THE IMPACT OF CHEMICAL HAZARDOUS

SITES ON RESIDENTIAL VALUES

by

PERRY G. WISINGER, B.B.A., M.B.A.

A DISSERTATION

IN

LAND-USE, PLANNING, MANAGEMENT AND DESIGN

Submitted to the Graduate Faculty of Texas Tech University in

Partial Fulfillment of the Requirements for

the Degree of

DOCTOR OF PHILOSOPHY

Approved

Dr. Paul Goebel Chairperson of the Committee

Dr. John Borrelli

Dr. Ronald Bremer

Dr. Gary Elbow

Dr. Eleanor von Ende

Accepted

John Borrelli Dean of the Graduate School

May, 2006

Copyright 2005, Perry Wisinger

ii

ACKNOWLEDGEMENTS

The author wishes to express his deepest gratitude to his friends whose timely

help with the data made this dissertation possible. Thomas and John, you came through

when needed most. Also the author would like to thank all the members of his committee

and in particular Dr. Bremer for his cheerful assistance with the statistical analysis. And

of course, the patience, drive, and guidance of Dr. Goebel kept this dissertation on track

despite many obstacles. And lastly, the author must acknowledge his family for their

endless faith and love. Janet, Jean, and Paul—thank you all.

iii

TABLE OF CONTENTS

ABSTRACT....................................................................................................................... iv

LIST OF TABLES............................................................................................................. vi

LIST OF FIGURES .......................................................................................................... vii

LIST OF ACRONYMS ..................................................................................................... ix

CHAPTER

I INTRODUCTION .......................................................................................1

II LITERATURE REVIEW ............................................................................8

III THEORETICAL DEVELOPMENT .........................................................25

IV DATA ........................................................................................................35

V METHODOLOGY AND RESULTS ........................................................56

VI SUMMARY AND CONCLUSIONS ......................................................100

REFERENCES ................................................................................................................106

iv

ABSTRACT

The Emergency Planning and Community Right to Know Act (EPCRA) has been

in existence for twenty years yet no comprehensive study has been performed studying

the impact it has on housing values surrounding disclosed sites. While many of the issues

have been studied individually, no previous study has investigated them in their entirety.

Additionally, no previous study has specifically investigated the impact on nearby

housing values of Risk Management Program sites or the impact that Tier Two sites have

on nearby housing values.

September 11, 2001 (9/11), shook the American consciences about their security,

but did it increase their fear of neighborhood environmental hazards? If publicly

available information is being used to value property, then residential values near

potential terrorist targets should have declined in the aftermath of 9/11. No previous

study investigated if a hedonic model could be used to measure the impact that potential

terrorist targets have on nearby housing values.

This study uses a hedonic model to test four hypotheses in Lubbock, Texas. The

first hypothesis questions if housing prices near EPA listed chemical hazards are lower,

ceteris paribus. This study finds that housing values are lower near Permitted Water

Discharge sites, Risk Management Program sites, and Hazardous Waste Handler sites.

The second hypothesis questions if housing prices are lower near EPA-designated Tier

Two sites, ceteris paribus. This study does not find that housing values are lower near

Tier Two sites. The third hypothesis questions if the negative impact of either EPA listed

sites or Tier Two sites grew after 9/11. This study finds the negative impact of being

v

between 2/3 of a mile and one mile of a Hazardous Waste Handler does grow after 9/11.

The last hypothesis questions if the new listing of chemical hazardous sites or the listing

of governmental enforcement action lowers nearby housing values. This study finds no

immediate impact on housing values resulting from the listing of new sites or

enforcement action.

vi

LIST OF TABLES

5.1 Descriptive Statistics..............................................................................................73

5.2 Combined Interquartile Range...............................................................................76

5.3 LnPrice Full Model Explanatory Variable Coefficients ........................................77

5.4 LnPrice Full Model Analysis of Variance .............................................................78

5.5 Reduced Model LnPrice Explanatory Variables Coefficients ...............................79

5.6 Reduced LnPrice Model Analysis of Variance......................................................80

5.7 LnPrice Reduced Model Test for Normality .........................................................82

5.8 Full Model Price Explanatory Coefficients ...........................................................83

5.9 Full Price Model Analysis of Variance..................................................................84

5.10 Reduced Price Model Explanatory Variable Coefficients .....................................85

5.11 Reduced Price Model Analysis of Variance ..........................................................86

5.12 Coefficient Inter-period Pair-wise Comparisons ...................................................86

5.13 Coefficient Intra-period Pair-wise Comparisons ...................................................87

5.14 Price Reduced Model Test for Normality..............................................................88

5.15 Adjusted Reduced Price Model Analysis of Variance...........................................88

5.16 Comparison of Adjusted Coefficients with Reduced Model Coefficients.............89

5.17 Adjusted Price Reduced Model Test for Normality ..............................................90

5.18 Comparison of Reduced Coefficients with Mixed Model Coefficients.................92

5.19 Inter-period Pair-wise Testing of Mixed Model Coefficients................................96

vii

LIST OF FIGURES

1.1 Typical Site Plan Attached to a Tier II or RMP filing.............................................5

4.1 Lubbock County Using MARPLOT......................................................................37

4.2 Simulated Dispersion Zone of Toxic Gas Leak Using ALOHA ...........................39

4.3 Schools and Hospitals in the City of Lubbock.......................................................40

4.4 Study Area .............................................................................................................40

4.5 Zip Code 79404......................................................................................................41

4.6 Zip Code 79405......................................................................................................42

4.7 Zip Code 79411......................................................................................................43

4.8 Zip Code 79412......................................................................................................44

4.9 2001 EPA Listed Air Release Sites .......................................................................46

4.10 New Air Release Site .............................................................................................47

4.11 New Hazardous Waste Handlers ...........................................................................47

4.12 2001 EPA Hazardous Waste Handlers ..................................................................48

4.13 2001 EPA Water Discharge Site............................................................................49

4.14 New Water Discharge Site.....................................................................................49

4.15 2001 EPA Toxic Release Inventory Sites..............................................................50

4.16 New Toxic Release Inventory Site ........................................................................50

4.17 2001 EPA Superfund Sites.....................................................................................51

4.18 Recent EPA Enforcement Action Sites..................................................................51

4.19 2001 Tier II Reporting Sites ..................................................................................52

4.20 2001 Risk Management Plan Sites ........................................................................53

4.21 MLS Zones within the Study Area ........................................................................54

5.1 Study Area Census Tracts......................................................................................59

5.2 One and One-Half Mile Range of Texas Tech University ....................................60

5.3 Period Ended September 11, 2001, Houses Sold...................................................62

5.4 Period Ended February 28, 2005, Houses Sold .....................................................63

5.5 Histogram of House Prices for the period ended September 11, 2001..................74

5.6 Histogram of House Prices for the period ended February 28, 2005.....................75

viii

5.7 Histogram of Combined House Prices...................................................................75

5.8 Plot of the Reduced LnPrice Model Residuals ......................................................81

5.9 Plot of the Reduced Price Model Residuals...........................................................87

5.10 Plot of the Adjusted Reduced Price Model Residuals ...........................................90

ix

LIST OF ACRONYMS

9/11 September 11, 2001

AIC Akaike Information Criteria

ALOHA Areal Locations Of Hazardous Atmospheres

BIC Bayesian Information Criteria

BLUE Best Linear Unbiased Estimator

CAA United States Clean Air Act

CAMEO Computer-Aided Management of Emergency Operations

CERCLA Comprehensive Environmental Response, Compensation, and Liability Act

CERLIS Comprehensive Environmental Response, Compensation, and Liability Information System

CBD Central Business District

CMA Comparative Market Analysis

CWA United States Clean Water Act

ECHO Enforcement and Compliance History Online

EPA United States Environmental Protection Agency

EPCRA Emergency Planning and Community Right to Know Act

GIS Geographic Information System

GNIS Geographic Names Information System

LMerr Lagrange Multiplier errors test

LMlag Lagrange Multiplier lag test

ML Maximum Likelihood

MLS Multiple Listing Service

MSE Mean-Squared Error

NFRAPR EPA No Further Remedial Action Planned Reports

NPDES National Pollutant Discharge Elimination System

NPL National Priority List

OLS Ordinary Least Squares

RCRA Resource Conservation and Recovery Act of 1976

x

RMP Risk Management Program

RTK Right-to-Know

SAR Spatial Autoregressive

SEER Scientifically Estimated Environmental Risks

TDSF Treatment, Disposal, and Storage Facilities

TIGER United States Census Bureau Topologically Integrated Geographic Encoding and Referencing

TRI EPA Toxic Release Inventory

USGS United States Geological Survey

VIF Variance Inflation Factor

1

CHAPTER I

INTRODUCTION

While the population slept on the night of December 2, 1984, 50,000 pounds of

highly toxic methyl cyanide gas leaked from a Union Carbide pesticide plant killing

3,000 people and injuring 200,000 more in a city of a million people, Bhopal, India. To

protect its citizens at home, the American government responded to this and other similar

disasters around the world by enacting the Emergency Planning and Community Right to

Know Act (EPCRA) in 1986. This far-reaching law requires the public availability of

information on chemical hazards and community emergency response plans addressing

these hazards.

Headlines of Love Canal and Three Mile Island achieved national attention and

the public is clearly apprehensive about exposure to toxic chemicals in their homes, but

does this apprehension translate to diminished residential values near environmental

hazards? Does a newly identified hazard result in property value loss? If so, which class

of hazards and how much of a loss does it cause? How far does the economic influence

of the environmental disamenity extend? Are nearby homeowners entitled to

reimbursement of any damages? The United States Environmental Protection Agency

(EPA) is making available a wealth of information regarding potential environmental

hazards but is the public using this information in valuing a residential purchase? These

are all important public policy concerns, but before any action can be taken economic

damages, if any, must be measurable. More and more, economic models are used to

value property for both property tax purposes and commercial lending. Are important

variables such as proximity to a potential chemical hazard being left out of these models?

With chants of “not in my backyard,” United States communities consistently

oppose the handling, storage, or disposal of waste near themselves for fear of declines in

property values and increased health risks. The safe handling, storage, and disposal of

hazardous materials is a national problem with estimated economic risk to real estate

alone from hazardous and toxic materials being $2 trillion [Mundy, 1992].

2

Waste is an unavoidable by-product of human activity and progress, but

unfortunately, some waste is severely toxic. In many places, municipal and industrial

waste disposal creates serious environmental problems threatening human health and the

stability of ecosystems. Under existing Superfund legislation, currently there are an

estimated 40,000 sites across the United States that may require environmental

remediation to meet acceptable pollution levels. As of March 2006, the EPA placed over

1,500 sites on its National Priority List (NPL) [EPA Envirofacts Data, 2006]. Cost of

each Superfund site cleanup averages $43 million, and already $30 billion has been spent

to clean up half these hazardous sites [Greenstone and Gallagher, 2005]. Aggregate loss

due to environmental hazards may exceed ten percent of the total U.S. real estate value

[Haight and Singer, 1993]. Real estate is critical to the American economy and accounts

for one-half of domestic private investment with 60% of that in housing [Miles, Berns,

and Weiss, 2000]. Anything that has a major impact on aggregate real estate activity and

value has national implications.

The Resource Conservation and Recovery Act of 1976 (RCRA) first provided

federal regulation of solid and hazardous waste management. It established both a paper

trail and a permit program covering Treatment, Disposal, and Storage Facilities (TDSF).

In 1980, Congress complimented RCRA by enacting the Comprehensive Environmental

Response, Compensation, and Liability Act (CERCLA) establishing the Superfund.

CERCLA requires the government to determine when a site constitutes a threat to health

or the environment. Moreover, in the absence of private initiative, it mandates the use of

Superfund monies to clean up a dangerous site [Skillern, 1995].

Individuals can be held liable for environmental clean-up costs for hazards they

did cause and sometimes for hazards they did not cause. Mere ownership of property

containing an environmental hazard can trigger liability, and sometimes hazards are not

identified until after title has passed. Because hazardous chemicals such as chlorinated

solvents used in dry cleaning, and hazardous metals such as mercury, copper, arsenic, and

cadmium can migrate through the soil and into the groundwater, land owners may be

liable for hazards that originated on adjacent property [Norse and Trieschmann, 1990].

3

According to Greenberg and Hughes[1993], two million Americans live within a

mile of a Superfund NPL site; one in six lives within four miles. Two Roper surveys for

the EPA reported over 60% of the public feels that both active and inactive hazardous

waste sites are “very serious” problems. It has been reported that residents near these

sites become chronically depressed, do not allow their children to play in the street, lose

faith in their elected officials, worry about the loss of property values, and hope for a

catastrophe so they can move. While environmental hazards stigmatize neighborhoods,

economic impacts are difficult to measure and the typical study can cost over $100,000

and take several months. Because they assess value for tax purposes, tax assessors judge

the relative importance of factors affecting property values. Responding to one survey,

tax assessors felt a hazardous-waste site lowered property values 18% up to one-fourth

mile, 9% up to one mile, and 2% over one mile; five percent were not sure of the impact,

and 66% thought a hazardous-waste site had no impact on property values within one-

fourth mile. And there are reasons to believe that not every environmental hazard will

lower property values. For example, some residents face limited housing options because

of racial or economic segregation. Accordingly, property values may not be able to drop

to reflect the presence of environmental hazards [Greenberg and Hughes, 1993].

To comply with EPCRA, the EPA currently requires the reporting of information

on potential chemical hazards to local, state, and federal agencies. In turn, the EPA has

made much of this information publicly available through its internet site. Information

about hazardous waste handlers, toxic chemical release sites, Superfund sites, and sites

requiring either an air release permit or water discharge permit is disclosed at the EPA

site. Also, hazardous sites the EPA has taken enforcement action against are disclosed.

Furthermore, the establishment of Local Emergency Planning Committees and State

Emergency Response Committees is required to make contingency plans based on the

filings by companies handling hazardous chemicals in the event of an accident.

Other potential hazardous sites are those handling so-called Tier Two chemicals.

EPA-designated Tier Two chemicals are those toxics held in sufficient quantities to

present a clear public hazard. Inventory reports on Tier Two chemicals must be

4

submitted annually to state agencies for use in emergency planning and are available to

the public upon request.

“Plant fire darkens city skies,” read the newspaper headlines following a recent

three-alarm chemical fire at a Tier Two site. The fire, of unknown origin, caused a

substantial number of five-gallon propane tanks to explode and threaten two 18,000-

gallon tanks. Lubbock, Texas, residents within a three-quarter mile radius were

evacuated [Lubbock Avalanche-Journal, February 28, 2006].

An average of 21 chemical accidents is reported daily in the United States. And

one in twenty results in immediate injuries, evacuations, or death. The serious accidents

generally involve anhydrous ammonia, chorine, sulfuric acid, sulfur dioxide, or

hydrochloric acid [Emergency Planning for Chemical Spills—Community’s Role in

Right-To-Know Law, April 28, 2005].

Sites handling large quantities of the most dangerous chemicals are required to

participate in the Risk Management Program (RMP). Those participating in the RMP are

required to file a report assessing the hazard and detailing the potential effects of an

accidental release including an evaluation of worst-case scenarios. The original estimate

of facilities expected to file was 66,000, but so far only 15,430 have filed RMP reports

[Kleindorfer, et al., 2003]. No previous study has considered the impact of either Tier

Two or RMP sites on nearby property values.

The World Trade Center and Pentagon terrorist attacks of September 11, 2001,

(9/11) increased public dialogue and government concern over pubic disclosure of the

location of hazardous chemicals. And shortly after 9/11, the EPA withdrew RMP data

from their internet site. Facilities handling large amounts of potentially hazardous

chemicals could be of interest to terrorists [Schierow, 2005]. Mohammed Atta, supposed

planner of the 9/11attack, had expressed an extraordinary and persistent interest in a

Tennessee chemical storage facility [The Safe hometowns Guide—Conduct an Inventory

of Chemical Site Hazards, 2002].

In light of 9/11, whether to make available sensitive information to the public is

an important public policy dilemma. RMP sites are clearly very dangerous, and would be

terrorists could easily make use of this information. For example, in an accidentally

released report from the U.S. Department of Homeland Security recently, chlorine tanks

were listed as major target of opportunity for terrorists [New York Times, March 16,

2005]. However, currently the public has a legal right to know where they are located.

Should this policy be changed? The USA Today editorial page recently argued the issue

[USA Today, March 14, 2005]. Perhaps an economic measurement of public use of such

information would assist policy makers. Has the public attitude toward chemical hazards

in their neighborhood changed since 9/11? If so, is this change in attitude detectable in

falling real estate values near environmental hazards. And if so, which hazards?

Figure 1.1 is a highly informative illustration of a typical site diagram of a

wastewater facility attached to a publicly available Tier Two or RMP filing. And while

the purpose of such an attachment is to diagram the site for emergency-response

personnel, unfortunately, it also provides would-be terrorists with much useful

information. Because chlorine is commonly used for water purification, large amounts

are found in communities throughout the United States. According to the official Xinhua

news agency, about nine people were killed and 150,000 evacuated in Chongquing,

China following a chlorine gas leak from a chemical plant [BBC News, April 17, 2004].

The Department of Home Land Security estimates that blowing up a chlorine tank could

kill 17,500 Americans and injure more than 100,000 [USA Today, March 16, 2005].

5 Figure 1.1 Typical Site Plan Attached to a Tier II or RMP filing (Demo)

6

To help communities understand the threat that chlorine and other hazardous

chemicals present and to assist emergency planners, the EPA has made publicly available

powerful software tools. LandView VI, powered by the MARPLOT mapping system, is

a viewer for EPA, United States Bureau of the Census, and United States Geological

Service (USGA) data and maps. Originally designed to aid communities with emergency

planning, LandView software was thought to also help study environmental equity by

comparing hazard location with demographic characteristics. However, as a very

functional tool it has additional research uses. LandView VI is a very good Geographic

Information System (GIS) software package that has been seldom used.

Hazardous waste handlers, toxic chemical release sites, Superfund sites, sites

requiring an air release permit, sites requiring a water release permit, sites that

enforcement action has been taken against, Tier Two sites, and RMP sites all constitute

hazards that have a potential effect on surrounding land values. September 11, 2001,

shook the American consciences about their security, but did it increase their fear of

neighborhood environmental hazards? If publicly available information is being used by

homeowners in valuing property then residential values near potential terrorist targets

(particularly Tier Two and RMP sites) should have declined in the aftermath of 9/11. No

previous study has tested the importance of this independent variable in their models.

One purpose of this study is to test the theory that relates the value of residential

housing to the proximity of chemical hazards for residential sales. The next purpose of

this study is to test the theory that relates the value of residential housing near a chemical

hazard before being listed by the EPA to the value of residential housing near that hazard

after being listed by the EPA. A closely related purpose of this study is to test the theory

that relates the value of residential housing near a chemical hazard before enforcement

action to the value of residential housing near that hazard after enforcement action

commences. Another purpose of this study is to test the theory that relates the value of

residential housing near chemical hazards before 9/11 to the value of residential housing

near chemical hazards after 9/11. In each case the independent variable is defined as the

residential sales price and the intervening variables of house and other neighborhood

characteristics are statistically controlled. The last purpose of this study is to establish

7

the usefulness of LandView VI software in measuring the economic impact of certain

chemical hazards on real estate values.

Outside of NPL Superfund sites, very few chemical hazardous sites receive much

press unless there has been a serious accident. Accordingly, most of the population is

probably not aware of the presence of these hazards within their communities. Does the

mere listing of these hazards on the EPA internet site provide the public with the

knowledge required in accurately assessing land values? And in the case of Tier Two

sites, does the housing market take into account information available only on request

from governmental agencies?

The remainder of this study is divided into five chapters. The analysis begins in

Chapter II with a review of pertinent literature. There the reader finds prior research on

the impact of environmental hazards on land values along with the impact of 9/11 on

urban space. The analysis continues in Chapter III with the theoretical development of

the basis for the study and the statement of hypotheses.

Lubbock County Texas is the study site and Chapter IV presents the data sources

for the study. Data comes from the EPA Envirofacts Data Warehouse internet site, the

RTKnet (Right-To-Know) internet site, the MapQuest internet site, the Texas Tier Two

Chemical Reporting Program, LandView VI, the Navica Multiple Listing Service (MLS)

system, and the Lubbock MLS Sold Volumes. Chapter V discusses the methodologies

for testing the hypotheses and the results. And Chapter VI summarizes the study and

recommends areas for further research.

8

CHAPTER II

LITERATURE REVIEW

Selecting the General Linear Model, most researchers studying the impact of

environment hazards on land values performed parametric regressions using Ordinary

Least Squares (OLS). The resulting hedonic price model generally measured the

negative impact of hazards on residential housing with the most recent investigations

regressing individual house sale prices against house and lot characteristics,

locational/neighborhood characteristics, and an environmental hazard. Generally, the

databases included information gathered by the United States census and various

governmental environmental agencies with the Environmental Protection Agency (EPA)

being the most prominent. On many occasions, this information was supplemented by

data from a Multiple Listing Service (MLS) database or by local property tax records. If

a single model spanned years, typically the researcher adjusted the sales price for

inflation to achieve inter-year comparability. Additionally, models used either a spline

function (time-kinked) or discrete periods for performing an effects study. Some

researchers tested different price gradients based on community wealth. An area of great

controversy because of the lack of a theoretical basis for specification, models were

typically specified as linear, linear-log, log-linear (semi-log), log-log, or even quasi-log

with early attention given to Box and Cox [1964] transformations. Another area of

considerable diversity was on how to specify the hedonic model with independent

variables.

Perhaps the first to investigate the impact of manmade hazards on land values,

Ridker and Henning [1967] discovered that ambient air pollution lowered property values

based on analysis of 167 census tracts in St. Louis, Missouri. Using a linear form and the

census tract as the basic unit of measure, this regression model resulted in a negative air

pollution coefficient statistically significant with a 97% confidence level. Demographics

were from the 1960 U.S. census and air pollution estimates were derived from samples

gathered by local officials.

9

Numerous follow-up studies were done confirming the negative impact of air

pollution on land values. Anderson and Crocker [1971] increased the database tested to

include the cities of Washington, D.C., Kansas City, and St. Louis and expanded the

definition of air pollution to include suspended particulates while limiting the dependent

variable to the median value of owner-occupied housing. Rather than census tracts,

Deyak and Smith [1974] used 100 Standard Metropolitan Statistical Areas as the basic

units of measure with demographic data provided by the 1970 census and pollution data

by the EPA; additionally, that model used a log-log specification. Goodwin [1977]

designated air-pollution levels as little, moderate, or high for modeling purposes and

introduced using median census-tract monthly rent as the dependent variable.

Brookshire, et al. [1982] validated survey results by comparison with hedonic modeling

of actual sales.

A number of other models also enriched the understanding of the relationship

between higher land values and clean air. For example, Smith [1978] found that different

multidimensional price gradients existed for high and low income groups. For testing the

Muth [1969] hypothesis of the relationship between income and access to the Chicago

Central Business District (CBD), Diamond [[1980a] and continued in Diamond [1980b]],

used a log-log model on data from a sample of 414 mortgage files of a single institution.

Shechter and Kim [1991] used the contingent valuation method in surveying the residents

of Haifa, Israel on their willingness to pay for cleaner air. Meta-analysis of 37 previous

studies by Smith and Huang [1993] confirmed by probit estimation the negative,

statistically significant relationships between housing prices and air pollution. And

Giannias [1996] introduced the use of Storage and Retrieval of Aerometric Data to

measure air quality.

On the other hand, Palmquist [1984] found conflicting results in modeling seven

U.S. cities: Atlanta, Denver, Houston, and Miami supported the contention that air

pollution lowered property values while Louisville, Oklahoma City and Seattle did not.

Moreover, follow-up studies by Wall [1972], Wieand [1973], Smith and Deyak [1975],

Berry and Bednarz [1975], Berry [1976], Palm [1978], Jackson [1979], Li and Brown

10

[1980], Linneman [1980], McDonald [1980], Roback [1982], and Dale-Johnson [1983]

did not find convincing evidence that higher air pollution levels lowered land values.

By the late 1970s, researchers started questioning historic approaches and began

testing new experimental designs using different statistical approaches. Schnare [1976]

introduced the use of a second-stage linear-log price model for analyzing the

relationships between census-tract ethnic composition, median urban housing prices, and

air quality. Expanding the definition of air pollution to include photochemical oxidant

levels, Nelson [1977] selected the log-log hedonic form after testing it using Box-Cox

transformation against the performance of other forms. For hedonic modeling of the

negative impact of air pollution, and other environmental hazards, on land values,

Bender, Gronberg, and Hwang [1980] advocated using the quadratic Box-Cox form in the

absence of a priori knowledge. Many researchers relied on Box-Cox for model

specification; however, Cassel and Mendelsohn [1985] selected two models measuring

the impact of air quality on land values to express reservations about the Box-Cox

flexible functional form approach for hedonic analysis. The large number of coefficients

resulting from Box-Cox reduces the accuracy of individual coefficients, Box-Cox cannot

be used with negative numbers, and Box-Cox has limited predictive power.

Cobb [1984] and Graves, et al. [1988] argued that incorrectly specified hedonic

models might produce results that are misleading. In both, altering model specification to

include or exclude doubtful variables changed independent variable coefficients in

amount, sign, and statistical significance. Graves, et al. [1988] noted independent

variables may be divided into three categories. Focus variables, such as air quality, are of

research interest. Free variables, such as lot size, have established relevance. And

doubtful variables, such as mean census-tract income, are of questionable value. The

affect of air quality on land values research by Krumm [1980], Dale-Johnson [1982],

Cobb [1984], Cassel and Mendelsohn [1985], Atkinson and Crocker [1987], Graves, et

al. [1988] and Huh and Kwak [1997] all emphasized model specification.

Atkinson and Crocker [1987] suggested Bayesian methods to break the

collinearity problem among candidate covariates and reduce “data mining,” i.e., the

unethical inclusion and exclusion of doubtful variables to obtain desired results.

11

Specification uncertainty, sampling uncertainty, and measurement error can wreak

cumulative havoc on regression coefficients. To illustrate this point, two hedonic models

including air quality were prepared. By varying data to reflect measurement error,

significant changes in regressed coefficient sign and amount were demonstrated. And

Huh and Kwak [1997] noted in modeling Seoul, Korea, that different functional forms

have pronounced impact on resulting hedonic models.

Also in the late 1970s, research progressed from studying the impact of air

pollution on land values to studying the impact of other environmental hazards. Epp and

Al-Ani [1979], Rich and Moffitt [1982], and Mendelsohn, et al. [1992] concluded

surface-water pollution levels influenced adjacent land values. Epp and Al-Ani [1979]

discovered that high acidity and perceived low water quality each lowered residential

values in Pennsylvania. For testing the effects of a specific event, Rich and Moffitt

[1992] prepared a hedonic model that included a statistically significant dummy variable

for land sales occurring on the Massachusetts’ Housatonic River after point-source

pollution cleanup efforts started. The Mendelsohn, et al. [1992] model resulted in

dummy variables significant at the 99% level for periods following public awareness of

toxic pollution of the New Bedford, Massachusetts harbor and the resulting loss of

surrounding property value. Additionally, Michael, Boyle, and Bouchard [2000]

concluded that surface-water pollution affecting water clarity lowered adjacent land

values.

Perhaps the first to test for reductions due to industrial mishaps, Webb [1980]

concluded the Three Mile Island (TMI) accident had lowered surrounding values based

on survey results. However, Nelson [1981] and Gamble and Downing [1982] both found

no measurable reduction in surrounding land values following the April 30, 1979, nuclear

accident there. Nelson [1981], modeling only 47 house sales sold after April 30, 1979,

did not produce statistically significant results. A second model in the same study using

53 sales produced similar results.

In another study of the effects of a nuclear accident on land values, Kinnard, et al.

[1991] concluded that real estate prices within six miles of a large uranium processing

plant seventeen miles north of a major Midwestern city did not fall following the

12

December 1984 public announcement of an accidental release of several hundred pounds

of low-level radioactive powder. Introducing a time-kinked hedonic pricing variable into

the model produced internally inconsistent results.

By contrast, Carroll, et al. [1996] claimed that real estate values declined

following a major environmental mishap by studying the before and after impact of the

July 27, 1988, Pepcon chemical plant explosion in Henderson, Nevada, and the

subsequent decision to relocate the plant 100 miles distant. Following the plant

explosion, an 18% reduction coefficient was observed, but local real estate prices

rebounded by 38% when Pepcon announced the rebuilt plant would be relocated.

Comparisons indicated the discrete-distance-from-the-plant model produced better results

than the continuous-distance quadratic model.

By the mid 1980s, research had expanded to waste disposal sites. Smith and

Desvousges [1986a], Smith and Desvousges [1986b], Hoehn, Berger, and Blomquist

[1987], Blomquist, Berger, and Hoehn [1988], Kohlhase [1991], Ketkar [1992], Thayer,

Albers, and Rahmatian [1992], Smolen, Moore, and Conway [1992a], and Smolen,

Moore, and Conway [1992b] found that hazardous waste sites also lowered nearby land

values. Asking how much more they would pay for an identical house located at one-

mile increments farther away from a hazardous waste site, Smith and Desvousges [1986a

and 1986b] surveyed 268 suburban Boston residents. Using an option-price model,

Smith and Desvousges [1986b] noted that respondents would pay significantly more for

reduced exposure to hazardous waste. Kohlhase [1991] controversially concluded the

deleterious effect of a toxic waste site on housing prices is reversible if the public is

informed by the EPA of cleanup completion; using a repeat sales technique for 45

observations produced positive yet statistically insignificant results. Ketkar [1992]

produced a hedonic price model based on estimated prices to determine the impact of

hazardous waste sites on property values rather than actual market transactions; cross-

section census data from 64 municipalities and data from the New Jersey Department of

Environmental Protection were stepwise regressed. Thayer, Albers, and Rahmatian

[1992] concluded housing prices increase as distance to a nearby waste site grows and

13

that distance from a hazardous waste site is more valuable than from a nonhazardous

waste site.

Hoehn, Berger, and Blomquist [1987] and continued in Blomquist, Berger, and

Hoehn [1988] performed cross-sectional models using county-based data as the unit of

observation. Using a linear form, the model regressed actual or imputed monthly housing

expenditures on a number of structural, neighborhood, climatic, and pollution variables,

including total suspended particulates, National Pollutant Discharge Elimination System

(NPDES) effluent discharges, landfill waste, number of Superfund sites, and the number

of Treatment, Disposal, and Storage Facilities (TSDF). All the pollution coefficients

were statistically significant.

Smolen, Moore, and Conway [1992a and 1982b] reported that hazardous waste

landfills negatively affect residential values at greater distances than had previous

research. Distance from the hazardous site was significant at the 99% confidence level

up to 2.6 miles, but notwithstanding the researcher claim, another set of hedonic pricing

models produced merely mixed impacts for greater distances. However, Zeiss and

Atwater [1989] found no statistically significant impact of waste disposal facilities on

property sales prices.

Michaels and Smith [1990] argued that a single hedonic price function was

unlikely to provide an adequate model of the relationship between the equilibrium prices

and the structural and site characteristics of homes in a large, complex market such as

Boston, Massachusetts. Therefore, four submarket models (premier, above average,

average, and below average) and a composite model were prepared. These models

included a variable indicating time of waste site discovery that was statistically

significant; however, results were too mixed to draw definitive conclusions about

comparative property devaluation due to proximity to hazardous sites.

McClelland, Schulze, and Hurd [1990], Reichert, Small, and Mohanty [1992], and

Nelson, Genereux, and Genereux [1992], concluded landfills lowered nearby property

values. McClelland, Schulze, and Hurd [1990] surveyed Los Angeles, California,

residents about their health risk judgments over a community landfill. Using both home

surveys and individual hedonic pricing models, Reichert, Small, and Mohanty [1992]

14

concluded that five landfills in Cleveland, Ohio had a 5.5-7.3% negative impact on

expensive housing while only a 3-4% negative impact for less expensive housing located

within several blocks of a landfill; although, an initial effort at a single pooled cross-

sectional model produced disappointing results. Nelson, Genereux, and Genereux [1992]

found that land values in the Minneapolis-St. Paul urban region rose with respect to

distance away from three separate landfills and effects varied depending on their

operational status.

Nevertheless, Bleich, Findlay, and Phillips [1991], and Guntermann [1995] both

stated landfills need not lower nearby property values. By comparing residential property

sales in neighborhoods with landfills with similar sales in neighborhoods without a

landfill, Bleich, Findlay, and Phillips [1991] controversially concluded that a well-

designed and well-managed landfill in the Los Angeles San Fernando Valley had not

lowered surrounding property values. Also, Guntermann [1995] concluded that open

solid-waste landfills lowered industrial land values within 1,000 feet while closed solid-

waste sites had not, and that landfills sold for less than other industrial-zoned properties.

And although not finding statistical evidence of a negative impact by landfills,

nevertheless, Halstead, Bouvier, and Hansen [1997] argued that different functional

forms might be necessary to correctly specify the hedonic price model and that Box-Cox

transformation was essential to finding the best data fit.

Perhaps the first to investigate a specific Superfund site, Kinnard and Geckler

[1991] claimed that market response to proximity to three New Jersey Superfund sites

was a direct function of the speed and apparent effectiveness of any remediation effort

following designation. This conclusion was based on a positive dummy coefficient

significant at the 95% level for periods after January 1, 1984, although the sites were first

designated as Superfund in October 1984.

By contrast, Kiel [1995] found no clear evidence that an official announcement of

intent to remediate a site led to an immediate rebound in price. According to Reichert

[1997], diminution in property values was directly related to proximity to an industrial

excess landfill the EPA had placed on the Superfund list. Claiming a Cobb and Douglas

[1928] form, the log residential selling price was regressed on a series of logs of

15

continuous housing characteristics and various dummy variables. The landfill had a

quick, significant, and permanent negative impact on housing prices that lessened as the

distance to the hazard grew.

But is landfill impact constant over time? In contrast to Zeiss and Atwater

[1985], Kiel and McClain [1995a] and continued in Kiel and McClain [1995b]

discovered that Boston, Massachusetts, incinerators lowered residential prices, but the

amount varied over time. Divided into discrete time periods to test for event effects, their

hedonic model included dummy variables for the pre-rumor phase, the rumor phase, the

construction phase, and for on-going operations. All coefficients were significant at the

99% confidence level. Additionally, both an income capitalization model and repeat-

sales technique were employed to confirm that even after seven years, uneven

appreciation rates relative to the distance to the incinerator indicated the local market had

not fully adjusted to the hazard presence.

Rich and Moffitt [1992], Mendelsohn, et al. [1992], Kinnard and Geckler [1991],

Kiel [1995], Zeiss and Atwater [1985], Kiel and McClain [1995a], and Kiel and McClain

[1995b] all used dummy variables in their models in an attempt to capture the effect of an

event. The problem with this approach is that dummy variables may capture changes not

envisioned in the model design, such as, general changes in real estate price levels.

Simons, Bowen, and Sementelli [1997] and continued in Simons and Bowen

[1998] found that some leaking underground storage tanks in Cuyahoga County, Ohio

lowered nearby property values. Both a Box-Cox and a linear specification produced the

nearly identical results that leaking registered tanks were significant at the 95%

confidence level in lowering residence values, but proximity to leaking nonregistered

tanks was not significant. Using multiple analysis of variance, the study also noted that

contaminated commercial properties sold for 30-40% less, were one-third less likely to

have sold, and more than twice as likely to have included seller financing.

However, Dotzour [1997] concluded that discovery of groundwater contamination

had no impact on housing prices in Wichita, Kansas, because average house sales prices

in the area did not decline following the announcement of contamination. Perhaps

because the groundwater was not used for drinking purposes, prices were not impacted.

16

And while local lenders continued to make mortgage loans on single-family housing,

lending decreased on commercial properties.

Two important ideas were recently introduced to creating and evaluating hedonic

models: Geographic Information System (GIS) and spatial autocorrelation. Maybe the

biggest challenge in recent years to face researchers is spatial autocorrelation. Most

researchers use OLS regression analysis to create a hedonic model. However, to find the

Best Linear Unbiased Estimator (BLUE) coefficients, OLS requires that regression errors

be randomly distributed to avoid autocorrelation problems. Since the normally unrelated

independent variables frequently share similar locational characteristics, correlation of

their error terms is common thus violating the requirement of randomness. This violation

of the statistical requirement for a BLUE coefficient results in many hedonic models

being less reliable than previously thought. Clearly, OLS models involving spatial

analysis should be tested for spatial autocorrelation. Unfortunately, earlier research

involving environmental hazards did not test their models for spatial autocorrelation.

This study proposes to overcome that shortcoming by testing for, and correcting if

necessary, spatial autocorrelation. For those less familiar, Bateman, et al. [2004] offers

an excellent discussion of many applications of GIS to environmental and resource

economics. The powerful combination of GIS technology and increased emphasis on

sophisticated statistical techniques in hedonic modeling has resulted in new developments

within the field of spatial econometrics. As defined by Anselin [1999],

Spatial econometric methods deal with the incorporation of spatial interaction and spatial structure into regression analysis. The field has seen a recent and rapid growth spurred both by theoretical concerns as well as by the need to be able to apply econometric models to emerging large geocoded data bases. While using GIS to estimate distances, Rosiers, Theriault, and Villeneuve [2000]

failed to overcome spatial autocorrelation problems using factor analysis. And their next

study, Theriault, et al. [2003] was largely dedicated to a discussion of testing for spatial

autocorrelation in hedonic models.

Wubneh and Shen [2004] measured the impact of manufactured housing on

adjacent residential property values. Of interest is the methodology this recent study

17

used; they used GIS to create discrete concentric rings about the target of interest,

manufactured homes. Their buffer rings had a radius of 300 feet, 600 feet, and 900 feet,

and they grouped residential homes sold within each ring for use in their hedonic price

model. This approach has the advantage of reducing the number of variables to be

regressed.

McCluskey and Rausser [2001] concluded that media coverage of a hazardous

waste site and high-risk perception decreased surrounding values in Dallas, Texas and the

degree of long term stigma depended on proximity to the site. Using discrete time-period

analysis, their study covered the twelve-year period from when the EPA found health

risks and ordered cleanup, until 1993 when the site was placed on the NPL Superfund

list. In a continuation of their study, McCluskey and Rausser [2003] concluded that long-

term stigma extends up to 1.2 miles from the hazardous site. They further note that

households in higher-income neighborhoods require a larger discount to live near a

remediated hazardous waste site.

Kiel and Zabel [2001] concluded the increase in surrounding land values caused

by cleaning up a Superfund site in Woburn, Massachusetts exceed the likely costs. For

their study, data from U.S. Census Bureau Topologically Integrated Geographic

Encoding and Referencing (TIGER) files was geocoded using the GIS program ArcView.

Using a spline function for their multiyear study, they determined that during the period

information on the Superfund site toxicity was not made public, the impact on housing

prices was not significant.

And Hurd [2002] found the pronounced losses caused by being labeled a

Superfund site substantially recovered after remediation occurred. In his effect study, he

created separate hedonic models for the period 1983-85 and 1994-96 and compared the

results. His models regressed the dependent variable Consumer Price Index adjusted

home sales price against housing characteristics, the neighborhood characteristic of

distance to highway, and two variables for the hazard: homes within 1,000 feet and the

inverse distance of homes over 1,000 feet. Still, Greenstone and Gallagher [2005]

concluded the benefits of Superfund clean-ups as measured through the housing market

are substantially lower than the mean cost of clean-up.

18

The findings of Hite, et al. [2001] suggest that closing a landfill will not

necessarily mitigate property-value impacts. Although no test for spatial autocorrelation

was performed they made interesting observations. The presence of an environmental

hazard could result in lower property taxes in the long run by lowering property values.

Because of declining tax revenues, schools and law enforcement could be negatively

affected. If taxes are raised to sustain the level of public services then housing density

may be expected to increase. Individuals fleeing the location may add to urban sprawl.

And disadvantaged socioeconomic groups may migrate to areas near hazards to take

advantage of lower housing prices.

In concluding land devaluation was caused by proximity to non-Superfund

hazardous sites, Ihlanfeldt and Taylor [2004] used the ArcView GIS to measure the

distances to Georgia Environmental Protection Division’s Hazardous Site Inventory, the

EPA’s Comprehensive Environmental Response, Compensation, and Liability

Information System (CERCLIS), and the EPA No Further Remedial Action Planned

Reports (NFRAPR) sites. Their approach involved estimating separate property price

gradients for proximity to listed hazardous waste sites both before and after the sites are

listed and then testing whether these gradients were statistically different from each other.

Because of the overlap, their first step was to combine the Georgia Hazardous Site

Inventory data with the CERCLIS data to form a single list. Next after testing several

model specifications, they settled on regressing the dependent variable of property price

against dummy variables indicating the year of sale, property characteristics, inverse

distance to the list sites if the sale occurred before the site was listed, inverse distance to

the list sites if the sale occurred after the site was listed, inverse distance to NFRAPR

sites if the sale occurred before the site was listed, inverse distance to NFRAPR sites if

the sale occurred after the site was listed, and the inverse distance to NFRAPR site if the

sale occurred after the site was delisted. From this model they concluded the proximity

distance measurement lost its usefulness at between 1.5 and 2 miles from the hazard. The

researchers used White’s hereroskedastic-consistent covariance matrix estimator to

correct for heteroskedasticity. Using a robust Lagrange multiplier to test for spatial

autocorrelation, they concluded that one model suffered from spatial autocorrelation

19

while the other did not. To adjust for this shortcoming, another model was estimated

based on the assumption of a spatial autoregressive (SAR) process for the error term.

The results of this SAR model were highly similar to their original OLS model. While

their methodology is similar to the current study effort, their study did not include a

number of independent variables, e.g., Risk Management Sites, Tier Two sites,

Hazardous Waste Handlers, Toxic Release Inventory sites, Permitted Water or Air

Discharge sites. Additionally, their study made no attempt to measure the 9/11 effect.

Hwang [2003] did his dissertation investigating the effects of Scientifically

Estimated Environmental Risks (SEER), the perceived risk of floods, hurricanes, and

hazardous material releases (EPA Toxic Inventory Release data), and hazard mitigation

measures along with other locational and neighborhood amenities on housing prices. He

used a mail survey to obtain his pricing and consumer attitude data along with market

values estimated by the Harris County Appraisal District for the basis of his hedonic

model. His hedonic model regressed the dependent variable of estimated housing values

against structural characteristics, neighborhood characteristics, locational characteristics,

city, hazard mitigation activities, SEERs, and risk perception. All GIS data were

referenced from the Texas Statewide Mapping System and census information was from

the 2000 Summary Tape Files 3 developed by the U.S. Bureau of Census. Since

proximity to a Toxic Release Inventory (TRI) facility was significant at the 95%

confidence level while the risk perception of floods and hurricanes was not, he concluded

that people living near hazardous material facilities tend to be more concerned about

chemical hazards than natural hazards such as floods and hurricanes. Additionally, all

the structural, neighborhood, and locational characteristics were statistically significant at

the 95% confidence level.

Like Hwang [2003], Decker, Nielsen, and Sindt [2005] used a hedonic model for

concluding TRI sites lowered nearby housing prices. Of additional interest is their

experimental design. For testing the impact of higher priced homes (>$157,000) they

used an interactive variable to see if it produced a statistically significant result, which it

did.

20

While Wubneh and Shen [2004], McCluskey and Rausser [2001 and 2003], Kiel

and Zabel [2001], Hurd [2002], Hwang [2003] and Decker, Nielsen, and Sindt [2005]

were all well-designed studies, a common weakness was the absence of any specific tests

for spatial autocorrelation. Like Ihlanfedt and Taylor [2004] the current study will test

and correct for, if necessary, spatial autocorrelation.

On the other hand, Soto [2004] made extensive allowances for spatial

autocorrelation in her Hedonic model estimating the impact various non-hazardous

attributes had on rural land values in Louisiana. She determined that due to presence of

spatial autocorrelation in the data, it was not possible to make accurate statistical

inferences based on OLS estimation. Accordingly, her price models were estimated by

maximum likelihood (ML) using a nearest-neighbors-weight matrix to compensate.

For investigating the impact environmental hazards have on land values, the meta-

analysis of Yiu and Tam [2004] highlights the choice to be made in choosing

methodologies: pair-wise or hedonic. Hedonic modeling is superior in that it requires a

much smaller data-base. Furthermore, their meta-analysis points out the choice between

three assumptions on the spatial structure of an urban place: monocentric, polycentric and

the no a priori. The monocentric gradient has been found not to be significant after

correcting for spatial autocorrelation, and the polycentric is best suited for very large

urban areas. The no a priori assumption, however, allows for the most flexibility when

designing a hedonic model.

Sirmans, Macpherson and Zietz [2005] performed a mega-analysis on empirical

studies of the specification of hedonic pricing models. Their study reveals the twenty

independent variables appearing nationwide most often were lot size, log of lot size,

square feet, log of square feet, if brick, house age, number of stories, number of

bathrooms, number of rooms, number of bedrooms, number of full bathrooms, presence

of fireplace, presence of air conditioning, presence of basement, number of garage

spaces, presence of deck, presence of swimming pool, distance to (CBD, golf course,

etc.), time on market, and any time trend.

Simons, and Saginor [2006] also performed a meta-analysis of environmental-

contamination hedonic models. They statistically compiled and analyzed the result of

21

some 75 peer-reviewed articles and case studies. Perhaps most interesting was their

recommendation for future research. They suggested using regression analysis to

determine if environmental laws affect the real estate market. Additionally, they asked if

the role of terrorism could also be analyzed by comparing sales before September 11,

2001, (9/11) with sales after 9/11.

All the previous studies reviewed were concerned with types of economic

disamenities and their impact on surrounding property values. The types of

environmental hazards along with methodologies, spatial assumptions, model

specifications, and statistical approaches were all discussed. Studies of the impact of air

pollution, water pollution, industrial mishaps, waste disposal sites, waste handlers,

Superfund sites, leaking underground tanks, groundwater contamination, TRI sites, and

the impact of enforcement action on property values were all reviewed. Based on this

review, the hedonic method combined with the no a priori assumption was found to be

preferred. The most common model specification made housing values a function of

house/lot characteristics, neighborhood/locational characteristics, and distance to the

environmental hazard(s). Distance to an environmental hazard was modeled using either

a continuous function or a discrete-distance measurement with discrete-distance

measurements having certain advantages, such as, there being no need to specify the

distance functional relationship. When investigating a large variety of environmental

hazards, a large geographic area was covered. For statistical purposes, OLS models were

designated as linear, log-linear, or log-log with log-linear being favored. For studying the

before-and-after effect of a specific event, either spline functions or discrete-time periods

were used; however, the spline function is normally restricted to a continuous-distance

function design. The literature revealed a weakness in OLS regression. Because of the

possibility of regression errors being spatially related, OLS results should be tested for

spatial autocorrelation, and if necessary, another statistical regression approach used.

While information was obtained from the census or a MLS database, actual house sales

prices from MLS databases should be more accurate than census-based average housing

values. In recent studies, GIS software was used to provide better spatial measurements.

22

The remainder of this literature review will concentrate on the general real estate impact

of the 9/11 terrorists attack.

Direct property losses from the September 11, 2001 attacks in New York City

were $20-30 billion [Dermisi, 2006]. In his discussion of the effects of terrorism, Mills

[2002] anticipates major future impacts on U.S. real estate and urban development.

While he believes the qualitative effects could be forecast, quantitatively the uncertainty

is enormous. For example, the increasing dangers of terrorist attacks should lower CBD

land values, but by how much is uncertain. And in the absence of massive government

intervention, he anticipates that suburbanization will accelerate if terrorism continues.

This belief is clearly a challenge to the monocentric assumption in hedonic modeling of

land values.

In his essay, Savitch [2003] stated that 9/11 is among the most significant events

of the 21st Century and suggested that 9/11 constitutes a critical event for urban studies.

With past attacks in Paris, Milan, Rome, Belfast, Londonderry, Manchester, London,

Barcelona, Madrid, Tokyo, and even Oklahoma City, urban terrorism has a substantial

history. But, because 9/11 encapsulated and catalyzed a ten-year trend toward urban-

based terror, it was a tipping point that created a new paradigm. He anticipated this 9/11

paradigm will cause massive economic ramifications affecting the use of urban space.

Because the aim of modern terrorism is to kill large numbers of people, cities are

expected to be the future battleground. The shock of collective homicide is enormous

and cities provide large numbers of potential victims. Specifically, since “chemoterror”

requires a relatively confined space to be effective, the high population densities within

cities are a prime target for a chemical attack. He used the analogy of living in high

crime areas to forecast that 9/11 would depress city populations with the mostly likely to

flee being middle-class, well-educated residents.

According to Eisinger [2004], survey results from the U.S. Conference of Mayors

reveal the new responsibilities local officials feel to meet terrorist threats. In cities of

every size and region, police are being reassigned based on vulnerability assessments of

likely targets.

23

Based on her survey results of college students, Bosco [2003] reported that

student anxiety about security in their future workplace increased significantly after 9/11.

There was a 22.1% increase in the number of students being either somewhat or very

concerned and a 23.7% decrease in the number of students not at all concerned about

workplace security.

But did these attitudes reflect a decrease in demand for housing near chemical

hazards? No empirical research was found that attempted to measure the 9/11 impact on

housing values near potential terrorist targets. Matthew Kahn [2004] of Tufts University

in a paper written for Ashgate Press posed the question if terrorist risk could be

quantified in a hedonic model. This study seeks to investigate that question.

While a complete review of the pros and cons of public chemical site disclosures

is beyond the scope of this study, for those interested “The Benefits and Costs of

Environmental Information Disclosure: What Do We Know About Right-to-

Know?,”[Beierle, 2003] and “Environmental Information Disclosure: Three Case of

Policy and Politics,” [Beierle, 2003] are recommended reading. Additionally, the reader

may find of interest the April 27, 2005, written statements provided the U.S. Senate

Committee on Homeland Security and Governmental Affairs on chemical facilities

extreme vulnerability by Chairman Collins [Collins, 2005], Ranking Member Lieberman

[Lieberman, 2005], Senator Corzine [Corzine, 2005], the General Accounting Office

[Stephenson, 2005], U.S. Chemical Safety and Hazard Investigation Board [Merritt,

2005], The Brookings Institution [Falkenrath, 2005], and the Council on Foreign

Relations [Flynn, 2005].

The literature review finds the impact on land values of air pollution, water

pollution, hazardous waste facilities, Superfund sites, TRI facilities, and the impact of

enforcement action for legal violations have all been previously studied. However, of the

studies reviewed only Ihlanfeldt and Taylor [2004] proved they overcame the problem of

spatial autocorrelation. Accordingly, many of these studies needed to be repeated. The

current study investigates the above hazards, and two new ones, impact on housing

values while testing for spatial autocorrelation and, if needed, using a statistical approach

more advanced than OLS. No one has studied the impact that either Tier Two sites or

24

RMP sites have on nearby land values. And lastly, no one has studied how 9/11 might

have affected the impact that potential chemical hazards have on surrounding housing

values. The analysis continues in Chapter III with the theoretical development of the

basis for this study and the statement of hypotheses.

25

CHAPTER III

THEORETICAL DEVELOPMENT

According to the United States Environmental Protection Agency (EPA), there

are many hazards falling into various classes. Air emissions facilities, hazardous waste

handlers, Superfund sites, Toxic Release Inventory (TRI) facilities, and wastewater

discharge facilities are all listed on a single EPA Geographic Information System (GIS)

web site; enforcement action is listed on a closely related EPA web site. According to

the literature review, prior research conflicted over the effect environmental stigma has

on nearby residential housing. None had investigated the simultaneous impact of a large

variety of hazards on single-family housing in a small geographical area. None had ever

studied the impact of either Tier Two sites or Risk Management Program (RMP) sites on

nearby housing prices. And none had investigated the potential impact of September 11,

2001, (9/11) on the environmental stigma of chemical hazards. This research adds to the

body of knowledge by measuring the relative economic impact of Tier Two sites, RMP

sites, and 9/11 using a hedonic price model.

The hedonic price model was developed by Court [1939] and theoretically

enhanced by Rosen [1974] to estimate the values of individual attributes of a complex

product such as housing. According to the literature, housing values are based on

house/lot and locational/neighborhood attributes. House/lot attributes are endogenous to

housing values whereas locational/neighborhood attributes are determined by positive or

negative externalities that either increase or decrease home values, respectively.

According to Rosen [1974], the hedonic model is a simulated market clearing

function created by the interplay of buyers and sellers. Basically what it does is allocate

the price of a complex product to its individual components thereby revealing their

marginal values. The relationship between housing value and housing attributes can be

formally stated as:

P(A) = P(A1, A2, A3,…., Ai) (1)

where P(A) is the house sales price and (A1, A2, A3,…., Ai) are the various attributes. For

studying the impact of environmental hazards on housing values, the linear form of the

hedonic model can be restated as:

P = α + β1X1 + β2X2 + β3X3 + ….. βiXi (2)

where P is a vector of observed sales prices, α is the intercept, βi are the coefficients, and

Xi are the quantity of each house attribute.

Theoretically, housing values are normally divided into three categories for

studying the impact of environmental hazards: house/lot attributes,

locational/neighborhood attributes, and hazard attributes with hazard attributes being the

independent variable of focus. Accordingly, the final form of the hedonic model for

investigating the impact of chemical hazards on housing values in Lubbock County,

Texas, is more formally stated as:

∑∑∑===

+++=n

1iii

n

1iii

n

1iii EDLCSBP α (3)

where

is the sales price of each house, P

α is the intercept,

∑=

n

1iiiSB is the sum of i house/lot attributes for each house,

is the sum of i locational/neighborhood attributes for each house, ∑=

n

1iiiLC

∑ is the sum of i hazardous site attributes for each house. =

n

1iiiED

There are two traditional ways to allow for the diminishing impact an

environmental hazard has on land values as the distance from the hazard increases:

continuous-distance and discrete-distance. As previously discussed in the literature

review, the discrete-distance approach is more flexible because it does not impose a

preconceived formula on the diminishing affect. Additionally, Carroll, et al. [1996]

26

27

reported better results using the discrete-distance model than from the continuous-

distance model because of better model fit.

One mile is chosen as the maximum distance to measure the impact of hazards on

land values. While some studies, such as McCluskey and Rausser [2003] and Ihlanfeldt

and Taylor [2004], indicated the effect was noticed at slightly greater distances, to choose

brackets either too big or extending too far away could compromise the integrity of

model results. As more fully discussed in the Methodology Chapter, each bracket

requires an independent variable coefficient; and, the larger the number of independent

variables, the larger the required sample size for statistical purposes. While more is

better, the minimum number of points needed to get an idea of a mathematical

relationship is three. Two points can only determine either a positive or negative

correlation, whereas three points will give a better idea of the relationship if it is

nonlinear. Therefore, a discrete-distance model dividing the mile distance into thirds is

chosen. This choice results in a model that measures the impact on housing values of

environmental disamenities up to 1/3 of a mile, between 1/3 and 2/3 of a mile, and

between 2/3 and one mile. Similar to the approach Wubneh and Shen [2004] used, the

number of chemical hazards within each discrete 1/3 mile band about each house sold is

counted and grouped by hazard class. In effect, this approach measures the density of

environmental hazards about each individual house sold.

For study purposes, the hazardous sites investigated are divided into two

categories: listed and reporting. “Listed” sites are those listed by the EPA on their

internet site. “Reporting” sites are those reporting to governmental agencies but are not

listed by the EPA on their internet site. Listed sites are further subdivided into two

groups: previously listed and newly listed. “Previously listed” sites were listed by the

EPA on their web site as of 9/11. “Newly listed” sites are those sites listed by the EPA

since 9/11. The discussion of previously listed sites follows with air emissions facilities.

The federal Clean Air Act (CAA) requires each state plan to improve air quality

in areas that does not meet national air-quality standards. This plan is to include an

inventory of existing sources of air pollution and an accurate estimate of the amount each

source emits. The EPA then organizes that information into a unified database for use in

28

regulating by means of permits the pollutants released into the air. This unified national

database concerning airborne pollution is named the Aerometric Information Retrieval

System, and facilities with air emissions permits are listed by the EPA on their web site

[www.epa.gov/enviro/].

Hazardous waste is any by-product that potentially poses a substantial hazard to

human health or the environment when improperly managed. The Resource Conservation

and Recovery Act of 1976 (RCRA) makes generators, transporters, treaters, storers, and

disposers of federally-recognized hazardous waste provide information concerning their

activities to state environmental agencies. These agencies then provided the information

to the EPA, and the EPA lists on their web site these hazardous waste handlers.

Historically, waste were often dumped on the ground, in rivers, or left out in the

open resulting in thousands of uncontrolled or abandoned hazardous-waste sites.

Abandoned warehouses, manufacturing facilities, processing plants, and landfills are

common hazardous-waste sites. To combat growing concern over health and

environmental risks posed by these sites, in 1980 Congress established the Superfund

Program to locate, investigate, and clean up these sites. The EPA in cooperation with

individual states and tribal governments administers the Superfund Program and lists

those sites on their web site. Superfund sites are divided into two classes: National

Priority List (NPL) and Non-priority.

Congress passed the Emergency Planning and Community Right to Know Act

(EPCRA) to inform communities and citizens of chemical hazards in their areas. To

comply, businesses report the locations and quantities of any of more than 600 chemicals

stored on-site to state and local governments. The EPA tracks these toxic chemicals using

the TRI database, which stores data by facility, by year and chemical, and by medium of

release whether air, water, underground injection, land disposal, or offsite. The TRI sites

listed by the EPA on their web site are those with environmental releases.

Municipal and industrial wastewater treatment facilities frequently release water

into rivers, streams, lakes, and other waterways. Under the Clean Water Act (CWA), the

EPA supervises such direct discharges into navigable U.S. waters. And under the

National Pollutant Discharge Elimination System (NPDES) program, these facilities (also

29

called "point sources") are required to obtain permits regulating their discharge.

Facilities with water discharge permits are listed by the EPA on their web site.

The CAA Amendments of 1990 require the EPA to publish regulations and

guidance for chemical accident prevention at facilities using extremely hazardous

substances. The Risk Management Program Rule (RMP Rule) was written to implement

this requirement. The Rule requires all facilities storing on-site any of 77 toxic or 63

flammable substances above a threshold quantity (varying from 250 to 20,000 pounds) to

develop a RMP plan. This plan supposedly includes a hazard assessment that details the

potential effects of an accidental release, an accident history of the last five years, and an

evaluation of worst-case and alternative accidental releases. Additionally, the plan is

required to describe a prevention program that includes safety precautions, maintenance,

monitoring, and employee training measures. And lastly, it is to include an emergency

response program that spells out emergency health care, employee training measures, and

procedures for informing both the public and emergency response agencies in the event

of an accident. Every five years, these plans are required to be revised and resubmitted

[Elliott, Keindorfer, and Lowe, 2003].

The RMP plan is supposed to reduce chemical risk at the local level by informing

fire, police, and other emergency response personnel who respond to chemical accidents.

And it is designed to be useful to citizens in understanding nearby chemical hazards. The

EPA anticipates that making the RMPs available to the public will stimulate

communication between industry and the public. And this improved communication will

improve accident prevention and improve emergency responses. By June 21, 1999, a

summary of the facility's RMP was to have been submitted to the EPA for public

disclosure. RMP*Info (a national database providing up-to-date information on RMP

plans submitted) was listed on the EPA's webpage, but was removed shortly after 9/11.

Still, RMP data was available from the EPA in federal reading rooms where no copies

could be made. However, as of 9/11, RMP sites were listed by the EPA on their web site

making it a previously listed site.

Within the chosen study area air emissions facilities, hazardous waste handlers,

Superfund sites, TRI sites, water discharge facilities, and RMP sites were all listed on an

30

EPA internet site as of 9/11. What was the impact these listed chemical hazards had on

surrounding housing values as of 9/11? For housing values within the study area, the first

hypothesis is stated as:

H1O: “Previously listed” chemical hazardous sites had no negative impact on

surrounding housing values as of 9/11.

H1A: “Previously listed” chemical hazardous sites had a negative impact on

surrounding housing values as of 9/11.

If the null hypothesis is rejected, then one can conclude that at least one group of

“previously listed” chemical hazardous sites had a negative impact on surrounding

housing values as of 9/11.

Although RMP sites are the most dangerous, there are other sites that handle

extremely hazardous chemicals. As briefly mentioned in the TRI description, under

ERPCA storing quantities of any of more than 600 very toxic chemicals requires filing a

Tier Two report with state and local emergency planning officials. The purpose of the

Tier Two reporting program is to protect the public health and environment by providing

current and accurate information about hazardous chemicals and their health effects and

by ensuring the regulated community complies with the requirements of the applicable

laws and regulations. Generally, Tier Two reports are the state repository for emergency

planning letters (one-time notifications to the state from facilities which have certain

extremely hazardous chemicals in specified amounts) and annual hazardous chemical

inventory reports. Tier Two Forms contain facility identification information and

detailed chemical data about hazardous chemicals stored at the facility. Emergency

response personnel, such as fire fighters, can use this information to plan response

strategies in the event that an emergency situation arises. While Tier Two data are not

available on the internet, community right-to-know programs have been established

under both federal and state laws and, so accordingly, private citizens in the community

31

can request and receive copies of Tier Two data. Accordingly, Tier Two sites are

considered reporting sites for purposes of this study.

Information from Tier Two sites is reported each calendar year. Since no greater

detail is available, for purposes of this study sites reporting for the year 2001 are

considered to have existed on 9/11. For housing values within the study area, the second

hypothesis is stated as:

H2O: Tier Two sites had no negative impact on surrounding housing values as of

9/11.

H2A: Tier Two sites had a negative impact on surrounding housing values as of

9/11.

If the null hypothesis is rejected, then one can conclude that as a group Tier Two sites

reporting for the year 2001 had a negative impact on surrounding housing values as of

9/11.

While RMP data has been withdrawn from the EPA internet site, Tier Two

disclosure basically remains constant at the state and local level. Does the public use this

information in valuing property? What impact did 9/11 have on public perception of

neighborhood environmental hazards? Did land values near hazardous sites decline after

9/11? No previous study has considered the impact of 9/11 on housing values near

environmental hazards.

According to the literature, there are two approaches for performing an event

study using a hedonic model: spline-function and discrete-periods. While both have

merit, the discrete-periods approach is best suited to a discrete-distance model. Roughly,

what the discrete-period method requires is two hedonic models for measuring a

changing impact of environmental disamenities: one prior to the event and one after. The

two models can then be compared to see if the impact changes between the two models.

To illustrate, examine the two hedonic models below:

32

P1 = α + β1X1 + β2X2 + β3X3 + ….. βiXi (3)

P2 = α + β1X1 + β2X2 + β3X3 + ….. βiXi (4)

Assume that P1 represents the before-event model and P2 represents the after-event

model. Further, assume that β3 represents the negative impact of a particular disamenity

in both models. A pair-wise comparison of β3 will reveal any changes in the impact of

the disamenity between models and, therefore, between the different time periods. In the

two models, every coefficient in each model has a corresponding coefficient in the other.

For study purposes, pair-wise comparisons are the two coefficients for the same

hazards: one before and one after 9/11. Used by Ihlanfeldt and Taylor [2004], Hurd

[2002], and McCluskey and Rausser [2001], this approach allows for testing the next

hypothesis. For housing values within the study area, the third hypothesis is stated as:

H3O: The negative impact of a “previously listed or reported” chemical

hazardous site category as of 9/11 did not increase after 9/11.

H3A: The negative impact of a “previously listed or reported” chemical

hazardous site category as of 9/11 increased after 9/11.

If the null hypothesis is rejected, then one can conclude that either the negative impact of

at least one group of chemical hazardous sites listed on 9/11 had or the negative impact

that year 2001 Tier Two sites had on surrounding housing values increased after 9/11.

The EPA on occasion takes enforcement action against some regulated facilities.

Four key components of the enforcement process are documented in EPA databases for

larger facilities and many smaller facilities. First is the occurrence of a monitoring event

such as an inspection/evaluation or a self-report. Second is the determination of a

violation. Third is the occurrence of a government enforcement action to address

violations. And lastly, are there penalties associated with enforcement actions? The

information provided on Enforcement and Compliance History Online (ECHO) covers

33

facilities regulated as CAA stationary sources, as CWA permitted dischargers under

NPDES, and as RCRA hazardous waste sites. ECHO (www.epa.gov/echo/) reflects

state/local and federal compliance and enforcement that has been entered into EPA's

national databases. These sites are also listed by the EPA on their web site. Do nearby

housing values decrease as a result of the new listing of enforcement action?

Additionally, the EPA listing of hazardous sites is a dynamic process with new

sites being regularly added. Do nearby housing values decrease as a result of the listing

of new air emissions facilities, hazardous waste handlers, Superfund sites, TRI facilities,

or wastewater discharge facilities sites?

Both the listing of recent enforcement action and the listing of new chemical

hazardous sites require a hedonic events test. How does the impact a particular site had

before being listed compare with the impact that same site has after being listed? This

question is tested using nearly the same method used for testing the third hypothesis—

pair-wise comparisons of coefficients covering the site before and after being listed by

the EPA. For housing values within the study area, the fourth hypothesis is stated as:

H4O: “Newly listed” sites have no negative impact on surrounding housing

values.

H4A: “Newly listed” sites have a negative impact on surrounding housing values.

If the null hypothesis is rejected, then one can conclude that at least one group of newly

listed sites has a negative impact on surrounding housing values.

In this chapter the chemical hazardous sites to be investigated are described and

the theoretical model for measuring the impact of these hazards on housing values is

developed. Also in this chapter all the hypotheses are stated. The first hypothesis

focuses on the impact that “previously listed” sites had as of 9/11. The second hypothesis

addresses the impact that Tier Two sites had as of 9/11. The third hypothesis concerns

measuring the impact 9/11 had on chemical environmental disamenities, and the fourth

34

hypothesis addresses the impact that “newly listed” sites have on housing values. The

next chapter introduces the data for this study and specifically identifies the study area.

35

CHAPTER IV

DATA

This chapter presents the data and Geographic Information System (GIS) software

for testing the four hypotheses. It also describes the chosen study area within Lubbock

County, Texas. The Lubbock Multiple Listing Service (MLS) database together with the

2000 year Lubbock MLS Sold Volumes identifies the houses sold during each study

period within the study area and provides the sales price of each house along with their

characteristics. Data on chemical hazardous sites are provided by the Environmental

Protection Agency (EPA) Envirofacts Data Warehouse internet site

(http://www.epa.gov/enviro/), the RTKnet (Right-to-Know) internet site

(http://www.rtknet.org), and the Texas Tier Two Chemical Reporting Program.

LandView VI is used to provide the needed census data and together with MapQuest

(http://www.mapquest.com/) provide address location. MARPLOT is used to plot both

the chemical hazardous sites and the houses sold. Additionally, MARPLOT is used to

make important spatial measurements. In their unpublished paper, Hunter, et al. [1995]

used LandView II to locate hazardous waste generators and RTKnet to locate Superfund

sites.

LandView software evolved from Computer-Aided Management of Emergency

Operations (CAMEO) software developed by the EPA and the National Oceanic and

Atmospheric Administration to help implement the Emergency Planning and Community

Right to Know Act (EPCRA). First released in 1991, CAMEO DOS (Disk Operating

System) included a mapping program named MARPLOT, which plotted street maps

based on Topologically Integrated Geographic Encoding and Referencing (TIGER) files.

The original LandView product, released in 1993, combined census street maps,

boundaries, and demographic statistics (LandView database manager) with the

MARPLOT map viewer. LandView II, released in 1995, added EPA-regulated sites to

1990 census socioeconomic demographic data. Upgraded with additional census data,

EPA data, and data from other federal agencies and with the ability to run on

Windows/Macintosh cross-platform system, LandView III was released in 1998. While

36

LandView IV, released in 2000, contained a faster program interface and the ability to

connect to on-line government databases for the most current information, it was still

based on 1990 census data. Curing that weakness, LandView V was released in 2002

based on 2000 census data. The latest version, LandView VI was released December

2003.

The LandView VI database function allows users to calculate census 2000

demographic and housing characteristics for a user defined radii and create simple

thematic maps. Furthermore, users can browse and query the Census, EPA or United

States Geological Survey (USGS) databases and map the results. And it can locate a

street address or intersection.

Along with emergency contact information, a complete CAMEO database

provides for a site map and information on chemical inventory, chemical identification

information, and response information such as chemical properties, reactivity, health

hazards, First Aid, protective clothing, and firefighting.

MARPLOT is a general-purpose mapping application designed to be easy to use,

fast, and to consume as little disk and memory space as possible. It allows users to

create, view, and modify maps quickly and easily. For any point of interest, MARPLOT

provides exact longitude and latitude coordinates. And it allows users to link objects on

these computer maps to data in other programs including CAMEO and ALOHA (Areal

Locations Of Hazardous Atmospheres). Map data for MARPLOT integrate Geographic

Names Information System (GNIS) geographic boundaries (states, counties, cities,

congressional districts, and so on), with data from selected federal lands from the USGS

National Atlas (roads, water bodies, railroads, parks, and so on), demographic data from

the Bureau of the Census, and information on EPA-regulated sites. The MARPLOT

mapping function creates layered maps showing Census 2000 legal and statistical entities,

EPA Envirofacts internet sites, and United States Geological Survey GNIS features in

large detail. It allows users to vary the map scale and control the layers. Users can

search for objects and they can add information to maps. Also, LandView VI can

retrieve information from its database on user-selected map objects. LandView VI is both

a comprehensive census database and an umbrella program that can integrate the

mapping program MARPLOT, the chemical database CAMEO, and the atmospheric

dispersion calculator ALOHA. After establishing the data sources for the study, the next

step is selecting the study location.

In the middle of the Southern High Plains far removed from other major urban

influences is Lubbock County, Texas. According to the 2000 census contained in

LandView VI, there were 242,628 people living there with 92,685 households and a

median household income of $32,198. Seventy-eight point four percent of its residents

had graduated from high school with 24.4% having bachelors degrees or higher too, but

12% of the families lived below the poverty level. Lubbock County’s population was

1.3% Asian, 7.7% black, and 27.5% Hispanic. Of the 100,595 housing units in Lubbock

County, 3,481 houses were built before 1940 and the majority built before 1980; the

median value was $69,100. As shown in Figure 4.1, located within Lubbock County is

the City of Lubbock.

Figure 4.1 Lubbock County Using MARPLOT

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38

According to the 2000 census, there were 202,225 people living the urbanized

Lubbock City area with 84,956 households and a median household income of $31,689.

Seventy- nine point five percent of its residents had graduated from high school with

26.3% having bachelors degrees or higher too, but 12.2% of the families lived below the

poverty level. Lubbock City’s population was 1.8% Asian, 9% black, and 27.5%

Hispanic. Of the 84,906 housing units in the City of Lubbock, 2,562 houses were built

before 1940 and the majority built before 1980; the median value was $69,000. In

addition to providing city maps as shown above, MARPLOT when combined with

ALOHA can simulate chemical accidents.

By inputting site characteristics, weather conditions, and the amount of the

released chemical toxin, ALOHA can plot an air dispersion footprint that is easily

transferred to a MARPLOT map. In Figure 4.2, the innermost ring indicates the

hypothetical area in which people could be dangerously ill within an hour from a 5,000

pound anhydrous ammonium leak in Lubbock, Texas. According to RTKnet, one of the

Risk Management Program (RMP) sites in the City of Lubbock reported having an

anhydrous ammonium storage capacity of 480,590 pounds.

Figure 4.2 Simulated Dispersion Zone of Toxic Gas Leak Using ALOHA

Additionally for the above simulation, MARPLOT could identify critical

locations such as schools and hospitals as demonstrated in Figure 4.3. Using its census

data base, LandView VI could estimate the number of Lubbock households and

individuals exposed.

39

Figure 4.3 Schools (green) and Hospitals (red) in the City of Lubbock

Totally within Lubbock County, the chosen study area comprises the four

contiguous zip code areas 79404, 79405, 79411, and 79412. Together they cover about

31 square miles of Lubbock County. The four zones combine for a population of 33,725

people and 6,031 housing units according to the 2000 census. This small area is selected

for study because it has a relatively dense human population and at least one of the

chemical hazards discussed previously. It should be noted here that a ring with a mile

radius covers 3.14 square miles—more than 10% of the study area.

Figure 4.4 The Study Area (bounded by red)

40

The largest in size Zip code 79404, shown in Figure 4.5, had a population of

10,230 people with 3,429 housing units and a population density of 393.1 people per

square mile according to the 2000 census. A minority rich community, it was 52.3%

Hispanic and 27% black.

Figure 4.5 Zip Code 79404

The smallest in size, zip code 79405 shown in Figure 4.6, had a population of

3,154 people with 1,150 housing units and a population density of 4,805.1 people per

square mile. Also a minority rich community, it was 70.4% Hispanic and 11.2% black.

41

Figure 4.6 Zip Code 79405

Shown in Figure 4.7, Zip code 79411 contained 5,307 people with 2,451 housing

units and a population density of 6,029.4 people per square mile. It was 40.8% Hispanic

and 7.3% black.

42

Figure 4.7 Zip Code 79411

The most populated zip code 79412, shown in Figure 4.8, had a population of

15,034 people with 6,031 housing units and a population density of 4,488.9 people per

square mile. It was 42.9% Hispanic and 10.5% black.

43

Figure 4.8 Zip Code 79412

The EPA has numerous internet sites to assist individuals in locating environment

hazards. Window to My Environment (www.epa.gov/enviro/wme/) is an integrated GIS

internet site that identifies many hazards near a specific location along with other useful

information. Envirofacts Data Warehouse makes available information about waste,

water, toxics, land, air, radiation, compliance, and other information, such as brownfields

(i.e., a site, the expansion, redevelopment, or reuse of which may be complicated by the

presence or potential presence of a hazardous substance, pollutant, or contaminant) along

44

45

with maps. Having advanced capacities, the experienced user can access data by

location, hazard type, or facility.

RMP data for the year 2001 is obtained from the non-governmental RTKnet.org

internet site. Operated by OMB (Office of Management and Budget) Watch, the Right-

to-Know Network provides free access to many environmental databases. Through

RTKnet, specific factories and their environmental effects, permits issued under

environmental statutes, and civil lawsuits filed can be identified. Additionally, databases

can be accessed by geographic region. A user manual and other materials are available

on line to help users. RTKnet was established in 1989 to support the EPCRA, and

RTKnet obtains its operating funds through donations. For their research, Hunter and

Sutton [2004] used RTKnet to locate hazardous waste facilities. The calendar year 2001

(the latest year available for this study) Tier Two data is available from the Texas Tier

Two Chemical Reporting Program, which is administered by the Texas Department of

State Health Services.

Using the MARPLOT GIS software program, this study plots the air emissions

sites, hazardous waste handler sites, Superfund sites, TRI sites, water discharge sites, and

the recent enforcement action sites affecting the study area along with RMP sites and Tier

Two sites. The location of each hazard along with rings indicating 1/3 of a mile, 2/3 of a

mile, and one mile are shown below. Those listed as 2001 were reported by the EPA

prior to 9/11; new or recent sites were listed by the EPA shortly after 9/11. Actual street

locations are established using Landview VI and the internet MapQuest address locator

site.

There are three air release sites identified prior to 9/11 affecting the study area

shown in Figure 4.9.

Figure 4.9 2001 EPA Listed Air Release Sites

As shown in Figure 4.10, there is one new air release site affecting the study area

listed by the EPA after 9/11.

46

Figure 4.10 New Air Release Site

There are 14 new hazardous waste handlers, shown in Figure 4.11, listed by the

EPA after 9/11 affecting the study area.

Figure 4.11 New Hazardous Waste Handlers

There are 103 hazardous waste handlers, shown in Figure 4.12, identified prior to

9/11 affecting the study area.

47

Figure 4.12 2001 EPA Hazardous Waste Handlers

There are two water discharge sites, shown in Figure 4.13, identified prior to 9/11.

48

Figure 4.13 2001 EPA Water Discharge Sites

There are three new water discharge sites affecting the study area, shown in

Figure 4.14 (upper center), listed by the EPA after 9/11.

Figure 4.14 New Water Discharge Sites

As shown in Figure 4.15, there are four TRI sites identified prior to 9/11.

49

Figure 4.15 2001 EPA Toxic Release Inventory Sites

As shown in Figure 4.16, there is one new TRI sited listed by the EPA after 9/11.

Figure 4.16 New Toxic Release Inventory Site

There is one Superfund site, shown in Figure 4.17, identified prior to 9/11.

50

Figure 4.17 2001 EPA Superfund Sites

As shown in Figure 4.18, there are five enforcement sites listed by the EPA

covering 2002 and 2003.

Figure 4.18 Recent EPA Enforcement Action Sites

As shown in figure 4.19, there are 54 Tier Two sites reporting for the year 2001.

51

Figure 4.19 2001 Tier II Reporting Sites

There are three RMP sites identified for the year 2001 shown in Figure 4.20.

52

Figure 4.20 2001 Risk Management Plan Sites

In addition to locating the hazards affecting the study area, houses sold before and

after 9/11 must also be located to determine their proximity to the hazards. Navica MLS

is a paid-subscriber internet-based MLS system that has multiple real estate search

capabilities. Using property search, it can search for property listings matching a specific

criterion such as MLS zones or sales during a specific period of time. It can search for

listings that match an address or search for listings that match an existing search criterion.

It can search for listings by a range of MLS numbers, or it can search for listings by

office or agent. Additionally, it can search for listings that match specific property tax

information.

There are many options when viewing search results. Navica can display results

describing property characteristics (such as size, number of rooms, number of baths, etc.)

in either a brief generic spreadsheet or with full or user-selected details. Alternatively, it

can display the same results in a Microsoft Excel spreadsheet. Using the Thumbnail

option, a small photograph of the listing along with limited information is produced. It

can display tax information for the selected listing. Navica can display a map from which

a radius search or a specific location search can be performed. And it can display listings

53

in a Comparative Market Analysis format (CMA). A CMA is useful for comparing a

selected property to existing active or sold listings.

Custom brochures including pictures can be created using Navica. Additionally,

it can generate reports showing market share, the current market, market summary, sold

market, sold price, and historical data.

As shown in Figure 4.21, the residential sales of interest for this study are within

MLS zones two, six, and ten. Using Navica, residential sales prices for both the time

periods of interest and within the study area are obtained. Additionally, Navica has the

detail necessary to design a hedonic model based on actual housing characteristics and

actual sales prices. Navica only maintains its online data for five years, but according to

Navica there are 323 residential sales between March 21, 2000, and September 11, 2001.

And there are at least 852 sales between January 1, 2002, and March 21, 2005, within the

study area. Records for earlier sales are obtainable from the year 2000 Lubbock MLS

Sold Volumes.

Figure 4.21 MLS Zones within the Study Area

Data from many sources are needed to test the four hypotheses. The hedonic

model requires residential sales prices, house/lot addresses and characteristics,

locational/neighborhood attributes, and hazardous site locations. The data are gathered

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55

from MLS databases, LandView VI, the EPA Envirofacts Data Warehouse internet site,

the RTKnet internet site, the MapQuest internet site, and the Texas Tier Two Chemical

Reporting Program. The next chapter discusses the methodology for testing the four

hypotheses.

56

CHAPTER V

METHODOLOGY AND RESULTS

This chapter discusses the research methodologies used to test the four hypotheses

and the results of these tests. The calculations for the tests begin with the final

specification of the model. After the model is fully specified, data must be applied to

mold the final model for testing the hypotheses. This involves adapting the model to fit

the data gathered and testing both the log-linear and the linear models for fit. After the

superior model is derived, it is subjected to stringent tests to determine what type of

statistical analysis is most appropriate.

The design of this study is to create a hedonic model based on the results of

multiple regression for testing the four hypotheses. The regression form of the hedonic

model can be formally stated as:

P = α + β1X1 + β2X2 + β3X3 + ….. βiXi + ℮ (5)

where P is a vector of observed sales prices, α is the regression intercept, βi are the

regression coefficients, Xi are vectors of the house attributes, and ℮ is a vector of random

errors. The next step is to select the independent variables, that is, the housing attributes

to be included in the hedonic model.

According to Douglas [1987], there are six major pitfalls in regression analysis.

They are specification errors, measurement errors, simultaneous equation relationships,

multicollinearity, hetroscedasticity, and autocorrelation. Specification errors fall into two

categories: use of wrong function form and omission of important independent variables.

Measurement errors result from incorrect measurement of model variables. Regression

analysis assumes that a single equation explains the relationship; however, price level is

the result of simultaneous solutions to both demand equation and supply equation.

Multicollinearity arises when independent variables are covariant, that is, one varies as

another does. However, this covariance does not reduce the usefulness of a model when

restricted to predictive uses and not explanatory purposes. Residual error terms should be

57

random because regression analysis presumes homoscedasticity of the residual error;

however, hetroscedasticity occurs when error terms exhibit a systematic relationship with

the magnitude of any independent variable. Moreover, autocorrelation is indicated by

any sequential pattern in error terms.

Sirmans, Macpherson and Zietz [2005] stated that for studies covering the

southwest U.S., the most common independent variables were house size, lot size, house

age, number of bathrooms, number of bedrooms, presence of fireplace, presence of air

conditioning, time on market, and presence of swimming pool. The independent house

variables Hwang [2003] used were lot size, living area, age of house, and the presence of

a fireplace. In designing a hedonic housing model of the City of Lubbock, Corgel,

Goebel, and Wade [1982] selected house size, number of bathrooms, presence of two-car

garage, presence of air conditioning, age of the house, month of sale, presence of

fireplace, presence of brick, and presence of cedar roof. Their results indicated that

number of bathrooms, month of sale, and type of roof where not statistically significant at

the 95% level of confidence while the other variables were significant. House size, house

age, number of fireplaces, presence of central air conditioning, number of cars garage,

presence of brick, and presence of swimming pool seem to be the best candidates for

structural independent variables. These seven independent variables convey much

information while generating minimal multicollinearity. Next we incorporate the

locational/neighborhood independent variables into the model.

Locational/neighborhood variables are the most difficult to choose. There is little

in the literature to assist because these variables are so study-area specific. While

commonly included, the distance to a Central Business District (CBD) independent

variable requires the assumption of a monocentric city. And the literature indicates this is

a safe assumption only in large metropolitan areas, which Lubbock, Texas is not. Within

Lubbock, there are two obvious places where proximity might be important: Texas Tech

University and South Plains Mall. While Texas Tech University is adjacent to the study

area, South Plains Mall is too far away to have any impact. And while major employers

such as the medical district might be important, in modeling Lubbock it is important to

note that most driving times are less than 25 minutes.

58

Ihlanfeldt and Taylor [2004] in modeling Atlanta, Georgia, chose distance to

CBD, nearest highway exit, the international airport, and the nearest subway station for

locational variables. While appropriate for Atlanta, they do not seem appropriate for

Lubbock, Texas.

Hwang [2003] in modeling Harris County, Texas, chose what city, distance to

CBD, distance to airport, and distance to park for locational variables. Distance to park

seems to be the only one with possible relevance for modeling Lubbock, Texas.

In modeling a small urban area, it is important to consider locational and

neighborhood independent variables jointly because they are so closely related. For

neighborhood variables, Ihlanfeldt and Taylor [2004] included population densities,

employment densities, non-white population percentage, real median household income,

and the percent of vacant land. Each was computed on a census-tract basis. Vacant land

percentage and employment densities were statistically significant for apartment property

values. Neither of those, however, seem well suited for modeling home sales. As his

neighborhood characteristics Hwang [2003] selected percentage of whites and household

income and both were statistically significant at the 99% confidence level. But there is

generally a great deal of colinearity between percentage of whites and household income

and therefore only one, if either, should be selected.

One of the major problems of using spatially related data in a regression model is

the impact that nearby sites may have on each other, and an unacknowledged relationship

can result in spatially lagged error terms and possible reliability problems with the

resulting model. To combat this problem, one of the locational/neighborhood variables

included in the model is the census tract median value of surrounding property. The

census tracts within the study area are shown in Figure 5.1

Figure 5.1 Study Area Census Tracts

There are two locational/neighborhood variables included in the study model:

distance to Texas Tech University and census-tract median house value. And while

somewhat arbitrary, it is felt that measuring the influence of Texas Tech University on

housing prices should be limited to 1.5 miles. The formula for calculating this

adjustment is as follows: adjustment factor equals (1.5 miles minus distance to Texas

Tech University in miles) divided by 1.5 miles. The study area covered by this

independent variable is shown in Figure 5.2. Next we incorporate the independent

variables of focus into the model.

. 59

Figure 5.2 One and One-Half Mile Range of Texas Tech University

The focal independent variables are proximity of hazardous waste handlers, Toxic

Release Inventory (TRI) sites, Superfund sites, sites requiring an air release permit, sites

requiring a water release permit, sites that enforcement action has been taken against,

Tier Two sites, and Risk Management Program (RMP) sites. Hazardous waste handlers,

TRI sites, Superfund sites, air release sites, water release sites, Tier Two sites, and RMP

sites all existed in the study zone prior to September 11, 2001 (9/11). All the hazards

plus those within one mile of the study area constitute the study zone because hazards

outside the study area but within a mile could influence the sales price of homes within

the study area. Additionally, new hazardous waste handlers, new TRI sites, a new air

release site, a new water release site, and new sites that enforcement action had been

taken against all came into being after 9/11. Because of the number of overlapping

60

61

hazards, a discrete-distance design is selected for the initial model. Each site has three

bands drawn about it: one with 1/3 mile radius, one with between 1/3 and 2/3 mile radius,

and the outmost with between 2/3 and one mile radius. This design has the same effect as

drawing the three bands about each house sold and counting the number of hazards by

classification within each band but is much easier to map since the number of hazardous

sites is much lower than the statistically required number of houses sold. Regardless of

approach, since there are two time periods and twelve classes of sites within three radii,

there are 72 independent focus variables of research interest.

Model design covers two time periods: before and after 9/11. The first study

period covers house sales for the eighteen-month period ending 9/11. Figure 5.3 shows a

plot of houses sold prior to 9/11. The second study period covers house sales for the

eighteen-month period ending February 28, 2005. Figure 5.4 shows a plot of houses sold

after 9/11. To allow for generalized movement in housing prices, a dummy variable is

used to mark the separate time periods. Therefore, the contemplated study model

consists of seven house/lot variables, two locational/neighborhood variables, 72 focus

variables, and a housing price adjustment variable for a total of 82 independent variables.

According to Tabachnick and Fidell [1996], the simplest rule of thumb for

estimating required sample size for a multiple regression equation is 8 times the number

of independent variables plus 50. Accordingly, a minimum of eight observations is the

cutoff for determining if an explanatory variable is viable and therefore includable in the

model. After gathering 700 observations of house/lot variables, fewer than eight have

swimming pools. Therefore, that variable is dropped from the model. After plotting 600

houses sold, it is obvious that 34 focus variables will not have sufficient observations for

inclusion in the model. Accordingly, the testable study model consists of one dummy

time-period variable, six house/lot variables, two locational/neighborhood variables, and

38 focus variables for a total of 47 independent variables. Since there are 47 independent

variables, the minimum number of house sales required is 426. A minimum of 213

observations need to come from housing sales prior to 9/11, and a minimum of 213

observations need to come from the period after 9/11. The final step in designing the

research model is to choose the functional form.

Figure 5.3 Period Ended September 11, 2001, Houses Sold

62

Figure 5.4 Period Ended February 28, 2005 Houses Sold

From the review of the literature, there are basically only two functional forms to

choose from: linear and log-linear. The advantages of the linear form are that it is

simpler to apply and understand. In fact, the following linear form has an intuitive feel to

it:

P = α + β1X1 + β2X2 + β3X3 + ….. β47X47 + ℮ (6)

63

Using the linear form, the coefficients can easily be expressed as either dollar amounts or

percentages of the sales price without adjustment. Because of its simplicity the linear

form should be chosen unless there are compelling reasons to do otherwise.

The other model is the log-linear form. Using the log-linear form requires taking

the natural log of the sales price before performing the regression procedure. The

following formally illustrates the log-linear form:

ln P = α + β1X1 + β2X2 + β3X3 + ….. β47X47 + ℮ (7)

While more complex the log-linear form enjoys some advantages over the linear form for

hedonic pricing models. As noted by Hwang [2003], the log-linear form usually helps

minimize heteroskedasticity and it narrows the dependent variables range by a significant

amount making it less sensitive to outliers. Additionally, after testing both the linear and

log-log models using Box-Cox, Ihlanfeldt and Taylor [2004] also chose the log-linear

functional form. Because of these advantages, the log-linear form is the proposed

functional form for this study. The final form can therefore be more formally stated as:

(8) εα ++++Α+= ∑∑∑∑====

38

1iii

2

1iii

6

1iii

1

0ii EDLCSBPln

where

is the natural log of each house sales price, Pln

α is the coefficient intercept,

is the price level dummy variable for sales after 9/11, ∑=

Α1

0ii

is the sum of the six structural attributes for each house, ∑=

6

1iiiSB

is the sum of the two locational/neighborhood attributes for each house, ∑=

2

1iiiLC

is the sum of the 38 hazardous site proximities for each house, and ∑=

38

1iiiED

64

ε is the error term associated with each house.

Also, since each environmental variable covering a specific geographic area for

the study period before 9/11 has a corresponding environmental variable covering the

same geographic area for the study period after 9/11, and visa verse, then by definition

for the period before 9/11, and (9) 018

1=∑

=iiiED

for the period after 9/11. (10) 0ED38

19iii =∑

=

Similar to the model used by Ihlanfeldt and Taylor [2004], this is a composite model

allowing a statistical comparison of corresponding environmental coefficients before 9/11

with environmental coefficients for the same hazard group after 9/11. Additionally, the

data lines up nicely to allow before and after testing of three hazard groups first listed by

the Environmental Protection Agency (EPA) after 9/11. By comparing each pair of

corresponding environmental regression coefficients, the third and fourth research

hypotheses can be tested.

For study purposes, pair-wise comparisons are the two coefficients for the same

hazards: one before and one after 9/11. This procedure allows potential comparison of

nine price gradients on 9/11 with nine price gradients after 9/11. Only pairs with at least

one statistically significant coefficient will be considered in performing pair-wise

analysis. To test the third and fourth research hypotheses, the individual pair-wise

coefficients must have at least one statistically significant coefficient and both

coefficients must be statistically different from each other. T-test can be used to test for

statistical differences.

In trying to overcome errors of specification, this study is attempting to include

the variables of established value while testing new ones to help further the body of

knowledge on proper specification. To minimize measurement errors, data sources of

65

66

established accuracy are being used. And for hetroscedasticity, model residuals will be

tested for autocorrelation.

According to Pindyck and Rubinfeld [1991], the Gauss-Markov theorem states

the OLS estimator of each multiple-regression coefficient is only the Best Linear

Unbiased Estimator (BLUE) if: 1) the standard multiple-regression model is specified, 2)

the independent variables are nonstochastic, 3) no exact linear relationship exist between

two or more of the independent variables, 4) the error term has a zero expected value and

constant variance for all actual amounts, 5) error amounts are uncorrelated, and 6) the

error amount is normally distributed. However, according to Dubin, Pace and Thibodeau

[1999], housing market model residuals are commonly spatially autocorrelated violating

the Ordinary Least Squares (OLS) assumptions for BLUE. And while autocorrelation

will not affect the unbiasedness or consistency of the OLS regression coefficients,

according to Pindyck and Rubinfeld [1991], it does affect their efficiency. Ihlanfeldt and

Taylor [2004] noted that if errors are spatially dependent, inference based on t-statistics

will be misleading. This means confidence intervals will be understated using OLS in the

presence of spatial autocorrelation. While OLS is the only statistical approach that can

produce BLUE, if spatial autocorrelation is present another statistical approach is needed.

According to a research paper by Florax, Folmer, and Rey [2001], the classical

approach towards hedonic specification is summarized as follows:

1. Estimate the initial model y = Xβ + е by means of OLS.

2. Test the hypothesis of no spatial dependence due to an omitted spatial lag or

due to spatially autoregressive errors, using the Lagrange Multiplier errors test

(LMerr) and Lagrange Multiplier lag test (LMlag), respectively.

3. If both tests are not significant, the initial estimates from step 1 are used as the

final specification. If only one test is significant, adjust the model for that

spatial dependence. Otherwise proceed to step 4.

4. If both tests are significant, estimate the specification pointed to by the more

significant of the two tests. For example if LMerr > LMlag, then correct the

error specification. If LMerr < LMlag, then correct the lag specification.

67

According to a chapter drafted by Anselin [1999], the Moran’s I is slightly better

for small samples and indistinguishable from LMerr in medium and large sample sizes.

Also according to Anselin [1992], the static Moran’s I is the most familiar for testing for

spatial autocorrelation errors. Usually, all the explanatory variables that affect the

dependent variable cannot be explicitly specified. The omitted variables are summarized

in the error disturbance. Sometimes reviewing the residual distribution can lead to the

discovery of additional independent variables that should be added. Anselin [1999]

refers to these errors as nuisance with the object being to first detect it and then correct

for it. Maximum Likelihood (ML) estimation is an acceptable method for dealing with

spatial lag and spatial error regression models according to Anselin [1999], and ML

differs from OLS in that the assumption of normality for the error term is not required by

ML estimation.

To follow how spatial autocorrelation errors are tested and corrected, the

spatially-weighted matrix W must be clearly understood. W is an (n by n) matrix where

n is the number of observations made. For example, if 508 house sales were observed

then the matrix would have 508 rows and 508 columns. This results in a matrix that

references each site with every other site. The distance, or inverse distance, of each site

from the other is placed in the corresponding matrix cell. This naturally results in a

diagonal of zeros as each site is cross-referenced with itself. Judgment based on a review

of the OLS error terms and the neighborhood characteristics of the study area must be

used to determine the maximum distance that neighbors significantly influence each

other. Too small a distance and the error correlations will not be captured. Too large a

distance will diminish the significance of the independent variables. Also the covariant

relationship has to be empirically determined. After choosing the appropriate

relationships, the spatially-weighted matrix W is created. Because the proposed model

covers two discrete time periods, the composite model will have to subdivide the spatially

weighted matrix DE into its two component matrices as follows to avoid including time

as well spatial autocorrelation errors:

(11) εα ++++++= ∑∑∑∑∑=====

38

19iii

18

1iii

2

1iii

6

1iii

1

0iEDEDLCSBAPln

Assuming equal distribution of the 508 observations, this subdivision results in

two (254 x 254) spatially-weighted matrices W. Because this is the largest size matrix

the Microsoft spreadsheet program Excel can handle, 508 observations of house sales are

to be used in the model. This number is well above the minimum required number of

426.

To achieve the row-standardized spatial weights matrix, the preferred way to

implement the Moran test, each row must be proportionally adjusted to equal 1.

According to Anselin [1992], the normalizing factor is

∑ ∑ =i j ij nw (12)

where wij are the elements of the spatial weights matrix and n is the number of

observations. In our illustration n equals 254. From this procedure

∑∑ ∑=

−

−−

i2

i

i jij ij

)x(

)x)(x(w*Iμ

μμ (13)

where

I* is Moran’s I, and

μ is the mean of the x variable with observation xi at location i.

And in reduced form,

e'eWe'e*I = (14)

where e is a (n x 1) vector of residuals and W is the (n x n) normalized spatial weights

matrix.

68

In most cases, Moran’s I range from -1 and +1, with zero meaning no spatial

autocorrelation. Inference for Moran’s I is obtained from its z-value. This z-value can be

converted to a confidence level. Only with a 95% confidence level can the possibility of

spatial autocorrelation errors be rejected.

This spatial dependence in the errors is specified as:

y = Xβ + е (15)

with

е = λWе + ξ (16)

where

X is the number of independent variables by n matrix of observations,

β is the (n x 1) vector of OLS coefficients,

е is the (n x 1) vector of errors from the OLS regression,

W is the (n x n) spatial-weighted matrix,

ξ is a (n x 1) vector of residuals, and

λ is the spatial autogressive process parameter with a possible range of zero to

one. The higher λ is, the greater the corrective weight.

Great diligence is required in estimating λ. Soto [2004] started at 0.05 and

increased λ in increments of 0.05 in finding her final model.

Moran’s I tests for spatial autoregressive errors, but according to Anselin [1992]

there can also be spatial lag dependence. He believes a spatially lagged dependent

variable among the independent variables is akin to the inclusion of an endogenous

variable in systems of simultaneous equations. In Anselin [1999], the Lagrange

Multiplier test for spatial lag errors takes the form

Dne'e

Wy'e

LM

2

lag⎥⎥⎥⎥

⎦

⎤

⎢⎢⎢⎢

⎣

⎡

= (17)

69

where

)W'WW(Tr)WX)('X)X'X(XI()'WX(D 22

1

++⎥⎦

⎤⎢⎣

⎡ −=

−

σββ (18)

and

e is the OLS vector of residuals,

W is the spatial-weighted matrix,

y is the vector of OLS dependent values,

n is the number of OLS observations,

X is the matrix of OLS observations,

β is the vector of OLS coefficients,

I is an identity matrix,

σ2 is the variance of e, and

Tr is the matrix trace operator, i.e., the sum of the diagonal elements.

Inference for the LMlag is drawn from the likelihood-ratio one-tail Chi-square (X2)

distribution. Again the null hypothesis that there is no lagged spatial autocorrelation is to

be rejected at the 95% confidence level.

Formally, according to Anselin [1992], substantive spatially lagged dependence

can be expressed as a mixed regressive spatial autoregressive process as follows:

y = qWy + Xβ + ε (19)

where

y is the (n x 1) vector of dependent variables,

W is the (n x n) spatial-weighted matrix,

X is the (n x n) matrix of observations,

β is a (n x 1) vector of estimated coefficients from the OLS regression,

ε is a (n x 1) vector of residuals, and

q is the spatial lag autoregressive parameter with a possible range of zero to one.

70

71

The higher q is, the greater the corrective weight.

However, this spatial-lag model is inappropriate for this research project. In the

model Equation (19), the vector of prices of houses sold is allocated spatially to remove it

from the residuals vector. This allocation presumes to capture the contribution the

surrounding property values makes to the house sold. Another way to look at the

equation is as follows:

y - qWy = Xβ + ε (20)

What this does in effect is reduce the dependent variable by the contribution that

other houses sold made to house sold based on their value and distance. It does not

capture the contribution of all nearby surrounding properties, only the houses sold

included in the model. A better way to capture this effect is to include the median value

of surrounding properties, and this is the approach this research study takes by including

the census-tract median house value in the model.

To create the needed models for testing the hypotheses, a statistical package will

have to be used that can test for and, if needed, correct, spatial autocorrelation. Both Soto

[2004] and Chernih and Sherris [2003] used the mixed procedure of SAS to model their

hedonic spatial econometric models.

SAS is a statistical program widely used in academic research and is the world’s

leading private information delivery system for accessing, managing, analyzing, and

presenting data (http://www.sas.com). The most current version, SAS 9, is built on the

cornerstones of scalability, interoperability, manageability and usability. SAS 9 can

easily perform OLS regression using the regression procedure, and under the mixed

procedure it should be able to regress a spatially autocorrelated adjusted model using the

method of restricted maximum likelihood, also know as residual maximum likelihood.

The mixed procedure is very powerful and even allows for a more flexible specified

covariance matrix of the error term as discussed above. The mixed model is

72

y = Xβ + Zγ + е (21)

where

y are the dependent variables,

X is the observation matrix,

β is a vector of coefficients,

Z is a matrix of known design,

γ is a vector of unknown random-effects parameters, and

е is a vector of error terms.

This is a mixed model because it contains both fixed-effects parameters, β, and random-

effects parameters, γ.

For the OLS model and the mixed model, SAS can provide the t- and F-statistics

for making inferences about fixed effects. Additionally, SAS can calculate distance

intervals between observations based on their individual longitudes and latitudes needed

for the spatially weighted matrix. While correlation by itself can never prove cause-

effect, it does lend credible evidence to support a plausible explanation. A basic

assumption is the model will be nearly correct as specified.

In this chapter thus far, the model was fully specified and the statistical procedure

for testing the four hypotheses was discussed. The OLS model contains 47 independent

variables. Moran’s I will be used to test the regression model for spatial autocorrelation

errors. In the event the OLS model fails this test, the mixed procedure will be used. The

remainder of this chapter will focus on the statistical results.

73

Table 5.1 Descriptive Statistics

11-Sep-01 28-Feb-05 Combined Count 254 254 508 Range $137,900 $154,500 $155,900 Minimum $7,100 $8,500 $7,100 Maximum $145,000 $163,000 $163,000 Mean $53,295 $61,950 $57,622 Median $46,925 $55,000 $49,950 Mode $25,000 $65,000 $47,000 Standard Deviation $27,285 $32,076 $30,061 Standard Error $1,712 $2,013 $1,334 Skewness 0.862 0.845 0.899 Kurtosis 0.396 0.132 0.39

Two hundred and fifty-four observations were made for the 18 month period

ended September 11, 2001, and another 254 were made for the 18 month period ended

February 28, 2005. Residential house prices sampled ranged from a low of $7,100 in the

period ended September 11, 2001, to a high of $163,000 in the period ended February 28,

2005.

For the period ended September 11, 2001, the average value of a residence sold

within the study area was $53,295 according to the sample taken. And as shown in Table

5.1, the sample average value for the period ended February 28, 2005, was $61, 950—an

increase of $8,655. However, the overall average of the two sample periods was

$57,622. The combined median value, a measure of the halfway point, was $49,950 and

the combined mode, a measure of the most frequent observation, was $47,000.

According to the census the overall median value for Lubbock County homes was

$69,100 in the year 2000, nearly $20,000 more than the study area in the year 2001.

Standard deviation quantifies variability and clearly increased from September 11,

2001, (9/11) to February 28, 2005, and is reflected in the increase in standard error as

well. Standard error is a measure of how far a sample mean is likely to be from the true

population mean of the study area and was only $1,334 for the combined periods.

Skewness characterizes the degree of asymmetry of a distribution about its mean. Both as calculated in Table 5.1 and as shown in Figure 5.5, Figure 5.6, and Figure 5.7,

house prices have a significant positive skew. This is also confirmed in that price means

are considerably higher than the medians for each period.

Figure 5.5 Histogram of House Prices for the period ended September 11, 2001

Histogram

0204060

$7,1

00

$25,

486

$43,

873

$62,

260

$80,

646

$99,

033

$117

,420

$135

,806

Bin

Freq

uenc

yHistogram

0204060

$7,1

00

$25,

486

$43,

873

$62,

260

$80,

646

$99,

033

$117

,420

$135

,806

Bin

Freq

uenc

yHistogram

0204060

$7,1

00

$25,

486

$43,

873

$62,

260

$80,

646

$99,

033

$117

,420

$135

,806

Bin

Freq

uenc

yHistogram

0204060

$7,1

00

$25,

486

$43,

873

$62,

260

$80,

646

$99,

033

$117

,420

$135

,806

Bin

Freq

uenc

y

Kurtosis characterizes the relative peakedness or flatness of a distribution

compared to the normal distribution and normal distributions produce a kurtosis statistic

of about zero. While values above two may be considered suspect (Brown, 1997), the

values shown in Table 5.1 indicate the sample of houses prices is suitable for OLS

regression analysis.

74

Figure 5.6 Histogram of House Prices for the period ended February 28, 2005

Histogram

0204060

$8,5

00

$29,

100

$49,

700

$70,

300

$90,

900

$111

,500

$132

,100

$152

,700

Bin

Freq

uenc

yHistogram

0204060

$8,5

00

$29,

100

$49,

700

$70,

300

$90,

900

$111

,500

$132

,100

$152

,700

Bin

Freq

uenc

yHistogram

0204060

$8,5

00

$29,

100

$49,

700

$70,

300

$90,

900

$111

,500

$132

,100

$152

,700

Bin

Freq

uenc

yHistogram

0204060

$8,5

00

$29,

100

$49,

700

$70,

300

$90,

900

$111

,500

$132

,100

$152

,700

Bin

Freq

uenc

yHistogram

0204060

$8,5

00

$29,

100

$49,

700

$70,

300

$90,

900

$111

,500

$132

,100

$152

,700

Bin

Freq

uenc

yHistogram

0204060

$8,5

00

$29,

100

$49,

700

$70,

300

$90,

900

$111

,500

$132

,100

$152

,700

Bin

Freq

uenc

yHistogram

0204060

$8,5

00

$29,

100

$49,

700

$70,

300

$90,

900

$111

,500

$132

,100

$152

,700

Bin

Freq

uenc

yHistogram

0204060

$8,5

00

$29,

100

$49,

700

$70,

300

$90,

900

$111

,500

$132

,100

$152

,700

Bin

Freq

uenc

yHistogram

0204060

$8,5

00

$29,

100

$49,

700

$70,

300

$90,

900

$111

,500

$132

,100

$152

,700

Bin

Freq

uenc

yHistogram

0204060

$8,5

00

$29,

100

$49,

700

$70,

300

$90,

900

$111

,500

$132

,100

$152

,700

Bin

Freq

uenc

yHistogram

0204060

$8,5

00

$29,

100

$49,

700

$70,

300

$90,

900

$111

,500

$132

,100

$152

,700

Bin

Freq

uenc

yHistogram

0204060

$8,5

00

$29,

100

$49,

700

$70,

300

$90,

900

$111

,500

$132

,100

$152

,700

Bin

Freq

uenc

yHistogram

0204060

$8,5

00

$29,

100

$49,

700

$70,

300

$90,

900

$111

,500

$132

,100

$152

,700

Bin

Freq

uenc

yHistogram

0204060

$8,5

00

$29,

100

$49,

700

$70,

300

$90,

900

$111

,500

$132

,100

$152

,700

Bin

Freq

uenc

yHistogram

0204060

$8,5

00

$29,

100

$49,

700

$70,

300

$90,

900

$111

,500

$132

,100

$152

,700

Bin

Freq

uenc

yHistogram

0204060

$8,5

00

$29,

100

$49,

700

$70,

300

$90,

900

$111

,500

$132

,100

$152

,700

Bin

Freq

uenc

y

As shown below in Table 5.2, the interquartile range for the combined sample of

house prices was $37,975.

Figure 5.7 Histogram of Combined House Prices

Histogram

0

50

100

$7,1

00

$28,

359

$49,

618

$70,

877

$92,

136

$113

,390

$134

,650

$155

,910

Bin

Freq

uenc

y

Histogram

0

50

100

$7,1

00

$28,

359

$49,

618

$70,

877

$92,

136

$113

,390

$134

,650

$155

,910

Bin

Freq

uenc

y

Histogram

0

50

100

$7,1

00

$28,

359

$49,

618

$70,

877

$92,

136

$113

,390

$134

,650

$155

,910

Bin

Freq

uenc

y

Histogram

0

50

100

$7,1

00

$28,

359

$49,

618

$70,

877

$92,

136

$113

,390

$134

,650

$155

,910

Bin

Freq

uenc

y

75

76

Table 5.2 Combined Interquartile Range

Quantile Estimate

100% Maximum $163,000 99% 142,000 95% 118,500 90% 104,900 75% Third Quarter 73,725 50% Median 49,950 25% First Quarter 35,750 10% 25,000 5% 19,000 1% 11,500 0% Min $ 7,100

_____________________________

The results of the SAS regression run on Equation (8) using OLS are shown in

Table 5.3 below. The full model regresses the natural log of the house price against the

explanatory variables. T-values are resultant of dividing the parameter by the standard

error, with the higher the value the better. P-values are probabilities based on the t-

values. The lower the p-value the better and the standard for testing hypotheses in this

study is a p-value of 0.5 or less. Many of the t-values are far too low to be of any value,

but that is to be expected from the full model. It is merely a starting point in deriving a

much more useful reduced model.

The variance inflation factor (VIF) measures the impact of collinearity among the

independent variables, and generally a VIF value greater than ten is of concern. Since

this model has values greater than ten, this is another indication of the need for a model

that reduces the number of explanatory variables.

77

Table 5.3 LnPrice Full Model Explanatory Variable Coefficients

Parameter Standard

ecnriaVa

VariableEstimate Error t Value

| t| > Pr Inflation

Intercept 9.96677 0.16195 61.54 <.0001 0 Feb 2005 dummy variable 0.34348 0.0941 3.65 0.0003 14.02984 House Size (square feet) 0.0004292 0.00003258 13.17 <.0001 2.28843 House Age (years) -0.00551 0.00152 -3.61 0.0003 2.68354 No. of Covered Parking 0.112 0.022 5.09 <0.0001 1.74033 No. of Fireplaces 0.12609 0.03466 3.64 0.0003 1.86549 Central Air? 0.23324 0.03246 7.19 <.0001 1.5534 Brick? 0.05586 0.03537 1.58 0.115 1.95109 TTU Distance Adjustment 0.27183 0.09222 2.95 0.0034 2.85652 Census Tract Median Value 0.00000163 0.00000321 0.51 0.6116 2.67126 2001 Air Release Facilities 2/3 mile -0.21492 0.14937 -1.44 0.1509 2.19187 2001 Air Release Facilities 1 mile -0.09434 0.07789 -1.21 0.2264 3.15579 2001 Hazardous Waste Handler 1/3 mile -0.02375 0.01844 -1.29 0.1983 2.38072 2001 Hazardous Waste Handler 2/3 mile -0.02039 0.01276 -1.6 0.1108 5.11073 2001 Hazardous Waste Handler 1 mile 0.0052 0.00736 0.71 0.4804 8.02111 2001 Risk Management Program 1 mile -0.06766 0.11947 -0.57 0.5714 4.07226 2001 Tier Two Sites 1/3 mile 0.05369 0.03406 1.58 0.1156 2.05347 2001 Tier Two Sites 2/3 mile -0.01248 0.02211 -0.56 0.5728 4.22666 2001 Tier Two Sites 1 mile 0.00513 0.01553 0.33 0.7412 6.2682 2001 Toxic Release Reported 2/3 mile 0.11031 0.10931 1.01 0.3134 2.4848 2001 Toxic Release Reported 1 mile 0.05576 0.05801 0.96 0.337 3.50457 2001 Permitted Water Discharge 1 mile -0.15951 0.08332 -1.91 0.0562 1.90192 2001 New Enforcement Action 2/3 mile -0.10886 0.13751 -0.79 0.429 5.2211 2001 New Enforcement Action 1 mile -0.00843 0.07718 -0.11 0.9131 10.08621 2001 NewHazardousWasteHandler 1/3 mile -0.00963 0.05715 -0.17 0.8663 1.6378 2001 NewHazardousWasteHandler 2/3 mile -0.03414 0.03565 -0.96 0.3388 2.9842 2001 NewHazardousWasteHandler 1 mile 0.00821 0.02825 0.29 0.7714 3.67387 2001 NewPermittedWaterDischarge 2/3 mile 0.08346 0.15683 0.53 0.5949 4.50073 2001 NewPermittedWaterDischarge 1 mile 0.02059 0.06404 0.32 0.7479 5.59913 2005 Air Release Facilities 2/3 mile -0.04757 0.153 -0.31 0.756 3.69989 2005 Air Release Facilities 1 mile -0.02531 0.08401 -0.3 0.7634 2.71731 2005 Hazardous Waste Handler 1/3 mile -0.01732 0.01966 -0.88 0.3787 2.41866 2005 Hazardous Waste Handler 2/3 mile -0.0088 0.01389 -0.63 0.5267 6.616 2005 Hazardous Waste Handler 1 mile -0.00656 0.0068 -0.97 0.3347 7.66582 2005 Risk Management Program 1 mile -0.14509 0.08011 -1.81 0.0708 2.49063 2005 Tier Two Sites 1/3 mile -0.03172 0.02728 -1.16 0.2455 1.61821 2005 Tier Two Sites 2/3 mile -0.04035 0.02017 -2 0.046 3.81108 2005 Tier Two Sites 1 mile -0.01856 0.0135 -1.37 0.1698 4.42109 2005 Toxic Release Reported 2/3 mile -0.01555 0.08449 -0.18 0.8541 2.2813 2005 Toxic Release Reported 1 mile 0.0377 0.06042 0.62 0.533 3.68598 2005 Permitted Water Discharge 1 mile -0.07607 0.10367 -0.73 0.4635 3.66694 2005 New Enforcement Action 2/3 mile 0.04356 0.11739 0.37 0.7107 3.64723 2005 New Enforcement Action 1 mile -0.03587 0.07284 -0.49 0.6227 10.67394 2005 NewHazardousWasteHandler 1/3 mile 0.07806 0.05974 1.31 0.192 1.57835 2005 NewHazardousWasteHandler 2/3 mile 0.01455 0.03801 0.38 0.702 4.57124 2005 NewHazardousWasteHandler 1 mile 0.03717 0.02964 1.25 0.2105 4.13203 2005 NewPermittedWaterDischarge 2/3 mile -0.10858 0.11943 -0.91 0.3637 2.4409 2005 NewPermittedWaterDischarge 1 mile 0.08505 0.08208 1.04 0.3007 10.25056

78

Table 5.4 provides an analysis of variance for the full model. Mean squares are

calculated by dividing the sum of squares by the degrees of freedom. The F-value is the

result of dividing the mean square model by the mean square error. P-values are based

on F-values. The higher the F-value, the lower the p-value and the better the model fit.

R-Square is another measure of fit with the closer to one the better the fit. Adjusted R-

Square uses the R-square statistic and adjusts it based on the residual degrees of freedom.

It is frequently used to compare two nested models. While at 0.7598 the R-Square is

adequate for drawing statistical inferences from, it is not as high as expected.

The root-mean-squared error (MSE) of the residuals, adjusted for the number of

the coefficients estimated, is the standard error of the estimate in a regression model.

The lower the standard error, the smaller the confidence interval of estimated

coefficients. This model produced a root MSE of 0.28311. The coefficient of variation is

the root MSE divided by the mean of the dependent variable and measures the amount of

variation of the explanatory variables; the smaller the value the better. This model

produced a coefficient of variation of 2.61619. The next step is to use the SAS

Regression Procedure to help derive the best reduced model.

Table 5.4 LnPrice Full Model Analysis of Variance

Sum of Mean Source DF Squares Square F Value Pr > F Model 47 116.59584 2.48076 30.95 <.0001 Error 460 36.86860 0.08015 Corrected Total 507 153.46443 Root MSE 0.28311 R-Square 0.7598 Dependent Mean 10.82132 Adj R-Sq 0.7352 Coeff Var 2.6161%

Enjoying more theoretical support than step-wise regression, Akaike (AIC) and

Bayesian (BIC) information criteria are frequently used to compare alternative models.

Since too many interrelated independent variables can penalize models resulting in

reduced coefficient significance, this widely accepted systematic method can be used for

eliminating insignificant explanatory variables from the model. AIC and BIC are

parsimony criteria used for determining the better model based on goodness of fit, the

79

number of adjustable parameters, and the total number of observations. Between rival

models, the model with the lowest AIC or BIC best explains the data. SAS calculates the

AIC and BIC statistics for all combinations of explanatory variables to assist the user in

establishing the best reduced model. Each possible model is graded according to AIC

and BIC statistics--the lower the AIC and BIC statistics, the better the model. The SAS

user reviews the various independent variable combinations of each potential model and

their corresponding AIC and BIC statistics. From this review, the user selects the model

variables with the lowest score for further analysis. In this case, the model with the

lowest combined AIC and BIC statistics only includes the variables shown in Table 5.5.

After establishing the best reduced model using AIC and BIC statistics, another

regression is run resulting in the coefficients shown in Table 5.5 below.

Table 5.5 Reduced Model LnPrice Explanatory Variable Coefficients

Parameter Standard Variance Variable Estimate Error t Value Pr > |t| Inflation Intercept 10.08481 0.07759 129.98 <.0001 0Feb 05 0.31833 0.0467 6.82 <.0001 3.56396House Size 0.000433 3.04E-05 14.23 <.0001 2.05579House Age -0.00649 0.00128 -5.06 <.0001 1.96575# of Covered Parking 0.10895 0.0206 5.29 <.0001 1.57424# of Fireplaces 0.13413 0.03274 4.1 <.0001 1.71687Central Air? 0.23483 0.03036 7.73 <.0001 1.40185Brick? 0.0498 0.03293 1.51 0.1311 1.7438TTU Adjustment 0.24775 0.07078 3.5 0.0005 1.7356201 Tier Two Sites 2/3 mile -0.03431 0.01419 -2.42 0.016 1.7940501 PermittedWaterDis 1 mile -0.15672 0.06546 -2.39 0.017 1.2109101 NewHazardWaste 2/3 mile -0.05116 0.02559 -2 0.0461 1.5852805 RiskManagemntProg 1 mile -0.15241 0.05543 -2.75 0.0062 1.2298405 Tier Two Sites 1/3 mile -0.04759 0.02397 -1.99 0.0476 1.2877505 Tier Two Sites 2/3 mile -0.04792 0.01319 -3.63 0.0003 1.6824505 Tier Two Sites 1 mile -0.01982 0.00922 -2.15 0.032 2.12701

The standard set for statistical significance was previously established at p-values

being 0.05 or lower. In this reduced model, all of the coefficients have p-values below

0.05 except whether the house was made of brick. Variance inflation factors are within

tolerance and there are six environmental variables of statistical significance. All of the

coefficients have the expected positive or negative sign. But, there are a few surprises.

80

First, there was a negative impact in 2001 for Permitted Water Discharge sites, but no

impact in 2005. If anything, it would have been expected to increase. Next, there was a

negative impact in 2001 for New Hazardous Waste Handlers; that was before the Waste

Handler had even been designated. And finally, the 1/3 mile band surrounding the 2005

Tier Two Sites have slightly less impact than the 1/3 to 2/3 mile band. And of course, the

parameter estimated values are a little difficult to understand because this is a log-linear

model.

As shown in Table 5.6, reducing the model does little to lower the R-Square and

the reduced model looks promising. The mean value for the dependent variable LnPrice

remains constant. The reduced model improves the root-mean-square error. And while

the coefficient of variance drops in the reduced model, it does not do so significantly.

Table 5.6 Reduced LnPrice Model Analysis of Variance Sum of Mean Source DF Squares Square F Value Pr > F Model 15 115.23092 7.68206 98.86 <.0001 Error 492 38.23351 0.07771 Corrected Total 507 153.46443 Root MSE 0.27877 R-Square 0.7509 Dependent Mean 10.82132 Adj R-Sq 0.7433 Coeff Var 2.5761% _____________________________________________________________________

Finally, the model residuals must be tested for normality. According to the SAS

Univariate Procedure, the residuals as shown in Figure 5.8 have a skewness of -0.848 and

a Kurtosis of 2.995.

SAS provides several tests of the hypothesis that the analysis variable values are a

random sample with a normal distribution. These tests, which are summarized in Table

5.7, include the following:

• Shapiro-Wilk test

• Kolmogorov-Smirnov test

• Anderson-Darling test

• Cramér-von Mises test

Figure 5.8 Plot of the Reduced LnPrice Model Residuals

Tests for normality are particularly important because the commonly used indices

are difficult to interpret unless the data are at least approximately normally distributed.

Furthermore, the computation of confidence limits is predicated on the assumption of

normality. Consequently, the tests of normality should always be computed. All four

SAS test results shown in Table 5.7 confirm with at least 99% confidence the possibility

of a non-normal distribution cannot be rejected. And a normal distribution is a basic

assumption of OLS regression. Because the Moran’s I statistics are 0.288 (p-value 0.23)

and 0.223 (p-value 0.18) for the log-linear model period components (before/after 9/11,

respectively), the possibility of spatial autocorrelation cannot be rejected with 95%

confidence.

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82

Table 5.7 LnPrice Reduced Model Test for Normality

Test --Statistic-- --------p Value--------- Shapiro-Wilk W 0.958438 Pr < W <0.0001 Kolmogorov-Smirnov D 0.06503 Pr > D <0.0100 Cramer-von Mises W-Sq 0.659698 Pr > W-Sq <0.0050 Anderson-Darling A-Sq 4.077318 Pr > A-Sq <0.0050 _____________________________________________________________

The literature review reveals that both the log-linear and the linear model are

commonly used in hedonic modeling. Many of the more recent research studies

discovered the log-linear model worked best for them following comparisons with the

linear model. For example, after testing both the linear and log-log models Ihlanfeldt and

Taylor [2004] chose the log-linear functional form. Accordingly. the next step is to

compare this log-linear model with a pure linear model as shown in Equation (6). This

step is done to insure the log-linear is the better specified model, and because both the R-

Square is a little disappointing and the residual distribution appears non-normal. The

results of the SAS regression run of the full Price model on the parameter estimates are

shown in Table 5.8 below.

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Table 5.8 Full Model Price Explanatory Variable Coefficients

Parameter Standard Variance Variable Estimate Error t Value Pr > |t| Inflation Intercept -602.231 7769.604 -0.08 0.9383 0 Feb 2005 26232 4514.176 5.81 <.0001 14.02984 House Size 27.58425 1.56316 17.65 <.0001 2.28843 House Age -159.656 73.08123 -2.18 0.0294 2.68354 # of Covered parking 6129.631 1055.511 5.81 <.0001 1.74033 # of Fireplaces 9298.212 1662.909 5.59 <.0001 1.86549 Central Air? 7565.887 1557.235 4.86 <.0001 1.5534 Brick? 2859.261 1696.933 1.68 0.0927 1.95109 TTU Adjustment 12197 4423.958 2.76 0.0061 2.85652 Census Tract Median Value 0.05886 0.15397 0.38 0.7024 2.67126 2001 Air Release Facilities 2/3 mile -2563.4 7165.77 -0.36 0.7207 2.19187 2001 Air Release Facilities 1 mile -1716.22 3736.69 -0.46 0.6462 3.15579 2001 Hazardous Waste Handler 1/3 mile -461.878 884.5827 -0.52 0.6018 2.38072 2001 Hazardous Waste Handler 2/3 mile -291.397 612.1727 -0.48 0.6343 5.11073 2001 Hazardous Waste Handler 1 mile -185.928 353.2258 -0.53 0.5989 8.02111 2001 Risk Management Program 1 mile -2604.07 5731.589 -0.45 0.6498 4.07226 2001 Tier Two Sites 1/3 mile 930.2879 1634.037 0.57 0.5694 2.05347 2001 Tier Two Sites 2/3 mile -40.9967 1060.926 -0.04 0.9692 4.22666 2001 Tier Two Sites 1 mile 87.53152 745.2339 0.12 0.9066 6.2682 2001 Toxic Release Reported 2/3 mile 1970.06 5244.046 0.38 0.7073 2.4848 2001 Toxic Release Reported 1 mile 890.2294 2782.948 0.32 0.7492 3.50457 2001 Permitted Water Discharge 1 mile -5215.51 3997.124 -1.3 0.1926 1.90192 2001 New Enforcement Action 2/3 mile -902.77 6596.683 -0.14 0.8912 5.2211 2001 New Enforcement Action 1 mile 1690.33 3702.689 0.46 0.6482 10.08621 2001 New Hazardous Waste Handler 1/3 mile 1579.874 2741.772 0.58 0.5647 1.6378 2001 New Hazardous Waste Handler 2/3 mile -454.627 1710.34 -0.27 0.7905 2.9842 2001 New Hazardous Waste Handler 1 mile 1202.702 1355.256 0.89 0.3753 3.67387 2001 New Permitted Water Discharge 2/3 mile -711.492 7523.505 -0.09 0.9247 4.50073 2001 New Permitted Water Discharge 1 mile -1555.78 3072.242 -0.51 0.6128 5.59913 2005 Air Release Facilities 2/3 mile 1118.551 7340.167 0.15 0.8789 3.69989 2005 Air Release Facilities 1 mile 996.3614 4030.429 0.25 0.8049 2.71731 2005 Hazardous Waste Handler 1/3 mile -1237.5 943.0247 -1.31 0.1901 2.41866 2005 Hazardous Waste Handler 2/3 mile -1107.78 666.4205 -1.66 0.0971 6.616 2005 Hazardous Waste Handler 1 mile -880.443 325.9909 -2.7 0.0072 7.66582 2005 Risk Management Program 1 mile -9151.9 3843.006 -2.38 0.0177 2.49063 2005 Tier Two Sites 1/3 mile -1946.64 1308.866 -1.49 0.1376 1.61821 2005 Tier Two Sites 2/3 mile -631.392 967.4363 -0.65 0.5143 3.81108 2005 Tier Two Sites 1 mile -757.445 647.4555 -1.17 0.2427 4.42109 2005 Toxic Release Reported 2/3 mile 220.8989 4053.261 0.05 0.9566 2.2813 2005 Toxic Release Reported 1 mile 1328.782 2898.812 0.46 0.6469 3.68598 2005 Permitted Water Discharge 1 mile -1692 4973.599 -0.34 0.7339 3.66694 2005 New Enforcement Action 2/3 mile 3178.726 5631.685 0.56 0.5727 3.64723 2005 New Enforcement Action 1 mile 2284.252 3494.521 0.65 0.5137 10.67394 2005 New Hazardous Waste Handler 1/3 mile 4617.947 2865.86 1.61 0.1078 1.57835 2005 New Hazardous Waste Handler 2/3 mile -473.393 1823.359 -0.26 0.7953 4.57124 2005 New Hazardous Waste Handler 1 mile 1592.98 1422.069 1.12 0.2632 4.13203 2005 New Permitted Water Discharge 2/3 mile -6367.52 5729.37 -1.11 0.267 2.4409 2005 New Permitted Water Discharge 1 mile -1291.47 3937.858 -0.33 0.7431 10.25056

Again, many of the t-values are far too low to be of any value, but that is to be

expected from the full model. It is merely a starting point in deriving a much more useful

reduced model. However, as shown in Table 5.9, at 0.8148 the R-Square is higher

indicating a better model fit than the log-linear model. Using the same criterion as before

a reduced model is selected.

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Table 5.9 Full Price Model Analysis of Variance

Sum of Mean Source DF Squares Square F Value Pr > F Model 47 3.733165E11 7942905305 43.06 <.0001 Error 460 84852717709 184462430 Corrected Total 507 4.581693E11 Root MSE 13582 R-Square 0.8148 Dependent Mean 57622 Adj R-Sq 0.7959 Coeff Var 23.5703% _______________________________________________________________________

SAS calculates the AIC and BIC statistics for all combinations of explanatory

variables with each possible model being graded according to AIC and BIC statistics--the

lower the AIC and BIC statistics are, the better the model. The model with the lowest

score is selected for further analysis. In this case, the variables from the model with the

lowest combined AIC and BIC statistics are selected along with the variables required for

pair-wise comparative testing. After establishing the best reduced model using AIC and

BIC statistics, another regression is run resulting in the coefficients shown in Table 5.10

below.

The results of the SAS regression run on the reduced linear price model are shown

in Table 5.10. In this model there are only nine coefficients that are statistically

significant at the 0.05 p-value level; sixteen coefficients are not although 15 are

significant at the 0.1 p-value level. That house age is not significant at the 0.05 level is

an indication the houses were mostly close to the same age. Still, there are only three

environmental variables significant at the 0.05 level: 2005 Hazardous Waste Handlers

between 1/3 and 2/3 mile away, 2005 Hazardous Waste Handlers between 2/3 mile and

one mile away, and 2005 Risk Management Program sites between 2/3 and one mile

away. But is the model better than the reduced LnPrice model?

85

Table 5.10 Reduced Price Model Explanatory Variable Coefficients

Parameter Standard Variance Variable Estimate Error t Value Pr > |t| Inflation Intercept 26.24887 3989.92 0.01 0.9948 0 Feb 2005 24141 3202.815 7.54 <.0001 7.3209 House Size 27.9514 1.48121 18.87 <.0001 2.12996 House Age -126.774 65.34133 -1.94 0.0529 2.2237 # of Covered parking 6274.596 1002.924 6.26 <.0001 1.62873 # of Fireplaces 9460.158 1593.173 5.94 <.0001 1.77495 Central Air? 7414.918 1472.79 5.03 <.0001 1.44033 Brick? 2950.59 1608.161 1.83 0.0672 1.8164 TTU Adjustment 12424 3882.178 3.2 0.0015 2.28019 2001 Permitted Water Discharge 1 mile -5876.71 3454.554 -1.7 0.0896 1.47261 2005 Permitted Water Discharge 1 mile -133.638 3579.064 -0.04 0.9702 1.96837 2001 Hazardous Waste Handler 1/3 mile -471.073 739.5954 -0.64 0.5245 1.72514 2001 Hazardous Waste Handler 2/3 mile -377.377 446.2978 -0.85 0.3982 2.81572 2001 Hazardous Waste Handler 1 mile -278.611 250.3967 -1.11 0.2664 4.17823 2005 Hazardous Waste Handler 1/3 mile -1411.61 839.0558 -1.68 0.0931 1.98479 2005 Hazardous Waste Handler 2/3 mile -1310.06 485.3306 -2.7 0.0072 3.6373 2005 Hazardous Waste Handler 1 mile -950.622 254.955 -3.73 0.0002 4.86049 2001 Risk Management Program 1 mile 953.8472 3536.611 0.27 0.7875 1.60718 2005 Risk Management Program 1 mile -7799.9 2766.805 -2.82 0.005 1.33823 2001 Tier Two Sites 1/3 mile 1248.06 1432.774 0.87 0.3841 1.63654 2005 Tier Two Sites 1/3 mile -1613.75 1181.851 -1.37 0.1727 1.36765 2001 New Hazardous Waste Handler 1/3 mile 1555.181 2352.847 0.66 0.5089 1.25023 2005 New Hazardous Waste Handler 1/3 mile 4571.542 2415.424 1.89 0.059 1.16221 2001 New Hazardous Waste Handler 1 mile 1103.877 1061.081 1.04 0.2987 2.33444 2005 New Hazardous Waste Handler 1 mile 1658.904 973.596 1.7 0.089 2.00764

According to Table 5.11, this reduced price model has a better F-value and R-

Square and, accordingly, a better data fit. Contrary to the findings of many previous

studies, the log-linear model does not apparently provide a better fit. Perhaps this should

be of no surprise. Corgel, Goebel, and Wade [1982] built a successful hedonic model of

the Lubbock housing market using the linear model.

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Table 5.11 Reduced Price Model Analysis of Variance

Sum of Mean Source DF Squares Square F Value Pr > F Model 24 3.722185E11 15509104085 87.15 <.0001 Error 483 85950769004 177951903 Corrected Total 507 4.581693E11 Root MSE 13340 R-Square 0.8124 Dependent Mean 57622 Adj R-Sq 0.8031 Coeff Var 23.1506% _____________________________________________________________________

Using SAS, a number of statistical pair-wise tests are run on the reduced price

model coefficients to determine if the coefficients are statistically different from each

other. Table 5.12 shows the results of comparing coefficients covering the same

geographical area for the periods ended September 11, 2001, with February 28, 2005.

Both the impact of Hazardous Waste Handler sites covering the area of 2/3 of a mile to

one mile are statistically different in 2001 than in 2005, and the impact of Risk

Management Program sites covering the area of 2/3 of a mile to one mile are statistically

different in 2001 than in 2005 at the 95% confidence level.

Table 5.12 Coefficient Inter-period Pair-wise Comparisons F Value Pr > F2001 to 2005 Permitted Water Discharge sites in 2/3 to 1 mile band 1.39 0.23842001 to 2005 Hazardous Waste Handler sites inside 1/3 mile 0.71 0.39892001 to 2005 Hazardous Waste Handler sites in 1/3 to 2/3 mile band 2.08 0.14952001 to 2005 Hazardous Waste Handler sites in 2/3 to 1 mile band 4.06 0.04452001 to 2005 Risk Management Program sites in 2/3 to 1 mile band 3.99 0.04622001 to 2005 Tier Two Sites inside 1/3 mile 2.42 0.12052001 to 2005 New Hazardous Waste Handler sites inside 1/3 mile 0.82 0.36482001 to 2005 New Hazardous Waste Handler sites in 2/3 to 1 mile band 0.15 0.6992

Table 5.13 shows the results of comparing coefficients covering the same time

periods but different geographic areas. From the 0.0158 p-value, it can be inferred the

impact that Hazardous Waste Handler sites between 2/3 and one mile away had a

different impact than Risk Management Programs sites between 2/3 and one mile away

did for the period ended February 28, 2005. So far this model is providing data

indicating that several environmental variables are both significant and different from

other environmental variables. The next step is to test for normality to see if valid

inferences can be drawn from this model.

Table 5.13 Coefficient Intra-period Pair-wise Comparisons

F Value Pr > F

2001 comparing Hazardous Waste Handlers 1/3 mile vs 2/3 mile bands 0.01 0.9168 2001 comparing Hazardous Waste Handlers 2/3 mile vs 1 mile bands 0.03 0.8534 2005 comparing Hazardous Waste Handlers 1/3 mile vs 2/3 mile bands 0.01 0.9075 2005 comparing Hazardous Waste Handlers 2/3 mile vs 1 mile bands 0.37 0.5419 2005 comparing New Hazard Waste Handlers 1/3 mile vs 1 mile bands 1.28 0.2589 2001 comparing New Hazard Waste Handlers 1/3 mile vs 1 mile bands 0.03 0.8554 2005 comparing 1 mile band for HazardWasHands vs RiskManagmntProg 5.87 0.0158

Figure 5.9 is a normality plot of the residuals of the Reduced Price Model. Again

as shown in Table 5.14, the results of statistical tests imply this is a non-normal

distribution. But the Central Limit Theorem holds the distribution of the mean

approaches a normal distribution as the sample size increases regardless of population

distribution pattern. So, a sample size of 508 should produce a normal distribution.

Figure 5.9 Plot of Reduced Price Model Residuals

87

88

One reason the distribution could be non-normal is because of the undue influence

of a few unique observations. Influential observations are those, according to various

criteria, that appear to have a large influence on parameter estimates. Accordingly, the

next step is to see if observational outliers are having an undue influence on the residuals.

This is a common statistical procedure and is done by inspecting the Student Residual

statistic to see which residuals scored high. Observations scoring a Student Residual

larger than two in absolute value may need some attention. Eight suspect observations

with scores above three are removed and the regression is run again.

Table 5.14 Price Reduced Model Test for Normality

Test --Statistic-- ---------p Value-------- Shapiro-Wilk W 0.956292 Pr < W <0.0001 Kolmogorov-Smirnov D 0.053362 Pr > D <0.0100 Cramer-von Mises W-Sq 0.436692 Pr > W-Sq <0.0050 Anderson-Darling A-Sq 2.879129 Pr > A-Sq <0.0050 ____________________________________________________________________

As shown in Table 5.15, the R-Square improves to 0.8496 and appears good for

drawing statistical inferences. The purpose of this exercise is to see if removing the

outliers affects the model in a significant way. This common procedure is a fairly robust

way of determining if the non-normal distribution of residuals is significantly impacting

model results.

Table 5.15 Adjusted Reduced Price Model Analysis of Variance

Sum of Mean Source DF Squares Square F Value Pr > F Model 24 3.443811E11 14349211182 11.77 <.0001 Error 475 60983587837 128386501 Corrected Total 499 4.053647E11 Root MSE 11331 R-Square 0.8496 Dependent Mean 56402 Adj R-Sq 0.8420 Coeff Var 20.0892% _______________________________________________________________________

As shown in Table 5.16, no variable that was previously significant is now

insignificant. Additionally, no variable that was previously insignificant is now

significant. However, there appears to be substantial changes in coefficients that were

89

previously significant at the 0.1 p-value. So the non-normal distribution of residuals

looks to be at least partially a result of influential observations. Table 5.16 Comparison of Adjusted Coefficients with Reduced Model Coefficients

Adjusted Reduced Parameter Parameter Variable Estimate Pr > |t| Estimate Pr > |t| Intercept 824.9321 0.8112 26.24887 0.9948 Feb 2005 20070 <.0001 24141 <.0001 House Size 29.62354 <.0001 27.9514 <.0001 House Age -158.547 0.0054 -126.774 0.0529 # of Covered Parking 4865.65 <.0001 6274.596 <.0001 # of Fireplaces 8044.616 <.0001 9460.158 <.0001 Central Air? 7867.389 <.0001 7414.918 <.0001 Brick? 3809.136 0.0062 2950.59 0.0672 TTU Adjustment 10915 0.0011 12424 0.0015 01 Permitted Water Discharge 2/3 to 1 mile -5355.76 0.0688 -5876.71 0.0896 05 Permitted Water Discharge 2/3 to 1 mile 1051.61 0.7306 -133.638 0.9702 01 Hazardous Waste Handlers to 1/3 mile -378.011 0.5479 -471.073 0.5245 01 Hazardous Waste Handlers 1/3 to 2/3 mile -461.07 0.2249 -377.377 0.3982 01 Hazardous Waste Handlers 2/3 to 1 mile -244.863 0.2516 -278.611 0.2664 05 Hazardous Waste Handlers to 1/3 mile -847.708 0.2395 -1411.61 0.0931 05 Hazardous Waste Handlers 1/3 to 2/3 mile -962.449 0.0221 -1310.06 0.0072 05 Hazardous Waste Handlers 2/3 to 1 mile -718.288 0.001 -950.622 0.0002 01 Risk Management Program 2/3 to 1 mile 258.5001 0.9316 953.8472 0.7875 05 Risk Management Program 2/3 to 1 mile -6669.98 0.0048 -7799.9 0.005 01 Tier Two Sites to 1/3 mile 1863.413 0.129 1248.06 0.3841 05 Tier Two Sites to 1/3 mile -1054.31 0.2973 -1613.75 0.1727 01 New Hazardous Waste Handler to 1/3 mile 738.2033 0.7128 1555.181 0.5089 05 New Hazardous Waste Handler to 1/3 mile -1129.78 0.5993 4571.542 0.059 01 New Hazards Waste Handler 2/3 to 1 mile 1127.858 0.2115 1103.877 0.2987 05 New Hazards Waste Handler 2/3 to 1 mile 627.1269 0.4537 1658.904 0.089

As shown in Figure 5.10, removing those eight observations does not make the

distribution of residuals normally distributed.

Figure 5.10 Plot of the Adjusted Reduced Price Model Residuals

As shown in Table 5.17, SAS tests imply the distribution of residuals is still non-

normal. This result is consistent with spatial autocorrelation of residuals therefore

requiring testing with Moran’s I. Since valid observations cannot be easily discarded, the

eight observations are restored for computing Moran’s I, i.e., Moran’s statistic is

computed based on the full 508 observations. Because the Moran’s I statistics with

respect to the linear model period components (before/after 9/11) are 0.003 (0.002 p-

value) and 0.009 (0.007 p-value), respectively, the statistical assumption of spatial

autocorrelation causing the non-normal distribution is rejected. Unlike the findings of

many recent studies, significant spatial autocorrelation appears not to be present in this

linear model.

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Table 5.17 Adjusted Price Reduced Model Test for Normality

Test --Statistic--- -------p Value------ Shapiro-Wilk W 0.993626 Pr < W 0.0334 Kolmogorov-Smirnov D 0.044581 Pr > D 0.0169 Cramer-von Mises W-Sq 0.153085 Pr > W-Sq 0.0224 Anderson-Darling A-Sq 0.813066 Pr > A-Sq 0.0371 _______________________________________________________________________

One of the assumptions of using OLS not previously addressed is the assumption

of equal variance between the two time periods. According to the descriptive statistics,

the period after 9/11 has more variance than the period before 9/11. This inconsistent

variation between periods could possibly taint inferences drawn from OLS. OLS is only

a fixed effects model. With the addition of random effects to the fixed effects, the mixed

procedure of SAS is more robust than OLS in addressing this issue. In the mixed model,

random parameters are tested and the model is regressed using ML rather than OLS.

Accordingly, the next step is to re-compute the price model on all 508 observations using

the SAS mixed procedure. The SAS mixed model is

y = Xβ + Z1γ + Z2 γ + е (22)

where

y are the 508 house prices,

X is the (508 x 25) observational matrix,

β is a (25 x 1) vector of coefficients,

Z1 is the (254 x 254) spatially weighted matrix for September 11, 2001,

Z2 is the (254 x 254) spatially weighted matrix for February 28, 2005,

γ are two (254 x 1) vectors of unknown random-effects parameters, and

е is a vector of 508 error terms.

The SAS Spatial Power function σ2ρdij is chosen for degrading the spatial correlation

impact over distance in producing the two spatially weighted matrices.

This mixed price model results in a Null Model Likelihood Ratio Test Chi-Square

of 28.39 and a p-value of less than 0.0001 while the variance for the period ended

February 28, 2005, is roughly double that of the period ended September 11, 2001. This

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means this model is good for drawing statistical inferences. The coefficient estimates of

the mixed model are shown in Table 5.18. Because this model is both more robust and

conservative, this mixed model is the model that will be used for testing the hypotheses. Table 5.18 Comparison of Reduced Coefficients with Mixed Model Coefficients

Reduced Mixed Parameter Parameter 95% 95% Variable Estimate Pr > |t| Estimate Pr > |t| Lower UpperIntercept 24167 0.9948 26741 <.0001 18030 35452 Sept 2001 -24141 <.0001 -24652 <.0001 -31126 -18177 House Size 27.9514 <.0001 26.2492 <.0001 23.572 28.926 House Age -126.774 0.0529 -113.19 0.077 -238.7 12.326 # of Covered Parking 6274.6 <.0001 6743.23 <.0001 4868.7 8617.8 # of Fireplaces 9460.16 <.0001 9219.07 <.0001 6288 12150 Central Air? 7414.92 <.0001 7108.34 <.0001 4328.8 9887.9 Brick? 2950.59 0.0672 3341.37 0.0305 315.48 6367.3 TTU Adjustment 12424 0.0015 9862.39 0.0066 2754.6 16970 01 PermWatrDisch 2/3 to 1 -5876.71 0.0896 -5703.86 0.0469 -11330 -77.934 05 PermWatrDisch 2/3 to 1 -133.638 0.9702 318.74 0.9377 -7692 8329.2 01 HazWasteHandlrs to 1/3 -471.073 0.5245 -392.97 0.5215 -1597 810.65 05 HazWasteHandlrs to 1/3 -1411.61 0.0931 -1498.34 0.117 -3373 376.29 01 HazWasHands 1/3 to 2/3 -377.377 0.3982 -457.22 0.2169 -1184 269.45 05 HazWasHands 1/3 to 2/3 -1310.06 0.0072 -1410.96 0.0111 -2499 -323.07 01 HazWastHandrs 2/3 to 1 -278.611 0.2664 -247.96 0.2407 -662.7 166.76 05 HazWastHandrs 2/3 to 1 -950.622 0.0002 -961.01 0.0009 -1524 -398.45 01 RiskManmtProg 2/3 to 1 953.847 0.7875 81.7228 0.9779 -5713 5876.2 05 RiskManmtProg 2/3 to 1 -7799.9 0.005 -8206.53 0.0095 -14402 -2010.9 01 TierTwoSites to 1/3 1248.06 0.3841 1095.41 0.357 -1239 3429.8 05 TierTwoSites to 1/3 -1613.75 0.1727 -1794.35 0.1854 -4453 864.19 01 NewHazWasteHand to 1/3 1555.18 0.5089 1713.22 0.3796 -2115 5541.4 05 NewHazWasteHand to 1/3 4571.54 0.059 4663.22 0.0916 -756.9 10083 01 NewHazWasHand 2/3 to 1 1103.88 0.2987 1000.48 0.2551 -724.8 2725.7 05 NewHazWasHand 2/3 to 1 1658.9 0.089 1523.67 0.172 -665.2 3712.5

According to the mixed model results, the intercept of $26,741 is statistically

significant along with house size, number of covered parking spaces, number of

fireplaces, the presence of central air conditioning, and whether brick. Additionally,

proximity to Texas Tech University is statistically significant. Permitted Water

Discharge sites have a statistically significant negative impact on housing prices that

were between 2/3 and a mile away for the period ended September 11, 2001. Hazardous

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Waste Handlers between 1/3 of a mile and a mile have a statistically significant negative

impact for the period ended February 28, 2005. And lastly, Risk Management Program

sites between 2/3 of a mile and one mile have a statistically significant negative impact

on housing prices also for the period ended February 28, 2005.

The mean value of the combined housing sample is $57,622. Rather strangely,

the model indicates a $24,652 increase in value for houses built during the period ended

February 28, 2005; however, since the average house only increased by $8,655 within the

overall model, the additional model increase of $15,997 seems to reflect an increased

sensitivity of the public to attributes having a negative impact on housing prices. By

raising the intercept point value, the model increases its sensitivity to the environmental

variables.

Before proceeding with analyzing model results, it must be understood by the

reader that regression analysis creates a probabilistic model, not a deterministic model.

In other words, each coefficient represents a range of values and the size of the range

depends upon the required reliability of the figure; the greater the confidence, the larger

the range. So a p-value of 0.05 produces a confidence level of 95% and a larger range

than a p-value of 0.1 and a confidence level of 90%. These ranges are demonstrated at

the 95% confidence level in Table 5.18.

According to model results, during the study period the average incremental value

of an additional square foot of housing space is $26.25 for houses sold within the study

area. The negative impact of each year of house age is not significant at the previously

establish p-value of 0.05; however, it is significant at the 0.1 p-value and has an

incremental impact of minus $113.19 for each year of age.

Adding another covered parking space adds $6,743, and adding a fireplace adds

$9,219. Having central air conditioning adds $7,108, and being a brick structure adds

another $3,341. From within 1.5 miles, every tenth of a mile closer to Texas Tech

University adds another $657 to the value of a home.

For the period ended September 11, 2001, there is only one environmental hazard

studied that has a statistically significant impact on housing prices. Being between 2/3 of

a mile and one mile of a Permitted Water Discharge site has a negative impact of $5,703

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or nearly 11% of the September 11, 2001, sample-mean house value. However, there is

no measurable negative impact for the same hazard for the period ended February 28,

2005. This result is thought to be due to an odor control program completed between the

two study periods at a water treatment plant.

For the period ended February 28, 2005, there are two classes of hazards that have

a statistically significant negative impact: Hazardous Waste Handlers and Risk

Management Program sites. While unfortunately the data does not support a statistically

significant value for having a Hazardous Waste Handler within 1/3 of a mile of a home, it

does have significant negative values associated with having them within 1/3 to 2/3 miles

of a home and for having them within 2/3 to one mile away. The negative impact of a

Hazardous Waste Handler being within 1/3 to 2/3 mile is $1,411 or 2.2% of the February

28, 2005, sample-mean house value. The negative impact of a Hazardous Waste Handler

being with 2/3 to one mile is $961, or 1.6% for each handler. The negative impact of a

Risk Management Program site being within 2/3 of a mile to one mile is $8,206, or over

13% of the February 28, 2005, sample-mean house value.

With model results finalized, the hypotheses can be tested. Do environmental

chemical hazardous sites decrease surrounding property values? For housing values

within the study area, the first hypothesis is stated as:

H1O: “Previously listed” chemical hazardous sites had no negative impact on

surrounding housing values as of 9/11.

H1A: “Previously listed” chemical hazardous sites had a negative impact on

surrounding housing values as of 9/11.

If the null hypothesis is rejected, then one can conclude that at least one group of

“previously listed” chemical hazardous sites had a negative impact on surrounding

housing values as of 9/11. To reject the null hypothesis, at least one of the “previously

listed” hazard coefficients must be negative and significantly different from zero for the

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period ended 9/11. Recall that “previously listed” sites are those listed by the EPA on

their web site as of 9/11.

Since Permitted Water Discharge sites that were listed by the EPA by 9/11 have a

statistically significant negative impact on nearby houses as of 9/11, the null hypothesis

must be rejected. Indeed, as shown in Table 5.18, being within 2/3 of a mile to a mile of

this “previously listed” chemical hazardous site has a negative impact of $5,703 on

surrounding house values as of 9/11.

Hoehn, Berger, and Blomquist [1987] and continued in Blomquist, Berger, and

Hoehn [1988] concluded that Permitted Water Discharge sites lowered nearby property

values. That conclusion is supported by this study.

A logical question that arises is whether a chemical hazard being listed by the

EPA makes a difference on the impact that hazard has on surrounding property values?

Tier Two sites as a whole are at least as hazardous as listed Hazardous Waste Handlers,

yet they are not listed by the EPA. However, this information is available upon request

from state and local officials and is not listed by the EPA. So for housing values within

the study area, the second hypothesis is stated as:

H2O: Tier Two sites had no negative impact on surrounding housing values as of

9/11.

H2A: Tier Two sites had a negative impact on surrounding housing values as of

9/11.

If the null hypothesis is rejected, then one can conclude that as a group Tier Two sites

reporting for the year 2001 have a negative impact on surrounding housing values as of

9/11. To reject the null hypothesis, at least one Tier Two coefficient must be negative

and significantly different from zero for the period ended 9/11. According to the reduced

LnPrice model results shown in Table 5.5, Tier Two sites have a negative impact.

However, since according to the best model results as shown in Table 5.18 Tier Two sites

have no statistically significant negative impact on surrounding housing values as of

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9/11, the null hypothesis cannot be rejected. Because Tier Two sites are not listed by the

EPA, this seems to suggest that listing by the EPA is important in lowering land values.

The next logical question is did the negative impact increase after 9/11? For

housing values within the study area, the third hypothesis is stated as:

H3O: The negative impact of a “previously listed or reported” chemical

hazardous site category as of 9/11 did not increase after 9/11.

H3A: The negative impact of a “previously listed or reported” chemical

hazardous site category as of 9/11 increased after 9/11.

If the null hypothesis is rejected, then one can conclude that either the negative impact of

at least one group of chemical hazardous sites listed on 9/11, or the negative impact that

year 2001 Tier Two sites, had on surrounding housing values increased after 9/11. To

reject the null hypothesis, the coefficients for any of “previously listed” sites or Tier Two

sites for the period ending after 9/11 must be negative and significantly greater than its

corresponding coefficient for the period ending 9/11.

Clearly, the negative impact of Tier Two sites did not increase after 9/11, but

determining whether Hazardous Waste Handlers or Risk Management sites did is not so

easy. Neither is statistically significant before 9/11 and yet both are statistically

significant afterwards. According to Table 5.12, both groups of hazards did indeed

increase the effect of their negative impact after 9/11. But this pair-wise test was

performed before adjusting for the unequal variance. Correcting the model for unequal

variance expands confidence intervals making rejecting a null hypothesis more difficult.

By examining the corrected confidence intervals in Table 5.19, the third

hypothesis can be statistically tested. For this test, the confidence interval for each pair

must not include zero. Only one upper-lower confidence interval does not include zero.

This pair-wise statistical test indicates the negative impact that a Hazardous Waste

Handler being between 2/3 of a mile and one mile away did indeed increase after 9/11.

And it grew by 290%. So based on the mixed model results, it can be concluded at the

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95% confidence level that the negative impact of either “previously listed” or Tier Two

sites did increase after 9/11. Hoehn, Berger, and Blomquist [1987] and continued in

Blomquist, Berger, and Hoehn [1988] concluded the number of Hazardous Waste

Handlers lowered nearby property values. Again, their conclusion is supported by this

study.

Table 5.19 Inter-period Pair-wise Testing of Mixed Model Coefficients

Standard Effect Estimate Error t Value Pr > |t| Alpha Lower Upperpwd3*Fe05 -6065.98 4887.39 -1.24 0.2152 0.05 -15669.00 3537.20hwh1*Fe05 1123.61 1129.84 0.99 0.3205 0.05 -1096.40 3343.63hwh2*Fe05 994.64 661.32 1.5 0.1332 0.05 -304.78 2294.05hwh3*Fe05 714.83 333.66 2.14 0.0327 0.05 59.22 1370.44rmp3*Fe05 8251.51 4215.73 1.96 0.0509 0.05 -31.93 16535.00tts1*Fe05 2888.9 1784.27 1.62 0.1061 0.05 -617.00 6394.79nhw1*Fe05 -2951.87 3336.5 -0.88 0.3767 0.05 -9507.71 3603.97nhw3*Fe05 -526.25 1414.7 -0.37 0.7101 0.05 -3305.97 2253.47

The results of the second hypothesis seem to suggest that listing by the EPA is

important in lowering land values. To confirm this possibility, the listing of a new site by

the EPA should have an impact on surrounding property values. So for housing values

within the study area, the fourth hypothesis is stated as:

H4O: “Newly listed” sites have no negative impact on surrounding housing

values.

H4A: “Newly listed” sites have a negative impact on surrounding housing values.

If the null hypothesis is rejected, then one can conclude that at least one group of newly

listed sites has a negative impact on surrounding housing values. To reject the null

hypothesis, the coefficient for any of the “newly listed” sites for the period ending after

9/11 must be negative and significantly greater than its corresponding coefficient for the

period ending 9/11.

“Newly listed” are those new sites listed by the EPA on their web site after 9/11

including those sites having enforcement action taken against them. However, the null

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hypothesis cannot be rejected. Test results, as shown in Table 5.18, indicate that sites

newly listed after 9/11 do not cause surrounding housing values to drop regardless of

hazard category. In other words, there does not seem to be an immediate market

response to the EPA listing new hazards or listing enforcement action.

As discussed previously in the literature review, many studies concluded that air

pollution lowered nearby housing values; however, many studies did not find evidence of

this relationship. Within the chosen study area, this study does not find solid statistical

evidence that Air Release Facilities lower nearby housing values.

Another question is about potential environmental problems verses actual

problems. In the case of toxic releases, this is an actual problem, yet it does not cause a

blip on the statistical analysis in this study. However, both Hwang [2003] and Decker,

Nielsen, and Sindt [2005] concluded housing prices were lower near Toxic Release

Inventory sites. Likewise, actual enforcement action does not cause an immediate

response in housing prices either in this study. However, no previous study specifically

studied the impact on housing values of merely listing all enforcement penalties on the

EPA web site.

The results of this statistical study are consistent with increased public awareness

and concern about proximity to Hazardous Waste Handlers after 9/11. There are other

signs that public awareness and concern is growing. Three hazard measurements are

significant for the period ended February 28, 2005 while only one is significant for the

period ended September 11, 2001. Being near either a Risk Management Program site or

a Hazardous Waste Handler is significant at the 95% confidence level for the period

ended February 28, 2005. It cannot be said with relative statistical certainty that the

negative impact of being near a Permitted Water Discharge facility decreases during the

study period.

One of the concerns of this experimental design is the distance to measure. Using

the two points that are both statistically significant for the Hazardous Waste Handlers

indicates a maximum distance of about 1.5 miles using a linear model.

The research methodologies used to test the four hypotheses within the study area

have been discussed and the results of the empirical analyses examined. The first null

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hypothesis is rejected. There is strong evidence that Permitted Water Discharge sites had

a negative impact on surrounding housing values as of 9/11. The second null hypothesis

could not be rejected. There is no strong evidence that Tier Two sites had a negative

impact on housing values as of 9/11. The third null hypothesis is also rejected. There is

strong evidence the negative impact of Hazardous Waste Handlers between 2/3 of a mile

and one mile have on surrounding housing values grows after 9/11. But the fourth null

hypothesis can not be rejected. There is no strong evidence that “newly listed” sites have

a sudden negative impact on surrounding housing values.

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CHAPTER VI

SUMMARY AND CONCLUSIONS

Following the December 2, 1984, Union Carbide pesticide plant accident that

killed 3,000 people and injured 200,000 more in Bhopal, India, Congress created the

Emergency Planning and Community Right to Know Act (EPCRA) in 1986. The

purpose of this legislation is to inform individuals of the chemical hazards within their

community and stimulate dialogue with state and local governmental officials to protect

the local population from similar accidents. In compliance with this act the

Environmental Protection Agency (EPA) collects data nationally about chemical hazards

and makes this information available to the public on its web site. The EPA currently

makes public information about hazardous waste handlers, toxic chemical release sites,

Superfund sites, and sites requiring either an air release permit or water discharge permit.

Also, hazardous sites the EPA has taken enforcement action against are disclosed on the

EPA web site. However, the EPA removed information from its web site on the most

dangerous chemical facilities called Risk Management Program sites following the

terrorist attack of September 11, 2001 (9/11) although this information is still publicly

available from another web site. Additionally, state and local officials gather information

on certain hazardous chemical facilities, called Tier Two sites, and this information is

available only upon request from state or local officials.

EPCRA has been in existence for twenty years yet no comprehensive study has

been performed studying the impact it has on housing values surrounding disclosed sites.

While many of the issues have been studied individually, no reported study has

investigated them in their entirety. Specially, no reported study has investigated the

impact on housing values of Risk Management Program sites, the generic listing of

enforcement action sites, or investigated the impact of listing new hazardous sites on the

EPA web site has on nearby housing values. Additionally, no reported study investigated

the impact that Tier Two sites have on nearby housing values.

September 11, 2001, shook the American consciences about their security, but did

it increase their fear of neighborhood environmental hazards? If publicly available

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information is being used to value property then residential values near potential terrorist

targets should have declined in the aftermath of 9/11.

Both Mills [2002] and Savitch [2003] hypothesized the terrorist attack of 9/11

would have a pronounced impact on real estate values, and Matthew Kahn [2004] posed

the question if terrorist risk could be quantified in a hedonic model. No reported study

investigated if a hedonic model could be used to measure the impact that potential

terrorist targets have on nearby housing values—especially in middle America.

Simons, and Saginor [2006] suggested using regression analysis to determine if

environmental laws affect the real estate market. Additionally, they asked if the role of

terrorism could also be analyzed by comparing sales before September 11, 2001, (9/11)

with sales after 9/11. This study addresses both issues.

In response to EPCRA, the EPA in cooperation with other federal agencies

created the comprehensive software program LandView VI to assist emergency planners

and responders. No previous study reported used LandView VI to investigate the impact

that chemical hazards have on surrounding housing values.

This study is the first examination of the impact of Risk Management Program

sites and Tier Two sites have on nearby housing values. It is the first examination of the

impact that sites newly listed on the EPA web site have on surrounding housing values.

And it is the first to use the features of LandView VI in combination with a hedonic

model.

This study uses data from 508 sales evenly divided between before and after 9/11

listed by a Multiple Listing Service (MLS) within the Lubbock County study area.

Additional data comes from the EPA Envirofacts Data Warehouse internet site, the

RTKnet (Right-To-Know) internet site, the MapQuest internet site, the Texas Tier Two

Chemical Reporting Program, and LandView VI.

For testing the hypotheses, the data for this study is used to create both a log-

linear and a linear hedonic price model using the statistic computer program SAS 9. The

linear model is found to be superior. One of the concerns of this study is if spatial

autocorrelation is present in the data for the hedonic model. Empirical analysis of the data

indicates that spatial autocorrelation is not present in the linear model; however, due to

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the unequal variance of the two study periods the SAS mixed procedure is used to model

the data and test the hypotheses The first hypothesis questions if housing prices near

EPA listed chemical hazards were lower, ceteris paribus, as of 9/11. This study finds

that housing values are lower near Permitted Water Discharge sites. The second

hypothesis questions if housing prices are lower near Tier Two sites, ceteris paribus, as

of 9/11. This study does not find that housing values are lower near Tier Two sites. The

third hypothesis questions if the negative impact of either EPA listed sites or Tier Two

sites grows after 9/11. This study finds the negative impact of being between 2/3 of a

mile and one mile of a Hazardous Waste Handler does grow after 9/11. The last

hypothesis questions if the new listing of a chemical hazardous sites or the listing of

enforcement action after increases the negative impact of being near a chemical

hazardous site. Following an EPA listing, this study finds the negative impact does not

suddenly increase bringing up a question of market efficiency.

Efficient Market theory holds that both buyers and sellers are fully informed at to

relevant market conditions at the time of sale. Therefore, if a chemical hazard is present,

then home owners and prospective buyers and can be expected to know about the hazard

and factor that in their pricing decisions. A major objective of this study is to determine

if the EPCRA negatively impacts housing prices. Some preexisting chemical hazards

appear to negatively impact nearby housing values; however, in no case does listing a

new hazardous site appear to negatively impact value. So either the public is aware of

the EPA lists and does not consider the lists important, or buyers are purchasing

residences unaware of the nearby chemical hazards. Either way, the results seem to lead

to the conclusion that it is not the legislative act that lowers values, but rather the

individual sites themselves. It is probable that buyers are making purchases based only

on information available locally totally unaware of the information available on the EPA

internet site. Since local information consists of hazardous site aesthetics and word-of-

mouth risk assessments, is there in reality an efficient housing market?

However, all conclusions drawn from test results must take into consideration that

the study area contains a very large minority population and below average housing

values. Different demographics could produce different results.

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This study presents as many questions as it answers and cites the opinion of others

in attempting to answer them. The study area within Lubbock County was selected

because it has a large number of chemical hazardous sites. This area also has, as a whole,

below average housing values. This begs the question if the hazards lowered the value or

did the hazards locate there because the values are lower. Because study results indicate

that housing values do not immediately decrease following the announcement of a new

hazard, one might be tempted to conclude that hazards tend to locate on lower priced

property. However, there is another explanation. Portnov, Odish, and Fleishman [2005]

suggest that property values decline over time following the creation of an environmental

disamenity. They suggest that capital reinvestment in an area suffers as a result of the

disamenity leading to a slow decline in value. Unfavorable environmental conditions

could lead to higher turnover of housing and to a gradual decline in the social-economic

status of the residents.

Another question is whether it is in the public’s best interest to openly disclose the

presence of potential terrorist targets. Lyman [2005] answers this question by stating that

one of the lessons of 9/11 is to “call a hazard a hazard. And be ready to protect the public

against all environmental risks.” And Dermisi [2006] adds “that future terrorist attacks

are more likely to be prevented or their effects more easily mitigated if there is a close

collaboration between real estate organizations and law enforcement not only at the

national level but also, more importantly, at the city level.”

To summarize, this study is divided into five chapters. The discussion begins in

Chapter I with an introduction to the problem of the impact of chemical hazardous sites

on surrounding housing values and the possible September 11, 2001, (9/11) effect.

The analysis continues in Chapter II with a review of pertinent literature. There

the reader finds prior research on the impact of environmental hazards on land values

along with the possible impact of 9/11 on urban space.

In Chapter III the theoretical development of the basis for the proposed study and

the statement of hypotheses are established. In that chapter the chemical hazardous sites

to be investigated are described and the theoretical model for measuring the impact of

these hazards on housing values is developed. The impact of all the environmental

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hazards listed by the Environmental Protection Agency (EPA) on their internet site along

with the impact of Tier Two sites on housing values within the study area is to be

measured using a hedonic model. Also in this chapter all the hypotheses are stated. The

first hypothesis focuses on the impact that “previously listed” sites had as of 9/11. The

second hypothesis addresses the impact that Tier Two sites had as of 9/11. The third

hypothesis is concerned with measuring the impact 9/11 had on environmental

disamenities, and the fourth hypothesis addresses the impact that “newly listed” sites had

on housing values.

In Chapter IV the source of data are discussed. Data from many sources are

needed to test the four hypotheses. The hedonic model requires residential sales prices,

house/lot addresses and characteristics, locational/neighborhood attributes, and hazardous

site locations. The data is to be gathered from the Lubbock Multiple Listing Service

database, LandView VI, the EPA Envirofacts Data Warehouse internet site, the RTKnet

(Right-to-Know) internet site, and the Texas Tier Two Chemical Reporting Program;

and, the study site is four contiguous zip code areas in Lubbock County, Texas.

In Chapter V, the model is fully specified and the statistical procedures for testing

the four hypotheses are discussed. Moran’s I is proposed to test the model for spatial

autocorrelation errors. And in the event the model fails this test, an alternative model is

described. SAS 9 is the statistical package for statistically testing the model.

Chapter VI discusses the results of applying the methodology established in

Chapter V to the data described in Chapter IV for testing the hypotheses listed in Chapter

III. The methodology first produces a model using the statistical process Ordinary Least

Squares (OLS). This OLS model is tested for violation of statistical assumptions

including spatial autocorrelation of the error terms. Because the OLS model suffers from

unequal variance between time-period problems, a mixed model using Maximum

Likelihood methodology is produced to overcome the variance problem.

The resulting model is used to test the four hypotheses. The first hypothesis is

tested to determine if proximity to any chemical hazard is statistically significant as of

9/11. The second hypothesis is tested to determine if proximity to Tier Two sites is

statistically significant. The third hypothesis is tested to determine if any part of several

105

negative price gradients for the period ending after 9/11, is statistically larger then the

same gradients for the period ended 9/11. The final hypothesis is tested to determine if

any “newly listed” site group decreased surrounding housing prices after being listed by

the EPA. The results of these tests indicate that housing values are lower near some

chemical hazardous sites; however, housing prices are not lower near Tier Two sites.

The tests also indicate that for Hazardous Waste Handlers the negative impact of being

near a chemical hazardous site grew after 9/11; however, the mere listing of a site by the

EPA on the internet does cause nearby housing values to suddenly decline.

Each model has all of its independent coefficients tested for statistical

significance and the results of these tests are discussed. Also the quality of the model

overall is statistically measured and these results are discussed. Additionally, the results

of the hypotheses tests are thoroughly discussed, and the conclusions drawn from this

study are compared with previous studies.

The following are recommendations for future research. First, the major

limitation of this study is that it has limited external validity because the study is limited

to Lubbock, Texas. Accordingly, it is recommended that this study be extended to other

geographic areas and the results compared. Next, future study could see if the trend of

declining property values near chemical hazards continues within the study area by

performing this study again at a future time. While outside of scope of this investigation,

a review of the data implies that higher-price property may react differently then lower-

price property. Accordingly, the scope of future study could be increased to compare the

reaction to the presence of chemical hazards in higher social-economic neighborhoods

with the reaction in lower social-economic neighborhoods to see if different price

gradients exist regarding the chemical hazardous sites introduced by this study. Lastly,

this study calls into question whether the housing market is truly efficient regarding the

EPCRA. Future study could investigate that question.

106

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