Staff Insights

Key Insights

• The Energy Efficiency Premium (hereafter called the premium) - that is, the increase in property price associated with a single grade increase in the BER rating - was on average 1.6% between 2015 and 2024. The premium decreased from 1.9% in 2015 to 1.3% in 2024.

• In 2024, 59% of properties for sale in Daft had a BER ranging from B3 to D1. If a property were to improve its BER from a D1-rating to a B3-rating, the four BER-grade difference would amount to a premium of 5.2%.

• The change in the premium over time differs by market segment. The premium is highest for lower-priced properties. However, the premiums converged over the ten years, decreasing for lower-priced properties and rising for higher-priced properties, potentially driven by higher-income households having greater capacity to prioritise energy efficiency.

Introduction/About This Study

Household Energy Efficiency

The Irish Building Energy Rating (BER) scheme provides property-level energy efficiency (EE) grades, ranging from A1 to G, based on expected energy consumption.^[1] The BER also plays a key benchmarking role within the national Climate Action Plan (DCEE, 2025) including a target of completing 500,000 residential energy retrofits to B2 (or above) standard by 2030. Energy efficiency improvements lead to multiple direct and indirect benefits. From a public, economy-wide perspective, a more efficient building stock leads to lower national greenhouse gas emissions, lower future EU fines from missed targets, and lower reliance on non-domestic energy supplies which can be sensitive to geopolitical developments.

From the householder perspective, higher EE can lead to improved health and comfort, and lower energy bills through reduced energy use and renewable energy (SEAI, 2024).^[2] More efficient properties are also more resilient to future energy price and supply shocks. An additional cost saving is potentially available to mortgaged households through lower interest rates ("green mortgages"), which, in Ireland, are generally available for properties with BER of B3 or higher.

An additional benefit accruing to property owners is the potential higher property value associated with EE upgrades. Numerous national and international studies show that EE is capitalised into property values. A series of Irish studies find that more energy efficient properties command higher prices under the BER system, typically in the range of 2–3% per grade improvement (Hyland et al., 2013; Stanley et al., 2016). Experimental evidence shows that improved energy cost information (rather than physical energy units) leads to higher demand for efficiency (Carroll et al., 2024). The relationship between EE and property value is referred to as the "energy efficiency premium" (premium) in the literature.

Financial Sector Linkages

From a banking perspective, EE has potential benefits for banking sector credit risk, with research showing that EE decreases the probability of default (PD) and loss given default (LGD). These could be systemically relevant factors in the long run given that mortgaged properties represent a high share of banking exposures -- 49% of total domestic credit in 2025 Q3 (CBI, 2026).

Lower PDs may be driven by the savings from reduced energy bills and, in some cases, reduced insurance costs-- this route impacts borrowers with low levels of disposable income the most (Billio et al., 2021). Kaza et al. (2014) report that only 9% of Energy Star homes in the US defaulted (15% of non-Energy Star homes).^[3] Meanwhile, the share of highly efficient properties in the UK with mortgage payment arrears is 7% and 18% lower than those of medium and low EE properties, respectively (Guin and Korhonen, 2020).

Numerous research studies of property sales data show an economically and statistically significant premium (Brounen and Kok, 2011; Hyland et al., 2013; Kahn and Kok, 2014; Khazal and Sønstebø, 2023; Stanley et al., 2016; Taruttis and Weber, 2022; Walls et al., 2017). These findings have potential credit risk implications for the banking sector through the LGD channel. In the event of a default, the bank's ability to recoup losses (liquidate the collateral) is higher due to the higher values associated with high-EE properties (Sanderford et al., 2015). This effect applies even during periods of stress in the real estate market (Hajnal et al., 2022).

Distributional Considerations

Energy efficiency varies significantly by income. Using the Pobal HP Deprivation Index (Pratschke, 2022), Figure 1 demonstrates that the share of A-rated properties is highest in more affluent areas. Disadvantaged communities have lower rates of efficiency, which reduces their energy resilience, exposing a population that is already vulnerable to rising energy costs. These communities are more at risk of energy poverty, with poorer health outcomes caused by inadequate heating and increased financial vulnerability due to stricter budget constraints (Estévez and Tovar-Reaños, 2026; Kelly et al., 2026; Kinnear and Julienne, 2025). Income restrictions also limit energy efficiency improvements, with over 70% of those who have investigated ways to reduce energy consumption through such improvements lacking sufficient funds to proceed (SEAI, 2017).

Research Setting and Main Findings

Prior literature in Ireland estimates the premium, but the change in the premium over time is yet to be studied. Our research estimates the change in the premium using listed prices in Irish second-hand residential sales between 2015 and 2024 using daft.ie data and a large number of household and location characteristics.

After accounting for other factors that affect property prices, our analysis shows that more energy efficient properties have an average premium of 1.6% over the ten years relative to a similar but inefficient counterpart.

The average premium decreases over the ten years from 1.9% in 2015 to 1.3% in 2024. This varies by market segment: for example, lower-priced properties have a decreasing premium over the ten years while more expensive properties have a rising premium.

Measuring the Energy Efficiency Premium

The Dataset

Our analysis uses listed prices from property sales data from daft.ie, which contains 90% of all property advertisements in Ireland (daft.ie, 2024). The dataset contains a range of property details, including locational features, structural characterises and energy attributes.^[4] To ensure that the EE premium is not capturing higher quality levels in properties, we build on Gillespie et al.'s (n.d.) research by including a measure of the internal condition or quality of the property in our analysis. This variable is categorised into luxury, excellent condition, good condition, needs, work, and derelict. This ensures that the premium is not overstated due to the underlying condition of the property. Our final dataset contains 437,710 observations.^[5]

We use the 15-grade BER rating outlined in Figure 2, which is a forecast of annual expected energy units per metre squared (kWh/m²/year). The BER is calculated by registered BER assessors using a common methodology designed for the Irish building stock. Ratings are primarily driven by characteristics such as property insulation, space and water heating system and microgeneration (predominantly solar) (SEAI, 2014). Importantly, the BER is based on "typical occupancy" which assumes that energy service demand (for example, the target internal temperature in the whole property) is constant across grades, which may overestimate consumption at lower grades due to cost-related pressures. The EE improves as the BER moves from G to A, so a B1 is more efficient than a B2.

Homeowners can improve the BER of their property by increasing the EE of the property. 210,112 properties increased their BER by at least one full letter category between 2009 and 2025, 50% of which improved their BER from an initial rating of C or below to an A or B rating in their most recent assessment. 18% of the total stock of BER rated properties were rated A in 2025, while 99% of properties built between 2020 and 2025 received A ratings (CSO, 2026).

The Analysis

Our analysis measures how energy efficiency affects listed property prices in the Irish second-hand residential market, and how this relationship has changed over time.^[6] This impact of energy efficiency on property prices is referred to as the "premium". Our first analysis describes the average value increase across all BER grades, while our second approach splits the BERs into high (A2-B3), mid (C1-D2), and low (E1-G) efficiency groups to differentiate across BER segments. These will be labelled A/B, C/D, and E/G, respectively. We also explore premium trends across different parts of the property price distribution (for example, low-, mid-, and high-value segments).^[7] Importantly, each of our analyses include an extensive list of locational and property characteristics, which allow us to better isolate the relationship between efficiency and property price.

We also avail of transaction prices from the Property Price Register. However, this results in a much smaller dataset, possibly due to Eircode matches with the daft.ie dataset.^[8]

The Irish Energy Efficiency Premium

The Average Premium in Irish Listed Prices

Our results show that property values are higher at higher EE levels. Our first set of results (Figure 3) show a premium of 1.9% in 2015, meaning that for each one-unit improvement along the BER scale (for example, B2 to B1) there is a 1.9% increase in the listed price of the property. In 2015, the price for an A2-rated property would be approximately 24.7% higher than the price for a G-rated property.

There is an average premium of 1.6% per BER grade improvement (average effect between 2015 and 2024). The premium remains positive but declined from 1.9% in 2015 to 1.3% in 2024. Given average listed prices in 2024 (€395,247), each BER grade increase raises listed prices by €5,138 (1.3%). If the EE rises from a G to an A2 BER, the listed price would rise €66,794 (thirteen BER grade increases, each raising price by €5,138). The average BER improvement carried out in Ireland is from a D2 to an A2 (SEAI, n.d.), a nine BER-grade improvement, which according to our analysis would be capitalised into a property price premium of 11.7%, or €46,242 in 2024. We run the same analysis on transacted and listed prices. The final results indicate that the premiums using listed and transaction prices do not differ greatly and the trend over time moves in the same direction for this sample.^[9]

The Average Premium by Price Distribution

While the average premium has declined, this general finding masks different market segment trends. We estimate the premium at a low-, medium-, and high-price point (25th, 50th and 75th percentiles, respectively) of property valuations.^[10] The premium for the lower price point at the 25th percentile is higher than medium-priced or high-price properties. However, the premiums have been converging over time. At the lower price point, the premium declined from 2.5% to 1.6%. In the context of the average BER upgrade (D2 to A2), this equates to a reduction in the premium from 22.5% in 2015 to 14.4% in 2024. This market segment appears to be driving the main results (pink line).

In contrast, the premium increased for higher value properties (75th percentile) from 0.6% in 2015 to 1.5% in 2024. Therefore, for the average BER increase (D2-A2), the premium has increased from 5.4% in 2015 to 13.5% in 2024. The premium on medium-priced properties has remained relatively stable, albeit with an increase in 2023 and 2024. The increase in the premium at the top end of the price distribution may reflect the availability of high-EE properties among higher-priced listings.

The Premium for BER Categories

Relative to the benchmark group (C/D-rated properties), A/B-rated properties have a premium of 7.2% (average of 6.15% over the ten years), while E/G-rated properties are discounted by 6.8% in 2015 (average discount of 5.15%). The premium for efficient properties decreased from 7.2% to 5% between 2015 and 2021 and then rose slightly to 5.6% by 2024 (Figure 4). The discount for E/G-rated properties reduced over the period from 6.8% in 2015 to 3.3% in 2024.

Policy Environment During Period of Study

During our analysis period, there were a number of policy changes and external shocks which may have changed how EE is valued (Figure 5). For example, carbon taxes were introduced in Ireland in 2010 to encourage individuals and firms to invest in low-emission technologies. Rates have risen from €20 per tonne of carbon dioxide in 2019 to €56 per tonne in 2024, with commitments to raise it to €100 per tonne in 2030 (OECD, 2021; Parliamentary Budget Office, 2024). Furthermore, many Irish mortgage providers started providing lower green interest rates ("green mortgages") for energy efficient properties. Uptake has grown significantly since inception, rising from 15% of new originations in 2020 to just below 40% in 2024. Finally, energy prices rose sharply in 2022 following the military attack by Russia on Ukraine, with electricity prices increasing from 28.23 cent per KWh in 2021 Q4, to 37.48 cent per KWh in 2023 Q4 (SEAI, 2025). While we do not examine the relationship between these variables and the premium in this study, in-depth research on the role these are playing in determining the EE is a promising avenue for future work.

Conclusion

Our results indicate that there is an energy efficiency premium of 1.9% for each one-unit improvement a property's EE in 2015. The premium has declined over time from 1.9% in 2015 to 1.3% in 2024, with an average premium of 1.6% per BER unit improvement in this period. The change in the premium over the ten-year period depends on the market segment: more expensive properties have had an increase in their premium over the ten years, while lower-priced properties have had a decrease in their premium over time, marking a reduction in the importance of EE for lower-priced properties.

These results have relevance for the Central Bank of Ireland's supervisory and financial stability mandates, given the important role that property values play as collateral on residential mortgages. Variations in energy efficiency premiums across BER categories directly affect property collateral values and credit risk, with properties losing premium status facing potential market devaluation.

References

Endnotes

1. Authors' affiliations: Hannah Ortega-McCormack (Department of Economics, Trinity College Dublin and Climate Change Unit, Central Bank of Ireland), James Carroll (Climate Change Unit, Central Bank of Ireland), Tom Gillespie (Department of Economics, University of Galway), and Ronan C. Lyons (Department of Economics, Trinity College Dublin). Contact CCU_Division2@centralbank.ie. We thank Rory McElligott, Nicola Fontana, Carol Newman, Francis O'Toole, Fergal McCann, and participants at internal Central Bank of Ireland seminars for helpful comments. All views we express in this Insight are those of the authors and do not necessarily represent the views of the Central Bank of Ireland. ↑
2. Up to 30%, 35%, and 10% of a home's heating can be lost through the attic, walls, and doors and windows, respectively. Insulating a property can lead to energy cost savings. Heat pumps reduce running costs, while solar panelling can heat 50-60% of a home's hot water per year, while solar electricity reduces electricity bills (SEAI, 2026). ↑
3. Energy Star is the US measure for energy efficiency. Properties that adhere to the US EPA energy efficiency specifications can seek an Energy Star label for their property (U.S. Environmental Protection Agency, n.d.) ↑
4. The model controls for the size of the property (number of bedrooms and bathrooms), the property type (apartment, bungalow, detached, semi-detached, terraced) and the region (Connaught/Ulster, Dublin City, Rest of Leinster, Munster, and Other Cities), a condition variable (luxury, excellent condition, good condition, needs, work, and derelict), as well as several vintage (period, Edwardian, Victorian, Georgian, French doors, high ceiling), aspect (southwest, south, west), location (cul de sac, beach, views), feature (site size, garden, sea-view, green view, balcony, bay window, utility, conservatory, granny flat, jacuzzi, fitted or walk-in wardrobe, wet-room, en-suite, garden, garage, corniced walls, presence of brands), and distance (distance to major road, to central business district, to primary and post-primary schools, to golf courses, to power lines, to forestry and nature reserves, to rivers, canals, the coast and lakes) variables. We included yearly time fixed effects and electoral district location fixed effects. ↑
5. Some property types were deleted as they were not deemed to be representative of the market. As newly built properties make up a very small proportion of the dataset and are required by legislation to become increasingly more energy efficient, our analysis only includes second-hand properties. We also delete studios and sites, as they amount to a very small proportion of the dataset. Properties listed as A1s were deleted as A1 properties make up <1% of the sample. ↑
6. We employ a statistical model that relates property prices to their characteristics (a log-linear hedonic price model), whereby the logged listed price is regressed on the characteristics of the house, time, and location using an OLS model. The main variable of interest is the BER. A vector of household characteristics (age, size, dwelling type, etc.), locational fixed effects, and yearly time fixed effects are controlled for. Location fixed effects are included at the electoral district level. To understand the change in the energy efficiency premium over time, we interact the BER with the year. Standard errors are robust and clustered at electoral district and year level. ↑
7. We run quantile regressions to estimate the relationship between the variables at different points in the price distribution, not just the mean. The results for different points on the price distribution can differ greatly from the mean results. Using the Stata command rifhdreg, we run yearly quantile regressions to obtain results for the 25^th, 50^th, and 75^th price percentiles. A pooled rifhdreg regression would apply the aggregated price percentile from across the price distribution for the ten years. By running rifhdreg conditional on each year, we are ensuring that the results capture any temporal variation. ↑
8. To ensure that the sample for transacted prices represents the locational distribution of properties in the daft.ie dataset, we generate a representative sample that captures the urban/rural distribution of properties in the daft.ie dataset. This ensures that the analysis is not subject to sample selection bias. ↑
9. The analysis on listed and transacted prices is run using the representative sample so as to identify how the two types of prices respond to energy efficiency when using the same sample. ↑
10. A percentile represents the position in a ranked dataset, indicating the percentage of data points that fall below it. For example, the 25^th percentile in listed price represents the point at which 25% of properties are below and 75% of properties are above. ↑