The Value of Energy Efficiency and the Role of Expected Heating Costs

Abstract

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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Notes

  1. 1.

    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.

  2. 2.

    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).

  3. 3.

    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.

  4. 4.

    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.

  5. 5.

    For houses older than 3 years, a “consumption-based” EPS can be calculated which is based on energy use in the past 3 years.

  6. 6.

    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.

  7. 7.

    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).

  8. 8.

    Figures reported by the German Association of Energy and Water Industries (BDEW), “Beheizungsstruktur des Wohnungsbestandes in Deutschland 2014”.

  9. 9.

    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.

  10. 10.

    Matching was done without replacement and inexact, using the Match function from R package Matching.

  11. 11.

    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.

  12. 12.

    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.

  13. 13.

    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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Correspondence to Andreas Mense.

Appendices

Appendix A: Tables

See Tables 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 and 11.

Table 1 Heating costs in gas-heated houses
Table 2 Summary statistics for the gas prices and climate sample
Table 3 Reporting the energy performance score
Table 4 Baseline regression results
Table 5 Local gas prices and climate regressions
Table 6 Local gas prices and climate: robustness checks
Table 7 Means in the heating types sample
Table 8 Heating types—logistic regressions
Table 9 Heating type regressions
Table 10 Building age regressions
Table 11 Building age, quality, and eps

Appendix B: Figures

See Figs. 1, 2, 3 and 4.

Fig. 1
figure1

Gas prices and climate factors in German ZIP codes. a Gas prices b climate factors Source: online contract offers; own calculation. German Weather Service. (Color figure online)

Fig. 2
figure2

Costs of different fuel types, relative to natural gas. Source: Federal Ministry for Economic Affairs and Energy; own calculations

Fig. 3
figure3

Kernel density estimates for the energy performance score

Fig. 4
figure4

Energy labels for real estate offers in Germany. Source: BBSR/energieeinsparverordnung. The label in the background (“Endenergiebedarf”) is based on a standardised projection of energy use. It containts a scale (A+ to H) that indicates EPS in steps of 25, and the exact EPS (see the blue label “Endenergiekennwerte”). Additionally, information on energy-related building characteristics is provided below the scale; this information is not available in the data set. The label up front is based on past use. It is structured similarly, but does not contain additional information. (Color figure online)

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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

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Keywords

  • Climate
  • Energy efficiency
  • Heating fuel prices
  • House price capitalisation

JEL Classification

  • R3
  • Q4
  • Q5