# RECORD c457882224ad7235eecc5a3f2b95232d76a155e7
# TITLE: Unleashing the hidden green value: assessing the impact of energy certification and environmental amenities on real estate through hedonic modelling
# YEAR: 2025.0  VENUE: Aestimum  DOI: 10.36253/aestim-17418
# AUTHORS: Carolina Bonardi Pellizzari; T. Tempesta; C. Franceschinis; Mara Thiene; D. Vecchiato
# PAGES: 16  CANDIDATE TABLE PAGES (1-based): [9]
# ABSTRACT: Rapid population growth and urbanization have intensified energy demand and climate vulnerability in cities, elevating the importance of urban green spaces and energy-efficient properties, which impact real estate values. This study uses a hedonic pricing model to assess how environmental amenities and energy efficiency influence property prices. We analysed sales contracts from 2022–2023 in Padua (Italy), enriched with spatial data on key environmental factors. Findings reveal a notable price premium associated with proximity to green spaces and waterways: for each hectare of park area, properties located within 100 m experience a 5.4% price increase, while each additional 100 meters from a waterway reduces apartment value by 3.7%. Additionally, apartments in energy class A or higher command a 30% higher price per square meter with respect to other energy classes. This research offers valuable insights into how urban green spaces and energy efficiency shape real estate values in rapidly urbanizing settings.


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Aestimum 87: 71-86, 2025
Firenze University Press 
www.fupress.com/ceset
A E S T I M U M
CENTRO STUDI DI ESTIMO E DI ECONOMIA TERRITORIALE - Ce.S.E.T.
ISSN 1592-6117 (print) | ISSN 1724-2118 (online) | DOI: 10.36253/aestim-17418 
Citation: Bonardi Pellizzari, C., 
Tempesta, T., Franceschinis, C., 
Thiene, M., & Vecchiato, D. (2025). 
Unleashing the hidden green value: 
assessing the impact of energy 
certification 
and 
environmental 
amenities on real estate through 
hedonic modelling. Aestimum 87: 71-86. 
doi: 10.36253/aestim-17418 
Received: March 4, 2025
Accepted: June 16, 2025
Published: December 31, 2025
© 2025 Author(s). This is an open 
access, peer-reviewed article pub-
lished by Firenze University Press 
(https://www.fupress.com) and distrib-
uted, except where otherwise noted, 
under the terms of the CC BY 4.0 
License for content and CC0 1.0 Uni-
versal for metadata.
Data Availability Statement: The 
datasets used and/or analyzed during 
the current study are available from 
the corresponding author on reason-
able request.
Conflicts of Interest: The authors 
declare no conflict of interest. The 
funders had no role in the design of 
the study; in the collection, analyses, 
or interpretation of data; in the writing 
of the manuscript, or in the decision to 
publish the results.
ORCID:
CBP: 0000-0002-6019-9686
TT: 0000-0002-6445-4744
CF: 0000-0001-5641-7823
MT: 0000-0003-3265-5426
DV: 0000-0002-4652-3961
Unleashing the hidden green value: assessing 
the impact of energy certification and 
environmental amenities on real estate through 
hedonic modelling
Carolina Bonardi Pellizzari*, Tiziano Tempesta, Cristiano France-
schinis, Mara Thiene, Daniel Vecchiato
Department of Land, Environment, Agriculture and Forestry (TESAF), University of 
Padua, Italy
E-mail: carolina.bonardipellizzari@phd.unipd.it, tiziano.tempesta@unipd.it, cristiano.
franceschinis@unipd.it, mara.thiene@unipd.it, daniel.vecchiato@unipd.it
*Corresponding author
Abstract. Rapid population growth and urbanization have intensified energy demand 
and climate vulnerability in cities, elevating the importance of urban green spaces and 
energy-efficient properties, which impact real estate values. This study uses a hedon-
ic pricing model to assess how environmental amenities and energy efficiency influ-
ence property prices. We analysed sales contracts from 2022–2023 in Padua (Italy), 
enriched with spatial data on key environmental factors. Findings reveal a notable 
price premium associated with proximity to green spaces and waterways: for each hec-
tare of park area, properties located within 100 m experience a 5.4% price increase, 
while each additional 100 meters from a waterway reduces apartment value by 3.7%. 
Additionally, apartments in energy class A or higher command a 30% higher price per 
square meter with respect to other energy classes. This research offers valuable insights 
into how urban green spaces and energy efficiency shape real estate values in rapidly 
urbanizing settings.
Keywords:	 building’s energy performance, hedonic model, urban environment.
JEL codes:	 Q49, Q51, R30.
1. INTRODUCTION
The world’s population is three times larger than it was in the mid-twen-
tieth century, growing from 2.5 billion in 1950 to 8 billion in 2022, and it is 
projected to reach up to 9.7 billion in 2050 (United Nations, 2022). Around 
55% of this total currently lives in urban areas and this share is projected to 
reach 68% by 2050 (United Nations, 2019). 
The increasing number of people in the cities coupled with the conse-
quences of climate change, such as higher temperatures, floods, and exces-
sive drought is increasing the vulnerability of the population living in urban 


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79
Assessing the impact of energy certification and environmental amenities on real estate through hedonic modelling
built apartments typically belong to the highest ener-
gy classes. However, the energy class is in some ways a 
proxy for the age of the property and therefore, by omit-
ting this last characteristic, the effect of the energy class 
can be overestimated. From the estimated model in 
which the age of the property is inserted, it emerges that: 
(i) apartments belonging to energy class A or greater 
increase the price per square meter by 30 %. We reduced 
the energy classes in the model to two classes (>=A, all 
other classes) because in further exploratory analyses we 
found that apartments belonging to other energy classes 
did not have a statistically significant effect on the price.
As to the effect of age, it is interesting to note that in 
the city of Padua this variable presents a parabolic trend 
(Figure 2 and Table 3). Including both the linear and 
squared terms allows us to account for the possibility 
that price does not decline uniformly with age. Instead, 
the relationship may follow a U-shaped curve (Figure 2), 
where price initially decreases with age (captured by the 
negative linear coefficient in Table 3), but beyond a cer-
tain point, the effect of age becomes less negative or even 
slightly positive (captured by the positive squared term 
in Table 3). This pattern can reflect architectural or his-
torical value in older buildings, or improvements made 
over time. More specifically, it causes the price of the 
property to decrease until the age of 67, after which the 
price per square meter increases. It can be deduced that, 
at least generally, the oldest properties built before the 
Second World War are more appreciated by the market 
for their architectural characteristics or for their location 
closer to the city centre.
The last intrinsic characteristic analyzed, the year 
of installation of the heating system, presented a posi-
tive effect on the price, regardless of the energy class to 
which it belongs (0.69% for each additional year).
Regarding the extrinsic non-environmental char-
acteristics, the model highlights that proximity to some 
services increases the price of the property. The price 
decreases by 1.23% as the distance from the train sta-
tion increases by 100 m. The number of schools within 
200 m increases the unit price by 12.2% and the number 
of supermarkets by 9.6%. These estimates align with the 
urban income theory, which posits that the willingness to 
pay for a property also depends on future costs associated 
with accessing the diverse range of essential services.
Concerning the environmental characteristics, two 
variables are significantly correlated to the price: the 
area occupied by urban parks within a radius of 100 m 
and from 100 to 200 m and the distance from water-
ways. When it comes to the urban parks variable, it 
should be noted that their effect is statistically significant 
up to a distance of 200 m. It can also be seen how the 
effect decreases as the distance of public greenery from 
the home increases: one hectare of park located less than 
100 m from the home increases the price by 5.4%, this 
premium drops to 3.9% for parks placed at a distance 
between 100 and 200 m. The distance from waterways 
also has a significant effect on the price of houses: for 
Table 3. Hedonic model results. Dependent variable: log of apartment prices per square meter (€/m2).
Variables
Coefficients
p-value
Price (€/m2)
% variation
Constant
-5.651
Apartment surface (m2)
-0.00347
****
-0.34
Energy class A
0.265
**
30.34°
Age of the apartment
-0.0166
****
Squared age of the apartment
0.000125
****
Year in which the heating system was installed
0.00691
***
0.69
Bathrooms number
0.173
***
17.35
Elevator
0.161
****
17.42°
Train station distance (Km)
-0.123
**
-12.32
Number of schools within a radius 200 m
0.122
**
12.18
Number of supermarkets within a radius 200 m
0.0965
***
9.65
Urban parks area (ha) within a radius of 100 m
0. 0541
***
5.41
Urban parks area (ha) between 100 and 200 m
0. 0393
**
3.93
Waterway distance (Km)
-0. 366
****
-36.6
Mediation
0.11860
**
12.59°
Observations = 222 / R-squared = 0.551 / Adjusted R-squared = 0.521 / St. Error of the model = 0.378.
° Calculated using Equation (4).
Note: * p<0.10, ** p<0.05, *** p<0.01, **** p<0.001.


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80
Carolina Bonardi Pellizzari et al.
every additional 100 m of distance from the waterway, 
the apartment price is reduced by 3.7%.
Finally, the model demonstrates that a subjective 
characteristic unrelated to the property itself or the pro-
vision of economic, social, and environmental services 
can influence house prices. Specifically, when an apart-
ment was sold through a real estate agency (mediation), 
the premium was on average 12.6% higher. This could be 
due to Italian legislation, which mandates that the price 
declared in the sales deed must match the actual price 
paid if a real estate agent is involved. Alternatively, it is 
possible that real estate agents are able to increase the 
sales price or that they predominantly handle higher-
priced properties, resulting in a higher average unit price.
5. DISCUSSION
From the analysis of the transaction contracts 
and geospatial data, some clues regarding the effect of 
environmental characteristics on property prices have 
emerged. The findings indicate a statistically significant 
influence of both energy efficiency and extrinsic envi-
ronmental factors on apartment prices in Padua, repre-
sented by price premiums. 
First, the price premium of 30% found for best-per-
forming apartments (EPC=A) is consistent with the liter-
ature which has been showing the increased importance 
of energy performance on real estate. The estimated 
price premium found in this study is somewhat similar 
to the one of Massimo et al. (2022) in the Italian munici-
pality of Reggio Calabria where a 41.5% premium was 
found for residential properties presenting EPC A or B 
compared to other energy classes. Using an intermediary 
baseline (EPC=D) in the same city explored in the pre-
sent study, Copiello and Coletto (2023) found a premium 
price of 55% for apartments belonging to EPC=A4 and 
a decrease of 29% for those with EPC=G. Price premi-
ums were also found in other parts of Italy, such as in 
the municipality of Bolzano, where a premium of 6% 
was found for properties moving from the worst ener-
gy performance class (G) to the best (A) (Bisello et al., 
2020). In Bari, studies using transaction prices identified 
premiums from 27% to 29.41% in properties presenting 
EPC>A (Manganelli et al., 2019; Morano et al., 2020) 
and a decrease of 27% for the worst-performing dwell-
ings (EPC=G) (Morano et al., 2020).
In the context of examining the influence of energy 
performance on the real estate market, a relevant obser-
vation arising from our study is the correlation identified 
between the apartment’s age and its EPC rating. With-
in our sample, newly built apartments exhibit superior 
energy performance, introducing the potential for con-
trasting conclusions. Divergent approaches exist in the 
literature: some studies incorporate both age and EPC 
in their models (e.g. Barreca et al. (2021); Manganelli et 
al. (2019); Morano et al. (2020)), while others opt for a 
singular variable, with EPC serving as a proxy for addi-
tional factors such as a building’s age or simply because 
other attributes are not significantly affecting price (see 
Bisello et al. (2020); Copiello and Coletto (2023); Mas-
simo et al. (2022)). Certain authors consider this correla-
tion problematic, as omitting variables correlated to the 
EPC may lead to overlooking the impact of the selected 
variable on price (Olaussen et al., 2017). 
Regarding extrinsic environmental variables, our 
findings suggest a positive effect of the proximity to 
water, illustrated by a 3.7% decrease in price for every 
additional 100 meters from the waterway, which aligns 
with the research of Bonetti et al. (2016) who identified 
an even stronger price decrease in Milan (Italy), of 7.4% 
for houses for an additional meter of distance from the 
canal. Taking into consideration the effect of blue spac-
es in general, our findings are somewhat aligned with 
Anderson and Vahe (2018), who identified a premium 
price of 0.9% for those properties located within 200 m 
from a small blue space in the UK. In the Netherlands, 
a 1.91% discount in price was found for properties locat-
ed 25-50 m from the nearest water when compared to 
properties located less than 25 m away and the discount 
increased to -4.04% for properties positioned 200-400 
m from the nearest water body (Koster, 2022). Another 
study in the Netherlands estimated a premium price of 
7% for a house located within 1000 m from a lake, and 
this premium got up to 28% if the house was by the lake 
with a nice view (Luttik, 2000). 
Finally, the effect of green urban areas is evidenced 
in our results by a 5.4% premium for an additional hec-
tare of urban park located within 100 m of the apart-
ment (and 3.9% for a distance between 100 and 200 m) 
aligns with existing literature regarding size and prox-
imity of green features. The positive correlation between 
the size of the closest green parks and property value 
has been consistently supported by various studies. For 
instance, an additional 1% of green space within 500 
m of apartments in Poland increased the sale price by 
3.95 PLN per square meter (Czembrowski and Kronen-
berg, 2016). Similarly, in Finland, an additional 1 km 
in the distance to the nearest forested area resulted in a 
decrease of 5.9% in the price of the dwelling (Tyrväin-
en and Miettinen, 2000). In Costa Rica, Piaggio (2021) 
reported a 4% premium price for each additional 1% 
increase in size of the closest neighborhood park. Simi-
larly to our findings, the authors also observed that dis-