Effect of Energy Efficiency Rating on House Pricing - Essay Example

Effect of energy efficiency rating on house pricing. When measuring the energy efficiency rating of a house there is allocation of point scores different design features. The design feature which include the building fabrics, window design and orientation enable a rating ranging from 0 to 10 to be applied. The rating scheme has its origin in computer models involving heat flow in a building shell with its application being strictly applied thermal performance of the building shell. In the rating a higher star number implies higher thermal efficiency for the dwelling. A higher star rating has an implication that that there is less consumption of energy use in heating and cooling with the end result being a low greenhouse emissions and provision of greater comfort. In 2009 the council of Australian Governments introduced a scheme where it is mandatory to disclose information about energy performance of a house, greenhouse and water performance when the house are being sold and this is helping home buyers and renters to make informed decisions. In rating a house the size and type of house play no role. This has an implication that small and big houses have the potential of having the same energy rating. In Australia Energy efficiency rating ( EER) calculation involve an accredited and professionally trained individuals who have the ability to use FirstRate which is a computer program specifically design for the purpose. The program has the ability of generating points that are based on the design information and features that include the floors, ceilings, external walls, windows, air leakage, orientation, glazing, zoning, and thermal mass and cross ventilation. Some of the building feature that increase the point scores are insulated ceilings, walls and double glazed windows while windows lacking curtains or drapes, and unsealed cracks and gaps around doors and other vents brings about reduction in the point scores. The sum total of the point scores is the determinant of the EER value of a dwelling. It is a requirement that when anyone is selling or leasing a house there should be a disclosure of the EER value in the sales advertising of the house. An EER incorporates the energy efficiency features of a house, but the features reflect characteristics that may have an influence on the house price for reason other than their effect on energy demand. A good example is where brick walls, timber flooring and attic ceilings have an influence on the energy efficiency in addition these features have aesthetic appeal which adds value to the. There are also thermal features that are omitted in the EER score yet they have a thermal energy efficiency impact. A good example is the presence of a heating system that is efficient and flexible in and energy efficient hot water system being installed in the house. Other energy saving feature of a house which have been omitted are efficient lighting system and appliances. Data set The mandatory requirement for disclosure of information regarding energy performance and sales of houses has resulted in availability of this information. With availability of this information there is a unique opportunity to establish the existence of a relationship between house’s prices and their energy efficiency rating. In order to enable the development of the data set, ACTPLA was tasked to do an examination on all house data files that matched house sales records of 2005 and 2006. The relevant house characteristics that are required for analysis and which were collected include the following: House size Block size Area of window Floor/wall material Storey number Cross ventilation potential Presence of shading schemes. Sales information collated from the same houses from the ACT Government included: settlement date transfer date; and Transfer price. The ACTPLA and EER data records were linked by use the suburb, block and section information after cleaning and checking and removing of unexplained data. By use of ABS house price index for the ACT it was possible to eliminate the impact of house price inflation with the aim of producing a good cross-sectional dataset. The assumption is that after eliminating the inflation impact on price. The remaining variation in price is assumed to be contributed by house characteristics. The study comprised of over 5000 homes that were sold in the act in the years 2005 and 2006 with the final data set being made up of 2 386 and 2 719 house records for 2005 and 2006 respectively. the two years were used due to the fact that they were a good representation of the latest complete data for a mature market. Method of analysis In order to establish the relation between energy performance and price there was use of Hedonic analysis was put into use. This is an economic valuation technique which is used in working out the implicit price of house variables, through decomposition of items under research (price in this case) into the constituent variables. This will then facilitate the estimation of the value of each variable. Variables considered There were 5 main categories of variables that were put consideration: 1. Structural variables- under this design and construction features were put under consideration 2. Distance variables- under this category of variables there was description of the relative location of shops, hospitals, schools and the central business district. 3.Neighbourhood variables- that covered major social and economic conditions sin the neighborhood of the home 4. Locational variables- Gave explanation to elements of suburb prestige 5. Energy efficiency variables In the study care was taken to enable major variables (such as size, location, and construction of house, and value of land) to be treated in a manner that was consistent to that of the energy efficiency variables. House price Modelling Statistics modeling involve analysis of data objects and establishing their link to other data objects. The modeling involved detached houses that were sold in the ACT in 2005 and 2006 in relation to distance, land socio-economic, neighbourhood and EER data by use of the five hedonic models. Models 1 to 3 addressed the hypotheses of the study. Models 4 and 5 played the role of validating the use of EER in Models 1 to 3. Model 1 was the basic model and included EER as an explanatory variable. Model 2, Is an extension of Model 1. It played the role of accounting for the non-thermal attributes of EER. EER was inclusion played the of an explanatory variable as well as in addition other individual energy efficiency-related variables that have non-thermal effects. Model 3, also an extension of the Model 1, Gave an account for thermal features separately. In addition to the ones included in Model 2, some of the energy efficiency-related variables accepted to have thermal effects were included. In this model there was a modification of the EER variable in order to rule out any random error contained in the EER of the basic model. Model 4, there is modification of Model 1 and there is treatment of the EER as a fitted variable. Model 5, there is modification of Model 1 and EER treated as a derived principal component score. Factors underlying EER that add value to a house for reasons other than energy efficiency From model two the result suggested that some EER factors that have non-thermal associations with price as well as having thermal associations. Double glazed windows, utility doors and wall and ceiling vents were the EER factors that were found to have statistical significance. Double glaze window are energy efficiency feature that is highly visible (in contrast to wall insulation) in addition of serving as a noise barrier and this makes it capable of adding value to the house. Feature like wall and ceiling vents are common in houses which were build in 1970’s and 1980’s and have a negative impact on pricing of a house due to trends and fashions in house market. After adding the three factors into the model, the model still indicated that there was a significant relationship between price of a house and its EER value. Another observation was that the changes observed in the values of the coefficients of other house characteristics were minimal. Presence of brick wall, largest window facing north, timber flooring were other EER non-thermal attributes that were put into consideration in the actual model, but were found to be non- significant. In model 1 the adjusted R2 was found to have a value of 0.82 which was a clear indication that the model gave a very good approximation of the relationship that exist between the house price and the explanatory variables. This value is higher when compared to the R2 value of Chen et al. (2004), Hansen (2006) and Hill and Melser (2006) There is a statistically significant relationship between house price and EER In model 1 the result indicated that there was a positive association between EER and house price with the result showing a strong significant relationship at a = 0.01 level of significance and t-value of 4.87. From the significance association it was found that on average, the detached house price that was sold in ACT in 2005 had a change of about 1.23% for a unit change in the EER if all other variables were held constant. Having used the EER star rating as a continuous variable in the model, there was a fundamental assumption that across the range of 0-10 of the EER star rating, for every increment of 0.5 there is 1.2% increase in the price of a house that is constant. In the real house market situation this may not be true due to the fact that moving from 1.0 t0 1.5 may have a different degree of association, in terms of the effect towards the buying decisions of consumers’ , as a move from 4.5 to 5. In order to the later argument, there was re-estimation of the model but this time the EER star rating being treated as a categorical variable but not as a continuous variable. After this alteration the estimated coefficients revealed that belonging to a higher EER categories have premium prices as compared to houses in lower EER categories when all other factors are held constant. As an example, it was found that a house belonging to the EER 1 category could attract a premium of 1.6% as compared to the reference category. There is a rise in the premium as when the EER category goes up although there is a decline in marginal addition. The extension of the basic model in order to facilitate the inclusion of some individual energy efficiency variables (model 3), the association of the EER star rating with house price was still statistically significant although there was a slight decrease with there was only marginal significance at a=0.10 level of significance. There is therefore an implication that the EER coefficient was sensitive to addition separate variables that were energy related. The value of estimated EER coefficient was found to be greater when the model estimate was done using 2006 data. In the data set the EER coefficients in model 1 and in model 3 were found to be significant. The EER coefficient was also found to be stable even when some energy efficiency related variables which are believed to have non-thermal effects were added. Conclusion From studies it is clear that there is a significant relationship between the EER and the price of a house. The findings are based on modeling involving hedonic regression of the factors that have influence on the house pricing. Administrative sources provided the useful dataset used in the analysis. The use of econometrics methods that utilized available data set ensured that robust result indicating the association between house price and EER was produced. References Chen, L., Zhao, S., Romanis, P. and Lim, P.P. (2004), Exploring Methods for Constructing a Housing Price Index, Australian Bureau of Statistics Research Paper, cat. no. 1352.055.067. Hansen, J. (2006), Australian House Prices: A Comparison of Hedonic and Repeat-Sales Measures, Reserved Bank of Australia Research Discussion Paper 2006-03, May 2006. Hill, R. and Melser, D. (2006), The Anatomy of a Housing Boom: Sydney 2001-2003, Draft paper, 21 November 2006. Read More