1# RECORD 0479cb200b63ed215ca478f6e8431d745dfbbdfd2# TITLE: The Influence of Trees and Landscaping on Rental Rates at Office Buildings3# YEAR: 2003.0 VENUE: Arboriculture & urban forestry DOI: 10.48044/jauf.2003.0324# AUTHORS: Robert J. Laverne; K. Winson-Geideman5# PAGES: 10 CANDIDATE TABLE PAGES (1-based): [7, 8]6# ABSTRACT: The environmental and economic benefits of trees have been studied relative to a variety of interests including their influence on real estate value. This study investigates the effect of trees and landscaping on office rental rates, based on a comparison of 85 office buildings that comprise 270 individual and unique leases in the Cleveland, Ohio, U.S., metropolitan area. Data that describe the quantity, functionality, and quality of landscaping were gathered from each of the buildings including landscape maturity, the percentage of ground cover (trees, turf, pavement, etc.), and functional attributes (building shade, noise buffer, space definition, recreation, visual screen, and aesthetics). Multiple regression analysis in the form of a hedonic equation was conducted to isolate the economic effects of landscaping. Office attribute data including lease information, physical attributes, and distance variables were used to calibrate the basic model, and landscaping data were added to the hedonic equation to determine if individual and/or interactive variables had any effect on contracted rental rates. The individual analysis of the variables showed a strong positive effect for those buildings with good landscaping aesthetics and building shade provided by trees. Conversely, landscaping that provides a good visual screen produced significant negative impacts on rental rates.789===== PAGE 1 (score 15, numeric-paren 0, table-words 0, var-words 15) =====101128112Journal of Arboriculture 29(5): September 200313LITERATURE REVIEW14A variety of benefits and costs associated with urban forests15and landscape trees have been explored (Dwyer et al. 1992;16Nowak 1993; McPherson et al. 1999), including energy17conservation (McPherson and Rowntree 1993; Laverne and18Lewis 1996; Simpson and McPherson 1996; Simpson 1998),19ozone reduction (Nowak et al., in review), air quality (Scott et20al. 1998; Beckett et al. 2000), carbon sequestration (Ip 1996;21McPherson 1998; McPherson and Simpson 1999), storm-22water management (Xiao et al. 1998), social involvement23(Sommer et al. 1994; Sommer et al. 1995), and even the24impact of trees on the reduction of domestic violence25(Sullivan and Kuo 1996). Some of these benefits are quantifi-26able in terms of economics, while others can be measured27only by social or environmental values.28Increasing attention has been devoted in the economic29and real estate literature to the study and measurement of30the impact of environmental externalities on property values31(Des Rosiers et al. 2000; Taylor and Smith 2000). Much of32this research has focused on the value added through trees33and landscaping to residential properties. Peters (1971) was34among the first to do so when he reported that shade trees35contributed 19% (US$57,000) to the total appraised value36of a 2.8 ha parcel. Payne (1973) used traditional valuation37techniques to conclude that the market value of a single-38family home received a 7% premium due to arborescent39vegetation, provided there were less than 30 trees on the40lot. Morales (1980) used regression analysis to compare the41sales price of homes with a substantial amount of tree cover42to those with no tree cover. The values derived from the43regression analysis showed that good tree cover added44about 6% (US$2,686) to the property value of the homes.45Anderson and Cordell (1988) surveyed 844 single-family46residential properties in Athens, Georgia, U.S., and found47that landscaping with trees was associated with a 3.5% to484.5% increase in sales price. Henry (1994) found that homes49that received an excellent landscaping rating from a local50landscaping professional could expect a sales price of about514 to 5 percentage points higher (depending on the size of52the lot) than equivalent houses with good landscaping.53Homes that had a landscaping appeal far below (fair or54poor) neighboring homes with excellent landscapes had a55sales price 8% to 10% below equivalent homes with good56landscaping appeal. Based on these and other studies, the57Council of Tree and Landscape Appraisers (2000) suggests58that “well-maintained landscapes can contribute up to 20%59to the value of an improved residential property.”60Morales et al. (1976) combined factor analysis and multiple61linear regression techniques to study 60 residential sales in62Manchester, Connecticut. Four factors were developed that63explained 83% of the variation in the data. These explanatory64variables reflected location, house size, date of sale, and tree65cover, respectively. The authors concluded that good tree cover66could raise total sale price by as much as 6% to 9%.67Additional work was conducted by Ulrich (1986), who68hypothesized that the liking for urban scenes usually69increased when trees and other vegetation were present.70Referring to recent research in the field, he concluded that71trees and other vegetation could be linked directly to health72and, in turn, be related to economic benefits of visual73quality. Compared to research focused on trees in residen-74tial areas, there is relatively little literature that reports on75the effect of trees on commercial real estate. One study76conducted by Wolf (1999) presented 32 photographic77scenes of retail properties to two groups of participants78(business professionals and consumers) who were asked to79THE INFLUENCE OF TREES AND LANDSCAPING ON80RENTAL RATES AT OFFICE BUILDINGS81by Robert J. Laverne1 and Kimberly Winson-Geideman282Abstract. The environmental and economic benefits of trees have83been studied relative to a variety of interests including their84influence on real estate value. This study investigates the effect of85trees and landscaping on office rental rates, based on a comparison86of 85 office buildings that comprise 270 individual and unique87leases in the Cleveland, Ohio, U.S., metropolitan area. Data that88describe the quantity, functionality, and quality of landscaping89were gathered from each of the buildings including landscape90maturity, the percentage of ground cover (trees, turf, pavement,91etc.), and functional attributes (building shade, noise buffer, space92definition, recreation, visual screen, and aesthetics). Multiple93regression analysis in the form of a hedonic equation was conducted94to isolate the economic effects of landscaping. Office attribute data95including lease information, physical attributes, and distance96variables were used to calibrate the basic model, and landscaping97data were added to the hedonic equation to determine if individual98and/or interactive variables had any effect on contracted rental99rates. The individual analysis of the variables showed a strong100positive effect for those buildings with good landscaping aesthetics101and building shade provided by trees. Conversely, landscaping that102provides a good visual screen produced significant negative impacts103on rental rates.104Key Words. Benefits of trees; real estate value.105106107===== PAGE 7 (score 25, numeric-paren 0, table-words 4, var-words 10) =====108109287110Journal of Arboriculture 29(5): September 2003111because there was no evidence they were not representative112of the market. Scatter plots were examined for113heteroscedasticity and no fanning was evident. The sum-114mary statistics for each of the variables is found in Table 2.115SUMMARY OF FINDINGS116As can be seen from Table 3, the overall performance of the117model is theoretically sound. With eight structural charac-118teristics, eight lease variables, six location variables, and119eight landscaping variables, the model has an R2 of .7311.120Most of the regression coefficients are consistent in sign and121magnitude with theoretical expectations. A few notable122exceptions include those leases executed between 1998 and1232001, because the market shows some stagnation during124those years. The distance to the city center is also insignifi-125cant, as is building age. Log-linear forms of these variables126were tested but failed to provide better overall perfor-127mance. These variables remained in the equation because128they are consistent with other literature, as did property129size, transaction size, and number of floors. An attempt was130made to include the effective age of the building (2001 less131year of renovation) rather than the building’s physical age,132but inconsistency in the data excluded their use.133The inclusion of the landscaping variables provides134interesting results. As would be expected, landscaping with a135good aesthetic value added approximately 7% to the136average rental rate of a building. Good building shade was137also highly valued, positively impacting rental rates by about1387%. Each of these variables is significant at the 95% level,139and the percentage increase is consistent with residential140Continuous descriptors141Minimum142Maximum143Mean144Median145Std. dev.146Contract rate ($/ft2)1475.251482514916.42150161513.39152Contract rate ($/m2)15356.51154269.1155176.73156172.2215736.49158Effective tax rate ($/$1,000 valuation)15940.3716089.816153.9516254.5516311.08164Property (building) size (ft2)1651092616642696016779630.591684863116956155.70170Property (building) size (m2)171101517239666173739817445181755217176Transaction (office) size (ft2)1772401781340011794860.62180211918110594.22182Transaction (office) size (m2)1832218412449185452186197187984188Lease term (months)189519012019149.671926019325.40194Number of floors1951196151974.5819841992.72200Building age (years)20112024120317.89204202059.17206Distance/public square (mi)2076.274520820.467420913.2121014.3992112.87212Distance/public square (km)21310.1021432.9421521.2621623.172174.61218Distance/highway interchange (mi)2190.19242203.67232210.912220.48772230.75224Distance/highway interchange (km)2250.312265.912271.472280.782291.21230Dummy variables231Mean232Total N233Mean234Total N235South2360.244423766238Execution date 19952390.103724028241East2420.348124394244Execution date 19962450.155624642247Southwest2480.340724992250Execution date 19972510.166725245253West2540.066725518256Execution date 19982570.122225833259Class A2600.6259261169262Execution date 19992630.144426439265Class B2660.314826785268Execution date 20002690.203727055271Class C2720.02962738274Execution date 20012750.103727628277Class unspecified2780.02592797280Multiple leases2810.8148282220283Medical2840.00372851286Gross lease2870.9296288251289Sublease2900.040729111292Triple net lease2930.070429419295Continuous landscaping variables296% of total # of leases (270)297Total N298Poor visual screen2990.437300118301Good visual screen3020.563303152304Poor noise barrier3050.685306185307Good noise barrier3080.31130984310Poor space definition3110.833312225313Good space definition3140.16731545316Poor aesthetics3170.741318200319Good aesthetics3200.25932170322Poor building shade3230.563324152325Good building shade3260.437327118328Table 2. Descriptive statistics.329330331===== PAGE 8 (score 34, numeric-paren 0, table-words 4, var-words 13) =====332333288334Laverne and Winson-Geideman: Influence of Trees and Landscaping on Rental Rates335literature. Landscaping that functions as a noise barrier or that336provides good space definition has no measurable impact, nor337does incremental increases in the amount of canopy cover,338turf, or flower beds. Somewhat surprising was the negative339impact of landscaping that provided a good visual screen.340Resulting in a loss in rent of approximately 7.5%, this number is341significant at the 95% level and has a t-value of –2.4080.342CONCLUSIONS343This report investigates the effect of landscaping on the base344rent of office buildings and is based on a detailed field345survey of 85 buildings consisting of 270 individual and346unique leases. All buildings are located within Cuyahoga347County and are considered a part of the Cleveland, Ohio,348metropolitan area. Conducted during the summer of 2001,349this survey focuses on the landscaping visible from all sides350of a property and captures 11 different attributes dealing351with ground and tree cover including functional aspects,352maturity, and amount of landscaping. Some of the attributes353were discarded due to the lack of theoretical justification or354explanatory impact. The remaining landscaping features355were then added to an array of structural, lease, and356distance variables to determine the effects of landscaping357and trees on office rental rates.358It appears that landscaping does have a positive impact359on rental rates, although quality is essential. As would be360expected, landscaping that is aesthetically pleasing provides361an increase in office rental rates (Vandell and Lane 1989).362This finding is consistent with residential literature that363shows a positive impact of 3% to 6% (Henry 1994). The364effect of good building shade is also365consistent with literature that shows a3666% to 9% increase in residential367property values (Morales et al. 1976).368Finally, the effect of trees that provide a369good visual screen negatively impacts370rental rates. At first blush, it appears371that this finding is contrary to intuitive372judgment, but when compared to373Payne’s (1973) conclusions regarding374excessive tree cover, it becomes much375more palatable. This finding may376indicate that office tenants value377visibility over privacy.378In conclusion, our research findings379are consistent with other literature on380the subject. The model shows a clear381relationship between quality landscap-382ing and office rental rates.383SUGGESTIONS FOR FURTHER384RESEARCH385The current study has some limitations,386as conclusive as it might appear. First,387the data set could be expanded to388include office space represented by389firms other than Grubb & Ellis. The use390of a single broker could produce391potentially biased results, although the392proprietary nature of the data effec-393tively limits the ability to obtain infor-394mation from a variety of sources.395Second, a comparative study of the396effects substantial, mature tree cover to397sites with no tree cover should also be398conducted, rather than attempting to399draw conclusions from a percentage400increase in the amount of landscaping.401This study was unable to define “substan-402Variable403Value404Std. error405t value406Pr (>|t|)407Intercept408228.193140923.35084109.77244110.0000412Effective tax rate413–1.01754140.2017415–5.04344160.0000417South418–3.90654196.6429420–0.58814210.5570422Southwest423–38.14534246.1350425–6.21764260.0000427West428–40.28464297.2999430–5.51854310.0000432Class B433–27.16644344.8036435–5.65544360.0000437Class C438–56.56214399.2304440–6.12784410.0000442Class unspecified443–80.54104449.6517445–8.34484460.0000447Medical44811.004244921.68794500.50744510.6124452Sublease453–24.21434546.6888455–3.62014560.0004457Property size4580.00014590.00054600.22314610.8236462Transaction size463–0.00294640.0015465–1.96244660.0509467Execution date 1995468–19.40044695.0509470–3.8414710.0002472Execution date 1996473–17.74584744.7636475–3.72534760.0002477Execution date 1997478–10.76334794.3868480–2.45364810.0149482Execution date 1998483–0.61934844.8579485–0.12754860.8987487Execution date 19994887.72254894.61204901.67444910.0954492Execution date 20014935.99404944.91114951.22054960.2235497Lease term4980.14274990.06215002.29635010.0225502Multiple leases5033.68045044.07465050.90335060.3673507Triple net lease508–39.82775096.1043510–6.52465110.0000512Number of floors5131.53745141.17035151.31375160.1902517Building age518–0.16625190.2331520–0.71325210.4764522Distance/public square5230.88135240.66715251.3215260.1878527Distance/highway interchange5284.08915291.87945302.17575310.0306532Good visual screen533–14.00945345.8179535–2.4085360.0168537Good noise barrier5385.18355393.56975401.45215410.1478542Good space definition5430.21945445.69195450.03865460.9693547Good building shade54811.60085495.36705502.16155510.0317552Good aesthetics55312.45855546.13695552.03015560.0435557Percent canopy cover558–0.39905590.3503560–1.13895610.2559562Percent beds5630.51595640.42835651.20455660.2296567Percent turf568–0.05145690.2058570–0.24975710.8030572Residual standard error: 20.16 on 237 d.f.573Multiple R2: 0.7311.574F–statistic: 20.13 on 32 and 237 d.f.; the p–value is 0.575Table 3. Regression results.576577578===== PAGE 9 (score 14, numeric-paren 0, table-words 0, var-words 14) =====579580289581Journal of Arboriculture 29(5): September 2003582tial” due to a lack of variation in the amount of canopy cover at583the observed sites, although we were able to qualify the584effectiveness of that cover. Third, a study of neighborhood585characteristics may also provide insight into the importance of586landscaping and tree cover. For instance, sites located in587heavily wooded neighborhoods or near parks may charge588higher rents than those that are not. Finally, a cost–benefit589analysis should be conducted to determine the effect of590planting and maintenance costs on the overall model.591LITERATURE CITED592Anderson, L.M., and H.K. Cordell. 1988. Influence of trees593on residential property values in Athens, Georgia (USA):594A survey based on actual sales prices. Landscape Urban595Plann. 15:153–164.596Beckett, K.P., P. Freer-Smith, and G. Taylor. 2000. Effective597tree species for local air-quality management. J. Arboric.59826:12–19.599Bollinger, C., K.R. Ihlanfeldt, and D.R. Bowes. 1998. Spatial600variation in office rents within the Atlanta region. Urban601Stud. 35(7):1097–1118.602Clapp, J.M. 1980. The intrametropolitan location of office603activities. J. Region. Sci. 20:387–399.604Council of Tree and Landscape Appraisers. 2000. Guide for605Plant Appraisal (9th ed.). International Society of606Arboriculture. Champaign, IL. 143 pp.607Des Rosiers, F., M. Theriault, and P. Villeneuve. 2000. Sorting608out access and neighbourhood factors in hedonic price609modeling. J. Property. Invest. Fin. 18(3):291–315.610Dwyer, J.F., E.G. McPherson, H.W. Schroeder, and R.W.611Rowntree. 1992. Assessing the benefits and costs of the612urban forest. J. Arboric. 18:227–234.613Glasock, J.L., S. Jahanian, and C.F. Sirmans. 1990. An614analysis of office market rents: Some empirical evidence.615J. Am. Real Estate Urban Econ. Assoc. 105–119.616Henry, M.S. 1994. The contribution of landscaping to the price617of single family houses: A study of home sales in Greenville,618South Carolina. J. Environ. Hortic. 12(2):65–70.619Ip, D.W. 1996. Community tree planting: Early survival and620carbon sequestering potential. J. Arboric. 22:222–228.621Laverne, R.J., and G.M. Lewis. 1996. The effect of vegetation622on residential energy use in Ann Arbor, Michigan. J.623Arboric. 22:234–243.624Martin, C.W., R.C. Maggio, and D.B. Appel. 1989. The625contributory value of trees to residential property in the626Austin, Texas, metropolitan area. J. Arboric. 15(3):72–75.627McPherson, E.G. 1998. Atmospheric carbon dioxide reduction628by Sacramento’s urban forest. J. Arboric. 24:215–223.629McPherson, E.G., and R.A. Rowntree. 1993. Energy conservation630potential of urban tree planting. J. Arboric. 19:321–331.631McPherson, E.G., and J.R. Simpson. 1999. Carbon Dioxide632Reduction Through Urban Forestry: Guidelines for633Professional and Volunteer Tree Planters. USDA Forest634Service General Technical Report PSW-GTR-171. 237 pp.635McPherson, E.G. J.R. Simpson, P.J. Peper, and Q. Xiao. 1999.636Benefit–cost analysis of Modesto’s urban forest. J. Arboric.63725:235–248.638Mills, Edwin S. 1992. Office rent determinants in the Chicago639area. J. Am Real Estate Urban Econ. Assoc. 20(2).640Morales, D.J. 1980. The contribution of trees to residential641property value. J. Arboric. 6(11)305–308.642Morales, D., B.N. Boyce, and R.J. Favretti. 1976. The contribu-643tion of trees to residential property value: Manchester,644Connecticut. ASA Valuat. October–November.645Nowak, D.J. 1993. Compensatory value of an urban forest:646An application of the tree-value formula. J. Arboric.64719:173–177.648Nowak, D.J., K.L. Civerolo, S.T. Rao, G. Sistla, C.J. Luley, and649D.E. Crane. (In review). The impact of urban trees on650ozone in the Northeastern United States. Atmos. Environ.651Payne, B.R. 1973. The twenty-nine tree home improvement652plan. Nat. Hist. 82:74–75.653Peters, L. 1971. Shade and ornamental tree evaluation. J.654For. 69:411–413.655Rosen, S. 1974. Hedonic prices and implicit markets: product656differentiation in pure competition. J. Polit. Econ.65782(1):3455.658Schmitz, W.G. 1988. The name of the game in office space.659Commerc. Invest. Real Estate J. May/June.660Scott, K.I., E.G. McPherson, and J.R. Simpson. 1998. Air661pollution uptake by Sacramento’s urban forest. J. Arboric.66224:224–234.663Simpson, J.R. 1998. Urban forest impacts on regional664heating and cooling energy use: Sacramento County case665study. J. Arboric. 24:201–214.666Simpson, J.R. and E.G. McPherson. 1996. Potential of tree667shade for reducing residential energy use in California. J.668Arboric. 22:10–18.669Sommer, R., F. Learey, J. Summit, and M. Tirrell. 1994. The670social benefits of resident involvement in tree planting. J.671Arboric. 20:170–175.672Sommer, R., J. Summit, F. Learey, and M. Tirrell. 1995. Social673and educational benefits of a community shade tree674program: A replication. J. Arboric. 21:260.675Sullivan, W.C. and F.E. Kuo. 1996. Do trees strengthen urban676communities, reduce domestic violence? Arborist News6775(3):33–34.678Taylor, L., and V.K. Smith. 2000. Environmental amenities as679a source of market power. Land Econ. 76(4):550–568.680Ulrich, R.S. 1986. Human responses to vegetation and681landscapes. Landscape Urban Plann. 13:29–44.682Vandell, K.D., and J.S. Lane. 1989. The economics of683architecture and urban design: Some preliminary684findings. J. Am. Real Estate Urban Econ. Assoc. 17(2).685Webb, R., and J.D. Fisher. 1996. Development of an effective686rent (lease) index for the Chicago CBD. J. Urban Econ.68739(1):1–19.688