ESRI Discussion Paper Series No.356

The Impact of the Rise and Collapse of Japan’s Housing Price
Bubble on Households’ Lifetime Utility

Takeshi Niizeki and Fumihiko Suga

August 2020

Economic and Social Research InstituteCabinet Office
Tokyo, JapanThe views expressed in “ESRI Discussion Papers” are those of the authors and not those of
the Economic and Social Research Institute, the Cabinet Office, or the Government of Japan.
(Contact us: https://form.cao.go.jp/esri/en_opinion-0002.html)

ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

The Impact of the Rise and Collapse of Japan’s Housing Price Bubble
on Households’ Lifetime Utility†
Takeshi Niizekia,b and Fumihiko Sugaa,c,*a

Economic and Social Research Institute, Cabinet Office, Japanb
Faculty of Law and Letters, Ehime UniversitycFaculty of Economics, Kyushu University

May 2020
Abstract
This study estimates the impact of the dramatic changes in housing prices during Japan’s bubble
from the late 1980s to the 1990s on households’ asset accumulation and utility over the life-cycle.
We construct a life-cycle model explaining households’ consumption/saving and housing
decisions under collateral and borrowing constraints. We estimate this model using data from theFamily Income and Expenditure Survey (FIES), which includes data on households’ housing
wealth estimated from objective information. Using the estimated model, we then conduct a
counter-factual simulation in which we assume that housing price remained constant during the
bubble period. Doing so allows us to quantify the gains/losses of lifetime utility due to the housing
price boom and bust. We find that 38.6% of households experienced an increase in lifetime utility,
which averaged 3.3%, while 61.4% experienced a decrease in lifetime utility, which averaged
2.4%. On average, Japan’s housing price boom and bust caused a 0.2% loss in lifetime utility,
which is equivalent to 1.3% of lifetime income.
Keywords: Asset Bubbles, Housing
JEL classifications: D12, D31, R21† The paper was presented at the 2019 Spring Meeting of the Japanese Economic Association in Tokyo on June 6th,2019. We are grateful to Tomoaki Kotera, the discussant at the conference, for his valuable comments. We alsopresented the paper at the 2019 Asian Meeting of the Econometric Society and at several research meetings andseminars held in Japan. We appreciate the comments and questions received from participants of these conferences andworkshops. We would also like to thank Taiyo Fukai, Junya Hamaaki, Kazuhito Higa, Masahiro Hori, Koichiro
Iwamoto, Keiko Murata, and other ESRI colleagues for their support, and Ralph Paprzycki for his excellent English
editing service. Special thanks go to the Statistics Bureau of Japan for providing us with the microdata from the Family
Income and Expenditure Survey (FIES). This work was supported by JSPS KAKENHI Grant Number 17H06925. The
views expressed in this paper are those of the authors and do not represent those of the institutions to which the authors
belong.
* Corresponding author: Suga.

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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

1. Introduction
What is the most expensive good that households purchase? The answer, for most Japanesehouseholds, is a house. To buy a house, it is quite common for households to save money to makea down-payment and borrow five to eight times their annual income. Moreover, the value of ahouse tends to affect how much the bank will lend. Therefore, fluctuations in housing prices overtime can have a significant impact on households’ lifetime resources.
The fluctuations in housing prices during Japan’s bubble were dramatic. Figure 1 shows theaverage housing wealth held by Japanese households.1 As can be seen from the figure, housing
wealth increased rapidly, especially in the Tokyo area, from the late 1980s to the early 1990s.
Households’ housing wealth almost doubled within five years and then gradually declined overten years. This dramatic change in housing wealth primarily reflects changes in land prices, which
are shown in Figure 2. Since in Japan residential real estate prices primarily reflect the price ofthe land on which a house sits, the dramatic changes in housing wealth were largely driven bychanges in land prices. Land prices are depicted in Figure 2 and show a similarly steep rise in thelate 1980s and gradual decline in the 1990s.
How did Japanese households react to such dramatic changes in housing prices? Figure 3
presents home-ownership rates by age across different cohorts. Homeownership increases by age,
and a typical Japanese household buys a house when the head is aged between 35 and 45. The
larger markers in Figure 3 are for observations falling into Japan’s bubble period (1988-1992).
The figure suggests that homeownership rates did not change in response to the boom and bust of
housing prices. That is, despite the dramatic increase in housing prices, there was no discernible
change in the life-stage (age of the household head) at which households typically buy a house.

1 We construct the data on households’ housing assets using the FIES and other data. The specific procedure is
explained in Section 4.

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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

Therefore, instead of delaying buying a house, households may have reduced consumption orsettled for a lower-quality house, receiving poorer housing services for a prolonged period. Since
most Japanese households tend to prefer a new house and the average price of a house is five toeight times households’ income (see, e.g., Ito, 1991), Japan’s housing price boom and bust may
have had a significant impact on households’ lifetime utility.
The goal of this study is to quantify the impact of Japan’s housing price boom and bust on
Japanese households using household data. Specifically, we use data from the Family Income andExpenditure Survey (FIES) conducted by the Statistics Bureau, Ministry of Internal Affairs andCommunications. The FIES data contains detailed information about households’ consumption,
income, and financial assets. Moreover, it provides information about household dwellings, suchas the location, size, and type (apartment or house). We combine the FIES data with other datasets
to estimate the objective value of households’ housing wealth. To concentrate on households thatwere most likely to be affected by the fluctuations in housing prices, we focus on households witha head born between 1951 and 1955 and living in the Tokyo Urban Employment Area (UEA). We
construct a structural model that describes the consumption/saving and housing behavior of thesehouseholds and estimate the model using the indirect inference method. Using the estimatedmodel, we then conduct a counter-factual simulation in which housing prices are held constantfrom 1987 to 1999. The simulation result indicates that 38.6% of the households experienced alifetime utility gain, which averaged 3.3%, while the remaining 61.4% experienced a lifetime
utility loss, which averaged 2.4%. On average, Japan’s housing price boom and bust caused a
0.2% loss in lifetime utility, which is equivalent to 1.3% of lifetime income.
The remainder of this paper is organized as follows. Section 2 outlines the background to ourstudy and provides an overview of the previous literature. Section 3 then presents the structuralmodel that we estimate, while Section 4 describes the dataset we employ and Section 5 discusses
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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

the empirical methods. Next, Sections 6 and 7 respectively present the results of the estimationand the counter-factual simulation. Finally, Section 8 concludes.

2. Background and Related Literature
How can we assess the impact of temporal fluctuations in housing prices? The easiest way would
be to evaluate the impact on the basis of capital gains/losses. If, for example, a household paid
$500,000 for a house during the bubble period and the price of the house dropped to $300,000,
the bubble caused a loss of $200,000. The problem with this approach is that the endogeneity of
housing choice is ignored: even if there had been no bubble, the household might have paid
$500,000 but would have got a better house for that money.
Another approach is to take advantage of the information about the relationship between
households’ housing wealth and consumption. According to the life-cycle/permanent income
hypothesis (LC-PIH), households’ consumption reflects changes in their available life-cycle
resources. Attanasio and Weber (1994) proposed an approach to analyze how households’
consumption responds to changes in housing prices. Hori and Niizeki (2017) applied the same
approach to FIES data to estimate the marginal propensity to consume by regressing consumption
on housing wealth. This approach is useful for examining whether housing wealth should be
regarded as life-cycle assets and whether the LC-PIH holds. However, it is unclear by what margin
life-time utility is increased or decreased due to the boom and bust of housing prices. If, forexample, a household changed only the quality of the house it purchased in response to the change
in housing prices, consumption may not reflect the change in life-time utility. Therefore, to assess
the long-term impact of the housing price boom and bust, we employ a so-called “structural”
approach, estimating a structural model to conduct counter-factual simulations. Such an approach4

ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

has at least two advantages. First, counter-factual simulations are not subject to the Lucas critique.
And second, we can evaluate the impact of the housing price boom and bust on the basis ofhouseholds’ lifetime utility.
We estimate a life-cycle model using the indirect inference method. Indirect inference is asimulation-based technique that has been employed in numerous studies estimating a structuralmodel. Simulation-based estimation techniques were first used to estimate the life-cycle modelby Gourinchas and Parker (2002) in their pioneering study using the method of simulated
moments (McFadden, 1989). Similar estimation methods have been employed by French (2005),
Laibson et al. (2007), and French and Jones (2011), among others. 2
The estimation of life-cycle models with housing wealth, on the other hand, is not very
common, since the model structure becomes complex when housing wealth is incorporated. Oneof the few studies to do so is that by Attanasio et al. (2012), who, using U.K. data, construct andestimate a life-cycle model that incorporates households’ housing choice under borrowing andcollateral constraints. Another study is that by Li et al. (2016), who, using U.S. data, estimate alife-cycle model with housing wealth to measure the elasticity of substitution between nondurable and housing goods. Our study is the first to employ this approach to examine the longterm impact of a housing price boom and bust on households’ lifetime utility.
Theoretical studies on economic bubbles (e.g., Hirano and Yanagawa, 2017) find that
financial deregulation can result in the emergence of a bubble. This means that policy makers
potentially face a trade-off between the benefits of financial deregulation and the risk of a bubble.
Against this background, understanding the impact of Japan’s housing price boom and bust on
households’ lifetime utility can provide important insights on the consequences of asset pricebubbles.

2

A study applying this approach to Japanese data to estimate a life-cycle model is that by Abe and Yamada (2009).

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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

3. Life-cycle Model
We construct a life-cycle model that incorporates consumption/savings and housing decisions
under realistic borrowing and collateral constraints. The basic setup of the model, especially thebudget constraint, is based on Attanasio et al.’s (2012) model.
Households live T periods. Since the number of periods considerably affects the time it takes
to compute the model, we set one period in the model to three years in order to save computation
time. We assume that the initial period starts at age 25-27 and household members live until age
82-84 (T = 20). In each period, households make decisions about consumption 𝑐𝑐𝑡𝑡 and housing

ht to maximize their lifetime utility:𝑇𝑇max � 𝛽𝛽 𝑡𝑡−1 𝐸𝐸[𝑢𝑢(𝑐𝑐𝑡𝑡 , ℎ𝑡𝑡 )].
ct ,ℎ𝑡𝑡

𝑡𝑡=1

For simplicity, we assume that the choice of dwelling is discrete. Households can choose not to
own a dwelling (ℎ𝑡𝑡 = 1 ), to own a low-quality dwelling (ℎ𝑡𝑡 = 2), to own a medium-quality

dwelling (ℎ𝑡𝑡 = 3), or to own a high-quality dwelling (ℎ𝑡𝑡 = 4). The quality of houses is definedon the basis of their value in each period, and we do not distinguish between houses andapartments. For simplicity, we assume that a household can own at most one house and has nobequest motive.
The current payoff depends on consumption 𝑐𝑐𝑡𝑡 and housing choice ℎ𝑡𝑡 and is given byu(𝑐𝑐𝑡𝑡 , ℎ𝑡𝑡 ) = γ�ln 𝑐𝑐𝑡𝑡 + 𝜙𝜙 ln 𝑓𝑓(ℎ𝑡𝑡 ) − 𝐹𝐹1[ℎ𝑡𝑡 ≠ ℎ𝑡𝑡−1 ]� + η(ht )where 𝑓𝑓(ℎ𝑡𝑡 ) is the utility derived from housing, F is the cost of moving, and 𝜂𝜂(ℎ𝑡𝑡 ) is an

idiosyncratic utility shock that takes different values for each housing choice, following the type6

(1)

ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

1 extreme value distribution. 3 The utility from housing is given by𝑖𝑖𝑖𝑖 ℎ𝑡𝑡 = 1
𝑖𝑖𝑖𝑖 ℎ𝑡𝑡 = 2.
𝑖𝑖𝑖𝑖 ℎ𝑡𝑡 = 3𝑖𝑖𝑖𝑖 ℎ𝑡𝑡 = 4

1
1 + 0.25𝜇𝜇𝑓𝑓(ℎ𝑡𝑡 ) = �
1 + 0.5𝜇𝜇
1 + 0.75𝜇𝜇Households’ budget constraint depends on their homeownership status. If a household
does not own a home, the budget constraint is written as

At+1 = 𝑅𝑅𝑡𝑡+1 �𝐴𝐴𝑡𝑡 + 𝑤𝑤𝑡𝑡 − 𝑐𝑐𝑡𝑡 − 𝑞𝑞𝑡𝑡 𝑑𝑑1𝑡𝑡 −�𝑗𝑗∈{𝑙𝑙,𝑚𝑚,ℎ}(𝑗𝑗)

𝑝𝑝𝑡𝑡 𝑑𝑑𝑗𝑗𝑗𝑗 �(2)where 𝐴𝐴𝑡𝑡+1 represents the household’s financial assets at the beginning of period 𝑡𝑡 + 1 (assets

carried over from period t), 𝑅𝑅𝑡𝑡+1 is the gross mortgage/interest rate, 𝑤𝑤𝑡𝑡 stands for thehousehold’s income, 𝑞𝑞𝑡𝑡 is the housing rent, 𝑑𝑑𝑗𝑗𝑗𝑗 (𝑗𝑗 = 1, 2, 3, 4) are dummy variables that take(𝑗𝑗)

a value of one if ℎ𝑡𝑡 = 𝑗𝑗 and zero otherwise, and 𝑝𝑝𝑡𝑡

are the prices of low-, medium-, and high-quality houses (𝑗𝑗 ∈ {𝑙𝑙, 𝑚𝑚, ℎ}). The gross interest rate that a household pays or receives, 𝑅𝑅𝑡𝑡+1 ,depends on the amount of financial assets the household holds at the end of period t. If thehousehold carries over negative financial assets into the next period, 𝑅𝑅𝑡𝑡+1 is the mortgage rate(𝑏𝑏)

�𝑅𝑅𝑡𝑡+1 = 𝑅𝑅𝑡𝑡+1 �; if the household carries over positive financial assets, 𝑅𝑅𝑡𝑡+1 is the interest rate(𝑑𝑑)

on bank deposits �𝑅𝑅𝑡𝑡+1 = 𝑅𝑅𝑡𝑡+1 �.

When a household owns a j-quality house (𝑗𝑗 ∈ {𝑙𝑙, 𝑚𝑚, ℎ}), the budget constraint can be

written as

(𝑘𝑘)

(𝑗𝑗)

At+1 = 𝑅𝑅𝑡𝑡+1 �𝐴𝐴𝑡𝑡 + 𝑤𝑤𝑡𝑡 − 𝑐𝑐𝑡𝑡 − 𝑞𝑞𝑡𝑡 𝑑𝑑1𝑡𝑡 − ∑𝑘𝑘≠𝑗𝑗 𝑝𝑝𝑡𝑡 𝑑𝑑𝑘𝑘𝑘𝑘 + 𝑝𝑝𝑡𝑡 �1 − 𝑑𝑑𝑗𝑗𝑗𝑗 �� .

Let 𝑠𝑠𝑡𝑡 denote a household’s savings or financial assets at the end of period t, that is, 𝑠𝑠𝑡𝑡 ≡(3)

𝐴𝐴𝑡𝑡+1.
𝑅𝑅𝑡𝑡+1

3 We incorporate idiosyncratic utility shocks to randomize the housing choice. The reason is that the randomness
from the idiosyncratic income shocks is not sufficient to replicate the observed pattern of housing asset accumulation.

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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

For simplicity, we assume that mortgage payments 𝑚𝑚𝑡𝑡 are not fixed and given by
𝑚𝑚𝑡𝑡 = (𝑅𝑅𝑡𝑡 − 1)𝑠𝑠𝑡𝑡−1 .

(4)

If a household owns a j-quality house (𝑗𝑗 ∈ {𝑙𝑙, 𝑚𝑚, ℎ}) at the beginning of period t and chooses tocontinue to live in that house, the household does not pay any housing costs except the mortgage

payments 𝑚𝑚𝑡𝑡 . In this case, the household’s savings 𝑠𝑠𝑡𝑡 can be written as(5)

𝑠𝑠𝑡𝑡 = 𝐴𝐴𝑡𝑡 + 𝑤𝑤𝑡𝑡 − 𝑐𝑐𝑡𝑡 .

If a household that owns a j-quality house (𝑗𝑗 ∈ {𝑙𝑙, 𝑚𝑚, ℎ}) at the beginning of period t buys a k-quality house (𝑘𝑘 ≠ 𝑗𝑗), it has to sell the existing (j-quality) house at the beginning of period t andpay for a new house:

(𝑗𝑗)

(𝑘𝑘)

(6)

st = 𝐴𝐴𝑡𝑡 + 𝑤𝑤𝑡𝑡 − 𝑐𝑐𝑡𝑡 + 𝑝𝑝𝑡𝑡 − 𝑝𝑝𝑡𝑡 .

If a household that owns a j-quality house (𝑗𝑗 ∈ {𝑙𝑙, 𝑚𝑚, ℎ}) at the beginning of period t wants to sell

the house and not buy a new one (renting a house instead), the budget constraint can be written
as
(𝑗𝑗)

(7)

st = 𝐴𝐴𝑡𝑡 + 𝑤𝑤𝑡𝑡 − 𝑐𝑐𝑡𝑡 + 𝑝𝑝𝑡𝑡 − 𝑞𝑞𝑡𝑡 .

We assume that a household’s borrowing limit depends on the value of the dwelling it owns:
st ≥ 0(𝑗𝑗)

st ≥ −𝜆𝜆ℎ 𝑝𝑝𝑡𝑡

(8)

if ℎ𝑡𝑡 = 0

if ℎ𝑡𝑡 = 𝑗𝑗, 𝑗𝑗 ∈ {𝑙𝑙, 𝑚𝑚, ℎ}(9)where 0 < 𝜆𝜆ℎ < 1. Equation (8) indicates that borrowing is allowed only when the householdowns a dwelling. 4 Equation (9) represents the collateral constraint, that is, the purchased house(𝑗𝑗)

is pledged as collateral and the household makes down payment (1 − 𝜆𝜆ℎ )𝑝𝑝𝑡𝑡 . Furthermore, we
impose the assumption that the borrowing limit depends on the household’s annual income, thatis,

4 Since in Japan the majority of households’ debt consists of housing loans, this assumption is not as strong as it may
appear.

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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"(10)

st ≥ −𝜆𝜆𝑤𝑤 𝑤𝑤𝑡𝑡 .

Moreover, we assume that households are not allowed to die in debt, that is, 𝑠𝑠𝑇𝑇 ≥ 0. Thus, there

is a natural debt limit at each period as an implicit constraint.

Household income 𝑤𝑤𝑡𝑡 depends only on the age of the household head:ln 𝑤𝑤𝑡𝑡 = 𝛼𝛼0 + 𝛼𝛼1 𝑡𝑡 + 𝛼𝛼2 𝑡𝑡 2 + 𝜈𝜈𝑡𝑡 ,(11)

𝜈𝜈𝑡𝑡 = 𝜈𝜈𝑡𝑡−1 + 𝜉𝜉𝑡𝑡

where 𝜉𝜉𝑡𝑡 ∼ 𝑁𝑁�0, 𝜎𝜎𝜉𝜉2 � and 𝜈𝜈1 ∼ 𝑁𝑁�0, 𝜎𝜎𝜈𝜈21 �. Note that 𝜈𝜈𝑡𝑡 follows a random walk and 𝜉𝜉𝑡𝑡 is an

idiosyncratic permanent income shock. Although the income function is simple, persistenthousehold characteristics, such as the household head’s educational attainment, are captured by𝜈𝜈𝑡𝑡 .

The income process differs across households, while households are faced with the same(𝑚𝑚)housing price and mortgage rate (interest rates). The price of a medium-quality house 𝑝𝑝𝑡𝑡isalso assumed to be exogenous and follows a random walk:

𝑝𝑝

(𝑚𝑚)

ln 𝑝𝑝𝑡𝑡

(𝑚𝑚)

𝑝𝑝

(12)

= ln 𝑝𝑝𝑡𝑡−1 + 𝜖𝜖𝑡𝑡

𝑝𝑝

The house price shock 𝜖𝜖𝑡𝑡 follows normal distribution 𝜖𝜖𝑡𝑡 ∼ 𝑁𝑁(0, 𝜎𝜎𝑝𝑝2 ). Since we discretize thestate variable and solve households’ utility maximization problem at each state point, the numberof continuous state variables we use is an important determinant of the time it takes to computethe model. To keep the number of continuous state variables small, we therefore assume thatthe
prices of low- and high-quality houses form a log-linear relationship with the price of mediumquality houses:
(𝑙𝑙)

(𝑙𝑙)

(𝑙𝑙)

(𝑚𝑚)

ln 𝑝𝑝𝑡𝑡 = 𝜁𝜁0 + 𝜁𝜁1 ln 𝑝𝑝𝑡𝑡(ℎ)

ln 𝑝𝑝𝑡𝑡

(ℎ)

= 𝜁𝜁0

(ℎ)

(𝑚𝑚)

+ 𝜁𝜁1 ln 𝑝𝑝𝑡𝑡

.

(13)

(14)

In theory, one would expect housing rents to be related to housing prices. In practice, however,this does not appear to be the case. Figure 4 shows housing rents in the Tokyo UEA. The figure
indicates that housing rents remained relatively constant even during the bubble period. We
9ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

therefore assume that housing rents depend only on time:
(𝑞𝑞)

(𝑞𝑞)

(𝑞𝑞)

qt = 𝜁𝜁0 + 𝜁𝜁1 𝑡𝑡 + 𝜁𝜁2 𝑡𝑡 2

(15)

As shown in Figure 5, mortgage rates changed dramatically during the bubble period. If weassume deterministic mortgage and interest rates, households in the model would behave as if
they knew in advance that mortgage rates would suddenly drop. We therefore incorporate
uncertainty about mortgage and interest rates in the model, i.e.:(𝑏𝑏)

ln 𝑅𝑅𝑡𝑡(𝑏𝑏)= ln 𝑅𝑅𝑡𝑡(𝑅𝑅)+ 𝜖𝜖𝑡𝑡(𝑅𝑅)

We assume that shocks to mortgage rates follows normal distribution ϵt(16)∼ 𝑁𝑁(0, 𝜎𝜎𝑅𝑅2 ). So as not

to increase the number of continuous state variable, we assume that the interest rate on savings isdetermined by the mortgage rate:(𝑑𝑑)

ln 𝑅𝑅𝑡𝑡

(𝑑𝑑)

= 𝜁𝜁1

(𝑑𝑑)

(𝑑𝑑)

+ 𝜁𝜁1 ln 𝑅𝑅𝑡𝑡

.

(17)

4. Data
We use the FIES data to estimate the model. The FIES is a monthly survey currently covering
about 9,000 households per month. We use data for the period from 1983 to 2012 containing
observations on 500,000 households in total. The survey tracks each household for three months
in the case of one-person households and six months in the case of two or more-personhouseholds. 5 Respondents keep a diary on their monthly income and expenditures. The FIES data
is designed to provide basic information needed to construct the consumer price index (CPI) andother important statistics related to consumption and the prices of goods. As mentioned earlier,
we focus on individuals born between 1950 and 1955 who lived in the Tokyo UEA during the

5 The FIES has a panel structure, but the information about assets is available only for the first period. We therefore
cannot take advantage of the panel structure and use the data as cross-sectional data.

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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

observation period. Furthermore, we focus on households with two or more members, because
information about financial assets is not available for one-person households. In the model,
households accumulate financial assets and borrow money to buy a house on their own; that is,
obtaining a house through inheritance is not included in the model. We therefore drop those livingwith a household member older than the head, because those living with their parents are likely
to share and inherit their house.
As a result of dropping observations based on these criteria, our sample consists of 4,478
households. Because one period in the model corresponds to three years, we divide theobservation period overall into 10 subperiods. The sample size for each subperiod ranges from
300 to 550 households.

4.1

Household income

Although the FIES data is panel data, each household is tracked for six months only, so that themonthly income data is subject to seasonality. Moreover, the seasonally adjusted income data isquite noisy, and the fit of the income function regression is poor. Therefore, we use the previous
year’s income, multiplied by the average ratio of households’ disposable income to pretax
income. 6
Figure 6 shows the mean household disposable income by age across the three cohorts
shown in Figure 3. We can see that life-cycle income is hump-shaped and that the shape differs
across cohorts. As previous studies (e.g., Hamaaki et al., 2012) have pointed out, the life-cycle
income path of younger cohorts in Japan is flatter than that of older cohorts. This pattern may
reflect the collapse of the traditional Japanese seniority wage system,

6

We calculate the ratio of disposable income to pretax income from seasonally adjusted monthly income. Specifically,
we calculate this ratio for each household as the average over the six month-period the household is tracked and used
this as the disposable income of the household.

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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

4.2

Financial assets

Figure 7 shows the mean of financial assets by household heads’ age across the three cohorts.
Larger markers indicate the period of the asset price bubble (1988-1992). The figure suggests thathouseholds’ financial asset holdings did not change much during the asset price bubble. This isperhaps because the majority of financial assets held by Japanese households are bank deposits.
For this reason, we do not investigate the effect of the bubble in financial markets on households’
life-time utility and exclusively focus on the bubble in housing prices.

4.3

Housing wealth

While the FIES provides rich information about households’ characteristics, income,consumption, and financial assets, it unfortunately does not contain information on the value ofhouseholds’ housing wealth. However, the dataset does offer detailed information about theapproximate address, type (apartment or house), age, land area, and floor space of households’
dwellings. Combining this information with other data sets such as official land price data allows
us to estimate the value of the dwelling in which a household lives and thus provides us with anobjective estimate of households’ housing wealth. 7 The benefit of these estimates over subjective
housing asset values, if they were available, is that they likely more accurately reflect the housing
price boom and bust. That is, those who bought their house before the bubble and were notinterested in selling it may not have been aware of the dramatic change in the housing wealth theyheld.
To estimate land values, we use households’ address and match this to the price of

7

Note that having to rely on this approach means that housing assets other than the household’s home are notincluded in housing wealth.

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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

residential land in the area. Land prices are taken from the “Land Market Value Publication”(Chikakoji) provided by the Ministry of Land, Infrastructure, Transport and Tourism. We multiply
the land area by the unit land price of the closest point for which land prices are available.
We estimate house values using information about the type of dwelling, the age of thehouse, and the floor area. Information about the link between housing construction costs, the typeof dwelling, the municipality, and the floor area are taken from the “Annual Report of Building
Construction” (1953-2012). Employing such information, we estimate the price of a new housein a particular locality and then estimate its value at the time of the survey taking depreciation
based on the age and type of dwelling into account. 8
Figure 8 shows households’ housing wealth by age across the three cohorts. The figure
indicates that households accumulate housing wealth until age 45-48. The rapid increase in
housing wealth for those in their 30s and 40s across all three cohorts reflects the increase inhomeownership rates. In addition, for the two older cohorts, the house price boom during the
bubble economy also contributed to the increase in their housing wealth, as indicated by the larger
markers representing years during the bubble period. In the model, we discretize housing wealthinto three levels: low-, medium-, and high-quality dwellings. We define low-quality dwellings as
the 25th percentile, medium-quality dwellings as the median, and high-quality dwelling as the75th percentile of the values of housing wealth. Thus, the prices of low-, medium-, and highquality houses in the model correspond to the 25th, 50th, and 75th percentile of the housing wealthdistribution. Note that households of other cohorts living in the Tokyo UEA are included when
we calculate the value of housing wealth.

8

For more detailed information about the estimation procedure, see Hori and Niizeki (2017).

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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

5. Estimation Procedure
This section describes our estimation procedure. It should be noted that some of the parametersin the model cannot be estimated from the data, so that we set them a priori. Specifically, the valueof the slope parameter in the utility from housing, μ, is set to 1, because it is not clear whether or

not it is separately identified from 𝜙𝜙. We also need to set the two parameters associated with theborrowing limits. The first is the loan-to-value ratio (1 − λh ), which is set to 80%, a typical value
in Japan ( see e.g. Moriizumi, 1996). Moreover, since we do not have information about the limitof the debt-to-income ratio, we set it to 6. 9 We calculate the variances of the shocks to housing
prices and mortgage rates directly from the FIES data. The parameters are summarized in Table
1.

5.1. Estimation of the parameters in the income, housing price, and mortgage rate processes

We assume that household income, housing prices, and interest rates are all exogenous. We
therefore estimate the parameters in Equations (11), (13), (14), (15), and (17) directly from thedata.
To estimate the household income function, we regress the log of annual disposable
income on age and age squared. We then estimate the variance of the income shock, σξ, and theinitial variance of the error term, σν1 , using the residual from the log wage regression. Let 𝑢𝑢�𝑖𝑖𝑖𝑖

be the residual from the log wage regression of individual i in period t. It is typically observedthat cross-sectional wage variation increases by age. Since, in our model, the increase in the crosssectional variance of household income is attributable to the idiosyncratic income shock ξ, thevariance of the error term of the household income function can be written as

9

We calculate the debt-to-income ratio using the FIES data and find that the 99th percentile of the debt-to-income
ratio in the FIES data is approximately 6.

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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"2
E�𝑢𝑢𝑖𝑖𝑖𝑖|𝐴𝐴𝐴𝐴𝐴𝐴� = 𝜎𝜎𝜈𝜈1 + 𝜎𝜎𝜉𝜉 (𝐴𝐴𝐴𝐴𝐴𝐴 − 24).

(18)

2
Thus, to estimate σξ and σν1 , we regress the squared residuals 𝑢𝑢�𝑖𝑖𝑖𝑖
on a constant term andAge − 24 . The coefficient on Age − 24 can be regarded as an estimate of the income shock

variance. Since the initial period of the model starts at age 25, the constant term can be regarded
as the variance of income in the initial period.

5.2. Estimation procedure of the structural parameters
Let us refer to the other parameters, such as discount factor β, as structural parameters. We
estimate the structural parameters employing the method of indirect inference proposed by
Gouriéroux et al. (1993), taking the parameters estimated directly from the data as given. The
indirect inference estimator of the structural parameter minimizes the “distance” between theordinary least squares (OLS) estimates of the following auxiliary model obtained from the FIES
data and those from the data generated from the simulations with the structural model:
10

(𝐴𝐴)

(𝐴𝐴)

(19)

𝐴𝐴𝑖𝑖𝑖𝑖 = � 𝜔𝜔𝑡𝑡 𝑑𝑑𝑖𝑖𝑖𝑖 + 𝑢𝑢𝑖𝑖𝑖𝑖
10

𝑡𝑡=1

(𝐻𝐻)

(𝐻𝐻)

𝐻𝐻𝑖𝑖𝑖𝑖 = � 𝜔𝜔𝑡𝑡 𝑑𝑑𝑖𝑖𝑖𝑖 + 𝜔𝜔𝑤𝑤 𝑤𝑤𝑖𝑖𝑖𝑖 + 𝑢𝑢𝑖𝑖𝑖𝑖 .
𝑡𝑡=1

(20)

where 𝐴𝐴𝑖𝑖𝑖𝑖 represents a household’s financial wealth, 𝑑𝑑𝑖𝑖𝑖𝑖 is a time dummy representing three-year intervals, 𝐻𝐻𝑖𝑖𝑖𝑖 represents households’ housing wealth, and 𝑤𝑤𝑖𝑖𝑖𝑖 is households’ income.

The estimation procedure is as follows. First, we estimate the auxiliary model using the

FIES data. Let 𝝎𝝎 denote the parameter vector of the auxiliary model obtained from the FIES

data. Second, we solve the dynamic programming problem of households by backward induction
for the set of structural parameters Θ ≡ {𝛽𝛽, 𝜙𝜙, 𝛾𝛾, 𝐹𝐹, 𝜎𝜎𝐴𝐴 } to obtain the policy and value functions. 10
10

To solve the dynamic programming problem, we discretize the continuous state variables and linearly interpolate

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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

Using these functions, we conduct simulations to obtain simulated data. Using the simulated data,
we then estimate the auxiliary model. Let ω(Θ) be the vector of OLS coefficients of the auxiliarymodel obtained from the simulated data. Finally, we calculate the distance between thecoefficients obtained from the FIES data, 𝝎𝝎, and those obtained from the generated data, 𝝎𝝎(Θ).

The distance is defined by[𝝎𝝎 − 𝝎𝝎(Θ)]′ 𝑊𝑊[𝝎𝝎 − 𝝎𝝎(Θ)](21)

where W is a weighting matrix whose diagonal elements are the inverse of the variance of thecorresponding elements of 𝝎𝝎 − 𝝎𝝎(Θ) . The indirect inference estimator is the minimizer ofEquation (21).

5.3. Identification of the structural parameters
We incorporate utility shock 𝜂𝜂(ℎ𝑡𝑡 ) (in Equation (1)) to randomize households’ housing choices,while 𝛾𝛾 dictates the relative magnitude of the utility shock and utility from consumption andhousing. A smaller 𝛾𝛾 indicates a weaker link between households’ housing choice and statevariables, and vice versa. This is why we include 𝑤𝑤𝑖𝑖𝑖𝑖 in Equation (20). By matching the 𝜔𝜔𝑤𝑤

obtained from the FIES data with the 𝜔𝜔𝑤𝑤 from the simulated data, we ensure that 𝛾𝛾 is uniquely

identified.

If we did not include utility shock 𝜂𝜂(ℎ𝑡𝑡 ), we would not need to include 𝑤𝑤𝑖𝑖𝑖𝑖 in Equation (20).

In that case, matching the coefficients in the auxiliary model is equivalent to matching the meanof financial and housing wealth in each period. Therefore, let us consider how the structuralparameters other than 𝛾𝛾 are identified by matching the mean of financial and housing wealth ineach period.

the policy and value functions. Furthermore, to approximate the expected values with respect to the random shocks that
follow a normal distribution, we use Gauss-Hermite quadrature. We use 20 sample points and impose the assumptionthat the shocks cannot be smaller than the smallest sample point.

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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

We can identify the structural parameters, except for 𝛾𝛾, if, in the model, the levels and growth

rates of financial and housing wealth are determined when the values of these parameters are
given. Let us consider the identification condition for each parameter. First, the standard deviationof financial assets in the initial period determines the levels of financial and housing wealth.
Therefore, we can identify 𝜎𝜎𝐴𝐴1 by matching the means of financial and housing wealth in thefirst or the first and second periods if the relative magnitudes of financial and housing wealth are
determined by other parameters. Second, the relative magnitudes of financial and housing wealthare determined by the weight of utility from housing 𝜙𝜙, where 𝜙𝜙 is identified from the levels offinancial and housing wealth. Third, the discount factor 𝛽𝛽 dictates the relative importance ofconsumption across different points in time. Thus, the value of 𝛽𝛽 is determined when the growthrate of financial assets is given. Finally, the moving cost 𝐹𝐹 dictates the frequency of housepurchases and sales, and the frequency determines the growth rate of housing wealth. Therefore,
the moving cost 𝐹𝐹 is identified by matching the growth rates of housing wealth.

6. Estimation Results
6.1

Reduced-form estimation

This section presents our estimation results. We start with the reduced-form estimation. As
mentioned above, parameters governing the exogenous processes that determine income, housing
prices, and mortgage (interest) rates are estimated directly from the data. The values of theseparameters are summarized in Table 1.
In order to graphically show the fit of the model, we draw the fitted regression lines ofincome and housing prices. Figure 9 shows estimated household income over the life-cycle. As
can be seen from the figure, the predicted income captures the hump-shaped life-cycle income
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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

well. Note that, in the model, household income halves at age 60 and remains constant thereafter.
Figure 10 shows the estimated prices of low- and high-quality housing. To save computation time,
we assume that the prices of low- and high-quality housing form a log linear relationship withmedium-quality housing. Although this may appear to be a strong assumption, the predicted prices
of low- and high-quality houses are very close to the actual prices.

6.2

Structural estimation

The estimates of the structural parameters are summarized in Table 2. Note that the estimateddiscount factor 𝛽𝛽 discounts the value for one period in the model. Since one period in the modelis three years, the annual discount rate is approximately 3.14%.

To provide an overview of the fit of the structural model, we draw the means of assets heldby actual and simulated households: Figure 11 shows the means of actual and predicted financial
assets in each period, while Figure 12 shows the means of actual and simulated housing wealthin each period. Solid lines depict the means calculated from the FIES data, while dashed lines
depict those calculated from the generated data. The model fit seems good for the most part,
except for financial wealth at age 36 to 45. Since the sample consists of households that wereaged 36 to 45 during the bubble, the estimated model underestimates households’ financial asset
holdings during the bubble period. Households in the FIES sample held more financial assets thansimulated households during the bubble period, perhaps because some of the households inherited
financial or housing wealth from their parents and did not borrow as much to purchase a house as
the model predicts. Despite this discrepancy, overall the estimated model successfully replicatesthe pattern of financial and housing wealth accumulation.
In order to take a closer look at the housing behavior of households, we present the shares ofrenters as well as low-, medium-, and high-quality housing owners in Figure 13. As can be seen,18

ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

renters gradually tend to become home-owners, and the share of households living in betterdwellings increases as households get older.

7. Counter-factual Simulation
To quantify the effect of the housing price boom and bust, we conduct a counter-factual simulationin which housing prices are held fixed from the first period (1983-85) to the 11th period (19982000). The actual and counter-factual housing prices of medium-quality housing are shown in
Figure 14. In each period, households make decisions about their consumption/saving andhousing based on the belief that housing prices are uncertain and follow a random walk. In the
counter-factual simulation, however, the realized housing prices are constant over time. Moreover,in the simulation, the variance of housing price shocks is replaced with the variance of housing
price shocks calculated from data before the housing price boom (1983-1986) and after the bust(1997-2012). Since housing price fluctuations were smaller during these periods before and after
the bubble, the variability of shocks to housing prices is smaller than the actual variability. Thismeans that in the counter-factual simulation, households are faced with less uncertainty and morestable housing prices.
Figures 15 and 16 present the results of the counter-factual simulation. They showhouseholds’ accumulation of financial and housing wealth based on the counter-factual housing
prices shown in Figure 14. Based on counter-factual housing prices, the housing wealth ofhouseholds under 45 years of age is smaller than based on actual prices. Figure 17 shows theshares of renters and home-owners and indicates that the share of renters is lower in the counterfactual simulation and households tend to live in higher-quality housing. Thus, the gap in housing
wealth of those aged under 45 between the simulations based on actual and counter-factual
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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

housing prices is for the most part due to the gap in housing prices. As can be seen from Figure
15, under counter-factual housing prices, households tend to hold less financial wealth after age
30. Why do households hold more debt in the counter-factual simulation in which they can buy ahouse at a lower price? As can be seen from Figure 17, households buy a better house in the
counter-factual simulation. Moreover, in the counter-factual simulation, consumption is 1.93%
higher than in the simulation with actual housing prices. In summary, in the counter-factual
simulation, households borrow more money to buy a better house and consume more.
As mentioned above, one of the greatest advantages of structural estimation is that we
can assess the effect of the housing price boom and bust on the basis of households’ utility. We
find that the impact of the housing price boom and bust is heterogeneous across households.
Japan’s housing price boom and bust had a negative impact for 38.6% of households and apositive impact for 61.4%. The average utility gain for those that experienced a positive effectwas 3.3%, while the average utility loss for those that experienced a negative impact was 2.4%.
Moreover, consumption of those that benefited from the housing boom and bust increased by
1.4%, while consumption of those who experienced a utility loss decreased by 4.1%. On average,
the lifetime utility from consumption and housing in the counter-factual simulation is 0.2% higher
than in the simulation with actual housing prices. Thus, the loss in lifetime utility due to the
housing price boom and bust is 0.2%. To lower lifetime utility by 0.2% in the no-bubble
simulation, lifetime household income would need to decrease by 1.3%. Therefore, the loss oflifetime utility is equivalent to 1.3% of lifetime income.

8. Conclusion
We constructed a theoretical model illustrating households’ financial and housing asset
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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

accumulation over the life-cycle. In each period, households make decisions with regard to theirconsumption/saving and housing under realistic collateral and borrowing constraints and facing
uncertainty about their income, housing prices, and interest rates. We estimate the model using
the method of indirect inference, so that OLS estimates of the auxiliary model calculated from thesimulated data mimic those calculated from the FIES data. The overall fit of the model is good,
but the model under-estimates households’ financial assets during the bubble period. Using theestimated model, we conducted a counter-factual simulation assuming that housing prices
remained constant from the mid-1980s through the 1990s. Doing so enabled us to quantify the
impact of Japan’s housing price boom and bust on households’ lifetime utility.
The estimated impact was heterogeneous across households: 38.6% of households
experienced an increase in lifetime utility, which averaged 3.3%, while 61.4% experienced adecrease in lifetime utility, which average 2.4%. On average, Japan’s housing price boom andbust caused a 0.2% loss of utility, which is equivalent to 1.3% of lifetime income. Moreover, wefound that those who experienced an increase in lifetime utility experienced an increase in
consumption as well, and vice versa. The amount of the changes in consumption and lifetime
utility, however, are quite different. This indicates that the changes in consumption may notprecisely reflect the changes in lifetime utility due to housing price fluctuations. Thus, for the
purpose of evaluating the impact of housing price fluctuations on lifetime utility, a structural
approach is more appropriate than a reduced-form approach that relies on the regression of
consumption on housing wealth.

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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

References[1] Abe, N., & Yamada, T. (2009). Nonlinear income variance profiles and consumption inequality
over the life cycle. Journal of the Japanese and International Economies, 23(3), 344-366.

[2] Attanasio, O. P., Bottazzi, R., Low, H. W., Nesheim, L., & Wakefield, M. (2012). Modelling thedemand for housing over the life cycle. Review of Economic Dynamics, 15(1), 1-18.

[3] Attanasio, O. P., & Weber, G. (1994). The UK consumption boom of the late 1980s: aggregate
implications of microeconomic evidence. The Economic Journal, 104(427), 1269-1302.

[4] French, E. (2005). The effects of health, wealth, and wages on labour supply and retirement
behaviour. The Review of Economic Studies, 72(2), 395-427.

[5] French, E., & Jones, J. B. (2011). The effects of health insurance and self-insurance onretirement behavior. Econometrica, 79(3), 693-732.

[6] Gouriéroux, C., Monfort, A., & Renault, E. (1993). Indirect inference. Journal of Applied
Econometrics, 8(S1), S85-S118.

[7] Gourinchas, P. O., & Parker, J. A. (2002). Consumption over the life cycle. Econometrica,
70(1), 47-89.

[8] Hamaaki, J., Hori, M., Maeda, S., & Murata, K. (2012). Changes in the Japanese employment
system in the two lost decades. ILR Review, 65(4), 810-846.

[9] Hirano, T., & Yanagawa, N. (2016). Asset bubbles, endogenous growth, and financial frictions.
The Review of Economic Studies, 84(1), 406-443.

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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"[10] Hori, M., & Niizeki, T. (2017). Housing wealth effects in Japan: evidence based on household
micro data. ESRI Discussion Paper Series No.339.

[11] Ito, T. (1991). The Japanese Economy. MIT Press.
[12] Laibson, D., Repetto, A., & Tobacman, J. (2007). Estimating discount functions with
consumption choices over the lifecycle. NBER Working Paper No. w13314. National Bureau
of Economic Research.

[13] Li, W., Liu, H., Yang, F., & Yao, R. (2016). Housing over time and over the life cycle: astructural estimation. International Economic Review, 57(4), 1237-1260.

[14] McFadden, D. (1989). A method of simulated moments for estimation of discrete responsemodels without numerical integration. Econometrica, 57(5), 995-1026.

[15] Moriizumi, Y. (1996). Credit rationing and public housing loans in Japan. Journal of Housing
Economics, 5(3), 227-246.

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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

Table 1. Pre-set parameters
Slope parameter governing the utility from housing μ

1.0

Borrowing constraint λw

6Collateral constraint λh

0.8Standard deviations of shocks and disturbances
Income shock σxi

0.0045

Housing price shock σp0.1029Mortgage rate shock σR

0.1529Initial income disturbance σν1

0.0812

Housing price shock (no bubble case) σp

0.0565

Parameters in household income function
Constant α0-2.9045

Age of household head squared α2-0.0019

Age of household head α10.1902

Parameters in housing equations(𝑙𝑙)Constant ζ0-0.1069(𝑙𝑙)

Log of medium-quality house ζ1(ℎ)

Constant ζ00.8968-0.0394(ℎ)

Log of medium-quality house ζ1

1.168

Parameters in housing rent function(𝑞𝑞)

Constant ζ00.3213(𝑞𝑞)
Time variable (year - 1982) ζ1

0.0415(𝑞𝑞)

Time variable (year - 1982) squared ζ1-0.0009

Parameters in the equation relating mortgage and interest rates(𝑑𝑑)

Constant ζ0-3.1297(𝑑𝑑)

Mortgage rate ζ1

1.3752

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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

Table 2. Estimates of structural parameters
Discount factor 𝛽𝛽

0.9086

Moving cost F

36.8319Weight on deterministic part of utility function γ

0.0989Weight on utility from housing 𝜙𝜙

3.2378Std. dev. of initial distribution of financial assets σA

2.0524

Notes: The discount factor is for three years. Standard errors are notcalculated, because it is computationally too burdensome.

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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

Figure 1: Households’ housing wealth, 1983 to 2012
Source: Family Income and Expenditure Survey, Statistics Bureau, Ministry of Internal Affairs and Communications.

Figure 2: Land prices in residential areas, Tokyo, 1983 to 2012
Source: “Land Market Value Publication” (Chikakoji), Ministry of Land, Infrastructure, Transport and Tourism.

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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

Figure 3. Homeownership rate, 1983 to 2012
Source: Family Income and Expenditure Survey, Statistics Bureau, Ministry of Internal Affairs and Communications.

Figure 4. Housing rent
Source: Family Income and Expenditure Survey, Statistics Bureau, Ministry of Internal Affairs and Communications.

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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

Figure 5. Mortgage and deposit interest rates, 1983 to 2012
Source: Monthly Report of Recent Economic and Financial Developments, Bank of Japan.

Figure 6. Annual disposable income, 1983 to 2012
Source: Family Income and Expenditure Survey, Statistics Bureau, Ministry of Internal Affairs and Communications.

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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

Figure 7. Household financial wealth, 1983 to 2012
Source: Family Income and Expenditure Survey, Statistics Bureau, Ministry of Internal Affairs and Communications.

Figure 8. Housing wealth, 1983 to 2012
Source: Family Income and Expenditure Survey, Statistics Bureau, Ministry of Internal Affairs and Communications.

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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

Figure 9. Model fit of household income function
Source: Family Income and Expenditure Survey, Statistics Bureau, Ministry of Internal Affairs and Communications.

Figure 10. Model fit of housing wealth values
Source: Family Income and Expenditure Survey, Statistics Bureau, Ministry of Internal Affairs and Communications.

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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

Figure 11. Model fit: financial assets

Figure 12. Model fit: housing wealth31

ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

Figure 13. Predicted share of renters and homeowners (by quality of housing)

Figure 14. Actual and counter-factual price of medium-quality housing

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ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

Figure 15. Counter-factual simulation: financial assets

Figure 16. Counter-factual simulation: housing wealth33

ESRI Discussion Paper Series No.356"The Impact of the Rise and Collapse of Japanʼs Housing Price Bubble on Householdsʼ Lifetime Utility"

Figure 17. Counter-factual simulation: share of renters and homeowners

34