50 Dimke et al.: Values of Landscape Trees on Residential Property in Cincinnati, Ohio nities of varying socioeconomic levels in the city of Cin- cinnati, Ohio, U.S. It was hypothesized that tree cover would have a positive effect on the sale price of homes. METHODS AND MATERIALS This study was conducted within six communities in Cin- cinnati, Ohio. These six communities are of varying socio- economic backgrounds and include Bond Hill, Carthage, Clifton, Hyde Park, Kennedy Heights, and North Avondale. Most of the properties in these areas are 60- to 80-year- old single-family homes on small urban lots. More affluent areas, such as Clifton and Hyde Park, have some properties with larger lots. For this study, apartment buildings were excluded, as well as sales at non-market prices. Tax asses- sor records were obtained from home sales between the years 2000 and 2005. One hundred property sales were randomly selected from each of the six communities. The initial data was collected during the winter when the deciduous foli- age was not present. Dominant genus, caliper of dominant genus, estimate of percent tree cover, and overall property maintenance was recorded. Researchers also noted wheth- er the dominant genus was evergreen or deciduous. The data collection was repeated during the summer months when trees were in full canopy. All trees were included in the data collection for both summer and winter evaluations. For data collection, a physical inspection and assess- ment of each of the 600 properties was performed. All trees were inventoried. Dominant genus was also determined by assessment of most prevalent canopy cover and noted as to deciduous or evergreen. For this analysis, baldcypress (Taxodium distichum) and dawn redwood (Metasequoia glyptostroboides) were classified as deciduous and Ameri- can holly (Ilex opaca) was considered an evergreen. For simplified analysis, deciduous genera were recorded as zero and evergreen genera recorded as one. Caliper of dominant genus was noted. The number of trees on the property was recorded. Canopy cover was then estimated by ocular esti- mate. If the canopy of a tree on an adjacent property over- hung the property being inspected, this tree canopy was included in the estimate of cover but not included in the tree count. Only trees planted on the property or street trees on the property easement were included in the tree count. Property maintenance was recorded on a scale of 1 to 5, with 5 being the best. A property was given a rating of 3 if it was on par with the average maintenance level of its neighborhood, a rating of 4 if it was above average, and a rating of 5 if maintenance was exceptional. A property received a rating of 2 if it was below average and 1 if the property was in total disrepair. This was an assessment of the general upkeep of the exterior of the home and yard. Finally, impact of the landscape was assessed on a scale of 1 to 5, with 1 being poor and 5 being excellent. The quality of the landscape design, plantings, and main- tenance of the plantings was evaluated. This rating meth- od was similar to the property maintenance rating, with 3 given to properties on par with their neighbors, 4 as above average and 5 for exceptional properties. Again, 2 was given to properties below average and 1 given to over- grown, poorly landscaped sites. Tree health and structure along with placement were considered when rating impact. ©2013 International Society of Arboriculture Property maintenance and landscape impact were found to be highly correlated; therefore only property mainte- nance was considered when developing the hedonic model. The six hundred properties, 100 from each of the six com- munities, were first evaluated in the winter of 2005–2006. The following property characteristics were selected as the explanatory variables: sale date: number of days on mar- ket prior to sale, square footage: size of living space in square feet, number of acres: lot size in acres, number of bedrooms, 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, Clifton, Carthage, or North Avondale; coded 1 = yes, 0 = no). The community of Bond Hill was held constant while the other five communities—Hyde Park, Kennedy Heights, Clifton, Carthage, and North Avondale—were variables in the model (Table 1). This allowed a comparison of the differences in the communities relative to Bond Hill. Evaluation of the six hundred properties was repeated in the late spring and summer of 2006 when the tree canopy was in full cover. A second evaluation was performed to determine if full canopy added more value than a winter site with no leaf cover. The community of Bond Hill was held constant, while the other five communities were variables in the model (Table 2). Table 1. Results of the analysis of the winter data. Data col- lected winter 2005–2006 in the Cincinnati, Ohio, communities of Bond Hill, Carthage, Clifton, Hyde Park, Kennedy Heights, and North Avondale. R2 = 0.681, adjusted R2 = 96.29, n = 600. Variable Sale date Square footage # acres # bedrooms Style height Year built Total baths Tree cover Hyde Park Kennedy Heights Clifton Carthage North Avondale Coefficient 12.35 37.11 170,457 4,298.29 -10,708 481.47 30,328 561.2 161,315 -1,561.78 95,447 308.01 6,789.17 t-ratio 3.2 6.28 7.35 1.07 -1.22 2.75 5.52 2.91 13.89 -0.14 7.78 0.03 0.54 = 0.674, F-value P-value 0.0015 <0.0001 <0.0001 0.286 0.2244 0.0062 <0.0001 0.0037 <0.0001 0.8918 <0.0001 0.9793 0.588 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). The hedonic method was utilized to estimate the value of each of the property attributes, including tree cover. A hedonic model can be computed from data about property prices and attributes. Implicit prices for different housing characteris- tics are estimated by multiple regression analyses. Attributes such as square footage and lot size are held constant while evaluating the effect of another variable such as tree cover. The monetary value of each characteristic can be cal- culated by observing the difference in the market price of commodities sharing the same attributes (Morancho
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