Global COE Hi-Stat Discussion Paper Series 310

Research Unit for Statisticaland Empirical Analysis in Social Sciences (Hi-Stat)

Imputed rent estimate of detached houses with regional
characteristics and housing quality adjustments: A case study
using Japan’s housing and land survey data

Hi-Stat Discussion Paper

FUJISAWA Mieko
INUI Tomohiko
HIROMATSU Takeshi

May 2024

Hi-Stat

Institute of Economic Research
Hitotsubashi University
2-1 Naka, Kunitatchi Tokyo, 186-8603 Japan
https://gcoe.ier.hit-u.ac.jp

Imputed rent estimate of detached houses with regional characteristics and housing
quality adjustments: A case study using Japan’s housing and land survey data

FUJISAWA Mieko†
INUI Tomohiko ǂ
HIROMATSU Takeshi§

Abstract
The problem of imputed rent estimation in national accounts has been widely discussed among
statisticians in Japan. The imputed rent of owner-occupied housing is estimated using prevailing rental
prices. Thus, it is important to determine whether a more accurate quality adjustment would raise theestimated imputed rent of owner-occupied properties. This study examined the determinants of housing
quality and developed an alternative estimation method for the imputed rent of owner-occupied
detached houses. Specifically, we used a hedonic model to identify the regional characteristics andhousing quality factors specific to individual houses that influence imputed rent estimation. Comparing
our results to the current estimation results used in the Japanese Statement of National Accounts reveals
that our imputed rent estimation results, which reflect regional characteristics and housing quality, are
3.84% higher than that estimated using the current methods. These findings highlight the need toincorporate a subdivision of the analysis area as well as the quality of city planning and housing into
the imputed rent estimation method.
Keywords: Imputed rent, Hedonic model, Quality adjustment, Detached houses, System of National
Accounts
JEL Classification: E01, R10, R32―――――――――――――†

Kanazawa University College of Human and Social Sciences School of Economics
Email：fujisawa@staff.kanazawa-u.ac.jp
ǂ Gakushuin University Faculty of International Social Sciences
Email：tomohiko.inui@gakushuin.ac.jp§
Institute of Information Security Graduate school of 1Information Security
Email：tkhirom@iisec.ac.jp

1. Introduction
The total value of imputed rent is incorporated into the Statement of National Accounts (SNA)
and is a component of gross domestic product (GDP). Imputed rent is an estimate of the rent the owners
would pay if they rented the properties they occupied. The estimation of this value does not use market
prices per se; instead, imputed rent is valued by using the market prices of rental housing. The
methodologies used to make such estimates are determined by each country’s statistics bureau,
following the recommendations of the United Nations (1977). There is no internationally standardized
method for these estimates; instead, countries develop their own methods consistent with their uniquesituations. After adoption, countries will update their methods as their situations change.
The methodologies used in other countries’ estimates are outlined in Eurostat-OECD (2012),
Eurostat (2010), and Frick and Grabka (2002). In the United Kingdom, statisticians have also worked
on improving methods for estimating imputed rent (Yu and Ive 2008) and the hedonic models used toadjust for housing quality (Richardson and Dolling 2005).
The Japanese SNA, which includes imputed rent, is estimated according to the Cabinet Office’s“Explanatory Notes on the Methodology for Calculating the Statement of National Accounts” 1(Cabinet Office 2011). However, it has been noted that the derived imputed rent neither reflects market
prices nor considers regional characteristics or housing quality (e.g., Arai 2005). In light of these issues
and in line with the 2008 United Nations recommendation (SNA2008; United Nations 2008), a
discussion on an optimal estimation method for imputed rent, including the use of a hedonic model ora similar method, is required.
Based on a summary by Balcazar et al. (2017), two model approaches are used to estimate
imputed rent based on actual conditions (a hedonic model and a non-hedonic model). The former uses
current government statistical data, whereas the latter requires a re-evaluation of user costs (Yates 1994)
or self-evaluation data (Fessler et al. 2016).
Furthermore, housing quality adjustment, which is promoted by SNA2008, needs to be
discussed. As every house is different, Hill (2013) argues that we should account for these quality
differences through housing price indexes. Housing quality adjustment methods include the hedonic
model and repeat-sales method. Hill and Syed (2016), who used the hedonic model, found that thequality difference between owned and rented houses in Sydney was approximately 18%. In addition,
Baltagi and Li (2015) confirmed the difference between owned and rented houses in Singapore using
the repeat-sales method. However, the repeat-sales method is subject to methodological constraints, andthe results can only be verified using data such as condominiums of similar quality.
To eliminate such constraints in the repeat-sales method, this study uses the hedonic model for
housing quality adjustment in the estimation of imputed rent in the SNA. Although many studies on
hedonic models for housing have been conducted (Chin and Chau 2003), there is no consensus on thespecific form of the hedonic price function (Lisi 2013). Therefore, we empirically investigate thehedonic model for housing. Fujisawa et al. (2021) conducted an estimation analysis using the Housing
and Land Survey data and confirmed the use of the hedonic model to estimate imputed rents forapartments and detached houses in Tokyo.
There are two main arguments to improve imputed rent estimation: the first is to propose a new
imputed rent estimation method based on new data (e.g., Shimizu et al. 2009), and the second is tomodify and improve the current situation (e.g., Fujisawa et al. 2021). This study is based on the latter,
and we tried to estimate imputed rent of detached houses with strong individuality and then verify its
feasibility using the method developed by Fujisawa et al. (2021).
The objective of this study is to introduce an alternative estimation method for detached houses
that reflects housing market characteristics consistent with current estimation methods (current
estimates) by utilizing Cabinet Office data (2011). Specifically, our study estimates imputed rents ofdetached houses that consider regional characteristics and housing quality and identifies differences
An edition of the Explanatory Notes on the Methodology for Calculating the Statement of National
Accounts is published for each base year in Japan; the most recent being the 2011 base year edition. In
addition, annual estimates and quarterly preliminary GDP estimates (QE) differ, and editions are also
published for annual and QE estimates. (Accessed: October 22, 2020)
https://www.esri.cao.go.jp/jp/sna/data/reference1/sakusei_top.html.
2
1

from current imputed rent estimates. Subsequently, we propose an improved estimation method that
adjusts for housing quality.
The remainder of this paper is organized as follows. Section 2 gives a brief overview of theexisting issues and the proposed improvement to the imputed rent estimate in Japan. Section 3 describes
the analytical method, the new estimation, and the data used in this study. Section 4 describes the resultsof the analysis and estimates. Based on the results, we discuss the estimation design for imputed rent.
Finally, section 5 concludes this study and presents its key implications as an alternative of the imputed
rent estimation method.
2. Existing issues and proposed improvement plan
2.1 Existing issues
The importance of imputed rent has been widely discussed among Japanese statisticians. Arai(2005) has pointed out that regional characteristics and housing quality are not taken into considerationand that, by their very nature, imputed rents are nationwide and subject to data limitations.
A problem with the current estimation method is failure to reflect market prices in imputed rentby using the continuous rent price in the estimation. This is highlighted by Shimizu et al. (2010), whonote that the current estimate uses continuous rent data from the “Housing and Land Survey” of the
Ministry of Internal Affairs and Communications (MIC), which differ from market rental price data.
Dieweret and Shimizu (2016) investigated continuous rent and prepared estimates using private sector
rental housing data. According to them, the trends in these indicators differ from the trends in imputed
rents. Although this issue of continuous rent is significant, the immediate task is to adjust the quality of
imputed rent to SNA2008.
Reflecting Arai (2005) pointed out, there was improvement in the estimation method from
uniformly across a country to each prefecture by Japanese government. However, Shimizu et al. (2013)stated that it is impossible to determine the effects of various market characteristics on rent in current
prefecture-level estimation. Thus, there is a need for a method for estimating imputed rents that is more
realistic and considers both regional characteristics and housing quality.
2.2 Proposed improvement plan
Current imputed rent estimates differ in methodology depending on whether they are calculated
in base or non-base years of the Housing and Land Survey. Estimates in the base year (base year
estimates) are developed using Housing and Land Survey data on (1) prefecture, (2) house structure,(3) attributes of the period from housing completion (completion period classifications), and (4) total
floor space of owner-occupied housing. Since the Housing and Land Survey only presents data in fiveyear intervals, estimations for non-base years need to be developed from other data sources. To create
adjusted estimates that capture changes from base year estimates, sources such as the “Statistics of
Housing Construction Starts” by the Ministry of Land, Transport and Tourism are used for the period
between base years.
In this study, we examine the process of adjustment for “regional characteristics” and “housing
quality” in base year estimates, which serve as the basis for the five-year estimates. If base year
estimates can be made more accurately, the accuracy of the estimates for non-base years willconsequently improve.
2.2.1 Issues with regional characteristics
The current method of calculating average rent per square meter—by dividing the rent by floor
space in each prefecture and multiplying this by the total floor space of owner-occupied houses—neither
distinguishes between commercial and residential areas nor adjusts for the quality of regional
characteristics. In other words, it does not reflect “geographical neighborhoods” or “regional
characteristics,” as pointed out by Ptacek and Baskin (1996). As addressed in the analysis by Arévalo
and Ruiz-Castillo (2006), geographical variables representing regional characteristics should be taken
into account.
As regional attributes in a single prefecture cannot be expressed using a single term, it isnecessary to make estimates in line with each regional attribute for each sub-area. According to thetheory of urban additive functions developed by Ricardo (1817), Mills (1972), and others, the fact that3

rent values decline as one moves farther away from city centers must also be factored into the estimates.
These urban additive functions have previously been empirically validated (e.g., Koramaz andDokimeci 2012). Using data from the Housing and Land Survey can solve this problem without
requiring the acquisition of new data; that is, these data makes it possible to distinguish between
municipalities and zones and to divide prefectures into three or four smaller areas, creating scope for
improvement in rent estimates.
Regional characteristics include neighborhood environments (housing environment factors).
For example, Boyle and Kiel (2001) demonstrated that housing prices are affected by the surrounding
environment (neighborhood variables such as air quality). Earlier studies have found that the omitted
variable bias effect of housing environment factors on land prices is significant (e.g., Hill 2013; Shimizu
et al. 2013). As such, a method for estimating imputed rent, taking into account these factors, is also
required. The Housing and Land Survey data include zoning, distances to nearby facilities, floor–arearatios (FAR), etc. It is possible to incorporate these variables as housing environment factors andanalyze them.
2.2.2 Housing quality issues
According to Arévalo and Ruiz-Castillo (2006), indicators of housing quality are important, but
the current estimation method casts doubt on this. The question here is whether only the variables ofstructural attributes of individual houses and completion period classifications can be used to capture
housing quality, or whether other variables can also be used to more accurately measure housing quality.
In the current estimation, the possibility that completion period classification variables compensate foreverything related to housing quality except housing structure cannot be dismissed.
It is necessary to examine which variables representing housing quality should be incorporated
into estimates, subject to the limitations of the Housing and Land Survey data. Specifically, referring to
Hill (2013), we focus on construction types, housing types, structure types, building age, and the gap
between rental and owner-occupied housing.
Construction types must be controlled for when estimating imputed rent because building costs
and housing attributes differ depending on the type of construction. However, the current estimates do
not account for construction types. For example, in Tokyo, detached houses and apartments account for
62.4% (6,204,776 ㎡) and 34.2% (3,424,682 ㎡) of the total floor space (9,993,036 ㎡) of housing(Table 1), respectively. Therefore, a method of estimating imputed rent should at least distinguishbetween detached houses and apartments. It is important to develop an estimation method for detachedhouses, which account for the majority of imputed rent in Japan. In the Housing and Land Survey,
housing is classified into four construction types: detached houses, rowhouses, apartments, and others.
Thus, it is possible to develop an estimation method specifically for detached houses.
Housing types of detached houses are separated into dedicated housing and housing combinedwith businesses (combined-use housing), although both categories are treated in the same manner incurrent estimates. As shown in Table 1, combined-use housing accounts for only 3.8% of all housing.
However, due to the characteristics of this type of housing, it is often located in high-rent areas; thus,
the impact of this housing type on rent estimates is likely to be significant. The Housing and Land
Survey data include dedicated and combined-use houses in each construction type. Therefore, it ispossible to distinguish between dedicated and combined-use detached housing and to create an
estimation method that incorporates these differences.

4

Table 1 Total floor space of houses in Tokyo
Housing types

Dedicatedhousing

Combination-use
housing

Construction types

WoodenFireproofed

Structures
SRC

S

OthersDetached house

1,130,958

4,168,841

303,421311,105

11,967

Attached house

21,526

132,927

38,257

24,873

1,462

Apartment house

19,237

90,535

3,143,777

158,709

340

Others

3,117

3,808

38,06814,328365

Subtotal

1,174,838

4,396,111

3,523,523

509,015

14,134

Detached house

48,812

132,640

65,526

57,697

1,197

Attached house

559

4,059

1,144

1,401

-

Apartment house

465

498

6,010

5,111

-

Others

4,988

13,562

20,502

11,21727

Subtotal

54,824

150,759

93,18275,426

1,224

1,229,662
12.3%

4,546,870
45.5%

3,616,705
36.2%

584,441
5.8%

15,3580.2%

Total
Composition ratio

Note: SRC and S are abbreviations for steel-framed reinforced concrete and steel-framed, respectively.
Source: Individual data from MIC, “2013 Housing and Land Survey”
5

Total
Composition ratio
5,926,292
59.3%
219,0452.2%
3,412,598
34.1%
59,6860.6%
9,617,621
96.2%
305,872
3.1%
7,163
0.1%
12,0840.1%
50,296
0.5%
375,415
3.8%
9,993,036
100.0%

Structure types may be wooden structures, which include wooden-frame, fireproofed woodenframed structures, and others, or non-wooden structures, which include steel-framed reinforced concrete(SRC) and steel-framed (S) structures. However, current estimates consider housing structure; themethod is simple and consists of only two categories: wooden and non-wooden structures. The
Construction Research Institute categorizes structures as reinforced concrete (RC), SRC, and S
structures, and points out that the prices and trends differ for each.2 There are five structural categories
in the Housing and Land Survey, and it is possible to categorize detached houses into three types ofstructures: wooden, fireproofed wooden, and non-wooden.
Building age is used as a proxy variable for housing deterioration. Respondents of the Housing
and Land Survey form select a completion period classification. Thus, building age cannot be obtained
from the survey, but information on completion period classifications can be obtained. Based on theassumption that housing values do not fall at a linear rate but instead decline with age following a
convex curve, this classification based on years of age—ranging from the year of housing completion
to four years earlier, every three years from five to seven years earlier, every five years from eight to 20
years earlier, and every ten years thereafter, with the same classifications applied to houses 65 years orolder. In current estimates, these completion period classifications, which have more ambiguity thanreal building ages, are used to measure not only the aging of houses but also housing quality. Because
building practices respond to revisions to laws and regulations, such as the Building Standards Law,
there is a high correlation between the age and quality of housing; thus, it is assumed that the age of thebuilding indicates the quality of the house. However, unlike previous periods when housing quality
improved due to revisions to the Building Standards Law and other factors, housing quality has notnecessarily been correlated with building age in recent years. This makes completion periodclassifications a poor proxy variable for housing quality. Under the constraint of unknown real building
ages, it is very important to substitute these completion period classifications with building ages andthen use housing quality data drawn from the Housing and Land Survey to clearly distinguish between
age-related deterioration and house quality and to clarify the relationships between these two factors
and rent.
Sato (2013) defined the gap in quality between rental and owner-occupied housing and notedthat current imputed rent estimates do not consider the effect of differences in quality between the two
types of housing. Determining the imputed rent of owner-occupied housing based on the rental valueof rental housing from the Housing and Land Survey data is difficult because quality adjustments must
be made as housing is an asset. In other words, there is a quality difference between rental housing,which prioritizes income and expenditures, and owned properties, which focuses on quality construction,
but this difference is unclear because there are no data linking difference in quality to differences inrent. In this study, we focus on differences in construction costs by structure2 and assume that the qualityof housing is represented in the housing structure.
3. Method of analysis and data
The method of analysis has two stages: first, to gauge the influence of the relationship between
rent and the factors of regional characteristics and housing quality, we conducted an analysis using thehedonic model. Next, we used the results of this analysis to yield a new estimation of imputed rent.
Below, we explain the model used in the analysis and the estimation method we employed to estimate
imputed rents. We then describe the data terms of the Housing and Land Survey as well as the dataprocessing.
3.1 Analysis modelIn our analysis, we incorporate regional characteristic and housing quality factors in the hedonic
model using the ordinary least squares method to estimate the parameters of each variable. We also
applied an alternative approach to building structures. We used real values rather than completion periodclassifications for building ages and employed variables that can be used to judge housing quality
Construction Cost Index by the Construction Research Institute (Accessed: October 22, 2020)
https://www.esri.cao.go.jp/jp/sna/data/reference1/h23/kaisetsu.html.
62

beyond age-related deterioration (Model 1). Moreover, as a robustness check, an analysis was
performed using the completion period classifications for building ages (Model 2). The equation usedin the model is as follows:𝑛𝑙𝑛𝑌 = 𝛽0 + ∑ 𝛽𝑖 𝑋𝑖 + 𝜀
𝑖=1（i = 1,2,3…,n）（1）
Here, 𝑙𝑛𝑌 denotes the explained variable, log-transformed rent per ㎡; 𝛽0 and 𝛽𝑖 represent theconstant term and coefficient, respectively; and i denotes the number of explanatory variables. X is avector of explanatory variable. In addition, to assess the robustness of the results to changes in thespecification of the completion period classification, the building age variable is used in Model 1 andthe completion period classification variable is used in Model 2.
3.2 Estimation method
This study focuses on issues related to regional characteristics and housing quality and attempts
to estimate imputed rent with an alternative approach in line with the current estimation method.
Specifically, the imputed rent is estimated by taking into consideration the area in which the house islocated and the house’s construction and structure types. We selected houses from four areas, twoconstruction types, and three structure types.
According to MIC (2013c), the data used as the basis for the current estimates are the results ofthe 2013 Housing and Land Survey. The data were divided by survey district, multiplied by the productof the inverse of the extraction rate, summed, and further multiplied by a set ratio to match the
population of the municipality as of October 1 of the relevant year. In this study, however, we estimatedand compared the results within that range based on the data used in the analysis. Therefore, theestimated amount of this study is a reduced version that differs from the population ratio of analytical
data only.
3.3 Data and processing methods
We use individual survey data for Tokyo from the 2013 Housing and Land Survey in our
analysis. We separated the survey data into two types: data showing regional characteristics and data
showing housing quality. These were confirmed using the questionnaire (survey form) from the 2013
Housing and Land Survey (MIC 2013a) and its glossary of terms (MIC 2013b).
The data on regional characteristics included distance from the city center (Surrounding central,
Suburban, and Exurban dummy) and location (Outside sewage treatment areas, FAR, the Road width infront of the house, and Distance from public transportation). Different types of housing quality
variables—dating (Building ages or Completion period classifications), structure (Wooden and S/RC
structure dummy), housing type (Store combination dummy), and space (Number of floors and Floor
area)—were used to indicate the quality of the individual housing factors. Table 2 shows these variables
and their descriptive statistics. The concrete processing method for the data is as follows.

7

Table 2 Data and descriptive statistics
Variable name [Unit/Category]

Frequency

Min

Max

Standard
deviation
100,839
59,428Average

Housing rent［Yen］
Surrounding central dummy［0:Not applicable, 1:Applicable area］

3335

10,000450,000

4302

0.000

1.000

0.327

0.469

Suburban dummy［0:Not applicable, 1:Applicable area］
Exurban dummy［0:Not applicable, 1:Applicable area］
Outside sewerage treatment area［0:Inside treatment area, 1:Outside treatment area］

4302
4302

0.000
0.000

1.000
1.000

0.226
0.165

0.418
0.371

4302

0.000

1.000

0.071

0.257

FAR［%］ [FAR=Gross floor area/Area of the plot]
Road width［1:Less than 2m, 2:4m, 3:6m, 4:10m, 5:10m or more］
Distance from transportation［Less than: 1:200m, 2:500m, 3:1km, Less than 2km from thestation and from the bus stop: 4:Less than 100m, 5:200m, 6:500m, 7:500m or more, 2km
or more from station and from the bus stop: 8:Less than 100m, 9:200m, 10:500m,
11:1km, 12:1km or more from the bus stop］

4293
4147

50.0001.000

700.000
5.000

213.564
2.579

114.471
0.909

4302

1.000

12.000

3.834

2.394

Building age［Year］
Completion period classification［Less than: 1:1 year, 2:2 years, 3:3 years, 4:4 years, 5:5 years,
6:8 years, 7:13 years, 8:18 years, 9:23 years, 10:33 years, 11:43 years, 12:43 years, 13:53
years, 14:54 years or more］

3190

0.000

67.50030.84117.629

3190

1.000

14.000

9.793

2.670

Wooden dummy［0:Not wooden,1:Wooden］
S/RC structure dummy［0:Not S/RC structure, 1:S/RC structure］
Store combination dummy［0:dedicated housing, 1:Combined-use housing］

4294
4294

0.000
0.000

1.000
1.000

0.347
0.078

0.476
0.268

4302

0.000

1.000

0.060

0.238

Number of floors［Story］
Floor space［㎡］

4302
4302

1.000
5.000

4.000
1,000

1.905
80.2450.500
48.2898

As the distance from the city center cannot be understood in terms of the real values from thedata of the Housing and Land Survey, we divide the regions into smaller areas using municipal codes
to capture differences among four areas: central (12 wards), surrounding central (11 wards), suburban(17 cities, including Tachikawa), and exurban (three towns and a village, excluding islands, and ninecities including Hachioji) areas (Figure 1). With the central area as the reference point, we use threedummy variables (surrounding central, suburban, and exurban dummy) in our models. The dummy
variables take a value of 1 for the corresponding areas when the data are applicable to the area.

Exurbanareas

Suburbanareas

Centralareas

Surrounding central areas

Figure 1 The four main areas in Tokyo
Next, we use a dummy variable to identify whether an area lies outside any sewage treatmentareas. We use real data on FAR from the Housing and Land Survey. Similarly, we develop an ordinalscale for front-of-house road width using data from the same survey and use the existing categories inthe survey to establish ordinal scales for distance from public transport, including the walking distanceto a rail station or bus stop.
We use building ages and completion period classification as variables and verified the validityof housing age between the two variables. The building age variable is made from the median values ofthe periods comprising the completion period classifications.
Regarding housing structure, wooden and S/RC structure dummy variables are created andcompared with the fireproofed wood structure variable, which is the benchmark. We use a dummy
variable based on data from the survey to distinguish between combination-use and dedicated housing.
For the number of floors and the floor space, we conduct a logarithmic transformation using thenumerical data of the Housing and Land Survey and analyzed its results.
4. Results of the analysis and estimation errors
In this section, we review the results of the analysis and the new estimation of imputed rents.
Based on these results, we discuss how to develop an alternate estimation method.
4.1 Results of analysis
Table 3 shows the results of our analysis. The degrees of freedom adjusted coefficient ofdetermination of Model 1 is 0.350, whereas the variance inflation factor (VIF) for each variable is lessthan 10, indicating that multicollinearity is not an issue in our estimations. Excluding missing values,
the sample size was 2,332. Although this analysis used the limited data provided by the Housing andLand Survey, the degrees-of-freedom–adjusted coefficient of determination would be expected toincrease if, for example, distances from the city center are accurately measured by the variables.
However, to improve the estimation method in line with the current estimation method, we focus hereon the results in Table 3 rather than the missing variables. As the same variables are statisticallysignificant in both Models 1 and 2, we hereafter focus on the results of Model 1.
The regional characteristic that positively affected rent is a wide road adjacent to the premises;9meanwhile, regarding factors specific to individual houses, rent is positively affected when the house
had an S/RC structure, was combination-use housing, and had a greater number of floors. In particular,
the coefficients for S/RC structure, combination-use housing, and number of floors are statisticallysignificant at the 1% level in Model 2 as well as 1.
Conversely, the factors exerting a negative impact include regional characteristics, such as
being located in a surrounding central, suburban, or exurban area; outside sewerage treatment areas; ata large distance from public transportation; or FAR. Factors specific to individual houses that have anegative impact included a wooden structure, an old building age, and a large floor space. All thesecoefficients are statistically significant at the 1% level.
From the above results, differences between regional characteristics over the three smaller areas
are evident, and housing rent is lower in suburban areas. Furthermore, land attributes, such as FAR,
distance from public transport, location outside a public sewerage treatment area, and the width of roads
adjacent to the plot also affected rents. Proximity to public transport increases rent; in contrast, being
located outside urban areas lowers rents. The results indicate that some kind of adjustment to accountfor these smaller areas is required in the current estimate. Furthermore, the FAR has a negative effect at
the 1% significance level. This result suggests that consumers’ assessments of their living environments
are reflected, to some extent, in rents because of the greater comfort of residential-use districts comparedwith higher FAR areas, such as commercial-use districts. These variables related to urban planningshould also be taken into account, as the current estimate does not consider sub-areas, districts, and thelike.
Although older construction dates reduce rents, they do not encompass all aspects of housing
quality among the statistically significant housing variables. Indeed, the coefficient for the building age
or completion period classification variable is smaller than expected, as shown in Table 3, whichsuggests that using this variable as the only proxy for housing quality is inadvisable. However, they arestatistically significant at the 1% level. These findings are evidence that housing age is an importantand indispensable variable.
The fact that the wooden and the S/RC structure dummy are statistically significant indicatesthat differences in rents by structure, among fireproof wooden, wooden, and S/RC structures, are large
for detached houses. This confirms that the estimation should consider more detailed classifications
than the current building structure classification on detached houses. Moreover, increased floor spacedecreases rents due to diminishing marginal utility. Additionally, we confirm that dating is not the only
statistically significant housing quality variable that impacts rents.

10

Table 3 Analysis results
Model 1
Surrounding central dummy
Suburban dummy
Exurban dummy
Distance from transportation
Outside sewerage treatment area
FAR
Road width
Building ages
Completion period classification
Wooden dummy
S/RC structure dummyStore combination dummy
Number of floors
Floor space
Constant term
Adjusted coefficient ofdetermination

CoefficientStandard error

***

0.033
0.037
0.041
0.005
0.047
0.000
0.012
0.001

-0.094-0.197-0.379-0.014-0.191-0.001
0.032-0.008-0.043
0.108
0.144
0.143
-0.66910.442

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

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

0.026
0.042
0.047
0.045
0.021
0.107

Model 2t

VIF

CoefficientStandard error-2.871-5.350-9.146-2.823-4.029-5.765
2.642-11.545-1.654
2.587
3.088
3.181
-31.21497.899

2.051
2.452
2.652
1.270
1.283
1.541
1.070
1.184
1.340
1.084
1.088
1.612
1.183

-0.089-0.189-0.365-0.013-0.194-0.001
0.030

***

0.033
0.037
0.042
0.005
0.048
0.000
0.012

-0.045-0.049
0.108
0.138
0.172
-0.66410.612

0.350

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

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

0.004
0.026
0.042
0.047
0.045
0.022
0.114
0.345

Note: 1) n=2,332
2) Superscripts ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively.

11t-2.699-5.130-8.784-2.637-4.077-6.0722.521-10.622-1.866
2.587
2.952
3.842
-30.889
92.715

VIF
2.049
2.448
2.644
1.269
1.283
1.535
1.070
1.131
1.337
1.084
1.087
1.590
1.180

4.2 Confirmation of differences in estimates
As the results of our analysis demonstrate that older building ages has a negative impact on rents,
the validity of using completion period classifications in the current estimates is confirmed. We alsofind that factors specific to individual houses must be taken into account. Therefore, in subsequent
imputed rent estimates, we included the building age or completion period classification and focusedon the regional characteristics and quality of housing by type and structure, comparing these estimateswith the current imputed rent estimates.
Table 4 shows the imputed rents calculated with the current estimation method. These rents are
estimated by multiplying the gross floor space for each structure and completion period classificationby the average rent per square meter. This total imputed rent is compared with our estimated imputed
rent value (Table 5 and 6), calculated using the theoretical value of the hedonic model’s coefficient to
identify any differences.
Table 4 Current estimate
Completion
Average
Structure
periodTotal floor space
Imputed rent
㎡ unit rent
classification
Wooden1
70,217
1,732.7274
121,666,917+Fireproofed
2
116,584
2,045.6535
238,490,467
3
114,592
1,843.8928
211,295,365
4
108,793
1,703.9406
185,376,812
5
101,967
1,900.6880
193,807,449
6384,254
1,602.7184
615,850,948
7
662,549
1,828.7304
1,211,623,474
8
636,394
1,473.5258
937,742,956
9
517,632
1,659.8737
859,203,734
10
962,7021,569.3250
1,510,792,297
11
838,807
1,471.7466
1,234,511,386
12
404,868
1,550.0452
627,563,720
13
135,305
1,360.4764
184,079,253
14418,581
1,414.2116
591,962,098
S/RC
1
11,152
2,052.0369
22,884,316
2
12,9001,865.9862
24,071,222
3
14,171
1,453.7037
20,600,435
4
19,5312,089.9809
40,819,417
5
15,8412,866.9057
45,414,654
6
48,1881,889.9839
91,074,546
7
92,3952,074.4861
191,672,142
8
105,727
1,785.2118
188,745,088
9
79,6962,004.8768
159,780,664
10
148,774
1,650.0797
245,488,964
11
86,5891,523.6278
131,929,405
12
38,166
1,781.0470
67,975,439
13
13,9812,020.2020
28,244,444
14
44,4201,967.9455
87,416,141
Total
6,204,776
10,070,083,751
Note: The reason for the differences in total floor space between Table 1 and this table is a loss of data
due to detail division.
12

In addition to detailing housing structure, we estimate imputed rents by taking into accountindividual housing factors, such as combined use housing and dedicated housing, building age, and subareas, as shown in Table 5. This results in an imputed rent of ¥10,459,526,292 (approximately 100
million USD based on 100 JPY per USD). Comparing this value with the imputed rents shown in Table
4, we find that the current estimates underestimate rent by 3.84% (¥386,442,540). This is assumed tobe due to the high ratio of gross floor space in the 23 wards (central and surrounding central areas),
which have the highest rent per square meter and indicate underestimation when the Tokyo prefecturalarea average is used.
This result shows that it is possible to conduct an estimate using the theoretical value of thecoefficient of the hedonic model. Moreover, we can verify that regional characteristics and housing
quality can be put into an estimation of imputed rent. This confirms the importance of dividing estimates
into sub-areas, detail structures, and housing types.
Table 5 Estimate results of Model 1
Area

Housing type
Dedicatedhousing

CentralCombinationuse housing
Dedicatedhousing
Surrounding
Combinationuse housing
Dedicatedhousing
Suburban
Combinationuse housing
Dedicatedhousing
Exurban
Combinationuse housing

Structure

Total floor
space

WoodenFireproofed
S/RC
WoodenFireproofed
S/RC
WoodenFireproofed
S/RC
WoodenFireproofed
S/RC
WoodenFireproofed
S/RC
WoodenFireproofed
S/RC
WoodenFireproofed
S/RC
WoodenFireproofed
S/RC

174,115493,737
204,186
14,00532,95445,842
346,3031,252,569
239,697
17,145
46,79752,081
342,476
1,376,882
116,294
8,361
30,672
15,829263,863
1,043,373
49,614
8,921
21,072
7,988
6,204,776

Total

Average㎡ unit rent
1,725.5818
1,751.4468
1,766.2560
1,740.6901
1,766.7816
1,781.7204
1,673.3890
1,698.4717
1,712.8330
1,688.0403
1,713.3426
1,727.8296
1,650.4173
1,675.1557
1,689.3198
1,689.8225
1,704.1106
1,634.9723
1,620.7816
1,645.0758
1,658.9855
1,634.9723
1,659.4792
1,673.5107-

Imputed rent
300,449,675
864,754,104
360,644,747
24,378,365
58,222,520
81,677,627
579,499,637
2,127,453,028
410,560,923
28,941,451
80,179,296
89,987,095
565,228,316
2,306,491,703
196,457,754
14,128,606
52,268,479
25,879,977
427,664,298
1,716,427,647
82,308,908
14,585,588
34,968,545
13,368,004
10,456,526,292

Using the coefficients of Model 2 and theoretical values based on completion periodclassification, we estimate our imputed rents by taking individual housing factors into account.
Table 6 shows the estimate results and an imputed rent of ¥10,049,749,034, which is 0.2 % lessthan the current estimates. There is a difference of 3.86% from the estimation results using the building
age data. This implies that estimation results vary depending on how building age data are handled, andcompletion period classification is applied, even when estimation is done by house type or structure.
13

Table 6 Estimate results of Model 2
Area

Housing type
Dedicatedhousing

CentralCombination-use housing
Dedicatedhousing
Surrounding
Combination-use housing
Dedicatedhousing
Suburban
Combination-use housing
Dedicatedhousing
Exurban
Combination-use housing

Structure

Total floor
space

Average㎡ unit rent

174,115493,737
204,186
14,00532,95445,842
346,3031,252,569
239,697
17,145
46,79752,081
342,476
1,376,882
116,294
8,361
30,672
15,829263,863
1,043,373
49,614
8,921
21,072
7,988
6,204,776

1,693.5163
1,722.2679
1,736.8958
1,707.7445
1,736.7377
1,751.4885
1,645.1496
1,673.0800
1,687.2901
1,658.9715
1,687.1366
1,701.4661
1,576.1391
1,602.8980
1,616.5120
1,589.3812
1,616.3649
1,630.0933
1,483.8298
1,509.0214
1,521.8381
1,496.2963
1,521.6996
1,534.6240-

WoodenFireproofed
S/RC
WoodenFireproofed
S/RC
WoodenFireproofed
S/RC
WoodenFireproofed
S/RC
WoodenFireproofed
S/RC
WoodenFireproofed
S/RC
WoodenFireproofed
S/RC
WoodenFireproofed
S/RC

Total

Imputed rent
294,866,588
850,347,380
354,649,797
23,916,962
57,232,454
80,291,734
569,720,235
2,095,648,191
404,438,384
28,443,066
78,952,931
88,614,055
539,789,823
2,207,001,350
187,990,644
13,288,816
49,577,144
25,802,746
391,527,770
1,574,472,197
75,504,475
13,348,459
32,065,254
12,258,576
10,049,749,034

The results of current estimation and Tables 5 and 6, respectively, show a positive and anegative relationship. We see a difference in the coefficients between the two analyses. Specifically, thecoefficients of the age variable in Models 1 and 2. As pointed out by Karato et al. (2015), the relationshipbetween age and price of houses is non-linear; we simply choose an optimum estimation.
The estimate results in Table 6 are already discussed in previous study (Fujisawa et al. 2021),
therefore, we selected Model 1, and the following discussion is based on the results of Table 5.
4.3 Discussion
Two issues need to be addressed regarding the imputed rent estimates made (Table 5) in thisstudy. The first is the validity of the analytical model, and the second is the validity of the estimation
method.
4.3.1 Discussion of the analytical model
Concerning the analytical model, the degrees-of-freedom-adjusted coefficient of determination
is 0.350; thus, we cannot exclude the possibility of omitted variable bias. For example, Fujisawa et al.
(2021) pointed out that important variables, such as the distance from the city center and location in thecentral business district rather than the nexus of the sub-areas, may be necessary. As the Housing andLand Survey does not include this information, this is a limitation that also applies to the current
estimates. However, the estimates in this survey, which employ sub-area classifications, also present a14

direction for improvement over the current estimation method. This can be regarded as Ricardian land
rent, as proposed by Ricardo (1817), and this can be also confirmed from simple tabulations of the
Housing and Land Survey that show that rents decline with distance from the city center.
The application of hedonic analysis enables the understanding of the actual rent conditions. The
results suggest that estimation methods should include more detailed variables for factors specific toregional characteristics and individual houses. The findings also show that building age is a criticalvariable. If the method of evaluation is cost-based, the concept of depreciation rate for building
deterioration could be included (Bokhari and Geltner 2018; Diewert et al. 2015), but this method is notapplicable for Japan SNA, which estimates imputed rent from rent prices in the market. Our results
indicate that imputed rent estimates need the building age variable not completion period classification.
Next, the results also show that the building age classifications are not an appropriate proxyvariable for housing quality and confirm the need to incorporate factors specific to individual houses.
The proposed method of analysis provides a minimal response to the arguments of Sato (2013);
nevertheless, the hedonic model needs to be redeveloped. Changing the estimation method every five
years by adapting the hedonic model to a base year also warrants consideration. This study was
conducted using the limited Housing and Land Survey data, and it is necessary to examine the variables
related to individual housing factors that should be used in the model in more detail.
4.3.2 Discussion of the estimation method
Our results confirm the need to consider regional characteristics, housing type, and structure
when estimating imputed rent.
Regional characteristics require accurate estimation when a system of small areas are
introduced in our estimate. As shown in Table 5, the total floor space within the 23 wards isoverwhelmingly large, and it is reasonable to conclude that the current estimates do not capture the full
extent of the real rent by multiplying simple averages by total floor space. In the future, the introductionof a weighted average method based each structure should be considered.
Housing type is used in the estimation method to adjust for housing quality, and the results
confirm it as a significant variable. Thus, subject to data availability, estimates should at least be createdusing housing type and construction type as proxy variables for housing quality. In the current estimates,for example, average rent per meter square is obtained excluding combined-use houses and thenmultiplied by total floor space, which is totaled for both combined-use and dedicated housing. The
results show that excluding combined-use housing leads to the underestimation of rents; thus, it isnecessary to account for combined-use housing while creating estimates. In such a case, discussions on
incorporating the quality of housing and living environments, based on the results of Hill (2013), arerequired. Hill and Syed (2016) confirmed that the difference between owner-occupied and rented houses
was 18%; the result of our estimation in Table 5 is only 3.84% higher than the estimate produced by thecurrent, less effective method. One reason could be that the new estimation was subject to data
constraints. It is therefore suggested that the quality of housing should be adjusted using a morecomprehensive method rather than at a micro level that only considers detached houses. In the future, amore comprehensive hedonic analysis and comparison of the estimation results are required.
We primarily use the structures-based approach in our estimation. As many previous studies(e.g., Hill 2013; Diewert and Shimizu 2016) pointed out, conducting estimates based on structure isimportant because it reflects the quality of housing. Our approach has highlighted the possibility ofmore accurate estimates than the current methods; future estimates can include weighted averages basedon each structure, at least.
5. Conclusions
The SNA2008 calls for a more accurate estimation method for imputed rents, which are a nonmarket good, that reflects the circumstances of each country. Concerning estimating the imputed rent,
this study followed the current “Explanatory Notes on the Methodology for Calculating the Statementof National Accounts” and examined whether data from the Housing and Land Survey, which is also
used in current estimates, could reflect regional characteristics and housing quality. Especially, we tried
to estimate imputed rent of detached houses with strong individuality.
In this study, we clarified the influence of regional characteristics and factors specific to15

individual houses on detached houses using a hedonic model analysis to propose an alternate estimation
method. Theorical values from the obtained coefficients were used to estimate imputed rents that reflect
regional characteristics and housing quality, and these values were compared to current imputed rent
estimates. Our results indicate that imputed rent estimates need include the building age variable, but
not completion period classification. As for the novelty of the study, it is shown that hedonic model
analysis on detached houses is possible using Housing and Land Survey data, as higher estimation
results than the current estimation were obtained. The alternative estimation method presented herein
is an improvement on the current method and represents a realistic proposal for implementation.
The estimates in this study are created in a simplified manner based on our analysis. Although
we highlight the differences between our estimates and those obtained using current methods, regional
characteristics and housing quality adjustments, including age variables resulting from different
estimates of building age and completion period classification, should be investigated more broadly. An
ongoing discussion on investigations into new methods of estimation is required.
To consider a more rigorous estimation method, there is scope for future consideration of
estimates that adapt the hedonic model. This has the additional advantage of being able to supplement
data deficiencies in the model in regions where data collection is difficult. Going forward, the methodby which statistical surveys are conducted should be reviewed. The adoption of a better and more
rigorous hedonic model, including improvements to the current questionnaire, requires thoroughconsideration and careful research and discussion. These issues are to be addressed in the future.
Acknowledgments
This work was supported by the Japan Society for the Promotion of Science (Grant Number: 17K03666).
Based on the provisions of the Statistics Act, questionnaire information related to the housing and land
statistical survey was used. We would like to express our gratitude here.
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18