# RECORD 21199d311319fd0034523954fb23eaaf34739428
# TITLE: Assessing the Impact of Urban Improvement on Housing Values: A Hedonic Pricing and Multi-Attribute Analysis Model for the Historic Centre of Venice
# YEAR: 2017.0  VENUE: nan  DOI: 10.3390/buildings7040112
# AUTHORS: P. Rosato; M. Breil; C. Giupponi; Raul Berto
# PAGES: 24  CANDIDATE TABLE PAGES (1-based): [13, 14]
# ABSTRACT: The Hedonic Pricing Method is one of the principal assessment methods for evaluating services and resources not normally exchanged on the market. However, the method is often unable to account for the great variety of qualities in an urban context and faces scarce and heterogeneous market data. This paper presents a model for the valuation of benefits generated by environmental and urban improvement investments adopting a mixed hedonic-multi-attribute procedure for modeling a value function of urban real estate values. The peculiarity of the model is that the independent variables are aggregated indicators, which synthetize more detailed characteristics. Using the expertise of real estate agents, all relevant variables influencing real estate values were weighted and synthetized in a set of cardinal indicators. Next, market prices were used to calibrate a hedonic function that transforms the cardinal indicators into real estate values. The valuation model was integrated into a GIS for mapping the housing value, and its variation induced by urban investment. The proposed model pointed out plausible and robust results, in particular, the possibility to use any available information, such as location, position, technical and economic characteristics of buildings, and organize it in a flexible and transparent way, and to keep evident the role of each characteristic through the hierarchical structure of the model. The model was applied to the real estate market of Venice to test the effects of the MOSE project (Electromechanical Experimental Module) for the protection of Venice from high tides. The results of the application showed a relevant increase in real estate values in the center of Venice, especially related to property in ground floor units, of about 1.4 billion €.


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buildings
Article
Assessing the Impact of Urban Improvement
on Housing Values: A Hedonic Pricing and
Multi-Attribute Analysis Model for the Historic
Centre of Venice
Paolo Rosato 1,*
ID , Margaretha Breil 2
ID , Carlo Giupponi 3
ID and Raul Berto 1
ID
1
Department of Engineering and Architecture, University of Trieste, 34127 Trieste, Italy; rberto@units.it
2
Fondazione Eni Enrico Mattei, 30124 Venice, Italy; margaretha.breil@feem.it
3
Department of Economics, Ca’ Foscari University, 30121 Venice, Italy; cgiupponi@unive.it
*
Correspondence: paolo.rosato@dia.units.it; Tel.: +39-040-558-3569
Received: 31 July 2017; Accepted: 23 November 2017; Published: 30 November 2017
Abstract: The Hedonic Pricing Method is one of the principal assessment methods for evaluating
services and resources not normally exchanged on the market. However, the method is often unable
to account for the great variety of qualities in an urban context and faces scarce and heterogeneous
market data. This paper presents a model for the valuation of benefits generated by environmental and
urban improvement investments adopting a mixed hedonic-multi-attribute procedure for modeling a
value function of urban real estate values. The peculiarity of the model is that the independent
variables are aggregated indicators, which synthetize more detailed characteristics. Using the
expertise of real estate agents, all relevant variables influencing real estate values were weighted
and synthetized in a set of cardinal indicators. Next, market prices were used to calibrate a hedonic
function that transforms the cardinal indicators into real estate values. The valuation model was
integrated into a GIS for mapping the housing value, and its variation induced by urban investment.
The proposed model pointed out plausible and robust results, in particular, the possibility to use any
available information, such as location, position, technical and economic characteristics of buildings,
and organize it in a flexible and transparent way, and to keep evident the role of each characteristic
through the hierarchical structure of the model. The model was applied to the real estate market
of Venice to test the effects of the MOSE project (Electromechanical Experimental Module) for the
protection of Venice from high tides. The results of the application showed a relevant increase in real
estate values in the center of Venice, especially related to property in ground floor units, of about
1.4 billion €.
Keywords: hedonic pricing method; multi-attribute analysis; real estate market; urban improvement;
MOSE project
1. Introduction
The evaluation of urban improvements is a relevant issue for urban planning and public
decision making. Urban improvements are usually evaluated using either multiple-criteria analysis
methods, which allow for the coherent assessment of quantitative and qualitative data and produce
qualitative outputs in terms of preferred solutions, or cost–benefit analysis, which base judgments on
quantitative results in terms of benefits generated by public investments. The application of cost-benefit
approaches offers the potential advantage of assessing and comparing public benefits and co-benefits
of investments in urban infrastructures, especially in “soft” or “green” infrastructures with more
traditional assessments of infrastructure.
Buildings 2017, 7, 112; doi:10.3390/buildings7040112
www.mdpi.com/journal/buildings


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Buildings 2017, 7, 112
13 of 24
Table 1. Summary of cardinal variables of the sample.
Variable
Min
Max
Average
Std. Dev.
Price (€)
70,000
2,500,000
402,956
271.518
Price (€ m−2)
1679
9000
4528
1.298
Residential area (m2)
27
400
92
56
Height above sea level (cm)
85
210
145
28
Table 2. Summary of nominal/ordinal variables of the sample.
Variable
Freq.
%
Cum. (%)
Urban Areas
Cannaregio
57
28.9
28.9
Castello, San Pietro, via Garibaldi
6
3.0
32.0
Sant’Elena
2
1.0
33.0
Dorsoduro
26
13.2
46.2
Giudecca/Sacca Fisola
25
12.7
58.9
San Marco/Canal Grande
13
6.6
65.5
S. Polo
24
12.2
77.7
S. Croce
17
8.6
86.3
Castello San Lio
5
2.5
88.8
Castello S. Martino
19
9.6
98.5
Case Muner, Baia del Re
1
0.5
99.0
Santa Marta
2
1.0
100.0
Distance from Venice access
>20 min
107
54.3
54.3
10–20 min
48
24.4
78.7
<10 min
42
21.3
100.0
Entrance
Shared
145
73.6
73.6
Independent
52
26.4
100.0
Lift/Floor
Lift or ground floor
63
32.0
32.0
No lift/2nd floor
102
51.8
83.8
No lift/3rd floor
24
12.2
95.9
No lift above 3rd floor
8
4.1
100.0
Brightness
Very bright
59
29.9
29.9
Bright
96
48.7
78.7
Little bright
42
21.3
100.0
View
Unique
10
5.1
5.1
Great
24
12.2
17.3
Good
83
42.1
59.4
Mediocre
70
35.5
94.9
Poor
10
5.1
100.0
Maintenance
Excellent
62
31.5
31.5
Good
69
35.0
66.5
Mediocre
51
25.9
92.4
Poor
15
7.6
100.0
Finishes
Fine
63
32.0
32.5
Ordinary
90
45.7
77.7
Cheap
44
22.3
100.0
Table 3. Pearson correlations test of the touristic sample (85 cases; Sig. 2-code in brackets).
Variables
Characteristics
Position
Technical
Characteristics
Location
−0.052
−0.007
(0.637)
(0.952)
Position
–
0.283
–
(0.09)


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Table 4. The value function of touristic market segment, robust standard errors with respect to
heteroskedasticity, HC1 variant.
Variables
Coefficient
Std Error
Ratio
p-Value
Constant (β0)
753.305
1169.05
0.6444
0.5212
Location characteristics (Cl)
4963.65
1300.15
3.818
0.0003
Position characteristics (Cp)
2057.32
1044.43
1.970
0.0523
Technical characteristics (Ct)
3430.75
1195.61
2.869
0.0052
Note: Dependent variable market price (€ m−2); N. cases: 84
85; R-square
0.259846; Adjusted R-square
0.232433;
F(3, 81)
9.886302; P-value(F) 1.3 × 10−5.
Table 5. Pearson correlations test of the resident samples (112 cases; Sig. 2-code in brackets).
Variables
Characteristics
Position
Technical
Characteristics
Location
−0.224
0.164
(0.18)
(0.085)
Position
–
0.244
–
(0.10)
Table 6. The value function of residents’ market segment, robust standard errors with respect to
heteroskedasticity, HC1 variant.
Variables
Coefficient
Std Error
Ratio
p-Value
Constant (β0)
1211.81
323.094
3.751
0.0003
Location characteristics (Cl)
794.464
360.194
2.206
0.0295
Position characteristics (Cp)
1254.08
381.914
3.284
0.0014
Technical characteristics (Ct)
1950.08
350.888
5.558
<0.0001
Note: Dependent variable market price (€ m−2); N. cases
112; R-square
0.398214; Adjusted R-square
0.381498;
F(3, 81)
19.30655; p-value(F) 4.27 × 10−10.
According to the OLS stepwise analysis, economic characteristics did not significantly influence
the market price in the sample. Real estate values in the most attractive touristic areas were strongly
influenced by location (district) and technical characteristics (finishes and state of repair). In the areas
of prevalent local residential demand, a high and significant constant was observed. Where those
wanting to live in Venice had a significant “base” they were willing to pay, to which the effects of the
other characteristics were added. In these cases, positional and technical characteristics also appeared
to be significant, but with different priorities. Technical characteristics had the highest coefficient,
followed by position and location characteristics. For residents, the building characteristics appeared
to be much more important than its location. Buyers seemed to be willing to sacrifice the prestige
of a good location (higher presence of tourists) for good technical characteristics (maintenance and
finishing); however, this was dependent on the high refurbishment costs and the conflicts between the
resident’s life quality and tourist congestion.
3.3. The Geographic Model
The value functions developed in the previous paragraph were implemented in a GIS model
to represent the spatial distribution of present property values and simulate changes induced by
hypothetical urban improvements. In this study, GIS was used exclusively to represent the spatial
distribution of features and values. No spatial statistical analysis was performed that could be
an interesting development of the evaluation model. The GIS organized and geo-referenced the
information to represent the state of each building in Venice in relation to the characteristics (xi)
implemented in the valuation model. The layers adopted a raster grid of 1 m2 and were implemented
using data collected for preparing the master plan for the historic center of Venice. Assuming the quality


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of the available information, some simplifications and elaborations were made. First, the available
information referred to entire buildings rather than single flats, which are the most common property
type purchased. For missing data, an interpolation between nearby points was necessary. Regarding
the technical characteristics of the buildings, poor information was available to precisely define the
state of indicator, so a uniform (most probable) state of repair was assumed for those buildings lacking
specific information. The value functions applied to layers mapping the Venice property characteristics
allowed us to map the aggregate indicators (cl and cp) and, finally, the real estate value. In the following
paragraphs, some examples are illustrated.
3.3.1. The Distribution of Location Characteristics
The noise exposure of buildings noise exposure was assessed using the acoustic classification
plan developed by the municipality of Venice. The document shows that noise was essentially due to
boat traffic in the most important canals, and the presence of tourists and recreation facilities (mainly
bars) in the most congested areas. Plan mapping provided a classification in zones with similar noise.
Therefore, buildings in the various zones were assigned a relative noise class.
Flooding in Venice is an occurrence that happens as the result of a complex interplay of
metrological and oceanographic phenomena. High tides are favored especially where strong winds
from the south/south-east, such as the sirocco, blow for a long time. These winds push additional sea
water into the lagoon through the three lagoon outlets, which add up to water flowing into the lagoon
as a result of tidal flows and rising water levels flood Venice’s streets and squares. The phenomenon of
high tides has been being systematically monitored in the municipality of Venice since the 1870s and
allows for modeling frequencies of different flood levels.
The frequency of flooding was estimated with an interpolation between the level quotas given by
the Municipality of Venice. The classification was made according to the frequency with which the
areas flooded. According to the flood frequency, six classes were set, from the worst case of three or
more floods per year, up to the best situation of less than one flooding in 100 years [60]. Figure 7 shows
the annual distribution of tides higher than 110 cm for the period between 1872 and 2016.
According to the experts’ judgments, properties in the historic center of Venice were first classified
according to the six urban districts (Sestieri). This partition had to be further differentiated into 15.
During the panel discussion, the touristic/residential vocation of the various areas of the center of
Venice was also explored.
To qualify each building regarding its accessibility from public transport, a spatial-temporal
relation was created assuming a speed of 4 km h−1 for pedestrians. Measuring the minimum distance
from each building to the public boat stops along public walkways (using the network of canals as a
barrier), a map of times needed to reach the nearest boat stop was created. This map was integrated
with the travelling times of public transport from each boat stop towards the main access points to
Venice from the mainland (the Piazzale Roma car park and S. Lucia railway station) at a speed of
7 km h−1. The result consisted of a layer of travel time from each building to the access point from
the mainland.
The judgment on the density of services available was based on the linear distance towards public
services (hospital and other sanitary services, schools and public administrations) in the historic center,
and on the density of private services (using a classification of density of commercial services made by
the local authority).