The German Energy Performance of Buildings Directive requires sellers on the housing market to provide detailed information on expected yearly energy consumption per square meter (energy performance, EPS). This paper uses variation in local fuel prices and climate, fuel types, and building ages to analyse the relationship between expected energy cost savings from energy efficient building structure and house prices in a data set of listing prices from all regions of Germany. Results suggest that heating cost considerations are less relevant than previously thought.
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Table 9 in Busse et al. (2013, p. 245) exemplifies this dilemma. It displays a range of plausible assumptions about discount rates and demand elasticities. As interpreted by the authors, this table supports their conclusion that myopia are absent. Allcott and Wozny (2014, p. 782, Fn. 9) use the same table to show that their own results and the results of Busse et al. (2013) support the presence of myopia.
For instance, there is evidence of uninformed consumer choices in low-cost situations if part of the price information is visible and part of it is hidden ( see Chetty et al. 2009, inter alia).
It is not possible to decide whether there are statistically significant differences because the authors only report significance levels and also do not indicate the type of covariance matrix that was used in their calculation.
Lising prices could be seen as final transaction prices measured with error. Even if this measurement is unbiased, it potentially increases confidence intervals around coefficient estimates. The error is unobservable in our case.
For houses older than 3 years, a “consumption-based” EPS can be calculated which is based on energy use in the past 3 years.
The results are robust to the choice of alternative measures for local climate, such as average local winter or autumn and winter temperatures. These results are available from the author upon request.
In 2015, only 0.2% of private households’ energy use fell on air conditioning according to the German Federal Environmental Agency [Umweltbundesamt], see Ziesing (2016).
Figures reported by the German Association of Energy and Water Industries (BDEW), “Beheizungsstruktur des Wohnungsbestandes in Deutschland 2014”.
Note that a regression of log price per square metre on covariates including log living area is equivalent to the more common regression of log price on covariates including log living area.
Matching was done without replacement and inexact, using the Match function from R package Matching.
Income is not available at the level of postal codes from official statistics. Private data suppliers might rely on housing prices as a proxy for local income, so that using such data would contaminate the regression.
In the estimation, this was repeated 200 times. In each repetition, 50 draws were made from a normal distribution centered around the coefficient estimate, with a standard deviation equal to the estimated standard error. The reported coefficient estimates and standard errors in column (2) of Table 10 are the empirical means and standard errors of these \(200 \times 50\) draws.
Fuerst et al. (2015) report coefficient estimates for A or B rated buildings and find a premium over E-rated buildings of 5.7% for the full sample.
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Mense, A. The Value of Energy Efficiency and the Role of Expected Heating Costs. Environ Resource Econ 71, 671–701 (2018). https://doi.org/10.1007/s10640-017-0179-7
- Energy efficiency
- Heating fuel prices
- House price capitalisation