52 Dimke et al.: Values of Landscape Trees on Residential Property in Cincinnati, Ohio Summer Results This set of explanatory variables accounts for 68% (R2 = 0.681) of the variation in housing price (P ≤ 0.05) (Table 2). The vari- able coefficients from the summer data also indicate that the Hyde Park location had the greatest impact on property values. The Clifton location had the second highest impact followed, in order, by square footage of living space, number of acres, total number of baths, sale date, tree cover, and year built. The coef- ficient signs of all significant variables were positive, as expected. Summer results were very similar to the winter results with loca- tion of the property being the most important factor in determining sales price home. The summer results indicated that the price per square foot of living space was seven U.S. dollars lower than win- ter results and the cost per acre was $11,000 higher. Winter analy- sis found the number of bedrooms was the best fit, while summer analysis used the total number of rooms as the best fit. Possible explanations for these differences may include the large number of sites evaluated, changes in tree cover, such as tree plantings or loss, and variation associated with visual estimations of tree cover. As with the winter results, summer results indicate that tree canopy is important to home buyers in these communities. The average effect of tree canopy across all communities indicates that for each percentage increase of tree cover, sales price increased by $580.92. The mean property value for the 600 sites analyzed was $166,357, while the mean percentage of tree cover was 27.1%. This indicates that the average value of tree canopy across the 600 prop- erties was $15,743 or 9.5% of the summer sale price of the home Combined Evaluations The decision to purchase a property is often decided a number of weeks ahead of the actual closing date. It takes time to se- cure financing, conduct property inspections, and possibly sell another house. The average length of time between contract signing and the actual closing date is approximately 45–60 days (Abel 2008). In order to determine if this had an influence on the significance of tree cover both the winter data and the sum- mer data were combined and 60 days was subtracted from the actual closing date. If estimated date of signing fell between No- vember 1st and April 30th the contract date was considered win- ter. If the signing date fell between May 1st and October 31st it was considered a summer contract. Since two sets of data were collected for each property the set of data that did not fall into the actual contract signing time was dropped from the analysis so the total number of observations remained 600 properties. The cover variable represents the baseline actual cover present and visible when houses were sold in the summer. For houses sold in the winter, the cover-winter variable is an adjustment to this baseline to reflect that the canopy was still present, but had less visual impact. The combined set of explanatory variables accounts for 69% (R2 = 0.686) of the variation in sale price of the properties (P ≤ 0.05) (Table 3). The variable coefficients indicate that the Hyde Park variable had the greatest explanatory power followed by the Clifton variable. The remainder of the significant variables, in rank order, included number of acres, living square foot- age, total number of baths, summer tree cover, and year built. The coefficient sign for all significant variables was positive. Analysis found that the effect of tree cover for summer sales was an increase in sale price of $780 per one percent of tree cover. The winter sales adjustment was a decline of $111 per one percent ©2013 International Society of Arboriculture Table 3. Results of the analysis of the combination of summer and winter data. Data collected in 2005–2006 in the Cincin- nati, Ohio, communities of Bond Hill, Carthage, Clifton, Hyde Park, Kennedy Heights, and North Avondale. R2 justed R2 = 0.686, ad- Variable Sale date = 0.678, F-value = 91.23, n = 600. Coefficient 1.21 Square footage # acres # bedrooms Style height Year built Total baths Tree cover Cover winter Hyde Park Kennedy Heights Clifton Carthage North Avondale 41.15 190,977 4,639.63 -9,441.05 583.3 33,210 783.98 -111.27 183,574 -384.22 109,657 -2,754.67 9,935.87 6.35 7.47 1.05 -0.98 3.03 5.53 3.14 -0.47 14.38 -0.03 8.12 -0.21 0.72 t-ratio 0.29 P-value 0.7752 <0.0001 <0.0001 0.2932 0.3291 0.0025 <0.0001 0.0018 0.6385 <0.0001 0.9756 <0.0001 0.8322 0.4714 Note: Sale date: number of days on market prior to sale; square footage: size of living space in square feet; number of acres: lot size in acres; number of bed- rooms; style height: one story or two story; year built: house age in years; baths: assigned 1 point for a whole bath and 0.5 point for a half bath; cover: estimated percentage of tree cover; and neighborhood (Hyde Park, Kennedy Heights, Clif- ton, Carthage, or North Avondale; coded 1 = yes, 0 = no). of tree cover. This adjustment would be interpreted as one percent of tree cover adds $669 ($780 - $111 = $669) to the winter sale price but the cover winter variable was not significant. The effects of tree cover for winter and summer sales are not significantly dif- ferent from one another. There may be a slight tendency for cover to add less value for winter sales, but its effect is weak at best. The possible explanation may be that home buyers cannot visual- ize tree canopy accurately without the leaf cover being present. The average effect of tree canopy across all six communi- ties was an increase of $783.98 per one percent increase in tree cover. The mean sale price across the 600 sites was $188,730, with the mean canopy cover of 25.8%. This indicates the average value of tree canopy is $20,226 or 10.7% of the sale price of the home. All monetary values in this model are reflected in 2007 prices. Again, this value is consistent with previous findings. Testing for Quadratic Effects A model was developed to determine if there was significant concavity to the effect of tree cover on sale price. If the mod- el indicated a quadratic relationship, then the optimal percent- age of tree cover could be determined. This model indicates that there does not seem to be significant concavity to the ef- fects of tree cover (Table 4). The explanatory variables account for 69% (R2 = 0.686) of the variation in this model (P ≤ 0.05) (Table 4). Living space square footage, number of acres, to- tal number of baths, along with the two communities of Hyde Park and Clifton were found to be significant in determining sales price. The year built was also insignificant for concave effects. The relationship between tree cover and sales price appears to be approximately linear. There does not seem to be an optimal percentage of tree cover. Evergreen Versus Deciduous Previous research has indicated that homeowners do not seem to have a preference for evergreen species over deciduous species of
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