Valuation of lake resources through hedonic pricing

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Valuation of lake resources through hedonic pricing
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Feather, Timothy Dale, 1961-
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Subjects / Keywords:
Water resources development   ( lcsh )
Lakes -- Economic aspects   ( lcsh )
Geography thesis Ph. D
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Thesis:
Thesis (Ph. D.)--University of Florida, 1992.
Bibliography:
Includes bibliographical references (leaves 96-101).
Statement of Responsibility:
by Timothy Dale Feather.
General Note:
Typescript.
General Note:
Vita.

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University of Florida
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Full Text









VALUATION OF LAKE RESOURCES THROUGH
HEDONIC PRICING















By

TIMOTHY DALE FEATHER


DISSERTATION PRESENTED TO THE GRADUATE SCHOOL
THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF



























Copyright 1992

by


Timothy D.


Feather













ACKNOWLEDGEMENTS


Though this dissertation is my contribution to the field, there are many who have

served as important advisors and supporters during its development who I would like to


acknowledge.


The efforts of my graduate committee chair, Edward Malecki,


and other


committee members


, Cesar Caviedes, Timothy Fik and Warren Veissman are very much


appreciated.


Peter Waylen and James Heaney,


who were important advisors during the


initial conceptualization of this effort, are duly recognized.


note of Dr.


I would like to make special


Waylen's support during the period of my comprehensive examinations.


John Crissey of the Orange County Property


Appraiser's Office was a critical


supporter of this research and provided the necessary property value data for the analysis.


William Hansen and Michael Krouse of the U.S.


Army Corps of Engineers'


Institute for


Water Resources are recognized for directing financial and technical backing for this

research.


moral


technical


support


provided


management


staff


Planning


Management


Consultants


Limited


recognized.


Without


their


commitment to this endeavor it would have been very difficult to conclude.


would like to thank my family,

continually--but lovingly--asking,


Lastly


especially my wife and parents for their support, for

When I might be finished?
















TABLE OF CONTENTS


ABSTRACT


CHAPTERS


INTRODUCTION


Economic Analysis in Water Resource Management
Statement of the Research Problem .


AND JUSTIFICATION


Issues in Environmental Appraisal.. .
General Approach and Data Needs


. . 5


* S * . 5
* . S S S 5


Economic Techniques in Environmental Analysis
Impact of Environmental Features on Land Rent .
General Applications in Property Value Analysis .
Water Resource Applications in Land Value Analysis
Application to Flood Control .. . .


Application to Irrigation Projects ......
Application to Water Resources in General


Final Comments


THE MODEL


. . 6
. S 9


S S S S a S S 51


S S S S S S S S S S S S S S S S S S S 5 26


S S S S S S S S S S S S S S S S S S S 5 5 28


Theoretical Overview


S S S S S S S S S S S S S S S S S S S 28


Assumption of Like Willingness-to-Pay among Households


LITERATURE REVIEW


I I I I)I1X


ACKNOWLEDGEMENTS iii

LIS'I' OF FIGURES ................... ................... vi

LIST OF 1~ABLES ................... ................... viii








Lake Characteristics Data


Filtering the Data


. MODEL CALIBRATION AND RESULTS


Chapter Overview . . . . . . . 49
Lakefront-Nonlakefront . . . . . . . 50
Lake Characteristics Impact . .. .. . .. .. 52


Land Rent Gradient


DISCUSSION OF RESULTS ......

Hypotheses Results . . .
Beneficiaries of Lake Resources .
Performance of Trophic State Index
Hedonic Valuation as a Planning Tool


VIII.


. * . * . 57


* . . *. a 88
. S S S 88


CONCLUSIONS AND RECOMMENDATIONS


REFERENCES


. a * a . a S a a 96


APPENDICES


APPENDIX A


APPENDIX B


APPENDIX C

APPENDIX D

APPENDIX E


Model Profiles of Past Work


Lake Quality Model Database


S. 102


. . a A.. 117


Distance Program in BASIC . . . . 119

Land Rent Gradient Model Database . . . 123

BOX-COX Procedure Program in SAS ........ ... 128


BIOGRAPHICAL SKETCH . . ....


. . 132














LIST OF FIGURES


Figure II-1


Techniques to Evaluate Environmental Change


Figure II-2


General Principles of Environmental Economics.


. . .. 10


Figure II-3


Theoretical Impact of Desirable Lake on Normal


Land Rent Gradient


* * 13


Figure m-1i

Figure III-2

Figure 1-3

Figure 111-4

Figure IV-1

Figure IV-2

Figure IV-3

Figure IV-4

Figure IV-5

Figure V-l


Figure V


Figure V-3


Marginal Price and Marginal Implicit Price Curves .

Intersection of Household Demand and Hedonic Pricing

Willingness to Pay for Change in Lake Characteristic .

Assumption of Aggregate Market Demand Curve


.* .. 30

. 33

.* .34


.t f f S S 36


Sample Property Appraiser's Record


Variability of Temperature and Dissolved Oxygen in Lakes


Stages of Lake Eutrophication .....

The Florida Trophic State Index

Filtering Property Appraiser's Database

Models Evaluated in This Research Effo:


. . . . 43


.* S S . 47

rt . S. . 50


Sale Price versus Unit Price for Lake Quality Analysis


Sale Price versus Lot Size for Lake Quality Analysis


. .. 55


* S 5 54


Figure V-4


Lake Regions in Study Area .


. .. . . 60








Figure V-8


Residual versus Independent Variables


. . 74


Figure VI-1


Observed and Estimated Relationship between Property


Value and Distance


. S S * . . 0 78


Figure VI-2


Figure VI-3


Theoretical Land Rent Surface for Long Lake


Theoretical Isovalue Lines for Long Lake


S. 80


. S 8


Figure VI-4


Figure VI-5

Figure VI-6

Figure VI-7


Observed and Estimated Relationship between Property


Value and TSI


.. 83


TSI Stimulated Changes in Value for Long Lake . . 84

Theoretical Land Rent Surface for Four Lake Regions ........ 85

Theoretical Isovalue Lines for Four Lake Regions .......... 86














LIST OF TABLES


Table IV-1


Selected Data from Property Appraiser'


Databases


Table V-1


Comparison of Mean Property Values of Lakefront


and Nonlakefront Parcels.


S* 53


Table V-2


Descriptive Variables Used in Lake Characteristics Analysis


. 56


Table V-3


Pearson Correlation Coefficients and Significance Statistics for


Variables in Lake Characteristics Analysis


. * a a 56


Table V-4


Parameter Estimates for Lake Quality Model


S. 58


Table V-5


Descriptive Statistics of Variables Used in Land Rent


Gradient Model


Table V-6


Pearson Correlation Coefficients and Significance Statistics for


Variables in Land Rent Gradient Analysis


Table V-7

Table V-8

Table V-9


Table VI-1


. a a a . S a 66


BOX-COX Procedure Summary for Land Rent Gradient Model


Parameter Estimates for Lake Quality Model

Collinearity Diagnostics from SAS ....


Summary of Hypothesis Results


.. 69


. a 72


Sa a a . .. a a 76


65


71















Abstract of Dissertation Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Doctor of Philosophy

VALUATION OF LAKE RESOURCES THROUGH
HEDONIC PRICING

By

Timothy Dale Feather


December 1992


Chairman:


Edward Malecki


Major Department:


Geography


Society places


value on


many water resource


features


which are difficult or


impossible to associate with monetary worth.


With many water resource management


decisions


based


benefit-cost


analysis,


identification


possible


economic


components of a project is desirable.


To partially accommodate this analytical need,


hedonic


pricing


techniques


have


been


developed;


assume


value


environmental goods is capitalized in proximate land values.


Thus,


the value of the


environmental resource is implicitly assigned through analysis of land values.

The hedonic pricing technique is used to estimate the demand for various lake


resources.


The successes and failures of past empirical applications of hedonic valuation








collected for Orange County, Florida.


The Orange County Property Appraiser's database


was accessed to obtain property characteristic data and numerous sources were sought


to obtain physical lake characteristic data.


The locational attributes of the lakes and


parcels were placed into a geographic information system to elicit the impact of lake

amenities on theoretically normal land values.


Three separate but associated empirical analyses were conducted.


First, lakefront


property values were compared to nonlakefront values and lakefront properties were


found to be 41


percent greater.


Next, lake quality and size were introduced to the


analysis


where


lakefront


properties


from


a cross-section


of lakes


were


evaluated.


Trophic state index and lake size were shown to be negatively and positively related to


property value,


respectively.


The last phase of the analysis shows parcel distance from


lakeshore


to decrease


property


value.


This


statistically


significant relationship


supports the general theory that lake impacts on property values diminish with distance

from the lakeshore.

The empirical hedonic relationships were shown through a series of graphics to


fiusurate


local


beneficiaries


resources.


statistical


results


methodologies can be used to evaluate some of the benefits attributed to lake resources

that water resource planners should consider in management decisions.














CHAPTER I
INTRODUCTION


process


determining


benefit


or value


certain


water


resource


characteristics


not yet


been


established


or clearly


accepted


water


resource


planners.


On the one hand


, when used as an input to production,


water resource benefits


are derived as production cost savings.


For example, the benefits or savings can accrue


from irrigating instead of transporting water in some agricultural production settings.


Similarly,


valuation of flood control benefits from a water resource is also relatively


straightforward. A dollar value can be computed to represent foregone flood damage.

On the other hand, valuation of quantifications of certain benefits can elude traditional


solutions.


The quandary occurs because society places worth on certain features of water


resources that have no direct monetary value, such as aesthetic value and some recreation


benefits.


In other words


, these are perceived benefits that are not bought or sold in a


market setting.


Therefore the challenge to water management decision makers is to


justifiably place value on unpriced goods.


What is the value of a water resource such as a lake?


be spent on cleaning up a waterway?


How much money should


These questions must be answered by those who


manage


environment.


response


to this


challenge


use of


a benefit







resource,


is measured


through


priced


complementary


goods,


property


values.


development and application of this technique are the topics of this research effort.


Economic Analvsi


in Water Resource Management


Economic analysis in water resource management has evolved significantly since


the U.S.


Army Corps of Engineers was initially ordered


to keep account of project


benefits and costs as mandated by the Rivers and Harbors Act of 1902.


The Flood


Control Act of


1936


supplemented


the Act of


1902,


making approval


of a project


contingent upon benefits outweighing costs.

decisions was generally accepted, making b


The idea of economically justifying policy


ienefit-cost analysis a popular and necessary


agent for allocation of government monies to water projects.


In the late


1940s, representatives from several federal water resource agencies


created a guide for planners and managers to benefit-cost analysis, referred to as the


"Green Book" (U.S.


in the late


Interagency Committee on Water Resources 1950).


1950s and early


Subsequently


1960s individual valuation procedures, as well as the total


process, underwent close examination and formalization by water resource economists,


engineers,


and policymakers (Eckstein 1958; Krutilla and Eckstein 1958; McKean 1958;


Hirshliefer et al.


1961; Maass et al.


1962).


Techniques were redefined and became


rooted in widely acceptable economic theory.


The "Green Book" was revised in 1958,


and other similar methodological "guides" were written (Sewell et al. 1962; Howe 1971).


The 1960s and 1970s, often referred to as the "Environmental Era"


(Veissman


Wetly


1985),


were


marked


increased


concern


maintenance







federal government mandated environmental impact statements


for all proposed


projects,


with the passage of the National Environmental Policy Act of 1969.


In 1970,


the intrinsic and extrinsic values of the environment were proclaimed by the federal


government,


Environmental


Protection


Agency


was


established


to set and


administer policy to maximize that value.

such as the Water Pollution Control Act of 19

(ratified in 1978 and 1981, respectively), en


Other important legislative advancements,

'72 and Executive Orders 10244 and 12291


iphasized water quality value and the need


for efficient management of water resources.

The federal government delegated greater water management responsibility to


state and local governments during the 1980s.


To support management decisions at all


government levels,


the U.S.


Water Resources Council (1983) published the latest set of


"principles and guidelines"


1983


, which continued the long line of guidelines that


started


with


"Green


Book."


environmental


management


decision-making


process, methodologies,


and assumptions involved are, as they have been for decades,


under constant scrutiny and development.

Statement of the Research Problem


The purpose of this study is to identify the empirical relationship between lake


water resource attributes and the land values of surrounding residential properties.


econometric terms,


the study will


assign


monetary


benefit estimation of lake water


resources via hedonic pricing.


Specific statistical hypotheses are developed in Chapter







means of


estimating


demands"


encourages


further work and application


of the


methodology.


The intent of this study is to show that hedonic pricing can successfully


be applied to lake resources, and that a strong relationship between the presence of water

resources and property values exists and should be considered in the planning setting.


This study, while sensitive to the successes and failures of past studies,


demonstrates that


valid theory exists and that modeling applications are feasible, resulting in a versatile tool

for water resource managers.














CHAPTER II


LITERATURE REVIEW


AND JUSTIFICATION


This


chapter


discusses


pertinent


environmental


resource


concepts


related


environmental appraisal.


Additionally, it provides a research background and defines the


current


research


paradigm


water


resource


management


to which


research


contributes.


The first section is a general discussion of the methodological proclivity


toward analyzing environmental change.


The second section details the role of land


values in


environmental analysis.


Two


more sections


provide critical appraisal


applications of land value analysis for evaluation of the general environment and water


resources, respectively.


Last, a brief statement of justification is made for this study


based on the stimuli of previous work.

Issues in Environmental Appraisal


General Approach and Data Needs

Management of environmental resources is a continuous and dynamic challenge.

Social and physical segments of the environment are affected and must be understood and


considered.


Thus the need for interdisciplinary research is quite apparent.


White (1961)


proffered a six-point framework for environmental analysis:


(1) estimation of available


resources,


(2) expansion of the


"range of choice," (3) assessment of technologies, (4)







of technology.


The practical range of choice is set by the culture and institutions that


permit, prohibit, or discourage a given choice.


The actual selection within these limits


depends on the manager's perception of the elements of the decision.

Environmental managers find that an important limiting factor in developing this


"practical


range


choice"


availability.


Heaney


(1988)


argues


methodological


advancement


overtaken


availability


reliable


data,


consequently


data collection efforts should


be intensified.


similar plea has been


extended into the regional paradigm of geography (Johnston 1983).

collection efforts involve the synthesis of systematically collect

relational databases and development of expert systems. Westcoat (


role of description and data collection in water resource


contributed to each of the following steps of Westcoat'


Contemporary data


xd data by means of


1984) emphasizes the


e planning. Geographers have


problem-solving scheme.


Problem


Definition:


Initial


identification


problem


characteristics,


appropriate


scales


analysis,


linkages


with


other


resource


management factors


Description:


Survey of the perceived range of choice among different


social groups; integrated surveys of resource distributions, environmental
processes, and social phenomena affected by the problem


Analysis:


"Mapping"


resource


management


alternatives,


their


configurations in the landscape,


and their effects on the domain of choice


Synthesis:


Expansion of the range of choice through identification of


unforeseen or misperceived linkages among resource users,
waste flows, and institutions

Economic Techniaues in Environmental Analysis


use areas,


Management of environmental change falls within the realm of economic theory


and a series of evaluation techniques have been created.


This section summarizes some




7





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federal government defines seven types of water resource outputs (U.S.


Water Resources


Council 1983):


flood control, navigation,


agricultural production,


commercial fishing,


water


supply,


recreation


hydroelectric production.


Many


water projects


have


multiple purposes and therefore affect humans through a combination of the defined

channels.

Techniques used to measure the impacts of changes in the environment relating


to humans are broadly categorized as market value based,


survey based,


and surrogate


value based (Bentkover et al.


1985; Freeman


1979; Hufschmidt et al.


1983) and are


shown


Figure


lI-i.


Market


value


techniques


upon


market


transactions


determine value. Their use is dependent upon the existence of a market for the pertinent

environmental good. If prices determined by the market do not exist, survey-based


techniques can be used to create hypothetical markets.


Carefully worded questionnaires


are developed that ask respondents what they would be "willing-to-pay" for the set of


quantities of goods.


The last group of techniques-surrogate value based--use priced


complementary goods to determine the value of a good that is not defined in the market.


The selection of the appropriate technique is generally based upon project purpose,


shown in the third tier of Figure II-1.


Development


analytical


methodologies


been


prominent


environmental valuation and allocation analysis in the recent past.

above techniques, methodologies such as linear programming, option


In addition to the


uization models, and


trade-off analysis (Cohon 1978; Goodman 1984) are used to provide the best solution,







(Walsh 1986).


The gross economic benefit of a good is estimated as the area under the


demand curve and is designated by area (abc) in Figure II-2 (top).


The techniques


discussed


above and


shown


Figure


are applied


to estimate demand


curves.


Assuming a price (or cost) P'


, the net benefit (or consumer surplus) of the good is


equivalent to the area (abc) under the demand curve less the area (decb) representing


costs.


If the quality of an environmental good is altered,


this will affect demand.


net benefit (or disbenefit) caused by a change in the environmental good is derived by


comparing


respective


consumer


surplus


estimates.


example,


the quality


swimming is enhanced through cleaner lake water, the demand curve for swimming at


the lake will likely shift to the right.

shown in Figure II-2 (bottom), is equ


The economic gain from this increased demand,


livalent to the consumer surplus of the altered state


(abe) less the consumer surplus of the unaltered state (cde) represented by the shaded

area.


Impact of Environmental Features on Land Rent


Following the suggestions for more data sources and development of economic

techniques for environmental analysis, research centered around the response of land


rents to the environment has taken place.


This section introduces the theory, and the


sections


describe


applications


of land


value


techniques


environmental


analysis.

Natural and man-made features of the landscape affect household utility and are


considered during residential choice decisions.


If demand for the goods and services








P











pY


Consumer Surplus


Demand


Initial Consumer Surplus


Initial Demand

\Altered Demand

Consumer Surplus


Under Altered


Demand







of land values across the landscape is studied by many disciplines, including economics,


geography,


and regional science.


Most researchers cite Von Thunen's (1821) work on


agricultural land values as the seminal work in land rent theory. He postulates that land

rents decrease with distance from the central business district (CBD), ceteris paribus.


This gradient was the result of market compensation for increased transportation costs.

Early advances in urban land rent theory centered on a central business district were


made


Hoyt


(1939),


included


neighborhood


status


variables,


along


with


accessibility,


as determinants of land rent.


Several formalized mathematical explanations


of urban land rent structure appeared in the 1960s.

or firm utility through a set of bid-rent functions.


* Alonso (1964) maximizes household

Muth (1969) discusses urban land rent


equilibrium


presents


theoretical


implications


relaxing


some common


assumptions found in standard urban land rent models (e.g.


featureless landscape).


common.


monocentric city and a


Empirical verification using the urban landscape of Chicago was


Richardson (1976) presents a comprehensive overview of the many extensions


to this formal mathematical approach to land value modeling.

Impacts of environmental features have been introduced into the development of


theory,


example,


Papageorgiou


(1973)


Thrall


(1987).


good


displaying uniform impact among all households is categorized as a


"public good" for


which


Mishan


(1971)


Samuelson


(1954)


provide


theoretical


underpinnings


evaluation


purposes.


environmental


good


such


as a clean


has a spatially


nonuniform influence on households and is considered a special type of public good: an







an imaginary direct path from the central business district will inflate the land rents in

the immediate vicinity of the lake in a manner similar to what is shown in Figure II-3.


A related econometric technique, referred to as


"hedonic pricing,"


substitutes


external effects of the environment on land value to estimate value for the environmental


good.


The value of the environmental good at hand (e.g., lake,


river, forest, air, sound)


is assumed


to be


implicitly


captured


adjacent property


values.


Thus


price


differential due to the presence of the environmental good is assumed to be the surrogate


value of that environmental good.


Theoretical background and econometric explanation


are presented by Griliches (1971) and Rosen (1974).


Property value impacts and hedonic


valuation are the central methodological theories of the present research.

General Applications in Property Value Analysis

The value of many types of environmental goods has been measured through


property values.


The majority of studies have focused on


the value of air quality.


Ridker and Henning (1967) provided the first application of evaluating the land value-air


quality relationship.


They reveal a positive relationship between the two:


as air quality


improves, so do property values.


Some researchers found this relationship to be just


marginally evident (Smith and Deyak 1975; Milliman and Sipe 1979).


A close look at


these studies reveals data definition problems or limited variability in air quality


. There


are many other successful applications of the property value technique to air quality

many of which are summarized by Freeman (1979).


Harrison and Rubinfeld'


(1978a) analysis of air quality in Boston pays particular
















Effect of Lake


w/oLake


Lake
Distance from Central Business District


Source:


Adapted from Thrall (1987)


Figure II-3

Theoretical Impact of Desirable Lake on
Normal Land Rent Gradient


accessibility index,


and socioeconomic class variables causes errors in the dependent


variable of between 20 and 30 percent.


Definition of


submarkets


for accessibility,


income


socioeconomic


status


found


to decrease


estimated


air improvement


benefits as compared with the aggregated "basic" equation.

Benefits from air aualitv improvements across income erouos are examined in







resources.


The variable indicated riverside tracts to be of significantly higher value than


those tracts not along the Charles,


thereby implying a positive demand for the amenities


of the river.

Havlicek et al. (1971) evaluate negative external effects on nearby land values of


waste disposal sites examined near sanitary landfills in Fort Wayne, Indiana.


Variables


considered are size of house and lot, number of bathrooms and bedrooms, age of house,


ownership-tenant occupancy


, year of sale, sale price.


Distance from the disposal site and


degrees (angle) from the prevailing wind describe the relationship between the parcel and


the disposal site.


Each degree from the direction of the prevailing wind is associated


with a $10.30 increase and a $0.61 increase in value is found for each foot away from


the disposal site.


Since the areal units are not reported,


it is unclear if these values are


in dollars per acre, dollars per front-foot, or some other unit.

Effects of hazardous waste dump sites have been the subject of many studies.

Payne et al. (1987) conduct an analysis of the property value response to proximity to


a radioactive waste site.


Awareness of the site was intensified by heightened publicity


in the region and resulted in a decrease in the value of older homes within a two-block


region


of the disposal


Damages


to nuclear


waste disposal


locations and


accidents are reviewed in Hageman (1981).


Using the Delphi technique, a panel of


experts reveal many cases where residents were compensated for decreased


property


values


as a result


nuclear


waste


in the


vicinity


their


land.


Though


documentation and evidence are convincing,


due to the nonempirical method of research







techniques as well.


Rubin and Yezer (1987),


who evaluated natural hazards in general,


report the land value response to the hazard to be significantly less in the case of an


expected disaster compared with an unexpected disaster.


Effects of flooding hazards are


discussed below.

Water Resource Applications in Land Value Analysis

Land value analysis as applied to water resource evaluation has been subject to


limited research.


Some attention has been through simple description or recognition that


resource-impacted land value differentials do in fact exist.


Other applications have been


more aggressive empirically


ranging


to formal


econometric application


of hedonic


valuation.


This section summarizes these applications.


Because the empirical results of


past hedonic pricing studies are critical to the development of the models in the present


research


, model profiles were created that list the dependent and independent variables,


the functional form of the mathematical equation,


goodness of fit statistics.


the regression parameter estimates, and


These profiles are found in Appendix A and are cataloged


alphabetically by author.

Application to Flood Control


Flood control projects, in broad terms,


provide benefits related


to inundation


reduction


land use intensification


, and location as outlined by the U.


Water Resources


Council


(1983).


Each of these categories of benefits can be measured through land


values.


Flood-free versus


flood-prone land is an


obvious example of a land


value


differential caused by water resources.


Generally a flooding hazard is expected to be





16

found the rate of land value recovery to hinge upon the magnitude and frequency of


flooding.


This confirms the findings of Rubin and Yezer's analysis of natural hazards


discussed above.

The federal government subsidizes residents of qualified floodplains through the


Flood Insurance


Administration.


Beyond


subsidy,


a price differential


remains


between floodplain and nonfloodplain lands.


Thunberg and Shabman (1990) derived a


willingness


flood


control


relief


anxiety


community


disruptions-these findings were developed while controlling for flood insurance impacts.

In evaluating a potential flood control project for the Passaic River in New Jersey, it was

discovered that nonfloodplain residential lands possess an average market value 30-40


percent higher than in the floodplain (U.S.


Army Corps of Engineers 1987).


The land


value


analysis


used


to derive


possible


residential


intensification


benefits


proposed project.


These benefits are considered in the assessment of project-related


regional economic impacts (Apogee Research et al.


1990).


Antle (1977) presents a case study of the Chester Creek Basin in Pennsylvania.

The impact of flooding on average land value was estimated to be approximately $5,100


per floodplain parcel.


Another important result was the identification of other important


variables as determinants of land value.


Property size, township, number of floors, and


transportation


location


were


found


to be


statistically


significant


multiple


regression model.


An apparent shortcoming of this study,


which is common to many


analyses of this sort, is data availability.


Various sources of property value data were







As part of a closer


look at the impact of flooding on


property net of flood


insurance, Donnelly (1989) finds an average floodplain parcel to be valued $6,000 less


than property outside the floodplain.


The statistical model appears to be very sound,


all parameter estimates are statistically significant and the r-square value is strong at


0.84.


The author further explains how adjustments are made to some of the independent


variables to control for multicollinearity, though no empirical justification is made for


the linear functional form of the final model.


Annual flood insurance payments are


analyzed at a 10 percent interest rate for the average property-the resultant value being


approximately $3,500.


The difference between the $6,000 and $3,500 is the residual


negative impact of floodplain property.


Donnelly refers to this as a "hassle premium,"


which echoes the findings of Thunberg and Shabman (1990) discussed above.

Application to Irrigation Projects

Milliman (1959) discusses the theoretical possibilities of measuring the primary


benefits of irrigation through increased agricultural land values.

require estimation of net returns from the crops being irrigated,


of yield,


Existing approaches

involving assumptions


output factors, factor costs, and coefficients of production for future seasons.


Milliman


suggests


use of


value


method


could


require


as many


assumptions as the "existing" methods, and that accuracy of the results may be adversely


influenced by data problems,


such as inaccurate land value and land


use data.


concludes that choice of the appropriate technique would have to be made on a case-by-

case basis.







calibrated:


one for reservoir land and the other for nonreservoir land.


Differences


between the equations summed over all tracts of land near a reservoir are considered the


value enhancement attributed


to the reservoir.


The general


conclusion


is that


reservoir presence enhances property values.


Generally the statistical results of the


Knetsch


model are encouraging.


structure of the Knetsch model


,though,


appears to have a few shortcomings.


Except for


the distance variable in the reservoir model


, each variable is stated as having a linear


influence on land values;


this does not allow for nonlinearities


, or "leveling off," of


influence on the dependent variable. Another problem lies in the use of two models to

estimate the influence of a reservoir on land values. A more desirable approach would


have been to use one function that allows the inclusion of both reservoir and nonreservoir


properties.


Finally,


the reservoir model most likely possesses multicollinearity between


reservoir/nonreservoir


distance


variable,


which


raises


concerns


about


accuracy of the parameter estimates.

David (1968) expands on the work of Knetsch in a study of Wisconsin lakes.

Improvements in the independent variables employed include knowledge of water quality


topography variables


model.


Water


quality parameters are based


upon


"good,"


"moderate,"


"poor"


classifications


made


representatives


from


state


environmental agencies.


Average lakefront slope is included as a measure of topography


and ease of access,


population,


and the presence of swamp and other lakes are also


included.


included.







squared value.


The "value-of-improvements" variable accounted for approximately


percent of the variance in


the Knetsch


study.


David's objective is to focus on the


relationship


between


lake characteristics


property


values.


variables except


"access to lake"


are found to be statistically significant.


David's study suffers from


poor-quality


environmental


study


area.


Some


necessary


were


unavailable, resulting in numerical aggregations and simplifying assumptions.


Pendl


(1971) suggests important factors in lakeshore property appraisal are lake type, size,


nutrient content, depth,


clarity,


and shoreline.


The value of riparian rights might also


be considered


, as discussed by Holden (1973).


David's


justification


excluding


"value


improvements"


variable


unclear.


The physical characteristics of a lake probably have little affect on property


prices relative to other variables such as "value-of-improvements."


of lake characteristics


To identify the effect


, other variables that are capitalized into property values must be


identified and controlled for through inclusion in the model.


The omission of "value of


improvements" from the model appears to only lower the r-squared value (David 1968).

A formal econometric approach is used by Brown and Pollakowski (1977) in the


valuation of shoreline property.


They estimate implicit price functions via hedonic price


regressions


for waterfront-housing services.


Variables used in the model emphasize


housing structural characteristics of housing.

water is a distance to water variable (or setback).


The only parameter directly related to

By assuming identical utility functions,


the marginal implicit price function is used as a marginal willingness to pay curve.







household benefits with the general public's utility for open space.

Pollakowski model contained no water characteristic variables. Thou

of the study was valuation of open space (indicated by setback), w


The Brown and


igh the main intent

rater characteristic


variables would have added considerable insight into the open space values.

Dornbush and Barranger (1973) perform a nationwide property value analysis and

find that abatement of pollution in all waters to a level "not inhibiting to desirable life

forms or practical users and which are aesthetically agreeable" would increase the capital


values of aggregate property value by approximately $1.3 billion.


They sampled twelve


areas


adjacent


to five


water bodies


have experienced


significant water


quality


improvement from 1960 to


1970.


A regression equation was developed in each area


using property value change as the dependent variable and independent variables of lot


size,


distance


to water


body,


distance


to local


features


were


considered


influential to property values (e.g.,


distance to local park, distance to school,


distance


to shopping center).


The Dornbush


and Barranger


study


suffers


from a lack of


generality in


models.


Variables, such as "distance to State and Commercial Street intersection," are


included that are unduly restrictive in geographic application.


Though recognition of a


transportation hub's influence on land values is desirable, the utilization of such variables

(in that form) severely limits the model's external validity.

A somewhat separate component of the same study is an examination of the public


perception of water quality.


Residential property owners were interviewed as to how





21

Epp and Al-Ani (1979) also evaluated the relationship between perceived and


technical water quality.


Perceived water quality was arranged through a survey asking


yes-no


question


as to whether


thought


level


water


quality


inhibited


recreational or aesthetic use of the water resource.


The parameter estimate indicated


negative perceptions of water quality were associated with lower property values.


Many


technical


measures


water


quality


were


examined


, including


dissolved


oxygen,


biochemical oxygen demand,


nitrate, and phosphorous.


The only technical water quality


parameter


found


to be an


important explainer


of property


value


was


It was


transformed into categories of 5.5 or lower and greater than 5.5.


The more acidic (5.5


or lower)


value


was


significantly


associated


with


lower property value.


Thus


perceived and technical water quality variables had a consistent impact on property value.


The value of urban water parks is measured by Darling (1973).


Two methods of


valuation are compared and contrasted: property value method and an interview method.

The study constitutes a respectable comparison of the two methods with actual empirical


verification


, which is oftentimes absent from this type of analysis.


Furthermore


author provides valuable insights for further research.


The interview method


, which is often referred to as contingent valuation,


on survey data to develop a demand curve


for the water park.


relies


The property value


method employed generally followed the approaches described above. Variables used

in the property value model are property value, improvements, size, crime, neighborhood


quality,


distance to water, and an inflation variable.


The general conclusion is that urban







However, results did not support this.


In two of the three areas analyzed, the property


value method produces a much higher value then the interview method.


In the third


area, the opposite is the case.


Questionnaire bias is a likely source of error.


Meticulous


questionnaire design is vital in estimating demand of a good.


Smith and Desvouges


(1986) provide a comprehensive discussion and application of interviewing techniques in

water resource valuation.

Data concerning property value transactions were difficult to obtain in Darling

(1973); thus a mix of assessed value and selling price was necessary to calibrate the


property value model.


This inconsistency would likely introduce additional errors in the


results and may have contributed to the misalignment of the demand estimates for the two

methods.

The area of greatest concern regarding Darling's study is variable selection (or


lack thereof). First, the study employs use of assessed value of property versus actual

market transactions. Actual sales price is certainly the metric of choice, as assessed


valuation techniques often inhibit inclusion of unique parcel characteristics and do not


reflect actual


market demand.


Also,


inclusion of a technical measurement of water


quality would have been useful for water resource management application (which is

often based upon technical water quality goals).

Addressing the allocation of the Kissimmee River Basin in Florida among user

groups, Reynolds et al. (1973) measure the value of the river to proximate landowners.


Two analyses are conducted.


The first, in a similar manner to Darling (1973),


measures







The second


analysis


is a survey


that asks


respondents


the value of


their


lakefront


property.


When asked what they felt the value would be if the lake were drained,


price dropped 48 percent.


The authors attribute the apparent difference between the


results of the two analyses to the fact that the second analysis includes structures on the

property that "hide" the influence of water on the land value.

Another comparison of water resource valuation techniques is provided by d'Arge


Shogren


(1989).


Following


Darling


(1973),


compare


the property


value


technique with a contingent valuation approach.


They also interview realtors, in a third


tier of the analysis, to gather another perspective to the valuation question.


The basic


focus of this study was to evaluate the differences in demand around two glacial lakes


called East Okoboji and West Okoboji in Iowa.


The water quality in West Okoboji is


substantially


higher


Okoboji.


Thus


Okoboji


case


study


provides


seemingly pure opportunity to compare the demand for higher quality lake attributes.

Estimates from the survey realtors attribute 23 percent of house value to water


quality,


while the hedonic price attributes 21


percent.


These two approaches were


expected to be close to one another, and the results support this hypothesis.

The authors also hypothesize that buyers are able to adjust the amount of water


quality they want as part of their bundle of goods by simply adjusting location,


which


causes the rent gradient for water quality to be concave downward.


This


, coupled with


the "thin" market in the Okoboji region,


causes the willingness-to-pay estimates for water


quality to be exceeded by the hedonic price estimates.


This hypothesis is supported, as





24

willingness-to-pay estimate that Darling (1973) found when comparing it with a hedonic

method.

The purpose of a study by Rich and Moffitt (1982) was to determine a portion of

the regional benefits associated with a water pollution control program through hedonic


valuation.


Regional benefits are calculated to be $600,000 for the 26.5 square miles that


were defined as the study area.

hedonic regression analysis, w


The total regional benefit is based on the results of the


which assigns $37 per acre for riparian land and $31


acre


nonriparian


land.


models


developed


a pre-


postabatement


categorical variable that serves as the operational determinant of the $600,000 abatement

benefits.


Rich


Moffitt's


binary


indication


riparian


not statistically


significant, since they have a limited number of observations (N


- 49).


It may have


been worthwhile to replace the binary variable with a continuous distance to water body


variable.


Also


, aligned with the discussion surrounding d'Arge and Shogren (1989),


technical measurement of water quality might have enhanced the engineering application

of the results.

Falcke (1982) closely follows the econometric theory and procedure of hedonic

pricing presented by Rosen (1974) to measure water resource benefits and also follows

the work of Dornbush and Barranger (1973) in derivation of a perceived water quality


index.


Survey data show that laypersons and technical experts often have differing


conceptions of the conditions of a water body; that is,


in an extreme case, residents felt







that have


undergone


significant


water


quality


change


was conducted.


Site-specific


equations are calibrated,


variable.


with the percent of property price change as the dependent


Each equation uses distance from the water body and perceived water quality


change as independent variables,


as well as a subset of the following variables:


distance


to school


, distance to shopping, location on busy street, location on corner lot, previous


property value, lot size, distance to new highway,


distance to nearest highway access,


distance to environmental nuisance, distance to other new facilities like a bridge, boat-


launching area, or country club.


The "distance-to-water-body" parameter estimate for


each site is regressed against perceived water quality change,


access, and region indicator.


water body type, public


This statistically meshed the site-specific equations into a


single function.

Some applications of hedonic valuation focus on the damaging impact of water


resources on property values.


The impact of flooding was discussed above.


Khatri-


Chetri and Hite (1990) examine the negative effects of reservoir regulation schedule on


residential


property values.


this case,


the needs for hydropower


caused


greater


variability in reservoir stage,


which impacted the utility of waterfront property owners.


Each one foot decrease in stage from normal pool caused about $5,434 decreases in sales


price per acre.


Young and Teti


(1984) examined the impact of degraded


water on


property values in the St. Albons Bay region of Vermont.


In comparison of two water


resource sites that provided a marked differential in


water quality,


the lower water


quality caused approximately 20 percent lower property value.


Young and Teti did not





26

higher property value, Jack Faucett Associates (1991) related beach erosion to decreases


in property value.


This decrease in property value was felt not only by oceanfront


residents, but also residents throughout the community (but to a lesser extreme).


This


analysis was used to justify erosion control measures in oceanfront communities.

Final Comments


Upon


examination


literature on


pricing


environmental


goods


through


hedonic pricing using property values, the following issues are apparent:


* In nearly each study,


quality of data was a


significant hinderance.

* Applications of water resource valuation are
less prevalent.

* Studies that did examine water resources


have


, for the most part, ignored hedonic


pricing theory.
have not explicitly used water quality or characteristic data in the
models.
have not attempted the development of a model that could be
applied in areas other then the study site.
rarely consider distance to water as a continuous variable.


The general exception to the above observations is the Falcke (1982) study.


The present


study is designed to overcome the common data problems while working within the


econometric


bounds


of hedonic


pricing.


This


study


advances


Falcke's


results


examining cross-sectional data in an attempt to control for the shifting housing markets


and other exogenous forces that alter land values over time.


It also concentrates on a


single geographic area rather than sites throughout the


United States.


Demand and





27

A considerable effort is made to compile the database for statistical analysis, both


in terms of the variables chosen and the data gathered to represent these variables.


models


, data, and results of this analysis are presented in the following chapters.














CHAPTER fI
THE MODEL


This chapter develops the formal model used in assigning hedonic value to water


resources.


Propriety


technique and


approach


are demonstrated


as are the


assumptions required under the model.

theoretical principles supporting hedonic


(1982).


The first subsection provides an overview of the

Pricing, based upon Freeman (1979) and Falcke


The second subsection discusses issues surrounding the calibration of the model.


Theoretical Overview


direct


market


value


or price


exists


many


environmental


goods


services.


Hedonic-pricing


techniques


utilize observed


property


values


to indirectly


estimate the price of environmental goods.


In particular, this study applies the technique


to the estimation of demand for various lake characteristics and permits positive and

negative benefits related to changes in these characteristics to be calculated.

The most empowering assumption of the hedonic technique is that the good being


measured


is realized by the consumer and is part of


the bundle of


goods the


provides.


Two further assumptions about the housing market are made:


(1) a single


housing market dictates housing choice in the study area; and (2) the housing market is

in equilibrium (buyers and sellers are optimally satisfied with each transaction in which







characteristic at hand is derived.


Second, a


willingness-to-pay function,


or inverse


demand


curve,


derived.


point


intersection


these


curves


is the


equilibrium price of the good being measured.

The hedonic (implicit) price function can be stated mathematically as


= f(S,,LL,,Wj)


where:


land value at site i with lake
characteristics j
set of site characteristics at site i
with lake characteristics j
set of location characteristics
at site i with lake characteristics j
level of lake characteristics j


The form of this function varies but is generally multivariate.


Thrall (1988) provides an


appraisal of theoretical issues pertaining to land rent function development.


Box and Cox


(1964) make significant progress on proper functional form assignment, and Halvorsen


Pollakowski


(1981)


present


an application


procedures.


Value of a desirable water characteristic, such as water quality,


increases with the


level of quality up to a point where the benefits of increased water quality begin to "tail


off" as shown in Figure rI-1 (top).


Thus the hedonic price function (of a "desirable"


good) generally increases at a decreasing rate reflecting marginally diminishing utility.


Differentiating the calibrated hedonic price


function


with respect to


the lake


characteristics defines the marginal implicit price,


BP/5W


= V(W)


Figure l-1 shows the hedonic (imDlicit) price (too) and marginal implicit trice (bottom)











V(W)


V(W)n


w







The second step is to derive willingness to pay curves, or inverse demand curves,


for lake characteristics.


Individual households or groups of households possess different


tastes and preferences for the good W.


The marginal implicit price denotes the aggregate


market value assigned to an additional unit of W; it does not directly account for


individual household demand for W. A sing

which, given its socioeconomic makeup, i:

estimate demand for that particular group.


;le observation for each household i is made,

s an insufficient number of observations to

Grouping of households by income class


(following Harrison and Rubinfeld 1978) provides an aggregated demand estimate by

group/individual household type.

A variety of possibilities exist as to the shape and empirical nature of willingness-


to-pay curves (Freeman 1974,


1979; Rosen 1974; Bartick 1988).


It is assumed here that


the lake characteristics are independent of a household's willingness to pay.


This means


that lake characteristics are considered exogenous to their implicit price and can be

estimated without regard to a supply-side function (as assumed by Harrison and Rubinfeld


1978).


Thus the willingness-to-pay curve can be estimated by the function below.


= P(S.,Li,W, ,H)


where:


willingness to pa:
household/group i


level


of water


characteristic


J by


P = willingness-to-pay function
Si = site characteristics at site i
L == location characteristics at site i
Wg = observed marginal implicit expenditure on lake characteristic
j by household/group i
Hi = set of household characteristics for household/group i





32

and the marginal implicit price function defines the equilibrium state for the household


in terms of lake characteristics (Figure III-2).


Households will buy quantities of W at


the aggregate marginal implicit price, moving along their willingness-to-pay curve to the


point where the two curves intersect.


This is the level of lake resource the household


will choose to obtain.

The benefits received by household/group i through a nonmarginal change in lake

characteristics from W1 to W2 are the integral of the willingness-to-pay function from W,


to W,.


The aggregate benefits are the sum of this integral for each household.


i-i


w,
f Pi(w)dw


where:


regional


economic benefit due


to the change in lake


characteristics for household/group i
initial lake characteristic level
lake characteristic level after change
willingness to pay for household/group i
number of households/groups in region


The economic benefit is depicted graphically as the area under the demand curve between


initial


final


states


(area


aba!


Figure


111-3).


Summing


each


household/group


affected


provides


aggregate


benefit


changing


characteristic W.

Assumption of Like Willingness-to-Pay among Households

Due to the complexity of revealing individual/group demand for various levels of


lake resources. an alternative annroach will be taken in this analvsis.


The maior




33





p..ij









V(W)m




W



















V(W)m


W


W







represents all individual households'


willingness to pay.


Falcke (1982) uses a form of


this assumption to assume that households at equal distances from the water resources


possess like willingness-to-pay functions.


Freeman (1979) recommends this method as


an approximation of benefits.


The assumption is graphically depicted in Figure III-4.


Assume that V(W), and


Pg represent the marginal implicit price and household willingness-to-pay functions for


(water


resource),


respectively.


If the


initial


state of water resource,


were


enhanced to W,, the actual household benefit would be represented by the area (abcd).


The assumption of this analysis,


given that P(W) will not be formally defined,


is that


V(W), represents household willingness to pay.


Consequently the household benefit


resulting from a move from

potential errors may occur.


Wi to W, would be represented by the area abedd).


First


Two


, if the demand is more responsive to price than the


implicit


price


function,


benefits


overestimated


area


Alternatively if the demand is less responsive to price following P,, the benefits will be


underestimated


regression;


the area


error


(bef).


Calibration


term


of V(W).


randomly


distributed


employ


both


least-squares


positively


negatively,


around


regression


line.


Therefore


many


overestimates


underestimates of benefits will cancel.


(bec).





















V(W)


W


W













CHAPTER IV
THE DATA


Data requirements for a hedonic valuation study are crucial to successful and


acceptable


application.


Omission


critical


component


dependent


variable-some form of property value-can cause harmful bias in parameter estimates.


The vast requirements


for data oftentimes cause researchers


to shy away from


approach.


But in recent times


, development of geographic information systems (GIS) and


access to large databases, such as property assessors'


records


, allow integrated access to


a comprehensive vector of parcel level attributes.


Though the data used in the present research were not rooted in a GIS,


amount of effort went into gathering the best data available.


a great


In fact, a GIS employing


many


of the same data used in


this study


has since


been developed by the agency


supplying the property value data.

This chapter describes the data used to test the model presented in the previous


chapter.


The source, necessary formatting,


and filtering of the data are presented.


intent of this chapter is to clearly indicate the evolution of the data and to conclude with

the data set required to calibrate the models that are presented in the following chapter.

Chief Data Sources





38

Residential development has been quite significant, especially in the Orlando area, again

rendering a desirable sample of transacted residential land values.

Orange County officials are especially enthusiastic about this study and have


provided important insights and data.


Both the director of the Orange County Property


Appraiser's Office and the chairman of the Orange County Commissioner's Office were


contacted about data needs for this study and, subsequently,


collaborated in providing the


parcel-level


property


used


effort.


While


acknowledgment


their


contribution to this study is certainly warranted,


the main point here is that identification


a good


source


support


those


who


maintain


data,


provide


tremendous advantage to the research effort.


In fact


, because the hedonic approach is


so data


intensive,


discovery


a strong


database


considered an ex post justification for choice of study site.

Property Assessor's Database


associated


support


could


The Orange County


Property


Appraiser's database is stored on


three 9-track


magnetic tapes.


Each of the 253.000 records in the database is


1,641 columns wide.


County


Appraiser's


Office


responsible


updating


maintaining


database-transactions are made and recorded daily.


shown in Figure IV-1


An example of a single record is


. These hard-copy records, kept on file at the County Appraiser's


Office, are made available to the general


public.


This research effort requires data


describing the locational structures and economic features of property,


IV-1.


as noted in Figure


The data shown in Figure IV-1 are categorized in Table IV-1 according to these


























ii

C r-
g9







C.
*0

nol
CIM
*c
C-


Ce-
a, 4-i
o n
O- I

twJ44
o ee
I~J


a u


I


s
4;


8
i
e 4)


-a


*.


C


C
9

=

-


i
I.


-al,



A:I


I aI
IrI
*u
1P*
Ir~~~n Irrn~ I


* S
In Is.!U


--C

I-
ufl-r *
jtl;~b
- -.


C.
-ena


*I-I
II -
U' II


-i,
11I


I,


Ill.


iL ;.i.


C


I"
Q
3



-1e
Ott


I-


0


I
I


I;.'
n1
S
a







I~ fl


-II

II,9...


In

I,.; i i



nt/
~I i..


-III


,0~i Ii
'4%


-9g


~'~''~'I


n


I
I

I






Ii




t


ii
rri :x







Table IV-1

Selected Data from Property Appraiser's Databases


Locational
Parcel code
Parcel address
Lakefront indicator

Structural
Structure type
Building characteristics/size
Parcel characteristics/size

Economic
Historical sale values
Historical sale dates
Transaction type
Assessed property value


Lake Characteristics Data


Associated with the property value data are characteristics of the environmental


resource at hand.


This study is aimed at describing the implicit value of lake resources;


therefore data describing lake resources are needed.


There are


7.748 lakes in Florida


(Shafer et al.


1986).


In terms of data describing these lakes, many have only locational


(latitude and longitude) and surface area measurements.


are name

(1982).


d.


In fact, only 3,261 of the lakes


Water quality data for 788 of Florida's lakes are compiled by Huber et al.


Another substantive source of water quality data is the annual water resource


assessment required of each state by the Federal Environmental Protection Agency (Hand


et al.


1988).


Data for


101 lakes in Orange County exist, ranging from simply a size







Choice


technical


water


parameters


model


should


made


conscientiously.


Heaney (1988) describes the difficulty in using single measures of water


quality to analyze water management effectiveness.


case in Florida,


Dierberg et al.


(1988) describes the


where lake management practices have had an impact on only


7 of 43


lakes.


This small impact is attributed partially to ineffective lake management strategies,


but the main question raised concerns technical water quality measurement practices.

Looking at the seasonal variability raises concern as to the applicability of annual


averaging of water quality parameters.


Stratification and mixing cause seasonal trends


in temperature and dissolved oxygen (a common measure of water quality),


not only


temporally but also by depth of sample,


as shown in Figure IV


(Tchobonoglous and


Schroeder 1985).

Not only are there problems with the technical measurement of water quality


a layperson' s


perception of water quality adds another dimension of complexity (Falcke


1982; Dombush and Barranger


1973).


Water resource characteristics being purchased


as part of the "bundle of goods" constituting property value should be measured in terms


that laypersons can understand.


For example,


a change in concentration of dissolved


oxygen in a lake may not be recognized in terms of milligrams per liter (the scientific

unit measure of dissolved oxygen) by a layperson; but if it causes a change in the amount


of algae and weeds in the lake, this can easily be recognized.


suggest easily perceivable water quality characteristics as color, odor


Lant and Mullens (1991)


algae, litter


temperature.








Temperature,


5 10 0 5 10 0 5 10


Dissolved Oxygen, g/m3


Source:


Tchobonoglous and Schroeder (1985)


Figure IV


Variability of Temperature and Dissolved Oxygen in Lakes


Schoeder 1985),


and the three main phases, or states, are shown in Figure IV-3.


oligotrophic state of eutrophication is the youngest.


It can be thought of as relatively


clean water, but so clean that it cannot support the threshold of food and nutrients to


sustain large populations of life forms.


Oligotrophic


lakes are


"in-waiting"


for the


natural growth and decay to take place that will cause food production to increase, and


the trophic state will move to mesotrophic.


A mesotrophic lake will support the largest


level of life in terms of oonulation and diversity: an oDtimal balance of nutrients and life










Mesotrophic
* balance between food
and consumption
* high population
and diversity


Oligotrophic

* low food/population
threshold


SEutrophic
* nutrients dominate
Sfood\population out
of balance


Natural Aging


Figure IV-3

Stages of Lake Eutrophication


causes dominance of algae and plant growth.


The highly variable oxygen availability


characterized by the eutrophic state can sometimes cause fish kills.

There are many facets of eutrophication that are easily perceived by laymen,

mainly because the eutrophic trend begins with a generally clean appearance and evolves


green,


soupy


state.


example,


differences


between


Lake


Tahoe,


oligotrophic lake, and Lake Okeechobee,


to the common man.


a highly eutrophic lake, are certainly evident


Changes in the perceived water quality attributes suggested


above-color. aleae. odor. temnerature-can be detected by laymen and are indicators





44

Trophic state indices (TSI) are used to enumerate the level of eutrophication in


a lake.


These indices typically range from 0 to 100, where 0 indicates very good water


quality and


100 very poor water quality.


TSIs are designed to reflect a doubling or


halving of algae biomass with each 10-unit change in index (Carlson 1977).


Though the


environmental


engineering


community


uses


TSIs


with


caution


, they


are generally


accepted as a representative indicator of lake trophic state.


Consequently water quality


management programs are often evaluated in terms of TSI (Dierberg et al. 1988).

Huber et al. (1982) provide an in-depth analysis of various constructs of TSI. A


combination of physical, chemical,


and biological parameters meshed through a statistical


weighting procedure laced with assumptions is the typical means of TSI development.


Huber et al.


evaluated the more popular indices,


paying particular attention to their


statistical validity in application to Florida lakes.


The recognized TSI configuration for


Florida lakes is provided in Figure IV-4.


water


quality


metric


used


study.


Salient


points of


justification are as follows:


first, lake eutrophication can be perceived by the general


public,


which


is a


necessary


component


hedonic


valuation;


second


combination of several technical water quality parameters that limit (but definitely does

not eliminate) the metric's volatility tied to sampling patterns; third, TSIs are used by the

scientific community in evaluating water quality management programs (e.g., Shannon


and Brezonik 1972).


Therefore the suggestion of Brezonik (1976) that


TSI is helpful in conveying lake quality information to the non- and
semi-technical public







PHOSPHORUS


- LIMITED LAKES (TN/TP


>30)


TSI (AVG)


= 1/3 [TSI (chl a) +


TSI(SD) +


TSI(TP)]


Where:


TSI(Chl a)


= 16.8+


14.4 In chl a,


(mg/m3)


TSI(SD)


= 60.0


- 30.0 In SD,


TSI(TP)


= 23.6 In TP


- 23.8,


(ug/1)


NITROGEN


-LIMITED LAKES (TN/TP


<10)


TSI(AVG)


= 1/3 [TSI (chl a) +


TSI(SD) +


TSI(TN)]


Where:


TSI(TN)


= 59.6 +


21.5 In TN,


(mg/1)


- BALANCED LAKES (10




TSI(AVG)


1/3[TSI(chl a) +


TSI(SD)


+ 0.5(TSI(TP) +


TSI(TN)]


Where:


TSI(TN)


= 56 +


19.8 In TN,


(mg/1)


TSI(TP)


Also:


= 18.6 In TP


TSI(CARLSON)


- 18.4,


(ug/1)


= 0.65TSI(Florida) +


23.2


Note:


chl a
SD
TP
TN
In


Trophic State Index
Chlorophyll a
Secchi Disk
Total Phosphorus (unfiltered)
Total Nitrogen
Natural Logarithm


Source:


Huber et al.


1982


Figure IV-4


NUTRIENT





46

Filtering the Data

The property appraiser's database yields sales transactions and parcel description


over


253,000


parcels


of land


study


area.


Unfortunately,


required


associated lake characteristic data are not as comprehensive.


Therefore the sample


selected for analysis is controlled by availability of water quality data.

Three separate but tangential analyses are conducted in this research effort, each


using different data

following chapter.

follows in the next


sets. I


Detailed documentation of these analyses is provided in the


Specific definition of the data set employed for these analyses also

chapter. Prior to the portioning design of the data set for the three


analyses, three levels of filtering of the property appraiser's database are conducted,


is diagramed in Figure IV-5. The first "global" filter isolates single-family residential

parcels from the population of parcels. This follows nearly all the studies directed at

hedonic valuation of water resources. Nonresidential users of land will in most cases


place a different value on land, depending on their business, industrial output, or other


purpose.


Also


, property tax schedules typically vary by land use type.


These differences


in land use purpose and resultant tax responsibility are capitalized into land value, and


this causes market value segmentation.


To control for this,


single family residential


properties are selected for the analysis.

Another reason for disaggregating parcels by land use type is that the exogenous


impact of water resources varies.


Residential occupants benefit from the recreation and


aesthetic opportunities that a water resource provides (assuming it is attractive and not










ALL PARCELS
253,347


RESIDENTIAL


91,314


SOLD IN 1983
12,639


VACANT
3,214


Figure IV-5

Filtering Property Appraiser's Database


different markets.


Comparison of the value of a variety of water-related services would


be statistically unwieldy and is not the intent of this work; therefore only single-family


residential properties are analyzed.


Thus it should be noted that the application of the


results of this work should be applied to water resources surrounded by residential land

uses only.


residential


parcels


are subsetted


further in


a second


"global"


filter that


isolates properties sold in 1983.


First


, let us address the issue of selection of a single







examined.


Only vacant parcels are considered because the focus of the research is the


locational relationship with the lake resource.


Inclusion of developed parcels would


introduce variance in market price of property that is not needed.


Many other studies


include structural characteristics that typically are easily explained in terms of square


footage and age.


This


in turn


, inflates the explanatory power of the calibrated model.


By using only vacant parcels,


the present effort and resultant model will explain the


locational value of the lake resources.

A last note on the data is that only qualified market transactions are considered.


"qualified" sale is one that reflects on actual market transaction.


On the other hand,


"unqualified"


sales,


which are


relatively


common,


are formulated


under nonmarket


conditions.


For example,


a father may sell his daughter a parcel of land for $100.


This


is actually a gift, but the transaction is recorded in the property appraiser's database.


is recorded


, though,


as an "unqualified" sale.


Actual sales price is used instead of assessed value.


(1976) for a discussion of these issues.


See Berry and Bednary


Though the goal of most assessment techniques


is to reflect market values


, they are sometimes biased to meet political goals.


This study


uses actual qualified market sales to avoid this bias and get a true representation of what

the market bears for a particular parcel.













CHAPTER V
MODEL CALIBRATION AND RESULTS

Chanter Overview


The influence of lake resources is looked at incrementally in this chapter from a


very simple perspective to a multidimensional perspective,


as shown in Figure


V-i.


Thus three hypotheses are tested with as many models:


Hypothesis 1


Land value of lakefront property is greater than nonlakefront
property.


Hypothesis


The effect of lake characteristics


(size and


water


quality) is


realized in land values.


Hypothesis 3:


Water resource related impact on land value will diminish with


distance from the water source.

First, the question of whether the present lake resources influence land value-yes


or no?


address


question,


lakefront


property


values


are compared


with


nonlakefront property values.


An affirmative answer to this question moves us to the


next dimension:


Is lake quality recognized in land values?


If so, lakes of varying


attributes are correlated to adjacent property values.

The final dimension builds upon the previous two while adding space or location.


proximity


the parcels


of land


lakeshore,


other traditional


rent-


influencing components of the urban landscape are considered (e.g.,


business district).


distance to central


The calibration of this third model constitutes the hedonic value










MODEL 1


Lakefront


Non Inke front


MODEL 2

Lake
Characteristic
Impacts


MODEL I

Effects of
Distance- Land
Rent Gradient


Figure V-l

Models Evaluated in This Research Effort

The three models

1. Lakefront-nonlakefront

2. Lake characteristics

3. Lake influence land rent gradient


are presented in this chapter individually.


As mentioned in the previous chapter


each


model works from a separate data set.

as are the statistical arguments, and fi


The definition of the respective data set is given


final model results are presented for each.







the presence of lake resources exists at all must be settled.


It is hypothesized that a


desirable


water


resource


enhance


proximate


property


values.


This


simple


relationship has been proven in the past (e.g.


Knetsch


1964; David


1968; Reynolds


1973) and is shown to hold true in this study.


This question


is addressed


comparing


means of the


groups of


data:


lakefront parcels versus parcels not on lakefront. From the global filter presented in the

previous chapter, there are 3,241 single-family residential, vacant parcels sold in 1983.


A very convenient "special use" code in the property appraisers database allows specific


identification of lakefront property.


Of the 3,241 parcels, 174 are lakefront.


Statistical


Analysis System's (SAS) PROC TEST procedure is used to test the means of the two


groups.


The results shown in Table V-l indicate a strong difference in property value


between the two groups.


The near lakefront parcel selling price is $26,085 compared


with $15,406 for nonlakefront property.


This difference, as shown by the t-statistic,


highly significant.

It should be noted that the t-statistic reported in Table V-l is an approximation


used in


the case where the variances of the two groups are different.


The statistic


reported


Table


indicates


difference


to exist


between


lakefront and


nonlakefront samples.


Thus the t-statistic is approximated as:


- X / [(SF/n,


+ Sf/n)]o.s


where:


the samnle means of samnla


and 7


in thit per lalcefrnt and


Or,


*





52

In the case of (statistically) equal variances between the two groups, a pooled variance

term is used:


-pci


where:


= pooled variance of the two groups


All other variables were defined above.


Equation


(1) yields a more conservative t-


statistic (more difficult to reject null hypotheses),


but strong significance is still shown.


Thus


existence


resources


increases


residential


property


values.


Nonlakefront property is valued at about 59 percent of lakefront property.

(1964) found nonreservoir land to be 54 percent the value of reservoir land, a

Chetri and Hite (1990) indicated this value to be about 40 percent. The d


Knetsch


nd Khatri-


differential


between lakefront and nonlakefront found in the present effort is probably conservative


because the presence of a lake influences more than lakefront parcels only.


theory describes (Thrall 1982),


As land rent


and is shown later in this report, a distance-decay effect


occurs over the landscape; so parcels not on the lakefront, but very near it, will receive


some inflationary influence by a lake's presence.


This distance-decay influence is not


realized in the statistics of Table V-1 because of the way the samples are defined-either


lakefront or not.


Therefore it is expected that the mean nonlakefront value, ($15,406)


harbors some of the distance decay impact that, in turn,


biases the nonlakefront mean


upward.


Lake Characteristics Imoact







Table V-l

Comparison of Mean Property Values of
Lakefront and Nonlakefront Parcels


Sales Price


Mean


Standard
Deviation


Lakefront parcels
Nonlakefront parcels


3.067


26,085.2
15,406.0


22,942.3
12,383.4


= 3.43
=-6.00


Prob
Prob


> F
> T


- 0.0000
= 0.0001


Shogren 1989) illustrates a significant increase in demand for cleaner lake water quality.

This increased demand was found to be capitalized in land values.

For this portion of the analysis, the question of whether lake characteristics are


revealed in property values is examined. The lake characteristics examined are TSI and

lake size and are regressed on lakefront parcels only. Most of the 174 observations of


lakefront parcels (see Figure IV-5) were next to


lakes for which no


TSI data


were


available.


Therefore the original 91,314 single-family residential observations (shown


in Figure IV-5) were accessed to obtain observations for 1982 and 1984.


The final data


set used in this analysis contains 45 observations for lakefront parcels around 19 lakes


sold during the years 1982 through 1984.


This data set is provided in Appendix B.


Examination


simple


plots of the raw


data reveals


some obvious outliers.


Selling price versus selling price per square foot of lot and selling price versus square





54

plots occur because (1) there was an error made in recording the data in the property

appraiser's database; and/or (2) these observations represent property transactions that


are unique.


In either case


, these data points are empirically separate from the remaining


points for reasons outside the realm of where this analysis is targeted.


Therefore they


are removed from the data set, causing the final database to contain 42 observations.

Variable name assignment and descriptive statistics of each are shown in Table


Simple correlation among all the variables are shown in Table V-3.


variables


Each of the


, with the exception of the year-of-sale categorical variables, exhibits significant


correlation to ACTPR, at the 1 percent significance level.


The direction and magnitude


correlations


vary.


FTSQ


positively


correlated


ACTPR,


an obvious


relationship that is verified here. The negative sign of the TSI variable indicates that TSI

is higher for lower-priced parcels. This is expected, as lake quality generally decreases


with increasing


TSI.


The SIZE correlation coefficients indicate selling price around


larger lakes is higher compared with selling price around smaller lakes.


sale variables


The year-of-


, and Y84 simply reflect time-dependent inflation.


TSI is significantly correlated to parcel size.


There is no physical explanation for


There is also a somewhat significant relationship between lake size and TSI.


this is interesting,


While


there is no apparent reason why larger lakes would be more eutrophic


than smaller lakes.


correlation


matrix


alone


provides


affirmation


impact


characteristics


on property


values.


Lake


water


quality


are both


strongly
































8.1 Pdo (3) Fe Eqsr Pus


Figure V-2


Sale Price Versus Unit Price for
Lake Characteristics Analysis


La aim


Figure V-3


300


n00


4n0







Table V-2

Descriptive Variables Used in Lake Characteristics Analysis


Description Variable Mean Std. Dev. Minimum Maximum


Parcel selling price ACTPR 66,445 51,107 5,000 175,000

Lake trophic state index TSI 49.5 10.9 33.0 75.0

Parcel footage FTSQ 24,070 18,792 2,436 88,305

Lake acreage SIZE 554 597 1 1,757


Table V-3

Pearson Correlation Coefficients and
Significance Statistics for Variables
in Lake Characteristics Analysis


ACTPR TSI FTSQ Y82 Y83 Y84 SIZE

ACTPR 1.0000 -0.5028 0.5456 -0.2648 -0.0187 0.2391 0.4215
0.0 0.0007 0.0002 0.0901 0.9060 0. 1271 0.0054

TSI -0.5028 1.0000 -0.6233 0.0822 -0.2155 0.1696 -0.2686
0.0007 0.0 0.0001 0.6045 0.1704 0.2829 0.0854

FTSQ 0.5456 -0.6233 1.0000 -0.3251 0.3067 -0.0697 0.4323
0.0002 0.0001 0.0 0.0356 0.0482 0.6609 0.0042

Y82 -0.2648 0.0822 -0.3251 1.0000 -0.4920 -0.2828 -0.2677
0.0901 0.6045 0.0356 0.0 0.0009 0.0695 0.0864

Y83 -0.0187 -0.2155 0.3067 -0.4920 1.0000 -0.6958 0.0837
0.9060 0.1704 0.0482 0.0009 0.0 0.0001 0.5980

VQlA n 3)IQI n 1^0^ _n nfhO7 _n 1V'Q _n flhl Q 4w n no







that is


regressed


upon


by the remaining variables.


year


sale variables are


included


, with 1983 being the base case and therefore part of the intercept.


The PROC REG procedure in SAS is used to produce the ordinary least-squares


parameter estimates to the multivariate model.


signs of each parameter estimate are as expected.


characteristic variables.


The results are shown in Table V-4.


TSI is the stronger of the two lake


In fact, the t-statistic for SIZE is only 1.2260, which makes it


only


weakly


significant.


on the


other


hand


significant


at the


percent


significance level.


Each unit of TSI increase causes the selling price to drop about


$1,549.


Unit change in FTSQ causes the selling price to increase $0.76.


The t-statistic


for FTSQ reveals a moderately strong relationship at most.


It is significant at the 12


percent level.


Y82 is insignificant,


is the strongest variable in the model,


picking up time-related variance in the selling price.


The functional form of the model shown in Table V-4 is linear.


Double-log and


semilog forms of the model were examined with marginal improvement in R-square (0.47


and 0.49


, respectively).


These configurations significantly reduce the strength of the t-


statistic for TSI.


Since the objective of this portion of the analysis is simply to prove the


existence of a relationship between lake characteristics and property value,


sacrifice of


a few percent points of goodness-of-fit is considered warranted for stronger parameter

estimates for the variables of interest.

Land Rent Gradient


The final model evaluated incorporates locational variables in explaining property







Table V-4

Parameter Estimates for Lake Quality Model


Parameter Standard
Variable Estimate Error T-Statistic Prob > |I TI

INTERCEP 107,938.00 45,867.92 2.353 0.0242

TSI -1,549.22 749.07 -2.068 0.0459

FTSQ 0.76 0.48 1.602 0.1179

Y82 -2,697.02 18,705.98 -0.144 0.8862

Y84 32,102.00 14,694.66 2.185 0.0355

SIZE 14.51 11.84 1.226 0.2281


Dependent variable = selling price
N = 43
F-statistic = 6.2370
Prob > F = 0.0030
R-square = 0.4642


peak at the CBD (see the theoretical land rent gradient shown in Figure I1-3).


A land


rent gradient reflecting reality has more peaks and valleys than the single CBD peak.

This portion of the analysis defines the peaks and valleys caused by water resources

while trying to control for other influences on property value.

In the previous two sections, the existence of a lake and its characteristics have


both been proven to impact land value.


The ability to indicate lakefront or nonlakefront


has been facilitated by a special code in the property appraiser's database.


For this







The parcel number contains the township, range,


places the parcel in a square-mile area.


and section delineation, which


The expected influence of a lake may vary


locally, and therefore further locational definition is needed.


The parcel number also


contains subdivision, block, and lot specification that, unlike the township, range, and


section numbers, are not tied to a numerically consistent map location.


These specific


attributes serve as an index to the property appraiser's parcel maps that were supplied


by the Orange County Property Appraiser's Office on microfilm.


are each quarter-corners,


Maps on the microfilm


or one-half mile by one-half mile squares, a map scale that


allows identification of individual parcels.


defined in terms of X-Y


Thus each parcel of land considered was


coordinates.


Referring back to Figure IV-5,


it is indicated that 3,241 parcels are available,


which are located randomly throughout the study area. This defines the starting point

from which the final sample of observations is drawn. As expected, the lakes with TSI

data limit the number of applicable parcel observations. A region of at least one mile


around each


for which


were available was


specified.


The one-mile


specification followed the one-mile-square sections defined on the detailed county map.

The sections that bordered the lake of interest, plus one more section beyond the border

sector, make up the region associated with the particular lake of interest. The relatively

large band of surrounding land ensured consideration of all possible parcels that might


be impacted by the lake of interest.

regions of interest overlapped. The


Many of the regions blended together because their


e final lake regions considered for analysis are shown





60




















Uq

o aj





VIP,,

9~ ar~



C..

048F
~9,







Approximately 1,300 parcels were sought out in the parcel maps.


These were subsetted


further because (1) several parcels were located in an individual subdivision for which

a random number of parcels were selected; (2) the parcel was situated next to a lake for


which no


TSI data were available;


(3) the parcel


was not found on the map.


Thus


information for approximately 570 parcels was taken from the parcel maps.


coordinates of the approximate center of each parcel were recorded.


The X-Y


The unit of size


measurement


recorded


the property


appraiser's database


is not consistent for all


parcels and in many cases cannot be converted to a consistent area metric.


many parcels are recorded as "one lot."


"lot."


For instance,


There is no way of knowing the size of this


Therefore while finding the X-Y location of parcels, lot areas were measured and


recorded in square feet.


All lakes within the region were considered because,


have TSI data recorded


considered in the statistical analysis.


though the lake may not


, it is a competing amenity within the region and needs to be


The result is a set of X-Y boundary coordinates for


the 96 lakes in the study area.


The intent of defining all objects of interest according to X-Y


coordinates is to


compile a simple geographic information system


(GIS).


This not only provides the


capability of determining relative distance among all the objects,


but graphic capabilities


as well.


Three other pieces of information are added to the GIS.


X-Y coordinates for


the major shopping malls are places on the database.


junctures are entered.


Similarly, major transportation


Major transportation and shopping hubs serve as proxies for CBD.






62





















8 rr
S- 01
i




..0 0

00





++
e e*




o +


a, + ..
+ +
+<









2 +

S e + -
*S r
0^ ^ "'
S9 a, U

3 A- + -4

6 0

Fb .>
I' + C-,
I-s a.



--- BBt+ +





*, +
A-~~ .~'6**IBB JH r







A computer program,


written in BASIC


between each parcel and its first,


second


was used to calculate the shortest distance


, and third closest lakes; nearest shopping center;


nearest


transportation


hub;


the CBD.


The code


for this


program


is found in


Appendix C.


This set of distances for each parcel,


compiled with parcel size, sales


information, lake size, and TSI,


makes up the variables used in the analysis.


Observations


parcels


closest


lakes


for which


no TSI


were


available


were


eliminated.


Close


examination


sales


price


revealed


several


observations that were sold for $100 and one parcel for $114.


The next highest sales


price was $5,000,


and the prices increase continually from there.


One explanation for


this is that they may be unqualified sales,


such as sales to family members for a nominal


charge.


It should be noted, though,


that this was controlled for during the initial filtering


process (described in Chapter


IV) by including only


NQ,"


or qualified sales,


in the


working database.


These may have been miscoded.


In any event,


these observations are


removed from the database.

Examination of selling price versus selling price per square foot, shown in Figure


V-6, reveals a couple of observations that are suspect.


These points are approximately


$18 per square foot, where the next highest values are about $10 per square foot and then


the values continually decrease.


As stated above, these points consist of effects outside


the intent of the model (most likely errors in coding) and are therefore deleted.


The final


data set contains 153 points and is provided in Appendix D.


Variable name assignment and descriptive statistics are shown in


Table


V-5.


























0 2 4


hk Pier NSm 01 N. F

Figure V-6

Sales Price Versus Unit Price for
Land Rent Gradient Analysis


this stage.


The distance to lake variables LAKE1D and LAKE2D both have strong


negative


correlation


coefficients


with


ACTPP


supports


their


hypothesized


relationship.


HUB and SHOP each indicate decreasing property value with increasing


distance.


Proximate


shopping


transportation


areas


are typically


considered


convemence.


The negative correlation coefficients of HUB and SHOP support this


notion.


Looking


correlation


among


explanatory


variables


warns


potential


multicollinearity.


CBD


HUB


and SHOP are all correlated with one another.


This is


not surprising,


since SHOP and HUB possess the same tvoe of convenience benefit that


















































LO.

4)




4)


\t rl
*-






U,


bO


'44-


r -a


en
*V



N
0








-4
S
erl






'.0
-4






0
en
0











U
|l



^ ^-




























02
C)






ra~


~~ 00 S\ C O0 O~C
en- en ott 00 00


I I
'fl .-~~~~-0\Qft~ C'\8(M
I I

In N~01 InOr tO000


00% 00Q


oo-OVI-r--tnrI 0\ en-moo
OOt-4e 00%0 02 eno oC Onvro




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(10l (MOu cflO% 000'r



eq 'nO N\ t) CM CM 00'C
eq men Oe ne Me


\0 (1r ON en, 0O-40N



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en mne en-' Cr rOO VOIn


0% Q5 0%0 N5N'OOn1 cit-'

tnI In CM (AS -\0c 0(1 Oen
In N eq In 0* NNO~na \
-o~d~6t6 66d66 6d
~~~~~ I;d;c idd 'dd9 d







large U.S.


cities.


LAKE1D and LAKE2D are correlated simply because, as distance to


the nearest


lake increases,


distance to the second


nearest lake also increases.


correlations between TSI


SIZE


, and FTSQ have no apparent physical justification.


multivariate


regression


model


developed


with


primary


focus


statistically strong parameter estimates for the independent variables.


concern is directed at controlling for correct functional form,


The next level of


goodness of fit, and


multicollinearity and heteroskedasticity.


Inclusion of LAKE1D and LAKE2D


which were shown to be correlated


caused


volatility


with


LAKE1D


parameter


estimate.


Therefore


LAKE2D


lesser


important variable, is dropped from tt

insignificant variables in the model.


SIZE1 and SHOP were found to be


The final model has ACTPR as the dependent


variable and FTSQ, TSI1


LAKE1D


CBD


, and SHOP as independent variables.


Following the suggestions of Halvorsen and Pollakowski (1981) and Milon et al.


(1984),


consideration of the Box and Cox (1964) procedures (referred to as BOX-COX)


for functional form selection is made.


Milon et al.


(1984) customize BOX-COX for


application to water resource amenities that is used in this research.


+
iE
i-I


(0,x)


i-i


where:


sale price
vector of nonwater-related attributes
vector of water-related attributes
number of independent variables
parameter estimates
BOX-COX transfnnnormatinn fatortn


X 0[0


izh"


wQ








This is an iterative procedure that varies functional form by changing X and


. The


choice of


functional


form


made


based


upon


maximum


log-likelihood


statistic.


Lj(0,X)


-AK lno2 (X,0)


= log-likelihood statistic
K = number of observations
P = sale price
o2 = standard deviation
,\X = BOX-COX transformation factors

FTSQ and ACTPR are found to be linearly correlated and therefore share equal


transformations through the procedure.


= log(y)


for the dependent variable ACTPR and FTSQ.


Thus


*0


= 0


The remaining independent variables are


transformed as follows:


= log(x)


=0


= 0


4M 4 .a aa-


where:


J-1


I







TABLE V-7

Box-Cox Procedure Summary for Land
Rent Gradient Model


0 -1 -1,399.0
0 0 -1,384.4
0 1 -1,356.5
0 2 -1,366.5
1 -1 -1,515.0
1 0 -1,508.9
1 1 -1,493.5
1 2 -1,496.9
2 -1 -1,703.2
2 0 -1,701.1
2 1 -1,694.3
2 2 -1,694.0



2 a2
1- a
0 log(a)
-1 1/a


ordinary least-squares parameter estimates.


Programming in SAS is used to create the


BOX-COX mechanics and develop the log-likelihood statistic.


in Appendix E.


This SAS code is listed


The log-likelihood statistic for the iterations of 0 and X is provided in


Table V-7.


The results indicate 0


=09,


as the best functional form.


Thus the land


rent gradient model takes the form:







Since increasing TSI is expected to cause ACTPR to decrease, the sign of #,


should be


negative.


FTSQ


ACTPR are


expected


move


same


way,


associated with FTSQ should be positive.


LAKE1D


SHOP


, and CBD are all expected


to cause decreases in ACTPR and


, therefore, f3,


and 0s should be negative.


The ordinary least square regression results for the final model,


shown in Table


, indicate all variables are strong indicators of


ACTPR,


the t-tests indicate


statistical significance.


The F-test and r-square values also indicate a strong model.


expected parameter estimate signs of each variable except CBD are revealed.

The sign of CBD indicates that increased distance from the city center causes


increased property value.


This is not a surprise because the correlation coefficient for


CBD


and ACTPR is positive.


measure of CBD


distance as


a convenience is


somewhat


antiquated


(discussed


above).


fact,


suburbanization


center


congestion has caused the traditional land rent gradient to be reversed in many cities.

This increasing land rent gradient with distance from the CBD is in the study area and

is statistically significant.


A couple of technical items regarding the final model should be noted.


First,


was not considered as a functional form because the parameter estimates were


very


small


on the


order


which


created


difficulties


interpretation


conducting


sensitivity


analysis.


improvement


in the


likelihood


statistic


marginal, and the decision to sacrifice the "best" functional form for ease of application

and interpretation was considered justified.


= -1







Table V-8

Parameter Estimates for Lake Quality Model


Variable


INTERCEP


TSI1


LOGFTSQ

LAKE1D

SHOP


CBD


Parameter
Estimate


5.740308


-0.012500


0.000120

-0.000120

-0.000050371


0.000031232


Standard


Error


0.69457899

0.00573203

0.00002243

0.00002243

0.00000780


0.00000753


T for H,:


Parameter


8.264

-2.181

8.798

-5.366

-6.459


4.149


Prob


0.0001

0.0308

0.0001

0.0001

0.0001


0.0001


Dependent variable
N
F-statistic


Prob


r-square


= log of selling price
= 153
= 97.0430


= 0.0001


= 0.76


research, and its inclusion in the model is considered important for a prior reasons.


large sample size helps to minimize the collinearity impact on the parameter estimates.


The degree of collinearity gauged through derivation of a condition index,


described by


Belsley et al.


of 9.2


(1980), indicates an acceptable level of collinearity.


shown in Table


The condition index


, is considerably less than the rule-of-thumb value of 30


recommended by Belslev et al.


The result is CRD is an imnnrtant cnntrihutnr in the final


> F







Table V-9

Collinearity Diagnostics from SAS


Number


Eigen
Value


Condition
Number


Proportion of Variance


TSI1


FTSQ


LAKE1D


SHOP


CBD


2.39380


0.0118


0.0348


0.0408


0.0070


0.0087


1.23383

0.93209

0.41202

0.02826


1.39289

1.60257

2.41038

9.20386


0.0844

0.0236

0.177

0.7025


0.1583

0.1617

0.6230

0.0222


0.0085

0.4840

0.4428

0.0240


0.0131

0.0041

0.0008

0.9750


0.0014

0.0015

0.0108

0.9775


A necessary condition of a properly defined least squares regression model is that


error


term


randomly


distributed.


This


condition


referred


heteroskedasticity.


Any pattern in


the error term indicates


homoskedasticity which


causes specification errors in the parameter estimates.


Following Hannett and Murphy


(1985),


predicted


(the model's prediction of selling price for each observation) is


plotted against the model's error term in Figure V-7.


very limited homoskedasticity.


No strong pattern exists, indicating


Plots of the error term versus the independent variables


are shown in Figure V-8, further indicating randomness in the error term.


A final consideration in evaluating the model's robustness involves examination

of autocorrelation based upon the location of each observation. This is termed spatial






73












0










0 0








0o o o o b
o o
o 09 o0 0 oo

oo o yoo 0o
oo oo oo


So 0

o o oo

O -
0



0
O O



0 A0





0 w0 000 00 0* o


U!dY Jo CO>tA pop jp ad


























a



J4


oaMOD


O




0 OOOD


S


O O

O ODXOO


aED


S


:1



I'



2i


Ft)pd P ow'sQE~


8.



21


1'pyp pwzpwpws


0


IOllp'fP3~I POZi~P13PnlS







randomness of the model error in terms of each observation's location.


If a pattern


exists, model specification problems may exist and appropriate statistical remedies are


required.


The Moran coefficient (Moran 1948) is used to test for spatial autocorrelation.


il


W. e.e


ill

i-i


where:


Moran coefficient


Wg = inverse of the distance between points i and j
e1 = model error for observation i
ei = model error for observation j

The Moran coefficient can range between -1 and +1 where a value of 0 indicates a


purely random pattern. A z-statistic is used to determine if there is a non-zero indicator

of spatial autocorrelation. The Moran coefficient value for the present model is 0.05,


which is very close to zero, and the associated z-statistic indicates there is no significant

spatial pattern in the error term.














CHAPTER VI
DISCUSSION OF RESULTS


The first two chapters provided a setting for the present effort by describing the


problem in environmental benefits estimation,


theory, and past empirical application of


hedonic valuation.


Chapters II through V presented the data, methods,


and final models


as part of the present research.


This chapter


discusses


these


results by examining


specific components of


model:


the distance decay


gradient and


water


quality


influence.


These results are presented in terms of the theory


past work, and application


issues.


Hypotheses Results

Three hypotheses were introduced at the beginning of Chapter V


Hypothesis 1:


Land


value


lakefront


property


greater


Hypothesis


Hypothesis 3:


nonlakefront property.
The effects of lake characteristics (size and water quality)
are realized in land values.
Water resource-related impact on land value will diminish


with distance from the water source.

Each hypothesis was proven true through presentation of analysis and models in


Chapter V


Results relating to the hypotheses are summarized in Table VI-1.


Therefore


lake characteristics are capitalized in proximate land values, and the magnitude of the


impact varies according to distance to lake-


water amalitvy


and lake sirze


The nrnduct







Table VI-1

Summary of Hypothesis Results


Hypothesis
Number


Description


Type of
Analysis


Selected
Results


Lakefront


Comparison of means


Nonlakefront


Nonlakefront property is
59 percent of lakefront property


Lake quality


Multiple regression


TSI is negatively correlated
with property value; lake size


is positively


correlated


with


property value


Land rent gradient


Multiple regression


TSI and distance are negatively
correlated with property value


Beneficiaries of Lake Resources


issue


"who


benefits


pays?"


challenging


issue


environmental valuation

or local taxing issues.

question is addressed.


I.


The "who pays" part is often convoluted with political agendas


This issue is not addressed here, rather the "who benefits"

The types of benefits provided by lake resources are generally


recreation and aesthetics.


Water resources can also provide, for example,


flood control


benefits


but the focus here is on recreation and aesthetics.


It is important to emphasize here that the benefits calculated through hedonic


value represent only a part of the total benefits picture.


The magnitude of the benefit to


those in proximity, as measured through this hedonic valuation procedure, is valid and







a significant distance (approximately one mile or more) are not considered here.


Thus


any lake resource benefits derived from hedonic valuation are a partial estimate of the

total benefit value of the lake.

According to the parameter estimate on LAKE1D (distance in feet to the nearest


lake),


impact


of the


lake diminishes


with


distance.


Thus


as distance


to lake


increases, the benefit received from the lake decreases. This is illustrated in the scatter

plot of observed land values to lake distance in Figure VI-1 (top). A line drawn through

this scatter plot would obviously have a negative nonlinear slope. Recognizing there are


other influences on


property value,


this line cannot be interpreted literally,


but the


general trend exists.


Dombush and Barranger (1972) indicate the impact of lakes to be


negligible beyond 4,000 feet from the lake boundary.


trend of data shown in Figure VI-1.

calibrated land rent model, the lake


This appears to be the general


To look at the relationship more closely through the


: distance impact is shown in Figure VI-1 (bottom).


This curve


is created


holding independent variables constant (at their respective


averages) while varying LAKE1D.


This plot shows a nonlinear-decreasing relationship.


The change in slope is less pronounced than expected.


At 4,000 feet on the x-axis, the


curve continues to decrease, where according to Dombush and Barranger (1972) the line


should be parallel with the x-axis.


These results indicate that the lake impact in Orange


County


Florida, goes beyond 4,000 feet.


This should be viewed cautiously,


though, as


only


a few


observations


sample


have


LAKE1D


greater


4,000


Extrapolation beyond observed LAKE1D values should be done only for theoretical











200,000




150,000




100,000




50,000




0


2,000 4,000 6,000


8,000


Lake Distance, Feet


22,000

20,000


18,000

16,000

14,000

12,000

10,000

8,000

6,000


2,000


Lake Distance, Fe


Figure VI-1







The point along the curve in Figure


VlI-


(bottom) at which the property value is 59


percent of the value of the lakefront property (lakefront property is where distance from


the lake


equals 0) is at approximately 4,400 feet.


This point on the curve compares


favorably with Dombush and Barranger (1972),


but the remaining portion of the curve beyond


4,400 feet indicates benefits greater than those reported by Dornbush and Barranger

(1972).


The empirical


curve shown


in Figure


VIls


(bottom) can


be expanded three-


dimensionally to develop a surface, which is termed the land rent surface. This concept

is often referred to in theory but is rarely shown with actual empirical data. This is the


case because continuous data on distance are


typically not employed.


With


use of


continuous data in the present effort,


empirical land rent surfaces can be explored.


land rent surface produced in the following figures are a means to present the concept


and results of the land rent gradient model.


The precise shape of the surfaces are a


function of the interpolation used in the mapping software.


Therefore any empirical


analysis should be based on the mathematical models versus data pulled from the land

rent surface maps.


The land rent surface for Long Lake is shown in Figure


VI-2.


The inset of


Orange County shows


the location of Long Lake.


Where limited precise empirical


information can be taken from the surface, the surface provided in Figure VI-2 is shown


two-dimensionally in Figure VI-3 through isovalue lines (where like land values are

connected through interpolation). The values associated with each line are in $1,000,



























S.?



eqI







82












q
N. I




-I-I













m bO
I-l


4)
0o















SS







aa





83

deflationary impact of distance is indicated by the lake appearing as a plateau at an

elevation higher than the remaining points.

Lake quality as measured through TSI has a negative relationship with property


value.


The observed data for TSI and property values are shown in Figure VI-4 (top)


which indicates a decreasing trend.


Property value versus TSI is shown in Figure VI-4


(bottom) by holding all independent variables at their mean and varying TSI.


the inverse relationship according to the calibrated model.


This shows


The relationship is nonlinear


as was specified through the semilog functional form of the statistical model.


Movement


of 10 TSI results in about a 20 percent impact on price.

The change in land values associated with a changing TSI from 66 to the 54.5

(the sample average in this study) for the Long Lake region is shown in Figure VI-5.

This causes increases in property value throughout the Long Lake region that are more


pronounced near the lakefront.


The isovalue line


from


the lake indicates a $2,750


increase in property value associated with the enhanced water quality conditions.


isovalue lines decrease with distance from the lake


, which follows the trend of decreasing


lake impact with lake distance shown in the statistical model.

To further illustrate the impact of distance and TSI of lake resources on land


values, a four-lake region is shown in Figure VI-6 and Figure VI-7.


Lakes Underhill,


Como


, Giles, and Arnold with a TSI range of 62 to


75 show the varying demand for


higher-quality lakes.


TSI for Lakes Underhill and Giles is 62 and is 65 and 75 for Lakes


Arnold and Como, respectively.


The higher plateaus are shown at the lesser eutrophic,











200,000




150,000




100,000




50,000




0


30 40 50 60 70


Trophic State Index


28,000

26,000

24,000

22,000

20,000

18,000

16,000

14,000

12,000

10,000


20 30 40 50 60
Trophic State Index


FiOnre VT-4






85
























rl bO

I.f


4)64)
I-'


0 0


LL4




U







0*jj




U) -






86




















,I I
a Jjbr


















6W U)CW~-










Performance of Troohic State Index


highly significant in


both


the lake quality


model and the


land rent


gradient model.


The difficulties in finding an appropriate water quality metric were


discussed in Chapter IV


and the decision to use TSI was made.


The results described


above indicate TSI is recognized in the residential property market and is therefore,


applicable in hedonic valuation framework.


As the engineering community becomes


more comfortable with TSI development, the exact form will likely change, which would


in turn affect the parameter estimates.


Certainly


though,


this study supports the use of


TSI as an indicator of water quality in the hedonic valuation framework.

Hedonic Valuation as a Planning Tool

Probably one of the more encouraging results to surface from this research is that

hedonic valuation can readily be used by water resource managers in benefit estimation.

This point has not received attention until now because the intricacies of the data and


statistical analysis have received a majority of the discussion. If the study area of

interest has an active GIS with parcel level property value information, the planner has


a tremendous advantage.


Provided below is a summary of the suggested procedure for


conducting hedonic valuation for lake resources.


Step 1: Determine the Purpose of Application

Generally, this method is used to estimate a portion of the benefits attributed to


lake resources as discussed earlier in this chapter.


If a particular application is intended,


this should be explored fully to determine exactly which benefits are being measured.







Step 2:


Determine Study Area


The study area is dependent on the purpose of the analysis (step 1).


is on a single site,


If the focus


then possibly a region of ten miles or so around the lake is required.


If a wider regional demand specification is derived, then a county or multiple county


study area is needed.


The most likely situation will be that the planner will be controlled


the amount of


data available.


Databases


with


parcel


level information are not


typically formed for more than one county.


Step 3:

A clean


Define Data Sources and Create Working Databases

, robust, and complete database is essential for application to hedonic


valuation

of sale.


I.


Property data should include sales price, size of parcel,


The relative distances between parcels and lakes are needed


type of sale, and time

I. It is also important


to include distance to other important factors that may influence property value (e.g.,

distance to shopping).


Technical lake characteristic data are required as part of the database.


TSI is a


recommended starting point based upon the successful application in the present effort.

Lake characteristics data are typically available through environmental or regulatory


agencies at the local,


state


or federal levels.


All avenues should be pursued,


as lake


characteristic data, especially describing water quality,


are scarce.


If a GIS is available and the property appraiser's information is part of it, a large


part of the data gathering is complete.

of nonmarket transactions. This resex


It is important to exclude the property value data


irch also shows that examination of vacant parcels







property value data.


If a relatively small region is being examined,


the property quantity


data needed may be quite manageable,


Step 4:


even if they are gathered from hard copy sources.


Calibrate Model


Given the database developed in step 3,


as the dependent variable.


run a statistical model using sale price


This may require careful statistical insight-the processes


presented in Chapter V (land rent gradient model) can be used as a guide.


Parameter


estimates should be compared with past work.


Most importantly


check that the model


provides realistic results and recognize its limitations.


Step 5:


Examine Benefits of Alternative Projects


The effects of proposed engineering projects on the lake are then evaluated in


terms of the model.


If TSI is a parameter, the TSI values, before and after the project,


are plugged into the model and the difference is a measure of the project benefits.


assumptions and benefit calculations should be carefully documented.


This will allow


for reasonable application of the results to benefit-cost analysis and will also aid in future

application of the model.