# The Influence of Energy Certification on Housing Sales Prices in the Province of Alicante (Spain)
## 3 Results
### Regression Analysis
\begin{tabular}{c c c c c c c c c c c} \multicolumn{11}{c}{**Estimates**} \\ \cline{2-11} \multicolumn{1}{c}{**Variable**} & \multicolumn{2}{c}{**1**} & \multicolumn{2}{c}{**2**} & \multicolumn{2}{c}{**3**} & \multicolumn{2}{c}{**4**} & \multicolumn{2}{c}{**5**} & \multicolumn{2}{c}{**6**} & \multicolumn{2}{c}{**7**} & \multicolumn{2}{c}{**8**} & \multicolumn{2}{c}{**9**} \\ \cline{2-11} \multicolumn{1}{c}{**ABCD/CF/Ref/Ref/**} & \multicolumn{1}{c}{**A/Ref/Ref/**} & \multicolumn{1}{c}{**A/Ref/C/Ref/Ref/**} & \multicolumn{1}{c}{**A/BC/Ref/Ref/**} & \multicolumn{1}{c}{**A/BC/Ref/C**} & \multicolumn{1}{c}{**A/BC/Ref/C**} & \multicolumn{1}{c}{**A/BC/Ref/C**} & \multicolumn{1}{c}{**A/BC/Ref/C**} & \multicolumn{1}{c}{**A/BC/Ref/Ref/C**} & \multicolumn{1}{c}{**A/BC/Ref/Ref/Ref/Ref/C**} & \multicolumn{1}{c}{**A/BC/Ref/Ref/Ref/Ref/Ref/C**} \\ \hline \multicolumn{11}{c}{_Market characteristics (\(I\))_} & & & & & & & & & \\ \hline \(E\)_projection_ & \(-0.028\)**\(\,\)**(\(\,\)**(  
Currently, studies carried out have been based on distinct scenarios and the literature has revealed a certain diversity in terms of determining the letter (or set of letters) of reference for measuring and comparing the impact of energy qualifications on housing prices (Figure 8). This circumstance hinders comparisons between the premium resulting from going from one value to another within the ABCDEFG qualification scale. To facilitate comparison between studies, some authors have recommended that letters not be grouped and that D be considered the letter of reference [32; 65], since it is in the middle of the scale. Based on this background, the results of the model obtained for the estimate 4 are presented, since it is the one that complies with the recommendations of using letter D as a reference. As for the characteristics of the housing, the model estimates that for each additional year (in terms of age of the housing), the asking price will be reduced a mean of 0.29%. As for size, the estimated impact implies that an increase of one square meter in surface area results in a 0.58% price increase, whereas the addition of another bedroom leads to a reduction of 2.09%. However, an additional bathroom represents a mean price increase of 22.44%. If the property has extras, such as built-in wardrobe, air conditioning or a terrace, the mean impact on prices estimated by the model is 0.25%, 8.42% and 2.81%, respectively. The results reveal that a home situated on an additional story has a price increase of 0.02%. Using as a reference a second-hand home in good state, the model estimates a mean discount in the asking price of 9.49% for a second-hand home that needs renovation. On the other hand, if the home is a new construction, the results reveal a price increase of 20.78%. Within the typology of homes and using apartments as the reference, a duplex or attic apartment has a price increase of 1.30% and 9.45%, respectively, whereas studio apartments have a discount of 23.56%. The values obtained in the estimation of the parameters related to the building characteristics, such as having an elevator, garage, storage space or swimming pool, imply a mean price increase of 19.48%, 10.18%, 4.88% and 9.38%, respectively. On the other hand, having a garden has a contrary effect, leading to a mean price reduction of 0.16% (not significant). As for characteristics related to location, for properties situated in neighborhoods with a higher gross development (those in which there are more homes per sector surface area), the model estimates a mean reduction in price of approximately 1.80%. As for the geographic distances, they are all statistically significant, except for the distance to pharmacies. The results reveal that for each kilometer that the housing is distanced from pharmacies or level 2 schools (secondary and high schools), the price decreases by 0.50% and 1.18%. The opposite occurs when the housing is distanced from health centers, hospitals and level 1 schools (infant and primary schools). Homes that are in coastal towns have a price increase of 16.57%.