# The Impact of Urban Transit Systems on Property Values: A Model and Some Evidences from the City of Naples

Authors: Mariano Gallo
Journal: Journal of Advanced Transportation
 (2018)

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## Abstract

A hedonic model for estimating the effects of transit systems on real estate values is specified and calibrated for the city of Naples. The model is used to estimate the external benefits concerning property values which may be attributed to the Naples metro at the present time and in two future scenarios. The results show that only high-frequency metro lines have appreciable effects on real estate values, while low-frequency metro lines and bus lines produce no significant impacts. Our results show that the impacts on real estate values of the metro system in Naples are significant, with corresponding external benefits estimated at about 7.2 billion euros or about 8.5% of the total value of real estate assets.


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## Body

## 1. Introduction

Urban transit systems play a fundamental role for the social and economic development of large urban areas, as well as significantly affecting the quality of life in such areas. Mobility and accessibility are two important factors that influence everyday life, social inclusion, and the competitiveness of firms and commercial operators. The quality of urban transit systems also affects real estate values: the higher the quality and quantity of transit system services in an urban area, the higher the active and passive accessibility of the area, and the higher the average real estate values.When a public authority invests in transportation infrastructures and/or in transit services, it generates some external (or social) costs and benefits; a cost[benefit] is considered “external” if it is produced by subject A and borne [enjoyed] by subject B, without any compensation between the subjects [1–3]. Usually, the external costs of transportation are associated with air pollution, greenhouse gas emissions, noise, accidents, and congestion. While there is extensive literature on such external costs [4–8] which are nowadays taken into account in some transportation planning processes, less attention is devoted to external benefits that are hardly ever explicitly considered in economic and social analyses of transport investments. Besides the high direct benefits due to improved accessibility, reduced generalised trip costs, and the lower environmental impacts produced by less use of private cars, investments in transit systems, especially in railways and metros, may generate an appreciable increase in property values in the zones served; this benefit should be explicitly considered inside cost-benefit analyses. Note that even if the increases in property values mainly regard most of the users that live near rail stations, they have to be considered external benefits since they are enjoyed by all property owners, regardless of whether they use the transit system or live near the stations. In terms of social equity, especially if there are major differences between the transit supply in different urban areas, an evident disequilibrium is created; indeed, some inhabitants have available a high level-of-service transit system and, moreover, if property owners, also a benefit on the value of their properties. On the other hand, people living in areas not served by good quality transit systems suffer a double disadvantage: on transportation systems and property values. Since transit systems are subsidised strongly with public money and, then, by the whole society, a more equitable transit supply should be an important objective of transport policy. This imbalance can in part be compensated by introducing local taxes on property [9]. Another aspect of the problem is related to the optimal taxation in the presence of (positive or negative) externalities; this topic is outside the aims of this paper, but the reader may refer to Cremer et al. [10], Auerbach and Hines [11], and Christiansen and Smith [12].In order to consider explicitly in transportation planning the impacts of transit system investments on real estate values as external benefits, we need to estimate the hedonic price of the presence of transit systems. According to hedonic theory [13], it is assumed that the price of a complex good, such as a house, can be expressed as a function of its extrinsic and intrinsic attributes: the coefficient of each attribute represents its implicit (hedonic) price. Calibration of hedonic pricing models, taking the availability of transit systems into explicit consideration, allows us to estimate the hedonic price of the presence of such systems in an area or in proximity to an area.In the literature, much evidence can be found of the impact of transit system availability on real estate values. Obviously, most papers refer to specific case studies since it is very difficult to generalise models and analyses. Almost all research findings have underlined a positive correlation between the availability of transit systems (nearby) and property values, especially with respect to high-quality systems, whether railways [14–26] or bus rapid transit lines [19, 27–30]. Fewer have also studied the negative effects of transit system proximity [31, 32], due to externalities (mainly noise). Brandt and Maenning [33] and Szczepańska et al. [34], instead, studied the influence of road noise on real estate values. Interesting meta-analyses were proposed by Mohammad et al. [35], examining 102 estimates from 23 studies for the impact of rail projects on land and property values, and by Debrezion et al. [25].From the modelling point of view, almost all papers propose hedonic price models (HPMs) for estimating property values and their correlation with transit system availability. Regarding the functional forms of the models in question, several types have been proposed: multiple linear regression (MLR) models [13, 14, 30, 31, 36, 37], also with a semilogarithmic form [16–18, 22, 23, 33, 38]; a multilevel model was proposed by Cervero and Kang [27], a quantile regression model was by Bohman and Nilsson [15], a Box-Cox linear transformation regression was by Chen and Haynes [16], a cross-sectional model was by Sun et al. [22], spatial econometric models were by Ibeas et al. [36], Efthymiou and Antoniou [32], Chen and Haynes [16], and Mulley et al. [19], a DID (Difference-in-Differences) estimator was by Dubé et al. [26], random utility models were by Jun [29], and a spatial Durbin model was by Zhong and Li [24].This paper proposes a (nonspatial) hedonic model for evaluating the impacts of urban transit systems on property values. In the literature, numerous spatial hedonic models can be found [39–43] that may be able to improve the results, but their application requires very small zones or, at the limit, single buildings; as will be described in the following, the available zoning is not suitable for the application of this approach. Other approaches proposed in the literature require more detailed data that are not available for the case study in the object.The main objectives of this paper are (i) to estimate the impacts of transit systems on real estate values in the city areas; (ii) to evaluate the corresponding external benefits; (iii) to evaluate the effects of some interventions on the Naples metro system on real estate values and on the corresponding external benefits for the areas concerned.The remainder of the paper is organised as follows: in Section2 the case study and the data used are described; in Section 3 the data are analysed and the hedonic model is formulated and calibrated; the impacts on real estate values and on the corresponding external benefits are estimated in Section 4; some analyses of two future scenarios are reported in Section 5; Section 6 concludes the paper.


## 2. Case Study and Data

The main objective of this paper is to estimate the impact of transit systems on real estate values in the city of Naples (Italy) and to evaluate the corresponding external benefits. The proposed approach can be applied, with the necessary modifications, to other large towns and cities. Naples is the largest city in southern Italy with about one million inhabitants (962,003 at the last census). It is the capital of the region of Campania and is close to some of the world’s best known tourist destinations. Naples and its metro system were recently studied by Pagliara and Papa [20] who evaluated the impact of metro investments on property values and residents’ location, analysing the changes produced by the opening of new stations with a pre- and postanalysis in the zones affected. In this paper, instead, we propose a model applicable to the whole city for estimating the impact of transit systems on property values and we explicitly estimate the external benefits produced by the presence of a metro system.For the aims of this paper, three kinds of data were required:(i)
census data on population, density, quality of buildings, number and area (in m2) of buildings, retail businesses;(ii)
real estate values;(iii)
transit system data.All census data were obtained from the Italian National Institute of Statistics (ISTAT) and refer to the last national survey [44]. The available data are aggregated into 4,307 census subzones (see Figure 1). The data that we used in our model were resident population, density, number of residential buildings classified by state of conservation (very good, good, medium, and poor), total area of residential buildings, and retail employees (as a proxy of retail businesses in the city zones).Figure 1
Census zones [44].Real estate values were obtained from the Real Estate Market Observatory (OMI) [45], which is a database provided by the Italian Revenue Agency about real estate values in all Italian cities, subdivided by homogeneous zones. In this database, Naples is partitioned into 65 zones, as reported in Figure 2; the dimensions of the zones are not compatible with the use of spatial hedonic models. For each OMI zone, the database provides some data; in our study we refer to the following data: (a) minimum and maximum real estate values for residential buildings in a normal state of conservation; (b) kind of zone (central, semicentral, peripheral, suburban, and rural). The median real estate values for OMI zones are summarised in Figure 3; the classes of zones are reported in Figure 4. From the database of OMI zones, we eliminated five zones (R1, R2, D28, D31, and E41) for which no real estate values were associated, since there were too few real estate sale contracts to be statistically significant or they were not present. Therefore we refer to a database of 60 OMI zones.Figure 2
OMI zones [45].Figure 3
Median real estate values.Figure 4
Classification of OMI zones.Data on the transit supply system were collected directly from the operators and involved the following services:(i)
4 funiculars (Centrale, Chiaia, Montesanto, and Mergellina);(ii)
7 metro lines (Line 1, Line 2, Cumana, Circumflegrea, and 3 Circumvesuviana lines);(iii)
76 bus lines.In Table1, the list of railway lines with the corresponding average daily frequencies is reported. In Table 2, the average daily frequencies of bus lines are summarised. It can be noted that average bus line frequencies are very low for urban services, except for a few lines; it is due to a strong financial crisis suffered in the last decade by the municipality that owns the urban bus firm and by the region that finances the largest part of transit services. The metro and bus lines are illustrated in Figures 5 and 6, respectively. Figure 5 also shows the metro lines currently under construction (black lines).Table 1
Railway lines.
Line From–to Service time and headways Average daily frequency [train/h] Line 1 Garibaldi–Piscinola 6:20–6:40 (20′) 7.00 6:40–21:44 (8′) 21:44–23:02 (14′)  Line 2 Pozzuoli–Gianturco 5:20–23:20 (8′) 7.50  Cumana railway Montesanto–Torregaveta 5:21–22:30 (20′) 3.00  Circumflegrea railway Montesanto–Torregaveta 5:12–21:43 (20′) 3.00  Circumvesuviana railway (suburban) Napoli–Sorrento 6:09–21:39 (28′) 2.14 Napoli–Baiano 6:04–20:18 (26′) 2.31 Napoli–Sarno 6:32–20:02 (39′) 1.54 Napoli–Poggiomarino 5:55–19:55 (36′) 1.67 Napoli–San Giorgio 6:11–18:11 (36′) 1.67  “Central” funicular Augusteo–Piazza Fuga 6:30–00:30 (10′) 6.00  “Chiaia” funicular Parco Margherita–Cimarosa 6:30–00:30 (10′) 6.00  “Montesanto” funicular Montesanto–Morghen 7:00–22:00 (10′) 6.00  “Mergellina” funicular Mergellina–Manzoni 7:00–22:00 (10′) 6.00Table 2
Bus lines.
Line code Average daily frequency [bus/h] 1 5.00 2 3.33 4 4.00 12 2.07 20 2.07 128 2.22 130 3.00 132 2.50 139 2.72 140 3.53 147 3.33 150 2.73 151 5.00 154 2.86 168 3.75 178 2.14 180 1.67 181 3.33 182 2.61 183 1.94 184 3.00 185 2.61 191 2.73 192 3.00 193 1.18 194 2.61 195 2.50 201 3.75 202 3.16 203 2.61 503 1.88 640 2.40 Alibus 2.86 C1 1.94 C12 2.73 C13 2.40 C14 0.92 C16 3.53 C18 1.54 C18E 1.88 C2 1.76 C21 2.86 C24 1.20 C25 2.40 C27 2.07 C31 2.14 C33 2.50 C33E 2.50 C36 1.20 C38 1.62 C40 2.22 C41 2.07 C44 2.07 C5 0.97 C51 1.54 C52 1.54 C59 0.94 C6 2.31 C63 2.50 C65 2.22 C68 2.31 C76 2.31 C91 2.00 C94 1.30 C96 1.36 C98 1.13 C99 1.71 E1 2.14 E2 2.31 E6 1.62 R2 6.67 R4 5.00 R5 2.07 R6 4.00 R7 2.40 V1 1.25Figure 5
Metro network.Figure 6
Bus services.This case study is very appropriate for the proposed analysis since the Naples transit system covers the various areas of the city with very different levels of service: (a) some zones are served by medium/high-frequency metro lines or funiculars, besides some bus services; (b) other zones are served by low-frequency metro lines, besides some bus services; (c) some zones are served only by low-frequency bus lines; (d) some zones are only partially served by low-frequency bus lines; (e) outlying zones are only marginally served by the transit system. These differences may significantly affect the real estate values of the various zones; indeed, in cities where the transit service is more equally distributed among zones the impact on property values may be very low or very difficult to quantify.Once all transportation system data have been gathered, we can construct a database that associates the corresponding data to each OMI zone, which we adopt as zoning since they correspond to real estate values. As for census data, all the data regarding the particular census subzones are associated with each OMI zone. Transportation data are associated with the OMI zones in the following ways:(1) a metro station is associated with a zone if it is contained in the zone or if the zone boundary is no farther than 250 m from the station; (2) a bus line is associated with a zone if it crosses the zone or runs along a road forming the zone boundary. In Table 3, all data associated with each OMI zone that are used to draw up the hedonic model are described, some of which are obtained with simple elaboration from starting data.Table 3
Data.
Name Type Unit of measurement Description OMI Alphanumeric - The code of the zone as provided by the OMI database Med_REV Integer € The median real estate value as provided by the OMI database Pop Integer Inhabitants Population in the OMI zone Area Real number Km2 Surface area of the OMI zone Density Real number Inhabitants/km2 Population density in the OMI zone BD_1 Integer Number of buildings Number of buildings with a “very good” conservation state in the OMI zone BD_2 Integer Number of buildings Number of buildings with a “good” conservation state in the OMI zone BD_3 Integer Number of buildings Number of buildings with a “medium” conservation state in the OMI zone BD_4 Integer Number of buildings Number of buildings with a “poor” conservation state in the OMI zone % BD_1 Percentage % Percentage of buildings with a “very good” conservation state in the OMI zone % BD_2 Percentage % Percentage of buildings with a “good” conservation state in the OMI zone % BD_3 Percentage % Percentage of buildings with a “medium” conservation state in the OMI zone % BD_4 Percentage % Percentage of buildings with a “poor” conservation state in the OMI zone % BD_1_2 Percentage % Percentage of buildings with a “very good” or “good” conservation state in the OMI zone Retail_emp Integer Employees Employees in retail businesses in the OMI zone Retail_emp_d Real number Employees/km2 Density of employees in retail businesses in the OMI zone HF_rail_st Integer Stations Number of high-frequency (≥6.00 trains/h) railway stations assigned to the OMI zone LF_rail_st Integer Stations Number of low-frequency (<6.00 trains/h) railway stations assigned to the OMI zone CF_bus Real number bus/h Cumulated frequency of all bus lines assigned to the OMI zone Central Boolean 0/1 1 if the zone is classified as “central” in the OMI database Semicentral Boolean 0/1 1 if the zone is classified as “semicentral” in the OMI database Peripheral Boolean 0/1 1 if the zone is classified as “peripheral” in the OMI database Suburban Boolean 0/1 1 if the zone is classified as “suburban” in the OMI database Rural Boolean 0/1 1 if the zone is classified as “rural” in the OMI database Hill Boolean 0/1 1 if the zone belongs to one of the hill districts of the city (Vomero, Posillipo, Camaldoli, Colli Aminei) Sea Boolean 0/1 1 if the zone is seafront Prestige Boolean 0/1 1 if the zone is commonly assumed to be prestigious (Posillipo, Chiaia)Moreover, the zones are also classified in function of three variables referring to some important features of the area:Sea, if the zone is seafront;Hill, if the zone belongs to one of the hill districts of the city (Vomero, Posillipo, Camaldoli, and Colli Aminei);Prestige, if the zone is commonly assumed to be prestigious (Posillipo, Chiaia). Figures 7–9 report the classification of OMI zones regarding these variables.Figure 7
Classification of OMI zones by the variable Sea.Figure 8
Classification of OMI zones by the variable Hill.Figure 9
Classification of OMI zones by the variable Prestige.


## 3. Data Analysis and Model Calibration

Before specifying and calibrating the model, we examined the dataset in order to explore the correlation between the real estate values and the variables associated with the OMI zones. First of all, we calculated the correlation coefficient,rjx,y, between the real estate values, yi, and each variable, xi,j, where i indicates the OMI zones and j the independent variable. The correlation coefficient rjx,y can be positive, between 0 and 1, or negative, between -1 and 0, and the more its value is proximal to 1 [-1], the higher the positive [negative] correlation is between the variables. In Table 4, the values of the correlation coefficient for each independent variable are reported. The variablesPop,Surface,BD_1,BD_2,BD_3,BD_4, andRetail_emp were not considered since they were used only for calculating other variables; the variablerural was not considered since in these areas there are no real estate values available.Table 4
Correlation coefficients.
Variablev r j x , y Density 0.0075 % BD_1 0.2137 % BD_2 0.2126 % BD_3 - 0.2649 % BD_4 - 0.1468 % BD_1_2 0.2758 Retail_emp_d 0.2956 HF_rail_st 0.7162 LF_rail_st - 0.1570 CF_bus 0.3661 Central 0.6633 Semicentral 0.2407 Peripheral - 0.0911 Suburban - 0.6585 Hill 0.5055 Sea 0.3742 Prestige 0.6136Upon analysing the results, it can be noted that density is not at all correlated with real estate values (the value is very close to 0); probably, it is due to the almost equal (high) density in all zones. As regards the other variables, only certain variables presented a significant correlation(rjx,y>0.5[<−0.5]); these variables areHF_rail_st,Central,Suburban,Hill, andPrestige. It is worth noting that low-frequency railway stations are not correlated to real estate values: the value is very low, in absolute value, and even negative. We may assume that all variables with an absolute value of the correlation coefficient lower than 0.25 are not correlated with property values and, then, are not useful as explanatory variables. This first analysis highlights that theHF_rail_st variable is highly correlated with real estate values (it presents the maximum absolute value of the correlation coefficient). The correlation between possible explanatory variables is examined, reporting the correlation coefficient matrix in Table 5. Examining the matrix, a strong (negative) correlation is, obviously, between variables%BD_3 and%BD_1_2, since one is nearly complementary to the other; in the specification of the model, both variables will not be jointly considered. The other variables highly correlated areHF_rail_st versusCentral(0.69), since in central areas there are more metro stations, andSuburbanversusCF_bus(-0.55) since buses cover almost equally the other areas except for the suburban ones, where the transit service is very marginal. However, about these other correlations, the calibration of the model and, in particular, the t-stat tests will be able to verify if the variables are significant even if considered jointly in the model.Table 5
Correlation coefficient matrix.
% BD_3 % BD_1_2 Retail_emp_d HF_rail_st CF_bus Central Semicentral Peripheral Suburban Hill Sea Prestige % BD_3 1.00 - 0.96 0.20 - 0.13 0.02 - 0.05 - 0.20 0.12 0.10 - 0.17 - 0.08 - 0.24 % BD_1_2 - 0.96 1.00 - 0.19 0.13 - 0.10 0.05 0.13 - 0.10 - 0.07 0.20 0.09 0.25 Retail_emp_d 0.20 - 0.19 1.00 0.14 0.15 0.23 - 0.02 0.12 - 0.27 0.09 0.02 0.10 HF_rail_st - 0.13 0.13 0.14 1.00 0.45 0.69 0.06 - 0.22 - 0.41 0.41 0.08 0.33 CF_bus 0.02 - 0.10 0.15 0.45 1.00 0.34 0.37 - 0.05 - 0.55 0.02 - 0.01 0.11 Central - 0.05 0.05 0.23 0.69 0.34 1.00 - 0.25 - 0.26 - 0.36 0.19 0.17 0.39 Semicentral - 0.20 0.13 - 0.02 0.06 0.37 - 0.25 1.00 - 0.29 - 0.40 0.27 - 0.01 0.02 Peripheral 0.12 - 0.10 0.12 - 0.22 - 0.05 - 0.26 - 0.29 1.00 - 0.42 - 0.10 - 0.02 - 0.15 Suburban 0.10 - 0.07 - 0.27 - 0.41 - 0.55 - 0.36 - 0.40 - 0.42 1.00 - 0.30 - 0.10 - 0.20 Hill - 0.17 0.20 0.09 0.41 0.02 0.19 0.27 - 0.10 - 0.30 1.00 0.06 0.09 Sea - 0.08 0.09 0.02 0.08 - 0.01 0.17 - 0.01 - 0.02 - 0.10 0.06 1.00 0.40 Prestige - 0.24 0.25 0.10 0.33 0.11 0.39 0.02 - 0.15 - 0.20 0.09 0.40 1.00Starting from this preliminary analysis, we formulated and calibrated a hedonic linear regression model (LRM) for estimating real estate values. The general formulation of the model is the following:(1)EY∣X=β0+β1X1+β2X2+⋯+βjXj+⋯+βpXp,where EY∣X is the expected value of the dependent variable Y, in our case the real estate value (REV) of a zone, conditional upon the terms on the right side of equation, Xj, which are the independent variables; β0 is theintercept, which is a parameter invariant with the values assumed by the independent variables, Xj; βj are the parameters (or coefficients) of the model, which have to be calibrated; Xj are the independent variables.This model has to be specified (the independent variablesXj to be considered in the model have to be identified) and calibrated (the values of parametersβs that are best able to reproduce the observed values of Y have to be estimated).We assume that the 60 real estate values,yi, corresponding to the 60 OMI zones, for which the data are available, are the observed data that we collect in a vector Y. The values of independent variables assumed in correspondence of each observation, which are usually called predictors, xi,j, are collected in a matrix X; there are always 60 rows in this matrix, while the number of columns depends on the number of model parameters. We also introduce the vector of the regression coefficients,  β,  and the vector of statistical errors, e. Vectors and the matrix are reported below, where subscript p indicates the number of parameters:(2)Y=y1y2⋮yi⋮y60X=1x1,1x1,2⋯x1,j⋯x1,p1x2,1x2,2⋯x2,j⋯x2,p⋮⋮⋮⋮⋮⋮⋮1xi,1xi,2⋯xi,j⋯xi,p⋮⋮⋮⋮⋮⋮⋮1x60,1x60,2⋯x60,j⋯x60,pβ=β0β1β2⋮βj⋮βpe=e1e2⋮ei⋮e60.Adopting this matrix notation, the multiple linear regression model can be written as(3)Y=Xβ+e.Indicating with x′i the ith row of the matrix X, we can write(4)yi=x′iβ+eiand hence(5)ei=yi-x′iβ.The values of coefficients βs can be estimated with the well-known ordinary least squares (OLS) method that minimises the sum of the square residuals:(6)β∧=Argβmin⁡Σiyi-x′iβ=Argβmin⁡Y-XβTY-Xβ,where Σiyi-x′iβ is usually indicated with RSSβ. Indicating with SYY the term Σiyi-y-2, a very important indicator of the goodness of the model is thecoefficient of determination, R2, that is given by(7)R2=1-RSSSYY.This indicator measures the proportion of variability in the variables Yi which is explained by the linear regression model; the closer R2 to 1 (statistical errors equal to 0 and perfect reproducibility of the phenomenon), the higher the goodness of the calibrated model.The coefficient of determination cannot be the only indicator for evaluating the goodness of a model. Indeed, it does not always decrease (usually increases) with the number of parametersβj, even if some are not actually useful for explaining the phenomenon. The other indicators that have to be used to evaluate the model are thetests of hypotheses that are able to measure whether the parameters adopted in the model are actually significant for reproducing the phenomenon. Below, we use as tests of hypotheses the F-test, obtained by the analysis of variance, and the t-test, regarding the significance of each independent variable. We will assume that a model is acceptable if the significance F is close to 0 (at least <0.05) and if the t-test of each coefficient βj is higher [lower] than t95[-t95] for positive [negative] βj, where t95 is the value of the t-distribution corresponding to the degrees of freedom (df) of the model with a confidence of 95%.We calibrated several models by adopting an adding method for identifying the parameters to consider in the model. Limiting the analysis only to variables where the absolute value of the correlation terms is greater than 0.25, we started with a model with only one df (one parameter) considering that with the higher value of correlation coefficient (see Table4). We calculated the corresponding values of R2, significance F, and t-test.If the model obtained is significant in terms ofF and t-test, we add another variable to the model, the next in terms of correlation coefficient. The new added variable is maintained in the model if all significance tests are verified; otherwise it is eliminated and the procedure continues with another variable. When all variables are examined, we try to add to the model some variables previously discarded (the significance of a variable can change with different df, since the t95 value changes). Since R2 always increases (or, at least, it does not change) when we add a variable, the final model obtained with this procedure will respect all significance tests and will have the highest value of R2 of all models examined. In Table 6, the results of the procedure are summarised: globally, we tested 12 models.Table 6
Models.
1 2 3 4 5 6 7 8 9 10 11 12 % BD_3          x % BD_1_2         x Retail_emp_d        x    x CF_bus      x HF_rail_st x x x x x x x x x x x x Central  x         x x Suburban   x x x x x x x x x x Hill     x x x x x x x x Sea       x x x x x x Prestige    x x x x x x x x x  df 1 2 2 3 4 5 5 6 6 6 6 7  t 95 6.314 2.920 2.920 2.353 2.132 2.015 2.015 1.943 1.943 1.943 1.943 1.895 R 2 0.513 0.567 0.672 0.808 0.846 0.849 0.868 0.876 0.872 0.871 0.883 0.889 Signif.F ≅0 ≅0 ≅0 ≅0 ≅0 ≅0 ≅0 ≅0 ≅0 ≅0 ≅0 ≅0 t-stat0 21.237 22.271 22.227 28.165 29.918 20.252 30.945 24.713 13.981 18.838 32.288 25.867 t-stat1 7.816 4.097 6.429 6.258 5.285 - 0.994 5.895 1.932 1.359 - 1.084 3.292 1.605 t-stat2  2.678 - 5.249 - 6.293 - 6.291 5.240 - 6.578 6.003 5.964 5.919 2.678 3.462 t-stat3    6.304 3.706 - 5.948 3.811 - 6.112 - 6.671 - 6.573 −6.576 2.425 t-stat4     7.210 3.204 2.955 3.898 3.524 3.624 4.379 - 6.172 t-stat5      7.039 5.877 3.082 3.005 2.981 2.904 4.397 t-stat6        5.932 5.458 5.529 5.626 3.011 t-stat7            5.683 Sign. tests Yes No Yes Yes Yes No Yes No No No Yes NoThe results reported in Table6 identify number 11 as the best model, which is as follows:(8)EREV=2179.69+100.61∗HFrailst+394.23∗Central-602.76∗Suburban+570.31∗Hill+454.50∗Sea+1046.27∗Prestige.Table 7 summarises all statistical tests and the analysis of variance (ANOVA) for the calibrated model. The residuals are reported in Table 8, while in Figure 10 the comparison is shown between the real estate values (REVs) obtained from the OMI database and the corresponding values estimated with the proposed model.Table 7
Statistical tests for model 11.
Regression statistics MultipleR 0.940 R2 0.883 AdjustedR2 0.870 Standard error 304.246 Observations 60  ANOVA df SS MS F SignificanceF  Regression 6 37198377 6199729 66.976 5.6 1 E - 23 Residual 53 4905988 92565.81 Total 59 42104365  Coefficients Standard error t-stat p value Lower 95% Upper 95%  Intercept 2179.69 67.51 32.288 1.5 6 E - 36 2044.29 2315.10 HF_rail_st 100.61 30.57 3.292 0.001776 39.30 161.92 Central 394.23 147.24 2.678 0.009851 98.91 689.55 Suburban - 602.76 91.67 - 6.576 2.1 7 E - 08 - 786.62 - 418.90 Hill 570.31 130.23 4.379 5.6 4 E - 05 309.11 831.52 Sea 454.50 156.48 2.904 0.005354 140.64 768.36 Prestige 1046.27 185.98 5.626 7.0 9 E - 07 673.23 1419.30Table 8
Residuals for model 11.
OMI zone OMI REV Model REV Residuals B10 3650 3446.07 203.93 B11 2975 3446.07 - 471.07 B12 4250 4049.73 200.27 B13 3750 2976.37 773.63 B14 4550 4577.74 - 27.74 B15 3075 3278.20 - 203.20 B16 3575 3720.80 - 145.80 B17 4150 4123.24 26.76 B18 3325 3028.42 296.58 B8 2325 2976.37 - 651.37 B9 3075 3076.98 - 1.98 C21 2250 2179.69 70.31 C22 2150 2280.30 - 130.30 C23 2225 2380.91 - 155.91 C24 4800 4653.21 146.79 C25 2075 2179.69 - 104.69 C26 2275 2481.52 - 206.52 C27 2325 2179.69 145.31 C28 3000 2380.91 619.09 C29 3250 3152.45 97.55 C30 2000 2179.69 - 179.69 C31 3250 2750.01 499.99 C32 2800 2280.30 519.70 C33 3075 3051.84 23.16 D21 1625 2179.69 - 554.69 D22 1650 2179.69 - 529.69 D23 2150 2179.69 - 29.69 D24 2150 2179.69 - 29.69 D25 2150 2179.69 - 29.69 D26 2400 2179.69 220.31 D27 1900 2280.30 - 380.30 D29 2650 2179.69 470.31 D30 2375 2380.91 - 5.91 D32 2500 2634.19 - 134.19 D33 2650 2179.69 470.31 D34 2325 2280.30 44.70 D35 2225 2380.91 - 155.91 D36 2150 2850.62 - 700.62 E27 1400 1576.93 - 176.93 E28 1575 1576.93 - 1.93 E29 1650 1576.93 73.07 E3 1700 1576.93 123.07 E30 1750 1576.93 173.07 E31 1700 1576.93 123.07 E32 1450 1576.93 - 126.93 E33 1825 1778.15 46.85 E34 1825 1576.93 248.07 E35 1775 1778.15 - 3.15 E36 1400 1576.93 - 176.93 E37 1525 1576.93 - 51.93 E38 1625 1576.93 48.07 E39 1650 1576.93 73.07 E40 1875 1576.93 298.07 E42 1300 1677.54 - 377.54 E43 1750 2031.43 - 281.43 E44 1625 1576.93 48.07 E45 1575 1576.93 - 1.93 E46 1700 1576.93 123.07 E47 1575 1576.93 - 1.93 E48 1400 1576.93 - 176.93Figure 10
Model versus real estate values.The analysis of the results shows that the model presents a good coefficient of determination (over 0.88) which indicates that over 88% of the variability in REVs is explained by the considered variables.An important result to underline is that low-frequency rail stations and (low-frequency) bus lines have no significant influence on REVs. By contrast, the high-frequency rail stations have a significant and nonnegligible impact on REVs, as will be seen in the next section. The lack of significance of low-frequency services (bus and metro) on REVs is probably due to the fact that these services cover more or less all areas of the city and, then, their presence has not a practical impact on property values. With the calibrated model, the lower REVs of few unserved areas are explained by the variableSuburban that, actually, regards only zones where the transit service is very scarce or absent.


## 4. Impact of the Naples Metro System on Real Estate Values and Estimation of External Benefits

With the model calibrated in the previous section it is possible to estimate the impact of the Naples metro system on REVs and the corresponding external benefits. The predicted contribution of high-frequency rail stations to REVs is reported in Table9 for each OMI zone; the same table also reports the percentages of this contribution. The percentage contributions for areas containing at least one high-frequency rail station are summarised in Figure 11, while Figure 12 reports the absolute values of the contributions. It can be noted that the contribution to REVs is significant for many zones: for two zones it is higher than 20%, for nine other zones it is higher than 10%, and for nine others it is higher than 5%.Table 9
Contribution of high-frequency rail stations to real estate values.
OMI zone Model REVs Contribution of HF_rail_st variable % B10 3446.07 301.83 8.8% B11 3446.07 301.83 8.8% B12 4049.73 905.50 22.4% B13 2976.37 402.44 13.5% B14 4577.74 503.05 11.0% B15 3278.20 704.27 21.5% B16 3720.80 100.61 2.7% B17 4123.24 503.05 12.2% B18 3028.42 0.00 0.0% B8 2976.37 402.44 13.5% B9 3076.98 503.05 16.3% C21 2179.69 0.00 0.0% C22 2280.30 100.61 4.4% C23 2380.91 201.22 8.5% C24 4653.21 402.44 8.6% C25 2179.69 0.00 0.0% C26 2481.52 301.83 12.2% C27 2179.69 0.00 0.0% C28 2380.91 201.22 8.5% C29 3152.45 402.44 12.8% C30 2179.69 0.00 0.0% C31 2750.01 0.00 0.0% C32 2280.30 100.61 4.4% C33 3051.84 301.83 9.9% D21 2179.69 0.00 0.0% D22 2179.69 0.00 0.0% D23 2179.69 0.00 0.0% D24 2179.69 0.00 0.0% D25 2179.69 0.00 0.0% D26 2179.69 0.00 0.0% D27 2280.30 100.61 4.4% D29 2179.69 0.00 0.0% D30 2380.91 201.22 8.5% D32 2634.19 0.00 0.0% D33 2179.69 0.00 0.0% D34 2280.30 100.61 4.4% D35 2380.91 201.22 8.5% D36 2850.62 100.61 3.5% E27 1576.93 0.00 0.0% E28 1576.93 0.00 0.0% E29 1576.93 0.00 0.0% E3 1576.93 0.00 0.0% E30 1576.93 0.00 0.0% E31 1576.93 0.00 0.0% E32 1576.93 0.00 0.0% E33 1778.15 201.22 11.3% E34 1576.93 0.00 0.0% E35 1778.15 201.22 11.3% E36 1576.93 0.00 0.0% E37 1576.93 0.00 0.0% E38 1576.93 0.00 0.0% E39 1576.93 0.00 0.0% E40 1576.93 0.00 0.0% E42 1677.54 100.61 6.0% E43 2031.43 0.00 0.0% E44 1576.93 0.00 0.0% E45 1576.93 0.00 0.0% E46 1576.93 0.00 0.0% E47 1576.93 0.00 0.0% E48 1576.93 0.00 0.0%Figure 11
Contribution of high-frequency rail stations to real estate values%.Figure 12
Contribution of high-frequency rail stations to real estate values (€).For estimating the corresponding external benefits, some hypotheses have to be assumed. From the ISTAT census data on total areas of residential buildings and on the number of buildings belonging to different classes of “conservation state,” we may calculate the area (m2) of residential buildings attributable to each class. Regarding the OMI REVs, the minimum and maximum values are available; we assume that the ISTAT “very good” classification corresponds to an expected REV equal to the maximum OMI REV, while the “poor” classification corresponds to the minimum OMI REV. The other two classes correspond to a linear interpolation of the two extreme data, assuming uniform distances between classes. Estimation of external benefits is obtained by applying the percentages estimated in Table 9 to the current REVs. In Table 10, the results of this analysis are reported for all OMI zones. Thus the external benefits, in terms of REVs, can be estimated at €7.2 billion (8.53% of the total value of real estate assets), with an average benefit per inhabitant amounting to €7,490 or an average benefit for each residential unit (100 m2) of €23,034. These benefits express the increase in real estate values enjoyed by property owners; such estimates can be useful to policy-makers, to study equalisation actions, and to transportation planners, to evaluate the total benefits of interventions, including the external ones.Table 10
Estimation of external benefits.
OMI Resident building (m2) BD_1 (m2) BD_2 (m2) BD_3 (m2) BD_4 (m2) REV BD_1 (€/m2) REV BD_2 (€/m2) REV BD_3 (€/m2) REV BD_4 (€/m2) Total REV (€ million) Metro REV contrib. (€ million) B10 1,393,303 185,774 848,943 336,985 21,602 4,400 3,905 3,395 2,900 5,339 467.65 B11 225,398 13,543 116,085 88,031 7,739 3,600 3,188 2,763 2,350 680 59.57 B12 1,096,268 59,050 811,289 207,957 17,972 5,100 4,539 3,961 3,400 4,868 1,088.55 B13 294,826 13,805 133,115 137,060 10,846 4,500 4,005 3,495 3,000 1,107 149.65 B14 1,052,991 154,419 741,211 151,478 5,883 5,500 4,873 4,227 3,600 5,123 562.94 B15 1,236,737 39,755 326,685 781,279 89,017 3,700 3,288 2,863 2,450 3,676 789.64 B16 205,626 5,469 127,969 64,532 7,656 4,300 3,822 3,329 2,850 749 20.26 B17 657,613 131,523 425,577 96,236 4,277 5,000 4,439 3,861 3,300 2,932 357.77 B18 239,533 27,416 118,324 76,478 17,316 4,000 3,555 3,096 2,650 813 - B8 874,051 15,984 278,451 513,999 65,617 2,800 2,487 2,164 1,850 1,971 266.44 B9 529,521 26,667 229,522 239,046 34,286 3,700 3,288 2,863 2,450 1,621 265.10 C21 460,933 48,816 321,057 86,366 4,694 2,700 2,403 2,097 1,800 1,093 - C22 547,236 14,967 132,833 202,992 196,444 2,600 2,303 1,997 1,700 1,084 47.83 C23 1,012,735 41,107 330,727 442,838 198,062 2,700 2,387 2,064 1,750 2,161 182.61 C24 1,125,018 228,652 582,377 305,520 8,469 5,800 5,140 4,460 3,800 5,714 494.22 C25 257,763 38,282 137,814 80,391 1,276 2,500 2,220 1,931 1,650 559 - C26 1,211,187 49,030 594,485 527,069 40,603 2,750 2,437 2,114 1,800 2,770 336.96 C27 244,131 15,624 179,680 44,920 3,906 2,800 2,487 2,164 1,850 595 - C28 1,560,064 61,538 1,184,185 282,741 31,600 3,600 3,204 2,796 2,400 4,882 412.60 C29 2,154,220 255,034 1,418,059 437,717 43,410 3,900 3,471 3,029 2,600 7,355 939.00 C30 222,178 29,169 92,471 90,609 9,930 2,400 2,136 1,864 1,600 452 - C31 46,990 280 12,866 32,166 1,678 3,900 3,471 3,029 2,600 148 - C32 251,375 122,546 83,792 43,991 1,047 3,400 3,004 2,596 2,200 785 34,63 C33 725,595 166,840 451,726 99,159 7,870 3,700 3,288 2,863 2,450 2,405 237.91 D21 150,463 5,614 103,303 34,809 6,737 1,950 1,736 1,515 1,300 252 - D22 46,602 590 19,467 24,776 1,770 2,000 1,769 1,531 1,300 76 - D23 650,172 9,132 308,649 303,170 29,221 2,600 2,303 1,997 1,700 1,390 - D24 446,193 7,794 395,533 42,216 649 2,600 2,303 1,997 1,700 1,017 - D25 218,929 8,519 27,260 163,558 19,593 2,600 2,303 1,997 1,700 445 - D26 541,086 17,722 253,097 259,189 11,076 2,900 2,570 2,230 1,900 1,301 - D27 225,659 16,073 112,508 90,006 7,072 2,300 2,036 1,764 1,500 435 19.21 D29 671,634 49,751 407,170 153,180 61,534 3,200 2,837 2,463 2,100 1,821 - D30 407,391 11,178 299,333 94,396 2,484 2,850 2,537 2,214 1,900 1,005 84.92 D32 9,418 409 3,481 4,709 819 3,000 2,670 2,330 2,000 23 - D33 309,237 - 192,330 115,021 1,886 3,200 2,837 2,463 2,100 833 - D34 98,633 4,759 18,169 66,621 9,085 2,800 2,487 2,164 1,850 219 9.68 D35 287,795 23,072 114,754 138,433 11,536 2,700 2,387 2,064 1,750 642 54.26 D36 435,528 44,393 275,291 110,589 5,254 2,600 2,303 1,997 1,700 979 34.56 E27 77,481 2,460 11,889 60,263 2,870 1,700 1,502 1,298 1,100 103 - E28 511,087 40,895 244,126 217,347 8,719 1,900 1,686 1,465 1,250 818 - E29 494,260 34,495 418,576 40,159 1,030 2,000 1,769 1,531 1,300 872 - E3 270,574 51,751 168,701 46,454 3,667 2,050 1,819 1,581 1,350 491 - E30 192,859 8,517 96,125 82,132 6,084 2,100 1,869 1,631 1,400 340 - E31 496,806 35,658 252,981 200,939 7,228 2,050 1,819 1,581 1,350 861 - E32 142,406 8,018 69,485 60,895 4,009 1,750 1,552 1,348 1,150 209 - E33 631,618 8,073 338,129 278,292 7,124 2,200 1,953 1,698 1,450 1,161 131.35 E34 537,587 44,578 237,218 236,156 19,635 2,200 1,953 1,698 1,450 991 - E35 328,820 26,577 227,036 71,532 3,676 2,150 1,903 1,648 1,400 612 69.26 E36 166,475 20,273 74,465 53,022 18,714 1,700 1,502 1,298 1,100 236 - E37 54,480 7,106 19,937 26,845 592 1,850 1,636 1,415 1,200 84 - E38 550,268 33,750 297,389 205,922 13,206 1,950 1,736 1,515 1,300 911 - E39 1,042,468 125,221 475,059 431,231 10,957 2,000 1,769 1,531 1,300 1,765 - E40 122,213 13,262 67,533 38,357 3,060 2,250 2,003 1,748 1,500 237 - E42 1,169,908 13,840 428,233 604,900 122,934 1,550 1,385 1,215 1,050 1,479 88.68 E43 659,668 21,185 387,180 251,302 - 2,100 1,869 1,631 1,400 1,178 - E44 775,193 69,333 315,757 346,665 43,438 1,950 1,736 1,515 1,300 1,265 - E45 168,456 6,289 50,312 83,554 28,301 1,900 1,686 1,465 1,250 254 - E46 430,411 27,911 345,211 52,883 4,407 2,050 1,819 1,581 1,350 775 - E47 139,821 477 40,562 95,918 2,863 1,900 1,686 1,465 1,250 213 - E48 194,576 45,709 81,772 46,128 20,967 1,700 1,502 1,298 1,100 283 -  Total          84,459 7,205


## 5. Scenario Analyses

The proposed model and procedure were also applied to two future scenarios; the first (Scenario 1) considers the completion of Line 1 of the metro network in Naples and of Line 6, which is now entirely closed. Currently, Line 1 (a more detailed description can be found in [46]) starts from Piscinola and ends in Piazza Garibaldi (railway central station); under the full project, instead, the line will be extended to form a circle, including Capodichino Airport and the city’s business district (Centro Direzionale); Line 6 is currently closed, even if part of it is completed, and is expected to be extended as far as the metro station Municipio of Line 1. Finally, we also consider the opening of Duomo station on Line 1 which is currently under construction. For both lines, we assume that the operation will provide a daily average frequency equal to or greater than 6 trains/h. The OMI zones affected by the project are B14 (+4 stations), E29 (+3 stations), B16, C28, D24, and E34 (+2 stations), and B18, B8, B9, D21, D22, D34, E36, E42, and E46 (+1 station); zone R2 is also affected, although it is excluded by the model since it only concerns the airport.Scenario 2 considers, besides the interventions provided in Scenario 1, also an increase in the frequencies in the “Cumana” and “Circumflegrea” metro lines up to 6 trains/h. Thus their stations will become of high frequency. Compared with Scenario 0, Scenario 2 involves the following OMI zones: B14 (+5 stations), C28 (+4 stations), D23, D26, D29, D33, and E29 (+3 stations), B16, D24, E28, E34, and E39 (+2 stations), and B11, B15, B17, B18, B8, B9, D21, D22, D30, D34, E36, E42, and E46 (+1 station). The scenarios are illustrated in Figure13.Figure 13
Scenarios.For both scenarios, the calibrated model is used to estimate the REVs. The results concerning the contribution of all scenarios to REVs are summarised in Table11; the same results are reported in Figure 14. By contrast, Table 12 reports the estimation of external benefits. These results show that the interventions provided in Scenario 1 increase the external benefits by about 1.6 billion euros, while under Scenario 2 the benefits could increase by up to €3.5 billion with respect to Scenario 0. In Scenario 1, the external benefits increase up to €9,169 per inhabitant and €28,197 per residential unit of 100 m2; in Scenario 2 to €10,662 per inhabitant and €34.084 per residential unit of 100 m2.Table 11
Contribution of high-frequency rail stations to real estate values for all scenarios.
OMI Scenario 0 Scenario 1 Scenario 2 Model REV HF_rail_st contribution % Model REV HF_rail_st contribution % Model REV HF_rail_st contribution % B10 3446.07 301.83 8.8% 3446.07 301.83 8.8% 3446.07 301.83 8.8% B11 3446.07 301.83 8.8% 3446.07 301.83 8.8% 3546.68 402.44 11.3% B12 4049.73 905.50 22.4% 4049.73 905.50 22.4% 4049.73 905.50 22.4% B13 2976.37 402.44 13.5% 2976.37 402.44 13.5% 2976.37 402.44 13.5% B14 4577.74 503.05 11.0% 4980.19 905.50 18.2% 5080.80 1006.11 19.8% B15 3278.20 704.27 21.5% 3278.20 704.27 21.5% 3378.81 804.88 23.8% B16 3720.80 100.61 2.7% 3922.02 301.83 7.7% 3922.02 301.83 7.7% B17 4123.24 503.05 12.2% 4123.24 503.05 12.2% 4223.85 603.66 14.3% B18 3028.42 0.00 0.0% 3129.03 100.61 3.2% 3129.03 100.61 3.2% B8 2976.37 402.44 13.5% 3076.98 503.05 16.3% 3076.98 503.05 16.3% B9 3076.98 503.05 16.3% 3177.59 603.66 19.0% 3177.59 603.66 19.0% C21 2179.69 0.00 0.0% 2179.69 0.00 0.0% 2179.69 0.00 0.0% C22 2280.30 100.61 4.4% 2280.30 100.61 4.4% 2280.30 100.61 4.4% C23 2380.91 201.22 8.5% 2380.91 201.22 8.5% 2380.91 201.22 8.5% C24 4653.21 402.44 8.6% 4653.21 402.44 8.6% 4653.21 402.44 8.6% C25 2179.69 0.00 0.0% 2179.69 0.00 0.0% 2179.69 0.00 0.0% C26 2481.52 301.83 12.2% 2481.52 301.83 12.2% 2481.52 301.83 12.2% C27 2179.69 0.00 0.0% 2179.69 0.00 0.0% 2179.69 0.00 0.0% C28 2380.91 201.22 8.5% 2582.13 402.44 15.6% 2783.36 603.66 21.7% C29 3152.45 402.44 12.8% 3152.45 402.44 12.8% 3152.45 402.44 12.8% C30 2179.69 0.00 0.0% 2179.69 0.00 0.0% 2179.69 0.00 0.0% C31 2750.01 0.00 0.0% 2750.01 0.00 0.0% 2750.01 0.00 0.0% C32 2280.30 100.61 4.4% 2280.30 100.61 4.4% 2280.30 100.61 4.4% C33 3051.84 301.83 9.9% 3051.84 301.83 9.9% 3051.84 301.83 9.9% D21 2179.69 0.00 0.0% 2280.30 100.61 4.4% 2280.30 100.61 4.4% D22 2179.69 0.00 0.0% 2280.30 100.61 4.4% 2280.30 100.61 4.4% D23 2179.69 0.00 0.0% 2179.69 0.00 0.0% 2481.52 301.83 12.2% D24 2179.69 0.00 0.0% 2380.91 201.22 8.5% 2380.91 201.22 8.5% D25 2179.69 0.00 0.0% 2179.69 0.00 0.0% 2179.69 0.00 0.0% D26 2179.69 0.00 0.0% 2179.69 0.00 0.0% 2481.52 301.83 12.2% D27 2280.30 100.61 4.4% 2280.30 100.61 4.4% 2280.30 100.61 4.4% D29 2179.69 0.00 0.0% 2179.69 0.00 0.0% 2481.52 301.83 12.2% D30 2380.91 201.22 8.5% 2380.91 201.22 8.5% 2481.52 301.83 12.2% D32 2634.19 0.00 0.0% 2634.19 0.00 0.0% 2634.19 0.00 0.0% D33 2179.69 0.00 0.0% 2179.69 0.00 0.0% 2481.52 301.83 12.2% D34 2280.30 100.61 4.4% 2380.91 201.22 8.5% 2380.91 201.22 8.5% D35 2380.91 201.22 8.5% 2380.91 201.22 8.5% 2380.91 201.22 8.5% D36 2850.62 100.61 3.5% 2850.62 100.61 3.5% 2850.62 100.61 3.5% E27 1576.93 0.00 0.0% 1576.93 0.00 0.0% 1576.93 0.00 0.0% E28 1576.93 0.00 0.0% 1576.93 0.00 0.0% 1778.15 201.22 11.3% E29 1576.93 0.00 0.0% 1878.76 301.83 16.1% 1878.76 301.83 16.1% E3 1576.93 0.00 0.0% 1576.93 0.00 0.0% 1576.93 0.00 0.0% E30 1576.93 0.00 0.0% 1576.93 0.00 0.0% 1576.93 0.00 0.0% E31 1576.93 0.00 0.0% 1576.93 0.00 0.0% 1576.93 0.00 0.0% E32 1576.93 0.00 0.0% 1576.93 0.00 0.0% 1576.93 0.00 0.0% E33 1778.15 201.22 11.3% 1778.15 201.22 11.3% 1778.15 201.22 11.3% E34 1576.93 0.00 0.0% 1778.15 201.22 11.3% 1778.15 201.22 11.3% E35 1778.15 201.22 11.3% 1778.15 201.22 11.3% 1778.15 201.22 11.3% E36 1576.93 0.00 0.0% 1677.54 100.61 6.0% 1677.54 100.61 6.0% E37 1576.93 0.00 0.0% 1576.93 0.00 0.0% 1576.93 0.00 0.0% E38 1576.93 0.00 0.0% 1576.93 0.00 0.0% 1576.93 0.00 0.0% E39 1576.93 0.00 0.0% 1576.93 0.00 0.0% 1778.15 201.22 11.3% E40 1576.93 0.00 0.0% 1576.93 0.00 0.0% 1576.93 0.00 0.0% E42 1677.54 100.61 6.0% 1778.15 201.22 11.3% 1778.15 201.22 11.3% E43 2031.43 0.00 0.0% 2031.43 0.00 0.0% 2031.43 0.00 0.0% E44 1576.93 0.00 0.0% 1576.93 0.00 0.0% 1576.93 0.00 0.0% E45 1576.93 0.00 0.0% 1576.93 0.00 0.0% 1576.93 0.00 0.0% E46 1576.93 0.00 0.0% 1677.54 100.61 6.0% 1677.54 100.61 6.0% E47 1576.93 0.00 0.0% 1576.93 0.00 0.0% 1576.93 0.00 0.0% E48 1576.93 0.00 0.0% 1576.93 0.00 0.0% 1576.93 0.00 0.0%Table 12
Estimation of external benefits for all scenarios.
OMI Scenario 0 Scenario 1 Scenario 2 Total REV (€ million) Metro REV contrib. (€ million) Total REV (€ million) Metro REV contrib. (€ million) Total REV (€ million) Metro REV contrib. (€ million) B10 5,339.24 467.65 5,339.24 467.65 5,339.24 467.65 B11 680.15 59.57 680.15 59.57 700.01 79.43 B12 4,868.42 1,088.55 4,868.42 1,088.55 4,868.42 1,088.55 B13 1,106.81 149.65 1,106.81 149.65 1,106.81 149.65 B14 5,122.70 562.94 5,573.05 1,013.29 5,685.64 1,125.88 B15 3,675.57 789.64 3,675.57 789.64 3,788.38 902.45 B16 749.16 20.26 789.68 60.77 789.68 60.77 B17 2,932.43 357.77 2,932.43 357.77 3,003.99 429.32 B18 812.87 - 839.88 27.01 839.88 27.01 B8 1,970.55 266.44 2,037.16 333.05 2,037.16 333.05 B9 1,621.49 265.10 1,674.51 318.12 1,674.51 318.12 C21 1,092.86 - 1,092.86 - 1,092.86 - C22 1,084.16 47.83 1,084.16 47.83 1,084.16 47.83 C23 2,160.67 182.61 2,160.67 182.61 2,160.67 182.61 C24 5,714.40 494.22 5,714.40 494.22 5,714.40 494.22 C25 558.88 - 558.88 - 558.88 - C26 2,770.34 336.96 2,770.34 336.96 2,770.34 336.96 C27 594.93 - 594.93 - 594.93 - C28 4,882.05 412.60 5,294.65 825.21 5,707.25 1,237.81 C29 7,355.42 939.00 7,355.42 939.00 7,355.42 939.00 C30 452.31 - 452.31 - 452.31 - C31 147.54 - 147.54 - 147.54 - C32 784.87 34,63 784.87 34.63 784.87 34.63 C33 2,405.48 237.91 2,405.48 237.91 2,405.48 237.91 D21 251.71 - 263.33 11.62 263.33 11.62 D22 75.85 - 79.35 3.50 79.35 3.50 D23 1,389.67 - 1,389.67 - 1,582.10 192.43 D24 1,016.59 - 1,110.43 93.85 1,110.43 93.85 D25 444.86 - 444.86 - 444.86 - D26 1,300.89 - 1,300.89 - 1,481.03 180.14 D27 435.41 19.21 435.41 19.21 435.41 19.21 D29 1,820.85 - 1,820.85 - 2,072.99 252.14 D30 1,004.78 84.92 1,004.78 84.92 1,047.24 127.38 D32 23.13 - 23.13 - 23.13 - D33 832.90 - 832.90 - 948.23 115.34 D34 219.44 9.68 229.13 19.36 229.13 19.36 D35 642.00 54.26 642.00 54.26 642.00 54.26 D36 979.20 34.56 979.20 34.56 979.20 34.56 E27 103.42 - 103.42 - 103.42 - E28 818.38 - 818.38 - 922.81 104.43 E29 872.27 - 1,039.23 166.96 1,039.23 166.96 E3 491.35 - 491.35 - 491.35 - E30 340.02 - 340.02 - 340.02 - E31 860.71 - 860.71 - 860.71 - E32 208.57 - 208.57 - 208.57 - E33 1,160.69 131.35 1,160.69 131.35 1,160.69 131.35 E34 990.58 - 1,116.99 126.40 1,116.99 126.40 E35 612.07 69.26 612.07 69.26 612.07 69.26 E36 235.72 - 250.76 15.04 250.76 15.04 E37 84.44 - 84.44 - 84.44 - E38 910.97 - 910.97 - 910.97 - E39 1,765.28 - 1,765.28 - 1,990.54 225.26 E40 236.69 - 236.69 - 236.69 - E42 1,478.59 88.68 1,567.27 177.36 1,567.27 177.36 E43 1,178.00 - 1,178.00 - 1,178.00 - E44 1,264.69 - 1,264.69 - 1,264.69 - E45 254.49 - 254.49 - 254.49 - E46 774.71 - 824.14 49.43 824.14 49.43 E47 213.33 - 213.33 - 213.33 - E48 283.46 - 283.46 - 283.46 -  Total 84,459.03 7,205.25 86,074.29 8,820.51 87,915.89 10,662.11Figure 14
Contribution of high-frequency rail stations to real estate values for each scenario.


## 6. Conclusions

In this paper, we specified and calibrated a hedonic model to estimate the impact of transit systems on real estate values and the corresponding external benefits for the city of Naples. The analyses led to some important conclusions regarding the case study.High-frequency metro lines (≥6 trains/h) produce significant impacts on real estate values, while no such correlation was found with low-frequency metro lines and bus lines. This underlines that only high-quality transit systems affect property values in Naples.The estimated value of external benefits correlated to this aspect is high both in terms of absolute value (€7.2 billion, €7,490 per inhabitant and €23,034 per 100 m2 residential unit) and in terms of percentage value (8.53% of total real estate assets). In some areas, the contribution to real estate value is very high, reaching as much as 22% of the total amount. These benefits may be explicitly considered in cost-benefit analyses.The proposed model and procedure applied to two future scenarios are able to estimate the corresponding increases in the external benefits regarding real estate values. Our results show that interventions on the network with a view to increasing the number of high-frequency metro lines may produce an appreciable increase in real estate values.Future research will be addressed to test other approaches in the case study, considering log-linear models and hedonic spatial models and evaluating socioeconomic impacts of a single transit line on the crossed zones. Moreover, to test the procedure on other European cities, recalibrating the model as required, and to consider this approach in cost-benefit analyses will be the object of future research too.



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*Source: 1767149-2018-04-05.xml*