The value of urban parks in the city of Turin: an application of the Geographically Weighted Regression

to air and water management, to ameliorating the well-being of local population. Starting from the concept of Total Economic Value, the paper investigates the method of the Geographically Weighted Regression (GWR) to estimate the value of two urban parks in the city of Turin: Dora Park and Valentino Park. The GWR grounds on to the Hedonic Pricing approach and permits to investigate the spatial patterns of the key variables under investigation. The results of the model show that proximity to parks influence positively real estate prices, and that it emerges a positive Willingness To Pay for environmental goods and services such as those provided by urban green areas. Abstract

The value of urban parks in the city of Turin: an application of the Geographically Weighted Regression

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Urban green areas offer a variety of benefits, such as air purification, biodiversity preservation, climate regulation, water management, to name a few, which can be classified as environmental services (MEA, 2005).Of particular importance are also the social and recreational benefits offered by green areas in cities, including the promotion of a positive mental attitude, psychological effects, increase in physical activities and social interaction, which generate positive psychological effects in their users (TEEB, 2012).The results of a recent review by D'Alpaos and Andreolli (2020) identify indeed green urban areas as one of the most important determinants in citizenship's perception of a highstandard urban quality and liveability of cities.
In this context, specific evaluation tools have been developed for assessing the value of urban green spaces to be accounted for in the valuation of urban transformation and regeneration projects and support policy makers in public decision-making processes.In particular, the so-called Total Economic Value (TEV) approach is widely recognized in the literature as a cornerstone in the valuation of urban green areas and, more broadly speaking, in the valuation of environmental goods and natural resources.The theoretical framework underpinning the TEV notion decomposes it into different value components, representing use and non-use values (Pearce and Turner, 1990;Pearce et al., 2006).In so far, stated preference methods and revealed preference methods developed by environmental economists are usually adopted to Urban green areas provide a wealth of benefits that range from maintenance of natural ecological processes to air and water management, to ameliorating the wellbeing of local population.Starting from the concept of Total Economic Value, the paper investigates the method of the Geographically Weighted Regression (GWR) to estimate the value of two urban parks in the city of Turin: Dora Park and Valentino Park.The GWR grounds on to the Hedonic Pricing approach and permits to investigate the spatial patterns of the key variables under investigation.The results of the model show that proximity to parks influence positively real estate prices, and that it emerges a positive Willingness To Pay for environmental goods and services such as those provided by urban green areas.Abstract examination and admits that the relationship between the independent and dependent variables varies according to location.To the authors' knowledge, this work represents the first application of a GWR model for the evaluation of urban parks in the city of Turin.By emphasizing properties located within a 1 km distance from urban parks, this study seeks to determine the inherent influence of a park mere presence on housing values.Such an approach highlights the unique contribution of each park to its immediate urban environment.In scenarios where urban planning resources are constrained, discerning whether a park markedly affects housing prices can offer valuable critical insights and contribute to informing decisions about resource allocation for parks enhancement and upkeep, or relative to the introduction of new green spaces in a city's urban texture.In this respect, the GWR served as a vital tool for unveiling nuanced, local-specific influences and providing a deeper understanding of park-related effects in different city areas.The remainder of the paper is organized as follows: section 2 describes the urban parks under investigation; section 3 presents the methodological background of GWR; section 4 illustrates the data used as model inputs; section 5 provides the model estimates and discusses the main findings; finally, section 6 summarizes the main conclusions of the works and proposes further developments for future research.

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As previously mentioned, our research focal point is the city of Turin.This choice was made essentially because Turin is one of the greenest city in Italy and its greening process has a long-standing history.Data from the 2021 green infrastructure master plan ('Piano Strategico dell'Infrastruttura Verde') of the city of Turin reveal that 37% of the area involved in the master plan (i.e., 48 km² out of 130 km²) is dedicated to green spaces.This percentage results into the remarkable quota of 55.43 m² of green space per resident.This city is endowed with 18.2 square kilometres of public green space, offering an average of almost 20 square metres of lush greenery per inhabitant.This figure exceeds the European average of 18.2 square metres per inhabitant and more than doubles the World Health Organisation's recommended minimum of 9 square metres.The expansion of urban green spaces has been particularly remarkable in the last three decades.Globally, Turin boasts 517 recreational green areas, which overall span 11,095,526 square meters and represent 34% of all public and private green spaces, thus covering 8.5% of the city area (Municipality of Turin, 2021).assess TEV components (Bottero et al., 2023).Among the different TEV valuation methods, the present paper investigates the potential of the hedonic pricing method for the valuation of urban green areas (Rosen, 1974).The Hedonic Price Model (HPM) is a valuation technique utilized to estimate the monetary value of real estate attributes based on market price transactions (Rosen, 1974;Wilhelmsson, 2000;Espey et al., 2000;Melichar and Kaprová, 2013;Wu et al., 2016;Park et al., 2017;Abbasov, 2018;Chen 2019;Xi et al., 2023).The methodology assumes that the market price of the real estate assets can be derived from their characteristics or attributes, and it can be expressed as the weighted sum of the implicit prices (also defined as "hedonic prices") of each characteristic (Lancaster, 1966;Rosen, 1974;Wilhelmsson, 2000).Real estate characteristics can be classified in two main groups: structural characteristics (such as building typology, lot size, number of rooms, etc.) and neighbourhood characteristics that include socioeconomic characteristics, accessibility to urban amenities and the level of public services (Can, 1990).The usual econometric model consists in regressing observed real estate market price transactions against real estate attributes by implementing a multilinear regression model, in which the estimated coefficients define the implicit marginal prices related to the asset attributes considered.
Traditional HPM provides implicit marginal prices of the characteristics, which do not vary across the entire market area.Consequently, it fails when the marginal prices of a significant number of relevant characteristics vary over space (spatial heterogeneity), especially in large markets (Schnare and Struyk, 1976;Bourassa et al., 2003;Bitter et al., 2007).In reference to spatial heterogeneity, it is widely acknowledged that real estate markets are influenced by elements not uniformly distributed over space.To address this issue, specific models such as the Spatial Lag Model, the Spatial Error Model or the Spatial Autoregressive Models have been proposed in the literature to account and mitigate the negative effect of potential biases in regression estimates generated by spatial dependence and spatial heterogeneity (Anselin, 1988).As far as urban parks are concerned, several applications of spatial hedonic models exist in the literature and demonstrate that proximity to green areas is a determinant of housing prices (Kim et al., 2019;Dell'Anna et al, 2022;Bottero et al., 2022).In the present paper, starting from the case of the city of Turin (North-west of Italy), we propose an application of a specific spatial hedonic model called Geographically Weighted Regression (GWR) for valuing the contribution of proximity to parks to real estate prices.GWR (Brundson et al., 1996) is a geo-statistical method that represents an extension of ordinary least squares regression (OLS) that permits to address the issue of the heterogeneity in the area under journal valori e valutazioni No. 34 -2023 The value of urban parks in the city of Turin: an application of the Geographically Weighted Regression The beginning of the greening process of the city began in 1559, when the capital of the Duchy of Savoy was transferred to Turin.Subsequently, the Savoy family expanded the boundaries of the city and began to build suburban villas surrounded by gardens full of plants, such as the Valentino Park.The process continued through the 19th century and led to the construction of tree-lined avenues that connected the various parts of the city.It is worth mentioning, though, that over the last twenty years, the city of Turin has undergone an urban regeneration process that has led, in turn, to the construction of new green infrastructures in addition to historic parks.This process testifies the growing awareness of policy makers and urban planners of the recreational and environmental value of urban green areas and their potential in improving urban quality and citizens' wellbeing.Figure 1 displays the mapping of public green areas in the city of Turin, including recreational areas, cultivated areas, and forested areas.As the aim of this paper is to investigate whether and to which extent the presence of a park may increase the marginal price of some dwelling attributes by implementing a GWR model.The dwellings under investigation are located at a 1-km maximum Euclidean distance from a park, as we assumed that the market price of properties located farther are no longer affected by the proximity to the park.We consider a property sited one kilometre from a park be at an easy walking distance to the park, which, consequently, permits residents to enjoy readily and get the benefits provided by the park (Stessens et al., 2017).
journal valori e valutazioni No. 34 -2023 We focus our analysis on housing sited around two different parks in Turin.In detail, we consider Valentino Park (Parco del Valentino) and Dora Park (Parco Dora).These parks are in two urban contexts, which differ in social and morphological characteristics.Valentino Park is the best-known park in Turin and is located in the southeast of the city (Figure 2).This Park is in a strategic and favourable position due to two main reasons: first it is near the city centre, one kilometre away from 'Torino Porta Nuova', the principal railway station in the city and, second, its eastern border extends along the left bank of the Po River overlooking the hill area, one of wealthiest neighbourhoods in town.The origins of Valentino Park date back to a very remote period: the toponym 'Valentino' had been in use for the area since the Middle Ages, and, from the seventieth century, it has indicated the Valentino Castle ('Castello del Valentino') and the surrounding area.The public opening of the park took place in the second half of the 19th century, when a new urban phase of the city started.This phase distinguished for a strong growth of the city population, a significant expansion of the local construction industry, and an emerging need for green areas for recreational purposes.Barillet-Deschamps was indeed inspired by the principles of the landscape park when designing the park's system of avenues, groves, artificial valleys, and the riding track.Even before its completion, the park became the setting for major national and international exhibitions held from 1829 to 1961.In addition to Valentino Castle, UNESCO World Heritage site and headquarters of the School of Architecture of the Politecnico of Turin, there are many points of cultural interest within the park: the Medieval Village, the 'Giardino Roccioso', the Botanical Garden of the University of Turin, the Fontana dei Mesi, the Fontana Luminosa, the 'Palazzina della Promotrice delle Belle Arti', 'Torino Esposizioni', and various rowing companies.The park provides nice walking paths and opportunities for sport and recreation, and it can be reached via public and private transport, thanks to the numerous parking lots sited in the park.Valentino Park is also accessible to bicycles and offers to riders the choice among several cycle paths that extend for its entire area.From an ecosystem perspective, Valentino Park encompasses a rich tree heritage and an attractive bird life.At the west border, it is surrounded by the historic district of San Salvario.This neighbourhood, intended as a residential area for the Turin bourgeoisie, was developed around the mid-19th century.Nonetheless, in the 1900s, the neighbourhood appearance began to change because of the industry expansion and the rise of FIAT, which attracted an evergrowing population and generated a sharp increase in housing demand.Around the 60s and 70s, San Salvario district underwent a rapid expansion following the economic boom that led to an immigration wave journal valori e valutazioni No. 34 -2023 The value of urban parks in the city of Turin: an application of the Geographically Weighted Regression especially from Southern Italy.In contrast to demand of existing prestigious housing, demand of new, less costly buildings grew significantly.The neighbourhood was strongly affected by these migratory phenomena, which resulted in a heterogeneous building stock in terms of construction period.The most widespread building typology is that of a multi-story building, as in the rest of the city.San Salvario is well-served by public transport and the only metro line in town runs west of the area.Contrary to Valentino Park, Dora Park is a postindustrial park built at the beginning of the 2000s as per the urban regeneration process that involved the city in recent years (Figure 3).Dora Park extends for about 430,000 m 2 , straddling the two banks of the Dora River.Until the 90s, the area hosted the production plants of FIAT and Michelin, which  Spatial heterogeneity of marginal prices is due to several factors.The real estate market is a segmented and characterized by a set of sub-markets defined based on physical dwelling characteristics and the geographical area where the dwelling sites (Bourassa et al., 2003).Indeed the real estate market intrinsic segmentation deals spatial heterogeneity of prices.The market demand for some real estate characteristics may be highly inelastic due to neighbourhood factors, building typology, and scarcity.This inelasticity leads, in turn, to increasing marginal prices (Schnare and Struyk, 1976;Bitter et al., 2007).The most important characteristic that is a key for real estate market price estimation is the building's location (Can, 1990;Chinloy, 1991;Bourassa et al., 2003;Bitter et al., 2007;Rymarak and Sieminska, 2012;Theurillat, 2015;Saenko et al., 2018).Location can make real estate assets unique (Bitter et al., 2007), due some particular structural characteristics of the building and neighbourhood factors: this fact deals spatial heterogeneity of market prices.As the real estate market is characterized by spatial autocorrelation of prices, house prices are estimated on the basis of sale prices of immediate properties.In this context it has been recognized that the OLS, which is the estimator of the hedonic price regression, does not allow for autocorrelation (Payton et al., 2008).Based on the above considerations, buildings' location emerges as the most relevant factor in the valuation of an asset market price.Consequently, it should be considered as an independent variable into hedonic models.
Although it is possible to use dummy variables to identify a specific sub-market and/or to perform regression analyses into specific sub-market areas, housing sub-markets are often problematic to define and investigate (Bourassa et al., 2003;Bitter et al., 2007).The Geographically Weighted Regression (GWR) is a regression technique that accounts for local variation of the implicit marginal prices related to data observation points (Brunsdon et al., 1996).Thanks to GWR it is thus possible to estimate an implicit marginal price for each specific location of data observation points, differently from traditional HPMs, which permit to estimate a unique average implicit marginal price for all data observation points (Dell'Anna et al. 2020).By implementing this methodology, analysts can capture spatial heterogeneity effects of implicit marginal prices.Many contributions in the literature adopted GWR to analyse real estate markets and compared it with other methodologies to test its robustness.Pàez (2005) performed a simulation exercise to compare GWR and the Expansion Method (Casetti, 1972) and found that GWR permits to reproduce map patterns in a satisfactory way and, in some conditions, provided better results than the Expansion Method.The author concluded that GWR, at least in average term, is not a model that reproduces an artificial spatial variability.Farber and Yates (2006)  In their regression, they considered nine attributes i.e. five housing/structural characteristics, two neighbourhood characteristics, and two accessibility characteristics.Their objective was to compare the four different regression approaches in terms of goodnessof-fit (by evaluating the R 2 coefficient of determination) and residual spatial autocorrelation.The authors found that GWR exhibited the highest R 2 value (equal to 91.9%), whereas HPM exhibited the lowest R 2 value (equal to 66.7%).The spatial autocorrelation analysis revealed that GWR was the model that best accounted for the spatial variation of hedonic prices as well.In addition, the findings by Farber and Yates (2006) revealed the importance of spatial heterogeneity in several housing attributes.Bitter et al. (2007) analysed the real estate market in Tucson city in Arizona (USA) by using GWR and Spatial Expansion method (SExM).They found a complex spatial pattern of marginal prices related to housing attributes.GWR guaranteed a better representation of the spatial pattern of marginal prices compared to SExM.The two models provided strong evidence of marginal price variations across Tucson real estate market (Bitter et al., 2007).Tang et al. (2011) utilized GWR to study the spatial pattern and structural determinants of Shanghai's residential housing prices.In particular, they investigated the effects of greening rate and age of the buildings on housing prices.The GWR performed by Tang et al. (2011) revealed a specific spatial structure (in terms of goodness of fit) underpinning market prices, and also found that GWR provided better estimates than HPM.Manganelli et al. (2014) adopted GWR to identify homogeneous real estate market areas of residential buildings in the city of Potenza (Italy).The authors provided useful implications in terms of taxation policy, planning decisions, and territorial transformations.Bujanda and Fullerton (2017) applied GWR to estimate the market price premium generated journal valori e valutazioni No. 34 -2023 The value of urban parks in the city of Turin: an application of the Geographically Weighted Regression by proximity and accessibility to transportation infrastructures in the real estate market in El Paso city (Texas, USA).A spatial heterogeneity of implicit prices emerged and in some cases its impact resulted negative (Bujanda and Fullerton, 2017).Mittal and Byahut (2017) implemented GWR to assess the market price premium of visual accessibility of scenic landscapes in detached houses in Worcester (Massachussets, USA).Massimo et al. (2019) used GWR model to analyse the real estate market in Reggio Calabria (Calabria Region, Italy).The authors estimated the market price premium for green buildings both in terms of selling price and rental price.As to the specific focus of the present paper, it is worth mentioning that a variety of applications of GWR in the literature show a positive correlation between housing prices and green areas both in urban and in peri-urban contexts (Wu et al. 2022;Wang and Chen, 2020;Murkin et al., 2023;Wang et al. 2022;Sylla et al., 2019;Bottero et al., 2019).From the methodological point of view, the formulation of GWR was firstly developed by Brunsdon et al. (1996): where   , is the constant of the model at regression point ,   (  !"  ) is the regression coefficient of variable K at regression point ,   is the vector of the K continuous independent variables at regression point ,  is the error term of the model at regression point  and P  is the dependent variable related to the regression point .The GWR regression coefficients are estimated as follows: !"%$! where  (  !"   ) is the spatial weights matrix,  is the transposed () matrix of observed attributes and  is the response variable vector.The spatial weights matrix  (  !"  ) takes into account spatial relationships among the observation points in the dataset.In order to define the structure of the spatial weights matrix, we adopted a Gaussian function as the kernel function: where   is the Euclidian distance between the -th regression point and the -th observation point and  is the bandwidth parameter.To estimate the latter, the Cross Validation (CV) score algorithm was adopted (Cleveland, 1979;Bowman, 1984;Brunsdon et al., 1996).

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The database used in this study comprises asking prices for apartments located in multi-family buildings in the city of Turin.In the Italian context, the acquisition of housing transaction data (i.e., the final agreed-upon price of sold properties) is particularly challenging due to limited accessibility to real estate transactions.As a result, our study relies on asking prices as a viable alternative to gauge market trends and property valuations.The full dataset has been regularly built over the years thanks to the collaboration with an online real estate agency (www.immobiliare.it).This collaboration has made it possible to build a dataset that amounts to about 15,000 real estate advertisements published between 2014 and 2018.As previously stated, we considered only the buildings located at the maximum distance of one kilometre from the parks in question, for which we identified two subsamples: the former consisting of 719 observations sited near Dora Park, and the latter of 721 observations sited near Valentino Park (Figure 4).The dataset provides information on eight independent variables that describe the characteristics of the dwelling (SURF, FLOOR, ELV, BOX, EL, ST, SG, YEAR), two location variables (LAT, LON), which identify the geographical position of the dwelling, and the dependent variable, i.e. the dwelling's market price (PRC).Table 1 report a detailed description of the dependent and independent variables, respectively.In Appendix A, the descriptive statistics of the dataset observations near Dora Park (Table A .1) and Valentino Park (Table A.2) are presented.

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The results of GWR conducted on the observations relative to Dora Park and Valentino Park are detailed in Tables 2 and 3, respectively ! .Notably, the GWR-based R 2 of the Dora Park model is equal to 0.77 (adjusted R 2 equal to 0.76), while the GWR-based R 2 of the Valentino Park model is R 2 =0.88 (adjusted R 2 equal to 0.86).Although both models incorporate the same variables, the correlation with housing prices differ between the two parks, because of the distinctive urban characteristics of their respective surrounding areas.Specifically, the area around Valentino Park features journal valori e valutazioni No. 34 -2023 77 1 To obtain a complete overview of the problem under investigation, the traditional model of the Ordinary Least Squared has been implemented for both the sub-samples.The results are reported in the Appendix (Table A.3 and Table A.4).The value of urban parks in the city of Turin: an application of the Geographically Weighted Regression homes that are bigger in size, age, and better in quality standards compared to those near Dora Park.This uniformity potentially enhances the accuracy of the model's predictions.Such varied urban intricacies account for the disparity in the R 2 values of the two models.

journal valori e valutazioni
As stated before, GWR permits to investigating the spatial variability of the local coefficients of predictor variables (elasticities).In relation to Dora Park, the differential impact of ST on assets' prices is more evident in the southern area of the park (Figure 6).In Campidoglio district this relationship is stronger (i.e., larger non-negative coefficients) than in other areas.This close relationship is due to the lack of recently built properties in this area, in which existing buildings were built starting in the mid-nineteenth century up to the 70s.The remaining zones surrounding the park, on the other hand, were subject, totally or partially, to new real estate developments related to the regeneration process, which Dora Park has underwent.As to Valentino Park, a higher elasticity occurred in the surrounding north-western area, mainly characterized by historic buildings, and a limited number of newly constructed buildings.As to variable SG3 (luxurious dwelling), from the analysis it emerged that the average elasticity between the two areas under investigation is quite similar (148527.27EUR for Dora Park vs. 153801.15EUR for Valentino Park).Nonetheless, based on the mapping of local coefficients, interesting conclusions can be drawn.As already mentioned in Section 2, the Dora Park area underwent an extensive urban regeneration process, which resulted in a renewal of the building stock and an increase in property values.This phenomenon is particularly evident in the north-eastern area of the Park.GWR implementation made it possible to identify new residential developments around the park, such as the 'Le Isole del Parco' project by 'Isola Associati Architects', built in conjunction with the creation of Dora Park.The project involved the construction of eight residential blocks, set on a 6-meter plate above street level.Architectural design choices and construction materials give the blocks a peculiar urban identity.As already mentioned in Section 2, the urban context, in which the Valentino Park is located, is very different.The area host historical buildings, with fine finishes, and is crossed by two important road axes: 'Corso Massimo d'Azeglio' and 'Corso Guglielmo Marconi'.As illustrated in Figure 7 (right panel), GWR figures of variable SG3 ouline the standout character and uniqueness of the buildings sited along these roadways.'Corso Massimo d'Azeglio' has always been one of the most elegant streets in Turin, due to its proximity to the banks of the Po River, Valentino Park, and the surrounding hills.Although, because of speculation, 'Corso Massimo d'Azeglio' has lost most of its elegant and stately nineteenth-century buildings, thanks to its privileged position, 'Corso Massimo d'Azeglio' has always been chosen for reside by Turin's upper class.By contrast 'Corso Guglielmo Marconi' is among the oldest and most fascinating avenues of 'San Salvario' district.It offers a beautiful view on the Valentino Castle, as well as it hosts prestigious, highquality historic buildings.

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In this paper, GWR is implemented to investigate the value of urban parks and green urban.In detail, we considered two of the parks in the city of Turin, namely Valentino Park and Dora Park.These parks are located in different urban areas and characterized by different features in terms of quality of the buildings and presence of infrastructures.The results of the estimation model show that there is a proximity effect between urban parks and house prices.

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The value of urban parks in the city of Turin: an application of the Geographically Weighted Regression This effect is more evident for Dora Park, which is located in a peripheral area of the city that has underwent a deep urban regeneration process in recent years.This positive effect reflects in the increasing growth trends of real estate prices in the Dora Park area, thus confirming the findings of the present research.In addition to their well-known aesthetic and recreational functions, green areas help to mitigate pollution to different environmental matrices (air, water, soil), improve the microclimate of cities, and preserve biodiversity.However, to date, due to the public-good nature of green areas, these functions and related benefits are scarcely integrated into open space management policies and, more generally, into local urban planning.
In the past, urban green areas were considered in planning processes through specific indicators that aim to measure the available green areas and their different types.
This information is useful for verifying the endowment of green spaces in cities and ensuring compliance with urban planning standards required by national regulations, but it is little informative about the actual significance of this 'natural capital' for urban sustainability, the benefits generated in terms of ecological balance, the socio-cultural development as well as the economic vitality and prosperity of cities.
Finally, it would be of scientific interest to develop additional estimations on panel data panel to validate the robustness of the results and verify for potential biases (Wooldridge, 2010).
starting from the 90s moved to suburban areas.Consequently, industry shut down and the area was re-converted.The Dora Park project is part of a complex transformation plan of the city, namely the Urban Redevelopment Program, which involved the Spina 3 area.Government funds and the formal commitment of the City Council to the construction of strategic public infrastructures favoured the reconversion of the above zone.In 2003, the Municipality awarded via an international call the design of the park to a multidisciplinary group specialized in the regeneration of post-industrial districts jointly with the landscape architect Peter Latz.In 2007, the park construction was listed among the celebratory works for the 150th anniversary of the Unification of Italy.Thus, co-financing investments for almost 70 million euros, the Italian Government and the Municipality of Turin made it possible to undertake the project construction in several stages.The park designers shed light on the industrial past vocation of the area.Natural elements, such as trees, bushes, leisure areas and side-by-side events, are fit into a systematic whole between pillars, concrete blast furnace towers and metal stair.The municipality built a large covered area (300 metres long and 45 metres wide) surrounded by 30-metre high steel pillars in the Vitali area.Several artists participated in the regeneration of the neighbourhood.Dora Park extends into three districts: north to Madonna di Campagna, east to Borgo Vittoria, and south to San Donato.The building stock around the park is diversified, and it mainly consists of buildings built primarily between the 50s and 80s.In accordance with the master plan of the city, along the stretch of Spina 3, large new buildings were designed by internationally known architecture firms in the immediate proximity of the new park.Adjacent to Dora Park lies a significant hub of innovation and research known as the Environment Park, a scientific park serving as a focal point for environmental technology advancements and sustainable initiatives in the region.Furthermore, the Santo Volto church, an architectural masterpiece by the celebrated Swiss architect Mario Botta, is sited close to the park.The San Volto church, with its distinctive design, seamlessly blends modern architectural aesthetics with spiritual sanctity, making it a prominent landmark in the area.The ensemble of Environment Park, San Volto church, and Dora Park enrich the tapestry of cultural, technological, and architectural facets of Turin.
of Dora Park (on the left) and Valentino Park (on the right), and of the related assets investigated in the analysis.indicates the presence (i.e., 1) or not (i.e., 0) of the elevator BOX Dummy variable that indicates the presence (i.e., 1) or not (i.e., 0) of the garage EL Set of dummy variables expressing the energy performance label of the dwelling (ELA, ELB, ELC, ELD, ELE, ELF, ELG) that corresponds to the building's energy performance labels A, B, C, D, E, F, and G, respectively.ST Set of dummy variables that indicate the state of maintenance of the dwelling, grouped into 4 levels: ST0 = to be restored, ST1 = good condition, ST2 = renovated, ST3 = new dwelling SG Set of dummy variables that indicate the market segment the dwelling belongs to, grouped into 4 categories: SG0 = affordable dwelling, SG1 = middle-class dwelling, SG2 = high-class dwelling, SG3 = luxurious dwelling YEAR Set of dummy variables that indicate the year in which the dwelling's market transaction took place, Y1=2014, Y2=2015, Y3=2016, Y4=2017, Y5=2018 PRC Dwelling's asking price in euros [EUR] For example, the average of the elasticities of SURF is 2222.15EUR in the Dora-Park subsample, and is 2865.39EUR in the Valentino-Park sub-sample.Consequently, ceteris paribus, an increase by one square meter in the dwelling's ground floor determines a higher price increase in the assets close to Valentino Park compared to Dora Park, and exhibits a a very large variability in the marginal price (i.e., standard deviation equal to 261.41 EUR).This result is in line with expectations, given the average prices of residential assets in the areas.Looking north of Valentino Park (i.e, to the central neighbourhood in Turin), it emerges a peak of the local price elasticities.The north-western area, as depicted in Figure 5 (left panel), stands out.In this area, architectural landmarks from the early 19th century dominate.Conversely, the western sector displays high marginal SURF prices, thus reflecting its diverse housing landmark, which spans from late 19 thcentury constructions to the 20 th -century additions driven by the economic upswing of the city.South of Valentino Park, smaller marginal SURF prices emerge.Yet, close to Dora Park, SURF marginal prices vary journal valori e valutazioni No. 34 -2023 79 234567#?#8#Local GWR estimates for variable SURF (the left-hand side of the graph maps properties located near Valentino Park, while the right-hand side maps properties located near Dora Park).234567#J#8#Local GWR estimates for variable ST3 (the left-hand side of the graph maps properties sited next to Valentino Park, while the right-hand side maps properties sited next to Dora Park).slightly (i.e. standard deviation equal to 23.87 EUR).Consequently, the model's results indicates a relatively stable coefficient distribution around Dora Park, as visualized in Figure 5 (right panel).The mean coefficients related to ST3 are similar in both the sub-samples: 42,041 EUR for dwellings close to Dora Park, and 40,041.18EUR for those close to Valentino Park.
journal valori e valutazioni No. 34 -2023 80 234567#K#8#Local GWR estimates for variable SG3 (the left-hand side of the graph maps properties located next to Valentino Park, while the right-hand side maps properties located next to Dora Park).