Copyright American Planning Association Autumn 1993Land use controls affect the relative cost of development at different sites and consequently the spatial array of housing prices. Some consumers may alter their residential choice in response to the changes in relative prices. Consequently price increases due to land use controls may have repercussions beyond the regulated area. For example, a greenbelt program may induce development beyond the greenbelt (Nelson 1988). Such repercussions, which may not be in accordance with the stated objectives of the controls, have not received much attention in the growth control literature.
The extent and magnitude of such spatial effects may determine whether a proposed set of regulations will achieve its objectives as well as the distribution of the growth controls' burden. Consequently a priori estimates of the spatial effects are of interest to planners and jurisdictions concerned with the economic and distributional implications of proposed growth control programs. This paper outlines an approach for analyzing the likely spatial effects of land use regulations, based on estimates of relevant households' preferences and willingness-to-pay (WTP) for various residence attributes, which can be derived through survey techniques.
This article describes the application of this approach to Maryland's Chesapeake Bay Critical Area (CBCA). This study identifies the households most likely to be affected by the CBCA and their preferences and willingness-to-pay for various residential attributes. By relating these preferences and attitudes to the CBCA impacts on housing prices, the study estimates the likely spatial effects. This analysis indicates that the CBCA program will neither lead to greater sprawl nor have regressive effects, as long as water access is maintained for the less affluent households who cannot afford waterfront residences. This realization may be crucial for maintaining political support for the program.
CONCEPTUAL CONTEXT AND METHODOLOGICAL APPROACH
Consumers searching for residences are faced with an array of alternative locations, residences, and prices determined by the actions of developers and government. These interactions are shown in Figure 1.(Figure 1 omitted)
Interactions among government, consumers, and producers occur as part of two intertwined processes. The market-based development process determines which residences will be built, where, at what cost, and prices. This is the process land use controls try to affect (Moor 1983). Land use controls, established thorough the political process, structure the location choices for the development process. Planners need to be able to assess the likely effects different land use measures will have on various activities and their locations (Faludi 1987; Moor 1983).
THE EFFECTS OF LAND USE CONTROLS
Land use controls encompass a variety of measures, such as density controls, use restrictions, fees, and review requirements. If the measures require production factors timing, or costs different from what would have occurred in an unrestricted market, price effects can be expected By raising development costs, even in a spatially differentiated manner, growth controls almost invariably raise housing prices (Katz and Rosen 1987; Singell and Lillydahl 1990; Schwartz et. al. 1981). However, there is no agreement regarding the magnitude of such price effects (Fischel 1990; Lillydahl and Singell 1987).
This lack of agreement arises because price effects are a function of the degree to which consumers can find substitutes for the regulated places and uses (Elliott 1981; Lillydahl and Singell 1987). If many substitutes exist nearby producers will not be able to obtain higher prices for developed property. Consequently the price of vacant land in the regulated area will drop, and fewer plots will be developed. In this case the primary spatial effect would be a shift of consumers away from the regulated area, leading to accelerated development in substitute areas. In the absence of nearby substitutes, producers can capitalize the higher costs of regulations in housing prices. In this case the controls would result in a significant price premium for home buyers. Naturally not all home buyers may be able or willing to pay such a premium, and some would locate elsewhere, either to less desirable areas or to less desirable homes within the same area. If the areas receiving this limited spillover are also constrained, prices may rise there too, creating a price spillover as well.
Empirical evidence from various locations have tracked incidences of growth controls causing price premiums (Katz and Rosen 1987), the spillover of development (Dowall 1984; Schwartz et al. 1981), or price spillovers (Pollakowski and Wachter 1990). The questions planners need to pursue are which of these effects will occur in any specific case and where will the development spillover take place. Planners can then analyze the effectiveness of a proposed program in achieving its goals and its implications for consumers and various local jurisdictions.
Most theoretical studies discuss spillover effects in the context of a city-suburb-rural spatial continuum, focusing on whether land use controls induce greater decentralization or more concentration (Sheppard 198; Thrall 1987; White 1975). Yet, none of these studies nor the scant post facto empirical evidence is sufficient for predicting what the actual effects are likely to be (Fischel 1990; Chinitz 1990). Consequently the nature of these effects continues to be a source of controversy (Fischel 1991). Moreover, analyses of spillover effects should not be limited to the concentration-decentralization dimension, as spillovers may occur in other dimensions, such as between similar communities within the same suburban ring or among different types of residences within the same community.
IDENTIFYING CONSUMER REACTIONS TO LAND USE CONTROLS
Two variables determine the extent, direction, and type of spatial spillover: (1) the degree to which consumers can find substitutes to residences whose prices are rising because of proposed regulations; and (2) the characteristics of those substitutes (Feitelson 1989). Figure 2 depicts schematically this study's approach to identifying likely consumer reactions to proposed land use controls. The identification of areas and types of development to which the regulations pertain is usually straightforward.(Figure 2 omitted)
The comparison of price trends in these areas to those in areas where the regulations do not pertain is the basis for most studies analyzing the controls' price effects (Schwartz et al. 1986). Yet estimates will be accurate only if the control group areas are indeed unaffected by the regulations. In other words, if there are any spillovers between the regulated area and the control areas the estimates may be biased.
The analysis of possible spillovers requires several steps. First, the study must identify potential consumers seeking residences in the regulated area, as this is the group most likely to be affected by higher prices. However, not all consumers are equally sensitive to such price premiums. While some will pay the additional premium, others may alter their residential consumption to avoid it. The second step, therefore, segments potential consumers according to their sensitivity to the controls' price premiums. This segmentation allows assessment of consumer trade-offs in response to the controls' price effects.
As housing is a multiattribute good, consumer reactions can be viewed as trade-offs among various housing characteristics (Lancaster 1966). To analyze such tradeoffs, consumer preferences and WTP for housing attributes need to be known. These can be elicited through survey-based techniques termed decompositional multiattribute preference models (Louviere and Timmermans 1990; Timmermans 1984).
Any given set of controls is likely to affect the marginal implicit prices of some attributes. Consequently consumers will face a different array of implicit attribute prices after a set of regulations has been enacted than before. Once the study identifies consumers sensitive to additional price premiums and elicits their preferences and WTP, then it can determine the trade-offs between different housing types that consumers are likely to make in response to the controls' price effects. Then, the study can analyze the spatial implications of these trade-offs. This analysis applies this approach to the Chesapeake Bay Critical Area.
THE CHESAPEAKE BAY CRITICAL AREA
The Chesapeake Bay Critical Area (CBCA) law, enacted by Maryland in 1984 in response to the deterioration of the bay's natural resources, strives to reduce adverse impacts on the bay's water quality and on natural habitats by regulating development along the shore and on adjacent land. The legislation designated a one thousand-foot strip of land around the tidal water of the Chesapeake and its tributaries as a critical area, for which a regulatory resource protection program was enacted (see Figure 3).(Figure 3 omitted) This program consists of plans and ordinances prepared by the local jurisdictions according to criteria developed by the Chesapeake Bay Critical Area Commission (Davis 1987). The commission approves the programs, which are enforced by the local jurisdictions. The criteria required local jurisdictions to classify the land within their critical area according to three categories: intensely developed areas, resource conservation areas, and limited development areas. For each category the guidelines specified minimum and desirable limitations and conditions for both development and redevelopment. The guidelines were incorporated into local regulations. The criteria also specified the conditions and limits for transforming lower intensity areas into higher intensity categories.(1)
The concerns of several jurisdictions led the commission to initiate an economic impact study of the CBCA. The first part of the study analyzed the effects of the law on land and housing prices around the bay, construction rates, the rate of sales, and the fiscal situation of jurisdictions around the bay. Beaton (1988) found that the CBCA mainly resulted in additional premiums for waterfront residences (above those apparent before the CBCA). This finding was expected as, with most coastal regulations, the CBCA affects primarily the construction of units near the water, especially on the waterfront. The premiums were apparent mostly in areas subject to rapid development, in particular the Annapolis area.
The second part of the study also explored a closely related follow-up question: How would different groups of consumers react to the price effects? Would they:
* Pay more for the same waterfront residence and consume less of other goods?
* Purchase the same type of residence or better, but forgo any relationship to the bay, or not move at all?
* Purchase a similar waterfront or water-related residence, but at a less attractive location, perhaps farther from work?
* Buy a less attractive unit, perhaps older, smaller, or on a smaller lot, but retain the same level of bay-related housing attributes?
* Forgo some but not all relationship to the bay, while maintaining the general location, house type, and quality; for example, buy a residence with a view of the water rather than on the waterfront?
The research reported here identifies which consumers will react according to which alternative. The answer contributes to the analysis of whether the program is likely to achieve its goals, as some of the reactions may counter the CBCA intent. For example, if consumers choose to retain water frontage and are willing to locate farther from work, the law may accelerate development in the more remote rural counties, thus leading to more intensive use of the still undisturbed parts of the bay.
THE DATA
Analysis of reactions of potential residential consumer to the CBCA's price effects should focus on household that would have purchased residences within the critical area had the CBCA not been enacted. This is the group directly affected by the critical area premium. However. as it is impossible to identify this group accurately, the study used a proxy group of home buyers who had recently purchased residences in the counties surrounding the bay before the enactment of the CBCA. As there is no reason to believe that characteristics of this group would have been significantly different had the CBCA not been enacted, recent buyers can be assumed to be representative of households that might have bought a similar residence in the critical area had the price structure stayed the same.(2)
THE LIKELY SPATIAL EFFECTS OF THE CBCA
Consumers affected by a specific set of growth controls are those desiring a residence whose price has been affected by the regulations. Land use regulations usually pertain to well-defined places and regions. Thus, affected consumers can be identified by the area in which they desire to reside. In the CBCA case, households directly affected by the law are those desiring a residence within the critical area, and would have bought such a residence had the CBCA not been enacted. These households will have to react to the law in one of the ways outlined above.
Figure 4 depicts the process of identifying from the sample the group that is a proxy for consumers directly affected by the CBCA, and among them those proxy households whose residential consumption is most likely to be affected by the critical area premiums.(Figure 4 omitted) The critical area includes both waterfront and nonwaterfront residences. Previous research (Feitelson 1992) and key informants (realtors, developers, planners, and public officials) from the bay area revealed that proximity to the water without water access or water view is not a valued characteristic.(3) Consequently the study stratified the proxy groups by the relationship of the residence they bought to the bay:
* Households that purchased residences on the waterfront;
* Households whose residences have assured access to the bay, but are not on the waterfront;
* Households that bought residences with a view of the water, but have no assured access to it;
* Households whose residences have no relationship with the bay.
The only households necessarily affected by the CBCA are waterfront buyers, as all waterfront homes are within the critical area. Residences with assured water access or view of the bay may be found both within and outside the critical area. Consequently, the implicit price of water frontage is the most likely to go up as a result of the CBCA (Beaton 1988). Thus, only waterfront residences are assumed to be directly affected by the CBCA (i.e., that only the solid arrows in Figure 4 are true).
A comparison of the four groups (Feitelson 1992) shows that buyers of waterfront residences tend to be older and wealthier than all other groups. A high proportion of households purchasing waterfront residences consists of two married adults, older than the sample average, with no dependents. These findings were not unexpected.
In contrast, buyers of residences with assured water access do not differ substantially in terms of income, age, or family composition from households that purchase residences with no relation to the bay. The main difference between purchasers of water-related housing and buyers of residences with no water amenities is in their involvement in water-related leisure activities. Purchasers of residences on the waterfront or with assured access to the water are more likely to own boats and use the bay for leisure activities than other households.
To identify the households whose residential consumption is most likely to be affected by higher critical area premiums, the survey asked respondents how much more they would have been willing to spend on their house had it not been available at the price they paid. Presumably the less a household is willing to add the greater the likelihood its residential consumption would have been altered had the CBCA been in effect.
Table 1 stratifies waterfront buyers into five subgroups according to their willingness to add to the price of their residence. Households not willing to add more than $10,000 to the prices paid for their waterfront residence earn approximately $12,000 to $20,000 per year less than households willing to add over $10,000.(Figure 1 omitted) Willingness to add also seems to increase with the intensity of use of the bay for leisure activities, as summarized by the water index.
Households willing to add less than $10,000 cite desire to be near water less frequently as the main reason for initiating residential search than other subgroups. Approximately 70 percent of all buyers willing to add 20,000 or less cite water frontage as the main consideration in choosing a residence, compared with 88.2 percent of the buyers willing to add more than $20,000. There is remarkably little difference in demographic characteristics among the five subgroups. The mean age of respondents in all subgroups is slightly over fifty years, and the average household size is between two and three persons.
Thus, the home buyers directly affected by higher waterfront premiums are older and wealthier than the rest of the population, and tend to use the bay extensively for leisure purposes. Among this group, the less wealthy households with lower frequencies of water-related leisure activities are the ones most likely to change their residential consumption as a result of an increase in waterfront premiums.
HOW ARE CONSUMERS LIKELY TO REACT?
Analyses of consumer reactions to the CBCA's price effects require information on both the desires and WTP of households for water-related housing attributes. While nonbudget-constrained desires define households' long-term attitudes toward attributes (Shlay 1985) and, thus, initial house hunting goals (Phipps et al. 1985), WTP drives short-term residential decisions (Stahl 1985) and the trade-offs made in the housing market.
A factorial survey provided information on consumer preferences and WTP for residential attributes.(4) Each respondent evaluated the desirability of ten housing vignettes on a scale of one (very undesirable) to fifteen (very desirable), and their WTP for the scenario on a scale of $0 to 400,000. The dimensions, detailed in Table 2, describe the possible trade-offs households can make in response to the CBCA price effects.(Table 2 omitted) The levels are the specific values that a dimension may take.
The first households expected to change their residential consumption as a result of the CBCA are prospective waterfront buyers for whom pre-CBCA prices were the maximum they were willing to pay for waterfront housing. These households (described in the first column of Table 1) are marginal waterfront buyers. The reactions of this group to the CBCA's price effects would be structured by their preferences and willingness to pay for various residential characteristics. The preferences and WTP valuations of marginal waterfront buyers were estimated using ordinary least square (OLS) linear regressions, and compared to the preference and WTP valuations of all waterfront buyers (see Table 3).(Table 3 omitted)
The most striking difference between marginal waterfront buyers and other waterfront buyers is the lower valuations of water frontage by the marginal waterfront buyers. While marginal waterfront buyers view water frontage as a desirable attribute, they accord it lower ratings than the average waterfront buyer. They are unwilling to pay any statistically significant premium for water frontage. However, marginal waterfront buyers have higher desirability valuation and WTP for access to navigable water than other waterfront buyers. These findings imply that while marginal waterfront buyers are perhaps willing to give up water frontage, they will strive to purchase residences with assured water access.
Marginal waterfront buyers also differ from other waterfront buyers in terms of their preferences for some nonwater-related housing attributes. In contrast to all other groups analyzed (including nonwaterfront buyers) marginal waterfront buyers prefer duplexes and town houses over single-family homes, and are willing to pay more than the other groups for them. Marginal waterfront buyers express preferences for areas with many young couples or single people, rather than retirees. These preferences do not arise among other groups. Slightly more sensitive to distance from work and to residence size (number of bedrooms) than other waterfront buyers, marginal waterfront buyers are willing to pay higher premiums for an additional bedroom or a reduction in driving time to work. Marginal waterfront buyers are willing to pay slightly higher premiums than other waterfront buyers for a new residence, over medium- or low-quality second-hand residences.
These findings suggest marginal waterfront buyers are a distinct group in terms of their preferences. Faced with higher premiums for waterfront residences, marginal waterfront buyers may search for high-density residences with assured access to the water in areas with a relatively young population. They will not, however, be willing to settle for smaller residences, lower quality residences, or for residences farther from work.
THE SPILLOVER EFFECTS
The likely shift in residential consumption of marginal waterfront buyers toward higher density dwellings with assured water access can probably be accommodated within the intensely developed areas designated in local programs, the grandfathered areas, and areas where higher density development may be allowed through the counties' growth allocations. As marginal waterfront buyers place high values on proximity to work, they may be reluctant to move to counties farther from employment centers. As they desire to have assured access to the bay, these buyers also seem unlikely to move far from the bay into the interior.
If the trade-offs identified here are indeed true, the CBCA can be expected to cause only limited spatial spillover, at least as long as it affects prices only moderately. It probably would not result, by itself, in spillover into the rural counties on the eastern shore, or in substantial spillovers into the interior. Rather, developments with assured access to the bay (but not on the waterfront) in the suburban and exurban counties can be expected to absorb most of the spillover.
Because the share of waterfront homes in the suburban and exurban housing markets is small (5 percent and 12 percent, respectively), the spillovers that will occur in these counties are not expected to affect price levels beyond the critical area. Thus, the CBCA will effect only households living in the area, but not other households locating in suburban or exurban counties (those proxied by nonwaterfront buyers). In other words, the possibility of widespread effects beyond the critical area or on the development of rural counties is unlikely.
This finding, however, should be qualified. If price premiums escalate, the residential consumption of households proxied by those willing to add modest amounts to the price they actually paid may also be affected. However, as this group did not express any overriding desire for low-density areas, such a premium escalation need not necessarily lead to widespread spillover into rural counties. Yet, such a spillover cannot be ruled out, as this group attached higher importance to water frontage and less to distance to work than in the case of the marginal waterfront buyers.
This study identifies a broad segment of the population that uses the bay extensively for leisure purposes but does not reside on the waterfront. Households in this group purchased residences with assured water access, not necessarily in the critical area. As this group does not differ substantially from home buyers whose residences do not posses any relationship to the bay, it seems that at least before the enactment of the CBCA there were no substantial barriers to bay access.
The CBCA legislation need not affect access to the bay.(5) Still, restrictive interpretation of some provisions by local jurisdictions may reduce access. If residences outside the critical area retain bay access, most of the population will not be affected directly by the CBCA.' Otherwise, an additional analysis, similar to the one conducted here for waterfront buyers, will be needed to identify the repercussions for buyers of residences with assured water access. Such a study can be expected to identify wider impacts than those found here.
A MORE TIMELY ANALYSIS
This article offers a practical method for analyzing the likely spatial effects of growth control programs. The empirical findings demonstrate that growth controls need not necessarily lead to sprawl. In the case of the CBCA, the group whose residential consumption is most likely to be affected is most reluctant to increase its commuting range. This finding is a function of the preferences of this specific group. Thus, the effects of growth controls on sprawl cannot be generalized, but have to be analyzed for each case and by consumer subgroups.
The CBCA study identified a significant group of buyers that purchased residences not on the waterfront but with assured access to the bay, and whose income levels and demographic characteristics are similar to those of average (nonwaterfront) buyers in the Chesapeake Bay region. The study concluded that as long as bay access is not impeded, the CBCA will probably not have major regressive effects. This finding shows land use controls should not be analyzed only as complete sets. Rather, the implications of different components of growth controls and of the way growth controls are administered should be analyzed for different population groups.
By focusing on consumer preferences this study circumvents one of the major problems in previous analyses--allowing enough time to have passed to observe effects (Nelson 1986). The price impacts of land use controls are a function of demand for residences in the regulated area, which is affected by demographic and macroeconomic factors and of producer activities. These factors and activities evolve over time, as does the administration of the growth controls. (For example, the attitude toward variances may change over time.) Thus, different price effects can be expected at different times after the enactment of the regulations. Research based on consumer preferences, which are relatively stable over time, can determine the likely directions of consumer reactions, regardless of the actual timing of the price effects. Moreover, the research can determine the directions and type of consumer reactions in relation to the magnitude of the price effects. With this information planners can administer growth controls so that the price effects will be in a range commensurate with the program's goals and acceptable distributional impacts. This will improve the political acceptability of the program and the likelihood that its desired benefits will be realized.
NOTES
1. The guidelines limit total conversion of land to higher intensities to 5 percent of the resource conservation area (RCA) within each jurisdiction. Of this allocation only half(2.5 percent) can be conversion of RCA land (the rest being conversion of limited development areas to intensely developed areas). For a discussion of the criteria, their development, and relationship to the program's goals, see Davis (1987) and Sullivan (1989).
2. The study's sample was the list of all home buyers in the period before the enactment of the CBCA local programs (1985 to 1986) in the counties surrounding the bay (excluding Baltimore), as reported in the property tax files. While buyers at the time were probably aware of the CBCA, its implications for any specific property were yet unknown. As the analysis required that home buyers' reactions be gauged, the sample included only transactions involving improved parcels and individual home buyers and deleted all commercial buyers. As prior experience of searching and buying is important for eliciting true estimates, the study included only households that purchased their residence in an arms-length sale.
A pilot study included over one hundred respondents in Queen Anne's County. Then the study randomly sampled 1,600 home buyers from the other fifteen counties. For the analysis to be pertinent to all counties around the bay, the study stratified the sample by county, so that sufficient observations would be available in counties (mostly on the eastern shore) where the total number of transactions was low. The study sampled one hundred to two hundred names from each county. To account for differences in samples and response rate between counties the study weighted the results to reflect the total number of buyers. Both the pilot and baywide surveys were conducted by mail, using the total design method (Dillman 1978). The study received 1,076 questionnaires for a total response rate of 69.6 percent.
3. This statement may not be true everywhere. In other places water view may be available from greater distances (mountainous terrain) or access may be guaranteed along the entire beach. In such cases a value may be placed on proximity. Unfortunately there are no studies of the value of proximity to water as a function of these variables.
4. The premise of the factorial survey approach is that judgment of an object (residence) is an algebraic function of the values placed by respondents on its attributes (Rossi and Anderson 1982). To elicit the influence of various attributes on residential preferences and WTP in ranking various housing vignettes two additive models were specified, expressing desirability rankings and WTP as a function of the values respondents place on the levels that describe each situation:
(Equation omitted)--1
where D sub ik is the residential desirability rating of vignette k by respondent i, X sub jk are the characteristics contained in vignette k, e sub ik is a random error, and (Characters omitted) is respondent i's mean desirability ranking.
(Equation omitted)--(2)
where W sub ik is respondent i's WTP for vignette k and (Characters omitted) his mean evaluation. Personal mean evaluations are introduced into the regressions to control for the systematic variation in the use of scales between individuals, as some use only one part of the scale while others use the whole scale or another part of it (Goodman 1989).
The residential attributes' coefficients derived from the linear regressions estimating this model represent the marginal contribution of each characteristic (level) to the respondents' valuations, controlling for differences among respondents in mean ranking (basic attitude toward housing).
5. The criteria allow the construction of private piers and of public facilities for water recreation activities throughout the area, though subject to some guidelines. New community piers and marinas are limited to intensely developed areas and limited development areas, though existing ones may be extended into resource conservation areas. New developments, however, may not build marinas or community piers in the resource conservation areas. Therefore, bay access may be limited in new developments adjacent to critical areas that have been designated as resource conservation areas.
6. The CBCA initiative may, however, increase access costs.
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Feitelson is a lecturers in the department of geography at Hebrew University in Jerusalem, Israel. His research interests include growth management, land use economics, the economics of transportation, and environmental policies. He has worked as a planner and an consultant in the private and public sectors in Israel and the United States. He has recently been involved in the preparation of the Israeli national master plan for immigration absorption, particularly its environmental aspects.
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