62 Kellogg et al.: Tree Preservation in a Weak Land Development Market Region RESULTS AND DISCUSSION Quantitative Analysis: Housing Value and Canopy Regression analysis was used to explore the re- lationship between tree canopy and the price of newly constructed housing in and all the subsequent study area between 2009 and 2011. The model- ing strategy was to first identify a satisfac- tory base model. The variables included in the base model, regres- sion analyses, fall into three general categories: 1. Data about the house: year built, lot size, living area, rooms, and baths. 2. Data about the neighborhood: density, demographics, housing conditions, school district quality. 3. Data about location and accessibility: county, proximity to jobs, highways, and the county seat. Once a base model was identified, research- ers explored the price impact of tree canopy in several ways. It is an important modeling distinc- tion that the regression process in this regard was exploratory. Researchers did not enter the model- ing process to test a well-formulated expectation of the manner in which, or the degree to which, trees or tree canopy might impact house price. In particular, while previous research identifies the advantages of having some canopy, the current study didn’t anticipate those advantages holding equally throughout the full range of possible canopy (that is, 1% to 100% canopy). Thus, several mod- els were tested. The two primary canopy variables tested were the square footage of the lot that was covered by tree canopy, and the percent of the lot that was covered by tree canopy, using data derived through NAIP. A description of the data used in the regression modeling is given in Table 2, along with their sources, the abbreviations used in the regression results, and their descriptive statistics. Present here are three variations on the modeling exercise. Model A estimates the relationship between canopy and housing value across the entire study area in aggregate. Model B explores this relationship county by county. Model C investigates disparate impacts across large and small lots to capture the effect on compact and non-compact development. ©2017 International Society of Arboriculture and Model A (canopy size and canopy coverage) Table 3 shows the results of Model A, the first to include measures of tree canopy. County indica- tor variables are included to account for differ- ences in the average price level by county. The structural characteristics of the house perform as expected. The size of the lot, the amount of livable area in the house, the number of rooms, and the number of bathrooms are all positive and significant. This means that the greater the amount of these variables, the higher the associ- ated sale price. Neighborhood variables are in- cluded to account for the influence of the various conditions that surround the sold house. Higher neighborhood education levels are associated with higher prices, which could reflect an under- lying relationship between education and income. The presence of vacant housing is not signifi- cant in the model, and the impact of renter occu- pancy, although small, is positive and significant. Interestingly, in the context of new development, the higher the neighborhood population den- sity, the higher the selling price of the home. This is likely contrary to the perception of sprawling, low density, high priced, exurban development, in that all things being equal, new construction yielded a higher price in higher density neighbor- hoods. It is important to note, however, this does not reflect the density of the housing development itself, but that of its entire neighborhood. And given the timing of the census data, relative to the time of the study, it is likely that the density mea- surement excludes the newly constructed home. The measures of school district quality are aligned qualitatively, relative to the leſt-out cat- egory of Academic Watch/Continuous Improve- ment, but their significance varies. Similarly, the access to jobs measure is positive but not significant, while access to the nearest high- way ramp and county seat are both significant. The analysis shows that both measures of canopy used—square footage and percentage—are signifi- cant. The square footage of canopy is positive and significant, indicating that tree canopy is valued. Higher amounts of tree canopy are associated with higher sales prices. Conversely, the percent- age of the lot covered by canopy is negative. Taken together, these forces work in opposition to each other: for a particular lot, the more canopy it
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