CSIS Discussion Paper No. 67

Economic Value of Urban Landscapes

November 2005

Xiaolu Gao1 and Yasushi Asami2

1

Corresponding author. Institute of Geographical Sciences and Natural Resources Research,

Chinese Academy of Sciences. Jia 11, Datun Road, Chaoyang District, Beijing 100101,
China. Email: gaoxl@igsnrr.ac.cn
2 Center for Spatial Information Science, University of Tokyo. Kashiwanoha 5-1-5, Kashiwa,
Chiba 277-8568, Japan. Email: asami@csis.u-tokyo.ac.jp

1Economic Value of Urban Landscapes

Abstract
To support decision-makings in landscape management and related policies, anobjective methodology for evaluating urban landscapes is necessary. In particular, it isanticipated to show the economic value of urban landscapes so that the social benefits ofregulations on landscapes can be verified. This paper developed a three-step framework
for assessing the economic value of urban landscapes, i.e. conducting standardizedlandscape survey, extracting critical evaluation factors, and identifying the marginal
effect of the factors with a hedonic approach. Using Tokyo and Kitakyushu city data,
which are typical of large metropolitan areas and local cities, it was empiricallydemonstrated that the compatibility of buildings and the greenery of neighborhood are
distinctively perceived factors, and in both cities, compatibility and greenery weresignificant determinants of land prices. Although Tokyo and Kitakyushu city differ inlandscape features and many other ways, the economic impacts of urban landscapes
were very alike. These results empirically confirmed the usefulness of the three-step
approach for evaluating urban landscapes, and because the improvement ofcompatibility and greenery require for the collaboration of residents, the results, in a
general sense, implied the importance of coordination at neighborhood level.

Keywords: urban landscape, economic value, compatibility, greenery21. Introduction
In recent years, there is a growing concern for the beauty of urban landscapes.
Improving urban landscapes is taken as an effective way for enhancing the competitive
ability of cities and a way for revitalizing old dilapidated areas. A variety of public
policies have been made to fulfill this purposes, e.g. land use regulations on the height
and appearance of buildings, landscape controls on views and advertisement, anddesignation of conservation areas (Nishimura et al., 2000). An example in Japan is theimplementation of ‘Landscape Laws’ in 2004. The laws provided legal foundation forthe protection of the rights of urban landscapes, and through a so-called ‘landscapecertification’ program, human judgments on the comprehensive quality of landscapes
were legitimated. It is widely accepted that the above policies have deep influences on
urban planning and landscape management. However, in practice, the evaluations ofurban landscapes are often mixtures of personal views, and there is still no formal
framework of evaluation. For these reasons, the effectiveness of the policies ispotentially limited. Therefore, it is of critical significance to establish an objective
methodology for evaluating urban landscapes.
In particular, since planning regulations on the heights, shapes, or locations ofbuildings out of landscape reasons will inevitably limit the development right of land, it
is strongly anticipated to clarify the social benefits of regulations and quantitatively
show the benefits. This necessitates analyses on the economic values of urban
landscape.
So far, quite a few frameworks for landscape evaluation have been raised. For
example, Gómez-Sal et al. (2003) proposed to evaluate landscape from ecological,
productive, economic, social and cultural perspectives and defined scenarios incomparison with which particular landscape planning or management projects can beevaluated. Prato (2000) proposed to evaluate the sustainability of landscapemanagement plans by considering the biophysical and economic attributes of plansemphasized differently by private and public decision-makers. Angileri and Toccolini

3

(1993) assessed the visual quality of rural landscapes for which they defined fiveaspects in landscape perceptions, i.e. relief, vegetation, density of built-up areas, size ofcultivated fields and presence of character elements such as hedgerows or small woods.
Based on field survey, they drew landscape evaluation maps for study areas. These
works strongly suggest that empirical studies are indispensable in landscapeevaluations.
In the literature, a big variety of approaches were employed in empirical works, forexample, evaluations for the physical space and compositions of specific historical sites(Carter and Bramley, 2002; Coeterier, 2002; Lichfield, 1988), evaluations of urban
landscapes with semantic differential technique in terms of human perceptions(Garcia-Mira et al., 1997; Green, 1999; Imamoglu, 2000), evaluations for the quality ofdesign with respect to physical criteria of landscape (Fukahori and Kubota, 2003;
İpekoğlu, 2006), and so on. In addition, some addressed the economic values oflandscapes, for example, with psychological experiment (Fukahori and Kubota, 2003),
contingent valuation method (Willis and Garrod, 1993), etc. Methodologically speaking,the above approaches have been well established.
However, although these approaches allow for evaluations from specific aspects, it
is not easy to use these methods to comprehensively evaluate urban landscapes, and theresults are sometimes not easy for understanding. On the other hand, widely usedmethodologies for evaluating the economic impact of environmental goods such ashedonic approach was rarely used in the present literature of urban landscape.
In this paper, we attempt to bridge this gap. The main concern of this study is
twofold. The first is to identify evaluating structure of urban landscape, and toempirically investigate whether the values of urban landscape are reflected by landmarket. This requires for a framework for objectively measuring the ‘beauty’ of urban
space, as well as an analytical framework for associating the perceptions for physicalenvironments with economic values. The second concern relates to the generalization of
the evaluation method. Since the perception of urban landscape is likely to differ among4

cities, districts, and individuals with different preferences, to what degree amethodology is valid and applicable is a critical issue. Through this work, we want toestablish an empirical approach for assessing urban landscape.
We selected Tokyo and Kitakyushu city for investigation, which are typical of large
metropolitan areas and local central cities, respectively. The two cities havewell-managed data on planning and urban environment, most of which are integrated
with GIS.
A sample of residential sites was chosen in each city. We conducted standardized
surveys on the landscape of these areas, i.e. in the survey subjective factors that may
lead to diverse results were controlled as much as possible. Upon analysis, it was
identified that compatibility and greenery are most distinctively perceived factors inurban landscapes, and in addition, they are significant determinants of residential land
prices. Comparison analysis of the Tokyo and Kitakyushu samples revealed that theirevaluation structures are quite alike, though the two cities have very different
geographical and social economic conditions. This strongly suggests the objectivity andusefulness of the developed method for assessing urban landscapes.

2. Methodology
A three-step approach was developed.
First, design a framework, by which one can qualitatively catch the physicalcharacteristics of urban landscapes, and then survey on sample sites and theirsurrounding areas to collect objective data.
Second, extract critical factors for landscape evaluation by employing principal
component analysis (PCA).
Lastly, adopt a hedonic approach to examine if the principal characteristics ofurban landscape are critical determinants of land prices.

2.1 Sample and data

5

In Tokyo, the sample sites were drawn from 1996-97 issues of Weekly Housing
Information, which provided information on a large amount of houses and lands for sale.
The sample was limited to transacted vacant land properties in western part of the 23
wards of Tokyo. The study area covers nine wards (Suginami, Nerima, Shinjuku,
Shibuya, Nakano, Setagaya, Meguro, Ota, and Shinagawa). We chose only a part ofTokyo for the convenience of survey, and because transacted sites were relatively
densely distributed in this area. In addition, the sample sites were confined to land use
zones designated mainly for residential use. The size of sample with complete data is272. Among them, 203 sites are in low-story residential zones, 37 in high-medium
residential zones and 32 in residential zones.
The sample in Kitakyushu city was drawn from an administration survey databasefor 1333 properties purchased in fiscal year 2003. Similar criteria were used as that inTokyo, which yielded a sample of 187 vacant sites distributed across the whole city. The
proportion of samples in low-story residential zones, high-medium residential zones andresidential zones is approximately 2:1:2.
The databases for the two samples were carefully constructed. First, both include
the price of the sites. For the Tokyo sample, Weekly Housing Information provided thefinal list prices of each. The administration survey data of Kitakyushu city provided theprices given by purchasers in questionnaire. Since they are not real prices, the accuracy
is a bit low, but we assumed that they were close to real prices. Besides, the data on theTokyo sample from the database of Gao et al. (2005) was used. They included detailed
information on sample lots such as sizes, shapes, the width and direction of front roads,
locations and accessibility to public transportation, a variety of neighborhood
environmental attributes, as well as land use and social economic indices of chome (i.e.,
district) such as building density, gross floor-area-ratio, population density, proportionof wooden-made structures, and so on. For Kitakyushu city, the living environment data
provided by Kitakyushu city, including the ratings on 18 aspects of living environmentof 1488 chomes, were used in addition to the administrative survey data.

6

A list of the above data can be found in Appendix 1.

2.2 Landscape survey
Table 1 shows the landscape survey sheet. With regard to the content, we learned
from Arai (2001), which proposed to evaluate urban landscape from three aspects:
neighborhood scene, street scene and planning activities (e.g. public involvement inlocal affairs and the implementation of ‘district planning’ and covenant).
In this survey, we focused more on evaluations of neighborhoods and streets, withspecific interest on the physical aspect of urban environment and for which data areeasy to be collected by observation. Along this line, we designed an 11-factor evaluation
system shown in Table 1. The factors beginning with ‘A’ are indicators ofneighborhoods and those with ‘B’ are indicators of streets. Furthermore, each factor has
several specific items, each rated with points (+1, 0, -1, etc.). The indices of the 11
factors were aggregated from the points.
Table 1 Factors for evaluating urban landscape
Factors
A1 Continuity of external walls(+1, 0, -1)
A2 Conformity in colors and materials(+1, 0, -1)
A3 Compatibility of buildings styles(+1, 0, -1)
A4 Beauty of skylines constructed by buildings(+3, +2, +1, 0, -1)

A5 Openness and the scale of buildings(+1, 0, -1, -2)
A6 Visually nice and continuous greenery(+2, +1, 0)
B1 Greenery of walls and trees(+1, 0, -1)

Items
Continuous
Average levelNot continuous
Harmonious
Average level
not harmonious
Sharing common features
Average level
Little common features
Building height in order
Similar roof shape
beautiful rhythm with other buildings
Skylines in disorder
Open and relaxedCompressed street space
Dull without change
forming network
Visual continuous
Continuously greened walls
Average level
Mostly concrete blocks walls

7

Point
+1
0
-1
+1
0
-1
+1
0
-1
+1
+1
+1
-1
+1
-1
-1
+1
+1
+1
0
-1

B2 Greenery of open pedestrian spaces(+2, +1, 0, -1)
B3 Favorable pedestrian space(+1, 0, -1, -2, -3)

B4 Friendly outdoor space(+1, 0, -1)
B5 Decorations and street furniture(+2, +1, 0)

Having well-greened parks and playgrounds
Many trees along street
Deserted land scattered by garbage
Pleasant streetscape
Chaotic scenes with garbage bins or bicycles
Advertises in disorder
Illegal parking that disturb pedestrian use
Friendly street space
Isolated street without living atmosphere
Street furniture, sculpture, waterscape, etc.
Well-designed lightening, etc.

+1
+1
-1
+1
-1
-1
-1
+1
-1
+1
+1

To keep objectivity in the survey, a detailed manual was made. The evaluations
were based on neighborhood areas within 20-25 m from the borders of each sample site.
Detailed criteria for giving points are provided, including both verbal descriptions andpictures for reference. For an instance, the criteria for A1 (the continuous of external
walls) are shown in Appendix 2. Besides, a 1-hour training was delivered toinvestigators, who came from two local investigation companies and had noprofessional experience on architecture and urban design.
The site surveys were conducted for the Tokyo sample in May 2004 and for the
Kitakyushu sample in March and April of 2005. The investigators were instructed towork on each site for 15 to 20 minutes, and finish about 10 sites every day. For eachsample site, two people implemented the evaluation. The second was asked to confirm
the points given by the first. In case that they cannot agree with each other, the scores
given by each were recorded. In addition, they were asked to take six to eight pictures
and a 15-second video for each sample area. These were later used to justify whether theevaluation results were biased and in case of different opinions appeared, which of theinvestigator’s result was better. In fact, 94% of the points given by the investigators
were unanimous and reasonable, and no significant difference was detected between the
results in two cities. A member in one of the 15 groups of Tokyo investigators tended torate landscape lower than his colleague. This tendency and other discrepancies in thesurvey were justified with the pictures and videos.
As results, the databases of urban landscapes in Tokyo and Kitakyushu city were8

established. Figure 1 shows the means of the indices of the 11 factors. The figures inbracelets are t-values of the differences between the two cities.
1.2(1.21)

Tokyo

1.0

Kitakyushu

0.8(2.89)

0.6
0.4
0.2

(-2.53)(-6.50)(-0.41)(0.41)(-0.83)(-1.5)(4.42)(2.3)(-0.62)

0.0-0.2

A1 A2 A3 A4 A5 A6 B1 B2 B3 B4 B5Figure 1 Comparison of the survey results in two cities

A screening of the data shows that the average levels are similar in factors such as
A1 (continuity of external walls), A2 (conformity in colors and materials), A3(compatibility of building styles), A4 (beauty of skylines constructed by buildings), B1(greenery of walls and trees) and B5 (decorations and street furniture) but significantly
different in other factors such as A5 (openness and the scale of buildings), A6 (visually
nice and continuous greenery), B2 (greenery of open pedestrian spaces), B3 (favorable
pedestrian space), and B4 (friendly outdoor space). The mostly varied factor A5 hadreflected the fact that many samples in Kitakyushu city were located in newly
developed areas with many vacant lands. As a whole, the results reasonably suggested
the characteristics of large metropolitan areas as compared to local cities, wherebuilding density is generally higher and residential areas are featured by less open spaceand greenery.

2.3 Principal Component Analysis (PCA) of landscape data
It was found that many of the 11 variables on urban landscape were significantly
correlated. We performed a PCA in the second step to alleviate the correlation problem

9while to understand the evaluation structure better.
For the Tokyo data, the 11 factors were firstly classified into four categories
according to their correlations with a graphical modeling method. As a result, we havefour groups of variables, (A1, A2, A3, A4), (B1, B2, B6), (B3, B4, A5), and (B5). From
the features they represented, we could tell that they relate to the compatibility ofbuildings, greenery, sense of familiarity, and the effort in blocks to preserve or create
unique characteristics, respectively. Then, PCA was conducted within each category.
This generated one principal component with eigenvalue larger than 1 for each of theformer three categories, which were named compatibility, greenery and familiarity.
They accounted for 72.8%, 66.6% and 58.2% of the classified variables,
respectively. B5 in the fourth category was omitted because eigenvalue of the factor
strongly correlated to this variable in the whole data is very small. The variables wereclassified prior to PCA because the two variables signified in later discussed hedonic
modeling kept more information of original data (44.7%) than the one significant
variable did if principal components without classification were input to hedonic model(41.4%).
In parallel, three principal components with eigenvalues larger than 1 weregenerated with the Kitakyushu data. Table 2 details the results.

Table 2 Principal components of landscape factors
Tokyo

Kitakyushu city

Principal Component

1

2

3

1

2

3

Eigenvalue

4.436

1.313

1.035

4.317

1.43691.016

Percent (%)

40.33

11.93

9.41

39.24

13.06

9.23

Cum Percent (%)

40.33

52.26

61.68

39.24

52.31

61.54

Evaluation concept

Compatibility

Greenery

Familiarit Compatib
y

ility

Greenery

Decoration

A1 Continuity of external walls

0.336-0.208-0.3060.372-0.234-0.004A2 Conformity in colors and materials

0.370-0.298-0.1550.392-0.269-0.06810

A3 Compatibility of buildings styles

0.381-0.158-0.2040.388-0.221-0.028buildings

0.383-0.206-0.2070.397-0.2350.079

A5 Openness and the scale of buildings

0.231-0.073

0.361

0.280

0.056

-0.002greenery

0.305

0.476

-0.097

0.220

0.608

-0.055

B1 Greenery of walls and trees

0.249

0.491

-0.197

0.322

0.289

0.032

B2 Greenery of open pedestrian spaces

0.275

0.487

0.037

0.226

0.548

0.144

B3 Favorable pedestrian space

0.280-0.086

0.479

0.246

0.084

-0.203B4 Friendly outdoor space

0.287-0.221

0.383

0.236

-0.056-0.079B5 Decorations and street furniture

0.119

0.187

0.492

0.048

-0.051

0.957

A4 Beauty of skylines constructed by

A6 Visually nice and continuous

In both samples, the three principal components explained for about 40%, 12%,and 9% of the variances of the 11 variables. In total, they keep 62% of the total
information of each dataset. The eigenvectors (lower part of Table 2) revealed that thestructures of the first and two components were fairly alike in Tokyo and Kitakyushu
city. Since the first principal component strongly correlates to factors representing thecompatibility of buildings (A1, A2, A3, A4), we considered it to be a scale forcompatibility. In the same way, the second principal component was deemed to be ascale of greenery. The third of the Tokyo sample, associated with B3, B4 and B5, wasregarded as familiarity. That of the Kitakyushu sample with strong association with B5
was named decoration.
The results suggested that compatibility and greenery are the most distinctive
features in the cognition of urban landscapes. Even in different cities, it has no muchchange. In metropolitan areas, familiarity is emphasized, probably because the flavor of
people in large metropolitan areas is inversely affected by intense mixtures of industrialuse, heavy traffic, and so on. In contrast, decoration in Kitakyushu city may havereflected the endeavors of local government and communities to preserve and create
local characteristics.

11

2.4 Hedonic analysis on landscape factors in Tokyo
In the third step, we performed hedonic analyses with the datasets of Tokyo andKitakyushu. In this process, the principal components of urban landscape were used as
independent variables to identify, if any, their impact on land prices.
For the Tokyo sample, we used the following linear regression functions.
UnitP = intercept + ∑ α i × X i + ∑ b j × ( X j / S ) ,
i

(1)

j

where, UnitP is a vector of unit price derived by dividing total land prices by lot size, Xi（i=1 to m）is a vector of the i-th independent variable with m being the number of

independent variables, S is a vector of lot size, and ai and bj are regression coefficients.
The dependent variable UnitP is approximately normally distributed. Terms Xi and Xi /S
were both entered because we assumed that the influence of some variables may changewith lot size.
The raw data of independent variables were transformed to suitable forms throughpostulating and validating various assumptions repetitively. For example, the width of
the front road, w1, was replaced by ln(w1), thinking that as w1 increases, its marginal
influence on UnitP should decay. The width of second front road, w2 (w2=0 if second
front road does not exist), was transformed to ln(w2-1) so that the new variable iscontinuous even if a second front road does not exist. After transformation, the fitting of
the model had been improved.
The specification in Equation (1) was tested against a variety of alternative functions
such as linear model regressing on Xi, log-linear regression model on Xi, and so on. The
fitting of the model in Equation (1) was satisfactory (highest R2 and lowest AIC), andthe prediction errors yielded by cross-validation test were smaller than other tested
models.
As the result of incremental stepwise regression, a model with 21 variables wasestablished (Table 3). The correlations of the 21 variables were weak except for thatbetween cul-de-sac and cul-de-sac/S. This was easy to understand because they were
integrated terms. However, statistical tests with some variables removed, and with12

random errors added to the correlated terms showed that the estimates of the model
were stable. This suggests that this model has not suffered much from multi-collinearity
problem.

Table 3 Regression model for unit price (in Tokyo)

Variable1
2
3

Definition

Intercept
Line-Seibu*
Line-Keio
Line-Tokyutoyoko

Along Seibu railway lines, 1; otherwise,0Along Keio railway lines, 1; otherwise,0Along Tokyutoyoko railway lines, 1;
otherwise, 0
4 Multiple line
Close to multiple railway lines, 1;
otherwise, 0
5 UpFAR1If effective FAR of a site is between
60-100%, 1; between 110-270%, -1;
otherwise, 0
6 UpFAR2with effective FAR of a site less than
210%, 1; beyond 220%, -1
7 UpFAR3
with effective FAR between 110-160%, 1;
between 170-210%, -1; otherwise, 0
8 t_station
Time to the nearest train station (minute)
9 Irregular shape
If for an irregular shaped site, S>=70㎡,
ln(S-70); otherwise, 0
10 Frontage/S
Frontage sharing with main front road (inmeters)/Sln(width of main front road (in meters))
11 ln(w1)
If width of the second main front road
12 ln(w2-1)
w2>=2.0 m, ln(w2-1); otherwise, 0
13 Cul-de-sac/S
Cul-de-sac dummy/S
14 Cul-de-sac
With a cul-de-sac front road, 1; otherwise,
0
15 Chome-elevation/S Average elevation of chome (m)16 Chome-popden
Population density of chome (person/ha）
17 Chome-BCR<=40%/ with average building coverage ratio lessS
than 40%, 1/S; otherwise, 0
18 Chome-wooden
Proportion of wooden structure buildings
in chome (%)
19 Unpleasant facility with unpleasant facility in neighborhood,1; otherwise, 0
20 Compatibility
First principal component of landscape
21 Greenery
Second principal component of landscape
0.699
R2
0.672
Adj. R2N
232

13

Impact on
Stdunit priceP-valut
Scope（thousand Errore2
Yen/m ）
734.5 0.0432 17.01 <.0001-120.2 0.0155 -7.77 <.0001 Neighborhood-40.2 0.0132 -3.05 0.0025 Neighborhood64.2 0.0159

4.03 <.0001 Neighborhood

33.4 0.0120

2.78 0.0058 Neighborhood-60.3 0.0122 -4.95 <.0001

Lot-112.7 0.0234 -4.84 <.0001

Lot

18.9 0.0085

2.21 0.0279

Lot-9.5 0.0013 -7.38 <.0001

Lot-24.3 0.0037 -6.51 <.0001

Lot

505.5 0.2092

2.42 0.0164

Lot

49.8 0.0168

2.96 0.0034

Lot

50.0 0.0135

3.69 0.0003

Lot

4998.3 2.0419

2.45 0.0151 Neighborhood-100.2 0.0258 -3.88 0.0001 Neighborhood
83.6 0.0357 2.34
0.02
-0.4 0.0001 -3.03 0.0027

District
District-4243.1 1.4081 -3.01 0.0029

District-2.0 0.0009 -2.21 0.0283

District-119.9 0.0284 -4.23 <.0001 Neighborhood
7.9 0.0033
8.4 0.0040

2.4 0.0171 Neighborhood
2.1 0.0368 Neighborhood

* Dummy variables for railway lines were based on JR Chuo line.

It was found that the determinants of land price include railway lines (line-Seibu,
line-Keio, line-Tokyutoyoko), accessibility to multiple railway lines (multiple line) andtime to the nearest train station (t-station). For example, properties along Tokyutoyoko
line, where is famous for wealthy residents and high-class houses, are 64 thousand
Yen/m2 more expensive than standard residential areas along Chuo line (based on which
the dummy variables of railway lines were generated).
Significant variables also include attributes of land such as frontage (frontage/S),
irregularity (flag) and floor-area-ratio regulated by planning controls (upFAR1, upFAR2,
upFAR3), the attributes of front roads such as width (ln(w1) and ln(w2-1)) andcul-de-sac, as well as the attributes with regard to blocks, which involve average
elevation (chome-elevation/S), population density (chome-popden), building coverage
ratio (chome-BCR<=40%), the proportion of wooden structure buildings(chome-wooden), and the presence of unpleasant facilities.
The estimates of the above variables as well as their signs are reasonable and theresults are of interest from many viewpoints. However, detailed explanations areomitted to leave space for the investigation of variables on landscape.
Two principal components were significant. One is compatibility, and the other isgreenery. Land prices increase for 7.9 thousand Yen/m2 if the value of compatibility
increases for one point and for 8.4 thousand Yen/m2 if the value of greenery increases
for one point. These results have some important implications. First, they demonstrated
the positive effect of landscapes on land prices since compatibility and greenery are
positively associated with landscape factors as shown by the signs of eigenvectors in
Table 2. Secondly, since the unit land price in Tokyo is averagely 600 thousand Yen/m2(specifically, the mean of this sample is 602.4 thousand Yen/m2), the difference of one
point in compatibility or greenery accounts for more than 1-1.5% of the total prices. We
should say that the influence by landscape amenity on land price can not be ignored.
In addition, the results suggested the importance of cooperative activities for14

improving landscape qualities. According to the nature of the significant variables, they
can briefly be classified to those associated with individual lots, those withneighborhoods, and those with even broader areas. This property is shown in theright-most column in Table 3. Because compatibility and greenery are attributes ofbuilding groups and neighborhoods, their improvement or management, to a large
degree, depends on the efforts of whole residents rather than solely individuals.

2.5 Hedonic analysis of landscape factors in Kitakyushu city

A similar modeling procedure as above was followed with the Kitakyushu data. As
a result, we got a log-linear regression model for unit land price.
ln(UnitP)= intercept + ∑ ai × X i ,(2)i

By stepwise regression, a model with 25 variables was established (Table 4). Thismodel explains for 63.8% of the variance of ln(UnitP). Statistical tests showed that
there were no big multi-collinearity problems in this model and the estimates werestable. In addition, the signs and the estimates of the variables were consistent withexpectation.
In this model, two principal components of urban landscape factors, compatibility
and greenery were significant at 0.01 and 0.1 levels, respectively. This result again
demonstrated the economic values of urban landscape. From the elastic coefficients, weknow that land price can increase for 2.9% if compatibility is one-point higher, and for
2.7% if greenery is one-point higher.

Table 4 Regression mode for ln(UnitP) in Kitakyushu city

No.

Variable

1

Intercept
Away from main road

Definition

Away from main road, 1;
otherwise, 1

Elastic
coefficienttP-value
Scope(impact onunit price）
11.0167 0.1414 77.91 <.0001-0.1399 0.0396 -3.53 0.0005
0.869 Neighborhood

Impacton Std Errorln(UnitP)15

2

Away from commercial Away from commercial area,
area
1;otherwise, -1
3 Road direction1
Not in east, 1; otherwise, -1
4 Road direction2
In north, north-east or south,
1; in east, 0; otherwise, -1
5 Road direction3
In north, 1; in north-east or
south, -1; otherwise, 0
6 Without sidewalk
Without sidewalk, 1;
otherwise, 0
7 Road circulation
No bad, 1; otherwise, -1
8 Line-not Chikuho
Not along Chikuho line, 1;
otherwise, -1
9 Line-not Hitahikosan Not along Hitahikosan line,
1; otherwise, -1
10 Line-not monorail
Not along monorail, 1;
otherwise, -1
11 t-bus stop
Time to the nearest bus stop
12 Solid land baseSolid land base, 1;
otherwise,-1
13 regulated FAR
FAR restricted by land use
regulations
14 Regular shape
Regular shape, 1; otherwise,-1
15 Distance-shopping
Within 200-500 m to thenearest shopping center, 1;
otherwise, -1
16 Chome-fire disasterIn high degree of danger, 1,
otherwise, -1
17 Chome-fire prevention Well prepared for fire
prevention, 1; otherwise, -1
18 Chome-public
Public transportation is bad,transportation
1;average, 0, good, -1
19 Chome-medical facility Poor or average medical
facilities, 1, good, -1
20 Chome-home careToo few or too many homecare facilities, 1, average, -1
21 Chome-daily facility
Insufficient daily facilities,
1;average or above, -1
22 Chome-popden
Population density in chome(person/ha)
23 Housing density
Density of houses in chome(house/ha)
24 Compatibility
Principal component oflandscape25 Greenery
Principal component oflandscape
R2
0.686
2
Adj R
0.638
N
187

-0.1175 0.0554-2.12 0.0355

0.889 Neighborhood-0.0562 0.0296-0.0580 0.0194-1.9 0.0595-3 0.0032

0.945
0.944

Lot
Lot

-0.0467 0.0316-1.48 0.1413

0.954

Lot

-0.0565 0.0206-2.74 0.0067

0.945 Neighborhood

0.1478 0.0380
0.0657 0.0315

3.89 0.0001
2.08 0.0389

1.159 Neighborhood
1.068 neighborhood-0.1024 0.0431-2.38 0.0186

0.903 Neighborhood-0.1329 0.0295-4.51 <.0001

0.876 Neighborhood-0.0004 0.0001
0.1729 0.0795-4.42 <.0001
2.17 0.0311

1.000 neighborhood
1.189
Lot

0.0011 0.0003

3.71 0.0003

1.001

Lot

0.1046 0.0214

4.89 <.0001

1.110

Lot

-0.0432 0.0182-2.38 0.0187

0.958 Neighborhood-0.0452 0.0171-2.64 0.0092

0.956

District0.0352 0.0192

1.83

0.069

1.036

District-0.1657 0.0378-4.38 <.0001

0.847

District-0.0490 0.0266-1.84 0.0672

0.952

District-0.0733 0.0212-3.46 0.0007

0.929

District-0.1485 0.0406-3.66 0.0003

0.862

District

0.0029 0.0008

3.76 0.0002

1.003

District-0.0048 0.0028-1.68 0.0947

0.995

District

0.0286 0.0084

3.41 0.0008

1.029 Neighborhood

0.0262 0.0150

1.75 0.0827

1.027 Neighborhood* Dummy variables for railway lines were based on JR Kagoshima line.

3. Comparison of the evaluation in two cities

16

The empirical results on the evaluation of urban landscapes in Tokyo andKitakyushu city are quite identical. Both demonstrated that compatibility and greeneryare mostly concerned attributes in landscapes, and in either case, they were significant
determinants of land prices.
Although the evaluations for 11 factors being investigated were quite different (asshown in Fig. 1), t-tests showed that the levels of compatibility in the two cities did nothave significant difference (t= -0.25), and neither did the levels of greenery in the twocities (t= 0.099).
In fact, the average unit price levels of the Tokyo and Kitakyushu samples are602.4 and 73.2 thousand Yen/m2, respectively. Accordingly, the impact of compatibility
and greenery at the average unit price value in Kitakyushu city are 2.12 and 1.98
thousand Yen/m2. The estimates are comparable in scale with the results in Tokyo,
which are 7.9 and 8.4 thousand Yen/m2. With respect to absolute values, the economic
impacts of compatibility and greenery in Tokyo are higher, but with respect to the ratios
to land prices, that in Kitakyushu city are a bit higher.
Because PCA factors were linear combinations of the product of eigenvectors andstandardized landscape factors, we decomposed the estimates for compatibility andgreenery in 11 dimensions. Thereby, the marginal effects of the 11 landscape factors onunit price were computed. Table 5 lists the results.

Table 5 Comparison of the results in Tokyo and Kitakyushu cityTokyo

A1 Continuity of external walls (1,0,-1)
A2 Conformity in colors and materials(1,0,-1)
A3 Compatibility of buildings styles(1,0,-1)

Kitakyushu city

Marginal
effect on unit
price(thousandYen/m2)+4.73

Elastic
coefficient(impact on
average unit
price）
1.0079Averagemarginal effecton unit price(thousandYen/m2)+0.44

Elastic
coefficient(impact onunit price）+5.17

1.0086+0.41

1.0057+5.78

1.0096+0.54

1.007417

1.0060A4 Beauty of skylines constructed bybuildings (3,2,1,0,-1)
A5 Openness and the scale of buildings(1,0,-1,-2)
A6 Visually nice and continuous greenery(2,1,0)B1 Greenery of walls and trees (1,0)
B2 Greenery of open pedestrian spaces(2,1,0,-1)
B3 Favorable pedestrian space(1,0,-1,-2,-3)
B4 Friendly outdoor space (1,0,-1)
B5 Decorations and street furniture (2,1,0)+2.76

1.0046+0.26

1.0035-*1.0000+1.00

1.0137+6.10

1.0101+2.39

1.0326+10.871.0180+2.85

1.0390+7.90

1.0131+2.4071.0327-1.0000+1.62

1.0221-1.00001.0000

+0.83
+0.01

1.01131.0002* ‘-‘ means that the impact of the factor is not significant.

Table 5 confirms that the influences of landscape factors on land prices are
significant, and that the absolute effects of landscape factors are generally larger inTokyo while the effects with respect to unit price is somewhat higher in Kitakyushu city.
For instance, solely by 1-point increase in A1 (continuous external walls), the unit
prices can rise for 0.6-0.8%. The effects associated with greenery-related factors areeven larger.
The results provide important policy implications. One of special interests, forexample, is the results with B1 (greenery of walls and trees). In well greened areas inTokyo, land price is 10.87 thousand Yen/m2 higher than that of other areas. In
Kitakyushu city, the marginal effect is 2.85 thousand Yen/m2, which is also the largest
among considered factors. Actually, in many Japanese cities, local governments
encourage residents to greening their walls and fences along street by providing
subsidies or reducing tax. There are more and more examples in enlightened areas
where residents make agreements among themselves to green walls. Our analysis
strongly demonstrates that these activities were valuable.

4. Concluding remarks

As more and more people pursue for the beauty of urban landscapes, understanding
their economic values, especially their impact on land prices is valuable. It may
substantially raise the incentives of residents for preserving or creating landscape beauty.
18

It also suggests that planning policies that purposefully encourage and induce people to
do so are valuable. In addition, with the impact of landscape improvement or landscapedestroy being clarified, it makes possible to adjust benefits among residents and others,
and still, it may help public landscape management sectors to optimize their budget
plans.
The analyses confirm the usefulness of the three-step procedure for landscapeanalysis, i.e., with standardized survey, PCA and hedonic analysis. Although Tokyo andKitakyushu city differ a lot in geographical and social economic conditions, land prices
levels, etc., their evaluation structures for urban landscape are amazingly similar. In
both cases, the compatibility of buildings and the greenery of neighborhood are
distinctly emphasized and in market, they are strongly evaluated. This implies theimportance of keeping compatibility and greenery levels in a general sense.
Throughout the analyses, it is noted that landscape aesthetics could hardly beachieved solely by individual efforts; instead, collaborative efforts in neighborhood
level are extremely important. Therefore, activities leading to collaborative
improvement of landscapes, such as ‘district planning’ made by local government, or‘building and landscape covenant’ made by residents should further be encouraged inlandscape management policies.

Acknowledgement

We are very grateful to the members of Evaluation of Residential Environment researchgroup who gave us much help on collecting data and valuable comments. This research
was sponsored by Ministry of Land, Transportation and Construction. it was also
supported by the Joint-research program of Center for Spatial Information Science,
University of Tokyo.

19

Appendix 1 Data for two samples

Tokyo (valid sample size: 272)
Railway lines
Time to the nearest station
Time to Yamanote line (railway line surrounding
central areas)
Lot size
Frontages
Shape of lot (irregular or not)Landform of lot
Number of front roads
Direction of front road
Prerequisites of development
Gas
Width and lanes of front road
Right of road (public or private)
Pavement of road
Slope of roadCul-de-sac
Building setback along roadFence and walls
Noise and vibrations
Adjacent land use (farmland, factory, parking lots, large
open space, etc.)
Unpleasant facilities (waste treatment, cemetery, etc.)
Mixture of land use
Mixture of different height buildings
Distance to public facilities (park, school, hospital,
shopping center, etc.)
Available sunshine duration (delimited by surrounding
buildings)
Land use zone
FAR and building coverage ratio designated by zoning
Effective FAR
Beauty area designated by planning
Requirement for building setback
Economic rank of chome
Planning activities (district plan)
High criminal occurrence area
Average elevation of chome

Kitakyushu city (valid sample size: 187)
Railway lines
Time to the nearest station
Lot size
Frontages
Shape of lot (irregular or not)Landform of lot
Number of front roads
Direction of front road
Width and lanes of front road
Right of road (public or private)
Pavement of road
Slope of roadCul-de-sac
Building setback along roadFence and walls
Adjacent land use (farmland, factory, parking lots, etc.)
Unpleasant facilities
Mixture of land use
Mixture of different height buildings
Distance to public facilities (park, school, hospital,
shopping center, etc.)
Land use zone
FAR and building coverage ratio designated by zoning
Evaluation of chome on vulnerability to fire disasters
Evaluation of chome on activities against fire disasters
Evaluation of chome on dangers to natural disasters
Evaluation of chome on hazard
Evaluation of chome on criminal-prevention
Evaluation of chome on pollution and noise
Evaluation of chome on public transportation
Evaluation of chome on accessibility to artery roads
Evaluation of chome on welfare facilities
Evaluation of chome on medical facilities
Evaluation of chome on daily facilities
Evaluation of chome on education facilities

20Population density in chome, changing rate of population Evaluation of chome on commercial facilities
Evaluation of chome on parks and public space
Proportion of road in chome
Evaluation of chome on coverage of greenery
Proportion of vacant land in chome
Evaluation of chome on open space
Building coverage ratio in chome
Evaluation of chome on planning regulations in terms
Density of wooden structure buildingsof landscape beauty
Evaluation of chome on sustainability of environment
Density of dilapidated old buildings
Evaluation of chome on balance of population

Appendix 2 Evaluations on A1 (continuity of external walls)

This factor focuses on the walls and external walls of buildings that are higher than
1.5 m (above eye-line) to see if they are well-aligned along street. Three situations are
separated.
+1 point: Most walls and buildings along street are well-aligned. They account for
more than 4/5 of the total in each side of the street. An example is shown in theleft of Fig. 2.
0 point: More than half of walls and buildings are well-aligned but no more than 4/5.
An example is shown in the middle of Fig. 2.
-1 point: There are many vacant lands, parking lots, houses without gate-walls, orlarge buildings such as apartments or office buildings in the neighborhood.
Specifically, less than 1/2 of walls are aligned along street. An example is shownin the right of Fig. 2.

+1 point

0 point
Figure 2 Pictures for reference in landscape survey

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