The effects of wetlands and other factors on rural land values. (Features).
The value of land is affected by its attributes and characteristics. These characteristics include soil quality or productivity; tract size; the number of acres in cropland, pasture, orchards, woods, and wetland; and the quantity and quality of buildings or other property improvements. All these features affect the income-earning potential of land. The location of the tract--i.e., the distance to markets, road access, and the proximity to metropolitan areas and municipal services--also influences value and can indicate whether there will be a nonagricultural demand for the land. This article explores how wetlands and other physical and locational factors impact rural land prices.
A number of studies have been conducted to identify factors that contribute to rural land values. (1) These studies have primarily focused on factors that affect the agricultural income stream or the nonagricultural demand for land. This study builds on those earlier studies and examines the hypothesis that wetlands adversely affect rural land values by restricting the use of the land.
During the settlement and development of the United States, wetlands were regarded as unhealthy environments and areas of low production. Public policy encouraged the drainage of these lands and their conversion to more productive uses. In recent years, however, the public has come to recognize that wetlands provide spawning and breeding grounds for fish and shellfish, habitats for birds and wildlife, and breeding grounds for waterfowl. Wetlands also facilitate flood mitigation and stream abatement, remove nutrients and pollutants from water, and serve as recreational areas and habitats for endangered and threatened species. (2) Now environmentalists and policymakers attempt to preserve these resources for the public's benefit.
In the private land market, however, wetlands are usually not recognized as a benefit since their advantages accrue to the public and cannot be captured by individual landowners. Landowners usually cannot alter or disturb wetlands, so the income-earning potential of these lands is restricted; consequently, wetlands are seen to have a negative impact on property value. Regulations to protect wetlands have also led some landowners to claim a legal "taking" since they are prohibited from using their land for income-generating uses. (3)
The identification and delineation of wetlands is controversial. There is no single, indisputable, ecologically sound definition for wetlands, primarily because wetlands are diverse and the demarcation between dry and wet environments lies along a continuum. (4) Ponds and wet areas around rivers and streams are commonly considered wetlands by the public, but many areas that do not fit this description are subject to regulatory protection as wetlands by federal, state, and local governments. Wetlands are generally defined on the basis of hydric soil, hydrophytic vegetation, and hydrology. (5) The U.S. Fish and Wildlife Service, which established the National Wetlands Inventory in 1974 to provide scientific information on the characteristics and extent of the nation's wetlands, uses the following definition:
... wetlands are transitional between terrestrial and aquatic systems where the water table is usually at or near the surface or the land is covered by shallow water. For the purposes of this classification, wetlands must have one or more of the following three attributes: (1) at least periodically the land supports predomi- nantly hydrophytes; (2) the substrate is predominately undrained hydric soil; and (3) the substrate is nonsoil and is saturated with water or covered by shallow water at some time during the growing season of each year. (6)
As areas become urbanized, population growth increases the competition between extensive and intensive land uses. When population increases, more land is needed for home sites, roads, airports, schools, commercial and industrial sites, parks, open space, and other uses. Urban areas expand into rural areas and the increased competition for rural land increases property values. The anticipation of higher net returns provides economic incentives for converting land to more intensive uses. Higher returns indicate higher capitalized values, and more intensive uses outbid extensive uses for control of the land. Just as urban uses replace agricultural land and wetland uses, more intensive agricultural uses replace other, extensive agricultural uses. Many land conversions are irreversible, and the extensive uses that are displaced by urbanization or the relocation of intensive agricultural uses may not be developed elsewhere.
Historically, wetlands have been perceived as wastelands-undeveloped swamps that could be drained to make the land available for higher economic uses. Most of the wetlands that have been lost in the U.S. were converted to agricultural and urban development. Dahl (7) estimates that Florida lost 46% of its wetlands from the 1780s to the 1980s, a loss of more than 45,000 acres per year. Because laws and regulations to protect wetlands have been enacted in recent years, the rate of wetland conversion has declined markedly. The federal government has tried to develop and implement "no net loss" policies. Data from the Florida Department of Environmental Regulation indicate that only 2,500 acres of wetlands were destroyed or lost in Florida from 1989-1990. (8)
The public's changing perception of wetlands has had several implications for rural landowners. First, landowners are sensitive to wetland delineation. For landowners, the delineation of an area as wetlands implies state and federal "jurisdiction" over their property and interference with their private decision making. Second, land use regulations are associated with a myriad of forms and documents, delays, consultant fees, and parcel restrictions, which may add significantly to the cost of land use changes. Third, most landowners allocate their land resources to obtain the highest economic rents possible. In the absence of wetland restrictions, landowners could alter the wetlands and generate higher returns to their lands. Some argue that the existence of wetland regulations prevents the landowner from altering the wetlands and potentially receiving a larger stream of future income. The benefits and costs associated with wetlands influence the rural land market and are ultimately reflected in rural land valu es.
A Conceptual Model
The diverse physical, economic, and institutional factors that affect rural land values have been the subject of extensive study. Previous studies can be divided into those that examine sales price data for individual parcels of land and those that analyze aggregate data such as time series estimates of land values collected on a state, regional, or national basis and cross-sectional data from the Census of Agriculture. (9) The principal variables in studies based on sales data have been physical characteristics such as soils and productivity variables, size, location with respect to roads and distances to cities or other nonagricultural attractions, land use, the value of buildings or other improvements, and taxes. In the studies that have used aggregate data, the principal explanatory variables have included income, government payments, average size, land use (proportion of land in cropland, pasture, or irrigation), population pressure (density), taxes, and trend or regional variables.
The analysis of sales data has tended to focus primarily on location and productivity variables that enhance agricultural income or rural land values rather than factors such as wetlands, which may not contribute to the expected future income of the tract. In addition, the identification and delineation of wetlands is controversial, and only limited data are available.
In this study, sales data were analyzed to estimate how wetlands and other physical and economic variables affect rural land prices. Theoretically, the value of land should equal the sum of the discounted future net returns to land. Since farm income data or productivity measures were not available for each tract of land in this study, land use variables were used as proxy measures to reflect the income-producing capacity of each tract. Land with an intensive use is expected to generate higher economic net returns and higher capitalized land values. Citrus production, for example, is an intensive land use that represents a long-term investment with the potential for an extended stream of high future income. Less intensive uses would be expected to produce lower net returns per acre and have lower capitalized land values. Wetlands represent a less intensive use of land and are expected to result in lower capitalized land values. Brown (10) determined that cropland acreage was positively related to rural land v alues while wetland acreage was negatively related. It was hypothesized that rural land value would increase as the proportion of land in intensive uses (such as citrus production) increased and decreased as the amount of land in wetlands increased.
Capital improvements tend to increase the productivity of land and are usually capitalized into land values. Access to water (the presence of an irrigation well) is important for the production of irrigated crops. Citrus and vegetables are among the agricultural crops in the study area that require irrigation. F. Xu and others (11) concluded that irrigation systems had a significant positive effect on rural land values. Buildings may be another important capital improvement. Buildings are considered long-term investments that may improve the income-producing capacity of some properties. Reynolds and Tower (12) concluded that increases in building value caused rural land prices to increase at a decreasing rate. It was hypothesized that the value of buildings value and the presence of irrigation wells would have a positive effect on rural land values.
Location theory indicates that a negative relationship should exist between rural land values and the distance from markets and population centers. Land rent is expected to decrease as distance from the market or urban center increases; buyers will pay less for tracts located greater distances from the market. Previous studies indicate that this relationship is nonlinear. (13) Since the nonagricultural demand for land declines as distance from a population center increases, this hypothesis applies to the demand for both nonagricultural and agricultural land. The presence of road frontage was also hypothesized to have a positive effect on rural land values. Parcels with road frontage provide accessibility to markets and urban centers. Road frontage may also increase the development potential of a parcel.
The per-acre sales prices of land were expected to vary with the size of the tract. Smaller tracts of land have more potential buyers, and the price of smaller tracts may increase as buyers compete for these tracts. Palmquist and Danielson (14) contend that the price per acre should vary inversely with the size of the tract because of the legal and political costs of subdividing land. The study assumed that a negative relationship exists between the size of a tract and rural land values.
The main objective of the study was to identify and estimate the effects of locational and physical characteristics on rural land, focusing specifically on the effects of wetlands on rural land values. The following model was developed to estimate the impact of land use, capital improvements, location, size, and wetland variables on rural land prices:
P = f (LU, C, LOC, S, W)
P = sales price of land per acre;
LU = land use variables;
C = capital improvements;
LOC = location variables;
S = size of tract; and
W = wetland variables.
The study area included four counties in southwest Florida: DeSoto, Hardee, Highlands, and Manatee. Data on land sales were obtained from the Farm Credit Service of Southwest Florida. The data, which relate to 212 sales of rural land transacted from 1988 through 1993, included location, the presence and value of buildings, purchase price, and selected physical characteristics of each parcel. (15) Data on the quantity and type of wetlands were obtained from National Wetland Inventory maps for each parcel. (16) Three wetland systems were identified from these maps: 1) Palustrine -- wetlands that are dominated by trees, shrubs, and persistent emergents; 2) Riverine -- rivers, channels, and drainage ditches; and 3) Lacustrine -- flooded lakes, reservoirs, and intermittent lakes. Palustrine wetlands were the predominant wetland system in the study area and they were broken into four wetland classes. Wetland variables include the total acreage of wetlands in each parcel and the amount of acreage classified in each wetland system and class.
In quantifying the effects of various parcel characteristics on rural land values, previous studies have employed a nonlinear functional form to reflect the nonlinear relationships among parcel characteristics and land values. (17) For example, the price of rural land is expected to decrease at a decreasing rate as the parcel size and distance from a population center increases. The Box-Cox transformation indicated the exponential form to be the correct specification. The following model was used:
ln P = [alpha] + [[beta].sub.1][C.sub.1] + [[beta].sub.2][C.sub.2] + [[beta].sub.3][C.sub.3] + [[beta].sub.4]Well + [[beta].sub.5]CIT
+ [[beta].sub.6]RD + [[beta].sub.7]CV + [[beta].sub.8](1/DIS) + [[beta].sub.9](1/Size)
+ [[beta].sub.10]WLA + [[beta].sub.11]WRI + [[beta].sub.12][WPA.sub.1]
+ [[beta].sub.13][WPA.sub.2] + [[beta].sub.14][WPA.sub.3] + [[beta].sub.15][WPA.sub.4] + [epsilon]
The definition and mean value of the dependent and independent variables are presented in Table 1 and [epsilon] is the stochastic disturbance term.
The natural log of the land price per acre was used as the dependent variable, and least squares regression was used to estimate the coefficients of the model. The estimated coefficients and t-values are presented in Table 2. Since the sales used in this study occurred over several years, a time variable was included in the preliminary analysis to determine whether land values were increasing or decreasing during the study period. The coefficient was not significant and the time variable was not used in the final forms of the model. Two models are presented in Table 2: Model 1 measures the effect of wetlands with a total acres of wetland variable (W), and Model 2 disaggregates the wetlands into six variables so that the effect of different types of wetlands on rural land prices can be evaluated. The coefficients in Model 2 were used to evaluate the effects of the other variables on rural land values in the discussion that follows. To interpret the effects on rural land prices caused by changes in the explanat ory variables, elasticities were calculated from the estimated coefficients for the continuous variables. The elasticities represent the change in rural land prices resulting from a specified change (from the mean) in the explanatory variables. The coefficients for the size and distance variables were estimated in both logarithmic and inverse forms and the coefficients were significant in both forms. Since economic theory suggests that beyond a certain range either variable should have little or no additional effect on the per-acre price, the inverse forms were used in this analysis.
Dummy variables were specified for each county to test for a difference among counties. Manatee County was used as the control county in the study. The coefficients for DeSoto and Hardee counties were not statistically significant, indicating that rural land prices in DeSoto and Hardee counties were not significantly different from Manatee County. The coefficient for Highlands County was positive and significant, indicating that rural land values were 25.6% higher here than in Manatee County. The effect of a discontinuous variable (dummy variable) on rural land prices in this type of model is [([e.sup.[beta]i] - 1) x 100]%. (18) A higher proportion of rural land sales in Highlands County involved citrus land, and citrus land values are considerably higher than the values of land in other agricultural uses. (19)
An irrigation well is a capital improvement usually associated with more intensive land uses and higher economic returns. The estimated coefficient for the Well variable indicates that rural land prices are 19.1% higher for parcels with irrigation wells. Citrus production is an intensive land use with a high investment cost and a potentially long stream of future income. The estimated coefficient for the Cit variable indicates that a 1% increase in citrus acreage resulted in a 1.49% increase in rural land prices. Buildings are long-term investments that may enhance a property's income-producing potential, and higher potential future income should result in higher property value. The sign of the estimated coefficient for the contributory value (CV) confirms that the value of the rural property should increase as CV increases. The elasticity calculated from the estimated coefficient for CV indicates that a 1% increase in CV resulted in a 0.027% increase in rural land prices (when evaluated at the mean). The pri ce elasticity measure suggests that only a small portion of the value of buildings was capitalized into land value.
Road frontage provides access to the property and may enhance the development potential of the parcel. The estimated coefficient for the Rd variable indicates that rural land prices were 21.5% higher for parcels with road frontage than for parcels without frontage. Economic theory suggests that an inverse relationship exists between returns to land and the distance of the parcel from the market due to transportation and other costs. To test this relationship, the distance from each parcel to the county seat, the largest city in each county, was measured. The distance variable was specified in its inverse form (1/Dis) to capture the asymptotic relationship between distance and sales price. (20) The effect of distance on the per-acre price of rural land is illustrated in Figure 1. Within a few miles of the city, the per-acre price declines rapidly as distance increases. After 10 miles, the impact of distance to the city has very little impact on the per-acre price.
Previous studies have also found a negative, nonlinear relationship between parcel size and rural land values. (21) The Size variable was specified in its inverse form. Figure 2 illustrates the effect of parcel size on rural land prices, indicating that, beyond a certain point, additional increases in size have little effect on the per-acre price of a parcel. The relationship between parcel size and per-acre price shown in Figure 2 was revealed by holding all other variables at their mean values. For smaller parcels, price per acre decreases substantially as size increases. Beyond 100 acres, the negative effect on per-acre price is minor relative to increases in acreage.
One of the objectives of this study was to determine the effects of wetlands on rural land values. Data on the quantity and type of wetlands in each parcel in the sample was collected from National Wetlands Inventory (NWI) maps. A total of 14,535.5 acres of wetlands was found on 187 of the 212 rural properties in the study (17% of the land area). As mentioned previously, there were three wetland systems in the study area: Riverine, Lacustrine, and Palustrine.
Wetlands are considered a low-intensive land use. Hydric soils in wetlands make cultivation of most agronomic crops difficult without some alteration. From an agricultural production standpoint, wetlands in their unaltered state limit the potential income-producing capacity of the land, which lowers its capitalized value. The total acres of land in wetlands (W) variable was used to test the overall effect of wetlands on rural land values in Model 1. The estimated coefficient for total wetlands was significant and indicates that a 10% increase in wetland acres resulted in a 0.206% decrease in rural land prices. Figure 3 illustrates how an increase in the acres of wetlands in a parcel affects the sales price.
Each wetland system is different in its characteristics and the functions it maintains. Therefore, the effect on land values may depend on the wetland type. In Model 2, wetland acres were disaggregated into 1) Riverine, 2) Lacustrine, and 3) Palustrine. Since Palustrine wetlands accounted for more than 99% of the wetlands in the study, this system was divided into four classes. Although the wetland (W) coefficient in Model 1 and previous studies suggests that the presence of wetlands affects rural land values negatively, (22) there was no a priori expectation of how specific types of wetlands might affect rural land values.
Riverine wetlands are commonly described as rivers, channels, or drainage ditches. They are normally considered unusable and contribute little or no productive value to the land. Riverine wetlands occupied only about 9.5 acres of the total wetland acreage in this study Although these wetland systems are usually small, they have a significant negative effect on rural land prices. The Riverine wetland coefficient indicates that a 10% increase (at the mean) in Riverine wetlands resulted in a 0.066% decrease in rural land prices.
Lacustrine wetlands are usually situated in a topographic depression and lack trees, shrubs, and persistent emergents. The Lacustrine system includes permanently flooded lakes, reservoirs, and intermittent lakes. Lacustrine wetlands consist of unconsolidated bottom and aquatic bed dominated by plants such as duckweed and water lettuce, which grow on or below the water's surface. There were 49 acres of Lacustrine wetlands in the study. The estimated coefficient was not statistically significant.
Palustrine wetlands accounted for more than 99% of the wetlands in the study. Palustrine wetlands are defined as all nontidal wetlands dominated by trees, shrubs, persistent emergents, emergent mosses, or lichens and all such wetlands that occur in tidal areas. (23) Palustrine wetlands include the vegetated wetlands traditionally called marsh, swamp, bog fen, and prairie. Using the data collected, these wetlands were divided into four Palustrine wetland classes: 1) scrub-shrub, 2) forested, 3) unconsolidated bottom, and 4) emergent.
Scrub-shrub wetlands are commonly described as thicket and wetland areas dominated by woody vegetation less than 20 feet high. (24) The scrub-shrub type of wetland is seasonally flooded and located on the outer fringes of the Palustrine wetlands. There were 586.1 acres of scrub-shrub wetlands in the study, which accounted for about 4% of the total wetland area. The scrub-shrub coefficient [(WPA.sub.1)] was only significant at the 0.20 level. (In other formulations of the model, this coefficient was significant at the .05 level.) The scrub-shrub coefficient indicates that a 10% increase in acres of land in the scrub-shrub class resulted in an increase in rural land prices of about 0.12%. A positive effect may result from the fact that buyers and sellers may not recognize these lands as wetlands since they are normally flooded only seasonally and are located on the fringe of the wetland area. These lands were not viewed as having a negative effect on rural land values because they were not always considered wet lands and did nor occupy large areas in this study.
Forested wetlands are characterized by woody vegetation that is at least 20 feet high. There were 7,380.75 acres of forested wetlands in the 212 sales and this type of wetland accounted for 54% of the wetlands in the study. The forested wetlands coefficient was negative and significant. The [WPA.sub.2] coefficient indicated that a 10% increase in the acres of forested land resulted in a 0.185% decrease in rural land prices. Figure 4 illustrates how an increase in forested wetlands affects rural land prices.
Unconsolidated Bottom Wetlands
Unconsolidated bottom wetlands are commonly characterized as ponds. They accounted for less than 1% of the wetland acreage in the study. The coefficient for unconsolidated bottom wetlands [(WPA.sub.3)] was not significantly different from zero.
Marshes, depressions, and drainage areas are commonly identified as emergent wetlands. There were 5,789.9 acres of emergent wetlands in the study, which accounted for 40% of the total wetland acreage. The coefficient for emergent wetlands [(WPA.sub.4)] indicated that a 10% increase in acres of land in emergent wetlands resulted in a -0.164% decrease in rural land prices. Figure 5 illustrates the effect of emergent wetlands on rural land prices.
Summary and Conclusions
Appraisal theory and analytical studies indicate that several variables affecting rural land values may have nonlinear relationships to rural land prices. In quantifying the effects of various parcel characteristics, a nonlinear functional form was specified. The natural log of the per-acre sales price was used as the dependent variable, and least squares regression was used to estimate the effect of various parcel characteristics on rural land prices. Location, parcel size, capital improvements, the proportion of land in intensive uses, and land area in wetlands explained more than 80% of the variation in sales prices.
Arguments can be made for both the positive and negative effects of wetlands on rural land values. On the one hand, wetlands make certain activities--such as agricultural production--infeasible without drainage or other alterations and government rules and regulations restricting development limit potential future income. On the other hand, some individuals may be willing to pay a higher price for acreage with wetlands because of their aesthetic appeal. This study classified wetlands into wetland systems and classes to test the effect of various types of wetlands. The results indicate that different types of wetlands may have different effects on rural land values. When all of the wetlands were aggregated into a total wetlands variable, a significant negative impact on rural land prices was indicated. When wetlands were broken into different systems and classes, the impact ranged from a small positive effect to significant negative effects.
The Riverine wetland system, commonly characterized as rivers, channels, and drainage ditches, had a significant negative effect on rural land prices. The Lacustrine wetland system had no significant effect on rural land values. The Palustrine wetlands, which comprised more than 99% of the wetlands in the study, were divided into four classes. The forested and emergent classes of Palustrine wetlands accounted for 94% of the wetlands in the study and had significant negative effects on rural land prices. The presence of the scrub-shrub class, which represented about 4% of the wetlands, had a positive effect on rural land prices. A study of how wetland types and wetlands proximity influenced residential property values in Minnesota indicated "that scrub-shrub and open-water wetlands are preferred to forested and emergent-vegetation wetlands." (25)
Locational factors were important determinants of rural land prices in this study. Distance to the county seat and road frontage were used in the analysis to represent locational effects, and both variables were highly significant. The effect of the distance variable on rural land values decreased at a decreasing rate as distance increased. The effect of the size of the tract had a similar negative relationship with rural land prices.
The proportion of land in higher-intensive uses (citrus production) reflects a potentially larger stream of future income and was highly significant. The presence of an irrigation well, a capital improvement with higher income potential, was also highly significant. The value of buildings (CV) had a small positive effect on rural land prices. The price elasticity indicates that only a relatively small portion of the value of buildings was capitalized into rural land values.
The results of the study may help property appraisers, land buyers and sellers, and policymakers evaluate the impact of certain parcel characteristics on rural land values. In estimating the effects of wetlands on rural land values, this study probably underestimates the benefits of wetlands to the public. The public benefits of wetlands are not captured by private landowners and, therefore, are probably not reflected in the sales prices of properties. The study's conclusions, however, will be useful to policymakers in assessing the impact on landowners of policies and regulations affecting wetlands.
One shortcoming of this study was that Riverine and Lacustrine wetlands were underrepresented in the sales selected for analysis. There were only 7.5 acres of Riverine wetlands and only 49 acres of Lacustrine wetlands in the study. Future studies should include more wetlands of these types. Future studies should also gather additional data on wetland characteristics and the attitudes of buyers and sellers toward wetlands with certain characteristics. This additional data could be used in a two-stage hedonic model to identify and estimate the inverse demand function for different wetland characteristics.
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Table 1 Mean Value and Definition of Variables Used in the Analysis Variable Mean Value Definition P 3,565.00 Price of land per acre (dollars) [C.sub.1] 0.3585 Dummy: County variable (1 if in DeSoto County, 0 otherwise) [C.sub.2] 0.2877 Dummy: County variable (1 if in Hardee County, 0 otherwise) [C.sub.3] 0.1981 Dummy: County variable (1 if in Highlands County, 0 otherwise) Well 0.4481 Dummy: Well (1 if present, 0 otherwise) Cit 0.1387 Land in citrus (proportion) Rd 0.8349 Dummy: Road frontage (1 if present, 0 otherwise) Size 403.41 Parcel size (acres) CV 53.19 Contributory value of buildings per acre (dollars) Dis 12.48 Distance to county seat (miles) W 68.58 Land in wetlands (acres) WLA 0.231 Land in Lacustrine wetland system (acres) WRI 0.045 Land in Riverine wetland system (acres) [WPA.sub.1] 2.76 Land in Palustrine scrub-shrub class (acres) [WPA.sub.2] 36.94 Land in Palustrine forested class (acres) [WPA.sub.3] 0.605 Land in Palustrine unconsolidated bottom class (acres) [WPA.sub.4] 27.31 Land in Palustrine emergent class (acres) Table 2 Estimated Coefficients and t-Values for the Analaysis of Rural Land Values in Southwest Florida Model 1 Parameter Variables Estimate t-Value Intercept 6.7911 [C.sub.1] (1 = DeSoto County, 0 -0.0247 -0.26 otherwise) [C.sub.2] (1 = Hardee County, 0 -0.1640 (*) -1.77 otherwise) [C.sub.3] (1 = Highlands County, 0 0.2429 (***) 2.64 otherwise) Well (1 = well present, 0 0.1872 (***) 2.83 otherwise) Cit Proportion of iand in citrus 1.4968 (***) 16.92 Rd (1 = road present, 0 otherwise) 0.2064 (***) 2.82 1/Size, Inverse of the parcel size 15.2086 (***) 3.20 in acres CV, Contributory value of buildings 0.0005 (***) 4.06 per acre 1/Dis, Inverse of distance to 1.8763 (***) 4.97 county seat in miles W, Acres of land in wetlands -0.0003 (***) -3.03 WLA, Acres in Lacustrine wetlands WRI, Acres in Riverine wetlands [WPA.sub.1], Acres in Riverine wetlands [WPA.sub.2], Acres in Palustrine scrub-scrub wetlands [WPA.sub.3], Acres in Palustrine unconsolidated bottom wetlands [WPA.sub.4], Acres in Palustrine emergent wetlands [R.sup.2] .811 [R.sup.2.sub.adj] .802 Model 2 Parameter Variables Estimate t-Value Intercept 6.7696 [C.sub.1] (1 = DeSoto County, 0 0.0111 .12 otherwise) [C.sub.2] (1 = Hardee County, 0 -0.1491 -1.61 otherwise) [C.sub.3] (1 = Highlands County, 0 0.2279 (**) 2.37 otherwise) Well (1 = well present, 0 0.1748 (***) 2.62 otherwise) Cit Proportion of iand in citrus 1.4892 (***) 16.61 Rd (1 = road present, 0 otherwise) 0.1948 (***) 2.63 1/Size, Inverse of the parcel size 17.2601 (***) 3.55 in acres CV, Contributory value of buildings 0.0005 (***) 3.98 per acre 1/Dis, Inverse of distance to 1.8941 (***) 5.03 county seat in miles W, Acres of land in wetlands WLA, Acres in Lacustrine wetlands 0.0547 .37 WRI, Acres in Riverine wetlands -0.1463 (**) -1.99 [WPA.sub.1], Acres in Riverine 0.0044 1.49 wetlands [WPA.sub.2], Acres in Palustrine -0.0005 (***) -2.61 scrub-scrub wetlands [WPA.sub.3], Acres in Palustrine 0.0170 .75 unconsolidated bottom wetlands [WPA.sub.4], Acres in Palustrine -0.0006 (***) -2.33 emergent wetlands [R.sup.2] .818 [R.sup.2.sub.adj] .804 (*)Denotes significance at the 0.10 level. (**)Denotes significance at the 0.05 level. (***)Denotes significance at the 0.01 level.
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(16.) U.S. Department of the Interior, Fish and Wildlife Service, User's Guide to Notional Wetlands Inventory Mops. (Bloomington, MN: National Wetlands Inventory, 1993).
(17.) Reynolds and Tower, 1978; L. J. Hushak and K. Sadr, "A Spatial Model of Land Market Behavior," American Journal of Agricultural Economics (61, 1979): 697-702; Chicione, 1981; Dunford et al, 1985; and J. S. Shonkwiler and J. E. Reynolds, "A Note on the Use of Hedonic Price Models in the Analysis of Land Prices at the Urban Fringe," Land Economics (62, 1986): 58-61.
(18.) Chicione, 1981.
(19.) J.E. Reynolds, "The Florida Land Market Report 1999 Survey Results," Florida Food and Resource Economics (143, July-August, 1999).
(20.) Reynolds and Tower, 1978.
(21.) Chicione, 1981, and Reynolds and Tower, 1978.
(22.) Brown, 1976; E. Reenstierna, "The Appraisal of Wetlands," Journal of the American Society of Farm Managers and Rural Appraisers, (44:2,1980): 58-62 and P. E. Norris, K. E. Ahern, and S. R. Koontz, "Wetland Regulation Impacts on Agricultural Land Prices in Two Oklahoma Counties," selected paper, Southern Agricultural Economics Association Meetings, Nashville, TN, 1994.
(23.) Cowardin et. al., 1979.
(25.) C. R. Doss and S. J. Taff, "The Influence of Wetland Type and Wetland Proximity on Residential Property values," Journal of Agricultural and Resource Economics (21, 1996): 120-129.
John E. Reynolds, PhD, is a professor and teaches rural property appraisal in the department of food and resource economics at the Univerity of Florida. He received his MS and PhD degrees from Iowa State University. He is past president of the Florida chapter of the American Society of Farm Manager and Rural Appraisers. Contact: email@example.com.
Alex Regalado is currently a senior legislative analyst with the Office of Program Policy Analylsis and Government Accountability, an office of the Florida legislature. He received his MS degree from the University of Florida and specialzes in enviornmental policy issues. Contact: firstname.lastname@example.org.
Excerpt from Papers and Proceedings published by Valuation 2000 in July 2000.
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|Author:||Reynolds, John E.; Regalado, Alex|
|Date:||Apr 1, 2002|
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