The German Energy Savings Act (Energieeinsparverordnung) requires sellers on the housing market to provide detailed information on expected yearly energy consumption per square meter. This paper uses variation in local fuel prices and climate, fuel types, and building ages to analyze 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 agents are aware of the investment dimension of energy efficiency improvements, but not all important aspects are taken into account.
logPi = Xib + ft + yd + k + d ×EPSi + hi
Pi is the price per square metre of house i,
EPSi is its energy performance score, and Xi is a vector of housing characteristics, including heating type (base category: gas heating).
ft and yd are time and district fixed effects.
The log price is the dependent variable, and the EPS coefficient is negative and highly significant. It implies a reduction of the price by approx. 0.11% as EPS increases by 1% (at sample mean). In column (2), the dependent variable is the price per square metre. The EPS effect is slightly smaller (-0.07% at sample mean) and model fit is somewhat worse.... A jump from an A-rated building (30
The overall picture is reasonable. Higher quality, younger, detached houses on larger lots are offered at a higher price per square metre.
Taken as a whole, the results suggest that, in parts, energy efficiency is taken into account in an economically meaningful way by sellers of residential houses in Germany. However, potential cost savings are not always and everywhere calculated correctly, providing support for the idea of “rational inattention” (Sallee, 2013). Variation in local gas prices or climate did not influence the value of EPS (Section 6.1). Additionally, the were no significant differences in the value of EPS across heating fuel type, even though the price of electricity was at least three times the price of gas in the past 24 years. Given the large potential savings in Taken as a whole, the results suggest that, in parts, energy efficiency is taken into account in an economically meaningful way by sellers of residential houses in Germany. However, potential cost savings are not always and everywhere calculated correctly, providing support for the idea of “rational inattention” (Sallee, 2013). Variation in local gas prices or climate did not influence the value of EPS.... Additionally, there were no significant differences in the value of EPS across heating fuel type, even though the price of electricity was at least three times the price of gas in the past 24 years. Given the large potential savings in this case, this latter result cannot be explained by rational inattention alone.
One important finding of this paper is that building age alters the value of EPS considerably. Earlier papers have estimated one single coefficient for samples that typically include buildings of all vintages and heating fuel types – although some have looked at sub-samples of different house types (Fuerst et al., 2015; Hyland et al., 2013). Consider the coefficient of eps × avg. gas price in column (1) of Table 5, indicating that a one Euro increase in expected yearly heating costs per square metre decreases listing prices by approx. 23 Euro=m2. At sample means, a change from an A-rated building (30 ≤ EPS < 50) to an E-rated building (160 ≤ EPS < 200) increases expected heating costs by approximately 9:09 Euro/[m2 · a]. The decrease in prices amounts to 208:98 Euro, or 10:2% of the sample mean. This is very close to the values reported in other studies, e.g. 9:3% in Hyland et al. (2013) or 10:2% in Brounen and Kok (2011). Note that both studies use a selection model because EPS is not reported in all observations. The suspected upward bias of EPS in OLS estimation does not seem to be large.
Once the sample is restricted to buildings younger than eight years, the estimated coefficient doubles in size, cf. Table 9. However, this sub-sample has a higher sample mean of 2285 Euro/m2 so that the premium of A- over E-rated buildings amounts to 13:0% at sample mean. In any case, from the perspective of an investor or construction company, the results from Table 9 are much more important than knowing how EPS is capitalised on average, i.e. in the whole sample. If a house owner wants to improve energy efficiency of the building substantially, it is very likely that the building is seriously retrofitted rather than renovated. The results presented here suggest that the premium will be much higher in that case. They are much closer to the policy-relevant question of how to foster energy efficiency investments in an effective manner.
by Andreas Mense, FAU Erlangen-Nürnberg
Social Science Research Network (SSRN) www.SSRN.com
Date posted: October 21, 2016
Keywords: Energy Efficiency, House Price Capitalization, Climate, Heating Fuel Prices, Information Inefficiency