The Residential Value of Energy Efficient

HousingPresentation at The ERES (European Real Estate Society ) Conference in Vienna, July 4 2013 Mia Wahlström, PhD student

Introduction

• In 2006, Sweden adopted a law (based upon EU directives) on energy performance certification of buildings to reduce the use of energy and the emission of climate gases.

• For single-family housing the law prescribes that each owner has to have the prescribed certificate no later than at the time of selling the house.

• In this way each potential buyer will have data about the current use of energy as well as about building attributes related to the use of energy. the certificates include expert advice for reducing the use of energy.

• The idea is that the added information is assumed to make the owners and users involved more aware of their energy consumption and options to reduce it which in turn should lead to a smaller or at least a more efficient energy use in the residential and commercial sector.

Research Questions

In this paper we will make use of the information provided by the Swedish performance certificates to look closer at the energy consumption among owners of single-family housing. We look at factors likely to influence their use of energy and at the relationship between the price of a house and its energy performance. More specifically, the following two questions will be addressed: • What role does preferences and household characteristics play for

energy consumption as compared with the energy related attributes of their house? Is the energy consumption also influenced by the deficiencies that are indirectly indicated by the improvement measures suggested by the certification expert?

• What is the price premium for energy efficient housing in Sweden? Are households willing to increase their bids for a given housing alternative, the lower they anticipate their energy cost to be? Are the bids also affected by the suggested improvement measures or rather the corresponding costs?

Literature review

Summarizing, earlier literature suggests that:– Household composition is a significant factor, determining energy

consumption.– A household with “green” attitudes and/or behavior seem to have

lower energy consumption then households with “non-green” attitudes and/or behavior.

– Households are willing to increase their bids for a given housing alternative if they anticipate lower future energy consumption.

A main contribution of our research is perhaps the richness of the data set it is based upon. This gives us the possibility to gain further insight to how different energy-related features and information co-vary with household and building characteristics in the complex process of bidding for and buying a house.

The Swedish Case

• A long tradition of an ambitious environmental policy -> a fairly high degree of environmental awareness among Swedish consumers can be expected. We think this makes it especially interesting to compare with earlier work in for example California and the Netherlands.

• No standardized “green” label. Recent research (Netherlands and California) focused on the capitalization of green-label information in the price of labeled homes. In Sweden, different labels have developed on the market, making it difficult for buyers to understand and evaluate the energy information. During 2009-2010 most Swedish house buyers had to take the details of energy efficiency into account when evaluating future energy performance of a house. Are they interested enough to take this information into account?

• A cold country. In Sweden the temperature varies a lot across regions.

As an example the number of frosty days differs from 60 in the south to 261 in the north. As a consequence, the variance in energy consumption is relatively large. It is of specific interest to analyze the climate’s effect on energy consumption with respect to global warming. 

Theoretical framework

• The hedonic price model analyze implicit prices (values) for the various attributes of houses or other goods, including implicit prices for environmental attributes.

• We assume that the joint set of attributes purchased can be divided into those providing direct consumer satisfactions, those that are inputs to the production of comfort in dwellings and those that are locational and spatial, see equation (1): Vj=V(X1j, X2j,Tj) (1)

where Vj is the market value of house j, X1jis a vector of variables providing direct consumer satisfaction, X2j is a vector of attributes, including energy consumption, needed to produce comfort and Tj represents the outdoor climate.

• In spite of this complexity the following utility maximizing framework will be assumed to reflect the most important factors influencing the price bid of a household i for a house j: Uij = Ui(X1j, X2j, OGi, Tj) (2)

where X1j is a vector of housing attributes such as living area, interior quality and lot size providing direct consumer satisfaction. The vector X2j represents energy related attributes - including energy use - that creates comfort and OG i is the income left over for consumption of other goods after paying the annualized cost for the house and for the energy consumed.

Theoretical framework (continued)• The budget constraint all potential buyers need to consider is given by equation

(3) expressing the volume of other consumption, OGi, as the difference between the income Yi, the annualized housing cost equivalent to the bid Viand the yearly energy expenditure, Ei :

OGi = Yi–rVi– Ei (3)

where r reflects the capital cost. • In addition to data on energy consumption, attributes and climate, we have

information about the income, age and composition of each household. The suggestions made by the certification experts provide further information about the housing attributes. Ej = Ej (X1j, X2j, Tj, Hj, Aj) (4)

where Ej is the energy consumption the last year for the seller of house j, H j is a vector of household characteristics and Aj is a vector of the expert measures suggested for house j.

• The result from this estimation can hopefully also be used to ameliorate the interdependence problems involved when estimating the hedonic price function. The two-stage econometric approach adopted will be presented later.

Data

• We make use of a unique database created through matching information provided by the single family housing certificates issued in 2009 and 2010 with three other sets of data. The resulting database contains over 80 000 observations described by about 200 variables.

• The certification data has been matched (by the Swedish Central Bureau of Statistics) with three other sets of data. – One is based upon the real estate tax assessments

and provides data on sales prices and additional information about the housing attributes.

– The other two provide information about the (selling) households and various characteristics of the neighborhood (municipality and parish) where the houses are located.

Our database contains all houses sold during 2009-2010 = approx. 2 % of all single-family houses. One difference between the sold and unsold houses during these years concerns construction year. The houses sold are to a higher degree built during the 60s and 70s (and even 50s), while the unsold houses more often are from before 1940.  

Data, variables

For each house, the data base holds information on a total of approximately 200 original variables, related to the following topics: • Building characteristics- interior/exterior• Energy equipment and consumption, in total and per energy

type• Proposed actions in order to reduce the energy consumption

and the associated cost • Area Characteristics• Household characteristics• House contract price and assessed value

Before starting the analyses, we cleaned the data so that only full-time (all-year) single- & double family houses are included with a positive transaction price (> 0 SEK). Some outliers were also taken out from the data, e.g. houses with a property plot < floor area.

Summary statistics• Most houses are from the 60s or 70s and almost 50% have an open

fireplace, electric stove or stove. The average floor area is approx. 160 sqm and average property plot around 1000 sqm. Most are detached, single-storey houses made of wood. Most of the houses have one bathroom and normal kitchen standard. Approximately half of the houses are located in the Stockholm, Gothenburg or Malmoe.

• The average selling price is ~239 800 EUR in total or ~1962 EUR per sqm. The median is lower ~207 070 EUR, indicating a skewed distribution.

• The average yearly energy consumption is around 18 500 kWh, of which ≈ 10 500 kWh electricity. The most common energy source is electricity direct heating or water filled radiators, but heat pumps are installed in between 15-20% of the houses. The most frequent expert advice in order to reduce energy consumption concerns hot water saving actions.

• The households are typically small, with 1-2 members. 35% are single households and 15% are single, elderly households. Approximately 40% are households with children. All households own at least one car and 5% at least one “green” car. The average (median) disposable household income is approximately 61 000 EUR and 43 600 EUR for the household head.

HypothesesBased on earlier work, the theory and data available, we formulate the following hypotheses:• Energy consumption per capita will decrease with household size and

increase with income• The households’ general attitudes concerning the environment will

influence the energy consumption. Environmentally aware households will have a lower energy consumption-

• The quality of the technical energy equipment influences energy consumption and the price of the house (i.e. has a positive value when bidding on a house).

• The observed energy consumption influences the price on a house put out for sale (i.e. has a positive value when bidding on a house).

• The expert’s advice on how to reduce energy consumption and the associated cost of proposed actions/investments will be taken into account when bidding on a house.

Empirical strategy and Econometric model

The hypothesis have been tested through statistical hypothesis testing in two steps. First we estimate the model of energy consumption and after that we estimate the hedonic price model. Applying a linear log-log model on energy consumption (E), gives:• lnEi = lnβ0+ β1ih1i+β2ih2i+β3ih3i+β4iWki + εi (5)

where h1i is a vector of building i’s attributes not related to energy, h2i a vector of the energy-related attributes, and h3i a vector of the household characteristics. Finally, Wki is a vector of the locational and climatic attributes. The vectors include both continuous and binary/discrete variables. The continuous variables have been transformed into ln-form before they are included into the regression.

Applying a linear log-log model on transaction price (V) for house i:• lnVi = lnβ0+β1h1i+β5h5i+β6ilnE(Ei)+lnVi + εi (6)

where h1i is a vector of the building i’s attributes not related to energy, h5i is a vector of the location-related attributes, and E(Ei) is the expected energy consumption. Finally, the local housing market is affecting the house price, reflected in the mean price in the local area of house i (Vi). The vectors include both continuous and binary/discrete variables. The continuous variables have been transformed into ln-form before they were included into the regression.

Estimations and Testing of Hypotheses (continued)

The energy consumption may be of larger interest in Sweden due to the lack of a standardized “green” labeling system of single-family houses. When estimating the hedonic price function we need to consider the variables influencing energy consumption in order to use valid instrumental variables in the price function.  a)Regression results with Yearly energy consumption per capita, kWh

(ln) as dependent. Building characteristics, energy attributes/facilities, household characteristics/attitudes and climatic variables are independents that are included into the model in different steps.

b)Regression results with Total house price (ln) as dependent. Locational and building characteristics as well as energy attributes and consumption are independents. Energy consumption per capita was instrumented with “households with kids” and “single household”.

Regression results - energy consumption Building characteristics

• When energy consumption is modeled by building characteristics only, house vintage is a strong factor. Houses built before 1980 have a much higher energy consumption compared to new houses (built after 2000).

• Size, in square meter (ln), is another strong independent. 1% larger house (about 1,5 sqm on a 150 sqm villa) -> 0,36% higher energy consumption.

• “Additional insulation recommended” is a proxy for relatively poor insulation quality, which corresponds to approximately 11% higher energy consumption. Houses with simple type of windows equivalently have a higher energy consumption.

• A new roof, facade or kitchen equipment has a reducing effect on energy consumption. The new-kitchen effect seems to be unrealistically high, which could imply that there are other things built into this, e.g. a more comprehensive renovation.

• Living in a row-house or a semi-detached house is significantly less energy demanding. The climatic zones have strong effects. In the Northest part the energy consumption is far higher compared to the southern part.

Regression results - energy consumption (continued) Building characteristics and energy attributes/facilities

• When the energy attributes are introduced into the model, the R-squared almost doubles. The coefficients for the building characteristics (in particular construction year) and climatic zone are decreased substantially.

• Heat pumps in general and ground sourced heat pumps in particular are very strong energy savers. A house with a ground sourced heat pump consumes over 50 % less energy than a house without such a pump. • Houses that use direct electricity have lower energy consumption. One explanation could be that direct electricity is relatively expensive and therefore an incitement to be more energy efficient.

• Further, houses with modern ventilation systems are less energy consuming than other houses. An oiled-fired boiler, gas and bio-fuels all have the opposite, i.e. a positive significant, effect on energy consumption per capita

Regression results - energy consumption (continued) Household characteristics

• We start the analysis with only a few household variables; age, size and foreign/Swedish background. As expected the size of the household has a strong reducing effect on energy consumption. The more people sharing a home, the lower is the energy consumption per capita. The R-square is strong (0,57) compared to what it was in the previous model with only building and energy characteristics.

• We still observe a relatively weak but positive effect (3,5%) of elderly households. Households with kids, on the other hand, have a reducing effect on energy consumption per capita. Education seems to have a significant small reducing effect, implying that households with higher education have lower energy consumption. Finally, we add disposable income (ln), to the analysis. The income show to have a significant positive effect; 1% increase in income corresponds to 3,5% increase in energy consumption. In other words; the more you earn the more you spend!

• Finally, car ownership and political environment were added to the analysis. Owning at least one green car seem to have a small (but still marginal) reducing effect on energy consumption and the same goes for the share of green votes in the parish where the house is located. This last addition to the analysis gives us a hint that other, more attitude-/value-related characteristics influence energy consumption. However, the impact is still quite small compared to household composition and income.

Regression results - energy consumption (continued) The full model: building, energy and household characteristics

• In this final regression the strongest variable is “Single household”. Living alone in a house generates almost twice the energy consumption per capita, compared to larger households. On the other hand households with children has lower energy consumption per capita compared to households without.

• Disposable income still has a relatively small positive effect on energy consumption; a household head with 10 % larger income has a 0,2 % higher energy consumption. When we control for household characteristics, construction year is not as strong as before, but nonetheless, houses built before 1920 consumes around 40% more energy than newly built houses.

• Heat pumps are still strong independents, with a reducing effect on energy consumption while Chips, pellets or briquettes, Oiled-fired boiler (and Gas) have almost as strong opposite effect on energy consumption.

• The size of the house has a stronger effect in this final model, compared to the first two. A house that is 10% larger (e.g. + 15 square meters on a 150 square meters house) consumes about 6% more energy per capita and year.

• The variables “Two or more bathrooms”, “At least 1 green car” and “Share of green votes in parish” are no longer significant in the model.

Estimating the Hedonic Price Function

• We have concluded that energy consumption to a large extent is influenced by household characteristics, which we must take into account when estimating the hedonic price function .

• Our hypothesis is that the price is a function of (among other things) the energy consumption during the previous year. The energy consumption in turn is a function of household characteristics, in particular the household size. In order to control for this we perform instrument variable regressions, where the energy consumption per capita is instrumented by the variables “single household” and “households with kids”.

Estimating the Hedonic Price Function (continued)• Location seems to be important in several ways. The mean price of houses

sold in the local municipality is one strong independent and the local parish influences the price through the median income and density. The denser and richer the parish, the higher the price. Further, the house’s proximity to beach/shoreline is a very strong independent in the model. The price of a house with a private shoreline or beach is almost twice as expensive as a house located more than 150 meters from the shoreline.

• Construction year is another strong factor influencing price. Houses built between 1920 and 1960 have the lowest prices, compared to new houses (built after 2000). The size of the house, is of course a very important factor in the price model. An additional 10% of living area corresponds to a price premium of 5 %. E.g. an additional 15 sqm on a 150 sqm villa yields a price premium of ~13 600 EUR on the average 272 500 EUR villa, equal to 870 EUR per extra sqm. The property plot has a much weaker but still positive effect on price.

• Other building characteristics with a positive effect on price are: new facade, open fireplace, high kitchen standard, new kitchen equipment and multiple bathrooms. Compared to the coefficient of a high kitchen standard, the coefficient of an open fireplace seems irrationally high. The reason is at least partly the ”coziness value” associated with this feature.

Estimating the Hedonic Price Function (continued)• Heat pumps in general, have a relatively strong positive effect on the

price. A ground sourced heat pump corresponds to a price premium of 15%. On an average 272 500 EUR villa, this adds up to over 40 000 EUR, which seems unrealistically high even though the investment often is substantial. The reason might be that there are other qualities built into this feature, such as a good standard and energy performance over all. It could also imply that in Sweden, where there is not one standardized green labeling of houses, the heat pump communicates a kind of green label value.

• The house’s energy consumption per capita has a negative coefficient, which means houses that have had lower energy consumption in the past sell at a higher price than houses with higher consumption per capita. The price premium is + 0,07 % for 1% lower energy consumption (kWh) per capita.

• The energy certificates also contain recommendations on how and how much the energy consumption could be reduced. The estimated reduction of energy from recommended action no 1 (the most cost-efficient action) has a positive effect on price. A 1% increase in the estimated energy savings (kWh) corresponds to a 0,02 % increase in price. This is a hint that the recommendations are actually taken into account in the bidding process, even though the influence is quite weak.

• The prices were generally higher in year 2010 compared to 2009.

Conclusions

1. “Energy consumption per capita will decrease with household size and increase with income” – supported. Especially household size influences the energy

consumption/capita. Income is a much weaker, but still significant factor.

2. “The households’ general attitudes concerning the environment will influence the energy consumption. Environmentally aware households will have a lower energy consumption” – difficult to draw conclusions about. Ownership of “green” cars had

a small significant effect on energy consumption. A “green” majority in the election of 2010 also seem to have a reducing effect.

3. “The quality of the technical energy equipment influences energy consumption and the price of the house (i.e. has a positive value when bidding on a house)” – accepted, especially for heat pumps that have a strong reducing

effect on consumption and a strong positive effect on price.

Conclusions (continued)

4. “The observed energy consumption influences the price on a house put out for sale (i.e. has a positive value when bidding on a house)” – accepted, but the effect is quite weak. The reason probably has to do

with the difficulty for the buyer of the house to calculate how much of the consumption that is due to the previous owners behavior and how much is due to the house’s building and energy characteristics.

5. “The expert’s advice on how to reduce energy consumption and the associated cost of proposed actions/investments provide further information about the energy performance, are related to the energy consumption and hence, will influence the bids” – accepted, at least when it comes to the estimated reduction of energy

for the most cost-efficient action. However, the effect is relatively weak.