2015 a 1 Eng the Effect of Road Traffic Noise on the Prices of Residential Property 11 Pp

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The effect of road traffic noise on the prices of residentialproperty – A case study of the polish city of Olsztyn

Agnieszka Szczepańska, Adam Senetra ⇑, Monika Wasilewicz-Pszczółkowska

Department of Planning and Spatial Engineering, University of Warmia and Mazury in Olsztyn, Prawocheńskiego 15, 10-724 Olsztyn, Poland

a r t i c l e i n f o

Article history:

Available online 20 March 2015

Keywords:

Road traffic noise

Prices of residential property

Acoustic map

a b s t r a c t

The key factors that determine the prices of real estate are location, technical standard of

property as well as the local environment. In urban agglomerations, road traffic noise has a

considerable impact on the purchasing decisions made by apartment buyers. This is a

widespread problem in Central-Eastern Europe. The main objective of this study was to

verify the working hypothesis that apartment prices are correlated with traffic noise levels

in Olsztyn, the capital city of the Region of Warmia and Mazury in north-eastern Poland.

The study was carried out in four principal stages. Firstly, traffic noise intensity was

determined for apartments (objects of real estate transactions concluded in 2013), based

on an acoustic map for the city of Olsztyn. The map was developed in line with the provi-

sions of Directive 2002/49/EC of the European Parliament and of the Council of 25 June

2002 relating to the assessment and management of environmental noise. Secondly, the

values of the Noise Depreciation Sensitivity Index (NDSI) were calculated. NDSI determines

the percentage change in property prices per dB increase in noise levels. The distribution of

unit prices of apartments was mapped relative to noise levels, and the relationships

between the analyzed variables were assessed. Thirdly, linear correlations between the

unit prices of apartments and noise levels were analyzed. The strength and direction of

relationships between the analyzed parameters were determined based on Pearson’s

correlation coefficient. In the last stage, the distribution of the unit prices of apartments

was mapped by ordinary kriging, a geostatistical estimation method. The research

hypothesis was confirmed by comparing the spatial distribution of traffic noise levels mea-

sured in stage 1 with the spatial distribution of apartment prices.

2015 Elsevier Ltd. All rights reserved.

Introduction

A landscape is generally regarded as a visual phenomenon, but local scenery can also be experienced with the involve-

ment other senses: hearing, smell and even touch. This approach is referred to as multisensory landscape perception.

Research results indicate that the visual landscape is complemented by an acoustic landscape where visual experiences

are enhanced through sound. The acoustic environment is studied by various fields of science, including musicology, cultural

anthropology, physics and geography. The concept of a multisensory landscape emerged in the 1980s. In this approach, land-

scape is perceived not only as the visual reality, but also as the acoustic reality, where sound is an additional stimulus that

complements a sensory experience.

http://dx.doi.org/10.1016/j.trd.2015.02.011

1361-9209/ 2015 Elsevier Ltd. All rights reserved.

⇑ Corresponding author. Tel.: +48 895234948.

E-mail address: [email protected] (A. Senetra).

Transportation Research Part D 36 (2015) 167–177

Contents lists available at ScienceDirect

Transportation Research Part D

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e/ t r d

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Soundscape is a term that describes the acoustic environment. The concept was introduced by Schafer in the 1970s, and it

was fully explored in his book, The Tuning of the World, published in 1977. In his studies of soundscape evolution, Schafer

describes urban soundscapes as complex sonic environments and as cues about socio-cultural life throughout history

(Raimbault and Dubois, 2005). In line with a different approach, ‘‘soundscape shall be defined tentatively as the totality

of sound phenomena that lead to a perceptual, esthetic and representational comprehension of the sonic world’’

(Augoyard et al., 1999). This definition of sound, an element of the local landscape, spurred research aiming to analyze vari-

ous sound qualities. In this context, sound is most often associated with noise. Road traffic noise, in particular in an urban

setting, is recognized as one of the greatest annoyances in literature (Hedfors, 2003; Raimbault and Dubois, 2005; Yang and

Kang, 2005). Urban dwellers search for residential locations that are removed from sources of noise, mostly heavily traveled

highways, industrial plants and large retail outlets.

Traffic noise is regarded to be one of the main environmental problems in Europe. The achievement of a high level of

health and environmental protection is one of the objectives of European Union policy, and the relevant goals are outlined

in Directive 2002 /49/EC of the European Parliament and of the Council of 25 June 2002 relating to the assessment and management

of environmental noise. In the light of the above Directive, Member States are under obligation to monitor the acoustic

environment and develop protection programs for areas where noise levels exceed the allowable limits. In Poland, acoustic

maps are developed and updated pursuant to the provisions of environmental protection laws. The above measures support

assessment of exposure to noise, identification of noise sources and areas that are particularly susceptible to noise. Acoustic

maps combined with information about real estate prices provide reliable data for evaluating the effects on noise on transac-

tion prices and real estate values.

The objective of this study was to evaluate the correlation between noise emissions and prices on the local real estate

market in Olsztyn, the capital city of the Region of Warmia and Mazury in north-eastern Poland. Two residential estates com-

prising apartment blocks were analyzed. The first estate is situated in a downtown area characterized by high road traffic

intensity. The second estate is set remotely from the city center, but it is adjacent to a main transit corridor that connects

the downtown area with the largest housing districts.

Literature review

Soundscape

Urban centers have always been the main sources of noise pollution. Cities traditionally combine various types of human

activity and functions, and they are characterized by high population density. Throughout centuries, the expansion and

introduction of new urban functions and continued population growth created new sources of noise and increased noise

levels. Civilizational change contributes to growing levels of noise pollution in the environment. Most urban components,including people, produce noise which makes up the city’s acoustic landscape. The urban soundscape comprises background

noise, sounds produced by mechanical equipment, human activity and human presence, speech, communication and noises

of nature (Garrioch, 2003; Kang and Servign, 1999; Lebiedowska, 2005; Neudorf, 2009). The acoustic landscape has to be

analyzed in a broader context of different sensory experiences and our knowledge of the surrounding space (Botteldooren

et al., 2011; Raimbault and Dubois, 2005; Yang and Kang, 2005).

Some sounds which are an intrinsic element of the urban landscape are regarded as noise which is a source of annoyance.

The most common sources of urban noise are road traffic, rail traffic, air traffic and sites of industrial activity (De Coensel

et al., 2005; Dubois et al., 2006; Garrioch, 2003; Raimbault and Dubois, 2005).

Not all urban are perceived negatively, and some sounds have positive connotations and can contribute to the quality of

the urban soundscape. Sounds of nature and their sequences (melodies) lead to positive perceptions of space. They are an

important component of emotional experiences that create the atmosphere of a place (Bernat, 2011).

In a Polish study conducted with the use of a multiple choice questionnaire, the respondents’ favorite natural sounds were

chirping of birds (90.9%), gushing water (21.2%) and rustling of trees (24.2%). Unpleasant sounds included traffic andmachine noise (55.2% and 39.9%, respectively), urban noise (13.8%), followed by sounds of sudden weather phenomena,

shooting sounds, arguing, shouting, loud music, fire engine and police car sirens (Kowalczyk, 2008).

Road traffic noise and its consequences

The urban environment is permeated by the noise of human activity, including traffic noise. Road traffic noise is caused by

two main factors: tire-pavement interactions and noise generated by a vehicle’s engine and exhaust system. According to

estimates, tire-pavement interactions are the main source of road traffic noise produced by passenger cars traveling at

speeds higher than 55 km/h and goods vehicles moving faster than 70 km/h (subject to vehicle weight, age and weather con-

ditions). Noise levels, noise tolerance and the effect of noise on the human auditory system are determined by a combination

of factors. Noise levels higher than 55 dB can be a source of annoyance, and the allowable noise limit is set at 65 dB.

According to statistical data, around 250 million Europeans suffer from noise-related disturbances (Directive 2002/49/

EC...., 2002; OECD Environmental...., 2008).

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Road traffic noise has an adverse effect on the urban environment. It contributes to health problems, such as sleep dis-

turbance and loss of productivity, but it also has adverse economic consequences by driving down the prices of real estate

(Papi and Halleman, 2004). The existing road networks rely on noise models developed for the contemporary vehicle fleet

and current traffic intensity levels. Future models should account for noise emissions from new generation vehicles and

changes in road traffic intensity (Stead et al., 2012).

The Report from the Commission to the European Parliament and the Council on the implementation of the

Environmental Noise Directive (2011) indicates that noise pollution is often linked to urban areas. Its authors also observe

that noise exposure in Europe presents an increasing trend compared to other environmental stressors. The main driving

forces for environmental noise exposure are urbanization, growing demand for motorized transport and inefficient urban

planning. Noise pollution can disturb sleep, affect the cognitive function of school children, cause physiological stress reac-

tions, cardiovascular disease and psychiatric disorders. Economic costs of noise pollution include devaluation in house prices

and productivity losses, whereas social costs are related to premature death or morbidity. The social cost of traffic, rail and

road noise across the European Union has been estimated at EUR 36 billion per year, most of which is related to passenger

cars and goods vehicles.

The economic consequences of noise pollution, namely the drop in real estate prices, are directly linked with social

aspects – growing incidence of health problems in areas exposed to high levels of noise. This problem is most visible in

the housing sector where rest and recreation are very important functions.

Noise and real estate prices

In literature, the impact of noise on real estate prices is generally investigated in the housing sector. Most studies analyze

statistical correlations between changes in noise intensity and changes in real estate value. The majority of research focuses

on two sources of noise in the urban environment: air traffic noise resulting from the proximity of airports (Bell, 2001;

Collins and Evans, 1994; Marmolejo-Duarte, 2008; Marmolejo-Duarte and Roca-Cladera, 2008; Karanikolas et al., 2011;

Levesque, 1994; Nelson, 2008; Pennington et al., 1990) and road traffic noise (Bateman et al., 2001; Benoit and Lanoie,

2007; Blanco and Flindell, 2011; Brandt and Maennig, 2011; Carles et al., 1999; Karanikolas et al., 2011; Kim et al., 2007;

Nelson, 1982, 2008; Taylor et al., 1982; Wilhelmsson, 2000).

The Noise Sensitivity Depreciation Index (NSDI) is one of the most popular indicators for describing the impact of road

traffic noise on real estate prices. NSDI determines the percentage change in property prices per dB increase in noise levels

(Blanco and Flindell, 2011). NSDI is generally calculated with the use of direct methods such as the Hedonic Pricing (HP)

method.

According to the State-Of-The-Art on Economic Valuation of Noise, the Final Report to the European Commission DG

Environment (Navrud, 2002), there are two main approaches to measuring the economic value of noise annoyance:

– An economic value per decibel per year measured by the Noise Sensitivity Depreciation Index (NSDI), defined as the aver-

age percentage change in property prices per decibel.

– An economic value per year per person (or household) annoyed by noise.

In a world-wide study of road traffic noise, NSDI was determined in the range of 0.08–2.22 per cent (Wilhelmsson, 2000;

Bateman et al., 2001; Bell, 2001; Navrud, 2002; Kim et al., 2007; Nelson, 2008; Marmolejo-Duarte and González-Tamez,

2009; Brandt and Maennig, 2011; Blanco and Flindell, 2011).

The influence of noise on property prices can also be evaluated by relying on the HP method to survey market partici-

pants’ preferences regarding improvements in environmental quality. The second indicator, willingness-to-pay (WTP) per

decibel per household per year, has been estimated in the range of EUR 7 per 10 dB reduction in noise levels to EUR 99

per 50 per cent drop in noise intensity (Feitelson et al., 1996; Navrud, 2002; Marmolejo-Duarte, 2008; Wardman and

Bristow, 2004; Nellthorp et al., 2007).

Both approaches can be used to describe the preferences of property market participants, but they differ in the type of the

analyzed preferences. The HP method is applied to evaluate revealed preferences: the consumers’ actual behaviors aimed at

the accumulation of market goods provide information about their choice of non-market commodities. This method relies on

statistical dependencies between the level of non-market goods (noise levels) and the value of market commodities (prop-

erty price). The WTP method surveys declared preferences, and it is based on the consumers’ direct responses. Revealed pre-

ferences indicate the price that the consumers would be willing to pay for a given commodity or changes therein, such as a

reduction in noise levels. However, whereas a reduction in noise levels has an indisputable effect on house prices, the values

given by the above indices are characterized by significant uncertainty.

It should be noted that noise also influences rental rates on the market of residential property, but its effect is less pro-

found than on the market of property intended for sale (Feitelson et al., 1996; Navrud, 2002). The above can probably be

attributed to temporal residence in rented property and the absence of a legal title to property.

Sociodemographic factors, including age, education, material status and level of environmental awareness in a household,

play an important role in property valuation. Those parameters shape consumer behaviors on the property market and influ-

ence the prices of individual property (Wardman and Bristow, 2004; Nellthorp et al., 2007; Marmolejo-Duarte and González-

Tamez, 2009).

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Acoustic mapping methodology

An acoustic map was developed for the city of Olsztyn in line with the provisions of Directive 2002/49/EC of the European

Parliament and of the Council of 25 June 2002 relating to the assessment and management of environmental noise. The cited

directive defines environmental noise as unwanted or harmful outdoor sound created by human activities, including noise

emitted by means of transport, road traffic, rail traffic, air traffic, and noise from sites of industrial activity. A strategic noise

map is defined as a map developed for the global assessment of noise exposure from different noise sources in a given area or

for overall predictions for a given area. European cities are also under obligation to develop acoustic maps pursuant to the

provisions of their respective national laws. In Poland, acoustic maps are regulated by the Environmental Protection Law

(2001). Managers of transport facilities have to measure noise levels and develop acoustic maps in areas where environmen-

tal noise limits are exceeded.

The following indicators are used to describe levels of environmental noise in an acoustic map [Regulation of the Minister

of the Environment . . .., 2012]:

Fig. 1. Location of the research object. Map of Olsztyn – analyzed sites. Source: http://msipmo.olsztyn.eu/imap/.


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1. Lnight – long-term indicator of average night-time noise, determined on every night of the year.

2. Lden – long-term indicator of average noise level A, expressed in decibels, determined on every day of the year in view of

the time of day. This indicator is expressed by the below formula (1):

Lden ¼ 10lg 12

24100;1Lday þ

4

24100;1ðLeveningþ5Þ þ

8

24100;1ðLnightþ10Þ

ð1Þ

where:

Lday – long-term indicator of average noise level A, expressed in decibels (dBA), determined during the day throughout the

year (from 6 a.m. to 6 p.m.).

Levening – long-term indicator of average noise level A, expressed in decibels (dBA), determined during the evening

throughout the year (from 6 p.m. to 10 p.m.).

Lnight – long-term indicator of average noise level A, expressed in decibels (dBA), determined during the night throughout

the year (from 10 p.m. to 6 a.m.).

Indicator Lnight is one of the parameters used to calculate Lden, and it is the second indicator applied in the process of

designing acoustic maps.

Pursuant to the provisions of the Regulation of the Minister of the Environment (2012), the maximum values of Lden are

set at 68 dB for road traffic noise in residential estates and 70 dB in downtown areas of cities with a population higher than

100,000.

The long-term indicator of average noise level (Lden) for apartments was determined 4 m above ground along building

facades most exposed to road traffic noise in the following intervals of dBA values: 30–45, 46–50, 51–55, 56–60, 61–65,

66–70, 71–75, 76–100.

Description of the studied market

The effects of road traffic noise on real estate prices have been studied in Olsztyn, the capital city of the Region of Warmia

and Mazury in north-eastern Poland (Fig. 1). Olsztyn has a population of 200,000. Its suburban region comprises mostly sin-

gle-family houses, and it stretches within a 20 km radius from the city’s administrative boundaries. The suburban region has

an estimated population of 120,000, and most of its inhabitants commute to Olsztyn on a daily basis. Due to Olsztyn’s sig-

nificance as a regional center of economic activity, an absence of a ring road and the region’s proximity to Russian and

Lithuanian borders and the Baltic Sea, the city’s downtown districts are exposed to heavy transit traffic, including in the

direction of Latvia, Estonia, Finland, Belarus and Ukraine. Olsztyn is one of the main transport hubs leading to destinations

in eastern and north-eastern parts of Europe. Olsztyn has 242 km of paved roads per 100 km2

. Population density is 1998people per 1 km2.

Two residential estates comprising apartment buildings, which are characteristic of this part of Europe, were analyzed.

The Śródmieście estate (Fig. 1) is exposed to high levels of road traffic which is generated mainly by goods vehicles traveling

between Central Europe and northern and north-eastern parts of the European continent (Russia, Lithuania, Latvia, Estonia,

Finland, Belarus, Ukraine). Some traffic routes intersecting Śródmieście have the status of national transit roads (Fig. 1).

The second analyzed estate was Nagórki (Fig. 1) which is situated far from sources of road traffic noise. The main sources

of internal noise are local access roads and public transport vehicles. The road network in Nagórki comprises roads which do

not have the status of national transit roads (municipal roads from downtown Olsztyn to the largest residential estates in the

city) and local access roads (Fig. 1).

The analyzed estates are representative of the entire city. They are typical residential estates composed of homogeneous

apartment buildings. The estates differ in their location in the city and, consequently, their exposure to noise. They represent

different living standards in the same urban center.

Methodology and results

The study was carried out on the assumption that apartment buyers are guided in their purchasing decisions by the build-

ing’s location relative to the road. This assumption was made in view of the fact that one of the analyzed estates is situated in

Table 1

Linear correlation between unit prices of apartments and noise levels – Ś ródmieście residential estate.

Variable Correlations (unit prices of apartments in EUR/m2 – noise level in dBA); N = 79 correlation coefficients are

significant at p < .05000

Average SD Unit price in EUR/m2 Noise level in dBA

Unit price in EUR/m2 903.43 101.89 1.00 0.61

Noise level in dBA 55.76 8.87

0.61 1.00


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downtown Olsztyn where road traffic intensity is very high. The second estate is bounded by municipal roads which lead

from central Olsztyn to the largest residential estates in the city. The levels of road traffic noise increase significantly during

rush hours.

Data relating to 118 apartment transactions conducted between January and December 2013 was analyzed in this study.

To unify experimental samples and eliminate other price-shaping factors, the analyzed data was sorted to produce transac-

tions concerning apartments with the same legal status, apartments in buildings erected based on the same constructiontechnology and in similar condition, apartments with similar area, situated in distinct housing estates. The one of the differ-

entiating factor was location relative to a road. Significant variations in real estate prices resulting from changes in market

supply and demand were not observed in the analyzed period, the time trend in prices reached 0%. Unit prices per square

meter are expressed in EUR based on the average EUR/PLN exchange rate quoted by the National Bank of Poland as at 23

December 2013 (EUR 1 = PLN 4.1638). The analyzed prices ranged from EUR 727–1128/m2, and the average unit price

was determined at EUR 895/m2.

The study was carried out in four principal stages:

Table 2

Linear correlation between unit prices of apartments and noise levels – Nagórki residential estate.

Variable Correlations (unit prices of apartments in EUR/m2 – noise level in dBA); N = 39 correlation coefficients are

significant at p < .05000

Average SD Unit price in EUR/m2 Noise level in dBA

Unit price in EUR/m2 876.72 76.19 1.00 0.51

Noise level in dBA 51.44 6.32 0.51 1.00

Unit price of apartments in EUR/m2 = 1379.70 - 8.54 * x

35 40 45 50 55 60 65 70 75

Noise level in dBA

700

750

800

850

900

950

1000

1050

1100

1150

U n i t p r i c e i n E U R / m 2

Fig. 2. Distribution of unit prices of apartments relative to noise levels – Śródmieście residential estate.

Unit price of apartments in EUR/m2 = 1191.18 - 6.11 * x

35 40 45 50 55 60 65

Noise level in dBA

700

750

800

850

900

950

1000

1050

1100

U n i t p r i c e i n E U R / m 2

Fig. 3. Distribution of unit prices of apartments relative to noise levels – Nagórki residential estate.


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1. Noise levels in buildings were determined based on the acoustic map of Olsztyn. The data shown in Figs. 5 and 7 indicates

that the main roads generate very high levels of noise. In selected buildings of the Śródmieście estate, noise levels were as

high as 71–75 dBA. In Nagórki, maximum noise levels were determined in the range of 61–65 dBA. The above findings

indicate that transit traffic generates higher levels of noise than local traffic.

2. The distribution of unit prices of apartments was mapped relative to noise levels. The data shown in Figs. 2 and 3 indi-

cates that apartment prices decrease with a rise in noise pollution. In Śródmieście (Fig. 2), apartment prices increased by

EUR 8.54/m2 per 1 dBA decrease in noise levels. In Nagórki (Fig. 3), a 1 dBA drop in noise levels increased apartment prices

by EUR 6.11/m2. NSDI was determined at 0.94% in the Śródmieście and at 0.70% in the Nagórki residential estate. The

above NSDI values are consistent with those reported in other studies (see Section 4), and indicate that apartment prices

are affected by traffic noise levels.

3. Linear correlations between unit prices of apartments and noise levels were analyzed. A statistical correlation between a

dependent variable (unit prices of an apartment) and an independent variable (noise level) was determined by Pearson’s

linear correlation analysis. Pearson’s correlation coefficient is a measure of the strength of a linear relationship between

Fig. 4. Distribution of unit prices of apartments in the Śródmieście residential estate.



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two variables. Its absolute value indicates the strength of a correlation between the analyzed parameters. The direction of

that relationship is described by the sign of the correlation coefficient (+ or ). In the discussed study, Pearson’s correla-

tion coefficients were determined at 0.61 for the Śródmieście estate (Table 1) and 0.51 for the Nagórki estate (Table 2).

Those results point to correlations between the investigated attributes. A negative value of the correlation coefficient

indicates that the examined correlation is inversely proportional, meaning that unit prices of apartments decrease with

an increase in noise levels.

4. The distribution of unit prices of apartments was mapped (Figs. 4 and 6) by ordinary kriging. Isoline interpolation tools

available in ArcGIS 10 software were used. Geographic Information Systems and the relevant applications feature

advanced tools for analyzing, visualizing and managing spatial data. Kriging is a geostatistical estimation method which

accurately estimates the values of the analyzed variables. Kriging estimates are assumed to be a weighted, linear

combination of random regionalized variables. The following value is a kriging estimator represented by a random func-

tion Z (si) (2):

Fig. 5. Acoustic map of Olsztyn – Śródmieście. Source: http://msipmo.olsztyn.eu/imap/.


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Z ðs0Þ ¼

Xni¼1

w i Z ðsiÞ ð2Þ

where: Z ⁄(s0) – estimated value in location s0. Z (si) – observed value of the analyzed variable in location si. wi – kriging

weights (calculated on the assumption of minimum error variance; the sum of weights has to be equal to 1).

The weights are calculated on the assumption of minimum variance is estimation errors. In ordinary kriging, the sum of

weights has to be equal to one. The use of kriging methods in analyses of price distribution on the real estate market is

widely discussed in literature (Bourassa et al., 2007; Páez, 2009).

In the Śródmieście estate, the lowest prices (Fig. 4) were reported for apartments exposed to high levels of noise (Fig. 5)

and situated along a national transit road. A similar situation was encountered in the Nagórki estate, where apartments situ-

ated along the municipal transport corridor and the major access road, the main sources of noise in the analyzed estate

(Fig. 7), fetched the lowest prices (Fig. 6). Higher prices were quoted for apartments situated inside the estate where noise

levels are considerably lower. The national transit road was a considerable source of noise, and apartments located in its

immediate proximity were characterized by the lowest prices in Śródmieście (EUR 727/m2). By comparison, the lowest price

in Nagórki was lower at EUR 736/m2. A trend was observed with regard to maximum apartment prices which reached EUR

1128/m2 in Śródmieście and EUR 1090/m2 in Nagórki. The difference between maximum and minimum prices was signifi-

cantly higher in the estate adjacent to the national transit road (EUR 401/m2). In the estate bordering the municipal transport

corridor, the above difference was only EUR 354/m2. The above disproportions clearly indicate that the Śródmieście estate is

exposed to much higher noise levels, and this weakness is not compensated by its convenient downtown location. It should

be noted, however, that the maximum prices of apartments situated further away from sources of noise in the Śródmieście

estate were higher than those in Nagórki (difference of EUR 38/m2), which is a characteristic trend in downtown locations.

Maps illustrating the distribution of unit prices of apartments (Figs. 4 and 6) indicate that noise exposure has a negative

effect on apartment prices in the analyzed estates.

Fig. 6. Distribution of unit prices of apartments – Nagórki residential estate.


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The differences in the values of the Pearson’s correlation coefficient between two estates indicate that the significance of

noise levels varies in different parts of the city. The above could be attributed to differences in buyers’ sociodemographic

characteristics (income, age), differences in location, exposure to noise (one of the key determinants of property value)

and local attributes (Feitelson et al., 1996; Wardman and Bristow, 2004).

Conclusions

The article presents the results of the first stage of research. The next analysis, in the same analyzed site, will be carried

out after putting into use the ring road. The works on the Olsztyn’s ring road began in 2014.The results of this study indicate that noise pollution is an important determinant of real estate prices. Acoustic maps

developed for large cities in the European Union are an abundant source of analytical data. GIS tools were used in this study

to identify the correlations between road traffic noise and prices on the local real estate market based on the spatial dis-

tribution of noise sources and apartment prices.

The presented isoline maps are a rich source of information about the spatial distribution of environmental factors such

as road traffic noise and industrial noise. Isoline maps help urban planners to minimize the annoyances experienced by big

city residents. They support the implementation of various planning and legislative measures to prevent real estate deval-

uation, including the introduction of restrictions on the construction and expansion of residential estates in areas most

exposed to traffic noise.

References

Augoyard, J.F., Karlsson, H., Winkler, J., 1999. Report and resolution of the soundscape research study group. In: Proceedings of from Awareness to Action,‘Stockholm, Hey Listen!’. Royal Swedish Academy of Music/World Forum for Acoustic Ecology, Stockholm, Sweden, pp. 130–135.

Fig. 7. Acoustic map of Olsztyn – Nagórki residential estate. Source: http://msipmo.olsztyn.eu/imap/.


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2015 a 1 Eng the Effect of Road Traffic Noise on the Prices of Residential Property 11 Pp

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