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clarify this cautionary note. With that caution in mind, property analysis is one of the most
powerful tools available in understanding the influence of highway projects on nearby residents
and businesses, and the applicability of this tool, which already is high, will increase in the future
as new data and GIS tools become available.
Importance of impacts. In some cases, the collection of sufficient data to carry out an analysis
of the probable effects of a transportation project on nearby property values may be quite costly.
It may be prudent to begin with a simpler approach, like the first method listed below, to gain
insight into the approximate magnitude of these effects. If the effects are unlikely to be sizable, it
generally is not necessary to collect the data necessary to apply the more rigorous Method 3.
METHODS
Table 12-1 provides a summary of the methods presented in this chapter.
Table 12-1.
Summary of methods for analyzing land prices and property values
Assessment Appropriate Use Data Expertise
Method level uses when needs required
1. Market Screening/ Project/ Small project or Low Data
studies and detailed corridor where a more collection
expert resource-intensive and
opinion analysis is either not interview
feasible or not
necessary
2. Property Detailed Project/ Projects and Medium Property
comparisons corridor alternatives appraisal
and assessments where
appraiser sophisticated models
opinion are not available
3. Hedonic Detailed Project/ Large projects or High Statistical
regression corridor/ policies where methods
system changes in land and
property values are
expected
Method 1. Market studies and expert opinion
This method can be the simplest and least data intensive of the methods described in this chapter.
The application of this method can be as simple as having a real estate expert advise on the likely
property value impacts of a highway project and then incorporating that assessment into an
environmental justice analysis. Yet, as with almost any method, agencies can use variations that
are more systematic and hence can provide more reliable inferences.
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There are, broadly speaking, two types of expert judgment that can be obtained. In the first case,
one can consult real estate experts, usually local professionals who are knowledgeable about
local property markets. In the second case, an agency can contract with a firm or expert to
conduct a market study. The two cases are likely to be distinguished by the rigor and amount of
systematic analysis employed, even though both will rely heavily on expert understanding of the
property markets.
In some cases, a market study might combine expert analysis with the techniques from Method 2
(property appraisals) and Method 3 (hedonic regression). Experts might form their opinion, in
part, based on appraisals or hedonic studies. So there can be cases where it is difficult to cleanly
separate which of the three methods described in this chapter is being used, and the boundaries
between the methods can blur at times. Yet having said that, the three methods described in this
chapter--expert opinion, appraisals, and hedonic regression--each involve increasing levels of
sophistication and are each appropriate in different circumstances, and so it is sensible to
describe each separately.
When to use. The method of expert opinion is most appropriate in cases where a more resource-
intensive analysis is either not feasible or not necessary. For small projects, with likely small
environmental justice implications, an expert analysis might be sufficient. Similarly, smaller
agencies with limited technical capabilities will find that careful use of expert opinion can go a
long way toward illuminating the potential environmental justice impacts of a project.
This method can be used as a preliminary stage in the analysis, to identify the location of
possible positive or negative impacts. If, for example, the use of expert opinion suggests that the
property value impacts might lead to importantly large environmental justice concerns, the other
two methods (appraisals or hedonic regression) can be used to understand the magnitude of the
property value impacts and how those positive or negative impacts will be incident on different
groups.
Analysis. The data collection should yield information about where property values will be
expected to increase and where they will be expected to decrease as a result of the highway
project. In some cases, agencies might even have estimates of magnitudes of property value
increases or decreases at particular locations. The analysis involves comparing that information
to the spatial distribution of low-income or minority tenants (owners and renters of residences
and also possibly business space) to understand if those groups are disproportionately affected by
the highway project.
Data needs, assumptions, and limitations. The data collection involves canvassing real estate
experts to assess the likely impact of a highway project on nearby properties. This can include
focus groups or systematic surveys of experts. Less systematically, an agency might simply
discuss the impact with a few experts and carefully document the results. In some cases, such
less systematic assessments might be sufficient.
It is also necessary to obtain data on minority, low-income, and other protected populations'
characteristics, such as is available from the census. Information on housing unit occupancy and
owner/renter tenancy are also available from the census. This method provides general
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information on the potential for a project to affect property values. While expert opinion might
not be able to precisely quantify the magnitude of land price or property value impacts, expert
opinion can be quite useful in assessing where property values might be positively or negatively
influenced by a highway project. Typically, this method will be regarded as less systematic than
the other two methods described below. Certainly, agencies should carefully document how
experts arrived at their opinion, so that the agency can defend the analysis, if necessary, in the
future.
Results and their presentation. Generally, the results of this method will be contained in a
report summarizing the insights of the experts contacted. Two levels of analysis are possible. In
the simplest case, the experts may provide an indication of the properties that would be affected
if the highway project were to be carried out. A more extensive analysis may reveal the experts'
opinions as to the approximate magnitude of effects on property values.
Assessment. The reliability of this method depends crucially on the knowledge of the experts
and the care taken to interpret their opinions. Agencies should seek to understand not only the
experts' opinions, but also how reliable (or precise) they believe their analysis to be. Overall, this
method is best used either when the scope of the project or the expected environmental justice
implications are of a small enough magnitude that large resource investments are not justified or
when the agency does not have the means to conduct a study using the other methods described
below.
Method 2. Property comparisons/appraiser opinion
The method of property comparisons is widely used by property appraisers to determine values
of houses and commercial properties. To conduct the analysis, an appraiser finds recently sold
properties within the same vicinity and with characteristics similar to the property being
appraised. The properties comparable to the property to be appraised are known as "comps."
Their sale prices are adjusted to yield the appraised value of the property in question. In practice,
the comparison criteria include dwelling age, various physical characteristics, and a broad range
of location characteristics.
When to use. Since this method is based largely on appraisers' judgment, property value impacts
of transportation projects of any size can be evaluated in this way. Appraisers' judgment may
well be the best estimates of property values when sophisticated models are not readily available.
The agency should give special attention to finding comparison properties near transportation
projects that are similar to the project being analyzed.
Analysis. To use this method for assessing environmental justice impacts of transportation
projects, appraisers must include a precise measure of how transportation accessibility is
capitalized into property value. If there are numerous recent property sales, it may be easy to find
comps with similar accessibility characteristics. Otherwise, a gradient must be developed, such
that it can explain variation in property value by variations in accessibility. For instance, past
empirical studies of the relationship between house values and distances to highways revealed a
gradient of approximately $1 to $4 per foot, which implies that each foot of distance away from a
highway will reduce house values by $1 to $4 (Boarnet and Chalermpong 2001). In this way,
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even when comps with similar accessibility characteristics cannot be found, other comps can be
used, and the values of those comps can be adjusted using an accessibility gradient.
Data needs, assumptions, and limitations. Data to be collected include physical characteristics
(such as number of rooms, floor area, quality of construction, piping condition), transportation
accessibility, amenities (such as school quality), and disamenities (such as crime and noise).
More importantly, location characteristics that involve transportation accessibility must be
evaluated and recorded. These include, for example, distance to the transportation facility of
interest, such as highway ramps or transit stations. Other neighborhoods in the region with
similar transportation accessibility characteristics must then be identified. Appraisers can then
search for comps using the information about properties to be appraised. Market rents and sale
prices of these comps are then used to estimate the values of properties in question.
Special care must be given to identify good comps. Since human judgments can vary widely, this
method may not be suitable if similar properties are difficult to find. Without good comps, this
method, which relies solely on appraisers' judgment, may not yield an accurate forecast of
changes in property values.
Results and their presentation. For the potentially affected properties, each appraiser provides
a report on the likely change in value, based on comparable properties. If two or more appraisers
are used, the analyst can compile a brief report providing a range in impacts on the values of
affected properties.
Assessment. Difficulties in finding similar properties or land with similar accessibility and other
characteristics may limit the use of this method. Some location characteristics that may affect
property values, such as air and noise pollution, may be difficult to compare, and thus cannot be
assessed. Yet the advantages of this method are that all metropolitan areas have a large number
of property appraisers and property appraisal techniques have become highly standardized. By
using a panel of several appraisers, a transportation agency can obtain a range of estimates that
will help the agency cope with differences in interpretation that are inherent in this method.
Method 3. Hedonic regression
Hedonic regression is a statistical method for evaluating impacts of various kinds on house and
residential property prices. These factors range from basic attributes of houses, such as size,
number of rooms, and age, to location attributes, such as accessibility to highways and other
amenities and disamenities, including the quality of public services, local tax burdens, and public
safety. The method utilizes a wide range of data to estimate how a change in a given factor
increases or decreases house prices.
Environmental justice analyses of new highway projects can be carried out by applying this
method to forecast the highway-induced changes in prices of houses that are owned or rented by
people in protected population groups. For example, increased levels of traffic noise caused by a
new highway can reduce the desirability of houses nearby and therefore reduce sales prices of
these houses. If the owners or tenants belong to protected population groups, actions may need to
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be taken to compensate them for the losses or disamenities. The losses can often be quantified
using the output of the regression model.
When to use. The regression method is appropriate when the project in question is of significant
scale. A new grade-separated highway, for example, will not only change transportation
accessibility for residents in the area, but also may have major environmental impacts. These
changes will be translated into changes in house prices in the surrounding areas, which can be
evaluated using the hedonic method. However, smaller projects, such as a road widening in a
commercial district, will likely have minor impacts on house prices, and the method may not be
able to detect any significant changes in house prices.2 Also, this method requires extensive data
as well as technical skills, both of which may not be available in smaller agencies.
Analysis. A multiple regression technique is used to isolate effects that various factors have on
house prices. These factors are called explanatory (independent) variables because they are used
to explain the variation in house and property prices (the dependent variable). The influence of
each factor (independent variable) on property values is reflected by its coefficient; that is, if the
coefficient of an independent variable, such as floor area, is positive, an increase in that variable
(larger floor area) will increase the property value, all else being equal. Statistical knowledge and
judgment are required for hedonic modeling to make decisions such as which factors should be
used as explanatory variables.
The estimation of different specifications of hedonic regressions is carried out by commercial
software, such as SPSS, SAS, STATA, or GAUSS. The performance of each estimated model is
reflected by two measures the R2 (goodness of fit of the overall equation) and t-statistics of the
coefficients. The R2 ranges from 0 to 1 and shows how well the independent variables together
explain the variation in the dependent variable (property prices). The t-statistics show how
precisely each coefficient is estimated.
Environmental justice analyses for highway projects can be carried out as follows. First, identify
and quantify changes in independent variables that result from the project. For example, a new
highway may reduce the distance from a house to the nearest highway.3 The highway project
might also increase the noise levels and create visual blight. Changes in property values can be
determined from the coefficients of a hedonic model. For example, if the coefficient on distance
to the nearest freeway is -$1 per foot, and the highway project will reduce the distance from a
particular house to the nearest freeway by 2,000 feet, then that house would gain value equal to
(-$1) x (-2,000) = $2,000. If noise levels at the house rise by 5 decibels (dB) due to the highway
project and if the coefficient on noise in a hedonic regression indicates that property prices are
reduced by $100 per dB, then the noise impact due to the highway project will reduce the
house's value by (-$5) x (100) = -$500. Thus, ignoring visual effects, the net effect of the
changes in accessibility and noise caused by the completion of the highway project increases the
house's value by $1,500.
2
Note that a road widening in a commercial district might have larger effects on the prices of commercial property.
3
The straight-line distance to the nearest highway is one measure of accessibility. Others include street-network
distance to the nearest highway and distance to the nearest mass transit station.
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The hedonic regression method is used to explain the variation in property prices by their
characteristics. For example, a house that is 500 feet away from a rail transit station is more
expensive than the one that is 5 miles away partially because of its better accessibility to transit.
Similarly, a house near the beach is more expensive than the one farther inland, and a house in a
good school district is more expensive than the one in an under-performing school district. Each
of the proceeding characteristics--access to transit, proximity to a beach, and school
quality--are location-specific. GIS software, such as ArcView, is a useful tool that can help
quantify the value of these variables.4 Agencies can use GIS software to generate many of the
location-specific variables, which in turn can be used for regression analysis of property prices.
The most obvious variables needed for transportation analysis are accessibility measures, often
approximated by distance from a property to a transportation facility (straight-line and street-
network distance have both been used). You can use GIS software to measure the distance from
each house to the nearest transportation facility. Other location-related variables are available
based on local jurisdictions. For example, average SAT scores, which can be used as a measure
of school quality, are often available from school districts. GIS software can be used to match
house locations to school district boundaries. Similarly, GIS software can match house locations
to other jurisdictional boundaries, so that data available from various jurisdictions can be used,
often as control variables, in an environmental justice analysis of highway projects. For example,
you could assign a city's average crime rate (which reflects the city's public safety) to a house
according to where the house is located.
The procedures for creating independent variables using GIS are summarized below.
Step 1 Geocode the street addresses of houses. You can match (geocode) each house's street
address, obtained from a source such as tax assessor's records, to the GIS street-network map.
The geocoding process can be automated to match many addresses at the same time, and GIS
software packages typically include routines for address matching and geocoding.
Step 2 Verify match accuracy. You can examine the accuracy of the address match by either
using diagnostics from the GIS software or by comparing a small number of computerized
matches to printed maps. For example, one can randomly draw 100 houses and compare the GIS
match with published street maps. If relatively few of the GIS matches are inconsistent with the
paper map, then the automated address matching can be judged to be relatively accurate.
Step 3 Create independent variables. The spatial join feature of ArcView can be used to
determine the distance (straight-line or street-network) from a property (from Step 2) to a
transportation facility. The same feature can also be used to assign amenity/disamenity
characteristics that follow jurisdictional boundaries. This requires GIS maps for jurisdictions on
which the data are based, such as school districts or municipalities.
Some other aspects of data preparation are listed below:
· The accuracy of the raw data should be verified. Data quality can vary across different
sources and even across time periods for the same source.
4
ESRI's ArcView for desktop computers can be obtained from http://www.esri.com/.
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· Home sales prices should be adjusted for inflation, using, for example, the consumer
price index for housing in the region or metropolitan area. This index can be obtained
from the U.S. Bureau of Labor Statistics' Web site, http://stats.bls.gov/.
· Non-arms-length transactions should be excluded from the analysis to ensure that sales
prices reflect market transactions. For example, transactions, in which buyers and sellers
share the same last name should be dropped, or at least scrutinized closely, as those sales
might reflect non-arms-length transfers of property from, for example, parents to
children. The sales price in such cases might not reflect the market value of the home if,
for example, the seller is willing to sell the house below market rate to a relative.
Data needs, assumptions, and limitations. Due to the nature of the statistical method, the
hedonic regression technique requires input data that cover a reasonably wide range of
properties. House and property sales records are the main source of data for hedonic analysis.
These data generally include property sale prices, rents, physical characteristics of property,
street address, sellers and buyers, loan amount, etc. Such records can be obtained from local
government agencies, such as tax assessor and collector's offices or appraisal data support
companies that compile such data from these agencies. Measures of accessibility can be
generated from street addresses, using GIS software. In addition to these sources, information
about amenities and disamenities can be gathered from local authorities, such as local police
departments and state departments of justice for crime, and school districts for school quality.
Finally, information about environmental impacts is available in environmental quality reports,
which are commonly required in large highway projects.
Visual effects, being unique to each project and location, might be difficult to assess in a
generalizable way using a hedonic regression approach. Also, there may be variation in different
types of effects across places. For example, some neighborhoods may value quietness more than
others. As a result, the model estimated from data in one neighborhood may not be transferable
for use in other places.
Results and their presentation. This method produces regression equations that show the
additive effects of various attributes on residential property values. If the appropriate data are
available and included in the analysis, it is possible to estimate the effects of a change in a
transportation facility on residential properties located at varying distances from the facility.
These results can be easily presented to a nontechnical person in a way that gives him or her
considerable insight.
Assessment. The hedonic method is more objective and more firmly grounded in theory than the
other methods described in this chapter. Changes in property values can be attributed directly to
each different effect from a transportation project. However, there are substantial technical and
data requirements associated with this method. Moreover, some effects, such as visual quality,
cannot be easily quantified, and thus are difficult to analyze using hedonic regressions.5
5
Rough measures, such as dummy variables for visually blighted areas, can be used as independent variables. For
example, a dummy variable for visual blight can be specified, such that it takes a value of 1 if a highway can be seen
from a house and 0 otherwise. In this way, the effect of visual intrusion from highways on house prices can be
isolated.
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