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Implementing hedonic pricing models for valuing the visual impact of wind farms in Greece
Economic Analysis and Policy 64 (2019) 248–258
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Implementing hedonic pricing models for valuing the visual impact of wind farms in Greece ∗
Konstantinos Skenteris a , Sevastianos Mirasgedis a , , Christos Tourkolias b a
Institute for Environmental Research & Sustainable Development, National Observatory of Athens, I. Metaxa & Vas. Pavlou, GR-15236 Palea Penteli, Greece b Center for Renewable Energy Sources & Saving, 19th km Marathonos Avenue, GR-19009, Pikermi Attiki, Greece
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Article history: Received 28 January 2019 Received in revised form 16 September 2019 Accepted 16 September 2019 Available online 18 September 2019 Keywords: Environmental impact Visual impact Wind energy Hedonic pricing Energy externalities
a b s t r a c t Although wind energy is a pollution-free and infinitely sustainable form of energy, there is considerable concern over certain environmental effects resulting from its development. Criticism focuses primarily on the visual impact of wind turbines and transmission lines, which may result in perceived deterioration of the landscape and also harm other economic activities such as tourism and real estate. This study applies the hedonic pricing method to estimate the value of environmental externalities associated with large-scale exploitation of wind power at a local level. It examines the characteristics of approximately 1,800 sales of single-family homes surrounding 17 existing wind facilities in two Greek islands, namely Evia and Kefalonia. Four different hedonic price models are developed and applied, with diverging results in the two areas. We find that in Evia, the per unit floor area sales price decreased for dwellings located within a 2 km radius of the wind farms, while in Kefalonia, the distance of the house to the wind turbines had no statistically significant effect on the sales price. © 2019 Economic Society of Australia, Queensland. Published by Elsevier B.V. All rights reserved.
1. Introduction The exploitation of renewable energy source (RES) technologies, particularly wind and photovoltaic systems, is a key pillar of the European policy for tackling climate change. Thus, while in 2000, the installed capacity of wind farms in Greece was only 226 MW, in 2017, it reached 2377 MW, with the generated electricity amounting to 7.4% of the total domestic electricity demand. The EU 2030 climate and energy framework sets three key targets for the year 2030: (i) at least 40% reduction in greenhouse gas emissions compared with 1990 levels, (ii) at least a 32% share for renewable energy, and (iii) at least a 32.5% improvement in energy efficiency. In this context, the utilization of wind energy in the power generation systems is expected to increase in the coming years. The draft National Energy Climate Plan of Greece (submitted to the European Commission in January 2019) foresees that the installed capacity of wind farms will reach 6600 MW by 2030 (MEE, 2019). Several other modeling exercises (see for example, WWF, 2017; European Commission, 2016) suggest that the increase in wind energy installations in Greece will continue beyond 2030, reaching an installed capacity ranging from 8000 to 10,500 MW by 2050. The large-scale utilization of wind energy has raised considerable concern over certain environmental impacts that result from wind power development (see for example, Wang and Wang, 2015; Dai et al., 2015; Abbasi et al., 2014; Leung and Yang, 2012) and in particular, over the visual impact of wind turbines and transmission lines, which results ∗ Corresponding author. E-mail addresses: [email protected] (K. Skenteris), [email protected] (S. Mirasgedis), [email protected] (C. Tourkolias). https://doi.org/10.1016/j.eap.2019.09.004 0313-5926/© 2019 Economic Society of Australia, Queensland. Published by Elsevier B.V. All rights reserved.
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in the deterioration of the landscape and may harm associated economic activities such as tourism and real estate. These concerns have led to local reactions against wind energy, which intensified in the last decade as the number of installed wind farms and the size of turbines increased. It is noteworthy that the average commercial size of onshore wind turbines being manufactured today is around 2.5 MW to 3 MW, with blades of about 50 m length and a tower height of 70 m to 90 m, the offshore ones being even larger. The environmental degradation associated with wind energy development typically includes noise; visual impact, including shadow flicker and reflectance; bird and bat fatalities; and electromagnetic interference (Wang and Wang, 2015; Dai et al., 2015; Jain, 2011). Among them, the most significant environmental concerns are regarding the visual impact and the associated aesthetic degradation of the landscape (see for example, Kontogianni et al., 2014), as wind turbines must be sited in exposed places, which are highly visible. However, being visible does not necessarily equate with being intrusive. Although aesthetic issues are, by their nature, highly subjective, individuals with a negative attitude toward wind energy are expected to assess visual impact less tolerably (Kaldellis et al., 2006). The analysis of the visual impact associated with wind farm development presents significant methodological difficulties as it depends on turbine and site characteristics, as well as on the level of exposure of visual receptors. According to a classification made by Mirasgedis et al. (2014), the methods usually implemented, independently or in combination, to analyze the aesthetic impact of wind energy comprise: (i) the zone of theoretical visibility approach (see for example, Manchado et al., 2013), which defines the land area from which a wind farm can be totally or partially visible (as the visual impact decreases with the distance, different zones of theoretical visibility can be defined, representing different levels of visual burden); (ii) the estimation of appropriately designed indices, which incorporate specific parameters (e.g., population in the neighboring areas and number of wind turbines) influencing the visual impact of wind farms (see for example, Sklenicka and Zouhar, 2018; Tsoutsos et al., 2009); (iii) field surveys that record public preferences (e.g., Kontogianni et al., 2014) and the evaluation of future changes in the landscape by utilizing various tools such as photomontage and video montages; and (iv) the monetization of the visual impact on the basis of appropriate environmental valuation techniques. With regard to noise impacts, the changes made in recent years to the design of wind turbines has resulted in a substantial reduction in the noise emitted. It is noteworthy that the level of perceived noise from a modern wind turbine at a 200 m distance is less than the background noise of a small provincial city and certainly not a source of discomfort. Wind turbines can also interfere with radar and aircraft navigation systems, as well as with other communications links such as television signals or mobile phone networks, although these issues can be addressed with careful siting and design. In any case, the potential noise impacts and electromagnetic interferences are limited to areas located near the wind farms. Lastly, bird and bat deaths are one of the most controversial issues related to wind turbines, with concerns raised by several environmental groups especially in cases where wind farms are installed in the habitats of rare and endangered birds, despite years of operation of several large wind facilities with only minor impacts. Recently, due to the development of advanced methods for siting wind power and monitoring for avian impacts, the risk of bird collisions has been reduced, with the number of annual bird fatalities ranging from 0.02 to 0.6 per turbine (Wang and Wang, 2015). This study utilizes a well established technique in environmental economics to investigate quantitatively the environmental impact of large-scale development of wind power at the local level, primarily addressing the issue of visual intrusion and aesthetic degradation of the landscape. Specifically, the hedonic pricing method (HPM) is implemented to examine to what extent the environmental impact caused by the development of wind farms, particularly the resulting alteration of the landscape, affects housing prices in a wide area around the wind farms. The results of the analysis can provide useful information on the economic magnitude of the environmental impacts attributed to wind farms (i.e., the external cost of wind energy), which might be useful for assessing this technology in relation to others in the context of long-term energy planning. The HPM is applied in two Greek islands, namely Evia and Kefalonia, where a large number of wind turbines have been installed during the last 15 years and several new projects are planned. Compared with other techniques used for estimating the environmental externalities of wind energy, such as the contingent valuation method or choice experiments (see for example, Westerberg et al., 2013; Krueger et al., 2011; Dimitropoulos and Kontoleon, 2009; Mirasgedis et al., 2014), the HPM can provide more realistic estimates, as it is based on consumer preferences already recorded in the market rather than on surveys where respondents may conceal expectations or rely on misleading information. However, the application of the HPM requires the collection and processing of large volumes of data, especially the actual house purchase prices over a long period. This presents significant difficulties in Greece as in the past, a large part of these transactions (totally or partially) were realized outside the banking system. For these reasons, the presented application of the HPM is one of the first done in Greece to value an environmental good and one of the few in Europe related to wind energy. The structure of this paper is as follows: Section 2 presents a literature review of studies that use techniques of environmental economics, particularly the HPM, to value the environmental impact of wind farms, while Section 3 describes in more detail the HPM used in this study. Section 4 focuses on the application of the method, providing information on the two case studies examined. Section 5 presents the selection of the appropriate hedonic price models and the basic results of the analysis. Finally, Section 6 summarizes the main findings of the study and provides the conclusions.
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2. Literature review Various techniques of environmental economics have been utilized to assess the visual disturbance and landscape alteration, as well as other environmental impacts resulting from the installation of wind farms (Knapp and Ladenburg, 2015). In most cases stated preference methods, such as the contingent valuation method and choice experiment models (Bergmann et al., 2006; Ladenburg and Dubgaard, 2007), have been conducted. In Greece, the contingent valuation method has been implemented by Koundouri et al. (2009) to investigate the benefits to the local community of a small wind park development in Rhodes, and by Mirasgedis et al. (2014) to quantify environmental costs of a large-scale use of wind energy in the South Evia region. Moreover, Dimitropoulos and Kontoleon (2009) developed a choice experiment model to examine the factors that affect citizens’ willingness to receive compensation for the installation of wind farms on two Greek islands, namely Naxos and Skyros. On the contrary, applying revealed preference methods to evaluate the environmental effects of wind farm installation is not that common as it is difficult to gather detailed statistics on house purchase prices. Moreover, significant computational burden is required to simulate and quantify the impact of installed wind turbines on property values for each examined area (in terms of proximity and visibility). Currently, the few studies that used the HPM for evaluating the impact of installed wind farms on the values of nearby properties have led to contradictory conclusions. In the United Kingdom, Dent and Sims (2007) found that property values of terraced and semi-detached houses located within a mile of a wind farm were affected, even though interviews conducted with estate agents from the area revealed no negative attitudes toward wind farms upon purchase of the nearby houses. In New York, USA, Heintzelman and Tuttle (2012) studied the impact of wind power on property values by analyzing over 11,000 sales transactions. Their results revealed that proximity of within a half mile zone from the wind installations results in a reduction of property values by as much as 35% and for properties within a mile, by 8% to 15%. A similar study (Sunak and Madlener, 2012) conducted in North Rhine-Westphalia, Germany found that property values decreased by 21.5% to 29.7% for distances up to 1 km from the examined wind farms, remaining statistically significant at distances up to 1.5 km. Lately, Heintzelman et al. (2017) conducted a parcel-level hedonic analysis of property sales transactions and concluded that after the construction of the wind farm, values decreased significantly for properties in New York with a view of and/or in close proximity to the turbines, while no negative impacts were identified on properties in Ontario, Canada. The latter conclusion is consistent with the findings of several studies that property values are not affected by nearby wind farms. Specifically, Sims et al. (2008) analyzed 201 sales transactions of properties located within half a mile of a wind farm in Cornwall, UK, and found no link between the presence of the wind farm and house values. Laposa and Müller (2010) studied the impact on property values from the potential installation of a wind farm in North Colorado, USA, through an analysis of sales transactions received before and after the announcement of the wind farm’s installation. They also found no statistically significant impact on the real estate market. Approximately 7500 sales transactions of detached houses were analyzed by Hoen et al. (2011) around 24 wind farms located in the United States using four different models. The results of their analysis revealed that neither the view of the wind turbines nor the distance from them affected the prices of the dwellings. Similarly, the impact of wind turbines on house values located within a mile was examined by Lang et al. (2014) in Rhode Island, and no statistically significant negative impacts on house prices were identified in either the post-public announcement or post-construction phases of the wind parks. McCarthy and Balli (2014) analyzed 945 sales transactions in Ashhurst, New Zealand and found no significant statistical impact of proximity to wind farms on property values. Moreover, Hoen et al. (2015) collected over 50,000 sales transactions of properties within 10 miles from wind farms and found no statistical evidence that the property values were affected by the turbine in the post-construction or post-announcement/pre-construction periods. Similarly, no significant statistical decrease in the values of properties near wind farms was identified by Castleberry and Greene (2018) from their analysis of 23,000 sales transactions in Western Oklahoma. Finally, Jensen et al. (2018) conducted a large-scale analysis using hedonic pricing models to investigate how on-shore and off-shore wind turbines affect property values in Denmark. The price of properties within a distance of three kilometers from a wind farm was found to be negatively affected in the case of on-shore wind turbines, while no significant effect was demonstrated in the case of off-shore wind farms located at a distance of 9 km from the closest traded home. 3. The hedonic pricing method The HPM is one of the oldest methods used to value non-market goods (such as environmental changes or services) and is based on the idea that an individual’s decision to purchase goods or services depends on the features or attributes of those goods, some of which may be environmental aspects (Hanley et al., 1997). Specifically, it exploits the value of a surrogate good or service to measure the implicit price of a non-market good. The HPM is commonly used in the context of property and labor markets (Bolt et al., 2005). In the former, the assumption is that the environmental quality is an attribute of real estate and its price reflects people’s preferences for environmental quality. In the latter, the health risk is assumed to be an attribute of a job and consequently, the wage rate reflects the willingness to be compensated for taking such risks. This study focuses on housing prices to value the environmental impacts and particularly, the aesthetic degradation of the landscape associated with the installation of wind farms in a specific area.
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As previously mentioned, the HPM is based on the idea that a person’s willingness to pay for a property reflects the attributes of that house, including indicative parameters such as the number of bedrooms and bathrooms, the backyard’s size, and the proximity to schools and other infrastructure. Some of these attributes may concern environmental aspects. For example, individuals may be willing to pay a premium for a house located close to a park or prefer having a discount on a house that is located close to a source of environmental burden (e.g., air pollutants). Using statistical analysis and more often, multiple regression models, it is possible to isolate the effect of the features that are envisaged to be valued. Consequently, the first step in implementing the HPM is to identify those attributes that are likely to determine the pricing of the house in the market, which is then used to specify the hedonic price function. Generally, three groups of elements are expected to affect the house price (Freeman et al., 1993; Kong et al., 2007; Bolt et al., 2005):
• Physical characteristics of the property (e.g., year of construction, size of the apartment, number of rooms, and availability of garage or storage space),
• Neighborhood characteristics (e.g., the existence of good public services, such as transportation, proximity to commercial areas, school or hospital, and the level of crime),
• Environmental characteristics (e.g., the view, proximity to forests or parks, proximity to the sea, and the level of air pollution or noise). The mathematical function that describes the value of properties is of the form: P = f (S1 , . . . , Sm, Ni, . . . , Nn, Q1 , . . . Qr)
(1)
where P is the price of the property; S1 , . . . Sm , its physical characteristics; N1 , . . . , Nn the characteristics of the wider area that the property is located (i.e., neighborhood characteristics); and Q1 , . . . , Qr the environmental features in the reference area, such as noise, ambient air quality, proximity to a park, and the view. To determine the form of the hedonic price function a large amount of data is necessary, comprising information on the prices and characteristics of different properties in a given area and for a specific period. It is a prerequisite that the real estate market in the reference area is well functioning and the people are well-informed on the differences in environmental variables across neighborhoods (Bolt et al., 2005). This guarantees that the property prices reflect the differences in environmental attributes. The coefficients of the independent variables included in the hedonic price function are estimated through regression analysis. The developed function provides the implicit price of a property, from which the change in a house price due to the marginal change in one of the characteristics included in the model can be inferred. The implicit price can therefore be interpreted as the additional cost of purchasing a house that is marginally ‘‘better’’ in terms of a particular characteristic. Theoretically, the observed price of a property is the result of an interaction between the supply and demand for properties. However, in this analysis, we are interested in estimating the demand curve for a specific environmental aspect that affects the prices of properties. By applying Eq. (1), the implicit price of this environmental aspect, which is an approximation of its welfare effects, can be obtained. The estimation of a demand curve requires conducting a second regression in which the implicit price for the environmental aspect in question is the dependent variable and individuals’ characteristics are the explanatory variables. However, most studies do not consider this last step in the analysis due to the additional data required with regard to the individuals’ characteristics (Hanley et al., 1997). For relatively small changes in the environmental aspect in question, it is considered as an acceptable approximation to estimate the welfare changes directly from the hedonic price function (Bolt et al., 2005). Hedonic price models, if properly structured, can provide useful information on the value of specific environmental goods. However, there are a number of limitations in the use of the HPM. These include (Boardman et al., 2011; Hanley et al., 1997) the following.
• Information: The model assumes that all individuals have prior knowledge of the potential positive and negative
•
•
•
•
externalities triggered by the purchase of a house. Even if this is the case, individuals may have different attitudes against environmental risks, which may either overestimate or underestimate welfare changes. Structure of the hedonic price function: The economic theory does not provide any specific guidance regarding the form of this function. Furthermore, the omission of an independent variable that affects property prices and at the same time is associated with another independent variable included in the model may result in incorrect estimates of model coefficients. Multicollinearity: In some cases, it is possible that large houses are only found in areas where the examined environmental aspect has good performance and small houses are found in urban areas where this particular aspect has poor performance. In this case it would be impossible to separate the environmental aspect in question and the size of the house accurately. Prices and price changes: The model assumes that market prices adjust immediately to changes in attributes. In reality, there is likely a lag associated with this, especially in areas where house sales and purchases are rare. Furthermore, market prices can incorporate expectations of possible future changes in environmental quality. Measurement validity: The quality of the measures used in the independent ‘‘explanatory’’ variables is of key importance. If proxy measures are used, this could result in estimating inaccurate coefficients through regression analyses.
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K. Skenteris, S. Mirasgedis and C. Tourkolias / Economic Analysis and Policy 64 (2019) 248–258 Table 1 Basic characteristics of the two cases analyzed in this study. Characteristics
Evia case study
Kefalonia case study
Number of wind farms Total installed capacity (MW) Population of the wider area (inhabitants) Area in consideration (km2 ) GDP per capita (e/capita)a 2006 2011 2016 Sectoral distribution of GVAa Agriculture, forestry, and fishing Manufacturing Services Number of transactions Total Within a 0–2 km zone Within a 2–4 km zone
13 83.9 40,210 1000
4 97.5 35,800 773
17,693 14,789 13,322
21,080 16,934 14,520
2006: 9%, 2011: 8%, 2016: 8% 2006: 45%, 2011: 40%, 2016: 49% 2006: 46%, 2011: 52%, 2016: 43%
2006: 10%, 2011: 15%, 2016: 17% 2006: 4%, 2011: 7%, 2016: 9% 2006: 86%, 2011: 78%, 2016: 74%
400 39 33
1416 15 32
a
This concerns the whole Evia Island in the first case study and the Ionian Islands, which comprise Kefalonia, in the second case study.
• Market limitations: The model ideally requires that a variety of different houses are available so that individuals are able to obtain the particular house of their choosing, with a combination of characteristics they desire. However, this is not always the case. 4. Case studies As previously mentioned, this study estimates the economic value of environmental effects, particularly the visual impact of large-scale exploitation of wind energy at a local level. To this end, the HPM is applied on two island regions in Greece, namely South Evia (Municipality of Karystos) and Kefalonia. Both areas present particularly rich wind energy potential and attract significant amounts of wind energy investment. At the same time, their residents are familiar with the potential disturbances generated by wind farms, which, to the extent they exist, are likely integrated in the operation of the local real estate market. Specifically, in South Evia, 13 wind farms covering an area of approximately 56 ha with a total capacity of 83.9 MW were installed during the period of this study. These wind farms were installed gradually in the reference area over the period from 2001 to 2008. The population in the neighboring areas (covering more than 1000 km2 ) is approximately 40,000. Correspondingly, in Kefalonia, one of the most important tourist destinations in Greece with worldwide reputation, four wind farms with a total capacity of 97.5 MW have been installed in an area of approximately 11 ha. The island of Kefalonia has an area of 773 km2 , and its population is about 35,000. The analysis of the environmental effects of wind farms in the two reference areas was based on 400 home sales in the case of South Evia and 1415 home sales in the case of Kefalonia. All these transactions were carried out during the period from 2006 to 2016. The data required for the analysis (i.e., house prices, location, and housing characteristics) were derived from the real estate database of the Bank of Greece. The characteristics of the two case studies are summarized in Table 1, while Table 2 shows that in both cases, the majority of the transactions were made after the installation of the wind farms. The selling price of the dwellings, adjusted per square meter of floor area, has been correlated with parameters related to the characteristics of the dwelling, the broader characteristics of the area where the dwelling is located, and several environmental characteristics. The latter include, among others, the variable ‘‘distance of the dwelling from wind farms’’ on the assumption that to the extent that the development of wind energy causes environmental impacts due to noise and/or visual nuisance, these will be more pronounced in the areas adjacent to the wind farms. It is noteworthy that in the case of Kefalonia, the wider area of wind farms is not inhabited and consequently, the number of transactions in a distance up to 1000 m from the nearest wind farm is small, while there is no house purchase within a 500 m zone. In the case of Evia only two transactions concern the sale of dwellings within a distance of approximately 500 m from the nearest wind farm. Under these circumstances, the variable ‘‘distance of the dwelling from wind farms’’ (included in the developed hedonic price models) it is considered that quantifies the visual impact of the wind farms as noise effects to the surrounding dwellings more than 500 m from a wind turbine are negligible. The calculation of this variable was performed using a geographic information system (GIS), digitizing as polygons the smallest geographic administrative structures in which the data derived from the database of the Bank of Greece could be registered. Specifically, in each case study, the polygons of urban areas that include the dwellings that were sold were digitized using the background of the forest maps in Greece, in conjunction with the boundaries of urban plans or the settlements. The polygons of the wind farms were identified on the basis of the coordinates of the peaks of their polygons as given by the geoportal of the Greek Regulatory Authority for Energy. The distance recorded for each observation was
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Table 2 Number of transactions per year and capacity of the installed wind farms in the area of interest in the corresponding time period. Year
Evia case study
Kefalonia case study
Annual number of sales 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
27 42 52 79 59 51 27 26 13 11 13
Total
400
Installed capacity in the year of sales (MW) 78.8 78.8 78.8 83.9 83.9 83.9 83.9 83.9 83.9 83.9 83.9
Annual number of sales 77 267 256 221 230 130 79 42 24 53 37
Installed capacity in the year of sales (MW) 97.5 97.5 97.5 97.5 97.5 97.5 97.5 97.5 97.5 97.5 97.5
1416
Fig. 1. Wind farms and residential areas with home transactions in South Evia.
the distance between the centripetal points of the corresponding polygons, considering the nearest wind farm that was in operation during the transaction period. Figs. 1 and 2 present the two study areas, depicting the installed wind farms and the residential areas where home sales were recorded during the reference period. 5. Results The equation of the hedonic price models developed in the context of this study is as follows: lnΥi = c + b1 Ai + b2 Pi + b3 Wi + b4 Qi +b5 Li + b6 Fi + b7 Si + b8 Ii + n1 Vi + n2 M1i + n3 M2i + n4 M3i
+ n5 M4i + g1 Dc i +g 2 Dd1i +g 3 Dd2i + g4 Sei + g5 Sk1i + g6 Sk2i + g7 SV i + ei
(2)
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Fig. 2. Wind farms and residential areas with home transactions in Kefalonia.
where c, b1 to b8 , n1 to n5 and g1 to g7 are the coefficients to be estimated from the regression analysis, and e is the residual term. In all models the dependent variable is the selling price of the dwellings per unit floor area, expressed in a natural logarithm form in order to reduce heteroscedasticity (Makridakis et al., 1998). The explanatory variables used in the models comprise the following parameters:
• Physical characteristics of each dwelling, namely the floor area (A) of the property in order to reflect the different preferences for large or small dwellings (continuous variable), the availability of parking space (represented by a dummy variable P that is equal to 1 if the dwelling has a garage and 0 otherwise), the availability of a warehouse (represented by a dummy variable W that is equal to 1 if there is a warehouse and 0 otherwise), the construction quality of the dwelling (represented by a dummy variable Q that is equal to 1 if in the database of the Bank of Greece there is an indication of high quality of the reference dwelling and 0 otherwise), the age (L) of the dwelling (continuous variable), the floor (F) of the building where the residence is located (categorical variable), the area (S) of the private land where the dwelling is located (continuous variable), and the realization of renovation work in the dwelling under consideration (represented by a dummy variable I that is equal to 1 if such activities took place recently and 0 otherwise), • Parameters that reflect the characteristics of the broader area where the dwelling is located, as well as the characteristics of the local real estate market (neighborhood characteristics); specifically, the models incorporate the mean value of the house prices (V) in the area in question (in eper m2 of floor space) as a measure of its attractiveness (continuous variable), as well as the period of the transaction represented by four dummy variables (M1 to M4 that divide the 11-year study period into five separate periods of two or three years, reflecting the broader
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fluctuations of the real estate market and the price changes due to the 2009 to 2016 financial crisis in Greece and other parameters, • A number of environmental characteristics: As previously mentioned, the main parameter used for assessing the environmental impact of the wind farms in this analysis is the distance between the dwelling that is sold and the nearest wind farm at the time of transaction. In both case studies, this variable is incorporated into the models developed either as a continuous variable (Dc ) or through two dummy variable (Dd1 and Dd2 ) that define three separate zones (i.e., 0–2 km, for which Dd1 = 1 and Dd2 = 0; 2–4 km, for which Dd1 = 0 and Dd2 = 1; and beyond 4 km where Dd1 = 0 and Dd2 = 0) around the wind farms examined. It should be noted that different widths of zones were examined during the development of the hedonic price models; eventually, those leading to the models with the best performance were chosen. In addition, the proximity of the dwelling in question to the sea has also been incorporated in the models in the case study of Evia through a dummy variable (Se ) that is equal to 1 if the dwelling is at a distance of up to 800 m from the sea and 0 otherwise. Correspondingly, two dummy variables have been included in the case of Kefalonia to model the proximity to the sea, namely Sk1 , which is equal to 1 if the dwelling is at a distance of up to 200 m from the sea and 0 otherwise, and Sk2 , which is equal to 1 if the dwelling is in a zone that is from 200 to 500 m from the sea and 0 otherwise. Lastly, an additional environmental variable is incorporated into the models through a dummy variable (SV), which represents the overall quality of the view from the house. Using 400 transactions in the case of Evia and 1416 transactions in the case of Kefalonia, the regression coefficients were estimated for four different hedonic price models (two for each case study), and the results are summarized in Table 3. The two models, namely E1 for Evia and K1 for Kefalonia, which incorporate the distance between the dwellings and the nearest wind farm as a continuous explanatory variable, provide a satisfactory interpretation of house price fluctuations. More specifically, in the case of Evia the developed hedonic price model incorporates 13 independent variables and the distance of the dwellings sold from the nearest wind farm was found statistically significant, leading to increased house sales prices as their distance from the nearest wind farm grows. Meanwhile, the developed model in the case of Kefalonia showed that the distance from the nearest wind farm does not affect the prices of dwellings in a statistically significant way (even at a confidence level of 90%). This difference may be attributed to the fact that Evia’s wind farms have a relatively small size and are visible from several villages and towns located in the wider area, while the wind farms in Kefalonia are concentrated in a relatively isolated and extremely sparsely populated area. The adjusted R2 of the two models were estimated at 51% in the case of Evia and 39% in the case of Kefalonia. The relatively low explanatory capacity of the models are likely attributable to the fact that the data included in the Bank of Greece’s database allow the identification of the dwellings’ location at a community level rather than at a building block level, which provides more detailed information of their environmental and neighborhood characteristics (e.g., view, visibility of wind farms, and proximity to infrastructures). Furthermore, despite the fact that the two models incorporate as independent variables a significant number of physical characteristics of the properties, several others (e.g., number of rooms and type of heating means available) that are expected to influence house prices are not included because relevant information is not available from any database in Greece. Nevertheless, the overall performance of the two models is quite satisfactory, and they incorporate as independent variables several characteristics expected to affect house prices, thus estimating reasonable signs for the corresponding coefficients. In the case of Evia, where the distance of the dwelling from the nearest wind farm was found to be statistically significant, housing prices were found to increase by 2.4% per km from the wind farms. However, hedonic price models that include the distance of homes from wind farms as a continuous variable are unable to determine the maximum distance for which wind turbines affect property prices, thus hindering the relevant environmental damage estimation, as property prices cannot grow continuously as the distance from the wind farms increases. To resolve this issue, two new hedonic price models were developed for the two cases (the E2 model for Evia and the K2 model for Kefalonia), where the distance of the dwellings from the nearest wind farm is modeled through a number of dummy variables that show to what extent the reference dwelling is located in a specific zone around the nearest wind farm. For example, if a transaction concerns a house located at a distance of 3 km from the nearest wind farm, the dummy variable that characterizes the zone with a distance of 2 km to 4 km around the corresponding wind farm takes the value 1, while the other relevant dummy variable (Dd1 ) is equal to 0. These two new models incorporate 16 and 15 independent variables for Evia and Kefalonia, respectively, and the estimated coefficients are presented in Table 3. The explanatory capacity of the new models is similar to the previous set of models, identifying as statistically significant several characteristics that influence the prices of dwellings in the reference areas. In the case of Kefalonia, none of the dummy variables used to model the distance of dwellings sold from the nearest wind farm were found to be statistically significant. Meanwhile, in the case of South Evia, it was found that prices of dwellings are negatively affected if they are at a distance of up to 2 km from a wind farm. For the transactions in this zone, it was found that the sales price of the dwellings reduced by 14.4%. This result is, to a large extent, comparable to that obtained by model E1. Specifically, considering that the aesthetic impact of wind farms disappears at 7.5 km (i.e., the average of the distance 5 km to 10 km found by Betakova et al. (2015), using the E1 model revealed that the sales price of a dwelling is reduced by 14.4% if it is 1 km away from the closest wind farm and by 12.4% if the corresponding distance is 2 km. Based on these results, an overall assessment of the environmental damage caused by installed wind farms in South Evia was attempted, considering the decline in house prices located in the 0 km to 2 km zone from the wind farms in the reference area. The analysis relies on the following assumptions:
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Table 3 Hedonic price models developed for estimating the environmental impact of wind farms in South Evia and Kefalonia. Variable
Constant Average price of the dwellings locally (in e/m2 ) Within an 800 m zone from the sea (Yes/No) Within a 200 m zone from the sea (Yes/No) Within a 200 m to 500 m zone from the sea (Yes/No) Distance from the nearest wind farm (in km) Nearest wind farm in the range of 0 km to 2 km (Yes/No) Nearest wind farm in the range of 2 km to 4 km (Yes/No) Building age (years) Transaction made in the period from 2008 to 2009 (Yes/No) Transaction made in the period from 2010 to 2011 (Yes/No) Transaction made in the period from 2012 to 2013 (Yes/No) Transaction made in the period from 2014 to 2016 (Yes/No) Floor Warehouse (Yes/No) Garage (Yes/No) Excellent building quality (Yes/No) Excellent site/view (Yes/No) Total floor area of the dwelling (m2 ) Recently renovated (Yes/No) Area of private land where the dwelling is located (m2 ) Number of observations Number of variables Adjusted R2
South Evia
Kefalonia
Model E1 coefficients
Model E2 coefficients
Model K1 coefficients
Model K2 coefficients
6.869*** 0.0004***
6.940*** 0.0005***
6.660*** 0.0005***
6.724*** 0.0005***
−0.064
−0.109*
– –
– –
– 0.044* −0.101***
– 0.059** –
0.024** –
0.003 –
–
−0.155**
–
−0.099
–
0.015
−0.007*** 0.072
−0.007***
−0.005***
−0.005***
0.078
0.033
0.036*
−0.069
−0.060
−0.134***
−0.119***
−0.314***
−0.306***
−0.397***
−0.399***
−0.537***
−0.526***
−0.360***
−0.337***
0.020 0.345*** 0.260* 0.147** – −0.002*** – –
0.017 0.356*** 0.283** 0.153** – −0.002*** – –
0.023 0.162*** 0.018 0.119** 0.057 −0.001*** – 0.000***
0.017 0.168*** 0.042 0.106* 0.035 −0.001*** – –
400 13 0.51
400 14 0.51
1416 16 0.39
1416 15 0.36
–
−0.091
***Significance at the 99% level. **Significance at the 95% level. *Significance at the 90% level.
• The percentage reduction in house prices estimated using the E2 model concerns all dwellings within a 0 km to 2 km zone from the wind farms in the reference area,
• The annualized value of dwellings is calculated at a discount rate of 1.5% and takes the life of each residence to equal 60 years,
• The load factor of the wind farms installed in South Evia is approximately 30%. Based on the above, the external environmental cost associated with the large-scale development of wind energy in southern Evia amounts to e2.59/MWh and zero for the Kefalonia Island. 6. Concluding remarks In this study, the HPM was applied to assess in monetary terms the environmental impact and particularly, the visual intrusion associated with the large-scale utilization of wind energy at a local or regional level. Attributing monetary values to environmental goods, services, and specific impacts appears to be a powerful tool for integrating environmental concerns into the relevant decision-making procedures. Moreover, the implementation of the HPM enables a more realistic economic valuation of environmental goods based on an analysis of consumers’ behavior in the actual market by examining price fluctuations of surrogate goods (in the present study, the sale prices of dwellings). Therefore, the valuation is not based on consumers’ theoretical preferences recorded in a survey but on an analysis of their behavior in the real estate market and the additional amounts they pay to acquire a dwelling with environmental benefits or the price reductions they demand to acquire a dwelling that faces environmental pressures. The method was applied to two Greek islands (Evia and Kefalonia), both of which have rich wind energy potential and in which a significant number of wind turbines have already been installed. In the case of South Evia, where the installed capacity of wind farms reached 83.9 MW during the examined period, the results of the analysis showed that house prices are negatively affected at a distance of up to 2 km from the wind farms, while there is no statistically significant
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effect on prices beyond this zone. Meanwhile, in the case of Kefalonia, where the installed capacity of wind farms was 97.5 MW during the period under consideration, the results of the analysis showed that house prices are not affected by the wind farms. The main difference between the two cases is that in Kefalonia, wind farms are concentrated in a relatively isolated and extremely sparsely populated area, while, in the case of Evia wind farms are dispersed in quite a large area, and in many cases wind turbines have been installed at close distances from houses. The hedonic price models developed in this study have a rather moderate explanatory capacity since several independent parameters that are expected to affect property prices were not incorporated into the models due to lack of data. In particular, the failure to integrate the visibility of wind farms from the residences sold as an explanatory variable constitutes a shortcoming of the developed models, even though the effect on the results may be limited since in the areas of interest multi-story buildings are uncommon. However, the overall statistical significance of these models is high, providing evidence that the estimated effect on house prices of the independent variables included in these models is reliable. Focusing on the case of South Evia it was estimated that house prices at distances of up to 2 km from the installed wind farms are reduced by about 14.4%. This is consistent with the results of several other relevant studies that found a statistically significant correlation between the installation of wind farms and house prices in a wider area, highlighting that this impact is limited to 1 km to 2 km distances from the wind farms (Dent and Sims, 2007; Heintzelman and Tuttle, 2012; Sunak and Madlener, 2012). Taking into account the total number of houses located within 2 km of wind farms in southern Evia, it was estimated that the marginal environmental cost at these levels of wind energy penetration is approximately e2.59/MWh. This is slightly lower compared with the environmental damage estimated in Mirasgedis et al. (2014) for the same area (e2.71/MWh) when using a contingent valuation method. In conclusion, the visual intrusion associated with large-scale exploitation of wind energy in an area creates externalities, but these are considerably lower compared with external costs caused by fossil-fueled power generation technologies. Specifically, Porchia et al. (2008) estimated that the external costs of various power generation technologies in Greece amount to 33/MWh for lignite-fired power plants, e8.2/MWh for natural gas-fired combined cycle units, and e22.6/MWh for fuel oil steam generators. As such, large-scale exploitation of wind energy (as a substitute for electricity generated by fossil fuels) demonstrates significant environmental benefits that can be valued in monetary terms. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. References Abbasi, T., Premalatha, M., Abbasi, T., Abbasi, S.A., 2014. Wind energy: Increasing deployment, rising environmental concerns. Renew. Sustain. Energy Rev. 31, 270–288. Bergmann, A., Hanley, N., Wright, R., 2006. Valuing the attributes of renewable energy investments. Energy Policy 34, 1004–1014. 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Implementing hedonic pricing models for valuing the visual impact of wind farms in Greece ∗
Konstantinos Skenteris a , Sevastianos Mirasgedis a , , Christos Tourkolias b a
Institute for Environmental Research & Sustainable Development, National Observatory of Athens, I. Metaxa & Vas. Pavlou, GR-15236 Palea Penteli, Greece b Center for Renewable Energy Sources & Saving, 19th km Marathonos Avenue, GR-19009, Pikermi Attiki, Greece
article
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Article history: Received 28 January 2019 Received in revised form 16 September 2019 Accepted 16 September 2019 Available online 18 September 2019 Keywords: Environmental impact Visual impact Wind energy Hedonic pricing Energy externalities
a b s t r a c t Although wind energy is a pollution-free and infinitely sustainable form of energy, there is considerable concern over certain environmental effects resulting from its development. Criticism focuses primarily on the visual impact of wind turbines and transmission lines, which may result in perceived deterioration of the landscape and also harm other economic activities such as tourism and real estate. This study applies the hedonic pricing method to estimate the value of environmental externalities associated with large-scale exploitation of wind power at a local level. It examines the characteristics of approximately 1,800 sales of single-family homes surrounding 17 existing wind facilities in two Greek islands, namely Evia and Kefalonia. Four different hedonic price models are developed and applied, with diverging results in the two areas. We find that in Evia, the per unit floor area sales price decreased for dwellings located within a 2 km radius of the wind farms, while in Kefalonia, the distance of the house to the wind turbines had no statistically significant effect on the sales price. © 2019 Economic Society of Australia, Queensland. Published by Elsevier B.V. All rights reserved.
1. Introduction The exploitation of renewable energy source (RES) technologies, particularly wind and photovoltaic systems, is a key pillar of the European policy for tackling climate change. Thus, while in 2000, the installed capacity of wind farms in Greece was only 226 MW, in 2017, it reached 2377 MW, with the generated electricity amounting to 7.4% of the total domestic electricity demand. The EU 2030 climate and energy framework sets three key targets for the year 2030: (i) at least 40% reduction in greenhouse gas emissions compared with 1990 levels, (ii) at least a 32% share for renewable energy, and (iii) at least a 32.5% improvement in energy efficiency. In this context, the utilization of wind energy in the power generation systems is expected to increase in the coming years. The draft National Energy Climate Plan of Greece (submitted to the European Commission in January 2019) foresees that the installed capacity of wind farms will reach 6600 MW by 2030 (MEE, 2019). Several other modeling exercises (see for example, WWF, 2017; European Commission, 2016) suggest that the increase in wind energy installations in Greece will continue beyond 2030, reaching an installed capacity ranging from 8000 to 10,500 MW by 2050. The large-scale utilization of wind energy has raised considerable concern over certain environmental impacts that result from wind power development (see for example, Wang and Wang, 2015; Dai et al., 2015; Abbasi et al., 2014; Leung and Yang, 2012) and in particular, over the visual impact of wind turbines and transmission lines, which results ∗ Corresponding author. E-mail addresses: [email protected] (K. Skenteris), [email protected] (S. Mirasgedis), [email protected] (C. Tourkolias). https://doi.org/10.1016/j.eap.2019.09.004 0313-5926/© 2019 Economic Society of Australia, Queensland. Published by Elsevier B.V. All rights reserved.
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in the deterioration of the landscape and may harm associated economic activities such as tourism and real estate. These concerns have led to local reactions against wind energy, which intensified in the last decade as the number of installed wind farms and the size of turbines increased. It is noteworthy that the average commercial size of onshore wind turbines being manufactured today is around 2.5 MW to 3 MW, with blades of about 50 m length and a tower height of 70 m to 90 m, the offshore ones being even larger. The environmental degradation associated with wind energy development typically includes noise; visual impact, including shadow flicker and reflectance; bird and bat fatalities; and electromagnetic interference (Wang and Wang, 2015; Dai et al., 2015; Jain, 2011). Among them, the most significant environmental concerns are regarding the visual impact and the associated aesthetic degradation of the landscape (see for example, Kontogianni et al., 2014), as wind turbines must be sited in exposed places, which are highly visible. However, being visible does not necessarily equate with being intrusive. Although aesthetic issues are, by their nature, highly subjective, individuals with a negative attitude toward wind energy are expected to assess visual impact less tolerably (Kaldellis et al., 2006). The analysis of the visual impact associated with wind farm development presents significant methodological difficulties as it depends on turbine and site characteristics, as well as on the level of exposure of visual receptors. According to a classification made by Mirasgedis et al. (2014), the methods usually implemented, independently or in combination, to analyze the aesthetic impact of wind energy comprise: (i) the zone of theoretical visibility approach (see for example, Manchado et al., 2013), which defines the land area from which a wind farm can be totally or partially visible (as the visual impact decreases with the distance, different zones of theoretical visibility can be defined, representing different levels of visual burden); (ii) the estimation of appropriately designed indices, which incorporate specific parameters (e.g., population in the neighboring areas and number of wind turbines) influencing the visual impact of wind farms (see for example, Sklenicka and Zouhar, 2018; Tsoutsos et al., 2009); (iii) field surveys that record public preferences (e.g., Kontogianni et al., 2014) and the evaluation of future changes in the landscape by utilizing various tools such as photomontage and video montages; and (iv) the monetization of the visual impact on the basis of appropriate environmental valuation techniques. With regard to noise impacts, the changes made in recent years to the design of wind turbines has resulted in a substantial reduction in the noise emitted. It is noteworthy that the level of perceived noise from a modern wind turbine at a 200 m distance is less than the background noise of a small provincial city and certainly not a source of discomfort. Wind turbines can also interfere with radar and aircraft navigation systems, as well as with other communications links such as television signals or mobile phone networks, although these issues can be addressed with careful siting and design. In any case, the potential noise impacts and electromagnetic interferences are limited to areas located near the wind farms. Lastly, bird and bat deaths are one of the most controversial issues related to wind turbines, with concerns raised by several environmental groups especially in cases where wind farms are installed in the habitats of rare and endangered birds, despite years of operation of several large wind facilities with only minor impacts. Recently, due to the development of advanced methods for siting wind power and monitoring for avian impacts, the risk of bird collisions has been reduced, with the number of annual bird fatalities ranging from 0.02 to 0.6 per turbine (Wang and Wang, 2015). This study utilizes a well established technique in environmental economics to investigate quantitatively the environmental impact of large-scale development of wind power at the local level, primarily addressing the issue of visual intrusion and aesthetic degradation of the landscape. Specifically, the hedonic pricing method (HPM) is implemented to examine to what extent the environmental impact caused by the development of wind farms, particularly the resulting alteration of the landscape, affects housing prices in a wide area around the wind farms. The results of the analysis can provide useful information on the economic magnitude of the environmental impacts attributed to wind farms (i.e., the external cost of wind energy), which might be useful for assessing this technology in relation to others in the context of long-term energy planning. The HPM is applied in two Greek islands, namely Evia and Kefalonia, where a large number of wind turbines have been installed during the last 15 years and several new projects are planned. Compared with other techniques used for estimating the environmental externalities of wind energy, such as the contingent valuation method or choice experiments (see for example, Westerberg et al., 2013; Krueger et al., 2011; Dimitropoulos and Kontoleon, 2009; Mirasgedis et al., 2014), the HPM can provide more realistic estimates, as it is based on consumer preferences already recorded in the market rather than on surveys where respondents may conceal expectations or rely on misleading information. However, the application of the HPM requires the collection and processing of large volumes of data, especially the actual house purchase prices over a long period. This presents significant difficulties in Greece as in the past, a large part of these transactions (totally or partially) were realized outside the banking system. For these reasons, the presented application of the HPM is one of the first done in Greece to value an environmental good and one of the few in Europe related to wind energy. The structure of this paper is as follows: Section 2 presents a literature review of studies that use techniques of environmental economics, particularly the HPM, to value the environmental impact of wind farms, while Section 3 describes in more detail the HPM used in this study. Section 4 focuses on the application of the method, providing information on the two case studies examined. Section 5 presents the selection of the appropriate hedonic price models and the basic results of the analysis. Finally, Section 6 summarizes the main findings of the study and provides the conclusions.
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2. Literature review Various techniques of environmental economics have been utilized to assess the visual disturbance and landscape alteration, as well as other environmental impacts resulting from the installation of wind farms (Knapp and Ladenburg, 2015). In most cases stated preference methods, such as the contingent valuation method and choice experiment models (Bergmann et al., 2006; Ladenburg and Dubgaard, 2007), have been conducted. In Greece, the contingent valuation method has been implemented by Koundouri et al. (2009) to investigate the benefits to the local community of a small wind park development in Rhodes, and by Mirasgedis et al. (2014) to quantify environmental costs of a large-scale use of wind energy in the South Evia region. Moreover, Dimitropoulos and Kontoleon (2009) developed a choice experiment model to examine the factors that affect citizens’ willingness to receive compensation for the installation of wind farms on two Greek islands, namely Naxos and Skyros. On the contrary, applying revealed preference methods to evaluate the environmental effects of wind farm installation is not that common as it is difficult to gather detailed statistics on house purchase prices. Moreover, significant computational burden is required to simulate and quantify the impact of installed wind turbines on property values for each examined area (in terms of proximity and visibility). Currently, the few studies that used the HPM for evaluating the impact of installed wind farms on the values of nearby properties have led to contradictory conclusions. In the United Kingdom, Dent and Sims (2007) found that property values of terraced and semi-detached houses located within a mile of a wind farm were affected, even though interviews conducted with estate agents from the area revealed no negative attitudes toward wind farms upon purchase of the nearby houses. In New York, USA, Heintzelman and Tuttle (2012) studied the impact of wind power on property values by analyzing over 11,000 sales transactions. Their results revealed that proximity of within a half mile zone from the wind installations results in a reduction of property values by as much as 35% and for properties within a mile, by 8% to 15%. A similar study (Sunak and Madlener, 2012) conducted in North Rhine-Westphalia, Germany found that property values decreased by 21.5% to 29.7% for distances up to 1 km from the examined wind farms, remaining statistically significant at distances up to 1.5 km. Lately, Heintzelman et al. (2017) conducted a parcel-level hedonic analysis of property sales transactions and concluded that after the construction of the wind farm, values decreased significantly for properties in New York with a view of and/or in close proximity to the turbines, while no negative impacts were identified on properties in Ontario, Canada. The latter conclusion is consistent with the findings of several studies that property values are not affected by nearby wind farms. Specifically, Sims et al. (2008) analyzed 201 sales transactions of properties located within half a mile of a wind farm in Cornwall, UK, and found no link between the presence of the wind farm and house values. Laposa and Müller (2010) studied the impact on property values from the potential installation of a wind farm in North Colorado, USA, through an analysis of sales transactions received before and after the announcement of the wind farm’s installation. They also found no statistically significant impact on the real estate market. Approximately 7500 sales transactions of detached houses were analyzed by Hoen et al. (2011) around 24 wind farms located in the United States using four different models. The results of their analysis revealed that neither the view of the wind turbines nor the distance from them affected the prices of the dwellings. Similarly, the impact of wind turbines on house values located within a mile was examined by Lang et al. (2014) in Rhode Island, and no statistically significant negative impacts on house prices were identified in either the post-public announcement or post-construction phases of the wind parks. McCarthy and Balli (2014) analyzed 945 sales transactions in Ashhurst, New Zealand and found no significant statistical impact of proximity to wind farms on property values. Moreover, Hoen et al. (2015) collected over 50,000 sales transactions of properties within 10 miles from wind farms and found no statistical evidence that the property values were affected by the turbine in the post-construction or post-announcement/pre-construction periods. Similarly, no significant statistical decrease in the values of properties near wind farms was identified by Castleberry and Greene (2018) from their analysis of 23,000 sales transactions in Western Oklahoma. Finally, Jensen et al. (2018) conducted a large-scale analysis using hedonic pricing models to investigate how on-shore and off-shore wind turbines affect property values in Denmark. The price of properties within a distance of three kilometers from a wind farm was found to be negatively affected in the case of on-shore wind turbines, while no significant effect was demonstrated in the case of off-shore wind farms located at a distance of 9 km from the closest traded home. 3. The hedonic pricing method The HPM is one of the oldest methods used to value non-market goods (such as environmental changes or services) and is based on the idea that an individual’s decision to purchase goods or services depends on the features or attributes of those goods, some of which may be environmental aspects (Hanley et al., 1997). Specifically, it exploits the value of a surrogate good or service to measure the implicit price of a non-market good. The HPM is commonly used in the context of property and labor markets (Bolt et al., 2005). In the former, the assumption is that the environmental quality is an attribute of real estate and its price reflects people’s preferences for environmental quality. In the latter, the health risk is assumed to be an attribute of a job and consequently, the wage rate reflects the willingness to be compensated for taking such risks. This study focuses on housing prices to value the environmental impacts and particularly, the aesthetic degradation of the landscape associated with the installation of wind farms in a specific area.
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As previously mentioned, the HPM is based on the idea that a person’s willingness to pay for a property reflects the attributes of that house, including indicative parameters such as the number of bedrooms and bathrooms, the backyard’s size, and the proximity to schools and other infrastructure. Some of these attributes may concern environmental aspects. For example, individuals may be willing to pay a premium for a house located close to a park or prefer having a discount on a house that is located close to a source of environmental burden (e.g., air pollutants). Using statistical analysis and more often, multiple regression models, it is possible to isolate the effect of the features that are envisaged to be valued. Consequently, the first step in implementing the HPM is to identify those attributes that are likely to determine the pricing of the house in the market, which is then used to specify the hedonic price function. Generally, three groups of elements are expected to affect the house price (Freeman et al., 1993; Kong et al., 2007; Bolt et al., 2005):
• Physical characteristics of the property (e.g., year of construction, size of the apartment, number of rooms, and availability of garage or storage space),
• Neighborhood characteristics (e.g., the existence of good public services, such as transportation, proximity to commercial areas, school or hospital, and the level of crime),
• Environmental characteristics (e.g., the view, proximity to forests or parks, proximity to the sea, and the level of air pollution or noise). The mathematical function that describes the value of properties is of the form: P = f (S1 , . . . , Sm, Ni, . . . , Nn, Q1 , . . . Qr)
(1)
where P is the price of the property; S1 , . . . Sm , its physical characteristics; N1 , . . . , Nn the characteristics of the wider area that the property is located (i.e., neighborhood characteristics); and Q1 , . . . , Qr the environmental features in the reference area, such as noise, ambient air quality, proximity to a park, and the view. To determine the form of the hedonic price function a large amount of data is necessary, comprising information on the prices and characteristics of different properties in a given area and for a specific period. It is a prerequisite that the real estate market in the reference area is well functioning and the people are well-informed on the differences in environmental variables across neighborhoods (Bolt et al., 2005). This guarantees that the property prices reflect the differences in environmental attributes. The coefficients of the independent variables included in the hedonic price function are estimated through regression analysis. The developed function provides the implicit price of a property, from which the change in a house price due to the marginal change in one of the characteristics included in the model can be inferred. The implicit price can therefore be interpreted as the additional cost of purchasing a house that is marginally ‘‘better’’ in terms of a particular characteristic. Theoretically, the observed price of a property is the result of an interaction between the supply and demand for properties. However, in this analysis, we are interested in estimating the demand curve for a specific environmental aspect that affects the prices of properties. By applying Eq. (1), the implicit price of this environmental aspect, which is an approximation of its welfare effects, can be obtained. The estimation of a demand curve requires conducting a second regression in which the implicit price for the environmental aspect in question is the dependent variable and individuals’ characteristics are the explanatory variables. However, most studies do not consider this last step in the analysis due to the additional data required with regard to the individuals’ characteristics (Hanley et al., 1997). For relatively small changes in the environmental aspect in question, it is considered as an acceptable approximation to estimate the welfare changes directly from the hedonic price function (Bolt et al., 2005). Hedonic price models, if properly structured, can provide useful information on the value of specific environmental goods. However, there are a number of limitations in the use of the HPM. These include (Boardman et al., 2011; Hanley et al., 1997) the following.
• Information: The model assumes that all individuals have prior knowledge of the potential positive and negative
•
•
•
•
externalities triggered by the purchase of a house. Even if this is the case, individuals may have different attitudes against environmental risks, which may either overestimate or underestimate welfare changes. Structure of the hedonic price function: The economic theory does not provide any specific guidance regarding the form of this function. Furthermore, the omission of an independent variable that affects property prices and at the same time is associated with another independent variable included in the model may result in incorrect estimates of model coefficients. Multicollinearity: In some cases, it is possible that large houses are only found in areas where the examined environmental aspect has good performance and small houses are found in urban areas where this particular aspect has poor performance. In this case it would be impossible to separate the environmental aspect in question and the size of the house accurately. Prices and price changes: The model assumes that market prices adjust immediately to changes in attributes. In reality, there is likely a lag associated with this, especially in areas where house sales and purchases are rare. Furthermore, market prices can incorporate expectations of possible future changes in environmental quality. Measurement validity: The quality of the measures used in the independent ‘‘explanatory’’ variables is of key importance. If proxy measures are used, this could result in estimating inaccurate coefficients through regression analyses.
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K. Skenteris, S. Mirasgedis and C. Tourkolias / Economic Analysis and Policy 64 (2019) 248–258 Table 1 Basic characteristics of the two cases analyzed in this study. Characteristics
Evia case study
Kefalonia case study
Number of wind farms Total installed capacity (MW) Population of the wider area (inhabitants) Area in consideration (km2 ) GDP per capita (e/capita)a 2006 2011 2016 Sectoral distribution of GVAa Agriculture, forestry, and fishing Manufacturing Services Number of transactions Total Within a 0–2 km zone Within a 2–4 km zone
13 83.9 40,210 1000
4 97.5 35,800 773
17,693 14,789 13,322
21,080 16,934 14,520
2006: 9%, 2011: 8%, 2016: 8% 2006: 45%, 2011: 40%, 2016: 49% 2006: 46%, 2011: 52%, 2016: 43%
2006: 10%, 2011: 15%, 2016: 17% 2006: 4%, 2011: 7%, 2016: 9% 2006: 86%, 2011: 78%, 2016: 74%
400 39 33
1416 15 32
a
This concerns the whole Evia Island in the first case study and the Ionian Islands, which comprise Kefalonia, in the second case study.
• Market limitations: The model ideally requires that a variety of different houses are available so that individuals are able to obtain the particular house of their choosing, with a combination of characteristics they desire. However, this is not always the case. 4. Case studies As previously mentioned, this study estimates the economic value of environmental effects, particularly the visual impact of large-scale exploitation of wind energy at a local level. To this end, the HPM is applied on two island regions in Greece, namely South Evia (Municipality of Karystos) and Kefalonia. Both areas present particularly rich wind energy potential and attract significant amounts of wind energy investment. At the same time, their residents are familiar with the potential disturbances generated by wind farms, which, to the extent they exist, are likely integrated in the operation of the local real estate market. Specifically, in South Evia, 13 wind farms covering an area of approximately 56 ha with a total capacity of 83.9 MW were installed during the period of this study. These wind farms were installed gradually in the reference area over the period from 2001 to 2008. The population in the neighboring areas (covering more than 1000 km2 ) is approximately 40,000. Correspondingly, in Kefalonia, one of the most important tourist destinations in Greece with worldwide reputation, four wind farms with a total capacity of 97.5 MW have been installed in an area of approximately 11 ha. The island of Kefalonia has an area of 773 km2 , and its population is about 35,000. The analysis of the environmental effects of wind farms in the two reference areas was based on 400 home sales in the case of South Evia and 1415 home sales in the case of Kefalonia. All these transactions were carried out during the period from 2006 to 2016. The data required for the analysis (i.e., house prices, location, and housing characteristics) were derived from the real estate database of the Bank of Greece. The characteristics of the two case studies are summarized in Table 1, while Table 2 shows that in both cases, the majority of the transactions were made after the installation of the wind farms. The selling price of the dwellings, adjusted per square meter of floor area, has been correlated with parameters related to the characteristics of the dwelling, the broader characteristics of the area where the dwelling is located, and several environmental characteristics. The latter include, among others, the variable ‘‘distance of the dwelling from wind farms’’ on the assumption that to the extent that the development of wind energy causes environmental impacts due to noise and/or visual nuisance, these will be more pronounced in the areas adjacent to the wind farms. It is noteworthy that in the case of Kefalonia, the wider area of wind farms is not inhabited and consequently, the number of transactions in a distance up to 1000 m from the nearest wind farm is small, while there is no house purchase within a 500 m zone. In the case of Evia only two transactions concern the sale of dwellings within a distance of approximately 500 m from the nearest wind farm. Under these circumstances, the variable ‘‘distance of the dwelling from wind farms’’ (included in the developed hedonic price models) it is considered that quantifies the visual impact of the wind farms as noise effects to the surrounding dwellings more than 500 m from a wind turbine are negligible. The calculation of this variable was performed using a geographic information system (GIS), digitizing as polygons the smallest geographic administrative structures in which the data derived from the database of the Bank of Greece could be registered. Specifically, in each case study, the polygons of urban areas that include the dwellings that were sold were digitized using the background of the forest maps in Greece, in conjunction with the boundaries of urban plans or the settlements. The polygons of the wind farms were identified on the basis of the coordinates of the peaks of their polygons as given by the geoportal of the Greek Regulatory Authority for Energy. The distance recorded for each observation was
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Table 2 Number of transactions per year and capacity of the installed wind farms in the area of interest in the corresponding time period. Year
Evia case study
Kefalonia case study
Annual number of sales 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
27 42 52 79 59 51 27 26 13 11 13
Total
400
Installed capacity in the year of sales (MW) 78.8 78.8 78.8 83.9 83.9 83.9 83.9 83.9 83.9 83.9 83.9
Annual number of sales 77 267 256 221 230 130 79 42 24 53 37
Installed capacity in the year of sales (MW) 97.5 97.5 97.5 97.5 97.5 97.5 97.5 97.5 97.5 97.5 97.5
1416
Fig. 1. Wind farms and residential areas with home transactions in South Evia.
the distance between the centripetal points of the corresponding polygons, considering the nearest wind farm that was in operation during the transaction period. Figs. 1 and 2 present the two study areas, depicting the installed wind farms and the residential areas where home sales were recorded during the reference period. 5. Results The equation of the hedonic price models developed in the context of this study is as follows: lnΥi = c + b1 Ai + b2 Pi + b3 Wi + b4 Qi +b5 Li + b6 Fi + b7 Si + b8 Ii + n1 Vi + n2 M1i + n3 M2i + n4 M3i
+ n5 M4i + g1 Dc i +g 2 Dd1i +g 3 Dd2i + g4 Sei + g5 Sk1i + g6 Sk2i + g7 SV i + ei
(2)
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Fig. 2. Wind farms and residential areas with home transactions in Kefalonia.
where c, b1 to b8 , n1 to n5 and g1 to g7 are the coefficients to be estimated from the regression analysis, and e is the residual term. In all models the dependent variable is the selling price of the dwellings per unit floor area, expressed in a natural logarithm form in order to reduce heteroscedasticity (Makridakis et al., 1998). The explanatory variables used in the models comprise the following parameters:
• Physical characteristics of each dwelling, namely the floor area (A) of the property in order to reflect the different preferences for large or small dwellings (continuous variable), the availability of parking space (represented by a dummy variable P that is equal to 1 if the dwelling has a garage and 0 otherwise), the availability of a warehouse (represented by a dummy variable W that is equal to 1 if there is a warehouse and 0 otherwise), the construction quality of the dwelling (represented by a dummy variable Q that is equal to 1 if in the database of the Bank of Greece there is an indication of high quality of the reference dwelling and 0 otherwise), the age (L) of the dwelling (continuous variable), the floor (F) of the building where the residence is located (categorical variable), the area (S) of the private land where the dwelling is located (continuous variable), and the realization of renovation work in the dwelling under consideration (represented by a dummy variable I that is equal to 1 if such activities took place recently and 0 otherwise), • Parameters that reflect the characteristics of the broader area where the dwelling is located, as well as the characteristics of the local real estate market (neighborhood characteristics); specifically, the models incorporate the mean value of the house prices (V) in the area in question (in eper m2 of floor space) as a measure of its attractiveness (continuous variable), as well as the period of the transaction represented by four dummy variables (M1 to M4 that divide the 11-year study period into five separate periods of two or three years, reflecting the broader
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fluctuations of the real estate market and the price changes due to the 2009 to 2016 financial crisis in Greece and other parameters, • A number of environmental characteristics: As previously mentioned, the main parameter used for assessing the environmental impact of the wind farms in this analysis is the distance between the dwelling that is sold and the nearest wind farm at the time of transaction. In both case studies, this variable is incorporated into the models developed either as a continuous variable (Dc ) or through two dummy variable (Dd1 and Dd2 ) that define three separate zones (i.e., 0–2 km, for which Dd1 = 1 and Dd2 = 0; 2–4 km, for which Dd1 = 0 and Dd2 = 1; and beyond 4 km where Dd1 = 0 and Dd2 = 0) around the wind farms examined. It should be noted that different widths of zones were examined during the development of the hedonic price models; eventually, those leading to the models with the best performance were chosen. In addition, the proximity of the dwelling in question to the sea has also been incorporated in the models in the case study of Evia through a dummy variable (Se ) that is equal to 1 if the dwelling is at a distance of up to 800 m from the sea and 0 otherwise. Correspondingly, two dummy variables have been included in the case of Kefalonia to model the proximity to the sea, namely Sk1 , which is equal to 1 if the dwelling is at a distance of up to 200 m from the sea and 0 otherwise, and Sk2 , which is equal to 1 if the dwelling is in a zone that is from 200 to 500 m from the sea and 0 otherwise. Lastly, an additional environmental variable is incorporated into the models through a dummy variable (SV), which represents the overall quality of the view from the house. Using 400 transactions in the case of Evia and 1416 transactions in the case of Kefalonia, the regression coefficients were estimated for four different hedonic price models (two for each case study), and the results are summarized in Table 3. The two models, namely E1 for Evia and K1 for Kefalonia, which incorporate the distance between the dwellings and the nearest wind farm as a continuous explanatory variable, provide a satisfactory interpretation of house price fluctuations. More specifically, in the case of Evia the developed hedonic price model incorporates 13 independent variables and the distance of the dwellings sold from the nearest wind farm was found statistically significant, leading to increased house sales prices as their distance from the nearest wind farm grows. Meanwhile, the developed model in the case of Kefalonia showed that the distance from the nearest wind farm does not affect the prices of dwellings in a statistically significant way (even at a confidence level of 90%). This difference may be attributed to the fact that Evia’s wind farms have a relatively small size and are visible from several villages and towns located in the wider area, while the wind farms in Kefalonia are concentrated in a relatively isolated and extremely sparsely populated area. The adjusted R2 of the two models were estimated at 51% in the case of Evia and 39% in the case of Kefalonia. The relatively low explanatory capacity of the models are likely attributable to the fact that the data included in the Bank of Greece’s database allow the identification of the dwellings’ location at a community level rather than at a building block level, which provides more detailed information of their environmental and neighborhood characteristics (e.g., view, visibility of wind farms, and proximity to infrastructures). Furthermore, despite the fact that the two models incorporate as independent variables a significant number of physical characteristics of the properties, several others (e.g., number of rooms and type of heating means available) that are expected to influence house prices are not included because relevant information is not available from any database in Greece. Nevertheless, the overall performance of the two models is quite satisfactory, and they incorporate as independent variables several characteristics expected to affect house prices, thus estimating reasonable signs for the corresponding coefficients. In the case of Evia, where the distance of the dwelling from the nearest wind farm was found to be statistically significant, housing prices were found to increase by 2.4% per km from the wind farms. However, hedonic price models that include the distance of homes from wind farms as a continuous variable are unable to determine the maximum distance for which wind turbines affect property prices, thus hindering the relevant environmental damage estimation, as property prices cannot grow continuously as the distance from the wind farms increases. To resolve this issue, two new hedonic price models were developed for the two cases (the E2 model for Evia and the K2 model for Kefalonia), where the distance of the dwellings from the nearest wind farm is modeled through a number of dummy variables that show to what extent the reference dwelling is located in a specific zone around the nearest wind farm. For example, if a transaction concerns a house located at a distance of 3 km from the nearest wind farm, the dummy variable that characterizes the zone with a distance of 2 km to 4 km around the corresponding wind farm takes the value 1, while the other relevant dummy variable (Dd1 ) is equal to 0. These two new models incorporate 16 and 15 independent variables for Evia and Kefalonia, respectively, and the estimated coefficients are presented in Table 3. The explanatory capacity of the new models is similar to the previous set of models, identifying as statistically significant several characteristics that influence the prices of dwellings in the reference areas. In the case of Kefalonia, none of the dummy variables used to model the distance of dwellings sold from the nearest wind farm were found to be statistically significant. Meanwhile, in the case of South Evia, it was found that prices of dwellings are negatively affected if they are at a distance of up to 2 km from a wind farm. For the transactions in this zone, it was found that the sales price of the dwellings reduced by 14.4%. This result is, to a large extent, comparable to that obtained by model E1. Specifically, considering that the aesthetic impact of wind farms disappears at 7.5 km (i.e., the average of the distance 5 km to 10 km found by Betakova et al. (2015), using the E1 model revealed that the sales price of a dwelling is reduced by 14.4% if it is 1 km away from the closest wind farm and by 12.4% if the corresponding distance is 2 km. Based on these results, an overall assessment of the environmental damage caused by installed wind farms in South Evia was attempted, considering the decline in house prices located in the 0 km to 2 km zone from the wind farms in the reference area. The analysis relies on the following assumptions:
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Table 3 Hedonic price models developed for estimating the environmental impact of wind farms in South Evia and Kefalonia. Variable
Constant Average price of the dwellings locally (in e/m2 ) Within an 800 m zone from the sea (Yes/No) Within a 200 m zone from the sea (Yes/No) Within a 200 m to 500 m zone from the sea (Yes/No) Distance from the nearest wind farm (in km) Nearest wind farm in the range of 0 km to 2 km (Yes/No) Nearest wind farm in the range of 2 km to 4 km (Yes/No) Building age (years) Transaction made in the period from 2008 to 2009 (Yes/No) Transaction made in the period from 2010 to 2011 (Yes/No) Transaction made in the period from 2012 to 2013 (Yes/No) Transaction made in the period from 2014 to 2016 (Yes/No) Floor Warehouse (Yes/No) Garage (Yes/No) Excellent building quality (Yes/No) Excellent site/view (Yes/No) Total floor area of the dwelling (m2 ) Recently renovated (Yes/No) Area of private land where the dwelling is located (m2 ) Number of observations Number of variables Adjusted R2
South Evia
Kefalonia
Model E1 coefficients
Model E2 coefficients
Model K1 coefficients
Model K2 coefficients
6.869*** 0.0004***
6.940*** 0.0005***
6.660*** 0.0005***
6.724*** 0.0005***
−0.064
−0.109*
– –
– –
– 0.044* −0.101***
– 0.059** –
0.024** –
0.003 –
–
−0.155**
–
−0.099
–
0.015
−0.007*** 0.072
−0.007***
−0.005***
−0.005***
0.078
0.033
0.036*
−0.069
−0.060
−0.134***
−0.119***
−0.314***
−0.306***
−0.397***
−0.399***
−0.537***
−0.526***
−0.360***
−0.337***
0.020 0.345*** 0.260* 0.147** – −0.002*** – –
0.017 0.356*** 0.283** 0.153** – −0.002*** – –
0.023 0.162*** 0.018 0.119** 0.057 −0.001*** – 0.000***
0.017 0.168*** 0.042 0.106* 0.035 −0.001*** – –
400 13 0.51
400 14 0.51
1416 16 0.39
1416 15 0.36
–
−0.091
***Significance at the 99% level. **Significance at the 95% level. *Significance at the 90% level.
• The percentage reduction in house prices estimated using the E2 model concerns all dwellings within a 0 km to 2 km zone from the wind farms in the reference area,
• The annualized value of dwellings is calculated at a discount rate of 1.5% and takes the life of each residence to equal 60 years,
• The load factor of the wind farms installed in South Evia is approximately 30%. Based on the above, the external environmental cost associated with the large-scale development of wind energy in southern Evia amounts to e2.59/MWh and zero for the Kefalonia Island. 6. Concluding remarks In this study, the HPM was applied to assess in monetary terms the environmental impact and particularly, the visual intrusion associated with the large-scale utilization of wind energy at a local or regional level. Attributing monetary values to environmental goods, services, and specific impacts appears to be a powerful tool for integrating environmental concerns into the relevant decision-making procedures. Moreover, the implementation of the HPM enables a more realistic economic valuation of environmental goods based on an analysis of consumers’ behavior in the actual market by examining price fluctuations of surrogate goods (in the present study, the sale prices of dwellings). Therefore, the valuation is not based on consumers’ theoretical preferences recorded in a survey but on an analysis of their behavior in the real estate market and the additional amounts they pay to acquire a dwelling with environmental benefits or the price reductions they demand to acquire a dwelling that faces environmental pressures. The method was applied to two Greek islands (Evia and Kefalonia), both of which have rich wind energy potential and in which a significant number of wind turbines have already been installed. In the case of South Evia, where the installed capacity of wind farms reached 83.9 MW during the examined period, the results of the analysis showed that house prices are negatively affected at a distance of up to 2 km from the wind farms, while there is no statistically significant
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effect on prices beyond this zone. Meanwhile, in the case of Kefalonia, where the installed capacity of wind farms was 97.5 MW during the period under consideration, the results of the analysis showed that house prices are not affected by the wind farms. The main difference between the two cases is that in Kefalonia, wind farms are concentrated in a relatively isolated and extremely sparsely populated area, while, in the case of Evia wind farms are dispersed in quite a large area, and in many cases wind turbines have been installed at close distances from houses. The hedonic price models developed in this study have a rather moderate explanatory capacity since several independent parameters that are expected to affect property prices were not incorporated into the models due to lack of data. In particular, the failure to integrate the visibility of wind farms from the residences sold as an explanatory variable constitutes a shortcoming of the developed models, even though the effect on the results may be limited since in the areas of interest multi-story buildings are uncommon. However, the overall statistical significance of these models is high, providing evidence that the estimated effect on house prices of the independent variables included in these models is reliable. Focusing on the case of South Evia it was estimated that house prices at distances of up to 2 km from the installed wind farms are reduced by about 14.4%. This is consistent with the results of several other relevant studies that found a statistically significant correlation between the installation of wind farms and house prices in a wider area, highlighting that this impact is limited to 1 km to 2 km distances from the wind farms (Dent and Sims, 2007; Heintzelman and Tuttle, 2012; Sunak and Madlener, 2012). Taking into account the total number of houses located within 2 km of wind farms in southern Evia, it was estimated that the marginal environmental cost at these levels of wind energy penetration is approximately e2.59/MWh. This is slightly lower compared with the environmental damage estimated in Mirasgedis et al. (2014) for the same area (e2.71/MWh) when using a contingent valuation method. In conclusion, the visual intrusion associated with large-scale exploitation of wind energy in an area creates externalities, but these are considerably lower compared with external costs caused by fossil-fueled power generation technologies. 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