# RECORD d3e27cac15a096168d22a145ab49342c924dc96e
# TITLE: Bridging the information gap: what are the impacts of EPCs on Prague’s residential market selling prices?
# YEAR: 2025.0  VENUE: Financial Internet Quarterly  DOI: 10.2478/fiqf-2025-0026
# AUTHORS: Tomáš Podškubka; Barbora Rýdlová; Josef Arlt
# PAGES: 12  CANDIDATE TABLE PAGES (1-based): [7, 8]
# ABSTRACT: Abstract It is assumed that energy performance certificates (EPC) can promote energy efficiency, increase property values, and improve market liquidity by providing trustworthy energy performance information. This article will provide a comprehensive study of the impact of EPCs on the residential selling prices in Prague, the capital of the Czech Republic. For the analysis, the original dataset was extracted from the publicly accessible sreality.cz portal. The hedonic regression model was applied to evaluate how the EPC influences the selling price of apartments in Prague. The regression analysis results are entirely in line with the analyzed current literature. EPC ratings have been proven to significantly affect the selling price in Prague and several other locations. In the most affluent locations of Prague, i.e., typically historical locations, the EPC ratings have not proved to be a statistically significant price factor because these locations suffer from excess demand and limited supply of properties.


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 39 
 
Financial Internet Quarterly 2025, vol. 21 / no. 4, p. 39
-50 
10.2478/fiqf-2025-0026 
BRIDGING THE INFORMA
TION GAP: WHAT ARE THE                  
   
IMPACTS OF EPCS ON P
RAGUE ’S RESIDENTIAL MARKET
               
SELLING PRICES?
 
Tomáš Podškubka
1, Barbora Rýdlová
2, Josef Arlt
3 
Abstract 
It is assumed that energy performance certificates (EPC) can promote energy efficiency, increase 
property values, and improve market liquidity by providing trustworthy energy performance 
information. This article will provide a comprehensive study of the impact of EPCs on the resi-
dential selling prices in Prague, the capital of the Czech Republic. For the analysis, the original 
dataset was extracted from the publicly accessible sreality.cz portal. The hedonic regression 
model was applied to evaluate how the EPC influences the selling price of apartments in Prague. 
The regression analysis results are entirely in line with the analyzed current literature. EPC 
ratings have been proven to significantly affect the selling price in Prague and several other loca-
tions. In the most affluent locations of Prague, i.e., typically historical locations, the EPC ratings 
have not proved to be a statistically significant price factor because these locations suffer from 
excess demand and limited supply of properties.   
JEL classification: C31, Q40, R21, R31  
Keywords: Energy Performance Certificate, Energy Efficiency, Czech Housing Market, Residential Properties, Hedonic Regression Model  
Received: 30.01.2025                                                                                                                                                                                            Accepted: 28.04.2025   
Cite this: 
Podškubka, T., Rýdlová, B. & Arlt, J. (2025). Bridging the information gap: what are the impacts of EPCs on Prague’s residential market selling prices? 
Financial Internet Quarterly 21(4), pp. 39-50.   
© 2025 Tomáš Podškubka et al. published by Sciendo. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 
4.0 License.  
www.finquarterly.com 
University of Information Technology and Management in Rzeszów 
1 Department of Finance and Business Valuation, Faculty of Finance and Accounting, Prague University of Economics and Business, Czechia, email: 
tomas.podskubka@vse.cz, ORCID: https://orcid.org/0000-0002-8761-2661.  
2 Department of Finance and Business Valuation, Faculty of Finance and Accounting, Prague University of Economics and Business, Czechia, email: 
barbora.rydlova@vse.cz, ORCID: https://orcid.org/0000-0003-0701-730X. 
3 Department of Finance and Business Valuation, Faculty of Finance and Accounting, Prague University of Economics and Business, Czechia, email: 
josef.arlt@vse.cz, ORCID: https://orcid.org/0000-0001-8349-7419. 


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 45 
 
www.finquarterly.com 
University of Information Technology and Management in Rzeszów 
Tomáš Podškubka, Barbora Rýdlová, Josef Arlt  
Bridging the information gap: what are the impacts of EPCs on Prague’s residential 
market selling prices?  
Financial Internet Quarterly 2025, vol. 21 / no. 4 
further affect the F-test and can cause misleading inter-
pretation of the index of determination R2. Multicollin-
earity, the high correlation among the independent 
variables, can lead to inaccurate regression parameter 
estimates and overestimating their standard errors. It 
also causes problems with the interpretation of partial 
regression parameters. Namely, it isn't easy to under-
stand the individual impact of the particular independ-
ent variable, provided that the other independent vari-
able is constant when these variables are correlated. 
 
Results
 
The results of the regression analysis are presented 
in Table 3. All computations were performed using the 
software package EViews 13. The Jarque-Bera test 
(Jarque & Bera, 1980) rejects the hypothesis of error 
term normality in most cases. However, histograms, 
skewness and kurtosis of the error terms indicate that 
their distribution is not far from normal. Since the Cen-
tral Limit Theorem ensures that normality is not re-
quired if the sample size is large enough, it can be stat-
ed that non-normality does not distort the obtained 
results. The Breusch-Pagan-Godfrey (Breusch & Pagan, 
1979; Godfrey, 1978) and Glejser tests (Glejser, 1969) 
reject homoscedasticity hypotheses in most cases. 
However, for heteroscedasticity to cause diagnostic 
test distortion , it must be extreme (as noted by Lumley 
et al., 2002), which is not the case in this study. The 
data do not contain extreme values, and the distribu-
tions of the error terms is close to normal, as men-
tioned above. The Breusch-Godfrey autocorrelation 
test (Breusch, 1978; Godfrey, 1978) does not reject the 
hypothesis of no autocorrelation in all cases.  
Methodology
 
To evaluate how the energy performance class 
influences the selling price of apartments in Prague, the 
following hedonic regression model is applied:  
(1) 
where the dependent variable pi, indexed by apartment 
(i) represents the price. The independent variables xij 
represent area_net, sep_kitchen, sqm_per_-room, 
state, location and variable of interest EPC. The param-
eter c is the intercept and βj, j = 1, …, k are the partial 
regression parameters. In this model, for a unit in-
crease in the independent variable xij, the expectation 
of the dependent variable changes by the percentage 
100(eβj - 1), provided that all other independent varia-
bles are held constant. When |βj| < 0.1 this percentage 
approaches to 100βj. The Ɛi are observation-specific 
zero-mean random error terms, which are a set of 
equally distributed independent random variables, i.e. 
Ɛi ~ IID(0, σ2). 
It is assumed that the error terms of the regression 
model (1) are normally distributed. This assumption is 
necessary to perform correct t-tests for the parameters 
of this model, when it is violated, the testing criterion:  
 
can be biased. Heteroscedasticity, the failure to satisfy 
the condition of constant variances of the error terms 
(homoskedasticity), i.e., D(Ɛi) = σ2
i can bias the standard 
error estimates sβj, resulting in incorrect t-tests. The 
presence of autocorrelation of the error terms, i.e. E
(ɛiɛj) ≠ 0, i ≠ j, can also bias  the standard error esti-
mates. Both heteroscedasticity and autocorrelation can 
Number of 
Location 
Description 
4 
Bohnice, Čimice, Hloubětín, Hodkovičky, Hostivař, Kamýk, Košíře, Krč, Malešice, Motol, Prosek, 
Radlice, Řepy, Stodůlky, Střížkov, Štěrboholy, Veleslavín, Vokovice, Vysočany, Zličín 
5 
Běchovice, Čakovice, Černý Most, Dolní Chabry, Dolní Měcholupy, Dolní Počernice, Háje, 
Hlubočepy, Horní Měcholupy, Horní Počernice, Hostavice, Hrdlořezy, Chodov, Klánovice, Koloděje, 
Kolovraty, Kunratice, Kyje, Letňany, Lhotka, Modřany, Radotín, Ruzyně, Řeporyje, Slivenec, Such-
dol, Šeberov, Velká Chuchle, Vinoř, Zbraslav 
6 (least 
attractive) 
Dubeč, Cholupice, Jinonice, Kbely, Královice, Křeslice, Liboc, Libuš, Lipence, Lipenice, Lysolaje, 
Miškovice, Petrovice, Písnice, Pitkovice, Satalice, Sedlec, Sobín, Točná, Třebonice, Třeboradice, 
Uhříněves, Újezd nad Lesy, Újezd u Průhonic 
Source: Authors’ own work. 
1
ln
,
1,...,
k
i
j
ij
i
j
p
c
x
i
N


=
=
+
+
=



j
j
t
s

=
Table 3: Regression results of log price per square meter in Czech crowns in Prague 
  
Prague 
Location 
    
1 
2 
3 
4 
5 
6 
c 
11.619*** 
11.485*** 
11.691*** 
11.436*** 
11.435*** 
11.398*** 
11.292*** 
area_net 
1.26E-5 
7.83E-5 
0.000 
0.001** 
0.000 
-0.001*** 
-0.000 
sep_kitch 
-0.019 
-0.002 
-0.069 
-0.017 
-0.035 
0.022 
0.036 
sqm_room 
0.015*** 
0.013*** 
0.009*** 
0.013*** 
0.015*** 
0.018*** 
0.016*** 


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 46 
 
www.finquarterly.com 
University of Information Technology and Management in Rzeszów 
Tomáš Podškubka, Barbora Rýdlová, Josef Arlt  
Bridging the information gap: what are the impacts of EPCs on Prague’s residential 
market selling prices?  
Financial Internet Quarterly 2025, vol. 21 / no. 4 
EPC (-0.5355) distorts the results, increasing the param-
eter estimates for EPC (decreasing them in absolute 
value) and increasing the estimates of their standard 
errors. The solution is to exclude state from the model 
and analyze only the apartments for which state takes 
the value 1 (after reconstruction, project, under con-
struction, new construction). The results of this regres-
sion analysis are presented in Table 4.  
There is a certain risk of multicollinearity. The ex-
planatory variable sep_kitchen is correlated with the 
variable state (-0.4068) and with the EPC (0.4108), and 
the variable area_net is correlated with sqm_per_room 
(0.6532). However, the parameter estimates for both 
variables are, with two exceptions, statistically insignifi-
cant, and their exclusion has no effect on the model 
parameter estimates. The correlation of the state with 
  
Prague 
Location 
    
1 
2 
3 
4 
5 
6 
state 
0.180*** 
0.266*** 
0.073* 
0.180*** 
0.178*** 
0.128*** 
0.178*** 
location 
-0.056*** 
  
  
  
  
  
  
EPC 
-0.024*** 
0.021 
-0.001 
-0.016 
-0.502 
-0.064*** 
-0.026 
Mean dep 
12.024 
12.227 
12.154 
11.994 
12.034 
11.967 
11.942 
SD dep 
0.459 
0.506 
0.381 
0.421 
0.464 
0.493 
0.394 
R2 
0.491 
0.383 
0.359 
0.482 
0.548 
0.498 
0.487 
F-test 
672.913*** 
32.042*** 
40.147*** 
169.256*** 
309.155*** 
210.649*** 
57.127*** 
D-W 
1.995 
2.981 
1.886 
2.079 
2.115 
1.828 
2.398 
N 
4189 
264 
364 
914 
1281 
1059 
307 
Notes: 1) The LS point estimates of parameters of regression model (1). 2) Asterisks denote statistical significance: 
*** p < 0.01, ** p < 0.05, * p < 0.1. 3) Mean dep – mean of the dependent variable, SD dep – standard deviation of 
the dependent variable, R2 – index of determination, D-W – Durbin Watson test, N – number of observations 
Source: Authors’ own work. 
  
Prague 
Location 
1 
2 
3 
4 
5 
6 
    
c 
11.972*** 
12.080*** 
11.881*** 
11.814*** 
11.815*** 
11.728*** 
11.536*** 
area_net 
3.71E-05 
0.001** 
0.001 
0.001 
0.001 
-0.001*** 
-0.001 
sep_kitch 
-0.055* 
-0.189 
-0.022 
0.034 
-0.084 
0.013 
0.145 
sqm_room 
0.014*** 
0.006** 
0.008*** 
0.010*** 
0.015*** 
0.018*** 
0.016*** 
location 
-0.056*** 
  
  
  
  
  
  
EPC 
-0.080*** 
-0.039 
-0.028 
-0.065*** 
-0.139*** 
-0.142*** 
-0.055 
Mean dep 
12.156 
12.417 
12.201 
12.139 
12.186 
12.107 
12.030 
SD dep 
0.446 
0.499 
0.366 
0.385 
0.439 
0.502 
0.368 
R2 
0.393 
0.253 
0.301 
0.387 
0.467 
0.408 
0.423 
F-test 
329.703*** 
10.649*** 
24.976*** 
73.744*** 
178.399*** 
115.832*** 
39.508 
D-W 
1.960 
3.969 
1.265 
2.339 
1.886 
1.495 
1.989 
N 
2557 
131 
237 
472 
818 
678 
221 
Table 4: Regression results of log price per square meter in Czech crowns in Prague 
Notes: 1) The point estimates of parameters of regression model (1). 2) Asterisks denote statistical significance:             
*** p < 0.01, ** p < 0.05, * p < 0.1. 3) Mean dep – mean of the dependent variable, SD dep – standard deviation of 
the dependent variable, R2 – index of determination, D-W – Durbin Watson test, N – number of observations. 4) Sta-
te: new construction, after reconstruction, project, under construction 
Source: Authors’ own work. 
supply is limited, and property is scarce, which dimin-
ishes the signaling and information asymmetry power 
of EPCs and boosts the importance of location itself. 
These findings are directly supported by several au-
thors (e.g. Marmolejo-Duarte & Chen, 2019, Wilhelms-
son, 2019 and Taruttis et al., 2022). It is worth noting 
that the mentioned studies were performed in Spain, 
Norway and especially Germany, which is a neighboring 
market for the Czech Republic and the most important 
Results of the adjusted regression analysis con-
tained in Table 4 are entirely in line with the analyzed 
current literature. This paper has proven that EPC 
ratings affect the selling price in Prague, taken as one 
location, and in Locations 3, 4, and 5 with the parame-
ters t-test p-values lower than 0.01. On the other hand, 
affluent Prague Locations 1 and 2 achieved statistically 
immaterial results for EPC influence, which can be in-
terpreted such that in prominent locations, real estate 


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 47 
 
www.finquarterly.com 
University of Information Technology and Management in Rzeszów 
Tomáš Podškubka, Barbora Rýdlová, Josef Arlt  
Bridging the information gap: what are the impacts of EPCs on Prague’s residential 
market selling prices?  
Financial Internet Quarterly 2025, vol. 21 / no. 4 
a sophisticated analysis of the unique data from Pra-
gue, Czech Republic. Prague’s real estate market is 
quite specific as it is characterized by high prices and 
a significant affordability gap compared to other major 
EU cities. This analysis is framed by the theory of infor-
mation asymmetry and the signaling theory, as the 
EPCs should mitigate information asymmetry in the real 
estate market and signal the energy performance of 
buildings. Using the most frequent hedonic regression 
analysis, the relationship between energy efficiency 
measured by EPCs and property prices on data from 
Prague while focusing on several micro locations has 
been tested. 
Our results can be summarized in the following 
three points: 
1. EPCs have an impact on prices - EPC in Prague apart-
ments statistically significantly influences the price. 
This finding holds in 3 out of 6 micro-locations and 
the Prague region as a whole. 
2. The degree of influence differs by micro-location - In 
the most affluent locations of Prague, i.e., typically 
historical (central) locations, the EPC ratings have 
not proved to be a statistically significant price fac-
tor. This can be easily explained:  these locations 
suffer from excess demand and limited supply of 
properties and are usually viewed as seller markets. 
Buyers have no option but to disregard most typical 
price drivers and focus only on location. In addition, 
historical locations are composed of old dwellings, 
and meeting state-of-the-art building standards 
through complete refurbishment is a cost and tech-
nical issue due to heritage protection. 
3. Apartment size also impacts prices - The size of the 
apartment, measured by square meters per room, 
was the other statistically significant explanatory 
variable of real estate prices in Prague. This aligns 
with theory and market practice, as smaller flats sell 
at a premium per unit price.  
The findings of this study imply that policymakers 
should continue to promote energy efficiency through 
EPC regulations and strive to increase public awareness 
of their long-term benefits, as higher EPC ratings are 
associated with increased property values. Incentives 
for property owners to improve their EPC ratings could 
further enhance these benefits. However, the results of 
this study suggest that the policy makers should design 
the EPC related programs taking into account regional 
market specifics. In Prague’s historical center we ob-
served no market driven motivation of the real estate 
owners for energy performance improvement, since 
the transaction motives lies in the premium location, 
the price effect of energy inefficiency proving negligi-
ble.  Given the unique challenges faced by historical 
locations, support measures should be directed to-
wards these areas to help property owners compen-
trade partner encompassing almost 80% of Czech inter-
national trade within the EU. Considering this, compa-
rable regulation applies, and similar real estate 
patterns can be expected. Correspondingly, the model 
without separated locations, which was constructed for 
the whole Prague region, demonstrated the high statis-
tical significance of the parameter estimate of the loca-
tion factor, which is the most well-known value driver 
in real estate pricing (Fuerst et al., 2015 or Olaussen et 
al., 2019). 
The notion of asymmetrical effect of EPC is also 
presented by Fuerst et al. (2016), McCord et al. (2020), 
and Cespedes-Lopez et al. (2019). Their findings are, 
however, somewhat different because they claim that 
only properties with higher EPC scores display positive 
significant effects at the higher end of the price distri-
bution, but there are also brown discount effects evi-
dent for lower-rated properties within F and G-rated 
EPC properties, which contradicts findings in this paper. 
The other influential real estate price driver proved 
to be the size of the apartment as measured by square 
meters per room (sqm_room). This outcome is also 
logical and expected as smaller flats are typically sold at 
a premium to larger ones measured by unit price. The 
parameter estimates of this factor are statistically sig-
nificant in all regressions having p-value lower than 
0.01. 
The parameter estimates of the remaining two 
explanatory variables (area_net, sep_kitch) showed 
predominantly no statistical significance.  
 
Conclusions
 
The EPC regulation imposed in the EU aims to miti-
gate climate change and global warming issues. It is 
assumed that EPCs can promote energy efficiency, in-
crease property values, and improve market liquidity 
by providing trustworthy energy performance infor-
mation. However, increased energy efficiency is also 
associated with higher costs, so the economic rationale 
behind this idea must be sound. In any case, the energy 
efficiency of buildings will remain the central focus of 
EU policymakers, practitioners, and scholars, and the 
importance of EPCs will continue to increase. 
The literature review showed that many authors 
have already studied this issue. Nevertheless, the re-
sults remain inconclusive, and research gaps have been 
identified. The analyzed papers proved robust evidence 
that higher EPCs accompany higher real estate prices, 
though the extent of this impact varies by region, prop-
erty type, and methodological approach and is some-
what uneven. This paper will address the gap identified 
in the current literature, which is the need for more 
granular data from a different market and climate envi-
ronment (e.g., Marmolejo-Duarte & Chen, 2019, Wil-
helmsson, 2019; Taruttis et al., 2022) and provide