# RECORD 942f0ff361a49b68c1333c31381f78c02a3d0937 # TITLE: How different are dwellings whose energy efficiency impacts price formation? # YEAR: 2019.0 VENUE: IOP Conference Series: Materials Science and Engineering DOI: 10.1088/1757-899x/603/4/042015 # AUTHORS: Ai Chen; Carlos Marmolejo-Duarte # PAGES: 11 CANDIDATE TABLE PAGES (1-based): [7] # ABSTRACT: Housing energy-efficiency has become a relevant issue since it is mandatory to exhibit an Energy Performance Certificate (EPC) when transacting real estate in the European Union. A number of studies have focused on energy-efficiency marginal prices using hedonic models from cross-sectional and longitudinal perspectives. Some of them have found that the increase of relevance of EPC ranks (i.e. marginal prices) is not the same for all the A-G classes. This study aims to explore the differences in terms of architectonic and location attributes between the apartments depicting an increase of EPC ranking marginal prices regarding those where energy efficiency seems not to play a role in price formation. In doing so, a pooled spatial error hedonic model is done using selling information for multifamily housing in Barcelona for the years of 2014 and 2016. Furthermore, it is shown which EPC ranks do exhibit an increase in terms of marginal prices in the period studied. Finally, we compare the architectonic and locative attributes for the set of homes where the energy efficiency has increased in terms of price importance to the set of homes where it has not increased. The results suggest that dwellings with high and medium EPC ranks (e.g. A for 2014; and B, C and D for 2016) are more expensive, larger and boasting of better architectonic qualities than the rest of homes where EPC ranks failed to have a role in price formation. On the contrary, the location attributes are different: while A-ranked dwellings of 2014 are located on peripheries where new housing completions are placed; B, C and D-ranked homes of 2016 are located in more centric locations. These findings have implications for future analysis regarding the energy premium and energy poverty, since specific characteristics in different submarkets may have a different impact on housing prices. ===== PAGE 1 (score 3, numeric-paren 0, table-words 0, var-words 3) ===== IOP Conference Series: Materials Science and Engineering PAPER • OPEN ACCESS How different are dwellings whose energy efficiency impacts price formation? To cite this article: Ai Chen and Carlos Marmolejo-Duarte 2019 IOP Conf. Ser.: Mater. Sci. Eng. 603 042015 View the article online for updates and enhancements. You may also like The improved anode performance enabled by Ni2P@C embedded in echinus-like porous carbon for lithium-ion battery Zhuo Chen and Haibo Li - Enhancement of electron–phonon coupling due to increased magnetism and applied hydrostatic pressure in FeSe Xiao-Fang Ouyang, Ze-Yi Song and Yu- Zhong Zhang - Improving the performance of quantum well solar cells with photonic crystal Weiye Liu, Jiaping Guo, Ding Ding et al. - This content was downloaded from IP address 132.203.214.169 on 20/10/2026 at 20:22 ===== PAGE 7 (score 38, numeric-paren 0, table-words 2, var-words 23) ===== WMCAUS 2019 IOP Conf. Series: Materials Science and Engineering 603 (2019) 042015 IOP Publishing doi:10.1088/1757-899X/603/4/042015 6 EPC rankings (ranked “A” in 2014 while “B”, “C” and “D” in 2016) have a significant impact on housing price formation. Table 2. Spatial Error Estimation Results Spatial Error Model - Maximum Likelihood Estimation R Square 0.764 Log-likelihood -928 Sigma Square 0.075 AIC 1917 S.E of regression 0.274 Coefficients B Std. Error Z-Value Prob. Lambda 0.462 0.016 28.317 - (Constant) 10.125 0.056 182.398 - Year 2016 0.041 0.015 2.684 0.007 Structural architectonic characteristics of apartments and buildings Area 0.015 0.000 37.546 - Area^2 - 0.000 0.000 - 19.709 - Number of bathrooms 0.098 0.008 11.842 - Air conditioner 0.074 0.008 8.905 - Central heating 0.041 0.009 4.686 - Retrofitted apartment 0.034 0.010 3.437 0.001 Swimming pool 0.186 0.015 11.976 - Lift 0.089 0.008 10.452 - Inverse of building age 0.238 0.045 5.340 - Energy performance of apartments Energy class A 0.086 0.034 2.492 0.013 Energy class C - 0.011 0.029 - 0.375 0.708 Energy class D 0.000 0.019 0.020 0.984 Energy class E 0.007 0.013 0.572 0.567 Energy class F 0.019 0.016 1.132 0.258 Energy class A * Year 2016 0.067 0.044 1.515 0.130 Energy class B * Year 2016 0.107 0.041 2.619 0.009 Energy class C * Year 2016 0.106 0.041 2.597 0.009 Energy class D * Year 2016 0.105 0.026 4.071 - Energy class E * Year 2016 0.008 0.018 0.462 0.644 Energy class F * Year 2016 - 0.033 0.024 - 1.386 0.166 Locative attributes (transport, centrality and amenities) Commuting time 0.004 0.001 2.597 0.009 <200m from sea shore 0.131 0.029 4.532 - Highway ramp 0.081 0.022 3.712 - <800 m from railway station 0.033 0.011 3.120 0.002 Centrality index 0.025 0.003 8.680 - Average gross area floor ratio 0.048 0.005 9.029 - Socio-economic attributes PC1 High income 0.104 0.006 17.356 - PC2 Med income 0.072 0.007 10.979 - Note: independent variables/covariates are introduced using the stepwise method. Energy class G is the controlled group. ‘–’ indicated the significant is less than 0.000. Variables with grey colour are insignificant at 90% of confidence. Source: Own elaboration. 4.2. How different are the dwellings which energy efficiency gains relevance in price formation? In order to explore significant differences in terms of achitectonic and locative attributes first the 6,492 aparments are clustered in 4 groups. Group 1 (N = 73) is for the energy-labelled dwellings (ranking A) which impact significantly on housing prices in 2014 while the others are for Group 2 (N = 3,167) where no impact was found in the same year. Dwellings in the 2016 dataset are groupped in the same way, resulting in Group 3 (ranking B, C, D) where EPC ranking was found to have an impact on price formation and Group 4 where no impact was found- Each of such gropus are formed by 485 and 2,718 apartments respectively. Next, an ANOVA test (at 90% confidence level) is implemented among these groups) for each of the architectonic and locative attributes. 4.2.1. The specific characteristic performance in year periods. According to the first column of table 3, in the 2014 dataset all the architectonic attributes (excluded the number of bedrooms) are significantly different between homes clustered in Group 1 and 2. Conversely, in the remainder of accessibility and ===== PAGE 8 (score 58, numeric-paren 0, table-words 1, var-words 27) ===== WMCAUS 2019 IOP Conf. Series: Materials Science and Engineering 603 (2019) 042015 IOP Publishing doi:10.1088/1757-899X/603/4/042015 7 socioeconomic dimensions just the centrality index, population density and employment show significant differences. The average unit price for an “A”-ranked dwelling with a 90 square meters’ size is 2,208 euros per square meter in 2014, 300 euros more per square meter than that in Group 2. Similarly, physical attributes (e.g. number of bathrooms, area of outdoor spaces and living room) and amenities (e.g. air conditioning and heating) are more present in Group 1 where the probability to find a heated dwelling doubles in Group 2 in relation to Group 1. Regarding the accessibility and socioeconomic dimensions, the “A”- ranked dwellings in 2014 are located in metropolitan peripheries where the average centrality index is 10.73, less than the referenced Group 2. The same is true for population and employment densities. All in all, energy-efficient homes in 2014 are basically located at peripheral zones where buildings are constructed under newer construction codes requiring efficient thermal performances. Table 3. Statistical Description for the groups in 2014 and 2016 Variables 2014 2016 Group 1 (N=73) Group 2 (N=3,167) ANOVA TEST Group 3 (N=485) Group 4 (N=2,718) ANOVA TEST F Sig. F Sig. Architectonic Attributes Total_price 197,784 162,047 11.55 0.001 275,192 221,404 51.58 0.000 Unit_price 2,209 1,908 14.87 0.000 3,029 2,514 67.04 0.000 Superficie 89.84 83.94 3.20 0.074 90.64 86.40 7.37 0.007 No_bedrooms 2.89 2.92 0.06 0.801 2.82 2.89 2.21 0.137 No_bathrooms 1.52 1.29 14.82 0.000 1.50 1.33 38.34 0.000 Dum_air_conditioning 0.67 0.30 47.38 0.000 0.60 0.45 37.82 0.000 Dum_heat 0.92 0.42 75.30 0.000 0.81 0.66 41.27 0.000 Dum_reform 0.30 0.10 30.71 0.000 0.28 0.18 30.19 0.000 Dum_lift 0.86 0.46 48.34 0.000 0.79 0.65 36.59 0.000 Ages of buildings 30.84 45.55 49.25 0.000 40.70 46.45 21.66 0.000 Construction_before 1981 0.51 0.84 57.91 0.000 0.62 0.77 48.44 0.000 Construction_between 1982-2006 0.25 0.12 11.16 0.001 0.21 0.17 4.35 0.037 Construction_after 2007 0.25 0.04 68.77 0.000 0.17 0.06 69.12 0.000 Storied 2.47 2.14 2.82 0.093 2.11 2.19 0.46 0.497 Areas_outdoor 12.60 9.40 3.63 0.057 9.71 9.45 0.06 0.800 Areas_living 15.18 12.11 6.75 0.009 13.19 11.69 6.18 0.013 Dum_grand_terrance 0.18 0.07 13.35 0.000 0.12 0.14 0.48 0.487 Dum_swim_pool 0.08 0.04 3.02 0.082 0.15 0.10 8.03 0.005 Dum_gard 0.27 0.09 30.77 0.000 0.20 0.15 7.17 0.007 Accessibility Attributes Time_commuting 24.61 24.00 1.32 0.250 24.57 24.65 0.19 0.666 Dum_sea (in 200 meters) 0.03 0.01 1.49 0.223 0.04 0.04 0.26 0.608 Dum_highway 0.96 0.93 1.18 0.278 0.94 0.94 0.10 0.758 Dum_trans_stations 0.53 0.50 0.36 0.547 0.57 0.56 0.33 0.568 Index_Central 10.73 11.39 5.52 0.019 12.47 11.94 16.39 0.000 Ratio_floor_areas 1.84 1.99 0.87 0.350 2.43 2.33 1.54 0.214 Socioeconomic Attributes Proportion of university degree 9.77 10.88 1.49 0.222 15.85 14.21 11.75 0.001 Density of population 16,884 22,051 3.70 0.054 21,623 24,730 7.42 0.006 Density of employment 7,021 9,569 4.89 0.027 9,456 10,742 6.77 0.009 PCA High income -0.23 -0.11 1.64 0.201 0.32 0.13 13.32 0.000 PCA Med income -0.22 -0.24 0.03 0.865 0.05 0.01 0.73 0.393 Pr_Manager 7.28 7.90 1.40 0.237 10.26 9.39 10.92 0.001 Pr_professiones 9.95 11.01 1.33 0.249 15.67 14.14 10.55 0.001 Pr_technics 13.31 13.29 0.00 0.974 14.97 14.35 9.60 0.002 Pr_admin 11.14 10.98 0.25 0.614 11.32 11.31 0.01 0.935 Pr_commer 15.20 15.05 0.15 0.702 14.21 14.77 8.48 0.004 Pr_agricultura_fisher 0.67 0.75 0.64 0.425 0.67 0.69 0.33 0.568 Pr_craftman 18.06 17.47 0.62 0.430 13.69 14.82 11.51 0.001 Pr_operation 13.73 13.28 0.38 0.535 10.22 10.88 4.69 0.030 Pr_unquality 10.61 10.17 0.68 0.408 8.89 9.55 8.54 0.004 Notes: Variables with grey colour are insignificant at 90% of confidence. Source: Own elaboration. In the 2016 dataset for Groups 3 and 4, the same significant differences in the “architectonic attributes dimension” which are found in 2014, are identified but the number of storey, area of outdoor spaces and