The market value of energy efficiency in buildings and the mode of tenure - OPUS 4
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Article
Urban Studies
2017, Vol. 54(14) 3218–3238
Ó Urban Studies Journal Limited 2016
The market value of energy Reprints and permissions:
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efficiency in buildings and the DOI: 10.1177/0042098016669464
journals.sagepub.com/home/usj
mode of tenure
Konstantin A Kholodilin
German Institute for Economics Research (DIW Berlin), Germany
Andreas Mense
Friedrich-Alexander University Erlangen–Nuernberg, Institute of Economics, Germany
Claus Michelsen
German Institute for Economics Research (DIW Berlin), Germany
Abstract
Concerns about global warming and growing scarcity of fossil fuels require substantial changes in
energy consumption patterns and energy systems, as targeted by many countries around the
world. One key element to achieve such transformation is to increase energy efficiency of the
housing stock. In this context, it is frequently argued that private investments are too low in
the light of the potential energy cost savings. However, heterogeneous incentives to invest in
energy efficiency, especially for owner-occupants and landlords, may serve as one explanation.
This is particularly important for countries with a large rental sector, like Germany. Nevertheless,
previous literature largely focuses on the payoffs owner-occupants receive, leaving out the rental
market. This paper addresses this gap by comparing the capitalisation of energy efficiency in sell-
ing prices and rents, for both types of residences. For this purpose data from the Berlin housing
market are analysed using hedonic regressions. The estimations reveal that energy efficiency is
well capitalised in apartment prices and rents. The comparison of implicit prices and the net pres-
ent value of energy cost savings/rents reveals that investors anticipate future energy and house
price movements reasonably. However, in the rental segment, the value of future energy cost sav-
ings exceeds tenants’ implicit willingness to pay by a factor of 2.5. This can either be interpreted
as a result of market power of tenants, uncertainty in the rental relationship or the ‘landlord–
tenant dilemma’.
Keywords
Energy efficiency, house price capitalisation, rental/owner-occupied housing, hedonic analysis
Corresponding author:
Claus Michelsen, German Institute for Economics Research
(DIW Berlin), Mohren-straße 58, 10117 Berlin, Germany.
Email: cmichelsen@diw.de.Kholodilin et al. 3219
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অⲴ▌൘ᝯˈ䎵ࠪ㌫ᮠѪ 2.5DŽ䘉ਟ䙊䗷』ᡧⲴᐲ൪࣋䟿ǃ』䍱ޣ㌫Ⲵн⺞ᇊᙗᡆĀᡯь-』
ᡧഠຳāᶕ䀓䟺DŽ
ޣ䭞䇽
㜭᭸ǃᡯԧ䍴ᵜॆǃ』䍱ᡯ/ъѫ㠚տᡯǃ⢩ᖱ࠶᷀
Received August 2015; accepted August 2016
Introduction production input for firms, as consumption
The energy efficiency of real estate plays a good for households, and as financial asset
key role in policies directed towards low car- for investors (which also holds for energy-
bon economies. In industrialised countries, intensive appliances in general; See Davis,
for example, about 40% of total energy con- 2008). In the residential context, research
sumption is used for space heating and cool- particularly focuses on housing as owner-
ing (OECD, 2003). In most studies on occupied consumption good, i.e. the choice
residential energy consumption, energy is and valuation of the efficient production
understood as input for the production of technology of energy intense services
housing services like a warm home. Energy, (Quigley, 1984). However, most home own-
however, can be substituted by capital inputs, ers, even owner-occupants, understand their
i.e. energy efficiency investments, which have property also as financial asset. They might
been identified as cost-effective alternatives to expect, additional to cost savings, returns
energy inputs. Scholars in the fields of climate from investment in terms of capital gains
policy as well as energy economics identified when reselling their property. This is particu-
the so-called ‘energy efficiency gap’ – the find- larly true for the case of rental apartments.
ing that energy efficiency measures are under- Landlords are most likely not interested in
utilised compared to their potential energy energy savings per se – they are interested in
cost savings (see, e.g., Allcott and Greenstone, the value and economic benefits energy effi-
2012; Bardhan et al., 2013; Schleich and ciency generates in terms of sale price and
Gruber, 2008). The fact that so many house- rental income increases, as well as vacancy
holds do not exhaust the potentials of retrofit- risk reductions. As most studies argue, land-
ting appears puzzling to many authors (see, lords often cannot pass on the investment
e.g., Eichholtz et al., 2010, 2013; Mills and costs to tenants due to market imperfections,
Schleich, 2012; Nair et al., 2010). which is called the ‘landlord-tenant dilemma’
One reason might be the so far under- (see, e.g., Schleich and Gruber, 2008). As a
researched threefold character of real estate result, it is argued that landlords – compared
in the energy efficiency context: it serves as to owner-occupants – produce less energy3220 Urban Studies 54(14)
efficient homes, which is confirmed by and expectations. These questions are ana-
empirical studies (see Rehdanz, 2007). lysed using micro-data from Berlin’s housing
Thus, to comprehensively understand market. Thereby, we benefit from the grow-
investors’ rationale, particularly that of ing online market for residences and use
landlords, research should also analyse the data obtained from the leading online hous-
potential effects energy efficiency has on the ing market portals in Germany, immobilien-
selling price of a dwelling and the generated scout24.de, immonet.de and immowelt.de. In
rental income streams. However, while hedonic regressions, we then include the
changes in price, rental income, or the risk energy performance of buildings as an expla-
of vacancy must be considered as important natory variable, along with an extensive set
determinants of investment decisions, the of control variables. Energy performance is
influence of energy efficiency has only been measured as the annual energy consumption
selectively studied. Available insights are in kilowatt-hours per square meter of resi-
focused on US housing markets and are dential living space (kWh/[m2 a)]1, which
largely limited to the analysis of owner- allows us to directly compare willingness to
occupied residences. The findings suggest pay (WTP) and energy cost savings at cur-
that energy savings are efficiently capitalised rent prices.
in house prices. However, while there are The remainder of this paper is structured
several studies which analyse the impact of as follows. In the next section, we provide a
energy efficiency on office prices and rents, brief overview of the relevant empirical liter-
there are still very few papers that would ature on energy efficiency capitalisation in
empirically address economic benefits for real estate prices and rents. We proceed by
landlords, i.e., how energy efficiency affects summarising the underlying arguments,
rental income or selling prices (to our knowl- which constitute the ‘landlord-tenant’ or
edge, to date only Hyland et al. 2013, and ‘investor-user dilemma’. The third section
Fuerst et al., 2015, assess this aspect). For a outlines our empirical strategy, the methods
long time, this could have been explained by used, and describes the data employed in
a lack of data. However, this has changed our study. We then discuss the empirical
and a growing number of researchers are results and their implications.
evaluating the economic effects of ‘green’
real estate investments in different contexts
(e.g. Brounen et al., 2012; Eichholtz et al., Related literature
2010, 2013; Fuerst and McAllister, 2011).
The aim of the present paper is to com-
Empirical studies
pare the willingness of owner-occupants, The number of studies dealing with the
landlords, and tenants to pay for energy effi- effects of energy efficiency investments on
ciency and to gain deeper insights about the the value of real estate is limited. Most of
underlying investment rationale. In a first the recent literature focuses on commercial
step, we analyse how energy requirements real estate and analyses the effects of Energy
for space heating capitalise in rental and StarÒ and Leadership in Energy &
owner-occupied apartment prices. In a sec- Environmental Design (LEED) certification
ond step, we assess the impact of energy effi- schemes (e.g. Eichholtz et al., 2010, 2013;
ciency on rents. Based on this information Fuerst and McAllister, 2011). These studies
and actual energy prices, we evaluate in a found significant positive effects of environ-
final step whether homeowners’ calculations mental certification on real estate prices,
are grounded on reasonable discount rates office rents, and vacancy risk.Kholodilin et al. 3221
The first generation of studies on resi- the specific effects of energy efficiency can-
dential real estate point in the same direc- not be disentangled.
tion. These studies, conducted in the 1980s, The second generation, studies published
were all based on US real estate transaction since 2011, tried to resolve the paucity of
data. Potential effects of energy perfor- small samples by combining transaction
mance on residential property are, in most data with ‘green’ certification ratings:
cases, analysed based on very small samples Brounen and Kok (2011), Bloom et al.
of detached or semi-detached dwellings, (2011), Kahn and Kok (2014), Deng et al.
located in one single city or neighbourhood. (2012), Walls et al. (2013), and Hyland
All the studies rely upon hedonic regres- et al. (2013) all find positive impacts, espe-
sions, some specifying the functional form cially from LEED and Energy Star certifi-
using Box-Cox methodology. cations schemes. But these studies also have
The first study by Halvorsen and shortcomings. Since the certificates only
Pollakowski (1981) analyses sales price require minimum standards of energy effi-
spreads between homes having oil and gas- ciency, the exact value of energy savings
fired heating systems installed. The results cannot be identified in this context. Hyland
suggest that abrupt oil price shifts, like those et al. (2013) match their rating schemes
in the 1970s, are associated with an immedi- with the results of an engineering model, to
ate price decrease of houses using this energy compare the potential energy costs savings
source. Johnson and Kaserman (1983) and with the implicit prices. They find that sales
Dinan and Miranowski (1989) come to the prices equal 64%–79% of the net present
conclusion that a $1 decrease on the energy value (NPV) of energy cost savings, while
bill is capitalised in sales price increases that rents cover about 14%–55% of future
vary between $11.63 and $20.73 per m2. energy costs. Overall, Fuerst et al., (2015)
Laquatra (1986) estimates the implicit price provide supporting evidence for the effi-
for thermal integrity to be $2510 per unit, cient capitalisation of energy performance
indicating, with Horowitz and Haeri (1990), in house prices for the case of Wales. They
that energy savings are efficiently capitalised conclude, that the lower implicit returns on
in housing market transactions. However, energy efficiency for landlords compared to
these early studies mainly suffer from very owner-occupiers leads to a leveling of prices
small sample sizes and thus from a potential between energy efficiency classes.
loss of generality. To summarise, the existing literature indi-
The first study that uses a substantially cates that – at reasonable discount rates –
larger amount of transactions was con- energy efficiency is well capitalised in house
ducted by Nevin and Watson (1998). It is prices. However, the evidence is concen-
based on data from the American Housing trated on US housing markets. Notably,
Survey, covering 30 metropolitan statistical only few studies (Brounen and Kok, 2011;
areas. In multiple regressions, the authors Deng et al., 2012; Högberg, 2013; Hyland
analyse the impact of utility expenditures on et al., 2013) provide insights on European or
house prices and conclude that housing mar- Asian housing markets. Moreover, most
kets efficiently value energy cost savings. studies available analyse single-family
However, while the study employs a larger detached or semi-detached housing, which is
sample, it lacks accuracy. The paper relies most likely to be owner-occupied. There is
on total utility expenditure instead of energy only one study to date, Hyland et al. (2013),
costs. Thus, general maintenance costs and that covers house sales in the rental housing3222 Urban Studies 54(14)
segment, an important market in many (iii) Moreover, it is claimed that transac-
countries. This appears even more surpris- tion costs incurred when concluding
ing, given the emphasis of the literature on the rental contracts, which allow to
the discussion of the so-called ‘landlord- fully appropriate the returns of energy
tenant dilemma’. In this light, further studies saving investments from energy effi-
which empirically assess the effects of energy ciency investments to either the land-
efficiency on rental housing prices and rents lord or the tenant (depending on who
appear long overdue. invests in energy efficiency), are prohi-
bitively high (Schleich and Gruber,
2008).
The impact of the rental relationship on
house prices Typically, housing market mechanisms and
In the literature on energy efficiency invest- the resulting rent asking strategies by land-
ments, the specific problems in the rental lords are disregarded in the literature on
relationship are described as the ‘landlord- energy efficiency investments. However,
tenant dilemma.’ It is argued that neither these should also play an important role for
landlords nor tenants have sufficient incen- differences in the implicit price of rented out
tives to invest because both groups face sub- versus owner-occupied dwellings.
stantial market failures and market The most important insight in this context is
imperfections. The key problems identified the following one: even if landlords are able to
are asymmetric information, prohibitively credibly transmit the information about energy
high transaction costs, and uncertainty savings, this does not imply that tenants are
(Allcott and Greenstone, 2012; Davis, 2011; willing to pay the rent (REPS ) that covers total
Levinson and Niemann, 2004; Schleich and energy cost savings. This is because tenants can
Gruber, 2008). In this context, the following move and choose between alternative resi-
arguments are frequently presented. dences. Thus, landlords face a risk of vacancy
(r). This risk can be diminished by reducing
(i) Typically, tenants cannot evaluate the rents (see Stull, 1978). Consequently, rational
real quality of a dwelling due to limited landlords optimise the NPV from investment
technical understanding or missing NPV at a discount rate d for the investment
information on the efforts undertaken period T by maximising asked rents and simul-
by the landlord to produce a certain taneously minimising r:
quality.
(ii) A second potential source for reduced XT
(1 r)
NPV = REPS ð1Þ
WTP of tenants is that they apply rela- t=1
(1 + d)t
tively high discount rates to future
energy savings and energy price where r = f (R) and f 9(R).0, which implies
increases (Hassett and Metcalf, 1993). that maximisation of NPV is achieved for an
In addition, the length of the rental intermediate value of REPS .
relationship is frequently uncertain, Commonly, excess supply hands over
not least because tenants can – in case market power to tenants, at least to some
of strong surges in energy prices – extent.2 In the present context, the value of
move to a more energy efficient dwell- energy efficiency in a rental dwelling (every-
ing at comparably low transaction thing else constant) should be lower com-
costs. This decreases WTP of tenants pared to the value of energy efficiency in an
compared to owner-occupants. owner–occupied home, because owner–Kholodilin et al. 3223
occupants can fully benefit from energy cost term. Given that we expect the prices for
savings. owner-occupied and rental dwellings to be
In summary, all arguments presented different, both the coefficients for RPi and
indicate that landlords’ returns from energy the interaction term EPSi 3 RPi should be
efficiency investments are likely to be lower statistically significant.
compared to those of owner-occupants. In a second step, we use a data set of
Consequently, the NPV and hence the impli- rental apartments and regress the monthly
cit price of energy efficiency should be lower rental income per square meter (Ri ) on EPSi
than it is for owner-occupied dwellings as in order to assess tenants’ WTP for energy
well. cost decreases:
log Ri = g 0 + g1 EPSi + Xi0 d + vi ð3Þ
Empirical strategy
Based on the empirical findings and argu- where log Ri is the asked rent per square
ments presented in the literature, the empiri- meter for the ith dwelling; vi is the error
cal strategy to identify potential differences term. Both the model for rents and prices
between the capitalisation of energy effi- are estimated using the common semi-log
ciency in owner-occupied and rental dwell- specification (Goodman, 1978). The result-
ings relies on standard hedonic estimation ing coefficients can be approximately inter-
methods. First introduced by Rosen (1974), preted as percentage changes of rents and
hedonic regression models are frequently prices resulting from unit changes of the
applied instruments to evaluate the implicit independent variables. Further, the semi-log
prices of housing characteristics, local ame- specification has the advantage of having
nities, and accessibility (for recent applica- economically sound properties: in contrast
tions see, e.g., Ahlfeldt, 2013; Fuerst et al., to a linear model, where, for example, each
2011; Moro et al., 2013). additional bedroom would be valued
In equation (2), the dependent variable is equally, the value added in a semi-log model
the log price of a dwelling i per square meter varies proportionally with the size and qual-
(log Pi ). While controlling for several struc- ity of the estate. Based on the estimation
tural and locational attributes of the dwell- results and information on energy prices, we
ing (Xi ), we estimate the influence of the key evaluate whether investors’ calculations
explanatory variables of interest: the energy appear reasonable.
performance score (EPSi ) of a house mea-
sured as the annual energy consumption in
kilowatt-hours per square meter of residen- Data and stylised facts
tial living space (kWh/[m2 a]), a dummy
Housing market conditions and energy
indicating whether the dwelling is sold as
prices vary substantially across regions.
rental property (RPi ), an interaction term of
Accordingly, the value of energy efficiency
both variables (EPSi 3 RPi ), and control
should also show a distinct regional pattern.
variables Xi :
Since it is difficult to appropriately control
log Pi = a0 + a1 EPSi + a2 RPi + a3 EPSi for the specific regional impacts, we concen-
trate on the Berlin housing market, where
3 RPi + Xi0 b + ui ð2Þ
already beginning in 2005, the market condi-
where log Pi is the asked price per square tions became more favorable for real estate
meter of the ith dwelling and ui is the error investors.3224 Urban Studies 54(14)
Data sources and quality. Empirical real estate variable. Indicators on whether the apart-
research is data demanding. In the past, ments are built or not are used as regressors
detailed housing market analysis was not (e.g. future or current year as construction
possible due to a lack of information on real year, key words such as ‘new’ and/or ‘under
estate transactions (DiPasquale, 1999; construction’ etc.). The resulting variable
Eichholtz et al., 2013; Gyourko, 2009; ‘non–existent’ is the inverse probability that
Olsen, 1987). In this study, as alternative to the apartment is constructed in reality.3
conventional transaction information, we A third serious objection against using
use data collected from Internet rental and asked prices and rents in Internet ads is that
selling advertisements of apartments in they may deviate from the final, or transac-
Berlin. The data were downloaded on a tion, prices and rents. Although appraised
monthly basis from June 2011 through data are reported as a valid substitute for real
December 2014 from the three most popular transaction information (Hyland et al., 2013;
German real-estate websites: immobilien- Malpezzi, 2003), there are only few studies
scout24.de immonet.de, and immowelt.de. that evaluate the degree of a deviation from
The ads placed on the three websites contain transaction prices. The two most prominent
extensive information on numerous struc- studies for Germany are that of Faller et al.
tural and locational characteristics of the (2009) and Henger and Voigtländer (2014).
properties for sale/rent. Faller et al. (2009) investigate the differences
However, using Internet advertisements between offer and transaction prices for
in this context suffers from four major short- Northrhine-Westphalia. Their findings indi-
comings that are addressed in the empirical cate that on average the offers are 8% above
analysis. First, Internet data are often pla- the real transaction prices. Including controls
gued by invalid or duplicated observations. for housing characteristics did not turn out to
Some advertisements are likely to be pub- be an explanation for the observed differ-
lished on different websites simultaneously. ences. Significantly smaller gaps are found for
The duplicates can cause serious distortions urban locations and during market expan-
of the estimation results. Therefore, we sions, with marginal explanatory power for
applied a matching algorithm specifically some housing characteristics (Henger and
designed to identify duplicates in the data. Voigtländer, 2014).
Second, the websites might be used by More generally, systematic mis-pricing of
realtors or construction companies for mar- housing characteristics has been found to be
keting purposes. Some objects, especially very costly to the seller (Knight, 2002; Merlo
apartments offered for sale, are not con- and Ortalo-Magne, 2004), which is in line
structed yet and such ads are placed by the with theoretical models of seller behaviour
construction firms in order to attract new (e.g. Knight et al., 1994). It increases time on
customers. Hence, a substantial share of the market and reduces the final transaction
these dwellings only exists on paper and price. Both effects make it more likely that
might never be built. Not accounting for this measurement errors (differences between list-
would lead us to biased results. Therefore, ing and transaction prices) are unrelated to
we identified real new apartments by screen- housing characteristics. This is confirmed by
ing the free text description of the apart- empirical evaluations: among others, Knight
ments for sale in the ads. In a nutshell, this et al. (1994) and Semeraro and Fregonara
classification is based on the coefficients of a (2013) analyse listed prices and the respective
logit estimation where fake advertisements transaction data. Both studies find that coef-
are included as a binary 0/1 dependent ficients changed only slightly when movingKholodilin et al. 3225 from listed to realised prices. Three out of both numbers are rather small, and, most four coefficients for housing characteristics importantly, the results are highly robust to in Knight et al. (1994) were statistically equal the particular choice of the dependent across regressions, although all t-values were variable.5 greater than six. The only exception is the variable ‘living area’ where the change of coefficients was statistically significant, but Variable definitions and descriptive small. statistics Finally, there may be systematic differ- Table 1 presents the descriptive statistics on ences between advertisements including and apartments for rent and for sale. In Berlin, excluding information on the energy perfor- the ‘typical’ dwelling for sale is generally mance of a dwelling. Until May 2014, sellers larger and better equipped compared to a and landlords were not obliged to publish rental apartment. energy performance scores (EPS) in their offers. Therefore, it is necessary to compare the characteristics of both groups of ads: Rents and apartment prices. The dependent those containing EPS and those that do not. variables in equations (2) and (3) are the logs In case of systematic differences between of the asked selling price and the asked these two groups, estimation results exclu- monthly rent, respectively. Both measures sively based on ads including EPS would be are reported in euros per square meter. In biased. Indeed, tests reveal significant differ- the period under consideration, both prices ences between the groups. Therefore, it is and rents follow an upward trend – to important to use methods that correct for account for these price movements over this selection bias. time, we include dummies for each month. Despite these potential data imperfec- Again, since we analyse prices in an expand- tions, we opt for using the data from Internet ing market, we believe that potential bias ads and correct for the sample selection bias between realised prices/rents in transactions by estimating a Heckman two–stage model. and asked prices/rents in advertisements is The main reason is that alternative data, con- rather small. taining information on energy consumption, house prices, and rents at the micro level,4 Energy certificates and occupancy status. The simply do not exist. Moreover, we concen- first key explanatory variable is the energy trate upon a large city experiencing a housing performance of buildings – since 2009, it is market expansion in recent years, which mandatory for each landlord/seller of a implies significant market power of sellers dwelling to provide information on the heat- and landlords. According to the literature, ing energy requirements of a building if pro- discrepancies between asked and real prices/ spective tenants/investors ask for it rents should be relatively small in the first (European Commission, 2002). The German place, while there is only little evidence for a ‘Energy Performance of Buildings Directive’ potential systematic mis-pricing of housing (Energieeinsparverordnung, EnEV) allows characteristics. Moreover, we evaluated the for two alternative ways of obtaining such a differences between the listed and realised measure. The first one is based on real transactions prices for a subsample of our energy billing information. The so-called data. The comparison of 29,680 matched ‘consumption based’ energy certificates are transactions from Gutachterausschuss and normalised to the climatic conditions of the online ads reveals that differences between city of Würzburg in the year 2002.
Table 1: Sample averages and variable definitions. 3226
Variable Variable definition sale no EPS sale EPS rent no EPS rent EPS
EPS of rental apartments kWh/m2 p.a. 2 127.5 2 129.8
EPS of available to use apartments kWh/m2 p.a. 2 107.6 2 2
Price Euro/m2 2528.27 2394.86 7.47 7.85
Refurbished dummy 0.08 0.06 0.09 0.12
Age years 63.71 65.91 66.30 69.62
Vintage class until 1920 dummy 0.35 0.35 0.32 0.39
1920–1950 dummy 0.14 0.12 0.13 0.10
1950–1970 dummy 0.15 0.22 0.17 0.16
1970–1990 dummy 0.05 0.04 0.18 0.16
1990–2015 dummy 0.30 0.27 0.19 0.19
No. of floors total number of floors 14.34 14.96 15.91 15.22
Schools within 1 km radius 9.39 9.32 7.92 8.47
Railway stations within 1 km radius 1.02 1.04 0.81 0.86
Metro stations within 1 km radius 2.23 2.36 1.66 1.78
Supermarkets within 1 km radius 14.37 14.53 12.16 13.17
Distance to city center kilometer 5.02 4.83 6.68 6.48
Population density residents/km2 124.27 115.87 118.32 119.02
No. of rooms No. of rooms 2.76 2.62 2.39 2.35
Residential space m2 84.19 80.77 69.58 68.95
Quality unknown dummy 0.59 0.52 0.71 0.51
low dummy 0.01 0.02 0.01 0.01
average dummy 0.11 0.20 0.17 0.29
high dummy 0.27 0.23 0.11 0.18
luxury dummy 0.02 0.04 0.01 0.01
Fitted kitchen dummy 0.26 0.23 0.39 0.38
Elevator dummy 0.45 0.44 0.32 0.31
Guest bathroom dummy 0.22 0.24 0.10 0.11
Parking lots No. available 0.23 0.25 0.12 0.13
Cellar dummy 0.64 0.75 0.47 0.62
Access to garden dummy 0.22 0.21 0.12 0.15
Balcony dummy 0.62 0.67 0.57 0.60
Suited for disabled dummy 0.10 0.15 0.07 0.06
Suited for elderly dummy 0.09 0.15 0.08 0.06
Architectural monument dummy 0.08 0.12 2 2
Notes: descriptive statistics for spatial controls apartment types and the dummies for floors are available from the authors upon request.
Urban Studies 54(14)Kholodilin et al. 3227 To mitigate a potential user bias, which is occupation. The German ‘Homeownership the main point of criticism for this measure, Law’ (‘Wohneigentumsgesetz’, WEG), German performance based certificates are only Civil Code (‘Bürgerliches Gesetzbuch’, BGB), applicable for apartment buildings and must and the Berlin-specific ‘Tenant Eviction be calculated as the average of three subse- Regulation’ (‘Kündigungsschutzklauselveror quent heating periods. Furthermore, the dnung’) delegate substantial rights to the EPS refers to the entire building. If the size tenants living in an apartment, which should of the dwelling is small relative to the build- be sold for purposes of owner-occupation.6 ing’s size, this reduces user bias consider- Alternatively, a potential buyer can try to ably. The alternative ‘performance based’ compensate the tenant for agreeing to cancel measure is based on an engineer’s assess- the contract. This, however, is costly and ment of the thermal conductivity of a build- should be negatively capitalised in the prop- ing. The outcome is the theoretical heating erty price. In our estimation, a dummy vari- energy requirement of a house. Both able indicates whether the apartment refers to approaches are intended to be comparable the rental segment or can be used directly in in terms of their outcomes as they provide owner-occupation.7 measures for the annual heating energy requirement (in kilowatt-hours) per square Control variables. In the rich literature using meter of residential space. Arguably, in case hedonic methods in real estate appraisal, of apartment housing, the consumption various variables have been proven to be based measure is by far more frequently important predictors of the property prices. applied, since it is easy to calculate and In our study, we control – as far as possible cheaper in the certification process. – for the most frequently tested features Typically, EPS ranges from zero to 300 (see, for a comprehensive summary, e.g., kWh/[m2 a]. In our sample, we observe val- Malpezzi, 2003). The variables included are ues ranging from 0 to 340 for EPS in proper- summarised in Table 1. ties for sale, while in dwellings offered for Size and type of the dwelling: In almost rent EPS ranges from 0 to 385.7 kWh/ any study, the size of the dwelling is included [m2 a]. Another key insight is the substan- as explanatory variable for the (rental) price. tial difference of the EPS between apart- In the present paper, size is captured by the ments for rent and available to use number of rooms as well as the total area. dwellings. This is in line with previous stud- Moreover, the studies generally distinguish ies (Rehdanz, 2007) and can indeed be between the dwelling’s type: In particular, understood as first evidence for split incen- we control for potential effects if the apart- tives among the two groups of investors. ment is, for example, a loft, a penthouse, or The second key variable of interest is the a souterrain dwelling. occupancy status of the apartment for sale. Comfort: The general comfort of an apart- Typically, this variable is included in the ment can be characterised by different attri- ads, because it is an important selection cri- butes. Using dummy variables, we control terion for potential buyers. Since tenancy whether an elevator, a cellar, a fitted kitchen, law in Berlin – if the actual tenant wants to a guest bathroom, or a parking lot is avail- stay in the apartment – forbids a transfor- able and if access to a garden or a balcony is mation from rental to owner-occupation included. Moreover, we control if the dwell- within a period of seven years after the sale, it ing is suited for elderly or disabled people. is unlikely that investors aim to buy currently To capture potential differences in the qual- rented out dwellings for the purpose of owner- ity of a dwelling, we use the information in
3228 Urban Studies 54(14) the ads indicating whether the apartment is density. Moreover, we include dummy vari- of low, average, high, or luxury quality. ables for the 12 districts of Berlin. Building attributes: the age of a dwelling Finally, to capture the recent surge of is associated with a certain ‘natural’ quality house prices, we include a monthly time of housing. The housing built in different trend in the estimation. decades is characterised by specific architec- tural design, materials, and construction techniques employed as well as aspects of Methods urban planning that affect the quality of life As outlined in the section ‘Data sources and in the apartments. To account for potential quality’ we face a serious selection bias in our differences in the architectural design, we data. While it is obligatory to have an energy include measures that capture the vintage performance certificate for each dwelling for class of a building, the age of the building, sale or for rent since 2009, it was optional to whether it is an architectural monument, report the energy performance score in online and the size of the house approximated by advertisements until May 2014. Obviously, the number of floors. this creates incentives to report only EPS that General housing condition: The general indicate low energy costs. Thus, we have rea- condition is also important for the quality of son to believe that the estimates obtained a dwelling – it should clearly make a differ- from a sample restricted to only those obser- ence to potential tenants or buyers, whether vations including EPS would be biased. One an apartment is newly constructed, refur- way to correct for such bias is to calibrate a bished, or non-refurbished. Consequently, Heckman selection model (Heckman, 1979), we include dummies indicating the refurbish- as also proposed by Hyland et al. (2013) in ment status of a home. an analogous context. Accessibility: Standard urban economics The underlying intuition is that one theory suggests that accessibility is one of observes a property price or rent that reflects the most important predictors for house the energy performance of a building ade- prices and rents. As common variable to con- quately, only if the EPS was reported to the trol for this effect, the distance to the city cen- investor or the potential tenant. Otherwise, ter is used in many hedonic studies. We one observes a price, which does not expli- include the distance in kilometers to the clo- citly reflect energy efficiency. In this context, sest of the two main city centres of Berlin: EPS can be interpreted as an omitted variable either ‘Gedächtniskirche’ (West Berlin) or that potentially affects both the level of the ‘Rotes Rathaus’ (East Berlin). The coordinates price and the coefficients of other housing of advertised apartments are determined attributes, since increased energy efficiency using the official list of Berlin’s addresses.8 allows to substitute energy expenditures and Amenities: Moreover, we use the exact other housing services. Missing information coordinates to determine the endowment on the energy performance imposes uncer- with local amenities, which play an impor- tainty on potential buyers and tenants, which tant role for house prices and rents. We potentially results in decreased WTP for count the number of schools, supermarkets, other housing attributes. and metro stations at foot distance (within 1 The Heckman procedure accounts for km radius) as a measure for local infrastruc- such bias. In a nutshell, the concept consists ture endowment. To account for neighbour- of two steps. In the first stage, the binary hood characteristics, we add population choice of reporting an EPS is estimated in a
Kholodilin et al. 3229
Table 2: First stage results.
Model 1 Model 2
apartment prices apartment rents
EPS mandatory 0.62 *** (31.89) 1.18 *** (87.25)
Building characteristics
Refurbished 20.11 *** (23.65) 0.15 *** ( 8.37)
Age of building 0.01 *** ( 8.29) 0.00 ( 0.28)
Vintage class: before 1920 20.89 *** (27.85) 0.79 ( 1.71)
1920–1950 20.82 *** (29.37) 20.20 *** (25.27)
1950–1970 20.23 *** (25.20) 20.00 ( 0.01)
1970–1990 20.51 *** (29.50) 0.03 ( 1.54)
199022015 base category
No. of floors 0.02 *** (10.69) 20.01 *** (26.69)
Rental 0.30 *** (16.36) 2
Architectural monument 0.05 ( 0.16) 2
Spatial controls
Charlottenburg 20.07 *** (21.43) 20.27 *** (29.23)
Friedrichshain-Kreuzberg 0.01 ( 1.24) 20.05 (21.25)
Lichtenberg 20.39 *** (25.74) 20.04 (21.19)
Marzahn-Hellersdorf 20.12 ** (22.20) 0.03 ( 1.01)
Mitte 20.15 *** ( 3.08) 20.16 *** (24.39)
Neukölln 20.08 ** (21.97) 20.04 (21.35)
Pankow 20.09 (21.93) 20.04 (21.61)
Reinickendorf 20.16 *** (23.73) 20.13 *** (25.26)
Spandau 20.13 *** (23.00) 20.15 *** (25.97)
Steglitz-Zehlendorf 20.04 (20.92) 20.16 *** (26.98)
Tempelhof-Schöneberg 20.22 *** (25.35) 20.12 *** (25.03)
Treptow-Köpenick base category
Constant 21.16 *** (226.88) 21.08 *** (241.78)
total sample/uncensored obs. N = 32,157/7298 N = 83,848/13,366
Wald x2 23,152.46*** 10,931.64***
Notes: ***, ** indicate significance at 1% or 5% level of confidence, z statistics in parentheses.
probit framework. Based on these results, performance (see Table 2). As identifying
the inverse Mills ratio (l) is calculated for restriction, we add a dummy variable ‘man-
each observation and is used as additional datory’ to the first stage equation. The vari-
regressor in the second stage, where the able indicates whether the advertisement was
impact of EPS on house prices and rents is published before May 2014, or under the
estimated (for a more general description of regime that obliges the reporting of EPS in
the Heckman two-stage estimator, see online ads since May 2014. Our data reveal
Greene, 2007). A significant coefficient for l that the obligation to report EPS increased
indicates the presence of a selection bias. the share of ads containing such information
In our case, we estimate the probability of in the selling sample from roughly 27% to
reporting an EPS as a function of housing 47.5%.9 Moreover, the distribution is indeed
characteristics, such as the age of the dwell- biased towards better EPS before May 2014.
ing, the refurbishment status, or the height of We estimate the impact of energy effi-
the building, among others, which are all ciency on apartment prices and rents using
potential candidates to affect the energy equations (2) and (3), extended by the3230 Urban Studies 54(14)
inverse Mills ratio obtained from the selec- impact on the energy performance to tenants
tion equation and the Internet ads data. The prior to the construction activity. A calcula-
estimation results are reported in Table 3. tion of the effects is not mandatory for
Overall, both models have substantial expla- owner-occupants. Landlords therefore
natory power, indicated by the Wald x 2 -tests clearly have a higher incentive to update
and the adjusted R2 . their energy performance certificate, which
might explain the observed differences. This
interpretation is also strengthened by the
The decision to advertise information on fact that the rental status of a dwelling
energy efficiency increases the likelihood of reporting EPS in
the sales model. Finally, in Model 1, we find
The results of the first stage show the deci-
a very small effect of the age of the building.
sion to report EPS in advertisements of
Further, we controlled for the status of an
apartments for sale and for rent, see Table 2.
architectural monument in the sales model.
We regressed this decision on a set of build-
However, this turned out to have no influ-
ing characteristics and a dummy variable
ence on the likelihood to report EPS.
that indicates whether the dwelling is adver-
tised in a period when EPS is mandatory to
be reported in ads and controls for the spa-
Capitalisation of energy efficiency in prices
tial dimension. Our results show that the
most important predictor for EPS included and rents
in the advertisement is the change in the reg- Table 3 presents our estimation results for
ulation. For both groups of ads, the likeli- the effects of energy efficiency and occu-
hood to include EPS has substantially pancy status on house prices. The coefficient
increased with the obligation to report this of the inverse Mills ratio is highly signifi-
measure. Moreover, there are significant cant, indicating the non-random selection,
spatial differences within Berlin. General as expected. Moreover, the negative sign
housing characteristics also affect the likeli- indicates that simple OLS estimates that do
hood to include information on the energy not consider the issue of sample selection are
performance of buildings. biased towards higher coefficients for EPS.
The estimates for both groups differ to The key variables in equation (2) are
some extent when comparing the results for EPS, RP, and EPS 3 RP. All of them are
general housing characteristics. The impact statistically significant, at least at the 5%
of vintage is highly significant for each class level of confidence. The coefficient ‘Rental
in the sales model. By contrast, only build- property’, RP, is negative and indicates that
ings constructed between 1920 and 1950 a currently rented out dwelling costs 27%
have a lower likelihood to report EPS, while per m2 less compared to a dwelling, which is
buildings constructed before 1920 have a available to use. The coefficient can be inter-
higher likelihood to report EPS in the rental preted as the discount which is related to the
model (marginally significant). rental relationship. First, it is costly to get
Interestingly, there is a negative effect of rid of the current tenant. Second, the future
refurbishment in the sales model, while rental income is, compared to the utility
recent refurbishment increases the likelihood received in owner-occupation, subject to
to report EPS in Model 2. This might be uncertainty. Third, the rental externality
explained by the fact that landlords have to (Henderson and Ioannides, 1983; Iwata and
announce refurbishments and the expected Yamaga, 2008) creates uncertainty aboutKholodilin et al. 3231
Table 3: Estimates for apartment prices and rents (semi-log specification).
Model 1 Model 2
apartment prices apartment rents
Energy performance and mode of tenure
Energy performance score (EPS) 20.0005 *** (25.40) 20.0002 *** (26.01)
Rental property (RP) 20.24 *** (212.02) 2 2 2
RP 3 EPS 0.0003 ** (2.14) 2 2 2
attributes of the flat
Number of rooms 0.001 (0.24) 20.004 *** (23.69)
Residential space 0.001 *** (6.54) 20.001 *** (212.55)
Built-in kitchen 0.061 *** (9.30) 0.068 *** (20.57)
Elevator 0.105 *** (15.54) 0.006 (1.34)
Second bathroom 0.048 *** (5.89) 0.035 *** (6.29)
Parking lots 20.003 (20.41) 0.011 *** (4.52)
Cellar 0.030 *** (4.55) 20.011 *** (23.54)
Access to garden 0.008 (0.98) 0.022 *** (4.73)
Balcony 0.007 (1.30) 0.019 *** (6.09)
Suitable for disabled 0.000 (0.05) 20.027 *** (23.03)
Suitable for elderly 0.014 (1.43) 20.013 (21.55)
Quality: low 20.149 *** (28.00) 20.104 *** (27.93)
high 0.153 *** (18.14) 0.122 *** (28.71)
luxury 0.327 *** (18.79) 0.204 *** (12.93)
unknown 0.035 *** (5.14) 20.006 (21.68)
normal base category
Controls for apartment type yes yes
Controls for floor yes yes
Housing attributes
Architectural monument 20.001 (20.12) 2 2 2
Refurbished 0.020 ** (2.04) 0.021 *** (4.60)
No. of floors 20.006 *** (210.17) 20.003 *** (29.31)
Age of building 20.001 *** (23.11) 20.000 (21.04)
Vintage class: before 1920 0.019 ( 0.50) 20.033 (21.75)
1920–1950 20.139 *** (24.57) 20.056 *** (23.85)
1950–1970 20.295 *** (215.85) 20.083 *** (29.43)
1970–1990 20.145 *** (27.66) 20.118 *** (218.91)
built after 1991 base category
Local amenities
Schools 0.007 *** (9.89) 0.006 *** (12.47)
Railway stations 0.021 *** (7.66) 0.020 *** (12.58)
Metro stations 0.019 *** (11.10) 0.001 (1.45)
Supermarkets 0.001 (1.08) 0.003 *** (8.51)
Distance to city center 20.027 *** (220.29) 20.015 *** (223.64)
Population density 0.000 ** (2.13) 20.000 *** (22.66)
Controls for neighbourhood yes yes
Controls for district yes yes
Other controls
Non-existent 0.200 *** (9.11) 2 2 2
Time trend 20.008 *** (224.94) 20.005 *** (229.17)
Constant 8.156 *** (195.32) 2.311 *** (143.14)
Inverse Mills ratio 20.072 *** (23.39) 20.026 *** (24.88)
R-Squared 0.75 0.69
Notes: ***, ** indicate significance at 1% or 5% level of confidence, z statistics in parentheses. Full results are available
upon request.3232 Urban Studies 54(14)
the intensity of use by the tenant. Thus, it is higher apartment prices. Therefore, we esti-
unclear how much resources will be needed mated the capitalisation of energy perfor-
for renovation or refurbishment in the mance in rents by regressing the natural log
future. Altogether, these aspects are likely to of monthly net rents in euros per m2 on
reduce the expected net rental income and, EPS. The results reported in Table 3 indicate
consequently, the apartment’s value, as con- that the coefficient for EPS is negative and
firmed by our estimation. statistically significant at the 1% level of
The second variable of interest is the confidence. However, its magnitude is small.
energy performance of the building and its A decrease of annual energy costs by one
impact on apartment prices. As expected, euro leads to an increase of annual rental
EPS has a negative sign, which implies that income by roughly 0.23 eurocents per m2 (at
higher energy requirements of dwellings lead sample mean).
to higher price discounts. On average, for Overall, the coefficients for the control
each additional kWh/[m2 a] of energy variables are in line with expectations and
needed, the price is reduced by 0.05%. Based the results reported in previous studies. For
on an average natural gas price10 in the period example, the rental income for a ‘refur-
of observation of eight eurocents per kilowatt bished’ apartment is significantly higher
hour (see Michelsen et al., 2014; Techem AG, compared to the base, a non-renovated
2012), a one euro reduction of annual energy home. Increasing distance to one of Berlin’s
costs is associated with a 15.5 euro increase of city centres incurs price and rental discounts,
the per square-meter house price (at the sam- while closeness to most other local amenities
ple mean). This is in the range reported in increases tenants’ and investors’ WTP. In
previous studies (see e.g., Johnson and rented out apartments, attributes like a
Kaserman, 1983; Nevin and Watson, 1998) built-in kitchen, a second bathroom, a bal-
By contrast, the coefficient of the interac- cony, a parking lot, all increase the rental
tion term EPS 3 RP is positive but smaller in income. Also the controls for the general
magnitude compared to the estimate for quality meet the expectations: low quality
EPS. For a rented out dwelling, the pre- decreases the rental income and prices, while
mium for one euro lower energy costs the WTP for high quality or luxury dwell-
per m2 and year will attain 6.22 euros. ings is significantly higher, compared to the
Under the assumption that the currently base group, a dwelling of average quality.
tenant-occupied dwellings are very likely to
be further rented out (due to the legal set-
ting, see the section ‘Rents and apartment
Are house prices a good reflection of
prices’, whereas available to use dwellings energy cost savings and rental income?
are most probably to be sold to owner-occu- Whether the estimated prices for energy effi-
pants, this implies that the implicit price for ciency reflect energy cost savings and rental
energy efficiency is strongly affected by the income reasonably can be assessed in differ-
rental relationship and the associated uncer- ent ways. A first indication whether energy
tainty. WTP for energy efficiency in owner- efficiency investments differ from general
occupied dwellings is almost 2.5 times larger real estate projects is to calculate commonly
than in rented out dwellings. used indicators in real estate appraisal, like
The question is whether this is a rational the price-to-rent ratio (price divided by the
response of investors to a low WTP for gross annual rental revenue). The measure
energy efficiency of tenants or if the rental indicates how much risk investors are
income from energy efficiency would imply willing to take in terms of the length of theKholodilin et al. 3233
payback period. For rental apartments, the d = 0:046 is the annual discount rate.12 In
price-to-rent ratio for energy efficiency the first scenario (e = 0), the estimated
equals roughly 27.1. This is very close to the annual rental income flow for each reduc-
overall price-to-rent ratio of rental dwellings tion of energy costs by one euro corresponds
in our sample (27.7). Energy efficiency is to a NPV of rental income over 55 years of
obviously rated as being only slightly riskier in total 5.08 euro. This reflects roughly 80%
than real estate investments in general. of the estimated implicit per square meter
In absence of rental income for owner- price (6.22 euro) for a one euro energy cost
occupied dwellings, one can compare the cost saving in a tenant-occupied dwelling.
savings-to-price-ratio (15.5, see the section Given that scarcity of fossil fuels will
‘Capitalisation of energy efficiency in prices increase in the future, it appears reasonable
and rents’) with the average length of owner- to assume that energy costs and conse-
ship of a dwelling. This gives an indication quently rental income from energy efficiency
whether owner-occupants try to match energy investments should also rise over time
savings with their personal benefits or if they (Scenario 2). Assuming tenants’ WTP to be
expect an additional premium when reselling tied to energy price movements, and taking
their home. According to a recently published the past price movements e = 3:5%13 (roughly
study by the German Federal Institute for the average annual increase of the consumer
Research on Building, Urban Affairs and price for natural gas in the period from 2001
Spatial Development (BBSR), the median to 2011 in Germany) as a reasonable proxy for
owner of a single dwelling holds the apart- future heating energy cost development, the
ment for 15 years (see Cischinsky et al., 2015: NPV of energy cost reductions by one euro in
68). Thus, WTP matches the average length this scenario equal 10.71 euros. The estimated
of ownership closely. WTP equals almost 60% of the NPV. In other
Another approach, which reflects the words: if landlords are perfectly rational and
nature of an investment more adequately, is calculate the NPV with e = 3.5% and d =
to compare the NPV of energy cost savings/ 0.046, the corresponding remaining technical
rental income over the entire technical life- lifetime is roughly 31 years.
time with the price investors are willing to A different picture can be drawn for the
pay. Based on our estimation results, we first value of potential energy cost savings in
calculate the NPV of the rental income from owner-occupied dwellings. The NPV can be
energy efficiency under two scenarios: In the calculated analogously to equation (4), while
first case, we assume that the implicit WTP income is generated by energy cost savings
of tenants (REPS ) is constant over the entire (C) instead of rental income (REPS )
investment period. In a second scenario, we
T
X
expect that rental income increases analo- 1+e t
gously to the average energy price move- NPV = C ð5Þ
t=1
1+d
ment over the past 10 years, e. Generally,
the NPV of a standard investment project Assuming in a first scenario a price of
can then be calculated as follows: eight eurocents per kWh heating energy, the
NPV – all else identical to the rental housing
T
X
EPS 1+e t case – of future energy cost savings at con-
NPV = R ð4Þ
t=1
1+d stant fuel prices (see equation (5)) equals
22.11 euros over the entire technical lifecycle
where t is the time index; T = 55 is the maxi- of 55 years, which exceeds owner-occupants’
mum technical lifetime of a building;11 and WTP by 42%. The spread even increases3234 Urban Studies 54(14)
when assuming an annual run-up of energy use (most likely owner–occupied)
costs (e) by 3.5%: the NPV (46.57 euros) dwellings – roughly by a factor of 2.5.
exceeds investors’ WTP roughly by factor (ii) This can be interpreted as a rational
three. Under the assumption of constant response of landlords: the rental rela-
energy prices, owner–occupants expect a tionship substantially reduces the rev-
remaining technical lifetime of 20 years; enues (rents vs. cost savings) from
Under the assumption of increasing energy energy efficiency investments. A one
cost savings, the remaining lifetime is 15 euro reduction in energy costs corre-
years, which again meets the average length sponds to an increase of only 23 euro-
of ownership quite closely. cents in rental income. However,
The results indicate that owner-occupants whether this is a result of market imper-
and landlords indeed follow two different fections, as argued by the authors
investment rationales. While owner-occupants emphasising the existence of the ‘land-
seem to orient their WTP primarily on their lord–tenant dilemma’ or a result of an
own direct benefits from reduced energy costs unequal distribution of market power
(future sales seem to play a minor role), land- between landlords and tenants, must be
lords calculate their energy efficiency invest- left for future research.
ments in strong analogy to general real estate (iii) Both groups of investors follow differ-
investment projects. Overall, the calculated ent investment rationales. Landlords
measures seem to fall in a plausible range and apparently optimise their investment
generally confirm and extend the results of in strong analogy to general real estate
the recently published study of Hyland et al. investment projects. This also includes
(2013) on the Irish housing market. the implicit assumption that potential
investors in the future also have a pos-
itive WTP for income generated from
Conclusions energy cost savings. By contrast, this
idea is obviously less important for
In this study, we investigated investors’ owner-occupants who seem to orien-
WTP for energy efficiency in the Berlin tate their WTP on individual energy
apartment housing market. In line with pre- cost savings/consumer needs, while
vious studies, we found that energy efficiency taking less into account the value of
is capitalised in house prices. Moreover, these savings for future owners. The
investors seem to account for potential differences might also partially be
future energy and house price movements. explained by shorter refurbishment
While this is an established finding in the lit- cycles of owner-occupied dwellings.
erature around energy efficiency of owner- However, these effects should also be
occupied dwellings, up to date only few subject to future research.
insights existed on the capitalisation of
energy efficiency in rental apartment prices Overall, our results indicate rational beha-
and the underlying rationale of investors. In viour by both groups of real estate investors:
this context, the present study adds three key Energy price movements seem to be antici-
insights to the debate. pated, current and future revenues are well
capitalised in rental apartment prices.
(i) The implicit price of energy efficiency However, owner-occupants seem to be too
in a tenant-occupied dwelling is signifi- pessimistic about potential revenues from
cantly below the level of available to reselling their home. For policy makers, ourKholodilin et al. 3235
findings imply a differentiated treatment of Notes
rental and owner-occupied housing in future 1. kWh stands for energy consumption; m2 is
policies towards the ‘Nearly Zero-Energy the residential living space in square meters;
Buildings’ (NZEB) standard, as, for exam- and a denotes a period of one year.
ple, targeted in the European Union by the 2. In housing markets, at least some ‘natural’
year 2021. While landlords’ WTP seems to vacancy occurs due to household fluctuation
be a rational response to current and future and search activities (e.g. Gabriel and
revenues, information for owner-occupants Nothaft, 1988, 2001; Rosen and Smith, 1983).
Beyond that, higher vacancy rates can often
about the potential capital gains when resel-
be observed because housing is a durable
ling their home might increase individuals’
good and cyclical housing market imbalances
WTP today and thus willingness to install tend to be persistent over long periods of time
energy efficiency measures. A promising (Glaeser and Gyourko, 2005). Thus, it is
approach to overcome the potential misalign- likely that landlords frequently cannot realise
ment of investment horizons of energy effi- the maximum rent that equals energy cost
ciency projects and individual investment savings; this is, in fact, not a result of market
objectives is to implement instruments of on- imperfection but that of competition.
bill financing, like, for example, recently intro- 3. For more details on the identification of
duced in the UK or frequently offered to com- duplicates and the probability of the physi-
mercial or public investors by contracting- cal existence of a ‘non-existent’ dwelling, see
Kholodilin and Mense (2011).
providers. For landlords, information seems
4. The only comparable data set with the data
to be a minor problem: overcoming insecurity on single dwellings is that of the evaluators’
of tenants about the real energy cost savings – committee (Gutachterausschuss) for Berlin.
e.g. by credible energy performance certifica- However, it is much less detailed and does
tion – and thereby increasing the revenues for not include information on energy consump-
landlords can be a stimulus for green invest- tion and rents.
ments. As Allcott and Greenstone (2012) 5. We estimated the price model based on both
point out, direct subsidisation should only be price indicators and found that the differ-
implemented in absence of information ences for the coefficient estimates are minus-
inefficiencies. cule. In the following we present results
based on listed prices. Results for transac-
Future research in this field should also
tion prices are available on request.
consider the comparison of the effects of 6. In addition to the protection against eviction
EPS on house prices and rents under hetero- for seven years, tenants have a preemption
geneous market conditions. While the find- right to buy the apartment two months after
ings in our study hold for the growing Berlin the announcement of the sale.
market, there are still no studies concerning 7. It must be noted that this variable does not
the implicit price for energy efficiency in mar- exactly identify rental and owner-occupied
kets that are facing population decline and a apartments. While a change from rental to
less favorable market environment. It can be owner-occupation is difficult, the conversion
expected that rental revenues and apartment to a rental apartment can be easily pursued.
However, this has a mitigating effect on the
prices would vary substantially, as indicated
spread in the WTP for energy efficiency in
by the study of Hyland et al. (2013). owner-occupied and rental dwellings. Our
results therefore represent the lower bound
Funding of the potential difference.
The authors received no financial support for the 8. For the list of addresses, see www.statistik-
research, authorship, and/or publication of this berlin-brandenburg.de/regionales/rbs/rbsadr
article. esse.asp?Kat=4002.You can also read
