersey consisting of nine counties: Bergen, Essex, Hudson, Middlesex, Monmouth, Morris, Passaic, Somerset, and Union. Within this nine-county area, the 185 communities identified as containing vacant developable land in 1970 are included in the zoning sample. To estimate the interjurisdictional determinants of restrictive zoning, 175 additional New Jersey communities within a lOmile radius3 of the geographic center of those in the zoning sample are included in the analysis.4 The zoning variables employed in the study reflect various elements of local zoning as applied to vacant developable land. In line with the three-equation model described earlier, three measures of zoning are required. The first (PCTRES) is defined as the percentage of total vacant developable acreage zoned for single-family, multifamily, or mobile home use, and accounts for the distribution of developable land between residential and nonresidential uses. The second zoning variable (LOTINDEX) measures variation in lot size requirements’ within single-family zones as well as the amount of multifamily and mobile home development permitted in residential zones. The rationale for employing such a measure of zoning restrictiveness is based on the “Euclidean pyramid” prioritization scheme.6Named after the 1926 test case which established the constitutionality of zoning [27], this scheme designates particular land uses as “highest and best” and ranks other uses relative to this superior category. Mobile homes, which are rarely permitted, typically constitute the lowest-ranking residential use. Multifamily development is ranked just above mobile homes, with high-rise apartments ranked 2The zoning data for the study were provided by the New Jersey Department of Community Affairs, Division of State and Regional Planning. 3As interjurisdictional effects are likely to be strongest among communities within reasonable distance of each other, the rationale for selecting the supplementary sample was based on commuting distance. The lo-mile radius was selected on the basis of Census data indicating that intercounty commuting within the study area is quite common and that the majority of workers live within 10 miles of their jobs (see [24-261). 4While strict application of the lo-mile radius criterion would require including a number of New York State communities in the sample, data limitations prevent their inclusion. This is not considered a serious limitation, however, since the regional variables are constructed as weighted averages and should not be greatly affected by the omission of one or two communities. SSimilar measures of zoning restrictiveness based on lot width and floor area requirements were also constructed. In the empirical analysis, results using the three alternative measuresof residential zoning restrictiveness were similar. 6This approach to measuring local zoning follows Shlay 1201.

RESTRICTIVE SUBURBAN ZONING

7

lower than townhouses and garden apartments. Single-family houses on small lots are ranked above multifamily housing, followed by successively more restrictive (as measured by lot size) single-family zones. The variable (LOTINDEX) reflects these implicit rankings and is computed according to the following formula:

LOTINDEXj

= k=lN ‘J

where, for community j, wk = the weight (1 through 6) assigned to each level of lot size restrictiveness; Nkj = the number of vacant developable acres zoned for each level of restrictiveness; Nr, = the total number of vacant developable acres zoned for residential use. Any system for weighting zoning restrictiveness is somewhat arbitrary. This formula assigns more weight to restrictively zoned acreage and less weight to permissively zoned acreage,as shown in Table 2. In constructing this index, dividing by Nri controls for variations across communities in the amount of land allocated for residential use. Thus, the index measures the proportion of residentially zoned land allocated to each level of restrictiveness relative to the total amount of vacant developable land zoned for residential use. The index takes on values ranging from 100 to 0 and is continuous over that range. The sample mean of the index is 53, TABLE 2 Weighting Schemefor Residential Zoning Restrictiveness” Lot size

Weight

Less than f acre f Acre to less than f acre & Acre to less than 1 acre 1 Acre to less than 3 acres 3 Acres or more

20 40 60 80 100

‘By construction, acreage designated for multifamily use is weighted by zero. Only one community in the sample designated any land for mobile home use, and this acreage is also weighted by zero.

8

BARBARA

SHERMAN

ROLLESTON

and its standard deviation is 24. This considerable variation reflects the diversity of zoning across communities and the degree of local discretion available in selecting levels of zoning restrictiveness. The index of nonresidential zoning restrictiveness (BUSINDEX) also follows the Euclidean prioritization scheme. Here, the number of acres zoned for industrial, commercial, and office/research uses is weighted by 0, 70, and 100, respectively. The index of nonresidential zoning restrictiveness is computed as follows:

BUSINDEXi = ‘= lN CJ

where, for community j, wk = the weight (1 through 3) assigned to each category of nonresidential use; Nkj = the number of vacant developable acres zoned for each nonresidential use; NCj = the total number of vacant developable acres zoned for nonresidential use. The mean value of the nonresidential index is 33, and its standard deviation is 30. This distribution reflects both the large number of communities designating large amounts of land for industrial use and the fact that industrial zoning is “weighted down” in constructing the index. While the assignment of weights to each nonresidential use category is arbitrary, lack of detailed information on the exact nature of permitted uses within each nonresidential use category precludes a more refined measure. The construction of the index implies, for example, that the industrial use category permits “general” nonresidential uses which are less desirable in terms of pollution, odor, or aesthetics and that “clean” types of nonresidential use are included in the office/research category. On a continuum of zoning restrictiveness, commercial zoning is assumed to fall between office/research zoning and industrial zoning, but closer to office/research zoning. Allocating vacant land for commercial use may simply reflect desires to meet local needs. Similar to office and research zoning, on the other hand, it may reflect desires for “good” tax ratables without the attendant negative externalities associated with industrial development. IV. RESEARCH HYPOTHESES The zoning measures described above are employed as dependent variables in estimating this study’s three-equation model of local zoning behav-

RESTRICTIVE

SUBURBAN

ZONING

9

ior. This section defines the model’s explanatory variables and describes their hypothesized relationship to the zoning measures.The variables definitions are summarized in Table 3. This section begins by discussing hypotheses regarding the land-use allocation decision. Land-use patterns are believed to be important in decisions regarding the distribution of vacant land to residential and nonresidential uses. Current patterns of development, in other words, define the way that communities plan for future development. From the point of view of the single-family homeowner, nonresidential development is often perceived to generate negative externalities. Thus, nonresidential development is more likely to be permitted in communities where it already exists and where residents are already conditioned to its presence than in communities that are primarily residential. Even in mixed-use communities, however, nonresidential development may be desirable only up to a certain point, beyond which its negative externalities outweigh any of its advantages [ll]. These considerations suggest a negative relationship between the proportion of existing land in nonresidential use (BLAND) and the proportion of vacant land zoned for residential use (PCTRES), and a positive relationship between the squared term (BLANDSQ) and PCTRES. For fiscal reasons, communities deriving a larger proportion of their tax revenues from nonresidential property (BUSBASE) are likely to zone vacant land for similar nonresidential uses. This hypothesis is consistent with White’s [29] argument that large nonresidential property tax bases permit lower overall tax rates and make it difficult for new residential structures to “pay for themselves” unless of considerably higher value than existing residential properties. Market forces, however, suggest that such high values may be inconsistent with the nonresidential character of such communities, leading those communities to zone more of their vacant land for nonresidential uses. Following the land-use allocation decision, communities select levels of restrictiveness to apply within residentially and nonresidentially designated zones. Residential zoning restrictiveness is hypothesized to depend on fiscal, externality, and exclusionary considerations, on the simul~eously determined level of nonresidential zoning restrict&ness (BUSINDEX), and on the prior land-use allocation decision (PCTRES). With respect to the latter variable, the hypothesis is that levels of residential zoning restrictiveness are greater in communities that allocate larger proportions of their vacant land to residential use. This hypothesis is best understood by considering the zoning decisions of communities which designate large fractions of their vacant land for nonresidential use. Such communities are not likely to impose stringent requirements on the remaining land zoned for residential development because such requirements are most likely inconsistent with market forces accompanying nonresidential development. Thus,

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BARBARA SHERMAN ROLLESTON TABLE 3 Variable Names and Definitions”* b Definition

Name

PCTRES LOTINDEX BUSINDEX BLAND BLANDSQ BUSBASE RELFISC

Percentage of total vacant developable land zoned for residential use Index of residential zoning restrictiveness Index of nonresidential zoning restrictiveness Proportion of existing land in nonresidential use (BLAND)’ Proportion of total tax base derived from nonresidential property Local fiscal capacity relative to that in surrounding communities, defined as follows: FISCAP,

i # j, where, for community j,

FISCAP = estimated local fiscal capacity; i = ith community within lo-mile radius of community j; n

= number of communities within lo-mile radius of community j;

dij = distance between communities i and j

CHGFISC EDENS RELMIN

Percentage change in local fiscal capacity, 1967-1970 Population density within residentially developed land areas Percentage of minorities relative to that of surrounding communities, defined as follows: PROPMIN,

RANGE

i # j, where, for community j ,

PROPMIN = proportion of local population nonwhite; j, i, d,, and n are as defined previously Local income homogeneity, defined as follows:

QUART, MEDINCj

where, for community j,

QUART = interquartile income range; MEDINC = median household income

RESTRICTIVE SUBURBAN ZONING

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TABLE 3-Continued Name EMPACC

Definition Average number of persons employed in surrounding communities, defined as follows:

where, for community j ,

EMP = number of persons employed in community i; j, i, d,,, and n are as defined previously YEARS TVAC

Number of years since zoning ordinance was revised or amended Total amount of vacant developable acreage

“Data sources are available from author upon request. bAll data apply to 1970 except for BLAND, BLANDSQ, and EDENS which, because of data limitations, were constructed using 1966 land-use information.

a prior decision to allocate a larger proportion of vacant land to nonresidential use limits the likelihood of applying restrictive zoning to remaining residential land. The second endogenous zoning variable (BUSJ%DEX) accounts for the hypothesized simultaneity between residential and nonresidential restrictiveness decisions. Communities are likely to view residential and nonresidential zoning restrictiveness as complementary land-use tools that can be simultaneously applied to meet similar community objectives, suggesting a positive relationship between the two zoning indices. Of the three fiscal determinants of restrictive residential zoning, two (CHGFISC and BUSBASE) measure local fiscal characteristics, while the third (RELFISC) accounts for interjurisdictional fiscal pressures which may contribute to zoning restrictiveness. RELFISC and CHGFISC are based on measures of fiscal capacity which reflect the ability of local governments to raise revenues from their own sources. Following Akin [3], these variables are constructed using multiple regression techniques which express local fiscal capacity as a function of the ability of local governments to tax (1) resident income, (2) resident wealth, and (3) nonresidents. For each community, per capita own-source local revenues are regressed on per capita income (which measures resident income), per capita market value of residential taxable property, farm land, and vacant land (which measures resident wealth), and per capita market value of commercial, industrial, and telephone and telegraph property (which measures the ability to tax outsiders). The relative importance of each component of the local tax base is

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BARBARA SHERMAN ROLLESTON

determined by the computed regression coefficients.7 Thus, the expected value of the dependent variable, actual local revenues per capita, provides a weighted index of fiscal capacity (FISCAP) which is then used to construct the variables RELFISC and CHGFISC (see Table 3).8 RELFISC measures local fiscal capacity in each sample community relative to that in surrounding communities’ and accounts for the use of zoning as a competitive fiscal tool. The positive relationship expected between RELFISC and zoning restrictiveness reflects the hypothesis that communities consider their own fiscal “health” in comparison with that of surrounding communities. Concern over the distribution of the region’s net fiscal wealth and their share of it encourages relatively “healthier” communities to compete, via zoning, to maintain or improve their relative fiscal advantage. CHGFISC measures the three-year growth rate in local fiscal capacity” and also reflects the hypothesis that zoning is a competitive fiscal tool used by communities to obtain or retain a portion of the region’s fiscal wealth. Communities experiencing recent increases in fiscal capacity, therefore, are expected to practice more restrictive zoning so as to maintain that growth rate. The third fiscal variable (BUSBASE) represents that component of fiscal zoning most directly reflecting concerns over the fiscal impact of residential development. For new housing to pay for itself at the lower tax rates often made possible by nonresidential development, it must be of high enough ‘An advantage of the regression approach to measuring fiscal capacity is that it accounts for interrelationships among local tax bases where more traditional averaging techniques do not. The latter method assumesthat each local tax base is taxed at an average statewide rate. The regression approach, on the other hand, recognizes that the use of one base may preclude extensive reliance on another and controls for these interrelationships. In essence,all other tax bases are held constant while examining the marginal increment to local fiscal capacity resulting from a change in a given tax base. For a description of the averaging technique, see Advisory Commission on Intergovernmental Relations [l, 21. ‘The thrust of the Akin approach is that it is income, or claims upon income, that produces additions to local fiscal capacity (simple correlation between per capita community income and RELFISC is 0.74). Thus, the fiscal capacity variables used in this analysis incorporate the effect of community income on zoning restrictiveness. 91n constructing this variable, each surrounding community’s fiscal capacity is weighted by its distance from community j in the zoning sample (see Table 3). When this weighted index is divided by l/d,j, the result is a measure of average fiscal capacity for the surrounding communities within a lo-mile radius of community j which weights most heavily the communities closest to community j. Dividing local fiscal capacity in community j by this average results in a ratio which indicates whether local fiscal capacity is greater than, less than, or equal to the average fiscal capacity in surrounding communities. “Use of this growth rate variable implies that fiscal changes over a period of time prior to 1970 will be reflected in zoning policies in effect in 1970. Given the rapid growth rate experienced by northeastern New Jersey in the 196Os,3 years is considered a reasonable time period to capture recent changes in local fiscal capacity.

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13

value to generate the tax revenues necessary to balance expected costs (Fischel [7], Netzer [16], [29]). Communities with larger nonresidential tax bases, therefore, may direct residentially zoned vacant land toward highvalued housing, suggesting a positive relationship between BUSBASE and residential zoning restrictiveness. The nature of the data set employed in this study and the use of the community as the unit of analysis permit externality zoning incentives to be analyzed only as a community-wide phenomenon.” The externality variable used here (EDENS) measures population density within residentially developed land areas. Based on the hypothesis that vacant residential land is zoned to accommodate density levels similar to those which prevail in developed residential areas, EDENS should be inversely related to residential zoning restrictiveness. This hypothesis again suggests some conformity of zoning to market forces in densely populated communities and the desire of more exclusive communities to retain property values through restrictive zoning. The hypothesis that restrictive zoning is a result of explicit exclusionary motives is tested by two variables, RELMIN and RANGE. The first is a measure of local racial composition relative to that in surrounding communities and represents hypothesized interjurisdictional exclusionary zoning incentives. l2 If communities are sensitive to the racial makeup of surrounding communities, then they may use zoning to discourage potential in-migration that might disturb existing levels of racial homogeneity. Exclusionary zoning incentives are hypothesized to be greatest in communities with small minority populations relative to those in surrounding communities, suggesting an inverse relationship between RELMIN and zoning restrictiveness. Restrictive zoning may be motivated by the desire for income as well as for racial homogeneity. Income homogeneous communities may be more successful in guiding zoning toward desired goals, whereas more heterogeneous communities that must cater to competing demands and diverse constituencies may lack the political cohesivenessnecessary to enact and implement restrictive zoning policies [9, 281.Income homogeneity, in other words, may reflect community “like-mindedness” and the differential abilities of communities to impose restrictive zoning. Furthermore, by imposing restrictive zoning, income homogeneous communities can better control new development in a manner that ensures continued income uniformity. The income variable used here (RANGE) is constructed so that higher “This is the contrast to previous studies which focus on the micro-level implications of zoning on property values, using individual structures as the units of analysis. ‘*Like the fiscal capacity index (RELFISC), this variable is a ratio indicating whether the proportion of minorities in a given community is greater than, less than, or equal to the average proportion in surrounding communities.

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values represent greater income variation and thus should be inversely related to zoning restrictiveness. Communities in the path of metropolitan development may have strong incentives to practice restrictive zoning. In such communities, growth of any kind may be perceived as adverse, imposing substantial economic or social costs on residents. EMPACC, a measure of average employment in surrounding communities, is designed to capture a major contributor to suburban growth pressures.Communities close to larger employment centers may face greater demand for housing and may use restrictive zoning to thwart that demand, suggesting a positive relationship between EMPACC and residential zoning restrictiveness. A second variable (YEARS) reflecting suburban growth pressures measures the number of years since the local zoning ordinance was revised or amended and should be inversely related to zoning restrictiveness. Communities practicing restrictive zoning are likely to revise their ordinances often to keep them up to date with existing conditions and reflective of contemporary land-use objectives, particularly if development pressures are strong [18]. The empirical analysis of nonresidential zoning generally follows the same framework as that used to examine residential zoning restrictiveness, reflecting the view that both types of zoning are used to accomplish many of the same objectives. Two variables (BUSBASE and EDENS), however, are omitted from the nonresidential analysis as they pertain solely to residential zoning decisions. An additional variable (TVAC), defined as the total amount of vacant developable land in the community, appears in the nonresidential equation only. In communities with substantial portions of vacant land, nonresidential activities can be more easily segregated from residential areas, thus minimizing potential negative externalities [ll] and perhaps reducing concern over the exact nature of that development. Thus, nonresidential zoning is expected to be less restrictive in communities with greater quantities of vacant land. V. EMPIRICAL

RESULTS

The empirical model described earlier represents a block recursive equation system, characterized by simultaneity within one block, (2) and (3), and recursiveness between blocks, (1) and (2), (3). Equation (1) is estimated using ordinary least-squaresregression, while two-stage least-squares regression is used to estimate (2) and (3).13 The estimates of (1) are shown in t3 Maximum likelihood techniques such as Tobit provide superior parameter estimates when dependent variables are truncated or severely constrained (Tobin [23]). While the dependent variables used here are constrained to values within lower and upper bounds of 0 and 100, they are continuous throughout this range and are not characterized by severe truncation. The model was estimated using Tobit analysis and yielded little difference in parameter estimates or significance levels. For ease of interpretation, the least-squares regression results are presented here.

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RESTRICTIVE SUBURBAN ZONING TABLE 4 Regression Results: The Land-Use Allocation Decision (Dependent Variable = PCTRES, N = 185) Variable BLAND BLANDSQ BUSBASE Constant

Coefficient

Standard error

T value

- 96.35 166.80 - 75.51 92.26

46.59 120.88 13.35 3.07

- 2.07* 1.38 - 5.66** 30.05**

Adjusted R2 = 0.25 F = 21.86** *Significant at 0.05 level. **Significant at 0.01 level

Table 4. These results provide support for the externality (BLAND) and fiscal (BUSBASE) considerations hypothesized to influence land-use allocation decisions but do not support the nonlinear specification of the externality variable. As expected, communities with larger nonresidential tax bases and larger fractions of existing nonresidential land use allocate smaller fractions of vacant land to residential use. While the equation is significant at the 0.01 level, it accounts for only 25% of the variation in the dependent variable. The variation in the dependent variable not explained by the model suggests that there is a great deal of randomness across communities in the decision to allocate vacant land to residential use. The empirical analysis of residential zoning restrictiveness provides the most successful results of the study. Together, the explanatory variables account for a substantial portion (57%) of the variation across the sample communities in residential zoning restrictiveness. Results shown in Table 5 indicate that the prior land-use allocation decision (PC’6RES) and the simultaneously determined nonresidential zoning restrictiveness decision (BUSIGDEX) are significant predictors of residential zoning restrictiveness. The first relationship suggests that residential zoning decisions reflect market forces which influence the suitability of vacant land for particular levels of restrictiveness. The significant negative sign on BUSI%DEX may indicate that communities view residential and nonresidential zoning restrictiveness as substitute rather than complementary techniques for accomplishing local objectives. On the other hand, the lack of data on the exact nature of uses permitted within industrial zones suggests an alternative explanation. Contrary to what has been assumedhere, much of the industrially zoned vacant acreage may represent “clean” rather than “general” industrial uses. This possibility is suggested by the fact that 73% of nonresidentially zoned vacant land in the study area is designated for industrial use, while only 10% is allocated for office/research use. The

16

BARBARA SHERMAN ROLLESTON TABLE 5 Regression Results: The Residential Zoning Restrictiveness Decision (Dependent Variable = LOTINDEX, N = 183)” Variable P&ES BUSI%DEX RELFISC CHGFISC BUSBASE EDENS RELMIN RANGE EMPACC YEARS Constant

T value

Coefficient 2.89 - 1.11 44.66 -0.13 189.26 -0.39 -6.04 46.06 - 3.45 - 2.19 - 195.75

0.44 0.27 10.01 0.07 38.24 0.40 1.45 12.14 0.57 0.54 39.64

6.56** -4.11** 4.46** - 1.86* 4.95** -0.98 -4.17** 3.79** - 6.05**

-4.06** -4.94**

Adjusted R2 = 0.57

F= 25.37'* “Two communities in the zoning sample zoned all of their vacant developable land for nonresidential use and thus are excluded from the analysis of residential zoning restrictiveness. *Significant at 0.10 level. **Significant at 0.01 level.

results may indicate, therefore, that communities view zoning for “clean” industrial uses and restrictive residential zoning as complementary land-use tools. The results shown in Table 5 support the hypothesis that residential zoning is responsive to both local and nonlocal fiscal conditions. The ratio of local to surrounding fiscal capacity (RELFISC) has a significant effect on residential zoning restrictiveness, suggesting that communities use zoning to protect their fiscal advantage relative to other communities in the region. Recent growth in local fiscal capacity (CHGFISC) also affects residential zoning restrictiveness although not in the expected direction.14 Communities experiencing greater growth in fiscal capacity tend to practice less restrictive residential zoning. A possible explanation is that communities experiencing increases in fiscal capacity are those facing and accommodating growth pressures by permitting more dense and less restrictive development. The positive relationship between the portion of the local tax base derived from nonresidential property (BUSBASE) and residential zoning restrictiveness supports the traditional theory of fiscal zoning. This theory “Collinearity between CHGFISC and other explanatory variables is low and thus is unlikely to account for the unexpected sign. Tables of correlation coefficients can be provided by the author upon request.

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holds that communities consider the costs and revenues associated with new development and design zoning regulations to ensure that new growth produces fiscal gains or, at least, prevents fiscal losses. The empirical results do not support the hypothesized externality incentive for zoning as measured by EDENS. Although precluded by data limitations, a measure of housing density rather than of population density may provide a better test of this hypothesis. It is also possible that population density does influence zoning restrictiveness but in a nonsystematic manner. Specifically, both low- and high-density communities may practice restrictive zoning, the former for externality reasons and the latter in anticipation of the fiscal benefits associated with large lot size development. The results show that communities with smaller minority populations relative to surrounding communities (RELMIN) tend to practice more restrictive zoning, supporting the exclusionary zoning hypothesis and the view that nonlocal community characteristics are salient to local zoning decisions. Restrictive residential zoning may be viewed as an effective barrier to inmigration of minorities from nearby communities. Contrary to expectations, communities with greater rather than less income variability (RANGE) tend to practice more restrictive residential zoning. The high correlation between income variability and per capita income (0.67) provides a possible explanation for this result, and suggests that RANGE captures the positive effect of income level on zoning restrictiveness. Also contrary to expectations, proximity to large employment centers (EMPACC) contributes to less rather than more restrictive residential zoning. Rather than using zoning to discourage growth, communities in the path of development appear to accommodate that growth. This suggests that land-use patterns promoted through zoning conform to some extent to market pressures which encourage more dense development in areas of high housing demand. Finally, as expected, communities which more recently revised their zoning ordinances (YEARS) tend to zone more restrictively. As a reflection of growth pressures, such revisions may ensure that zoning regulations remain concurrent with contemporary local and regional economic and social conditions. The model of local zoning behavior proposed here is less successful in explaining nonresidential zoning decisions, accounting for only 17% of the variation across communities in nonresidential zoning restrictiveness.15One 151n estimating the nonresidential zoning restrictiveness equation, only two significant relationships were obtained. The proportion of vacant land zoned for residential use was significant and positive, perhaps reflecting local sensitivity to the perceived negative extemalities associated with nonresidential development. Relative fiscal capacity also was positive and significant, supporting the hypothesis that fiscally advantaged communities impose restrictive nonresidential zoning to preserve their advantage. Results of the nonresidential zoning equation may be obtained from the author upon request.

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ROLLESTON

explanation for this low explanatory power may be the difficulty, given data limitations, of accurately measuring nonresidential restrictiveness. As discussed earlier, the weighting scheme used to measure nonresidential zoning restrictiveness is rather arbitrary. Nevertheless, estimates obtained using different sets of weights were not substantially different. While the results do not appear to be sensitve to the particular weighting scheme employed, the underlying assumption that an industrial designation is the most permissive type of nonresidential zoning may not be correct in all cases. Another explanation for the nonresidential equation’s low overall explanatory power is the possibility of a community-specific dynamic element inherent in nonresidential zoning decisions. For example, nonresidential development within suburban communities may be particularly sensitive to the political power of special interest groups, whether they are developers, business people, homeowners, environmental groups, and/or local zoning or planning boards. The political determinants of zoning are likely to be quite diverse, however, and not easily captured using a cross-sectional approach. V. CONCLUSIONS The results of this study suggest that lack of attention to proper causal modeling of the zoning decisionmaking process is a serious weaknessin the existing literature. A major contribution of this study has been to develop an empirical framework suitable for directly examining alternative hypotheses regarding the determinants of zoning. The model developed here views zoning as a product of both local and regional community characteristics which generate fiscal, externality, and exclusionary zoning incentives. Application of this model to a sample of communities in northeastern New Jersey, using ordinary least-squares and two-stage least-squares regressions, provides evidence as to the role of these incentives. Difficulties in obtaining sufficiently detailed zoning data and thus in measuring all pertinent dimensions of zoning restrictiveness are the primary limitations of this study. Nevertheless, the zoning data used here provide a unique opportunity to conduct a comparative analysis of zoning across communities within a single metropolitan area. This approach is essential for examining the fiscal zoning hypotheses derived from models of residential location and public choice and for assessingother dimensions of interjurisdictional competition played out through the “zoning game.” This research provides evidence that fiscal considerations are important determinants of restrictive residential zoning. Suburban communities appear to be concerned over the cost-revenue implications of new residential development. The results suggest that communities encourage residential uses which, given local tax base composition, at least break even fiscally and thus pay for themselves. Furthermore, in highly fragmented metropoli-

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tan regions, communities with fiscal superiority over their neighbors appear to use zoning to preserve their relative fiscal advantage. Zoning thus offers communities a means to compete for net fiscal gains in the context of geographically bounded metropolitan regions. The results also suggest that zoning patterns are consistent to some extent with underlying market forces. Communities experiencing greater growth in fiscal capacity and those in the path of regional development, by virtue of their proximity to employment centers, accommodate those growth pressures by permitting more dense residential development. Similarly, attention to market forces may lead communities designating large proportions of their vacant land for nonresidential use to impose less restrictive zoning in remaining residential areas. This research also addresses the issue of exclusionary zoning and examines the hypothesis that suburban communities use zoning to “protect” themselves from potential nonwhite population growth generated from nearby communities. The results indicate that communities may indeed wish to retain existing levels of racial homogeneity and may view restrictive zoning as an effective tool for accomplishing this objective. The significant negative relationship between residential and nonresidential zoning restrictiveness, while contrary to expectations, may be explained by considering the extensive use of industrial zoning within those land areas designated for nonresidential development. Industrial zoning may be used as a general designation for acreage that communities wish to relegate to a “holding zone.” This designation prohibits residential development and, at the same time, may discourage other types of development. For example, there may be little demand for the land as industrial property, in which case the industrial designation effectively keeps the land off the market. This practice of “overzoning” for industry may reflect a “wait and see” approach which essentially freezes development until suitable proposals are presented for developing the land in accordance with local objectives. Particularly where the extent of industrial zoning is inconsistent with realistic expectations of future industrial demand, this practice may constitute a highly restrictive zoning tool. This research shows that strong incentives exist at the local level to use zoning for purposes that may well extend beyond the limits of legitimate land-use planning and general welfare considerations. Future research may be able to ascertain the extent to which legislative and judicial reforn@ can break down these incentives and thus alter established zoning practices 16For example, reduced reliance on local property taxation, tax base sharing, and cost sharing of municipal services have been suggested as means to reduce the fiscal incentive for restrictive zoning. Similarly, court-imposed “fair share” housing plans may reduce the ability of communities to carry out exclusionary zoning.

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within suburban communities. Until weakened, restrictive suburban zoning to disparities in wealth and poverty inequities in the spatial distribution of

and unless these incentives are will no doubt remain closely linked across metropolitan areas and to quality living environments.

REFERENCES 1. Advisory Commission on Intergovernmental Relations, “Measures of State and Local Fiscal Capacity and Tax Effort: A Staff Report,” U.S. Govt. Printing Office, Washington, D.C. (1962). 2. Advisory Commission on Intergovernmental Relations, “Measuring the Fiscal Capacity and Effort of State and Local Areas,” U.S. Govt. Printing Office, Washington, D.C. (1971). 3. J. S. Akin, Fiscal capacity and the estimation method of the Advisory Commission on Intergovernmental Relations, Nat. Tax. J., 26, 275-294 (1973). 4. R. F. Babcock, “The Zoning Game,” Univ. of Wisconsin Press, Madison, Wise. (1966). 5. E. J. Bra&man, B. J. Cohen, and D. M. Trubek, Measuring the invisible wall: Land use controls and the residential patterns of the poor, Yule Law J., 82, 483-508 (1973). 6. J. P. Crecine, 0. A. Davis, and J. E. Jackson, Urban property markets: Some empirical results and the implications for municipal zoning, J. Law Econom., 20 79-99 (1967). 7. W. A. Fischel, Fiscal and environmental considerations in the location of firms in suburban communities, in “Fiscal Zoning and Land Use Controls” (E. S. Mills and W. E. Oates, Eds.), Heath, Lexington, Mass. (1975). 8. W. A. Fischel, A property rights approach to municipal zoning, L.and Econom., 54,64-81 (1978). 9. B. W. Hamilton, Property taxes and the Tiebout hypothesis: Some empirical evidence, in “Fiscal Zoning and Land Use Controls” (E. S. Mills and W. E. Oates, Eds.), Heath, Lexington, Mass. (1975). 10. F. J. James and 0. D. Windsor, Fiscal zoning, fiscal reform, and exclusionary land use controls, J. Amer. Inst. Planners, 42, 130-141 (1978). 11. R. N. Lafferty and H. E. Frech, III, Community environment and the market value of single family homes: The effect of the dispersion of land uses, J. Law Econom., 381-394 (1978). 12. J. R. Logan, Industrialization and the stratification of cities in suburban regions, Amer. J. Social., 82, 333-348 (1976). 13. J. R. Logan, Growth, politics and the stratification of places, Amer. J. Social., 84, 404-416 (1978). 14. E. S. Mills, Economic analysis of urban land use controls, in “Current Issues in Urban Economics” (P. Mieszkowski and M. Straszheim, Eds.), Johns Hopkins Univ. Press, Baltimore (1978). 15. R. H. Nelson, “Zoning and Property Rights,” MIT Press, Cambridge, Mass. (1977). 16. D. Netzer, “Economics of the Property Tax,” The Brookings Institution, Washington, D.C. (1966). 17. New Jersey County and ‘Municipal Government Study Commission, “Housing and Suburbs. Fiscal and Social Impact of Multifamily Development,” Ninth Report, N.J. County and Municipal Govt. Study Commission, Trenton, N.J. (1974). 18. New Jersey Department of Community Affairs, Division of State and Regional Planning, “Zoning in New Jersey, 1967,” N.J. Dept. of Community AKairs, Trenton, N.J. (1972). 19. F. H. Reuter, Externalities in urban property markets: An empirical test of the zoning ordiance in Pittsburgh, J. Law Econom., 16, 313-350 (1973).

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20. A. B. Shlay, “Zoning for Whom? Estimating the Impact of Zoning on Census Tract Housing and Population for the Chicago SMSA,” Unpublished Ph.D. dissertation, University of Massachusetts (1981). 21. A. B. Shlay and P. H. Rossi, Keeping up the neighborhood: Estimating net effects of zoning, Amer. Social. Rev., 46, 703-719 (1981). 22. W. J. Stull, Community environment, zoning, and the market value of single family homes, J. Law Econom., 18, 535-557 (1975). 23. J. Tobin, Estimation of relationships for limited dependent variables, Econometrica, 26, 24-36 (1958). 24. United States Department of Commerce, 1970 Censusof Population, Vol. 1, Part 32, New Jersey, “Characteristics of the Population,” U.S. Govt. Printing Office, Washington, D.C. 25. United States Department of Commerce, Current Population Reports, Special Studies, Series P-23, No. 105, “Selected Characteristics of Travel to Work in 20 Metropolitan Areas: 1977,” U.S. Govt. Printing Office, Washington, D.C. (1981). 26. United States Department of Commerce, Current Population Reports, Special Studies, Series P-23, No. 90, “Selected Characteristics of Travel to Work in Paterson-Clifton-Passaic SMSA: 1975,” U.S. Govt. Printing Office, Washington, D.C. (1979). 27. Village of Euclid v. Ambler Realty Co., 272 U.S. 365 (1926). 28. M. J. White, Fiscal zoning in fragmented metropolitan areas, in ‘I Fiscal Zoning and Land Use Controls” (E. S. Mills and W. E. Oates, Eds.), Heath, Lexington, Mass. (1975). 29. M. J. White, Self-interest in the suburbs: The trend toward no growth zoning, Policy Anal., 4, 185-203 (1978). 30. D. Windsor, “Fiscal Zoning in Suburban Communities,” Heath, Lexington, Mass. (1979).