Essays on housing and real economy

     
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   
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Acknowledgements
I owe an enormous debt of gratitude to my supervisor, Associate Professor Sing
Tien Foo for his support, guidance and encouragement throughout the Ph.D.
program. His gentle personality and meticulous attitude to research will bene…t
my career as well as personal life.
Special thanks to Dr. Liao Wen-chi, who gives me a lot of help on my research,
especially for Chapter 3.
Great thankfulness is expressed to Associate Professor Fu Yuming, Associate
Professor Yu Shi Ming, Associate Professor Tu Yong, Professor Ong Seow Eng,
Professor Deng Yongheng, Dr. Lee Nai Jia, Dr. Li Nan, Dr. Seah Kiat Ying,
Dr. Li Pei, Dr. Qian Wenlan, Professor James D. Shilling, Dr. Chu Yongqiang
for their helpful comments, suggestions and discussions on my thesis and general
academic career.
I am grateful for helpful comments from the participants at the Asian Real
Estate Society annual conference 2010, Paci…c Rim Real Estate Society annual
conference 2012, and Global Chinese Real Estate Congress annual conference 2012.
I would like to thank all of my postgraduate peers. I enjoyed sharing ideas
and developing my research by conversing with them. They are included but
not limited: Wong Woei Chyuan, Omokolade Ayodeji Akinsomi, Liu Bo, Shen
Huaisheng, Li Mu, Wei Yuan, Liang Lanfeng, Xu Yiqin, Zhang Huiming, Shen
i
ACKNOWLEDGEMENTS ii

Yinjie, Liu Jingran, Peng Siyuan, Jiang Yuxi, Chen Wei, Wang Yourong, Guo
Yan, Li Qing, Radheshyam Chamarajanagara Gopinath, and etc.
I would also like to thank the administrative sta¤ members, Zainab Bte Abdul
Ghani, Zheng Huiming, Nor’aini Bte Ali, Wong Mei Yin, Ko Chen, who were so
instrumental in helping me get things done smoothly.
Generous …nancial support from National University of Singapore is highly
appreciated.
Finally, I thank my family, particularly my wife, Qiu Leiju, who has been
unconditional supportive and encouraging during the course of my study.
Contents
Acknowledgements i
Summary vi
1 Introduction 1
1.1 Research Background . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Overview of the Research . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Signi…cance of the Research . . . . . . . . . . . . . . . . . . . . . . 6
1.4 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . . 7
2 Housing, Wealth Composition and Expected Stock Return 9
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2 Related Literature . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
2.3 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.3.1 Environment and Preferences . . . . . . . . . . . . . . . . . 19
2.3.2 Housing Market . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.3.3 Pricing Kernel . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.4 Data and Measurement . . . . . . . . . . . . . . . . . . . . . . . . . 25
2.4.1 Measurement of Housing-Financial Wealth Ratio . . . . . . 26
2.4.2 Consumption Data . . . . . . . . . . . . . . . . . . . . . . . 30
iii
CONTENTS iv
2.4.3 Financial Data . . . . . . . . . . . . . . . . . . . . . . . . . 32

2.5 Long-horizon Forecasts . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.6 Cross-Sectional Test of the Linear Factor Model . . . . . . . . . . . 37
2.6.1 The Linear Factor Model and Fama-MacBeth Procedure . . 37
2.6.2 Results from Fama-MacBeth Procedure . . . . . . . . . . . . 39
2.6.3 Sensitivity Analysis . . . . . . . . . . . . . . . . . . . . . . . 45
2.6.4 Time-varying Consumption Betas . . . . . . . . . . . . . . . 47
2.7 Micro Evidence from Subprime Crisis . . . . . . . . . . . . . . . . . 52
2.7.1 Time-varying Stock Market Participation . . . . . . . . . . . 53
2.7.2 Determinants of Distress . . . . . . . . . . . . . . . . . . . . 55
2.7.3 Consequence of Distress . . . . . . . . . . . . . . . . . . . . 58
2.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
2.9 Appendix: Panel Study of Income Dynamics (PSID) Data . . . . . 61
3 Risk Attitude and Housing Wealth E¤ect 65
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
3.2 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
3.4 Empirical Tests and Results . . . . . . . . . . . . . . . . . . . . . . 80
3.4.1 Estimate Risk Attitude . . . . . . . . . . . . . . . . . . . . . 81
3.4.2 Estimate HWE by Risk Attitude . . . . . . . . . . . . . . . 87
3.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
3.6 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
3.6.1 Descriptive Statistics of Risky Assets Holding . . . . . . . . 97
3.6.2 First Step Probit Model of Heckman Correction . . . . . . . 97
4 Consumption and Wealth Accumulation over the Life Cycle: Does
CONTENTS v
Down Payment Matter? 100
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
4.2 Stylized Facts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
4.3 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
4.3.1 Demographics . . . . . . . . . . . . . . . . . . . . . . . . . . 109

4.3.2 Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
4.3.3 Preferences . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
4.3.4 Endowments . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
4.3.5 Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
4.3.6 Timing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
4.3.7 The Household’s Problem . . . . . . . . . . . . . . . . . . . 113
4.3.8 Equilibrium . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
4.4 Calibration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
4.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
4.7 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
4.7.1 Life-cycle Wealth and Non-housing Consumption . . . . . . 123
4.7.2 Numerical Computation Algorithm . . . . . . . . . . . . . . 125
5 Conclusion 127
5.1 Summary of Main Findings . . . . . . . . . . . . . . . . . . . . . . 127
5.2 Policy Implication . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
5.3 Limitation and Future Work . . . . . . . . . . . . . . . . . . . . . . 131
Summary
This thesis focuses on issues related to the roles of housing in real economy. Hous-
ing is the single most important consumption good, and the dominate wealth for
households. The unique characteristic in‡uences the behavior of households in
terms of consumption, saving, mortgage issuance, and asset return. This thesis is
composed of three main chapters.
Chapter 2 considers a consumption-based asset pricing model where housing is
explicitly modelled both as an asset and a consumption good. As a consumption
good, housing expenditure share is modelled as a novel risk factor. As an asset,
it is the major component of wealth other than …nancial assets. The ‡uctuation
of aggregate housing-…nancial wealth ratio, as a consequence of irrational housing
market, impacts the budget constraints of households. It increases household’s
exposure to risk and shifts the conditional distribution of consumption growth.

Using the United States data, I …nd that the ‡uctuation of housing-…nancial wealth
ratio is a strong predictor for the expected stock return. Conditional on this
factor, the covariances of the returns with the aggregate risk factors explain a
large ratio of the cross-sectional variations in annual returns of size and book-
to-market portfolio. The micro mechanism of this asset pricing model is also
supported by the micro data during subprime crisis.
Chapter 3 examines the housing wealth e¤ect— the positive consumption change
vi
SUMMARY vii
induced by house price appreciation— and whether it is dependent upon house-
holds’attitude toward risk. A simple theoretical model is introduced to highlight
a negative relationship between the wealth e¤ect and risk aversion. This chapter
empirically tests this negative relationship, using data from the U.S. Consumer
Expenditure Survey (CEX). The investigation involves two steps. In the …rst step,
I make use of households’demographic and their risky and liquid asset holdings
to estimate risk aversion. The Heckman correction model is applied to address
the issue of limited stock market participation. In the second step, I construct
pseudo panel data through grouping households by their birth years and their
predicted values of risk aversion, and then, I estimate the responses of households’
consumption changes to house price ‡uctuations by di¤erent risk-attitude groups.
Consistent with the prediction of the theoretical model, the estimation results
suggest a signi…cant negative relationship between the housing wealth e¤ect and
households’risk attitude. Households, who are less risk averse, experience greater
consumption changes in response to house price appreciation.
Chapter 4 explains the in‡uences of mortgage down payment requirement
in both the housing and the credit markets. The rapid expansion of mortgage
credit market since the mid-1990s reduces the mortgage down payment require-
ment for the U.S. households. Micro data over the life cycle show that the pat-
terns of consumption and wealth accumulation changed after the credit expansion:
non-housing consumption for older household increases signi…cantly, but younger

households own less wealth. This chapter develops a dynamic general equilibrium
model with constant housing supply, which …nds that decreases in down payment
requirement account for changes in the pro…les of consumption and wealth accu-
mulation for households. This model also implies that decreases in down payment
requirement cannot explain increases in homeownership rate since the mid-1990s.
List of Tables
2.1 List of Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.2 Summary Statistics of Hostorical Data . . . . . . . . . . . . . . . . 31
2.3 Long-Horizon Predictability Regressions . . . . . . . . . . . . . . . 36
2.4 Fama-Macbeth Regression Results . . . . . . . . . . . . . . . . . . . 40
2.5 Comparison of Asset Pricing Models . . . . . . . . . . . . . . . . . 42
2.6 Cross-Sectional Results with Lagged Consumption . . . . . . . . . . 46
2.7 Cross-Sectional Results with Lagged Housing-Financial Wealth Ratio 48
2.8 Cross-Sectional Results with Overlapped Return . . . . . . . . . . . 49
2.9 Consumption Beta . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
2.10 Time-varying Stock Market Participation and Foreclosure . . . . . . 53
2.11 Logit Regression on Determinants of Distress . . . . . . . . . . . . . 56
2.12 Stock Market Participation by Di¤erent Types Families . . . . . . . 58
2.13 Test of Consumption Insurance . . . . . . . . . . . . . . . . . . . . 59
2.14 Descriptive Statistics of PSID Data . . . . . . . . . . . . . . . . . . 64
3.1 Benchmark Values of Parameters . . . . . . . . . . . . . . . . . . . 74
3.2 Regression of Risky Asset Composition on Demographics . . . . . . 84
3.3 Cohort Regression of Consumption Change by Risk Attitude . . . . 93
3.4 Comparison of The Housing Wealth E¤ect across Risk Attitude
Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
viii
LIST OF TABLES ix
3.5 Robust Test of Cohort Regression Based on Risk Attitude Predicted
by Tobit Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
3.6 Descriptive Statistics of Risky Assets Holding . . . . . . . . . . . . 98

3.7 First Step Probit Model of Heckman Correction . . . . . . . . . . . 99
4.1 Parameters of Benchmark Model . . . . . . . . . . . . . . . . . . . 116
4.2 Aggregate Variables in Two States . . . . . . . . . . . . . . . . . . 119
4.3 Household Equivalence Scales . . . . . . . . . . . . . . . . . . . . . 125
List of Figures
2.1 Housing Wealth and Financial Wealth Growth: 1929-2009 . . . . . 28
2.2 Comparison of Housing-Financial Wealth Ratio Meaurements . . . 29
2.3 The Housing-Financial Wealth Ratio and 10-yrs Cumulative Excess
Return . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.4 Realized versus Predicted Returns for the Fama-French Portfolios . 44
3.1 Housing Wealth E¤ect by Relative Risk Aversion . . . . . . . . . . 75
3.2 Housing Wealth E¤ect by Initial LTV and Weight in Terminal Wealth 77
3.3 Structure of Consumer Expenditure Survey (CEX) data . . . . . . . 79
3.4 Reconstruction of Consumer Expenditure Survey (CEX) data . . . 81
3.5 Consumption and Income over Life Cycle . . . . . . . . . . . . . . . 90
4.1 Consumption of Household over Life-cycle in U.S. 1997 and 2005 . . 106
4.2 Wealth of Household over Life-cycle in U.S. 1994 and 2005 . . . . . 108
4.3 Consumption over Life-cycle under 20% and 10% Downpayment . . 121
4.4 Wealth over Life-cycle under 20% and 10% Downpayment . . . . . 122
x
Chapter 1
Introduction
1.1 Research Background
Housing, that provides necessary shelter, is the largest consumer expenditure in
U.S In 2009, personal housing consumption expenditures were about 1.5 trillion
dollars, or 15.8% of household budgets
1
. It is also the dominant wealth compo-
sition in household’s portfolio. Bertaut and Starr-McCluer (2002) show that res-
idential property accounted for about one quarter of aggregate household wealth

in the US in the late 1990s, while Banks and Tanner (2002) report that real estate
accounted for 35% of aggregate household wealth in UK in the mid-1990s.
With the development of economy, housing is not just a consumption and
investment good. It also in‡uences modern economic system through the …nance
market. The expansion of property backed …nancial derivatives integrate housing
market closely with other economic sectors. In 2009, the total mortgage related
security is about 9.18 trillion U.S. dollar compared with 34.74 trillion U.S. dollar
total value in US bond market
2
. The recent subprime mortgage crisis highlights
1
Data is from National Income and Product Account (NP IA) Table 2.4.5 “personal consump-
tion expenditures by types”.
2
Data source: Securities Industry and Financial Markets Association (SIFMA)
1
CHAPTER 1. INTRODUCTION 2
the pervasive in‡uences of housing in the economy. Studies show that this crisis
is triggered by declines in housing market, which consequently causes a dramatic
rise in mortgage delinquencies and foreclosures in the US, The adverse e¤ects spill
over to the …nancial markets around the world.
There is a substantial, mostly older literature on the modelling of housing in
real economy. This stream of literature takes the overall economic environment
as given and explores the e¤ects of income and interest rates on residential in-
vestment, housing price and other related real estate issues (e.g. Alberts 1962,
Fair, 1972; Ketchum 1954; de Leeuw and Gramlich, 1969). By including interest
rate and income as explanatory variables, the literature explicitly explores the
link from economic environment to housing market. However, the feedback e¤ect
from the housing market is widely neglected by most of the previous literatures.
Since the substantial weight of housing wealth in the real economy, changes in

housing market impact the whole economic system. The previous literature fails
to consider endogeneity issues between the housing market and the real economy.
Built on this stream of literature, the housing researches now expand to a
wider scope. The frontier research on housing covers three aspects. Firstly, the
performance of housing market can be taken as a sensitive indicator of economy.
As Leamer (2007) said “housing is the business cycle”, economic statistics show
strong correlations between the housing variables (e.g. residential investment,
housing consumption, housing price) and the macroeconomic variables (e.g. inter-
est rate, unemployment, in‡ation)
3
. This linkages o¤er a clear sign of economic
‡uctuation. One of implementations is asset pricing model. Cochrane (1991, 1996)
tests his production-based asset pricing model with both common and residential
/>3
The related research can refer to Green (1997), Gauger and Snyder (2003), Leamer (2007),
etc.
CHAPTER 1. INTRODUCTION 3
investments. He …nds residential investment performs better in his asset pricing
model. Following Cochrane’s work, a growing literature that incorporates housing
variables into the asset pricing framework (e.g. Piazzesi, Scheider and Tuzel, 2007;
Lustig and Nieuwerburgh, 2006; Chu, 2010) has been published.
The ‡uctuations in housing market also provide feedback e¤ects into the real
economy. The most important feedback e¤ect in the housing market is known as
the "housing wealth e¤ect". This research pioneers by Case, Quigley and Shiller
(2005). Their most widely cited paper …nds signi…cant housing e¤ects, which is
larger than the wealth e¤ects from the stock market. However a number of subse-
quent empirical researches examining the housing wealth e¤ect from both micro
and aggregate level show mixed results. The strength of housing wealth e¤ects
varies depending on the dataset and methodology
4

. Theoretically, the di¢ culty
of housing wealth e¤ect is the completely di¤erent impacts of housing price on
homeowners and renters. It is easy to understand that homeowners feel rich based
on their home’s appreciation and therefore consume more. The problem with this
line of thought is that increasing housing price means increasing housing cost for
renters, who experience the boom of housing market as a decrease in real wage. In
order to avoid this problem, housing wealth e¤ect is generally modelled in the con-
texts of life cycle, tenure choice and credit constraint. These models (e.g. Buiter,
2008; Campbell and Cocco, 2007) show that housing wealth will be of second or-
der importance for non-housing consumption. Housing value increases result in
4
Carroll and Slacalek (2006) reports larger and signi…cant housing wealth e¤ect in US market
based on aggregate data. Campbell and Cocco (2007) …nd housing wealth e¤ect in UK with
individual data. Gabriel, Bostic and Painter(2009) link CEX and SCF two micro datasets and
show positive housing wealth e¤ect in US. However, Attansio, Blow, Hamilton and Leicester
(2009) employ UK micro level dataset and …nd no housing wealth e¤ect. They argue that
correlation b etween housing wealth and consumption may arise from the same resp on se to third
factors. Calomiris, Longhofer and Miles (2009) question the methodology in Case, Quigley
and Shiller (2005) and Carroll and Slacalek (2006)’s paper. They claim housing wealth e¤ect is
mythical after handling the endogeneity issues. Following this idea, Sousa (2009) reports housing
wealth e¤ect is virtually nil in European countries.
CHAPTER 1. INTRODUCTION 4
higher collateral value and therefore relax borrowing constraints
5
. Housing wealth
indirectly a¤ects non-housing consumption through the collateral channel.
Since housing wealth and consumption have considerable weight in real econ-
omy, housing can play a role of media during economic cycle. Business downturn
deteriorate asset values, reduce debt capacity and depress investment, which will
collectively amplify the magnitude of declines in the downturn (Bernake and Gerl-

ter, 1989). Recent empirical evidences are reported by Chaney, Sraer and Thesmar
(forthcoming) for US and France, and Gan (2007a, 2007b) for Japan. The recent
subprime crisis shows the need for researches not to neglect the importance of
housing in the macroeconomic models. Household leverage, mortgage issuance,
and foreclosure are closely linked with asset price, investment and consumption
through housing market during and after this recession. (e.g. Mian and Su…, 2010;
Mian, Su…, and Trebbi, 2011).
1.2 Overview of the Research
In line with above cutting-edge researches, I discuss the linkage between housing
and real economy in this dissertation. In my study, housing is modeled into the
utility function of household; and also considered in the budget constraint at the
same time. Housing plays a dual-role in my research, and I propose a consumption
based asset pricing model, a typical household consumption model and a general
equilibrium model in the following three main chapters respectively. They describe
di¤erent impacts of housing on real economy.
Chapter 2 extends the consumption-based asset pricing model, where housing is
considered as both consumption and wealth composition. As consumption goods,
5
This is called "collateral e¤ect" in the research on corporate real estate. Higher real estate
asset value can increase …rms’debt capacity. e.g. Benmelech, Garmaise and Moskowitz, 2005;
Chaney, Sraer and Thesmar, forthcoming; Gan, 2007a, 2007b.
CHAPTER 1. INTRODUCTION 5
housing brings consumption composition risk into the asset pricing following Pi-
azzesi, Schneider and Tuzel (2007)’s work. As investment goods, over-investment
in housing will in‡uence household’s consumption when the market declines. The
‡uctuations of irrational housing market can add volatility into the stock market.
The micro data from subprime crisis shows a clear linkage between over-investment
in housing and distress in consumption. Using the U.S. market data, the housing
wealth composition factor is found to be a strong predictor of the expected stock
return.

Chapter 3 studies the consumption behaviors of heterogeneous households cor-
responding to changes in housing prices, which is also known as "housing wealth
e¤ect". The researches on housing wealth e¤ect ‡ourish recently, but the results
are still controversial. In my study, housing wealth e¤ect is linked to household’s
risk attitude. Through both the theoretical analysis and empirical testing using
Consumer Exp enditure Survey (CEX), I …nd that housing wealth e¤ect varies
across households with di¤erent risk attitudes. Households, who are less risk
averse, experience greater consumption changes in response to house price appre-
ciation.
Chapter 4 shows the patterns of consumption and wealth accumulation over life
cycle changed with the rapid mortgage credit expansion. Micro-data reports that
the non-housing consumption for older households increase signi…cantly, whereas
the wealth accumulates slowly for younger households after the mortgage credit
expansion in U.S An overlapping generation model is proposed and calibrated,
which explicitly shows that lower down payment requirement can impact both
housing and credit market. It accounts for the changes of consumption and wealth
accumulation pro…les.
CHAPTER 1. INTRODUCTION 6
1.3 Signi…cance of the Research
This research emphasizes the linkage between housing and real economy and con-
siders the dual-role of housing— both consumption and investment goods — into
consumption sector. A consumption based asset pricing model, a typical house-
hold consumption model and a general equilibrium model are proposed in this
dissertation respectively to describe the impacts of housing on real economy. It
potentially contributes to the current …nance, macroeconomic and real estate lit-
erature.
To …nance literature, it adds the ‡uctuation from housing market into as-
set pricing model and mitigates the poor empirical performance of consumption
asset pricing model (see Breedem, Gibbons and Litzenberger, 1989; Mehra and
Prescott, 1985). This dissertation also discusses the impacts of credit expan-

sion on both housing market and credit market. It enriches the studies on the
recent severe mortgage credit expansion since 1990s (see Chomsosengphet and
Pennington-Cross, 2006; Mayer and Pence, 2008; Greenspan and Kennedy, 2008).
To macroeconomic literature, it considers that the households’consumption
behavior over life cycle and how they response to the shock from housing market
and credit market. It is related to a growing literature on how durable goods a¤ect
the households’consumption over life cycle (See Cho and Sane 2006; Fernandez-
Villaverde and Krueger, 2011).
To real estate literature, this dissertation enhances the understanding on three
streams literature. Firstly, it is the part of literature that studies the linkage be-
tween real estate returns and stock returns (see Kullman, 2003; Chu, 2010); sec-
ondly, the results in this dissertation indicate a new angle for previous researches
on housing wealth e¤ects. Previous literature have presented encouraging empir-
ical evidences (see Case, Quigley and Shiller, 2005; Benjamin, Chinloy and Jud,
CHAPTER 1. INTRODUCTION 7
2004; Carroll, Otsuka and Slacalek, 2011), but the underlying mechanism is not
fully understood (See Campell and Cocco, 2007; Li and Yao, 2007; Gan, 2010).
Thirdly, it is also a complement of the researches on house ownership decision of
borrowing constrained households (see Zorn, 1989; Linneman and Wachter, 1989;
Brueckner and Follain, 1990).
More speci…cally, the signi…cance of each main chapter is presented as follows.
This thesis proposes a housing asset pricing model (Chapter 2) which includes both
housing consumption and housing wealth risk factors. The empirical test …nds that
this model can mitigate the poor performance of consumption based asset pricing
model (CCAPM). The housing pricing factors show higher explanatory power in
cross section and time serially.
Housing wealth e¤ects on consumption are examined by the risk attitudes of
households in the thesis (Chapter 3). This study …nds that housing wealth e¤ects
vary among households with heterogeneous risk attitudes. It adds a new angle in
understanding the cross-sectional variation in housing wealth e¤ects in previous

literature.
The thesis investigates the in‡uence of changes in down payment requirement
on consumption and savings behaviors of households in a general equilibrium struc-
ture (Chapter 4). Unlike prior studies, variables in the …nancial market and the
housing market are all endogenous. Households are impacted by changes in down
payment requirement through these two markets at the same time.
1.4 Organization of the Thesis
The remaining part of the thesis is organized as follows: Chapter 2 presents the
…rst story, entitled “Housing, Wealth Composition and Expected Stock Return”. It
CHAPTER 1. INTRODUCTION 8
explores the in‡uences of the housing market on the stock market. The impacts of
increasing housing prices on non-housing consumption are examined in Chapter 3,
entitled “Risk Attitude and Housing Wealth E¤ect”. Chapter 4 presents the third
story, entitled “Consumption and Wealth Accumulation over the Life Cycle: Does
Down Payment Matters?”This chapter investigates the consumption and saving
behaviors of households over the cycle corresponding di¤erent down payment re-
quirements. The …nal chapter concludes the thesis, highlights the limitations of
the study, and provides recommendations for further research.
Chapter 2
Housing, Wealth Composition
and Expected Stock Return
2.1 Introduction
Housing is widely accepted having a dual-role in real economy. It is the single
most important consumption good supplying amenity for households, and, at the
same time, the dominant asset in their portfolios. This research explicitly models
this characteristic in the utility function to explain the variations in both expected
returns across stocks and equity risk premium over time.
My research starts with Flavin and Yamashita (2002)’s point that housing is
one asset in household’s portfolio. In an uncertain environment, wealth composi-
tion should satisfy the mean-variance e¢ ciency structure(Flavin and Nakagawa,

2008). If the stock market is e¢ cient, as in the Fama’s (1970) hypothesis, the
‡uctuation of wealth composition, measured by housing-…nancial wealth ratio, is
triggered by irrational housing market. The ‡uctuation of wealth composition
in‡uences the budget constraints of households, and changes the distribution of
9
CHAPTER 2. HOUSING WEALTH AND ASSET PRICING 10
consumption growth. This is a possible channel through that irrational housing
market impacts the expected stock return.
This research use U.S. equity returns data to test the predictability. The
model …nds a signi…cant relationship between irrational housing market and risk
premia. Risk premia of consumption growth in a hot housing market is higher.
The consumption betas are time-varying. Conditional on the wealth composition,
the covariances of returns with aggregate risk factors explain 80% of the cross-
sectional variation in annual size and book-to-market portfolio returns.
Housing plays a dual-role in my asset pricing model. As a consumption good,
it is separated from the common consumption and modeled as an independent
argument in the utility function. In Piazzesi, Schneider and Tuzel’s (2007) general
equilibrium model, housing consumption introduces a novel risk factor: shock of
the non housing expenditure share. This new argument in stochastic discount
factor (SDF) is derived from the non separable utility function, and represents the
consumption composition risk.
As an investment good, housing is an asset in the households’wealth portfolio.
The change in wealth composition re‡ects the change in the expected risk premia
for individual asset. If housing market is irrational, a high housing-…nancial wealth
ratio implies that housing market is hot. Investors expect housing to have high
risk premium in future, and they will increase housing investment in their portfo-
lio. On the other hand, when their expectations are broken, some of the investors
will be su¤ered from tight budget constraints. Considering a full consumption in-
surance here: "If markets are complete or if there is some other mechanism or set
of institutions that implement a full-information Pareto-optimal allocation, then

an individual’s consumption should not respond to idiosyncratic income or wealth
shocks."(Cochrane,1991). Consumption growth is determined by the systematic
CHAPTER 2. HOUSING WEALTH AND ASSET PRICING 11
shocks only if the budget constraint is not bound; because households with tight
budget constraint do not have enough resource to satisfy this assumption. There-
fore, tight budget constraint will change households’consumption growth, which
introduces a new factor to asset pricing model. This mechanism bridges the wealth
composition and expected stock return.
The above describes the mechanism of how macroeconomic factors predict the
expected stock returns. Four key elements play the key roles in this process. They
are listed as follows:
Irrational Housing Market. The stock market e¢ ciency is a widely dis-
cussed topic, and Fama (1970) concludes that the e¢ cient market hypothesis is
not neglected after reviewing a large number of articles on market e¢ ciency re-
search. This hypothesis asserts that …nancial markets are "informationally e¢ -
cient", in other word, rational. Although the hypothesis is still controversial by
some empirical results and behavior …nance theory, economists will still accept
this hypothesis in their models’assumption
1
. However, housing market is known
to be less e¢ cient and driven by irrational behavior of households because of the
special characteristics of housing, such as high transaction cost, indivisibility, and
lack of short sell. This is supported by empirical evidence
2
.
Portfolio. "Modern portfolio theory is a theory of investment, which attempts
to maximize portfolio expected return for a given amount of portfolio risk, or
equivalently minimize risk for a given level of expected return, by carefully choos-
ing the proportions of various assets"
3

. It is so called "mean-variance e¢ ciency
1
The continuous discussion on e¢ cient market hyp othes is can b e found in Fama’s(1998) other
survey paper.
2
The most important paper testing e¢ ciency in housing market is published by Cash and
Shiller(1989,1990). They build repeated sale housing price index for four metropolitan areas,
and …nd very strong serial correlation. They conclude U.S. market for homes appears not to
be e¢ cient. Recently Shilling and Sing (2009) point out the irrational term in commercial real
market can explain 4 p erce nt of the variations among total 19 to 27 percent.
3
dern_portfolio_theory
CHAPTER 2. HOUSING WEALTH AND ASSET PRICING 12
framework". This leads to the development of the synthetic risk model known as
"CAPM theory" (Sharpe,1964; Lintner,1965), widely used in the stock market.
Flavin and Yamashita (2002) include housing as one of the assets in household’s
portfolio, and explain the owner-occupied housing decision based on this theoret-
ical framework
4
. In the mean-variance e¢ ciency framework, the composition of
portfolio is determined by the expected return, volatility of assets, and the risk
attitude of consumer. In a general equilibrium structure, the expected return and
the volatility of assets both source from the uncertain environment. The portfolio
composition is …xed if the risk attitude of consumer is constant. The composition
of portfolio re‡ects the expected returns of asset in an uncertain environment.
Consumption Insurance. This idea came from the permanent income hy-
pothesis in macroeconomic area. Modigliani and Brumberg (1954) and Friedman
(1957) note in their permanent income hypothesis and life cycle model that in-
dividuals tend to smooth their consumption over states of nature in order to
maximum their utility functions over the life cycle. The consumption insurance in

asset pricing model is viewed as a cross-sectional counterpart to the life cycle the-
ory
5
. The full consumption insurance implies that heterogeneous consumers are
able to equalize, state by state, their marginal rates of substitution. Therefore, the
equilibrium in a heterogeneous-consumer economy is isomorphic in its pricing im-
plications to the equilibrium of a representative-consumer economy (Wilson, 1968;
Constantinides, 1982). This theoretical fundamental underlies most of macroeco-
nomic asset pricing models, and thus the aggregate data can link with pricing
models with representative agent. The primary testable implications of equilib-
4
The researches on portfolio choice with exogenous returns in the presence of housing also
can be found in Yamashita (2003), Cocco (2005), and Flavin and Nakagawa (2008).
5
There is an extensive literature on the hypothesis of complete consumption insurance; see
Cochrane (1991), Mace (1991), Altonji, Hayashi, and Kotliko¤ (1992), and Attanasio and Davis
(1996).
CHAPTER 2. HOUSING WEALTH AND ASSET PRICING 13
rium in a representative-consumer are the set of Euler equations of consumption,
enlightened by Lucas(1978). However, the model performs poorly in explaining
security prices. Mehra and Prescott (1985) point out that the model predicts a
mean equity premium that is too low and a mean interest rate that is too high
given the observed low variability of aggregate consumption growth in U.S. mar-
ket, and they call this phenomenon "equity premium puzzle". Modi…cations are
suggested to mitigate the poor empirical performance of the model. Some start
from the assumption of full consumption insurance, and this research is one of
them.
Heterogeneous consumers and Limited Participation. If consumption
insurance is incomplete, representative agent makes no sense in asset pricing
model. The earlier studies suggest that the potential enrichment of the joint

assumption — heterogeneous consumers and incompletely consumption insurance
on asset pricing is illusory
6
. Constantinides and Du¢ e (1996) argue that the
previous models with heterogeneous consumers, that have failed to improve the
performance, have a common feature, that the individual income to aggregate
income is time series stationary. They relax this assumption by adding a factor,
consumption growth distribution, in the SDF. This theory is empirical supported
by U.S. Consumer Expenditure Survey (CEX) data (Brav, Constantinides and
Geczy, 2002). Their idea is adopted by most of models with heterogeneous con-
sumers
7
. That is, to de…ne a non-stationary individual endowment to aggregate
6
For example, Mankiw (1986), Lucas (1994) and Telmer (1993) calibrate economies in which
consumers face uninsurable income risk and borrowing or short-selling constraints, and conclude
that consumers are able to come close to the complete-markets rule of complete risk sharing, even
though consumers are allowed to trade in just one security in a frictionless market. Aiyagari and
Gertler (1991) and Heaton and Lucas (1992, 1995, 1996) added transaction costs or borrowing
costs in economies with uninsurable income risk and concluded that consumers are still able to
come close to the complete-markets rule of complete risk sharing, unless the ratio of the net
supply of bonds to aggregate income is restricted to an unrealistically low level.
7
For example, Jouini and Clotilde (2007) discuss the heterogeneous belief; Chien and Lustig
(2010) focuse on distribution of wealth; Gomes and Michaelides (2008) combine the idiosyncratic