Buildings contribute to approximately 28% of global energy-related emissions (IEA, 2020a). In the USA, 20% of the nation’s greenhouse gas emissions come from heating, cooling and powering households (Goldstein et al., 2020). In Germany, private households’ energy consumption causes approximately 31% of Germany’s total CO2 emissions (Destatis, 2021; Ritchie & Roser, 2020). In the UK, heating in the residential sector accounts for approximately 21% of CO2 emissions (National Statistics, 2020a, b). In Finland, building heating and electricity consumption caused 36% of all emissions, of which roughly two-thirds were from heating (SYKE, 2020).

Heat pumps are a key technology for decarbonising the heating sector. Currently, only 5% of global heat is delivered with heat pumps, but this may triple by 2030, and eventually, heat pumps could cover 90% of the global space and water heating needs for low emissions (IEA, 2020b). Ram et al. (2019) calculated that by 2050, over €7 trillion will be invested into individual heat pumps that heat or cool buildings. Currently, the European Commission (2022) and its member countries are planning major subsidy policies to encourage the adoption of heat pumps to displace natural gas for heating.

Heat pumps use electricity to draw energy from the surrounding air, water or ground (Staffell et al., 2012). They are highly energy efficient, as one unit of electricity can deliver many units of heating or cooling. Heat pumps can reduce emissions by over 90% compared to fossil fuel heaters if the electricity system powering them has low emissions (Vimpari, 2021). Although they have higher initial investment costs than traditional fossil-fuel-based heaters, heat pumps have lower operating costs due to their high efficiency in energy conversion. This high capital expenditure can hinder investment, even if high economic and environmental benefits are proven.

One important practical question for households is whether the upfront investment is reflected in house sales prices. The lifecycles of these investments can be up to 30 years, with payback periods ranging from 5 to 15 years, whereas homes are switched in shorter periods. A homeowner might wonder whether the annual energy savings are enough to cover the initial investment cost. If the heating system has a positive impact on sales prices, the homeowner does not have to rely only on energy savings to recoup upfront costs. This topic is related to a growing body of literature that has measured the effects of different energy efficiency measures’ impacts on housing prices using statistical methods. Previous research suggests that decreased energy costs are among the key reasons for price premiums for energy-efficient homes (e.g. Dastrup et al., 2012; Shen et al., 2021).

This paper adds to the literature by examining the effect of GSHP, a particular type of heat pumps,Footnote 1 on house transaction prices in Finland. A dataset of 19,008 transactions in eight large cities in Finland between 1999 and 2018 was used. For detached houses, four heating types dominate in these cities: direct electricity, district heating, oil and ground source heat pump (GSHP), with an approximate market share of 52%, 21%, 20% and 7%, respectively. Traditionally, district heating has been very cost-effective, but tightening environmental regulations and the cost-effectiveness of heat pumps have increased the competition for district heating companies. Figure 1 presents the average historical heating prices for these four heating types for the period 1998–2019, as well as housing price development in the Helsinki Metropolitan Area (HMA) and the rest of Finland (Statistics Finland, 2021a, b). For GSHP, an average coefficient of performance (COP) of three was used, i.e. one unit of electricity is required to produce three units of heating (Vimpari, 2021). Oil heaters are assumed to have an average efficiency of 80%, as older boilers may have an efficiency between 60 and 70% and modern boilers up to 95%.

Fig. 1
figure 1

Average historical heating prices and housing price development in Finland

According to Fig. 1, the oil price increased by 340%, electricity by 131% and district heating by 142%, whereas house prices in the Helsinki Metropolitan Area increased by 119% and in the rest of Finland by 60%. District heating was cheaper than oil and electricity, but GSHP was the most cost-efficient heating type. However, GSHP requires a high upfront investment compared to the other heating methods. For example, in the UK, for a detached house requiring 10 kW of heating capacity, approximately €1 600 must be invested for a direct electricity-based system, €2300 for a gas- or oil-based system, and €13,700 for a GSHP-based system (Scottish Government, 2021).

Given that GSHP requires a high upfront investment and significantly reduces heating costs, the following main hypothesis was set: There is a sales price premium for GSHP-heated houses compared to other heating systems. This was tested by constructing a hedonic price model.

Previous literature

There is a vast body of literature that utilised hedonic regression to estimate how different variables explain housing prices. Within the field of this study, i.e. how energy-related characteristics impact housing prices, this methodology has been used within the context of energy efficiency ratings, rooftop photovoltaics, solar heaters and, most recently, air source heat pumps,

Deng et al. (2012) examined whether high energy efficiency, as measured with the Green Mark label, impacts the sales prices of apartments in Singapore. The label was found to command a 4 to 6% premium. Kahn and Kok (2014) investigated how green labels such as Energy Star or LEED affect housing transaction prices. Their results suggested a 2 to 4% premium for energy-efficient homes, depending on location and building characteristics. Walls et al. (2017) examined house transactions in the USA. Spatial matching, propensity score matching and regression analysis found a 2% premium for Energy Star in Portland, Oregon, but no premium in Austin, Texas. Local certificates had larger premiums (4% and 9%, respectively), but these certificates often represended more qualities than energy-related improvements. Cerin et al. (2014) investigated the impact of the European Energy Performance Certificate (EPC) on housing prices in Sweden. They found that energy performance was not always rewarded in terms of price, depending on building age and price class. Fuerst et al. (2015) investigated whether a high-energy performance rating, as measured by the European energy performance rating (EPC), has an impact on housing prices in the UK. The findings suggest that an A/B rating commands a 5.0% premium and a C rating commands a 1.8% premium compared to the holdout rating of D. Similarly, Fuerst et al. (2016) examined whether high-energy performance ratings commanded a price premium for apartment transactions in Finland. A premium of 3.3% was found for the top three energy performance categories, which dropped to 1.5% when a set of neighbourhood characteristics was added to the model. In contrast, Yoshida and Sugiura (2015) analysed the transaction prices of green buildings in Tokyo. Their model suggests that a green building with renewable energy and recycled materials can result in a price discount due to higher lifecycle costs for the user.

Dastrup et al. (2012) examined the impact of rooftop photovoltaics (PV) on house transaction prices in California. They noted that the value was generated through energy savings and communicating that a home is green. A hedonic pricing model, as well as a repeat sales approach, found a 3.6% premium for homes with PVs. Similarly, Hoen et al. (2013) examined the impact of PV on house prices in California. A hedonic pricing model revealed a 3.6% price premium with a 1% significance. The price premium was found to be slightly higher than the system’s upfront costs. Qiu et al. (2017) examined the impact of PV and solar heaters on residential home values in Arizona. They employed semi-parametric, non-parametric and hedonic regression to test whether a treatment group with solar systems has a price premium compared to a control group. The study found a 17% premium on sales prices for properties with installed PV; no premium was found for solar heaters. The percentage premium is rather large due to low housing and land prices in Arizona.

Shen et al. (2021) estimated how the installation of an air source heat pump impacted house prices in the USA. They used different methods and found a premium of between 4.3 and 7.1% for heat pump transactions. The results also showed that the premium was higher in regions with more environmentally conscious and middle-class people, as well as in regions with a milder climate.


In hedonic price regression, a specific set of characteristics is used to form an equilibrium that defines the price of goods (Rosen, 1974). The common denominator in the previous literature is that first, a model containing a set of temporal, locational and building characteristics is used to form an equilibrium model, which is then enriched by a set of energy-related characteristics. Depending on the type of characteristics, these are set as continuous variables or categorical variables. Continuous variables, such as the dependent variable, price, are often defined in levels or in its natural logarithm transformation, which often may increase the explanatory power of the equation. Categorical variables are used to analyse the impact of a specific categorical (dummy) characteristic on price.

In this study, the following equation was used to estimate the price of a dwelling:

$$ln\left({P}_{itm}\right)= {\beta }_{0}+\sum_{j=1}^{J}{\beta }_{j}{B}_{ji}+\sum_{t=1}^{T}{\beta }_{t}{D}_{ti}+\sum_{l=1}^{L}{\beta }_{l}{S}_{li}+{\upepsilon }_{itm}$$

where ln(P) is the logarithmic for the price of transaction i at year t and month m, \({\beta }_{0}\) is the intercept, \({B}_{j}\) is a vector of j building variables, including heating type, \({D}_{t}\) is a vector of t temporal variables, \({S}_{l}\) is a vector of l locational variables and \(\upepsilon\) is the error term of the model.

This methodology was used to analyse whether having GSHP as a heating type increases transaction price of a detached house. This was studied with two different models, i.e. including one of the following variables in the building variables:

  1. 1)

    GSHP (true/false)

  2. 2)

    Heating type (categorical): direct electricity, district heating, GSHP or oil (hold-out: direct electricity)

Given that GSHP has the highest capital expenditure and is the cheapest form of heating, it should have a price premium against other heating types. The second model was used to test and estimate how different heating types perform individually against direct electricity, which is the most expensive heating type, as well as the most used for detached houses in Finland. Python Statsmodels (2022) was used to conduct the analysis, with ordinary least squares (OLS) as the method.

Data and descriptive statistics

The main dataset was a housing transaction database collected by the Central Federation of Finnish Real Estate Agencies (KVKL). It includes nearly two million transactions between 1999 and 2018 in Finland (KVKL, 2019), also including other building types, such as apartments and semi-detached houses, which are almost always run by housing cooperations in Finland. This data often does not include the heating type of the building. However, cities’ building departments maintain a technical building database that includes this information. Eight large Finnish cities provided this data, which was merged with the transaction data by using the exact street addresses of buildings within both databases (Building data, 2020). To ensure exact matching, the street addresses in the housing transaction database were cleaned using the Levenshtein distance method, which was used for comparing (and correcting) addresses with the official street addresses used in the cities’ building databases. Levenshtein (1966) is a method that calculates distances between words and can be used to clean data. Furthermore, Statistics Finland collects socioeconomic data in a raster database (raster size 250 m × 250 m) in Finland (Statistics Finland, 2021c). This data were added to the database based on the nearest publication year (Table 1).

Table 1 Hedonic model variables

The remaining data were then cleaned for outliers (above and below three standard deviations) based on floor area (sqm) and unencumbered transaction price (€). Cleaning was done separately for each city, as there are major differences in housing prices between the cities. Additional cleaning was done by removing transactions with the ‘unknown’ condition. Finally, new developments were also removed, as this study wanted to focus on the transactions of existing buildings. The final dataset included 19,008 transactions of detached houses in eight cities (Helsinki, Espoo, Vantaa, Turku, Tampere, Lahti, Kuopio and Oulu), home to over two million inhabitants. Tables 2 and 3 provide descriptive statistics of the data used.

Table 2 Summary statistics
Table 3 City-level price statistics (€/sqm)*

In the tables, the transaction prices were adjusted for 2020 using housing price index data available for the Helsinki Metropolitan Area and the rest of Finland (Statistics Finland, 2021b). However, for the hedonic regression model, this adjustment was not made because the temporal variables should capture the market conditions over time. Direct electricity dominates with a 52% share as a heating source, while district heating has a 20% share, oil 21% and GSHP 7%. Houses with GSHP are clearly higher valued than the average, and oil is the opposite. However, this difference fades away when looking at the prices at the city level, where GSHP prices are quite close to the mean prices of all the buildings. For other characteristics, some differences can be identified. Oil-heated houses are older and their condition is not as good as others. Figure 2 shows how different types of heating have evolved over time. Direct electricity and district heating have had a rather stable share of total house transactions, with shares of 50% and 20%, respectively. Oil’s market share in the transaction seems to be decreasing, while the share of GSHP has increased over the last few years.

Fig. 2
figure 2

Development of different heating systems

Hedonic regression results

The estimation results are presented in Table 4. In the first column, GSHP was tested against other heating types and in the second column separately against each heating type. The adjusted R2 numbers of 0.851 indicate high performance for both models.

Table 4 Hedonic OLS regression estimates of log sales prices

The building characteristic variables worked as expected. A larger floor area and better conditions increase the transaction price, whereas older buildings, longer distance from the CBD and leasehold decrease the transaction price. Our variable of interest, GSHP as a heating type, increases the transaction price of detached houses by 5.33%Footnote 2 (4.08 to 6.61%, with a 95% confidence interval) compared to other heating types. This finding is statistically significant at the 1% level. Thus, the main hypothesis was supported. This was further tested in the second column by inspecting each heating system individually against direct electricity. Again, GSHP commands a price premium of 4.85% (p < 0.01), while district heating commands a lower price premium of 1.27% (p < 0.05). Oil decreases the transaction price by 2.31% (p < 0.01). These secondary model’s findings were somewhat aligned with the heating costs presented in Fig. 1: GSHP is clearly less expensive than direct electricity, and district heating is also less expensive. On the other hand, oil is approximately on the same level as direct electricity, but its price has high volatility, requires effort from the owner to refill the oil boilers and produces local pollutants. These could be the reasons behind the negative price premium.

Table 5 provides robustness testing by first stepwise increasing the characteristics and then analysing how removing both age and/or condition changed the models. It is known that both can have a major effect on heating costs, as older buildings that are in a bad condition have lower energy efficiency.

Table 5 Sensitivity analysis of regression estimates

When a location is controlled, the GSHP premium drops dramatically. This is expected because in the dataset, GSHP houses had a larger market share in cities with higher prices (see Tables 2 and 3). Adding the lower-level, time-varying neighbourhood characteristics did not have a significant effect on either the performance or the estimates. When building characteristics were added, the performance increased by approximately 0.22 in both regression models, but the price premiums also decreased. This change was then further analysed by excluding first age, then condition and then both from the final model. The performance remained high, but interestingly, these exclusions decreased the price premium. This suggests that GSHP houses did not have some unseen conditional effect that accounted for the estimated GSHP price premium.

Further analysis was conducted by estimating how the premiums had developed over time and in different cities. Two regression models are created with interaction variables, see Table 6.

Table 6 Interaction variable estimations for GSHP houses

There positive premiums have been quite consistent since 2010, when the heating costs also started to increase more rapidly than housing prices, as presented in Fig. 1. However, the positive housing price developments were fuelled by higher loans and very low interest rates. Since heating costs are paid by available income rather than the debt that is used for buying the house itself, the increasing trend in premiums could be linked to the ratio between available income and energy costs. Figure 3 presents the indexed development of electricity prices and district heating (oil is excluded given the high volatility), as well as wages and salaries in Finland (Statistics Finland, 2021bd). Up until the financial crisis in 2007–2009, heating costs and wages followed each other. However, since the financial crisis, there has been a clear and growing gap between these indexes. Regarding the municipality interaction variable, 6 municipalities have a statistically significant positive price premium with quite a bit of volatility between the size of the premium.

Fig. 3
figure 3

Development of heating costs and wages in Finland

Economic return of GSHP in detached houses

The total economic and environmental performance of GSHP was estimated for an average detached house in Finland. At the end of 2020, the average selling price of an old, detached house in HMA was 3 369 €/sqm and in the rest of Finland, it was 1 497 €/sqm (Statistics Finland, 2021b). Based on building data statistics, the average house in HMA was constructed in 1984 and has a living area of 181 sqm (Statistics Finland, 2021c). The corresponding house in the rest of Finland was constructed in 1972 and has an area of 143 sqm. Using the above square metre prices, the respective values of these houses were estimated at €609 789 and €214 071. Thus, the premium of 5.33% would indicate a price premium of €32 484 in HMA and €11 404 in the rest of Finland.

Using the methodology presented by Vimpari (2021), the actual energy consumption of these buildings was estimated based on the floor area, construction year and location. For the rest of Finland, the city of Kuopio (located in the centre of Finland) was used as a reference point, as the climate is colder and heating requirements are higher compared to HMA, which is in the south. Oil was used as the current heating system. Table 7 presents details regarding current heating costs and emissions, as well as the numbers of whether GSHP is the heating system. Additionally, some details of GSHP are provided, together with key financial parameters, such as the payback period and the internal rate of return (IRR). The heating costs and emissions are higher for the building in the rest of Finland, even though they are 21% smaller. The locations in a colder region, as well as an older construction year with worse insulation, are the reasons for this. However, savings from GSHP are larger for the building in HMA, a key reason being that the coefficient of performance for GSHP is higher in southern Finland and electricity (distribution) prices are lower. Heating costs and emissions in HMA are reduced by approximately 71% and 94%, respectively, compared to the buildings in the rest of Finland, with 58% and 93%, respectively. In both cases, the payback period for the investment was approximately 10 years without considering the potential sales price premium. The lifecycle investment performance (IRR) was slightly better for the building in HMA.

Table 7 Overall economic and environmental analysis of GSHP in an average detached house

Finally, the number of years of energy savings on top of the price premium required to cover the capital expenditure of GSHP was calculated. In HMA, 0 years of energy savings plus the price premium cover the investment costs, whereas 5 years are needed in the rest of Finland. This highlights the relationship between the investment costs of a GSHP system and housing prices. The relative investment cost is approximately the same in both locations, but the sale prices have larger differences, as the average price for an old detached house is 125% higher in HMA than in the rest of Finland.


Previous literature utilising hedonic regression analysis has found price premiums for energy efficiency, rooftop PV and air source heat pumps in several markets. Heat pumps are a key technology for decarbonising the heating and cooling emissions of buildings, as pointed out in several research papers and industry reports. This study focused on GSHP, which is a key heating technology in colder regions. GSHP is more expensive than air source heat pumps; hence, analysing their impact on sales prices is an important addition to existing literature.

A hedonic model was built to examine whether a GSHP system commands a price premium over traditional heating types that have much lower capital expenditures than GSHP. The model was applied to a dataset of 19,008 house transactions in eight large Finnish cities.

For detached houses, a statistically significant 5.33% sales premium was found. Transforming this premium into monetary values, using the average sales price of detached houses in HMA and the rest of Finland, indicated respective premiums of €32,484 and €11,404. Based on housing market dynamics, this is an important aspect to highlight, as the capital expenditures of a GSHP system are similar across housing markets, but the value of houses is not. This would indicate that the investment cost of a GSHP system is captured in the sales price more easily in locations with higher sales prices. The energy savings analysis shed light on this, as the sales price premium could capture the investment cost of a GSHP system, whereas in the rest of Finland, 5 years of energy savings are required. Hence, in locations with lower prices, the owner would have to keep the house for a longer period to recoup the investment in the GSHP. This is important to understand when national energy aid policies are planned.

Previous literature, such as Kuminoff et al. (2010), has stated that there are challenges when interpreting the results of linear hedonic regressions. As the data do not identify the timing of the installed heating system, a model comparing the pre- and post-installation of GSHP cannot be constructed. Hence, there might be unobservable characteristics, especially those related to individual housing attributes that were not identified by the used model and may influence the found premium. Controlling for building characteristics, age and condition is done to mitigate this potential impact, as well as analysing the found premium through time.

According to the European Commission (2020), there are 40 million detached houses in Europe. Most of these homes are heated with either burning fossil fuels or using low-efficiency direct electricity. Thus, massive upfront investments are required from building owners in high-efficiency heat pumps. For homeowners to invest, it is important to understand the economics of these investments, both in terms of what they do to annual heating costs, as well as how they impact sales prices.

Similar research should be conducted in different markets to understand whether a premium can be found when the dominant heating type is based on gas boilers (as in many European or North American markets) and/or when electricity prices are higher. More findings from other datasets would strengthen the findings of this paper, as there might be some unobserved attributes that explain the price effect of the heating system.