# RECORD 670783d3e4613f6e96cb818330755422772653ef
# TITLE: The impact of energy certification on the real estate market: an analysis using Big Data and hedonic pricing
# YEAR: 2026.0  VENUE: Aestimum  DOI: 10.36253/aestim-19711
# AUTHORS: D. Vecchiato; Carolina Bonardi Pellizzari; Andrea Dominici; T. Tempesta
# PAGES: 19  CANDIDATE TABLE PAGES (1-based): [10, 12, 13, 18, 19]
# ABSTRACT: This study analyses the effect of Energy Performance Certificates (EPC) on residential buildings in Padua (Italy). Introduced by the 2002/91/EC Directive, EPCs became mandatory in European Union countries for buildings at sale or lease. Using web-scraping we collected 5,188 real estate offers in 2022 and 2023, of which 1,738 were in the ‘apartment’ category and suitable for data analysis. We examined EPC effects on prices both in aggregate terms (2022–2023 combined) and by year, to test short-run stability. Results confirm previous findings: a price premium emerges as energy classes improve, with the highest values for top EPC ratings. While most housing characteristics showed stable short-run effects, EPC classes revealed a stronger and more significant impact over time, especially for lower energy classes.


===== PAGE 1 (score 10, numeric-paren 0, table-words 0, var-words 10) =====

Aestimum 88: 51-69, 2026
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-19711 
Citation: Vecchiato, D., Bonardi 
Pellizzari, C., Dominici, A. & Tempesta, 
T. (2026). The impact of energy certification 
on the real estate market and its effect 
in the short-run: an analysis using Big 
Data and hedonic pricing. Aestimum 88: 
51-69. doi: 10.36253/aestim-19711 
Received: September 23, 2025
Accepted: January 23, 2026
Published: September 11, 2026
© 2026 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:
DV: 0000-0002-4652-3961
CBP: 0000-0002-6019-9686
AD: 0000-0002-6701-0858
TT: 0000-0002-6445-4744
The impact of energy certification on the real 
estate market and its effect in the short-run: an 
analysis using Big Data and hedonic pricing
Daniel Vecchiato, Carolina Bonardi Pellizzari, Andrea Dominici*, 
Tiziano Tempesta
Department of Land, Environment, Agriculture and Forestry - TESAF, University of 
Padua, Italy
E-mail: daniel.vecchiato@unipd.it; carolina.bonardipellizzari@phd.unipd.it; andrea.
dominici@unipd.it; tiziano.tempesta@unipd.it
*Corresponding author
Abstract. This study analyses the effect of Energy Performance Certificates (EPC) 
on residential buildings in Padua (Italy). Introduced by the 2002/91/EC Directive, 
EPCs became mandatory in European Union countries for buildings for sale or lease. 
Using web-scraping we collected 5,188 real estate offers in 2022 and 2023, of which 
1,738 were in the ‘apartment’ category and suitable for data analysis. We examined 
EPC effects on prices both in aggregate terms (2022–2023 combined) and by year, to 
test short-run stability. Results confirm previous findings: a price premium emerges as 
energy classes improve, with the highest values for top EPC ratings. While most hous­
ing characteristics showed stable short-run effects, EPC classes revealed a stronger and 
more significant impact over time, especially for lower energy classes.
Keywords:	 appraisal, hedonic pricing, energy, energy performance certificates, big 
data, web crawling.
1. INTRODUCTION
The last few decades have been marked by an unprecedented increase in 
energy consumption. According to the International Energy Agency’s (IEA) 
2021 report, demand grew even more sharply after COVID-19 years. Coupled 
with pressures to adopt renewable energy sources and challenges related to 
ongoing conflicts, such as the war in Ukraine, this has led to rising energy 
prices and further pressure on the energy transition (IEA, 2021).
To reduce energy consumption while maintaining performance, various 
technologies and policies have been developed to improve energy efficiency. 
In the housing sector, energy efficiency can be translated into technologies 
to improve the sustainability of apartments, houses, or entire buildings in 
terms of energy use and CO2 emissions. Examples include solar panels, mod­
ern heating systems, efficient lighting and appliances, wall insulation, and 


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60
Daniel Vecchiato et al.
Table 3. The hedonic models results.
 
Dependent variable: ln(P/m2)
Full sample
year=2022
year=2023
Constant
7.126***
7.185***
7.102***
[7.063, 7.189]
[7.100, 7.271]
[7.013, 7.191]
Surface (m2)
-0.003***
-0.003***
-0.003***
[-0.003, -0.003]
[-0.003, -0.002]
[-0.004, -0.003]
Bathrooms
0.149***
0.091***
0.170***
[0.118, 0.179]
[0.047, 0.136]
[0.129, 0.212]
Zone: Camin, Zona Industriale
0.082
0.060
0.091
[-0.017, 0.181]
[-0.102, 0.223]
[-0.036, 0.218]
Zone: Forcellini, San Camillo, Nazareth, Terranegra
0.417***
0.400***
0.441***
[0.350, 0.484]
[0.314, 0.485]
[0.342, 0.540]
Zone: Guizza, Crocifisso, Ponte Quattro Martiri, Voltabarozzo, Salboro
0.195***
0.181***
0.209***
[0.134, 0.255]
[0.102, 0.260]
[0.123, 0.296]
Zone: Piazza Mazzini, Ospedale Militare, Porta Trento, Stazione
0.402***
0.383***
0.423***
[0.319, 0.485]
[0.275, 0.490]
[0.303, 0.543]
Zone: Piazze, Duomo, Santo, Santa Sofia, Altinate, Savonarola, Ponte Molino
0.778***
0.806***
0.764***
[0.721, 0.835]
[0.731, 0.882]
[0.683, 0.845]
Zone: Sacra Famiglia, Basso Isonzo, Brusegana, Aeroporto, Paltana, Mandria
0.174***
0.175***
0.166***
[0.116, 0.232]
[0.101, 0.250]
[0.082, 0.251]
Zone: Sacro Cuore, Altichiero
0.199***
0.161***
0.234***
[0.124, 0.274]
[0.061, 0.260]
[0.128, 0.340]
Zone: Scrovegni, Portello, Ospedali, Stanga, Pio X
0.118***
0.253***
0.055
[0.052, 0.184]
[0.162, 0.344]
[-0.036, 0.146]
Zone: Specola, Riviere, San Giuseppe, San Giovanni
0.442***
0.408***
0.459***
[0.374, 0.511]
[0.308, 0.508]
[0.367, 0.551]
Zone: Torre, Mortise, Ponte di Brenta
reference
EPC: >=A
0.506***
0.473***
0.517***
[0.459, 0.553]
[0.410, 0.536]
[0.449, 0.585]
EPC: B
0.361***
0.312***
0.361***
[0.265, 0.458]
[0.202, 0.421]
[0.200, 0.522]
EPC: C
0.253***
0.183***
0.321***
[0.167, 0.339]
[0.080, 0.287]
[0.185, 0.457]
EPC: D
0.245***
0.165***
0.279***
[0.192, 0.299]
[0.089, 0.241]
[0.205, 0.352]
EPC: E
0.091***
0.077**
0.095***
[0.043, 0.139]
[0.016, 0.139]
[0.025, 0.166]
EPC: F
0.071***
0.026
0.097***
[0.029, 0.113]
[-0.032, 0.084]
[0.039, 0.155]
EPC: G
reference
Elevator (present)
0.103***
0.093***
0.093***
[0.070, 0.135]
[0.049, 0.136]
[0.047, 0.139]
Alarm System (present)
0.033*
0.087***
0.005
[-0.005, 0.070]
[0.037, 0.138]
[-0.049, 0.059]
Observations
1,738
705
1,033
R2
0.550
0.622
0.529
Adjusted R2
0.545
0.612
0.521
AIC
758.055
84.128
622.167
BIC
867.265
175.292
720.972
Note: 95% confidence intervals in brackets. * p < 0.10, ** p < 0.05, *** p < 0.01.


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61
The impact of energy certification on the real estate market and its effect in the short-run
price. A dwelling of EPC class >= A is worth, on aver­
age, 65.8% more than a similar counterpart in class G, 
while a dwelling with a B label presents a price pre­
mium of 43.5%. The influence of the EPC C, D, and E 
labels is lower, with a price premium of 28.8%, 27.8%, 
and 9.5%, respectively, compared to the G label. Class F 
has the lowest effect on the dwellings, with a price pre­
mium of 7.3%. The surface of the dwelling (in m2) pre­
sented a negative coefficient: this was expected and con­
sistent with theory given that the dependent variable in 
our model is the price per square meter and it usually 
decreases with the dwelling size. The higher the num­
ber of bathrooms, the higher the impact on real estate 
prices. Each additional bathroom increases the premium 
price by 14.9%. In the same direction, the presence of an 
elevator and an alarm system command a price premi­
um of 10.8% and 3.3%, respectively. All the zone estima­
tions are positive and statistically significant, except for 
“Camin, Zona Industriale”, which is not statistically sig­
nificant. Since the zone of reference in our model (zone: 
“Torre, Mortise, Ponte di Brenta”) was an industrial area 
located in the north-east part of the city, results sug­
gested that areas closer to the city centre present a posi­
tive effect on the price given factors such as accessibility 
and prestige. The price ranges from 12.5% of the zone 
“Scrovegni, Portello, Ospedali, Stanga, Pio X” to 117.7% 
of the zone “Piazze, Duomo, Santo, Santa Sofia, Altinate, 
Savonarola, Molino”, which is the most central, histori­
cal and attractive district of the city.
Regarding the second objective, namely to study the 
stability of the EPC effect in the short run, we adopted 
a sequential approach. First, we draw some general con­
siderations investigating the results of the single mod­
els of the two years considered (Table 3); second, we 
did a more detailed investigation to check if the results 
of the two years could be considered statistically dif­
ferent (Table 5). In the second model (Table 5) all vari­
ables (apart from the surface) have been interacted with 
a dummy indicating if the data were collected in 2023, 
keeping 2022 as the base level. Considering the results of 
the single models (general approach), from Table 3 it is 
possible to observe that some differences, despite being 
of small magnitude, between the two years considered 
emerge. More specifically, all EPC classes have a greater 
influence on dwelling prices in 2023 compared to 2022. 
A similar result can be observed for the number of bath­
rooms, and in 5 cases out of 8 if we look at the urban 
zone. The impact on price of the presence of an elevator 
Figure 4. Plot of the coefficients estimated in Table 3. Note: the constant term was omitted because it was out of scale compared to the 
other coefficients. The dependent variable is the dwelling price, expressed as ln(P/m2). 


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62
Daniel Vecchiato et al.
is nearly unchanged between the timespan considered, 
while the presence of an alarm system has a positive 
effect in 2022, and it is not significant in 2023.
Nevertheless, checking if the results of the two years 
could be considered statistically different (detailed anal­
ysis, Table 5), it is possible to notice that the dummy 
variable Year=2023 is not significant, indicating that the 
dwelling price does not depend on the year of the list­
ing prices, and, therefore, that prices are not influenced 
by market dynamics or inflation between the two years. 
Looking at the stability of the EPC effect in the short 
term, from our results, it emerges that such a difference 
is significant only for some EPC classes. More specifical­
ly, there is no statistically significant effect in the most 
energy efficient classes (>= A and B) with respect to G, 
while for the other classes, apart from class E, the effect 
is positive and increasing, and it ranges from 8% (class 
F) to nearly 15% (class C). It is therefore possible to 
observe that in 3 EPC classes out of 6, namely in 50 % of 
cases, there is a different effect of EPC between the two 
years examined. On the contrary, we can observe that all 
other variables have no significant differences between 
the two years, apart from the presence of an alarm sys­
tem (negative effect) and one case out of 9 for what con­
cerns the location of the dwelling.
5. DISCUSSION AND CONCLUSIONS
This study contributes to the growing body of 
research on the impact of energy performance cer­
tificates on residential property prices, with a specific 
focus on the city of Padua, Italy. Our results confirm 
the trend found by previous authors, where the premi­
um price increases passing from lower energy classes to 
the best performing EPC class, confirming the findings 
of other studies conducted on the impact of EPC on 
residential properties in Padua by Copiello and Boni­
faci (2015), Copiello and Donati (2021) and Copiello 
and Coletto (2023). More in detail, from our results 
the price for an apartment presenting EPC class >= A 
is 65.8% higher compared to the worst-performing 
apartments (belonging to class G). In comparison with 
the findings reported for the city of Padua, this out­
Table 4. Estimated percentage change based on the results of the hedonic models presented in Table 3.
 
Dependent variable: ln(P/m2)
% change†
(Full sample)
(year=2022)
(year=2023)
Surface (m2)
−0.309
−0.257
−0.325
Bathrooms
14.871
9.137
17.039
Zone: Camin, Zona Industriale
-
-
-
Zone: Forcellini, San Camillo, Nazareth, Terranegra
51.769
49.123
55.413
Zone: Guizza, Crocifisso, Ponte Quattro Martiri, Voltabarozzo, Salboro
21.506
19.821
23.303
Zone: Piazza Mazzini, Ospedale Militare, Porta Trento, Stazione
49.524
46.613
52.657
Zone: Piazze, Duomo, Santo, Santa Sofia, Altinate, Savonarola, Ponte Molino
117.745
124.002
114.665
Zone: Sacra Famiglia, Basso Isonzo, Brusegana, Aeroporto, Paltana, Mandria
18.970
19.172
18.085
Zone: Sacro Cuore, Altichiero
21.994
17.417
26.349
Zone: Scrovegni, Portello, Ospedali, Stanga, Pio X
12.514
28.815
-
Zone: Specola, Riviere, San Giuseppe, San Giovanni
55.657
50.403
58.229
Zone: Torre, Mortise, Ponte di Brenta
reference
EPC: >=A
65.833
60.429
67.656
EPC: B
43.504
36.551
43.475
EPC: C
28.799
20.131
37.865
EPC: D
27.777
17.907
32.130
EPC: E
9.516
8.038
10.013
EPC: F
7.323
-
10.173
EPC: G
reference
Elevator (present)
10.820
9.721
9.738
Alarm System (present)
3.307
9.139
-
Note: † For categorical and dummy variables, the percentage change was calculated using Eq. 3.
Reference levels and statistically not significant coefficients were omitted.


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63
The impact of energy certification on the real estate market and its effect in the short-run
Table 5. The hedonic models results to check if the year affects the estimated coefficients.
 
Dependent variable: ln(P/m2)
β
95% Conf. Int.
Constant
7.192***
[7.091, 7.292]
Surface (m2)
-0.003***
[-0.003, -0.003]
Bathrooms
0.122***
[0.081, 0.163]
Year = 2023
-0.096
[-0.225, 0.032]
2023 × Bathrooms
0.036
[-0.010, 0.082]
Zone
Camin, Zona Industriale
0.056
[-0.135, 0.247]
Forcellini, San Camillo, Nazareth, Terranegra
0.403***
[0.302, 0.503]
Guizza, Crocifisso, Ponte Quattro Martiri, Voltabarozzo, Salboro
0.180***
[0.087, 0.272]
Piazza Mazzini, Ospedale Militare, Porta Trento, Stazione
0.390***
[0.265, 0.516]
Piazze, Duomo, Santo, Santa Sofia, Altinate, Savonarola, Ponte Molino
0.808***
[0.720, 0.897]
Sacra Famiglia, Basso Isonzo, Brusegana, Aeroporto, Paltana, Mandria
0.179***
[0.092, 0.266]
Sacro Cuore, Altichiero
0.160***
[0.043, 0.277]
Scrovegni, Portello, Ospedali, Stanga, Pio X
0.254***
[0.148, 0.361]
Specola, Riviere, San Giuseppe, San Giovanni
0.407***
[0.290, 0.525]
Torre, Mortise, Ponte di Brenta
reference
Camin, Zona Industriale × 2023
0.035
[-0.188, 0.259]
Forcellini, San Camillo, Nazareth, Terranegra × 2023
0.036
[-0.100, 0.171]
Guizza, Crocifisso, Ponte Quattro Martiri, Voltabarozzo, Salboro × 2023
0.031
[-0.091, 0.153]
Piazza Mazzini, Ospedale Militare, Porta Trento, Stazione × 2023
0.032
[-0.135, 0.199]
Piazze, Duomo, Santo, Santa Sofia, Altinate, Savonarola, Ponte Molino × 2023
-0.050
[-0.165, 0.066]
Sacra Famiglia, Basso Isonzo, Brusegana, Aeroporto, Paltana, Mandria × 2023
-0.017
[-0.133, 0.100]
Sacro Cuore, Altichiero × 2023
0.077
[-0.076, 0.229]
Scrovegni, Portello, Ospedali, Stanga, Pio X × 2023
-0.198***
[-0.334, -0.063]
Specola, Riviere, San Giuseppe, San Giovanni × 2023
0.051
[-0.094, 0.195]
EPC
>=A
0.466***
[0.392, 0.540]
B
0.311***
[0.182, 0.439]
C
0.180***
[0.059, 0.301]
D
0.157***
[0.068, 0.246]
E
0.070*
[-0.002, 0.141]
F
0.022
[-0.046, 0.090]
G
reference
>=A × 2023
0.053
[-0.044, 0.149]
B × 2023
0.051
[-0.144, 0.247]
C × 2023
0.147*
[-0.027, 0.320]
D × 2023
0.124**
[0.012, 0.235]
E × 2023
0.029
[-0.067, 0.125]
F × 2023
0.076*
[-0.011, 0.162]
Elevator (present)
0.093***
[0.042, 0.144]
2023 × Elevator (present)
0.001
[-0.066, 0.068]
Alarm System (present)
0.089***
[0.029, 0.149]
2023 × Alarm System (present)
-0.087**
[-0.165, -0.010]
Observations
1,738
R2
0.560
Adjusted R2
0.550
AIC
758.230
BIC
971.190
Note: 95% confidence intervals in brackets. * p < 0.10, ** p < 0.05, *** p < 0.01.


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64
Daniel Vecchiato et al.
come is higher than that of 21.9% detected by Copiello 
and Bonifaci (2015), but it is consistent with the 61.7% 
highlighted by Copiello and Donati (2021) and with 
the band found by Copiello and Coletto (2023). Even if 
these studies have the same urban area in common with 
our application, a direct comparison of the results is 
not straightforward due to some differences. Even if all 
these studies used as data listing prices, the timing of 
data collection and the sample size were not the same. 
Moreover, we have to consider the different analyti­
cal approaches implemented and the independent vari­
ables included in the models. For example, Copiello and 
Bonifaci (2015) focused on residential buildings using 
listing prices collected from April to July 2013, namely 
around ten years before our application. Such a time 
gap might be one of the causes of the discrepancy of 
our results with those of the authors, since the impact 
of the EPC might have changed as a result of numer­
ous dynamics over nearly a decade. Such a hypothesis 
is supported by Barreca et al. (2021), who found that, 
contrary to empirical evidence, in the first years after 
the implementation of EPC in Italy, these labels are 
increasingly exerting a significant influence on price 
variations. If the data of Copiello and Donati (2021) 
refer to around a few years before our study (third quar­
ter of 2019), the authors analysed the data with a spa­
tial dynamic model. Findings of Copiello and Coletto 
(2023) are referred to 2022 but are limited to a specific 
period of the year (March-July), so including 321 obser­
vations. Moreover, the different analytical approaches 
implemented and the independent variables included in 
the models make it challenging to compare the results 
across studies. Lastly, it should be noted that we have 
adjusted our estimates as suggested by Halvorsen and 
Palmquist (1980) for discrete variables (see Eq. 3), and 
therefore the magnitude of the estimates, when great­
er than 25%, could be even greater in the case where 
estimates were not adjusted. Our findings showed that 
while for other characteristics results could be consid­
ered quite stable in the short run, for the EPC classes 
there seems to be a greater effect with time, an effect 
that proved significant for lower energy classes. Such 
a result, despite needing further validation, is quite 
interesting and could be explained with some consid­
erations. First, it might well be that, as highlighted by 
Olaussen et al. (2017), the EPC, being potentially highly 
correlated with other characteristics of the building or 
dwelling, captures, along with the premium price for 
energy efficiency, other aspects related to the dwelling, 
like for example its maintenance status or whether it 
was recently renovated. In this respect, it is possible that 
old dwellings with low EPC classes were partially ren­
ovated, with an improvement of the EPC class. Partial 
renovation could improve the EPC but not bring it to 
the highest levels, which could be an explanation for the 
difference between the two years found to be significant 
only for lower EPC classes, rewarding the niche market 
of partially renovated dwellings.
The observed price premiums associated with higher 
EPC ratings could serve as an incentive for homeown­
ers to invest in energy-efficient upgrades and renova­
tions. Beyond the potential for reducing energy costs 
and enhancing environmental sustainability, our find­
ings suggest that such investments may also translate 
into tangible increases in property values. This dual ben­
efit could motivate homeowners to prioritize energy effi­
ciency measures, contributing to the EU’s overall goal of 
decarbonizing the building stock.
Our study suggests some lines for future research. 
First, our results should be validated given that they 
could be ‘context-dependent’ both in space and time, 
and therefore similar studies would be necessary to 
validate our results considering different cities in differ­
ent periods. A further aspect that deserves attention is 
the understanding of the reliability of the use of listing 
prices in the appraisal practice, especially when using 
results in absolute values rather than relative percent­
ages. In this respect, it would be interesting to scientifi­
cally estimate the average discrepancy of listing prices 
from real transaction prices. For now, it is possible to 
affirm that the relative percentages of the premium pric­
es derived in listing price studies could be used, subject 
to further validation, in classical appraisal approaches 
like, for example, the Market Comparison Approach. 
Such analytical effort would be particularly relevant 
given that the use of Big Data collected with the web 
crawling technique resulted particularly effective and 
therefore allows to overcome the difficulty of operat­
ing with real transaction data, especially in the Italian 
or similar context, where data are available, but of dif­
ficult accessibility. Furthermore, Italian data need to be 
carefully studied, given that they depend on the data 
reported in transaction documents (they are not read­
ily available in a ‘database’ form), which do not follow a 
standard format, and often miss some important infor­
mation like the status of the dwelling, while for exam­
ple the EPC is nowadays always reported given that it is 
compulsory by law.
REFERENCES
Banca d’Italia. (2025). Sondaggio congiunturale sul mer­
cato delle abitazioni in Italia. Available at: https://


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68
Daniel Vecchiato et al.
Table A1. EPC classes comparisons between the study sample and Padua Municipality residential statistics of registered EPCs. Own elabo­
ration based on data available on Regione del Veneto (2025).
Energy label
Full sample
Study Sample
Municipal Registered EPCs *
year=2022
year=2023
year=2022
year=2023
n
%
n
%
n
%
n
%
n
%
>=A
418
24.1
159
22.6
259
25.1
649
13.3
924
14.8
B
42
2.4
24
3.4
18
1.7
129
2.6
221
3.5
C
54
3.1
29
4.1
25
2.4
220
4.5
381
6.1
D
169
9.7
58
8.2
111
10.8
494
10.1
699
11.2
E
233
13.4
106
15
127
12.3
799
16.4
1,085
17.3
F
345
19.9
120
17
225
21.8
1,330
27.3
1,542
24.6
G
477
27.4
209
29.6
268
25.9
1,247
25.6
1,407
22.5
all
1,738
100.0
705
100.0
1,033
100.0
4,868
100.0
6,259
100.0
Note: * data refer to the registered EPCs in the municipality of Padua for residential buildings (Regione del Veneto, 2025) of all types 
(apartments, detached houses, etc.). The considered municipality data included all registered EPCs, not only those for buildings on sale, 
while the study sample considered only residential buildings in the apartments category for sale.
Table A2. Variance Inflation Factor (VIF) analysis related to the Full sample model reported in Table 3.
Variable
VIF
1/VIF
Surface (m2)
1.94
0.515517
Bathrooms
2.01
0.498356
Zone:
Camin, Zona Industriale
1.24
0.808198
Forcellini, San Camillo, Nazareth, Terranegra
1.75
0.572506
Guizza, Crocifisso, Ponte Quattro Martiri, Voltabarozzo, Salboro
2.05
0.48838
Piazza Mazzini, Ospedale Militare, Porta Trento, Stazione
1.46
0.686173
Piazze, Duomo, Santo, Santa Sofia, Altinate, Savonarola, Ponte Molino
2.84
0.351695
Sacra Famiglia, Basso Isonzo, Brusegana, Aeroporto, Paltana, Mandria
2.29
0.437268
Sacro Cuore, Altichiero
1.62
0.618551
Scrovegni, Portello, Ospedali, Stanga, Pio X
1.82
0.548833
Specola, Riviere, San Giuseppe, San Giovanni
1.79
0.559873
EPC:
>=A
2.06
0.486187
B
1.11
0.9012
C
1.13
0.88326
D
1.27
0.786563
E
1.35
0.738194
F
1.41
0.707525
Elevator (present)
1.31
0.760979
Alarm System (present)
1.1
0.912921
Mean VIF
1.66


===== PAGE 19 (score 87, numeric-paren 51, table-words 5, var-words 7) =====

69
The impact of energy certification on the real estate market and its effect in the short-run
Table A3. The hedonic models result considering the EPC level D as reference level.
 
Dependent variable: ln(P/m2)
Full sample
year=2022
year=2023
Constant
7.371***
7.350***
7.380***
[7.296, 7.446]
[7.247, 7.453]
[7.277, 7.484]
Surface (m2)
-0.003***
-0.003***
-0.003***
[-0.003, -0.003]
[-0.003, -0.002]
[-0.004, -0.003]
Bathrooms
0.149***
0.091***
0.170***
[0.118, 0.179]
[0.047, 0.136]
[0.129, 0.212]
Zone: Camin, Zona Industriale
0.082
0.060
0.091
[-0.017, 0.181]
[-0.102, 0.223]
[-0.036, 0.218]
Zone: Forcellini, San Camillo, Nazareth, Terranegra
0.417***
0.400***
0.441***
[0.350, 0.484]
[0.314, 0.485]
[0.342, 0.540]
Zone: Guizza, Crocifisso, Ponte Quattro Martiri, Voltabarozzo, Salboro
0.195***
0.181***
0.209***
[0.134, 0.255]
[0.102, 0.260]
[0.123, 0.296]
Zone: Piazza Mazzini, Ospedale Militare, Porta Trento, Stazione
0.402***
0.383***
0.423***
[0.319, 0.485]
[0.275, 0.490]
[0.303, 0.543]
Zone: Piazze, Duomo, Santo, Santa Sofia, Altinate, Savonarola, Ponte Molino
0.778***
0.806***
0.764***
[0.721, 0.835]
[0.731, 0.882]
[0.683, 0.845]
Zone: Sacra Famiglia, Basso Isonzo, Brusegana, Aeroporto, Paltana, Mandria
0.174***
0.175***
0.166***
[0.116, 0.232]
[0.101, 0.250]
[0.082, 0.251]
Zone: Sacro Cuore, Altichiero
0.199***
0.161***
0.234***
[0.124, 0.274]
[0.061, 0.260]
[0.128, 0.340]
Zone: Scrovegni, Portello, Ospedali, Stanga, Pio X
0.118***
0.253***
0.055
[0.052, 0.184]
[0.162, 0.344]
[-0.036, 0.146]
Zone: Specola, Riviere, San Giuseppe, San Giovanni
0.442***
0.408***
0.459***
[0.374, 0.511]
[0.308, 0.508]
[0.367, 0.551]
Zone: Torre, Mortise, Ponte di Brenta
reference
EPC: >=A
0.261***
0.308***
0.238***
[0.203, 0.319]
[0.226, 0.390]
[0.159, 0.317]
EPC: B
0.116**
0.147**
0.082
[0.013, 0.219]
[0.024, 0.269]
[-0.083, 0.248]
EPC: C
0.008
0.019
0.042
[-0.085, 0.101]
[-0.097, 0.134]
[-0.100, 0.185]
EPC: D
Reference
EPC: E
-0.154***
-0.087**
-0.183***
[-0.214, -0.094]
[-0.170, -0.005]
[-0.267, -0.100]
EPC: F
-0.174***
-0.138***
-0.182***
[-0.230, -0.119]
[-0.219, -0.058]
[-0.256, -0.107]
EPC: G
-0.245***
-0.165***
-0.279***
[-0.299, -0.192]
[-0.241, -0.089]
[-0.352, -0.205]
Elevator (present)
0.103***
0.093***
0.093***
[0.070, 0.135]
[0.049, 0.136]
[0.047, 0.139]
Alarm System (present)
0.033*
0.087***
0.005
[-0.005, 0.070]
[0.037, 0.138]
[-0.049, 0.059]
Observations
1738
705
1033
Adjusted R2
0.545
0.612
0.521
AIC
758.055
84.128
622.167
BIC
867.265
175.292
720.972
Note: 95% confidence intervals in brackets. * p < 0.10, ** p < 0.05, *** p < 0.01.