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The Effect of Road Traffic onResidential Property Values:

A Literature Review andHedonic Pricing Study

Ian Bateman, Brett Day, Iain Lakeand Andrew Lovett

January 2001

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This study was commissioned by the Scottish Executive Development Department, Edinburghunder Mr. Mike Cartney. Two broad objectives were identified from the outset:

• to review the underlying theory and methodologies for the assessment and monetaryvaluation of the impact upon property prices of those road disamenity items which arecompensatable under the Part 1 of The Land Compensation (Scotland) Act 1973;

• to undertake an empirical study, based in Scotland, of the monetary value of thesedisamenities.

The authors wish to thank the following for comments upon previous drafts of this report:Mike Cartney, Scottish Executive; William Duthie, Valuation Office Agency; ProfessorDavid Pearce, University College London; David Hastie, Scottish Executive; Mike Waggott,Scottish Executive.

The views expressed in this report are the work of the authors only and should not be taken toreflect the views of the Scottish Executive or of any other Government Department.

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TABLE OF CONTENTS

EXECUTIVE SUMMARY

1 INTRODUCTION

1.1 ROADS AND ROAD TRAFFIC1.2 PART 1 OF THE LAND COMPENSATION (SCOTLAND) ACT 19731.3 DETERMINING COMPENSATION CLAIMS1.4 THE LIMITATIONS OF THE CURRENT SYSTEM

2 PRICE AND VALUE

2.1 INTRODUCTION2.2 PRICES AND MARKETS

2.2.1 THE PRICE OF A SIMPLE GOOD2.2.2 THE PRICE OF A COMPLEX GOOD2.2.3 UNPRICED GOODS AND SERVICES

2.3 ECONOMIC VALUE2.3.1 THE DIFFERENCE BETWEEN VALUE AND PRICE2.3.2 TYPES OF VALUE AND TOTAL ECONOMIC VALUE

2.4 SUMMARY AND CONCLUSIONS

3 THE TECHNIQUES OF NON-MARKET VALUATION

3.1 INTRODUCTION3.2 VALUATION AND POLICY MAKING3.3 VALUING ENVIRONMENTAL GOODS

3.3.1 �PRICING� METHODS3.3.1.1 Opportunity costs3.3.1.2 Costs of alternatives3.3.1.3 Mitigation behaviour3.3.1.4 Shadow project costs3.3.1.5 Government payments3.3.1.6 The dose-response method3.3.1.7 Summary: �Pricing� methods

3.3.2 �VALUATION� APPROACHES: EXPRESSED PREFERENCE METHODS3.3.2.1 The contingent valuation (CV) method3.3.2.2 Contingent ranking (CR) and stated preference (SP) methods

3.3.3 �VALUATION� APPROACHES: REVEALED PREFERENCE METHODS3.3.3.1 The travel cost (TC) method3.3.3.2 The hedonic pricing (HP) method

3.4 VALUATION TECHNIQUES AND THE PROJECT REQUIREMENTS3.5 SUMMARY AND CONCLUSIONS

4 THE HEDONIC TECHNIQUE

4.1 INTRODUCTION4.2 HEDONIC PRICES REVISITED4.3 THEORETICAL VALIDITY OF HEDONIC PRICING

4.3.1 DEFINITION OF THE HOUSING MARKET4.3.2 TIME AND MARKET EQUILIBRIUM4.3.3 ENVIRONMENTAL QUALITY IN THE HEDONIC PRICE MODEL

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4.4 ESTIMATION OF THE HEDONIC PRICE FUNCTION4.4.1 REGRESSION ANALYSIS, VARIANCE AND BIAS4.4.2 THE DEPENDENT VARIABLE4.4.3 THE EXPLANATORY VARIABLES4.4.4 FUNCTIONAL FORM4.4.5 TREATMENT OF MULTICOLLINEARITY4.4.6 SPATIAL DEPENDENCE

4.5 ESTIMATION OF WELFARE CHANGES4.6 SUMMARY AND CONCLUSIONS

5 EMPIRICAL STUDIES

5.1 INTRODUCTION5.2 THE VALUATION OF NOISE AND VIBRATION POLLUTION

5.2.1 MEASURING NOISE POLLUTION5.2.2 STUDIES OF NOISE POLLUTION5.2.3 NOISE POLLUTION AND OTHER VALUATION APPROACHES5.2.4 SUMMARY

5.3 THE VALUATION OF AIR POLLUTION5.3.1 MEASURING AIR POLLUTION5.3.2 STUDIES OF AIR POLLUTION5.3.3 SUMMARY

5.4 SUMMARY AND CONCLUSIONS

6 LITERATURE REVIEW: SUMMARY AND CONCLUSIONS

7 STUDY DESIGN ISSUES

7.1 INTRODUCTION7.2 THE USE OF A GEOGRAPHICAL INFORMATION SYSTEM IN THE STUDY7.3 STUDY AREA AND HOUSE PRICE INFORMATION7.4 DERIVING EXPLANATORY VARIABLES ON THE STRUCTURE,

NEIGHBOURHOOD AND ACCESSIBILITY OF THE SAMPLE PROPERTIES7.4.1 STRUCTURAL VARIABLES7.4.2 NEIGHBOURHOOD VARIABLES7.4.3 ACCESSIBILITY VARIABLES

7.5 INCORPORATING THE SEVEN COMPENSATABLE PHYSICAL FACTORSINTO THE STUDY DESIGN

7.6 ROAD NOISE7.6.1 ACCOUNTING FOR THE NON-COMPENSATABLE IMPACTS OF A NEW

ROAD

8 THE HEDONIC PRICING STUDY: DEFINING VARIABLES

8.1 INTRODUCTION8.2 STRUCTURAL VARIABLES

8.2.1 GROUND FLOOR AREA8.2.2 PLOT AREA8.2.3 PROPERTY TYPE8.2.4 OTHER STRUCTURAL VARIABLES

8.3 NEIGHBOURHOOD VARIABLES8.4 ACCESSIBILITY VARIABLES8.5 ENVIRONMENTAL VARIABLES

8.5.1 THE VISUAL IMPACT OF ROAD AND OTHER LAND USES8.5.2 ROAD TRAFFIC NOISE

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8.5.2.1 Calculating the noise level being emitted from each road8.5.2.2 The propagation of road noise to each property

9 THE IMPACT OF ROAD NOISE UPON PROPERTY PRICES

9.1 INTRODUCTION9.2 SPECIFICATION OF THE HEDONIC PRICE FUNCTION: SELECTION OF

VARIABLES9.3 SPECIFICATION OF THE HEDONIC PRICE FUNCTION: CHOICE OF

FUNCTIONAL FORM9.4 THE MODEL RESULTS AND INTERPRETATION

9.4.1 MODELLING PROCEDURE AND RATIONALE9.4.2 TABULATED RESULTS9.4.3 DISCUSSION OF THE MODELS

9.4.3.1 Structural Variables:9.4.3.2 Neighbourhood Variables9.4.3.3 Accessibility Variables9.4.3.4 Visual Amenity Variables9.4.3.5 Noise Pollution

9.5 STRENGTHS AND WEAKNESSES9.5.1 STRENGTHS

9.5.1.1 Theoretical Strength of the Analysis9.5.1.2 Quantity and Quality of the Assembled Data9.5.1.3 Stability of noise impact estimates

9.5.2 WEAKNESSES9.5.2.1 Omission of internal characteristics data9.5.2.2 The time series problem9.5.2.3 Modelling decisions

9.6 TRANSFERABILITY9.5.1 REPRESENTATIVENESS9.5.2 LIMITATIONS OF HEDONIC PRICING

10 RECOMMENDATIONS AND CONCLUSIONS

10.1 A NEW PROCEDURE FOR ASSESSING PART 1 CLAIMS10.2 EXTENDING THE MODEL: INTEGRATING NOISE COMPENSATION

ESTIMATES INTO ROAD PLANNING PROCEDURES10.3 CONCLUSIONS

APPENDICES

ANNEX A: MEASURES OF VALUE; CONSUMER SURPLUS, WILLINGNESS TO PAYAND WILLINGNESS TO ACCEPT

ANNEX B: THE BOX-COX TRANSFORMATION

ANNEX C: PRINCIPAL COMPONENTS ANALYSIS

ANNEX D: VARIABLES CREATED FOR THE STUDY

ANNEX E: ASSIGNING TRAFFIC VOLUMES TO ALL ROADS

ANNEX F: CALCULATING ROAD NOISE LEVELS

REFERENCES

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EXECUTIVE SUMMARYThis executive summary provides a brief overview of the research. After eachsubheading, the number in parentheses guides the reader to the most relevant sectionin the full report to consult for further details. There is also a section by sectionoverview of the full report at the end of this summary.

ES 1 Introduction (Section 1)

Part 1 of the Land Compensation (Scotland) Act 1973 states that where the use ofpublic works reduces the value of a piece of land, the legal owners can becompensated for this loss. This compensation is payable to claimants from whom noland is taken and who are debarred from bringing an action at common law fornuisance. This report will focus upon losses associated with the construction of newroads and, according to the law, compensation can only be paid for loss arising fromseven physical factors: noise, vibration, smell, fumes, smoke, artificial lighting andthe discharge onto the land of solid and liquid substances. Other impacts such asvisual disamenity are non-compensatable. Furthermore, any enhancement in propertyprice arising from the new road (e.g. from improved accessibility) is deducted fromthe compensation payable as a result of the seven physical factors identified above.Compensation payments are based upon any change in property price one year afterthe opening of the road.

Given this degree of complexity the assessment of claims is a skilled operation that isconducted by expert valuers. However, this same complexity has in recent years ledincreasingly to disputes regarding compensation assessments, resulting in severalinstances of litigation. In addition the proliferation of �no win, no fee� arrangementsbetween claimants and teams of valuers, has led to a substantial increase in thenumber of Part 1 claims and a resultant rise in the amount of taxpayers� money paid incompensation. These claims for compensation can be very large and the case is oftencited in the 1990's of the A27 in England that cost £20m to build but resulted in Part 1payments of over £22m.

Therefore, this study had two main aims. The first was to provide further empiricalinput to the compensation assessment process in an attempt to assist valuers indetermining the appropriate level of payment. In addition it was hoped to producecompensation values which are easier for claimants to understand.

In order to achieve this a variety of valuation techniques were considered (seeSections 3 and 4). It was decided that the most appropriate method was that ofhedonic property pricing. This relates property prices to a wide range of factors thatmay affect price. These include variables relating to the structure, neighbourhood,accessibility and environment of the property in addition to variables relating to theimpacts of nearby roads. Considering a wide range of variables was thought necessarybecause the physical impacts of a new road are likely to be related to a variety of otherfactors. It is only when all these other factors have been controlled for that the impactof road disamenities upon property prices can be quantified. These prices can then beused to estimate compensation payments under Part 1 of the Land Compensation(Scotland) Act 1973.

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ES 2 Appraising the claims procedure (Section 1.3 � 1.4)

ES 2.1 The current system for determining compensation payments(Section 1.3)

Part 1 claims for compensation can be lodged at any time between one and threeyears after the opening of a new trunk road. Any claims for compensation aresubmitted to the Scottish Executive Development Department (SEDD) who thenpass these to the Valuation Office Agency (VOA) who assess the compensationand negotiate with the claimants or their agents to try and reach an amicablesettlement.

The valuers determine the level of compensation, if any, that should be paid to theclaimant. Before assessing the compensation due, the valuer will carry out adetailed inspection of the property. It will be inspected both externally andinternally and notes taken on the construction, size, age, accommodation, standardof modernisation and condition, etc. The valuer will note any garden and otherground held with the property and its location in relation to the new road. Detailswill also be taken of any works carried out under the Noise Insulation (Scotland)Regulations 1975.

The valuer will also discuss with the claimant exactly which aspects of the newroad and its traffic have resulted in disamenities. This is an important informationgathering exercise since in one short visit the valuer is clearly not able to witness atfirst-hand all the impacts of the road (e.g. if the visit is made in the middle of theday the valuer cannot assess the impacts of noise from rush hour traffic).

The valuer will then utilise this information to determine the appropriate level ofcompensation. This consists of a number of discrete steps illustrated in Figure 1.Initially the valuer will interrogate the VOA�s database of property sales, which inScotland is based on information provided by the Registration of Title. Thedatabase is supplemented by sales information gleaned from other casework,professional contacts and relevant publications. Using this database, it may bepossible to find a record in the Register that reports the selling price of a similarproperty in a comparable location with respect to the new road. If a second salesrecord exists that catalogues the market price of another similar property that isunaffected by the new road, then a comparison of the two selling prices gives thevaluer an indication of the possible change in total market value of the claimant�sproperty.

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Figure 1: The current and suggested procedures for determining Part 1 claims

In large estates with many identical properties this may well be possible. However,where the housing is less homogenous it is difficult to find sales prices for similarproperties in comparable locations. In these cases the valuer may be able to makesome inferences from sales of similar yet more distant properties, but thiscompromise simply introduces further uncertainty into the valuation process.

The most difficult situation is one where no records of similar property sales areavailable. In these cases the valuer has to rely on professional knowledge andexperience to determine the depreciation arising from use of the new road.

Occasionally, the Scottish Executive provide measurements of the change in noiselevels and other environmental impacts resulting from a new road. However, thisinformation is rarely specific to individual properties. Moreover, no procedureexists that would allow valuers to relate these measures to changes in propertyprices.

Once an estimate of the total decrease in property price has been calculated, usingthe information gathered during the visit to the property, the valuer is charged withdetermining the relative importance of the seven physical factors in causing theprice of the house to fall.

The final stage in the process of claims assessment involves the valuer examiningwhether any of the positive effects of the new road, such as better accessibility orreduced noise levels on other roads, have increased the property price. These arethen offset against the compensation value calculated above.

Panel 2: Suggestedprocedure

Examine recent property sales toidentify the depreciation caused by

the road.

If this is notpossible produceestimate based

upon judgementand experience

Estimate % of depreciation due tothe 7 physical factors

Determine current house price fromdatabase of property sales if possible

Assess whether compensation should bereduced due to:� Improved accessibility� Lower noise from existing roads

Final level ofcompensation

Determine current house price fromdatabase of property sales if possible

Assess whether compensation should bereduced due to:� Improved accessibility� Lower noise from existing roads

Final level ofcompensation

Panel 1: Current procedure

Based upon noise increasecalculate depreciation using

results from the hedonic pricingmodel

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Once the VOA has determined the appropriate compensation level this informationis communicated to the claimant. A period of negotiation then ensues after whichthe householder can either accept the settlement or refer the claim to the LandsTribunal for Scotland.

ES 2.2 The limitations of the current system (Section 1.4)From a review of current procedures we feel that the process lacks four key piecesof information:

1. As outlined above, the database of property sales is used to estimate the totalchange in a property�s price resulting from the construction of a new road.However, Part 1 of the Land Compensation (Scotland) Act 1973 is restricted topaying only for the seven physical factors described previously. Using simplecomparisons of the selling price of just a handful of houses makes it difficult topartition an observed depreciation between compensatable factors, such as roadnoise, and non-compensatable factors such as visual impact.

2. The method used to assess claims is based upon the examination of recentproperty sales data from similar houses. This creates difficulties in areas wherethere are few property sales or many dissimilar houses. In these cases it is moredifficult to identify similar properties from which the impact of a road can bequantified.

3. Part 1 claims are only submitted after a new road has been built. Thereforewhen the VOA assess a claim they may not possess detailed information aboutconditions at the property before the road was built. This may make it difficultto assess the changes that have occurred at the property because of the newroad.

4. It is difficult to estimate the magnitude of Part 1 claims before the new road isconstructed, and hence it is difficult to take account of likely claims at the roaddesign stage.

ES 3 Literature Review (Sections 2-6)

The previous section has detailed the limitations involved in the procedures used todetermine compensation levels under Part 1 of the Land Compensation (Scotland) Act1973. The aim of our research was to provide valuers with an empirical study tosupplement the array of techniques they currently employ. This approach sought toprovide a transferable formula that would allow the appropriate level of compensationto be paid to any property affected by a new road to be estimated. The existence of alarge empirical study would also enable valuers to justify their proposed level ofcompensation payments in any Lands Tribunal.

In order to determine the appropriate level of compensation that should be paid avariety of approaches were considered. However, it was decided that a hedonicpricing technique was the best way forward. This involves a detailed empiricalanalysis of a database of property sales. Currently, valuers use a similar database aspart of the process to estimate the impact of the road scheme upon property prices. Bymuch the same process, but using large samples and statistical techniques, ourresearch aimed to estimate how each of the different factors that constitute the effectof a new road are likely to contribute to this impact.

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A hedonic pricing study involves obtaining the selling price of a large number ofproperties. For each property we then need to obtain information on four groups ofvariables:

• Structural variables (e.g. the number of rooms in each house);

• Accessibility variables (e.g. the proximity of schools);

• Neighbourhood variables (e.g. local unemployment rates);

• Environmental variables (e.g. aircraft noise).

These factors are then related to house prices in a statistical model. Once the differentfactors have been controlled for we can include variables relating to compensatableimpacts under Part 1 of the Land Compensation (Scotland) Act 1973. This willquantify the impact that road noise, as an example, has upon property prices.

ES 4 The study outline (Section 7)

A 50 km2 urban area in Glasgow was chosen as the study site for several reasons. Itwas decided to use an urban study site, as the high density of properties would allow asmaller study area thus reducing digital data storage requirements and, moreimportantly, processing time. Glasgow was chosen as it is a socially heterogeneouscity with a variety of road types and hence impacts. A sample of 3500 properties soldduring 1986 was extracted from a database of property sales (Registration of Title) forthe study area. The locations of the study area and the sample properties are illustratedin Figure 2. The use of 1986 data is not problematic, as the analysis will determinethe percentage change in property price per unit increase in road noise. Therefore anytemporal changes in property price are accounted for and the results should still beapplicable today. This will hold unless people's sensitivity to noise has changed overtime, but there is no evidence to suggest that this has occurred.

ES 5 The definition and derivation of explanatory variables(Section 8)

In order to estimate the effect of an individual variable upon property prices, thestructural, neighbourhood, accessibility, environmental as well as the road impactattributes of all the sample properties need to be calculated. In this study most of thevariables were calculated using Ordnance Survey (OS) digital map data and aGeographical Information System (GIS). A GIS is a system for capturing, storing,checking, manipulating, analysing and displaying spatially referenced data. Theprocedures used to define the explanatory variables will be considered briefly.

ES 5.1 Structural variables (Section 8.2)Structural variables define the fabric of each building and the plot upon which it isbuilt. A wide range of structural variables were calculated for each propertyincluding ground floor area, garden area and property type. These variables werecalculated using a GIS in combination with large scale OS Land-Line.Plus digitaldata that record the location of all significant ground features with a spatialaccuracy of 40 cm. The lines constituting building and plot outlines are clearly

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visible on the sample of these data in Figure 3. Using GIS these lines wereextracted and manipulated into the required structural variables.

However, such a method does not permit potentially important variables such asthe number of storeys, construction material, or age to be defined. Therefore theGIS analysis was supplemented with a simple external inspection of each propertyduring which these variables were recorded. One limitation with this method is thatvariables relating to a property�s interior still cannot be determined. Thereforepotentially important factors such as type of heating, and the property�s internalstate of repair were not included in the analysis.

It was also not possible to record the presence of double-glazing, which is ofteninstalled as a defensive measure against noise and increases the property price.Therefore, if noisy houses have installed double-glazing, and this variable is notaccounted for, then the analysis will produce a conservative estimate of the impactof noise upon property prices. It is likely that some of the neighbourhood variableswill be correlated with double-glazing and so will partially act as a surrogate.Nevertheless, we accept that this omission may cause a downward bias in ourestimate of the noise coefficient.

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Figure 2: The study area and sample properties

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Figure 3: OS Land-Line.Plus

ES 5.2 Neighbourhood variables (Section 8.3)Neighbourhood variables describe the characteristics of the local area in which theproperty is located and census data are a good indicator of these attributes. InScotland the smallest spatial unit for which census data are released is the outputarea which in this study encompassed approximately 55 households. Using the GISeach property was located within its output area and census derived variablesassigned to each property. These variables included the percentage unemploymentand the percentage of two car owning households. They were chosen on the basisthat previous studies had shown them to be related to the level of social deprivationin an area.

ES 5.3 Accessibility variables (Section 8.4)Accessibility variables define the ease with which local amenities can be reachedfrom the property and for this study schools, bus routes, railway stations, shops,parks, and the Central Business District were all considered. Through use of GISthree separate measures of accessibility, namely car travel time, walking distanceand straight-line distance were then calculated from each amenity to everyproperty.

0 150 metresBuilding outlinesOther linesPlot outlines

Reproduced from the OS Land-Line.Plus with permission of Her Majesty�sStationery Office © Crown Copyright

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ES 5.4 Environmental variables (Section 8.5)Environmental variables describe the environment surrounding each property. Forthis study several of these variables were specified including the level of aircraftnoise and the visual impact of a variety of land uses. An estimate of the aircraftnoise level at each property was produced by obtaining a map showing noisecontours around Glasgow Airport. Variables measuring the visual impact of avariety of land uses, including industrial areas and parkland, were also specified.The procedure used to define these is identical to that for the visual impact of roadand so is described in the next section.

ES 5.5 Road impact variables (Section 8.5)Having defined variables relating to the structure, neighbourhood, accessibility andenvironment of each property, variables relating to the impact of roads werespecified. Two groups of variables were defined, the first of which constitutes theseven physical factors for which compensation can be paid.

However, one problem with the current compensation process is the difficulty inapportioning property price depreciation between compensatable and non-compensatable factors. If we only included variables related to the seven physicalfactors in the property price model misleading results might be produced. Anexample would be if properties with high noise levels were also subject to a highvisual impact from roads. This would result in the estimate of the impact of roadnoise upon property prices also including a component related to the visual impactof the road. The solution to this difficulty is to include the non-compensatableimpacts of road, such as visual impact and accessibility, into the property pricemodel. These form the second group of variables.

ES 5.5.1 Compensatable road impacts (Section 8.5.2)Part 1 of the Land Compensation (Scotland) Act 1973 states that compensationis payable to householders who have experienced a drop in their property pricedue to the use of a new road. However, compensation is only payable for theimpacts of seven physical factors namely, noise, vibration, smell, fumes, smoke,artificial lighting and solid and liquid discharges. In theory we would wish toinclude all these factors in the property price model.

However, after careful consideration it was decided to only specify the level ofroad noise. The first reason for this is that, from a series of discussions withvaluers, it became apparent that most Part 1 claimants cite noise as the mainfactor for which compensation is sought. Therefore changes in noise causeannoyance and are thus likely to affect property prices.

However, even if a house and its occupants are negatively impacted upon byone of the physical factors, compensation can only be paid if the house price islowered. This will only occur if potential buyers perceive the factor and hencebid a lower price for the property. In a short inspection visit to the house it isunlikely that potential buyers will be aware of factors such as artificial lightingand land discharges. Therefore property prices are unlikely to be affected and sono compensation is due. Noise is a more obtrusive factor, and so it is likely tobe incorporated in the decision making process of most house buyers.

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As the noise level at any property increases it is likely that the other physicalfactors, such as vibration, smell and fumes, will also rise. Therefore weanticipate that a road noise variable will also act as a surrogate for the otherphysical factors. Finally, the study will concentrate upon one factor because itwould be prohibitively expensive to implement a claims procedure that involvedmeasuring the change in each of the seven physical factors at every property.

The road traffic noise level at each property was calculated by using theCalculation of Road Traffic Noise (CRTN) procedure. This process wassimplified slightly in order to account for the large number of sample propertiesand adapted so that most of the calculations could be performed in a GIS. Thesemodifications were also justified because many assumptions had to be made inassigning traffic flows and composition to each road. Therefore a moresophisticated modelling procedure would not necessarily have improved theaccuracy of the noise level ascribed to each property. In any case thesemodifications will only have a small overall impact upon the noise level at eachproperty. The noise calculation procedure is illustrated in Figure 4.

Data on the volume and composition of traffic along a number of roads in thestudy area were obtained from Glasgow City Council. These were used inconjunction with various interpolation techniques to produce traffic volume andcomposition estimates for all the roads in the study area. Estimates of roadspeeds were derived from national road speed statistics. The gradient of eachroad was calculated and the type of road surface estimated. All these data werethen used to calculate the noise level being emitted from each road. Each sampleproperty was matched to its nearest road and the distance between the road andproperty calculated. This was combined with the noise level for the road tocalculate a noise estimate for the property. These noise levels were thencorrected to account for reflections from other buildings and ground absorptionof the sound by making global assumptions about the nature of the study area.

This procedure will work well unless other roads, in addition to the nearest,have a large impact upon the noise level at any property. Therefore, if thenearest road to a property was a multi-carriageway road, then the noisecontributions from the other carriageways were calculated, and the noise levelscombined. A similar situation occurred if the nearest road to each property wasa minor road but there was a motorway, A or B class road within 100m of theproperty. In these cases, the contribution from the major road was calculatedand the noise levels combined.

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Figure 4: Calculation of noise levels at each property

ES 5.5.2 Non-compensatable road impacts (Section 8.5.1)The accessibility impactsRoads can have positive effects upon property prices if they permit easy accessto a variety of local amenities. Variables describing these impacts were definedas part of the wider accessibility assessment.

The visual impactObtaining measures of the visual impact of road for each property wasperformed in two steps. The first involved calculating the amount of road thatcould be seen from each property. Measures of impact were then derived byweighting the amount visible by its distance from the property.

To calculate what can be seen from each property a digital representation of theland surface was created known as a Digital Elevation Model (DEM). Thisconsisted of information on the height of the land as well as the heights andlocations of buildings.

Viewshed analysis is a GIS procedure used to determine the area of land visiblefrom any specific location based upon the elevation in the DEM. Viewshedswere calculated from a central point at the front and back of each property. Thearea of land visible from each property was then combined with a land use mapto calculate the amount of road visible from each property. An identical

The volume, composition and speed of traffic wasestimated for all road segments. The gradient was

calculated using GIS.

Each property was matched to its nearest roadsegment and the distance between the two calculated

In combination with the traffic information, a noiselevel was calculated for each property

Global assumptions were made to account for buildingreflections and noise absorption by the ground

Final noise level for each property

The noise contributions from other roads was calculated if:1. Nearest road was multi carriageway;

2. Property nearest to minor road but within 100m of major road.

For each road segment

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procedure was used to calculate the amount of industry, parkland, buildings,railways, open space and water visible from each property.

The impact of visible road or any other land use can be expected to decreasewith distance from the property. Therefore, measures of impact for road and theother land uses were created by weighting the amount of each land use visibleby its distance from the property.

ES 6 The statistical model (Section 9)

Using GIS the research project generated one of the most comprehensive hedonicproperty price datasets ever compiled. It contained 327 explanatory variables for eachof the 3500 properties. As highlighted in the previous section, detailed information isavailable on each property�s structural qualities, the characteristics of theneighbourhood, the accessibility of each property to amenities, the environment of theproperty and of course details of each property�s exposure to the impacts of roads.

With such richness in the availability of variables, the researcher is faced with adecision as to which characteristics should be included in the hedonic price functionand which (if any) should be left out. In making this choice, researchers usuallyfollow two guidelines. First and most importantly, we would wish to include thosevariables that we believe, a priori, will be major determinants of the pricescommanded by properties. For example, we would be rightly sceptical about ahedonic price function that failed to include a variable describing the size of theproperty, since we would expect larger properties to command higher prices. Second,it is common in hedonic data sets to have a large number of variables that areessentially descriptors of very similar characteristics of the property. For example,there were a large number of variables describing the characteristics of theneighbourhood of each property. A model with a large number of neighbourhoodvariables will be difficult to understand and tell us no more than a much simplerspecification. In such circumstances, researchers employ what is known as �Occam�sRazor�; they remove variables that add complexity without offering much tounderstanding.

ES 7 Results and interpretation (Section 9.4)

ES 7.1 Modelling results (Section 9.4)In order to produce a statistically robust model it was necessary to examine theeffect of the explanatory variables upon the natural logarithm of each propertyprice. The house price model is presented in Table 1.

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Table 1: Regression results: Models of the natural log of property price

Variable Coefficient Standard errorLog of floor Area(m2) .270 .013***Garden Area(m2) .00015 .00003***Perimeter/Cross-Section Area .012 .002***Number of Storeys in House-Type Properties -.163 .029***Semi-Detached Property (Dummy) -.0120 .034Terrace Property (Dummy) -.0016 .037Sub-Divided House Property (Dummy) -.426 .066***Four-Block Property (Dummy) -.521 .054***Flat Property (Dummy) -.536 .054***Tenement Property (Dummy) -.604 .053***Other Property Type (Dummy) -.425 .074***Number of Flats in Property -.010 .002***Basement Flat (Dummy) -.019 .042First Floor Flat (Dummy) .067 .013***Second Floor Flat (Dummy) .052 .013***Third Floor Flat (Dummy) .027 .015*Fourth Floor Flat (Dummy) .027 .021Stone Faced (Dummy) -.039 .021*Pre-War (Dummy) .039 .034Young Families (% Households) -.0022 .0004***Old Families (% Households) .0011 .0003***Elderly Living Alone (% Households) .0050 .0009***Non-Owning (% Households) -.0013 .0003***Unemployed (% Population) -.0014 .0008*Two Car Owning (% Households) .0096 .0006***Commonwealth Population (% Population) .00076 .0005Other Ethnic Population (% Population) -.00033 .0023Distance to Walk to Shops(m) .00033 .00007***Distance to Walk to Shops Squared(m) -.0000003 .0000001***Car Travel Time to City Centre (minutes) .078 .015***Car Travel Time to City Centre Squared (minutes) -.0034 .0008***Car Travel Time to Railway Station (minutes) .014 .018Car Travel Time to Railway Station Squared (minutes) -.0092 .0047**Obstructed View -.0000002 .0000001*View of Parkland .0078 .0102View of Industry -.0549 .014***View of Roads -.062 .007***Aircraft Noise (NNI) -.0025 .0014*Traffic Noise (L10 (18-hour) dB(A)) -.00202 .0008**Constant 8.947 .126***N 3544Adjusted r2 0.7089Notes for Table 1:Dependent Variable = Natural Log of Property Price*significant at the 90% level**significant at the 95% level***significant at the 99% level

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The coefficient column in Table 1 describes the effect that each explanatoryvariable has on the dependent variable (the natural logarithm of property price). So,for example, consider a variable such as Garden Area which is measured in m2.The coefficient has a value of 0.00015 that tells us that if the garden area of ahouse increases by 1m2 so the natural logarithm of property price increases by0.00015.

The next column provides a measure of the variability in our estimated coefficientknown as the standard error. The smaller this value in relation to its respectivecoefficient then the narrower the band of uncertainty within which the coefficientestimate lies and the greater the degree of confidence we have regarding thatestimate. This degree of certainty, or statistical �significance�, is indicated by theasterisks after the parentheses with more asterisks indicating a more significantresult. Typically statisticians accept a 95% certainty level as evidence of asatisfactorily significant relationship between the explanatory and a dependentvariable.

The last row of Table 1 presents the overall fit of the model given by the adjustedr2 variable which describes the proportion of property price variability (i.e. over70.89%) explained by the model.

The variable representing the level of traffic noise is negative with a significantcoefficient. This indicates that properties exposed to higher levels of noisepollution command lower prices, all else being equal. The use of the natural log ofproperty price as the dependent variable gives this coefficient a simpleinterpretation; the coefficient represents the percentage change in the price of aproperty that would result from a one-decibel increase in the level of traffic noisepollution.

Therefore the results of this analysis indicate that each decibel increase in trafficnoise decreases property price by .20%, the standard error shown in parenthesesindicates that we can be 95% confident that the coefficient takes a value that isgreater than -.04% and less than -.37%. Some of this range will be due toimperfections in the property price model, and 0.20% is our best estimate of theimpact of road noise upon property prices and, in our opinion, it is this value thatshould be incorporated into the compensation procedures.

ES 7.2 The reliability of the results (Section 9.5)We are confident about the reliability of these results for a variety of reasons. Thefirst is that the overall model explained approximateley 71% of the variability inproperty prices. This figure is similar to, or better than, that achieved in previoushedonic pricing studies. This indicates that we have been successful in accountingfor much of the diversity within our sample properties.

Nearly all of the explanatory variables in the final model had impacts uponproperty price which accord with prior expectations. Larger properties sell for apremium but if an area contains a high unemployment rate then this indicates asocially less affluent area and accordingly the properties sell for less money.Properties subject to high levels of aircraft noise also had lower prices. All theseimpacts accord with our prior expectations.

Many different versions of the property price model were created and it isencouraging to note that the impact of road noise upon property prices was

ES-15

relatively stable across these models and accords closely with that given in ourpreferred model. This indicates that the noise coefficient was not highly susceptibleto the inclusion of other variables in the model and so leads us to be moreconfident about the robustness of the result.

Finally the depreciation calculated in this study is broadly in line with that found inother hedonic pricing studies. In the main report 17 such studies are reviewed andeach decibel increase in road noise depressed property price by a mean of 0.4%with a standard deviation of 0.23. Therefore the results from this research arewithin the range reported in other studies albeit towards the lower end.

ES 7.3 Result transferability (Section 9.5.1.3)This study quantified the impact of road noise upon property prices in Glasgow. Ifthis value is to be incorporated into land compensation procedures it must betransferable to a much larger geographical area. In order to assess this severalissues must be considered.

ES 7.3.1 RepresentativenessIt is important to remember that the results of this analysis are only representativeof the range of data given in the sample and that care should be taken inextrapolating well beyond that range. For example, we can readily use our modelsto infer results for a house which is not in our sample but whose characteristics arewell represented in that sample. However, another property whose characteristicsare not well represented in the sample may not be so well described by modelsbased on those data. To push the point to the extreme, we would not expect theprice of a former lighthouse on a remote cliff top to be well predicted by a modelwhose sample data set is entirely drawn from a highly populated city centre.

The sample properties in this research were diverse and house prices ranged from£10,000 to more than £100,000. An analysis of the diversity of our sampledemonstrated it to be geographically spread over many different areas of Glasgowas illustrated in Figure 2. There were also many different housing types varyingfrom tenements to detached houses and terraced houses of all different ages. Theareas encompassed by the sample properties were heterogeneous in socio-economic terms and the percentage of households without access to a car in aproperty�s census output area (an indicator of the area�s wealth) varied from 5% to100%. In order to aid transferability it is crucial that the sample propertiesrepresented a full range of noise values. This was the case with large numbers ofproperties at all noise levels from 54 dB(A) (low noise) to 78 dB(A) (high noise).Therefore, these results should be applicable to the full spectrum of road noiseimpacts.

ES 7.3.2 Limitations of hedonic pricingThis research produced one hedonic pricing model for Glasgow and thereforeassumed that the whole area was one housing market. However, severalcommentators have argued that housing may be divided into a series ofsubmarkets. Within these, there may be different types of properties and differentpreferences from the people living within. These may lead to the impact of roadnoise upon property prices varying between different geographical areas. Forexample, one previous study demonstrated different impacts of noise upon

ES-16

property prices between the centre and outskirts of a city. Similarly, submarketshave been identified within the city of Glasgow, although they were not consideredin relation to noise measures.

The present study is based upon house price data from one year and so saysnothing regarding whether the impact of road noise upon property prices isconstant over time. We might expect our estimates to be invariant across timeunless the supply for houses with certain noise characteristics changes orindividuals� sensitivity to noise, and hence demand for peace and quiet, alters. Weare unaware of any studies of these aspects to date.

In summary, the sample of properties from which the results were drawn was verydiverse in terms of the environmental and social conditions represented. In theabsence of other comparable studies, these results might constitute acceptable ifrough guides to noise compensation values elsewhere. However, the studypresented in this paper only considers the market (or collection of submarkets)which constitutes the City of Glasgow. In order to test rigorously the transferabilityof these results, one would require at least one further study to be carried out in aseparate location. This would allow value transfers between sites and pooling ofdata to encompass a wider variability of observations.

ES 8 Implementation (Section 10)

ES 8.1 The new procedure (Section 10.1)Our preferred model of house prices predicts that each decibel increase in roadnoise depresses the prices of existing properties by 0.20%. Therefore when a newroad is built we would expect the resulting noise to depress property prices by anidentical amount. As argued earlier, while we have referred to the issue of roadnoise throughout our analysis, in fact this is a proxy for all of the seven physicalfactors for which compensation can be given under Part 1 of the LandCompensation (Scotland) Act 1973. Therefore when assessing compensation dueto changes in road noise the valuer will now require two pieces of information:

• a property�s current price (CP) at the claim date (i.e. 1 year after road opening);

• an estimate of the magnitude of the change in road noise affecting the propertyas measured in dB(A) on the L10(18-hr) scale (∆dB) at the claim date (i.e. 1year after road opening).

Using the notation developed above we can state a simple formula for thecalculation of compensation (COMP) due to a change of ∆dB as follows:

Therefore if we had a property with a price on the compensation date of £75,000which had been subject to an 8 decibel increase in noise then this household wouldbe due:

��

���

� ×∆×+

−= CP])20.0[100(

100COMPdB

CP

1181£

75000])820.0[100(

10075000COMP

=

��

���

� ××+

−=

ES-17

This formula suggests a new assessment procedure for valuers which is illustratedin Panel 2 of Figure 1 alongside the current procedure in Panel 1. Thisdemonstrates a simplified process for the valuer which makes the currentprocedures less ambiguous. In theory, the model presented in this paper could beused to calculate the property price removing the need for a valuer. However, ourhouse price model includes variables such as the property floor area, car traveltimes and the amount of road visible from the property. These variables arecomplex to define and thus it would be impractical to determine these for allproperties submitting a compensation claim. Therefore, the valuer is still requiredto determine the property price, a task that they are well experienced in. The valuerwill also need to quantify how any positive benefits of the new road, such asimproved accessibility, have increased the property price. These can then be offsetagainst the compensation payable.

In order to apply this approach the valuer will require information on the changein road noise at each property. When new roads are built an environmental impactstatement is often produced, which includes estimates of the changes in noiselevels associated with the new road. If these were produced for a time period oneyear after the opening of the new road (the date when compensation is payable),these noise levels could be used as the basis upon which compensation paymentsare determined. This procedure will work well as long as the predicted noise levelsare similar to the actual noise levels observed once the road is constructed. If thenew road has a vehicle flow that is very different from that predicted then a revisednoise level should be calculated, and this value communicated to the valuer.

An issue of particular importance to roads and highway authorities will be whetherthese procedures will lead to changes in the overall amount of Part 1 payments. Anunpublished report by the Department of Transport examined 8 road schemes and135 Part 1 claims across England. Through interviews with valuers it wasestimated that for each decibel increase in road noise awards of 0.311% of theprevious property price were being paid. This result is very similar to ourempirically derived value of 0.20%. If these results are applicable in Glasgow thenthey indicate that any awards calculated using our recommended formula wouldresult in very similar, but slightly lower payments.

ES 8.2 Extending the model: Integrating noise compensationestimates into road planning procedures (Section 10.2)

The previous section mentioned that the change in noise level at any property isoften estimated as part of an environmental impact statement. Therefore, thepotential may exist to combine this information with details of current propertyprices and our compensation formula to calculate an estimate of the level of Part 1claims before a road is constructed. If this can be done in the road planning stagethen the road design can be modified to reduce these claims before constructionstarts. If the costs of modification are less than the Part 1 savings then they shouldbe applied to the scheme. Such a process is known as a Cost Benefit Analysis(CBA). Such an approach calls for minor modification of the road planningprocedure a schematic diagram of which is shown in Figure 6.

ES-18

Figure 6: Assessing Part 1 claims in scheme design: A suggested approach

ES 9 Conclusions

The research project generated an extremely comprehensive property price data set,consisting of information on the structure, accessibility, neighbourhood andenvironment of 3500 properties in Glasgow. Variables were also created relating tothe impacts of nearby roads. The level of road noise was chosen as a surrogate for allseven physical factors for which compensation can be paid under Part 1 of the LandCompensation (Scotland) Act 1973. Using a hedonic property price model we wereable to estimate the impact of this variable upon property prices.

The results of this analysis demonstrated that each decibel increase in road noise at aproperty decreases its price by 0.20%. Noise was measured on the L10(18-hr) dB(A)scale in accordance with the procedures set out in the standard UK noise calculationmethod (CRTN). This result can be used to calculate Part 1 claims for compensationand, in accordance with the 1973 legislation, the values produced are independent ofany visual or accessibility impacts associated with roads.

Based on this large empirical study we have been able to recommend a new procedurefor the assessment of Part 1 claims. Using the results of this research should assist

Yes No

Final road design

Have all noise mitigationpossibilities been explored

Apply noise mitigation measures andcalculate new noise level

Calculate Part 1claims

Is money saved?

Yes No

Do not alterscheme

Alter scheme toinclude mitigation

Model noise fromnew road

ES-19

valuers in setting compensation levels and justifying them should any Part 1 claim becontested in the Lands Tribunal. One of the most useful conclusions from thisresearch is that because Part 1 claims can be predicted before a new road isconstructed, it should be possible to actively minimise the Part 1 claims during roaddesign.

ES 10 Report Layout

The initial sections of this report review past theoretical and empirical research in thearena of valuing road disamenities. Based on this review a methodology is developedto quantify the impact of road disamenities upon property prices. The final sectionsapply this methodology before recommending how the current compensationprocedures can be altered. The report has ten main sections.

Section 1 provides an outline of Part 1 of the Land Compensation (Scotland) Act 1973and appraises the current compensation procedures.

Section 2 seeks to familiarise readers with some of the basic concepts that underlie theeconomist�s understanding of the property market. Specifically, it examines theeconomic theory of how the price of a property is determined in a market and exploresthe relationship between price and another important economic concept, value.

Section 3 goes on to discuss the various analytical approaches that are available toresearchers to investigate the impact of changes in environmental amenities on pricesand values.

Section 4 takes the discussion a step further by concentrating in detail on oneparticular approach; the hedonic price technique. This approach would appear to beparticularly suited to investigating issues such as the assessment of compensationclaims.

Section 5 reports on empirical applications of the hedonic pricing technique.

Section 6 summarises the overall conclusions from the review.

Section 7 draws on the review to propose how the project should progress.

Section 8 presents the methodology and results from the hedonic pricing study used tocalculate the impact of road disamenities upon property prices .

Section 9 presents an analysis of the strengths and weaknesses of the study.

Section 10 concludes the study and discusses how these findings can be utilised todevise a revised claims assessment procedure.

1-1

1 INTRODUCTION

1.1 Roads and Road TrafficThe large growth in road transport over the last 30 years bears testament to theimportance of the motor vehicle in modern society. The expansion of vehicleownership and the roads network has provided people with considerable choiceover where to live in relation to their work and an ability to pursue a wide range ofsocial activity. At the same time, the construction of new roads contributes toeconomic growth by providing the transport infrastructure required for acompetitive economy. In some cases, local bypass schemes take traffic and itsnoise and fumes away from town centres, reducing congestion and speeding upjourney times. Indeed, in general, the construction of a new road will result inconsiderable benefits to road users.

Of course, not all the ramifications of building new roads are beneficial. Indeed,the construction of new roads and the flow of traffic that eventually uses theseroads have a number of undesirable impacts. Economists use the term externalitiesto describe the impacts of roads and road traffic; externalities because, in general,they are imposed by one group (e.g. those who use their cars on the new roads), onan external, third party (e.g. those who live or work near a road). Table 1-1provides a list of the various externalities arising from road transport.

One group that we might expect to suffer more than most from these externalitiesare those living in houses adjacent to a new road. Indeed the construction and useof a new road is likely to result in a number of unwelcome changes to their livingenvironment.

• For a start, local residents will have to suffer the noise, disruption and pollution ofthe construction work itself.

• Further, once the construction work is complete, residents might find that theviews from their properties have been impaired; homes that had once overlookedgreen fields may now face onto a busy trunk road.

• Also, the flow of traffic along the road may make it more time consuming,difficult and dangerous for residents to make short trips, say down to the localshops, if they involve crossing the road (the so-called barrier effect).

• Last, but by no means least, local residents will have to cope with a host ofexternalities produced by the traffic itself. Traffic travelling along the new roadwill generate both noise and air pollution, and residents may be afflicted by avariety of other problems including dust from the road, smells from vehicle fumes,polluted water run-off and vibration.

Of course the impacts will not all be bad, local residents, perhaps more than others,should benefit from the increased accessibility afforded by the new road.

From an economic point of view the new road may have two detrimental impactson local residents. First, it is likely to have a negative impact on their well being.People tend to dislike noise and air pollution and many of the other externalities ofroad transport. The road makes the time they spend in and around their homes thatbit less agreeable. In economic terms, we say that the �value� that the residents

1-2

derive from living in that location has declined. Second, the reduction in value thatpeople could derive from living in a particular location may well, but notnecessarily, reflect itself in a reduction in the �price� that a property next to a newroad could fetch if it were put up for sale on the open market.

Table 1-1: Externalities of Road Transport

Related to Vehicle Ownership and Infrastructure

Alterations in visual appearance

Overshadowing of property

Solid waste deriving from road construction

Vehicles withdrawn from service

Pollution of surface and ground water

Impact on land resources

Intrusion on wildlife habitats

Related to the Actual use of RoadsLOCAL GLOBAL

Barrier Effect Carbon Dioxide pollution

Dust pollution Methane pollution

Heavy Metal pollution Nitrogen Oxides pollution

Infrasound

Noise & Vibrations

Particulates

Accidents

SmellsSource: Bertrand (1997)

1.2 Part 1 of The Land Compensation (Scotland) Act 1973Part 1 of the Land Compensation (Scotland) Act 1973 states that where the use ofpublic works reduces the value of a piece of land, the legal owners can becompensated for this loss (in this report we will be concentrating upon claimsresulting from the use of new roads). This compensation is payable to claimantsfrom whom no land is taken and who are debarred from bringing an action atcommon law for nuisance. There are a number of important caveats in thisresponsibility;

• First, compensation is only payable on the depreciation in market value of theproperty, not on any other possible measure of loss in well being of households.

• Second, compensation is only payable on the depreciation in a property�s marketprice that can be attributed to the following seven physical factors resulting fromthe use of the road by road traffic

− Noise

1-3

− Vibration

− Smell

− Fumes

− Smoke

− Artificial lighting

− Discharge onto the land of solid and liquid substances

• As a consequence of the above, compensation should not be paid for propertyprice depreciation resulting from the visual disamenity generated by the presenceof the new road. Nor should the impacts of the construction process or the barriereffect be considered.

• In addition, only the direct effects of the new road are considered. For example, anew road may increase traffic on existing roads. This will result in disamenitiesfor local residents but these impacts are not compensatable under the Act.

• Finally, any enhancement in value arising from the road (e.g. property prices mayrise in an area if it becomes easier to commute to a nearby centre) should be set-off from the compensation payable because of the seven physical factors.

1.3 Determining Compensation ClaimsWhen a trunk road is constructed in Scotland, any Part 1 claims for compensationare submitted to the Scottish Executive Development Department (SEDD). TheSEDD then pass these to the Valuation Office Agency (VOA) who assess thecompensation and negotiate with the claimants or their agents to try and reach anamicable settlement.

The assessment of a compensation claim involves a number of procedures1.

• As an initial step, a valuer will carry out a detailed inspection of the property witha view to assessing the compensation payable. The property will be inspected bothexternally and internally and notes taken on the construction, size, age,accommodation, standard of modernisation and condition etc. The valuer willnote any garden and other ground held with the property and its location inrelation to the new road. Details will also be taken of any works carried out underthe Noise Insulation (Scotland) Regulations 1975.

• As part of the visit the valuer will also talk, relatively informally, with theclaimant, a conversation that would appear to fulfil an number of purposes;

− it allows the valuer to ascertain from the claimant exactly which aspects of thenew road and its traffic have resulted in disamenities. This is an importantinformation gathering exercise since in one short visit the valuer is clearly not ableto witness first-hand all the impacts of the road (e.g. if the visit is made in themiddle of the day the valuer cannot assess the impacts of noise from rush hour

1 Our information on current practice is based upon discussions with the VOA and from observations ofthe working practice of a Senior Valuer with the District Valuer�s Office, Scotland South EastEdinburgh. (Our thanks are due to Tom Nisbet who allowed us to shadow him around Glenrotheswhilst he assessed Part 1 Claims resulting from a new access road).

1-4

traffic). The valuer is able to assess from this conversation which, if any, of theseven physical factors resulting from the new road are the cause of disturbance tothe household.

− at the same time this conversation allows the valuer to explain to the claimanttheir rights under the Land Compensation (Scotland) Act 1973 and the process bywhich the claim will be assessed.

• Later, the valuer will interrogate the VOA�s database of property sales, which inScotland is based on information provided by the Registers of Scotland ExecutiveAgency. The database is supplemented by sales information gleaned from othercasework, professional contacts and relevant publications. Using this database, itmay be possible to find a record in the register that reports the selling price of asimilar property in a similar location with respect to the new road. If a secondsales record exists that catalogues the market price of another similar property thatis unaffected by the new road, then a comparison of the two selling prices givesthe valuer an indication of the possible change in total market value of theclaimant�s property. In large estates with many identical properties this may wellbe possible. However, where the housing is less homogenous problems will arisein finding sales prices for similar properties in similar locations. The valuer maybe able to make some inferences from sales of similar yet more distant properties,but this simply introduces uncertainty into the valuation process. The mostdifficult situation is one where no records of similar property sales are available.In these cases the valuer has to use solely his professional knowledge andexperience to estimate a property price and depreciation.

• Occasionally, measurements of the changes in noise levels and otherenvironmental impacts resulting from the road are provided by the ScottishExecutive. However, this information is rarely specific to individual properties.Moreover, no procedure exists that would allow valuers to relate these measures tochanges in property prices.

• Once an estimate for the total decrease in property price has been calculated, thevaluer must determine the percentage of this loss that results from the sevenphysical factors for which compensation can be paid. Using the informationgarnered during the visit to the property, the valuer is charged with assessing theimportance of the seven physical factors in causing the price of the house to fall.

• The final stage in the process of claims assessment involves the valuer examiningwhether any of the positive effects of the new road, such as better accessibility orreduced noise levels on other roads, have increased the property price. These arethen offset against the compensation value calculated above.

Once the VOA has determined the appropriate compensation level this information iscommunicated to the claimant. A period of negotiation then ensues after which thehouseholder can either accept the settlement or refer the claim to the Lands Tribunalfor Scotland.

1.4 The Limitations of the Current SystemThe current system of compensation assessment has been in place for 27 years andis undertaken by trained and experienced valuers. Their knowledge of the housingmarket and research into local property price movements allows them to assess the

1-5

possible compensation required by reference to evidence of local sales. Wheresuch sales are lacking the valuer relies on his judgement and experience of similarschemes in other localities.

However, several firms of valuers have recently identified a market niche bypursuing Part 1 claims on behalf of property owners on a �no win no fee� basis.Their strategy has been to contact all people living near to a new road, offering tosubmit a Part 1 claim on the property owner's behalf. Consequently, there has beena substantial expansion in the number of Part 1 claims and a resultant increase intaxpayers' money paid in compensation. These payments can be large and the A27in England cost £20m to build but resulted in over £22m of Part 1 claims.Therefore it has become important to ensure that the current level of Part 1payments are correct.

From a review of current procedures we feel that the process lacks four key piecesof information:

1. As outlined above, the database of property sales is used to estimate the totalchange in a property�s price resulting from the construction of a new road.However, Part 1 of the Land Compensation (Scotland) Act 1973 is restricted topaying only for the seven physical factors described previously. Using simplecomparisons of the selling price of just a handful of houses makes it difficult topartition an observed depreciation between compensatable factors, such as roadnoise, and non-compensatable factors such as visual impact.

2. The method used to assess claims is based upon the examination of recentproperty sales data from similar houses. This creates difficulties in areas wherethere are few property sales or many dissimilar houses. In some cases it is moredifficult to identify similar properties from which the impact of a road can bequantified.

3. Part 1 claims are only submitted after a new road has been built. Therefore whenthe VOA assess a claim they may not possess detailed information aboutconditions at the property before the road was built. This may make it difficult toassess the changes that have occurred at the property because of the new road.

4. It is difficult to estimate the magnitude of Part 1 claims before the new road isconstructed, and hence it is difficult to take account of likely claims at the roaddesign stage.

The aim of this research project is to provide valuers with an additional approachto assessing claims that will add to the array of techniques they currently employ.The research is based on a detailed empirical analysis of data from the Registrationof Title. Currently, valuers use the Register to estimate the total depreciation in aproperty�s price by comparing the selling prices of identical houses that have beenaffected and not affected by a new road. By much the same process but using largesamples and statistical techniques, the research aims to estimate how each of thedifferent factors that make up the total impact of a new road contribute to thisdepreciation. The analysis will seek to provide a transferable formula that willallow extrapolation to properties for which little directly comparable data isavailable. The existence of a large empirical study should be useful for the VOA toaid in their assessments of claims and to justify their valuations before LandsTribunal.

2-1

2 PRICE AND VALUE

2.1 IntroductionThe principal task of the research in this study will be to provide a guide to howthe price of houses might be influenced by changes in certain environmentalconditions that will be affected by the use of a new road.

Of course, to estimate how the price of a property might change it will be essentialto understand how that price is determined in the first place. In this chapter,therefore, we take a brief look at the economic theory of how price is determined ina market and examine its relationship with another important economic concept;value.

2.2 Prices and MarketsWe are all familiar with the concept of prices. When we walk into a corner storeto buy a loaf of bread and a pint of milk, or when we go into a travel agents tobook a summer holiday, we are faced by a set of prices that guide us in ourdecisions of what and how much to purchase.

If we were to think more closely about different goods and services and theprices that we pay for them, we might distinguish three categories;

Simple goods and services for which there is pretty much just one acceptedprice. A simple good or service would consist of a group of products that areso alike that the consumer makes no distinction between them. Examplesmight include basic foodstuffs (e.g. bread and milk), petrol or a service suchas window cleaning. Of course branding, advertising and marketingincreases the degree to which we differentiate between very similarproducts, but for ease of exposition let us ignore this.

Complex goods and services for which a range of prices exist. A complex goodor service is one that consists of a diversity of products that, while differingin a variety of characteristics, are so closely related in consumers� mindsthat they are considered as being one commodity. We would expect theprice of a complex good or service to depend on the individual products�exact characteristics. Examples of such goods include cars, holidays andhousing, the last of which forms the focus of this research project.

Unpriced goods and services. While, in everyday language, goods and servicesare usually considered to be those commodities or types of assistance thatwe can go out and purchase, the economic definition is far broader. Itencompasses, for example, environmental goods such as clean air or thevisual amenities that derive from unspoilt landscapes. In general, thesegoods do not have markets (we do not buy and sell clean air) and thereforethey do not command a price, yet it is undeniable that we derive well beingfrom their provision or existence. To introduce some terminology, this wellbeing is known to economists as utility and the level of utility that anindividual gets from consuming a good defines its value. Indeed, a keydistinction made by economists is between the value that an individual

2-2

derives from a good or service and the price that must be paid for it. Clearly,in the case of an unpriced environmental good like clean air, we can valuethese goods highly but not have to pay a penny for them. We shall return tothe distinction between price and value later in the Chapter.

Economics explains the mechanism by which the prices of goods and servicesare determined by the workings of the market. Or, in the case of unpriced goods,their lack of a price is explained through the failure of the market to functionproperly. Let us examine each of these categories of goods and services in moredetail.

2.2.1 The Price of a Simple Good

In the last section we introduced the concepts of value and utility, a thirdeconomic concept with which we should be familiar is that of willingness to pay(often abbreviated to WTP). These three concepts are very closely related. Wehave already defined value as a measure of the satisfaction or well being that anindividual derives from consuming a good. But in what units do we measurevalue? Well, two possible measures are utility and WTP.

Utility: Historically, the word utility has been used in economics to denote thesubjective sensations � satisfaction, pleasure, wish-fulfilment, cessation ofneed, etc. � which are derived from consumption. Since utility is a measureof subjective sensations it is, in effect, an individual-specific measure ofvalue. We can surmise from individuals� decisions the relative utilitiesprovided by consuming different goods and services, but we cannot measurethese utilities on an absolute scale nor compare them across individuals.

Willingness to Pay (WTP): A second measure of value is WTP. This describesthe maximum amount an individual would actually be prepared to pay toconsume a good or service (as distinct from what he would actually have topay which is determined by the good or service�s price). Unlike utility, WTPis a cardinal measure of value; it is measured in an absolute scale � money.An individual�s maximum WTP for a good or service gives us a measure ofhow greatly that good or service is valued by that individual. Since WTP ismeasured in monetary units, we can compare these amounts across differentgoods and services for the same individual and, for that matter, betweendifferent individuals.

In many ways, therefore, WTP is a more useful measure of value than utility.By way of example, let us consider the consumption of half litre bottles ofmineral water. On a hot summer�s day an individual may have a relatively largeWTP for a bottle of mineral water. Having consumed this bottle, however, histhirst will have been somewhat slaked so that whilst this individual may stilldesire another bottle, his WTP for a second bottle will be less than for the first.In more general terms a consumer�s WTP for any subsequent unit ofconsumption will, in all but a few rare instances, be lower than that of thepreceding unit. This effect is known as the rule of diminishing marginal utility;as consumption of a good increases so the utility an additional (marginal) unitprovides is less than that of the preceding unit. This trend will extend over alladditional units, so that our individual�s WTP for a third bottle will be even less

2-3

than the second, even less than that for the fourth and so on until he is no longerwilling to pay anything for a further bottle of water.

A common way to visually describe the relationship set out above is to graph anindividual�s WTP for each unit of consumption. This yields the Marginal WTPSchedule shown in Figure 2-1. We could think about this marginal WTPschedule in the opposite way. Rather than how much a consumer is willing topay for each successive unit of a good, we could think about how many units ofthe good a consumer would purchase at a given price. When presented in theseterms the relationship describes the Individual Demand Curve.

Figure 2-1: The Marginal WTP Schedule or Individual Demand Curve

Extending this analysis further, we could draw up an individual demand curve(marginal WTP schedule) for each consumer in the market. The top panel ofFigure 2-2 shows the individual demand curves for all the consumers (denotedby the number N) in a market for a simple good. Notice that the demand curvesdiffer in shape from consumer to consumer, since each consumer is likely tohave different preferences, personal characteristics and income.

As illustrated in Figure 2-2, by adding all these different individual demandcurves we can construct an Aggregate Demand Curve for the whole market.That is, by adding all the amounts demanded by the different individuals at eachprice, we could calculate the total demand from all the consumers in the marketat each price.

0Quantity

WTP /Price Marginal WTP Schedule / Individual

Demand Curve

2-4

Figure 2-2: Demand and Supply for a Simple Good

0

0

Price

Quantity

Household 1:

0

Price

Quantity

Household 2:

0

Price

Quantity

Household N:

� etc. �

Quantity

PriceAggregate

Demand Curve

0

0

Price

Quantity

Firm 1:

0

Price

Quantity

Firm 2:

0

Price

Quantity

Firm M:

� etc. �

Quantity

PriceAggregate Supply

Curve

2-5

Turning to the supply side of the market, our economic model assumes that thefirms supplying the good are motivated by the desire to make the maximumpossible profits. The principle of diminishing returns applies equally well tofirms as it does to consumers. As a firm starts to produce units of a good its costper unit usually initially decreases due to increasing productivity of the so-called �factors of production� (labour capital, expertise, etc.). However, asoutput expands to higher levels so the possibilities for productivity gainsdiminish and per unit costs tend to rise. Thus to maintain its returns, the priceper unit at which the firm is willing to produce goods will be higher the greaterthe quantity of goods it is producing. Note that firms will always prefer toexpand production when per unit costs are declining and consequently fixoutput levels somewhere within the range where these costs are increasing.Consequently many economic textbooks tend to ignore this initial downwardsloping part of the per unit cost curve and just focus on the upward slopingsection of the curve (for discussion see Turner et al, 1994).

Again, the inverse of this relationship, which describes how many units of thegood the firm would decide to produce at different prices, is given by the Firm�sSupply Curve. The bottom panel of Figure 2-2 shows the supply curves of allthe different firms (denoted by the number M) in the market. As before wecould add these different supply curves and generate an Aggregate SupplyCurve for the entire market.

Given our understanding of supply and demand; how is the price of a gooddetermined? Well, very simply, the market brings buyers and sellers together.Given the demand for the good expressed by the consumers at different pricesand the amount that firms would choose to supply at different prices the markettends towards an equilibrium price. We can show this by plotting the aggregatedemand curve and the aggregate supply curve on the same graph, as in Figure 2-3. The market price will settle at the point where demand for the good equals thesupply of the good. If the price were higher, then supply would outstrip demandand firms would have produced stock that they could not sell. Prices would thenfall as firms try to sell this excess supply. If the price were lower, then demandwould exceed supply and firms could increase their profits by increasing pricesto choke off excess demand for the good. This price (Pe), therefore, defines themarket equilibrium, the point at which demand and supply are identical(quantity Qe) and the market clears.

It s important to notice that for a simple good, the market dictates only oneequilibrium price. The reason for this is obvious � if the same good were beingsold at two prices in the same market then everyone would buy from the cheapersupplier.

2-6

Figure 2-3: Market Price and Aggregate Demand & Supply

The amount of a good that each individual purchases will be determined by theequilibrium market price and their own marginal WTP schedule. In Figure 2-4,the marginal WTP schedules of Households 1 and 2 from the top panel ofFigure 2-2, have been drawn on the same graph as the equilibrium market price.The final amount demanded by household 1 is given by quantity q1 and that byhousehold 2 is given by quantity q2.

Figure 2-4: Household Demand for a Simple Good; the Interaction of theEquilibrium Market Price and the Household's Marginal WTP Schedule.

0

Price

Pe

q2 Quantityq1

EquilibriumMarket Price

Marginal WTP /Demand Schedule

for Household 2

Marginal WTP /Demand Schedule

for Household 1

0Quantity

Price

AggregateDemand Curve Aggregate

Supply Curve

Pe

Qe

2-7

2.2.2 The Price of a Complex Good

Of course, not many goods can be considered entirely homogenous (i.e. eachindividual item of a good is identical) and many similar goods differ in theirindividual characteristics. Examples of such goods include cars, holidays andhousing, the last of which forms the focus of this research project. Obviously,products of this nature command a variety of different prices according to theirexact characteristics. The simple demand and supply model presented in the lastsection seems an inadequate description of how the price of complex goods suchas these are determined in markets.

Keeping with the housing example, the simple model would only be a genuinereflection of reality if it were possible to consider all properties with the samecharacteristics, say those with two bedrooms, a garage, a small garden and in asimilar neighbourhood, as separate commodities and define demand curves foreach different type of housing stock. But, houses vary in such a diverse varietyof characteristics and are so closely related in consumers� minds that it makeslittle sense to think of them as distinct commodities.

An alternative model was suggested in a seminal paper by Rosen (1974). Rosendescribed houses or similar heterogeneous products, as single commoditiesdifferentiated by the amounts of various characteristics they contain. We canimagine this market for properties as being one in which the consumers considera variety of somewhat dissimilar products which differ from each other in anumber of characteristics including, amongst many characteristics, number ofrooms, size of garden, distance to shops and, as we shall discuss in more detaillater, environmental characteristics such as levels of pollution or noise. Using ananalogy of Freeman (1993 p 371), �it is as if the urban area were one hugesupermarket offering a wide selection of varieties. Of course, the individualscannot move their shopping carts through this supermarket. Rather, theirselections of residential locations fix for them the whole bundle of housingservices. It is much as if shoppers were forced to make their choices from anarray of already filled shopping carts. Individuals can increase the quantity ofany characteristic by finding an alternative location alike in all other aspects butoffering more of the desired characteristic.�

The price of any one of these �shopping carts� will be determined by theparticular combination of characteristics it displays. Indeed, we could describeany particular property by the qualities or characteristics of its structure,environs and location. A succinct means of denoting this is as a vector ofvalues1; effectively a list of the different quantities of each characteristic of theproperty. In general, therefore, any house could be described by the vector,

z = (z1, z2, �, zn), (2-1)

where zi (i = 1 to n) is the level of any one of the many characteristics. Naturallywe would expect properties possessing larger quantities of good qualities tocommand higher prices and those with larger quantities of bad qualities tocommand lower prices. Again we can use a succinct piece of notation toillustrate this point;

P = P(z) (2-2) 1 Note that by convention vectors are written in bold type.

2-8

Which can be read as; the price of a property (P) is determined by or �is afunction of� the vector of values (z) describing its characteristics. This functionis known as the hedonic price function; �hedonic� because it is determined bythe different qualities of the differentiated good and the �pleasure� (in economicterms utility) these would bring to the purchaser.

As we will discuss in later chapters it should be possible to estimate the hedonicprice function by observing the selling prices of properties in a market. That is,if we have enough information on the selling prices of properties exhibitingdifferent characteristics we should be able to tease out how much eachindividual characteristic influences the total price of a property. To give anintuitive insight into how this could be achieved imagine that we took theproperties in a market that were identical in all characteristics apart from one,say size of garden, and plotted the quantity of that characteristic, for eachproperty against the price that property commanded. We might expect to draw agraph similar to that shown in Figure 2-5.

In Figure 2-5 the asterisks next to the other characteristics (i.e. z2*, z3*,�, zn*)indicate that these characteristics are held constant whilst the focuscharacteristic, size of garden (i.e. z1), changes. In this hypothetical case, thehedonic price function rises from left to right implying that the bigger aproperty�s garden the higher the price that property commands in the market.Notice also that the slope of the curve becomes progressively flatter; theincremental increase in a property�s market price resulting from its possessing abigger garden declines as gardens get progressively larger. This sort ofrelationship reflects a form of satiation; having a few square metres of gardenwill add considerably to the price of a house when compared to a house with nogarden at all, whilst a few extra square metres will make a negligible differencebetween the selling prices of two houses which already boast football pitchsized gardens.

Figure 2-5: The Hedonic Price Schedule for characteristic z1

Of course the relationship will not be identical to that graphed for every type ofcharacteristic. For example we might find that for another characteristic, say

0Quantity of

Characteristic z1

Price ofProperty

Hedonic Price ScheduleP(z1, z2*, z3*, �, zn*)

2-9

floor space, plotting house price against the quantity of the characteristic (againholding all other characteristics constant) results in a straight line rising fromleft to right. A straight-line relationship suggests that there is no satiation andthat the price commanded by a property increases uniformly in relation to thequantity of the characteristic that it possesses. In general, however, we wouldexpect that the greater the quantity2 of any favourable characteristic of aproperty, the more a potential property-buyer will have to pay for it.

Compared to a simple good, the market for a complex good does not return justone price; rather it returns a continuum of prices. However, as with the marketfor a simple good, we would still expect this continuum of prices to represent amarket equilibrium. That is, at the set of prices revealed by the hedonic priceschedule, demand would equal supply and the market would clear. Of coursethis follows basic logic, if a seller set the offer price on his property too highthen it would remain unsold, conversely if the price were too low then he wouldrisk losing out on potential profits.

Rosen explains the attainment of market equilibrium more formerly as theinteraction of buyers and sellers. The details of his model are beyond the scopeof the present discussion, in short, however, buyers wish to purchase theproperty that provides them with greatest quality at the lowest price, whilstsellers wish to sell their property at the highest price possible. The marketreconciles these conflicting goals by matching buyers to sellers such that thebuyers (within their limited budgets) cannot increase their utility by choosing adifferent property and the sellers cannot increase their profits by increasing theoffer price.

Since the equilibrium hedonic price schedule, as illustrated in Figure 2-5, isdetermined by the market interaction of buyers and sellers it also revealsinformation about the households� demand for the various characteristics of thecomplex good.

Figure 2-6: The Hedonic Price and Implicit Price Schedules for characteristic z1

2 For some characteristics, such as air pollution, it may be more natural to talk about quality rather thanquantity, though, in effect, both are measures of the level of provision of a characteristic.

0Quantity of

Characteristic z1

Price ofProperty

(P)

Hedonic Price ScheduleP(z1, z2*, z3*, � , zn*)

0Quantity of

Characteristic z1

ImplicitPrice of z1

(Pz1)

Implicit Price ScheduleP�(z1, z2*, z3*, � , zn*)

2-10

Imagine that we had already estimated the hedonic price function P(z) for theproperties in a given housing market. It would be possible to use theinformation from the hedonic price function to see how the quantity of aparticular characteristic influenced the overall price commanded by a property(as illustrated in Figure 2-5 and again in the left-hand panel of Figure 2-6).Indeed, we could plot another graph which traced the additional amount thatmust be paid by any household to move to a bundle with a higher level of thatcharacteristic, other things being equal (illustrated in the right hand panel ofFigure 2-6). This new function is known as the implicit price function and isdenoted Pz1.3 It is know as an implicit price function of a characteristic becauseit is revealed to us indirectly through the amounts people are prepared to pay forthe whole property of which the particular characteristic is only a part.

Purchasing households in the property market can be viewed as facing a seriesof these implicit price schedules, one for each of the various characteristics ofthe property. The household will get the most utility by simultaneously movingalong each implicit price schedule until it reaches a point where its WTP for anadditional unit of that characteristic just equals the implicit price of thatcharacteristic.4

The marginal WTP schedules of two households, i and j, for characteristic z1 arepresented in Figure 2-7. These marginal WTP schedules map the samerelationship between price and WTP as those shown for a simple good in Figure2-1. In the case of the simple good the marginal WTP schedule showed thequantity of the entire good that the household would purchase at differentprices. In the case of the complex good, the marginal WTP schedule shows thequantity of a certain characteristic that the household would wish to have in thebundle of characteristics that make up their ideal property, given the implicitprice schedule of that characteristic. It is clear from Figure 2-7, that if householdi were to purchase a house with more than q1i units of characteristic z1, then theimplicit price per unit of that characteristic would be higher than thehouseholds� WTP per unit (i.e. household i would be better off purchasing aproperty with less of characteristic z1).

3 Mathematically speaking, the implicit price is determined by taking the partial derivative of thehedonic price function (Equation 2-2) with respect to one of its arguments, for example z1. For thosenot familiar with calculus, the derivative of a function describes the rate at which the value of thatfunction (e.g. the hedonic price) changes as one of its arguments changes (e.g. the quantity ofcharacteristic z1). By convention this is denoted Pz1. Referring to Figure 2-6 we can see that at first thehedonic price function rises steeply (the rate of change is high) so that the implicit price of thecharacteristic (the extra amount paid to acquire a house with more of characteristic z1) is also high. Athigher levels of z1 the hedonic price function is flatter (the rate of change is low) so that the implicitprice of the characteristic is also low.4 It is worth noting that in the real world it is unlikely that a housing market will possess properties withevery possible combination of characteristics. In this case it may prove impossible for a household tofind a property that perfectly fulfils its requirements. In the terms of this discussion, for certaincharacteristics a household may be unable to find a property that has just the right quantity of eachcharacteristic such that its WTP for each characteristic is just equal to the implicit price of eachcharacteristic. The impact of this phenomenon on the hedonic price function is discussed further inSection 4.3.3.

2-11

Figure 2-7: Household Demand for a Complex Good; the Interaction of theImplicit Price Schedule and the Household's Marginal WTP Schedule

2.2.3 Unpriced Goods and Services

Our third category of goods and services consists of those which are not boughtand sold, and therefore do not command a market price. Examples of theseinclude many environmental goods such as air, sea views and access to a localwoodland. From an economic point of view, the key difference between thesegoods and services and those described above is one of ownership or, moreformally, property rights.

The simple and complex goods described previously were all examples of whatare referred to as �private� goods. A private good is one over which a seller hasprivate property rights. Since the seller owns the good, he can extract apurchase price from the consumer. On the other hand, unpriced goods andservices are frequently examples of �public� goods for which only commonproperty rights exist. Since there is no defined body that owns the good and isorganised to sell it, the good is not traded in a market and remains unpriced.These goods are often referred to as open-access because they have no marketprice associated with their use (although in practice as a result of location suchgoods are more readily available to some rather than others).

There is no reason to believe that households would not be willing to pay forsuch goods if they had to. Indeed, we could draw up a marginal WTP (demand)schedule for an unpriced good in the same way we drew one up for privategoods. This is illustrated in Figure 2-8. In the absence of a price, the demand forthe unpriced good is given by q1 for household 1 and q2 for household 2.

0 Quantity ofCharacteristic z1

ImplicitPrice of z1

(P�)

Implicit Price ScheduleP�(z1, z2

*, �, zn*)

Marginal WTP /Demand Schedule

for Household i

q1i q1j

Marginal WTP /Demand Schedule

for Household j

2-12

Figure 2-8: Household Demand for an Unpriced Good or Service

As we shall discuss in detail in the next section, economists and decision-makers are frequently interested in information that can be gleaned from thedemand curve for a good. A problem facing researchers is that for unpricedgoods it is extremely difficult to estimate demand curves such as those shown inFigure 2-8. For example, it is rare that an environmental good such as �peaceand quiet� will ever be directly traded in a market (e.g. it is unlikely that wecould pay a fee that would ensure that we enjoyed a certain level of peace andquiet in our homes). Since researchers cannot see how demand for such a goodchanges in response to different prices they are unable to construct a picture ofthe demand curve.

Over the years, researchers have developed a number techniques to overcomethis problem and these techniques form the subject matter of Chapter 3. Fornow, however, it is worth noting the basis for one of these techniques; thehedonic technique. The hedonic technique derives from the observation thatwhilst some environmental goods, take peace and quiet as an example, are nottraded directly in their own markets they may be traded indirectly in othermarkets. Indeed, for a complex good like housing, peace and quiet is likely to beone of the many characteristics that influence total price; people will likely paymore for houses in quieter areas than they would for houses in noisy areas.Using the logic described in the last section it should, therefore, prove possibleto estimate the hedonic price function for housing and from this ascertain theimplicit price of peace and quiet. With details of the demand and implicit pricefor peace and quiet, researchers have the key information necessary to estimatea demand curve. We shall return to the hedonic price technique in later chapters.

Price

0q2 Quantityq1

Marginal WTP /Demand Schedule

for Household 2

Marginal WTP /Demand Schedule

for Household 1

2-13

2.3 Economic ValueThough we have briefly addressed the point already, a fundamental issue requiringclarification arises from the common and persistent confusion between prices andvalues. Even the earliest economic commentators recognised that the price of agood need not correspond to its value. Adam Smith, writing more than 200 yearsago, noted the extreme disparity between the (very low) price of water and its (veryhigh) value. Yet even today the use of environmental goods and services iscommonly dictated by their price rather than their value. A contemporary exampleof such use is the dumping of UK sewage sludge in the North Sea. In so doing, thesea provides highly valuable but virtually zero priced waste assimilation services.The value of these services is only now becoming apparent because the phasing outof such dumping (Dutch Ministry of Transport and Public Works, 1990) has forcedthe companies involved to find costly incineration and landfill alternatives.

2.3.1 The Difference between Value and Price

Returning to the example of bottled water, a clear difference can now beestablished between price and value and thereby illustrate the economicmeaning of the latter. An individual�s decision to purchase and consume a bottleof mineral water indicates a surplus of value over price. This �consumer surplus�can then be added to the price paid to yield the economic value of that bottle ofwater. In economic parlance, value is thereby defined as the maximum amountthat the individual would have been willing to pay for the bottle of water. Thisdefinition of value can now be formalised as per Equation 2-35:

Value = WTP = Price Paid + Consumer Surplus (2-3)

Continuing with the example of the value of mineral water, let us now considerthe total value of all the bottles consumed by an individual during one hotsummer day. Suppose that our individual decides to purchase a second bottle ofwater. This tells us that again the value of that bottle either equals or exceeds itsprice. Again this value can be determined using Equation 2-3.

Of course, diminishing marginal utility will result in the consumer surplusrealised by the individual being smaller for the second bottle. This trend willextend over all additional units, so that for our individual the value of a thirdbottle may be just greater than its price (i.e. consumer surplus is reduced almostto zero) while for a fourth bottle value falls below price. In this example,therefore, our individual would purchase three bottles of water on this sunnyday, but no more.

The total value of mineral water consumption for our individual can now bedetermined by summing up the value (WTP) for each of the three bottlesbought. Notice that this exceeds the price paid by the sum of consumer surplus.

5 Equation 2-3 is in fact a simplification as it can be shown that consumer surplus is only anapproximation of the value gap between price paid and WTP (see Just et al., 1982, for furtherdiscussion).

2-14

We can simply illustrate the relationships set out above using the graph ofhousehold demand presented in Figure 2-4. Figure 2-9 presents a specific caseof Figure 2-4 showing the marginal WTP schedule (or demand curve) of ourindividual consuming bottles of mineral water. For ease of exposition, each unitconsumed is illustrated by a column indicating the total WTP of the individualfor that bottle of mineral water. The total WTP represented by each column hasbeen divided into a part that was paid and a part that represents consumersurplus.

The area under the demand curve and above the price line describes theproduct�s consumer surplus. Adding this to the price paid area under the priceline gives the total value of the goods consumed. The graph also shows that thequantity consumed is determined by the intersection of the demand curve andprice line. Beyond that point additional units have values which are lower thantheir price and are therefore not consumed.

Figure 2-9: The Marginal WTP Curve for Bottled Water, Illustrating theDivision of Total Value into Consumer Surplus and Price Paid

Our mineral water example refers to a �private� good, that is one that has privateproperty rights through which a seller can extract purchase price from theconsumer. However, the theory of economic valuation set out above can, inprinciple, also be extended to non-market, unpriced, �public� goods for whichonly common property rights exist. Again we can see that such public goods arevalued by individuals; in economic terms they have a WTP for consumption ofsuch goods. However, as the market price paid is zero (there may be a non-zerocost of getting to such goods, of which more later), value (WTP) in this case iscomposed entirely of consumer surplus.

Figure 2-10 describes the demand curve for such a good; walks in a publicwoodland. This is a specific example of the demand curves illustrated in Figure2-8. Here the entrance price is zero but our individual derives a significant valuefrom her first walk in the wood during a given period, say one week. As in the

0

Price

P

Bottles of MineralWater per Day

3

EquilibriumMarket Price

Marginal WTP /Demand Schedule

for Household 1

ConsumerSurplus

PricePaid

21

×

×

654 98 107

×

××

× × × ×

2-15

case of a private good, the value of subsequent walks declines relative toprevious visits. The demand curve mapped out need not be a straight line,indeed such a relationship is unlikely in practice. In Figure 2-10 the first visit isvery highly valued after which WTP initially declines rapidly but then at aslower pace. Such a demand curve might be representative of someone whotakes their dog to the wood, the initial walk being valued both because itprovides exercise for the dog (which the individual values) and for its aestheticqualities. After this additional visits are only of use because they provideexercise for the dog. Note that increasing visits above a certain level wouldagain drive the value of additional visits to zero6.

Figure 2-10: The marginal WTP curve for �walks in the woods�, illustrating totalvalue as the consumer surplus enjoyed by the consumer

2.3.2 Types of Value and Total Economic Value

Notice that in the above example our public good was in fact providing theindividual with two types of value: aesthetic beauty and a place to exercise adog. In fact a complex good such as a wood provides a variety of values.

Figure 2-11 shows how total economic value (TEV) can be broken down into itsconstituent parts and illustrates these with reference to certain of the valuesgenerated by our woodland example.

6 In fact the demand curve may be even more complex than this. For example it could be non-linear anddiscontinuous if the value of one visit per day is positive but an additional visit on any day is always ofzero (or negative) value.

Price

0q

Marginal WTP /Demand Schedule

Walks in theWoods per Week

ConsumerSurplus

2-16

Figure 2-11: Total economic value (with examples from the appraisal of a forestproject)

Source: Adapted from Bateman (1995)

The basic division of values within TEV is into either those which are, or arenot, derived from the valuing individual�s actual use of goods and services. Thefirst of these, known generically as Use Values can be further subdivided into:

Those which involve direct, present day use (note here that for ourexample a woodland can generate both market priced private goodssuch as timber and unpriced public goods such as recreation7);

the option value of future direct use (Weisbrod, 1964; Cicchetti andFreeman, 1971; Krutilla and Fisher, 1975; Pearce and Turner, 1990);

the bequest value of providing use and/or non-use values for presentand/or future others.

Pure Non-use Values are most commonly identified with the notion ofvaluing the continued existence of entities such as certain species offlora or fauna or even whole ecosystems (Pearce and Turner, 1990;Young, 1992).

7 Note also that by changing property rights and restricting entry, unpriced, open access public goodslike recreation can be transformed into market priced private goods.

Primary usevalue

Secondary usevalue

Option usevalue

Bequestvalue

Existencevalue

e.g. timberrevenues

e.g. creatingemployment

e.g. futurerecreation by

presentindividuals

e.g. futuregenerationsrecreation

e.g. preservingbiodiversityand wildlife

habitat

Utilitarianuse value

Total non-use valueTotal use value

Total economic value

2-17

In theory then, in valuing complex goods such as those provided by theenvironment, TEV should be assessed rather than just the more obvious directuse or market priced attributes of such goods (for examples see Barde andPearce, 1991).

2.4 Summary and ConclusionsIn this chapter, we have introduced some key concepts. We established that themarket operates to bring together buyers and sellers and establishes an equilibriumprice that clears the market. For simple goods, one price rules in the entire market.For complex goods an equilibrium or hedonic price schedule can be determinedthat describes the relationship between the total price of the good and the quantityof the different characteristics of the good. The hedonic price schedule can be usedto determine an underlying implicit price schedule for each individualcharacteristic of the complex good.

The actual quantity of a simple good that is demanded by any one household isdetermined by the point at which the household�s marginal WTP or demandschedule is just equal to the price of the good (illustrated in Figure 2-4). The actualquantity of a characteristic of a complex good that is demanded by any onehousehold is determined by the point at which the household�s marginal WTP ordemand schedule is just equal to the implicit price of that characteristic (illustratedin Figure 2-7). For some goods that do not have clearly defined property rights,there may be no market and the good may not have a price at all (illustrated inFigure 2-8).

Value, as defined by economists, is the maximum amount that consumers arewilling to pay for the goods they purchase. For most purchases this is in excess ofthe price charged and the consumer realises a consumer surplus. The total valuegenerated for an individual by purchases of a certain good is the sum of what wasactually paid plus the consumer surplus enjoyed on each purchase. This can bemeasured as the area underneath the marginal WTP or demand curve for all unitsof the good consumed (illustrated in Figures 2-9 and 2-10).

Some goods, including many of those provided by the environment, are not tradedin markets and do not command a price. However, these goods may conferconsiderable value on consumers. This value may originate from a variety ofsources, not just the straightforward consumption or use of the good. As aconsequence, in estimating the value of some complex goods, such as thoseprovided by the environment account should be taken of total economic value.

The difference between price and value is an important one, especially in socialdecision-making. For example, let us consider the decision as to whether or not anew road should be built that would destroy an area of woodland used by localpeople for recreational purposes. If the decision were made purely based upon anassessment of goods that commanded prices then the choice of whether to build theroad or not may come down to a comparison of the construction and maintenancecosts of the road and the time savings that would be enjoyed by the users of theroad. The loss of the woodland to local people would not even be taken intoconsideration, despite the fact that they might value it highly.

One of the fundamental rules of economic cost-benefit analysis, therefore, has beento compare options in terms of the value they generate to the members of society.

2-18

To allow such an approach to succeed, economists need to estimate demand curvesfrom which changes in value can be determined. This is eminently possible formarket goods where changes in demand in response to changes in price can beobserved and measured, but for non-market, unpriced goods the task is far fromsimple. In the next chapter we go on to look at the techniques that have beendeveloped by environmental economists to uncover the values of goods that are notdirectly traded in markets.

3-1

3 THE TECHNIQUES OF NON-MARKET VALUATION

3.1 Introduction

The concept of economic value, introduced in the last section, is an importantmeasure for decision-makers choosing between policy options. To ensure thesedecisions take full account of environmental concerns, it is important that valuesare attributed to the environmental consequences of projects. In this section, wereview various techniques that have been developed to value environmental goodsand assess which of these techniques are appropriate for the purposes of the presentproject.

3.2 Valuation and Policy Making

In Section 2, we introduced the concept of value. To recap, value was taken to be ameasure of the well being or utility that an individual obtains from a good. Wewould normally think of this utility as resulting from the consumption or use of thegood (e.g. visiting a woodland and enjoying its aesthetic beauty) though there is noreason why value might not also come from other quarters. Indeed, we showed thatfor a woodland people might get utility from knowing that the woodland is therefor them to use at some other time even if they don’t visit it at the moment, fromthe fact that others (e.g. their family and children) can visit the woodland eithernow or at some time in the future, or they may value the woodland even if no onewere to visit it simply because they believe that having natural areas is a goodthing.

Though as individuals we may measure the value we get from goods in terms ofthe utility that we derive from them, utility is a measure without real substance; itcannot be quantified in meaningful units and thus, as researchers or policy-makers,we can’t compare utility between, or aggregate it across, individuals. Alternativelyvalue can be measured in monetary units through assessing individuals’Willingness to Pay (WTP) for a good or service. It is assumed, in many cases, thatindividuals can express utility in terms of the maximum amount of money theywould be prepared to give up to enjoy that good or service.

If individuals value a good or service more than it costs to buy then, naturally, theywill purchase the good and enjoy the satisfaction that comes from consuming it. Sofor most purchases a consumer’s WTP will be greater than the purchase price andthey will enjoy a surplus of value that they do not have to pay for. The differencebetween what the consumer had to pay and the maximum they are willing to pay isknown, appropriately enough, as Consumer Surplus (CS). As shown in Figure 2-9we can estimate CS as the area that lies below the demand curve but above theprice line.

Frequently, policy makers are faced with choices that will impinge upon theprovision of goods and services presently enjoyed by individuals in society. Oneway of making decisions would be to sum up the changes in value experienced byall individuals from each policy option, and choose the option which generated the

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most value to society1. In other words, one measure of the value of a policy optionto society would be the change in total CS that it brought about.

Before continuing, we should note some reservations concerning the use of CS tomeasure changes in value. For a start, economic theory shows that CS, as measuredby the area under the demand curve, is only an approximation of the amount anindividual would actually be willing to pay for a change in the provision of a good.Second, up to this point we have only measured value in terms of WTP. However,we could equally well ask how much an individual is willing to accept (WTA) incompensation for a change in the provision of a good2. Theoretically there may bea difference in WTP and WTA values for the same change in provision and thismay be especially large for environmental goods (Hanemann, 1991). To maintainsimplicity and because in many cases CS is a good approximation of WTP andWTA (Willig, 1973; 1976; Randall and Stoll, 1980) we will continue using theterm CS to represent a measure of value in this discussion. The theoreticalrelationship between WTP, WTA and CS is discussed in detail in Annex A.

Choosing a particular policy option can influence the provision of goods andservices in two ways. It could result in an;

• Increase or decrease in the price of a good or service

• Increase or decrease in the quantity (or quality) of a good or service

To estimate how price or quantity changes may affect the CS derived from theconsumption of a good or service, we first need to have an estimate of its demandcurve. For market goods where changes in demand in response to changes in pricecan be observed and measured this is usually possible3.

For example, imagine we had estimated the demand curve for a particular marketgood. This is illustrated in Figure 3-1. Now, at the present price, P1, consumersenjoy a CS equal to the entire shaded area above the price line.

If a policy were introduced that increased the price of the good from P1 to P2, thenCS would contract to the lightly shaded triangle above the new price line. Theincrease in price has reduced the CS by an amount equal to the more darkly shadedarea defined by P1P2AB.

For purposes of policy however, the net loss of value to society is smaller than thisarea. Note that the shaded rectangle P1P2AC was originally part of CS butfollowing the price rise is part of payments made to the suppliers of the good. In

1 Clearly, WTP is dependent to an extent on people’s ability to pay. If policy makers were to base theirchoice between options solely on measures of the total change in value enjoyed by members of societythen they would be making the implicit assumption that the distribution of wealth and income isoptimal. Frequently, however, policy-makers have distributional goals and will wish to take these intoconsideration when making policy decisions. This could be achieved by weighting WTP values ofvarious groups in society differently to reflect these distributional goals. Alternatively, the policy makercould adopt measures to redistribute the net benefits of a project in order to address distributionalproblems.2Furthermore, as we can assess both gains and losses in provision we can define in total four equallyvalid measures of value: WTP to secure a gain; WTP to avoid a loss; WTA to suffer a loss; WTA toforgo a gain.3 It is important to note that this process is by no means bereft of uncertainty, with estimates ofaggregate WTP for some quantity of a market good (that is, the area under the demand curve)commonly having an error range of around ± 50% (Tinch, 1995)

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effect what was CS is now income to the suppliers. There is no overall loss invalue to society, that value has just been transferred from one party to another. Thereal loss to society therefore, is just the darkly shaded triangle, area ABC. Ineconomic terms this is known as the deadweight loss.

Figure 3-1: The Change in Consumer Surplus from a Change in Price

So much for a price change, what happens when a policy changes the level ofprovision of a good? This is often the case with environmental goods and services.We can imagine numerous policies that change the quality of air or water or thatchange the amount of natural land that is available for people to visit.

If we knew the demand curve for an environmental good4, say for example ‘peaceand quiet’ in the neighbourhood in which an individual lives, it might looksomething like that depicted in Figure 3-2. Remember the demand curve is simplythe marginal WTP schedule; at low levels of peace and quiet (i.e. when it isrelatively noisy in the neighbourhood) people are willing to pay a lot for morepeace and quiet, but as the level of peace and quiet in the neighbourhood increasespeople are willing to pay incrementally less to achieve a further unit reduction innoise pollution.

4 To avoid confusion, this could be either an aggregate demand curve or an individual demand curve(see Figure 2-1 of Section 2). If it is possible to directly estimate the aggregate demand curve (i.e. ademand curve for the whole population who value the environmental good) then our measure of thechange in CS resulting from a policy will give us a direct estimate of the change in value experiencedby the whole population. More commonly valuation will proceed by taking a representative sample ofindividuals from the relevant population and estimating an ‘average’ individual demand curve. Thechange in CS resulting from the policy for this ‘average’ individual can then be aggregated to the entirepopulation.

0 Quantity

Price

Q2 Q1

P1

P2A

CB

DeadweightLoss

Demand Curve

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Figure 3-2: The Change in Consumer Surplus from a Change in Quantity orQuality

As discussed in Section 2, environmental goods frequently do not have a price (e.g.we don’t pay for every hour of peace and quiet we enjoy) but their supply may beconstrained. In the case shown in Figure 3-2, the current level of peace and quiet isinitially fixed at Q1 and consumers enjoy a CS equal to the entire shaded area. If apolicy were introduced that increased noise pollution (e.g. a new road was builtthrough the neighbourhood) the level of peace and quiet would be reduced to Q2.Our measure of the loss of value of people living in the neighbourhood would begiven by the change in CS represented by the darkly shaded area Q1Q2AB.

Given the above, it is possible to take one of two approaches to valuation;

• We can begin by estimating the demand curve for an environmental good (likethat depicted in Figure 3-2) and then use this demand curve to estimate how CSwill be affected by changes in the provision of the good.

• Alternatively, we can avoid the first step of estimating a demand curve andattempt to directly assess the effect on CS of a particular change in provision ofthe environmental good. Referring to Figure 3-2, this would mean making adirect estimate of area Q1Q2AB.

Both approaches have their advantages. By estimating a demand curve, it ispossible to assess a whole range of possible changes in the provision of theenvironmental good. However, directly estimating a particular change may proveto be an easier task with a greater degree of accuracy.

Our objective in the remainder of this section is to look at the various techniquesthat have been developed to estimate changes in CS brought about by changes inthe provision of environmental goods, looking at both those that estimate demandcurves and those that directly estimate the value of a particular change.

0 Quantity / Quality

Price

Q2 Q1

A

B

ConsumerSurplus Loss

Demand Curve

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3.3 Valuing Environmental Goods

Research into methods for placing monetary values on changes in the provision ofenvironmental goods can be traced back to at least the 1940s (Bateman, 1993).However, burgeoning interest in the field over the past two decades has resulted inthe development of a wide variety of assessment methods. These can be classifiedin a number of different ways, none of which is entirely satisfactory (Pearce andMarkandya, 1989). In this paper we adopt a somewhat pragmatic initial divisionbetween;

• Valuation techniques; which estimate theoretically consistent values in linewith the demand curve and direct valuation approaches discussed above, and

• Pricing techniques; which include a range of generally simpler approaches.These approaches do not attempt to estimate value per se but produce estimateswhich in theoretical terms are analogous to prices in that they inform about thecost of attaining a given change in the provision of an environmental good5.

A further subdivision is made amongst the valuation techniques into;

• Revealed Preference Techniques, that generally involve the estimation of ademand curve for the environmental good, and

• Expressed Preference Techniques, that do not estimate a demand curve butdirectly assess the effect on CS of a particular change in provision of theenvironmental good.

Figure 3-3 illustrates this division and provides an overview of the methodsdiscussed subsequently.

3.3.1 ’Pricing’ Methods

3.3.1.1 Opportunity costs

One approach to ‘pricing’ is to examine what value would have to beforegone in order to, say, enhance a particular environmental asset. Acontemporary UK example is the on-going creation by the CountrysideCommission of a 'New National Forest' between Leicester and Burton-on-Trent in the English Midlands (Countryside Commission, 1990). The 150-square-mile site includes a large area of high quality agricultural land, theloss of which represents an ‘opportunity cost’.

5 In theory the ‘price’ information provided by such techniques could be used as the basis of aninvestigation of underlying demand curves and hence values. However, this would require repeatedobservations and is unlikely to outperform explicit ‘valuation’ methods.

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Strictly speaking this opportunity cost should be the net market price for thecrop and livestock products of the land ‘lost’ to the new forest. But becausethese products are subsidised by the Common Agricultural Policy (CAP) ofthe EC, the compensation paid to landowners would be the lost subsidisedvalue of production minus the costs of input. This is a distortion in economicterms, but it is politically necessary based on fairness. Resource prices aremore a function of the structure of markets and the political weight of majorinterest groups, rather than competitive relationships. Compensatingarrangements reflect these distorted prices, yet this ‘value’ sets the lowerlimit of the environmental benefit of the new forest.

3.3.1.2 Costs of alternatives

Where an environmental resource is being used (or its use is planned) as partof some development or production project, then one strategy for evaluationis to calculate the cost of using some alternative resource. One example is thesewage treatment alternatives to North Sea dumping cited in the DutchMinistry of Transport and Public Works (1990) report mentioned in Section2. A second example arises from the extension of the M3 motorway atTwyford Down, a recognised area of outstanding natural beauty in midHampshire, UK. Here a scheme for running the motorway through a tunnelunder the Down was costed at just under £70 million (Medley, 1992), a costwhich the DoT clearly felt outweighed the environmental benefits ofpreserving the area as the extension was subsequently achieved by digging alarge cutting through the middle of the Down thereby destroying the integralecological characteristics of the site; a decision which generated large scalepublic protest and a challenge in the European Courts regarding the adequacyof the scheme’s environmental impact assessment (EIA). This exampleunderlines a general problem with such an approach in that reliance uponpricing rather than valuation techniques places environmental goods at riskof under-valuation. More fundamentally it underlines the aforementionedissue of who should be the arbiter of (in this case implicit) valuation. Themove from a broad base of individuals to a single decision-maker can beseen, in this example, as having resulted in the omission or insufficientweighting of values held by a significant number of individuals.

3.3.1.3 Mitigation behaviour

Here the prices which individuals pay in order to mitigate environmentalimpacts are taken as simple monetary assessments of those impacts. Acommonly applied example is the use of double glazing costs as a proxy forthe disamenity value of noise intrusion. Such costs have been used in theassessment of road projects. However, this method is at best partial as it onlytakes account of noise intrusion within a house rather than also around itsenvirons. It also ignores the welfare loss due to an imposed change inpeople’s habits, e.g. their inability to leave windows open without sufferingfrom heightened traffic noise.

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3.3.1.4 Shadow project costs

Another ‘pricing’ option is to look at the costs of providing an equalalternative environmental good elsewhere. One such study (Buckley, 1989)examined such possibilities for a development project which threatened anexisting wildlife habitat. Here three options were highlighted; assetreconstruction (providing an alternative habitat site); asset transplantation(moving the existing habitat to a new site); and asset restoration (enhancingan existing degraded habitat). The costs of the preferred option can then beentered into the project appraisal as the ‘price’ of the threatened habitat.However, for such an approach to provide suitable compensation theecological adequacy of alternative or ‘shadow’ projects needs to bediscussed.

3.3.1.5 Government payments

The Government, as arbiters of public preferences, occasionally directlyvalue environmental goods and services by fixing subsidies paid directly toproducers (particularly farmers) for adopting environmentally benignproduction methods. Such values have been used as part of projectappraisals, one case being that for the Aldeburgh sea wall in Suffolk wherethe costs of wall renovation were assessed against various items one of whichwas the environmental value of protected land as proxied by ESA(Environmentally Sensitive Area) subsidies to local farmers (Turner et al.,1992).

3.3.1.6 The dose-response method

Statistical techniques can be used to relate differing levels of pollution (the‘dose’) to differing levels of damage (the 'response'). In one such study theenvironmental costs of a coal-fired power station were assessed byexamining various dose-response models relating acidic and photo-oxidantemissions to their impacts upon forest, crops, fisheries, etc. (Holland andEyre, 1992). Once the physical impact of these emissions upon, say, cropshas been calculated in terms of tonnes per annum lost then this can be given amonetary evaluation by multiplying this damage by the market price pertonne (although caveats regarding the distortion of prices in heavilyintervened markets such as agriculture again apply here). Box 3-1 illustratesthis particular case study.

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Box 3-1: Applying the dose-response method: pricing sulphur emissions

The following example is taken from Holland and Eyre (1992). Panel A shows sulphuremissions (measured in parts per billion) from a coal fired power station. The environmentalimpacts of such emissions are initially appraised in a simple scoping study, such as thatshown in Panel B, where experts identify broad areas of concern. Panel C focuses on just oneof these impacts; damage to crops. The dose-response curve illustrated in this panel showsthat, as the concentration of airborne sulphur increases, so crop yield falls. Panel D prices theoverall annual quantity of SO2 emissions from the power station by reading off the quantityloss relating to those emissions (from Panel C) and then multiplying the number of tonnes lostby the market price per tonne. Finally Panel D details an estimate of the social value (shadowprice) of those losses.

Panel A: Emission source: annual mean rural SO2 concentration for the West Burton‘B’ power station.

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Panel B: Scoping report: impacts matrix

Impacts

Pollutants Forest Crops Wild Plants Animals Fisheries

SO2 2 2 2 1 0

NO2 1 1 1 1 0

NH3 1 1 1 1 0

O3 3 3 3 1 0

Total acid 3 1 3 2 3

Total N 3 0 3 2 2

Key to impacts: 0 = low/no impact; 1 = some impact; 2 = severe impact; 3 = very severeimpact

Panel C: Typical sulphur - crop dose - response relationship

Source: Roberts (1984).

Panel D: Impact pricing matrix.

Scenario: Impacts of SO2 emissions upon selected crops arising from the construction of a1800 MW coal fired power station at West Burton

Crop Impact1 Market Shadow(tonnes lost) price2 (£) price3 (£)

Wheat 1,471 161,810 151,253

Barley 1,258 138,380 129,352

Notes:1 See Holland and Eyre (1992) for further details of application.2 Prices are those estimated for 1994 in Nix (1993), namely £110/tonne for both milling wheat and

malting barley.3 Shadow price adjustments are from Bateman (1996) which takes into account European

Community price subsidies detailed in OECD (1992) and EC (1992) and the price impacts ofsubsidiaries estimated by Roningen and Dixit (1989).

Y0

Y1

Yie

ld

C0 C1

Concentration of sulphur emissions

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3.3.1.7 Summary: ‘Pricing’ methods

Non-demand curve ‘pricing’ approaches can be useful in providing roughmonetary assessments of environmental goods and services that mightotherwise be treated as free. However, they are not without flaws andlimitations. The ‘opportunity cost’, ‘cost of alternatives’, ‘mitigationbehaviour’ and ‘shadow project costs’ approaches provide monetarybenchmarks against which the value of the environmental good in questioncan only be subjectively judged. These are not true valuations as theassessment only considers whether the environmental good is of greatervalue than the opportunity cost. This criticism also applies to the‘government payments’ approach but here we have the additional problemthat the benchmark value used is set not by the market but by government.

The dose-response approach does have potential for wide application(Schultz, 1986; Barde and Pearce, 1991), although some doubts have beenraised concerning the complexity of some dose-response relationships andconsequent problems of statistical estimation (Turner and Bateman, 1990).

3.3.2 ‘Valuation’ approaches: Expressed preference methods

Expressed preference methods directly ask people about their valuation ofenvironmental goods. In effect, they are directly estimating the area under thedemand curve for the good in question. This is generally achieved throughone-to-one interviewing (although other variants, including groupapproaches, are becoming more common).

3.3.2.1 The contingent valuation (CV) method

Here environmental evaluations are obtained by using surveys to ask peopledirectly what they are willing to pay for a given gain (or willingness toaccept for a given loss) of a specified good. The CV approach is notable,therefore, in that it does not estimate CS but the theoretically more correctmeasures of WTP and WTA (see Annex A for details of the differencesbetween the three measures). Indeed, the method can yield estimates of allfour of the measures of value described in Annex A.

Given its flexible one-to-one or group survey methodology, CV is also aninteresting tool with which to investigate a number of issues relating toeconomic theory including the WTP-WTA asymmetry discussed previously.

The CV method has been used extensively to assess the various componentsof TEV as expressed preference approaches in general appear to offer thebest potential for assessing non-use values. Box 3-2 presents a good exampleof a CV study analysing the values held by both users and non-users of ariver for a program of water quality enhancement measures.

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Box 3-2: Applying the contingent valuation method: The use and non-use value ofimproving river water quality

This example is taken from Desvousges et al. (1987) who examined both the use and non-usevalue of improving water quality in the Monongahela River, a major river flowing throughPennsylvania, USA. Analysts asked a representative sample of households from the area whatthey would be willing to pay in extra taxes in order to maintain or increase the water qualityin the river. The analysts conducted several variants of CV survey. In one variant householdswere asked how much they would be willing to pay for each of the following three possiblewater quality scenarios:

Scenario 1: Maintain current river quality (suitable for boating only) rather than allow itto decline to a level unsuitable for any activity (including boating).

Scenario 2: Improve the water quality from boatable to a level where fishing could takeplace.

Scenario 3: Further improve water quality from fishable to swimmable.

Results from the whole sample and from subsets of those who did or did not use the river aredetailed in panel A

Panel A: Willingness-to-pay (WTP) for three river quality scenarios

Water quality Average WTP of Average WTP of Average WTP ofScenario whole sample users’ group non-users’ group

($ p.a.) ($ p.a.) ($ p.a.)

Maintain presentboatable river quality 25.50 45.30 14.20

Improve fromboatable to fishable 17.60 31.30 10.80quality

Improve fromfishable to 12.40 20.20 8.50swimmable quality

A number of interesting conclusion can be drawn from these results. Considering the resultsfor the whole sample it can be seen that the stated WTP sums draw out a conventionaldemand curve for water quality, i.e. people are prepared to pay a relatively high amount for aninitial basic level of quality, but they are prepared to pay progressively less for increases tothat water quality. The figure shown in panel B draws out the demand curve indicated by theresults for the whole survey, i.e. for the average household.

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Panel B: Demand curve for water quality: Average WTP of whole sample

Considering panel A again; notice that the willingness to pay of non-users is not zero. This isdue to the fact that such households, while not personally wishing to visit the river,nevertheless do value its continued existence and even upgrading so that others can enjoy itsbenefits. As indicated previously, this non-use, ’existence’ value derives from people’saltruistic ‘public preferences’ showing that the concentration upon people's ‘privatepreferences’ as indicated in the prices of marketed goods, does not always fully capture theentire range of values (TEV) which people have for goods, particularly those provided by theenvironment.

3.3.2.2 Contingent ranking (CR) and stated preference (SP) methods

Whilst methodologically distinct, CR and SP (or conjoint analysis) methodsare sufficiently similar to be considered together. Both involve one-to-oneinterviews in which respondents make choices between goods, each of whichis described via a number of attributes of which one will usually be somemonetary or proxy monetary measure. In a CR experiment subjects are askedto give a preference rank order across a number of goods. For example, in arecent study Foster and Mourato (1997a, b) ask respondents to rank theirpreferences for various types of bread, one of which was produced viaconventional intensive farming methods while others were produced via‘green’ agriculture with lower levels of pesticide use. Each loaf wasdescribed in terms of various attributes including the price of a loaf andmeasures of the human health and environmental impacts of associatedpesticide use. By varying the price of these products and seeing how thisaffects respondents rank ordering the authors are able to infer a valuation forreductions in pesticide use and associated improvements in human health.

The SP method has most frequently been applied to the valuation ofrecreational assets or sites. Here, respondents, who may be interviewed on oroff site, are presented with descriptions of two or more sites each being

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characterised via a number of attributes of which one will be some monetaryanalogue (for example a travel cost or entrance fee6). Respondents are thenasked to choose which site they would prefer to visit. Analysts can then seehow respondents’ choices change as the site attributes and monetary amountsare varied and from this information infer the value placed upon eachattribute. Box 3-3 provides an example of how the SP method can be appliedand the wealth of information it can yield7.

SP models appear to offer three potential benefits over CV studies. First, theymore readily provide useful information regarding the attributes a respondentrates most highly in assessing a site (a factor which may aid in planning issues).Secondly, they appear to provide statistically stronger models of respondents’preferences. And thirdly, they are highly compatible with the revealedpreference, travel cost model.

Box 3-3: Applying the stated preference method: the value of recreational fishing

Here we refer to the work of Adamowicz et al. (1994) regarding the value of recreationalfishing given various rates of flow in the Highwood and Little Bow rivers in southwesternAlberta. However, as the scenario cards reproduced as Panel A indicate, a variety of attributesother than simply river flow were assessed.

Panel A: Example of a stated preference question as used in the Alberta experiment

Preamble: Suppose last August that you could have chosen only from the recreationalopportunities described below.

A. Standing Water B. Running Water C. Non-Water

Water Feature Natural Lake Stream

Terrain Mountain Foothills

Driving Distance 50km 50km

Fishing:

Types of fish available Pickerel, Pike & Rainbow Trout & Any other non-waterPerch Mtn Whitefish related recreational

Fish size Large Large activity or stay

Typical fishing success 1 fish every 35 mins 1 fish every 4 hrs at home.

Camping Facilities Designated Campsite Designated Campsite

Water Quality Good Good

Boating None None

Swimming Yes No

Beach No Yes

Day Use or Entry Fee

To Maintain Facilities None $6

6 Hence the links in Figure 4 between the SP method and both the CV and TC methods.7 This strength needs to be balanced against the methodological and analytical demands of the method.For further discussion see Adamowicz et al. (1999).

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1. If these recreational opportunities had been available to you last August, would youhave seriously considered visiting

Opportunity A � Yes � No

Opportunity B � Yes � No

2. Had the above opportunities been available last August, which one would you havemost likely chosen? (Check one and only one box)

� � �

A B C

Source: Adamowicz et al. (1994).

The full list of attributes together with regression coefficients for both standing and runningwater scenarios is given in Panel B (the essentials of regression analyses are explained inSection 4). The coefficients need to be interpreted with respect to the statistical definition ofattributes. However, generally a negative coefficient means that presence of the relevantattribute lowers the likelihood of a visit (vice versa for positive signs), while the absolute sizeof the coefficient indicates the impact of a unit change in the attribute. Therefore, for therunning water case, if only one species of fish is present (Mountain Whitefish) the site is lesspreferred to one with two species which is in turn less preferred to a site with three species.

Panel B: Attributes and relevant coefficients of the stated preference model

Attribute Description Standing Watera Running Watera

Terrainb Flat Prairie (= -1) versus -.367 -.415

Rolling Prairie (=1) -.071 -.100(.046) (.042)

Foothills (=1) .257 .125(.045) (.042)

Mountain (=1) .181 .390(.045) (.042)

Fish Size Large (=1) versus Small (= -1) .058 .090(.026) (.025)

Fish Catch Fish per unit time .062 .105(.028) (.026)

Fish Speciesb Mtn Whitefish (=1) versus n/a -.275

Rainbow Trout and Mtn Whitefish (=1) n/a .064(.043)

Rainbow Trout, Mtn Whitefish and Brown Trout (=1) n/a .107(.041)

Cutthroat Trout, Mtn Whitefish and Bull Trout (=1) n/a .103(.042)

Water Quality Good (=1) versus Bad (= -1) .394 .321(.027) (.025)

Facilitiesb None (= -1) versus -.353 -.277

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Day-Use Only (=1) -.200 -.109

(.046) (.043)

Limited Facility Campsite (=1) .305 .162(.045) (.042)

Fully Service Campsite (=1) .248 .225(.045) (.042)

Swimming Yes (=1) versus No (= -1) .274 .158(.026) (.025)

Beach Yes (=1) versus No (= -1) .198 .123(.026) (.024)

Distance Kilometres -.007 -.007(.0004) (.0004)

Notes: Standard errors given in the small italic parentheses.a Water Feature specific (running versus standing water) coefficients are estimated for most attributesexcept distance. n/a indicates not applicable and n/s indicates not significant and not included in themodel.b Attributes with multiple levels are coded using effects codes. The base level is assigned -1 for allcolumns representing the remaining levels. Each column contains a 1 for the level represented by thecolumn and a -1 for the base. The interpretation of these parameters is that base level takes the utility levelof the negative of the sum of the estimated coefficients and each other level takes the utility associatedwith the coefficient.

Source: Adamowicz et al. (1994).

Adamowicz et al. report resultant welfare measures in terms of $/trip which are thensubdivided into distance categories from the site (to allow comparison with a travel coststudy). These group means range from $4.29 to $8.06 per trip with a grand mean(unweighted) of $5.94/trip.

3.3.3 ‘Valuation’ approaches: Revealed preference methods

Revealed preference methods ascertain individuals’ valuations of environmentalassets by observing their purchases of market-priced goods that are necessary toenjoy the environmental good in question (e.g. purchasing petrol so that a daytrip to the country can be enjoyed). This information allows the analyst toestimate, through statistical procedures, the demand curve for the good inquestion and from this derive measures of CS for changes in provision of thegood.

3.3.3.1 The travel cost (TC) method

The TC method uses the costs incurred by individuals travelling to reach asite as a proxy for its recreation value, i.e. values are revealed fromindividuals’ purchases of marketed goods.

In effect, the travel costs incurred by an individual in visiting a site representthe vertical ‘price’ axis on a conventional demand curve diagram such as thatshown previously in Figure 2-1. These travel costs are composed of twoelements: the travel expenditure (petrol, fares, etc.), and the value of traveltime (for an introduction to the extensive literature in this area see Bateman,1993). These travel costs in part determine the number of visits (horizontal

3-17

axis, Figure 2-1). In effect, by surveying visitors to a site and asking them forinformation concerning their travel costs, the frequency of visits over a givenperiod and other determining factors (see Box 3-4), we can map out thedemand curve for the site. As with any good, the value of the site will beequal to the area under this demand curve.

The demand curve is derived from a ‘trip-generating function’ which simplyexplains the number of visits (V) as a function of a constant (α), the travelcost (TC) and any other relevant explanatory variables (X; a matrix of furthervariables, see Box 3-4 for an example) as shown in Equation 3-1.

V = a + b1 TC + b2X (3-1)

Here the coefficients b1 and b2 show the nature and strength of therelationship between the explanatory variables (TC and X respectively) andthe dependent variable (V). An example of such a trip generating function isprovided in Box 3-4 that concerns a TC study of the recreational value ofvisits to a UK forest.

Box 3-4: Applying the travel cost method: the value of recreational visits to a forest

During 1993 a sample of 351 visitor parties were interviewed as part of an on-site survey atLynford Stag, a recreational area within Thetford Forest, East Anglia. Information wasgathered concerning travel costs and various other factors liable to influence the frequency ofvisits. Travel costs were defined as the sum of travel expenditure (in turn defined as a per mileflat rate amount) and the value of travel time (defined as per many previous studies as being apercentage of visitors wage rate).

A trip generating function was estimated using maximum likelihood techniques, the bestfitting model (which allowed the value of time to be whatever percentage wage rate gave thebest statistical fit to the data) being detailed in Panel A:

Panel A: Trip generating function for visitors to Lynford Stag

ln(VISITS) = - 0.4853 - 0.2857 TC + 0.2643 HSIZE

(0.5923) (0.0883) (0.1994)

- 1.4729 HOLS + 1.7408 WORK + 2.277 LIVE

(0.5333) (0.4534) (0.3946)

+ 0.5050 SCENE - 0.4629 NT + 0.4416 TAX

(0.2417) (0.2370) (0.2465)

+ 0.6066 DOG

(0.2583)

Where:

VISITS = Number of party visits p.a.

TC = Travel Cost evaluated at 8p/mile and time cost at 2.5% of wage rate

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HSIZE = Household sizeHOLS = On holiday when interviewed (0-1)WORK = Works at forest (0-1)LIVE = Lives at forest (0-1)SCENE = Scenery rating (1-4)NT = National Trust member (0-1)TAX = Respondent is a taxpayer (0-1)DOG = Respondent’s main reason for visiting is dog walking (0-1)

Figures in small italicised brackets are standard errors

Source: Bateman et al. (1996) (provides further details regarding variable definitions).

The relationships described by the trip generating function accord with prior expectations.Respondents with larger household size (i.e. with children) visit the site more frequently (asshown by the positively signed coefficient on the HSIZE variable) than others. Similarlyhigher visit rates are associated with those who work or live nearby, rate the scenery of thesite highly, are taxpayers (an indicator of higher disposable incomes) or come to the forest towalk their dog. As expected those who were only in the area for a holiday visit the site lessover the year than others. Somewhat surprisingly those who were members of the NationalTrust also visited less although this may be a reflection of a wider recreational choice set forsuch respondents. However, as expected a strong negative relationship with travel cost wasobserved. The sign and size of the coefficient on TC indicate the nature of the underlyingdemand curve, integration of which yields an estimate of consumer surplus (see Bateman etal. (1996) for relevant formulae). These are detailed in the first row of the table given in PanelB (shown in bold) with consumer surplus per person per visit estimated at £1.32 (estimates ona per household and per annum basis are also given). However, the remaining rows of thetable illustrate the potential sensitivity of TC consumer surplus estimates to variedassumptions concerning the travel expenditure and travel time value elements of the travelcost variable. Increasing either of these can result in substantial inflation of consumer surplusestimates. These results underscore the need for careful analysis of TC data and theimportance of selecting the optimal model when calculating values.

Panel B: Thetford Forest TC Study: Consumer surplus (CS) estimates

Travel Expenditure Travel Time CS/household CS/household CS/person(pence/mile) (% of wage rate) per annum per visit per visit

(£) (£) (£)8p 2.5% 153.23 3.95 1.328p 0% 140.39 3.62 1.218p 43% 374.10 9.65 3.228p 100% 743.61 19.18 6.39

23p 0% 381.31 9.83 3.2823p 43% 570.56 14.71 4.9023p 100% 898.02 23.16 7.7223p 6% 402.83 10.39 3.46

Perceived1 0% 142.21 3.66 1.22Perceived1 43% 345.06 8.90 2.97

Note: bold type = best fitting model.

1. In this treatment respondents were asked to state their perceptions of travel expenditure

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3.3.3.2 The hedonic pricing (HP) method

The final valuation technique stems from the hedonic price schedule familiarto us from the discussion in Section 2. There it was shown that the price ofhouse is determined by its characteristics. Amongst these characteristics are anumber of environmental goods including landscape amenity, noise and airquality that we may wish to value.

The HP method requires a two-stage analysis. Stage one involves theestimation of the HP function. Statistical techniques are employed todiscover how variations in the prices commanded by houses are related to theamount of the environmental good enjoyed by the residents of thoseproperties. For example, suppose that we were interested in estimating thedisamenity value of noise from road traffic i.e. the value lost by residents of aproperty because of the noise resulting from road traffic. For the purposes ofexposition, it is possibly easier to present the analysis in terms of ‘peace andquiet’. Noise is, in effect, the lack of peace and quiet and, indeed, we canthink of noise and peace and quiet as opposites; a unit increase in noise isequivalent to a unit decrease in peace and quiet.

Our first-stage analysis would be to see how house price (Hp) variedaccording to the level of peace and quiet (PQ) experienced at each propertyas well as matrices of other relevant explanatory variables such as structuralcharacteristics (S; e.g. house size), neighbourhood characteristics (N; e.g.accessibility to workplaces), other environmental factors (E; e.g. pollutiondisamenity) and other relevant explanatory variables (X). To do this we needto estimate the HP function using the statistical function shown in Equation3-2.

Hp = α + b1PQ + b2S + b3N + b4E + b5X (3-2)

where

α = constant

b1 = coefficient on peace and quiet (PQ)

b2 = coefficient on structural characteristics (S)

b3 = coefficient on neighbourhood characteristics (N)

b4 = coefficient on other environmental variables (E)

b5 = coefficient on other explanatory variables (X)

The analyst is primarily interested in b1 the coefficient on the ‘focus’ variablePQ. Analysis of this equation indicates how the price of a house is influencedby the level of peace and quiet experienced at a property. We would envisagethat b1 would have a positive sign such that the greater the degree of peaceand quiet the higher the price of the house (Hp). Indeed we can interpret b1 asthe ‘price’ of peace and quiet; it is the amount that a household would haveto pay for a house that enjoyed one more unit of peace and quiet.

The second stage of the HP analysis is to estimate the demand curve for theenvironmental good. As discussed above, the demand curve relates the priceof a good to the quantity of that good chosen by a household. Therefore, tomap out the demand curve for the amenity value of peace and quiet, we relate

3-20

the level of peace and quiet (PQ) around houses to the price paid for (PB) andany other relevant variables (which we can denote as Z). This gives ourdemand curve Equation 3-3:

PQ = α + γ1PB + γ2Z (3-3)

where

α = constant

γ1 = coefficient on price paid for peace and quiet (PB)

γ2 = coefficient on other explanatory variables (Z)

Analysis of Equation 3-3 maps out the demand curve as the relationshipbetween the price of peace and quiet (PB) and its quantity (PQ) as shown bythe coefficient γ1. It is to be expected that γ1 < 0, revealing that thecharacteristic negative relationship between price and quantity demanded isas true for peace and quiet as for other goods.

Box 3-5: Applying the HP method: Valuing Air and Noise Pollution

The majority of HP studies have looked at the evaluation of environmental costs rather thanbenefits. A large number of these studies have examined either air or noise pollution in UScities, however such applications are sensitive to the extent to which individuals can perceivesuch costs. For example, gases such as carbon monoxide, whilst potentially very harmful(even lethal in exceptional circumstances) are imperceptible to individuals and can only bedetected using sensitive monitoring equipment. Furthermore, the occurrence of one airpollutant (or noise source) very often coincides with the incidence of others. This latter pointcreates problems for analysts attempting to distinguish one pollutant from another. A commonsolution to these problems is to focus upon a single pollutant but recognise that people’sperceptions may actually refer to the wider set of pollutants which occur with it. This factorshould be borne in mind when discussing HP results such as those for air pollution detailed inpanel A.

Panel A: HP valuations of air and noise pollution in US cities

City Study year Pollution % fall in propertyvalue per % increase

in pollution

St Louis 1960 Sulphation 0.06-0.101963 Particulates 0.12-0.14 Chicago 1964-67 Particulates and sulphation 0.20-0.50Washington 1970 Particulates 0.056-0.12

1967-68 Oxidants 0.01-0.02Toronto 1961 Sulphation 0.1Hamilton 1961-67Philadelphia 1960 Sulphation 0.1

1969 Particulates 0.12Pittsburg 1970 Dustfall and sulphation 0.09-0.15

1969

Los Angeles 1977-78 Particulates and oxidants 0.22

Source: Pearce and Markandya (1989)

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As we shall discuss in Section 5, the HP method has often been applied to thevaluation of environmental goods such as noise and air quality as reflected inlocal house prices (see, for example, Box 3-5). Wider applications (such as,for example, the valuation of the landscape impacts of landfill or wastefacility siting) are eminently feasible.

3.4 Valuation Techniques and the Project Requirements

As described in Section 1, new trunk roads generate a number of disamenities (e.g.increased air pollution, increased traffic noise, severance etc.). These disamenitiesare especially marked for those living in properties close by the new road. TheScottish Executive is responsible for compensating those affected by thesedisamenities but only in so much as they depress the market price of theirproperties.

Though we will discuss the issue in more detail in the next section, we can thinkabout the value of a house to its occupants as a monetary measure of the flow ofbenefits that they will enjoy from living in that property in that location. Amongstthese benefits are those that they derive from the environmental goods that will beaffected by the building of a new road. So, if the new road increases air pollution,the flow of benefits from clean air will be reduced. And, if the new road increasestraffic noise the flow of benefits from ‘peace and quiet’ will be reduced. Thus, wewould expect that for certain households, the building of a new road will reducethe value they enjoy from environmental goods by living in that property.

But Part 1 of the Land Compensation (Scotland) Act 1973 is not required tocompensate for changes in the value derived by households from living in aproperty but in changes in the market price of the property. How are the two likelyto differ?

In Section 2, we discussed the theoretical model that describes how the price of aproperty is set in the market. This theory underlies the Hedonic Price (HP)valuation technique discussed in the last section. The first stage of this techniqueestimates the HP schedule from which the implicit price schedule for any of theenvironmental goods that influence the price of a house can be derived. Forexample, imagine we had successfully executed the first stage of the HP valuationtechnique for the properties in a particular market. From this we could derive theimplicit price schedule for an environmental good, say ‘peace and quiet’. We couldgraph this implicit price function and it may look something like that shown inFigure 3-4. Stage 2 of the HP valuation technique uses the information from thefirst stage to estimate the individual demand curve for the same environmentalgood. If we took the demand curve for a particular house affected by the new roadwe could draw this on the same graph as the implicit price function illustrated inFigure 3-4. Before the road was built this household experienced q1 units of peaceand quiet. Following the construction of the road, increased noise pollution fromtraffic reduces this to q2.

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Figure 3-4: Changes in value and price from a fall in environmental quality

Though it might not be immediately obvious to those unfamiliar with graphs thatchart marginal curves, we can measure the reduction in the price of the house bythe lightly shaded area marked A in the diagram. To explain why this is, imaginethat we were at the initial level of peace and quiet, q1. If noise pollution was toincrease marginally, the level of peace and quiet enjoyed by the property would fallby one unit. By reading off the implicit price function at this new lower level wecould see by how much the market price of the house would decline for a one unitreduction in peace and quiet. If peace and quiet were to be reduced by another unit,we could again use the implicit price function to calculate how much this secondreduction would wipe off the price of the house. By adding these two reductions inprice together we would have calculated the fall in the price of the property for atwo unit decrease in peace and quiet. If we repeated this procedure for all the unitsbetween q1 and q2 we would be calculating the area marked A and in effect besumming up the total reduction in the price of the house resulting from the increasein noise pollution.

The change in value realised by the owner of the property can be measured as thearea under the demand curve, as we showed at the beginning of this section (Figure3-2). From Figure 3-4, this amounts to the entire shaded area. Clearly, the loss invalue to the property owner from the reduction in peace and quiet is greater thanthe fall in the price of the property, by the amount represented by the darkly shadedarea labelled B.

0 Quantity ofCharacteristic z1

ImplicitPrice of z1

(P’)

Implicit Price ScheduleP’(z1, z2

*, …, zn*)

q2 q1

Marginal WTP /Demand Schedule

for Household i

A

B

3-23

Some of the techniques described in this section are inappropriate for measuringeither the change in price of a house or the change in value realised by a householdwhen the construction of a new road alters local environmental conditions. For astart, it has been shown that the various ‘pricing’ techniques generally lack a solidtheoretical basis and, where alternative ‘valuation’ techniques exist, should beavoided.

At the same time, not all these ‘valuation’ techniques are themselves appropriate.The Travel Cost technique, for example, is only applicable when householdsexpress their preferences for an environmental good through the distance they areprepared to travel and the number of trips they are prepared to take to enjoy itsbenefits. Clearly, we cannot think in these terms for the environmental conditionsexperienced in and around the home.

As a result, only the HP valuation technique and the expressed preference methods,such as contingent valuation, would seem suited to addressing this issue. Whilstexpressed preference valuation techniques are specifically designed to estimatechanges in welfare (i.e. value) they are not appropriate for the measurement ofchanges in market prices.

Whilst most economic analyses are interested in measuring the welfare implicationof some changes, in this case the construction of a new road, the LandCompensation (Scotland) Act 1973 clearly specifies that compensation is onlypayable in respect of the change in property price induced by the new road. Onecan think of instances where a given individual who was particularly sensitive tonoise might suffer a considerable welfare loss from the new road. However, if thiswas not reflected in a property price loss then no compensation would be payable.A more empirical objection to survey based expressed preference methods in thiscontext is the difficult of accurately conveying (and respondents accuratelyperceiving) the true nature of long term noise level changes within a survey setting.For these reasons such approaches were not considered to be straightforwardlyapplicable to the present study.

Thus, it is only the HP valuation technique that provides an approach suited tomeasuring the change in property price that would result from environmentaldisamenities created by a new road. Of course, if we were also interested inchanges in value experienced by property owners from these same disamenities,then a number of approaches could be used. We could implement the second stageof the HP valuation technique to estimate a demand curve and use this to derivemeasures of value change. Alternatively, the expressed preference methods couldbe employed be directly employed to elicit these values.

The suitability of the HP valuation technique for the task of estimating changes inthe price of properties that might result from environmental disamenities caused bya new road, means that it is worth looking in more detail at how this approach isimplemented. As a consequence this will be the subject of the next section.

3.5 Summary and Conclusions

In this section we have examined how estimating the changes in CS enjoyed bymembers of society from the adoption of different policy options can guidedecision makers in choosing projects which provide the most value to society. It

3-24

was shown that changes in CS can be estimated for both market and, withsomewhat more difficulty, non-market (environmental) goods.

We went on to introduce a number of techniques that have been employed to putmoney values to changes in the provision of environmental goods. Some of thesetechniques estimate the value of changes (“valuation” techniques), others estimatesome measure of the cost of changes (“pricing” techniques). In general, the“pricing” techniques, whilst providing some indication of the cost of losing (orpossibly gaining) environmental amenities, do not have a sound theoretical basisand where possible “valuation” techniques should be preferred.

Whilst expressed preference valuation techniques are specifically designed toestimate changes in value they are not appropriate for the measurement of changesin market prices. Since it is changes in the price of properties afflicted by road-caused environmental disamenities that is of interest in this project, only one ofthese techniques is entirely appropriate; the HP valuation technique. The HPvaluation technique is, therefore, discussed in detail in the next section.

4-1

4 THE HEDONIC TECHNIQUE

4.1 Introduction

In the last section, we reviewed the diversity of techniques that have beendeveloped to price and value environmental assets. The discussion concluded thatonly two methods, the hedonic approach and the hypothetical market (and otherexpressed preference methods) approach, were in most respects suitable techniquesfor assessing the impacts on housing prices and household values of the seven‘physical factors’ stipulated in Part 1 of the Land Compensation (Scotland) Act1973. Of these two approaches, the hedonic valuation approach would appearpreferable for this research since it is the only technique designed specifically tomeasure the impact of changes in quality characteristics of a property on its marketprice. In this section, we provide a more detailed account of the hedonic approach,describing the underlying theory, highlighting its assumptions and shortcomings,and defining areas that will need attention in the proposed application.

4.2 Hedonic Prices Revisited

In Section 2, we established that we could describe any particular property by thequalities or characteristics of its structure, environs and location. A succinct meansof denoting this is as a vector of values; effectively a list of the different quantitiesof each characteristic of the property. In general, therefore, any house could bedescribed by the vector,

z = (z1, z2, …, zn), (4-1)

where zi (i = 1 to n) is the level or amount of any one of the many characteristicsdescribing a property.

Also, it was suggested in Section 2 that the price of a house (which we shall denoteP) was determined by the particular combination of characteristics it displays, sothat properties possessing larger quantities of good qualities command higherprices and those with larger quantities of bad qualities command lower prices.Again we can use a succinct piece of notation to illustrate this point;

P = P(z) (4-2)

Equation 2-2 can be read as; the price of a property is determined by or ‘is afunction of’ the vector of values describing its characteristics. This function is thehedonic price function. We can illustrate the hedonic price function in a graph,which shows how the quantity of any one characteristic (e.g. characteristic z1),effects the total price of the house given that all the other characteristics of thehouse don’t change (e.g. z2, z3, z4,…, zn are constant1). The hedonic price schedulefor characteristic z1 is illustrated in Figure 4-1.

1 By convention an asterisk is used to show that a characteristic remains constant whilst we concentrateon the effect of one particular quality on the price of a house e.g. z2*, z3*, z4*,…, zn* are unchanging asthe focus characteristic changes.

4-2

Figure 4-1: The Hedonic Price Schedule

The first stage of the hedonic price valuation technique is to estimate the hedonicprice function from data on the selling prices of a large number of properties in thesame housing market. Using a statistical technique known as regression analysis itis possible to tease out the relationship between the level of any one housingcharacteristic (or variable in statistical parlance) and the price of the property.However, before we discuss the empirical process of estimating the hedonic pricefunction, let us spend a little more time considering the theoretical underpinningsof the hedonic price schedule.

The Processes determining the Hedonic Price Schedule

Imagine a hypothetical housing market that consists of a large number ofhouseholds with different characteristics (e.g. number of members, income,children etc.), occupying a large number of properties that exhibit a wide variety ofdifferent characteristics. Imagine also, for now, that the households are distributedrandomly amongst the houses. In this case we would imagine that many of thehouseholds would not be entirely content with the property that they presentlyoccupy. Indeed a number of households would probably want to move house, sincethe flow of well-being that comes from the characteristics of the property theycurrently inhabit is not as high as that which would be provided by living inanother property.

Households assess the value they might realise from the different propertiesavailable in the market and bid amongst each other. If one household has theability (i.e. available income) and is prepared to offer more for a property than thevalue that the present residents realise from that property, then it will make sensefor a bid to be accepted. So begins a process of exchange in which households seekout their value-maximising residential choice (i.e. the house they are happiest withgiven their present income constraint). As the market settles, the prices of thedifferent properties become defined by the amount households are willing to pay tomove to a certain property and the willingness of incumbent households to acceptbids from other households for their properties. We would imagine that eventually

0Quantity of

Characteristic z1

Price ofProperty

Hedonic Price ScheduleP(z1, z2

*, …, zn*)

4-3

the market settles on a set of prices that define an equilibrium; at these prices eachhousehold is unable to increase the value they get from their property by moving toanother location. The prices attained when this equilibrium is reached will justclear the market given the existing stock of housing and its characteristics, andthese prices define the hedonic price schedule.

Changes in the conditions of demand (e.g. new households migrate into the marketarea) or supply (e.g. a new housing estate is built), will cause the market to adjustas households reassess their optimal residential location and a new schedule ofprices will result.

Obviously, this is a relatively simplified model of the interactions that constitute ahousing market and the establishment of a set of ruling prices for properties andimplicit prices for their characteristics. The realities of the housing market may besomewhat more complex and we shall discuss these realities and their implicationsin the next section. However, the simple model provides a useful basis from whichto examine some of the features of the market that are of interest to us.

Stability of the Hedonic Price Schedule over Space

The characteristics of the housing stock and of the households, the two keycomponents of the housing market, will differ, sometimes greatly, from market tomarket. Thus the model would suggest that the set of prices that define the hedonicprice schedule will be unique to a particular housing market. Indeed, a number ofeconomic analysts have placed the housing market in a wider context of the‘desirability’ of a particular area. If a particular area represents a desirable place tolive and work (e.g. if a region happens to have greater employment opportunitiesor a more favourable climate) it will attract households from less desirable citiesand regions. The decision to move to a given area is also a decision to purchaseland and housing. In-migration pushes up the demand for land and housing andincreases the price commanded by property. The market again adjusts. In the end aprocess of changing prices and expansion of the housing stock will allow themarket to come back into equilibrium. We should not be surprised, therefore, tofind the equilibrium Hedonic Price Schedule for two different housing marketsindicating two very different absolute prices for almost identical properties. Thiswould be expected by anyone familiar with the differences in house prices betweendifferent urban areas even within the same country.

Of course, this does not mean that the implicit prices that underlie this function arenot ‘relatively’ similar. Hence, it might not seem unreasonable to assume that inmarkets where the prices of properties are generally high that the implicit price of acharacteristic will also be high, whilst in markets where property prices aregenerally low the implicit price of the same characteristic is also low. Indeed, aswe shall discuss in Section 4, researchers, when comparing the results fromhedonic pricing studies, measure implicit prices in terms of the proportion of theprice of the average property in the market that can be attributed to certaincharacteristics. If this figure is shown to be relatively similar across all studies, itwould provide a means by which results could be transferred to markets whereindependent studies have not been carried out.

Stability of the Hedonic Price Schedule over Time

The simple model also suggests that house prices will go through a process ofreadjustment when conditions change. For example, the construction of a new road

4-4

will result in several impacts on the residents of local properties. Clearly, the initialperiod of road building will cause considerable disruption to local residents.Existing roads may have to be closed, construction vehicles, machinery and thebuilding work itself will cause pollution from noise, dust and fumes and all thesewill reduce the flow of benefits that residents enjoy from the location of theirproperty. Once the new road is completed, nearby residences may continue tosuffer from increased levels of noise and air pollution from higher levels of traffic.Of course not all the impacts of the new road will be detrimental to these localresidents. It is likely that they will derive some benefits from greater accessibility,reducing their journey times to certain locations. How then would our modelpredict house prices will respond to these changes in the local environment? First,we should perhaps look in a little more detail at how residents derive value fromthe property they inhabit.

As we have already established, a property provides well-being to a householdthrough its different qualities. The number of bedrooms it has, the size of thegarden, the proximity of a local school and so on, all confer more or less well-being on a household according to the household’s particular characteristics. Ofcourse we would include within these qualities, the amount of peace and quiet, thequality of air, and other characteristics of the local environment that might beaffected by road traffic. If we imagine a household considering purchasing aparticular property then we can envisage their maximum WTP for the house wouldbe the value of the flow of services they would receive from these qualities overthe years. In other words, the value of the house to a household is not just thebenefits they would get from living in the house now but also the benefits theyforesee from living in that house for all the years to come. We could represent thisrelationship as;

( ) ( ) ( ) ( ) ttt vvvV δδδ zzzzzz +++= ...,...,, 221121 (4-3)

Where V(.) is a function that gives the total value of the flow of benefitsthat the household foresees it will derive from living in aparticular property over the years

zt is the vector of characteristics of the property in period t

v(zt) is a function that gives the value of the benefits that thehousehold believes it will derive from living in the property inperiod t

δt is the discount factor in period t

The use of a discount factor possibly needs some more explanation. Put simply thediscount factor is a measure of how heavily a household weights benefits derivedin any time period. It is usually assumed that households, when viewing from thepresent day a flow of benefits stretching off in to the future, will weight benefitsthat they enjoy in the present time period greater than those they might enjoy in thenext time period. In the same way, they will weight benefits they hope to enjoy inthe next time period greater than those that might accrue in the time period afterthat and so on for all subsequent time periods. Indeed, we would normally envisagethe discount factor (δt) to progressively decline in value as later and later timeperiods are given less and less weight in the household’s calculation of the totalvalue from living in a property.

4-5

Equation 4-3 simply states that the total value that a household gets from living ina particular house will be the sum of the values it envisages getting from living inthat property for all the years to come. The values in each time period will dependon the anticipated characteristics of the house and its environs in that time period,and the household weights benefits or values that accrue in earlier time periodsgreater than those that accrue in later time periods.

Let us return to our new road construction example and trace through how ahousehold’s valuation of an affected property might change over time. In simpleterms we could imagine that the present time period and each of the future timeperiods is characterised by one of three states:

• Before construction of the new road the household enjoys the current set ofenvironmental conditions and the accessibility provided by the present roadnetwork. Let us label the value realised in every time period before roadconstruction begins £a.

• During construction of the new road the household is likely to experience aperiod in which both environmental conditions deteriorate and accessibility isdisrupted as a result of the building work and its associated activities. Let uslabel the value realised in every time period during road construction as £b.

• After construction of the new road the household will possibly experiencegreater noise and air pollution from increased traffic whilst at the same timeenjoying the increased accessibility afforded by the new road. Let us label thevalue realised in every time period after the road has been built as £c.

As we have already ascertained, the total value of a property to a household at anypoint in time will be equal to the discounted flow of the benefits it foreseesenjoying from living in that location. Thus, at a point in time before the roadproject is announced, the household expects to enjoy a value of £a from living inthe house in the present time period and in all future time periods. The discountedsum of this flow of values gives us their total valuation of the property i.e. themaximum they would be WTP to live in that house. We can call this amount £A. Inthe same manner, if we moved forward in time until after the road had been built,the household would expect to enjoy a value of £c in each of the time periods andwe can label the discounted flow of these values as £C.

Clearly over time, the household’s valuation of the property will begin at £A andend up at £B. But, how will the time path of this valuation move between these twosteady states? Well, this will depend on the relative values of £a, £b and £c. Twopossibilities are illustrated in Figure 4-2 which plots the household’s total valuationof the property at each point in time.

In Scenario 1, £a is greater than £c and hence the total value of the house beforethe road is built (£A) will be greater than the total value after the road is built (£C1).Such a scenario would reflect a situation in which the disamenities of the new road(e.g. increased noise and air pollution, obstructed views) outweigh the amenities(e.g. increased accessibility). In Scenario 2 the reverse is true; £a is less than £cand in this case £A is less than £C2. In both scenarios the assumption has beenmade that in a time period during the actual construction, the value realised fromthe property by the household is at its lowest (i.e. £b is less than both £a and £c).Though this is not necessarily true in the real world, it is representative of a

4-6

situation in which the household experiences considerable disruption and pollutionfrom the actual process of construction.

Figure 4-2: Time path of the value of a house to a particular household

Figure 4-2 illustrates how the time path of the value of a property to a household islikely to go through five distinct stages;

• Stage I: Before the road project is announced the household values benefitsfrom the property in all future time periods at £a and the total value of thehouse to them will be steady at £A.

• Stage II: The announcement of the road project brings about an immediatechange in the value of the house. The household foresees that for the next fewtime periods it will continue to enjoy £a worth of value from the property.However, it also knows that in the future it will have to suffer a period ofdisruption and pollution caused by the road’s construction and that the value itwill realise from the property in these time periods will fall from £a to £b.Since these reductions will occur in the near future, it weights them relativelyhighly. Further the household foresees future changes in the flows of benefitsfrom the property once the road has been completed and it values these at £cfor each time period after construction. In the household’s recalculation of thetotal value it gets from the property these are weighted least heavily since theywill only be experienced in the relatively distant future.

As commencement of construction approaches the value of the property to thehousehold falls as the periods of reduced benefits get closer and become more

Road ProjectAnnounced Construction

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heavily weighted in the household’s calculation of total value. Notice how thefall in value of the house is less steep in Scenario 2 than it is in Scenario 1despite the fact that we have assumed that the household would enjoy the samedisbenefits from construction in both scenarios. The reason for this is that inScenario 2 the greater future benefits of the new road partially compensate thehousehold for the disbenefits experienced during construction. Stage IIrepresents what is known as Planning Blight a period in which no actualconstruction is undertaken but the value of the property to the household isfalling.

• Stage III: The household’s value from the property reaches its nadir at the pointwhen construction begins and the period of maximum disturbance anduncertainty concerning future disruption is most immediate. Over the next fewtime periods it will only enjoy benefits valued at £b per period but in the futureit foresees these increasing again to the post construction level of £c. In ourhypothetical example the value of the property to the household begins toincrease as construction continues and the period of foreseen disruptionlessens. We might term this stage a period of Construction Blight.

• Stage IV: Once construction is completed the household may still be uncertainabout the true impact of the road and its traffic on the benefits they derive fromliving in that property. It may take some time for the household’s valuation ofthe property to settle again at a new stable level. The two time paths illustratedin Figure 4-2 reflect a situation where the households initially underestimatesthe benefits and/or overestimates the disbenefits of the new road.

• Stage V: Finally, the value of the house recovers to its new steady state of £Cwhere the household values the benefits from every future time period at £c. InScenario 1 this is an amount less than the value of the property to thehousehold prior to the road being built, in Scenario 2 it is greater.

It is important to remember that this is the valuation of one particular household.For other households the changes in their valuation of a property may follow adifferent time path. Given the uncertainties regarding the dynamic equilibratingprocess outlined above, and their importance to the determination of appropriatecompensation levels, we highlight this as a major focus deserving future research.

How then does the housing market adjust to these changes in the characteristics ofcertain properties? Let us begin by assuming that the housing market is initially inequilibrium and all households have selected the property that provides them withthe greatest possible value. Our model predicts that the construction of the newroad will disrupt this equilibrium and residents of properties affected by the newroad will find themselves in a less than optimal situation. In most cases they willfind that the value they derive from environmental amenities such as peace andquiet, clean air and unobstructed views will be reduced2, whilst that from theaccessibility of the property may increase. There are a number of responses that thehousehold could take. For a start it may implement what are known as defensive oraversive measures. Appropriate defensive measures might include investing indouble-glazing to reduce noise pollution in the house, or purchasing air purifiers toreduce pollution from car fumes.

2 Of course the new road may be a bypass that takes traffic away from a household’s property, in whichcase we would get the reverse effect.

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Alternatively the household may decide to sell the property. Of course, initially,the dip in value of the house caused by planning and construction blight may makeit difficult to find bidders for the property. However, following this period wewould envisage that other households will make offers for the property. We wouldexpect these households to be more noise and pollution tolerant than the presentinhabitants and possibly gain greater benefits from the increased accessibility ofthe properties.

An example is shown in Figure 4-3 where the construction of a new road hascaused a decrease in the quantity of characteristic z1 of the property from q1 to q2.The present residents of the property, household i, are no longer at their optimum.Indeed, at the new lower level, q2, household i is willing to pay the shaded area toincrease the level of the characteristic. It is possible that the household will expressthis by undertaking defensive expenditures (e.g. installing double glazing) of up tothe shaded area in cost to increase the provision of characteristic z1 back to q1.

Figure 4-3: Changes in value and price from a fall in environmental quality

If, on the other hand, household i, decides to sell the house then the property willbe purchased by a household such as household j. Household j is more tolerant oflow levels of characteristic z1, such that its demand curve for that characteristic islower than household i’s3. Indeed, we would expect household j to have a demand

3 For example, environmental qualities such as peace and quiet are often considered luxury goods.Households with higher incomes may well be WTP considerably more for peace and quiet thanhouseholds with lower incomes.

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curve that intercepted the implicit price schedule for characteristic z1 at exactly thenew level of provision (q2).

A similar story could be told for all the characteristics of the house that changefollowing the construction of the new road. It is clearly possible that for somequality characteristics, such as accessibility, the value provided by the location ofthe house will increase. In this case we would expect the present residents of thehousehold to be over-provided with this characteristic. It would make sense forthem to sell the house and cash in on the willingness of other households to pay forhigher levels of accessibility.

Some empirical evidence exists to support this description of how property pricesadjust to changes in environmental quality. Crawley (1973) presented an analysisof the adjustment of land values in response to the expansion of an airport. Hisevidence suggested that residential land values fell during periods of substantialchange, but that after the change they increased to approximately their previouslyestablished long-run trend. He explained the reason behind this phenomena, sayingthat during a “shock” period noise-avoiders sell their residential property, drivingdown the price; noise-indifferent people move in and some land is shifted to otheruses, thus in turn bidding up the price.

One further issue we should consider is the overall change in price of the house.We have shown that for certain characteristics, the part of the total price of thehouse that is paid for those characteristics will change following the constructionof a new road. For some characteristics these changes will give rise to a fall inprice, for others they will result in a rise in price. The overall impact will dependon the relative size of these individual characteristic price changes.

It seems reasonable to suggest that increases in noise and air pollution, issues ofseverance, and the obstruction of views will undoubtedly reduce the overall priceof the property. On the other hand this may be offset by changes in othercharacteristics, notably the accessibility of the property. This is an important issueas far as this piece of research is concerned. Part 1 of the Land Compensation(Scotland) Act 1973 stipulates that only decrements in the market price of thehouse caused by the seven physical factors will be compensated and that finalcompensation must be net of any increases in the worth of the property broughtabout by beneficial aspects of the new road.

In summary, changes in the characteristics of a small part of the housing stock willchange the value derived from these properties by their current inhabitants. Thiswill almost certainly be the case for houses in proximity to the course of aproposed new road. In the short term this might manifest itself in a period of blightin which the value of the flow of benefits from living in an affected property arereduced due to the disruption and pollution associated with construction activitiesand uncertainty concerning the impacts of the new road.

In the longer term, it is possible that affected households will no longer findthemselves at their optimal location and may decide to sell their property. Theselling price of the house will be determined by the hedonic price schedule4. Thepurchasing household will have greater tolerance of characteristics that have

4 If the characteristics of a sufficiently large section of the housing stock were to change we wouldexpect large scale readjustments in the housing market. Such large scale changes will result in a shift inthe hedonic price schedule. This would seem unlikely for a road building project.

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deteriorated as a result of the new road and higher demand for characteristics thathave improved.

4.3 Theoretical Validity of Hedonic Pricing

The hedonic model of the housing market, outlined in the previous section, formsthe basis of procedures designed to estimate the impact of the construction of anew road on the prices of properties. In short, data on the prices of properties areused to estimate the hedonic price function. In turn the hedonic price function isused to calculate the change in price of a property that results from alterations inthe characteristics of a property brought about by a new road.

It should be clear to most readers that the hedonic price model is a simplification ofthe processes that take place in the real world. Since this model forms thefoundation of our estimation procedure, it is important that the assumptions thatunderpin the model (and allow it to abstract reality) are highlighted and theirvalidity assessed.

In this section we take a brief look at these assumptions and draw conclusions onwhere and when the hedonic price model is a reasonable approximation ofconditions in the housing market and, therefore, where it will be appropriate toderive the hedonic price schedule from data on house prices in real housingmarkets.

4.3.1 Definition of the Housing Market

We have already established that each housing market is likely to have a uniquehedonic price schedule determined according to the particular characteristics ofthe households and housing stock that make up that market. One of our primaryconcerns in collecting price data, therefore, will be to ensure that we are lookingat just one market.

Hedonic studies have varied considerably in the geographical area they haveconsidered as one market. Some researchers have deemed it suitable to use datafrom house prices for an entire nation whilst, at the other extreme, some havefocused on areas no bigger than a single census tract. Obviously it is possible tomake one of two errors in collecting data for a hedonic study;

• Data is collected on house prices that come from one or more differenthousing markets

• Data is collected from only a small portion of an entire housing market

In the first case, we risk seriously biasing our estimates of the hedonic pricefunction. House prices from two different markets may follow very differentprice schedules. If we use data from both these markets, we will end upestimating a hedonic price function that is a poor reflection of both of the twounderlying price schedules. Using this single, biased estimate of the hedonicprice function to calculate changes in the prices of properties following changesin their characteristics will lead to inaccurate and misleading results.

In the second case where data is collected from only a small portion of thehousing market, there is a good chance that our estimates of the hedonic pricefunction will be imprecise. That is, data on only part of the market is unlikely to

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provide information on all the possible combinations and extremes of housingcharacteristics. Under such conditions, it will be difficult to define the true pathof the hedonic price schedule with accuracy.

Under what circumstances, then, would we expect housing markets to bedivided or, to use the technical term, segmented? Palmquist (1991) suggests thatsome form of barrier, be it geographical, or the result of discrimination or lackof information, must exist to prevent purchasers in one market participating inanother.

Fortunately, it is possible to use statistical techniques to test for segmentation.Put simply, if it is suspected that the house prices used in a hedonic study maycome from a segmented market then rather than estimating one hedonic pricefunction, researchers can estimate a separate function for each suspected marketsegment. It is then possible to test whether the separate functions are sufficientlysimilar to count as one market or whether they are significantly different andshould be treated separately.

Evidence from the hedonic literature using this sort of test has returnedambivalent results. For example, Butler (1980) tested to see whether a nationalhousing market existed by comparing data from 36 cities. Though he concludedthat the market in the sale and purchase of houses could not be considered asingle market, he found it impossible to reject the possibility that the houserental market was a single national market. Similarly, Smith and Huang (1995)surveyed hedonic pricing studies carried out between 1967 and 1988 andconcluded that the estimated hedonic price functions varied across cities due todifferences in local conditions. Other researchers have investigated thepossibility that segmentation exists in the housing market within a single urbanarea. Straszheim (1974), for example, found that geographical segmentation wasa feature of the housing market in San Francisco. On the other hand, Ball andKirwan (1977) found that clusters of different housing types in the Bristol areadid not result in separate submarkets with different hedonic prices.

Market segmentation may be a serious problem if data from more than onehousing market are used to estimate the hedonic price function. Certainly, itwould seem reasonable to conclude that there is a good chance that marketsegmentation exists between cities when the costs of gathering information onproperties in other cities and the costs of moving between cities raise significantgeographical barriers. Segmentation may still exist within a single urban areaand researchers using data from just one city should test for separate submarketsdefined by features such as geographical area, type of structure (e.g. flatscompared to houses) or household income.

4.3.2 Time and Market Equilibrium

Data for hedonic studies may come in one of a number of forms. One of themost common forms of hedonic datasets consists of a snapshot of house pricesat a point in time. In statistical terminology this is known as a cross-sectionaldataset. Alternatively, a dataset may comprise details of the sale prices of

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houses over a series of years. Though not conforming to the strictest statisticaldefinition, we could label this sort of data a panel dataset5.

Imagine for now that we are using a cross-sectional dataset to estimate thehedonic price function. That is we have a dataset that consists of information onthe prices of properties in a market at a particular point in time. Will it bepossible to define an accurate description of the hedonic price function fromdata of this type?

One of the fundamental assumptions of the hedonic pricing model is that thehousing market is in a state of equilibrium. That is, the model assumes that themarket settles on a set of prices at which each household is unable to increasethe value they get from their property by moving to another location. If thehousing market is in a state of equilibrium then house prices from a cross-sectional dataset should give an accurate basis from which to measure thehedonic price function. However, there are a number of reasons why we mightnot expect every cross-sectional dataset to represent a hedonic priceequilibrium. Following Freeman (1993), three key conditions must be fulfilledfor the market to be in constant equilibrium;

1. Households have perfect information. In other words, households areassumed to know the characteristics and prices of all the properties in themarket. If households are not aware of the prices and characteristics of allproperties then it is likely that the implicit price they pay for differentcharacteristics will vary from sale to sale.

It is much like walking into the first electronics shop on the high street andbuying the television in their range which best suits your purposes. If youare lucky you will have purchased a bargain, though on the other hand youmight walk a hundred yards up the road and find a shop selling the samemodel at a cheaper price. If purchasers do not have full information on theprice and characteristics of the products on sale in a market then a variety ofprices can co-exist for the same product.

Without perfect information it is likely that the hedonic price function willbe ill-defined or, to use economic terminology, can only be estimated withlarge variance.

2. Transaction costs are zero. Transaction costs are the expenses, on top of theprice of the property that the household incurs when moving house.Transaction costs in the property market are varied and not inconsiderable,consisting of items such as the time spent searching for properties, expenseson lawyers and surveyors, taxes and the costs of moving possessions fromone property to another.

Again, transaction costs may prevent the market from reaching equilibrium.Given the prevailing market prices, a household may want to live in aproperty with a different set of characteristics than their current residence.However, if the transaction costs are sufficiently high, they may negate thebenefits of moving. The household will stay where it is and the housingmarket will remain out of equilibrium.

5 More correctly a panel dataset would consist of observations of the prices commanded by a particularset of properties over a series of years.

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Once again, transaction costs will lead to the hedonic price schedule beingill-defined. Households may be content to reside in properties for whichtheir marginal WTP for certain characteristics diverges from the implicitprice of that characteristic.

3. The hedonic price schedule adjusts instantaneously to changes in demand orsupply conditions in the housing market. For the market to reachequilibrium, changes in the supply or demand for properties will be reflectedin changes in the hedonic price schedule as households seek out their valuemaximising residential location under the new conditions. Of course in thereal world, many issues including imperfect information and transactioncosts will result in this process of adjustment taking some time. During theperiod of readjustment, we might expect some variation in the implicitprices that are paid for characteristics of properties.

As before, if the property prices used in a cross-sectional hedonic analysisare taken from house sales during a period of readjustment in the market, wewould expect this to be reflected in greater variance in the estimate of thehedonic price function.

Clearly, it is unlikely that a housing market will be in a state of perfectequilibrium at any one point in time. In general, however, this will not be amajor shortcoming of the hedonic price method. In a state of disequilibrium wewould expect the prices paid for properties to sometimes be higher andsometimes be lower than the prices that would define a perfect equilibriumhedonic price schedule.

Thus we can still estimate the underlying hedonic price schedule from a marketin disequilibrium since on average the high and low prices in the data will tendto cancel each other out. Our estimate of the hedonic price schedule may not beas accurate as if data were taken from a market in equilibrium but it will not bebiased. We shall discuss in more detail issues of accuracy and bias in the nextsection.

The question is more vexed for “panel” datasets, that is, datasets that consist ofhouse prices aggregated from over a series of years. It is possible that over aperiod of years, changes in the conditions of supply or demand in the marketwill result in shifts in the hedonic price function. In other words, a panel datasetmay consist of house prices taken from temporally distinct housing marketswith different hedonic price schedules. The same problems will compoundestimation of the hedonic price schedule from data of this type as werehighlighted for data taken from geographically distinct markets in the previoussection. In short, estimation of the present hedonic price schedule will be biasedby inclusion of house price data from previous periods in which a different setof equilibrium hedonic prices ruled.

Again, it is possible to test for temporal separation of markets using statisticaltechniques. We can simply compare the hedonic price schedule estimated forone period with the hedonic price schedule estimated for another period. If theyare not significantly different then it is reasonable to assume that the conditionsof supply and demand in the housing market have changed little and that thehedonic price schedule has remained stable over time.

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Evidence from the hedonic literature suggests that temporal separation ofmarkets may be a problem. Edmonds (1985) found that the hedonic pricefunctions estimated from two separate Japanese datasets from 1970 and 1975were distinctly different. Palmquist (1980) found that the proposition that adataset covering 13 years of house prices in Washington in the USA,represented information on one equilibrium hedonic price schedule wasunacceptable. However, when adjacent pairs of years were used, the hedonicprice schedule appeared to be reasonably stable.

Overall, it would seem wise to regard the aggregation of data over time withsome caution. If the market has not been subject to any significant shocksduring the period, aggregation may be defensible and statistical techniques canbe used to test this hypothesis.

4.3.3 Environmental Quality in the Hedonic Price Model

The primary goal of this project is to ascertain the extent to which changes inthe environmental amenities enjoyed by a property, following the constructionof a new road, will influence its price. The (not unreasonable) assumption is thatincreases in noise and air pollution will be perceived by households asdisamenities and increasing levels of these disamenities will depress the price aproperty commands in the market.

One issue that concerned some early research using hedonic pricing methodswas whether or not households perceived environmental quality. If householdswere not aware of differences in the environmental characteristics betweenproperties, it would be highly improbable that the level of environmental qualitywould be capitalised into the selling price of a house. As we shall show in somedetail in the next section, a weight of evidence has amassed to suggest that thisis not so and that environmental quality is an identifiable quality component inthe hedonic price function.

A further theoretical concern is that there is insufficient variation inenvironmental quality across the properties in a market. To attain perfectequilibrium in the market, the theory underlying the model assumes thatproperties exhibiting all possible combinations of housing characteristics areavailable in the housing market. This is necessary for households to be able tolocate at a position of simultaneous equilibrium with respect to allcharacteristics (i.e. to find a property at which the quantity of each and everycharacteristic is such that their marginal WTP for each characteristic is justequal to its implicit price). However, the range of alternative housing types islikely to be fairly limited.

An example of this problem was provided by Harrison and Rubinfeld (1978).Having estimated the hedonic price function for housing in the Boston StandardMetropolitan Statistical Area, they were interested in calculating the marginalimplicit price of air pollution (specifically nitrogen oxide levels). Contrary toexpectation they discovered that high income households were locating inregions of high air pollution. One possible explanation of this phenomenon isthat some high-income households wished to locate in properties that providedboth low levels of pollution and high levels of another attribute (e.g. ease ofaccess to the cultural amenities found near the city centre). However, no

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properties satisfied both of these requirements, to the extent that thesehouseholds had to compromise on the level of air pollution to maximise thebenefits realised from other characteristics of the property.

The impact of these “gaps” in the characteristics of the available set ofproperties on our estimates of the hedonic price schedule will depend on thenature of the data. If the gaps are randomly distributed across the range ofpossible housing types then we would expect as many households to chooselower than ideal levels of a particular characteristic as to choose higher thanideal levels. Over the entire dataset these should cancel each other out and, onaverage, we should be able to find an estimate of the hedonic price function thatis not systematically biased away from the true underlying function (i.e. it willbe an unbiased estimate), though the confidence intervals on our estimate willbe relatively large (i.e. it will be estimated with greater variance). On the otherhand, if the gaps in characteristics are over particular ranges and combinationsof properties (e.g. no low air pollution properties which are located convenientlyclose to the city centre) then we might expect some form of systematic bias toenter our estimation of the hedonic price schedule. As Freeman (1993, pp385-86) concludes, “the problem is almost certain to exist for some subgroups insome urban areas. But we need not conclude that the aggregate estimates are sounreliable to be of no use …This is a problem to which empirical researchersmust be sensitive. Examination of the disaggregated behaviour of the modelsuch as that carried out by Harrison and Rubinfeld could be helpful inidentifying the existence of such problems and judging their seriousness.”

4.4 Estimation of the Hedonic Price Function

We come, at last, to examine the practicalities of using data to estimate the hedonicprice function. Provided we take heed of the theoretical concerns expressed in theprevious section, it should be possible to take data on the prices and characteristicsof houses, use these to discover the underlying hedonic price function and fromthis derive the implicit prices of the various quality characteristics of properties.

In this section we begin with a high level overview of the statistical tool, known asregression analysis, that is used to estimate the hedonic price function.Subsequently, we examine various details of this analysis to highlight approachesthat will improve the quality of the research output.

4.4.1 Regression Analysis, Variance and Bias

Imagine that in a hypothetical housing market, there exists a wide diversity ofproperties that vary in just two characteristics, for example floor space andenvironmental quality. For now let us assume that this market conforms to allthe assumptions of the hedonic price model and has reached a state ofequilibrium. In that case, we could apply Equation 4-2 to define a hedonic pricefunction of the general form given in Equation 4-4;

P = P(floor space, environmental quality) (4-4)

If we were to plot the quantity (or quality) of each characteristic displayed by aproperty against its price (whilst controlling for the other characteristic), we

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might expect the spread of points on the graph to look something like thatshown in Figure 4-4.

Figure 4-4: Plots of price versus quantity of property characteristics in a perfecthousing market

Notice how the establishment of equilibrium in the market ensures that thehedonic price function is clearly defined for both characteristics. In this case asa property’s floor space increases, the price of the property also increases at aconstant rate as shown in Panel A. This constant rate relationship is known as alinear relationship. Similarly, the price of a house increases as environmentalquality increases but at a declining rate as shown in Panel B. In other words,improvements in environmental quality when environmental quality is initiallypoor, will increase the price of a property by a relatively large amount; whilstwhen environmental quality is initially good, further improvements will bringabout a relatively small increase in the price commanded by the property. Thisdeclining rate relationship is known as a logarithmic (or log) relationship.

We could express these relationships in mathematical terms according toEquation 4-5;

Pi = β0 + β1( floor spacei) + β2 log(environmental qualityi) (4-5)

In other words the price of property i can be calculated from its floor space andenvironmental quality, where β1 is a positive constant (or number) whichdefines the exact rate of increase of property price with increasing floor space.In the same vein, the β2 constant describes the exact rate of increase of pricewith respect to a unit increase in the log of environmental quality. Finally, the β0

constant represents the price of the house when both floor space andenvironmental quality are zero.

In effect, Equation 4-5 is an exact specification of the general hedonic pricefunction given in Equation 4-4.

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Of course, in the real world we would not imagine all the assumptions of thehedonic price model to hold perfectly. Indeed, for a number of reasons includingpoor measurement of variables in the dataset and disequilibrium in the housingmarket we might expect our plots of each characteristic against property price(again, accounting for the other characteristic), to look more like those depictedin Figure 4-5.

Figure 4-5: Plots of price versus quantity of property characteristics with ‘real’data

Clearly, in Figure 4-5 the plots do not define exact relationships between thecharacteristics and house price. What we would like to be able to do is toestimate the underlying hedonic price function from data such as that shown inFigure 4-5. This is the purpose of regression analysis.

In simple terms, regression analysis begins from the premise that therelationship defined by Equation 4-5 is no longer exact. Indeed, our regressionmodel can be written as;

Pi = β0 + β1( floor spacei) + β2 log(environmental qualityi) + εi (4-6)

where εi is an error term that takes on a different value for each property andallows for the divergence of a given property price from the general expectedprice for a property with given characteristics. Using relatively simplemathematical procedures, regression techniques are used to estimate values ofβ0, β1 and β2 so that the error terms in Equation 4-6 are as small as possible. Inother words, regression analysis works out the line of best fit and these linesdefine our estimates of the hedonic price function. The lines of best fit for thedata in Figure 4-5 are shown transposed onto the plots in Figure 4-6.

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Figure 4-6: Lines of ‘best fit’ from regression analysis

Providing certain conditions are met, the regression estimates of β0, β1 and β2

will be the same as the ‘true’ values defined in Equation 5. To introduce a littlemore terminology, the constants β0, β1 and β2 we are trying to estimate throughregression analysis are known as the regression parameters.

At this stage, it is probably worth explaining in a little more detail the statisticalconcepts of variance and bias introduced in the last section. For the time beinglet us concentrate just on our regression estimate of the relationship betweenenvironmental quality and the price of a property.

Figure 4-7 presents two plots of the relationship between environmental qualityand property prices for two different sets of data. Both sets of data come fromhousing markets with the same underlying hedonic price function. Notice howthe spread of points in Panel B is significantly larger than that in Panel A. Againwe could use regression analysis to estimate the underlying relationship for bothsets of data. The best fit lines from these regressions are also plotted in Figure 4-7. What is important to note is that regression analysis predicts exactly the sameline of best fit from both sets of data. In terms of Equation 4-5, the parametersthat are estimated for both datasets are identical, however, the minimised valuesfor the error terms, εi, are greater for the data shown in Panel B than they are fordata shown in Panel A. In statistical parlance we would say that the parametersfor the Panel B data set are estimated with a larger variance.

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Figure 4-7: Variance and the estimation of lines of best fit

The concept of bias differs importantly from that of variance. Figure 4-8 showsa plot of environmental quality against the price of properties. This time the datacomes from two different markets with significantly different hedonic functions.Data from market segment A is marked with crosses, while that for marketsegment B is marked with circles and the hedonic functions for each market areillustrated by the broken lines.

Figure 4-8: Bias and the estimation of lines of best fit

If the researcher, in trying to estimate the hedonic price function for marketsegment A, erroneously includes data from market segment B, then regressionanalysis will result in an estimation of the hedonic price function represented by

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o

Line of Best Fitfor Segment A

Line of Best Fitfor Segment B

Line of Best Fit forCombined Data

PANEL A PANEL B

4-20

the unbroken line. Notice that this relationship is significantly different from thetrue hedonic price function for market segment A. The researcher’s estimatedfunction is said to be biased. Bias is a serious problem as it can lead tomisleading conclusions (whereas variance still leads to correct conclusions butwith lesser certainty). In this example an estimate of the impact of a reduction inenvironmental quality on the price of houses in market segment A based on thebiased (combined data) hedonic price function will tend to be smaller than thatpredicted by the true relationship.

4.4.2 The Dependent Variable

One of our first concerns in estimating a hedonic price function is themeasurement of property price. In statistical parlance property price is thedependent variable; its value is dependent on the quantities or qualities of theproperties’ characteristics.

Early hedonic pricing studies in the United States used census data, which isrelatively easy to obtain. As Freeman (1993) pointed out, however, there weresome problems in that the data are presented in aggregate format that reducesaccuracy and curtails the ability of the researcher to control for relevant housingand location characteristics. A further problem with US census data is that it ison house prices based on home-owners’ personal estimates. How closely theseestimates reflect the true price that a property would command in the housingmarket is debatable. For example, Nelson (1978) showed that personal estimatesof property values from US census data were 3% to 6% higher than those givenby professional valuers.

This suggests a second possible source of data on property prices; professionalvaluations. It is not uncommon for large datasets to be compiled on the valuesof properties for the purposes of taxation. Again, data from these sources are notentirely reliable as they are, after all, only best guesses at the actual sellingprices of properties (DoE, 1972).

Most commentators would agree, therefore, that by far the most preferredsource of data for hedonic property market studies are records of actual salesprices on individual properties. Fortunately, records of sales prices are availablefor Scottish residences through the Registration of Title, which lists the address,price and registration date of every property transaction.6 It is recommended thatthis data form the basis of any hedonic price analysis for Scottish properties.

4.4.3 The Explanatory Variables

In statistical parlance, the characteristics that are used to explain the sellingprice of a house are known, appropriately enough, as the explanatory variables.It is not surprising that in the estimation of an hedonic price function researchersmust account for a very large number of explanatory variables. We wouldexpect that the structural attributes of the accommodation itself, indicators of its

6 This data is not public record in England and Wales, though the Nationwide Building Society releasessome data for these countries for academic use. However, the location of the property in this dataset isonly approximate and would be of little use for the present study where impacts are likely to vary overa comparatively small area.

4-21

accessibility, variables describing the characteristics of the neighbourhood andmeasures of environmental quality will all be important determinants of houseprice. These different categories of variables are summarised in Table 4-1.

Table 4-1: Categories and examples of variables in the hedonic price function

Variable Category Examples of Variables in this Category

StructuralNumber of rooms; presence of garage; size of garden;presence of central heating; etc.

AccessibilityDistance to: bus stop; town centre; school; shopping centre;etc.

NeighbourhoodAverage age; race distribution; crime rate; quality ofsurrounding schools; etc

EnvironmentalNoise levels; air pollution levels; quality of views from theproperty; etc.

Source: Based on Tinch (1995)

Including accurate measurements of all the relevant explanatory variables in thespecification of the hedonic price function is extremely important. It is a fact ofregression analysis that leaving out or mismeasuring explanatory variables canlead to bias in the estimation of the regression parameters. Simply put, some ofthe changes in property price caused by omitted variables may incorrectly beattributed to other included variables. To introduce a further piece of statisticalterminology, this is known as omitted variable bias.

Though it is usually relatively easy to define the relevant characteristics of theproperty itself (i.e. the Structural variables in Table 4-1), the definition andmeasurement of the other variables may be somewhat more complex.

Accessibility, for a start, is a rather vague concept and in studies of hedonicpricing, various kinds of accessibility have been included such as distance tocentral business district, access to main roads, distance to schools, distance toenvironmental facilities, etc. As we have already discussed, accessibility is oftenassociated with proximity to roads that in turn will be associated to theenvironmental parameters of noise and air pollution with which we areconcerned. Whilst we would envisage accessibility increasing property prices,we would expect the opposite of pollution caused by traffic. For example,Harrison and Rubinfeld (1978), in their hedonic pricing study of air quality,computed regressions with and without accessibility measures. Their resultsindicated that the parameter estimate for air pollution changed significantlywhen accessibility variables were deleted, which implied that withoutaccessibility the parameter on air pollution reflected both disadvantages ofgreater pollution and advantages of greater accessibility. Consequently, ifaccessibility is ignored, the estimation may be biased especially when there is acorrelation between accessibility and the environmental characteristics.

Geographical Information Systems (GIS), an important recent development incomputing technology, provide an ideal tool for improving estimation ofaccessibility variables. GIS capture, store, check, manipulate, analyse anddisplay spatially referenced data (DoE, 1987). Since accessibility variables are

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inherently spatial, GIS have introduced much greater flexibility and precisioninto the estimation of accessibility variables. For example, it is possible to useGIS to calculate car travel times to important amenities that reflect the actualdistance travelled on the road network taking account of road speeds alongvarious road types. In the same way walking distances from a property to localamenities can be calculated precisely using the network of pedestrian routes(Lake et al., 1998).

Neighbourhood variables describe the characteristics of the local area in whichthe property is located. In general, census data are a good indicator of theseattributes. Again GIS are a fast and efficient means of matching properties tocensus data at different spatial scales. For example, neighbourhood variablescan be constructed from the smallest unit of the census, reflecting the directneighbourhood of the property. Alternatively, user defined neighbourhood areascan be defined such as the area within 5 minutes walking distance of theproperty. GIS, therefore, allows researchers to efficiently and accuratelyconsider the impacts of local as well as wider neighbourhood effects on thevalue of properties.

A final major concern is the definition and measurement of environmentalvariables. As several researchers have pointed out, the correct measure of anenvironmental variable will be the one that reflects households’ perceptions ofthe environmental (dis)amenity. This is not as easy to obtain as it might sound.For example, a measurement of noise pollution should reflect many facets ofthis disamenity, including its intensity, frequency, duration, variability, time ofoccurrence during the day and so forth. Though we shall look at the issue inmore detail in the next section, there is clearly no single, obvious measurecapable of reflecting all these aspects of noise pollution. It is important,therefore, that we consider carefully our measures of environmental variables.

GIS may also be a considerable help in deriving measures of environmentalvariables. For example, we would expect the quality of views from a property tohave an impact on its price. Since the construction of a new road may seriouslyimpair the views from properties, this will almost certainly be of interest in thisstudy. However, Part 1 of the Land Compensation (Scotland) Act 1973 does notallow compensation to be paid for impairment of views. Hence, our analysismust be able to isolate the impact of this environmental disamenity from that ofdisamenities for which compensation is payable. It is possible to use GIS tomake accurate assessments of the types of land use that are directly visible fromany individual property and that take account of the presence of other buildingsand the topography of the local environment.

To conclude, hedonic price studies must account for as many importantexplanatory variables as is possible. Failure to do so may lead to serious bias inthe estimation of the parameters for the variables that are included. Fortunately,the ability of GIS to calculate large quantities of spatial data rapidly andaccurately is a considerable technical advance in the compilation of explanatoryvariables. Its use will bring about important advances in hedonic price studiesover the coming years.

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4.4.4 Functional Form

In the previous two sections we have discussed the choice and measurement ofthe dependent and explanatory variables used in the hedonic price function. Afurther important task facing researchers is to establish the exact nature of therelationship between the dependent variable and the explanatory variables. Thisrelationship is known as the functional form. Let us return to our hypotheticalexample, in which the ‘true’ hedonic price function is given by;

Pi = β0 + β1( floor spacei) + β2 log(environmental qualityi) + εi (4-6)

As already noted, the relationship between floor space and property price is alinear relationship and that between environmental quality and property price isa logarithmic relationship. Imagine that rather than specifying the hedonicfunction as in Equation 4-6, the researcher attempts to estimate the followingregression;

Pi = β0 + β1( floor spacei) + β2 (environmental qualityi) + εi (4-7)

Now, instead of specifying the relationship between environmental quality andproperty price as logarithmic, the assumed relationship in this regression islinear. The impact of this mis-specification of the functional form is shown inFigure 4-9.

Figure 4-9: The effect of mis-specificaiton of functional form on estimation of thehedonic price schedule

EnvironmentalQuality

Price ofProperty

(P)

×

×

×

××

×

×××

×

×

×

×

×

×

×

×

×

××

×

×

× ×

×

××

×

×

×

××

×

×

True, logarithmicrelationship

Biased, linearrelationship

4-24

Notice, that the linear relationship is only a poor approximation of the truelogarithmic functional form. At low levels of environmental quality, the linearrelationship underestimates the increase in price of properties resulting from animprovement in environmental quality, whilst at high levels it overestimates theimpact of an improvement. Imposing an incorrect functional form on theregression equation leads to what is termed mis-specification bias.

Typically researchers will adopt one of four simple functional forms;

• Linear specification; both the dependent and explanatory variables enter theregression in their linear form.

• Semi-log specification; the log of the dependent variable is regressed againstlinear explanatory variables

• Log-linear; a linear dependent variable is regressed against the log of theexplanatory variables, and

• Log-log; both the dependent and explanatory variables enter the regression intheir log form.

Unfortunately, economic theory gives no clear guidelines on how to selectfunctional form. How then can researchers ensure that they are specifying thecorrect relationship between the dependent and explanatory variables? Ingeneral, most researchers have progressed by using their intuition to guess at aninitial specification of the functional form. Then, through a process of trial anderror, alternative transformations of the variables are examined7. Functionalforms that improve the fit of the model8 to the data are assumed to bear closerresemblance to the ‘true’ underlying hedonic price function.

However, a more accurate, though unfortunately more complex solution to thisproblem exists. That is to use what is known as a flexible functional form.Without going into too great detail, this approach involves the estimation of afurther set of regression parameters that dictate the best possible transformationof the variables.

A commonly employed flexible functional form is known as the Box-Coxtransformation. Using experimental data based on the housing market inBaltimore, Cropper, Deck and McConnell (1988) found the Box-Coxtransformation provided a consistently superior approximation of the truefunctional form than did other commonly employed transformations. The Box-Cox transformation is explained in detail in Annex B.

7 The transformation of a variable refers to the mathematical function that is used to change a variablesuch that the relationship between the dependent variable and an explanatory variable can be expressedin a linear form. A number of transformations are possible. We have already introduced the linear andlogarithmic transformations but other possible transformations include taking the inverse of theexplanatory variable (which describes a declining relationship) and taking the square of the explanatoryvariable (which describes a U-shaped or inverted U-shaped relationship).8 The term ‘fit of the model’ refers to how well the researcher’s model explains the observed variationin the data. One commonly used method for judging the fit of the model is through the R2 statistic thatmeasures what portion of the total variation in the data is explained by the estimated regressionfunction as opposed to being subsumed in the regression error term.

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4.4.5 Treatment of multicollinearity

A further problem that researchers often encounter in their attempts to estimatethe hedonic price function is known as multicollinearity. Multicollinearityoccurs when two or more explanatory variables have a very similar relationshipwith the dependent variable. This is a problem that occurs frequently withenvironmental variables. For example, we would envisage that both noisepollution from traffic and air pollution from traffic fumes will have a negativeimpact on the price of a property. Unfortunately, the two are highly correlated.Higher levels of traffic result in greater noise pollution and higherconcentrations of exhaust fumes. Regression analysis finds it extremely difficultto tease apart the separate influences on property prices of these two distinct butclosely related disamenities of living in close proximity to a road.

There is no easy solution to the problem of multicollinearity and in its presenceestimated regression parameters may be implausibly large or in the worst case,have the wrong sign (e.g. the regression parameter estimated on a variable thatwe would expect to increase the selling price of the house, may actually indicatethe opposite relationship).

It is sometimes possible that problems of multicollinearity can be overcomethrough more accurate measurement of variables. For example, if data on noisepollution from roads takes account of local features such as trees and banks thatact to dissipate traffic noise, then the correlation of noise pollution with airpollution may be less distinct. Once again, the power of GIS may be invaluablein this respect through improving the accuracy of variable measurement.

Another possible approach is simply to circumvent the problem by combininghighly correlated variables into one index. This is the basis of a procedureknown as principal components analysis (see Annex C). However, whilstavoiding possible problems resulting from multicollinearity, this approachmakes it more difficult for us to estimate the separate influence on propertyprices of individual variables of interest.

4.4.6 Spatial Dependence

One final issue in the estimation of hedonic price functions is that of spatialdependence or correlation. Spatial dependence results from the fact thatproperties in close proximity to each other often share very similarenvironmental, accessibility and neighbourhood characteristics. If it werepossible to include all these characteristics as explanatory variables, thensimilarities in the selling prices of neighbouring properties would already beaccounted for. Unfortunately, this is infrequently the case and some of thecorrelation between the prices of properties in close proximity is not explainedin the estimated hedonic price equation.

Traditional regression analysis does not account for this correlation. Whilst, ingeneral, this does not lead to biased results, it does lead to greater variance inour estimation of the regression function. If we can account for some of thesimilarity in the prices of properties in close proximity through spatialcorrelation, it will allow us to remove some of the white noise from the data anddraw clearer inferences on the variables that we have included in our analysis.

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Recently, researchers have begun to address this problem and borrowingprocedures developed by geographers have developed regression techniques thataddress the issue of spatial dependence (e.g. Anselin, 1993; Irwin andBockstael, 1998; Bell and Bockstael, 1997). In simple terms, these techniquesproceed by including an element into the regression analysis that relates eachproperty included in the data set to each other property according to the distancebetween the two. Again, GIS provides an easy and accurate means of collectingthe data on distances between properties to include in this analysis.

The estimation of a spatial correlation model is a relatively complex procedureand there is no guarantee that for a given data set it will be possible to obtainmeaningful results or for that matter any results at all. Given that spatialcorrelation does not lead to systematic bias in parameter estimates, estimation ofsuch models should be considered a nicety that should take a lower priority tothe accurate identification of functional form and the handling ofmulticollinearity.

4.5 Estimation of welfare changes

As we have already seen, the area under the implicit price schedule will be equal tothe change in house price associated with an environmental change. However, thisdoes not equal the household’s WTP for this change. In order to determine this thearea under the demand curve of each household must be examined. Therefore, thetrue change in value to a household from, say a fall in environmental quality fromq1 to q2, will be given by the area under the demand curve not the area under theprice line (or in this case implicit price curve). The shaded area in Figure 4-10illustrates the underestimation in welfare change (loss) that would result fromusing the area under the implicit price curve to value a fall in environmentalquality.

Figure 4-10: Consumer Surplus for a Non-Marginal Change in EnvironmentalQuality

Implicit Price ScheduleP’(z1, z2

*, …, zn*)

0 Quantity ofCharacteristic z1

ImplicitPrice of z1

(P’)

q2 q1

Demand Schedulefor Household i

4-27

Although Part 1 of the Land Compensation (Scotland) Act 1973 makes clear thatcompensation is payable on price changes rather than welfare changes,nevertheless economists are frequently interested in estimating these welfarechanges (household WTP). To facilitate this, there is a second stage to the HPMthat attempts to identify the individual demand curve. To identify any demandcurve we need information on individuals’ demand for a good at different prices.For a normal good there are two ways we can collect this information:

• Observe individuals’ demand for a good in one market when the price of thegood changes over time.

• Observe individuals’ demand for a good in different markets where the good istraded at different prices.

Returning to Figure 4-3 we can imagine household i and j to be representative oftwo groups of households in the market. Notice that the demand curves for thesetwo groups are different. The fact that their demand curves are different is becausethe households in the two groups have different characteristics. For simplicity letus assume that all households in the market are identical apart from the fact thatthey differ in their incomes. In Figure 4-3, for example, households with demandcurves like that shown for j are richer than those for i; they are willing to paygreater amounts for the characteristic of the property (e.g. peace and quiet) at alllevels of that characteristic. In our simplified case, it is income that shifts thedemand curve for the characteristic in or out. This shifting of the demand curveresults in households with different incomes facing different implicit prices.

It should be clear, therefore, that we would never observe two households with thesame income facing different prices. The end result is that we are unable todistinguish the separate influences of price and income on the demand for acharacteristic. In general, the only way we can identify the demand curve is to haveobservations on households with the same characteristics facing different implicitprices. The problem comes down to that faced when estimating demand curves fornormal goods. We need information on demand when prices change over time orprobably more realistically, as illustrated in Figure 4-11, from different propertymarkets where the implicit price schedules are different.

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Figure 4-11: Second Stage of the HPM; Identification of the Demand Curve

4.6 Summary and Conclusions

We have covered a large amount of ground in this section, describing in detail theworkings of the hedonic model, assessing the assumptions of this model and whatthose tell us about the validity of the hedonic pricing technique. Finally we haveaddressed the empirical issues that underlie statistical estimation of the hedonicprice function. Several conclusions can be drawn from this discussion. First andforemost, it would appear that, provided we are careful in our selection of data, theassumptions of the hedonic price model are not so unrealistic as to make ourestimations of the hedonic price schedule from real world data meaningless.Second, as far as the actual estimation of the hedonic price schedule is concerned, anumber of possible problems and pitfalls have been highlighted. However, givencareful consideration and use of state of the art techniques, including;

• the use of GIS to calculate accurate explanatory variables

• the employment of flexible functional forms and

• allowing for spatial dependence

it would appear that none of these problems are insurmountable.

0 Quantity ofCharacteristic z1

ImplicitPrice of z1

(P’)

Implicit Price ScheduleMarket 1

Demand Schedulefor Household i

Implicit Price ScheduleMarket 2

Implicit Price ScheduleMarket 3

5-1

5 EMPIRICAL STUDIES

5.1 IntroductionPart 1 of the Land Compensation (Scotland) Act 1973 stipulates that the roadsauthority should pay compensation for the depreciation in the value of propertiesthat results from seven physical factors (noise, vibration, smell, fumes, smoke,artificial lighting and discharge onto the land of solid and liquid substances) arisingfrom the use of a road. Compensation is paid only for the reduction in the open-market price of a property that is directly attributable to these physical factors andnet of any enhancement of value arising from the new road.

In Sections 3 and 4 we discussed the methodological toolkit available toresearchers to estimate how characteristics of a property�s environment, such asthose represented by the physical factors resulting from road use, influence itsprice. Also, it was established that the hedonic pricing valuation techniqueprovided the most appropriate methodology to address this issue.

Two issues raised in the last section are extremely relevant to the estimation ofproperty price depreciation brought about by the seven physical factors resultingfrom road use.

• First, the issue of whether an environmental disamenity will be capitalised inthe price of a house. Put simply, only physical factors that are perceived by ahousehold and which significantly affect the enjoyment they gain from livingin a particular location will influence house prices. Thus, whilst the impacts ofnoise and vibration and those from air pollution (fumes, smoke and smells)may well be capitalised in the price of a house, it is less obvious that thoseresulting from artificial lighting or liquid and solid discharges will impact onproperty prices. Indeed, as far as the authors are aware, no studies to date haveattempted to isolate the influence of these factors on property prices.

• Second, it is very likely that a number of the physical factors arising from roaduse will be highly correlated with each other and with other factors resultingfrom the proximity of a road. In some ways this is advantageous as it should bepossible to enter just one rather than several measures in the regressionanalysis; for example, we could include a variable for air pollution in generalthat picked up the combined impacts of fumes, smoke and smells on the priceof a house. In other ways it may cause problems, especially when correlationexists with factors not deemed compensateable in Part 1 of the LandCompensation (Scotland) Act 1973. For example, road noise may be highlycorrelated with the visual disamenity of having a road running past the front ofa property (i.e. those close to main roads may suffer both from noise pollutionand the visual pollution of having to watch cars and trucks travel past theirwindows). If we do not include a variable reflecting the visual disamenity ofroads then it is likely that our estimate of the impact of noise pollution will betoo large, erroneously including the impacts of overlooking a road.

In light of these two issues, the vast majority of hedonic pricing studies that havedealt with the impact of road traffic on housing prices have concentrated on one (or

5-2

occasionally both) of two variables; namely air and noise pollution. In this section,therefore, we review the previous empirical research into the impacts of noise andpollution on property prices. The section describes how the different researchprojects were undertaken, the major issues dealt with in the different analyses,presents the empirical results and discusses their accuracy and similarity.

5.2 The Valuation of Noise and Vibration Pollution

5.2.1 Measuring Noise Pollution

In the last section we hinted at the difficulties of establishing a measure of noisepollution that truly reflected the impact noise has on households� lives. Broadlydefined, noise can be described as unwanted sound and vibration (Litman,1995). The qualifier �unwanted� covers a broad gamut of impacts that rangefrom physiological (e.g. sleep disturbance), to pathological (i.e. auditoryimpairment) to psychological (Gent and Rietveld, 1993). The extent of theseimpacts will depend not only on the magnitude of the noise pollution but on itsintensity, frequency, duration, variability and time of occurrence during the day(e.g. night noise pollution may cause more value loss than daytime noise).

On top of this, the nature of noise pollution is truly multi-faceted. For a startvehicles produce noise from a whole variety of sources (e.g. mechanicalmovement, exhaust emission, tyre-road contact, aero-dynamic disturbances,bodywork vibration, brake friction, theft alarms, warning horns etc.). To furthercompound the difficulties, the level of noise pollution will depend on a numberof parameters (Department of Transport, 1988) namely:

• Characteristics of the traffic itself, such as the type of vehicles (inparticular, the share of motorbikes and HGVs), the fluidity of traffic(closely related to the number of obstacles on the road such as trafficlights), speed, drivers� behaviour and so forth;

• Parameters related to the type, state and quality of the road;

• Characteristics of the environment: width of road, distance and height ofreceptor

A by-product of this complexity is that there is no �correct� way to measurenoise. Different noise exposure indices all try to convert the measured level ofnoise into a figure that reflects the perceived annoyance and hence take intoaccount intensity, frequency, duration, variability, time of day and so forth(Nelson, 1978).

As a starting point the level of noise at any one point in time can be measuredon a logarithmic scale using so-called �A-weighted� decibels, dB(A). The dB(A)scale approximates the sensitivity of the human ear by weighting more heavilymedium and high frequencies. Table 5-1 provides some examples and broadratings of noise pollution using the dB(A) scale.

5-3

Table 5-1: The dB(A) scale with some examples

dB(A) Rating Sources Effects140 Gunshots, Explosions Instant Auditive Trauma130 Jet Aircraft Taking Off

120 Pain above this Level110 Pneumatic Drill100 Discotheque90 Close by a Lorry80 Busy Cross-Roads Interference with Work75 Very Bad70 Bad Interior of Car Interference with Speech65 Quite Bad60 Moderate Window on Busy Road55 Tolerable

50 Quite Good Quiet Street Normal45 Good40 Excellent Calm Office Interference with Sleep30 Library20 Studio, Whispering Sensation of Calm10 Desert0 Limit of Audibility

Source: Adapted from Gent and Rietveld (1993), Soguel (1991) and Tinch (1995)

Clearly, a measure that only reflects the level of noise (usually represented bythe letter L) at any one point in time is not an adequate reflection of the truediversity of noise pollution resulting from road traffic. An improved measurewould be one that reflected the distribution of noise over the day. Onepossibility is to use the L10, L50 or L90 measures, that gauge the noise levelexceeded 10%, 50% and 90% of the time and are known respectively as thepeak, mean and ambient noise levels.

Better still is the equivalent continuous sound level or Leq measure that has beenthe favoured measure in many hedonic price studies. The Leq measure providesa single figure that, as its name suggests, reflects the distribution of soundthroughout the day.

Calculated in the same way as Leq, the day/night equivalent sound level (Ldn)attempts to reflect the added annoyance of noise at night by weighting noisepollution at this time more heavily.

A further measure is that of the noise pollution level (NPL). The NPL attemptsto account for the added irritation of variability in the noise pollutionexperienced at any one location over the day. This is achieved by adding to theLeq measure of noise pollution at a site a term that reflects the variability ofnoise levels.

5.2.2 Studies of Noise Pollution

A large number of hedonic price studies have investigated the impact of noisepollution on property prices. In general, this noise pollution has come from oneof two sources; road or air traffic. Of course, our main interest is in studiesfocusing on noise from road traffic but we shall look briefly at studies of airtraffic noise pollution as well.

5-4

A list of studies is provided in Table 5-2. In order to facilitate comparison of theresults of hedonic price studies researchers often quote a Noise SensitivityDepreciation Index (NSDI). Originally introduced by Walters (1975), the NSDIwas adopted for comparative purposes by Nelson (1980, 1982) in his majorreviews of hedonic price studies of airport and traffic noise. For two residentialproperties that differ only in their level of noise exposure, the absolute amountof housing depreciation per decibel can be defined as

exposurenoiseindifferenceexposurenoisefromvaluepropertyinreduction=D

NSDI is then defined as

exposurenoiseindifferencepricehouseinondepreciati%total

valueproperty=×= 100DNSDI

NSDI can be regarded as a percentage change in price arising from a unitincrease in noise. NSDI for the various studies is shown in the fifth column ofTable 5-2. Values in the studies listed range from 0.08 (Palmquist, 1980 and1981) to 2.22 (Gamble et al., 1974). The simple mean for these studies is anNSDI of around 0.55. In other words, these studies suggest that an increase innoise pollution of 1 dB(A) will reduce the value of a property by just over ½ ofone percent.

The variety of NSDI values presented in Table 5-2 should not come as anysurprise. Theoretically, we would not expect different housing markets to havethe same hedonic price function and, therefore, would not expect applications ofthe hedonic pricing technique in different cities in different years to returnidentical results.

In a recent study, Bertrand (1997) used a statistical technique known as meta-analysis1 to compare 16 estimates from nine different hedonic pricing studies ofnoise pollution carried out in the USA, Canada, Switzerland and Finland.Extrapolating his results, he estimates the average NSDI to be 0.64%.Bertrand�s results also provide insights into how the hedonic price functionvaries from market to market. In line with expectations, the greater the averagelevel of noise in a market and the greater the income of the market�shouseholds, then the higher the implicit price that is paid for noise pollutionreductions.

1 Meta-analysis can be defined in its simplest form as the analysis of analyses. In short it attempts tosynthesise and explain the results returned from a number of similar original studies.

5-5

Table 5-2: Hedonic pricing studies of loss in property value from Road Trafficnoise (% depreciation in house prices per 1 dB(A) increase in noise level)

Source Study StudyYear Study Area Noise

Measure NSDI

Allen, 1980� 1977-79 North Virginia, Va., USA L10 0.15

1977-79 Tidewater, Va., USA L10 0.14

Anderson and Wise, 1977� 1969-71 Towson, Md., USA. NPL 0.43

1969-71 North Springfield, Va., USA NPL 0.14

Bailey, 1977� 1968-76 North Springfield, Va., USA Log ofDistance 0.3

Gamble et al., 1974� 1969-71 Bogotoa, N.J., USA NPL 2.22

1969-71 Rosendale, Md., USA NPL 0.24

1969-71 North Springfield, Va., USA NPL 0.21

1969-71 All three areas NPL 0.26

Grue et al.,1997 Oslo, Norway � Obos Leq 0.24

Oslo, Norway � Flats Leq 0.21

Oslo, Norway � Houses Leq 0.54

Hidano et al., 1992* Tokyo, Japan Leq 0.7

Hall et al., 1978� 1975-77 Toronto, Canada Leq 1.05

Hall et al., 1982 Toronto, Canada � Arterial Leq 0.42

Toronto, Canada � Expressway Leq 0.52

Hammar, 1974 Stockholm, Sweden Leq 0.8 �1.7

Iten and Maggi, 1990 Zurich, Switzerland - 0.9

Langley, 1976� 1962-72 North Springfield, Va., USA NPL 0.22

Nelson, 1978� 1970 Washington, D.C., USA Ldn 0.87

Palmquist, 1980, 1981� 1962-76 Kingsgate, Wa., USA L10 0.48

1958-76 North King County, Wa., USA L10 0.3

1950-78 Spokane, Wa., USA L10 0.08

Pommerherne, 1988 1986 Basel, Switzerland Leq 1.26

Renew, 1996a Brisbane, Australia Leq 1.0

Soguel, 1991 1990 Neuchatel, Switzerland Leq 0.91

Vainio, 1995 Helsinki, Finland Leq 0.36

Vaughan & Huckins, 1975� 1971-72 Chicago, USA Leq 0.65� Reviewed in Nelson (1982)* From Bertrand (1997) who notes that figure is presented with caution

The use of a single statistic to compare studies conceals considerableheterogeneity in the exact method of their application. As an example, each ofthe studies deals with noise in a slightly different manner. Whilst the majority of

5-6

studies have plumped for the Leq measure of noise (as shown in Column 4 ofTable 5-2), the method by which the noise pollution impacting on a particularhouse is assessed can be very different from study to study. A number of studiesadopt the noise contour approach whereby data from various monitoring pointsare used to construct bands of similar noise pollution across the urbanenvironment. The noise pollution experienced by any particular property willdepend on the band in which it falls. Studies using this approach includeGamble et al. (1974) and Palmquist (1992). More advanced measures of noisepollution can be achieved by using models that take account of the exactcharacteristics of a particular dwelling. Data from these models are likely to bemuch more accurate. Studies taking this approach include Pommerehne (1987),Soguel (1991) and Vainio (1995).

Studies also vary considerably in the choice and accuracy of the explanatoryvariables used in the regression analysis and in the choice of functional form.For example, Vaughan and Huckins (1975) in an hedonic price study usingindividual housing sales in the Chicago urban and suburban areas in 1971-72,included variables reflecting structural characteristics (e.g. sq. feet of livingspace, number of garages, lot width and age of dwelling), neighbourhoodcharacteristics (e.g. total number of lots on the block – a crowding measure,number of visible broken windows – a blight measure and available recreationland), accessibility characteristics (e.g. distance to the central business districtand distance to Lake Michigan) and environmental characteristics (i.e. noisepollution and air pollution – a composite measure of sulphates and particulates).In contrast, Allen (1980) again using individual housing sales but this time fortwo towns in Virginia only included structural variables (e.g. sq. feet of floorspace, sq. feet of lot, no. of bathrooms, no. of fireplaces, age of property etc.) aswell as a measure of noise pollution. A more detailed description of a hedonicpricing study for noise pollution is provided in Box 5-1.

Box 5-1: Soguel’s (1991, 1994) hedonic pricing study of noise in Neuchatel, Switzerland

Soguel (1994) conducted a hedonic pricing study in the town of Neuchatel in Switzerland.Rather than using housing prices, the dependent variable was monthly rent, net of charges. Anumber of authors have suggested that using rental charges simplifies the interpretation ofhedonic pricing regressions, since the dependent variable is a monthly measure of theproperties’ value and not, as explained in Section 4, the capitalised value of expected futurerents (Palmquist, 1991). There were various explanatory variables in the data set, of which 13covered the structure and condition of the building and services provided, 23 thecharacteristics of the apartment and 5 the location of property. Soguel employed the Leq

measure of noise pollution.

The best specification of the model involved applying the log transformation to both thedependent variable and most of the explanatory variables. The noise variable was nottransformed. The final model reported by Soguel contained significant coefficients on 17variables, including the coefficient estimated on the noise pollution variable. Based on theexplanatory variables, the model was capable of predicting rental charges with reasonableaccuracy (in statistical terms it returned a high R2 of 0.8, or 80% of the variability in the datacould be explained by the regression).

The coefficient on the noise variable suggested a NSDI of 0.91. That is, a unit dB(A) increasewill reduce rents by 0.91%. This result can be used to construct the implicit marginal pricefunction for noise by using data on mean monthly rent at each level of noise pollution. Thisdata is shown converted into 1993 £ in Table 5-3 (from Tinch, 1995).

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Table 5-3: Implicit marginal WTP per dB per month

Noise Mean Monthly Implicit MarginaldB(A) Rent (1993 £/m) Price (£/dB/m)

45 243 2.22

50 232 2.12

55 222 2.03

60 212 1.94

65 202 1.84

70 193 1.77

75 184 1.69

80 176 1.61

Notice that, in absolute terms, the implicit marginal price of noise is falling as noiseincreases. The implicit marginal price schedule suggested by this data is plotted in Figure 5-1.The idea that the price of noise is lower at higher levels of noise pollution may go somewhatagainst our prior expectations. However, it is important to remember that the marginalimplicit price function is not the same as the marginal WTP function. The WTP functions fortwo hypothetical households have also been traced on Figure 5-1. Notice how the WTPfunctions appear to be reversed (i.e. WTP is increasing as the quantity of the X-axis variableincreases) but this is merely a result of the fact that we are dealing with noise pollution whichis a �bad�, rather than its inverse �peace and quiet�, the corresponding �good�.

Figure 5-1: The implicit price and WTP functions for noise

Taking household i as an example, as noise pollution increases from qi the implicit price ofnoise falls. At the same time, however, the marginal WTP of the household actually increases.Thus, we cannot interpret this result as saying that a 1 dB increase is less annoying at 80dB(A) than at 50 dB(A). It is more annoying for the same household.

0 NoisePollution

ImplicitPrice ofNoise

Implicit PriceSchedule

Marginal WTPHousehold i

qi qj

Marginal WTPHousehold j

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How then can we explain the existence of a declining marginal price for noise pollution?Well, as Soguel points out, incomes differ substantially among the households in this studyand it is likely that preferences vary also. Those less sensitive to noise will be more likely tolocate in noisy areas; and those living in noisier areas, with cheaper rents, tend to be peoplewith lower incomes and with a correspondingly lower ability to pay for reduced noise or otherenvironmental improvements. In the figure, therefore, we might expect household i to have ahigher income than household j, and hence be able to locate in areas where noise pollution islower.This result has some interesting ramifications. In simple terms it suggests that households inlow noise pollution areas should be compensated more for increases in noise than shouldthose in high noise pollution areas. The equity implications of this are substantial given thatthose in low noise pollution areas also tend to be the relatively wealthy.

Source: Soguel (1991, 1994), Tinch (1995)

A number of hedonic studies have focused on the impacts of aircraft noise onproperty prices. Though qualitatively different from road traffic noise, it isworth reviewing these studies to show the extent to which hedonic techniqueshave been successful in isolating the impacts of environmental noise pollution.

Nelson (1980) summarised twelve property value studies carried out in the1960s and 1970s. Eight of these studies were conducted in the United States,two in Canada and one each in both of Australia and London. In the last twentyyears, a number of further studies have been carried out in the United States,Canada and the UK. These studies are listed in Table 5-4.

The studies detailed in Table 5-4 have returned a variety of NSDI scoresranging from 0.29 up to 2.3. The mean NSDI score for these studies is 0.87though this falls to 0.64 when the relatively high figures reported by the Paik(1972) and Yamaguchi (1996) papers are removed. In other words, the studiesreviewed suggest that, as a crude average, a 1 dB increase in aircraft noise willreduce the value of a property by 0.64 of one percent. This is roughly in linewith Nelson’s (1980) conclusion that “… the noise discount is commonly 0.5-0.6% …”.

Schipper (1996) has carried out a more formal statistical test of these resultsusing meta-analysis (see Footnote 24). He finds that the implicit price of aircraftnoise pollution is influenced by a number of factors including the timing,country and specification of the original noise studies. His findings suggest thatas a baseline the NSDI is around 0.33% whilst for studies in the United Statesthis rises to 0.65%.

Only Pennington et al. (1990) failed to return a significant and negativecoefficient on the noise variable. Pennington et al. (1990) undertook a hedonicprice study using property data of actual market transactions covering the periodApril 1985 to March 1986. They found that, following the inclusion ofneighbourhood variables, the noise variable became statistically insignificant.Pennington et al. concluded that differences in property values could beattributed solely to neighbourhood and other characteristics.

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Table 5-4: Hedonic pricing studies of loss in property value from Aircraft Noise(% depreciation in house prices per 1 dB(A) increase in noise level)

Source Study StudyYear Study Area NSDI

Abelson, 1979� 1972-73 Marrickville, Sydney, Australia 0.4

1972-73 Rockdale, Sydney, Australia 0.5

Collins and Evans, 1994 1985 Manchester, UK -

De Vany, 1976� 1970 Dallas, USA 0.8

Dygert, 1976� 1970 San Mateo, San Francisco, USA 0.5

1970 Santa Clara, San Jose, USA 0.7

Emerson 1969, 1972� 1967 Minneapolis 0.58

Gautrin, 1975� 1968-69 London Heathrow, UK 0.62

Levesque, 1994 Winnipeg, USA 1.3

McMillan et al., 1980� 1975 Edmonton, Canada 0.51

Maser et al., 1977� 1971 Rochester, N.Y., USA � City 0.88

1971 Rochester, N.Y., USA � Suburban 0.61

Mieskowski & Saper, 1978� 1969-73 Etobicoke, Toronto, Canada 0.52

Nelson, 1978� 1970 Washington, USA 1.06

Nelson, 1979 1970 San Francisco, USA 0.58

1970 St. Louis, USA 0.51

1970 Cleveland, USA 0.29

1970 New Orleans, USA 0.4

1970 San Diego, USA 0.75

1970 Buffalo, USA 0.52

O�Byrne et al., 1985 1980 Atlanta, USA 0.69

1970 Atlanta, USA 0.64

Paik, 1972� 1960 New York, USA 1.9

1960 Los Angeles, USA 1.8

1960 Dallas, USA 2.3

Pennington et al., 1990 1985 Manchester, UK 0.47

Price, 1974� 1960-70 Boston, USA 0.83

Uyeno et al., 1993 1987 Vancouver, Canada 0.65

Yamaguchi, 1996 1996 London Heathrow, UK 1.51

1996 London Gatwick, UK 2.30� Reviewed in Nelson (1980)

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The Pennington et al. (1990) result was challenged by Collins and Evans(1994). They studied the noise effect of Manchester Airport with the same dataset but using a non-regression analytical technique known as the artificial neuralnetwork (ANN) approach. Though it is impossible to judge the significance ofcoefficients using the ANN approach, Collins and Evans distinguished asizeable noise effect despite the fact that this was dwarfed in importance by theimpact of neighbourhood characteristics.

Concerning functional form, the majority of researchers have opted for the�traditional� semi-log form (i.e. the dependent variable, sales price, is entered inlog form and the aircraft noise variable is entered in linear form). However,there is no theoretical reason to believe that this is the optimal specification,indeed, Levesque (1994) employed the Box-Cox transformation and showedthat the functional form implied by his data was significantly different from thesemi-log.

As for the use of explanatory variables, all the studies contain structuralcharacteristics whilst treatments of neighbourhood and accessibilitycharacteristics differ from one study to another. For example, the Levesque(1994) study did not include neighbourhood characteristics, whilst, as alreadymentioned, the Pennington et al. (1990) study claimed that includingneighbourhood characteristics made the noise coefficient insignificant. On theother hand, O�Byrne et al. (1985), studying the impact of noise pollution fromAtlanta International Airport, obtained more or less the same noise effect withand without neighbourhood characteristics included in the regression.

With regard to accessibility characteristics, three of the aircraft noise studies(Levesque, 1994; Nelson, 1980 and Uyeno et al., 1993) included these variables,each confirming the importance of including accessibility variables in hedonicpricing regressions. Both Nelson (1980) and Uyeno et al. (1993) attempted toaccount for the importance of accessibility to the airport as a focal point foremployment, transportation and commercial services. However, the latterresearchers dropped this variable from their final specification because theyfound it an insignificant factor in determining property prices. Conversely,Nelson found this accessibility variable to be significant and concluded thatmajor bias could be introduced into hedonic pricing studies if it is ignored.

5.2.3 Noise Pollution and other Valuation Approaches

Confirmation of the robustness of the hedonic pricing method can be soughtthrough comparison with figures derived from other valuation methodologies.For example, JMP Consultants Ltd. (1996) recently carried out research for theDepartment of Transport valuing the nuisance from road traffic. One approachthey adopted (and in their opinion �the most plausible and practical tool for thevaluation of nuisance arising from changes across a broad spectrum� p 256.) isto ask the opinion of expert property valuers. Using a large sample theyconcluded that the best estimate of the NSDI was 0.29% per dB increase ordecrease in noise pollution. This result falls in the range of values commonlyreported from hedonic studies but is somewhat lower than the average of valuesreported in the hedonic literature.

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A further point of comparison can be found in studies that have employed morethan one methodology to investigate a single valuation problem. For example,Pommerehne (1987), Soguel (1991 and 1994) and Vainio (1995) have used thecontingent valuation approach (see Section 2) to produce results that they cancompare with those derived from their hedonic analyses.

The Pommerehne (1987) study in Basel, Switzerland produces remarkablysimilar results. Estimating households� WTP to reduce noise pollution by half,the hedonic price method returns a result of 79 Swiss Fr per month compared toa value of 75 Swiss Fr per month derived from the contingent valuation survey.In a similar manner, the Soguel study in Neuchatel, Switzerland produces highlycomparable results. Again valuing households� WTP to reduce noise pollutionby half, the research estimates a value of 60 Swiss Fr per month from thehedonic pricing method (Soguel, 1991) and a value of between 56 and 67 SwissFr per month from the contingent valuation method (Soguel, 1996).

The Vainio (1995) study in Helsinki, Finland is not so favourable. ThoughVainio has to make a number of assumptions to compare his two datasets, heconcludes that a change in noise pollution levels from Leq 65 to Leq 55 would bevalued at FIM 18,420 using the hedonic pricing method and at almost threetimes this amount (FIM 51,600) using the contingent valuation approach.

Support for the accuracy of the hedonic pricing of noise pollution from thesecomparative studies is favourable but by no means conclusive.

5.2.4 Summary

The evidence presented in this section provides considerable support for thecontention that noise pollution is capitalised into the price of a house. Theestimated impact of noise on house prices would appear to vary considerablyfrom study to study. This is not surprising considering studies are taken fromdifferent markets and at different times and, therefore, will be estimating quiteseparate equilibrium hedonic price functions.

The values quoted as NSDIs range from 0.08% to 2.22% for road traffic noiseand from 0.29% to 2.3% for aircraft noise. Statistical analyses of these resultssuggest that figures of 0.64% for road traffic pollution and 0.33% for air trafficpollution (or 0.65% in the United States) could be taken as average values forthe percentage decrease in housing prices following a 1 dB increase in noisepollution.

Though evidence from comparative studies using other valuation techniquesfails to conclusively confirm the accuracy or otherwise of the hedonic pricingmethod, the greatest confirmation of the validity of the approach is in theconsistently significant results reported by researchers.

5.3 The Valuation of Air PollutionTable 5-5 shows the contribution of road traffic to total emissions of the principalair pollutants in the UK. Road traffic is clearly an important source of air pollution.However, because of the spatial distribution of transport and other emittingactivities, transport emissions in the urban environment are, relatively, even moreimportant than the figures suggest (London Research Centre, 1993).

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Table 5-5: Contribution of Road Transport to air pollution in the UK, 1991.(Figures presented as %�s of total UK emissions)

Source NOx CO VOCs� CO2 SO2 PM10� BS�

Cars 29 80 23 12 1 10 6

HGVs and LGVs 20 8 7 6 1 14 32

Other 3 2 7 1 0 3 6

Total from RoadTransport 52% 90% 37% 19% 2% 27% 42%

Source: Maddison et al. (1996)�VOCs = Volatile Organic Compounds, PM10 = Particulate Matter under 10µm in diameterand BS = Black Smoke

The impacts of air pollution on households in the urban environment are manifold.Probably of major concern is the health impact of air pollution. Indeed, Calthrop(1995) estimates that 6,665 premature deaths resulted from exposure to airpollution from the road transport sector in the UK during 1993.

Though research is continuing, it would appear that small particulate matter in theair is one of the primary causes of mortality from air pollution (e.g. Ostro, 1994;Bown, 1994). Particulate matter (often measured as PM10 � particulate matter witha diameter of under 10 µm or TSP � total suspended particulates) is made up oftiny particles of dust which are generated by motor vehicles either through thewear of brakes and tyres or from the reaction of chemicals in vehicle exhausts.

Many lesser health impacts are discussed in the literature on air pollution. Theseinclude respiratory hospital admissions (RHA), emergency room visits (ERV),restricted activity days (RAD), minor restricted activity days (MRAD), asthmaattack, acute respiratory symptoms, chronic bronchitis, eye irritation, and so on.

Air pollution has many impacts beyond those on health. It also plays a role indamaging buildings, soiling clothing, reducing visibility, creating unpleasantodours and so forth.

Given the range and possible severity of the impacts of air pollution resulting fromroad traffic, it is reasonable to assume that households may value cleaner air andthat this may be capitalised into the price of housing.

5.3.1 Measuring Air Pollution

One of our major concerns in attempting to measure the impact of air pollutionon housing prices is how we might define variables for inclusion in the hedonicprice regression that accurately reflect households� perceptions of their exposureto air pollution. In many cities there exists a reasonably extensive air monitoringnetwork that can give objective measures of the concentrations of pollutants.However, the question remains whether an objective measure of pollutionconcentration is a good proxy for household perceptions.

Indeed, it is only possible to value effects in so far as people perceive themaccurately, which they are unlikely to do perfectly. Some effects will beoverlooked, because households are not aware of all the risks to which they are

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exposed, or because they are not aware of the links between an effect and theirexposure to air pollution. For example, lead from car exhausts has been shownto have serious health impacts especially on the young. It is unlikely, however,that households have any real perception of the levels of lead pollution to whichthey are exposed. Of course, this is not generally going to impact on ourestimation of the hedonic price function. Air pollutants that are not recognisedby households, or are recognised but are not perceived as being dangerous, willsimply return insignificant coefficients.

On the other hand, correlation in the spatial concentrations of air pollutantsemitted by road traffic may result in estimation problems. The correlation thatexists amongst many pollution variables implies that a single pollution variablein an equation is likely to pick up some of the effects of other pollution. On topof this a pollution variable may also be correlated with other road traffic impactssuch as soiling, noise and visual disamenity.

The problem with including more than one pollution variable, or with includingpollution and noise variables, is that the multicollinearity can result in distortedor less precise estimates of the coefficients. If a study of only one pollutionvariable is conducted, some of the effects of any other pollution and most or allof the effects of noise will be picked up. Unfortunately, it is not always possibleto determine for any particular approach exactly which impacts are being fullyvalued, which partially, and which are being overlooked.

Given this statistical problem a pragmatic alternative is to include only one ortwo pollution variables in the hedonic price regression. In this situation theconfidence associated with the coefficients estimated will be greater, but thecoefficients themselves will be biased. This bias is, however, deliberatelyinduced, with the aim that the coefficient on just one variable should pick up theeffects of several.

The air pollutant that probably fulfils this general role best is particulate matter.Not only are particulates related to health impacts but they are also clearlymanifested to households through visibility impacts and levels of soiling.Fortunately, particulate concentrations are also highly correlated with otherpollutants emitted from road traffic. Indeed, the Ministry of Transportation andHighways in British Columbia have concluded that �transport planning shoulduse particulate pollution as the primary measure of local air pollutants and as aproxy for other harmful emissions such as NOx, VOCs and CO� (Bein et al.,1994).

5.3.2 Studies of Air Pollution

Some of the earliest studies of hedonic pricing were interested in ascertainingthe possible impacts of air pollution on property prices. Ridker and Henning(1967) began the process with an application that looked at sulphate airpollution in St. Louis, USA. Since then, a large number of studies have beenundertaken. Though many authors (e.g. Ridker and Henning, 1967; Andersonand Crocker, 1971) have found that air pollution levels influenced propertyprices a number of other studies (e.g. Smith and Deyak, 1975; Weiand, 1973)have been less successful.

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Pearce and Markandya (1989) provide details of a number of hedonic pricingstudies of air pollution (see Box 3-5 in Section 3). The studies listed haveinvestigated a variety of pollutants including particulates, oxidants, sulphatesand even dustfall. The studies are compared using a similar measure to theNSDI. Pearce and Markandya (1989) report the % fall in property value per %increase in air pollution. In general, this figure appears to lie around the 0.1%mark but ranges from a low of 0.01% up to maximum of 0.5%.

A more detailed review of the hedonic pricing of air pollution was carried outby Smith and Huang (1995). They took data from some 37 different hedonicpricing studies and extracted information on the estimated implicit price of aunit reduction in total suspended particulates (TSP). The studies used in thisanalysis are summarised in Table 5-6.

It is clear from Table 5-6 that a large amount of variation is exhibited by theseestimates. Some of the studies even suggest a negative implicit price, which isinconsistent with our expectations concerning the relationship between propertyprices and air pollution (i.e. a negative implicit price implies that the marketprice of a house will be higher if it is located in a more polluted area). Theaverage implicit price for a unit decrease in TSP is $109.90 (in 1982-84 US$)which is several times larger than the median value of $22.40. This implies thata small number of relatively high figures are increasing the average.

Smith and Huang (1995) carry out a meta-analysis on this data. Their findingsinclude the observation that, as expected, housing markets in areas of higherincomes and higher pollution have a significantly higher implicit price for airpollution reductions.

They also note that the functional form chosen by the researcher willsignificantly change the estimated implicit price. Specifically, studies thatimpose a linear specification return higher estimates of the implicit price of airpollution than do estimates that use the semi-log specification, which in turnreturns a higher implicit price than do either the log-linear or log-logspecifications (see Section 4.4.4 for a fuller description of these specifications) .

The importance of functional form on estimation of the hedonic price functionhas also been investigated by Graves et al. (1988). They experimented with awide variety of specifications of the hedonic price function to ascertain howspecification changes influence the coefficient estimated for air pollution.Graves et al. included two air pollution variables in their regressions; a measureof visibility and total suspended particulates (TSP). The hedonic price functionwas estimated several times including or dropping a variety of explanatoryvariables and using different transformations of the variables. Graves et al.found that as the specification changed the visibility coefficient ranged frompositive and significant, as expected, to insignificant and of mixed sign. On theother hand, the particulate coefficient was reasonably stable, particularly whenboth air pollution variables were included in the regression. The Box-Coxtransformation was shown to result in a consistently better model than otherimposed functional forms (see Section 4-4-4 for a discussion of the Box-Coxtransformation).

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Table 5-6: Hedonic pricing studies of loss in property value from Air Pollution(Implicit price of a unit decrease in TSP in constant 1982 to 1984 US$)

Source Study Study Area StudyYear

Implicit Price(1982-84 US$)

Anderson and Crocker, 1971 Washington, USA 1960 -5 to 169

Kansas City, USA 1960 16 to 32

St. Louis, USA 1960 17 to 33

Appel, 1980 New York 1970 159 to 191

Atkinson and Crocker, 1982 Chicago, USA 1964 366

Bender, Gronberg and Hwang,1980 Chicago, USA 1970 159 to 234

Berry, 1976 Chicago, USA 1968 -1

Brookshire et al., 1979 Southern California Air Basin 1977 577

Brookshire et al., 1982 Los Angeles, USA 1977 149

Brucato et al., 1990 Los Angeles 1972 141 to 191

San Francisco 1978 500

Egan, 1973 Hartford 1960 1,612 to 1,808

Jackson, 1979 Milwaukee, USA 1970 551

Krumm, 1980 Chicago, USA 1971 29

Li and Brown, 1980 Boston, USA 1971 3 to 11

McDonald, 1980 Chicago, USA 1970 -240 to 160

Nelson, 1978a Washington, USA 1970 0 to 1,522

Palmquist, 1984 Multiple Cities, USA 1977 .4 to 174

Palmquist, 1982b Multiple Cities, USA 1977 -89 to 109

Palmquist, 1983 Multiple Cities, USA 1977 -76 to 98

Polinsky and Rubinfeld, 1977 St. Louis, USA 1960 36 to 38

Smith, 1978 Chicago, USA - 116 to 138

Soskin, 1979 Washington 1970 74

Source: Smith and Huang (1995)

Some studies have attempted to include more than one measure of air pollutionor account for the other disamenities of road traffic such as noise pollution andvisual disamenities (e.g. Harrison and Rubinfeld, 1978; Nelson, 1978; Graves etal., 1988; and Vainio, 1995). In some cases, problems with multicollinearityhave made this impossible (see Box 5-2 for a case example). Others, such as theGraves et al. (1988) study described above, have been more successful and beenable to distinguish the influence of two or more environmental variables relatedto road traffic.

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Box 5-2: Harrison and Rubinfeld (1978) Hedonic Pricing Study of NOx in Boston

Harrison and Rubinfeld (1978) undertook a hedonic pricing study based on data from the1970 census of the Boston Standard Metropolitan Statistical Area. Their dependent variablemeasure was the median value of owner-occupied homes in a census tract rather than thepreferred measure of actual sales prices.

As far as explanatory variables are concerned, Harrison and Rubinfeld include two structuralvariables, eight neighbourhood variables, two accessibility variables and one air pollutionvariable. The pollution variable used is the concentration of NOx. As the authors note, theNOx variable is used to proxy air quality since the air pollution variables in their data set arehighly correlated and identifying their separate impact on housing prices would be extremelydifficult. The variables used in the study are described in Table 5-7.

Table 5-7: Variable definitions for Harrison and Rubinfeld (1978) hedonic pricing study

Variable Definition

Structural:

Rooms Average no. of rooms in houses in census tract

House Age Proportion of houses in census tract built before 1940

Neighbourhood:

Black Population Proportion of population who are black

Low Status Proportion of population from low status groups

Crime Crime rate in town

Large Zoned Area Proportion of town�s residential land zoned for high status large size lots

Industry Proportion of non-retail business area in town

Tax Full value property tax rate

Pupil-Teacher Ratio Pupil-teacher ratio in town school district

Charles River Dummy variable set to 1 for census tracts that bound the Charles River

Accessibility:

Employ distances Weighted distances to 5 employment centres in Boston

Radial Highways Index of accessibility to radial highways

Air Pollution:

NOx Annual average nitrogen oxide concentrations in pphm

Particulates Annual average particulate concentrations in pphm

The authors experimented with a number of functional forms and found that using the log ofthe dependent variable gave the best fit to the data. They also employed a simplified versionof the Box-Cox transformation to find the best transformation of the NOx variable, whichtranspired to be the square of the variable. The transformations employed and the parameterestimates from the study are listed in Table 5-8.

The regression estimates conformed well to prior expectations with virtually all thecoefficients having the expected signs and being statistically significant. Based on theexplanatory variables, the model was capable of predicting housing prices with reasonableaccuracy (in statistical terms it returned a high R2 of 0.81, or 81% of the variability in the datacould be explained by the regression).

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Table 5-8: Variable transformations and parameter estimates for Harrison andRubinfeld (1978) hedonic pricing study

Variable Transformation Coefficient

Constant Linear 9.76

Structural:

Rooms Square 0.0063

House Age Linear 8.98 × 10-5

Neighbourhood:

Black Population Square 0.36

Low Status Log -0.37

Crime Linear -0.012

Large Zoned Area Linear 8.03 × 10-5

Industry Linear 2.41 × 10-4

Tax Linear -4.20 × 10-4

Pupil-Teacher Ratio Linear -0.031

Charles River Linear 0.088

Accessibility:

Employment distances Log -0.19

Radial Highways Log 0.096

Air Pollution:

NOx Box-Cox -0.0064

(Particulates) (Box-Cox) -0.051

The NOx variable has a negative sign and is highly significant. The non-linear specification ofthe NOx variable suggested by the Box-Cox transformation, means that the impact of a 1pphm (part per hundred million) change in NOx concentrations on house prices depends uponboth the level of NOx and of the other variables. Taking the means of the data Harrison andRubinfeld estimate that a 1 pphm change in NOx alters median house values by $1,613.

The authors re-estimated the model replacing the NOx variable with a measure of theconcentration of particulates. Again the variable was highly significant. Harrison andRubinfeld note that the coefficients on the non-pollution variables are virtually the same usingeither of the two pollutants. This result adds credence to the view that the various pollutionvariables are reflecting households’ aversion to pollution generally rather than to individualpollutants.

One further interesting observation from this study was that changing the specification of thenon-pollution variables in the housing value equation alters the results substantially. When thetwo accessibility variables were deleted the coefficient on NOx

2 changed from –0.0064 to –0.0036. Since NOx concentrations are highly correlated to proximity to major employmentcentres and radial highways, deleting the accessibility variables results in the parameter forNOx reflecting not only the disadvantages of higher NOx concentrations but also theadvantages of greater accessibility.

Source: Harrison and Rubinfeld (1978)

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5.3.3 Summary

Researchers have achieved considerable success in using the hedonic pricingmethod to distinguish the impact of air pollution on housing prices. In general,to avoid problems with multicollinearity, it is necessary to choose only onevariable, frequently particulates, to stand as a proxy for the variety of pollutantsthat result from road traffic.

Smith and Huang�s (1995) review of a number of studies measuring the implicitprice of particulates has shown that the implicit price determined by a housingmarket increases with both income and ambient levels of air pollution. This is agood result as it conforms to our prior expectations. However, they alsoconcluded that the functional form chosen by the researcher could significantlyinfluence the estimated implicit price.

A comprehensive investigation of this issue by Graves et al. (1988) concludedthat the Box-Cox transformation provided the best specification for theirhedonic pricing model.

From a survey of a large number of hedonic pricing studies, Smith and Huang(1995) provide two figures as an indication of the �average� implicit price of airpollution reductions. The more conservative of these is the median value of$22.40 per unit decrease in TSP.

5.4 Summary and ConclusionsAs this review has shown, a large weight of evidence has now been amassed tosupport the contention that both noise and air pollution are capitalised in the priceof property. In a very large number of studies, the hedonic pricing method has beensuccessfully employed to identify the size of this impact.

In noise pollution studies NSDIs (i.e. the percentage decrease in housing pricesfollowing a 1 dB increase in noise pollution) have been reported that range from0.08% to 2.22% for road traffic noise and from 0.29% to 2.3% for aircraft noise.With specific reference to road traffic noise, researchers have suggested that an�average� value lies somewhere in the lower part of this range. Nelson (1982)reviewing 14 studies for the United States and Canada concludes that the averageNSDI is around 0.4% whilst more recent work by Bertrand (1997) suggests theaverage figure may be as high as 0.64%.

Hedonic studies including air pollution have also returned a wide variety ofimplicit prices. Smith and Huang (1995) reviewing some 37 different empiricalstudies conclude that a measure of the average implicit price of air pollution is$22.40 per unit decrease in TSP (total suspended particulates).

Though calculating average figures is a useful exercise, we should not be surprisedto find that housing markets with different characteristics have different hedonicprice schedules and, therefore, different implicit prices for noise and air pollution.Indeed, research suggests that the implicit price of both noise and air pollution willbe higher in property markets where households are relatively more wealthy andwhere the general level of pollution is relatively higher (Bertrand, 1997; Smith andHuang, 1995).

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A major problem facing researchers is in distinguishing the different impacts onhousing prices of highly correlated variables. For example, air pollution resultingfrom road traffic takes a variety of forms (e.g. CO, NOx, particulate matter etc.).Whilst each pollutant may have different impacts on households� well-being (e.g.health impacts, soiling, physical corrosion of buildings etc.) the high level ofcorrelation between the concentrations of these pollutants makes it nigh onimpossible to distinguish their separate impacts on the price of properties. Ingeneral, this has resulted in researchers including only one or at most two (e.g.Graves et al., 1988) measures of pollution that act as proxies for all air pollutants.

The same problem may occur in distinguishing the separate impacts of air andnoise pollution emanating from road traffic. Only a handful of studies haveattempted to estimate hedonic regressions with both air and noise pollutionvariables specified (e.g. Nelson, 1978 and Vainio, 1995). Of course, this problem isto a certain extent due to the quality of data used in the analysis. For example, astudy that proxies both noise and air pollution by using the distance of the propertyfrom a main road will clearly be unable to distinguish the separate impacts of thetwo pollutants. However, as techniques for determining the exposure of differentproperties to noise and air pollution improve it may be possible to achieve moreaccurate results. One possibility is that GIS will provide just such a capability.

For the purposes of the current research, the correlation between noise and airpollution from road traffic is not a major constraint. Whilst it may prove difficult todistinguish the exact impact of each pollutant separately, it should be possible topick up their combined influence on housing prices. Of more concern is thecorrelation between these pollutants and the other impacts of living in closeproximity to a main road that do not warrant compensation under the LandCompensation Act. Most importantly, we would wish to distinguish the impacts ofnoise and air pollution from the visual disamenity of living close to a road and thebenefits of greater accessibility.

A number of researchers have investigated the impact of views on property prices(e.g. Mcleod, 1984, examined the impacts of river views on property prices inPerth, Western Australia and Anderson and Cordell, 1988, examined the effect oftrees on property prices in Georgia, USA) and it is clear that the views of, andfrom, a house can considerably influence its selling price (e.g. Mcleod, 1984,estimates a 28% increase in property prices resulting from river views andAnderson and Cordell, 1988, demonstrate that houses with five or more trees intheir front garden are around 4% more expensive). Hence, it would seemreasonable to assume that a property�s visual exposure to roads and road trafficwill impact on its market price.

As suggested in the last section, GIS have provided a means whereby much greateraccuracy can be introduced into the measurement of variables. Indeed, it is possibleto use GIS to accurately measure the visual exposure of properties to roads (Lakeet al., 1998). Such measures must be included in hedonic analyses if the impacts ofnoise and air pollution are to be distinguished from the visual disamenity ofoverlooking roads.

A final issue that has been important in the specification of hedonic models is thechoice of functional form. Smith and Huang�s (1995) review of a number of airpollution studies determined that the functional form chosen by the researchercould significantly alter the estimated implicit price. A comprehensive

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investigation of this issue by Graves et al. (1988) concluded that the Box-Coxtransformation provided the best specification for their hedonic pricing model.

In general, it would seem that the hedonic method can be successfully employed tomeasure the impact of noise and air pollution on property prices. As theory wouldsuggest, the empirical literature suggests that these impacts will vary from marketto market. Distinguishing the separate influence of noise and air pollution may berelatively difficult though it is essential to include comprehensive measures ofaccessibility and the visual disamenity of roads. If this is not done then it is likelythat the implicit prices estimated for noise and air pollution will erroneouslyinclude the impacts of these factors.

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6 LITERATURE REVIEW: SUMMARY AND CONCLUSIONS

1. The expansion of the road network and of vehicle ownership provides a number ofbenefits to society. These include contributing to economic growth and providingpeople with vastly increased choice and mobility. However, the construction ofnew roads, and the associated expansion of road traffic, can result in severalexternalities being imposed on local residents. Amongst these we would includethe disruption and pollution caused by construction work, the impact on the visualenvironment, the severance of communities by busy roads, and the noise and airpollution resulting from increased traffic.

2. Part 1 of the Land Compensation (Scotland) Act 1973 requires that the Secretaryof State for Scotland (Scottish Ministers) pays compensation to householderswhose properties decline in price as a result of these externalities. Compensationis only payable on depreciation in property value that can be attributed to certainphysical factors resulting from increases in road traffic (e.g. noise, vibration andair pollution). Compensation should not be paid for property price depreciationresulting from the visual disamenity generated by the presence of the new road.Nor should the impacts of the construction process or any barrier effect beconsidered. Finally, any enhancement in the price of a property arising from theroad (e.g. property prices may rise in an area if it becomes easier to commute to anearby centre) should be deducted from the compensation payable as a result ofthe physical factors.

3. House prices are determined in the property market through the interaction ofbuyers and sellers. Houses are complex goods that exhibit a large variety ofcharacteristics (e.g. number of rooms, size of garden, proximity to local amenities,quietness of neighbourhood etc.). Each characteristic of a house will influence theprice that house commands in the market. Economic theory suggests that themarket establishes an equilibrium price schedule for properties. This schedule isknown as the hedonic price schedule because we can disaggregate the total priceinto a series of implicit prices which reflect the benefits or pleasure (hencehedonic) that households gain from each of the individual characteristics.

4. The price of a property should not be confused with the value of a property to aparticular household. Value, as defined by economists, is the maximum amountthat consumers are willing to pay for the goods they purchase. In this particularcase the goods in question are the quantities or qualities of the differentcharacteristics of a property. In general, a household will only purchase a unit of agood if it values that unit more than the price it would have to pay for it. Thedifference between price paid and value is known as consumer surplus. Value canbe measured as the area underneath the marginal willingness to pay or demandschedule for all units of the good consumed.

5. Environmental economists have developed a number of methodologies designedto establish the value of environmental amenities. Some of these techniquesestimate the value of changes (�valuation� techniques), others estimate somemeasure of the cost of changes (�pricing� techniques). In general, the �pricing�techniques, whilst providing some indication of the cost of losing (or possiblygaining) environmental amenities, do not have a sound theoretical basis and wherepossible �valuation� techniques should be preferred.

6. There exists a further subdivision of �valuation� techniques into those that employsurveys to directly enquire of households how highly they value a good (expressed

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preference techniques) and those that observe the actual choices of households inmarkets to deduce their valuation of a good (revealed preference techniques).Whilst expressed preference valuation techniques are specifically designed toestimate changes in value they are not appropriate for the measurement of changesin market prices. Since it is changes in the price of properties afflicted byenvironmental disamenities caused by traffic that are of interest in this project,only one valuation technique is entirely appropriate; the hedonic pricing valuationtechnique.

7. The hedonic pricing technique, as its name suggests, relies on the revelation of ahousing market�s hedonic price schedule. Using data on the selling prices ofhouses in a single property market, statistical techniques can be employed toestimate the hedonic price schedule. In other words, by observing how much ispaid for houses with different characteristics, it should be possible to estimate howthe individual characteristics of a property influence its overall price. If thephysical factors resulting from traffic on a new road do have an impact on theprice of a house it will be revealed in the hedonic price schedule and estimates canbe made of the size of this impact.

8. Researchers have raised a number of theoretical concerns over the validity of thehedonic price valuation technique. However, provided care is taken in theselection of data, the assumptions of the hedonic price model are not so unrealisticas to make our estimations meaningless.

9. A large weight of evidence has now been amassed that supports the contentionthat both noise and sound pollution are capitalised in the price of property. In avery large number of studies the hedonic pricing method has been successfullyemployed to identify the size of this impact.

• In noise pollution studies NSDIs (i.e. the percentage decrease in housing pricesfollowing a 1 dB increase in noise pollution) have been reported that range from0.08% to 2.22% for road traffic noise. Researchers have suggested that an�average� value lies somewhere in the lower part of this range.

• Air pollution studies have also returned a wide variety of implicit prices. Smithand Huang (1995) reviewing some 37 different empirical studies conclude that ameasure of the average implicit price of air pollution is $22.40 per unit change inTSP (total suspended particulates).

10. Though calculating average figures is a useful exercise, we should not besurprised to find that housing markets with different characteristics have differenthedonic price schedules and, therefore, different implicit prices for noise and airpollution. Indeed, research suggests that the implicit price of both noise and airpollution will be higher in property markets where households are relativelywealthier and where the general level of pollution is relatively higher (Bertrand,1997; Smith and Huang, 1995).

11. The literature suggests that researchers are faced with a number of empirical andestimation problems in attempting to derive the hedonic price schedule fromhousing market data. These problems include;

• Dependent Variable. Researchers are agreed that the most preferred source of datais records of actual sales prices on individual properties. Every effort should bemade to ensure that this data is gathered from a single housing market bothgeographically and temporally. If doubts exist, statistical tests can be employed toprove or disprove the assertion that data is from a single market.

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• Explanatory Variables. A large number of characteristics of a property are likelyto affect its price. In general, we can divide these up into four categories;structural, accessibility, neighbourhood and environmental variables. Includingaccurate measurements of all the relevant explanatory variables in thespecification of the hedonic price function is extremely important. It is a fact ofregression analysis that leaving out or mismeasuring explanatory variables canlead to bias in the estimation of the regression parameters. For example, if wefailed to include a variable reflecting the visual disamenity of roads then it islikely that our estimate of the impact of noise pollution would be too large,erroneously including the impacts of overlooking a road.

Geographical Information Systems (GIS) provide an ideal tool for improvingthe measurement and compilation of explanatory variables. For example, it ispossible to use GIS to accurately measure the visual exposure of properties toroads (Lake et al., 1998). If reliable measures of both the visual disamenity ofoverlooking a road and the pollution generated by traffic are included in theestimated hedonic price function it should be possible to distinguish theseparate impact of the two. Similarly, GIS can be used to simply and calculateaccurately accessibility variables such as car travel times to importantamenities. The values calculated by GIS can reflect the actual distancetravelled on the road network taking account of typical speeds along differentclasses of roads.

• Multicollinearity. A major problem facing researchers is to distinguish thedifferent impacts on housing prices of highly correlated variables. For example,air pollution resulting from road traffic takes a variety of forms (e.g. CO, NOx,particulate matter etc.). Whilst each pollutant may have different impacts onhouseholds� well being (e.g. health impacts, soiling, physical corrosion ofbuildings etc.) the high level of correlation between the concentrations of thesepollutants makes it nigh on impossible to distinguish their separate impacts on theprice of properties. In general, this has resulted in researchers including only oneor at most two measures of pollution that act as proxies for all air pollutants.

Unfortunately, there is no easy solution to the problem of multicollinearity andin its presence estimated regression parameters may be implausibly large or inthe worst case, have the wrong sign. It is possible that problems ofmulticollinearity can be overcome through more accurate measurement ofvariables. For example, if data on noise pollution from roads takes account oflocal features such as trees and banks that act to dissipate traffic noise, then thecorrelation of noise pollution with air pollution may be less distinct. Onceagain, the use of GIS may be invaluable in improving the accuracy of variablemeasurement.

• Functional Form. Another matter that has been important in the specification ofhedonic models is the choice of functional form. This refers to the mathematicaltransformation that is assumed to best describe the relationship between eachexplanatory variable and the dependent variable. Smith and Huang�s (1995)review of a number of air pollution studies determined that the functional formchosen by the researcher could significantly influence the estimated implicit price.Fortunately, an accurate, though complex solution to this problem exists. That isto use what is known as a flexible functional form. This approach involves theestimation of a further set of regression parameters that dictate the best possibletransformations of the variables. A comprehensive investigation of this issue by

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Graves et al. (1988) concluded that a flexible functional form known as the Box-Cox transformation provided the best specification for their hedonic pricingmodel.

• Spatial Dependence. One final issue in the estimation of hedonic price functions isthat of spatial dependence or correlation. Spatial dependence results from the factthat properties in close proximity to each other often share very similarenvironmental, accessibility and neighbourhood characteristics. If it were possibleto include all these characteristics as explanatory variables, then similarities in theselling prices of neighbouring properties would already be accounted for.Unfortunately, this is infrequently the case and correlation between the prices ofproperties in close proximity is not explained in the estimated hedonic priceequation. Traditional regression analysis assumes that individual properties will beindependent observations, but if there is a high degree of spatial dependence thiscondition will be violated, leading to biased estimates of significance forexplanatory variables. Recently, researchers have begun to address this problemand borrowing procedures developed by geographers have developed regressiontechniques that account for spatial dependence (e.g. Irwin and Bockstael, 1998;Bell and Bockstael, 1997).

Research has shown that the hedonic price valuation technique can be successfullyemployed to estimate the impact that a change in environmental quality will have onthe prices of properties in a market. The quality and accuracy of the results from suchan analysis will depend to a large extent on how successfully the issues raised in thisdiscussion are tackled. Over recent years advances in estimation techniques andcomputer technology have allowed researchers to reach levels of sophistication inhedonic price modelling previously unimagined. For example, GIS provide a verypowerful tool for compiling large and accurate datasets. Similarly, models usingflexible functional forms and accounting for spatial dependence minimise thepossibility of mis-specification bias. The challenge that faces the researcher is to takeadvantage of these advances to estimate a hedonic price model that is well specifiedand provides results which prove logically defensible and robust.

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7 STUDY DESIGN ISSUES

7.1 OverviewSection 3 concluded that in order to determine the correct level of compensationthat should be given under Part 1 of the Land Compensation (Scotland) Act 1973 ahedonic pricing (HP) approach was necessary. The aim of this research was todetermine what the correct level of compensation should be. We have no priorexpectations about whether the current levels of compensation are too high or toolow.

A HP study involves obtaining the selling price of a large number of properties.For each property we then need to obtain information on its structural,neighbourhood and accessibility characteristics. Once these have been controlledfor in a property price model, we can include environmental variables such as thoserelating to compensatable factors under Part 1 of the Land Compensation(Scotland) Act 1973 and other characteristics. This will derive the amount thatphysical factors such as road noise currently impact upon property prices. Theseprices can then be transposed into the compensation levels that should be paid tohouseholds impacted upon by new road developments.

7.2 The use of a Geographical Information System in thestudy

Previous HP studies have been hindered by the time and effort required to collectexplanatory variables of sales price for a large number of properties. As anexample McLeod (1984) only had a sample size of 168 because he had to inspecteach house in turn. Similar constraints led to a sample of only several hundred forAnderson & Wise (1977) as their methodology involved placing noise loggers ateach property. As well as depressing sample size, data collection problems havealso led to studies where some of the factors affecting property prices were notspecified. For instance, Levesque (1994) did not control for neighbourhoodvariables when examining the impact of aircraft noise upon property prices. Thiscan lead to misleading results being produced.

In this research these constraints were overcome through use of a GeographicalInformation System (GIS). A GIS is a system for capturing, storing, checking,manipulating, analysing and displaying spatially referenced data (DoE, 1987). Oneadvantage of using a GIS is that, given suitable digital data, a wide variety ofvariables (e.g. measures of accessibility or structural characteristics such as floorarea) can be calculated in an automated manner. However, if a GIS merelyduplicated the procedures used in previous studies then its full potential would notbe realised. As a GIS also provides the functionality to calculate moresophisticated explanatory variables, it offers other means of improving uponprevious research (Lake et al., 1998). For instance, Anderson & Wise (1977)measured the visual impact of a road by recording whether it was visible from thesample properties. Using GIS more sophisticated measures of impact can bederived, including the amount of visible road and its distance from the property.

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Most of the GIS analysis used three large-scale digital map datasets produced bythe Ordnance Survey (OS) of Great Britain. The main source of information wasLand-Line.Plus, a vector database recording the locations of most ground featuresto a spatial accuracy of 40 cm. This included features such as road centrelines andthe outlines of individual buildings. Land height data was provided by Land-FormPROFILE and the ADDRESS-POINT dataset provided grid references for alladdresses in the study area.

7.3 Study Area and House Price InformationThe study area was defined to cover two areas to the south and north west ofGlasgow city centre. These areas were chosen for a variety of reasons. First, bychoosing an urban area the study yields a high density of property sales per unitarea, thus reducing computing requirements to acceptable levels. The secondreason is that Glasgow is a very diverse city in terms of its social makeup andhousing characteristics. This means that any estimates of property pricedepreciation due to roads may be more applicable to a range of housing types andsocial areas thus enhancing, although by no means ensuring, the transferability ofthe results. Finally the choice of Glasgow as a study area built upon an ongoingstudy by the authors thereby reducing the time and cost requirements for the study.

The basis of any HP study is information on property sales in the study area, and inScotland the Registration of Title records, the address and selling price of allproperty transactions. House price information was therefore extracted from thisregister for the study area providing a sample of over 4000 individual properties.The next stage in the study was the calculation of variables relating to the structure,neighbourhood and accessibility characteristics of all these properties.

7.4 Deriving explanatory variables on the structure,neighbourhood and accessibility of the sample properties

7.4.1 Structural variables

In many HP studies structural variables are obtained from a surveyor�sinspection of each property. However, this information was not readily availableand the cost of eliciting this through house visits or questionnaires would beprohibitively expensive. Therefore structural variables were specified through aGIS-based analysis of each property�s outline on Land-Line.Plus. This methodpermitted us to specify variables such as property type, building area and plotarea.

However, a purely GIS approach would not permit factors such as the numberof storeys, construction material, or property age to be included in the analysis.These variables can be determined through a simple external visual inspection(Lake et al., 1998). Therefore the GIS analysis was supplemented with a visualinspection of each property. All the structural variables, which were collected,are presented in Table 7-1.

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Table 7-1: Structural variables collected for each property

Method ofDetermination Variable

GIS: Floor area

Plot area

Aspect

Slope

Property type

Visual inspection: Number of storeys

Property age

Presence of basement

Presence of attic conversion

Construction material

Pitched or flat roof

7.4.2 Neighbourhood variables

Neighbourhood variables define the quality of a property�s surroundings, and inScotland the best source of social data on individual small areas is the 1991census (Lake et al., 1998). This was obtained by surveying every household inScotland and publishing the results by grouping properties together to preserveconfidentiality. The census contains a wide range of information varying fromthe amenities present in each house, to data on the social characteristics of theinhabitants. Some information is coded for all households while more detaileddata are only recorded for 10% of households (Openshaw, 1995).

In previous studies (Pennington et al., 1990; Garrod & Willis, 1992)neighbourhood variables were calculated at one spatial scale. However,although the immediate surroundings of a property may affect its price the widerneighbourhood may also have an impact. GIS has the flexibility to calculateneighbourhood variables at a variety of different spatial scales.

Scottish census data are released at two detailed spatial scales. The output areais the smallest census unit and, for the study area, consists of an average of 56households. The other unit is the postcode sector that encompassesapproximately 2770 households. Postcode sectors contain information that isnot released at the scale of output area for reasons of confidentiality. Thereforeneighbourhood variables were calculated for each property based upon thesetwo spatial scales.

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7.4.3 Accessibility variables

Accessibility variables define the ease with which people can travel to localamenities. Although a wide range of facilities may be selected, six were used inthis study. These were chosen on the grounds that they might affect propertyprices, and were relatively straightforward to locate. The selected amenitieswere:

• Grocers and newsagents;

• Public parks;

• The Central Business District;

• Railway and underground stations;

• Primary schools;

• Bus routes.Accessibility variables should relate to people�s perception of distance and thismay bear little resemblance to how people actually travel to the amenity. Theflexibility of GIS permitted us to calculate three different measures ofaccessibility from each property to each amenity. These were straight-linedistance, car travel time and walking distance. The HP model will demonstratewhich measure has the greatest correlation with property price.

7.5 Incorporating the Seven Compensatable Physical Factorsinto the Study Design

Part 1 of the 1973 Land Compensation (Scotland) Act states that compensation ispayable to householders who have experienced a drop in their property price due tothe use of a new road. However, compensation is only payable for the impacts of 7physical factors namely:

• Noise

• Vibration

• Smell

• Fumes

• Smoke

• Artificial lighting

• Solid and liquid dischargesIt is important to note that there are other impacts of road traffic, which are notcompensatable under the Act. These include visual impact and loss of privacy. Indetermining which of the seven physical factors to include in the HP model weneed to consider several points:

• How easy can quantitative measures of the factor be obtained?

• How often does the factor appear in land compensation claims?

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• How likely is the factor to affect property prices?

• Are some factors likely to be highly correlated?After careful consideration of all these points we concentrated on the physicalfactor �noise� for several reasons. The first is that from our discussions with theScottish Executive and the Valuation Office Agency it appears that most Part 1claims cite noise as the main factor for which compensation is sought.

Although a house may be negatively impacted upon by any of the physical factors,compensation is only payable if the house price is impaired. This will only occur ifpotential buyers perceive the factor and hence bid a lower price for the property. Itis highly likely that most buyers will not be aware of factors such as artificiallighting and land discharges irrespective of whether they affect the currentresidents.

It is likely that noise is the major road impact factor that people will perceive whenthey visit a house. It is also likely to be strongly related to many of the otherphysical factors such as vibration, smell, fumes and smoke. Therefore weanticipated road noise to be the main impact and expected that collinearity wouldmake it impractical to include the other factors in the same HP model.

We also felt that the study should concentrate upon one factor because, if everyphysical impact had to be recorded at each property for a claim to be processed,then the costs would become prohibitive. We were fully aware that any new systemof assessing Part 1 claims should not lead to a huge increase in the costs ofassessing the claim.

Therefore in this study we concentrated upon noise for the following reasons:

• Noise constitutes the vast majority of compensation claims

• It is intuitive that people will perceive noise when they are considering a propertypurchase

• Noise will act as a surrogate for many of the other factors

• It would be prohibitively expensive to quantify all the other impacts.

7.6 Road NoiseSection 5 briefly discussed the different factors determining the noise level at anyproperty. This study calculated a noise level at each individual property. Byincluding these values in the HP model we were able to quantify how road noiseaffects property prices. In order to calculate a noise level for each property data ontraffic volumes and compositions along roads were obtained from Glasgow CityCouncil. This only included a selection of all the roads in the study area. Thereforevarious interpolation techniques were used to obtain estimates of traffic volumeand composition for all roads. A traffic noise level was then calculated at eachproperty using the standard UK noise calculation method employed by theDepartment of Transport. This is known as the Calculation of Road Traffic Noise(CRTN). In order to implement CRTN various physical parameters such as thedistance from the road to the house need to be determined. All these werecalculated using large-scale digital data and the GIS.

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7.6.1 Accounting for the Non-Compensatable Impacts of a New Road

One problem with the current compensation process is the difficulty inapportioning property price depreciation between compensatable and non-compensatable factors. In the HP regression, if we wish to calculate the amountthat road noise by itself affects property prices we need to include in theregression variables relating to uncompensatable road impacts with which noisemay be correlated. Therefore variables relating to road accessibility and visualintrusion were calculated and incorporated into the model.

The accessibility impacts of roadsRoads have positive benefits as they permit easy access to a variety of localamenities. These impacts were covered by accessibility variables, which havealready been discussed.

The visual impact of roadsIn order to incorporate the visual impact of roads into the analysis we need tocreate measurements of the amount of road that is visible from each property.Using the large-scale digital data and a GIS we calculated �viewsheds� for eachproperty. These showed the area of land that was visible from each property bytaking into account the height of the land and the heights of features such asbuildings. These were then combined with the location of the roads to determinethe area of roads visible from each property. If these variables are included inthe HP model, the estimates that are derived for the impact of noise uponresidential property prices will take account of the visual impact of roads.

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8 THE HEDONIC PRICING STUDY: DEFINING VARIABLES

8.1 IntroductionIn Section 7.3 the justification was provided for locating the study area in theSouth and North West of Glasgow. This area is illustrated in Figure 8-1. Withinthe study area all property sales were extracted during the year 1986, a year chosenbecause of the ready availability of many of the datasets. The results, detailedsubsequently, demonstrate that noise accounts for a given proportion of a propertyprice rather than some constant amount. Therefore as property prices increasethrough time the quantity of the house price attributable to noise will also increasein absolute terms. The fraction of the house price attributable to noise would onlychange through time if people became more sensitive to noise, and there is noevidence to suggest that this is the case.

The property sales information was obtained from a copy of the Registration ofTitle held at the Land Value Information Unit at the University of Paisley. Thesewere cross-referenced with another copy of the Register obtained from the Centrefor Housing Research and Urban Studies at the University of Glasgow. In order toensure that our property sales data was accurate, only properties with identicalentries on both copies of the Register were selected for the study.

The process of concentrating upon two subsections of Glasgow and only selectingproperties with identical entries on two copies of the Registration of Title reducedour sample to 3868 properties or 26.7% of all the property sales in Glasgow thatyear. These addresses were spatially referenced using OS ADDRESS-POINT,which provides a unique grid reference for each postal address in the UK (Martinet al., 1994). The locations of all the sample properties are illustrated on Figure 8-1.

In order to estimate the effect that road noise has upon property prices, thestructural, neighbourhood, accessibility and environmental attributes, including theroad noise level, of all the properties, need to be calculated. This section discussesthe procedures used to define each of these attributes.

8.2 Structural variablesStructural variables define the character of each building and the plot upon which itis constructed. A wide range of structural variables were calculated for eachproperty including ground floor area, plot area and property type. The proceduresapplied to calculate such variables are described in this section. Most of thestructural variables were calculated from OS Land-Line.Plus digital map data.This source records the locations of ground features, such as buildings and fences,with a spatial accuracy of 40 cm (OS, 1996). Samples of these data are illustratedin Figure 8-2.

8.2.1 Ground floor area

To determine the ground floor area of a property it is necessary to know boththe outline of the building within which the property is located and the extent to

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Figure 8-1: The study area

REPRODUCED FROM THE ORDNANCE SURVEY ADDRESS-POINT WITHPERMISSION OF HER MAJESTY'S STATIONERY OFFICE © CROWN COPYRIGHT

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which the building is subdivided into different properties, a terrace row being anexample of a building subdivided into different properties. On Land-Line.Plusthe boundaries of individual buildings are coded as building outlines. Thedividing walls between buildings are coded as �general line� and �peck� detail.This is illustrated in Figure 8-2.

Automatic procedures were developed to extract these lines from Land-Line.Plus. These were then converted into polygons to define the floor area ofeach individual property. The accuracy of the method was assessed by visuallyinspecting each of the floor area polygons. This demonstrated that most of thefloor areas (97.2%) were correctly delineated. The 2.8% that were not correctwere manually adjusted by extending and deleting lines as appropriate.

8.2.2 Plot area

Plot areas are not delineated as separately labelled lines on Land-Line.Plus.However, the landscape features associated with plot boundaries, such as hedgesand fences, are recorded as general line detail along with other features such aspaths and garden sheds (see Figure 8-2). As a consequence, the delineation ofplot boundaries involves separating the relevant elements of general line detailfrom the other lines covered by this coding.

In defining the plot areas a careful balance had to be struck between automationand manual editing. Greater automation increases the speed at which plot areascan be defined, but too much automation can lead to inaccuracies. Theprocedure developed defined the majority of boundaries through a set ofautomated processes involving relatively straightforward simplifications of thedata while manual editing was used to determine the remaining more complexdelineations. The automated procedures can be divided into several stages,these being illustrated in Figure 8-3.

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Figure 8-2: An example of data taken from OS Land-Line.Plus

0 100 200 Meters

KeyBuilding outlinesGeneral line detailRoad centrelinesRoad edgesRailway lines

N

EW

SREPRODUCED FROM THE ORDNANCE SURVEY 1:1250 Land-Line.Plus WITH PERMISSION OF THE CONTROLLER OF HER MAJESTY�S STATIONERY OFFICE (c) CROWN COPYRIGHT

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Figure 8-3: The definition of plot areas

Basic plot area information can be obtained from the general line and peckdetail given in Land-Line.Plus (step 1 in Figure 8-3). However, when theselines were extracted the derived coverage contained many dangling arcs, such aswhere garden paths had been joined to houses. Any of these lines less than 4min length were deleted (step 2), which led to further dangling arcs in locationssuch as property entrances. Joining together all dangling arcs within 4m of eachother (step 3) closed these. Following this, island polygons (polygons notcontiguous to any other polygon), representing features such as garden sheds,were deleted using the criterion that they consisted of one line starting andending at the same point (step 4). Finally, single unconnected lines were

1) General line and peck detailextracted from Land-Line.Plus

2) Small dangling arcs are deleted

3) Dangling arcs snapped

4) Island polygons are deleted

5) Unconnected lines deleted, plotareas defined

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deleted using the criterion that both nodes would only be joined to one line (step5).

These automated processes correctly delineated over half of the plot boundaries.Those that were not so delineated could be classified into one of the three casesillustrated in Figure 8-4. Case 1 in Figure 8-4 concerns houses with open plangardens (i.e. gardens with no firm boundary features such as hedges). Here theassumption was made that the missing boundary would be a linear extension ofany existing boundaries. Other situations where buildings or road edges formedthe plot boundary (cases 2 and 3 respectively) were corrected by adding theappropriate line.

Figure 8-4: Difficulties in defining plot areas

8.2.3 Property type

Basic property type was defined by identifying the polygon representing theground floor area of each property and examining its connectivity with otherproperties. Once the ground floor area of each property had been delimited (seeSection 8.2.1) the attributes of the arcs making up each property, includingdetails of the polygons either side of each line, were exported into a database.Detached houses were identified on the criterion that they would consist of onlyone line. Mid terrace houses consisted of four lines, two of which borderedother houses. From the remaining properties, the semi-detached and end terracehouses could be classified. Both of these were represented by two lines, one ofwhich bordered another property, the difference between them being that if the

Case 2: Buildings formplot outline

Case 3: Road edge formsplot outline

Case 1: Open plan frontgardens

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bordering property was a mid terrace, then the property under consideration hadto be an end terrace, otherwise it was semi-detached.

A second stage in determining property type involved intersecting theADDRESS-POINT database with the floor area polygon to identify the numberof addresses within each property. Results from this procedure enabled eachproperty to be further classified. For example, a semi-detached property withtwo adjoining addresses was coded as a maisonette, while a detached propertywith a large number of ADDRESS-POINT records was interpreted as a towerblock.

8.2.4 Other structural variables

Through the use of Land-Line.Plus further structural variables were calculatedfor each property including the ratio of floor perimeter to the square root ofarea, which provided an indication of shape (Blamire and Barnsley, 1996). Theslope and aspect of the land upon which the property was built were alsorecorded. These latter measures were derived from the digital elevation model,which is discussed in Section 8.5.1.

During the analysis, it was decided to supplement the GIS procedures describedabove with a simple external inspection of each property during which variablessuch as construction material and number of storeys were noted. This was usedin case some of the variations in property price were not being explained by thevariables determined solely on the basis of GIS and large-scale digital data.One limitation with the entire methodology was that variables relating to aproperty�s interior could not be recorded. Therefore potentially importantfactors such as type of heating, double-glazing, and the property�s internal stateof repair were not included in the analysis.

A total of 53 structural variables were ultimately created for each property asdetailed in Annex D.

8.3 Neighbourhood variablesNeighbourhood variables describe the characteristics of the local area in which theproperty is built and census data are a good indicator of these attributes. InScotland census data are released at two different spatial scales. The smallest ofthese is the output area (OA) which on average encompasses approximately 56households. Data are also released at a larger scale; that of the postcode sector, anarea which covers approximately 3700 households. Using the GIS each propertywas matched to census derived variables at three spatial scales varying from OA, toall OAs within 200m of the property, and finally to the postcode sector level.These allowed the consideration of local as well as wider neighbourhood effects.Census data were then calculated for each of these areas. These processes areillustrated in Figure 8-5 using the example of percentage unemployment.

8-8

Figure 8-5: The calculation of neighbourhood variables at different spatialscales: Unemployment rates

Approximately forty neighbourhood variables were calculated at each of the threespatial scales for each property using data from the 1991 UK census. Theseincluded variables relating to the social quality of the surrounding area (such as thepercentage unemployment, the percentage of single parents and the percentage oftwo car households) and the quality of the local housing stock (such as thepercentage of one room houses, and the percentage of households without centralheating). Annex D contains a complete list of all the neighbourhood variables usedin this study.

8.0% unemployment

Sample property Output area

6.5% unemployment

7.5% unemployment

9.5% unemployment

6.0% unemployment

8.0% unemployment

8.5% unemployment

9.0% unemployment

7.0% unemployment

Weighted averageunemployment = 7.5%200 m

Spatial scale 1: output area

Spatial scale 2: wider output area

Spatial scale 3: Postcode Sector

Postcode Sector

8-9

8.4 Accessibility variables

Accessibility variables define the ease with which local amenities can be reachedfrom each property. For this study primary schools, bus routes, railway stations,shops, parks, and the city centre were all considered as potentially pertinent localamenities. The addresses of these facilities were recorded using various localdirectories, and an accurate grid reference obtained for each through use of theADDRESS-POINT and Land-Line.Plus data. Three separate measures ofaccessibility were then calculated from each property to the nearest of each type ofamenity, these being car travel time, walking distance and straight-line distance.So, for each property we calculated the straight-line distance, car travel time andwalking distance to the nearest shop, the nearest park etc. etc.

Figure 8-6: Different measures of accessibility from each property (using theexample of proximity to shops).

Straight-line distance is merely the ‘as the crow flies’ distance between each houseand the nearest of each amenity. This is illustrated on panel 1 of Figure 8-6. The

Shop

Property

Shop

Property

Shop

Property

145 m

20sec

22sec

8sec

6sec

5sec20sec

46m 36m

37m76m

38m96m

51m

36m

Straight line distance from property tonearest shop = 145 m

Minimum travel time from property tonearest shop = 39 sec

Minimum walking distance fromproperty to nearest shop = 173m

RoadPathMeasured route

Panel 1

Panel 2

Panel 3

8-10

accessibility measures based upon car travel times are calculated from the roadnetwork. For each section of road, the time that a car would take to travel along itslength is estimated on the basis of the length of the road and the likely speed of thevehicle (road speeds being based upon DOT, 1993). The shortest travel timebetween each property and the nearest example of each amenity type is thencalculated (panel 2). Walking distance is calculated by using a road network,amended to exclude routes unsuitable for pedestrians (e.g. motorways) andsupplemented with pedestrian only routes such as paths and footbridges. The latterwere identified from Land-Line.Plus. The distance along each section of thisnetwork was then calculated and the shortest walking distance from each propertyto its nearest example of each amenity determined (panel 3).

In total 18 accessibility measures were generated for each property. These arepresented in Annex D.

8.5 Environmental variablesHaving defined variables relating to the structure, neighbourhood and accessibilityof each property, environmental variables were specified. A complete list of thesevariables is presented in Annex D. In addition to defining the road noise level ateach property we also calculated variables relating to the visual impact of a road.This would ensure that any depreciation in property price due to road noise isindependent of the visual impact of the road.

8.5.1 The visual impact of road and other land uses

Measures of the visual impact of roads for each property were calculated using athree-stage process. The first involved calculating the area of land visible fromeach property. This was then combined with a land use map to identify the typeof view that was visible. Finally measures of impact were derived by weightingeach visible cell by its distance from the observer such that cells nearer to agiven property are accorded greater weight in the assessment of visual impact(Howes and Gatrell, 1993).

To calculate what can be seen from each property a 3 dimensional digitalelevation model (DEM) was created for the study area. This was composed oftwo components, the first of which was land elevation. Land height contoursfor the study area were extracted from Land-Form PROFILE for the study area(step 1 in Figure 8-7). This is a dataset produced by the OS and containselevation contours taken from 1:10 000 scale maps (OS, 1996). Byinterpolating between the contours, a 3 dimensional model of the land surfacecan be produced as illustrated in step 2 of Figure 8-7. This model has an aerialphotograph draped over the top of it to demonstrate the nature of the study area.

The study area is predominantly urban and so buildings will have a large impactupon what can be seen from each property. These were accounted for byextracting the locations of all buildings from Land-Line.Plus (Step 3 in Figure8-7). Each building was then visually inspected, via a site survey, and thenumber of storeys recorded. Each storey was then assumed to be 3m high andthis information combined with the locations of the buildings to generate a 3dimensional model of the buildings (Step 4). The buildings and the land surfacewere then combined to produce a 3 dimensional model of the study area (Step

8-11

5). This combined model was then converted to a 1 square metre grid where thevalue of each grid cell represents its height. This was determined byinterpolating from the three dimensional model. This latter step was necessaryto enable the GIS to calculate the area of land visible from each property.

Figure 8-7: Creating a 3D urban model

The 1m grid of the land surface can be used to calculate the area and land typevisible from each property. The procedure followed to calculate this isillustrated in Figure 8-8 and described as follows. The grid of land heights isillustrated in Panel (1) of Figure 8-8 alongside a black square highlighting thelocation of a sample property. On the basis of these two pieces of informationthe GIS was used to calculate the extent of land visible from each property as isillustrated for our sample site in Panel (2). The viewsheds were calculated 45degrees either side of the perpendicular to each building to simulate the viewfrom a property�s window.

(1) Contours extracted from Land-Form PROFILE

(3) Building locations extracted fromLand-Line.Plus

(2) 3D-land surfacecreated from 1

(4) 3D model of buildingheights created from 3

(5) Panels 2 and 4 combined tocreate an overall 3D land surface

REPRODUCED FROM THE ORDNANCE SURVEY 1:1250 Land-Line.Plus AND Land-Form PROFILEWITH PERMISSION OF HER MAJESTY'S STATIONERY OFFICE © CROWN COPYRIGHT

8-12

The resulting viewsheds were combined with a land use map to illustrate thedifferent land uses visible from each property (Panel 3). The land use map wascreated by extracting the lines representing various land uses from Land-Line.Plus. This delineated roads alongside other land uses such as buildings,vegetation, railway, industry, parks, and water.

We would expect the impact of any land use upon a property�s view to decreasewith distance from the property. Therefore each land use was extracted in turnfrom the viewsheds (Panel 4) and each visible cell was assigned a valuecorresponding to its distance from the property. This was achieved bycalculating the distance from each sample property to all the visible cells (Panel5) and weighting each cell accordingly. This meant that a cell, which was 10 mfrom the sample property, was assigned a value of 0.1 and a cell, which was 100m from the sample property, was assigned a value of 0.01. This is illustrated inPanel 6. These values were then summed to produce a measure of the visualimpact from that land use.

In addition to weighting each visible cell by its distance from the property,further visual impact measures were calculated by weighting each visible cell bythe square of its distance from the property and also by calculating the area ofeach visible land use.

Variables measuring the visual impact of roads, railways, parks, industry,vegetation, water, and buildings were calculated from the front and back of eachsample property.

8.5.2 Road traffic noise

The road traffic noise level at each property was calculated in accordance withthe standard CRTN method used by the Department of Transport (DOT, 1988).This produced noise levels on the L10 (18-hr) noise scale. It involved 2 mainstages: first calculating the amount of noise being emitted from each road, andsecond modelling of how this noise will travel to each individual property.

8.5.2.1 Calculating the noise level being emitted from each road

The noise level being emitted from each road is a function of the trafficvolume, its composition and speed, as well as the gradient and surface of theroad.

The road traffic volume along each of the 9389 road segments in the studyarea was calculated from a variety of sources. For many of the main roads inthe study area the volume was estimated from traffic counts obtained fromGlasgow City Council (589 roads or 6.3%). For those major roads wheretraffic data were not available volumes were estimated from the StrathclydeIntegrated Transport Model (SITM) produced by the Strathclyde PassengerTransport Executive. Minor roads proved more problematic as most did nothave any traffic counts and were not represented on the SITM. However, afew minor roads did have traffic count data and a predictive model wascreated to link these to factors that may describe how busy the road is (suchas the distance from various amenities).

8-13

Figure 8-8: Deriving measures of visual impact

1. Grid of land and buildingheights 2. Viewshed is calculated

4. Each land use extracted inturn2

3. Viewshed overlaid with landuse map1

5. Apply a distance decayfrom each house

6. A grid of weighted cells isproduced

7. These cells are summed to derive a viewscore for each land use

Sampleproperty

Higherelevation

Notes: 1 In this example the following land uses are illustrated• = residential land use, • = open space, •= roads2 In this example residential properties are selected

REPRODUCED FROM THE ORDNANCESURVEY 1:1250 Land-Line.Plus AND Land-Form PROFILE WITH PERMISSION OF HERMAJESTY'S STATIONERY OFFICE ©CROWN COPYRIGHT

Viewpoint

8-14

Based on this model traffic volumes were ascribed to all the minor roads inthe study area. All the traffic volumes were standardised to 1986 levels andto 18-hour traffic counts. A more complete description of the assignment oftraffic volumes to individual roads is presented in Annex E.

The road traffic noise level is also affected by the percentage of heavyvehicles. Thirteen traffic counts were obtained from Glasgow City Councilwhere the composition of the traffic was recorded. From these observationsthe percentage of heavy vehicles on each class of road was calculated. Theseare presented in Table 8-1. No compositional counts were available forminor roads. However, there are unlikely to be many heavy vehicles usingthese roads and so it was assumed that the percentage of heavy vehicles waszero. These road class averages were then transferred to all roads.

Table 8-1: Percentage of heavy vehicles on each road typeRoad class Number of

countsAverage %heavy vehicles

Motorway 2 13.39A Road 3 13.77B Road 6 8.38C Road 2 5.60Minor Road 0 0

In order to calculate the noise level, the speed of the traffic was also required.Traffic speed can be estimated from a model that relates the volume of thetraffic and the class of road to the likely speed of traffic upon it. Trafficspeeds were estimated based upon the speed flow relationships presented inIHT & DOT (1987).

The gradient of the road also affects the noise level with higher gradientsbeing associated with elevated noise levels. In order to determine thegradient, the road network was combined with the 3 dimensional model ofland heights (see Section 8.5.1) to estimate the slope and aspect of the landupon which each road was built. The direction of the road (e.g. Northeast→ Southwest) was then calculated and combined with the slope and aspectof the land to produce a gradient for each section of road. The road type alsoaffects the noise level being emitted from each road. All roads were assumedto have impervious and bituminous road surfaces.

On the basis of the traffic volume, the percentage of heavy vehicles, thespeed of the vehicles, the gradient of the road and the road surface, the levelof noise being emitted from each road was calculated according to theformulae specified in CRTN (DOT, 1988). Further details are provided inAnnex F.

8.5.2.2 The propagation of road noise to each property

After the noise level on each section of road was calculated, each house wasassigned to its nearest road and this basic noise level assigned to the property(Step 1 in Figure 8-9). This noise level then needs to be adjusted to accountfor the changes in sound level that will occur between the road and the house.All the procedures were based upon those recommended in CRTN.

8-15

The further the road from the house the lower the sound due to air absorptionand the dispersion of the sound. Using the GIS the horizontal and verticaldistance between the house and road were calculated and the noise leveladjusted accordingly (Step 2). The road noise will also be lowered as it isabsorbed by the ground, a process that depends upon the nature of the groundsurface between the road and the house. As the study area is predominatelyurban it was assumed that the ground surface between the house and the roadwas hard and the road noise adjusted accordingly (Step 3). The noise level ata property can be increased if there are buildings on the opposite side of theroad to the house from which noise will be reflected. Due to the fact that thestudy area was predominantly urban it was assumed that buildings covered50% of the opposite side of the road to each property. Using this informationthe noise levels were adjusted accordingly (Step 4). Noise barriers shieldednone of the roads in the study area and so these did not have to beincorporated into the analysis.

The procedures detailed so far will produce a good estimate of the road noiseat each property. However, other roads in addition to the nearest may havean impact upon the noise level. This was accounted for in two ways. If thenearest road to a property was a multi-carriageway road (Step 5), then thehouse was also matched to the second carriageway and the noise from thiscalculated (Step 5a). This noise was then adjusted as before to account for itsattenuation between the road and the house. The second situation was if thenearest road to each property was a minor road but there was a major road(Motorway, A or B class) within 100m of the property (Step 6). In thesecases the property was matched to this major road and the noise contributioncalculated by determining the angle of view between the property and themajor road (Step 6a). This noise was then adjusted as before to account forits attenuation between the road and the house. All the noise levels were thencombined to determine an overall noise level for each property (Step 7).Further details of this procedure are provided in Annex F.

8-16

Figure 8-9: Calculating a noise level for each property

2) Distance correction

3) Ground cover correction

4) Correct for reflections

6) Is road within 100m of a majorroad but closest to a minor road

6a) Match each house tothis road and assign noiselevel while correcting for

the angle of view

5a) Match each house tosecond carriageway and

assign noise level from thisroad

5) Is nearest road multi carriageway ?

7) Combine contributions from allsegments to get final noise level

YES

YES

Adapted from DOT (1988)

1) Match each house to itsnearest road and assign

noise level

9-1

9 THE IMPACT OF ROAD NOISE UPON PROPERTY PRICES

9.1 IntroductionAs outlined in Section 8, the application of a GIS supplemented by a simple sitesurvey allowed the construction of a hedonic price dataset consisting of some 300variables describing the characteristics of over 3,500 properties in the Glasgowurban area. This dataset forms the basis of the statistical analysis presented in thissection. As far as possible, the analysis has taken into account both theoreticalconsiderations and drawing upon the experiences of past empirical research. Theobjective of the analysis, of course, is to ascertain how noise pollution resultingfrom road traffic influences the market price of a property.

9.2 Specification of the Hedonic Price Function: Selection ofVariables

Through the use of GIS the information collected for this project has generated oneof the most comprehensive hedonic property price datasets ever compiled. Ashighlighted in the previous section, detailed information is available on eachproperty�s structural qualities, the characteristics of the direct neighbourhood andwider region, the accessibility of each property to amenities, the quality of the viewfrom each property and of course details of each property�s exposure to noisepollution. With such richness in the availability of variables, the researcher is facedwith the fortunate conundrum as to which characteristics should be included in thehedonic price function and which (if any) should be left out. In making this choice,researchers usually follow two guidelines.

• First, and most importantly, we would wish to include those variables that webelieve, a priori, will be major determinants of the prices commanded byproperties. For example, we would be rightly sceptical about a hedonic pricefunction that failed to include a variable describing the size of the property, since,all else equal, we would expect larger properties to command higher prices.

• Second, it is common in hedonic datasets to have a large number of variables thatare essentially descriptors of very similar characteristics of the property. Forexample, many of the myriad variables compiled in this dataset to describe thecharacteristics of the neighbourhood are essentially different though similarindicators of the socio-economic standing of the area. Thus, while includingindicators of a neighbourhood�s unemployment levels, non property owningresidents, and two car owning households will probably provide a reasonablyaccurate picture of the socio-economic standing of the neighbourhood, addingsimilar socio-economic indicators (e.g. details of the number of children ininstitutions, percentage of people with a university degree, percentage of workersin the construction industry, percentage of multiple earning households etc.) willadd to the complexity of the model, but little to our understanding of thecharacteristics that influence house prices. In such circumstances, researchersemploy what is known as �Occam�s Razor�; they remove variables that addcomplexity without offering much to understanding and use a few variables tocapture the influence of many similar variables (e.g. using variables describingrates of unemployment, property non-owners and two-car households as indicators

9-2

for the many variables which characterise the socio-economic characteristics of anarea).

Following these guidelines, the 300 plus possible variables were whittled down toa set of under 50 variables that, in the researchers� opinion, represented theessential elements of the hedonic price function. The variables and the qualitiesand characteristics of the property that they are intended to represent are listed inTable 9-1.

Table 9-1: Variable Categories and Variables include in the Specification of theHedonic Price Function

Variable Category Variables

Structural Variables:

a. Dimensions of Property Floor area; Garden area; Property shape

b. Type of Property Detached House; Semi-detached House; TerraceHouse; Subdivided House; �Four-Block� House;Flat; Tenement Flat

c. Characteristics of �House-Type�Property

Number of Storeys

d. Characteristics of �Flat-Type�Property

Number of Flats in property, Floor on which flatlocated

e. Appearance of Property Age of property; Building fronting material

Neighbourhood Variables:

a. Ethnic Composition ofNeighbourhood

Percentage of population in local neighbourhoodwho are of:Commonwealth Background; Other Ethnicbackground

b. Age Composition ofNeighbourhood

Percentage of households in local neighbourhood,that can be classed as:Young Families; Old Families; Elderly PeopleLiving Alone

c. Socio-Economic Composition ofNeighbourhood

Percentage of households in local neighbourhoodnot owning their properties; Percentage of workingpopulation in local neighbourhood that areunemployed; Percentage of households in localneighbourhood owning two cars.

Accessibility Variables:

a. Walking Distance to Amenities Local Shops

b. Car Travel Time to Amenities City Centre, Railway Station

Visual Amenities:

a. Overall view Afforded fromProperty

Area of land visible from the property

b. Views of Different Land Uses Views of Roads, Parkland and Industry

Noise Variables:

a. Aircraft Noise Aircraft Noise Levels

b. Road Traffic Noise Road Traffic Noise Levels

9-3

A number of comments should be made concerning the choice of variablesoutlined in Table 9-1:

• Since the structural characteristics of a property are likely to be highly importantdeterminants of its market price, the majority of structural variables available inthe data set were included in the hedonic price function.

• No variables existed describing the internal characteristics of properties, and thismay be considered a shortcoming of the dataset.

• Neighbourhood characteristics were taken as census details of the population inthe property�s Output Area (OA); the smallest unit of the Scottish Census,consisting, on average, of some 56 properties. It seems likely that these detailswill best characterise the property�s immediate neighbourhood. Variablesdescribing the characteristics of the wider neighbourhood proved, in general, to beextremely similar in magnitude to those for the OA and when included in thehedonic price function added little to the explanatory power of the model.

• A number of different combinations of neighbourhood variables were tested in thehedonic price function. Those selected are included to capture three importantfacets of the neighbourhood; its ethnic, age and socio-economic composition.

• Accessibility variables were included for local shops, the city centre and railwaystations. Variables for accessibility to bus routes were not included because thesetended to be very collinear with those for accessibility of local shops. Variablesfor the accessibility of schools and parks were not included in the finalspecification of the hedonic price function since they never proved to besignificant.

• Accessibility variables were taken as walking time for accessibility to shops andcar travel time for accessibility to the city centre and to railway stations, as it wasassumed that these modes of transport would be the commonest used in reachingeach of the three different amenities.

• All the visual (dis)amenity variables included in the model are for views from thefront of the property.

• The variables for views of land uses use a square distance decay that weightsimmediate land uses more heavily than more distant ones. The two alternativemeasures of visual (dis)amenity (i.e. total area of each land use visible and total ofeach land use visible weighted by distance) were included in alternativespecifications of the hedonic price function. These alternative measures performedless well, returning non-intuitive results (e.g. in one specification using total areaof each land use, the model predicted that the more road visible from a propertythe higher the market price of that property).

• To avoid over-specification of the model only variables reflecting very differentland uses (parkland, road and industry) were included in the model (e.g.vegetation and buildings were not included). Too few observations existed onproperties with views of the sea, watercourses, pylons or railways to safelyinclude them in the specification of the hedonic price function.

9-4

9.3 Specification of the Hedonic Price Function: Choice ofFunctional Form

As well as selecting which variables should be included in the hedonic pricefunction, the researcher must also choose the functional form which best portraysthe relationship between a property�s market price and each of the variablesdescribing its characteristics (see Section 4.4.4 for a detailed discussion).

There is no reason, of course, why we would not expect these relationships to beextremely complicated. Figure 9-1, for example, depicts the relationship that mightexist between property prices and garden size. In this hypothetical depictionproperty prices increase in three distinct �steps�.

Figure 9-1: A Hypothesised Hedonic Price Schedule for Size of Garden

Clearly properties with very small gardens, or what we have termed �backyards�,will command higher prices than those with no garden at all. Naturally, propertieswith larger backyards command relatively higher prices still, but, in thehypothetical relationship shown in Figure 9-1, the rate of increase in property pricedeclines as the size of a property�s backyard increases (there is only so much roomneeded in a backyard to have barbecues in the Summer, hang out washing andstore household rubbish!). However, once the property�s garden reaches a certainsize it is highly likely that its perceived functionality might change. Gardens of aparticular size are large enough for property owners to grow and tend a lawn,flower beds and possibly a vegetable patch. In Figure 9-1 this change infunctionality is shown by the second step, where �backyards� become �gardens�.Again, property prices increase relatively rapidly as �garden� size increases but,again, the hypothetical relationship depicted here suggests that the rate of increasesteadily declines. We might imagine that the increased requirements of mowing,watering, raking and weeding will progressively reduce the attractions of

Size ofGarden

Price ofProperty

Hedonic PriceSchedule

�Backyards� �Gardens� �Grounds�

9-5

purchasing a property with a larger garden. Figure 9-1 includes a third step thatportrays yet another change in functionality that occurs when a garden becomes solarge that it might better be termed a property�s �grounds�.

As shown in this hypothetical example, the relationship between property pricesand property characteristics may be highly complex. Again, researchers are facedby a problem; if they attempt to replicate the complexity of the �true� relationshipby using elaborate functional forms, they run the risk of over-complicating themodel. Rather, the key to quality modelling is to distil the essential nature of theunderlying relationships. In this hypothetical case, the researcher would need toinclude five or more terms for garden size in the hedonic price function to comeclose to replicating the �true� functional form. As shown in Figure 9-2, however,including just one, linear term provides a reasonable approximation to the �true�functional form. The researcher is able to reveal the fundamental nature of therelationship without over complicating the model.

Figure 9-2: Approximation of the Hedonic Price Schedule

Indeed, the simple linear functional form provides a reasonable approximationwhenever increasing levels of a property characteristic lead, in general, to eitherincreases or decreases in property prices. For this reason, the majority of variablesincluded in the model are included in simple linear form.

For some variables, intuition suggests that other functional forms may be moreappropriate. As an example, consider the influence of accessibility to local shopson property prices. Clearly, the convenience of living close to shops exerts apositive influence on property prices. On the other hand, the disamenities ofcongestion, parking difficulties and pollution associated with living close to acommercial area, will exert downward pressure on housing prices. The influence ofthe proximity of shops on property prices, therefore, will be the result of theinteraction of these two opposing forces. As illustrated in Figure 9-3, we mightexpect that very close to commercial areas the negative disamenities of congestion

Size of Garden

Price ofProperty

Hedonic PriceSchedule

Researcher�sApproximation

9-6

and pollution will dominate, such that moving away from shopping areas, propertyprices will initially rise. At some point, however, the impact of the increasinginconvenience of travelling longer distances to shops is likely to become moreinfluential. We might expect property prices to plateau and eventually decline asthe inconvenience of greater travel times outweighs the advantages of lowercongestion.

Figure 9-3: Hypothetical Relationship between Property Price and Accessibilityto Shops

The functional form illustrated in Figure 9-3 can be replicated in a statistical modelby including the characteristic variable in the hedonic price function in both linearand squared forms. This functional form has been adopted for all the accessibilityvariables.

One further decision facing the researcher is the functional form of the dependentvariable; property price. The distribution of property prices in the dataset isreproduced in Figure 9-4.

Accessibility toShops

Price ofProperty

Hedonic PriceSchedule

Congestiondisamenities

outweighconvenience of

accessibility

Convenience ofaccessibilityoutweighscongestion

disamenities

9-7

Figure 9-4: Histogram of property prices

Clearly, the distribution of property prices in the dataset is skewed to the right; thebulk of properties have relatively low prices whilst a relatively small number ofproperties have very high prices1. This shape of this distribution is indicative of alog-normal distribution and provides some support to the assertion that thedependent variable should be included in hedonic price function in log form.

In Section 4.4.4 and further in Annex B, we discussed the use of a flexiblefunctional form called the Box-Cox transformation. It is possible to employ theBox-Cox transformation to determine whether the hedonic price function is bestapproximated using simple property price or the log of property price. The resultsof this test show that the log model is far closer to the �best� transformation ofproperty price that the linear model.2

1 Note also, that relatively few properties have sold for just over £30,000, the price at which stamp dutybecomes payable. It is apparent that potentially more valuable properties are sold at under this mark toavoid stamp duty, the difference in price being made up by artificially inflating the price paid forinterior furnishings such as carpets and curtains. In the dataset some 260 properties (6.55%) sold forexactly £30,000. It was deemed that this price was probably a poor reflection of the true market valueof the property and as a consequence these 260 properties were removed from the sample.2 If the linear model was most appropriate then we would expect to estimate a Box-Cox parameter onthe dependent variable with a value around 1, whilst if the log model were more appropriate the Box-Cox parameter would take a value approaching 0. In this case, the Box-Cox parameter estimated on thedependent variable (Property Price) has a value of 0.134 and a standard error of 0.026. Clearly, the logmodel is a better approximation to the functional form than the linear model. Note, however, that theBox-Cox parameter is significantly different from the value 0 ( t = 5.163, p = 0.000) such that the logtransformation is still not the �best� transformation but merely an approximation. It was decided toproceed with the log model since employing the more complicated transformation suggested by theBox-Cox parameter, considerably complicates the model and confuses the interpretation of thecoefficient estimates.

Std. Dev = £13990Mean = £28162N = 3968Minimum = £10010Maximum = £150000

Property Price (£)

150,000

140,000

130,000

120,000

110,000

100,000

90,000

80,000

70,000

60,000

50,000

40,000

30,000

20,000

10,000

Freq

uenc

y

1000

800

600

400

200

0

Std. Dev = 13912.75Mean = 27872N = 3868.00

9-8

The final set of variables employed in the models along with their functional formsare listed in Table 9-2. The complete definitions of these variables including theirunits of measurement are given in Annex D. Table 9-2 also describes theresearcher�s a priori expectations of how each variable will impact on a property�sprice. For example, we would expect to estimate a positive coefficient on avariable such as garden size included in linear form, since larger gardens, all elseequal, should increase the price a property commands in the market. An importanttest of the quality of the estimated models will be the extent to which the signs ofthe estimated coefficients conform to these a priori expectations.

Table 9-1: Variables included in the Hedonic Price Models and a-prioriexpectations of their influence on house prices

Variable Code A Priori Expectations ExpectedSign

Log of floor Area LNAREA Larger properties will command higher prices +ve

Garden Area GARDEN Properties with larger gardens will command higher prices +ve

Perimeter/Cross-Section Area PR_AREA

A large perimeter to floor area ratio reflects greatercomplexity in the shape of the property. It is expected thatmore complex construction will proxy for quality of theproperty.

+ve

Number of Storeysin House TypeProperties

STOREYSGiven that two properties have the same floor area it is likelythat those with fewer storeys (e.g. a bungalow) will bepreferred to those with more storeys.

-ve

Detached Property(Dummy) T_DETAC

In the models, detached houses are taken as the baselineproperty type. The coefficients estimated on the other propertytype dummy variables, reflect the relative difference in pricebetween that property type and a detached house with exactlythe same characteristics.

Not Included

Semi-DetachedProperty(Dummy)

T_SEMI A semi-detached property, all else equal, will likely commanda lower price than an equivalent detached property -ve

Terrace Property(Dummy) T_TERR A terraced property, all else equal, will likely command a

lower price than either a semi-detached or detached property -ve

Subdivided HouseProperty(Dummy)

T_SUBHOU A subdivided house property, all else equal, will likely have alower price -ve

Four-Block Property(Dummy) T_4BLCK Expected to have a similar coefficient to a sub-divided house -ve

Flat Property(Dummy) T_FLAT A flat, all else equal, is likely to command a lower price than

house type properties -ve

Tenement Property(Dummy) T_TENE Expected to have a similar coefficient to a flat -ve

Other Property Type(Dummy) T_OTH

This variable represents all other property types. It is likely, allelse equal, that these will command lower prices than detachedhouses.

-ve

Number of Flats inProperty NUMBER

It is expected that, all else equal, the greater the number ofother flats in a single building, the lower the price commandedby a flat type property.

-ve

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Variable Code A Priori Expectations ExpectedSign

Ground Floor Flat(Dummy) FLOOR_G

In the models, ground-floor flats are taken as the baseline forflat type properties. The coefficients estimated on the other flattype dummy variables, reflect the relative difference in pricebetween a ground floor flat and flats at other levels in thebuilding.

Not Included

Basement Flat(Dummy) FLOOR_B A basement flat, all else equal, is expected to command a

lower price than a ground floor flat -ve

First Floor Flat(Dummy) FLOOR_1

A first floor flat, all else equal, is expected to command ahigher price than a ground floor flat, for reasons of securityand privacy.

+ve

Second Floor Flat(Dummy) FLOOR_2

It is unclear, a priori, whether a second floor flat, all elseequal, will be more or less desirable than a ground floor flat;advantages of security and privacy trading off against thedisadvantage of ease of access

No priorexpectation

Third Floor Flat(Dummy) FLOOR_3

It is likely that disadvantages of access will be greater still onthe third floor such that the price commanded will likely belower than for a third floor flat than a second floor flat but wehave no prior expectations relative to a ground floor flat.

No priorexpectation

Fourth Floor Flat(Dummy) FLOOR_4 As for a third floor flat. No prior

expectation

Stone Faced STONEThere is no prior expectation as to whether stone facedproperties will command higher or lower prices than brick-faced properties

No priorexpectation

Pre-War PRE_WAR

A priori, it is unclear as to whether older properties willcommand higher or lower prices than newer properties. Olderproperties may have more �character� but have poorer facilitiesand require more upkeep

No priorexpectation

CommonwealthPopulation(% Pop)

COMMONW

It is possible that properties in neighbourhoods with largercommonwealth populations will command lower prices,though this is likely to be a reflection of socio-economicdifferences in the populations.

No priorexpectation

Other EthnicPopulation(% Pop)

OETHNIC As for commonwealth population. No priorexpectation

Young Families(% Households) YNGFAML

Neighbourhoods with larger populations of young families arelikely to be less affluent such that, all else equal, propertieswill command relatively lower prices.

-ve

Old Families(% Households) OLDFAML

In contrast, neighbourhoods with larger populations of olderfamilies are likely to be more affluent such that, all else equal,properties will command relatively higher prices.

+ve

Elderly LivingAlone(% Households)

ELDALONE

We have no prior expectations as to whether properties inneighbourhoods with larger populations of elderly peopleliving alone will, all else equal, command lower or higherprices.

No priorexpectation

Non-Owning(% Households) NOTOWNER

The percentage of households in a neighbourhood that do notown their property is likely to proxy for low socio-economicstanding. We would expect that properties in areas with highnon-owning populations will, all else equal, command lowerprices.

-ve

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Variable Code A Priori Expectations ExpectedSign

Unemployed(% WorkingPopulation)

UNEMP

Similarly, the neighbourhood unemployment rate is anotherproxy for low economic standing. Properties in areas of highunemployment, all else equal, will likely command lowerprices.

-ve

Two Car Owning(% Households) TWOCAR

In contrast, the number of two car owning households in aneighbourhood is a measure of relative affluence. We wouldexpect, all else equal, that greater car ownership will beindicative of wealthier neighbourhoods in which propertiescommand relatively greater prices.

+ve

Distance to Walk toShops WALKSHOP +ve

Distance to Walk toShops Squared WALSHO2

The distance to walk from a property to a shop is included inlinear and squared forms. This functional form allows therelationship between the price of a property and the distance tothe nearest shops to take the form of an inverted �U�. Wewould expect that living very close to a shop would bedisadvantageous due to the corollary disamenities ofcongestion and pollution associated with a commercial area.However, the further a property is from a shopping area, thegreater the disamenity of travelling to the shops. The inverted�U� relationship would suggest that there is an optimaldistance for a property to be located in relationship toshopping facilities.

-ve

Car Travel Time toCity Centre CARCENTR +ve

Car Travel Time toCity Centre Squared CARCENT2

For similar reasons, we would expect there to be an optimaldistance for a property to be located in relation to the citycentre; too close and congestion and pollution are likely toreduce the price commanded by a property, too far and thedisamenity of travelling a long distance to the city centre willreduce the relative price of the property.

-ve

Car Travel Time toRailway Station CARRAIL +ve

Car Travel Time toRailway StationSquared

CARRAIL2

Again, we would expect the trade off between congestion andpollution disamenities with accessibility to dictate an optimallocation of property in relation to a railway station -ve

Obstructed View FL0

Properties with more expansive views are likely, all elseequal, to command higher prices. We would expect, therefore,for properties that have more obstructed views to be lessvaluable.

-ve

View of Roads F_S1_VS

It is likely that roads and the traffic that travels on themrepresent a visual disamenity over and above the air and noisepollution that is caused by road traffic itself. Hence, wewould expect that the greater the area of road that is visiblefrom a property the lower the price it would command in themarket.

-ve

View of Parkland F_SPK_VSIn contrast, it is assumed that views of parkland represent avisual amenity; properties that overlook parks are likely tocommand higher prices, all else equal.

+ve

View of Industry F_SID_VSAgain, industrial sites are likely to represent a visualdisamenity; we would expect properties that overlookindustrial areas to command lower prices, all else equal.

-ve

Aircraft Noise NOISENoise pollution is a disamenity. We would expect propertiesexposed to greater levels of aircraft noise, will commandlower prices, all else equal.

-ve

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Variable Code A Priori Expectations ExpectedSign

Traffic Noise AIRNOISESimilarly, we anticipate that properties exposed to greaterlevels of traffic noise will command lower prices, all elseequal.

-ve

9.4 The model results and interpretation

9.4.1 Modelling Procedure and Rationale

In refining our optimal models of house price formation a variety of initialmodels were investigated following the principles of statistical modellingbriefly referred to above. However, it is interesting to note that the central resultof interest, the impact of road noise upon property prices, is relatively stableacross these models and accords closely with that given in our best fitting modeldescribed subsequently.

Four separate models are presented here. In each of the models, the full set ofstructural variables have been included as well as the variables representing aproperty�s exposure to noise pollution from both road traffic and aircraft.Indeed, Model I includes just these variables. Model II extends the specificationof the hedonic function to include variables describing neighbourhoodcharacteristics. Only the variables defined for the immediate neighbourhood ofthe property have been included, since variables describing the characteristics ofthe wider area tended to be very similar to those for the immediate environs.Model III includes three measures of accessibility; distance to walk to thenearest shopping area, time to travel by car to the city centre and time to travelby car to the nearest railway station. In many of the hedonic pricing studiesreviewed in earlier sections of this report these accessibility variables are notincluded. The final model, Model IV is the best specification of the model,including the structural, neighbourhood and accessibility variables as well asvariables describing the visual (dis)amenities of land uses (the viewshedsdiscussed in Section 7.6.1) around the property. As far as the authors are aware,this is the first study in which these potentially important variables have beenincluded.

9.4.2 Tabulated Results

We can summarise the four models discussed above as follows:Model I: Structural and Noise variables.

Model II: Structural, Neighbourhood, and Noise variables.

Model III: Structural, Neighbourhood, Accessibility and Noise variables.

Model IV: Structural, Neighbourhood, Accessibility, Visual Amenity andNoise variables.

The results from the estimation of these four models are presented in Table 9-3.

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Table 9-3: Regression Results: Models of the Natural Log of Property Price

Variable ModelI

ModelII

ModelIII

ModelIV

Log of floor Area .316(.014)***

.286(.013)***

.278(.013)***

.270(.013)***

Garden Area .00031(.00003)***

.00017(.00003)***

.00017(.00003)***

.00015(.00003)***

Perimeter/Cross-Section Area .019(.002)***

.015(.002)***

.014(.002)***

.012(.002)***

Number of Storeys in House-Type Properties

-.242(.032)***

-.209(.029)***

-.169(.029)***

-.163(.029)***

Semi-Detached Property(Dummy)

.043(.037)

.024(.034)

-.00197(.034)

-.0120(.034)

Terrace Property(Dummy)

-.0187(.0408)

.061(.037)

.011(.038)

-.0016(.037)

Sub-Divided House Property(Dummy)

-.641(.072)***

-.493(.066)***

-.427(.067)***

-.426(.066)***

Four-Block Property(Dummy)

-.770(.059)***

-.531(.055)***

-.508(.055)***

-.521(.054)***

Flat Property(Dummy)

-.839(.058)***

-.586(.055)***

-.549(.055)***

-.536(.054)***

Tenement Property(Dummy)

-.948(.057)***

-.673(.054)***

-.625(.054)***

-.604(.053)***

Other Property Type (Dummy) -.749(.082)***

-.494(.076)***

-.449(.075)***

-.425(.074)***

Number of Flats in Property -.013(.002)***

-.011(.002)***

-.011(.002)***

-.010(.002)***

Basement Flat(Dummy)

-.019(.047)

-.00043(.043)

-.013(.042)

-.019(.042)

First Floor Flat(Dummy)

.045(.015)***

.056(.013)***

.062(.013)***

.067(.013)***

Second Floor Flat(Dummy)

.039(.015)***

.041(.013)***

.046(.013)***

.052(.013)***

Third Floor Flat(Dummy)

-.0010(.0168)

.0084(.0153)

.014(.015)

.027(.015)*

Fourth Floor Flat(Dummy)

.016(.023)

.0096(.0211)

.017(.021)

.027(.021)

Stone Faced -.0166(.0215)

-.078(.020)***

-.064(.021)***

-.039(.021)*

Pre-War -.020(.037)

.048(.034)

.030(.034)

.039(.034)

Young Families(% Households)

-.0029(.0004)***

-.0025(.0004)***

-.0022(.0004)***

Old Families(% Households)

.0015(.0003)***

.0014(.0003)***

.0011(.0003)***

Elderly Living Alone(% Households)

.0049(.0009)***

.0052(.0009)***

.0050(.0009)***

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Non-Owning(% Households)

-.0016(.0003)***

-.0016(.0003)***

-.0013(.0003)***

Unemployed(% Population)

-.0025(.0008)***

-.0013(.0008)*

-.0014(.0008)*

Two Car Owning(% Households)

.0104(.0006)***

.0097(.0006)***

.0096(.0006)***

Commonwealth Population(% Population)

.00008(.00046)

.0010(.0005)**

.00076(.0005)

Other Ethnic Population(% Population)

-.0057(.0023)**

-.0011(.0023)

-.00033(.0023)

Distance to Walk to Shops .00041(.00007)***

.00033(.00007)***

Distance to Walk to ShopsSquared

-.0000004(.0000001)***

-.0000003(.0000001)***

Car Travel Time to City Centre .090(.015)***

.078(.015)***

Car Travel Time to City CentreSquared

-.0041(.0008)***

-.0034(.0008)***

Car Travel Time to RailwayStation

.034(.018)*

.014(.018)

Car Travel Time to RailwayStation Squared

-.014(.005)***

-.0092(.0047)**

Obstructed View -.0000002(.0000001)*

View of Parkland .0078(.0102)

View of Industry -.0549(.014)***

View of Roads -.062(.007)***

Aircraft Noise -.0057(.0011)***

-.0043(.0010)***

-.0014(.0014)

-.0025(.0014)*

Traffic Noise -.0084(.0009)***

-.0057(.0008)***

-.0042(.0008)***

-.00202(.0008)**

Constant 9.704(.100)***

9.459(.093)***

8.813(.121)***

8.947(.126)***

N 3544 3544 3544 3544

R2 0.6258 0.6922 0.7011 0.7089

Notes for Table 9-3:Dependent Variable = Natural Log of Property Price*significant at the 10% level**significant at the 5% level***significant at the 1% level

The first column in Table 9-3 (Variable) gives the name of each explanatoryvariable. Those variables which are identified (in brackets) as dummy variables

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either take the value 1 where the case applies or 0 when it does not. So thedummy variable Semi-Detached Property takes a value of 1 for houses whichare semi-detached and 0 for other properties. Other variables are treated ascontinuous i.e. as if they can take any value. Clearly in reality, such variablesonly take those values which are given in our sample. Furthermore, a fewvariables, such as that describing the number of storeys in a house, only take afew integer values. While such data are commonplace in statistical analyses it isimportant to remember that the models estimated on the basis of thisinformation only represent the range of data given in the sample and that weshould be wary of extrapolation well beyond that range. For example, while wecan readily use our models to infer results for a house which is not in our samplebut whose characteristics are well represented in that sample, another propertywhose characteristics are not well represented in the sample may not be so welldescribed by models based on that data. To push the point to the extreme, wewould not expect the price of a former lighthouse on a remote cliff top to bewell predicted by a model whose sample data set is entirely drawn from a highlypopulated city centre.

The remaining four columns of Table 9-3 detail the four models describedpreviously. Here each cell gives two numbers, the upper being the coefficientdescribing the effect which each explanatory variable has on the dependentvariable (the natural logarithm of property price). So, for example, consider avariable such as Garden Area which is measured in m2 (see Annex D). In ModelI this variable has a coefficient of 0.00031 which tells us that if the garden areaof a house increases by 1m2 so the natural logarithm of property price increasesby 0.00031. Notice that as we move from Model I to the more detailed modelsso the coefficient on Garden Area becomes smaller. This is a common andexpected result. In simpler models the limited number of explanatory variablesare the only ones available to account for the variation in property pricesobserved in the data set. Therefore each variable tends to capture the variationdue not only to its own action upon house price but also omitted variables whichhave a similar relationship with house price. So, for example, it might be that inModel I, aside from telling us about the size of a property�s garden, the GardenArea variable is also acting as a proxy for some omitted variable such as theneighbourhood variable Two Car Owning which is an indicator of the socio-economic status of an area (i.e. it might be that areas where ownership of two ormore cars is high also have properties with large gardens but in Model I only thelatter variable is present). As the models are expanded so coefficients oftendecline in absolute value (i.e. whether they have a positive or negative sign theirvalue tends towards zero). It is the more detailed models such as Model IVwhich, when carefully constructed according to Occam�s Razor, are more likelyto give coefficient estimates which more accurately reflect the effect of a givenexplanatory variable upon property prices. Therefore we would place lessemphasis on the coefficient upon Garden Area in Model I (.00031) than inModel IV (where it has more than halved to .00015).

The lower figure in each cell (given in parentheses) provides a measure of thevariability of our estimated coefficient known as the standard error. The smallerthis value in relation to its respective coefficient then the narrower the band ofuncertainty within which the coefficient estimate lies and the greater the degreeof certainty we have regarding that estimate. This degree of certainty, or

9-15

statistical �significance�, is indicated by the asterisks after the parentheses withmore asterisks indicating a more significant result. Typically statisticians accepta 95% certainty level (i.e. a 5% chance that this result has occurred throughsome random process) as evidence of a satisfactorily significant relationshipbetween the explanatory and dependent variable (indicated by two asterisks inTable 9-3).

Moving from Model I to Model IV involves the addition of the various groupsof variables indicated previously. Discussion of these results is presented in thefollowing section. The last row of Table 9-3 presents the overall fit of the modelgiven by the R2 variable which describes the proportion of property pricevariability explained by each model. As expected structural variables areresponsible for the largest proportion of price variation. The overall degree of fitof our most detailed model (IV) is very high as is typical in many good qualityhedonic pricing studies.

9.4.3 Discussion of the Models

In this section we discuss the various models presented in Table 9-3. Ratherthan discuss these in isolation from each other, which ignores the obviousimprovements in model fit as we progress from Models I to IV we will discussthe various groups of variables (Structural, Neighbourhood, Accessibility,Visual Amenity and Noise) of which they are composed.

9.4.3.1 Structural Variables:

In general, and across all four models, the structural variables are significant(i.e. there is little statistical chance that the relationship observed between thevariable and the price of a property has occurred purely through chance) andhave signs that accord with our a priori expectations. As we would expectproperties with larger floor area, larger gardens and more complex shapescommand higher prices. Also, houses with fewer storeys, all else equal, arepriced higher. The dummy variables designating the different property typesfollow our expectations. Detached, semi-detached houses and terrace housescommand relatively higher prices than sub-divided houses or four-blockproperties which in turn command higher prices than flats or tenements. Thedummy variables indicating the floor on which a �flat-type� property (i.e. a flator a tenement flat) is located indicate that basement flats command lower pricesthan those situated on the ground floor, though in none of the models is thissignificant. On the other hand, as expected, first floor flats are consistently andsignificantly more valuable than ground floor flats. The same is true of secondfloor flats, though the price premium for a second floor flat when compared toan equivalent ground floor flat is less than that for a flat situated on the firstfloor, reflecting the increased inconvenience of access. Flats situated on higherfloors tend also to be priced higher than ground floor flats though the premia aresmaller and tend not to be significant.

Though we had no prior expectations of how stone facing or the age of theproperty would influence its price, the models suggest that stone face propertiescommand relatively lower prices whilst properties constructed previous to 1945

9-16

command relatively higher prices. Again, these relationships are not particularlystrong and tend to be insignificant.

Model I provides the simplest specification of the hedonic price function inwhich only the structural characteristics of properties are used to explaindifferences in the prices they command in the market. It is interesting to notethat the R2 statistic for this model is .63, indicating that some 63% of thevariation in prices can be explained solely through the structural characteristicsof the property.

9.4.3.2 Neighbourhood Variables

Again, the neighbourhood variables conform well to our prior expectations.Indicators of neighbourhoods of low socio-economic standing (% of householdswith young families, % of working population unemployed, % of householdsnot owning their property) significantly reduce the relative price of a property,whilst indicators of affluence (% of households with old families, % ofhouseholds owning two cars) significantly increase the price of the property, allelse equal. Also, all four models indicate that properties in neighbourhoods thathave a high percentage of households consisting of elderly people living alone,command higher prices. It is assumed that this variable proxies forneighbourhoods of relatively high socio-economic standing. The variablesincluded to reflect the commonwealth and other ethnic populations living in theneighbourhood of a property tend not to be significant.

9.4.3.3 Accessibility Variables

Variables indicating each property�s accessibility to shopping areas, the citycentre and railway stations were included in both Models III and IV. In general,the parameters on these models are highly significant. In accordance with ourprior expectations, the inclusion of both linear and squared forms of thesevariables results in a relationship between property prices and proximity tofacilities that resembles an inverted �U�. The prices of properties very close tothese facilities are relatively low, indicative of the increased congestion andpollution associated with shopping areas, the city centre and railway stations.Moving away from these facilities, property prices initially rise, reach anoptimum and then fall as the disamenity of travelling longer distances tofacilities becomes relatively more important. As an example, the relationshipbetween property price and walking distance to the nearest shopping area isillustrated in Figure 9-4.

9-17

Figure 9-4: Relationship between walking distance to shops and property price

0

0.02

0.04

0.06

0.08

0.1

0.12

0 200 400 600 800 1000

Walking Distance to Shops (m)

% Change in Property Price

9.4.3.4 Visual Amenity Variables

Model IV is the only of the four models to include the variables indicating thevisual amenity qualities of each property. Once again, the coefficients on thesevariables follow our prior expectations; the more obstructed the view from aproperty, the lower the price it commands in the market, all else equal, the moreparkland that can be seen from a property the greater the property price, all elseequal, and the more a property�s view is dominated by roads or industrial areasthe lower its price, all else equal. Whilst the coefficient on views of parkland isnot significant, those on views of industrial areas and roads are highlysignificant.

9.4.3.5 Noise Pollution

Finally, and most importantly for this project, we come to a discussion of theinfluence of noise pollution on the price of a property. Variables indicating bothtraffic noise and aircraft noise were included in all four models. Bothconsistently return negative and significant coefficients; as would be expected,properties exposed to higher levels of noise pollution command lower prices, allelse equal.

Let us concentrate on the coefficient estimated on the traffic noise variable. Theuse of the natural log of property price as the dependent variable gives thiscoefficient a simple interpretation; the coefficient represents the percentagechange in the price of a property that would result from a one decibel increase inthe level of traffic noise pollution. Clearly, the value of this coefficient differsmarkedly across the four models. In Model I, which includes only structuralvariables, the coefficient takes a value of -.0084; in other words, a one decibelincrease in traffic noise pollution suffered at a property would reduce the price itcommanded in the market by 0.84%.

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In Model II, neighbourhood variables were included as well as thecharacteristics of the property itself. The coefficient on the traffic noise variabledeclines to -.0057. It seems likely that noise is related to the socio-economicstanding of the neighbourhood of a property. Indeed, examination of thecorrelation between noise and the neighbourhood variables reveals that areas oflower socio-economic standing tend to suffer higher levels of traffic noisepollution. If the neighbourhood variables are excluded from the model, thecoefficient on traffic noise erroneously includes an element reflecting the socio-economic standing of a property�s neighbourhood.

In Model III, which includes accessibility variables as well as those describingeach property�s structure and neighbourhood, the coefficient on the noisevariable once again reduces in absolute magnitude. Clearly, the ease of access aproperty enjoys to facilities is in some way related to the level of noise pollutionendured at the property's location. Accounting for accessibility gives a clearerindication of the impact on house prices that can be attributed solely to trafficnoise pollution.

Model IV presents the most complete specification of the hedonic price functionin which structural, neighbourhood and accessibility variables arecomplemented by the addition of variables indicating the visual (dis)amenity ofthe land uses surrounding a property. As described earlier one of the visualdisamenities that is accounted for in Model IV is that presented by views ofroads and the traffic that flows along them. Since the overall impact of roads onproperty prices comprises elements of both visual disamenity and pollutiondisamenity we would envisage that accounting for visual impacts will isolatethat part of the overall disamenity that results solely from traffic pollution.Indeed, in Model IV, the coefficient on the traffic noise variable declines,attaining a value of -.00202 (or -.0020248 to 7dp) .

Therefore the results of this analysis indicate that each decibel increase in trafficnoise decreases property price by .20%, the standard error shown in parenthesesindicates that we can be 95% confident that the coefficient takes a value that isgreater than -.04% and less than -.37%. Some of this range will be due toimperfections in the property price model, and 0.20% is our best estimate of theimpact of road noise upon property prices. It is this value that should beincorporated into the compensation procedures.

9.5 Strengths and WeaknessesTo conclude this section we provide some consideration of the various strengthsand weaknesses that can be identified in our analysis.

9.5.1 Strengths

9.5.1.1 Theoretical Strength of the Analysis

Many UK hedonic pricing studies have been conducted using estate agent,valuers or property owner�s estimates of property values. The potential biasesinherent in such an approach, particularly when applied to complex and

9-19

subtle impacts such as those induced by the environment of the propertymean that this is an undesirable approach to the issue at hand. Our study hasrelied entirely upon actual sales price information which provides thenecessary market price validation required to ensure that results areempirically defensible and suitable for policy implementation andcompensation purposes. This ensures that the study conforms well to therequirements set out by economic theory for producing defensible valueestimates.

9.5.1.2 Quantity and Quality of the Assembled Data

The analysis is based upon a very large dataset of over 3,500 property salesmaking this the largest study of its type conducted in the UK to date. Thisavailability of data substantially improves the statistical accuracy of theestimated models and empirical coefficients.

The property price dataset has been matched by use of the mostcomprehensive and detailed data regarding the physical and socio-economiccharacteristics of the neighbourhood, as well as the accessibility andenvironmental characteristics of individual properties. A variety of structuralvariables were supplemented by visual inspection of properties. These highquality data were processed using a variety of sophisticated spatial analysistechniques facilitated by a geographical information system. One of theinnovative features of this study is that extensive efforts were made toseparate the various noise and related pollution aspects of road disamenityfrom its visual impact. As such it provides the most sophisticated estimate ofthe impact of traffic noise on property prices yet to be published.

The resulting dataset comprises a combination of variables used to explainproperty prices which is as comprehensive as any that has been used inprevious hedonic property price research.

9.5.1.3 Stability of noise impact estimates

The estimate on the traffic noise variable is consistently negative, statisticallysignificant and relatively stable across the various detailed models presentedhere and in other specifications investigated by the authors. We can concludethat within this dataset, noise pollution has a significant and negative impacton property prices.

9.5.2 Weaknesses

9.5.2.1 Omission of internal characteristics data

Details of the internal characteristics of properties were not available to theresearchers. We would expect that variables reflecting each property�s stateof repair and internal features (e.g. number of rooms, bathrooms, centralheating, double glazing etc.) would be important in explaining the pricecommanded by a property. However, so long as none of these variables arecorrelated with exposure to traffic noise pollution, the absence of data oninternal characteristics should not bias our estimate of the coefficient on

9-20

traffic noise pollution. Clearly, this is not the case with double glazing. Theresearchers intend to undertake further work on this issue with the aim ofquantifying the effects involved.

9.5.2.2 The time series problem

In Section 4.2 (Figure 4-2) it was suggested that when a road is constructedthe value of a property may go through a series of distinct stages beforereaching a new equilibrium. In this study we have measured the impact ofroad noise upon existing properties many of which will be at equilibriumpoints rather than undergoing transition. Part 1 claims for compensation areassessed one year after a road has opened in an attempt to permit propertyprices to stabilise before compensation is paid. However, one year is a fairlyarbitrary date and to our knowledge there are no studies which have verifiedthe accuracy of this time period. The implications of this are that, if after oneyear the property prices have not attained equilibrium, then the application ofcompensation payments determined by this research will reflect the longterm effects of road noise rather than necessarily those being suffered byhouseholds at the time of compensation. We would identify this as a areadeserving of further research.

9.5.2.3 Modelling decisions

A statistical model reflects to a large extent the assumptions and modellingdecisions made by the researcher. A different researcher, using the samedata, may derive very different conclusions. A different researcher trying toprove one particular point will almost certainly be able to do so. However,we are confident that the models that we have presented in this report are agenuine and fair reflection of the pattern of relationships between propertyprices and property characteristics.

9.6 TransferabilityThis study quantified the impact of road noise upon property prices in Glasgow. Ifthis value is to be incorporated into land compensation procedures it must betransferable to a much larger geographical area. In order to assess this severalissues must be considered.

9.6.1 Representativeness

This hedonic pricing study has estimated the impact of road noise upon propertyprices. The aim of the research is to produce estimates that can be transferred toother properties across Scotland. However, this can only be achieved withconfidence if our sample of properties was diverse in terms of the housing types,social areas and the noise levels it covered.

As an example, noise may have a different effect upon property prices atdifferent absolute noise levels. Therefore if the sample of properties used forthe HP study consisted totally of houses with noise levels of between 58 and 68

9-21

dB(A) then there may be difficulties in transferring the results to areas wherethe noise levels are over 70 dB(A).

Therefore in any HP study it is important to obtain a large sample of propertieswhich represent a wide range of housing types, in many different areas. Theaim of the following paragraphs is to examine the diversity of the dataset andhence the limitations of our results. This has been achieved by examining thedistribution of a selection of carefully chosen variables.

Panels 1 to 5 in Figure 9-5 demonstrate that the sample contained a wide rangeof property types located in a variety of different areas. Panel 1 illustrates thatthere was a large range of property prices varying from just over £10,000 tomore than £100,000. Thus a full spectrum of house prices have been includedin the sample. The percentage of households without access to a car in any areacan be regarded as an indicator of the area�s wealth. Panel 2 demonstrates alarge diversity in wealthy areas varying from those with 5% of no car ownership(most deprived) to those with almost 100% car ownership (less deprived).

Panels 3 and 4 demonstrate the housing types covered by the sample. Panel 3illustrates that while over 60% of the properties were tenements, there weresignificant numbers of other property types. Panel 4 demonstrates that most ofthe properties in the study were pre 1919 with smaller but significant numbersin the other age bands. Panel 5 describes the numbers of properties in areaswith varying percentages of rented accommodation. This provides an indicationof an area�s wealth (i.e. more owner-occupiers represents greater wealth) as wellas its housing type, with areas of high renting more likely to be formerly publichousing.

Panel 6 is one of the most important and demonstrates the range of noise valuesamongst our sample properties. There is a very even spread with manyproperties experiencing very low road noise levels around 54 dB(A) and othersexperiencing very high noise levels around 78 dB(A). Therefore there was awide range of noise levels represented in the dataset indicating that our resultsshould be applicable to the full spectrum of noise impacts.

In summary therefore, Figure 9-5 illustrates that the sample properties had awide variety of characteristics and confirms our earlier suppositions about thediversity of the dataset.

9-22

Figure 9-5: The diversity within the property dataset

Panel 3: A histogramof property types

Panel 2: A histogramof the percentage ofproperties in outputareas with varyingpercentages of non-carownership

Panel 1: A histogramof property prices

0

2

4

6

8

10

12

14

16

18

20

5 15 25 35 45 55 65 75 85 95Percentage of households in OA with no car

Perc

enta

ge o

f hou

ses

0

10

20

30

40

50

60

70

Semi-detached

Detached Terraced Tenement Flat Subdividedhouse

4 in a block Other

Perc

enta

ge o

f hou

ses

0

5

10

15

20

25

10000 20000 30000 40000 50000 60000 70000 80000 90000 >100000

House price (£)

Perc

enta

ge o

f hou

ses

9-23

Figure 9-5: The diversity within the property dataset (cont.)

Panel 6: A histogramof noise levels

Panel 5: A histogramof the percentage ofproperties in outputareas with varyingpercentages of rentinghouseholds

Panel 4: A histogramof property ages

0.0

10.0

20.0

30.0

40.0

50.0

60.0

70.0

80.0

90.0

Pre 1919 1919-1945 Post 1945

Perc

enta

ge o

f hou

ses

0

5

1 0

1 5

2 0

2 5

3 0

5 1 5 2 5 3 5 4 5 5 5 6 5 7 5 8 5 9 5P e rc e n ta g e o f h o u s e h o ld s re n t in g h o u s e in O A

Perc

enta

ge o

f hou

ses

0

5

10

15

20

25

30

35

40

50 54 58 62 66 70 74 78 85

Noise level in dB(A)

Perc

enta

ge o

f hou

ses

9-24

9.6.2 Limitations of hedonic pricing

This research produced one hedonic pricing model for Glasgow and thereforeassumed that the whole area was one housing market. However, severalcommentators have argued that housing may be divided into a series ofsubmarkets. Within these, there may be different types of properties anddifferent preferences from the people living within. These may lead to theimpact of road noise upon property prices varying between differentgeographical areas. For example, one previous study demonstrated differentimpacts of noise upon property prices between the centre and outskirts of a city.Similarly, submarkets have been identified within the city of Glasgow, althoughthey were not considered in relation to noise measures.

The present study is based upon house price data from one year and so saysnothing regarding whether the impact of road noise upon property prices isconstant over time. We might expect our estimates to be invariant across timeunless the supply for houses with certain noise characteristics changes orindividuals� sensitivity to noise, and hence demand for peace and quiet, alters.We are unaware of any studies of these aspects to date.

In summary, the sample of properties from which the results were drawn wasvery diverse in terms of the environmental and social conditions represented. Inthe absence of other comparable studies, these results might constituteacceptable if rough guides to noise compensation values elsewhere. However,the study presented in this paper only considers the market (or collection ofsubmarkets) which constitutes the City of Glasgow. In order to test rigorouslythe transferability of these results, one would require at least one further study tobe carried out in a separate location. This would allow value transfers betweensites and pooling of data to encompass a wider variability of observations.

10-1

How then can this value be incorporated into the current procedure used by the

VOA to add a greater amount of empiricism into the calculation of Part 1 claims?

Our preferred model of house prices predicts that each decibel increase in road

noise depresses the prices of existing properties by 0.20%. Therefore when a new

road is built we would expect the resulting noise to depress property prices by an

identical amount. As argued earlier, while we have referred to the issue of road

noise throughout our analysis, in fact this is a proxy for all of the seven physical

factors for which compensation can be given under Part 1 of the Land

Compensation (Scotland) Act 1973. Therefore when assessing compensation due

to changes in road noise the valuer will now require two pieces of information:

• a property s current price (CP) at the claim date (i.e. 1 year after road

opening);

• an estimate of the magnitude of the change in road noise affecting the property

as measured in dB(A) on the L10(18-hr) scale (∆dB) at the claim date (i.e.

1-year after road opening).

Using the notation developed above we can state a simple formula for the

calculation of compensation (COMP) due to a change of ∆dB as follows:

Therefore if we had a property with a price on the compensation date of £75,000

which had been subject to an 8-decibel increase in noise then this household would

be due:

This formula suggests a new assessment procedure for valuers which is illustrated

in Panel 2 of Figure 1 alongside the current procedure in Panel 1.

��

���

� ×∆×+

−= CP])20.0[100(

100COMP

dBCP

1181£

75000])820.0[100(

10075000COMP

=

��

���

� ××+

−=

Our preferred model of house price formation (Model IV in Section 9) predicts that

each decibel increase in road noise depresses the prices of existing properties by

approximately 0.20%. Therefore when a new road is built we would expect the

resulting noise to depress property prices by an identical amount. As argued in

Section 7, road noise is a proxy for all of the seven physical factors for which

compensation can be given under Part 1 of the Land Compensation (Scotland) Act

1973. It is precisely because the effect of road noise is collinear with all of the

other compensatable items that it is used as a proxy for all of these factors.

10 RECOMMENDATIONS AND CONCLUSIONS

10.1 A new procedure for assessing Part 1 claims

10-2

Figure 10-1: Existing and proposed Part 1 claims procedure

This demonstrates a simplified process for the valuer. In theory, the modelpresented in this paper could be used to calculate the property price removing theneed for a valuer. However, our house price model includes variables such as theproperty floor area, car travel times and the amount of road visible from theproperty. These variables are complex to define and thus it would be impractical todetermine these for all properties submitting a compensation claim. Therefore, thevaluer is still required to determine the property price, a task that they are wellexperienced in. The valuer will also need to quantify how any positive benefits ofthe new road, such as improved accessibility, have increased the property price.These can then be offset against the compensation payable.

In order to apply this approach the valuer will require information on the changein road noise at each property. When new roads are built an environmental impactstatement is often produced, which includes estimates of the changes in noiselevels associated with the new road. If these were produced for a time period oneyear after the opening of the new road (the date when compensation is payable),these noise levels could be used as the basis upon which compensation paymentsare determined. This procedure will work well as long as the predicted noise levelsare similar to the actual noise levels observed once the road is constructed. If thenew road has a vehicle flow that is very different from that predicted then a revisednoise level should be calculated, and this value communicated to the valuer.

The estimation of changes in road noise is not straightforward. One approachwould be to place noise recording equipment at each property before and after theroad was built. However, this would be both time consuming and costly, as manynoise measurements would have to be taken to ensure that all potentiallycompensatable properties were covered. Noise measurements can also be

Panel 2: Suggestedprocedure

Examine recent property sales toidentify the depreciation caused by

the road.

If this is notpossible produceestimate based

upon judgementand experience

Estimate % of depreciation due tothe 7 physical factors

Determine current house price fromdatabase of property sales if possible

Assess whether compensation should bereduced due to:� Improved accessibility� Lower noise from existing roads

Final level ofcompensation

Determine current house price fromdatabase of property sales if possible

Assess whether compensation should bereduced due to:� Improved accessibility� Lower noise from existing roads

Final level ofcompensation

Panel 1: Current procedure

Based upon noise increasecalculate depreciation using

results from the hedonic pricingmodel

10-3

unreliable as they are affected by a variety of factors such as the wind, and do notonly record road noise.

We therefore recommend the use of noise modelling software to calculate thechange in noise level. This will be based upon the predicted traffic flow andcomposition on the new road and uses modelling approaches similar to thosedescribed in Annex F. Non-road ambient noise or wind does not affect thisprocedure and so the difference in noise level with and without the road is likely tobe more accurate.

An issue of particular importance to roads and highway authorities will be whetherthese procedures will lead to changes in the overall amount of Part 1 payments. Anunpublished report by the Department of Transport examined 8 road schemes and135 Part 1 claims across England. Through interviews with valuers it wasestimated that for each decibel increase in road noise awards of 0.311% of theprevious property price were being paid. This result is very similar to ourempirically derived value of 0.20%. If these results are applicable in Glasgow thenthey indicate that any awards calculated using our recommended formula wouldresult in very similar, but slightly lower payments.

10.2 Extending the model: Integrating noise compensationestimates into road planning procedures

The previous section discussed the use of noise modelling software as a means ofproviding the estimates of road noise change necessary to the compensationcalculation procedure. Such software is now often used at the road planning stageas part of the Environmental Impact Assessment (EIA) for a new road. Figure 10-2 presents an example of a road noise change map from an EIA of the M80extension.

Figure 10-2: A noise change map for the M80 extension

10-4

Road noise change information such as that illustrated in Figure 10-2 can be usedin conjunction with the estimated coefficient on property price impact to allowplanners to calculate expected Part 1 compensation payments prior to thedevelopment of a new road. Furthermore, the net cost of a variety of noisereduction systems (such as noise barriers, whisper concrete or road realignment)can be assessed taking into account the compensation saving benefits of each andthereby allowing identification of an optimal system.

As an example of such a procedure, Table 10-1 illustrates the financial viability ofa noise barrier in terms of both its direct costs and compensation saving benefits. Inthis hypothetical example the barrier constitutes a net benefit. However such aresult need not automatically imply that such a scheme should be sanctionedbecause (i) other alternatives should be considered as these may provide higher netbenefits, (ii) all compensation payments should be adjusted for the expectedaccessibility and other beneficial effects of the new road and (iii) in the event notall houses may claim for compensation within the required timespan (although thisraises a moral dilemma concerning whether such factors should be allowed toinfluence assessments).

Table 10-1: The financial viability of a new noise barrierWithout noise barrier With noise barrierEstimated house

price withoutroad

Increase innoise level

Projected Part 1claim

Increase innoise level

Projected Part 1claim

Savings on Part 1claims

Property 1 £30,000 10 dB(A) £615 5 dB(A) £308 £307Property 2 £30,000 10 dB(A) £615 5 dB(A) £308 £307Property 3 £30,000 10 dB(A) £615 5 dB(A) £308 £307Property 4 £30,000 6 dB(A) £369 2 dB(A) £123 £246Property 5 £36,500 3 dB(A) £224 1 dB(A) £75 £149Property 6 £41,000 2 dB(A) £168 0 dB(A) £0 £168Property 7 £41,000 1 dB(A) £84 0 dB(A) £0 £84Property 8 £41,500 2 dB(A) £170 0 dB(A) £0 £170Property 9 £45,000 3 dB(A) £277 0 dB(A) £0 £277Property 10 £45,000 3 dB(A) £277 0 dB(A) £0 £277

Projected savings in Part 1 claims £1862

Cost of noise mitigation £1000

Projected overall saving £862

The above example was used for illustrative purposes and, if applied in practice,other costs and benefits could also be incorporated. As an example, a reduction innoise levels may lead to fewer claims for compensation meaning loweradministration charges as fewer Part 1 claims need to be dealt with. On the costside it should be noted that in generating each scenario there are administrationcosts in mapping the noise and determining the property prices along the route.Both these could be incorporated into the CBA.

The CBA involves cost and benefits that occur at different time periods. A noisebarrier would be constructed to prevent Part 1 claims in a couple of years.Therefore one would probably wish to discount these future savings as is commonwith most CBA.

Such an approach calls for minor modification of the road planning procedure, aschematic diagram of which is illustrated in Figure 10-3. It should be noted that, aswith any new procedure, there may be certain administrative and related costimplications. Our recommendation is that serious consideration should be given tothis approach.

10-5

Figure 10-3: Assessing Part 1 claims in scheme design: A suggested approach

10.3 ConclusionsPart 1 of the Land Compensation (Scotland) Act 1973 states that compensation canbe paid to households where the physical factors associated with a new road lead toa reduction in the property price. The level of compensation payments arecurrently determined by the Valuation Office Agency, and are mainly based uponthe Registration of Title and the expertise and skill of their staff.

The aim of this research was to quantify how the physical factors associated with anew road affect the property price. It was hoped that such information would makethe assessment of Part 1 claims more straightforward. Ways that these resultscould be incorporated into the current assessment of Part 1 claims were theninvestigated. The first aim was satisfied through the use of a hedonic pricingstudy, which relates current property prices to a wide range of factors that mayaffect the price. These included variables related to the structure, neighbourhood,accessibility and environment of the property in addition to variables relating to theimpacts of nearby roads. This process was necessary because the physical impactsof roads are likely to be related to a variety of other factors. Therefore all theseneed to be controlled for before the portion of the property price attributable to the

Yes No

Final road design

Have all noise mitigationpossibilities been explored

Apply noise mitigation measures andcalculate new noise level

Calculate Part 1claims

Is money saved?

Yes No

Do not alterscheme

Alter scheme toinclude mitigation

Model noise fromnew road

10-6

road can be determined. The hedonic pricing study was based in Glasgow on asample of over 3,500 properties.

This study found that property prices were depressed by 0.20% for each decibelincrease in road noise. Noise was calculated on the L10(18-hr) scale in accordancewith the procedures set out in the standard UK noise calculation method namelyCRTN. This value means that a new technique should be developed by the VOAto assess Part 1 claims. This will involve the valuers using their experience andjudgement to calculate the value of the property without the new road. The valuerwill then multiply 0.20%/decibel by the increase in noise at each property todetermine an initial compensation level. This will provide a benchmark uponwhich the valuer should base his claim for compensation. He/she may still have toadjust it downward to account for the benefits of the road such as betteraccessibility. The above procedure requires that the valuer be notified about theincrease in noise level before visiting a property to assess the Part 1 claim. Thebest source of information for this would be the environmental impact statement,produced before the road was constructed.

This research has produced a large database of properties where the physicalimpacts of roads upon property prices have been quantified. Therefore it will be ofgreat value not only to the valuer in determining Part 1 payments, but it should alsoplay a role in justifying compensation payments should the case come before aLands Tribunal. Currently, as there is no such study, these cases usually involvetwo valuers arguing against each other on the basis of their own judgement.

This study has also recommended that Part 1 claims be determined at the roaddevelopment stage from information gathered for the environmental impactstatement. This should allow the estimation of Part 1 claims before the new road isconstructed. This implies that, while a road is being designed, changes can beincorporated to reduce the magnitude of Part 1 claims. As an example noisereduction systems could be incorporated into the design where these cost less thanthe expected associated savings in Part 1 claims allowing road to be redesigned soas to optimise noise management strategies.

A-1

ANNEX A: MEASURES OF VALUE; CONSUMER SURPLUS,WILLINGNESS TO PAY AND WILLINGNESS TO ACCEPT

Economists involved in making decisions concerning the provision or pricing ofgoods and services in the real world are often interested in measuring changes in thevalue enjoyed by individuals when the provision or price of a good or servicechanges.

In Section 2.2.1, we described two measures that could be used to quantify thischange in value;

• Change in Utility(∆U); a subjective measure of the change in value experiencedby an individual when the price or provision of a good or service changes, and

• Willingness to Pay (WTP) and Willingness to Accept (WTA); a monetary measureof changes in well-being describing the maximum amount an individual wouldactually be prepared to pay or the minimum they would be willing to accept inorder to achieve or to avoid a given change in the provision or price of a good orservice.

In Section 2.3.1 a further possible measure of this change in value was introduced;

• Change in Consumer Surplus (∆CS); like WTP and WTA this is a monetarymeasure of changes in well being.

One great advantage of using ∆CS to measure changes in value is that (unlike theother two measures) we can quantify it directly from the observed behaviour ofindividuals. Indeed, CS can be measured as areas under the demand curve (for a fullerdescription of why this is see Section 2.3.1). ∆CS is illustrated for an increase in priceof a simple good (e.g. half litre bottles of mineral water) from P1 to P2 in Panel A ofFigure A-1 and for a decrease in provision of an unpriced good (e.g. an environmentalgood such as clean air) from Q1 to Q2 in Panel B of Figure A-1.

Figure A-1: Changes in CS resulting from an increase in price and reduction ofprovision

QuantityQuantity

Price

PANEL A PANEL B

P1

P2

Q1Q2

∆CS∆CS

A-2

Now, if ∆U, WTP, WTA and ∆CS are to be considered theoretically consistentmeasures of value, then it must hold that a constant relationship exists between allthree according to Equation A-1;

∆U = α.WTP/A =α.∆CS (A-1)

Unfortunately, the last equality in Equation A-1 does not hold. That is ∆CS is not atheoretically correct measure of changes in value. To see why this is, we need tounderstand in more depth the relationship depicted by the demand curve.

We have already established that the demand curve traces the observed quantities of agood that are consumed by an individual1 at different prices. At a higher price wewould expect an individual to consume less of a good, at a lower price we wouldexpect them to consume more.

These changes in demand for a good when its price changes can be shown to resultfrom both an income and a substitution effect. When the price of a good changes, thereal income of the consumer changes. The purchasing power of his money incomewill be greater when the price of the good falls, it will be less when the price rises.When the price of a good falls, therefore, we would expect consumers to buy morebecause they can afford to buy more (the income effect). Existing buyers willprobably increase their purchases and new buyers, who did not purchase at the higherprice, will tend to enter the market.

A fall in the price of a commodity also makes it relatively cheaper when comparedwith competing goods. There will probably be some �switching� of purchases awayfrom the now relatively dearer substitutes towards the commodity that has fallen inprice. This is the substitution effect. Both the income and substitution effects causeconsumers to buy more of a product when it becomes cheaper. Consumers becomemore able and more willing to buy the product. The opposite effects will apply whenthe price of a good rises. Consumers� purchasing power will fall, reducing their abilityto buy the product and they are likely to switch to substitute products that havebecome relatively cheaper.

The important fact for this analysis is that a change in the price of a good will bringabout a change in a consumer�s real income. Thus, when the price of a good falls, anindividual will find his real income has increased. At the new, lower price, his moneywill buy relatively more goods and services, he enjoys a higher purchasing power, andhence he will realise a higher level of overall utility. The reverse is true of a price rise;his real income will fall and he will end up with a lower level of overall utility. Thissituation is illustrated in Figure A-2, where a price rise from P1 to P2 causes the totalutility experienced by the individual to fall from U1 to U2.

Returning to Equation A-1, we can now use Figure A-2 to prove that ∆U ≠α.∆CS. If∆CS were a theoretically correct monetary measure of ∆U then it follows that takingthe monetary amount ∆CS away from the consumer at the lower prices, P1, when thepurchasing power of income is relatively high, would reduce the overall utility heenjoys by ∆U. At the same time, it should also be true that giving the consumer themonetary amount ∆CS at the higher prices, P2, when the purchasing power of incomeis relatively low, would increase his overall utility by the same amount, ∆U. Clearly, 1 Or by an entire market of individuals in the case of the aggregate demand curve

A-3

both statements cannot be true at the same time; taking a fixed amount of incomeaway from an individual when money can buy relatively more goods and servicesmust lead to a bigger ∆U than giving him the same amount of income when moneycan buy relatively fewer goods and services.

Figure A-2: Changes in overall utility resulting from an increase in price

We would envisage, therefore, that ∆CS is an underestimate of ∆U when looked atfrom the lower prices when money is relatively valuable, and an overestimate whenlooked at from the higher prices when money is relatively less valuable.

Clearly the problem with using the area under the ordinary demand curve to measurechanges in value is that it incorporates income effects; the purchasing power ofincome changes as prices change and we move along the demand curve. However,there is an alternative demand curve that we could construct known as the Hicksiandemand curve. Unlike the ordinary demand curve, the Hicksian demand curve is onewhere utility is held constant. It is also often termed the compensated demand curve.This terminology arises from the fact that the Hicksian demand curve is constructedsuch that compensation is made which eliminates the income effects of a price changeso as to keep the consumer�s utility constant. Hence the consumer is �compensated�for the price change, and his utility is the same at every point on the Hicksian demandcurve. In contrast to the situation with an ordinary demand curve, the consumer is notworse off facing higher prices than lower prices since his income is compensated.

In Figure A-3 the Hicksian demand curve equivalent to the level of utility, U2,experienced at the higher price, P2, has been overlain on the ordinary demand curve.Since this demand curve traces only the pure substitution effect, increases in price donot lead to as rapid a decline in demand.

Quantity

Price

P1

P2

∆CS

U2

U1

A-4

Figure A-3: The Hicksian demand curve and WTP

The Hicksian demand curve maps out a graphical representation of our secondmeasure of value; WTP and WTA. Imagine that our consumer were faced by theincrease in prices illustrated in Figure A-3 but could avoid this increase by paying outmoney. How much of his income would he prepared to give up? Clearly he wouldonly give up as much of his income as would reduce his overall utility from its presentlevel, U1, to the level he would have to endure if the price of the good rose, U2. Thisamount is illustrated in Figure A-3 by the lightly shaded area labelled A. Theindividual�s WTP to avoid a price rise is known as an Equivalent Variation; it is theamount of money that the individual would have to pay out in the absence of the priceincrease, so that his utility would fall to the same level as he would have experiencedif the price increase had taken place. It is a monetary amount �equivalent� to thechange.

However, this is only one measure of the welfare change that might result from thisprice change. Alternatively, if the increase in prices were imposed on the individualwe could ask the question, how much would they be willing to accept to compensatefor the price increase? Whilst facing the higher prices, this would amount to apayment that would increase his level of utility from its current level, U2, to thatpreviously enjoyed when the prices were lower, U1. This is illustrated by the areaA+B in Figure A-4. The individual�s WTA compensation for the increase in prices is aCompensating Variation; it is the increment to income that allows the individual toreturn to their original utility level after a change (i.e. it compensates for the change).

Quantity

Price

P1

P2U2

U1U2

HicksianDemandCurve

AB

A-5

Figure A-4: The Hicksian demand curve and WTA

Figures A-3 and A-4 illustrate two of the four possible Hicksian measures of value,namely WTP to avoid a loss and WTA compensation for a loss. Two other measuresalso exist, WTP to secure a gain and WTA in lieu of a gain. Which should be used asthe welfare measure depends on the initial allocation of property rights in eachsituation; where no property rights have been defined, this is a political decision.

Consider estimating the benefits of a policy decision to reduce noise from traffic. Thefull property right might be allocated to motorists, effectively giving them the right tocreate as much noise as they wish. Then the WTP of residents to have noise levelsreduced should be measured. Alternatively, the residents might be allocated the rightto quiet; then their WTA for not having the reduction in noise levels should be used.

Notice that neither WTP or WTA measures are the same as CS. Since CS does not giveus a theoretically valid measure of changes in well being brought about by changes inthe provision or price of goods and services the validity of its use in welfare analysisis somewhat suspect. However, bounds can be calculated for the extent to which thethree measures differ (Willig, 1976) which show that for small price changes themeasures may differ by only a few percent. In many cases, therefore, CS is areasonable approximation of welfare change.

For environmental goods, it is often changes in the level of provision which are ofinterest, rather than changes in prices (e.g. air pollution levels in a town). The sametheoretical methodology applies to changes in the quantity or quality of a good and forthe sake of brevity is not expounded here. However, it is worth noting that the Willigbounds do not extend simply to this case. It has been shown (Hanemann, 1991) thatwhere no few close substitutes exist for a good, there can be substantial divergencebetween WTP, WTA and CS.

Quantity

Price

P1

P2U2

U1

U1

HicksianDemandCurve

BA

B-1

ANNEX B: THE BOX-COX TRANSFORMATION

In Section 4, we introduced the concept of transforming a variable. A transformationis simply a mathematical function that converts one number into another number.Transformations are useful in regression analysis because they allow us to describecomplicated relationships between an explanatory variable and the dependent variablein a linear form. As we have already seen, when the relationship between anexplanatory variable and the dependent variable is best described by an upwardsloping curve that becomes progressively flatter, we can use the logarithmictransformation to describe the relationship in linear form. This is illustrated in FigureB-1.

Figure B-1: The logarithmic transformation

Of course many other possible relationships could exist between an explanatoryvariable and the dependent variable. Amongst some of the more commonly usedtransformations used in regression analysis are the;

• Inverse transformation (i.e. Y = β.1/X); which linearises a relationship defined bya declining slope that becomes progressively flatter

• Square transformation (i.e. Y = β.X2); which linearises a relationship defined by aU-shaped curve (or inverted U-shaped curve if the parameter estimated on theexplanatory variable is negative)

• Cube transformation (i.e. Y = β.X3); which linearises a relationship defined by asigmoid-shaped curve.

Most of the transformations that are commonly used in regression analysis areimposed by the researcher before estimation of the model according to hisassumptions concerning how the dependent and explanatory variable are related. Forexample, if we were regressing income against age, we might expect that at relativelyyoung ages income would rise steadily but at some point this would level off and atolder ages income would begin to decline. Our expectation of this inverted U-shapesuggests that we should include a variable of age-squared in our regression.

0 X

Y

0 Ln(X)

Y

B-2

Of course, by deciding in advance the transformation which describes the relationshipbetween the dependent variable and the explanatory variable we are imposing anassumption on our model. It is possible however, to allow the model to dictate thetransformation that allows the regression equation to best fit the data. Models thatallow for this are said to have a flexible functional form.

One transformation that allows for a flexible functional form is the Box-CoxTransformation (Box and Cox, 1964). Imagine that in our hedonic regression we areuncertain of the relationship between the dependent variable (P; price of house) andone of the explanatory variables (x1). As a first effort, we might attempt a logtransformation of this variable. This would amount to estimating the regression givenby Equation B-1;

( ) iiii XxP εεεεββββββββ ++= 11 ln (B-1)

where Pi is the price of house ix1i is the explanatory variable on which we are focussing attention

Xi is a vector of other explanatory variables

β1 is the parameter on the x1 variable that we wish to estimate

β is a the vector of parameters on the X variables that we also wish toestimate, and

εi is the normal regression residual.

This model could be estimated using a straightforward Ordinary Least Squares (OLS)estimator. However, we might be unhappy with the imposition of the logtransformation on the x1 variable. Instead, we could attempt to transform this variableusing the Box-Cox transformation;

[ ]

λλλλ

λλλλλλλλ 11

1−= xx (B-2)

where λ is the Box-Cox parameter that we wish to estimate as part of our model.

Notice that the transformation is not fixed but relies on the value of λ. If λ were totake on a value of 1 then the transformation would simply collapse back to a straightlinear transformation (i.e. x1 � 1) . If, however, λ takes on a value approaching zerothen the model will result in the log transformation (i.e. ln x1). Since λ can take onany value the Box-Cox allows for a wide diversity of transformations of which thelinear and log forms are special cases.

Our regression would now take the form of Equation B-3;

[ ]iiii XxP εεεεββββββββ λλλλ ++= 11. (B-3)

B-3

We now wish to estimate not only the β1 and β parameters but also the Box-Coxparameter λ. Since this model is no longer linear in the parameters, we can no longeruse a straightforward OLS estimator. Instead models involving the Box-Coxtransformation are usually estimated using Maximum Likelihood (ML) techniques.

If our estimate of λ is significantly different from both zero and one, we can concludethat the more complicated transformation dictated by the Box-Cox transformation fitsthe data better than either the linear or logarithmic forms.

Of course, it is possible to transform more than one explanatory variable using theBox-Cox. Indeed, it is possible to transform all the explanatory variables and thedependent variable using the Box-Cox. Unfortunately, as more parameters are added,the model becomes more and more complex and may prove difficult to estimate.

C-1

ANNEX C: PRINCIPAL COMPONENTS ANALYSIS

This technique can be used to identify relationships between groups of variables. If thereare strong correlations within a large set of variables it may be unnecessary to include allof them in a statistical investigation and better to employ several indices that reflect themain underlying dimensions of variability. Principal components analysis provides ameans of achieving this objective, generating new composite measures (known ascomponents) where each input variable is strongly correlated with one (or a few) of thenew components, but not with the remainder (see Norusis, 1985; Webster and Oliver,1990; or Dunteman, 1994 for further details). There can be as many components asinput variables, but it is usual for a high proportion of the variance in the original data tobe accounted for by a much smaller number of components. Scores on the componentscan be calculated for each original observation (e.g. a property) and since thecomponents are orthogonal (i.e. uncorrelated with each other) and standardised (i.e. witha mean of zero and a standard deviation of one) these new measures are particularlysuitable as inputs to other statistical techniques such as regression analysis.

Principal components are usually defined by first transforming the input variables to zscores and then deriving eigenvectors from the matrix of correlations between thestandardised measures. The first component extracted always represents the maindimension of variability in the input data and can be thought of as the 'average' variable.It also accounts for the largest proportion of variance in the data and the amountassociated with subsequent components progressively decreases.

Relationships between the input variables and each component can be assessed byexamining the pattern of component loadings. These coefficients represent the strengthof correlations between variables and components, ranging from -1 to +1. A value closeto either extreme signifies a strong association (positive or negative), while a coefficientof zero suggests no relationship. If the loadings of all input variables on a componentare squared and summed then the total (known as an eigenvalue) represents the amountof input variance accounted for by that component. When standardised measures areused the overall variance in the data is equal to the number of input variables, so dividingan eigenvalue by this figure gives the proportion of total variance associated with acomponent. It is also common to use eigenvalues when deciding how many componentsshould be selected to adequately summarise the input information. Several criteria havebeen suggested for this purpose, but one of the most widely used is that proposed byKaiser (1960). He argued that components with eigenvalues under 1.0 should not beretained on the grounds that they contained less information than a single standardisedinput variable. There is an obvious logic to this rule, but care does need to be taken thatit does not result in too parsimonious a description of the data.

One common problem in principal components analysis is that the first few componentsextracted tend to be somewhat general in nature and have high correlations with manyinput variables. As a consequence, it can be difficult to place substantive interpretationson the components. A solution is often to mathematically rotate the selected componentsso that the pattern of correlations with the original variables is altered. The most widelyused rotation procedure is the varimax method. This aims to maximise the loadings of avariable on one or two components and minimise those on others. The relationshipbetween input variables and components should therefore be clearer after a rotation. Italso should be noted that eigenvalues will change after a rotation, so the relativeimportance of components may be different.

C-2

The final stage in a principal components analysis is the calculation of componentscores. These represent values for each original observation (e.g. a property) on the newcomposite components. There are several methods for calculating component scores (seeDunteman, 1994), but the score on a particular component for an individual observationessentially reflects:

i) The values for that observation on the input variables.

ii) How those variables are correlated with the relevant component.

Once component scores have been derived they can be further analysed to assesssimilarities between observations. If there are only two significant components it isstraightforward to plot the individual observations on a scatter diagram and definegroups by eye. When there are three or more principal components such visualapproaches tend to be impractical and it is more common to use the scores as inputvariables for other statistical techniques.

D-1

ANNEX D: VARIABLES CREATED FOR THE STUDY

STRUCTURAL VARIABLES DEFINED USING GIS - 27 variablesAREA2 Area of the property in m2

AREA_PLO Plot area of the property in m2

PERIMETE Perimeter of property in mPERM_PLO Perimeter of property�s plot in mPR_AREA Ratio of the property perimeter to the square root of the areaLOOKOUT Aspect of property in degrees from SouthOLDHOUS = 1 if the property is not a flat and was built before 1919; = 0 otherwiseOLDTENEM = 1 if the property is a tenement built before 1919; = 0 otherwiseNUMBER Number of ADDRESS-POINT seeds in propertySLOPE Slope angle of the land upon which the property is built in degreesT1 = 1 if the property is a detached house; = 0 otherwiseT10 = 1 if the property is a single end tenement; = 0 otherwiseT11 = 1 if the property is a ground floor flat; = 0 otherwiseT12 = 1 if the property is an uncertain tenement; = 0 otherwiseT13 = 1 if the property is a non tenement property which is probably a flat; = 0 otherwiseT14 = 1 if the property is a subdivided house; = 0 otherwiseT2 = 1 if the property is a mid terrace property; = 0 otherwiseT3 = 1 if the property is an end terrace property; = 0 otherwiseT4 = 1 if the property is a semi detached property; = 0 otherwiseT5 = 1 if the property is a four in a block property; = 0 otherwiseT6 = 1 if the property is a basement flat; = 0 otherwiseT7 = 1 if the property is a whole floor flat; = 0 otherwiseT8 = 1 if the property is a regular tenement; = 0 otherwiseT9 = 1 if the property is another flatted property; = 0 otherwiseNON GIS STRUCTURAL VARIABLES - 23 variablesATTIC_BA = 1 if the property has an attic and a basement; = 0 otherwiseATTIC_ON = 1 if the property is an attic only; = 0 otherwiseALLEY_PR = 1 if the property has an alley to the side of it; = 0 otherwiseBASE_ONL = 1 if the property has a basement only; = 0 otherwiseCOL_WHIT = 1 if the property has white stone facing; = 0 otherwiseCOL_PAIN = 1 if the property has painted stone facing; = 0 otherwiseCOL_RED = 1 if the property has red stone facing; = 0 otherwiseF1919_19 = 1 if the property was built between 1919 and 1945; = 0 otherwiseFLATROOF = 1 if the property has a flat roof; = 0 otherwiseFLOORPOS If a flatted property, the floor levelINTNSF = 1 if the property was built between 1919-1945 and has no stone facing; = 0 otherwiseINTSF = 1 if the property was built between 1919-1945 and has stone facing; = 0 otherwiseMIDHOUS = 1 if the property was built between 1919-1945 property; = 0 otherwiseNEWHOUS = 1 if the property was built post 1945 and is not a flat; = 0 otherwiseOLDSFR = 1 if the property was built pre 1919 property with red stone facing; = 0 otherwiseOLDSFW = 1 if the property was built pre 1919 property with white stone facing; = 0 otherwisePITCHROO = 1 if the property has a pitched roof; = 0 otherwisePOST_194 = 1 if the property was built post 1945 property; = 0 otherwisePRE_1919 = 1 if the property was built before 1919; = 0 otherwiseREGHOUSE = 1 if the property is not a flat; = 0 otherwiseSF = 1 if the property is stone faced; = 0 otherwiseSTOREYS Number of storeys of propertyTENEMENT = 1 if the property is labelled as a tenement in the Registration of Title; = 0 otherwiseACCESSIBILITY VARIABLES - 18 variablesWALKBUS Walking distance to nearest bus route in mWALKCENT Walking distance to the centre of Glasgow in mWALKPARK Walking distance to the nearest park in mWALKRAIL Walking distance to the nearest railway station in mWALKSCHO Walking distance to the nearest school in mWALKSHOP Walking distance to the nearest shop in mCARBUS Car travel time to the nearest bus route in minutesCARCENTR Car travel time to the centre of Glasgow in minutesCARPARK Car travel time to the nearest park in minutesCARRAIL Car travel time to the nearest railway station in minutesCARSCHOO Car travel time to the nearest school in minutesCARSHOPS Car travel time to the nearest shop in minutesEDBUSR Euclidean distance to nearest bus route in mEDCENTRE Euclidean distance to the centre of Glasgow in mEDPARK Euclidean distance to the nearest park in mEDRSTAT Euclidean distance to the nearest railway station in m

D-2

EDSCHOOL Euclidean distance to the nearest school in mEDSHOPS Euclidean distance to the nearest shop in mENVIRONMENTAL VARIABLES - NOISE AND GENERAL - 6 variablesAIRNOISE Aircraft noise level in NNINOISEFIN Road noise level in dB(A)DIS_MABC Distance to the nearest motorway, A, B or C road in mDIS_RD Distance to the nearest road in mDIS_MA Distance to the nearest motorway or A road in mPYLON_D Distance to nearest electricity transmission line in mENVIRONMENTAL VARIABLES - BACK VISUAL IMPACTS 1� LINEAR DISTANCE DECAY - 22 variablesB_L13_VS Sea viewscoreB_L15_VS Outside study area viewscoreB_L1_VS Road viewscoreB_L3_VS Water viewscoreB_L5_VS Vegetation viewscoreB_L7_VS Buildings viewscoreB_L8_VS Unclassified viewscoreB_L_PY_V Pylons viewscoreB_LID_VS Industry viewscoreB_LPK_VS Park viewscoreB_LRL_VS Railway viewscoreENVIRONMENTAL VARIABLES - BACK VISUAL IMPACTS1 � SQUARE DISTANCE DECAY - 21 variablesB_S13_VS Sea viewscoreB_S15_VS Outside study area viewscoreB_S1_VS Road viewscoreB_S3_VS Water viewscoreB_S5_VS Vegetation viewscoreB_S7_VS Buildings viewscoreB_S8_VS Unclassified viewscoreB_S_PY_V Pylons viewscoreB_SID_VS Industry viewscoreB_SPK_VS Park viewscoreB_SRL_VS Railway viewscoreENVIRONMENTAL VARIABLES - BACK VISUAL IMPACTS1 � AREA VISIBLE - 12 variablesB_PY_ARE Area of pylons visible in m2

BL0 Area of land not visible in m2

BL1 Area of road visible in m2

BL13 Area of sea visible in m2

BL15 Area out of study area visible in m2

BL3 Area of water visible in m2

BL5 Area of vegetation visible in m2

BL7 Area of buildings visible in m2

BL8 Area of unclassified visible in m2

BLIND Area of industry visible in m2

BLPARK Area of park visible in m2

BLRAIL Area of railway visible in m2

ENVIRONMENTAL VARIABLES - FRONT VISUAL IMPACTS1 � LINEAR DISTANCE DECAY - 22 variablesF_L13_VS Sea viewscoreF_L15_VS Outside study area viewscoreF_L1_VS Road viewscoreF_L3_VS Water viewscoreF_L5_VS Vegetation viewscoreF_L7_VS Buildings viewscoreF_L8_VS Unclassified viewscoreF_L_PY_V Pylons viewscoreF_LID_VS Industry viewscoreF_LPK_VS Park viewscoreF_LRL_VS Railway viewscoreENVIRONMENTAL VARIABLES - FRONT VISUAL IMPACTS1 � SQUARE DISTANCE DECAY - 21 variablesF_S13_VS Sea viewscoreF_S15_VS Outside study area viewscoreF_S1_VS Road viewscoreF_S3_VS Water viewscoreF_S5_VS Vegetation viewscoreF_S7_VS Buildings viewscoreF_S8_VS Unclassified viewscoreF_S_PY_V Pylons viewscoreF_SID_VS Industry viewscoreF_SPK_VS Park viewscoreF_SRL_VS Railway viewscore

D-3

ENVIRONMENTAL VARIABLES - FRONT VISUAL IMPACTS1 � AREA VISIBLE - 12 variablesPY_AREA Area of visible pylons in m2

FL0 Area of land not visible in m2

FL1 Area of road visible in m2

FL13 Area of sea visible in m2

FL15 Area out of study area visible in m2

FL3 Area of water visible in m2

FL5 Area of vegetation visible in m2

FL7 Area of buildings visible in m2

FL8 Area of unclassified visible in m2

FLIND Area of industry visible in m2

FLPARK Area of park visible in m2

FLRAIL Area of railway visible in m2

NEIGHBOURHOOD VARIABLES - LOCAL BASE STATISTICS - 47 variablesBIGHL Percentage of houses with more than 7 roomsBRNNCL Percentage of residents born in new commonwealthCAR2L Percentage of 2 car householdsCHD_CWL Number of children per child care workerCHDL5L Percentage of children aged under 5CHLDIHL Number of children in institutions per 1000 childrenCONSTRL Percentage of workers in the construction sector (lower bound)CONSTRU1 Percentage of workers in the construction sector (upper bound)CRWDL Percentage of households with > 1 person per roomDINKYL Percentage of double income families with no childrenFECONL Percentage of people working in the �free� economyG40HRL Percentage of full time workers working > 40 hrs per weedHGHEDL Percentage of people with a degree or higher degreeHGHWGL Percentage of people in occupations with gross wages > £23,705HSNDL Percentage of homeless peopleIL_HPL Ratio of people with limiting long term illness to no. of health care workersINFECL Percentage of people working in the informational economyLCKAML Percentage house houses lacking basic amenitiesLOWSCL Percentage of households with working head in social classes IV or VLPENL Percentage of lone pensioner householdsMANUFACL Percentage of workers in the manufacturing sectorMIDCLSL Percentage of households with working head in social classes 1-3MIGRL Percentage of residents with a different address 1 year before censusMULEARNL Percentage of multiple earning householdsMVEFML Percentage of recently moving familiesMVEPNL Percentage of recently moving pensionersNOCARL Percentage of households without access to a carNONMANL Number of non manual workers per manual workersNOSCHL Percentage of 17 year olds not in full time educationNOWNL Percentage of households not owning their homeNSLFL Percentage of residents in non self contained accommodationOETHNICL Percentage of people of other ethnic backgroundsOLDFAML Percentage of residents aged 35-54 with children < 16PRCHDL Percentage of children in household with no earnersPRIMARYL Percentage of workers in the primary sectorSCRPMNL Percentage of men aged 35-54 who are unemployed or on a schemeSERVICEL Percentage of workers in the service sectorSNGPRL Percentage of single parentsTRDFML Percentage of lone and cohabiting parents in relation to all familiesUNEMPL Percentage unemploymentVCNTL Percentage of vacant or second homesWKCLSL Percentage of residents in working class occupations (SOC 4-9)WORKPARL Percentage of working parents with children 0-4YNGFAML Percentage of residents 16-34 with children < 16YTSL Percentage of 16 and 17 year olds on a government schemeYUNEMPL Percentage of unemployed 16 and 17 year oldsNEIGHBOURHOOD VARIABLES - SMALL AREA STATISTICS - 25 variablesCOMMONW Percentage of people born in new commonwealthDINKY Percentage of double income families with no childrenELDALONE Percentage of elderly people living aloneMULTEARN Percentage of multiple earning householdsNOCAR Percentage of households with no access to a carNOCH Percentage of households with no central heatingNOTOWNER Percentage of residents not owning their homeNOWC Percentage of residents with no exclusive use of WC, bath or showerOETHNIC Percentage of other ethnic backgrounds

D-4

OLDFAM Percentage of residents aged 35-54 with children < 16ONEPARNT Percentage of lone parent householdsOVERCROW Percentage of households with > 1 person per roomPOORCHLD Percentage of children in households with no earnersROOM_1 Percentage of houses with 1 roomROOM_2 Percentage of houses with 2 roomsROOM_3 Percentage of houses with 3 roomsROOM_4 Percentage of houses with 4 roomsROOM_5 Percentage of houses with 5 roomsROOM_6 Percentage of houses with 6 roomsROOM_7 Percentage of houses with 7 roomsTWOCAR Percentage of 2 car householdsUNDER5 Percentage of people < 5UNEMP Percentage unemploymentVACANT Percentage of vacant and second homesYOUNGFAM Percentage of residents 16-34 with children < 16NEIGHBOURHOOD VARIABLES - SMALL AREA STATISTICS FOR 200M BUFFER AREA - 39 variablesCOMMONWW Percentage of born in new commonwealthDINKYW Percentage of double income families with no childrenELDALON1 Percentage of elderly people living aloneFAMILYW Percentage of lone and cohabiting parents in relation to all familiesLONGHOU1 Percentage of people working > 40 hours per weekMANUFACW Percentage of people working in the manufacturing sectorMIGFAMW Percentage of recently moving familiesMIGPENW Percentage of recently moving pensionersMULTEAR1 Percentage of multiple earning householdsNOCARW Percentage of households with no access to a carNOCHW Percentage of households with no central heatingNONMANW Percentage of non manual workers per manual workerNOSCHOO1 Percentage of young people 16-17 not in full time educationNOTOWNE1 Percentage of residents not owning their homeNOWCW Percentage of residents with no exclusive use of WC, bath or showerOETHNICW Percentage of other ethnic backgroundsOLDFAMW Percentage of residents aged 35-54 with children < 16ONEPARN1 Percentage of lone parent householdsOVERCRO1 Percentage of households with > 1 person per roomPOORCHL1 Percentage of children in households with no earnersPRIMARYW Percentage of people working in the primary sectorQUALDW Percentage of people with a degree or higher degreeROOM_1W Percentage of houses with 1 roomROOM_2W Percentage of houses with 2 roomsROOM_3W Percentage of houses with 3 roomsROOM_4W Percentage of houses with 4 roomsROOM_5W Percentage of houses with 5 roomsROOM_6W Percentage of houses with 6 roomsROOM_7W Percentage of houses with 7 roomsSCRAPME1 Percentage of men aged 35-54 who are unemployed or on a schemeSERVICEW Percentage of people working in service sectorTWOCARW Percentage of 2 car householdsUNDER5W Percentage of people < 5UNEMPW Percentage unemploymentVACANTW Percentage of vacant or second homesWKCLASSW Percentage of people in working class occupations (SOC 4-9)YOUNGFA1 Percentage of residents 16-34 with children < 16YTSW Percentage of people aged 16-17 on a YTS schemeYUNEMPW Percentage of 16-17 year olds unemployed1 viewscore units are discussed in Section 8.5.1

E-1

ANNEX E: ASSIGNING TRAFFIC VOLUMES TO ALL ROADS

The first stage in estimating a traffic volume for all roads in the study area was toobtain all the relevant traffic count data from Glasgow City Council. These mostlycover a period between 8 a.m. and 6 p.m. and are usually collected for a specificproject (e.g. a proposal to change a road junction). This means that their geographicdistribution is sporadic. Therefore in order to obtain a reasonable sample, it wasnecessary to collect data for several years either side of 1986. From these the averagetraffic flow between 8 a.m. and 6 p.m. was calculated for all the sample roads. Thisproduced traffic volumes for 589 individual road sections (6.3% of all the roads in thestudy area).

However, because the traffic counts spanned several years they had to be adjusted forchanges in traffic flow over this time. The volumes were standardised to 1986 levelsby using the adjustment factors in Table E-1. These are based on national trafficvolume increases (Department of Transport, 1991).

Table E-1: Multiplication factors to standardise traffic levels to 1986

Year Multiplicationfactor

1980 1.1751981 1.1491982 1.11241983 1.1051984 1.0671985 1.0411986 01987 0.9371988 0.881989 0.82

This produced an average traffic volume per hour based upon 10-hour traffic counts.In order to use the CRTN procedure to calculate a noise level, it was necessary toconvert this to an average traffic volume based upon 18-hour traffic counts. Thefactor to achieve this was supplied by the Strathclyde Passenger Transport Executiveand was calculated from the Strathclyde Integrated Transport Model (SITM). Thisestimates traffic volumes based upon a survey of actual driver trips, and was suppliedfor the year of 1996 based upon 24-hour road flows. An hourly average vehicle flowbased upon an 18-hour traffic count is 0.785026 that of a flow based upon a 10-hourtraffic count. However, most of the roads in the study area did not have an actualtraffic count and so a methodology was derived to estimate the flow on all roadsbased upon those where data were available. The first method was applied when twosections of the same road had known traffic volumes. In these cases linearinterpolation was used to estimate traffic flow on the intervening sections. This wasbased upon the number of junctions (Figure E-1; assignment with 2 measurements).If only one count existed for a road then this was copied to all the other sections(Figure E-1; assignment with 1 measurement). However, the procedures describedabove were only applicable for some roads with no traffic volume.

E-2

Figure E-1: Ways of assigning traffic volumes

However, after these procedures were applied there were some roads lacking a trafficvolume estimate. For most major roads in the study area traffic estimates have beenproduced from the SITM. Data from this were obtained for these roads with no trafficdata. The traffic volumes were converted to 1986 18-hour counts by comparing asample of roads that had actual traffic volumes and were also represented on themodel. This still left a handful of major roads with no traffic volumes and countswere assigned on the basis of the average volume for this class of road across thestudy area.

However, most of the minor roads were still not assigned a traffic volume. For thesewe defined a series of variables which we hypothesised would explain how busy theroad might be. The first set of variables were the distance from each road to a set ofamenities namely, shops, parks, railway stations, major roads, bus routes, schools, andthe centre of Glasgow. The next set of predictive variables was based upon thelocations of cars and the locations of amenities.

The number of cars belonging to households along each road was calculated from the1991 UK Census and the OS ADDRESS-POINT database. Using a GIS the path ofthese vehicles was then modelled assuming that these would all drive from their roadof residence to an amenity. The number of vehicles travelling on all roads was thencalculated. This was repeated for each of the 7 amenities in turn and produced whatwe term cumulative flow variables. These present an indication about how busy aroad is likely to be and are illustrated in Figure E-2.

100 veh/hr 90 veh/hr 80 veh/hr 70 veh/hr

Assignment with 2 measurements

100 veh/hr 100 veh/hr 100 veh/hr 100 veh/hr

Assignment with 1 measurement

100 veh/hr - Actual traffic count100 veh/hr - Assigned traffic count

Road section Road section Road section Road section

E-3

Figure E-2: Modelling traffic flow

These variables were used to try and explain the variations in traffic flow observedalong the 77 minor roads where we had actual traffic data. This was achieved using amultiple regression technique. However, this was problematic as most of the actualtraffic data consisted of observations next to major roads. Therefore it was difficult todevelop a model that accurately estimated traffic volumes away from major roads.This was overcome by assuming that all cul-de-sacs in the study area would have atraffic volume equal to our predicted cumulative flow.We therefore recalculated our predictive model using the 77 minor roads for whichwe had actual traffic data and 77 cul-de-sacs where the traffic flow equalled ourprediction of cumulative flow. These observations were regressed against outexplanatory variables and the results are presented in Box E-1.

2 2 2 2

44

06

422

226

0 12

4 42 2

44

26

2 No. of cars along each road4 Cumulative flow on each road

Direction of traffic flow Amenity e.g. Shop

E-4

Box E-1: Traffic assignment model

These suggest that as the distance from the centre of Glasgow increases the trafficflow decreases. It also indicates that as our predicted flow of cars to main roadsincreases the actual traffic volume also increases. Both these accord with our priorexpectations. Visualising the predictions upon small sections of the study area cantest the results of this model further. An example of such a prediction, based upon asmall area in South Glasgow, in presented in Figure E-3.

This figure illustrates smaller volumes away from main roads and larger volumeswhere main roads meet minor roads. Although we cannot tell how accurate thesepredictions are, they do seem to accord with prior expectations. This method wastherefore adopted as the best way of assigning traffic volumes to the remaining minorroads.

Figure E-3: The results of traffic modelling

lncars = 7.676 + 0.573 * lnmajor - 0.237 * lndistcen

(5.668) (9.795) (3.577)

Where:

lncars = Natural log of the number of cars

lnmajor = Natural log of the predicted flow to main road

lndistcen = Natural log of the distance to city centre

adjusted R2 = 39.4%

figures in brackets are t-values

metres

E-5

In order to calculate a noise level for each property, it was assigned to its nearest road.Table E-2 links the number of houses to the traffic assignment method on the road towhich it was matched. This table demonstrates that the majority of houses wereassigned to minor roads where the traffic volumes were assigned on the basis of ourtraffic model. Most other houses had roads classified on the basis of actual orinterpolated traffic measurements.

Table E-2: Assigning traffic data to individual properties

Traffic assignment method Number ofHouses

Percent of allhouses

Actual traffic data 332 9.6Interpolating between 2 observations 290 8.4Interpolating on 1 observation 228 6.6Interpolation from SITM 79 2.3On basis of road class 45 1.3Minor road traffic modeling 2482 71.9Total 3456 100

F-1

ANNEX F: CALCULATING ROAD NOISE LEVELS

Once the traffic volume, the percentage of heavy vehicles, the speed of the vehicles,the gradient of the road and the road surface have been determined, the level of noiseemitted from each road is calculated in a series of stages. The first stage calculates anoise level based solely upon the traffic flow and the formula to achieve this ispresented in Equation 1.

The traffic speeds that were assigned to each section of road in Section 8.5.2.1 werethen adjusted to account for the percentage of heavy vehicles and the gradient of theroad (Equation 2).

The next stage adjusts this noise level to account for the speed of the traffic and thepercentage of heavy vehicles. This is achieved using the formula in Equation 3. Thisnoise level is then adjusted to account for the gradient of the road using Equation 4.

1Equation)dayhour8vehicles/1(thousandflowtotal

:Where)(101.29)18( 1010

=

+− =

q

AdBqLoghrL �

2Equation(percent)gradient

vehiclesheavyofpercentage:Where

/100

)10015.13.2(73.0

==

���

��

� −+=∆

Gp

hkmGppv

4Equation(percent)gradient

:Where)(3.0correctionGradient

=

=

G

AdBG

3Equationclesheavy vehiofpercentagep

(km/h)speedv:Where

)(8.68)51(log10)50040(log33Correction 1010

==

−++++= AdBvp

vv

F-2

The procedures described above permits a basic noise level to be calculated for eachsegment of road in the study area. Once each of our sample properties has beenmatched to their nearest road we then need to examine how this noise will bemodified between the road and the individual property. As the sound travels from theroad it will be reduced as it spreads out and is absorbed by the air. Equation 5 isapplied to quantify these factors.

Sound may also be absorbed by soft ground surfaces such as grassland or cultivatedfields. However, in Section 8.5.2.2 it was assumed that all the ground surfaces in thestudy area were hard (e.g. pavements) and so no ground cover correction needed to beapplied. The noise level at a property can also be increased if there are buildings onthe opposite side of the road from which sound will reflect. This can be corrected forusing Equation 6.

In Section 8.5.2.2 it was assumed that buildings covered 50% of the opposite side ofthe road to each property. Therefore 0.75 dB(A) was added to the noise level at eachproperty to account for reflections.

The procedure defined above permitted a noise level to be calculated for the majorityof properties. However, as stated in Section 8.5.2.2 there are two exceptions to thisrule. The first is if the nearest road to each property is multi carriageway. The secondis if the nearest road to a property was a minor road, but there was a major road(Motorway or A class) within 100m of the property. In both these cases thecontribution from the other carriageway or road has to be considered. The proceduresto define these are identical to those described above. The only difference pertains tothe case where there is a major road within 100 m of the property. In this case thebasic noise level needs to be adjusted to account for the fact that there may bebuildings between it and the sample property. This was accounted for by estimatingthe angle of view of this new road from the sample property and applying Equation 7.

( )

( )[ ]

5Equationproperty toroadofedgefromdistancevertical

property toroadofedgefromdistancehorizontal5.3'

:Where)(5.13/'10correctionDistance

21

22

10

==

++=

−=

hd

hdd

AdBdLog

( )

6Equationbuildingsbyoccupiedpropertyeachfrom viewofangle theofproportion

:here)(5.1correctionReflection

=

=

rW

AdBr

F-3

The noise levels from all the component parts are then combined, using Equation 8, toproduce a final noise level for each property.

7Equation

)(180

10Correction 10 AdBLog ��

���

�= θ

8Equation

)(10

101

1010 AdBLnAntiLogLogLn

��

���

���

�

�= �

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