# RECORD 0479cb200b63ed215ca478f6e8431d745dfbbdfd
# TITLE: The Influence of Trees and Landscaping on Rental Rates at Office Buildings
# YEAR: 2003.0  VENUE: Arboriculture & urban forestry  DOI: 10.48044/jauf.2003.032
# AUTHORS: Robert J. Laverne; K. Winson-Geideman
# PAGES: 10  CANDIDATE TABLE PAGES (1-based): [7, 8]
# 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.


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Journal of Arboriculture 29(5): September 2003
LITERATURE REVIEW
A variety of benefits and costs associated with urban forests
and landscape trees have been explored (Dwyer et al. 1992;
Nowak 1993; McPherson et al. 1999), including energy
conservation (McPherson and Rowntree 1993; Laverne and
Lewis 1996; Simpson and McPherson 1996; Simpson 1998),
ozone reduction (Nowak et al., in review), air quality (Scott et
al. 1998; Beckett et al. 2000), carbon sequestration (Ip 1996;
McPherson 1998; McPherson and Simpson 1999), storm-
water management (Xiao et al. 1998), social involvement
(Sommer et al. 1994; Sommer et al. 1995), and even the
impact of trees on the reduction of domestic violence
(Sullivan and Kuo 1996). Some of these benefits are quantifi-
able in terms of economics, while others can be measured
only by social or environmental values.
Increasing attention has been devoted in the economic
and real estate literature to the study and measurement of
the impact of environmental externalities on property values
(Des Rosiers et al. 2000; Taylor and Smith 2000). Much of
this research has focused on the value added through trees
and landscaping to residential properties. Peters (1971) was
among the first to do so when he reported that shade trees
contributed 19% (US$57,000) to the total appraised value
of a 2.8 ha parcel. Payne (1973) used traditional valuation
techniques to conclude that the market value of a single-
family home received a 7% premium due to arborescent
vegetation, provided there were less than 30 trees on the
lot. Morales (1980) used regression analysis to compare the
sales price of homes with a substantial amount of tree cover
to those with no tree cover. The values derived from the
regression analysis showed that good tree cover added
about 6% (US$2,686) to the property value of the homes.
Anderson and Cordell (1988) surveyed 844 single-family
residential properties in Athens, Georgia, U.S., and found
that landscaping with trees was associated with a 3.5% to
4.5% increase in sales price. Henry (1994) found that homes
that received an excellent landscaping rating from a local
landscaping professional could expect a sales price of about
4 to 5 percentage points higher (depending on the size of
the lot) than equivalent houses with good landscaping.
Homes that had a landscaping appeal far below (fair or
poor) neighboring homes with excellent landscapes had a
sales price 8% to 10% below equivalent homes with good
landscaping appeal. Based on these and other studies, the
Council of Tree and Landscape Appraisers (2000) suggests
that “well-maintained landscapes can contribute up to 20%
to the value of an improved residential property.”
Morales et al. (1976) combined factor analysis and multiple
linear regression techniques to study 60 residential sales in
Manchester, Connecticut. Four factors were developed that
explained 83% of the variation in the data. These explanatory
variables reflected location, house size, date of sale, and tree
cover, respectively. The authors concluded that good tree cover
could raise total sale price by as much as 6% to 9%.
Additional work was conducted by Ulrich (1986), who
hypothesized that the liking for urban scenes usually
increased when trees and other vegetation were present.
Referring to recent research in the field, he concluded that
trees and other vegetation could be linked directly to health
and, in turn, be related to economic benefits of visual
quality. Compared to research focused on trees in residen-
tial areas, there is relatively little literature that reports on
the effect of trees on commercial real estate. One study
conducted by Wolf (1999) presented 32 photographic
scenes of retail properties to two groups of participants
(business professionals and consumers) who were asked to
THE INFLUENCE OF TREES AND LANDSCAPING ON
RENTAL RATES AT OFFICE BUILDINGS
by Robert J. Laverne1 and Kimberly Winson-Geideman2
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.
Key Words. Benefits of trees; real estate value.


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Journal of Arboriculture 29(5): September 2003
because there was no evidence they were not representative
of the market. Scatter plots were examined for
heteroscedasticity and no fanning was evident. The sum-
mary statistics for each of the variables is found in Table 2.
SUMMARY OF FINDINGS
As can be seen from Table 3, the overall performance of the
model is theoretically sound. With eight structural charac-
teristics, eight lease variables, six location variables, and
eight landscaping variables, the model has an R2 of .7311.
Most of the regression coefficients are consistent in sign and
magnitude with theoretical expectations. A few notable
exceptions include those leases executed between 1998 and
2001, because the market shows some stagnation during
those years. The distance to the city center is also insignifi-
cant, as is building age. Log-linear forms of these variables
were tested but failed to provide better overall perfor-
mance. These variables remained in the equation because
they are consistent with other literature, as did property
size, transaction size, and number of floors. An attempt was
made to include the effective age of the building (2001 less
year of renovation) rather than the building’s physical age,
but inconsistency in the data excluded their use.
The inclusion of the landscaping variables provides
interesting results. As would be expected, landscaping with a
good aesthetic value added approximately 7% to the
average rental rate of a building. Good building shade was
also highly valued, positively impacting rental rates by about
7%. Each of these variables is significant at the 95% level,
and the percentage increase is consistent with residential
Continuous descriptors
Minimum
Maximum
Mean
Median
Std. dev.
Contract rate ($/ft2)
5.25
25
16.42
16
3.39
Contract rate ($/m2)
56.51
269.1
176.73
172.22
36.49
Effective tax rate ($/$1,000 valuation)
40.37
89.8
53.95
54.55
11.08
Property (building) size (ft2)
10926
426960
79630.59
48631
56155.70
Property (building) size (m2)
1015
39666
7398
4518
5217
Transaction (office) size (ft2)
240
134001
4860.62
2119
10594.22
Transaction (office) size (m2)
22
12449
452
197
984
Lease term (months)
5
120
49.67
60
25.40
Number of floors
1
15
4.58
4
2.72
Building age (years)
1
41
17.89
20
9.17
Distance/public square (mi)
6.2745
20.4674
13.21
14.399
2.87
Distance/public square (km)
10.10
32.94
21.26
23.17
4.61
Distance/highway interchange (mi)
0.1924
3.6723
0.91
0.4877
0.75
Distance/highway interchange (km)
0.31
5.91
1.47
0.78
1.21
Dummy variables
Mean
Total N
Mean
Total N
South
0.2444
66
Execution date 1995
0.1037
28
East
0.3481
94
Execution date 1996
0.1556
42
Southwest
0.3407
92
Execution date 1997
0.1667
45
West
0.0667
18
Execution date 1998
0.1222
33
Class A
0.6259
169
Execution date 1999
0.1444
39
Class B
0.3148
85
Execution date 2000
0.2037
55
Class C
0.0296
8
Execution date 2001
0.1037
28
Class unspecified
0.0259
7
Multiple leases
0.8148
220
Medical
0.0037
1
Gross lease
0.9296
251
Sublease
0.0407
11
Triple net lease
0.0704
19
Continuous landscaping variables
% of total # of leases (270)
Total N
Poor visual screen
0.437
118
Good visual screen
0.563
152
Poor noise barrier
0.685
185
Good noise barrier
0.311
84
Poor space definition
0.833
225
Good space definition
0.167
45
Poor aesthetics
0.741
200
Good aesthetics
0.259
70
Poor building shade
0.563
152
Good building shade
0.437
118
Table 2. Descriptive statistics.


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Laverne and Winson-Geideman: Influence of Trees and Landscaping on Rental Rates
literature. Landscaping that functions as a noise barrier or that
provides good space definition has no measurable impact, nor
does incremental increases in the amount of canopy cover,
turf, or flower beds. Somewhat surprising was the negative
impact of landscaping that provided a good visual screen.
Resulting in a loss in rent of approximately 7.5%, this number is
significant at the 95% level and has a t-value of –2.4080.
CONCLUSIONS
This report investigates the effect of landscaping on the base
rent of office buildings and is based on a detailed field
survey of 85 buildings consisting of 270 individual and
unique leases. All buildings are located within Cuyahoga
County and are considered a part of the Cleveland, Ohio,
metropolitan area. Conducted during the summer of 2001,
this survey focuses on the landscaping visible from all sides
of a property and captures 11 different attributes dealing
with ground and tree cover including functional aspects,
maturity, and amount of landscaping. Some of the attributes
were discarded due to the lack of theoretical justification or
explanatory impact. The remaining landscaping features
were then added to an array of structural, lease, and
distance variables to determine the effects of landscaping
and trees on office rental rates.
It appears that landscaping does have a positive impact
on rental rates, although quality is essential. As would be
expected, landscaping that is aesthetically pleasing provides
an increase in office rental rates (Vandell and Lane 1989).
This finding is consistent with residential literature that
shows a positive impact of 3% to 6% (Henry 1994). The
effect of good building shade is also
consistent with literature that shows a
6% to 9% increase in residential
property values (Morales et al. 1976).
Finally, the effect of trees that provide a
good visual screen negatively impacts
rental rates. At first blush, it appears
that this finding is contrary to intuitive
judgment, but when compared to
Payne’s (1973) conclusions regarding
excessive tree cover, it becomes much
more palatable. This finding may
indicate that office tenants value
visibility over privacy.
In conclusion, our research findings
are consistent with other literature on
the subject. The model shows a clear
relationship between quality landscap-
ing and office rental rates.
SUGGESTIONS FOR FURTHER
RESEARCH
The current study has some limitations,
as conclusive as it might appear. First,
the data set could be expanded to
include office space represented by
firms other than Grubb & Ellis. The use
of a single broker could produce
potentially biased results, although the
proprietary nature of the data effec-
tively limits the ability to obtain infor-
mation from a variety of sources.
Second, a comparative study of the
effects substantial, mature tree cover to
sites with no tree cover should also be
conducted, rather than attempting to
draw conclusions from a percentage
increase in the amount of landscaping.
This study was unable to define “substan-
Variable
Value
Std. error
t value
Pr (>|t|)
Intercept
228.1931
23.3508
9.7724
0.0000
Effective tax rate
–1.0175
0.2017
–5.0434
0.0000
South
–3.9065
6.6429
–0.5881
0.5570
Southwest
–38.1453
6.1350
–6.2176
0.0000
West
–40.2846
7.2999
–5.5185
0.0000
Class B
–27.1664
4.8036
–5.6554
0.0000
Class C
–56.5621
9.2304
–6.1278
0.0000
Class unspecified
–80.5410
9.6517
–8.3448
0.0000
Medical
11.0042
21.6879
0.5074
0.6124
Sublease
–24.2143
6.6888
–3.6201
0.0004
Property size
0.0001
0.0005
0.2231
0.8236
Transaction size
–0.0029
0.0015
–1.9624
0.0509
Execution date 1995
–19.4004
5.0509
–3.841
0.0002
Execution date 1996
–17.7458
4.7636
–3.7253
0.0002
Execution date 1997
–10.7633
4.3868
–2.4536
0.0149
Execution date 1998
–0.6193
4.8579
–0.1275
0.8987
Execution date 1999
7.7225
4.6120
1.6744
0.0954
Execution date 2001
5.9940
4.9111
1.2205
0.2235
Lease term
0.1427
0.0621
2.2963
0.0225
Multiple leases
3.6804
4.0746
0.9033
0.3673
Triple net lease
–39.8277
6.1043
–6.5246
0.0000
Number of floors
1.5374
1.1703
1.3137
0.1902
Building age
–0.1662
0.2331
–0.7132
0.4764
Distance/public square
0.8813
0.6671
1.321
0.1878
Distance/highway interchange
4.0891
1.8794
2.1757
0.0306
Good visual screen
–14.0094
5.8179
–2.408
0.0168
Good noise barrier
5.1835
3.5697
1.4521
0.1478
Good space definition
0.2194
5.6919
0.0386
0.9693
Good building shade
11.6008
5.3670
2.1615
0.0317
Good aesthetics
12.4585
6.1369
2.0301
0.0435
Percent canopy cover
–0.3990
0.3503
–1.1389
0.2559
Percent beds
0.5159
0.4283
1.2045
0.2296
Percent turf
–0.0514
0.2058
–0.2497
0.8030
Residual standard error: 20.16 on 237 d.f.
Multiple R2: 0.7311.
F–statistic: 20.13 on 32 and 237 d.f.; the p–value is 0.
Table 3. Regression results.


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Journal of Arboriculture 29(5): September 2003
tial” due to a lack of variation in the amount of canopy cover at
the observed sites, although we were able to qualify the
effectiveness of that cover. Third, a study of neighborhood
characteristics may also provide insight into the importance of
landscaping and tree cover. For instance, sites located in
heavily wooded neighborhoods or near parks may charge
higher rents than those that are not. Finally, a cost–benefit
analysis should be conducted to determine the effect of
planting and maintenance costs on the overall model.
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