Journal of Advanced Transportation
Volume 2018 (2018), Article ID 1767149, 22 pages
https://doi.org/10.1155/2018/1767149

Research Article

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

Mariano Gallo

Dipartimento di Ingegneria, Università del Sannio, Piazza Roma 21, 82100 Benevento, Italy

Correspondence should be addressed to Mariano Gallo; gallo@unisannio.it

Received 9 October 2017; Revised 30 January 2018; Accepted 21 February 2018; Published 5 April 2018

Academic Editor: David F. Llorca

Copyright © 2018 Mariano Gallo. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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.

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 is considered “external” if it is produced by subject A and borne by subject , 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 Section 2 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 Table 1, 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′)
6.00

Table 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
1.25

Figure 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: 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; 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, , between the real estate values, , and each variable, , where indicates the OMI zones and the independent variable. The correlation coefficient can be positive, between 0 and 1, or negative, between and 0, and the more its value is proximal to 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 variables Pop, Surface, BD_1, BD_2, BD_3, BD_4, and Retail_emp were not considered since they were used only for calculating other variables; the variable rural was not considered since in these areas there are no real estate values available.

Table 4: Correlation coefficients.

Variable

Density

% BD_1

% BD_2

% BD_3

% BD_4

% BD_1_2

Retail_emp_d

HF_rail_st

LF_rail_st

CF_bus

Central

Semicentral

Peripheral

Suburban

Hill

Sea

Prestige

Upon 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 −0.5; these variables are HF_rail_st, Central, Suburban, Hill, and Prestige. 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 the HF_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 are HF_rail_st versus Central (0.69), since in central areas there are more metro stations, and Suburban versus CF_bus 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 -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

% BD_1_2

Retail_emp_d

HF_rail_st

CF_bus

Central

Semicentral

Peripheral

Suburban

Hill

Sea

Prestige

Starting 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:

where is the expected value of the dependent variable , in our case the real estate value (REV) of a zone, conditional upon the terms on the right side of equation, , which are the independent variables; is the intercept, which is a parameter invariant with the values assumed by the independent variables, ; are the parameters (or coefficients) of the model, which have to be calibrated; are the independent variables.

This model has to be specified (the independent variables to be considered in the model have to be identified) and calibrated (the values of parameters βs that are best able to reproduce the observed values of have to be estimated).

We assume that the 60 real estate values, , corresponding to the 60 OMI zones, for which the data are available, are the observed data that we collect in a vector . The values of independent variables assumed in correspondence of each observation, which are usually called predictors, , are collected in a matrix ; 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,  ,  and the vector of statistical errors, . Vectors and the matrix are reported below, where subscript indicates the number of parameters:

Adopting this matrix notation, the multiple linear regression model can be written as

Indicating with the th row of the matrix , we can write

and hence

The values of coefficients can be estimated with the well-known ordinary least squares (OLS) method that minimises the sum of the square residuals:

where is usually indicated with . Indicating with the term , a very important indicator of the goodness of the model is the coefficient of determination, , that is given by

This indicator measures the proportion of variability in the variables which is explained by the linear regression model; the closer 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 parameters , even if some are not actually useful for explaining the phenomenon. The other indicators that have to be used to evaluate the model are the tests 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 -test, obtained by the analysis of variance, and the -test, regarding the significance of each independent variable. We will assume that a model is acceptable if the significance is close to 0 (at least <0.05) and if the -test of each coefficient is higher [lower] than for positive [negative] , where is the value of the -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 Table 4). We calculated the corresponding values of , significance , and -test.

If the model obtained is significant in terms of and -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 value changes). Since 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 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

6.314
2.920
2.920
2.353
2.132
2.015
2.015
1.943
1.943
1.943
1.943
1.895

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.
≅0
≅0
≅0
≅0
≅0
≅0
≅0
≅0
≅0
≅0
≅0
≅0

-stat₀

32.288

-stat₁

3.292

-stat₂

2.678

-stat₃

−6.576

-stat₄

4.379

-stat₅

2.904

-stat₆

5.932
5.458
5.529
5.626
3.011

-stat₇

5.683

Sign. tests
Yes
No
Yes
Yes
Yes
No
Yes
No
No
No
Yes
No

The results reported in Table 6 identify number 11 as the best model, which is as follows:

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

Multiple
0.940

0.883

Adjusted
0.870

Standard error
304.246

Observations
60

ANOVA
df
SS
MS

Significance

Regression
6

Residual
53

Total
59

Coefficients
Standard error
-stat
value
Lower 95%
Upper 95%

Intercept

HF_rail_st

Central

Suburban

Hill

Sea

Prestige

Table 8: Residuals for model 11.

OMI zone
OMI REV
Model REV
Residuals

B10
3650

B11
2975

B12
4250

B13
3750

B14
4550

B15
3075

B16
3575

B17
4150

B18
3325

B8
2325

B9
3075

C21
2250

C22
2150

C23
2225

C24
4800

C25
2075

C26
2275

C27
2325

C28
3000

C29
3250

C30
2000

C31
3250

C32
2800

C33
3075

D21
1625

D22
1650

D23
2150

D24
2150

D25
2150

D26
2400

D27
1900

D29
2650

D30
2375

D32
2500

D33
2650

D34
2325

D35
2225

D36
2150

E27
1400

E28
1575

E29
1650

E3
1700

E30
1750

E31
1700

E32
1450

E33
1825

E34
1825

E35
1775

E36
1400

E37
1525

E38
1625

E39
1650

E40
1875

E42
1300

E43
1750

E44
1625

E45
1575

E46
1700

E47
1575

E48
1400

Figure 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 variable Suburban 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 Table 9 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 Figure 13.

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 Table 11; 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
10,662.11

Figure 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.

Conflicts of Interest

The author declares that he has no conflicts of interest.

Acknowledgments

The author is grateful to Antonio Pace for his helpful collaboration in defining the transit network model.

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