CSIS Discussion Paper No.93

Estimation of Hedonic Single-Family House Price Function
Considering Neighborhood Effect Variables*
Chihiro Shimizu†
Reitaku University
January 13, 2009
-AbstractIn this study, hedonic house price functions are formulated using the single-family house market in the
23 wards of Tokyo as the subject. In the formulation of hedonic models, the generation of omitted
variable bias is thought to occur in cases when, in addition to locational factors (i.e., factors involved indecision making when buying a property) and building structures which affect the house prices, local
environmental variables and the individual characteristics of house buyers, such as their income, are nottaken into consideration. However, since it is difficult to obtain local environmental information in asmall neighborhood unit and to observe individual characteristics of house buyers in the property market,
these variables have not been sufficiently considered in previous studies. In the current study, I aim to
improve the hedonic model by incorporating local environmental factors and data on family income in asmall neighborhood unit using a geographic information system (GIS). I demonstrate that, without
considering these variables, non-negligible levels of omitted variable bias are generated in the variables
that are major factors in determining house prices, such as ground area, front road width, distance to thenearest station, and the travel time to the central business district.
Keywords: Hedonic model, identification, omitted variable bias, Neighborhood Effect, Geographic
Information System
JEL Classification: C31 - Cross-Sectional Models; Spatial, R31 - Housing Supply and Markets.

*

I would like to thank Kiyohiko G. Nishimura(Bank of Japan), Atsuyuki Okabe,Yasushi Asami,Takatoshi Tabuchi(University of Tokyo),

Koji Karato(Toyama University) and YongHeng Deng(University of Sothern California) for discussions and comments.This research is partly
supported by the Grant in Aid for Research No. 19653025(Representative is Yasushi Asami) by Ministry of Education, Science and Culture
in Japan and CSIS(Center for Spatial Information Science of University of Tokyo) joint research.
†

Associate Professor, Department of International Economics and Business, Reitaku University，Visiting Research Fellow, Center for Spatial

Information Science (CSIS), University of Tokyo
Tel: +81-(0)4-7173-3439(Laboratory), E-mail: cshimizu@reitaku-u.ac.jp

11．Objective of the study
Economic growth and the progress of urbanization have promoted high-density land use and theconstruction of high-rise buildings, thereby generating urban problems such as traffic jams, theobstruction of insolation and ventilation, and the impairment of view. To cope with these
problems, policies for the preservation of high-quality urban space through the regulation of land
use and other measures have been actively adopted within cities. However, policies such as
land-use regulations cannot always be achieved with the agreement of citizens because
uncertainties arise about the effects of such policies. Accordingly, various attempts to measure
the effect of a policy such as land-use regulation in terms of economic values have been made.
The most representative technique for such attempts is a hedonic model that focuses on property
values.
However, in the formation of hedonic models, as indicated by Ekeland et al. (2004), several
problems still remain. The most important problem is the identification of estimated variables.
Namely, in the estimation using a hedonic model, price is determined as a result of the locational
activity (decision-making of a family or a company to buy a property or land) of a standard
household (family income) (Rosen (1974)); whereas in an actual city, heterogeneity of family
income should be taken as a prerequisite (Kanemoto and Nakamura (1986)). In this case, data
specific to the individual such as income, family types and so on is required. In addition, when ahedonic model is constructed for a wider space, neighborhood variables that can explicitly handle
the differences among neighborhoods must be incorporated as explanatory variables. However,1

in the actual construction of hedonic models, very few studies have appeared in which thesevariables are incorporated explicitly (Asami and Gao (2006)). This lack is due to the problem ofbias as a result of omitted variables in the estimation, which occurs because of the impossibility ofincorporating all variables that must be considered (Ekeland et al. (2004)).
However, if information on the attributes of a family such as income cannot be obtained directly,
then it may be natural to attempt to replace the information with collected statistical values andother observable variables. In addition, with the provision of spatial information and progress inspatial information processing technologies, it has become possible to observe data in a smallneighborhood unit which represent regional characteristics at a level much higher than before.
Under such circumstances, in this study, starting from a hedonic model in which only thebuilding attributes and locational characteristics are considered, I attempt to construct a model
wherein regional characteristics in a small neighborhood unit and family income attributes, whichhave not been considered before(in Japan), are taken into account.

Since such regional

characteristic variables and the family income attribute variables affect each other, they show
externalities. Therefore, I call them neighborhood effect variables. By comparing a model
incorporating these variables with a model that does not include them, I clarify whether or not
omitted variable bias is generated.
Results showed that, in the model considering neighborhood effect variables, the explanatory
power was improved compared with the simple linear model. At the same time, each of theneighborhood effect variables, which served as subjects of urban policies, was adopted with2

statistical significance. As neighborhood effect variables, I adopted the conditions of the use ofland and buildings and the building density in a unit of 500 x 500 (m2) mesh, as well as averagegross floor area and the ratio of office-worker households, which are both proxy indexes
representing income level and are collected in the national census. In addition, I measured theroad traffic noise at each point of the unit.
Furthermore, to consider regional characteristics that cannot be explained by the incorporationof these variables, I incorporated latitude and longitude coordinates as variables, and expanded theoriginal hedonic model.

In addition, I further improved the model by assuming a close

relationship between locational characteristics and land-use density. Namely, I obtained theland-use density in each zone defined by urban planning, and incorporated the cross terms of theland-use density with the ground area, floor space, front road width, age of the building, time to
the nearest station, travel time to the central business district (CBD), and the floor area ratio, all ofwhich have significant affect on house price, in the model to obtain an approximate model that
controls fixed effects.
As a result, I found that non-negligible bias was generated in the estimated parameters of not
only the variables representing locational characteristics such as the front road width, time to thenearest station, the a dummy variable for bus transportation (bus dummy), and travel time to the
CBD, but also the age of the building.

3

2．Hedonic model with consideration given to neighborhood externalities
2.1.Effect of neighborhood externality on price
Locational activities are affected by environmental factors in the broad sense of the term, suchas accessibility to transportation, possibility of development, or the conditions imposed on the useof buildings in the neighborhood. In addition, the possible use of buildings is restricted byland-use regulations. One purpose of land-use regulations is to ensure high-quality urban spaceby separating the land in accordance with its different uses and by eliminating effects that induce
external diseconomy. If these factors have some effect on people’s locational activities, thesefactors are also reflected in house prices. Namely, “capitalization” through so-called “votingwith your feet” is generated(Tiebout (1956)).
Thus, the house price is determined by factors such as the living environment resulting fromland-use regulations, locational characteristics such as travel time to the CBD, the conditions
imposed on local land use, the condition of the building including its age after construction andfloor space.

Various differentiations can be made in terms of performance and function.

Additionally, as formulated by Rosen (1974), a bid-rent function, which constitutes a hedonic
model, is determined by the characteristics of family income.
However, in many cases of hedonic-model estimation, characteristics of family income are
ignored, and estimation is often made using only variables that can be obtained. Additionally,
differences in geographical attributes are in most cases replaced by the travel time to the CBD orby dummy variables representing the local environment. However, when a gap appears in the4price structure in terms of space and unobservable geographical differences are a factor, the use of
these proxy variables is not sufficient. In the parametric polynominal expansion model proposed
by Jackson (1979) or in geographically weighted regression (for example, Zuang (2006)), theformulation of functions with consideration given to spatial heterogeneity is possible; however,back ground causes of heterogeneity in the conventional hedonic model cannot be dealt with
explicitly.
Under such circumstances, in this study, I aim to improve the hedonic model by organizing
attribute information on local land-use conditions and regional household characteristics asneighborhood effect factors.

2.2. Estimation model
In this study, I set up the following hedonic model using the market of single-family houses andland in the 23 wards of Tokyo as the analytical subject.

log DP / GA  a0   a1h X h   a2i Z i   a3・
j LD j   a4 ・
k RDk
h

i

j

k

(1)
  a5・l TDl   a6 m log Vm   a7 h , m X ・h Vm  a8u  a9 v 
l

DP
Xh

m

h,m

:Price of Detached House Price per ㎡(Yen):Main variables
X1 : GA / Ground Area/Square Meters
X2 : FS / Floor Space/Square Meters
X3 : RW / Front Road Widths(10cm)
X4 :Age /Age of Building5

X5 :TS / Time to Nearest Station
X6 :TT / Travel Time to Central Buisiness District
Zj:Other variables
Z1 : RT / Market Reservation Time(week)
Z2 : OC / other Characteristics
LDk :Location(Ward) Dummy(k=0…K)
RDl :Railway Dummy(l=0…L)
TDm :Time Dummy(m=0…M)
Vn:Neighborhood Effect Variables
V1 : FR / Floor Area Ratio/FAR
V2 : LR / Lot Area Ratio/LAR
V3 : ZD / Zoning Dummy
V4 : LU / Land Use Condition
V5 : HC / Household Characteristics
V6 : NOi / Noiseu,v :Longitude,latitude

In the model and in accordance with previous studies, I set the following variables as major
explanatory variables (Xh) that determine house prices: ground area (GA), floor space (FS), frontroad width (RW), age of building (Age), time to nearest station (TS), and travel time to the CBD(TT) (see, for example, Shimizu and Nishimura (2006), (2007)). The model composed of only
these basic variables is referred to as Model-1.
In addition, I also considered some dummy variables including a location (ward) dummy (LDj)and a railway dummy (RDk) as locational characteristics as well as a time dummy (TDl) to take
into account the difference in the transaction time, in addition to the variables for the time elapsed
between putting a house on the market to the conclusion of the contract (market reservation time,Z1), and the direction the windows face (Z2).
In addition to these variables, I expanded the model to one that takes neighborhood externalities

6

into account, which is the major aim of this study. First, to handle neighborhood externalities
explicitly, I incorporated variables (Vn) that represent neighborhood externalities.
Vn mainly comprises the following: zoning restrictions imposed by urban planning, which is
generally available information, the local land-use conditions (LU), which can only be obtained byactual on-site observation, and the residential household characteristics (HC), as well as the roadtraffic noise (NOi), which is a strong environmental externality.
First, as variables related to land-use regulations, I set the following three variables that areeasily obtained from publications and advertisements on the Internet: V1: floor area ratio (FR), V2:
lot area ratio (LR), V3: zoning dummy (ZD). The model in which these variables, which
represent generally observable residential characteristics, are incorporated is referred to as
Model-2.
Furthermore, I incorporated the following variables that can only be obtained by direct
observation of properties: V4: local land-use conditions (LU), V5: household characteristics (HC),and V6: road traffic noise (NOi). In addition, regarding small neighborhoods such as city blocks,
while I assume that a group of households having the same preference forms a region as a result of
spatial locational competition, I also assume that households having different preferences arelocated in different regions. In this case, the heterogeneity of variance between different regions
is anticipated. Accordingly, when I assume that households having different preferences arelocated in different regions, to deal with the heterogeneity of variance, I incorporated fixed effectsof the major variables (Xh). In this model, I incorporated the floor area ratio as a proxy variable7representing land-use conditions and land-use density, as well as its cross terms with the major
variables (Xh).
Even when control of the model by the incorporation of such neighborhood effect variables andfixing the effects of major variables are applied, it is still predicted that the problem of unobserved
variables remains.

To deal with this problem, I incorporated positional coordinate data

(longitude and latitude) in the manner of Jackson (1979).
The model in which the neighborhood effect variables (Vn), the floor area ratio and its crossterms with the major variables to deal with the heterogeneity of variance, and the coordinate data(u, v) are incorporated is referred to as Model-3.

2.3.Difficult-to-observe neighborhood effect factors
In defining variables based on local land-use conditions (LU), I used individual building datafrom the “Survey on Land Use in Tokyo” in fiscal 2001. In this survey, data on 1,662,088
buildings are arranged as geographic information system (GIS) data (polygon data), wherein theconditions of use, area, structure, and other parameters of buildings are presented. With these
data, I calculated (1) the number of buildings, (2) the average area of all buildings, and (3) the
standard deviation of building area in each square of side length 500 m, which corresponds to thetallying region of the national census. The average area serves as a variable with the samecharacteristic as the building density, and the standard deviation of the building area was assumed
as a proxy variable representing the appearance of the town. It is assumed that, in a region with a8small standard deviation, houses have similar sizes along well-ordered streets, whereas in a region
with a large standard deviation, the appearance of the town is not well-controlled.
As household characteristic (HC) data, I set the following items as variables in accordance with
the national census: (1) the number of households, (2) the ratio of households with elderly people(the number of households having an individual aged 65 years or older/total number ofhouseholds), (3) the ratio of households with an office worker (specialist and engineer employees+ management-level employees + clerical employees/total households). The ratio of householdswith an office worker is assumed as a proxy variable representing the differences in the income
and academic background in a region. This can be done because these employees are generally
known to have a more academic background and higher income level on average than householdswith other occupations.
I also incorporated data on the floor space per household. The average area calculated fromthe “Survey on Land Use in Tokyo” is the average area of buildings existing in a mesh regardlessof ownership, whereas the average area observed in the national census means the floor space
attributed to a household, which is different from the former area index. I consider that, while
the former is a proxy variable representing ambient environment, the latter can be used as a proxyvariable representing assets or income.
In addition, I incorporated road traffic noise as a variable representing the quality of the local
environment. Road traffic noise is not a cause of direct noise pollution; because a high degree of
air pollution also occurs in a region with a high level of road traffic, I judged that road traffic noise9is a representative index of local environmental quality. For the method used to measure roadtraffic noise, refer to the Appendix.

2.4. Data
2.4.1. Single-family house price
The analysis data used in this study is summarized in the following. As the subject of theanalysis, I used the data for transactions concluded in the 23 wards of Tokyo (621 km2) within one
year, from January to December 2000.
I used the prices of single-family houses and lands in “Residential Information Weekly”
published by RECRUIT, Co. as the main information source.

This magazine provides

information on the characteristics and asking price of properties and land on a weekly basis, andincludes the historical price data for individual properties from the time they are first placed in themagazine until they are removed because of sale or other reasons; the prices are renewed on a
weekly basis. Regarding the price information available in the magazine, three items are listed:i) the initial asking price (first offer price) when the house was placed on the market, ii) the priceupon removal from the magazine (estimated purchase price: first bid price), and iii) the actual
transaction price, which is collected as a sample for statistical purposes. The first asking pricerepresents the seller’s desired price rather than the market value. In contrast, the transaction
prices are enouph to estimate hedonic model considering neighborhood effects.
From the information published in Residential Information Weekly, I decided to use the price at10

the time of removal from the magazine upon the conclusion of the contract as the explained
variable of the model. The price at the time of removal from the magazine is the first priceoffered by a prospective buyer; such a bid is offered through the process in which several
particulars of characteristics and price are disclosed to the market via the magazine, and the priceis decreased until the buyer responds to that information in a manner opposite to that of an auction.
Thus, this price indicates the upper range of market price but it is extremely near transaction price.
(Comparing resuls of 962 samples we observed between ii) last listed price and iii) transaction
price, above the 95% samples are equal).

2.4.2. Data regarding house characteristics
The price of a single-family house is determined by information on the land and the building.
As the numerical data representing the attributes of the land and building, I adopted ground area
(GA), floor space (FS), and front road width (RW). The age of the building is the period from theconstruction of the building to the conclusion of the transaction. To take into account whether
the house’s windows are south-facing or not,I defined a south-facing dummy (SD). In

addition, when a house has an adjacent private road, this is handled by a private-road dummy(PD); when a transaction is only for land without a building, it is handled by a land dummy (LD).
Furthermore, the convenience of public transportation from each house location is represented
by the travel time to the CBD (TT) and the time to nearest station (TS). The travel time to the
CBD is measured in the following way. First, I defined the CBD. The metropolitan area of11Tokyo is composed of 23 wards with Tokyo as its center containing a dense railway network. I
designated terminal stations as the center of major business districts. The terminal stations arechosen to include six on the Yamanote Line: Tokyo, Shinagawa, Shibuya, Shinjuku, Ikebukuro,
and Ueno, as well as Otemachi as the central station of the Tokyo Metro (Teito Rapid Transit
Authority). Then, I investigated average travel times during the day from each station to theseven terminal stations, and set the minimum value as the travel time to the CBD for each
property.
Regarding the time to the nearest station, times for different means of transportation areavailable. There are three means of transportation: by foot, by bus, and by car. However, thedata for analysis only include houses within walking distance or bus-transportation distance.
Therefore, any difference in the distance between the former and latter is controlled by a dummy
variable (bus dummy: BD). In addition, the walking time (in minutes) is recorded when thehouse/land is within walking distance, and walking distance from the house/land to the bus stopand the onboard time from the bus stop to the nearest station (in minutes) are recorded in the caseof houses/land in a bus-transportation area. The time to the nearest station (TS) is defined as(walking time to nearest station) + (walking time to bus stop) + (onboard time from bus stop tonearest station). Then, for a bus-transportation area, the cross term of the constant dummy
variable with the time to the nearest station (TS) is incorporated in the bus dummy.
These variables are factors attributed to the location or buildings that are single-family houses;
regional price differences are expected to exist as well. Therefore, I set a ward dummy (WD) to12reflect differences in the quality of public services and differences in the prestige of regions.
Furthermore, since most of the residential ground developments in the Tokyo metropolitan area
have been carried out along railway lines, the price structure of houses differs along each railway
line; therefore, I defined a railway line dummy (RD). Moreover, I used a time dummy (TD) tocontrol differences in temporal price changes.
The sale price of each property is also affected by the fluidity and depth of the market. The
time taken until the contract is concluded is considered to be affected by the period and location
and by the level of activity of transactions in the market. I explain such market factors using
variables such as market reservation time (RT). The RT is the time between the date when aland/house is placed on the market by a seller and the date when the transaction is concluded.
Properties with a long market reservation time are regarded as having a price higher than theequilibrium price or participating in a thin market. Conversely, properties with a short market
reservation time are regarded as being in a market with high fluidity or having a price close to or
lower than the equilibrium price. For our purposes, I defined the market reservation time as thetime between the first listing of the property in the magazine and its removal from the magazine
due to contract conclusion.
Table 1 shows a list of the explanatory variables. The observation data consist of 13,822transactions concluded between January and December 2,000.

13

Table 1．List of analyzed data
Symbols
GA
FS
RW

AGE

TS

TT

Variables
Ground Area/square
metersFloor space/ square
metersFront Road Widths
Number of years sinceconstruction

Time to nearest station

Travel Time to central
business district

Contents

Unit

Ground Aream2

Floor space.

m2

Front Road Widths

10cm

Period between the date when the data is deleted
from the magazine and the date of construction ofyear

the building.
Time distance to the nearest station (Time by
Walk or Bus).
Minimum of railway riding time in daytime to
Terminal 7 stations in 2000.

minuteminutePeriod between the date when the data appear in
RT

Market reservation timethe magazine for the first time and the date ofweekbeing deleted.
Whether the time distance includes riding time ofBD

Bus dummybus 1,(0,1)

not including bus time including bus time 0.
The property is on the ground floor 1,
FDFirst floor dummy(0,1)
on the other floors 0.
Fenestrae facing south 1，

SD

South-facing dummy(0,1)
other directions 0.

BU

Building characteristics

CS

Census Data

ZD

Zonning dummy

Building density-number of buildings,Averagebuilding floor space, in 500×500mesh.
Number of household ,etc－－

Land use zonning i 1，
(0,1)
other land use 0.
k th administrative district 1,
LDk (k=0,…,K)

Location (Ward) dummy(0,1)
other district 0.

14

l th railway line
RDl (l=0,…,L)

1,

Railway line dummy(0,1)
other railway line 0.
m th month 1,

TDm (m=0,…,M)

Time dummy (monthly)(0,1)
other month 0.

2.4.3.Statistical distribution of single-family house prices
Table 2 shows descriptive statistics of the major variables.
The average single-family house price is 72.11 million yen, the minimum value is 10.50 million
yen, and the maximum value is 398.00 million yen, with a fairly large standard deviation of 43.84
million yen. The data include a wide range of single-family houses from small dwellings to theso-called over-100-million-yen large ones. The average unit price is approximately 0.68 million
yen/m2 with a small variation.
The minimum ground area is 10.11 m2 and the maximum area is 797 m2, showing a large
variation. The minimum floor space is 0 m2 for land-only transactions and the maximum valueis 649 m2, with an average of 77 m2.
Regarding the age of the buildings, while the average age is 5.28 years due to the inclusion of alarge number of newly built houses, the maximum value is 41.33 years, with a right-skewed
distribution.
Regarding the time to the nearest station, there are properties with a minimum value of 0 minutesthat are located in front of a station; the maximum value is 36 minutes, with an average value of
10 minutes. On average, while many properties are conveniently located, some are located in15

areas with inconvenient public transport.
The minimum travel time to the CBD is 1 minute, which indicates that there are properties
located adjacent to main terminal stations. The maximum travel time is 33 minutes, and theaverage time is 11 minutes. The variation is small because the subject of the analysis is the 23
wards of Tokyo, wherein which a dense railway network has been developed.

Table 2．Summary statististics of single-family house data.

Variables
DP:Price ofDetached house price

AverageStandard
Deviation

Minimum

Maximum

7,211.86

4,384.60

1,050.00

39,800.00

GA: Ground Area (㎡)114.99

75.07

10.11

797.35

DP/GA

76.83

69.37

0.00

649.08

FS: Floor space (㎡)

4.85

2.48

2.00

50.00

RW:Front Road Widths

5.28

8.75

0.00

41.33

Age:Age of Building(monthes)

9.64

4.36

0.00

36.00

TS: Time to the nearest station: (minutes)

11.45

6.08

1.00

33.00

2.39

2.53

0.00

35.00

68.67

24.08

12.00

392.00(10,000 Yen)

TT: Travel Time to Central BusinessDistrict (minutes)
RT: Reservation Time (week)
2000/01-2000/12

n=12,954

16

3．Estimated results
3.1.Formulation of hedonic models
I formulated Model-1 on the basis of eq. (1) using the market for single-family houses and landin the 23 wards of Tokyo. The results are shown in the following.

log DP / GA  4.655  0.001・GA  0.001・FS  0.016・RW  0.011・Age  0.010・TS  0.012・TT(324.255) (42.569)(15.276)(19.749)(47.013)(20.938) (26.633)

 0.169・BD  0.002・( BD  log TS )  0.029・SD  0.284・LD  0.0007・MR  1 j WD jj(4.025)(0.892)(6.316)(35.761)(1.029)  2 k  RDk   3l  TDl  
k

l

(2)
Adjusted R-square value: 0.645 (The numbers in parentheses indicate t values)
Number of observations: 12,954

Since the adjusted R-square value is 0.645, the formulated model has a fairly high explanatory
power as a primitive model. The model was basically linear, and the selection of variables
constituting the zoning dummy and railway line dummy was performed by a best-subset selection
procedure based on Malow’s CP when the major variables (Xh) were incorporated.
Using Model-1 as a starting model, I formulated other models wherein consideration is given toneighborhood effect variables (Table 3).
First, in Model-2, the urban-plan zoning dummies for house use, commercial use, and industrialuse, as well as the floor area ratio and the lot area ratio were added. After incorporating the17variables adopted in Model-1, I selected variables to be added by the best-subset selection
procedure. As a result, the commercial-use zoning dummy and the floor area ratio were adopted.
Next, I formulated Model-3 by incorporating the ambient land-use conditions (LU), thehousehold characteristics (HC) obtained in the national census, and the road traffic noise (NOi) as
neighborhood effect variables, and by adding the cross terms of the floor area ratio with major
variables as well as the latitude and longitude coordinates. After incorporating the variables
adopted in Model-2, I included the squared terms and cubed terms of the latitude and longitude
coordinates and added variables corresponding to V4, V5, and V6.

In concrete terms, I

incorporated the variables adopted in Model-2 and the cross terms of the floor area ratio with the
major variables and selected other variables by the best-subset selection procedure.
Table 3 shows the variables adopted. When the formulated models were compared, bothModel-2 (adjusted R-Square: 0.693) and Model-3 (adjusted R-Square: 0.712) showed improved
explanatory power as evaluated by the adjusted R-square value. In Model-2, the estimated valuesof the commercial-use zoning dummy and the floor area ratio have positive signs. However,neither estimate is statistically significant, even at the 5% significance level. When a comparison
is made with Model-3, only when other variables such as neighborhood effect variables, position
coordinates, and fixed effects are incorporated are the estimates of the commercial-use zoning
dummy and the floor area ratio statistically significant at the 5% significance level and 1%
significance level, respectively.
Regarding their signs, the commercial-use zoning dummy has the effect of decreasing the house18

price; we can see that house prices tend to be high in regions with a high floor area ratio. Sincemany regions have a poor living environment among commercial districts, I assume that such aneffect was exhibited in the model.

Table3. Estimated results of modified hedonic models.
Dependent Variable
DP: Price of Detached House (in log) per ㎡
Independent Variables
Model-2
X:Property Characteristics

Model-3

Coefficientt-value

Coefficientt-valueConstant

4.903

330.892-48.523-3.089

GA: Ground Area

-0.005-50.466-0.005-42.660

FS: Floor space

0.001

24.2640.001

12.686

RW:Front Road Widths

0.017

22.756

0.010

6.437

Age:Age of Building-0.011-47.183-0.009-19.166

TS: Time to the nearest station-0.009-20.190-0.007-7.637

BD: Bus Dummy-0.141-3.621-0.134-3.516

TS × BD

0.002

0.623

0.002

0.692

TT: Travel time to the CBD-0.011-26.178-0.007-8.835

Coefficientt-value

Coefficientt-value

SD:South Face Dummy

0.027

6.511

0.029

7.190

LD:Land Dummy-0.187-24.146-0.198-26.244

MR:Market Reservation Time-4.516E-04-0.653-4.383E-04-0.653

Coefficientt-value

Coefficientt-value

ZD1:Zoning Dummy(Commercial use)

0.005

0.555

-0.009-1.055ZD2:LR(Lot area ratio)

5.E-060.153

2.017E-04

2.757

Coefficientt-value

Coefficientt-value

BU1:Average building area－－

1.782E-03

12.752BU2:Standard deviation of building area－－

-1.576E-04-4.912BU3:BLD: Building Density－－

1.560E-04

4.286

BU4:BLD2－－-6.659E-08-3.858

CS1:Rate of Office worker－－

6.414E-03

7.155

CS2:Average building area per household－－

2.669E-03

12.314

Z:Other Property Characteristics

V1:Neiborhood Effects

V2:Neiborhood Effects by GIS

19

－

－

-1.858E-02-4.139Coefficientt-value

Coefficientt-valuelatitude－－3.384E-01

3.288

longitude3－－

4.658E-05

1.645

Coefficientt-value

Coefficientt-value

GA: Ground Area－－

-3.940E-07-1.425

FS: Floor space－－

9.231E-07

3.175

RW:Front Road Widths－－

1.696E-05

3.967

Age:Age of Building－－-1.111E-05-4.929

TS: Time to the nearest station－－-1.084E-05-2.379

TT: Travel time to the CBD－－-1.061E-05-3.071

EX:Noise
Unobserved Spatial Effects(u,v)

Fixed Effects by LR

Location (Ward) Dummy
LDj (j=0,…,J)

Yes

Yes

Yes

Yes

Yes

Yes

Railway/Subway Line Dummy
RDk (k=0,…,K)
Time DummyTDl (l=0,…,L)
Number of observations= 12,954

12,954

Adjusted R-Square= 0.693

0.712

3.2.Effect of neighborhood externalities on house price
Ambient land-use conditions: LU
According to Model-3 regarding ambient land-use conditions (LU), the estimated averagebuilding area per mesh (500 m x 500 m) had a positive sign and was statistically significant at the
1% significance level, indicating that an increase in the average building area positively affects the
single-family house price. In contrast, the estimated standard deviation of the building area had anegative sign and was statistically significant. In addition, while the estimated building density
had a positive sign, the estimated second-order term had a negative sign. In a region with a large
average building area and uniform areas of buildings, the local environment is pleasant and the20appearance of the town is well ordered, which causes house prices to increase. However, as thevariation in building area increases, the local environment deteriorates, resulting in a decrease inhouse price.
Regarding building density, i.e., the degree of concentration of buildings in a regional unit, theprimary term, which affects property values linearly, has a positive effect, whereas the estimatedsecond-order term had a negative effect and was statistically significant. This suggests that an
appropriate degree of building concentration has a positive effect on property values, whereas highconcentration has a negative effect.

Household characteristics: HC
Regarding the household characteristics surveyed in the national census, the office worker ratio,
as defined in section 2.3, and the average building area per household were adopted in Model-3.
Both of these variables were estimated to have positive signs and were statistically significant at
the 1% significance level. Since the academic background and income of people working inthese occupations are expected to be high, the office worker ratio variable is considered to serve asa proxy variable representing both the ability to buy a house and the academic background. The
average building area per household was adopted independently of the average building area (GIS
data), and similarly it was estimated to be a positive externality.
In houses in a region with a large average building area according to GIS data, the likelihoodthat attention is paid to the façade (appearance) of the houses, such as tidy hedges, is high, so that21

an externality due to such physical contributions to the urban space is expected; I consider this thereason why the estimated average building area has a positive sign and is statistically significant.
Meanwhile, at the level of the household unit, a large building area means a large area owned by ahousehold; accordingly, the variable of the average building area shows characteristics of a proxyvariable representing the assets and economic power of households. Thus, these two effects are
estimated independently.
In other words, this suggests that in a region of households with high income and considerable
assets, house prices tend to be high. Such a result is consistent with an economic model proposedby Rosen (1974), thus indicating that house prices are not determined only by physical factors suchas the houses themselves.

Environmental externalities/road traffic noise: NOi
It is natural to expect that house prices are strongly affected by environmental externalities suchas air cleanliness and noise, independent from the physical environment such as the town
appearance set as LU. Here, I also incorporated the road traffic noise (NOi) as an environmental
externality independent from ambient land-use conditions (LU) and household characteristics (HC).
Since the road traffic noise is determined by the amount of road traffic, I considered that it could
serve as not only the externality of noise but also as a proxy index representing air pollution andsafety. Results of the estimation showed that road traffic noise had a negative sign and wasstatistically significant at the 1% significance level, and that an increase in noise by 1 dB decreased

22the house price by 1.8%.

Other factors
The fixed effect was controlled by the cross terms of the floor area ratio with the major variables,
and the estimated values were statistically significant for all variables at the 1% significance level.
Regarding the coordinate values (u, v), the cubes of latitude and longitude were adopted. Thisfeature suggests that, in this model, geographical attributes remain that cannot be absorbed by theincorporation of travel time to the CBD, the land (ward) dummy, the railway line dummy, and theneighborhood effect factors.

3.3.Omitted variable bias
Through the above analyses, we have shown that neighborhood effect variables significantly
affect house prices. Consequently, if we do not take these variables into consideration, omitted
variable bias is generated in the values estimated by hedonic models. Therefore, I compared thethree models formulated in this study, i.e., Model-1, Model-2, and Model-3 (Table 4), andmeasured the bias levels.

Comparison between Model-1 and Model-2
When Model-1 and Model-2 are compared, we see that the ground area (GA) is estimated as
being too high in Model-1, but no difference is apparent in the estimated parameters of the other23

major variables between the two models. In concrete terms, while ground area was estimated as(-0.001) in Model-1, it was estimated as (-0.005) in Model-2.
The difference between these two models is due the presence or absence of the land-use
regulation dummy and the floor area ratio. Since the floor area ratio and land-use regulation
cause a change in the effectively available area, the occurrence of large differences between thestatistics estimated using the models with and without these variables is expected.
For example, the ground area has a strong correlation with the effective area of land available forconstruction. Meanwhile, an increase in the floor area ratio increases the effective area available
for construction in the vertical direction independent of the planar area; thus, there is a replacement
relationship between the ground area and the floor area ratio. Consequently, when the floor area
ratio and the commercial land-use regulation dummy were considered in Model-2, the estimatedparameter showing a decrease in the house price accompanying an increase in the area wassignificant.
Regarding the variables other than ground area, it is difficult to assume a strong correlation withthe floor area ratio or land-use regulations. Consistent with this hypothesis, the results showed nodifferences in the estimated parameters for the variables other than ground area between Model-1
and Model-2; thus, the correctness of this hypothesis has been verified.

Comparison between Model-2 and Model-3
Next, I compared Model-2 and Model-3. In Model-3 the variables LU, HC, and NOi are24added and the fixed effects of the floor area ratio as well as the major variables and the coordinate
data are incorporated. No changes were noted in the estimated values of ground area and floor
space, but bias was generated for not only the variables representing locational characteristics suchas the front road width, time to the nearest station, bus dummy, and the travel time to the CBD, butalso for the age of the building.
The absolute values of the estimated parameters of Model-3 for these location characteristics
and the age of the building were smaller than those of Model-2. In concrete terms, the estimatedvalues were as follows:

Front Road Widths(RW)
Time to the nearest station (TS)Bus dummy (BD)Time to the nearest station (TT)Age of Building (Age): Model-2(0.017) / Model-3(0.010);: Model-2(-0.009) / Model-3(-0.007);: Model-2(-0.141) / Model-3(-0.134);: Model-2(-0.011) / Model-3(-0.007);: Model-2(-0.011) / Model-3(-0.009);

andThis means that the effect of each variable considered as a neighborhood effect variable inModel-3 was integrated in the major variables in Model-2.
First, regarding RW, in residential districts in the 23 wards of Tokyo where houses are densely
built, it is predicted that, as RW increases, insolation and ventilation are improved and public
spaces are widened, leading to an improvement in the local environment. Therefore, whenneighborhood externalities are not considered, it is possible that such environmental factors wereabsorbed in RW in the estimation.

25

Table4．Comparison of estimated parameters.
Method of Estimation
OLS
Dependent Variable
DP: Price of Detached House (in log) per ㎡
Independent Variables
X:Property Characteristics

Model.1

Model.2

Model.3

Coefficientt-value

Coefficientt-value

Coefficientt-valueConstant

4.655

324.2554.903

330.892-48.523-3.089

GA: Ground Area

-0.001-42.569-0.005-50.466-0.005-42.660

FS: Floor space

0.001

15.2760.001

24.2640.001

12.686

RW:Front Road Widths

0.016

19.7490.017

22.756

0.010

6.437

Age:Age of Building-0.011-47.013-0.011-47.183-0.009-19.166

TS: Time to the nearest station-0.010-20.938-0.009-20.190-0.007-7.637

BD: Bus Dummy-0.169-4.025-0.141-3.621-0.134-3.516

TS × BD

0.002

0.892

0.002

0.623

0.002

0.692

TT: Travel Time to CBD-0.012-26.663-0.011-26.178-0.007-8.835

V1:Neiborhood Effects

Yes

Yes

Yes

V2:Neiborhood Effects by GIS

No

No

Yes

Z:Other Property Characteristics

Yes

Yes

Yes

Fixed Effects by LR

No

No

Yes

Unobserved Spatial Effects

No

No

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Yes

Location (Ward) Dummy
LDj (j=0,…,J)
Railway/Subway Line Dummy
LDk (k=0,…,K)
Time DummyTDl (l=0,…,L)
Number of observations=

12,954

12,954

12,954

Adjusted R-Square=

0.645

0.693

0.712

Regarding the variables representing the convenience of public transport such as “time tonearest station”, “bus dummy”, and “travel time to the CBD”, when environmental factors suchas the neighborhood externalities were not considered, the house price was estimated to decrease
with the physical distance from the CBD. However, as the distance from stations with large
26amounts of commerce (a large CBD) increases, environmental factors such as building density
and road traffic noise improve. When this is taken into account, as the distance from the centerof a city or from regions with a large amount of commerce increases, these environmental factors
may cause the house price to increase. Thus, when the neighborhood externalities are taken into
consideration, the effects of “time to nearest station”, “bus dummy”, and “travel time to the CBD”
are less negative.
Regarding the age of buildings, it is unlikely that house prices decrease uniformly with age inthe entire city. In districts where the environment is pleasant, the decrease in the house pricewith the time since the construction of the house is likely to be suppressed. Accordingly, by
taking into consideration the neighborhood effect variables representing the local environment, thenegative effect of age is reduced.
As described, we can see that, without taking into consideration the neighborhood effectvariables, non-negligible omitted variable bias is generated in the hedonic model of the housing
market in the 23 wards of Tokyo.

4．Concluding remarks
In this study, to estimate hedonic price functions, I focused on the problem of omitted variablebias and attempted to clarify the effect of the incorporation of neighborhood effect variables as ameans of reducing this bias. In concrete terms, starting from a hedonic model wherein only thebuilding attributes and locational characteristics are considered, I expanded the model by27

incorporating neighborhood effect variables and observed changes in the estimated parameters.
As a result of the analyses, the following items were clarified. Starting from Model-1, in
which the estimation is carried out using only generally available information, I incorporated
neighborhood effect variables into the model.

The effects of incorporating the variables

representing the land-use conditions (LU) were as follows.
-The estimated average building area in a mesh (500 m x 500 m) acted positively and wasstatistically significant. This may indicate that a large area per building acts as an externality,
thereby affecting house prices positively. However, the estimated standard deviation had anegative sign and was statistically significant.
-As a reason for this, I found that, when the areas of buildings surrounding a house are similar, theregion has a well-ordered appearance, which is a positive externality, whereas when the variationis large, the region does not appear well-ordered, leading to a negative externality.
-Regarding building density, while the estimated primary linear term had a positive sign, theestimated second-order term had a negative sign. An appropriate degree of building density
produces a positive effect, but when the density becomes too high, a negative externality is
induced.
Next, I considered the household characteristics among variables obtained in the national
census. I also introduced road traffic noise as another environmental externality.
-The indices of the number of office workers and the average gross floor space per household
were adopted, and their estimated values acted positively and were statistically significant. The

28

number of office workers is a proxy variable representing the levels of academic background andincome. The average building area per household is a proxy variable representing asset size.
From this result, the importance of taking into consideration the individual attributes ofhouseholds, such as income and academic background in the estimation using hedonic models, as
shown by Rosen (1974), was also demonstrated.
-With respect to the road traffic noise, it was shown that an increase in the noise level of 1 dB
decreases the property value by 1.8%.
Using these estimated results, I confirmed that omitted variable bias is generated in a model
wherein neighborhood effect variables are not considered.
Starting from Model-1, the commercial-use zoning dummy and the floor area ratio were addedin Model-2; the comparison between Model-1 and Model-2 showed that the estimated statisticalvalue of the ground area in Model-1 was approximately twice that in Model-2. No effects ofincorporating the variables of “time to nearest station”, “bus dummy”, “travel time to the CBD”,
and “age of building” were noted.
In addition, when Model-3, in which the neighborhood effect variables were incorporated, wascompared with Model-2, effects of “time to nearest station”, “bus dummy”, “travel time to the
CBD”, and “age of building” were observed in Model-3.
-The estimated parameters in Model-2 compared with those in Model-3 were 1.7 times greater for
the front road width and 1.3 times greater for the time to the nearest station, showing large
differences. The absolute values of the estimated parameters of the bus dummy, the travel time29

to the CBD (TT), and the age of the building in Model-2 were larger than those in Model-3. Thisdifference arises because, when the effects of the variables relating to the neighborhood effectvariables are not considered as in Model-2, such effects are integrated in variables such as “time tonearest station”, “bus dummy”, “travel time to the CBD”, and “age of building”, so that thesevariables are estimated to have high values. Thus, the problem that the generation of omitted
variable bias is at a non-negligible level was confirmed.
In this study, by considering the neighborhood effect variables represented by theenvironmental variables and the incomes of households in a small neighborhood unit, I aimed to
expand the initial hedonic model and to clarify their effect on reducing omitted variable bias. As
demonstrated by the series of estimated results, when the neighborhood effect variables were notconsidered in the estimation using hedonic models, bias was generated in the estimatedparameters.
The application of hedonic models as an evaluation method for the effects of urban policies has
been actively attempted in Japan. I hope that this study contributes to the evaluation of suchpolicies.

30

Appendix．Measurement of road traffic noise

I measured the road traffic noise by field investigation and using a prediction method in
accordance with ASJ Model 1998 by measuring and estimating equivalent noise levels LAeq,16h
during the daytime reference time period (from 6 am to 10 pm). This method of measurement
was chosen because I judged that the prediction would only yield the effects of roads on property
values as the subject of the analysis, in contrast with actual measurements wherein measurement
errors are generated due to variable factors (measurement day and time, measurement point). In
addition, prediction errors were expected to be smaller than measurement errors.

The measurement sites comprised 202 sites in Setagaya ward, Tokyo. Measurements were
performed at a point 1.2 m above the ground and 1-2 m from a building at the measurement site.
At sites where a reflection effect was predicted, a correction of -2 dB was applied to the measured
value. Measurements were performed between 2 pm and 6 pm on September 16 and 18, 2000,
with a measurement period of 5 min at each site. The measurement procedure was as follows:
signals (noise) received by an omnidirectional microphone (Rion NL-04 and NL-06) wererecorded on a data recorder (Sony-D10). Then, an equivalent noise level LAeq.,5min for the 5
min signals, in which sounds other than the traffic noise on the subject road were excluded fromthe signals obtained from 10 min measurement, was obtained by an integral-average noise meter.
This value was defined as the daytime equivalent noise level LAeq.,16h. We pressed the pause31button to stop the analysis; then after removing the unwanted sounds, we restarted the analysis.
Note that in the field investigation, in cases when measurement sites were at the back of a building
facing another road, not only the traffic noise from the subject road but also traffic noise fromvehicles passing the other road is included in the subject sound.

We used the road traffic noise, the amount of traffic of the road, and the distance from the roadto produce a prediction model estimating the road traffic noise. The result of the estimation is asfollows.
Noiseit =46．167 +0．832・(Rstr it／Rdst it)
Noiseit : noise level at site i at time t
Rstr it : amount of road traffic at site i at time t
Rdst i : direct distance from site i to the nearest main road
Adjusted R-Square: 0.923(The number in the parentheses is a t value.)
Number of Observation: 202

This model has a strong explanatory power with an adjusted R-square value of 0.923,
demonstrating that the model can be used as a prediction model.

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33