JOURNAL OF SUSTAINABLE REAL ESTATE 2022, VOL. 14, NO. 1, 95–112 ARES https://doi.org/10.1080/19498276.2022.2135188 American Real Estate Society Alexander Groh, Hunter Kuhlwein and Sven Bienert International Real Estate Business School, University of Regensburg, Regensburg, Germany KEYWORDS ABSTRACT Economic viability; energy Several studies have investigated the relationship between the energy performance of build- performance certificates; ings and housing prices. First, this paper identifies a price premium for energy efficiency generalized additive model; within the German rental market. Then, the indexed price differences and associated mar- hedonic pricing model; ginal benefits are compared with the marginal costs of energy retrofits. An extensive data- marginal cost base of Germany’s largest online platform for housing over a time span from 2016 to 2020 is used in a hedonic regression approach. In addition, to extract the marginal costs of energy consumption abatement, a dataset of 1048 rental units regarding green-retrofit measures is utilized. Although a significant green premium is identified in the rental market, the findings suggest that it is not high enough to compensate landlords for the money they have to spend to retrofit. The marginal costs exceed the marginal benefits by far. Furthermore, it is found that the German government’s recent plans to split the carbon tax between landlords and tenants do not change this because the price per metric ton of carbon is insufficiently high. Limitations with respect to the data basis and consequently to the interpretation of the results exist. Nevertheless, the findings can help both tenants and landlords in their decision- making, as well as policy makers in the implementation of decarbonization efforts. Introduction of the building sector. To meet this challenge, the European Commission presented its strategy for a The most recent Assessment Report of the so-called “Renovation Wave” for climate neutrality Intergovernmental Panel on Climate Change (IPCC) and market recovery on October 14, 2020, as part of made it clear once again that the world’s climate is the European “Green Deal” (European Commission, in danger and that drastic steps will be necessary to 2020). Accordingly, the annual building renovation stem the tide of global warming (Masson-Delmotte rate is to be at least doubled by 2030. Currently, et al., 2021). The building sector plays a particularly about 75% of buildings in the EU are not energy important role in this. After all, 27% of total global efficient, but 85–95% of today’s existing buildings energy-related CO emissions come from the oper- will still be in use in 2050. Tools like the Carbon ation of buildings, and a further 10% from the con- Risk Real Estate Monitor (CRREM) (see Hirsch et al., struction industry as of 2020 (United Nations 2019), and the wide availability of Energy Environment Programme, 2021). To achieve the goal Performance Certificates (EPCs) have increased both of the Paris Agreement (UNFCCC, 2015), to limit the transparency and the ability to identify buildings in global temperature rise to well below 2 Ccompared need of an energetic update. At the same time, the with pre-industrial times (and to make efforts to rate of annual energy retrofits in the residential limit the temperature rise to 1.5 degrees), the building stock in both Europe and Germany is at European Union (EU) submitted a Nationally only about 1% of the total stock (European Determined Contribution (NDC) which states that Commission, 2019). The Revised Energy the European economy should reach net zero by the year 2050 (European Commission). This goes Performance of Buildings Directive mandates that hand-in-hand with the widespread decarbonization the worst performing 15% of the residential CONTACT Alexander Groh firstname.lastname@example.org International Real Estate Business School, University of Regensburg, Universitatsstraße 31, 93953 Regensburg, Germany. 2022 The Author(s). Published with license by Taylor & Francis Group, LLC This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 96 A. GROH ET AL. building stock have to be upgraded until 2030 from This is the case because the new German govern- the current EPC label G to at least label F (European ment that is made up of three parties, namely Commission, 2021). Social Democrats (SPD), Green party and Liberals For Germany, the rented residential building (FDP) recently announced that the carbon tax bur- stock plays an outstandingly important role for cli- den will be split between landlords and tenants on mate impact reduction in comparison to other a consumption-independent basis (SPD et al., 2021). European countries, as the homeownership rate is This paper analyses how energy performance is below 50% and thus the lowest in the Eurozone transferred to the rent of an apartment and tests if (Andrews & Caldera-Sanchez, 2011; Eurostat, 2019). green premia are present. To do so, an extensive In contrast to owner-occupied dwellings, there is a dataset of rental listings in Germany is examined, problem with the energy efficient renovation of wherein the energetic conditions from Energy rented buildings that is frequently mentioned in the Performance Certificates (EPC) are utilized as central literature: The Split Incentive Problem or Landlord- exogenic variables. In addition, a dataset with Tenant Dilemma (Schleich & Gruber, 2008). This energy modernization data of multi-family houses in dilemma is indeed an obstacle to the renovation of Germany is used to calculate marginal costs for many rented buildings. However, one party, the energetic improvements in the context of building landlord, must invest the costs of a retrofit, he can- renovation. The central research question of the not benefit directly from the advantages this invest- present study is threefold. The first part explores ment brings. The tenant, on the other hand, the question of whether, in the German market for benefits directly from the energy renovation, as he rented apartments in multi-family buildings, there is faces lower heating costs and enjoys improved ther- a price premium for energy efficiency (green pre- mal comfort after a retrofit. But he, the tenant, has mium). Provided that a price premium is indeed no influence on the investment to achieve energy found, the subsequent research-focus is on whether efficiency. Consequently, there must be another the rent increase potential from an improvement in channel to compensate the landlord for the invest- energy efficiency is sufficient to offset the costs of a ment or he would not retrofit his property in the retrofit, over the expected useful life of the asset. If first place. Although there are tools that could be this is rejected, it is necessary to investigate used to reduce the split incentive problem, such as whether the regulatory framework that is currently green leases that include a cost-benefit sharing in place in Germany provides sufficient incentives mechanism, they require a certain amount of for the implementation of energy efficiency meas- expertise and are not very common in Germany ures. In this respect, it will be examined whether (Cajias et al., 2019). At the beginning of 2021, a uni- the current level and design of the CO tax on fossil form CO tax was introduced on fuels for heat gen- fuels for residential heating in Germany provide a eration, which is levied on the heating costs. The sufficiently strong incentive for owners of energetic- costs for the CO tax were intended to be shared ally poor multi-family houses to retrofit their proper- equally between tenants and landlords, but this rul- ties for energy efficiency. ing was overturned. For the time being, 100% of The paper is organized as follows. The next sec- the tax burden is borne by the tenant. Thus, a rent tion introduces the theoretical background and increase remains the most effective way for land- reviews literature on the topic. Then, the two data- lords to recover the investment costs for green ret- sets are described, whereas in the subsequent sec- rofits. In this regard, the question of whether higher tion, the methodology of both the statistical model rents can be achieved by improving the energy effi- estimation and derivation of marginal benefit and ciency of a property is important for landlords to marginal cost curves is presented. The results of the ask themselves before commissioning any measures hedonic pricing model as well as the derived curves (Fuerst et al., 2020). In an existing lease, it is pos- are placed in relation to each other and supple- sible to increase the rent within a narrow frame- mented by the influence of the assumed future work. Another aspect that increases the benefit of lower energy consumption of an apartment in the course of the CO taxation in the penultimate sec- future from the landlord’s perspective is tax savings. tion. The last section concludes the paper. JOURNAL OF SUSTAINABLE REAL ESTATE 97 Literature Review the region considered. By meta-regression, they aggregated the results of 66 prior studies and find In the field of research on the influence of energy that EPCs entail an overall price premium of 4.2%. efficiency on the price of buildings and the achiev- However, this varies when broken down by contin- able rents, a multitude of studies have emerged ent. Average premiums of 5.36% are observed in which can generally be divided into two main North America, 4.8% in Asia, and the lowest in strands. One refers to green labels which are based Europe, on average 2.3%. Similarly, Wilkinson and on certain characteristics, and the other focuses on Sayce (2020) in another meta-analysis examined, in absolute energy consumption to proxy for energy a European context, the relationship between EPCs efficiency. Early examples of both date back to the and capital (or rental) values. They once more verify 80s (Gilmer, 1989; Johnson & Kaserman, 1983). Since that the majority of research shows there is a posi- then, the price effect of green labels in commercial tive relationship between observed market price real estate has been investigated in many studies and energetic performance, but also that EPC rat- (Addae-Dapaah & Wilkinson, 2020; Fuerst & ings are not a strong value driver compared with McAllister, 2011; Kok et al., 2012; Robinson & other variables. In addition, they also confirm that McAllister, 2015; Simons et al., 2014; Wiley et al., energy upgrades can increase value, but point out 2010). Also, in an extensive body of literature, EPCs that this does not go so far that the costs outweigh have been utilized to implement energy efficiency the increase in value. By contrast, Copiello and in hedonic modeling, and test its price effect. The Donati (2021) conclude that investing in building first to demonstrate that higher energy efficiency, as energy efficiency can be economically viable up to measured by EPC ratings, is capitalized into pur- a certain extent, when comparing the marginal chase prices, was Brounen and Kok (2011) who benefit of a retrofit with the marginal cost to save studied transactions of about 32,000 residential 1 kWh. Their analysis is based on housing price data properties that occurred between 2008 and 2009 in for the town of Padua in Italy. Specifically, they the Netherlands. They found that properties with a point out that housing in the worst energy rating green label rated A, B, or C had a premium of 10%, bands can be profitably, meaning that marginal 5.5%, and 2.2%, respectively, relative to properties benefit exceeds marginal costs, refurbished up to an rated D. In subsequent studies, this fundamental energy performance index of about 50 kWh/m ato relationship of a significant price premium for green 40 kWh/m a, depending on whether or not tax buildings has been confirmed several times for dif- incentives are provided. It is important to note that ferent housing markets, but with varying premium this finding is based on substantial premiums of up levels (Cadena & Thomson, 2021; Cajias & Piazolo, to 61.7% from the lowest to the best EPC rat- 2013; Copiello & Donati, 2021; Dell’Anna et al., 2019; ing bands. Fuerst et al., 2016; Kholodilin, Mense, & Michelsen, Most of the aforementioned studies have focused 2017; Taltavull de La Paz et al., 2019). A few studies, primarily on purchase transactions and one cannot however, indicate that there is no significant, a neg- assume that the energy efficiency effects identified ligibly small or even a negative relationship. The on the residential sales market can be simply findings of Cerin et al. (2014), for example, suggest applied to the rental market, as these markets differ that energy efficiency causes a price premium only both in the degree of formalization of disclosure of for certain age and property-price classes in the rights (e.g. involvement of real estate agents and Swedish residential market. Interestingly, Yoshida notaries) and in the prevalence of compliance con- and Sugiura (2010) identified a price discount in the trols (Dressler & Cornago, 2017). However, the influ- Tokyo market for newly constructed green condo- ence of EPC ratings has also been investigated, miniums of 5.5%, while green condominiums on albeit to a lesser extent, in the context of rental average are traded at a premium. Also, meta-analy- apartments: Cajias and Piazolo (2013) identify a ses, such as that of Cespedes-Lopez et al. (2019), green premium in the German rental market for the have emphasized that the effects are not unambigu- energy classes “B,”“C,” and “D” of 13.3% (which is 2 2 ous and their strength depends strongly on the way on average e0.47/m ), 13.5% (e0.59/m ), and 16.3% the EPC rating is included in the analysis and on (e0.74/m ) higher rent as the reference class, the 98 A. GROH ET AL. lowest energy efficiency. Hyland et al. (2013) find a power are higher than for green awareness. significant lower premium for rental apartments Another recent study of Taruttis and Weber (2022) than for property sales. Their research, for which suggests a significant but very small premium for they used rental advertisement data from Ireland, energy efficient apartments by using data for the also suggests a significantly lower premium than German state of North Rhine-Westphalia. They then that found by Cajias and Piazolo (2013). For A-rated compare the identified premium to the energy cost dwellings, Hyland et al. (2013) find a gain of 1.8% savings associated with the increased energy effi- green premium relative to otherwise similar D-rated ciency and find that the savings exceed the pre- dwellings. Dressler and Cornago (2017) find, with mium by a factor of six, reflecting an inefficient data for rentals in the city of Brussels, that highly market in terms of the energy efficiency energy-efficient dwellings are associated with a of buildings. 4.8% rent premium when compared with low- Overall, the majority of studies suggest that a energy-efficient dwellings, which amounts to e50 green premium also exists in the rental market, but per month for the average apartment in their data- that the level of this premium differs according to set, which has 107 m of living space. In addition, various factors. This present paper is part of the they point out that disclosing energy-efficiency existing debate and aims to broaden it by compar- information for dwellings with intermediate energy- ing the efficiency gains from a retrofit with the asso- efficiency results in a discount, which they interpret ciated marginal costs and analyzing whether the as a strategic motivation not to disclose a dwelling’s monetary benefits justify the implementation of energy performance when it is not in the top retrofit measures from the landlord’s perspective. classes. Cajias et al. (2019) with a big dataset of nearly 760 thousand observations across over 400 Data local markets in Germany, estimated that rents for Aþ, A, B, and C-rated rental apartments are on aver- Data on Asking Rents age 0.9%, 1.4%, 0.1% , and 0.2% higher than the ref- The original dataset comprises more than two erence category D, whereas dwellings in the million observations of rental listings from the lead- categories below E, F, G, and H are subject to rent ing online platform in Germany for housing, discounts of up to 0.5%. By analyzing different ImmobilienScout24, for the time span 2016 to year subsamples, Cajias et al. (2019) also demonstrate 2020 and in cities with a population of more than that the Top 7 real estate markets show less sensi- 100,000. Data access was provided by the Research tivity to energy-efficiency, while in secondary mar- Data Centre Ruhr at the RWI – Leibniz-Institute for kets, the premium is enhanced by up to 1.4% Economic Research (FDZ Ruhr). The dataset is identi- points (for Aþ), while discounts are also increased fied at DOI: “10.7807/immo:red:hm:suf:v3.” by up to 1.8% points. In addition, the premiums for Since it is not transaction data or data from rent the A category increased over time from 0% in 2013 agreements, but from offerings on an online plat- to 1.4% in 2017 and the brown discounts for G and form, the information was entered by the platform H-rated apartments decreased over time. users. This means that it is subject to data entry Furthermore, and in line with Fuerst et al. (2020), bias. To deal with this issue, we cleared the data of evidence for a negative coherence of time on the implausible values such as zero or negative area, market and energy-efficiency of rental units is pro- and of missing values that are required for the esti- vided. In a more recent published scientific work for mation such as energy demand per square meter. Germany, some of the earlier findings were con- After data-clearing processes, we are left with firmed by Pommeranz and Steininger (2021), who 533,780 observations with full hedonic characteris- once more demonstrate that rents are lower for tics. The dataset contains information on unit char- apartments with higher energy consumption on acteristics which is needed for a Hedonic Pricing average. Furthermore, they suggest that in neigh- Model according to Sirmans et al. (2005). Namely borhoods with higher green awareness and higher purchasing power, lower rents for energy-inefficient rent, apartment size, energy demand per square apartments are paid, while the effects of purchasing meter, number of rooms, quality and if the features JOURNAL OF SUSTAINABLE REAL ESTATE 99 Elevator, Balcony, Guest WC, Built-in Kitchen, per year (kWh/m a) has to be in the range between Garden, and Cellar are applicable. The categorical 31 and 50 kWh/m a. variable for quality has the classifications simple, Accordingly, we construct a binary variable for normal, sophisticated and luxury, which we include each EPC-rating band. The summary statistics and as binary variables as well as equipment features Pearson Correlation Coefficients for the EPC-varia- bles generated in this way can be found in Table 3. (see e.g. Cajias et al., 2019; Pommeranz & As can be seen from Table 3, most apartments Steininger, 2021). We also add two socioeconomic variables to the dataset by including the number of are in EPC class D (24%), followed by E (18%) and C (16%) but only very small proportions of the build- households in a city and the average household income to control for unobserved market-specific ings are assigned to the very upper and very lower rating bands. Although the shares of the classes heterogeneity (Cajias et al., 2019). The socioeco- A þ and A are close to the share in the German nomic data were retrieved from GfK (http://www. gfk.com). Table 1 depicts the descriptive statistics. In building stock at 2% (Aþ) and 5% (A), the very low- est groups G and H are massively underrepresented. addition, Table 2 depicts the corresponding Pearson correlation coefficients. In the multi-family housing stock in Germany, their As indicated in Table 2, one indicator for an shares are around 7% (H) and 9% (G) as depicted in expected green premium is that energy consump- Figure 2, while in the data sample, they comprise tion and rent per square meter have a negative cor- only 2% (H) or 5% (G). A possible reason for this relation. To examine the relationship between low percentage of the lower rating bands in the energy demand and rental potential, the energy data may be that owners who advertise these apart- efficiency ratings, which are also called EPC bands ments for rent are aware that mediocre and low- or EPC classes, are used in addition to the absolute energy efficiency potentially reduce the rentability value per square meter. The EPC classes are not or results in a price discount, and therefore do not included in the data from the outset, but are calcu- include energy performance in the advertisement lated on the basis of the energy demand values Dressler and Cornago (2017). according to German legislation. Figure 1 shows the Table 3 also indicates that rent and energy con- sumption show a negative correlation coefficient of EPC rating bands from H (the worst) to Aþ (the best) like they are defined in the German Building 0.26, while the correlation coefficients of the dif- Energy Act (GEG). For example, for a building to be ferent EPC classes and the rent per square meter assigned to energy efficiency class A, its annual have changing signs. For classes A þ to C, the sign energy demand in kilowatt-hours per square meter is positive and from class D downwards it is Table 1. Descriptive statistics. Variable Unit Mean Std. Dev Min. Q5% Q30% Median Q70% Q90% Max. Asking rent e per month 751.864 529.959 101.5 269 435 599 849 1,380 12,000 Log asking rent Log e p.m. 6.441 0.583 4.62 5.595 6.075 6.395 6.744 7.23 9.393 2 2 Rent per m e/m p.m. 10.188 4.412 0.988 5.102 7.375 9.375 11.54 15.833 83.673 Energy consumption kWh/m p.a. 118.394 54.088 1.1 42 87 114.5 141.37 186.7 499.21 Log energy consumption Log kWh/m p.a. 4.655 0.525 0.095 3.738 4.466 4.741 4.951 5.23 6.213 Constructed Number 1972.583 32.725 1901 1910 1958 1972 1995 2016 2020 Age Number 45.014 32.703 0 0 22 46 60 99 119 Living Area m 72.711 31.382 10.1 32 56 67.39 81.6 111 482 Floor Number 2.185 1.825 1 0123445 Rooms Number 2.53 0.953 1 1 2 2.5 3 4 10 Households Number 530,232.9 632,779.6 50,781 62,580 137,849 303,140 396,228 2,008,823 2,008,823 Purchasing Power e/household p.a. 41,783.01 5,714.261 31,415.1 34,586.63 37,335.6 41,418.41 45,003.1 48,007.09 54,784.02 Elevator Binary 1 ¼ yes 0.358 0.48 000011 1 Balcony Binary 1 ¼ yes 0.715 0.451 001111 1 Guests WC Binary 1 ¼ yes 0.207 0.405 000001 1 Built-in Kitchen Binary 1 ¼ yes 0.448 0.497 000011 1 Garden Binary 1 ¼ yes 0.195 0.396 000001 1 Cellar Binary 1 ¼ yes 0.792 0.406 001111 1 Simple equipment Binary 1 ¼ yes 0.016 0.124 000000 1 Normal equipment Binary 1 ¼ yes 0.496 0.5 000011 1 Sophisticated equipment Binary 1 ¼ yes 0.434 0.496 000011 1 Luxury equipment Binary 1 ¼ yes 0.054 0.225 000000 1 100 A. GROH ET AL. Figure 1. German energy efficiency classes of residential build- ings according to German Building Energy Act. (Source: Own depiction). negative. The range of correlations is between 0.23 and 0.09. Surprisingly, the highest or lowest corre- lations are not at the extreme points of the energy- efficiency classes, i.e. at A þ and H, but at B with 0.23 and at D with 0.09. Due to the correlations, one would expect the presence of a green premium in the classes A þ to C. Green Retrofit Data The data used for this study regarding the cost of green retrofits and corresponding efficiency gains were collected by the General Association of the German Housing Industry (Bundesverband deutscher Wohnungs- und Immobilienunternehmen e. V.; GdW) from some housing companies and part- ners and kindly made available to us. The sample comprises exclusively multifamily buildings in Germany and includes observations on 1,048 resi- dential units in 27 properties with a total of 64,519 m of living space before and after retrofit measures. The data contain a description of the measures carried out, the year of the retrofit, reno- vation costs, the energy demand before and after refurbishment, and the energy consumption before and after refurbishment, whereas energy demand and consumption are included in relation to the liv- ing space. The term energy demand refers the amount of energy per square meter that is required to provide heat to the unit. It is calculated based on normative standard conditions which are defined in the GEG. The energy consumption, on the other Table 2. Pearson correlation matrix of variables. Variable i ii iii Iv v Vi Vii viii ix x xi xii xiii xiv xv xvi xvii xviii xix xx xxi i Asking rent 1 ii Log asking rent 0.91 1 iii Rent per m 0.62 0.65 1 iv Energy consumption 0.26 0.28 0.26 1 v Log energy consumption 0.30 0.33 0.30 0.93 1 vi Constructed 0.27 0.30 0.30 0.46 0.51 1 vii Age 0.27 0.30 0.30 0.46 0.51 1.00 1 viii Living Area 0.77 0.75 0.08 0.15 0.18 0.13 0.13 1 ix Floor number 0.11 0.08 0.11 0.05 0.03 0.03 0.03 0.04 1 x Number of Rooms 0.51 0.54 0.08 0.07 0.08 0.04 0.04 0.80 0.00 1 xi Households 0.27 0.30 0.32 0.04 0.03 0.06 0.05 0.10 0.10 0.01 1 xii Purchasing Power 0.29 0.35 0.46 0.04 0.07 0.21 0.20 0.06 0.02 0.01 0.24 1 xiii Elevator 0.33 0.34 0.37 0.36 0.39 0.46 0.46 0.16 0.26 0.01 0.13 0.11 1 xiv Balcony 0.27 0.34 0.13 0.20 0.21 0.30 0.30 0.29 0.01 0.23 0.08 0.10 0.23 1 xv Guests WC 0.49 0.50 0.17 0.17 0.20 0.23 0.23 0.55 0.02 0.44 0.02 0.13 0.20 0.22 1 xvi Built-in Kitchen 0.27 0.30 0.43 0.12 0.12 0.18 0.17 0.04 0.05 0.08 0.20 0.13 0.19 0.08 0.10 1 xvii Garden 0.03 0.03 0.02 0.01 0.02 0.01 0.01 0.06 0.16 0.08 0.04 0.02 0.07 0.00 0.06 0.03 1 xviii Cellar 0.06 0.08 0.05 0.06 0.05 0.09 0.09 0.11 0.06 0.15 0.13 0.09 0.03 0.16 0.11 0.02 0.10 1 xix Simple equipment 0.08 0.10 0.08 0.06 0.06 0.06 0.06 0.05 0.01 0.02 0.01 0.01 0.04 0.04 0.05 0.07 0.03 0.03 1 xx Normal equipment 0.45 0.51 0.43 0.28 0.32 0.29 0.29 0.30 0.01 0.15 0.11 0.19 0.28 0.21 0.28 0.25 0.05 0.05 0.13 1 xxi Sophisticated equipment 0.29 0.39 0.32 0.22 0.25 0.22 0.22 0.19 0.01 0.10 0.08 0.16 0.21 0.18 0.20 0.21 0.04 0.04 0.11 0.87 1 xxii Luxury equipment 0.41 0.34 0.29 0.17 0.20 0.17 0.17 0.27 0.04 0.13 0.08 0.08 0.18 0.09 0.21 0.14 0.04 0.03 0.03 0.24 0.21 JOURNAL OF SUSTAINABLE REAL ESTATE 101 Table 3. Summary statistics and Pearson correlation coefficients for EPC classes. Mean energy Mean rent 2 2 Mean kWh/m a e/m p.m. i ii Iii iv V vi vii viii ix xi i Rent (e/m ) 10.19 1 ii Energy (kWh/m a) 118.39 0.26 1 iii EPC – Aþ 0.02 19.2 13.18 0.11 0.29 1 iv EPC – A 0.05 40.9 13.78 0.19 0.34 0.04 1 v EPC – B 0.15 62.2 12.57 0.23 0.43 0.07 0.1 1 vi EPC – C 0.16 87.4 9.83 0.04 0.25 0.07 0.1 0.19 1 vii EPC – D 0.23 114.7 9.27 0.11 0.03 0.09 0.13 0.23 0.24 1 viii EPC – E 0.18 143.5 9.34 0.09 0.22 0.07 0.11 0.2 0.21 0.26 1 ix EPC – F 0.13 176.6 9.27 0.08 0.41 0.06 0.09 0.16 0.17 0.21 0.18 1 x EPC – G 0.05 219.2 9.19 0.05 0.43 0.04 0.05 0.1 0.1 0.13 0.11 0.09 1 xi EPC – H 0.02 287.7 8.82 0.04 0.45 0.02 0.03 0.06 0.06 0.08 0.07 0.05 0.03 BMWi (2020). 24% 25% 19% 20% 17% 17% 16% 15% 15% 15% 15% 15% 14% 13% 13% 15% 12% 12% 12% 9% 8% 10% 7% 7% 5% 4% 4% 4% 3% 3% 5% 2% 0% Detached and Semidetached Multifamily Total Houses A+ A B C D E F G H Figure 2. Frequency distribution of building efficiency classes according to the final energy demand in the German building stock (Source: Own depiction according to BMWi, 2020). Table 4. Descriptive statistics for green retrofit data. Variable Mean S Min. Q25 Median Q75 Max. Number of units 39 26 6 24 29 47 114 Total living area (m ) 2,389.57 1,662.66 378.42 1,310.60 1,927.10 2,983.61 7,725.66 Year of retrofit 2016 1.72 2013 2015 2017 2018 2019 Cost of retrofit (EUR/m ) 828.02 312.30 200.52 594.04 787.53 1,104.19 1,344.84 Energy demand before retrofit (kWh/m a) 236.88 77.00 124.97 166.31 249.00 291.05 387.40 Energy demand after retrofit (kWh/m a) 69.97 25.47 33.58 50.06 74.00 84.41 118.49 Energy consumption before retrofit (kWh/m a) 176.28 39.26 114.48 150.99 181.24 194.05 258.30 Energy consumption after retrofit (kWh/m a) 94.39 20.11 57.18 83.46 91.75 98.85 151.37 Energy demand saving (kWh/m a) 166.91 62.72 79.98 113.52 179.00 204.10 322.40 Energy consumption saving (kWh/m a) 81.89 35.85 25.44 53.47 89.16 101.97 173.33 hand, is based on values measured over a 3-year multi-family houses. In addition, because the year in period, before and after each retrofit, and in the which the retrofit measures were carried out varies, dataset are only adjusted for temperature differen- we extrapolate the retrofit to 2018, using the con- ces. Since the EPC classes according to the GEG do struction cost index for Germany provided by the not refer to the living space but to the usable German statistical office (Destatis, 2022a). We space, the energy consumption and demand must choose 2018, as this is the average year of the be converted to this. Here, we apply the simplified green premium data with which the retrofit costs conversion according to GEG § 82 para. 2, which for are compared later. Table 4 shows descriptive statis- this purpose specifies a conversion factor of 1.2 for tics of the data sample. 102 A. GROH ET AL. Table 5. Pearson correlation coefficients for green retrofit data. Variable I ii iii iv v vi vii viii ix I Number of units 1 Ii Total living area (m ) 0.95 1 iii Year of retrofit 0.12 0.02 1 Iv Cost of retrofit (e/m ) 0.32 0.30 0.16 1 V Energy demand before retrofit (kWh/m a) 0.34 0.35 0.33 0.12 1 vi Energy demand after retrofit (kWh/m a) 0.38 0.36 0.10 0.20 0.64 1 vii Energy consumption before retrofit (kWh/m a) 0.19 0.15 0.12 0.34 0.72 0.37 1 viii Energy consumption after retrofit (kWh/m a) 0.17 0.25 0.26 0.24 0.27 0.42 0.42 1 Ix Energy demand saving (kWh/m a) 0.26 0.28 0.36 0.23 0.95 0.37 0.72 0.15 1 X Energy consumption saving (kWh/m a) 0.12 0.02 0.28 0.50 0.64 0.17 0.86 0.10 0.70 Table 5 shows the Pearson correlation coefficients square meter as the energy proxy. Some studies for the retrofit data. It can be noted here that the argue that the standard HPM approach overesti- cost of retrofit per square meter correlates nega- mates the influence of energy consumption and that tively with both the number of housing units and using different alternatives such as including spatial dependencies (e.g. Bisello et al., 2020; Conway et al., the total usable space in the building, implying that 2010; Copiello & Donati, 2021) or applying nonlinear the average cost of retrofit decreases with the size estimation techniques like Generalized Additive of the building and that economies of scale may be Models (GAM) (Cajias, 2018; Cajias & Ertl, 2018) pro- achieved accordingly. However, the positive correl- duces better results than the standard approach. ation coefficients of the costs with the initial state Spatial Models could not be applied to our data, as before renovation in kWh/m a and the additional the dataset was cleared from addresses or granular state afterwards are particularly noteworthy. This location information by the provider for privacy rea- already indicates rising marginal costs of retrofitting sons. Accordingly, in a second step to apply a GAM with increasing energetic performance. approach, we use partial residual plots on our HPM estimates to identify possible nonlinear relationships Methods between predictor and response variables (Brunauer Hedonic Pricing and Generalized Additive Model et al., 2010). A visual inspection reveals that all non- categorical covariates suggest nonlinear modeling to The econometric approach to examining whether some degree. Consequently, these are modeled non- higher or lower energy consumption in rental multi- linearly within an additive mixed approach with family housing is associated with a significant price mixed covariates of parametric estimates and nonlin- premium involves two steps. Our first step is to esti- ear functions. We estimated four different model mate a hedonic pricing model (HPM), as empirically specifications of which two are solely linear. Two justified by Sirmans et al. (2005), which is the standard more are mixed linear and nonlinear covariates, methodology for examining value determinants in whereas non-linearity is accounted for by modeling housing. The baseline model is specified as follows: the nonlinear covariates with penalized splines. For Y ¼ Xb þ fðx Þ (1) each HPM and by means of the GAM approach, a fur- ther model is estimated in which the energy con- With apartment unit factors (i), energy consump- sumption is represented by the EPC rating bands. tion [EC] proxies (j), socioeco- nomicindicators (k), Since we are interested in the rent difference of the binary locational variables[L] on ZIP code level(l) and better classes compared with the worst performing binary time dummy controls [K] by listing year (t): buildings, we set the classes G and H as the refer- price log ¼ bX þ lEC þ dS þ hL þ kK þ e ence category. Due to the negative correlation i j k l t i between energy consumption and price per square (2) meter, this approach leads to the expectation that In doing so, we apply the ordinary least squares the regression coefficients of the binary variables for estimation method on the fully linear form and thus the classes A þ to F have a positive sign, and that an use the log of price per square meter as the response increase in the strength of the effect can be variable, and the log of energy consumption per observed with increasing energy efficiency. JOURNAL OF SUSTAINABLE REAL ESTATE 103 Marginal Benefit and Marginal Cost Curves Results and Implications Following Copiello and Donati (2021), MB for ener- The econometric analysis consists of three parts. We getic improvements in buildings can be calculated first estimate the price impact of energy efficiency as follows: ratings and then proceed to translate the results into the MBC. We then use the methodology DTB MB ¼ (3) described above to determine the MBC, which we DEpi subsequently compare with the MCC to assess the where DTB is the change in total benefit (TB), and profitability of retrofit measures. As a final subsec- hence, the price premium due to an increase in energy tion, we address caveats with regard to interpreting efficiency after a green retrofit. And DEpi is the change the results. in the energy performance index Epi which is meas- ured in kWh/m a. To apply this calculation procedure to the coefficients resulting from the estimation of the Hedonic Pricing Model Regression Results HPM for each energy efficiency class and then estimate Table 6 shows the regression results for energy- a marginal benefit curve (MBC), we use the average related variables. Full regression results with coeffi- savings between two classes resulting from the data cients for all included covariates can be found in set, and the associated rent premium at the point of the appendix in Table A1. Model (1) is the standard means for the reference category (EPC G & H). linear model with the numeric energy index param- Analogous to the calculation of the MB, the mar- eter log(energy/m ) as exogenic variable, model (2) ginal costs can be calculated as the quotient of total is the otherwise similar OLS model but with energy costs (DTC) per square meter to undertake the efficiency bands as exogenic variables, while model green retrofit measures and the resulting change in (3) is the counterpart to (1), but estimated in a GAM the energy performance index (DEpi): framework and likewise model (4) showing the cor- responding GAM model estimates to model (2) with DTC EPC rating bands. Spatial fixed effects on ZIP Code MC ¼ (4) DEpi level and year time dummies have been included in To derive the appropriate slope of the marginal all model estimations. cost curve, we relate the MC determined for each observation i to the respective intervention level IL Table 6. Regression results for energy-related exogenic varia- which is defined as average of energy performance b bles on log rent e/m . index before (Epi ) and energy performance index (1) (2) (3) (4) after (Epi ) retrofit: log(Energy per m ) 0.058 8.047 b a (0.001) (edf) Epi þ Epi i i Aþ 0.134 0.039 IL ¼ (5) 2 (0.002) (0.002) A 0.109 0.024 (0.001) (0.001) To make the curve determined, that is shifted B 0.069 0.021 towards a lower degree of energy efficiency com- (0.001) (0.001) C 0.009 0.008 parable with the MBC, it has to be shifted back (0.001) (0.001) toward a higher level of energy efficiency by factor D 0.002 0.003 (0.001) (0.001) S. S being defined as: E 0.003 0.001 (0.001) (0.001) 1 1 F 0.002 0.002 S ¼ DEpi (6) (0.001) (0.001) n 2 Spatial FE Yes Yes Yes Yes Time FE Yes Yes Yes Yes Thetwo curves derived inthis way canbeused to N 533,780 533,780 533,780 533,780 Adjusted R 0.926 0.927 0.934 0.934 graphically analyze the extent to which the implemen- Notes. Significant at 10, 5, and 1 percent levels; standard errors in tation of the measures pays off economically. The brackets below the estimated coefficients; effective degrees of freedom are reported for nonlinear estimates within nonlinear models. The esti- intersection of the MBC and MCC (if one is observed) mated coefficients are marked with “edf” in brackets below. The reported indicates the optimal level of energy reduction. significance shows the significance of smooth terms. 104 A. GROH ET AL. For all estimates, a significant influence of the MB for EPC classes MBC energy quality of the buildings on the rent was 0.6 found, confirming the results of previous studies. As 0.5 expected, the positive impact of really high energy quality is much greater than for slightly better 0.4 apartments according to the results of the linear regression. Surprisingly, the signs of the small but 0.3 significant coefficients for EPC classes D, E and F in 0.2 model (2) are negative. However, this result in the linear regression appears plausible when the correl- 0.1 ation coefficients of the EPC classes with the rent per square meter from Table 3 are included, 200 175 150 125 100 75 50 25 0 because the negative correlation for the classes G 2 Energy Demand (kWh/m a) and H is weaker than for E, F, and G. This relation- Figure 3. Derivation of the marginal benefit curve. (Source: ship, which is difficult to explain economically, is Own depiction). not found in the analogous GAM model (4), which suggests that the non-linear inclusion of several var- property. The assumption regarding the useful life iables has improved the model estimation. This is of the building is based on the legal requirements also supported by the higher adjusted R . From the of German tax law, which provides for straight-line estimated coefficients of binary variables in a semi- depreciation at 2% per year, corresponding to a logarithmic regression, the percentage effect is cal- period of 50 years until the building is fully depreci- ated. This NPV is finally divided by the absolute culated by applying the formula 100ðe 1Þ as change in the energy performance index from each stated explicitly for hedonic pricing models by EPC class to the reference category, as stated in Halvorsen and Palmquist (1980). Accordingly, in equation (3). This procedure yields the plot which is model (4), which is the basis for the following ana- presented in reversal scale in Figure 3. The step-by- lysis, the highest green premium for energy effi- step calculation can be derived from Table A2 in ciency class A þ is 3.98%, compared with the the appendix. reference category. The following categories A, B, C, Following the procedure outlined in Section show a green premium of 2.43%, 2.12% and 0.80%, “Marginal Benefit and Marginal Cost Curves” and while for D, E and F, only very small differences of defined by Equations (4) and (5), the marginal costs 0.3%, 0.1% and 0.2% were identified in comparison of energy demand adjustment are plotted against with worst performing classes G and H. the intervention level (IL), as depicted by the blue circles in Figure 4a and 4b, where Figure 4a con- Derivation of Marginal Benefit Curve and tains the values for the energy demand and Figure Marginal Cost Curve 4b contains the values for the actual consumption. To derive the marginal benefit of avoiding another The result suggests increasing marginal costs for unit of energy per square meter from the previously retrofits on higher levels of energy efficiency for identified green premium, we proceed with the both cases, which has also been observed in earlier average square meter rent within the reference cat- studies on energetic retrofits for different measures egory, which is at e9.09/m . This is increased by the and materials (Gustavsson & Piccardo, 2022; respective percentage of the green premium for the Timmons et al., 2016). To adjust the MCC (IL) that higher energy classes. The resulting increases in was plotted on the intervention level to the target future cash flows are discounted to a net present level to obtain the final MCC, it is shifted to the value (NPV) using a yearly discount rate of 3% and right by S (¼83.46 kWh/m a for forecasted energy assuming a 50-year useful life for the facility compo- demand and ¼40.94 kWh/m a for actual measured nents. The discount rate reflects the investor’s cap- consumption). ital return requirement and would, in practice, vary The comparison of the value range of the MBC according to the location or risk profile of the and MCC already clearly shows that the costs MB (€/kWh) JOURNAL OF SUSTAINABLE REAL ESTATE 105 (a) (b) MC on IL MCC (IL) MCC MC on IL MCC (IL) MCC 12 20 6 10 0 0 200 175 150 125 100 75 50 25 0 250225200175150125100 75 50 25 0 2 2 Energy (kWh/m a) Energy (kWh/m a) Figure 4. Derivation of the marginal cost curve for energy demand (a) and energy consumption (b). (Source: Own depiction). exceed the expected benefits by far for both dataset and shifting the MCC downward by 55%, demand and consumption. Since the actual meas- the observation of the irretrievability of the meas- ures from the owner perspective does not change. ured savings are on average far below the fore- casted levels, the marginal costs related to Also, the assumption-based Marginal Cost Curve for energy-related costs (MCCer; grey dotted line in consumption are at a higher level. In the following section, the analysis of the generated curves will be Figure 5a) does not intersect the MBC (green dotted line at bottom of Figure 5a). continued and extended by different approaches. The observation that energy-efficient refurbish- We focus here on energy demand. Knowledge of a ment does not pay off in monetary terms applies in possible discrepancy between forecasted and real- particular to rented housing, because of the split ized saving should be considered in investment incentive problem. This is illustrated with a calcula- decisions, but since data on the actual consumption tion example: To approximate the NPV of the reduc- are not available up front, and are therefore not tion in energy consumption by one kWh/a, we part of an investor’s decision-making. assume the natural gas price per kWh of the year 2020 of 6.2 Cent/kWh (Destatis, 2022b), before the Synthesis and Economic Evaluation of the CO Tax CO tax on fuels was introduced in Germany. Based The joint depiction of MBC and MCC in one plot on that we calculate the total cost benefit over a yields the conclusion that the MB from possible rent 50-year useful life, applying the discount rate men- increases is not sufficient to offset the retrofit costs tioned above and an energy cost progression of 2% from the owner’s point of view. The MBC runs which reflects the average annual increase for the under the MCC and does not intersect it. At this years 2005 to 2020 (Destatis, 2022b). We assume point, it should be noted that the preceding cost the price and price progression for natural gas, analysis is based on the full costs of the renovation because in the private household sector, natural gas measures, because only for 12 observations in the is the most important energy source on the heating data set the costs eligible for subsidies for energy- market, with a current share of around 44% € € efficient buildings (“forderfahige Kosten”) are (BMWK, 2022). This results in an NPV of e2.44 in known. These costs are defined as costs for meas- terms of energy cost saving for 1 kWh/m . The ures that explicitly increase the energetic quality of corresponding line (ECS) intersects the MCC at an a building. For these 12 observations, the average energy performance of about 185 kWh/m a, mean- share of energy-related costs is at 45% of full costs. ing that a retrofit would be expected to be eco- Even assuming this percentage for all retrofits in the nomically advantageous up to this point (Figure 5b). MC (€/kWh) MC (€/kWh) 106 A. GROH ET AL. (a) (b) MCC MBC MCCer MCC MCCer ECS 8 8 7 7 6 6 5 5 45% energy- 4 4 related 3 3 2 2 1 1 0 0 200 175 150 125 100 75 50 25 0 200 175 150 125 100 75 50 25 0 2 2 Energy (kWh/m a) Energy (kWh/m a) Figure 5. Marginal cost curves, marginal benefit curve and energy cost saving. (Source: Own depiction). This consideration assumes that owner-occupiers hereby is that the CO price, which is regulated in can retrofit at the same cost per square meter as the BEHG (“Brennstoffemissionshandelsgesetz”), is the real estate companies that provided the data only defined until 2026. In 2021, it was introduced for the analysis. However, this might in many instan- at e25 per metric ton (t) of CO and will gradually ces not be the case, as these companies are able to increase to e30 (2022), e35 (2023), e45 (2024), e55 benefit from economies of scale and bargaining (2025) and a range from e55 to e65/tCO in 2026. power. The intersection with the MCCer, however, is Subsequently, free pricing is to be established on reached at a much higher energetic level at about the market, unless it is decided in 2025 that defined 60 kWh/m a, due to the lower marginal costs. price corridors will be continued. The German Federal Ministry for the Environment, Nature The example does not claim to provide an exact estimation regarding the de facto profitability Conservation and Nuclear Safety (BMU) uses values of retrofit measures in practice, as it is based on from the BMU-funded project “Politik-Szenarien IX” averaged data and various assumptions. (“Policy Scenarios IX”) in its current model calcula- tions (Repenning et al., 2021). The “Policy Scenarios Nevertheless, the insight is quite clear that under- taking modernization efforts to increase building IX” project assumes a CO price of e65/tCO in 2026 2 2 energy efficiency is much more attractive, due to and an annual increase of e15/t to e125/t in 2030, the inclusion of energy cost savings in the owner- e200/t in 2035 and up to e275/t in 2040. We adopt this assumption and add the expectation that the occupied sector. With the potential to solve the landlord-tenant price will not increase further from 2045, when dilemma to some extent, a proposal of splitting the Germany is expected to have already achieved net CO tax between tenant and landlord was included carbon neutrality. To calculate this tax on a kWh of as a declaration of intent in the coalition agreement energy, we include the CO emission factor for nat- of the newly voted-in German federal government ural gas of 0.20431 kg/kWh (Department for in 2021 (SPD et al., 2021). The agreement states Business & Energy & Industrial Strategy, 2022). This that a percentage allocation of the tax will be conversion results in a kWh of natural gas being implemented, that will depend on the EPC class of taxed, for example, with 0.73 Cents in 2022, with a building. If this law has not been passed by June 3.0 Cents in 2030 or with 8.5 Cents in 2045 and 1, 2022, the distribution will be made on a parity after. The sum of the tax savings thus achieved for basis and regardless of the energy performance. 1 kWh over a 50-year period results in an NPV of Below, we analyse these two cases and again calcu- e1.10/kWh. In the case of parity distribution of the late a marginal benefit for saving 1 kWh of energy, tax, simply 50% of the calculated NPV, i.e. 55 Cent, but with inclusion of the carbon tax. One challenge are added to the MB for each EPC class, which shifts MB and MC (€/kWh) MB and MC (€/kWh) JOURNAL OF SUSTAINABLE REAL ESTATE 107 Table 7. Distribution of the CO -tax burden by EPC classes on tenant and landlord. Tax payed by Tax payed by Approximate Related emissions with 2 2 Emissions (kg CO /m a) Landlord (%) Tenant in (%) EPC class EF of gas (kg CO /m a) 2 2 <12 0 100 Aþ & A 4.65 & 9.89 1217 10 90 B 15.05 1722 20 80 C 21.16 22 27 30 70 –– 2732 40 60 D 27.75 3237 50 50 E 34.72 3742 60 40 –– 4247 70 30 F 42.74 4752 80 20 –– 52 90 10 G & H 53.05 & 69.63 (a) (b) MBC MCC MCCer MCCer ECS ECS + tax MBC + tax (50/50) MBC + tax (steps) 3.5 2.5 1.5 0.5 0 0 250225200175150125100 75 50 25 0 200 175 150 125 100 75 50 25 0 2 2 Energy (kWh/m a) Energy (kWh/m a) Figure 6. Marginal cost curves, marginal benefit curve and energy cost saving. (Source: Own depiction). it upwards. In April 2022, the parties comprising the Based on the input parameters just presented, German government agreed on how the gradual the modified MBCs for the two different cases of allocation of the CO tax for residential buildings imposing the carbon tax on the landlord are should be structured. A 10-stage model is proposed, derived. These are shown in Figure 6a. The course which provides that a poor energy performance of of the MBC is increased significantly in both cases. the unit or building leads to a higher cost burden However, the taxation is still insufficient to raise the for the landlord. This is based on the energy con- marginal benefit of saving 1 kWh in the rental sector sumption converted into CO emissions, not purely to such an extent that it offsets the cost of the on the classes of the energy certificate. The CO renovation for any level of energy efficiency. Both costs to be borne by the parties per residential unit MBCs that include CO taxation also run strictly are determined via the heating cost statement. For below the MCCer. apartments with a particularly poor energy balance In the owner-occupied sector, on the other hand, (52 kg CO /m a), landlords bear 90% and tenants the tax would be fully added to the energy cost 10% of the CO costs. However, if the building savings and thus further increase the economically meets the very efficient standard, landlords do not reasonable depth of renovation (Figure 6b). In gen- have to bear CO costs at all. To apply this informa- eral, it is highly questionable whether both investors tion to our calculation methodology, we use the and private users would apply such assumptions in just introduced CO emissions factor and multiply it their decision making. The fact that the tax is only with the average energy demand of each EPC class defined until 2026 and, as a consequence it is in our data (see Table 7). unclear how high it will be in subsequent years, MB and MC (€/kWh) MB and MC (€/kWh) 108 A. GROH ET AL. creates planning uncertainty which limits the incen- correlation between unobserved factors and prices, tive effect of the carbon tax. and between the unobserved factors and energy efficiency levels (Aydin et al., 2020). Finally, the analysis was based on used energy Limitations and Possible Model Extension per square meter, although the actual target value Some limitations to the analysis, which mainly con- of the goals does not refer to this, but to CO or cern the data used, should be considered when CO -equivalent emissions. Although an approxima- interpreting the results. For the analysis of the tion of the influence of CO taxation could be green premium in the rental market, it is important achieved by means of a conversion using the emis- to note that the rent data reflect asking prices sion factor for natural gas, the quantitative analysis which implies that there is no guarantee that a con- would gain in substance if it could be based on tract was actually concluded at this price. actual CO emissions per square meter. The current Nevertheless, as practice shows, leases in the resi- federal government has already expressed its inten- dential sector are rarely negotiated and mostly con- tion to digitize the EPC and focus increasingly on clude at the asking rent. Moreover, the data only CO , from which further research could benefit. But extend to 2020. Accordingly, both the CO tax and more importantly these steps would enable a more the recent tremendous price increases in the energy targeted implementation of retrofit measures in market in Germany of up to 30% (Destatis, 2022d) the future. that might induce adjustments on the demand side, i.e. tenants being more sensitive to the energetic Conclusion performance, are not reflected in this data. This study empirically investigated whether a green Regarding the cost side of the analysis, due to premium is paid for energy efficiency in the German the lack of information in the available data, it was rental market. The results show that this is indeed only possible to make a rough approximation of the the case for the very high-performance EPC classes, actual energy-related costs of renovation. It is while there is only a very small, almost negligible important to note that with massively increasing renovation rates, which are a necessary requirement premium for mediocre- and lower-performance for reaching both the EUs’ and Germany’s climate classes. In addition, a marginal cost curve for the targets, the typical occasion for energy-related reno- abatement of an additional kilowatt-hour of final vation cannot always be “normal” maintenance, but energy was derived from a dataset of green-retrofits of multi-family homes in Germany. rather intervention in the building substance out- A comparison of the marginal cost with the mar- side of the usual maintenance cycles, in order to ginal benefit derived from the identified green pre- specifically implement the necessary measures for mium shows that the monetary advantage resulting climate protection. For this reason, the application from possible rent increases is far from sufficient to of energy related cost components alone in eco- nomic profitability studies can be criticized. A fur- compensate for the costs of retrofit measures (if ther limitation is that public subsidies from the there are no public subsidies). Although the finding federal programs for energy-efficient building reno- of a green premium implies that the landlord-tenant vation could not be considered, as they are not part dilemma is not absolute, but that landlords can also of the provided data either. The observation of benefit to some extent from efficiency gains in rela- financially unviable retrofits from the landlord’s tion to rent, a comparison shows that the net pre- sent value from energy cost savings is many times point of view demonstrates that there is a substan- larger than that of additional rent. An inclusion of tial need for providing subsidies. Another drawback is related to the potential bias the planned split of the CO tax between both land- that can result from omitting unobserved housing lord and tenant in the analysis has shown that at characteristics that correlate with measures of the time of the study, this split is on average not energy efficiency. The presence of such determi- capable of providing a sufficient incentive for the nants can produce either a downward or an upward landlord to carry out green retrofits. The price per bias, which depends on the direction of the ton of CO appears to be too low for this purpose. 2 JOURNAL OF SUSTAINABLE REAL ESTATE 109 Regarding this finding, it is important to note that Furthermore, in the context of sharply increasing the tax payments to be made in the future were energy prices, it is essential to re-evaluate the discounted to 2021. The incentive effect of the tax behavior of tenants in the near future. Due to a possible short-term decline in demand for apart- therefore increases in influence, as the price rises over time. However, this is also partly countered by ments with poor EPC ratings, the green premium is rising construction costs. It is likely that when likely to increase as the benefits of low energy con- sumption and the awareness amongst the tenants higher renovation rates are achieved, costs will also grows. If data availability on both sides (housing increase further, due to increased demand for con- struction services during the next decade. costs and retrofits costs) allows for it, future The findings do not imply that the taxation com- research should further investigate the incentive effects for building retrofits. pletely fails to achieve its purpose, but they suggest The study results, even if imperfect and subject to that this form of taxation is not sufficient in the limitations, appear to be valuable for both tenants short term, to bring about a substantial increase in and investors in their decision-making, as well as for the renovation rate and that further measures are policy makers in the implementation of decarboniza- therefore necessary. An excessive increase in the tax tion efforts in the residential real estate sector. to correct this and to increase the renovation rate in the short term is not an advisable measure, because this would drive up the housing costs of all Notes households including those of owner-occupiers for 1. Note that EPC classes are not defined in the same way which the incentive is already stronger than for in all jurisdictions, but differ both in terms of their owners of rental stock. However, from today’s point number and the respective range of values. For example, of view, the inclusion of the tax in calculations is the EU defines classes A to G while Germany is using a associated with considerable planning uncertainty, differentiation of A þ to H. as the price per ton is only defined until 2026. It 2. According to German law, an apartment may be should in fact be specified until 2030 and even advertised with the inclusion of the EPC whereas it is mandatory to present the EPC to the prospective tenant beyond to take full advantage of realizable poten- at the latest at the time of the apartment tour. tials that could be activated by CO taxation. To 3. From 2020-Q4 to 2021-Q4, construction costs for the increase the renovation rate in the short term and maintenance of residential properties rose by over 14% to focus on the worst performing buildings where (Destatis, 2022c). the greatest efficiency gains are achievable, binding minimum standards as already proposed in the last Disclosure statement update of the Energy Performance of Buildings Directive (EPBD) appears to be a good alternative. 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(1) (2) (3) (4) log (Energy per m ) 0.058 8.047 (0.001) (edf) Aþ 0.134 0.039 (0.002) (0.002) A 0.109 0.024 (0.001) (0.001) B 0.069 0.021 (0.001) (0.001) C 0.009 0.008 (0.001) (0.001) D 0.002 0.003 (0.001) (0.001) E 0.003 0.001 (0.001) (0.001) F 0.002 0.002 (0.001) (0.001) log(area) 0.774 0.775 8.988 8.988 (0.001) (0.001) (edf) (edf) Age 0.001 0.0005 8.993 8.991 (0.00001) (0.00001) (edf) (edf) floor number 0.00003 0.0005 8.958 8.898 (0.0001) (0.0001) (edf) (edf) number of rooms 0.031 0.031 8.970 8.963 (0.0004) (0.0004) (edf) (edf) Elevator 0.031 0.027 0.017 0.017 (0.001) (0.001) (0.001) (0.001) Balcony 0.023 0.024 0.037 0.037 (0.001) (0.001) (0.001) (0.001) Guests WC 0.043 0.043 0.014 0.014 (0.001) (0.001) (0.001) (0.001) Built-in Kitchen 0.064 0.065 0.063 0.063 (0.001) (0.001) (0.0005) (0.0005) Garden 0.014 0.014 0.012 0.012 (0.001) (0.001) (0.001) (0.001) Cellar 0.027 0.025 0.014 0.014 (0.001) (0.001) (0.001) (0.001) Simple equipment 0.077 0.078 0.085 0.085 (0.002) (0.002) (0.002) (0.002) Sophisticated equipment 0.141 0.138 0.118 0.118 (0.001) (0.001) (0.001) (0.001) Luxury equipment 0.282 0.275 0.217 0.217 (0.001) (0.001) (0.001) (0.001) log(Purchasing Power) 1.051 1.029 6.281 6.137 (0.068) (0.067) (edf) (edf) log(Households) 0.109 0.110 1.082 1.089 (0.008) (0.008) (edf) (edf3) Spatial FE Yes Yes Yes Yes Time FE Yes Yes Yes Yes N 533,780 533,780 533,780 533,780 Adjusted R 0.926 0.927 0.934 0.934 Notes. Significant at 10, 5, and 1 percent levels; standard errors in brackets below the estimated coefficients; effective degrees of freedom are reported for nonlinear estimates within nonlinear models. The estimated coefficients are marked with “edf” in brackets below. The reported significance shows the significance of smooth terms. Table A2. Calculation of marginal benefit for each EPC class. Aþ AB C D E F Green Premium (%) 3.98 2.43 2.12 0.80 0.30 0.10 0.20 Green Premium (e/m p.m.) 0.36 0.22 0.19 0.07 0.03 0.01 0.02 Total Benefit (e/m ) 112.28 68.58 59.91 22.68 8.48 2.82 5.65 D Epi (kWh/m a) 235.21 213.57 192.25 166.98 139.76 110.97 77.80 Marginal Benefit (e/kWh) 0.48 0.32 0.31 0.14 0.06 0.03 0.07 2 2 Notes. The green Premium in e/m p.m. is calculated on the basis of an average rent of e9.09/m p.m. in the reference category. The total benefit is calculated as the NPV of monthly payments over a 50-year period with bullet payments and a discount rate of 3%. D Epi always corresponds to the average change compared to the reference category.
Journal of Sustainable Real Estate
– Taylor & Francis
Published: Dec 31, 2022
Keywords: Economic viability; energy performance certificates; generalized additive model; hedonic pricing model; marginal cost