Arboriculture & Urban Forestry 39(2): March 2013 Table 2. Results of the analysis of summer data. Data col- lected in summer 2006 in the Cincinnati, Ohio, communities of Bond Hill, Carthage, Clifton, Hyde Park, Kennedy Heights, and North Avondale. R2 = 0.681, adjusted R2 = 96.40, n = 600. Variable Sale date Square footage # acres Style height Total rooms Year built Total baths Tree cover Hyde Park Kennedy Heights Clifton Carthage North Avondale Coefficient 12.11 44.67 159,457 -6,655.4 -4,261.16 450.33 34,512 580.92 162,410 3,900.08 99,023 -2,038.77 12,360 t-ratio 3.13 6.75 6.6 -0.76 -1.72 2.56 6.17 2.6 13.93 0.34 8.05 -0.17 0.98 = 0.674, F-value P-value 0.0018 <0.0001 <0.0001 0.4495 0.0851 0.0106 <0.0001 0.0096 <0.0001 0.7333 <0.0001 0.8636 0.3265 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). 2003). It is theorized that goods are defined by the set of characteristics that form them and the price paid for that good is the sum of the price paid for each characteristic of that good (Morancho 2003). This is written as follows: [1] P = f(x1, x2 , x3 , xn , z) where P is the market price of the property and x1, x2 … xn represent the property characteristics, such as square footage and number of bathrooms. Tree cover, the envi- ronmental attribute evaluated in this study, is expressed as z. The environmental variable without a market price is referred to as the hedonic variable (Morancho 2003). Linear models have been used in previous hedonic re- search (Tyrväinen 1997; Morancho 2003). Linear models are in use due to their ease of interpretation, although there are many reasons to believe price and the environmen- tal variable may be non-linear (Morancho 2003). A linear model assumes that the marginal willingness to pay for an additional unit of an attribute (e.g., an extra percentage of tree cover) remains constant. In developing the model for this analysis, a quadratic model tested tree cover and age of the properties; they were not found to be significant. The basic regression analysis equation is as follows: , x3 [2] the property attributes, and εi where x1i, x2i b1, b2 , …, bn , bz Pi = b1x1i + b2x2i + b3x3i + . . . + bnxni + bz + εi , …, xni zi are the marginal willingness to pay for each of is the error term for the equation. , zi The property transactions selected for this research occurred over a five-year period, between 2000 and 2005. Sale prices were adjusted to third quarter 2007 prices of the Home Price Index for the Cincinnati Metropolitan Statistical Area. are the housing variables, parameters , RESULTS AND DISCUSSION Winter Results In developing the model it was found that tree cover and the impact (maintenance) rating were closely correlated. Main- tenance rating and landscape impact rating were also closely correlated. Landscape rating was dropped from the model and maintenance rating was used for further testing. When using both variables in the same model, tree cover lost its significance. The model was run using both tree cover and the impact rating separately and each was found to be sig- nificant if used individually. Both models had the same R2 value and since the impact rating is a subjective variable, tree cover was chosen for the model and is an accepted method for reducing covariates when using the hedonic method. When developing a hedonic equation, it is important to use a minimum number of variables as to prevent problems with multicollinearity. Multicollinearity may cause problems such as imprecise estimates and wrong sign on variables even though the R2 may be high (Hanley and Spash 1993). The winter set of explanatory variables accounts for 68% (R2 = 0.681) of the variation in housing price (P ≤ 0.05) (Table 1). The variable coefficients indicate that location of the house, such as the Hyde Park variable, had the greatest explanatory power, with the Clifton variable following second in explanatory power (Table 1). Number of acres, living square footage, total number of baths, sales date, tree cover, and year built had the remain- der of the explanatory power, listed in rank order. Sale date vari- able was included to show that the fewer days a property is on the market, the more value was added to the property. All coef- ficient signs were positive as expected. The other three neigh- borhoods—Kennedy Heights, Carthage, and North Avondale— were not significantly different from the constant, Bond Hill. Results of this research indicate that living in Hyde Park or Clifton has the largest positive impact on the price of a home. Analysis also shows an increase of USD $170,457 for each additional acre, but since most of the properties studied are on small urban lots, the value of the land, although still an important factor influencing price, is typically only a frac- tion of the per acre amount. Results also indicate that tree cover has a significant positive effect on home values in the six communities studied. The average effect of tree canopy across all communities indicates an increase of $561 per one percent increase in tree cover. The mean property value for the 600 sites studied was $166,357, while the mean percent- age of tree cover was 24.8%. This indicates that the average value of tree canopy is $13,913 or 8.4% of the sale price of the home as determined by the data collected during the win- ter months. These results are in line with previous findings. Morales (1980) found that good tree cover in Manchester, Connecticut, U.S., increased property values by six percent, while in a smaller study (Martin et al. 1989), tree cover in Austin, Texas, U.S., increased property values as much as 19%. In more recent studies, trees were also found to increase prop- erty values in Minnesota, U.S. (Sander et al. 2010) and Indi- ana, U.S. (Payton et al. 2008). Another study in Los Angeles, California, U.S., found residents would like additional trees but were not willing to pay for them (Saphores and Li 2012). 51 ©2013 International Society of Arboriculture
March 2013
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