CSIS Discussion Paper 131 (The University of Tokyo)

Do Urban Amenities Drive Housing Rent?∗
Chihiro Shimizu†· Shinya Yasumoto‡· Yasushi Asami§·
Terry Nichols Clark¶
July 26, 2014(Aug 16, 2014 revised)
Abstract
What brought the concentration of people to certain areas? And how much arehouseholds prepared to pay in exchange for being part of such concentrations? Focusing
on the Tokyo metropolitan area, which is one of the world’s largest urban areas, thispaper aggregates individual data relating to urban amenities in small areas and explores
its relationship to population concentration, as well as clarifying its relationship to rent
(housing service prices). It is understood from the obtained results that a concentrationof urban amenities produces population concentration and also raises housing rent. In
addition, it is shown that when measuring the degree of amenity concentration, it isthe diversity of amenities, not simply the total number of amenities that is important.
Concentration of diverse amenities enhances an area’s appeal, and as a result, households
will seek to reside there even if rents are high. Among the various types of amenities,
it was observed that amenities such as recreational classes, educational facilities andconvenience facilities such as restaurants have positive externality. On the other hand,a clear negative relationship was found between housing rent and amenities with negative
externality, such as cemeteries and video arcades.
JEL Classiﬁcation: C31 - Cross-Sectional Models; Spatial, R31 - Housing Supply andMarkets.
Key Words: amenity concentration; population concentration; housing service prices;hedonic approach; Geographic Information System (GIS).

∗ This research was conducted as the Tokyo case for an international amenity comparison project by the

Department of Sociology, University of Chicago. It is part of a joint research project with the Center
for Spatial Information Science of the University of Tokyo. In addition, during the planning stages ofthis paper, valuable comments were provided by Tomoya Mori from the Kyoto University. The authors
would like to hereby express their gratitude.
† Department of Economics,Reitaku University, School of Economics, University of British Columbia, andCenter of Spatial Science Information, The University of Tokyo: e-mail: cshimizu@reitaku-u.ac.jp.
‡ Graduate School of Medicine, The University of Tokyo; and Institute of Economic Research, Kyoto
University.
§ Department of Urban Engineering, The University of Tokyo.
¶ Department of Sociology, University of Chicago.

11 Introduction: Urban Amenities and Rent
What brings about the growth of cities?
In recent years, the concept of urban growth has gained increasing importance. Storper and
Scott (2009)[26] argued that the growth of cities is profoundly related to characteristics ofresidents in cities and how the population characteristics change over time. Cities are deﬁned
as places that attract many and diverse people, and they promote sharing, exchanging, andgenerating new information (Florida 2009[7], Jacobs 1969[14]). Because of these characteristics
of cities, Jacobs (1969)[14] and Florida (2009)[7] mentioned that cities can generate new ideas
and technologies that make possible their strong and stable growth.
In other words, the growth of cities today depends heavily on the skills of the people attracted
to them—especially their creativity, which generates new knowledge, ideas, and technology.
Clark (2004)[4] and Howkins (2002)[13] argued that, unlike past days, the most importantdriver of economic growth is not land or capital, but people’s creativity. Knowledge andideas, as the nature of public goods, are repeatedly and endlessly transmitted and shared
among people, and new ideas are generated by linking them to other ideas (Storper and
Scott 2009[26]). Based on this characteristic, cities—places where many people gather andinteract—are advantageous in terms of producing new creative knowledge and ideas, and it
encourages their continuous development (Jacobs 1969[14], Florida 2009[7]).
How, then, can we stimulate the migration and concentration of the creative class which
supports the growth of cities? It has been noted that, to decide where to live, creative people
have a tendency to place importance on cultural factors—especially lifestyle quality, typiﬁed byaccess to urban amenities (hereafter referred to as “amenities”)—rather than economic factors
such as high wages and low rent (Glaeser et al. 2004[11]; Adamson et al. 2004[1]). Vibrantmusic and arts communities, movie theaters, restaurants, grand buildings, high-quality schools,
libraries, museums, and so forth have been mentioned as speciﬁc examples of amenities which
improve people’s lifestyle quality (Silver et al. 2010[25]). In terms of the reasons why people
have come to focus on cultural consumption opportunities produced by such amenities, it has
been mentioned that there has been a shift from an economic structure mainly comprisedof labor-intensive companies to one now dominated by information- and knowledge-basedindustries, so people have more opportunities to take advantage of free time and leisure in
their daily lives (Fogel 2000[8], Glaeser et al. 2004[11]). In the wake of this trend, it has been
said that the role of cities has also shifted from being a “place of production” to a “place ofconsumption” (Glaeser et al. 2004[11]). In other words, the “cultural consumption” available
in a city has become a key factor in determining whether it can attract the creative class whowill support its development. Florida (2002)[6] has shown in particular that it is importantfor cities to provide for more diverse and plentiful cultural consumption in order to attract
the aﬄuent class and the creative class.
There are various indicators that show the degree of a city’s growth, such as increased employment opportunities, growth of the overall residential population or of the aﬄuent class
population, and rises in income or rent. Based on these indicators, many studies have established that amenities have an impact on the growth of cities. For example, past research has
shown that there is a strong association between amenities and the social attributes of local
residents. It is recognized that there is a relationship between the social attributes of local
residents and the accessibility of parks, green spaces, medical facilities, elementary schools,
and many other amenities, and it has been shown that in many cases, the aﬄuent class has
superior access to such amenities, and that amenities tend to attract this demographic group2

(Yasumoto et al. 2014[30], Comber et al. 2008[3], Christie and Fone 2003[2], Talen 2001[28]).
Furthermore, Glaeser et al. (2001)[10] have shown that in the United States, cities withmore amenities have greater population, whilst Navarro et al. (2012)[16] have demonstrated
that in Spain, higher income groups live in cities where opportunities for higher quality ofcultural consumption through amenities are available. Although several indicators of urban
growth (i.e. the total population or increases in the aﬄuent class, changes in people’s laborrelated circumstances such as income) have already tested in previous study, there are few
studies that focused on rent ﬂuctuations as a city growth indicator. If one considers a city’sland supply as ﬁxed, population ﬂuctuations will be reﬂected on the economic market as
rent ﬂuctuations. In other words, via the rental market, it is possible to measure how muchhouseholds are prepared to pay in exchange for a concentration of amenities.
Based on the above points, this paper analyzes how various types of amenities impact rent
and the characteristics of people drawn to live in the vicinity of these amenities. Speciﬁcally,
focusing on the Tokyo metropolitan area, which is one of the world’s largest urban areas,
it aggregates individual data relating to amenities at the small area level and explores its
relationship to population concentration, as well as clarifying its relationship to housing rent.
The results obtained show that along with a concentration of amenities, the diversity of theseamenities is a key factor. This result is consistent with Florida (2002)[6]’s ﬁnding that thediversity of amenities is important for the growth of cities.

2 Empirical Model and Data
2.1 Small Area Data
Prior to the analysis, we shall examine the spatial distribution of amenities, population,and housing service prices. For population-related statistics we used the 2010 census, while
for amenity-related data, we used Zenrin telepoint data,‘Tel Point Pack!’*1 In addition, foranalysis using small areas, it is important to consider what kind of spatial unit should be used.
For this paper, we decided to conduct analysis using a mesh of 500-meter square blocks. In
analysis that aims to clarify changes over time in the population composition for each area
based on information obtained from the census, how the census district boundaries changeover time is highly signiﬁcant. Also, for cross-sectional analysis, there were many cases wherecomparative analysis of all data at the same survey time was diﬃcult.
However, in cases where census data is not published based on mesh blocks, like in the U.S.,
and only census data compiled based on census districts whose boundaries change over time
(Census track, zip code, etc.) is available, it is diﬃcult to determine whether a given change
in population composition over time occurred because the census district boundary changed
or due to some other factor (Mohai 2008[15], Saha and Mohai 2005[20]). In the U.K. andother countries, there are many cases where mesh block statistics are not published, and if
conducting similar research in such countries, considerable resourcefulness and eﬀort may beneeded in order to circumvent this problem (for an example of such eﬀort, see Saha and Mohai
2005[20]). Moreover, in cross-sectional analysis, a similar problem may happen to combine
data for which the survey time varies.
With regard to this issue, as demonstrated by their actual use in the present research, amajor beneﬁt of the mesh block statistics available in Japan is that they make it possible tosigniﬁcantly reduce the costs of conducting research when attempting to develop analysis that
*1 Zenrin telepoint data is created from the Yellow Pages phonebook database.

junction with coordinates from NTT.

3

Data is supplied in con-

incorporates temporal changes from cross-sectional analysis of spatial diﬀerences.
In order to observe amenity concentration, it is important to ﬁrst clarify the deﬁnition. Thisis because each amenity may be diﬀerently related to social composition or social demand interms of its characteristics, and as a result the spatial density and location behavior willchange. As shown in Table 1, amenities extracted from Zenrin telepoint data are divided into
24 categories. This categorization was made in terms of the urban role of each amenity.
We will clarify the relationship between population concentration and rent (housing service
prices) in conjunction with this amenity-related data. We decided to use census data for
population data and individual data relating to contracted rent per dwelling for housing rent
data.
Table. 1 Categories of urban amenities
Category
Category1:Art/Galleries
Category2:Artists
Category3:Arts Instruction
Category4:Associations
Category5:Bars/Nightlife
Category6:Clothing/Fashion
Category7:Community/Gov’t
Services
Category8:Education/HealthCategory9:Foreign Gov’t Services
Category10:Literary Culture
Category11:Media Services
Category12:Museums/Aquariums/Zoos/Historic Sites
Category13:Music/Instrument
StoresCategory14:OTHER
Category15:Other Entertainment
Category16:Parks and Nature
Category17:Performance Arts
Category18:Religion
Category19:Restaurants/Food
Category20:Specialty Services
Category21:Specialty Stores
Category22:Sports and Recreation
Category23:Tourism
Category24:Visual Arts

Amenities
Museum, Museum of Art, Museum of Science
literary profession, artist
Vocational school, such as music, art and crafts, othertypes of school, lessons for hobby
Business cooperative, political organization
Karaoke, dance hall, bar, Beer hall
Clothing store, jewelry store, bags shop
Nursery, child care center, social welfare and nursing careservice business
Medical and other health business, elementary, middle
and high schools, university, private cram school
Foreign diplomatic oﬃces, United Nations-related facilities
Publishers, libraries, bookstores, and newspaper shops
Broadcasting industry, advertising agencies, TV program production
Zoo, botanical garden, aquarium, planetarium
Musical instruments, record·CD·DVD sales
Graveyard, parking lot
Movie theater, arcade games, theme park
Camping ground, ﬁshing pond, parks
Theatrical company, orchestra, dance troupe, live house
Christian church, and other religious groups
Restaurants, fast food, coﬀee shops, drive-in
Law ﬁrm, interpretation, translation, art repair industry
Sales of medicine, antiques, liquor, tobacco, and toys
Golf course, ski resort, tennis court, and other sports
facilities
Travel agency, Japanese-style hotel, hotel
Printing business, Design, Photographs

2.2 Spatial Distribution
Table 2 breaks down summary statistics for population indicators, housing service priceindicators, and amenity indicators. In addition, Figures 1 show population-related indicators,
4

Figures 2 shows the number of households by residence type, Figure 3 and 4 show spatial
distribution of amenities. The ﬁgures show the 50 km area surrounding Tokyo Station, which
is an economic center and transportation hub in Japan, in units of 10 km. In the following
sections, we will analyze population, housing service prices, and major amenities.*2(a) Total population(b) Second-generation baby boomer: aged 35-39(c) First-generation baby boomer: aged 60-64(d) Aged 65 or more (retired people)

Figure 1 Distribution of population groups in Tokyo metropolitan area

Population Distribution
With regard to population distribution, we observed ﬁrst-generation baby boomers (ages60-64), second-generation baby boomers (ages 35-39), post-retirements (ages 65+), along withthe total population. As a general trend, older people tended to be concentrated closer tothe city center. The second-generation baby boomers were not concentrated in speciﬁc areas
but instead distributed across the entire Tokyo metropolitan area. The background of thesetrends was that the generation that had purchased housing prior to the bubble and older
generations who tended to be comparatively wealthy were concentrated in the city center area
*2 The analysis is restricted to mesh blocks with a population of at least 1 in the 2010 census. In the Tokyo

metropolitan area, there were 36,870 corresponding 500 metre mesh blocks.

5where housing prices are high, while people of child-rearing age were spread out across thesuburbs.
Distribution of residence type
The proportion of single-person households is extremely high in the Tokyo metropolitanarea, and these are distributed across the entire area (Figure 2-(a)). The concentration is particularly high in the city center. Owned homes spread out from the city center to the suburbs
and are relatively uniformly distributed (Figure 2-(b)), private rental housing is concentrated
in the city center (Figure 2-(c)). In addition, there are a very large number of apartment residences, not only in the city center but also across the entire Tokyo metropolitan area (Figure
2-(d)).

(a) single-person households(b) Owned house(c) Praivate rental housing(d) Multi family household

Figure 2 Spatial distribution of housing types

Amenity Concentration
To understand the importance of amenity concentration, it is necessary to focus on boththe number of amenities and types of amenities (i.e., diversity). What also needs to beconsidered is that some amenities positively aﬀect housing service values, whilst others may
6have a negative eﬀect on housing values, and these diﬀerent types of amenities may be mixed
together in an area.
Table 2 reports summary statistics for various variables of the 36, 870 500-meter squaremesh blocks (mesh blocks where no people live were removed in advance). A notable point isthat for all of the amenity categories from 1 to 24, the minimum value was 0. There was alsosigniﬁcant deviation in the maximum value (ranged from 6 to 1, 293). This indicates that totest the relationship between distributions of amenities and housing services, it is necessary
to consider not only the concentration density but also the diﬀerences between areas where
an amenity exists and areas where no amenity exists. Accordingly, focusing on the proportionof zero-amenity areas by category (Table 3), 86% of mesh blocks are zero-amenity areas on
average. In other words, each category of amenity only exists in certain areas.
Therefore, based on Table 1, we looked at how many of the 24 amenity categories are
represented with facilities in each area (Figure 3-(a)) and the total number of facilities thatexist in each area when all types of amenity are aggregated (Figure 3-(b)). Compared to thespatial variation in population, one can see that amenities are heavily concentrated in the city
center and that there are signiﬁcant diﬀerences between areas.

(a) Number on scene(b) Sum of Amenity utilities

Figure 3 Diversity in amenities and amenity concentration

In particular, one can clearly understand that it is possible to enjoy a greater diversity ofamenities the closer one gets to the city center. Looking at the concentration level for individual amenity types, restaurants (Figure 4-(a): Restaurants/Food) are evenly represented inmost areas, but if limited to bars, pubs, and so on (Figure 4-(b): Bar/Night Life), one can seethat they are clustered in certain areas. In addition, clothing (Figure 4-(c): Clothing/Fashion)amenities are concentrated in an area that is broader than the city center. Compared to theseamenities that are present across a large spatial range in varying degrees of concentration,international-related government institution facilities such as embassies (Figure 4-(d): Foreign Gov’t Services) are concentrated in speciﬁc areas of the city center, while for museums,
aquariums, zoos, etc., the number of facilities is limited to begin with, and there are cases
where there is only one amenity every few kilometers.

7

Table. 2 Descriptive statics of major variables
Variable

MeanStandard
deviation

Min

Max

Data
source934.358
39.32880.31981.14573.975162.336
66.2961360.595
60.353114.865130.506
106.451243.337103.223

1
0
0
0
0
0
0

10, 423609
1, 063
1, 212
1, 2462, 5701, 334

*
*
*
*
*
*
*

0.246

0.047

0

1

**

0.018
0.02
0.194
0.309
1.52
0.889
0.173
1.217
0.005
0.317
0.283
0.003
0.088
0.08
0.033
0.046
0.041
0.111
4.387
1.873
2.055
0.391
0.115
0.537

0.191
0.169
0.794
1.724
12.157
6.447
0.559
3.645
0.136
2.478
2.561
0.069
0.616
0.473
0.318
0.262
0.428
0.446
16.752
5.75
5.017
1.636
0.849
3.357

0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0

13
6
21
66
1, 293
399
14
96
16
298
142
6
29
26
20
12
18
17
721
254
101
55
54
200

***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***

Population (unit: N of people)
Total
Aged 0-4: pre-school children
Aged 5-14: compulsory education
Aged 30-34: 2nd-generation baby boomers
Aged 55-59: the baby boomers
Aged 65 or more: retired people
Aged 75 or more: elderly people
Price of housing service (unit: 10,000 yen)Rent
Amenity (unit: N)
Category1
Category2
Category3
Category4
Category5
Category6
Category7
Category8
Category9
Category10
Category11
Category12
Category13
Category14
Category15
Category16
Category17
Category18
Category19
Category20
Category21
Category22
Category23
Category24

Number of Areas = 36, 870* Census (2005)，** Recruit Residential Information，*** Zenrin·Tele Point Pack

8

983.92%

34.00%

Category24

Total

36, 395

012, 53730, 941

34, 71630, 931

21, 425

21, 360

20, 061

33, 895

36, 12235, 48136, 10535, 036

35, 08936, 800

33, 835

32, 268

36, 768

25, 165

32, 50230, 70628, 72232, 170

33, 11236, 271

Number of areas = 36, 870

83.89%

58.11%

Category21

94.16%

57.93%

Category20

Category22

54.41%

Category19

Category23

97.97%

91.93%

96.23%

Category16

Category17

97.93%

Category15

Category18

95.17%

95.03%

99.81%

Category12

Category13

91.77%

Category11

Category14

99.72%

87.52%

68.25%

Category08

Category09

88.15%

Category07

Category10

77.90%

83.28%

87.25%

Category04

Category05

89.81%

Category03

Category06

98.71%

98.38%

Category01

Category02

0 ratio14, 974

5, 6272, 111

5, 76613, 776

14, 30113, 334

2, 973735

1, 388761

1, 8281, 75870

2, 8504, 460101

10, 849

4, 366

5, 577

7, 1584, 552

3, 737

599

474

0−9

5, 547

209

40

158

1, 420977

2, 446

2

13

1

4

6

23

0

126

122

1

747

2

411

664

119

21

0

1

10 − 29

1, 648

58

2

14

210

140

503

0

0

0

0

0

0

0

42

11

0

93

0

84

168

24

0

0

0

30 − 49

1, 197

29

1

1

38

76

359

0

0

0

0

0

0

0

13

7

0

16

0

65

100

5

0

0

0

50 − 99

769

6

0

0

1

16

149

0

0

0

0

0

0

0

4

2

0

0

0

26

49

0

0

0

0

100 − 299

Table. 3 Frequency distribution of amenity variables

111

0

0

0

0

0

16

0

0

0

0

0

0

0

0

0

0

0

0

1

7

0

0

0

0

300 − 499

73

0

0

0

0

0

2

0

0

0

0

0

0

0

0

0

0

0

0

0

1

0

0

0

0

500 − 999

14

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

0

1

0

0

0

0

1000 −

(a) Restaurants/Food(b) Bar/Night Life(c) Clothing/Fashion(d) Foreign Gov’t Services

Figure 4 Amenity concentration by amenity type

3 Impact of Amenity Concentration on Population Concentrationand Housing Service Prices
3.1 Hedonic modelingThis paper considers two issues. The ﬁrst is the relationship between amenity concentrationand population concentration. The second is the relationship between amenity concentration
and rent (housing services price).
In order to clarify how the amenity concentration impacts both population concentrationand housing service prices, the hedonic approach was applied in this study.
The hedonic model proposed by Rosen (1974)[19] developed a market equilibrium theory
for products diﬀerentiated according to the proposition of Tinbergen (1959)[29]. This theoryshows how diﬀerentiated assets such as housing can be analyzed from both an economic
theory and econometric model perspective. Speciﬁcally, the relationship between the productsupplier’s oﬀer function, the product consumers’ bid function, and the hedonic price function
structure is carefully scrutinized, and the product’s market price is deﬁned based on consumer
10and producer behavior.
In addition, the relationship between amenities and population is supported by capitalization
theory. Considering household housing selection behavior, it is thought that decisions are madebased on housing features and on the residential environment where housing exists that falls
within the household’s budget constraints. If we ignore housing features here, areas where
there is a high level of amenities exert strong attractiveness of location, with families thatdemand to live there. Therefore, if we assume land supply as ﬁxed, there will be a higher
population concentration in areas with a high level of amenities compared to other areas, andas a result, land and housing rent will be higher.
First, in estimating the hedonic function, there is a problem on selection of housing pricedata. In this paper, we decided to use newly contracted housing rent data for housing service
prices. In consumer price statistics and the like, the housing rent measured as an actual living
cost is the paid rent. However, since paid rent is not always determined based on current
market rent but is sometimes paid based on past leases, it is not determined as a result of the
behavior of market participants such as population movement.*3
Therefore, we will look at what kind of eﬀect an amenity has based on newly contracted
rents.
Based on the above considerations, we speciﬁed the two estimation models for housing rent
per property i, Pi shown below. With regard to continuous quantity, we decided to perform
logarithmic conversion for housing rent only since the distribution of antilogarithm of rent ishighly skewed, and there is a need to estimate the regression coeﬃcient of each variable as avalue of rent elasticity.
First, we will derive the eﬀect that the number of amenity types and the amenity concentration level (number of amenities) has on housing rents (Model 1).
Let Vi be the number of categories of facilities located in the mesh block, which property i
is included in and AMi be the number of amenity facilities located in the mesh block, which
the property i is included in.
The housing rent can be expressed as in Equation 1; the l characteristic vectors for theproperty, market condition and the neighborhood eﬀect, Xl,i . With regard to amenities, weconverted them into category data because the distribution was notably skewed.*4 In addition,
we controlled for mesh block’s m characteristic vectors for population factor (P OPm,i ) at the*3 Shimizu, Nishimura, and Watanabe (2012)[24] clariﬁes the diﬀerence between actual paid rent and market

rent and its structure.

*4 When it comes to conversion into category data, we faced the problem of how to specify the bandwidths.

In this regard, as proposed by Shimizu, Karato, and Nishimura (2014)[22], using an SWR (Switching
Regression Model), GAM (Generalized Additive Model), or the like may be considered, but one faces the
problem that interpretation is diﬃcult. Therefore, after conﬁrming the distribution of the total numberof amenities, we speciﬁed bandwidths at our own discretion. Speciﬁcally:
1: Not even 1 amenity is present.
2: Number of amenities is from 1 to 9
3: Number of amenities is from 10 to 29
4: Number of amenities is from 30 to 49
5: Number of amenities is from 50 to 99
6: Number of amenities is from 100 to 299
7: Number of amenities is from 300 to 499
8: Number of amenities is from 500 to 999
9: Number of amenities is 1, 000 or more
Diewert and Shimizu (2014)[5] also estimated hedonic functions by generating similar dummy variables
to analyze non-linear structure.

11

same time. ϵi is an iid normal disturbance.
log Pi = a0 + a1 Vi + a2 AMi +∑a3l Xl,i +∑a5m P OPm,i + ϵi

(1)

m

l

In addition, in order to ﬁnd the individual eﬀect of amenities, we inserted the type h(h = 1, 2, 3, . . . , 24, i.e., the 24 types of amenities) into the hedonic function as an individual
variable (Model 2). The total number of facilities per amenity categories h (H = {1, 2, ..., 24})expressed as CAMh,i .We can get 24 individual variables corresponding to amenities categories.
∑∑∑
a3l Xl,i +a5m P OPm,i + ϵi
(2)a2h CAMh,i +log Pi = a0 + a1 Vi +lhmFurthermore, as a method of adjusting for diﬀerences in distribution between amenities, weestimated Models 3 and 4 in addition to Models 1 and 2 by introducing various indicatorsthat normalized the amenity concentration level.

3.2 Amenity Concentration and Population Concentration
Prior to the analysis, we looked at the correlation between population and the various
amenities (Table 4). There is a certain positive relation between the total number of amenities
and total population, with a correlation coeﬃcient of 0.628. In addition, there is also apositive relationship between amenities and housing rent at 0.662. In other words, amenity
concentration generates population concentration and increases housing rent. Speciﬁcallythere is a strong correlation between amenities and a population of those aged 30 or more.
Since people begin buying housing from this age, there could be a certain relationship between
amenities and housing selection.
Furthermore, we estimated the relationship between population concentration and diversity of amenities (number of amenity types) and amenity concentration (Table 5). To focus
on the relationship between amenity diversity and amenity concentration, we set the total
population logarithmic value as an explained variable. We tried to derive the relationshipbetween amenities and population by controlling the convenience of each mesh block basedon its distance from Tokyo Station. Looking at the estimation results, both determinationcoeﬃcients adjusted for degrees of freedom had a certain explanatory power, at 0.534 and0.573. First, amenity diversity was estimated to be positive and signiﬁcant in all cases. We
did not see any signiﬁcant results for amenity concentration level. Furthermore, if we focusedon the number of each individual amenity, there were some coeﬃcients that were estimated as
positive and signiﬁcant and some that were estimated as negative. In other words, dependingon the type, an amenity may have a positive relationship with population concentration or it
may produce a negative eﬀect. As a speciﬁc example, educational facilities such as elementaryand junior high schools (Category 8) are supplied in areas where population is concentrated,and people gather in such areas. As a result, they are estimated to have a positive, signiﬁcantrelationship. However, cemeteries and the like (Category 14) are recognized to have a negativeeﬀect.
Drawing on this basic analysis, we will estimate a hedonic function using housing rent.

12

13

Total N of amunities
Category01
Category02
Category03
Category04
Category05
Category06
Category07
Category08
Category09
Category10
Category11
Category12
Category13
Category14
Category15
Category16
Category17
Category18
Category19
Category20
Category21
Category22
Category23
Category24

0.628
0.077
0.139
0.357
0.352
0.431
0.405
0.306
0.556
0.060
0.386
0.303
0.013
0.246
0.182
0.134
0.123
0.168
0.309
0.552
0.541
0.574
0.367
0.184
0.405

Total
population
0.601
0.069
0.129
0.349
0.347
0.414
0.390
0.290
0.540
0.048
0.375
0.290
0.016
0.236
0.171
0.131
0.118
0.153
0.296
0.527
0.501
0.550
0.361
0.166
0.393

Population(0 − 4)
0.595
0.067
0.124
0.344
0.339
0.407
0.384
0.291
0.542
0.044
0.368
0.281
0.014
0.229
0.164
0.124
0.120
0.144
0.293
0.520
0.501
0.548
0.351
0.159
0.386

Population(05 − 14)
0.639
0.082
0.144
0.374
0.374
0.445
0.422
0.303
0.570
0.068
0.404
0.324
0.021
0.260
0.189
0.145
0.127
0.180
0.318
0.563
0.537
0.582
0.388
0.188
0.425

Population(30 − 34)
0.618
0.078
0.141
0.361
0.357
0.429
0.408
0.295
0.552
0.062
0.389
0.306
0.021
0.247
0.183
0.135
0.126
0.170
0.311
0.545
0.522
0.565
0.369
0.182
0.407

Population(55 − 59)

Table. 4 Correlation between amenities and population

0.622
0.085
0.151
0.374
0.365
0.439
0.423
0.305
0.557
0.067
0.402
0.321
0.020
0.259
0.192
0.136
0.132
0.183
0.326
0.548
0.517
0.562
0.376
0.196
0.418

Population(≥ 65)
0.609
0.091
0.158
0.375
0.362
0.432
0.421
0.299
0.542
0.072
0.403
0.325
0.020
0.263
0.196
0.135
0.133
0.192
0.329
0.538
0.500
0.544
0.375
0.202
0.416

Population(≥ 75)
0.662
0.143
0.219
0.400
0.429
0.467
0.473
0.288
0.554
0.184
0.481
0.459
0.038
0.320
0.269
0.185
0.137
0.297
0.345
0.594
0.550
0.568
0.453
0.268
0.528

Rent

Table. 5 Population concentration and amenity concentrationcoeﬃcientt valuecoeﬃcientt value

6.161

223.4205.797

205.630

0.246
0.013

52.100
0.850

0.405

59.080−0.498−0.268−0.225−0.277−0.153−0.254−0.1130.204−0.678−0.292−0.274−0.754−0.408−0.444−0.535−0.385−0.449−0.174
0.029
0.020
0.098
−0.228−0.441−0.184−8.490−6.130−9.850−12.430−9.240−13.430−5.45010.260−6.070−13.800−11.650−4.100−14.590−13.950−11.300−10.480−10.210−7.600
1.780
1.080
5.270
−10.940−15.250−9.740

X: Neiborhood characteristics
T T : Distance to Tokyo station−0.019−76.62−0.017−68.960

Number of observations
Adjusted R square36, 870
0.534

Constant term
A: Eﬀect of amenities
Act :Number of amenity types
Aall : Total N of amenities
An :Category01
An :Category02
An :Category03
An :Category04
An :Category05
An :Category06
An :Category07
An :Category08
An :Category09
An :Category10
An :Category11
Category12
An :
An :
Category13
An :Category14
An :Category15
An :Category16
An :Category17
An :Category18
An :Category19
An :Category20
An :Category21
An :Category22
An :Category23
An :Category24

14

36, 870
0.573

4 Hedonic Function Estimation Results
4.1 Data
In estimating the hedonic function, data on housing and surrounding environment as wellas population concentration was collected. The details are presented below.
4.1.1 Housing rent data (P )，property or building attributes, market condition and the neighborhood eﬀect characteristics (X).
For housing, we gathered rent data contracted between January 2010 and December 2010. In
terms of the information source, we used condominium rent information published in Recruit’s
weekly housing listings magazine, Shukan Jutaku Joho. This magazine provides information
relating to quality and asking price on a weekly basis*5 . Out of the information published in
Shukan Jutaku Joho, we used the price information at the point in time when a listing wasdeleted from the magazine due to the contracted lease.*6
In addition, we used building ﬂoor space (S) and building age (Age) as quantitative data (X)representing building attributes. The building age is the period from the month the building
was constructed until the month the lease was contracted. We took into account whether thebuilding’s structure is reinforced concrete (RC) with an RC construction dummy, whether it
is a wooden construction with a wooden construction dummy,*7 whether it faces south witha south-facing dummy (South), and, similarly, whether it faces north with a north-facing
dummy (N orth).
Furthermore, it is necessary to be careful to use information collected via the propertytransaction market, since price levels may ﬂuctuate according to market conditions (Genesove
and Mayer 2001[9], Goetzmann and Peng 2006[12]). This is because every property’s sale priceis inﬂuenced by the speed of liquidity and market depth. It is known that not just the time
until a dwelling is leased but also the agreed price will be inﬂuenced based on whether thetransaction takes place in an area or time with an active market, or an area or time with few
transactions (M K). We employed a “market retention time” (M R) variable to adjust thesemarket feature. The market retention time is the period of time from when the lessor puts a
property on the rental market until a tenant is found.
*5 Recruit’s database includes historical information from the time a dwelling appears in the listings until it

is removed due to being leased, etc. In terms of price-related information, three types of data exist: i) the
listed price when the dwelling appears on the market (ﬁrst oﬀer price), ii) the price at the time when it isremoved from the listings (ﬁrst bid price), and iii) the lease (sale) price collected as a sample (transaction
price). The ﬁrst oﬀer price is not the market price but the seller’s desired price. On the other hand, thetransaction price may be aﬀected by individual circumstances accompanying the property transaction,such as the buyer being in a rush to make a purchase or the seller oﬄoading a property below market
value. When selecting data, it is necessary to bear these information characteristics in mind.
*6 The price at the point in time when a dwelling is removed from the listings is the initial buyer asking
price, based on a reverse auction-style process: information on the dwelling’s quality and price are
published via the listings, and the price decreases until a buyer appears. Therefore, while this is the top
price among buyers’ oﬀer prices, in comparison to transaction price information, it may be considered acompetitive market price that is not inﬂuenced by personal circumstances surrounding the transaction.
*7 In terms of the structure codes, the data was sorted as follows: 01 = reinforced concrete, 02 = steelreinforced, 03 = precast concrete, 04 = reinforced precast concrete, 05 = wooden construction, 06 = steel
frame, 07 = light-gauge steel, 07 = aerated concrete, 09 = block, 99 = other. Most single-family housing
is reinforced precast concrete, steel frame, or wood. That being the case, the wooden construction
dummy looks at the degree of price diﬀerence in the case of wooden construction, taking reinforced
precast concrete or steel frame as a base.

15

To estimate a hedonic function for a broad area, it is necessary to consider not only building characteristics but also spatial diﬀerences. In terms of surrounding environment factors
relating to spatial diﬀerences, the most typical example is the transportation accessibility ofeach housing location. Speciﬁcally, in the Tokyo metropolitan area where there are highly
developed train networks, housing rent changes considerably based on the accessibility of thenearest rail station. Therefore the following factors were considered in the hedonic pricing
estimation: time to nearest station (T S) and time to the central business district (T T ).
First, in terms of the time to the nearest station (T S), travel time by transportation method
is available. There are three transportation methods: walking, bus, and car.*8 In the case ofwalking distances, the walking time (minutes) is recorded, while in the case of bus distances,
the walking distance from the property address to the bus stop (minutes) and the time fromthe bus stop to the nearest train station (minutes) are recorded.*9 In addition, with regard
to bus distance, we considered a bus distance dummy (BU S). Furthermore, we distinguishedattribute price diﬀerences by transportation method (walking, bus) by inserting a cross-termof the bus distance dummy and T S. With regard to the time to the central business district(T T ), we used the average daytime travel time by train to Tokyo Station.*10
The above variables are related to the location and building, but it is expected that geographic diﬀerences in price also exist. Accordingly, we decided to reﬂect diﬀerences in public
services and the like and diﬀerences in the overall area (neighborhood status) by creating an
administrative district dummy (LD). In addition, since residential land development in theTokyo metropolitan area has taken place in tandem with railway line development, the pricestructure may vary by railway line. We therefore created a railway line dummy (RD).
Population Variable (P OP )It is recognized that a concentration of amenities has a certain relationship with population
concentration. However, a variable of total population may have potential to cause collinearity
with the hedonic function. Considering areal characteristics, it is recognized that there is acertain relationship between age groups and amenity concentration. To incorporate this issue,
we generated two variables. The ﬁrst is the old-age dependency ratio(Old). The second is the
population growth rate from 2005 through 2010. The old-age dependency ratio is an indicatorthat has theoretically and quantitatively been clearly shown to have a relationship with housing
demand and housing prices in a series of research by Nishimura (2011)[17], Nishimura andTakáts (2012)[18], and Takáts (2012)[27]. The old-age dependency ratio is deﬁned as the
population of aged 65or more divided by population of the working age (population aged 20
to 64). In other words, with regard to economic activity, this is the ratio between the agegroup that produces and the age group that does not produce but purely consumes. Previous
research (Takáts 2012[27], Saita, Shimizu and Watanabe 2013[21]) has shown that increases
in the old-age dependency ratio have the eﬀect of lowering housing prices. Along with thisaging rate-related indicator, we also inserted the population rate of change over the previous
5 years (T P OP (−5)).

*8 However, only the walking distance and bus distance were present in the analysis data.

We therefore
decided to control the diﬀerence in walking and bus transportation methods using a bus distance dummy
variable (BU S).
*9 Therefore, the time to the nearest station (T S) is deﬁned as the ”walking time” + ”Walking time to thebus stop” + ”time spent riding the bus from the bus stop to the nearest station.”*10 Excluding the morning and evening work and school commuting periods, we calculated the average timespent riding the train between 9 a.m. and 3 p.m. The data was supplied by Val Laboratory.

16

4.2 Hedonic Function Estimation Results
The hedonic function estimation results are reported in Table 6 and Table 7. In Model 1
housing rent increases due to both increases in the diversity of amenities (V ) and increases
in the number of amenities (AM ). This is consistent with the relationship to population
concentration that was shown earlier (Table 5). The model also shows that: increases in theold-age dependency ratio (Old) lowers housing rent, while the population growth rate increases
housing rent. This result is consistent with the ﬁndings of previous research. Moreover, theseresults were observed when surrounding environment characteristics and building characteristics (X) were controlled. Speciﬁcally, rents decrease as the distance from Tokyo Station at
the center of the Tokyo metropolitan area increases, while increases in building age and the
distance from the nearest station both lower rents as well. In addition, dwellings whose entrance faces south have a positive eﬀect, while north-facing ones have a negative eﬀect. These
results are consistent with ﬁndings on housing rent price formation characteristics shown inprevious research.
Model 2 looks at the eﬀects by amenity. Housing rent is high in areas where vocational
schools, recreational classes, etc., are concentrated (Category 3: Arts Instruction). It is possible that this type of indicator is a proxy showing the area’s cultural level. Moreover, housing
rents are high in areas where there are concentrations of educational facilities such as elementary/junior high schools, universities, and cram schools (Category 8: Education/Health),a concentration of international-related facilities (Category 9: Foreign Gov’t Services), parks(Category 16: Parks and Nature), and a concentration of restaurants and the like (Category
19: Restaurants/Food). Conversely, areas where there are concentrations of cemeteries, parking lots, etc. (Category 14: Other) and movie theaters and video arcades (Category 15: OtherEntertainment) lower rents.
In other words, there is a positive relationship between rent and the concentration of educational facilities, facilities for daily life such as restaurants, and amenities that improve theenvironment such as parks. On the other hand, rents are lowered by concentrations of facilities
that cause external diseconomy, such as cemeteries and parking lots, and facilities that are
conducive to disruption of the public peace, such as video arcades. These results should be
carefully considered because there are two possible causal processes to generate these results:
Amenity concentration may happen ﬁrst and it encourages population concentration and increases housing rent, or alternatively facilities such as parks and the like are ﬁrst supplied inareas where both population concentration and rent are high, or facilities such as cemeteries
are supplied in areas where housing rent is already low.
In Models 3 and 4, which used variables of normalized amenity indicators, the results wereconsistent with Models 1 and 2. In other words, the models estimated here may be considered
to have a certain robustness.

17

Table. 6 Housing values and amenity concentration 1
Model.1
coeﬃcientt value

Model.2
coeﬃcientt valueConstant term

10.819503.390

10.815504.350

A: Eﬀect of amenities
Act :Number of amenity types
Aall :
Total N of amenities
An :Category01
An :Category02
An :Category03
An :Category04
An :Category05
An :Category06
An :Category07
An :Category08
An :Category09
An :Category10
An :Category11
An :Category12
An :Category13
An :Category14
An :Category15
An :Category16
An :Category17
An :Category18
An :Category19
An :Category20
An :Category21
An :Category22
An :Category23
An :Category24

0.001
0.003
-

2.890
2.830
-

−0.001−0.014
0.005
0.011
0.003
−0.0020.006−0.0020.004
0.032−0.001
0.010
0.028
0.001
−0.006−0.0100.017
0.001−0.0100.010−0.0020.002−0.0070.017−0.009−1.680−3.500
1.720
6.870
1.700
−1.4004.390−1.2602.820
4.260−0.420
6.750
3.460
0.560
−2.920−3.4007.450
0.470−6.1706.880−1.1001.610−34.900
378.960−139.660−0.0110.005−2.7701.600−0.0120.005−3.0301.550

P OP : Population factors
Old:
Old age dependency ratio
T P OP : Relative change in population(−5)

18Model.1
coeﬃcientt value
X: Building and Neiborhood characteristics
S:
Exclusive area
Age:
Building age
T S:
Distance to closest rail stationBus:
Bus Dummy
T S × BusW D:
Wooden dummyRC:
Reinforced concrete dummySouth: South-facing dummy
N orth: North-facing dummy
MT :Market reservation time
TT:
Distance to Tokyo station

0.017−0.009−0.009−0.1620.003−0.0540.014
0.003−0.0250.00008−0.007

Others, dummy variables
Ward dummy
Railway line dummy

Yes
Yes

Number of observations
Adjusted R square50, 2720.891

19379.080−139.220−75.640−21.5806.820−33.8105.390
2.950−5.1509.570−36.130

Model.2
coeﬃcientt value−0.1590.003−0.0530.013
0.004−0.0250.000−0.0120.000
0.01298−0.009Yes
Yes
50, 272
0.892−21.370
6.650−33.540
5.050
3.190−5.1809.750−3.0301.550
3.130−74.930

Table. 7 Housing value and amenity concentration 2
Model.3
coeﬃcientt value

Model.4
coeﬃcientt valueConstant term

10.822503.070

10.815504.350

A: Eﬀect of amenities
Act :Number of amenity types
Aall :
Total N of amenities
An :Category01
An :Category02
An :Category03
An :Category04
An :Category05
An :Category06
An :Category07
An :Category08
An :Category09
An :Category10
An :Category11
An :Category12
An :Category13
An :Category14
An :Category15
An :Category16
An :Category17
An :Category18
An :Category19
An :Category20
An :Category21
An :Category22
An :Category23
An :Category24

0.001
0.004
-

5.670
4.260
-

0.001
−0.005
0.002
0.006
0.009
0.004
0.009
−0.001−0.001
0.006
0.000
0.008
0.002
−0.003−0.001−0.0020.005−0.002−0.0090.006−0.006−0.001−0.0070.017−0.0095.420−6.600
2.010
5.270
5.070
3.240
7.030
−2.090−0.8105.480−0.1603.290
2.930−2.930−1.110−2.5606.820−1.550−6.430
4.190−3.480−0.350−34.790379.950−140.060−0.0120.005−2.9301.510−0.0140.004−3.5901.110

P OP : Population factors
Old:
Old age dependency ratio
T P OP : Relative change in population(−5)

20Model.3
coeﬃcientt value
X: Building and Neiborhood characteristics
S:
Exclusive area
Age:
Building age
T S:
Distance to closest rail stationBus:
Bus Dummy
T S × BusW D:
Wooden dummyRC:
Reinforced concrete dummySouth: South-facing dummy
N orth: North-facing dummy
MT :Market reservation time
TT:
Distance to Tokyo station

0.017−0.009−0.009−0.1620.003−0.0540.014
0.003−0.0250.00008−0.007

Others, dummy variables
Ward dummy
Railway line dummy

Yes
Yes

Number of observations
Adjusted R square50, 2720.891

21379.020−139.220−75.610−21.600
6.820−33.8105.400
2.910−5.1909.630−35.960

Model.4
coeﬃcientt value−0.1600.003−0.0540.013
0.004−0.0250.000−0.0140.000
0.01052−0.009Yes
Yes
50, 272
0.892−21.4106.720−33.770
5.080
3.240−5.2309.730−3.5901.110
2.430−74.760

5 Conclusion: Do Amenities Impact Housing Concentration andHousing Service Prices?
What kinds of drivers encourage urban growth? Focusing on the Tokyo metropolitan area,
which is one of the world’s largest urban areas, this paper analyzed the relationship between
amenity concentration and housing rent. The reason we focused on the relationship with rent
here is that rent has a clear causal relationship with housing location behavior. If we assume
that land supply is ﬁxed, increases in location pressure for areas with a high level of amenities
are reﬂected in the market as an increase in housing rent. Conversely, the concentration of
amenities with negative externality decreases location pressure, which may be reﬂected in
the market via a decrease in housing rent. Furthermore, regardless of the country, rent isthe biggest household expenditure item, representing around 25% of household expenditures.
Given this, we believed there was considerable sense in focusing on housing rent.
First, let us summarize the obtained results.
When we examined the spatial distribution of amenity concentration, we found that someamenities were concentrated only in speciﬁc areas, depending on their type, and their spatial
variation diﬀered considerably. That being the case, it is recognized that it will be diﬃcult
to clearly measure the relationship with household utility level by using only the number ofamenities as an indicator. Based on this analysis, we decided to focus not just on the numberof amenities but also their diversity.
When we looked at the relationship of amenity diversity and concentration to populationand housing rent, we found that amenity concentration generates population concentration
and also drives up housing rent. In particular, we found that it was not the simple numberof amenities but the diversity of amenities that was important. Concentration of diverse
amenities enhances the appeal of an area and, as a result, households will seek to reside there,
even if it means paying higher rent. Among the various types of amenities, facilities such as
educational institutions, recreational classes and convenient facilities such as restaurants andthe like were observed to have positive externality. However, it was made clear that there is awell-deﬁned negative relationship between housing rent and amenities such as cemeteries andvideo arcades with negative externality.
An important implication is understanding the eﬀect of amenity diversity. The types ofamenities counted here included some with simple negative externality, some that do nothave a clear relationship, and so on. In that case, it is necessary to carefully elucidate thesigniﬁcance of diversity. In estimating the hedonic function, there is a possibility that theeﬀect of diverse amenities on rent may be biased by diversity level of amenities in surrounding
areas. Addressing this issue could provide an important view point for future works.
In addition, in sorting amenities into 24 groups, we grouped together amenities thought
to have similar characteristics in terms of their function into the same category, but thecriteria did not involve any objective indicators; categorization was performed based on thesubjective judgment by one of the authors specializing in urban sociology. Therefore, somealternative criteria to categories of amenities can be suggested, for example, the correlation
between various indicators showing urban growth, including rent, by categorizing amenities
according to criteria for the 15 groups suggested by the “entertainment machine” theory that
demonstrate amenities drive urban growth (Navarro et al. 2012[16]).

22

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