Abstract:
This paper investigates the price effect of EPC ratings on the residential dwelling prices in Wales. It examines the capitalisation of energy efficiency ratings into house prices using two approaches. The first adopts a cross-sectional framework to investigate the effect of EPC band (and EPC rating) on a large sample of dwelling transactions. The second approach is based on a repeat-sales methodology to examine the impact of EPC band and rating on house price appreciation. The results show that, controlling for other price influencing dwelling characteristics, EPC band does affect house prices. This observed influence of EPC on price may not be a result of energy performance alone; the effect may be due to non-energy related benefits associated with certain types, specifications and ages of dwellings or there may be unobserved quality differences unrelated to energy performance such as better quality fittings and materials. An analysis of the private rental segment reveals that, in contrast to the general market, low-EPC rated properties were not traded at a significant discount. This suggests a lower implicit price for energy efficiency when the owner is not responsible for covering the cost of energy in a dwelling.
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Results:
Turning to the variable of interest, EPC rating, and using band D as the ‘hold-out’ category, the pattern of price effects is consistent with a positive relationship between energy performance rating and sale price. For the whole sample model there are significant positive premiums for dwellings in bands A and B (11.3%) or C (2.1%) compared to dwellings in band D. For dwellings in EPC bands lower than D there are statistically significant discounts; -2.1% for band E dwellings, -4.7% for band F dwellings and -7.2% for dwellings in band G. The price impact varies depending on the type of property: a terraced dwelling rated B has sold for approximately 17.1% more per square metre than a terraced dwelling EPC rated D. The comparable figure for a semi-detached dwelling is 8.2%. Relative to the other dwelling types detached dwellings are likely to display the greatest degree of heterogeneity, particularly in rural areas. Recognising this, detached dwellings were categorised as urban or rural. Table 2 shows that the price impact is more marked and for urban dwellings in bands E and F than for rural dwellings in the same bands. This might be a result of purchasers willing to pay
higher prices for rural dwellings (perhaps because of their character and setting) regardless of their energy performance. In the last column in Table 3 the results of the estimation when energy efficiency score, rather than band, is used as the independent variable are displayed. The expected positive relationship between energy efficiency and dwelling sale price is also found.
higher prices for rural dwellings (perhaps because of their character and setting) regardless of their energy performance. In the last column in Table 3 the results of the estimation when energy efficiency score, rather than band, is used as the independent variable are displayed. The expected positive relationship between energy efficiency and dwelling sale price is also found.
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These estimated price premiums are much higher than for the comparable study conducted in England (Fuerst et al, 2015). One reason for this effect is the lower average house price in Wales. The findings for Wales are very similar to the results for the North East region of England where significant positive premiums were estimated for dwellings in bands A and B (14.4%) or C (2.7%) compared to dwellings in band D and statistically significant discounts for dwellings in band E (-2.5%) and F (-6.0%).
Deng et al. (2012) investigated the residential sales prices of 74,278 dwelling units in 1439 buildings in Singapore between 2000 and 2010. They applied a number of model specifications including OLS and GLS procedures and refined the sample using Propensity Score Matching (PSM). There were significantly different results depending on model specification. For the PSM regression, they estimated an average price premium for Green Mark of about 4-6%. The detailed breakdown was Platinum 14%, Gold Plus 2.3%, Gold 5.5% and Certified 0.1%. In an alternative specification, they estimated average price premium for Green Mark of about 14-21% (Platinum 21%, Gold Plus 15%, Gold 15% and Certified 10%).
In the US Bloom et al. (2011) reported that the ENERGY STAR homes in Colorado sold for $8.66 more per square foot than the non-ENERGY STAR homes. However, several modelling biases (e.g. effective controls for area fixed effects) and a small sample of properties (only 300 properties) weaken the argument substantially. Using a sample of 14,055 transactions (of which 6,781 were tagged as green) from the NTREIS housing transaction database for two urban centres in Texas (Frisco and McKinney), Aroul and Hansz (2012) used a standard hedonic procedure to estimate price premium of 2% for green transactions. When disaggregated into mandatory and voluntary green transactions, the respective premiums were 5% and 1%. It is unclear (albeit to the authors) how green and non-green buildings were differentiated given that there seems to have been a mandatory program. The study did not control for location and quality; green transactions may be associated with better quality neighbourhoods and meeting the green standards may be associated with higher specification homes. Kahn and Kok (2014) conducted a hedonic pricing analysis of all single-family home sales in California over the time period 2007 to 2012. Using a sample of matched properties based on the likelihood of having a green label and the local area weather condition, they found almost a 2% premium for green labels. While the perennial difficulty of measuring unobserved nonfinancial benefits of green label still remains, this study shows a robust positive association based on several alternative specifications. However, the results are based on comparing a relatively small ’treated’ sample with a substantially larger ‘non-treated’ sample.
Using a customised sustainability metric based on 36 variables to provide a sustainability score for each apartment, Feige et al. (2013) drew upon rental prices of a sample of 2453 residential apartments in Switzerland. Their results were inconsistent with some sustainability-related features having significantly positive effects, others having no statistically significant effect on price and some having a negative effect. It is notable that they found an unexpected negative relationship between energy efficiency and price. This was attributed to Swiss residential lease structures where landlords tend to recover the estimated cost of energy from tenants in advance. Hence, less energy efficient buildings may have appeared to have a higher rent since the energy cost is ‘bundled’ with rent.
Kholodilin and Michelsen (2014) also study the residential rental market and find for the Berlin housing market that energy efficiency savings are generally capitalised into prices and rents and that buyers are able to anticipate energy and house price movements sufficiently well. Another relevant finding for the present study is the significantly lower implicit prices of energy efficiency of rental dwellings compared to owner-occupied dwellings. The authors explain these differences as a sign of the market power of tenants or as a result of the split incentive problem. Similarly, Cajias and Piazolo (2013) find higher total returns and higher rents for energy-efficient dwellings in their study of the German housing market in the 2008-2010 period. They estimate that a one percent energy saving raises rents by 0.08 percent and the market value of a property by 0.45 percent.
In a recent study with an interesting focus on presale (dwellings bought from developers) and resale (dwellings sold by owners) prices, Deng and Wu (2014) compared a sample of 13,224 dwellings in 62 Green Mark developments with 55,983 dwellings in 1,375 non-GM developments in Singapore between 2000 and 2010. They applied a range of approaches including hedonic methods (supplemented by PSM) and difference-in-difference (DID) methods to investigate the price effects of the Green Mark certification. Similar to Deng et al. (2012), overall they estimate an average price premium of about 4-5%. In terms of the different levels of award, the estimated premium for the Platinum rating was 11%, the comparable figures for the Gold and Certified ratings were 5% and 1.6% respectively. There were significant differences between presale and resale price premiums with premiums for re-sales found to be substantially higher. Using a smaller sample of repeat transactions, DID approach estimates price appreciation premium for Green Mark dwellings of 2% to 3%. They infer from the results that developers are capturing a small part of the green premium.
However, without details of costs of achieving GM certification, similar to most previous price studies they were unable to assess whether the price premium compensated developers for additional costs.
Turning to energy performance ratings in Europe, Brounen and Kok (2011) investigated the relationship between EPC rating and sale price for 31,993 residential sale prices in the Netherlands in 2008-9. They found price premiums of 10%, 5.5% and 2% for homes in bands A, B and C respectively compared to homes in band D. For dwellings in bands E, F and G they identified discounts of 0.5%, 2.5% and 5% respectively. Their data set contained a broad range of control variables including dwelling size, insulation quality, central heating and level of maintenance.
However, their control for location was broad – at the province level – which may explain the low explanatory power of the four variants of the hedonic price model that were tested. A possible issue with the study is that it was based on sales of dwellings that had opted to have an EPC, a minority of total residential sales. Hyland et al (2013) analysed the impact of energy efficiency ratings in Ireland on residential asking prices and rental rates based on a rich data set of BER ratings (the Irish equivalent of the EPC) as well as property and price information. The authors found that A-rated properties achieve a sales premium of 9% and a rental premium of around 2% relative to D-rated properties. However, the analysis in Hyland et al (2013) does not appear to control for age of buildings and is thus in danger of misattributing age effects to energy efficiency effects.
Finally, in a study closely related to this paper, drawing upon a large sample of 333,095 English housing transactions with mandatory energy certificates (in which there were eight A-rated houses) Fuerst et al (2015) found:
The vast majority of houses were clustered in the middle EPC bands (C, D and E). Nearly half of all dwellings were in band D.
Flats tended to be the most energy efficient dwellings with approximately half placed in band C (40%) or B (9.8%).
There was a clear relationship between energy efficiency and age. Only 6% of dwellings built before 1900 had an EPC rating of C or better. The comparable figure for dwellings constructed since 2007 was 92%.
Significant positive price premiums were found for dwellings rated A/B (5%) or C (1.8%) compared to dwellings rate EPC D. Although they are small, for dwellings rated E (-0.7%) and F (-0.9%) statistically significant discounts were estimated.
Turning to price growth, the findings were less clear-cut. Dwellings in EPC band C experienced significantly higher price growth than those in band D. However, this was not the case for the dwellings in bands A and B, which experienced significant price depreciation compared to Drated dwellings. Dwellings in band E (-0.18%) and F (-0.26%) were also estimated to have had statistically significant lower rates of price growth compared to D-rated dwellings.
...
Hyland, Lyons and Lyons (2013) who find that the rental premium captures only 14-55% of the net present value of energy savings. Rehdanz (2007) and Kholodilin and Michelsen (2014) arrive at similar conclusions in their study of German housing markets. The implicit lower return on energy efficiency for landlords compared to owner occupiers thus leads to a levelling of prices between D, E, F and G bands, all else equal. A diverging result compared to the German studies is our finding of a significant premium for A, B, C rated properties which may be explained by the fact that the owner-occupied and rental tiers of the market are less segmented in the UK market and the Welsh market in particular. The fraction of 'dedicated' rental stock on the overall market is lower and most properties could be used for either owner occupation or as a rental investment which is not necessarily the case in Germany.
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2633539
In the US Bloom et al. (2011) reported that the ENERGY STAR homes in Colorado sold for $8.66 more per square foot than the non-ENERGY STAR homes. However, several modelling biases (e.g. effective controls for area fixed effects) and a small sample of properties (only 300 properties) weaken the argument substantially. Using a sample of 14,055 transactions (of which 6,781 were tagged as green) from the NTREIS housing transaction database for two urban centres in Texas (Frisco and McKinney), Aroul and Hansz (2012) used a standard hedonic procedure to estimate price premium of 2% for green transactions. When disaggregated into mandatory and voluntary green transactions, the respective premiums were 5% and 1%. It is unclear (albeit to the authors) how green and non-green buildings were differentiated given that there seems to have been a mandatory program. The study did not control for location and quality; green transactions may be associated with better quality neighbourhoods and meeting the green standards may be associated with higher specification homes. Kahn and Kok (2014) conducted a hedonic pricing analysis of all single-family home sales in California over the time period 2007 to 2012. Using a sample of matched properties based on the likelihood of having a green label and the local area weather condition, they found almost a 2% premium for green labels. While the perennial difficulty of measuring unobserved nonfinancial benefits of green label still remains, this study shows a robust positive association based on several alternative specifications. However, the results are based on comparing a relatively small ’treated’ sample with a substantially larger ‘non-treated’ sample.
Using a customised sustainability metric based on 36 variables to provide a sustainability score for each apartment, Feige et al. (2013) drew upon rental prices of a sample of 2453 residential apartments in Switzerland. Their results were inconsistent with some sustainability-related features having significantly positive effects, others having no statistically significant effect on price and some having a negative effect. It is notable that they found an unexpected negative relationship between energy efficiency and price. This was attributed to Swiss residential lease structures where landlords tend to recover the estimated cost of energy from tenants in advance. Hence, less energy efficient buildings may have appeared to have a higher rent since the energy cost is ‘bundled’ with rent.
Kholodilin and Michelsen (2014) also study the residential rental market and find for the Berlin housing market that energy efficiency savings are generally capitalised into prices and rents and that buyers are able to anticipate energy and house price movements sufficiently well. Another relevant finding for the present study is the significantly lower implicit prices of energy efficiency of rental dwellings compared to owner-occupied dwellings. The authors explain these differences as a sign of the market power of tenants or as a result of the split incentive problem. Similarly, Cajias and Piazolo (2013) find higher total returns and higher rents for energy-efficient dwellings in their study of the German housing market in the 2008-2010 period. They estimate that a one percent energy saving raises rents by 0.08 percent and the market value of a property by 0.45 percent.
In a recent study with an interesting focus on presale (dwellings bought from developers) and resale (dwellings sold by owners) prices, Deng and Wu (2014) compared a sample of 13,224 dwellings in 62 Green Mark developments with 55,983 dwellings in 1,375 non-GM developments in Singapore between 2000 and 2010. They applied a range of approaches including hedonic methods (supplemented by PSM) and difference-in-difference (DID) methods to investigate the price effects of the Green Mark certification. Similar to Deng et al. (2012), overall they estimate an average price premium of about 4-5%. In terms of the different levels of award, the estimated premium for the Platinum rating was 11%, the comparable figures for the Gold and Certified ratings were 5% and 1.6% respectively. There were significant differences between presale and resale price premiums with premiums for re-sales found to be substantially higher. Using a smaller sample of repeat transactions, DID approach estimates price appreciation premium for Green Mark dwellings of 2% to 3%. They infer from the results that developers are capturing a small part of the green premium.
However, without details of costs of achieving GM certification, similar to most previous price studies they were unable to assess whether the price premium compensated developers for additional costs.
Turning to energy performance ratings in Europe, Brounen and Kok (2011) investigated the relationship between EPC rating and sale price for 31,993 residential sale prices in the Netherlands in 2008-9. They found price premiums of 10%, 5.5% and 2% for homes in bands A, B and C respectively compared to homes in band D. For dwellings in bands E, F and G they identified discounts of 0.5%, 2.5% and 5% respectively. Their data set contained a broad range of control variables including dwelling size, insulation quality, central heating and level of maintenance.
However, their control for location was broad – at the province level – which may explain the low explanatory power of the four variants of the hedonic price model that were tested. A possible issue with the study is that it was based on sales of dwellings that had opted to have an EPC, a minority of total residential sales. Hyland et al (2013) analysed the impact of energy efficiency ratings in Ireland on residential asking prices and rental rates based on a rich data set of BER ratings (the Irish equivalent of the EPC) as well as property and price information. The authors found that A-rated properties achieve a sales premium of 9% and a rental premium of around 2% relative to D-rated properties. However, the analysis in Hyland et al (2013) does not appear to control for age of buildings and is thus in danger of misattributing age effects to energy efficiency effects.
Finally, in a study closely related to this paper, drawing upon a large sample of 333,095 English housing transactions with mandatory energy certificates (in which there were eight A-rated houses) Fuerst et al (2015) found:
The vast majority of houses were clustered in the middle EPC bands (C, D and E). Nearly half of all dwellings were in band D.
Flats tended to be the most energy efficient dwellings with approximately half placed in band C (40%) or B (9.8%).
There was a clear relationship between energy efficiency and age. Only 6% of dwellings built before 1900 had an EPC rating of C or better. The comparable figure for dwellings constructed since 2007 was 92%.
Significant positive price premiums were found for dwellings rated A/B (5%) or C (1.8%) compared to dwellings rate EPC D. Although they are small, for dwellings rated E (-0.7%) and F (-0.9%) statistically significant discounts were estimated.
Turning to price growth, the findings were less clear-cut. Dwellings in EPC band C experienced significantly higher price growth than those in band D. However, this was not the case for the dwellings in bands A and B, which experienced significant price depreciation compared to Drated dwellings. Dwellings in band E (-0.18%) and F (-0.26%) were also estimated to have had statistically significant lower rates of price growth compared to D-rated dwellings.
...
Hyland, Lyons and Lyons (2013) who find that the rental premium captures only 14-55% of the net present value of energy savings. Rehdanz (2007) and Kholodilin and Michelsen (2014) arrive at similar conclusions in their study of German housing markets. The implicit lower return on energy efficiency for landlords compared to owner occupiers thus leads to a levelling of prices between D, E, F and G bands, all else equal. A diverging result compared to the German studies is our finding of a significant premium for A, B, C rated properties which may be explained by the fact that the owner-occupied and rental tiers of the market are less segmented in the UK market and the Welsh market in particular. The fraction of 'dedicated' rental stock on the overall market is lower and most properties could be used for either owner occupation or as a rental investment which is not necessarily the case in Germany.
http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2633539
by Franz Fuerst 1, Patrick M. McAllister 2, Anupam Nanda 3 and Peter Wyatt 4
1. University of Cambridge - Department of Land Economy; City University of New York - Center for Urban Research
2. University of Reading - Department of Real Estate and Planning
3. University of Reading - School of Real Estate & Planning, Henley Business School
4. University of Reading - Department of Real Estate and Planning
Social Science Research Network (SSRN) www.SSRN.
July 20, 2015
Keywords: energy efficiency, housing markets, hedonic models, implicit prices, split incentive problem
1. University of Cambridge - Department of Land Economy; City University of New York - Center for Urban Research
2. University of Reading - Department of Real Estate and Planning
3. University of Reading - School of Real Estate & Planning, Henley Business School
4. University of Reading - Department of Real Estate and Planning
Social Science Research Network (SSRN) www.SSRN.
July 20, 2015
Keywords: energy efficiency, housing markets, hedonic models, implicit prices, split incentive problem
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