Final Report: A Consistent Framework for Valuation of Wetland Ecosystem Services Using Discrete Choice Methods

EPA Grant Number: R831598
Title: A Consistent Framework for Valuation of Wetland Ecosystem Services Using Discrete Choice Methods
Investigators: Milon, J. Walter , Scrogin, David , Weishampel, John F.
Institution: University of Central Florida
EPA Project Officer: Hahn, Intaek
Project Period: April 1, 2004 through March 31, 2006 (Extended to December 31, 2007)
Project Amount: $313,797
RFA: Valuation for Environmental Policy (2003) RFA Text |  Recipients Lists
Research Category: Economics and Decision Sciences

Objective:

The overall goal is to develop and test a consistent framework to estimate wetland services values. The diverse nature of wetland services negates complete valuation through a single method or data source. Our approach uses a dual modeling strategy to utilize revealed preferences (RP) from housing market models and stated preferences (SP) from a choice model for wetland ecosystem services inCentral Florida. There are four interrelated objectives to implement this strategy: 1) To estimate the revealed preference demand for proximity to wetlands and other water resources using hedonic pricing and discrete choice models of residential property values; 2) To estimate the demand for ecosystem services from different types of wetlands using a stated choice survey; 3) To develop a general valuation function for wetland ecosystem services, and 4) To estimate the implicit value of ecosystem services in wetland mitigation banking markets.

Summary/Accomplishments (Outputs/Outcomes):

Revealed Preferences for Wetlands

Housing Market Data and GIS Analysis

The property sales data used in the hedonic price and discrete housing choice analyses covered the period January 2000 through May 2005. The data were obtained from property tax appraisers in three Central Florida counties: Orange (Orlando), Polk (Lakeland), and Volusia (Daytona Beach). In addition to identifying the sales prices of single-family residential properties, dates of sale, and geographic locations, the data contained a variety of physical attributes commonly included in property value analyses. These included the number of bedrooms and bathrooms, the square footage of the structure under central air/heating, the square footage of the land (or parcel), the age of the home, and the presence of a pool. The sample of property sales and individuals used in the analysis was homeowners who received a ‘homestead exemption’ for property taxes. In Florida the exemption is assigned to a property if it is owned by a Florida resident who uses the property as their primary residence. With this sample of homeowners, we exclude nonresidents and individuals, partnerships, and corporations who purchase properties solely for investment purposes. This definition of the sample is also consistent with that employed in the stated choice wetland valuation survey.

To generate the spatial/environmental variables included in the hedonic and discrete choice models, property sales data were overlaid with GIS land use maps and maps identifying a variety of natural and human-made spatial attributes. Digital land cover/use maps were acquired from the two regional water management districts with jurisdiction in the respective counties: St. John’s River Water Management District (SJRWMD) for Orange and Volusia counties and Southwest Florida Water Management District (SFWMD) for Polk county. These maps include data based on medium and low altitude flight imagery collected in 2000 and 2003 at scales of 1:24,000 and 1:6,000 resulting in an image resolution of 1 meter. The data were analyzed and interpreted into cover and land use types by specialists within the water management districts based on the Florida Land Use and Cover Classification System (FLUCCS).

Using mapping tools in ArcInfo 9.0, single family residential property sales were identified within the landscape and geo-coded. The Euclidean distance between the centroid of each parcel and the edge of the nearest patch of each of the four wetland types and to the nearest lake, patch of upland forest, and patch of agricultural or rangeland. The wetland types are: (1) Hardwood Forests (loblolly bay, tupelo, and bottomland hardwoods); (2) Coniferous Forests (cypress, pond pine, and cabbage palm); (3) Freshwater Marshes (sawgrass, cattail, and other aquatic vegetation); and (4) Wet Prairies (emergent and sparse vegetation).

In addition to classifying wetlands according to the vegetation type, the wetlands were also classified by their potential to be transformed into another land use. Prior research suggests that the permanency of protection for environmental amenities and open space may influence the value of surrounding properties. Protected wetlands appear in two forms compared to Unprotected wetlands.  First, wetlands contained in the Florida Natural Areas Inventory (FNAI) are designated as being regionally significant and are not subject to development in the future because they are owned by a state or local government entity (FNAI Protected).  In addition, other protected wetland areas were identified using property appraisers’ land use codes that designated land set aside by developers and owned by neighborhood homeowner associations; these include conservation easements, undeveloped lands, and open spaces not available for future development (Other Protected).

The three counties compare in a number of ways. Orange is the smallest and most population-dense, Volusia is the most commercial-dense, and Polk is the largest and most agricultural-dense. In all three counties, about one-sixth of the landscape is comprised of wetlands; upland forest and water comprise the smallest portion of the total landscape coverage. In all counties, the average distance to the nearest wetland patch is more than 1,000 meters. While some properties are located adjacent to each of the wetland types, the nearest wetlands are miles away from some properties, most notably in Polk county. Across counties the acreage of the nearest patches of Hardwood Forest and Coniferous Forest wetlands associated with the average property sold are considerably larger on average than the nearest patches of Freshwater Marsh and Wet Prairie wetlands. In all cases, the smallest nearest patches are designated as Unprotected.

Comparing the property parcels, the average price of a property sold in Orange county (expressed in 2000 dollars) was about $45,000 greater than that of Volusia county and about $55,000 greater than Polk county. In terms of physical characteristics, the heated area of the mean property sold was comparable across the counties; however, the area of the land was notably smaller in population-dense Orange county relative to Volusia and Polk counties. The mean number of bedrooms and bathrooms was similar across counties, as was the proportion of properties with pools.

Hedonic Property Models and Implicit Prices for Wetlands

Hedonic property price models (linear and double log) were estimated using the property sales and GIS data for the three counties. In general, the hedonic property models performed well with high explanatory power. Results for the structural and location attributes of the properties indicated a large number of statistically significant relationships that were consistent with the general literature on hedonic property models. To test for differences in the implicit prices of wetland proximity between the three protection status categories, the distance variables were interacted with dummy variables that identify whether the respective wetland was publicly protected from future development (FNAI Protected) or informally protected through an easement or collective ownership by a neighborhood organization (Other Protected).

For the wetland relationships, results indicated that there were considerable differences in the property price effects of wetland types and protection status across the three counties. One wetland type may be either an amenity or disamenity depending on protection status and/or county.  The extent of surrounding residential and commercial development and whether the properties were located in a flood zone had little effect on these relationships. Due to the number of empirical specifications, it is difficult to summarize the individual coefficient results across all wetland types, protection status, and county. Complete details are reported in Section 2, Table 2 – 6, and Appendix 2 – C of the full report.

The most direct interpretation of the relationship between wetland types and property prices is provided by the ‘implicit prices’ of wetland proximity derived from the estimated coefficients in the hedonic property model. These implicit prices account for the multiple interactions within the model and measure the mean incremental ‘willingness to pay’ (positive or negative adjustment to the sales price) needed to acquire a marginal (one unit) change in an attribute. Table ES – 1 reports some of the mean implicit prices and 99% confidence intervals for a one-meter reduction in distance to each of the four wetland types, by protection status, across the three counties. Positive (negative) willingness to pay coupled with confidence intervals that contain solely positive (negative) values indicate amenity (disamenity) effects from proximity to the respective wetland type.

The results in Table ES – 1 indicate that wetland proximity tends to have relatively small overall effects on property prices regardless of the type of wetland. In some cases, however, wetland proximity had relatively larger effects that were directly related to the category of protection status. For example, larger amenity effects were found for proximity to Hardwood Forest ($3.41 per meter) and Coniferous Forest ($6.14 per meter) wetlands designated as FNAI Protected in Orange county. Alternatively, Hardwood Forest wetlands with FNAI protection had larger disamenity effects (-$5.26 per meter) in Polk county. For Freshwater Marsh wetlands, larger disamenity affects were found for proximity to Unprotected patches in Orange county (-$4.97 per meter), FNAI Protected patches in Volusia county (-$6.80 per meter), and Other Protected patches in Polk county (-$3.58).  Lastly, the most consistent–and perhaps most striking–findings reported in Table ES – 1 were associated with proximity to Wet Prairie wetlands. In all cases, proximity to FNAI Protected patches led to larger disamenity effects on property prices, most notably in Polk county (-$18.40 per meter). These implicit prices indicate that the effect of wetlands on property prices was very location specific and depended on the wetland type and protection status. It was not possible to conclude that wetlands, in general, were either amenities or disamenities in the Central Florida region.

Table ES – 1. Mean Implicit Price of 1 Meter Reduction in Property Distance to Nearest Wetland Type by Protection Status

Orange County         

Volusia County         

Polk County         

Nearest Wetlands

Mean

99% CI

Mean

99% CI

Mean

99% CI

Hardwood Forest

Unprotected

$1.96

[1.66, 2.26]

$1.60

[1.17, 2.03]

$0.56

[-0.17, 1.29]

FNAI Protected

3.41

[2.53, 4.29]

1.75

[0.92, 2.58]

-5.26

[-7.64, -2.88]

Other Protected

0.24

[-0.60, 0.13]

2.33

[1.44, 3.20]

0.77

[-0.75, 2.30]

Coniferous Forest

Unprotected

-0.08

[-0.42, 0.26]

-1.31

[-1.80, -0.82]

0.08

[-0.38, 0.52]

FNAI Protected

6.14

[3.47, 8.76]

-0.99

[-2.69, 0.72]

-0.62

[-2.05, 0.81]

Other Protected

---

---

-0.13

[-1.08, 0.82]

-0.15

[-0.85, 0.54]

Freshwater Marsh

Unprotected

-4.97

[-5.36, -4.58]

0.15

[-0.42, 0.72]

-0.31

[-0.94, 0.32]

FNAI Protected

-1.74

[-4.11, 0.64]

-6.80

[-12.00, -1.64]

3.81

[0.60, 7.05]

Other Protected

1.97

[1.41, 2.53]

2.46

[0.45, 4.47]

-3.58

[-4.82, -2.33]

Wet Prairie

Unprotected

1.16

[0.68, 1.64]

-0.76

[-1.44, -0.08]

-2.74

[-3.56, -1.93]

FNAI Protected

-7.03

[-13.30, -0.71]

-4.41

[-9.76, 0.91]

-18.40

[-22.99, -13.91]

Other Protected

-0.82

[-1.33, -0.32]

-2.82

[-5.83, 0.23]

3.33

[1.05, 5.64]

Discrete Choice Property Models

Discrete choice analysis was also used with the housing data described earlier as an alternative method to estimate demand and implicit prices for wetlands near residential property. The approach was to model the probability of housing choice as a function of the same structural and spatial attributes in the hedonic property price models. To generate choice sets for each property sale, a home buyer’s choice set was defined as the chosen alternative and 249 randomly drawn properties purchased/sold within a three-month window around the purchase/sales date of the respective property within the same county. For example, if an individual purchased a property in Orange county in April, then the choice set contains 250 properties, each of which sold in either March, April, or May within the same year.

Similar to the hedonic price analysis, conditional logit models were estimated using both linear and log specifications. Due to the variety of empirical specifications, it is also difficult to summarize the discrete choice model coefficient results across all wetland types, protection status, and county. Complete details are reported in Section 2, Table 2 – 7, and Appendix 2 – D of the full report. Overall, the bulk of the wetland and protection status coefficients were not statistically significant, indicating that the probability of choosing a property was not affected by the proximity of the property to wetlands nor of the protection status of the nearest wetlands. In fact, none of the coefficients were significant for the models estimated with the Volusia county data. However, two notable effects were found in Orange and Polk counties where the nearest Wet Prairie wetlands were perceived as a disamenity in the respective housing markets. This result was comparable to the disamenity effects for Wet Prairies in the hedonic price analysis.

Similar to estimation of the implicit prices from the hedonic models, the estimated discrete choice models may be used to estimate the compensating surplus (willingness to pay) associated with incremental changes in distance to wetlands using estimates of the marginal utilities of distance and income. However, as the wetland distance coefficients were insignificant in most cases and the price coefficient was insignificant in all cases (see Table 2-8 and Appendix 2-D), surplus estimation from the discrete choice models was not pursued. Also, one of the project’s original objectives to combine property-based discrete choice models with stated choice models for wetland valuation was not pursued.

Stated Preferences for Wetland Ecosystem Services

Choice Survey Design

A stated preference choice survey was developed to estimate residential property owner preferences and willingness to pay for wetland ecosystem services through a public land acquisition program across Orange, Polk and Volusia counties. The approach followed random utility theory and employed a conjoint-based (pairwise choice) survey to elicit preferences for public acquisition of specific land parcels. Each parcel was comprised, in part, of the same four wetland types used in the property valuation models (Hardwood Forest, Coniferous Forest, Freshwater Marsh and Wet Prairie). Each parcel also provides unique ecosystem services, such as serving as habitats for wildlife and recharging groundwater recharge supplies. To mitigate potential hypothetical biases that are a common criticism of stated preferences experiments, the survey design presented choice scenarios in the context of a land acquisition program representative of established programs in central Florida. In addition, the pairwise choice sets contained actual parcels of undeveloped private lands sampled from the landscape rather than hypothetical parcels constructed through factorial survey design methods commonly employed in stated choice surveys.

The sample included 600 homeowners recruited from the three counties using a random sample from the same property appraiser information described previously for the property valuation models. Subjects completed the survey at various university facilities equipped with desktop computers and internet connections to access a server that presented the survey and recorded responses.

The survey was comprised of three sections. First, the introductory section greeted participants with a short voice-scripted and visual description of the state and local land conservation initiatives and solicited opinions on state programs and spending. The section concluded with visual and verbal descriptions of the four wetland types and presented an example of one of the pairwise choice sets to instruct subjects about the response format. The second section contained a menu of six pairwise choices of parcel sites selected from a total of sixty-eight sites identified across the three counties. Pairwise combinations included within home county sites, home county and other county sites, and only other county sites to test whether the location (jurisdiction) of the sites influenced the site choice. For a given pair of sites, participants were presented with information sequentially through maps and aerial photos about: i) the county in which the sites were located; ii) the total site acreage and the number and percent of acres in each of the eight land categories; iii) the number and percent of acres in each of the four wetland categories; and iv) the ecosystem rating (or ratings) for each site. The maps and aerials photos were over-written with multi-media ‘flash’ technology to highlight and distinguish between the site attributes and to identify their location within the surrounding landscape and the respective counties. A final choice screen summarized the site attributes and cost per household/acquisition prices (randomly assigned from $10 to $125 in $5 increments) and allowed participants to review the maps and aerial photographs. After reviewing the information, subjects were asked to choose their most preferred of the two sites or to choose ‘neither’ site. Finally, the third section solicited socio-economic information from participants.

A key element of the survey design was the ratings for ecosystem services at each of the 34 parcel sites included as part of the pairwise choice information. Ratings for the thirty-four sites were developed for three services: Wildlife Habitat Quality, Groundwater Recharge Potential, and Surfacewater Connectivity. The rating protocol was based on Uniform Mitigation Assessment Method (UMAM) developed by the Florida Department of Environmental Protection for wetland mitigation assessment and utilized a variety of GIS data, assessment information from environmental agencies within Florida, and personal visits by the research team to each site. Site ratings were assigned on a 1 (lowest) to 10 (highest) scale for each of the three ecosystem services. Complete details on the rating protocol are provided in Section 3.2.2 of the full report. To assess the significance of these ecosystem service ratings in site choices, a ‘short’ version of the survey included only the Wildlife Habitat service and a ‘long’ version included all three ecosystem service ratings as part of the descriptive information for each parcel site.

Stated Choice Survey Results and Implicit Prices for Wetland Ecosystem Services

Response data were used to estimate econometric models for the short and long versions of the survey using a nested logit choice specification to account for the neither option in the site choices. Also, three different specifications of the land composition variables were used along with the socioeconomic characteristics of each respondent in the nested logit models.

As expected, the ‘price’ individuals would pay to acquire a site–cost per household–had a negative and significant effect on the probability of site choice in all cases. Similarly, the ecosystem service rating coefficients for Wildlife Habitat and Groundwater Recharge differed significantly from zero in all cases indicating that these ecosystem services were perceived as amenities by respondents. In contrast, the Surfacewater Connectivity coefficients were not significant. Results from the long version also indicate that Wildlife Habitat was perceived as a relatively more important ecosystem service than Groundwater Recharge.

Additional results for both the short and long version of the survey indicated that respondents preferred larger wetland parcel sites over smaller sites. However, this effect diminished in significance and magnitude in the short version. This suggests that respondents gave relatively more weight to a full set of ecosystem service ratings versus a single rating as a determinant of the site choice. Also, results indicated that only two types of land composition, Wetland Hardwood Forest and Wetland Prairie acreage, had significant and positive effects on the probability of site choice with both versions of the survey. Of the remaining six land types identified in the site choice information, only Upland Forest acreage had a significant effect in most of the estimated models.

Another interesting result was that with the short version of the survey, the probability of site choice diminishes significantly in all cases as the proximity of a site to the respondent’s residence increases. In contrast, no significant proximity effects are found with the long version. One possible explanation is that Wildlife Habitat and diversity were viewed as a local public good so proximity matters in the absence of any additional information about the services offered by the site. But, to the extent that Groundwater Recharge services were perceived as providing benefits that were independent of site location (at least regionally), the amenity effects of proximity may diminish when subjects were informed about the presence and quality of these additional services.

Other location issues that were tested included whether the level of development surrounding a parcel site (and visible to respondents in the aerial photos used in the survey) influenced site choice. Results indicated no significant difference in preferences for sites that were in lightly developed or rural areas. However, some models indicated that the choice probability was significantly less for urban sites with adjacent development relative to rural sites.

Finally, county-specific effects of parcel site location were examined with the hypothesis that respondents would be more likely to choose a site located within their county of residence versus a site located in either of the other two counties. Results did not support this hypothesis. Complete details on the estimated models are reported in Section 3, Table 3 – 4, and Appendix 3 – D of the full report.

As with the hedonic property model, implicit prices for wetlands can be derived from the estimated coefficients in the stated choice models. In this derivation, the implicit price measures the mean incremental ‘willingness to pay (WTP)’ to acquire a marginal (one unit) change in an ecosystem service taking into account the multiple interactions within the model. For the ecosystem services included in the survey, a marginal change is a one-unit increase (decrease) in the rating for a service at a representative site. Mean WTP and 99% confidence intervals for a one-unit increase in the site rating for the three ecosystem services are reported in Table ES – 2. Model 1 was estimated with only a total acreage variable and Model 2 included land composition variables (see Table 3 – 4); both models were estimated for the short and long versions of the survey.

For Model 1 in Table ES – 2, results from the short and long versions indicated that the mean WTP for a unit increase in the Wildlife Habitat service was about $23 for a representative site. Given that the mean acquisition price of a site (see Table 3-1) was about $60, changes in this attribute are therefore highly valued. With the long version, results also indicate that a marginal change in the Groundwater Recharge services was valued about $10 less than Wildlife Habitat services. Given that the mean habitat rating is about 7 and the mean recharge rating is about 5 (see Table 3-1), a one unit change in latter corresponds to a larger percentage change (20% versus 14%). Also, the WTP for Surfacewater Connectivity was considerably lower than both of the other services and could be equal to $0.00 since the estimated coefficient was not statistically significant. Similar qualitative results were found for Model 2 although the mean WTP for Wildlife Habitat services was considerably larger in the short version ($34 versus $24); the mean WTP for Groundwater Recharge was also higher in Model 2 that Model 1. Overall, the choice survey results indicated that respondents placed considerable weight on ecosystem services in their parcel site selection decisions and relative differences in WTP estimates can be attributed to alternative specifications of the choice model.

Table ES – 2. Mean Marginal Willingness to Pay for Wetland Ecosystem Services

Model 1

Model 2

Short

Long

Short

Long

Ecosystem Service

Mean

99% CI

Mean

99% CI

Mean

99% CI

Mean

99% CI

Wildlife Habitat

$24.34

(4.18)

13.58

35.09

$22.77

(5.50)

8.60

36.95

$34.22

(7.17)

15.76

52.69

$24.89

(6.51)

8.11

41.66

Groundwater Recharge

---

---

$13.66

(2.50)

7.22

20.10

---

---

$19.16

(3.37)

10.49

27.84

Surfacewater     Connectivity

---

---

$4.47

(2.14)

-1.04

9.97

---

---

$4.96

(2.81)

-2.28

12.21

Standard errors reported in parentheses.

Wetland Mitigation Bank Valuation

Stated Choice Valuation Functions and Wetland Mitigation Banking in Florida

To illustrate a potential application of the WTP for ecosystem services estimated from the stated choice models, a ‘valuation function’ was developed and evaluated in the context of wetland mitigation banking in Florida . A mitigation bank restores, creates, enhances, or preserves an off-site wetland area to create ‘credits.’ These credits can be sold by the banker to developers, transportation agencies, and others who plan to impact a wetland at another site and are required to provide compensation.  The State of Florida has been a leader in the use of wetland mitigation banking and has established data bases and assessment methodologies to evaluate and monitor individual mitigation banking sites.

Valuation functions were developed from the short and long versions of stated choice models by converting the coefficients for the total acreage, land composition and ecosystem service ratings variables into marginal willingness to pay (WTP) values. One valuation function using Model 1 with a total acreage variable for the parcel sites in both short and long versions is presented in Table ES – 3. The value for the total acres variable in the short version indicates that the average respondent (household) would be WTP $0.06 for an acre of land in the average wetland parcel site. For a 1,000 acre site, this would imply an individual WTP of $60 for the site. Furthermore, a one-unit increase from the average wildlife habitat rating would add $24.34 to the WTP of the average site. Thus, a 1,000 acre site with a 7.9 habitat rating (compared to the mean habitat rating of 6.9) would have a WTP of $84.34 for the average respondent. Similar calculations can be made with the long version. Additional valuation functions based on alternative model specifications are reported in Section 4 of the report to illustrate the sensitivity of the results.

Rather than constructing a hypothetical wetland area to apply the valuation function, actual wetland mitigation banks in Central Florida with complete data on acreage, land composition and ecosystem services were used. A ‘representative’ wetland mitigation banking site was developed from data for six private banking sites in Central Florida that included total acreage, pre- and post-mitigation land composition, and ecosystem service ratings. This representative wetland bank site was 1,900 acres and had ecosystem service ratings of 8.1 for Wildlife Habitat, 5.2 for Groundwater Recharge, and 7.5 for Surfacewater Connectivity. Note that these ratings were higher than the mean ratings of the same ecosystem services for the sites included in the stated choice survey.

Table ES – 3. Willingness to Pay for Wetland Ecosystem Services & Composition

Model 1                   

Short

Long

Mean

99% CI

Mean

99% CI

Ecosystem Service

Wildlife Habitat

$24.34

13.58

$22.77

8.60

(4.18)

35.09

(5.50)

36.95

Groundwater Recharge

---

---

$13.66

7.22

(2.50)

20.10

Surfacewater Connectivity

---

---

$4.47

-1.04

(2.14)

9.97

Land Composition (Acres)

Total Acres

$0.06

0.03

$0.03

0.00

(0.01)

0.09

(0.01)

0.07

Standard errors reported in parentheses.

Results using the valuation function WTP estimates from Table ES – 3 and the representative wetland bank characteristics are presented in Table ES – 4. For an average mitigation bank site of 1,900 acres with typical levels of ecosystem services, the average respondent’s WTP would be $111.87 and $52.43 with the short and long forms, respectively. Note that these estimates assume the representative site has an average level of ecosystem services. If the actual levels of service ratings for a representative site were used instead of average levels, the aggregate WTP would increase due to the positive incremental value of the ecosystem services. These results are also presented in Table ES – 4 where the marginal contribution of each ecosystem service is included depending on the survey version. For the short version of Model 1 that only included wildlife habitat as an ecosystem service, the added value of a 8.3 habitat rating (compared to a 6.9 average rating) is $33.11 per site so that the total WTP for an individual respondent would be $144.99 for a 1,900 acre site.

A similar approach would be used to value the enhanced groundwater recharge and surface water connectivity provided by the representative Central Florida banking site. Table ES – 4 shows that the 5.7 groundwater recharge rating would add $10.74 to the value of the site.   Note that the value per acre in the long version, however, has dropped to $52.43 so the cumulative WTP would be lower in the long version. Also, a surface water rating of 7.7 would add $3.34 to the value of the site although it should be noted that this value would not be statistically significant. The cumulative effect of the enhanced ecosystem ratings in the long version would be to change the total value of the 1,900 acre representative site to $97.50 per respondent.

Table ES – 4. Individual Willingness to Pay (WTP) Estimatesfor an Average Mitigation Site in Central Florida

Individual WTP

for Average Mitigation Bank Site

in Central Florida

Average

Mitigation Bank

Short

Long

Land Composition

Total Acres

1900.38

$111.87**

$52.43*

Ecosystem Service

Wildlife Habitat

8.3

$33.11**

$30.98**

Groundwater Recharge

5.7

---

$10.74**

Surfacewater Connectivity

7.7

---

$3.34

Subtotal Services

$33.11

$45.07

Total WTP

$144.99

$97.50

** and * indicate significance at the 1% and 5% levels, respectively.

Given that the survey design utilized a sample of single family, owner occupied households in Orange, Polk and Volusia counties, the sample individual WTP results for a representative mitigation bank site can be extrapolated to the population of single family households in these three counties. Using household population estimates for 2007 from the Florida Department of Revenue, the total WTP for a representative bank site would vary between $69.16 million with the short version and $46.5 million with the long version. On a per acre basis, the total WTP would vary between $36,398 and $24,476 per acre. Other stated choice models were also used as a valuation function; estimates of the individual and total WTP per site and the estimated values of mitigation banks for these alternative models are provided in Section 4 of the report.

Comparison of Stated Choice Valuation and Mitigation Bank Credit Pricing

One approach to evaluate the wetland parcel site WTP estimates from the valuation function is to compare these values with the private market values of bank credits for the same Central Florida wetland mitigation banks used to develop a representative bank site. Private mitigation credit prices are determined by the costs of creating the credits, the total number of available credits within the service area, and the potential savings to bank credit buyers relative to other wetland mitigation options such as onsite mitigation. Real estate market demand and supply within each service area have the most significant impact on credit prices since market conditions dictate the costs of acquiring land to create a mitigation bank and the demand for commercial and residential development determine the need for lands containing wetlands. Thus, bank credit prices are driven by market forces whereas the stated choice valuation function is driven by public preferences for wetlands and wetland ecosystem services.   

There was wide variability in annual and average credit prices for the period 2006 – 2008 for the private mitigation banks included in this study. The lowest credit price was $28,000 and the highest was $145,000 with an average price of approximately $69,000 to $78,000 over the three year period.  This average price compares with estimated price of $60,000 per credit across the U.S. in 2007. Assuming an average credit price of $75,000 and the average credits allowable for these banks, the total value of the average mitigation bank would be $36.9 million. Based on an average bank site of 1,900 acres, the implied value of the underlying acreage would be $19,421 per acre. Note that the underlying acreage within a banking site may include several land types so the average value of a mitigation bank should not be interpreted as the value of wetland acreage alone.

With the stated choice valuation functions, the value of a representative banking site varied from a low range of $37.8 to $48.3 million ($19,895 to $25,421 per acre) to a high range of $67.1 to $71.7 million ($35,316 to $37,737 per acre). This range in values resulted from different specifications of the underlying model used for the valuation function and whether the estimates were adjusted to reflect a higher level of ecosystem services for a representative mitigation bank relative to an average wetland site within Central Florida. Despite the wide range in the WTP estimates, the results indicate that a representative mitigation banking site provided a significant economic value to the Central Florida community.

Conclusions and Applications

The objectives of this project were to employ a combination of revealed preference and stated choice methods to value different types of wetlands and the ecosystem services they provide with an application to wetlands in Central Florida. The availability of high quality GIS landscape data coupled with large volume residential property sales data from three distinct real estate markets provided a data-rich setting to evaluate the merits of the alternative valuation methods.

The hedonic models identified several statistically significant relationships between property values and different types of wetlands. These relationships, however, were not consistent and indicated that wetlands can be viewed as both amenities and disamenities. Wetlands that were protected from future development were generally more highly valued than unprotected wetlands. These results suggest that the economic effects of wetland protection and the contributions of wetland ecosystem services to nearby properties are highly localized. Policy analysis focused on wetland protection should consider the variety of factors that could influence whether a specific type of wetland is viewed as an amenity or disamenity within a specific geographic area.

Application of discrete choice models using the same residential property and wetland data as the hedonic models were not very successful. Despite a variety of efforts to define alternative choice sets and model specifications, estimation results indicated few statistical significant relationships between property values and different types of wetlands. In addition, the discrete choice models were not well-behaved and many of the standard relationships between structural and neighborhood characteristics and property values were not statistically significant. As a result, the discrete choice models were not used to estimate wetland values.

The project also utilized the GIS landscape data to develop realistic choice sets using actual wetland sites in Central Florida for the stated choice experiments. This use of landscape data, rather than hypothetical factorial designs, coupled with video flash technology and internet delivery is one of the first applications of this approach for ecosystem services valuation purposes. It is a promising approach for future studies where there is sufficient variation in the landscape and the study area is large enough to identify a reasonable number of candidate sites.

Results from the stated choice experiments indicate that respondents clearly demonstrate a willingness to pay to acquire wetland sites and the ecosystem services provided by these sites. Of the three ecosystem services considered in this study, Wildlife Habitat Quality was consistently the most highly valued compared to Groundwater Recharge Potential and Surfacewater Connectivity. The latter service was not statistically significant in most models. The estimated values indicated the relative importance of these services for the survey population and can be used to establish priorities for wetland acquisition programs. Future research should consider other types of wetland ecosystem services that were not addressed in this project.

The valuation functions developed from the stated choice models also demonstrate the feasibility of using the results to value specific wetland parcels. In this study, the valuation functions were used to value a ‘representative’ wetland mitigation bank in Central Florida and the results were compared to the value of the representative bank based on actual prices paid for mitigation bank credits. Although there was significant variation in the estimated value depending on the model used for the valuation function, this comparison indicated that public values for wetlands were generally consistent with the private values for wetlands revealed through wetland mitigation markets in Central Florida. These valuation functions could be considered for benefit transfer applications in other geographic areas.

Journal Articles:

No journal articles submitted with this report: View all 5 publications for this project

Supplemental Keywords:

watersheds, groundwater, ecosystem indicators, cost benefit,, RFA, Scientific Discipline, Economic, Social, & Behavioral Science Research Program, Economics, decision-making, Ecology and Ecosystems, Urban and Regional Planning, Social Science, Economics & Decision Making, policy analysis, surveys, deliberative policy, decision analysis, web-based methods, discrete choice, hedonic price models, environmental values, environmental policy, aquatic ecosystems, public values, revealed preference, public policy, stated preference, wetlands preservation, economic tradeoffs

Progress and Final Reports:

Original Abstract
  • 2004 Progress Report
  • 2005
  • 2006 Progress Report