# RECORD 755578f10943061d8e5adfd02909b14166725d76
# TITLE: Influence of the Urban Green Spaces of Seville (Spain) on Housing Prices through the Hedonic Assessment Methodology and Geospatial Analysis
# YEAR: 2022.0  VENUE: Sustainability  DOI: 10.3390/su142416613
# AUTHORS: E. Ramírez-Juidías; J. Amaro-Mellado; J. Leiva-Piedra
# PAGES: 15  CANDIDATE TABLE PAGES (1-based): [8, 9]
# ABSTRACT: The city of Seville (Spain) is made up of a historical network of pre-existing city overlaps, which increase the economic and heritage value of certain urban areas. To date, green spaces are one of the most important variables in determining the economic value of housing. Thus, this paper uses the hedonic technique and geostatistical analysis with GIS as a methodological approach to infer the economic influence of urban green spaces on housing prices. Along with the traditional variables used to explain dwelling prices, the size of the green space has also been taken into account as an environmental variable affecting prices. The sample consists of 1000 observations collected from Seville. According to the findings, the most relevant variables depend on the hedonic model. Still, in general terms, a dwelling’s selling price is related to basic explanatory variables such as living area, number of rooms, age, and number of baths. The green area per inhabitant present in a dwelling’s district is also included as part of these basic explanatory variables. In conclusion, the hedonic linear model is the model that best fits housing prices where the values are similar to those obtained by kriging regardless of the district. Based on this research, each square meter of green space per inhabitant in a district raises the housing value by 120.19 €/m2.


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Citation: Ramírez-Juidías, E.;
Amaro-Mellado, J.-L.; Leiva-Piedra,
J.L. Inﬂuence of the Urban Green
Spaces of Seville (Spain) on Housing
Prices through the Hedonic
Assessment Methodology and
Geospatial Analysis. Sustainability
2022, 14, 16613. https://doi.org/
10.3390/su142416613
Academic Editors: Marco Devecchi,
Fabrizio Aimar and Matteo Caser
Received: 6 November 2022
Accepted: 9 December 2022
Published: 12 December 2022
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Attribution (CC BY) license (https://
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sustainability
Article
Inﬂuence of the Urban Green Spaces of Seville (Spain) on
Housing Prices through the Hedonic Assessment Methodology
and Geospatial Analysis
Emilio Ramírez-Juidías 1
, José-Lázaro Amaro-Mellado 2,3,*
and Jorge Luis Leiva-Piedra 4
1
Instituto Universitario de Arquitectura y Ciencias de la Construcción (IUACC), Universidad de Sevilla,
2 Reina Mercedes Avenue, 41012 Seville, Spain
2
Departamento de Ingeniería Gráﬁca, Universidad de Sevilla, 41092 Seville, Spain
3
Instituto Geográﬁco Nacional-Servicio Regional en Andalucía, 41013 Seville, Spain
4
Laboratorio de Investigación en Teledetección, Universidad Tecnológica del Perú, Intersección Avenida El
Progreso s/n-Vía de Evitamiento, Chiclayo 14001, Peru
*
Correspondence: jamaro@us.es
Abstract: The city of Seville (Spain) is made up of a historical network of pre-existing city overlaps,
which increase the economic and heritage value of certain urban areas. To date, green spaces are
one of the most important variables in determining the economic value of housing. Thus, this paper
uses the hedonic technique and geostatistical analysis with GIS as a methodological approach to
infer the economic inﬂuence of urban green spaces on housing prices. Along with the traditional
variables used to explain dwelling prices, the size of the green space has also been taken into account
as an environmental variable affecting prices. The sample consists of 1000 observations collected
from Seville. According to the ﬁndings, the most relevant variables depend on the hedonic model.
Still, in general terms, a dwelling’s selling price is related to basic explanatory variables such as
living area, number of rooms, age, and number of baths. The green area per inhabitant present in a
dwelling’s district is also included as part of these basic explanatory variables. In conclusion, the
hedonic linear model is the model that best ﬁts housing prices where the values are similar to those
obtained by kriging regardless of the district. Based on this research, each square meter of green
space per inhabitant in a district raises the housing value by 120.19 €/m2.
Keywords: hedonic method; GIS; geospatial analysis; urban green space; housing prices
1. Introduction
Green spaces serve essential environmental and recreational functions [1]. Forest and
urban vegetation, in edaphoclimatic terms, improve the environment in which they are
found and serve as the foundation for the conservation of fauna and ﬂora. Recreational
activities with a basis in nature are becoming more popular and, while the effects are on
a smaller scale, urban parks serve the same environmental and recreational functions as
larger forests and green spaces.
Gardens and city parks play the primary environmental function of absorbing carbon
dioxide (CO2) emissions. These emissions are primarily generated through the use of
private vehicles in urban transportation. Moreover, emissions have increased signiﬁcantly
in recent decades. It is worth noting that, in Spain, each person in a large city emits
4.62 tonnes of CO2 per year [2]. Given that one hectare of Mediterranean forest can absorb
approximately four tonnes of CO2 per year, a simple calculation leads us to conclude that
we require more than one hectare of green space per inhabitant (inh) to offset the pollution
generated by these emissions. The preservation of urban green spaces is thereby becoming
increasingly important in order to combat the growing contamination of our cities.
Additional factors, such as acoustic isolation, should be included in the list of the
environmental functions of urban green parks as some gardens serve as acoustic screens
Sustainability 2022, 14, 16613. https://doi.org/10.3390/su142416613
https://www.mdpi.com/journal/sustainability


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Sustainability 2022, 14, 16613
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the average price per m2 and was taken into account (Table 4) in accordance with Solano-
Sánchez et al. [39]. Under the predeﬁned hypothesis, a higher value per m2 in a district
implies a higher dwelling value. This index was created by assigning one value to the
city’s most expensive district and then assigning a proportional value to the remaining
zones. In order to be able to test and validate this study, a comparison of real price
(www.idealista.com) versus estimated price for each HPM model will be conducted.
Table 4. District Index (DI) value for each set of districts (Own elaboration).
Set of Districts
€/m2
DI
Triana
2715.69
0.865
Torreblanca-Sevilla Este-Parque Alcosa
1207.94
0.385
Santa Justa-Miraﬂores-Cruz Roja-San Pablo-Santa Justa
1919.3
0.612
San Jerónimo-Pino Montano
1188.24
0.379
Prado de San Sebastián-Felipe II-Bueno Monrreal-La
Palmera-Los Bermejales-Bellavista-Jardines de Hércules
2497.18
0.796
Nervión
2642.49
0.842
Macarena
1368.73
0.436
Los Remedios
2899.17
0.924
Cerro-Amate
1017.51
0.324
Centro
3137.89
1
Finally, a literature review was conducted to analyze the results obtained in this study
and to carry out a coherent discussion concerning the works published to date.
5. Results and Discussion
Following the data analysis, it was discovered that including the green area (m2/inh)
variable in the price equation allowed for the estimation of the inﬂuence of environmental
factors on dwelling market value. In order to ﬁnd out if the HPM presents multicollinearity,
it was decided that obtaining the Variance Inﬂation Factor (VIF) (Table 5) would be required.
As is well known, if the VIF value for each independent variable is greater than ten then
the multicollinearity is considered to be high.
Table 5. Independent variables and coefﬁcients of linear HPM model (r = 0.85, R2 = 0.721, p ≤0.001)
(Own elaboration).
Variables
Coefﬁcients
Stand. Error
Student’s t
Prob.
VIF
Constant
−120.443
14.795
−0.075
0.000
-
Size (m2)
30.032
0.039
1.7
0.000
1.622
Baths (n◦)
98.725
0.042
0.14
0.000
1.467
Age (years)
−53.31
1.42
−1.42
0.000
1.535
Green area
(m2/inh)
11.145
0.87
0.872
0.000
1.020
Regarding the HPM model selected as the most satisfactory, it should be mentioned
that the linear function form was found to be the most appropriate after performing the
data analysis. Like the VIF, Table 4 also displays the independent variables’ coefﬁcients.
As for the logarithmic model, in addition to the VIF value, Table 6 provides the
differences with respect to the linear model. Thus, the main difference is the dependence
on the number of rooms (Rooms) variable in the logarithmic model. In contrast, under
the linear model, the property’s value depends on the dwelling area (Size) variable. This
may be because of the relationship between both variables which causes the predominant
variable to cancel the slave variable in the requisite model depending on the selected model.


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Sustainability 2022, 14, 16613
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Table 6. Independent variables and coefﬁcients of the logarithmic HPM model (r = 0.816, R2 = 0.667,
p ≤0.001) (Own elaboration).
Variables
Coefﬁcients
Stand. Error
Student’s t
Prob.
VIF
Constant
6.56
1.49
4.36
0.000
-
Room
1.39
1.08
1.29
0.000
1.705
Baths (n◦)
0.26
0.6
0.432
0.000
1.523
Age (years)
−0.365
0.205
−1.78
0.000
1.380
Green area
(m2/inh)
−0.013
0.13
−0.1
0.000
1.000
If the reciprocal model is analyzed, Equation (8) is obtained (r = 0.647, R2 = 0.42,
p ≤0.05):
P = 5581.151 −344, 864.49·
 1
S

(8)
After the reciprocal model’s price estimation (Figure 3) is represented, it can be ob-
served that the price function shape in the limit, “P” trends towards “α = 5581.151”. The
term “−(β/α)” refers to the smallest amount of surface available in a dwelling (61.79 m2 in
this study).
Figure 3. Reciprocal model representation when “α > 0” and “β < 0”. (Own elaboration).
If the three models are compared based on the coefﬁcient of determination (R2), it
can be said that a higher value of this coefﬁcient implies a better estimate of the housing
price. For this reason, the linear model explains 5.4% more of the predicted price than
the logarithmic model and 30.1% more than the reciprocal model. On the other hand, the
logarithmic model explains 24.7% more of the estimated price than the reciprocal model.
Additionally, the Chow test [40] was used to ensure the models’ stability. It should
be noted though that the Chow test has its origin in economics. Nevertheless, it has also
been applied to other ﬁelds such as renewable energies [41], ﬂood risk assessment [42], and
spatiotemporal disease diffusion [43]. In this study, the results revealed that no structural
changes occurred exclusively in the parameters of either the linear or logarithmic models.
This can be seen in Figure 4, which compares the real price (from www.idealista.com
(accessed on 18 October 2022)) to the estimated price for each model used. As can be seen,
Figure 4 allows for the simulation of hypotheses that facilitate the validity of the study to
be tested.


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Sustainability 2022, 14, 16613
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Figure 4. Comparison of real (www.idealista.com (accessed on 17 October 2022)) versus estimated
prices for each HPM model. (Own elaboration).
When the models’ degree of ﬁt is perfect, they should appear as point clouds in a
diagonal line starting at the origin (1017.51; 1119.94 in this study), as shown in Figure 4. In
this case, the estimated values for linear and logarithmic models indicate that the linear
form is a good ﬁt. However, the reciprocal model presents a trend line that does not coincide
with the indicated origin. For this reason, the model does not have as much stability as the
linear and logarithmic ones.
Furthermore, with regard to the kriging, Figure 5 shows the geostatistical price interpo-
lation for the city of Seville. As is well known, in order to perform predictions using kriging
the forecasting system must be geometrically deﬁned at some point. This means that we
must establish the neighbourhood of the data that will intervene in the approximation. It is
possible to use all the dwelling prices (assuming a global neighbourhood) and a set of data
(considering a local neighbourhood) in the estimation. In practice, only house prices within
a predeﬁned circumference or ellipse centred on the estimated point are used. Obviously,
the proximity of the data for each point to be valued will differ, thereby necessitating
a different kriging equations system for each point on the map at which a prediction is
desired. On the other hand, this method allows for the synthetic representation of housing
price estimates in the form of a colour map. This representation is generated by performing
predictions on the nodes of a regular mesh. In this manuscript the housing price has been
estimated at each node of a 10-m-sided regular mesh.
The map in Figure 5 displays how the more valuable areas coincide with those closest
to the city centre. The least valued areas, on the other hand, are primarily in the districts of
San Jerónimo-Pino Montano, Torreblanca-Sevilla Este-Parque Alcosa, and Cerro-Amate.
This is primarily due to a shift in buyer preferences following the COVID-19 pandemic.
Following conﬁnement the percentage increase in the preference for housing (new or used)
with a balcony was around 80% [44]. As a result, the average percentage decrease in housing
prices was 5% across the districts, except for the Centro district which is dominated by
homes with small balconies and a communal roof terrace. In order to compare the kriging
values in terms of €/m2 with those of the analyzed models, Table 7 lists the average values
per district corresponding to the centroid established in the geospatial analysis.