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Valuation of Indirect Losses
Due to Proximity Damages on
Residential Property in Idaho
N04-03
KLK465

Authored By:

Ruby Miles Stroschein, Sarah E. Miles, and James Nelson

Date: August 13, 2003

Department of Agricultural Economics and Rural Sociology

1
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim Report

Executive Summary
Valuation of Indirect Losses Due to Proximity Damages on Residential Property in Idaho

Statewide transportation planning needs require forecasting and assessing property damages that
result from a road project. As the traffic flow and traffic demands in Idaho change, the Idaho
Transportation Department continuously evaluates transportation elements of comprehensive plans,
determines impacts of proposed land use changes, and determines the transportation needs for thestate. Meeting transportation needs often requires building or widening roadways, which necessitatesthat the state exercise their eminent domain right, the right to take private property for a public use
upon payment of just compensation.
Two basic forms of damages have been identified in eminent domain litigation: the taking of thephysical property; and concluded hypothetical damages occasioned by the taking to the remainder—
the remaining land and improvements as they exist at a point in time after the road project has been
completed. The problem in the past has been that the methods used to estimate the value of thesedamages employed limited comparable data, usually three to five direct comparisons, with subjective
adjustments applied based on experience and arbitrary judgment.
In this study, a six region forecasting model was developed to explain residential property values in
Idaho based on multivariate regression analysis. The model uses factors, or characteristics that
commonly affect the sales price of a home, and less common characteristics such as street-trafficclassification and setback from the street or road, to conclude what portion of home value isattributable to proximity and to street-traffic classifications.
A multi-regional or state wide model was developed and tested, as were separate models for eachregion. The regions from which data were collected and analyzed are: the Idaho Falls region, thePocatello region, the Boise region, the Lewiston region, the Moscow region, and the Coeur d’Aleneregion. The statewide model, which incorporates statstically estimated adjustments for each region,
was the strongest and most complete model. With it, statistically reliable as-is and hypotheticalestimates of residential property values can be calculated within the tested regions statewide forresidential properties that have been or will be affected by damages associated with designing newroutes or widening existing streets and roads. The model will also assist in providing morequantative benchmarks for assessing whether damages have even occured at all.

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ACKNOWLEDGEMENTS
This study would not have been possible without the contribution of many people. The following
people are acknowledged for their much appreciated contributions to this study:
Cindy Reno-Smith, employee of Idaho Transportation Department.
Teresa Kranner, ISU undergraduate for assistance in inputting data and for statisticalconsulting.
Dr. Joel Hamilton and Dr. Chris McIntoch, University of Idaho, for statistical consulting.
Tom S. Stroschein in his assistance in mapping out destinations for efficiently inspecting
properties and driving Sarah Miles and Ruby Stroschein to complete inspections.
Stan Moe, MAI and owner of Columbia Consulting, Coeur d’Alene, Idaho, for input and offering
information relating to the Coeur d’Alene Region.
Trey Knipe, MAI, the owner of Knipe Janoush Knipe, Boise Idaho, for input and analysis of themodel.
Members of the oversight committee for their conferencing in making the model.
Dr. Michael Dixon, NIATT for his contributions and suggestions in the process.

3
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportTable of Contents
Executive Summary………………………………………………………..

2

Acknowledgements…………………………………………………………

3

Table of Contents……………………………………………….………….

4

List of Tables……………………………………………………………….

4

List of Figures……………………………………………………………….

5

Map of Idaho………………………………………………………………..

6

Introduction………………………………………………………………….

7-10

Background
Problem Statement
Study Organization and Process
Literature Review…………………………………………………………….

10-12

Objectives……………………………………………………………………..

12-13

Statistical Analysis and Data………………………………………………..

13-14

Map of Major Idaho Population Centers……………………………………

15

The Model……………………………………………………………………..

16-22

Data…………………………………………………………………………….

23-26

How the Model Works………..……..………………..….…………………..

26-28

Regional Models………………………………………………………………

28-30

Combined City Regions / The Idaho Model………………………………..

31

Results of the Idaho Model…………………………………………………..

32-37

Model Input and Results
Model Calculations
Model Adjustment to Appraiser’s Value
Adjusted Value in the “After”
Conclusion…………………………………………………………………….

38-39

References……………………………………………………………………

40-42

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Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportAPPENDIX
Appendix A – Oregon Cost Manual………………………………………..

43-51

Appendix B – USPAP Mass Appraisal Guideline…………………………

52-60

Appendix C – Excel Instructions……………………………………………

61

Appendix D – A Guide to using Proximity Damage Valuation Model….

62-77

Appendix E –Eff. Use of Experts Including Daubert/Kumho Challenges

78-88

List of Tables
Table 1

Road Classifications

Table 2

Variables Analyzed and General Data Sources for Each Variable

Table 3Results of the Idaho Model

Table 4Model Input and Results

Table 5Model Calculations

Table 6

Model Adjustment to Appraiser’s Value

Table 7

Adjusted Value in the “After”

List of Figures
Figure 1

Map of the State of Idaho

Figure 2

Identification of Major Idaho Population Centers

Figure 3

Primary Data

Figure 4

Building Description

Figure 5

Amenities

Figure 6

Land Description

Figure 7

Road Data

Figure 8

Combined City Region

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Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportFigure 1. Map of the State of Idaho

6
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportIntroduction
Idaho’s transportation system is comprised of about 60,000 miles of road, about 4,000
bridges, 1,900 miles of rail lines, 125 public airports, and the Port of Lewiston. Of Idaho’s 58,588 milesof roads, approximately 9 percent, or 5,000 miles, are state–controlled, while 40 percent are federal.
Of the total miles of non-federal rural roads, 14 percent are state, 44 percent are county, less than one
percent are township, and 42 percent are municipal and other. The state highway system accounts
for 54 percent of the state’s vehicle miles of travel, while 41 percent of vehicle miles of travel occur on
the interstates. From 1984 to 1998, vehicle miles of travel on the state highway system has increased
more than 63 percent (Idaho’s Transportation System Defined).
As the demands of the traveling public in Idaho change, traffic flow and traffic demands inIdaho change. As a result, the Idaho Transporation Department (ITD) is responsible for developing a
20-year long-range plan as well as the 3-year State Transportation Improvement Program (STIP).
Working under the supervision of a Governor Appointed Board, ITD has six planning districts that workwith a variety of transportation planning organizations and groups, including six regional planning anddevelopment organizations. Meeting the state’s transportation needs often requires widening
roadways or designing new routes, which necessitate that the state exercise their eminent domainright. Eminent domain is the right of the state to take private property for a public use upon payment ofjust compensation.
In Real Estate Valuation In Litigation second ediition, (1995), James Eaton identifies two basic
forms of damages in eminent domain litigation. One is the taking of the physical property. The otheris the concluded damages occasioned by the taking to the remainder parcel. The amount of damages
is determined by computing values concluded by doing an appraisal on the property in its present
state (“as-is”) and an assumed or supposed (“hypothetical”) value of the property at a point in timeafter the road project has been completed (chapter 14).

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The Idaho Department of Transportation spends a great deal of taxpayer money tocompensate residential property owners for estimates of residual property damage resulting from aroad project.

More specifically, measuring damages caused by the remainder’s proximity to theimprovement being constructed, e.g., a highway, has not been empirically examined on a statewide or
regional level, and the relationship between estimates for just compensation for anticipated damages
and the actual loss of market value to the residential property has not been empirically identified.
In 1997, the right of way division of ITD organized a task force to consider the parameters of acomprehensive study in an effort to develop consistency and reliability in concluding residential
property proximity damages. After three years of gathering studies and literature from federal
agencies and other state transportation departments, the task force contacted the Univesity of Idaho
Agricultural Economics and Rural Sociology Department requesting a proposal to complete the study
for Idaho. In November 2000, the grant was issued and administered through NIATT, National
Institute for Advanced Transportation Technology, the transportation engineering division of the
Unversity of Idaho.
The Idaho Transporation Department is the lead agency for the research project with a fivemember technical oversight committee. The committee members include:•

Doyle Pugmire, Appraisal Coordinator, ITD•

Leonard Hill, Right of Way Manager, ITD•

Rick Machmeier, Right of Way, Appraisal Review, ITD•

Scott Frey, FHWA•

Karl Vogt, Attorney General’s Office, ITD

8Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportProblem Statement: The Real Property Aquisition Appraisal Process.
It has become essential for real estate appraisers to use a standard definition of “Proximity
Damage” as well as a standard formula in the value computations in order to avoid subjective andflawed estimates of value.
“There are many perspectives that lend themselves for proximity study… its physical orenvironment affects, its social affects,its health affects, etc.” (p. 2) The Appraisal Journal,
Transportation Research Record, Right of Way Journal, and Real Estate Valuation in Litigation
provide extensive information on the valuation process of typical and atypical properties, as well ascomplex and noncomplex appraisals. These publications describe regulations for eminent domain
appraisals, following Uniform Standards of Professional Appraisal Practice and Uniform Appraisal
Standards for Federal Land Acquisitions.
While appraisers do not conclude just compensation, they are required to measure thediminution in value based on material facts and circumstances that would influence a buyer orseller. (Eaton, 1995, p. 20) The Federal Highway Administration appraisal guidelines outline
technique and methodology, which state:
The sales comparison approach should be developed and relied upon whenever there isadequate market data.
The approach shall include adequate research to identify all pertinent similar properties forwhich sales, listings, or rental data are available.
All comparable information will be confirmed by the buyer, seller, broker or other person
having knowledge of the price, terms and conditions or the reason for not so confirming shallbe stated.
Significant adjustments for similarities and dissimilarities such as time, location, physical andeconomic characteristics, and motivation for the transaction shall be individually explained.
Substantial lump sum adjustments that cannot be quantitatively or qualitatively supported are
not acceptable.

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Using the specific methods defined by the Federal Highway Administration appraisal
guidelines, it becomes evident that proximity damages have a discernable affect on property values.
Proximity damages are specifically defined as “[a]n element of severance [compensable]
damages that is caused by the remainder’s proximity to the improvement being constructed, e.g., ahighway; may also arise from proximity to an objectionable characteristic of a site or improvement,e.g., dirt, dust, noise, vibration.” (Eaton, 1995, p. 314) Distinguishing proximity damages from otherfactors that effect value, e.g., square footage, condition, effective age, room count, lot size, are ideallyconcuded by measuring properties with identical or similar features. It is near impossible to findrecently sold properties that are substantially the same, with exception to proximity to thecharacteristics that create dirt, dust, noise, and vibration. As a consequence, the direct sales
comparison technique is highly limited in these types of appraiser problems.
Literature Review.
The major studies that have been performed with respect to proximity damages focused
primarily on “comparison control” research method and “before and after” research method.
Initial studies employed several other methodologies. One study conducted in the late 1970’s
addressed multiple regression process with proximity as one of the variables. All of the material
surveyed consisted of studies researching the socioeconomic impacts of freeway projects.
Private research firms for the State of California, and the State of Washington transportation
department conducted two of the studies reviewed. Six additional studies that were evaluatedfor methodology were research papers presented to the California Transportation Board. Mostof the publications were from the 1970 to 1980 era, a period of high growth and progressive
transportation modifications, primarily in freeway design and construction, including integration
with existing housing developments.
Most of the studies conducted found an absence of a reliable predictive model to approximate
damages, likely caused by a number of factors. However, these studies do point to recurring patterns

10
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim Reportin the effects of freeways on residential property values. Most of the studies completed were“comparison control” method in which an impact area adjacent or close to a freeway was compared toa control area farther removed from the freeway.
Professional research material was reviewed for additional information. Appraisal Journal is a
professional journal that discusses new valuation methods, and current concerns and developmentsin the field of real estate appraising. It often references the appraisal of atypical and complex
properties and methods. It does not address the problem of proximity valuation, but does recommend
use of before and after valuation techniques for appraisal problems for which no market indicators
exist.
The national refereed journal, Right of Way Journal provided more background to the problem
of residential proximity damages than any other available source. An article based on a speechpresented to the International Right of Way Association International Seminar in Baltimore, Maryland
on June 17, 1998, cites the process derived by Salt Lake City’s Property Management Department,
based on a study conducted in a portion of the city. The findings can be summarized as follows:
The council compared a selection of properties that had sold, been subject to a taking, andthen resold.
In addition to the traditional components of an appraisal, the appraisers for this study did a
comparison in the before and after, and included a residential front yard proximity study report.
Values on intrinsic damages derived from the market were concluded from the before andafter comparison of value.
The appraisers did consistently find a decrease in the market value of the properties in theafter condition, or when the distance between the residential property and the road decreased.
Damages were expressed in the form of a percentage of the before value.1

1

It should be noted that the street-traffic classification co-efficient in the Salt Lake City study remained
constant, and that the location of the house with respect to the distance from the road varied. In themodel presented in this report, the distance from road variable and the street-traffic classification variableboth vary.

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Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportA query was made by ITD in 2001 with all State Transportation Departments. Responses tothe query showed that no study has yet been performed that uses research multivariate regression
analysis to estimate the market value of road characteristics (expressed as independent variables) as
they affect the sales price of a residential property (the dependent variable). Multivariate regression
analysis involves selecting independent variables (I.V.’s) that, when working together, create an
outcome (the dependent variable). As an example, a 1,500 square-foot (first I.V.) above average
quality construction home (second I.V.) with three bedrooms (third I.V.), two bathrooms (fourth I.V.)
and a two-car garage (fifth I.V.) sitting on a 7,000 square foot lot (sixth I.V.) with a fifty-foot set-backfrom a road (seventh I.V.) with 500 to 1,000 cars per day (eighth I.V.) located in Moscow, Idaho (ninth
I.V.) creates a value of $137,000 (the dependent variable). The independent variables selected, andtheir reliability of predicting values, are selected by using statistical processes discussed later in the
Methods section.
Objectives
Compensation for proximity damages (reduced value of the remaining property after a road isbuilt or widened) is based on the assumption that the value of residential property is diminished as adirect result of proximity to a high traffic road. The methods being used to estimate the values of thesedamages employ limited comparable data, usually three to five direct comparisons, and subjective
adjustments based on perception and arbitrary judgment. An objective study based on a method inwhich numerous home sales are identified in an impact area adjacent or close to a high traffic road,and numerous home sales away from high traffic impact areas is needed to emperically conclude if
damages do exist, and to quantify such damages.
The general objective of this research was to determine what features or characteristics ofroads, if any, affect the sale prices of adjacent residential properties, and to quantify such effects.
Specific objectives of this study are:12
Proximty Damages Study in Idaho for the Idaho Transportation Department

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1. Identify significant independent variables that affect the values of single-family homes in major
population regions of Idaho, in order to isolate road related factors.
2. Evaluate models of value of single family homes for different regions of Idaho, relative tomodels for the state as a whole to determine what model or models could be most useful toestimate single family residential property values throughout Idaho.
3. Evaluate any empirical evidence of road effects to conclude a standardized method for
applying damage measurement in analyzing estimated losses of market value due to roadprojects.
Setting: The State of Idaho.
The following is a brief overview of the geographic information and demographics ofIdaho210 largest cities by population (2000):
Boise, 85,787; Nampa, 51,867; Pocatello, 51,466; Idaho Falls, 50,730; Meridian, 34,919; Coeurd'Alene, 34,514; Twin Falls, 34,469; Lewiston, 30,904; Caldwell, 25,967; Moscow, 21,291
Land area: 82,747 sq mi. (214,315 sq km)
Geographic center: In Custer Co., at Custer, SW of Challis
Number of counties: 44, plus small part of Yellowstone National Park
Largest county by population and area: Ada, pop. 312,337 (2001); Idaho, 8,485 sq mi.
State forests: 881,000 ac.
State parks: 27 (43,000+ ac.)2001 resident population est.: 1,321,006
2000 resident census population (rank): 1,293,953 (39). Male: 648,660 (50.1%); Female:
645,293 (49.9%). White: 1,177,304 (91.0%); Black: 5,456 (0.4%); American Indian: 17,645(1.4%); Asian: 11,889 (0.9%); Other race: 54,742 (4.2%); Two or more races: 25,609 (2.0%);
Hispanic/Latino: 101,690 (7.9%). 2000 population 18 and over: 71.5%; 2000 population 65
and over: 11.3%; median age: 33.2.
Idaho is the 13th largest state in the U.S. in land area, 11th smallest in population, and 11th least
densely populated. Approximately 73 percent of Idaho’s population and jobs, and just under 100

2

Though popularly believed to be an Indian word, “Idaho” is an invented name whose meaning is
unknown.

13
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim Reportpercent of the land, is non-urban. Approximately 94 perent of Idaho’s roads are rural. Federally
owned lands amount to 62 percent of the state’s land area.
12 percent of the state’s jobs are in the rural agriculture sector, and 1 percent are the mining
sector. Most jobs in Idaho’s rural areas are in sectors also common in urban areas: construction,transportation and utilities (12 percent); manufacturing (15 percent); business and trade (26 percent);and services and government (34 percent). (Idaho’s Transportation System Defined)
This study concentrated on the following six major population centers in Idaho: (Figure 1)
1.

The greater Pocatello - Bannock County region

2.

The greater Idaho Falls - Bonneville County region

3.

The greater Boise – Ada County region

4.

The greater Lewiston - Nez Perce County region

5.

The greater Moscow - Latah County region

6.

The greater Coeur d’Alene - Kootenai County region

14
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportFigure 2. Identification of Major Idaho Population Centers

Coeur d’Alene
RegionMoscow
RegionLewiston
RegionBoise Region

Idaho Falls
RegionPocatello
Region15
Proximty Damages Study in Idaho for the Idaho Transportation Department

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The Model.
The general model used in this study is a multivariate regression model with residential
property value as the dependent variable. Independent variables considered in the study are possible
factors explaining residential property values. These variables were specified based on two criteria,
as follows:1.

Variables generally considered to be consistent factors that affect residential
property values in the direct comparison approach appraisal method, under typicalsales conditions with a typically motivated purchaser and seller, and2.

Variables concluded to impact value related to road proximity, based on review offorty to fifty proximity damage files at the Idaho Transportation Department.

General sources of data on specified variables were:
1.

Real estate mutliple listing service (MLS) information from each of the six regions of
Idaho considered in this study.

2.

County assessors’ field sheets and computer data bases where MLS data were notcomplete.

3.

Idaho Transportation Department (ITD), COMPASS, Ada County Highway District,and local traffic engineering departments’ traffic count data. These data werecollected according to the street-traffic count classifcations shown in Table 1.

4.

On-site inspections of each property considered.

16
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportTable 1. Street-Traffic Count Classification
Model
Classification
Identificaiton

Street Use

Traffic Count

Base Case

Local-A

Residential

0-100 cars per day

1

Local-B

Residential

101-500 cars per day

2

Local C

Residential

501-1000 carps per day

3

Collector

Traffic Circulation

1001-5000 cars per day

4

Minor/Rural
Arterial

Through-travel,leaving, entering

5,000-10,000 cars per day

5

Principal Arterial*

Through-travel

10,000+ cars per day*Interstates are included in this classification.
The Federal Highway Guide for Functional Highway Classification notes that area definitions
for urban and rural areas have fundamentally different characteristics as to density and types of landuse, density of streets, and highway networks. Since data for this study were collected in areas ofgreater than 5,000 population, urban classifications apply. 3 COMPASS and Federal Highway street
classification information was correlated with traffic count data obtained from ITD, Ada County
Highway District, and local traffic engineering departments.
A list of independent variables analyzed and the general source of data for each of thesevariables were presented in Table 2. Data were collected on about 1,800 MLS listed residential homesales that represent about 10 percent of the market for the period analyzed (1998 through mid - 2002).

3

The word “road” may be interchangebly used with “street” within the report, both having the same meaning.
COMPASS and the Federal Highway Administration also use the term “street” and “road” interchangeably within their
publications.

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Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportTable 2. Variables Analyzed and General Data Sources for Each Variable*
Variable

General Data Source

Date of SaleMLS Data Sheets

Year built

MLS Data Sheets and Assessor

Effective age

On-site inspections and MLS

Quality of consturction

On-site inspections

Gross Living Area

MLS Data Sheets

Above Grade Bedroom CountMLS Data Sheets

Above Grade Bathroom CountMLS Data Sheets

Total Basement Square FeetMLS Data Sheets

Basement Square Feet Finished

MLS Data Sheets

Heating System

MLS Data Sheets

Cooling System

MLS Data Sheets

Number of Fireplaces

MLS Data Sheets

Patios/Decks

MLS Data Sheets

Fencing

MLS and on- site inspections

Automatic Sprinkling System

MLS Data Sheets

Shops & Outbuildings

MLS and on-site inspections

Car Storage (includes garages andcarports)

MLS and on-site inspections

Lot Size

MLS Data Sheets and Assessor

ZoningMLS Data Sheets and City P &Z

Location

MLS and On-Site
Inspections

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Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportTable 2, continued
Variable

General Data Source

Setback of house from road (curbto living area)

On-site Inspection –measurement using measuring
wheel

Fronts/backs to road*On-site inspection

Traffic Count**

ITD, ACHD, local traffic data

Speet Limit**

On-site Inspection

Number of lanes**

On-site Inspection

Road Classification

On-site Inspection*See setback explanation following.
**Traffic count, speed limit, and number of lanes data are used to conclude the overall roadclassification
All of the variables included in the study are considered to be in excess of Uniform Standardsof Professional Appraisal Practise and Uniform Appraisal Standards for Federal Land Acquisitions
requirements. Additional data collected and on file include: Addresses, legal descriptions whereavailable, MLS reference numbers, dates of sales, tax parcel numbers (when made available in the
MLS data), financing, sales concessions, list prices, and number of days on market.
The meanings of most of the variables specified in Table 2 are stright forward. However, thefollowing variables deserve additional explanation:-

Setback of home from the road. An onsite inspection was made for each property
to measure the distance, in feet, of the home set-back from the road travelway. If
there was more than one road abutting the site, the road with the most proximity
characteristics was used as the measuring point, measuring to the front, side, orback as it applied.

19
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim Report-Effective age. Onsite inspection and specific property information garnered from
realtor comments on the MLS data sheet were used to conclude the effective ageof the home. Specific information such as a new roof, new carpets, new heating
system, new kitchen cabinets,etc. were accounted for in concluding the effective
age of the home. Condition was originally considered as a separate variable, but
was found to be highly correlated with effective age, indicating that condition isoften inherent in effective age, causing multicollinearity, and was thus eliminatedas an independent variable.

-

Quality of Construction Classification. The quality of construction of the home isbased on classifications used by the Oregon Cost Manual, correlating classes 1-8
to “below average,” “average,” “above average,” and “good.” Oregon Cost Manualclass 8 homes are considered to be excellent quality construction homes and were
not used in any of the models. Oregon Cost Manual classifications ofconstruction are included in the Appendix, H through L. Parameters forconstruction quality are, from the Oregon Cost Manual:Classes 1 and 2 = “Below Average” Construction Quality
Class 1 Description: Structures in this class are built at low cost in keeping withthe overall simple design and modest construction. Emphasis is on basic shelter.
These houses fall far short of sound minimum building standards.
The structures often lack a planned design. Building additions are common andmay contain materials not compatible with the original construction, resulting in apoor plan and/or appearance. Undersized or overspaced structural members arecommon, leading to sagging and buckling of the building. Some desirable service
features are either lacking or of minimal quality. Interior components may consistof one small bedroom, one small bath, and a combination kitchen, dining room
and iiving room.
These dwellings usually are found in older deteriorating urban neighborhoods, inremote recreation areas, or in areas that lacked building code requirements.
Class 2 Description: Buildings in this class provide modest low-cost housing.
These structures fall below current building code requirements for overall
construction. Emphasis is on space, instead of style, design, appeal, or functional
utility.

20
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The design is usually a simple rectangle with very plain features. Many dwellings
have poorly adapted additions or enclosures of porch areas. Interior and exterior
cover materials are plain and inexpensive. One bathroom is standard with low
grade fixtures. The quality of workmanship and materials is generally not productof skilled labor.
Classes 3 and 4 = “Average” Construction Quality
Class 3 Description: Houses in this class are generally built to meet the
specifications of government financing programs (FHA and FmHA). Emphasis is
on functional utility rather than styling. These homes just meet the current
minimum building code.
A simple rectangular shape is most common. Exterior dimensions are usually inmultiples of four feet to minimize waste of building materials. There is no exterior
ornamentation. Front entries typically open directly into the living area. Interiorfeatures are plain and economical. Bathrooms feature economy grade fixtures.
Appliances may or may not be built in, and are the most affordable on the market.
The overall concept is to provide housing for the economy market.
Class 4 Description: These residences were generally built by contractors
following a stock plan. Emphasis is still on functional utility. However, thesehomes can have some styling features such as hardwood floors, brick veneer orother ornamentation.
The quality of materials and workmanship is fair. Usually the front exterior isdesigned to provide some curb appeal while other exterior walls are plain.
Windows, doors, plumbing and heating are normally comprised of “competitive”
grade materials. The class 4 home will have modest entry way. Bathroom fixtures
will be of fair quality. Built in appliances will be of fair quality, and the quantity will
depend on the floor plan. Service features such as cabinetry, electrical outlets andlighting are basic but not numerous.
Class 5 = “Above Average” Construction Quality
Class 5 Description: These buildings constitute an average quality home, built
for speculation, or on order by the volume builder. The dwellings reflect poplular
combinations of styling, design, functional utility, and convenience of floor plan.
These homes are acceptable to a broad portion of the market.
Exterior ornamentation such as brick veneer, railings, or cornice trim may bepresent. These homes will have a larger entry area, often multi-storied, with some
type of outside window area to give an even more expansive feeling. Typically,
windows will be larger and more numerous, with accent windows being common.
Bathroom fixtures will be of average quality and may include china lavatories, andentry level designer faucets. Built in appliances often include separate ovens andcooktops. Interior features may consist of a small amount of average qualtiy
hardwood paneling, or painted or stained wainscoating.
Classes 6 and 7 = “Good”
Class 6 Description: These dwellings provide housing with emphasis onconvenience of floor plan and overall attention to appearance detail. Care is taken
to achieve attractive architectural balance in terms of period or classic architectural

21
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim Reportstyle and design. The effect often is evidenced by greater irregularity of exterior
shape and roof design.
Workmanship and materials are of good quality. The exterior of the house has
ample ornamentation, such as good qualtiy brick veneer or similar styling features.
Windows will be of wood clad quality and design. Entry areas will be ample in sizeand geight, with good quality hardwood or tile floor cover. Baths feature goodquality fixtures that may include designer characteristics. Appliances will often
include double ovens, built in microwaves, downdraft cooktops, and trashcompactors. Millwork and trim will be of good qualtiy painted or stained hardwood,or comparable materials. Interior wall finishes are of good quality.
Class 7 Description: These residences are custom built. They usually aredesigned by professional home planners and built by specializing contractors,
possibly under architectural supervision. Special effort is made to bring out goodstyling and design features most outwardly noticeable in the exterior wall, roof andinterior construction detail. Care has been taken to ensure convenience in floor
plans, window placement, built-ins and adaptation of the house to the site.
All materials and labor are of better quality. The front of the house usually haslarge amounts of better quality brick veneer or other comparable materials withsimilar styling features and ornamentation. Windows are usually of wood andconstructed to integrate with the design of the house. The entry way will be large
with raised ceiling heights, and hardwood, tile, or marble floor cover. Three formal
rooms off the entry are common in this class house. Special interior detail may
include ample quantity of built-ins, solid core raised panel doors, and better quality
designer plumbing fixtures in the kitchen and baths.
Class 8 Descrition = not used in the study, but included in the report forclarification.
These homes are the best quality custom dwellings. They are professionallydesigned by an architect and constructed by well-qualified specialized builders, tothe individual desires of a client owner. The architect and contractor maintainquality control throughout construction. Design is not primarily governed by costconsideration and may feature special wall and roof designs to achieve a particularclassic style or period effect. Spacious entryways, lofted ceilings and varied floor
levels are common. Materials and workmanship are of superior quality. Care istaken to ensure optimum site adaptation. Great attention to detail will be foundthroughout these structures. The kitchen and baths feature the best quality
plumbing fixtures. Interior trim is decorative and intricate. Lighting systems andwindows are custom designed to enhance interior features or create special
effects. A large number of custom built features and convenience items generally
are present. These residences typically give a sense of grandeur. Due to theunlimited range of this class of house, the factor book only reflects the very
beginning of the cost scale.

22
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportData:
1,800± residential home sales that represent approximately 10% of the market for the period
covered were selected from the multiple listing services that cover the six regions of Idaho consideredin this study. In addition, some home sales on major and minor arterials and on connectors were
specifically selected for comparison. The parameters of the study include $40K to $600K homes thathave sold in the greater area of the six identified regions of Idaho between 1998 and 2003 – the timeparameters depended on the relative volume of home sales in each area. The Boise, Coeur d’Alene,
Moscow, and Lewiston regions have higher sales volumes, allowing for a narrower time range. The
data were initially entered into an Access data base. The following five figures demonstrate theinformation that was entered for the 1,800± homes.

23
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportFigure 3. Primary Data

Figure 4. Building Description

24
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportFigure 5. Amenities

Figure 6. Land Description

25
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportFigure 7. Road Data

The data were transferred to an Excel spread sheet and statistical analysis was performed
using MiniTab Statistical Software Package. MINITAB® is used by over 400 universities world-wide
and companies such as GE, 3M, Ford Motor Company, and leading Six Sigma consultants rely on
MINITAB to make data-driven decisions. MINITAB includes: basic and advanced statistics, regression
and ANOVA, SPC, DOE, reliability analysis, power and sample size, time series and forecasting, andGage R&R.
How The Model Works
The variables were selected as predictors of the value of a residential property in each city.
The focus of the study is on road characteristic variables, however, all factors considered to influencevalue were included to develop a more effective model. The goal of using multivariate regression
analysis is to isolate those effects being studied from the larger bundle of characteristics that cause
home values to increase or decrease. The value of these individual characteristics, that as a whole
explain most of the variability in sales price, are expressed in terms of coefficients, i.e. square feet of26
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim Reportgross living area, square feet of basement, square feet of finished basement, effective age of dwelling,
quality of construction and so forth.
The variables representing the base case for categorical variables are specified following eachmodel, i.e. Boise and Moscow are the base case in the state model and require no adjustment for
location. All other locations required a corresponding (-) adjustment for their respective location.
“Local –A” Street Classification indicates a residential street with a traffic count of 1-100 cars per day
and is the base case, requiring no adjustment. All other catetories require a corresponding adjustment
as follows:
The original structure of the Road Classification variable was in categories of 0-100 cars perday, 101-500, 501-1000, 1001-5000, 5001-10,000, and 10,001+. This categorization caused
problems for the Idaho Falls, Pocatello, Lewiston, Coeur d’Alene, Moscow, and Boise regression
analysis. These categories exhibited multicollinearity4 with many other variables, and removing
variables from the analysis resulted in large biased estimators for the traffic count categories; anotherproblem encountered was an incongruent pattern in coeffecient values and category significance. A
solution was found by re-categorizing the road classifications for each city until logical and significant
coefficients were obtained. For the Idaho model, all road classification categories were significant as
originally identified and no re-categorization was necessary. For the Lewiston model, street-traffic
count classification was deemed insignificant as a determinant of sales price at every possiblecombination of the street-traffic count classification variable.
Multicollinearity present in any of the models was dealt with by either re-categorization
techniques, as well as consolidation of many road factors into just two variables: distance of thehouse from the road or street, and road classification (see Table 1). It was found that characteristics

4

Multicollinearity is a statistical term that means two independant variables are highly correlated and exhibiting highly similareffects on the dependent variable (sales price).

27
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim Reportsuch as number of lanes and speed limit were highly correlated with the street-traffic countclassifications, resulting in large biased estimators.
Because some road factors are inherent in the existence of other road factors, the logicalcorrection for multicollinearity and/or insignificance was to remove one of the variables (usually the
one that was least significant). This was also justified by the insignificance of number of lanes andspeed limit as predictors of sales price. For example, a typical home buyer would not likely separate
the unappealing attributes of high traffic from the number of lanes or speed limit the road has. These
characteristics are generally considered together as one attribute of the property. In the same respect,regression analysis cannot separate the affect on value that traffic count, speed limit and number oflanes have separately. However, the damages from these road characteristics are captured in wholeby the road classification which accounts for the traffic count variable, likely because changes in thenumber of lanes and/or speed limit of a road result from changes in a road classification and traffic
count.
REGIONAL MODELSFor Pocatello, initial results yielded a significant setback variable for values of 60 linear
feet from road and less. However, further research indicated that the significance of the roadclassification variables, as grouped in the model shown in Appendix A, are directly dependent
on the exclusion of setback as an indicator of value. In essence, the value contained in setback
is being captured by the road classification variables and is thus intrinsically included in themodel. Similar results appeared in all individual Idaho regional models, however the setback
variable and road classification variables were individually significant in the Idaho State Model.
This is likely explained by the fact that the Idaho State Model contained sufficient data to beable to recognize the variation in the model for each variable, while the individual regionalmodels were not able to recognize the variation.

28
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim Report

The setback variable in the Boise Regional model does not hold a high level of significanceas a predominant factor affecting the sales price of a Boise residential property; also, it only
applies to setbacks of 150 feet or less. By including the setback variable, the significance level
for the road classification of “Local-B, 101 – 1000” falls as compared to the model where
setback is excluded. The setback and road classification variables are inter-related due to theintrinsic nature of road characteristics, which are often considered a bundle of features that
affect a home in similar ways.
In the Lewiston Regional model the traffic count variable was re-categorized in every
possible ordinal combination, but no statistically significant relationship between the traffic countvariable and sales price could be established. A potential reason for this is simple lack ofdissimilar observations in the dataset. However, the model predicts that setback does influence
the value of a home in Lewiston up to 100 linear feet, after which an increase in setback nolonger attributes to an increase in the value of the home. Lewiston is also the only region thatplaced significance on “shop” values.
As in the Lewiston Regional model, the Moscow Regional Model road classification
variable was re-categorized in every possible ordinal combination, with marginal significance at
the classifications of ‘Local-A and Local-B, below 10,000 cars per day’ and ‘Collector,
Minor/Rural Arterial, and Principal Arterial, above 1,000 cars per day.’ The model did notindicate setback as a significant variable, and as explained in the Pocatello region model,
appears to be intrinsic in the road classification co-efficient.
Additional regional anomolies occurred in properties in the Fort Russell District of
Moscow which are on the historical registry, are generally larger, better quality homes that arehighly sought after within the Moscow real estate market. Turnover is low within the district, withvalues increasing at a higher rate than the average rate. The indicator variable was included to29
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim Reportaccount for any differences that occur in sales price that are the direct effect of the property
being located within the Fort Russell district of Moscow.
Again, the road classification variable in the Coeur d’Alene Regional Model was recategorized in every possible ordinal combination, but no statistically significant relationshipbetween this variable and sales price could be established. Setback does influence the value ofa home in Coeur d’Alene up to 100 linear feet, after which an increase in setback no longer
attributes to an increase in the value of the home.
Property values in Coeur d’Alene are greatly a function of location, specifically proximity
to Lake Coeur d’Alene, golf courses, views of the mountains and lake, and locations within
gated communities. Because evidence of explicit differences in value exist with respect tolocations in the greater Coeur d’Alene area, an indicator variable was assigned to and reserved
for those homes in the most excellent locations of Coeur d’Alene. Homes in this categorytended to represent the highest valued homes within the area, with views and/or ammenities notcommon to the greater market. The coefficient was highly statistically significant as a predictorof sales price.

30
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportFigure 8. COMBINED CITY REGIONSIDAHO MODEL

Coeur d’Alene

Moscow

Lewiston

Idaho Falls
Boise

Pocatello

31
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportTable 3. RESULTS OF THE IDAHO MODELLN Sales Price = 6.97 - 0.0133 Effective Age + 0.466 LN Gross Living Area - 0.0121 Above Grade
Bedroom Count + 0.0384 Above Grade Bathroom Count + 0.0610 LN Basement Square Footage
Finished + 0.0353 Natural Log Basement Square Footage + 0.0712 No. Car Storage + 0.0754 LN Linear
Feet From Road - 0.0253 ‘Local-A,101 - 500 TC’ - 0.0377 ‘Local-B, 501 – 1,000 TC’ - 0.0617 ‘Collector,
1,001 – 5,000 TC’ - 0.0742 ‘Minor/Rural Arterial, 5,001 – 10,000 TC’ - 0.152 ’Principal Arterial, 10,001+
TC’ + 0.0705 LN Lot Size - 0.162 Coeur d’Alene - 0.340 Idaho Falls - 0.209 Lewiston - 0.296 Pocatello +0.0419 Above Quality Construction + 0.238 Good Quality Construction
Predictor
Constant
Effective Age
LN Gross Living Area
Above Grade Bedroom Count
Above Grade Bathroom CountLN Basement Square Footage Finished
LN Basement Square Footage
No. Car Storage
LN Linear Feet From Road
*Local A, 101-500 TC*Local B, 501-1,000 TC*collec, 1,001-5,000 TC*M/R Arterial, 5,001-10,000 TC*Princ Arterial, 10,000 + TC
LN Lot Size
*Coeur d'Alene*Idaho Falls*Lewiston*Pocatello*Above Quality Construction*Good Quality Construction

S = 0.1662

R-Sq = 87.1%

Coefficient SE Coefficient
6.972
0.1937-0.0133059
0.0008254
0.02872
0.46552-0.012134
0.009537
0.038410.013690.060960.011140.03533
0.018910.071165
0.009611
0.075380.02474-0.02530.01936-0.03769
0.02113-0.06170.02148-0.07419
0.02641-0.15233
0.026370.070510.01009-0.161550.03069-0.33966
0.02635-0.20863
0.02882-0.29591
0.026210.041930.012890.238430.03362T
36.00-16.12
16.21
-1.27
2.81
5.47
1.87
7.40
3.05
-1.31
-1.78
-2.87
-2.81
-5.78
6.99
-5.26
-12.89-7.24-11.29
3.25
7.09

P
0.000
0.000
0.000
0.204
0.005
0.000
0.062
0.000
0.002
0.192
0.075
0.004
0.005
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.000

R-Sq(adj) = 86.8%

The variables with asterisks represent the presence or absence of that attribute.

Thecoefficient is multiplied by 1 if the home has the attribute, otherwise 0. As with the other individual
Idaho regional city models, “Good Construction Quality” is a categorization that was assigned to only
Class 6 and 7 homes based on the Oregon Cost Manual. The road classification variables were all32
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim Reportsignificant in their original categorizations. Setback is significant at values of less than 100 linear feetfrom the road.
Each city-region was given an indicator variable to test for differences in sales price due towhich city the home is located in, where Boise was the original base case. After it was determinedthat home prices in Moscow are not significantly different from those of Boise, the base case was redefined to include both the Moscow and Boise sample. In use, the property should acquire theindicator variable value for that city or region in which it is most closely related, either in market
association or geographic location.
In the above model, dependent variables and some independent variables are expressed innatural logarithm form. Transformation of some variables to this format is necessary to meet theassumptions of Ordinary Least Squares Regression, namely that there cannot be non-linear
relationships or non-constant variance between the residential versus fitted values. However, innormal form, there is a non-linear relationship between sales price and multiple independent variables.
The independent variables requiring the natural logarithm transformation are those scalar variables
that have high ranges of value.
For those independent variables requiring the transformation (denoted by LN preceding thatvariable’s name), the coefficient represents the percentage change in sales price given a one percent
change in the value of the variable. For all other variables, the coefficient represents the percentagechange in sales price given a one-unit change in the variable. For indicator variables, the coefficient
represents the percentage change in sales price if that attribute does exist. For example, thepredicted value of a home declines by 15.23% if it is located on a Principal Arterial or Interstate withover 10,000 traffic count per day as compared to a home located on a Local-A street, with 0-100 traffic
count.

33
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim Report

The following table demonstrates how the model works. The home being tested is a goodquality, one story 1990’s era home and is located in Boise, Idaho. Variables of interest are shown in
the “Specific Variables of the Subject Property” column.
It is important to note that any extenuating factors that highly affect the value of the property inthe before condition and are not specified in the model may need to be accounted for. An example
would be a guesthouse located on the property, or a swimming pool. This would be accounted for in
the “Other Adjustments” category in the model.

34
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportTable 4. Model Input and Results

IdahoTransportation Department
Proximity Damages Determination
Results
Appraised Value - In the before:$125,000% Contributable to Land:

20%% Contributable to Improvements:

80%

Effective age of house:

5Gross Living Area:

Appraised Value in the Before
Concluded Model Value In the Before
Reconciliation of Values
Adjusted Model Value in the Before$125,000$ 121,134$

3,866$ 124,939

Adjusted Model Value in the After$ 116,499$

1350

GLA Bedroom count:3

Concluded Proximity Damages:

GLA Bathroom count:

2

Damages as a percent of value in the Before

8,440
6.75%

Basement Square Footage Total:

0

Portion of Damages Attributable to Land:

$

1,688

Basement Square Footage Finished:

0Portion of Damages Attributable to Improvements:

$

6,752

Number Car Storage:2

Construction Quality:

Good

Lot Size in Square Footage:

22500

Boise

Region:Linear Feet from Road (Before):
Road Classification (Before):

150

1001-5000 cars per day

Linear Feet from Road (After):Road Classification (After):

70

5001-10000 cars per day

This example is a 3 bedroom, 2 bath 1,350 SF home located in Boise on a 22,500 SF lot with
an effective age of 5 years and good quality construction. The home is currently located 150 feet froma road that has a classification of 1001-5000 cars per day. The road project will create a setback of
70 feet and a road classification* change. In the example used, this home would suffer 6.75%, or$8,440 due to proximity damages.
*Note: The road classification in the model is categorical data, not scalar data; meaning the road is
identified by category rather than by the specific number of cars per day.

35
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim Report

The following tables show how the values are concluded in the above example.
Table 5. Model Calculations

BEFORE

General Variable

Model

Specific Variable

Needing Log Form Coefficients Of Subject Property
Constant

Coefficient *Variable(Column C*Column E)

6.972

Effective Age

LN GLA

Converting Column D to LN Format(Where Specified in Column B)

X

6.972

-0.0133059

50.46552

1350

Above Grade BR Count-0.012134 3

3

Above Grade BA Count

0.03841 2

2

5-0.0665295

7.207859871

3.355402927-0.036402
0.07682

LN BSMT SF Fin

X

0.06096

1

0

0

LN BSMT SF

X

0.035331005.010635294

0.377701688

No. Car Storage
LN Sample Setback

0.071165 2X

20.07538

0.14233150

100-500-0.0253000501-1000-0.037690001001-5000-0.06171

1-0.0617

5000-10,000-0.0741900010,001+-0.15233

0000.07051

22500

10.02127059

0.706599789

CDA-0.16155

0

0

0

IF

-0.33966

000Lewiston-0.20863

000Pocatello-0.29591000Above Quality

0.04193000Good Quality

0.238431

10.23843

LnLotSizeX

Summed Results:

11.7046529

Exponent of Summed Results (Value):$

121,134.03

Table 6. Model Adjustment to Appraiser’s Value
BEFORE, ADJUSTEDGeneral Variable

Model

Specific Variable

Needing Log Form Coefficients Of Subject Property
Constant

Coefficient *Variable(Column C*Column E)

6.972

Effective Age
LN GLA

Converting Column D to LN Format(Where Specified in Column B)

X

6.972

-0.0133059

50.46552

1350

Above Grade BR Count-0.012134 3

3

Above Grade BA Count

0.03841 2

2

5-0.0665295

7.207859871

3.355402927-0.036402
0.07682

LN BSMT SF Fin

X

0.06096

1

0

LN BSMT SF

X

0.035331

00.071165 2

No. Car Storage

2000.14233

0.07538150

5.010635294

0.377701688

100-500-0.0253000501-1000-0.037690001001-5000-0.06171

1-0.0617

5000-10,000-0.0741900010,001+-0.15233

0000.07051

22500

10.02127059

0.706599789

CDA-0.16155

0

0

0

IF

-0.33966

000Lewiston-0.20863

000Pocatello-0.29591000Above Quality

0.04193000Good Quality

0.238431

10.23843

LN Sample Setback

LnLotSize

Other Adjustments, Dollar Value

X

X$3,866

Summed Results:

11.7046529

Exponent of Summed Results (Value):$121,134.03

Other Items

0.030927753

New Sum:

11.73558066

Exponent of Total (Total Value)

124938.9701

36
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportTable 7. Adjusted Value In the “After”
AFTER

General Variable

Model

Specific Variable

Needing Log Form Coefficients Of Subject Property
Constant

Coefficient *Variable(Where Specified in Column B)(Column C*Column E)

6.972

Effective Age
LN GLA

Converting Column D to LN Format

X

6.972

-0.0133059

50.46552

1350

Above Grade BR Count-0.012134 3

3

Above Grade BA Count

0.03841 2

2

5-0.0665295

7.207859871

3.355402927-0.036402
0.07682

LN BSMT SF Fin

X

0.06096

1

0

LN BSMT SF

X

0.03533

1

0

No. Car Storage
LN Sample Setback

0.071165 200.14233

0.0753870

4.248495242

0.320251571

100-500-0.0253000501-1000-0.037690001001-5000-0.06170005000-10,000-0.074191

1-0.07419

10,001+-0.15233

0000.07051

22500

10.02127059

0.706599789

CDA-0.16155

0

0

0

IF

-0.33966

000Lewiston-0.20863

000Pocatello-0.29591000Above Quality

0.04193000Good Quality

0.23843

1

LnLotSize

Other Adjustments, Dollar Value

X

2

0

X

$3,86610.23843

Summed Results:

11.63471279

Exponent of Summed Results (Value):112951.3855

Other Items

0.030927753

New Sum:

11.66564054

Exponent of Total (Total Value)116499.2997

The conclusions of the calculations in Tables 5, 6 and 7 relate to the “Results” section shown in theright hand side of Table 4. It is important to note that any extenuating factors that highly affect thevalue of the property in the before condition that are not common and thus are not specified in
the model. Reconciliation in this form utilizes common calibration techniques to account for
extreme differences between actual and predicted values.

37
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim Report

Conclusion
Many variables in the general method of residential property values used in this study wereconsistently significant among all cities, while other variables, such as the presence of a shop, weresignificant in some areas and not in others. The general theme of the street-traffic count classification
variables was significance of either street-traffic count classifications or setback, but not both. Other
road variables, including number of lanes and speed limit, were likely captured by street-traffic countclassifications, and were not significant on their own.
The Idaho Model adequately represents the general housing characteristics affecting all areasin the state. The original sample size is approximately 1,800± homes in total, representing a very
good sampling of the total number of residential homes within the state, capturing ranges in size,
quality, age, room count, and lot size up to 5 acres. Both setback and the street-traffic countclassification variables were significant in the Idaho Model, where value was sufficently captured inpart due to the variation and the large aggregate number of observations in the whole state.
Evaluation of this model for the purposes of the Idaho Transportation Department shows aneed for compensation to homeowners for intrinsic damages to property resulting from any decreases
in the setback value and/or increases in traffic count. Other compensation to homeowners will be in
the traditional form of actual land lost.
The R-squared value of the All Cities Idaho Model is 87.1%. According to this model, 87.1%of the variation in sales price of the home is explained by variation in the variables listed in theequation. Approximately 12.9% of the variation in sales price is unexplained by this model. This isconsistent with all individual regional city models. Through regression analysis, the researchers havederived, with notable accuracy, the factors that affect residential value within the six combined regionsof Idaho. Using this technique, many factors have been isolated that affect value by including them in

38
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim Reportthe model. By having the dependent variable be the selling price of the home instead of damages
incurred, we have derived a solid equation that is less arbitrary and more apt to account for differencesin property types.
Deliberate time and care have been taken to assure that this study meets the guidelines ofUniform standards of Professional Appraisal Practice Standard 6, Mass Appraisal Development andReporting. The complete form of Standard Six is in the Appendix M section of this report.
Finally, the researchers have been aware of, and continue to make deliberate efforts to ensurethat the study possess techniques that meet the “Daubert/Kumho” court test. In the Law Seminars
International presentation given at the Boise Eminent Domain and Inverse Condemnation seminar in
March 2003, Daniel R. Front of Holland & Hart LLP, Denver, Colorado identified four nonexclusive
factors to consider in exercising the trial judge’s “gatekeeping” obligation.
In Daubert v. Merrell Dow Pharmaceuticals, Inc., 509 U.S. 579, 589 (1993), the United States
Supreme Court held that Fed. R. Evid. 702 imposes a special obligation upon a trial judge toensure that expert testimony is not relevant, but reliable. In Daubert, the Supreme Court
identified four nonexclusive factors to consider in excercising this “gatekeeping” obligation: (1)whether a theory or technique can be and has been tested; (2) whether it has been subjected
to peer review and publication; (3) whether, in respect to a particular technique, there is a highknown or potential rate of error and whether there are standards controlling the technique’s
operation; and (4) whether the theory or technique enjoys general acceptance within arelevant scientific community. (Effective Use of Experts Including Daubert/Kumho Challenges
p. 1)
At the time of this publication, the University of Idaho College of Agricultural and Life
Sciences is sponsoring an ITD Proximity Damages Model Methods and Applications 8-hourcourse in Idaho Falls, Moscow, and Boise to instruct ITD fee and staff appraisers on
methodology and applications of the model; and is in the process of submitting a professional
article for publication in the Appraisal Institute Journal, ASFMRA Journal, and International
Right of Way Association. Valuationn Modeling for Appraisal Application lecture notes areincluded in the appendix.

39
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportReferences
Ada County Assessor. www.adacountyassessor.org/website/adapar.
Allen, Gary R. Highway Noise, Noise Mitigation, and Residential Property Value.
Charlottesville, Virginia: Virginia Highway and Transportation Research Council, 1981.
The Appraisal Guide. http://wwwcf.fhwa.dot.gov/realestate/apprgd.gtm#APRCHUSE.
Accessed November 7, 2002.
Burkhardt, John E., Armando L. Lago and Jerome Rothenberg. Highway Improvement as a
Factor in Neighborhood Change. Bethesda, Maryland: Resource Management
Corporation, 1971.
Burkhardt, Jon E. Community Reactions to Anticipated Highways: Fears and Actual Effects.
Highway Research Record, No. 470, 1973, pp. 22031.
Burkhardt, Jon E. Socioeconomic Reactions to Highway Development, August 10, 1983. (Paper prepared for presentation to the 63rd Annual Meeting of the Transportation
Research Board).
Coeur d’Alene Multiple Listing Service, aka Coeur d’Alene Association of Realtors. On-line
download of residential sales data.
County Profiles. State of Idaho Department of Commerce.
Eaton, J.D. Real Estate Valuation in Litigation. Appraisal Institute. Chicago 1995.
Federal Highway Guide for Functional Highway Classification
Frost, Daniel R. Effective Use of Experts Including Daubert/Kumho Challenges. Law Seminars
International. Presented March 28, 2003.
Gamble, Hays B., e. al. The Influence of Highway Environmental Effects on residential property
values. University Park, Pennsylvania: Institute for Research on Land and Water
Resources, 1974.
Gamble, Hays., Owen H. Sauerlender and C. John Langley. Adverse and Beneficial Effects ofHighways on Residential Property Values. Transportation Research Record, No. 508,
1974, pp. 37-48.
Granger, J.H. Residential Proximity Study. Washington State Department of Transportation,
District 3, 1982.
Greater Idaho Falls Multiple Listing Service, aka Greater Idaho Falls Association of Realtors.
Sold book data referenced by multiple listing number.

40
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportGreater Pocatello Multiple Listing Service, aka Greater Pocatello Association of Realtors. Soldbook data referenced by multiple listing number.
Hall, Fred L., Barbara E. Breston and S. Martin Taylor. Effects of Highway Noise on Residential
Property Values. Transportation Record, No. 686, 1978, pp. 38-43.
Hsu, Ann Yi-rung and James O. Wheeler. A Bibliography of Highway Impact Studies, 166-1976.
Council of Planning Librarians Exchange Bilbiography, No. 1401, 1977.
Idaho’s Transportation System Defined. http://www2state.id.us/itd/AboutITD/oversyst.htm,
accessed November 9, 2002.
Land Economic Studies, Department of Highways, State of Colorado.
Lang, William R. Residential Proximity Damage Study. International Right of Way Association,
October 1988, pp. 10-13.
Latah Multiple Listing Service aka Latah Association of Realtors. Sales and physical datacollected using Voyager software.
Lewis-Clark Multiple Listing, aka Lewis-Clark Association of Realtors. Sales and physical datacollected using Voyager software.
Linne, Mark R., MAI, CAE, M. Steven Kane, George Dell, MAI,SRA. A Guide to Appraisal
Valuation Modeling. Appraisal Institute. Chicago, 2000.
Monger, Christopher K. Proximity Study SR 515 State Highway Widening Project Dent,
Washington. November, 1990.
NADO Research Foundation Regional Transportation Online Resource Center, Idaho.
http://www.nado.org/rtoc/library/id.html. Accessed November 9, 2002.

Nelson, Roland D. Laurence G. Allen. Expressway Proximity Damages to Residential
Properties. International Right of Way Association. February 1983, pp. 14-19.
Oregon Cost Manual, Oregon Department of Revenue, Revenue Building, Salem, Oregon.
Revised April 1993.
Southwest Idaho Multiple Listing Service, aka Ada County Association of Realtors. Sales andphysical data collected using Voyager software.
Traffic Counts compiled by Ada County Highway District from traffic data collected by ACHD
and ITD.
Traffic Counts compiled by Idaho Falls and Bonneville County Engineers Department.
Traffic Counts compiled by Lewiston City and Nez Perce City Engineers Department.

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Interim ReportTraffic Counts compiled by Moscow City Engineers Department.
Traffic Counts compiled by Pocatello City Engineers Department
Uniform Appraisal Standards for Federal Land Acquisitions. Interagency Land Acquisition
Conference, Washington, D.C. 1992
Uniform Standards of Professional Appraisal Practice. The Appraisal Foundation. 1999, 2000,
2001, 2002.
Wiegal, Dennis B. Residential Proximity Study. Oregon Department of Transportation, Right of
Way Agent, September 1983.

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Interim ReportAPPEXDIX A - OREGON COST MANUAL CONSTRUCTION CLASSES 1 THROUGH 8

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Interim ReportAPPENDIX B - USPAP MASS APPRAISAL GUIDELINESTANDARD 6
In developing a mass appraisal, an appraiser must be aware of, understand, and correctly
employ those generally accepted methods and techniques necessary to produce andcommunicate credible appraisals.
Comment: STANDARD 6 is directed toward the substantive aspects of developing andcommunicating competent analyses, opinions, and conclusions in the appraisal of a universe ofproperties. Mass appraisals are used primarily for purposes of ad valorem taxation. But
depending upon the purpose of the appraisal and the availability of statistical data, mass
appraisal procedures may also be appropriate for the valuation of any universe of properties,
but only when written reports are made and the results of statistical testing are fully disclosed
and explained. The reporting and jurisdictional exceptions applicable to public mass appraisals
prepared for purposes of ad valorem taxation do not apply to mass appraisals prepared forother purposes.
Mass appraisals can be prepared with or without computer assistance and are often developedby teams of people. The validity of mass appraisal conclusions is frequently tested or contested
by single-property appraisals. Single-property appraisals should conform to STANDARDS 1 and
2 for real property and STANDARDS 7 and 8 for personal property. In the context of
STANDARD 6, the terms appraisal and mass appraisal both refer to the appraisal of a universeof properties, whether real property, personal property, or both.
The JURISDICTIONAL EXCEPTION may apply to several sections of STANDARD 6 because
ad valorem tax administration is subject to various state, county, and municipal laws. Standards
Rule 6-1
In developing a mass appraisal, an appraiser must:
Standards Rule 6-1 (a) be aware of, understand, and correctly employ those generally accepted
methods and techniques necessary to produce a credible appraisal;
Comment: Departure from this binding requirement is not permitted. Mass appraisal uses:
1. Division of tasks,
2. Standardized data collection and analysis,
3. Properly specified and calibrated valuation models, and
4. Standards and measurements of the accuracy of the data collected and values produced.
This rule recognizes that the principle of change continues to affect the manner in whichappraisers perform mass appraisals. Changes and developments in the real estate field have asubstantial impact on the appraisal profession. Revisions in appraisal theory and practice resultfrom:- changes in the cost and manner of constructing and marketing commercial, industrial,
residential, and other types of real estate;

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- changes in the legal framework in which real property rights and interests are created,conveyed, mortgaged, and taxed;- corresponding changes in appraisal theory and practice; and,- social and economic changes.
To keep abreast of these changes and developments, the appraisal profession is constantly
reviewing and revising appraisal methods and techniques and devising new methods andtechniques to meet new circumstances. For this reason it is not sufficient for appraisers tosimply maintain the skills and the knowledge they possess when they become appraisers. Massappraisers must continuously improve their skills to remain proficient.
Standards Rule 6-1 (b) not commit a substantial error of omission or commission thatsignificantly affects a mass appraisal;
Comment: Departure from this binding requirement is not permitted. Standards Rule 6-1(b) is
identical in purpose to Standards Rule 1-1(b).
Standards Rule 6-1 (c) not render a mass appraisal in a careless or negligent manner.
Comment: Departure from this binding requirement is not permitted. Standards Rule 6-1(c) is
identical in purpose to Standards Rule 1-1(c).
Standards Rule 6-2
In developing a mass appraisal, an appraiser must observe the following specific appraisal
guidelines:
Standards Rule 6-2 (a) consider the purpose and intended use of the appraisal; (29)--------------------------------------------29. See Statement on Appraisal Standards Number 9.
Standards Rule 6-2 (b) identify any special limiting conditions;
Comment: Although appraisers in ad valorem taxation should not be held accountable forlimitations beyond their control, they are required by this guideline to identify cost constraints
and to take appropriate steps to secure sufficient funding to produce appraisals that comply withthese standards.
Expenditure levels for assessment administration are a function of a number of factors. Fiscal
constraints may impact data completeness and accuracy, valuation methods, and valuation
accuracy. While appraisers should seek adequate funding and disclose the impact of fiscal
constraints on the mass appraisal process, they are not responsible for constraints beyond theircontrol.
Standards Rule 6-2 (c) identify the effective date of the appraisal;Standards Rule 6-2 (d) define the value being considered; if the value to be estimated is market
value, the appraiser must clearly indicate whether the estimate is the most probable price:

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Interim Report(i) in terms of cash; or(ii) in terms of financial arrangements equivalent to cash; or(iii) in such other terms as may be precisely defined; if an estimate of value is based on belowmarket financing or financing with unusual conditions or incentives, the terms of such financing
must be clearly set forth, their contributions to or negative influence on value must be describedand estimated, and the market data supporting the valuation estimate must be described andexplained;
Comment: For certain types of appraisal assignments in which a legal definition of market value
has been established and takes precedence, the Jurisdictional Exception may apply.
Standards Rule 6-2 (e) identify the real estate and personal property, as applicable;
Comment: The universe of properties should be identified in general terms and each individual
property in the universe should be identified with the information on its identity stored orreferenced in its property record.
Standards Rule 6-2 (f) in appraising real property:(i) identify and consider any personal property, trade fixtures, or intangible items that are notreal property but are included in the appraisal;
Comment: This guideline requires the appraiser to recognize the inclusion of items that are notreal property in the overall value estimate. Expertise in personal property (see STANDARD 7) orbusiness (see STANDARD 9) appraisal may be required to allocate each overall value to its
various components. Separate valuation of such items is required when they are significant to
the overall value.
(ii) consider whether an appraised physical segment contributes pro rata to the value of thewhole;
Comment: This guideline does not require the appraiser to value the whole when the subject of
the appraisal is a physical segment. However, if the value of the whole is not considered, theappraisal must clearly recognize that the value of the property being appraised cannot be used
to estimate the value of the whole by mathematical extension.
Standards Rule 6-2 (g) identify the property interest(s);(i) consider known easements, restrictions, encumbrances, leases, reservations, covenants,
contracts, declarations, special assessments, ordinances, or other items of similar nature;(ii) consider whether an appraised fractional interest or partial holding contributes pro rata to thevalue of the whole;
Comment: This guideline does not require the appraiser to value the whole when the subject of
the appraisal is a fractional interest or a partial holding. However, if the value of the whole is notconsidered, the appraisal must clearly reflect that the value of the property being appraisedcannot be used to estimate the value of the whole by mathematical extension.

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Interim ReportStandards Rule 6-2 (h) in appraising real property, consider the effect on use and value of thefollowing factors: existing land-use regulations, reasonably probable modifications of such
regulations, economic supply and demand, the physical adaptability of the property,neighborhood trends, and the highest and best use of the property; andComment: This guideline sets forth a list of factors that affect use and value. In considering
neighborhood trends, an appraiser must avoid stereotyped or biased assumptions relating torace, age, color, gender, or national origin or an assumption that race, ethnic, or religious
homogeneity is necessary to maximize value in a neighborhood. Further, an appraiser must
avoid making an unsupported assumption or premise about neighborhood decline, effective
age, and remaining life. In considering highest and best use, an appraiser should develop theconcept to the extent required for a proper solution of the appraisal problem.
Standards Rule 6-2 (i) recognize that land is appraised as though vacant and available fordevelopment to its highest and best use and that the appraisal of improvements is based on
their actual contribution to the site.
Comment: This guideline may be modified to reflect the fact that, in various market situations, asite may have a contributory value that differs from the value as if vacant.
Standards Rule 6-3
In developing a mass appraisal, an appraiser must:
Standards Rule 6-3 (a) identify and consider the appropriate procedures and market information
required to perform the appraisal, including all physical, functional, and external market factors
as they may affect the appraisal;
Comment: Such efforts customarily include the development of standardized data collection
forms, procedures, and training materials which are used uniformly on the universe of properties
under consideration.
Standards Rule 6-3 (b) employ generally accepted techniques for specifying property valuation
models; andComment: The formal development of a model in a statement or equation is called model
specification. Mass appraisers must develop mathematical models that, with reasonableaccuracy, represent the relationship between property value and supply and demand factors, as
represented by quantitative and qualitative property characteristics. The models may be
specified using the cost, sales comparison, or income approaches to value. The specification
format may be tabular, mathematical, linear, non-linear, or any other structure suitable forrepresenting the relationship between market value and observable property characteristics.
The appropriate approaches should be used in appraising a class of properties. The concepts ofaccepted techniques apply to both real and personal property valuation models.
Standards Rule 6-3 (c) employ generally accepted techniques for calibrating mass appraisal
models.

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Interim ReportComment: Departure from binding requirements (a) through (c) is not permitted. Calibrationrefers to the process of analyzing sets of property and market data to determine the specificparameters of a model. The table entries in a cost manual are examples of calibrated
parameters, as well as the coefficients in a linear or non-linear model. Models should becalibrated using generally accepted techniques, including, but not limited to, multiple linear
regression, non-linear regression, and adaptive estimation.
Standards Rule 6-4
In developing a mass appraisal, an appraiser must observe the following specific appraisal
guidelines, when applicable:
Standards Rule 6-4 (a) collect, verify, analyze, and reconcile such data as are necessary andappropriate to:(i) estimate cost new of the improvements;(ii) estimate accrued depreciation;(iii) estimate value by sales of comparable properties;(iv) estimate value by capitalization of income. i.e. rentals, expenses, interest rates,
capitalization of income. ie. rentals, expenses, interest rates, capitaliation rates and vacancy
data.
Comment: This rule requires appraisers engaged in mass appraisal to take reasonable steps toensure that the quantity and quality of the factual data that are collected are sufficient toproduce credible appraisals. For real property, systems for routinely collecting and maintaining
ownership, geographic, sales, income and expense, cost, and property characteristics data
should be established. Geographic data should be contained in a complete set of cadastral
maps compiled according to current standards of detail and accuracy. Sales data should becollected, confirmed, screened, adjusted, and filed according to current standards of practice.
The sales file should contain, for each sale, property characteristics data that arecontemporaneous with the date of sale. Property characteristics data should be appropriate to
the mass appraisal models being used. The property characteristics data file should containdata contemporaneous with the date of appraisal. It may contain historical data on sales. The
data collection program should incorporate a quality control program, including checks andaudits of the data to ensure current and consistent records.
Standards Rule 6-4 (b) base projections of future rental rates, expenses, interest rates,
capitalization rates, and vacancy rates on reasonable and appropriate evidence.
Comment: This guideline requires an appraiser, in developing income and expense statements
and cash flow projections, to weigh historical information and trends, current market factors
affecting such trends, and reasonably anticipated events, such as competition fromdevelopments either planned or under construction.
Standards Rule 6-4 (c) consider and analyze terms and conditions of any available leases.
Standards Rule 6-4 (d) consider the need for and extent of any physical inspection.
Standards Rule 6-5

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Interim ReportIn applying a calibrated mass appraisal model an appraiser must:
Standards Rule 6-5 (a) value improved parcels by accepted methods or techniques based on
the cost approach, the sales comparison approach, and income approach, as applicable;
Standards Rule 6-5 (b) value sites by generally accepted methods or techniques; suchtechniques include but are not limited to the sales comparison approach, allocation method,
abstraction method, capitalization of ground rent, and land residual technique;Standards Rule 6-5 (c) when estimating the value of a leased fee estate or a leasehold estate,consider and analyze the effect on value, if any, of the terms and conditions of the lease;
Comment: In ad valorem taxation the appraiser may be required by rules or law to appraise theproperty as if in fee simple, as though unencumbered by existing leases. In such cases, market
rent would be used in the appraisal, ignoring the effect of the individual, actual contract rents.
Standards Rule 6-5 (d) consider and analyze the effect on value, if any, of the assemblage ofthe various parcels, divided interests, or component parts of a property; the value of the wholeshould not be estimated by adding together the individual values of the various parcels, divided
interests, or component parts; andComment: When the value of the whole has been established and the appraiser seeks toestimate the value of a part, the value of any such part must be tested by reference toappropriate market
data and supported by an appropriate analysis of such data.
Standards Rule 6-5 (e) consider and analyze the effect on value, if any, of anticipated public or
private improvements, located on or off the site, to the extent that market actions reflect suchanticipated improvements as of the effective appraisal date; appraise proposed improvements
only after examining and having available for future examination;(i) plans, specifications, or other documentation sufficient to identify the scope and character ofthe proposed improvements;(ii) evidence indicating the probable time of completion of the proposed improvements; and(iii) reasonably clear and appropriate evidence supporting development costs, anticipatedearnings, occupancy projections, and the anticipated competition at the time of completion.
Comment: Ordinarily, proposed improvements are not appraised for ad valorem tax purposes.
Appraisers, however, are sometimes asked to provide estimates of value of proposed
improvements so that developers can estimate future property tax burdens. Sometimes
condominiums and units in planned unit developments are sold with an interest in unbuilt
community property, the pro rata value of which, if any, should be considered in the analysis ofsales data.
Standards Rule 6-6
In reconciling a mass appraisal an appraiser must:

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Interim ReportStandards Rule 6-6 (a) consider and reconcile the quality and quantity of data available andanalyzed within the approaches used and the applicability or suitability of the approaches used;
andStandards Rule 6-6 (b) employ generally accepted mass appraisal testing procedures andtechniques to ensure that standards of accuracy are maintained.
Comment: Departure from binding requirements (a) and (b) is not permitted. It is implicit inmass appraisal that, even when properly specified and calibrated mass appraisal models areused, some individual value estimates will not meet standards of reasonableness, consistency,
and accuracy. However, appraisers engaged in mass appraisal have a professional
responsibility to ensure that, on an overall basis, models produce value estimates that meetattainable standards of accuracy. This responsibility requires appraisers to evaluate theperformance of models, using techniques including, but not limited to, goodness-of-fit statistics,
hold-out samples, analysis of residuals, and appraisal-to-sale ratio data. They also should
review individual value estimates before they are used.
Standards Rule 6-7
A written summary report of a mass appraisal for ad valorem taxation or a written report of amass appraisal for any other purpose should clearly communicate the elements, results,
opinions, and value conclusions of the appraisal.
Documentation for a mass appraisal for ad valorem taxation may be in the form of (1) property
records (2) reports, (3) manuals, (4) regulations, (5) statutes, and (6) other acceptable forms.
Each written report of a mass appraisal for any purpose other than ad valorem taxation must:
Standards Rule 6-7 (A) clearly and accurately set forth the appraisal for any purpose other thanad valorem tazation must:
Standards Rule 6-7 (B) contain sufficient information to enable the person(s) who receive or relyon the report to understand it properly;Standards Rule 6-7 (C) clearly and accurately disclose any extra ordinary assumptions orlimiting condition that directly affects the appraisal and indicate its impact on value.
Furthermore, each written report of a mass appraisal for any purpose other than for ad valorem
taxation, and, when provided, a written summary report of a mass appraisal for ad valorem
taxation must:
Standards Rule 6-7 (a) state the purpose and intended use of the appraisal; (30)--------------------------------------------30. See Statement on Appraisal Standards Number 9.
Standards Rule 6-7 (b) disclose any assumptions or limiting conditions that result in deviationfrom generally accepted methods and techniques or that affect analyses, opinions, andconclusions;

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Interim ReportComment: One limiting condition that must be disclosed is whether or not any physicalinspection was made.
Standards Rule 6-7 (c) set forth the effective date of the appraisal;
Comment: In ad valorem taxation the effective date of the appraisal may be prescribed by law.
If no effective date is prescribed by law, the effective date of the appraisal, if not stated, ispresumed to be contemporaneous with the data and appraisal conclusions.
Standards Rule 6-7 (d) define the value to be estimated;Standards Rule 6-7 (e) identify the properties appraised including the property rights;
Comment: The report should document the sources for locating, describing, and listing theproperty. When applicable, include references to legal descriptions, addresses, parcel
identifiers, photos, and building sketches. In mass appraisal this information is often included inproperty records. When the property rights to be appraised are specified in a statute or court
ruling, the law should be referenced.
Standards Rule 6-7 (f) describe and justify the model specification(s) considered, datarequirements, and the models chosen;
Comment: The user and affected parties must have confidence that the process andprocedures used conform to accepted methods and result in credible value estimates. In thecase of mass appraisal for ad valorem taxation, stability and accuracy are important to thecredibility of value estimates. The summary report should include a discussion of the rationale
for each model, the calibration techniques to be used, and the performance measures to be
used.
Standards Rule 6-7 (g) describe the procedure for collecting, validating, and reporting data;
Comment: The summary report should describe the sources of data and the data collection andvalidation processes. Reference to detailed data collection manuals should be made, including
where they may be found for inspection.
Standards Rule 6-7 (h) describe calibration methods considered and chosen, including themathematical form of the final model(s); describe how value estimates were reviewed; and, if
necessary, describe the availability of individual value estimates;
Standards Rule 6-7 (i) in the case of real property, discuss how highest and best use wasdetermined;
Comment: The mass appraisal summary report should reference case law, statute or public
policy that describes highest and best use requirements. When actual use is the requirement,
the report should discuss how use-values were estimated.

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Interim ReportStandards Rule 6-7 (j) identify the appraisal performance tests used and set forth theperformance measures attained;
Standards Rule 6-7 (k) provide any additional information necessary to more fully explain theappraisal including departures permitted by the DEPARTURE PROVISION; andStandards Rule 6-7 (l) contain a signed certification by the appraiser in a manner consistent withapplicable laws, rules or regulations and generally accepted appraisal practices for mass
appraisals prepared for ad valorem taxation; and for mass appraisals prepared for otherpurposes, contain a signed certification in accordance with Standards Rule 6-8.
Comment: Departure from binding requirements (a) through (l) is not permitted.
Standards Rule 6-8
Each written mass appraisal for purposes other than ad valorem taxation must contain a signed
certification that is similar in content to the following form:
I certify that, to the best of my knowledge and belief:- the statements of fact contained in this report are true and correct.
- the reported analyses, opinions, and conclusions are limited only by the reported assumptions
and limiting conditions, and are my personal, unbiased professional analyses, opinions, andconclusions.
- I have no (or the specified) present or prospective interest in the property that is the subject of
this report, and I have no (or the specified) personal interest or bias with respect to the parties
involved.
- my compensation is not contingent upon the reporting of a predetermined value or direction invalue that favors the cause of the client, the amount of the value estimate, the attainment of a
stipulated result, or the occurrence of a subsequent event.
- my analyses, opinions, and conclusions were developed, and this report has been prepared, inconformity with the Uniform Standards of Professional Appraisal Practice.
- I have (or have not) made a personal inspection of the property that is the subject of thisreport. (If more than one person signs the report, this certification must clearly specify which
individuals did and which individuals did not make a personal inspection of the appraised
property.) (31)- no one provided significant professional assistance to the person signing this report. (If there
are exceptions, the name of each individual providing significant professional assistance must
be stated.)--------------------------------------------31. See Advisory Opinion AO-2. References to Advisory Opinions are for guidance only and do
not incorporate Advisory Opinions into the Standards Rules.

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Interim ReportAPPENDIX C – EXCEL INSTRUCTIONSEXCEL INSTRUCTIONS
Note: to install Excel’s regression function, you go to Tools, then Add-Ins, and check ‘Analysis
ToolPak.” You may be prompted to install the Analysis ToolPak using the Microsoft Office
software disc.

Correlation Matrix for Multiple Regression (Used to Test for MC)Tools
o Data Analysis
 Click on Correlation• Select input range box and retrieve data
• Select an output option (where to put results)• Verify that appropriate box is checked if the column labels were
included in the selected range of data**Excel is picky. Verify that all x variables are in adjacent columns with no non-numeric data
present**
Regression AnalysisTools
o Data Analysis
 Click on Regression• Select y (dependent) variable range box and retrieve data
• Select x (independent) variable(s) range box and retrieve data
• Verify that appropriate box is checked if the column label ere
included in the selected range of data
• Select the output range box and show Excel where on your sheet
you want the regression results**Again, Excel is picky. Verify that all x variables are in adjacent columns with no non-numeric
data present. This means that data has to be copy/paste with any insignificant variable columns
taken out of the data set before re-running regression**

When you are running your final regression (i.e. all variables are significant): under ‘Residuals’at the bottom of the Regression dialog box, check ‘Standardized Residuals’ and ‘Residual
Plots.’ Examine residual plots for patterns indicating non-normality.

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Interim ReportAPPENDIX D – A GUIDE TO USING PROXIMITY DAMAGE VALUATION MODELA GUIDE TO USING the Proximity Damage Valuation Model
Overview
Statewide transportation planning needs require forecasting and assessing property damages that
result from a road project. As the traffic flow and traffic demands in Idaho change, the Idaho
Transportation Department continuously evaluates transportation elements of comprehensive plans,
determines impacts of proposed land use changes, and determines the transportation needs for thestate. Meeting transportation needs often requires widening roadways, which necessitates the state toexercise their eminent domain right, the right to take private property for a public use upon payment ofjust compensation. (page 21, Real Estate Valuation in Litigation).
Two basic forms of damages have been identified in eminent domain litigation: the taking of thephysical property; and concluded hypothetical damages occasioned by the taking to the remainder—
the remaining land and improvements as it exists at a point in time after the road project has been
completed. The problem in the past has been that the methods used to estimate the value of thesedamages employ limited comparable data, usually three to five direct comparisons, and subjective
adjustments based on experience and ordinal judgment.
This study developed a six region forecasting model based on multiple regression analysis using
factors, or characteristics, that directly affect the sales price of a home; and indirect characteristics
such as street-traffic classification and setback from the street or road, to conclude indirect correlation
between value relative to proximity and to street-traffic classifications. Aggregation of the data from all
regions to form a state-wide model has resulted in a successful method of estimating intrinsic
damages due to road projects for the Idaho Transportation Department.
The home sales represent a systematic sample of the population in each region, however,conclusions from each model infer that the regions studied in Idaho have some sociologically anddemographic differences, yet the same major home attributes are important for all areas. An example
of a difference would be the importance of central air conditioning in Lewiston, where this is not an
important feature to home buyers in Pocatello or Idaho Falls.

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Interim ReportStreet or road characteristics, however, generally vary between the significance of setback orvariations of the street-traffic classification variables for each city, but not both. For the Idaho Model,
where all individual city observations were tested as a group, all street-traffic classification variables
and the setback variable (distance between the street or road and dwelling) are significant.
Objectives of the Study
Compensation for proximity damages (reduced value of the remaining property after a road is rebuilt
or widened) is based on the assumption that the value of residential property is diminished as a direct
result of proximity to a high traffic road. An objective study was needed to empiracally conclude first, if
proximity damages do exist, and second, the corresponding co-efficient that reflects the amount ofdamages, if any.
The Idaho Department of Transportation spends a great deal of taxpayer money to compensate
residential property owners for estimates of residual property damage as a result of a road project.
More importantly, the validity of the underlying assumption—that proximity damages are a fixed anduncorrectable problem—has not been empirically examined on a statewide or regional level, and therelationship between estimates for just compensation for anticipated damages and the actual loss ofmarket value to the residential property has not been empirically identified. The goal of this research
was to answer the question: What features or characteristics of a road, if any, affect the sales price ofresidential properties adjacent to that road?

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Interim ReportData Sources
Major pieces of data that the study required included detailed sales information on residential
properties that have sold in Pocatello, Idaho Falls, Boise, Lewiston, Moscow, and Coeur d’Alene since1998 as follows:•

COMPASS and Federal Highway street classification information correlated with traffic
count data•

Anaysis of demographic data for each region•

Onsite inspection of each property to measure the distance, in feet, of the home setback from the road.

•

Onsite inspection and specific property information garnered from Realtor commentson the MLS data sheet to conclude effective age of the home. It should be noted thatcondition is inherent in effective age, i.e. if the condition of a house is good, theeffective age is lower.

•

Classification of quality of construction based on Oregon Cost Manual, correlatingclasses 1-8 (see appendix for description of classes) to “below average,” “average,”“above average,” and “good.”

Inputs for the model were developed by identifying primary criteria considered to be consistentvariables affecting value that are used in the direct comparison appraisal method, under typical salesconditions with a typically motivated purchaser and seller.

Additional inputs concerning roadcharacteristics were researched by reviewing forty to fifty proximity damage settlement files at the
Idaho Transportation Department to conclude the variables impacting value related to road proximity.
The types of variables identified from these files were: distance the house is located from the road,classification of street (road) as defined by Federal Highways Administration correlated with a traffic
count classification. This classification also accounts for speed limit and number of lanes and whether
the road is a principal arterial, a minor arterial, a rural arterial, a collector, or local street. If the road is aprincipal arterial or collector street with greater than 5000 cars per day, it was identified whether thehome faced or backed this major roadway. The road variables were aggregated into the parametersof the model.

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Interim ReportModel Parameters
The parameters of the study include $40K to $600K homes that have sold in the greater area of six
regions of Idaho between 1998 and 2002 – the time parameters depended on the relative volume ofhome sales in each area. The higher sales volume, the stricter the parameters were on the age of theclosed sales in that area. Information was collected from each applicable Multiple Listing Service andCounty Assessor’s Office.
Data1,800± residential home sales that represent approximately 10% of the market for the period covered
were selected from the multiple listing services that cover the six regions of Idaho considered in thisstudy. In addition, some home sales on major and minor arterials and on connectors were specificallyselected for comparison. The parameters of the study include $40K to $600K homes that have sold in
the greater area of the six identified regions of Idaho between 1998 and 2003 – the time parameters
depended on the relative volume of home sales in each area. The Boise, Coeur d’Alene, Moscow,and Lewiston regions had higher sales volumes, allowing for a narrower time range. The data were
initially entered into an Access data base. The following five figures demonstrate the information thatwas entered for the 1,800± homes.

65
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportAcknowledgements and, Authors
The Idaho Transporation Department is the lead agency for the research project with a five-member
technical oversight committee. The committee members include:•

Doyle Pugmire, Appraisal Coordinator, ITD•

Leonard Hill, Right of Way Manager, ITD•

Rick Machmeier, Right of Way, Appraisal Review, ITD•

Scott Frey, FHWA

Authors of the study include:•

Ruby Miles Stroschein, Certified General Appraiser and employee of the University of Idaho•

Dr. James Nelson, a graduate professor at the University of Idaho in the College of Agricultural
Economics and Rural Socialology•

Sarah E. Miles, Appraiser Trainee and Statistical Analyst

The following table lists the variables that were identified for each property in the model.

66
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportVariable

General Data Source

Date of SaleMLS Data Sheets

Year built

MLS Data Sheets and Assessor

Effective age

On-site inspections and MLS

Quality of consturction

On-site inspections

Gross Living Area

MLS Data Sheets

Above Grade Bedroom CountMLS Data Sheets

Above Grade Bathroom CountMLS Data Sheets

Total Basement Square FeetMLS Data Sheets

Basement Square Feet Finished

MLS Data Sheets

Heating System

MLS Data Sheets

Cooling System

MLS Data Sheets

Number of Fireplaces

MLS Data Sheets

Patios/Decks

MLS Data Sheets

Fencing

MLS and on- site inspections

Automatic Sprinkling System

MLS Data Sheets

Shops & Outbuildings

MLS and on-site inspections

Car Storage (includes garages andcarports)

MLS and on-site inspections

Lot Size

MLS Data Sheets and Assessor

ZoningMLS Data Sheets and&Z

Location

MLS and On-Site
Inspections

Setback of house from road (curbto living area)

On-site Inspection –measurement using measuring
wheel

Fronts/backs to road*On-site inspection

City P

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Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportTraffic Count**

ITD, ACHD, local traffic data

Speet Limit**

On-site Inspection

Number of lanes**

On-site Inspection

Road Classification

On-site Inspection

All of the variables included in the study are considered to be in excess of Uniform Standards ofProfessional Appraisal Practise and Uniform Appraisal Standards for Federal Land Acquisitions
requirements. Additional data collected and on file include: Addresses, legal descriptions whereavailable, MLS reference numbers, dates of sales, tax parcel numbers (when made available in the
MLS data), financing, sales concessions, list prices, and number of days of market.
The meanings of most of the variables specified in the previous table are stright forward. However,the following variables deserve additional explanation:-

Setback of home from the road. An onsite inspection was made for each property
to measure the distance, in feet, of the home set-back from the road travelway. If
there was more than one road abutting the site, the road with the most proximity
characteristics was used as the measuring point, measuring to the front, side, orback as it applied.

-

Effective age determination. Onsite inspection and specific property information
garnered from Realtor comments on the MLS data sheet were used to conclude
the effective age of the home. Specific information such as a new roof, newcarpets, new heating system, new kitchen cabinets,etc. were accounted for inconcluding the effective age of the home. Condition was originally considered as aseparate variable, but was found to be highly correlated with effective age,

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Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim Reportindicating that condition is often inherent in effective age, causing multicollinearity,
and was thus eliminated as an independent variable.

Quality of Construction Classification. The quality of construction of the home is based onclassifications used by the Oregon Cost Manual, correlating classes 1-8 to “below average,”“average,” “above average,” and “good.” Oregon Cost Manual class 8 homes are considered to
be excellent quality construction homes and were not used in any of the models. Parameters
for construction quality are, from the Oregon Cost Manual. Construction classifications areattached for clarification.

Using Regression Modeling

The following itemized list corresponds to numbers 1 through 18 on the Excel Spreadsheet
Regression Model.

1. Enter the appraised value in the “before” condition, or prior to the road project.

2. Enter the approximate percentage of value that is contributable to the to the land in Cell
B6.

3. Enter the approximate percentage of value that is contributable to the improvements tothe land itself in Cell B7. Numbers 2 and 3 should not exceed 100% when summed.

4. Enter the effective age of the house in Cell B10. The effective age is one of the mostcritical steps in inputting data. Effective age is calculated based on the condition of thebone structure (frame and foundation), and reflects upgrades to the bone structure; both
long-lived and short-lived such as additions, windows, electrical, plumbing, roofing,
kitchens, bathrooms, floor coverings, heating systems, etc.

69
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportNote: The effective age determinate in the model are based on comments gleaned from
the MLS data sheet and from exterior on-site inspection.

5. Using the ANSI Standard, enter the total above grade gross living area (GLA) of thehouse, measured in square feet in B11.

6. Using the dropdown menu in Cell B12, enter the total above grade bedroom count.

7. Using the dropdown menu in Cell B13, enter the total above grade bathroom count.
Three-Quarter bathrooms are expressed as full bathrooms.

8. Enter the total square footage of the basement in Cell B14. If there is no basement,
enter “0”.

9. Enter the total square footage of the basement that is finished in Cell B15. Again, if thehome has no basement or if the home has an unfinished basement, enter “0”..

10. Using the dropdown menu in Cell B16, enter the number of cars this garage or carport
can store in a typical fashion. Note: There is no difference in the model between
garages or carports.

11. Using the dropdown menu in Cell B17, enter the quality of construction. This variable isa measure of the quality of construction, more thoroughly explained in the Single Family
Residential Conventional Construction Classes attached.

Note: The Oregon Cost Manual Construction Quality Classifications were followed inconcluding construction quality. Construction quality class 8 was generally outside theparameters of the Idaho data set. It is important to follow these guidelines.

12. Enter the size of the subject lot in Cell B18 in the before condition, measured in square
footage.

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Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim Report13. Using the dropdown menu in Cell B19, enter the region, or region closest to the subject.
Boise = Ada County Region
Coeur d’Alene = Kootenai County Region
Idaho Falls = Bonneville County Region
Lewiston = Nez Perce County Region
Moscow = Latah County Region
Pocatello = Bannock County Region

14. In Cell B20, enter the number of linear feet measured from the edge of the road or rightof way to the closest living area of the house, in the before condition.

15. Using the drop down menu in Cell B21, enter the road classification in the before.

16. In Cell B22, enter the number of linear feet measured from the edge of the road or rightof way to the closest living area of the house, in the after condition.

17. Using the dropdown menu in B23, enter the road classification in the after condition.

18. The Results Box is a summary of findings. Here should be the appraiser’s value in the
before, the model’s concluded value in the before, and a reconciliation of values. This isnecessary for two reasons:a. The model is based on natural logarithms for both the dependent variable andsome of the independent variables. This, simply stated, means that damages
are expressed as percentages of value, and the respective value that eachvariable adds to the whole is not a linear function. It is hence imperative that the71
Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim Reportappraiser’s concluded value in the before be similar to the model’s concluded
value so the percent base is accurate.
b. There are many variables affecting value that cannot and are not captured bycoefficients in the model. Reconciliation is necessary in order to prevent theoverstatement or understatement of value to again assure that the base fordamage calculation is accurate.

Proximity damages are shown as:

1. A total dollar amount
2. As a percentage of value in the before (a percentage diminution of total value)
3. As a dollar amount of damages contributable to land and improvements.

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Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportAll Cities - Idaho Model
The Idaho model was run multiple times with various categorizations, and under various
assumptions. The final result, shown on the next page, uses all original categorization of thetraffic count variables and setback values of 100 and less. Setback loses significance quickly atvalues any greater.
The Idaho Model is a log/log model, meaning that the dependent variable (sales price) is innatural logarithm form, as has been the case for all individual city models in order to correct for
non-constant variance. The difference between this model and the other models is that thosescalar independent variables that have a high range of value are also expressed in logarithmic
form. While transforming the dependent variable corrected for non-constant variance, atransformation on scalar independent variables with extreme ranges was necessary to correct
for non-linearity.
Correlation matrix for variables in final model, before transformation
Correlations: Sale Price, Effective Ag, Gross Living, Above Grade , Above Grade

Effective

All Cities - Idaho Model Correlations
Sale Price Effective Gross Liv Above Gr Above Gr Basement Basement No. Cars
-0.5630.000

Gross Liv

0.729
0.000

-0.4090.000

Above Gr

0.462
0.000
0.640
0.000

-0.3480.000-0.495
0.000

0.583
0.000
0.653
0.000

0.627
0.000

0.239
0.000
0.198
0.000

-0.1360.000-0.113
0.000

0.079
0.001
0.091
0.000

-0.019
0.427
0.007
0.781

0.021
0.396
0.025
0.298

0.627
0.000

No. Cars

0.477
0.000

-0.457
0.000

0.396
0.000

0.322
0.000

0.407
0.000

0.090
0.000

0.143
0.000

Sample Li

0.375
0.000

-0.266
0.000

0.303
0.000

0.206
0.000

0.267
0.000

0.103
0.000

0.113
0.000

0.225
0.000

OneHundA

0.092
0.000
-0.1080.000-0.1310.000-0.094
0.000
0.076
0.003
0.148
0.000

0.087
0.001
-0.1030.000-0.111
0.000

0.148
0.000
-0.0420.100-0.157
0.000

0.135
0.000
-0.0780.002-0.167
0.000

0.065
0.011
0.045
0.078
-0.032
0.209

0.062
0.016
0.017
0.507
-0.036
0.157

0.081
0.002
-0.0720.005-0.141
0.000

Above Gr

Basement
Basement

Five HundOne Thous

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Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportFive Thous-0.1610.000-0.156
0.000

0.177
0.000
0.169
0.000

-0.1240.000-0.0540.037-0.1350.000-0.0800.002-0.1640.000-0.105
0.000

0.019
0.463
-0.059
0.022

0.019
0.464
-0.0190.460-0.1230.000-0.061
0.019

LNLotSiz

0.434
0.000

-0.192
0.000

0.390
0.000

0.227
0.000

0.283
0.000

0.204
0.000

0.201
0.000

0.262
0.000

CDA 1

0.110
0.000
-0.1370.000-0.0940.000-0.1550.000-0.1600.000-0.026
0.282
0.145
0.000
0.068
0.005

0.021
0.375
-0.051
0.036-0.0760.002-0.093
0.000

0.015
0.522
-0.0620.011-0.1170.000-0.076
0.002

0.059
0.014
-0.1130.000-0.0950.000-0.0930.000-0.078
0.001
0.125
0.000
0.048
0.049
0.136
0.000

-0.041
0.088
0.220
0.000
-0.015
0.531
0.144
0.000

0.112
0.000
0.016
0.509
-0.1150.000-0.109
0.000

0.136
0.000
0.600
0.000

-0.2150.000-0.274
0.000

0.143
0.000
0.422
0.000

0.104
0.000
0.215
0.000

0.139
0.000
0.331
0.000

0.131
0.000
0.107
0.000

0.063
0.010
0.099
0.000

0.119
0.000
0.243
0.000

Sample Li OneHund A Five Hund One Thou Five Thou Ten Plus A LNLotSiz
OneHund A
0.052
0.045

CDA 1

TenPlus A

IF1
Lewiston
Pocaltello

Abv QualGood Qual

Five Hund-0.1020.000-0.2680.000

One Thous-0.0040.876-0.2810.000-0.2240.000

Five Thous-0.0780.002-0.1660.000-0.1330.000-0.1390.000

Ten Plus A

0.033
0.199

-0.1890.000-0.1510.000-0.1580.000-0.0930.000

LNLot Size

0.573
0.000

0.055
0.036

-0.0420.112-0.0360.170-0.0620.019-0.059
0.024

CDA 1

0.056
0.022

0.023
0.379

-0.0720.005-0.0740.004-0.0670.009-0.1200.000-0.042
0.092

IF1-0.043
0.075

0.092
0.000

0.006
0.829

0.022
0.385

-0.042
0.104

0.003
0.895

-0.0270.280-0.2010.000

74Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportLewiston-0.0630.009-0.0910.000-0.060
0.020

0.021
0.424

0.044
0.086

0.045
0.078

0.002
0.952

-0.1360.000

Pocatello-0.0350.154-0.079
0.002

0.130
0.000

0.036
0.161

0.152
0.000

0.030
0.238

0.045
0.069

-0.2000.000

Above B

0.082
0.001

0.045
0.078

-0.083
0.001

0.029
0.268

-0.007
0.800

0.003
0.897

0.108
0.000

-0.0930.000

Good B

0.181
0.000

-0.0720.005-0.0630.014-0.0490.059-0.071
0.006-0.071
0.006

0.136
0.000

0.128
0.000

Pocatello

Above B

Lewiston

IF1
Lewiston-0.2060.000

Pocatello-0.3030.000-0.2050.000

Above B

0.060
0.013

-0.030
0.211

0.017
0.476

Good B-0.119
0.000

0.016
0.511

-0.1020.000-0.2070.000

No multicollinearity is present.

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Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim Report

Regression Analysis – Log/Log Model: LNSalesPrice versus Effective Age, LnGLA, ...
The regression equation is
LNSalesPrice = 6.97 - 0.0133 Effective Age + 0.466 LnGLA - 0.0121 Above Grade Bedroom
Count + 0.0384 Above Grade Bathroom Count + 0.0610 LnBSMTSFFin + 0.0353 LnBSMTSF +0.0712 No. Cars + 0.0754 LNSampleSetback - 0.0253 OneHundA - 0.0377 FiveHundA - 0.0617
OneThousA- 0.0742 FiveThousA - 0.152 TenPlusA + 0.0705 LNLotSize - 0.162 CDA1 - 0.340 IF1 - 0.209
Lewiston1 - 0.296 Pocatello1 + 0.0419 AboveB + 0.238 GoodB
Predictor
Constant
Effective Age
LnGLA
Above Grade Bedroom Count
Above Grade Bathroom CountLnBSMTSF Finished
LnBSMTSF
No. Cars
LN Sample Setback
One Hund "101-500"
Five Hund "501-1,000"
One Thous "1,001-5,000"
Five Thous "5,001-10,000"
Ten Plus "10,000+ "LN Lot Size
CDA 1
IF1
Lewiston
Pocatello
Above B
Good BS = 0.1662

R-Sq = 87.1%

Coefficient
6.972-0.0133059
0.46552-0.012134
0.038410.060960.03533
0.071165
0.07538-0.0253-0.03769-0.0617-0.07419-0.15233
0.07051-0.16155-0.33966-0.20863-0.29591
0.041930.23843

SE Coef
0.19370.008254
0.02872
0.009537
0.01369
0.011140.018910.009611
0.02474
0.01936
0.021130.021480.026410.026370.010090.03069
0.02635
0.02882
0.026210.01289
0.03362T
36.00-16.12
16.21
-1.27
2.81
5.47
1.87
7.4
3.05
-1.31
-1.78
-2.87
-2.81
-5.78
6.99
-5.26
-12.89-7.24-11.29
3.25
7.09

P
0.000
0.000
0.000
0.204
0.005
0.000
0.062
0.000
0.002
0.192
0.075
0.004
0.005
0.000
0.000
0.000
0.000
0.000
0.000
0.001
0.000

R-Sq(adj) = 86.8%

Analysis of Variance
SourceRegression
Residual Error
Total

DF
20
791
811

SS
147.8628
21.8601
169.7229

MS
7.39310.0276

F
267.52P
0.000

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Proximty Damages Study in Idaho for the Idaho Transportation Department

Interim ReportResiduals Versus the Fitted Values(response is LNSalesP)
0.8
0.6

Residual

0.4
0.2
0.0
-0.2
-0.4
-0.6
-0.8

10

11

12

13

Fitted Value

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