# RECORD fb9d007b500b1eff5cd9c2ed633d123752bd4215
# TITLE: EPC Labels and Building Features: Spatial Implications over Housing Prices
# YEAR: 2021.0  VENUE: nan  DOI: 10.3390/su13052838
# AUTHORS: Alice Barreca; E. Fregonara; D. Rolando
# PAGES: 22  CANDIDATE TABLE PAGES (1-based): [15, 17, 18]
# ABSTRACT: The influence of building or dwelling energy performance on the real estate market dynamics and pricing processes is deeply explored, due to the fact that energy efficiency improvement is one of the fundamental reasons for retrofitting the existing housing stock. Nevertheless, the joint effect produced by the building energy performance and the architectural, typological, and physical-technical attributes seems poorly studied. Thus, the aim of this work is to investigate the influence of both energy performance and diverse features on property prices, by performing spatial analyses on a sample of housing properties listed on Turin’s real estate market and on different sub-samples. In particular, Exploratory Spatial Data Analyses (ESDA) statistics, standard hedonic price models (Ordinary Least Squares—OLS) and Spatial Error Models (SEM) are firstly applied on the whole data sample, and then on three different sub-samples: two territorial clusters and a sub-sample representative of the most energy inefficient buildings constructed between 1946 and 1990. Results demonstrate that Energy Performance Certificate (EPC) labels are gaining power in influencing price variations, contrary to the empirical evidence that emerged in some previous studies. Furthermore, the presence of the spatial effects reveals that the impact of energy attributes changes in different sub-markets and thus has to be spatially analysed.


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20 October 2026
POLITECNICO DI TORINO
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EPC Labels and Building Features: Spatial Implications over Housing Prices / Barreca, A., Fregonara, E., Rolando, D.. -
In: SUSTAINABILITY. - ISSN 2071-1050. - 13:5(2021), p. 2838. [10.3390/su13052838]
Original
EPC Labels and Building Features: Spatial Implications over Housing Prices
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DOI:10.3390/su13052838
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Sustainability 2021, 13, 2838
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showed the presence of spatial dependence between variables (Moran’s I = 0.346), thus the
OLS model could be biased.
Furthermore, results showed that, above the spatial regression models, the more
suitable spatial model tested was the Spatial Error Model (SEM), since AIC and Log
likelihood values are respectively lower and higher than in the Spatial Lag Model(SLM).
The Breusch-Pagan test on the spatial effects, calculated for testing the homoscedasticity
hypothesis, showed that the null hypothesis was confirmed (Table 3).
Table 3. Ordinary Least Squared (OLS) and Spatial Error Models (SEM), complete sample (Source: Authors’ elaboration).
New Housing Listing Prices per Square
Meter (LogLP)
New Housing Listing Prices per Square
Meter (LogLP)
Variables
Ordinary Least Squares (OLS)
Spatial Error Model (SEM)
Coefficients
Probability
Coefficients
Probability
NRM
−0.009
0.331
−0.021
0.001
***
NBT
0.070
0.000
***
0.034
0.001
***
LTR = 0
Omitted
LTR = 1
0.081
0.000
***
0.085
0.000
***
NVW
0.017
0.170
0.031
0.001
***
CSS = 0
Omitted
CSS = 1
0.128
0.000
***
0.047
0.001
***
PNT = 0
Omitted
PNT = 1
0.092
0.014
*
0.090
0.000
***
CBX = 0
Omitted
CBX = 1
0.019
0.293
0.057
0.000
***
LFT = 0
Omitted
LFT = 1
0.069
0.000
***
0.111
0.000
***
CTP = 1
0.016
0.392
−0.034
0.013
*
CTP = 2
−0.100
0.000
***
−0.070
0.000
***
CTP = 3
Omitted
MTL = 1
−0.053
0.031
*
−0.057
0.001
***
MTL = 2
Omitted
MTL = 3
0.099
0.000
***
0.096
0.000
***
MTL = 4
0.199
0.000
***
0.181
0.000
***
BLC = 1
Omitted
BLC = 2
0.194
0.000
***
0.100
0.001
***
BLC = 3
0.388
0.000
***
0.159
0.000
***
BLC = 4
0.606
0.000
***
0.205
0.000
***
BLC = 5
0.831
0.000
***
0.251
0.000
***
BLT = 0
Omitted
BLT = 1
0.088
0.005
**
0.096
0.000
***
ARC = 0
Omitted
ARC = 1
0.054
0.017
*
0.045
0.004
**
EPC = 3
0.025
0.585
0.062
0.050
*
EPC = 2
Omitted
EPC = 1
−0.033
0.018
*
−0.027
0.005
**
LAMBDA
-
-
0.797
0.000
***
Constant
6.900
0.000
7.184
0.000
Number of observations
2092.000
2092.000
Log likelihood
−478.616
22.744
AIC
1001.230
−1.487
R square
0.537
0.754
Adj R square
0.532
-
Breush-Pagan test
98.471
0.000
84.693
0.000
Likelihood ratio test
1002.719
0.000
DIAGNOSTICS FOR SPATIAL DEPENDENCE FOR WEIGHT MATRIX: LP_2015–2018 2092_ (row-standardized weights)
TEST
MI/DF
VALUE
PROB
Moran’s I (error)
0.346
27.118
0.000
Lagrange Multiplier (lag)
1.000
1279.516
0.000
Robust LM (lag)
1.000
560.604
0.000
Lagrange Multiplier (error)
1.000
722.496
0.000
Robust LM (error)
1.000
3.584
0.058
Lagrange Multiplier (SARMA)
2.000
1283.100
0.000
Signif codes: p ≤0.001 ‘***’; p ≤0.01 ‘**’; p ≤0.05 ‘*’; p ≤0.1 ‘.’; p ≤1 ‘ ’.
ResultsshowedthattheSEMmodelcanexplain75%ofthepricevariation(R squared = 0.754)
and regression residuals were not clustered. This result is rather good considering that
the aim of this study was not to predict property values, but just to study the influence


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of a set of characteristics on property prices. The significant variables with the higher
marginal prices were the presence of elevator (LFT (1) = 0.111), the renovated condition of
the unit (MTL (4) = 0.181), the category of the building, that means prestigious buildings
(BLC (5) = 0.250) and the coefficient on the spatially correlated errors (LAMBDA = 0.797),
which were able to manage the spatial dependence in the model.
On the other hand, in relation with the characteristics of the housing units, a clear,
even if small, influence of the EPC label and the presence of an air conditioning system
on housing prices emerged. In particular, low EPC labels from G to E (EPC (1) = −0.027)
significantly and negatively affected housing prices, while high EPC labels from B to A4
(EPC (3) = 0.062) had a positive influence on them, and the presence of air conditioning
had a positive small marginal coefficient (ARC = 0.045). These results are in line with the
recent literature related to the perception of the EPC label in the real estate market, which
is an ongoing research field [74]. For example, Chen and Marmolejo-Duarte highlighted
that in Barcelona, an energy performance improvement from label G to label A brought in
a growth of 8.6% of housing prices in 2014, and an increase of 10.6% from label G to label B
in 2016 [75]. Moreover, Dell’Anna et al. estimated an increase given by the EPC of 6.33%
for each rating level from G to A in Turin for the 2014–2018 time period [11].
By comparing the results of the current analysis with two previous researches based
on the same study area (Turin) with the same source of data (TREMO) [12,13] and with
other recent literature [54,75,76], it is possible to confirm that the attention on the EPC
label rose slightly in recent years and is starting to be monetized by the real estate market,
not only for the lower labels (E, F, G) but also for the higher ones (A-A4, B).
This means that the international energy policies and regulations transposed into
the national laws and legal constrains (EPC is mandatory for all transaction contracts
since 2012, and minimum energy requirements are mandatory for all types of building
interventions in Turin since 2007) applied on the residential building stock in the city of
Turin are finally starting to be recognized by the real estate market.
5.2.1. Spatial Clustering: “Northern Zone” and “Central Zone” Sub-Samples
A further step was carried out by applying the SEM on both the CZ and the NZ sub-
samples, which were previously defined. The CZ sub-sample included 886 observations,
while the NZ sub-sample included 660 observations. Both spatial regressions assumed the
logarithm of unitary listing price (LogLP) as a dependent variable, while, on the basis of
the significance of the independent variables (Stepwise selection), different intrinsic and
extrinsic characteristics were included in the two final models (Table 4).
By comparing the results of the two models, the following variables were significant
both in the NZ and in the CZ sub-sample: the presence of Terrace and Lift, the Maintenance
Level, the Building Type and the 1946–1975 Construction Time Period (CTP (2)). Further-
more, the presence of Custodian Service and Air Conditioning variables were significant
only in the NZ sub-sample, while the Allocation Level, the Number of Bedrooms and
Bathrooms, the Number of Views, and the classification of the unit as a Penthouse were
significant in the CZ sub-sample. Although the Building Category represents one of the
most significant variables, there are also some differences in its significance in the two
sub-samples: in the NZ sub-sample almost all levels were significant, while in the CZ only
higher levels (Noble (4) and Prestigious (5)) were significant. The analysis of the EPC label
highlighted that the high EPC labels (3) were significant only in the CZ sub-sample, while
low EPC labels (1) had a negative influence on the price formation process only in the NZ
sub-sample.


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Table 4. Spatial Error Model, Central Zone (CZ) and Northern Zone (NZ) sub-samples (Source: Authors’ Elaboration).
Listing Prices per Square Meter (LogLP)CENTRAL ZONE
Listing Prices per Square Meter (LogLP)NORTHERN ZONE
Variables
Spatial Error Model (SEM)
Variables
Spatial Error Model (SEM)
Coefficients
Probability
Coefficients
Probability
NRM
−0.033
0.000
***
CSS = 1
0.082
0.025
*
NBT
0.043
0.001
***
CBX = 1
0.063
0.025
*
LTR = 1
0.093
0.000
***
LTR = 1
0.105
0.001
***
NVW
0.029
0.019
**
LFT = 1
0.144
0.000
***
ALL = 2
0.06
0.001
***
MTL = 1
−0.093
0.002
***
ALL = 3
0.054
0.001
***
MTL = 3
0.089
0.001
***
PNT = 1
0.096
0.003
**
MTL = 4
0.21
0.000
***
LFT = 1
0.091
0.000
***
BLT = 1
0.217
0.000
***
MTL = 3
0.101
0.000
***
BLC = 4
0.266
0.000
***
MTL = 4
0.154
0.000
***
BLC = 5
0.281
0.001
***
BLT = 1
0.072
0.036
*
BLC = 2
0.125
0.009
**
BLC = 4
0.06
0.000
***
BLC = 3
0.18
0.000
***
BLC = 5
0.138
0.000
***
CTP = 2
−0.069
0.001
**
CTP = 2
−0.045
0.015
*
EPC = 3
0.039
0.544
EPC = 3
0.079
0.054
.
EPC = 1
−0.037
0.049
*
EPC = 2
0.031
0.034
*
ARC = 1
0.083
0.014
*
ARC = 1
0.024
0.245
LAMBDA
0.627
0.000
***
LAMBDA
0.65
0.000
***
Constant
6.919
0.000
***
Constant
7.581
0.000
***
N of observations
886
-
N of observations
660
-
Log likelihood
119.010
-
Log likelihood
−20.489
-
AIC
−202.021
-
AIC
74.977
-
R square
0.583
-
R square
0.603
-
Breush-Pagan test
36.143
0.004
Breush-Pagan test
36.118
0.003
Likelihood ratio test
209.338
0.000
Likelihood ratio test
167.224
0.000
Signif. codes: p ≤0.001 ‘***’; p ≤0.01 ‘**’; p ≤0.05 ‘*’; p ≤0.1 ‘.’; p ≤1 ‘ ’.
In conclusion, the results confirmed that different spatial clusters behave as different
sub-markets: they both include different listing prices and building categories and are able
to differently monetize the intrinsic characteristics of the buildings.
Firstly, there is a clear connection between the impact of EPC label on housing prices
and the quality of the buildings and apartments. Namely, dwellings with a high energy-
label (EPC = A), which slightly impacts housing prices, are more expensive and boast
the best architectonic attributes. Moreover, those dwellings with high EPC labels are
located in buildings that are older than 60 years and are probably totally refurbished, both
being mainly historical buildings listed by Superintendence of Archaeology, Fine Arts
and Landscape, and being regularly under ordinary maintenance by the owners, often
representative of the richest population.
Secondly, some attributes totally differed in the two sub-samples. The characteristic
related to the air conditioning system is emblematic: it is not monetized by the city centre
sub-market as it is a common dwelling’s feature, in order to guarantee a high internal
comfort to almost all residential units (mainly classy or prestigious). On the opposite hand,
air conditioning is a stronger discriminating factor for dwelling buying decisions in the
northern suburbs, where the housing quality is lower and for which the “housing added
services” seems to compensate by acquiring the greatest importance.
The other dimensional and physical features seem related to the micro-context of the
dwelling. In fact, in the city centre the building oldness and density allow the presence of
non-ordinary apartment plans, with no balconies or with a solely principal view. Further-
more, the building density means that classy buildings are widely closed, and medium or
low floor levels have low lighting and face the opposite buildings. Thus, the presence of a


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Sustainability 2021, 13, 2838
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terrace (in the penthouse) or more than one view are appreciated features, which emerged
in previous studies [77].
Even these last considerations which emerged from the study seem to confirm the
results reported in the most recent literature (previously mentioned in the Section 2),
which reveal an uneven importance of building features—green features included—in
view of the building energy performance and, jointly, to the reference sub-market. Then,
the results can allow us to argue that, on the one side, some attributes are able to capture
a part of the EPC and/or the energy performance features, in terms of marginal prices,
for reasons that should be furtherly explored (for example, by analysing the awareness
degree of the EPC potentialities among buyers); on the other side, to argue the presence of
“latent” variables able to act as a proxy of the building/dwelling energy rating, due to the
knowledge degree of the EPC potentialities which could be surely reinforced.
5.2.2. EPC Clustering: Low Energy-Efficient Dwellings Sub-Sample
A further step of the analysis was conducted by considering the LEE sub-sample,
consisting of 234 observations related to housing units with low EPC labels (E, F and G),
built in the 1946–1990 time period. The identification of this not-spatial sub-sample, based
on an attribute selection, moved from the results of the previous spatial regressions, which
highlighted that the low EPC labels (EPC = 1) and the buildings built between 1946 and
1990 (CTP = 2), to two variables that significantly and negatively influence the listing
price formation.
A spatial regression (SEM) was performed also on this sub-sample, assuming LogLP as
the dependent variable and different intrinsic features as independent variables. The results
(final model with only significant variables) are illustrated in Table 5.
Table 5. Spatial Error Model (SEM) Low Energy Efficient (LEE) Sub-Sample. (Source: Authors’
elaboration).
New Housing Listing Prices per Square Meter (LogLP)
Variables
Spatial Error Model (SEM)
Coefficients
Probability
LTR = 1
0.149
0.001
***
CSS = 1
0.146
0.003
**
CBX = 1
0.114
0.004
**
LFT = 1
0.090
0.016
*
MTL = 3
0.116
0.000
***
MTL = 4
0.189
0.000
***
BLC = 3
0.128
0.000
***
BLC = 4
0.214
0.000
***
BLC = 5
0.387
0.001
***
ARC = 1
0.143
0.005
**
LAMBDA
0.739
0.000
***
Constant
7.082
0.000
***
Number of observations
234
Log likelihood
0.356
AIC
21.287
R square
0.718
Breush-Pagan test
9.599
0.476
Likelihood ratio test
81.314
0.000
Signif. codes: p ≤0.001 ‘***’; p ≤0.01 ‘**’; p ≤0.05 ‘*’; p ≤0.1 ‘.’; p ≤1 ‘ ’
Above the numerous variables and their related levels, the final SEM can explain
the 72% (R squared = 0.718) of the price formation process; results showed the spatial
LAMBDA indicator (Lambda = 0.739), the prestigious (BLC (5) = 0.387) to medium level
(BLC (3) = 0.128) of building category the high level of maintenance (MTL (4) = 0.189),
and the presence of terrace, lift, box and custodian service as the most influential variables.


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Sustainability 2021, 13, 2838
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In conclusion, in this case the spatially correlated errors indicator (expressed by
the LAMDA variable) and the “building quality” (expressed by the prestigious building
category) are able to explain the greatest part of the price variability. It is interesting to
notice that in this sub-sample of energy inefficient housing, refurbished dwellings are also
included with a good level of maintenance and prestigious building category, confirming
that the energy efficiency and the techno-physical features related to the energy behavior of
a building, in the case of existing and historical buildings, are often unrelated to the building
architectural quality. Moreover, also for the sub-sample of existing assets, the “housing
added services” such as the presence of the appurtenance car box, the presence of lift and
custodian service are attributes appreciated by the market.
Finally, it is worth highlighting the presence of two variables probably more related
with the present climate changes: the presence of terrace/balcony and the presence of air
conditioning. Since the average temperatures increased significantly in Turin from +8.07 ◦C
(in 1963) to +10.69 ◦C (in 2018) [78], devices (passive and active) related to home cooling
are increasingly appreciated by the market.
6. Conclusions
This paper aimed to explore the pricing processes of the real estate residential proper-
ties, considering the influence of the EPC labels and relevant building/dwelling attributes
on prices in their spatial context. The study was developed by analysing a sample of
residential listing prices in Turin. Exploratory Spatial Data Analyses (ESDA) and two
regression models (the Ordinary Least Squares model and the Spatial Error Model) were
firstly applied on the whole data sample.
The results highlighted that low EPC labels (E, F and G) significantly and negatively
affect housing prices, while high EPC labels (B, A1, A2, A3 and A4) have a lower but
positive influence on them. Moreover, some intrinsic building/dwelling features emerged
as characteristics particularly able to influence the property price formation: the building
category and the housing unit maintenance level.
Secondly, the regression models were applied on three different sub-samples: two of
them were generated by a spatial approach—based on LISA cluster maps of LP—while
the third was identified by applying an attribute (not spatial) approach based on intrinsic
features, representative of the most energy inefficient housing units. The results showed
that high EPC labels are significant only in the “high-high” spatial cluster characterized
by a positive autocorrelation of high LP values, while low EPC labels have a negative
influence on the price formation process only in the “low-low” spatial cluster, character-
ized by a positive autocorrelation of low LP values. By comparing the influence of the
building/dwelling attributes on property prices in these two spatial sub-samples, some
variables resulted as always significant, but several differences emerged, which means that
different spatial clusters behave as different sub-markets.
The results achieved in the third sub-sample, which included low energy efficient
dwellings, on the one hand confirmed the great influence of prestigious “building category”
and high levels of maintenance, and on the other highlighted the relevance of other
variables, such as the presence of a terrace, lift, box, custodian service and air-conditioning
system. Therefore, the results allow for arguing that the EPC labels are acquiring an
explicative power on property prices, in line with some international studies. Specifically,
by comparing the results with two previous studies conducted in the city of Turin in two
different periods [12,13], a growing effect of EPCs on prices can be seen.
Furthermore, some building features related to the energy performance emerged
which deserve to be deeply explored in future, being connected to two relevant aspects: on
one side the capacity of certain features, including the “green” ones, to capture a part of
the EPC’s explicative power; on the other side, the eventual presence of “latent” variables
acting as a proxy of the building/dwelling energy rating.
Operatively, the methodological approach illustrated in this work can be considered
a support for building/dwelling retrofitting policies, considering the potentialities of