Economics Honors Paper - Tu Nguyen - 2015

Understanding the Causes of the Housing Bubble
in the United States
Tu Nguyen
Advisor: Dr. John Abell
Presented to the Department of Economics
in partial fulfillment of the requirements
for a Bachelor of Arts degree with Honors
Randolph College
Lynchburg, Virginia
May 9th, 2015

Understanding Housing Bubble -1-
Abstract
This paper attempts to explain the causes of the recent housing bubble in the United
States in a multidisciplinary approach. A review of the literature suggests that there are economic
and psychological theories behind the bubble. A housing bubble index is introduced to gauge the
existence of a bubble in the market. A 2-stage least squares regression analysis using the Newey
West Estimator method is used to empirically test these theories. The results indicate that
government housing subsidies and the media are significant determinants of the housing bubble
index. In addition, the lagged dependent variable being significant with a high t value suggests
that the housing bubble had a strong momentum and built a virtuous cycle within itself.
However, these findings must be taken with caution because it is still not known whether the
Newey West Estimator method can be applied to cointegrated time series data.

Table of Contents
Section I: Introduction …………………………………………………………………………... 3
Section II: Literature Review ……………………………………………………………………. 5
Section III: Data and Method …………………………………………………………………... 15
Section IV: Model ……………………………………………………………………………… 24
Section V: Results ……………………………………………………………………………… 29
Section VI: Limitations ………………………………………………………………………… 40
Section VII: Policy Implications ……………………………………………………………….. 42
Appendix A …………………………………………………………………………………….. 43
Appendix B …………………………………………………………………………………….. 45
References ……………………………………………………………………………………… 47

I. Introduction
Seven years have passed since 2008 but the effects of the financial crisis are still being
felt. On September 15, 2008, Lehman Brothers, the fourth-largest investment bank in the United
States at the time, collapsed after a great struggle to avoid bankruptcy. The collapse of Lehman
Brothers paralyzed the global financial system, threatening to bring it down. A number of large
financial institutions such as AIG and Citigroup faced the threat of bankruptcy, which was then
prevented by a huge bank bailout package by the federal government. However, the bank bailout
package from the government, together with an economic stimulus package in 2009, was not
enough to keep the US economy and the global economy from going into a recession. Three
economists at the Federal Reserve Bank of Dallas, Luttrell, Atkinson, and Rosenblum (2013),
estimated that this financial crisis has cost the U.S. economy at least 40 to 90 percent of one
year’s output, a value of $6 trillion to $14 trillion. The Global Financial Crisis of 2008 is
considered by many economists to be the worst financial crisis since the Great Depression.
Realizing the long-lasting damage of the recent crisis, one will naturally ask, what caused
the global financial crisis? There are many theories to explain the causes of the financial crisis of
2008. However, the general consensus is that the primary cause of the financial breakdown was
the credit crisis following the burst of the housing bubble. Steven Gjerstad and Vernon Smith
found that, historically, housing bubbles have been a leading indicator in eleven out of the
fourteen economic recessions since 1929 (Gjerstad & Smith, 2013). It does not require much
thought to realize that financial bubbles, in particular housing bubbles, have had significant
impacts on the economy and people’s lives. However, Yan, Woodard, & Sornette (2012) pointed
out that bubbles have been ignored at the policy level. To be specific, they mentioned that not
until the global financial crisis did government officials acknowledge the importance of

understanding and forecasting bubbles in general, including housing bubbles (Yan, Woodard, &
Sornette, 2012). Therefore, it is safe to claim that a thorough understanding of housing bubbles is
necessary to prevent another recession from happening in the future.
By far, the majority of the literature on housing bubbles focuses on developing theoretical
models to explain the phenomenon. The Log-Periodic Power Law bubble model or its
modifications are often employed to study the dynamics of bubbles and crashes (Yan et. al,
2012; Ohnishi, Mizuno, Shimizu, & Watanabe, 2011; Kivedal, 2013). Some other researchers
attempted to find out the causes of the housing bubble graphically by looking for the correlation
between certain macroeconomic indicators, which will be discussed in the following section, and
periods of housing booms (Liebowitz, 2009). A number of analysts have conducted empirical
research to identify the existence of housing bubbles in the market, but so far only a few
attempted to find out the causes of bubbles empirically (Escobari, Damianov, & Bello, 2012;
Mayer, 2011; Kohn & Bryant, 2011). In this paper, I will develop an econometric model to
explain the causes of housing bubbles in a multidisciplinary approach that hopefully might allow
policy makers to prevent another catastrophe from happening in the future by making smart
decisions when the bubble is still in its early stages.
The paper is laid out as follows: Section II will cover the literature on housing bubbles in
the United States, including the definition and some theories that aim to explain the causes of the
bubbles. Section III will go into detail on the method and data used in the study. Section IV will
describe the econometric model employed in the paper. Section V will detail the results and
implications of the quantitative analysis. Finally, Section VI will specify some limitations of this
study and provide recommendations and suggestions for future research.

II. Literature Review
So what exactly is a housing bubble, or even a bubble in general? The following is a
definition of bubbles which has been widely accepted among economists, by Charles
Kindleberger, a prominent economic historian and author of several books on financial crises:
A bubble may be defined loosely as a sharp rise in the price of an asset or a range of
assets in a continuous process, with the initial rise generating expectations of further rises
and attracting new buyers – generally speculators interested in profits from trading in the
asset rather than its use or earnings capacity (cited in Eatwell, Milgate, & Newman, 1987,
p. 281).
A housing bubble will then be defined accordingly, by replacing the word “asset” from
the above definition with “real estate”. The definition is straightforward. However, it is not an
easy task to determine whether a bubble is forming at a specific point in time, not to mention
trying to explain the causes of the bubble. For example, before the bursting of the housing bubble
in 2006, there was a strong debate among scholars around the existence of a bubble in the real
estate market. In fact, “there still does not appear to be a cohesive theory or persuasive empirical
approach with which to study bubble and crash conditions” (Vogel, 2009, p. i).
Since the bubble burst, researchers have started trying to explain what conditions led to
the bubble. However, a review of the literature suggests that a general consensus on the causes of
the housing bubble has not been reached. Ben Bernanke (2009), former chairman of the Federal
Reserve, cited the inflows of global savings into the United States as the main cause of the recent
housing bubble. He argued that the surplus of available funds led financial institutions to
compete for borrowers, thus making it easier for households and businesses to obtain credit. In
addition, the large inflows of global savings drove down interest rates in the United States, which

made it cheaper for investors to borrow money. As a result, more people took out loans to invest
in the real estate market. Furthermore, the lending process was poorly done as lenders took on
higher risks by approving subprime mortgage loans to borrowers with bad credit. Lenders might
have believed in borrowers’ ability to refinance loans because with rising house prices,
borrowers could just take out loans to buy a house with little down payment, add nominal
improvements, sell it quickly after a few weeks, and still profit from the price increase. The rapid
expansion of mortgage lending resulted in the housing boom in the U.S. Eventually, when
investors realized home prices were overvalued, the whole system collapsed. Borrowers with low
creditworthiness could not repay their loans, and large lenders declared bankruptcy.
Furthermore, Bernanke (2009) argued that the saving inflows from abroad not only
affected the mortgage market but also drove down returns on traditional long-term investments,
causing investors to look for alternatives. To meet the increasing demand for new investments,
the financial industry creatively designed securities that combined individual loans in intricate
ways. Later, these new securities turned out to involve hidden and significant risks that were not
fully understood by both investors and designers of the securities in the first place.
Contrary to Bernanke (2009) who believed foreign savings were the primary cause of the
housing bubble, many researchers pointed to the U.S. government as the culprit (Gerardi et al.,
2008; Liebowitz, 2009; Wallison, 2009). These scholars suggest that the underlying cause of the
bubble is believed to be the U.S. government’s efforts to increase home ownership, especially
among low-income and minority groups, represented through the Community Reinvestment Act
of 1977 (CRA) and the affordable-housing mission of Fannie Mae and Freddie Mac in the 1990s.
According to this line of thinking, instead of providing subsidies to these underserved groups,
through regulatory and political pressure, the government forced banks into making loans that

would not be normally advisable (Wallison, 2009). As a result, banks had to reduce their lending
standards to make mortgage financing accessible to more people. The decline in mortgage
lending standards allowed mortgages in some cases to require virtually no down payment, which
helped increase the homeownership rates. The Alternative Mortgage Transactions Parity Act of
1982 aided in the process by removing restrictions against mortgage loans with exotic features,
such as ARM and interest-only mortgages (Sherman, 2009). As a result, from 1995, when
lending quotas from CRA became effective, to 2005, the homeownership rate saw a surge from
64 percent to 69 percent:
Figure 1. Homeownership Rate in the United States
Source: U.S. Census Bureau
61.0
62.0
63.0
64.0
65.0
66.0
67.0
68.0
69.0
70.0
HomeownershipRate(Percent)
Homeownership Rate in the United States
(1985-2014)

The rise in homeownership in turn led to an increase in house prices, which eventually
created a bubble. When the bubble burst, many homeowners having received subprime
mortgages were not able to make their payments. The reason for these defaults is
straightforward. Low-income or bad credit borrowers took out no or little down payment home
loans because they believed that during the housing boom, their home price would rise
substantially, thus increasing their home equity1. As a result, they would soon be qualified for
traditional mortgage loans or they could turn around and sell their house for a higher return.
Unfortunately, the truth was harsh. One thing to notice is that the interest rate associated with
these no or little down payment loans was set to rise after one or two years. In addition, the Fed
started raising the interest rate in 2004 in an attempt to slow down the economy. These two
combined effects caused the loan payments to expand significantly. As a result, these borrowers
soon defaulted after the housing bubble burst in 2006. To be specific, it is estimated that
homeowners who used subprime mortgages to purchase their homes ended up in foreclosure
more than 6 times as often as those who used prime mortgages (Gerardi et al., 2008). As a result,
the financial system got into trouble. This would have been prevented with stricter underwriting
standards.
Moreover, lots of changes to the regulatory framework which took place in the late 20th
century might have contributed to housing bubbles in the U.S. (Sherman, 2009). A brief history
of financial deregulation in the United States is included in Appendix A.
Some researchers believed that interest rates played a significant role in fueling the
housing bubble. According to White (2009), the Fed’s expansionary monetary policy provided
the means for unsustainable housing prices and risky mortgage financing. From 2001 to 2006 the
1 Home equity: The amount of the house that the homeowner truly owns. If s/he sells the house and pays off bank
loans, the value of the home equity is the difference between the market value and the mortgage. The homeowner
can build up his/her equity if the home’s value rises.

Fed kept the federal funds rate well below its targeted level suggested by the Taylor rule2, which
is a monetary-policy rule that stipulates how much the Federal Reserve should adjust the nominal
interest rate to stabilize the economy in the short-term and still maintain its long-term growth.
The Taylor-rule gap – the amount by which the Taylor rule policy setting exceeded the actual
federal funds rate – reached 200 basis points in the period 2003-2005. During this period, there
were times when the real federal funds rate was negative. As a result, other market interest rates,
being heavily influenced by the federal funds rate, were also being kept low. At that time, a
person could profit by borrowing at the low interest rate to buy a residential property and by
keeping it for a period of time; the property’s price would keep up with the inflation rate, which
was higher than the interest rate. The considerable lowering of short-term interest rates by the
Fed not only helped increase the total dollar amount in mortgages but also made Adjustable-rate
mortgages (ARMs) cheaper relative to 30-year fixed-rated mortgages. As a result, the share of
new ARMs more than doubled from 2001 to 2004. The increase in riskier investments made the
market more vulnerable to a shock. In addition, since the value of real estate property, a long-
lived asset, depends on the discounting of its future cash flows, the fall in interest rates in the
2000s made real estate prices “seem like bargains” (White, 2009, p. 119). As a result, demand
for and prices of existing houses increased, and there was a surge in construction of new housing
on undeveloped land. A housing bubble had been formed.
Recently, Shiller (2005), among many scholars, has drawn on the study of human
psychology to explain the causes of the housing bubble. Shiller emphasized irrational exuberance
as the main culprit. Irrational exuberance is defined as a heightened state of speculative fervor. In
the case of the housing market, investors were confident that house prices would keep rising (or
2 Taylor rule: 𝑖 𝑡 = 𝜋 𝑡 + 𝑟∗
𝑡 + 𝑎 𝜋
( 𝜋 𝑡 − 𝜋∗
𝑡
) + 𝑎 𝑦( 𝑦𝑡 − 𝑦̅∗
𝑡
), where 𝑖 𝑡 is the nominal federal funds rate, 𝜋 𝑡 is the
inflation rate, 𝑟∗
𝑡 is the real federal funds rate, 𝜋∗
𝑡 is the target inflation rate, 𝑦𝑡 is the log of real output, 𝑦̅∗
𝑡
is the
log of potential output,and in most cases, 𝑎 𝜋 = 𝑎 𝑦 = 0.5.

at least could not drop) because land is limited. This belief drove prices up to levels way beyond
the underlying value that could not be explained by fundamentals.
According to Shiller, the housing bubble started with a sharp increase in house prices,
accompanied by great public excitement and a decline in credit underwriting standards. Seeing
the new opportunity, people wanted to participate in the market. They talked about these price
increases with their friends and their relatives. Shortly after that, the media featured stories of
people getting rich, causing more enthusiasm among the general public. As more people were
willing to participate in the housing market because of the herd mentality, prices were elevated
further. The increase in prices again attracted more and more people. As a result, house prices
were pushed up far above intrinsic values. A bubble had been formed.
Shiller’s argument is based on a fundamental theory in psychology, the conformity
theory. It suggests that people tend to change their opinions and perceptions in ways that are
consistent with group norms. They will be inclined to follow and mimic what others are saying
or doing. According to Deutsch and Gerard (1955), there are two main reasons for this human
behavior: informational influence and normative influence. Informational influence suggests
people conform because they want to make sound judgments, and they assume that when the
majority agree on something, the majority must be right. This happens when a person lacks
knowledge or is in an unclear situation and has to look to the group for guidance. The fact that
people usually take the judgments of others into consideration when making their own
conclusion is not surprising because since birth, they have learnt that “the perceptions and
judgments of others are frequently reliable sources of evidence about reality” (Deutsch and
Gerard, p. 635). On the other hand, normative influence leads people to conform because they
are afraid of the negative consequences of appearing deviant. Schachter (1951) pointed out that

individuals who deviate from group norms are usually disliked and rejected by others. Since it is
human nature to be liked and to want to be accepted by others, people will change their behavior
and opinion in order to fit in with the group.
In addition, there is another concept in psychology that needs a closer look, group
polarization. Group polarization refers to the tendency for groups through group discussion to
make decisions that are more extreme than initial tendencies (Moscovici & Zavalloni, 1969).
Therefore, if in the beginning the majority of group members lean toward a risky position on an
issue, the group’s position becomes even riskier after group discussion. It should also be noted
that the judgments expressed by the group consensus will usually be adopted by group members
as their personal opinions.
Finally, confirmation bias, a psychological tendency to search for and interpret evidence
in a way that confirms one’s initial beliefs, might have influenced how people behave during the
bubble. It has been found that people normally seek information that they consider supportive of
an existing hypothesis and avoid information that is not supportive of that hypothesis because
human beings tend to overestimate their own judgments. In addition, even ambiguous evidence
will be interpreted in a way that backs up one’s existing belief. Confirmation bias is also found to
be pervasive and strong (Nickerson, 1998).
These ideas from the psychology discipline may explain why there was a sharp increase
in speculators in the housing market in the early 2000s. An average person with poor knowledge
of the financial industry would still be willing to be a part of the housing hype as s/he heard
stories of the housing market from the media and from other people. S/he would be well-
convinced that since everyone was talking about it, the housing market was a great place to
invest. Indeed, no one wanted to miss out on this opportunity. On top of that, further interactions

between people with the same interest in the housing market would strengthen their initial
interest in real estate. Furthermore, even when there were warnings of a housing bubble by
prominent economists, people tended to avoid these warnings and look for additional evidence
that supported their existing belief in the profitability of their investments in the housing market.
As shown, word of mouth might have helped inflate the housing bubble. However, word
of mouth alone could not be capable of creating such strong hype in the real estate market. There
must have been a greater channel that spread the real estate hype among large groups of people.
Indeed, Shiller (2005) asserted that in general, “significant market events generally occur only if
there is similar thinking among large groups of people, and the news media are essential vehicles
for the spread of ideas” (p. 85). Under the mechanism of informational influence as described
above, the media could have contributed to the house price increase by featuring stories of
people getting rich quickly from the real estate market. As a result, it is probably no coincidence
that the first speculative bubble ever recorded – the Dutch tulip mania of the 1630s3 – occurred
shortly after the world’s earliest printed newspapers appeared in Europe (Stephens, 1994).
Recognizing the influence of the media on the housing bubble, Shiller (2007) noted that,
...the feedback that creates bubbles has the primary effect of amplifying stories that
justify the bubble; I called them “new era stories.” The stories have to have a certain
vividness to them if they are to be contagious and to get people excited about making
risky investments. Contagion tends to work through word of mouth and through the news
media. It may take a direct price-to-price form, as price increases generate further price
increases (p. 9).
3 After tulips were brought into the Netherlands from Turkey, they became a favorite flower in the country.In
addition, it took a long time to grow these tulips. As a result, they were highly sought after. Supply could not catch
up with demand. As a result, speculators began to enter the market, which helped drive the prices up even further
and created the tulip mania.

Agreeing with Shiller (2007), Soo (2013) created a sentiment index by studying news coverage
in 20 city markets covered by the Case-Shiller home price index. She found that sentiment has a
significant effect on housing prices. It can predict over 70 percent of the variation in national
house price growth.
Finally, a housing bubble can be understood graphically through a supply-demand model
that allows extrapolative expectations, expectations about the future values of something
extrapolated from its observed past values, as shown in Figure 2.
Figure 2. Supply-Demand Bubble Model

Initially, the market is in equilibrium at point A. Suppose there is a shock in the housing
market, causing demand to shift from D0 to D1. At the original price P0, consumers would now
like to buy Q1 instead of Q0 while suppliers still find it profitable to supply only Q0. This creates
a situation of excess demand from Q1 to Q0. In the basic supply-demand model, the excess
demand will lead to a price increase to P1 and, as a result, quantity supplied will increase while
quantity demanded will decrease; the new equilibrium would be at point B. However, in the
bubble model, there are extrapolative price expectations. Hence, the rising price from P0 to P1
will shift the demand curve to D2 and increase quantity demanded to Q2. Again, the higher
demand causes a shortage from B’ to B, which is greater than the initial shortage of Q1 – Q0. As
can be expected, instead of equilibrating the market, the increase in price drives it further away
from equilibrium. The larger shortage will put an upward pressure on price, causing it to rise to
P2. If expectations are extrapolative, demand will shift to D3, causing an ever greater shortage
from C’ to C. As has been shown, with extrapolative expectations, the more the price rises, the
more people want to buy and therefore the price and demand keep rising. Interestingly, if we
connect the quantity demanded at each price level, we will have a demand curve that is upward
sloping. This is a peculiar feature of the bubble model (Colander, 2009).

III. Data and Method
In this research, I am going to not only test whether some of the variables suggested by
previous researchers actually contributed to the housing bubble but also quantify the irrational
exuberance in the market.
First, before getting to the econometric model, I would like to define a proxy for the
housing bubble. A consensus has not been reached on the best method to estimate a housing
bubble. Many previous researchers used the ratio approach and some used the user cost
approach4. Among these, the ratio approach is the most commonly used method to study a
housing bubble. It generally includes two different ratios, price to rent ratio and price to income
ratio (Chen, 2012, p. 20).
In finance, the Price - Earnings ratio (P/E ratio) is an important metric when it comes to
valuing stocks. Generally speaking, a high P/E ratio reflects the expectation of higher growth in
the future. In addition, the higher the P/E ratio, the more overpriced the market. A review of the
literature suggests researchers have agreed that there is a similar P/E ratio for the housing
market, with “P” being a house’s current market value and “E” being the value at which it could
be leased. To be more specific, the earning of a house, E, is often calculated as the total values of
all the expected future rents discounted back to the present. Therefore, the house price-to-rent
ratio is a metric that reflects the relative cost of owning versus renting. Economic theory suggests
that if house prices rise way beyond rents, potential homebuyers will choose to rent, therefore
reducing the demand for houses. As a result, house prices will be brought down in line with
rents.
4 The user cost approach is a model that is built upon the proposition that the cost of renting should be equivalent to
the all-in risk-adjusted cost of homeownership.

The ratio is considered to be an important measure of a potential deviation of housing
prices from their fundamental values. The common argument in favor of this ratio is that if the
price-to-rent ratio remains high for a long period of time, it must be because house prices are
being sustained by unrealistic expectations of future gains rather than being supported by
fundamental rental value. This signals a potential bubble. This measure has been used by many
researchers to study house price bubbles (Kivedal, 2013; Leamer, 2002; Krainer & Wei, 2004;
McCarthy & Peach, 2005). For example, Leamer (2002) recommended using the housing P/E
ratio to proxy a bubble. If the P/E increases dramatically, there is a chance of a bubble in the
market. He found that P/Es in the San Francisco Bay Area rose faster relative to those in the Los
Angeles metro area during the period 1991-2002. This signaled a bubble in San Francisco
compared to Los Angeles. Similarly, Krainer and Wei (2004), using data from the Office of
Federal Housing Enterprise Oversight (OFHEO) and the U.S. Bureau of Labor Statistics, found
that in the early 2000s, house prices were departing from fundamentals, which are implied rental
values. However, they also warned that price-rent ratio can rise without signaling a bubble.
In this research, I also use the P/E ratio to proxy a housing bubble. However, it is a
daunting task to measure the prices and earnings of houses in the market on a national level, and
I could not find such data. Therefore, instead I have decided to use the S&P/Case-Shiller U.S.
National Home Price Index available from the S&P Dow Jones Indices website for ‘P’ and the
Owners’ Equivalent Rent of Residences Index (OERI) from the U.S. Bureau of Labor Statistics
for ‘E’. The S&P/Case-Shiller U.S. National Home Price Index, which covers nine major census
divisions, measures changes in the prices of single-family, detached residences by comparing the
sale prices of the same properties over time. It is a widely used barometer of the U.S. housing
market. OER is the implicit rent that owner occupants would have to pay if they were renting

their houses. For example, an OER index of 200 (relative to a base year value of 100 in 1982)
indicates that Owners’ Equivalent rents had risen 100 percent since 1982. This P/E ratio is
similar to what Krainer and Wei (2004) proposed in their study. However, I have decided to use
the S&P/Case-Shiller U.S. National Home Price Index instead of the OFHEO national house
price index as suggested by Krainer and Wei because the OFHEO data are only collected from
transactions or appraisals associated with mortgages securitized by Fannie Mae or Freddie Mac,
which are mostly prime loans. On the other hand, the S&P/Case-Shiller U.S. National Home
Price Index is computed using all available arm’s length transactions, including those financed
with other types of mortgages, such as Alt-A and subprime (Goetzmann et al., 2012, p. 6). As a
result, the S&P/Case-Shiller Index is more comprehensive and relevant to the purpose of this
study.
I then created a Housing Bubble Index by taking a ratio of those two indices. A value of 1
means the housing market is priced appropriately. Assuming that the real estate market is
forward looking, home price is essentially the present value of future rent payments. Hence, it is
expected that an appropriately priced market will result in a HBI value of 1. The farther the index
deviates from 1, the higher the chance there is a housing bubble. This happens when house prices
rise at a higher rate than rental values. A plot of the housing bubble index is presented below.

Figure 3. Housing Bubble Index
Source: S&P/Case-Shiller U.S. National Home Price Index and Owners’ Equivalent Rent
of Residences Index.
As can be seen in figure 3, the Housing Bubble Index seems to do a reasonable job of
measuring bubbles in the real estate market. It captures a small bubble in the late 1980s that,
according to Shiller (2005), reflects regional bubbles on the West Coast and the East Coast. In
addition, it shows the great boom and bust in the real estate market in the 2000s.
Studying the factors that led to the housing bubble, Kohn and Bryant (2011) found that
personal income was one of the significant variables in their models. Therefore, I decide to
include a variable for real disposable income per capita as a measure of housing affordability. I
hypothesize that higher real disposable income per capita will result in a higher chance that a
bubble will happen because people will be confident to spend more during good times. Data for
real disposable income per capita are obtained from the Federal Reserve Bank of St. Louis’
database.
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
1986-01-01
1987-03-01
1988-05-01
1989-07-01
1990-09-01
1991-11-01
1993-01-01
1994-03-01
1995-05-01
1996-07-01
1997-09-01
1998-11-01
2000-01-01
2001-03-01
2002-05-01
2003-07-01
2004-09-01
2005-11-01
2007-01-01
2008-03-01
2009-05-01
2010-07-01
2011-09-01
2012-11-01
2014-01-01
HousingBubbleIndex
Housing Bubble Index (1986 - 2014)

In addition, a variable that tracks the amount of government housing subsidies is included
in order to account for the various changes in governmental regulations that aimed at increasing
homeownership, especially the Community Reinvestment Act of 1977 and the affordable-
housing mission of Fannie Mae and Freddie Mac. Since there were so many changes in the
regulatory framework, the use of dummy variables to test for structural breaks is not efficient.
On the other hand, as suggested by the literature, these regulations aimed to increase
homeownership by allowing low-income and bad-credit borrowers to take out more affordable
home mortgages. Similary, housing subsidies have been granted to increase the accessibility to
public housing, especially for low-income households. As a result, the amount of government
housing subsidies might be a good and practical proxy for these regulatory changes. It is
assumed that whenever the Democratic Party is in power, the amount of government subsidies
for low-income households tend to increase and more regulation is expected. On the other hand,
when the Republican Party is in power, the government will likely cut back on government
subsidies and lax regulation is expected. This implies that there might be a positive correlation
between government housing subsidies and change in the regulatory framework. Consequently,
housing subsides is used to proxy for regulatory changes.
Like many other researchers, I believe that interest rates played an important role in
fueling the housing bubble. However, in my model, I will not include the federal funds rate as
suggested by White (2009). Since the federal funds rate is the interest rate banks charge each
other on interbank loans, ordinary market participants will not be able to borrow at that rate.
Therefore, it might not be a good proxy for the housing bubble. Instead, I am going to use the
spread between the rate on conventional, conforming 30-year fixed-rate mortgages and the rate
on treasury-indexed 1-year adjustable rate mortgages. The lower the spread, the more profitable

for investors to invest in the short term rather than in the long term, thereby increasing the
amount of speculation in the real estate market. Data for these interest rates are taken from
Freddie Mac.
Moreover, in an attempt to account for the psychological theory of the crowd, household
home mortgages liability (measured as a flow) will be included in the model. Initially, I would
have liked to have a variable that tracks the number of investors in the real estate market.
However, such data cannot be found. Therefore, the flow of the household home mortgages
liability will be used as a replacement. A plot of the household home mortgages liability is
included below.
Figure 4. Flow of the household home mortgages liability.
Source: Federal Reserve Flow of Funds Z1 release.
-400000
-200000
0
200000
400000
600000
800000
1000000
1200000
1400000
Jan-86
May-87
Sep-88
Jan-90
May-91
Sep-92
Jan-94
May-95
Sep-96
Jan-98
May-99
Sep-00
Jan-02
May-03
Sep-04
Jan-06
May-07
Sep-08
Jan-10
May-11
Sep-12
Jan-14
Flowofhouseholdhomemortages
liability(MillionsofDollars)
Flow of the household home mortgages
liability (1986 - 2014)

As can be seen from the plot, this series has a constantly rising trend through 2006,
suggesting rising household liabilities as they keep taking out more and more home loans. The
gradual increase from 1997 to 2006 might be explained by the herd behavior. People took out
more home loans to invest in the real estate market as they saw other people profiting from
housing. After the housing collapse and the financial crisis, the numbers fell quickly to zero or
even negative as people were scared and began paying off their debts.
Finally, I will test whether the media helped fuel the housing bubble by including a
variable that tracks the change in total articles from one year to another on the housing market. I
will follow the method used by Glynn, Huge, and Hoffman (2008). In their research, the authors
studied the impact of the media on the housing bubble by conducting a newspaper content
analysis on articles that were focused on the real estate market. They searched for articles
appearing in the New York Times, the Minneapolis Star Tribune, the Houston Chronicle, and the
San Francisco Chronicle because these are the representative newspapers for the four different
regions in the US – the Northeast, the Midwest, the South, and the West respectively. They
obtained desired articles by using the following search string on the Lexis-Nexis database:
((real estate OR housing w/5 bubble OR dream OR speculat!) OR (home sales OR
housing starts w/5 increas!) OR (housing w/5 sure bet) OR (hous! prices w/ rise OR
rising) OR (housing OR real estate w/ 5 good time OR good buy OR expan!) OR
(mortgage w/5 innovation) OR (reduce! w/5 down payment) OR (mortgage w/5 interest
only OR negative amortization OR no-doc OR predatory OR hybrid OR 2/28) OR
(Subprime w/ 5 increase OR popular!) OR (housing OR real estate OR mortgage w/5
crash! OR bust OR burst! OR freez! OR slump! OR slowdown OR meltdown OR
collaps! OR fall OR falling OR default OR tight! OR delinquent OR negative equity OR

crunch) OR (foreclosure w/5 increas! OR high!) OR (housing starts OR home sales OR
real estate sales w/5 decreas! OR fewer) OR (bankrupt! OR unemployed w/5 home
builder OR mortgage lender OR mortgage broker)) (p. 10).
Where:
OR means either term can be present
w/5 means terms must be within five words of each other. For example, housing w/5
bubble requires housing to occur within five words of bubble
! returns variations of a term. For example, speculat! means the search will return all
documents containing the following words: speculated, speculating, speculation, etc.
According to the authors, this search term string “was deemed suitable for appropriate
selection of a sample”, with the precision rate above or close to 80% (Glynn et al., p. 10). In this
paper, I will make some minor modifications to their method. First, instead of conducting a
newspaper content analysis5, I will simply count the total number of articles that the search
engine returns. Second, I will conduct a comprehensive study by looking at all the U.S.
newspapers instead of only the four as mentioned by the authors. Finally, I will divide their
search string into two separate parts and make 2 different variables: one that might inflate the
bubble and one that might deflate the bubble. I will then calculate the ratio between the two
variables. Here is the search string for the “inflation” variable:
((real estate OR housing w/5 bubble OR dream OR speculat!) OR (home sales OR
housing starts w/5 increas!) OR (housing w/5 sure bet) OR (hous! prices w/ rise OR
5 Newspaper content analysis is technique aimed at determining the meaning and purpose of newspaper articles by
studying and evaluating the details and implications of the content in each article.

rising) OR (housing OR real estate w/ 5 good time OR good buy OR expan!)OR
(mortgage w/5 innovation) OR (reduce! w/5 down payment) OR (mortgage w/5 interest
only OR negative amortization OR no-doc OR predatory OR hybrid OR 2/28)OR
(Subprime w/5 increase OR popular!)).
Below is the search string for the “deflation” variable:
((housing OR real estate OR mortgage w/5 crash! OR bust OR burst! OR freez! OR
slump! OR slowdown OR meltdown OR collaps! OR fall OR falling OR default OR
tight! OR delinquent OR negative equity OR crunch) OR (foreclosure w/5 increas! OR
high!) OR (housing starts OR home sales OR real estate sales w/5 decreas! OR fewer)
OR (bankrupt! OR unemployed w/5 home builder OR mortgage lender OR mortgage
broker)).
An increasing inflate/deflate ratio means that there is more news that favors the market than that
which hinders the market, and therefore there is a higher chance of the existence of a bubble.
The sample period for the regression analysis is 1986-I to 2014-III. All the data used are
quarterly data.

IV. Model
My model is as follows:
𝐻𝐵𝐼𝑡 = 𝛼0 + ∑ 𝛼1, 𝑖 𝐼𝑛𝑐𝑜𝑚𝑒 𝑡−𝑘
𝑘
𝑖=0 + ∑ 𝛼2,𝑖 𝑆𝑢𝑏𝑠𝑖𝑑𝑦 𝑡−𝑘
𝑘
𝑖=0 + ∑ 𝛼3,𝑖 𝑀𝑜𝑟𝑡𝑔𝑎𝑔𝑒𝑠𝑡−𝑘
𝑘
𝑖=0 +
∑ 𝛼4,𝑖 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡𝑡−𝑘
𝑘
𝑖=0 + ∑ 𝛼5,𝑖 𝑀𝑒𝑑𝑖𝑎 𝑡−𝑘
𝑘
𝑖=0 + ∑ 𝛼6,𝑖 𝐻𝐵𝐼𝑡−𝑘
𝑘
𝑖=1 + 𝜀1𝑡. (1)
𝑀𝑒𝑑𝑖𝑎 𝑡 = 𝛽0 + ∑ 𝛽1,𝑖 𝐻𝐵𝐼𝑡−𝑘 + ∑ 𝛽2,𝑖 𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟𝑡−𝑘
𝑘
𝑖=0 + 𝜀2𝑡
𝑘
𝑖=0 . (2)
with 𝐻𝐵𝐼𝑡 and 𝑀𝑒𝑑𝑖𝑎 𝑡 being the endogenous variables and the rest of the variables being
exogenous variables.
𝛼0 and 𝛽0 are intercept terms.
𝜀1𝑡 and 𝜀2𝑡 are error terms.
𝐻𝐵𝐼𝑡 is the value of the housing bubble index at time 𝑡.
𝐼𝑛𝑐𝑜𝑚𝑒 𝑡 is the level of real disposable income per person at time 𝑡.
𝑆𝑢𝑏𝑠𝑖𝑑𝑦𝑡 is the amount of housing subsidies given by the government at time 𝑡.
𝑀𝑜𝑟𝑡𝑔𝑎𝑔𝑒𝑠𝑡 is the amount of household home mortgages liability at time 𝑡.
𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡𝑡 is the difference between the rate on 30-year fixed-rate conventional
mortgages and the rate on treasury-indexed 1-year adjustable rate mortgages at time 𝑡.
𝑀𝑒𝑑𝑖𝑎 𝑡 is the inflate/deflate ratio mentioned above at time 𝑡.
𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟𝑡 is the consumer sentiment index
Kohn and Bryant (2011) conducted an econometric analysis using structural equation
modeling (SEM) to determine the factors that caused the housing bubble in the 2000s. Using
median asking price as a proxy for the house price boom, they found that for the pre-bubble

period, only personal income and vacancy rates were statistically significant. However, for the
bubble period from 1997 to 2007, CPI, housing inventory, population, vacancy rates, and median
asking rents were significant. In addition, the R2s in both periods were high, at or above .8. In
their research, Kohn and Bryant decided to use the SEM method to address the problems with
multicollinearity found in their earlier work.
Different from Kohn and Bryant, in my study, I propose a simultaneous-equation model
because I believe there is a bi-directional causality between 𝐻𝐵𝐼𝑡 and 𝑀𝑒𝑑𝑖𝑎 𝑡. This means the
media will help fuel the bubble, and the increase in housing prices will, in turn, lead to more
articles talking about the housing market. If the method of Ordinary Least Square (OLS) is
applied to each equation separately, disregarding the other equation in the system, the
coefficients estimated will be biased and inconsistent. A proof of why it is not appropriate to
apply the OLS regression to each equation separated is included in Appendix B.
Therefore, I will use the method of two-stage least squares (2SLS) developed by Henri
Theil (1953) and Robert Basmann (1957) to estimate the coefficients of all independent variables
in the system. The 2SLS method requires that both equations be identified. If 𝐻𝐵𝐼𝑡 is the only
independent variable in equation (2), this equation is under-identified. As a result, the consumer
sentiment index, a measure of consumers’ attitudes towards the current state of the economy, is
added as one of the explanatory variables in the second equation. It is believed that a high
consumer confidence will help inflate the media hype. Data for the consumer sentiment index is
obtained from the University of Michigan Consumer Sentiment Index from the Federal Reserve
Bank of St. Louis.
Having ensured that the system of regression equations is identified, I will now be able to
conduct a 2SLS regression. To apply this method, I will first substitute (2) into (1) and move

both the 𝐻𝐵𝐼𝑡 terms to the left hand side to obtain a reduced-form equation where only
exogenous or predetermined variables appear on the right hand side and then regress 𝐻𝐵𝐼𝑡 on all
the predetermined variables in the whole system. Doing so will help rid the model of the possible
correlation between 𝐻𝐵𝐼𝑡 and 𝜀2𝑡 . The new equation is of the form:
𝐻𝐵𝐼 𝑡 = П0 + П1 𝐼𝑛𝑐𝑜𝑚𝑒𝑡 + П2 𝑆𝑢𝑏𝑠𝑖𝑑𝑦𝑡 + П3 𝑀𝑜𝑟𝑡𝑔𝑎𝑔𝑒𝑠 𝑡 + П4 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡𝑡 + П5 𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟𝑡 + ε3,𝑡. (3)
Where ε3,𝑡 is the usual OLS residual. Again, I have simplified the model by excluding all the lag
terms. From the above equation (3), I will get
𝐻𝐵𝐼 𝑡
̂ = П0
̂ + П1
̂ 𝐼𝑛𝑐𝑜𝑚𝑒𝑡 + П2
̂ 𝑆𝑢𝑏𝑠𝑖𝑑𝑦𝑡 + П3
̂ 𝑀𝑜𝑟𝑡𝑔𝑎𝑔𝑒𝑠 𝑡 + П4
̂ 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡𝑡 + П5
̂ 𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟𝑡 . (3’)
by using Stata to run an OLS regression. Similarly, after substituting (1) into (2) and following
the steps outlined above, I get
𝑀𝑒𝑑𝑖𝑎𝑡 = П0 + П1 𝐼𝑛𝑐𝑜𝑚𝑒 𝑡 + П2 𝑆𝑢𝑏𝑠𝑖𝑑𝑦𝑡 + П3 𝑀𝑜𝑟𝑡𝑔𝑎𝑔𝑒𝑠 𝑡 + П4 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡𝑡 + П5 𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟𝑡 + ε4,𝑡. (4)
Running the OLS regression will give
𝑀𝑒𝑑𝑖𝑎𝑡
̂ = П0
̂ + П1
̂ 𝐼𝑛𝑐𝑜𝑚𝑒 𝑡 + П2
̂ 𝑆𝑢𝑏𝑠𝑖𝑑𝑦𝑡 + П3
̂ 𝑀𝑜𝑟𝑡𝑔𝑎𝑔𝑒𝑠 𝑡 + П4
̂ 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡𝑡 + П5
̂ 𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟𝑡 . (4’)
From here, I will estimate the second equation (2) by replacing the endogenous variable
𝐻𝐵𝐼𝑡by 𝐻𝐵𝐼𝑡
̂. I will then run an OLS regression on this modified equation to get an estimation of
𝑀𝑒𝑑𝑖𝑎 𝑡. Now it is appropriate to apply OLS because 𝐻𝐵𝐼𝑡
̂ is uncorrelated with the new error
term as the sample size gets larger. Similarly, I will estimate the first equation (1) by replacing
the endogenous variable 𝑀𝑒𝑑𝑖𝑎 𝑡 by 𝑀𝑒𝑑𝑖𝑎 𝑡
̂ and run an OLS regression to get an estimation of
𝐻𝐵𝐼𝑡.
Finally, the following ad hoc estimation of lagged variables test is used to determine how
far back the lag variables should be included in the model:
1. Run an OLS regression with no lag variables and record the adjusted R squared.

2. Take a variable that is believed to have a lag effect, say 𝐼𝑛𝑐𝑜𝑚𝑒 𝑡. Introduce the 1-
period lag, 𝐼𝑛𝑐𝑜𝑚𝑒𝑡−1, into the model.
3. Run the OLS regression again with the new variable and record the new adjusted R
squared.
4. If the adjusted R squared increases and the regression coefficients of the lagged
variables are statistically significant, then keep the new variable in the model.
Otherwise, remove it from the model.
5. If the new lag variable is believed to belong to the model, introduce the 2-period lag,
𝐼𝑛𝑐𝑜𝑚𝑒𝑡−1 in this case, into the model and repeat steps 3-5.
6. If the new lag variable is removed, move on to a different variable and repeat steps 2-
5.
Finally, for this econometric model, I predict that a higher personal income per capita
will result in a higher chance that a bubble will happen because people will be confident to spend
more in good times (positive coefficient). Second, as suggested by previous researchers, the
media also help fuel the housing bubble. Therefore, the coefficients on 𝑀𝑒𝑑𝑖𝑎 are expected to be
positive. Third, the lower the spread between the rate on 30-year fixed-rate conventional
mortgages and the rate on treasury-indexed 1-year adjustable rate mortgages, the higher the value
of the housing bubble index because investors are then encouraged to invest in the short term,
thus increasing speculation in the market. This implies a negative coefficient on 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡𝑡.
Fourth, the coefficients on 𝑆𝑢𝑏𝑠𝑖𝑑𝑦 are expected to be positive since a larger amount of federal
housing subsidies granted will lead to a higher demand for housing among low-income
households, thus a higher demand in general. Fifth, a higher amount of household home

mortgages liability poses higher risks to the market. As people take out more and more loans,
they will also purchase more houses. As explained in chapter 2, because of the herd mentality,
more people will be induced to be a part of the housing hype. As a result, house prices rise
significantly, increasing the chance that a bubble exists (positive coefficient). Sixth, the lagged
observations of the dependent variable are included to represent the dynamic nature of the
model. They are expected to have positive coefficients because the literature suggests that the
housing bubble has a strong momentum built in itself, which reinforces itself through a virtuous
cycle. Finally, in the second equation, the higher the value of the housing bubble index, the
higher the inflate/deflate ratio (positive coefficient). Moreover, the higher the consumer
sentiment index, the larger the media hype. As a result, the expected coefficient of 𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟𝑡 is
positive.
The expected signs for the first equation are summarized in the following table:
Table 1. Expected signs
Variables Expected Signs
𝐼𝑛𝑐𝑜𝑚𝑒 𝑡 +
𝑀𝑜𝑟𝑡𝑔𝑎𝑔𝑒𝑠𝑡 +
𝑆𝑢𝑏𝑠𝑖𝑑𝑦 𝑡 _
𝑀𝑒𝑑𝑖𝑎 𝑡 +
𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡𝑡 _
𝐻𝐵𝐼𝑡−𝑖 +

V. Results
First, the ad hoc estimation of lagged variables test was used. It was found that 𝐻𝐵𝐼𝑡−1,
which is the 1-period lag of the dependent variable, was the only significant lagged variable in
the model.
Second, it is suggested by the literature that the majority of time series data have a
problem of spurious correlation problem, in which there is a strong relationship between two or
more variables even though it is not caused by a real causal relationship. The reason is that time
series data typically increase over time. A spurious regression, in which the dependent variable
and one or more independent variables are spuriously correlated, will tend to inflate the t-scores
and the overall fit of the model. The most probable cause of spurious correlation is non-
stationary time series. Therefore, before running the regression, I ran an Augmented Dickey-
Fuller Test to determine if there were any non-stationary variables in the model. The null
hypothesis was that the variable considered had a unit root, meaning the variable was non-
stationary. It turned out that all but
𝑀𝑒𝑑𝑖𝑎 𝑡 and 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡𝑡 are non-stationary. This signaled a problem of spurious correlation.

Table 2. Augmented Dickey-Fuller test results
Variables t score
𝐼𝑛𝑐𝑜𝑚𝑒 𝑡 -0.724
𝑀𝑜𝑟𝑡𝑔𝑎𝑔𝑒𝑠𝑡 -1.718
𝑆𝑢𝑏𝑠𝑖𝑑𝑦 𝑡 -0.884
𝑀𝑒𝑑𝑖𝑎 𝑡 -4.249
𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡𝑡 -3.527
𝐻𝐵𝐼𝑡 -0.792
𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟𝑡 -2.282
The traditional approach to address the nonstationarity problem is to take the first differences
of every variable and use them in place of the original variable in the equation. For example,
𝐻𝐵𝐼𝑡 would be replaced by 𝐻𝐵𝐼𝑡 − 𝐻𝐵𝐼𝑡−1. However, “using first differences tends to throw
away information that economic theory can provide in the form of equilibrium relationships
between the variables when they are expressed in their original units” (Studenmunt and Cassidy,
2011, p. 424). As a result, the author suggests not using first differences until the model has been
tested for cointegration. A set of variables is said to be cointegrated if there is a long-run
equilibrium relationship between the variables. In addition, if the set of variables is cointegrated,
it is possible that linear combinations of non-stationary variables can actually be stationary. As a
result, after figuring out that there was a problem of nonstationarity, I decided to test the model

for cointegration. It was found that the nonstationary variables were cointegrated as can be seen
in Table 3. Therefore, the equation can be estimated in its original units.
Table 3. Augmented Dickey-Fuller test of residuals
Variables t score
𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙𝑠 (1st equation) -8.198***
𝑅𝑒𝑠𝑖𝑑𝑢𝑎𝑙𝑠 (2nd equation) -5.326***
*** = 0.01 significance level
I then ran a 2-stage least squares regression using the procedure outlined above. Results for the
first equation are included in Table 4.

Table 4. 2SLS OLS Regression Results for Equation 1.
Dependent variable: Housing Bubble Index (HBI)
Number of observations 115
R-squared 0.9896
Adjusted R-squared 0.9890
Variable Coefficient t score
𝐼𝑛𝑐𝑜𝑚𝑒 𝑡 2.71e-06 1.75*
𝑀𝑜𝑟𝑡𝑔𝑎𝑔𝑒𝑠𝑡 -2.51e-08 -0.71
𝑆𝑢𝑏𝑠𝑖𝑑𝑦𝑡 -1.65e-06 -2.34**
𝑀𝑒𝑑𝑖𝑎 𝑡 .1085954 1.9*
𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡𝑡 -.0043091 -0.62
𝐻𝐵𝐼𝑡−1 .9535441 31.78***
* = 0.10 significance level
** = 0.05 significance level
*** = 0.01 significance level
Results for the second equation are summarized in Table 5.

Table 5. 2SLS OLS Regression Results for Equation 2.
Dependent variable: Media
R-squared 0.2581
𝐻𝐵𝐼𝑡 .659465 4.41***
𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟𝑡 .0109252 5.25***
Running the Breusch-Godfrey LM test for autocorrelation for both equations gave:
Table 6. Breusch-Godfrey test results
---------------------------------------------------------------------------
lags(p) | chi2 df Prob > chi2
-------------+-------------------------------------------------------------
4 | 68.548 4 0.0000
---------------------------------------------------------------------------
4 | 44.625 4 0.0000
H0: no serial correlation
This means both of the equations have autocorrelation problems, which makes OLS not
the best linear unbiased estimator method. Hence, the Newey West Estimator, a method of

correcting serial correlation in the error terms, is used to solve this problem. The Newey West
approach, however, requires stationary variables. Yet, the literature does not support or oppose
the use of Newey West for cointegrated time series data. As a result, I conducted two regression
analyses, one with Newey West approach that used original units and one with first differences.
Results for the Newey West analysis are given in Tables 7 and 8.

Table 7. 2SLS Newey West Regression Results for Equation 1.
R-squared 0.9896
𝐼𝑛𝑐𝑜𝑚𝑒 𝑡 2.71e-06 1.60
𝑆𝑢𝑏𝑠𝑖𝑑𝑦𝑡 -1.65e-06 -2.36**
𝑀𝑒𝑑𝑖𝑎 𝑡 .1085954 1.77*
𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡𝑡 -.0043091 -0.63
𝐻𝐵𝐼𝑡−1 .9535441 25.15***

Table 8. 2SLS Newey West Regression Results for Equation 2.
R-squared 0.2581
𝐻𝐵𝐼𝑡 .659465 1.35
𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟𝑡 .0109252 4.56***
In order to make it easier to understand the magnitude of these coefficients, I have
calculated the mean value of the change of the housing bubble index from one quarter to the next
quarter, which is 0.0018 unit.
As can be seen from Table 4 and Table 7, the t values of all the variables in the model
decreased. This was expected because the autocorrelation problem tends to inflate the t values.
Three of the variables in my model were significant. The coefficient of 𝑀𝑒𝑑𝑖𝑎 𝑡 suggested that a
one-unit increase in the inflate/deflate ratio would cause the HBI index to rise by .1085954 unit.
As the literature suggests, human beings tend to follow the majority opinion. Therefore, when
there are more articles that favor the housing market than those that oppose it, more people are
willing to participate in the market. The increase in demand will then help increase home prices
further. In addition, 𝑆𝑢𝑏𝑠𝑖𝑑𝑦 𝑡 was significant and had the expected sign. The most interesting
result, however, was that the lagged dependent variable, 𝐻𝐵𝐼𝑡−1 was statistically significant with

a very high t-score. This implied that the housing bubble index had a strong momentum from one
period to the next.
In sum, the model did a fairly good job in explaining the housing bubble. An adjusted R-
squared of 0.99 suggested 99% of the change in the housing bubble index was explained by the
variables in the model. In addition, the Ramsey RESET test showed that there were no omitted
variables. It is also interesting to note that 𝐻𝐵𝐼𝑡−1 was statistically significant at a very high
level, indicating the virtuous cycle built in a housing bubble. This implied that when there is a
sign of a housing bubble, policy makers should act quickly to prevent the bubble from
developing through its own feedback loop. However, these findings must be taken with caution
because it is still not known whether the Newey West Estimator method can be applied to
cointegrated time series data.
As mentioned above, I also ran a 2SLS OLS regression analysis using first differences.
Results for this analysis are presented in Table 9 and Table 10.

Table 9. 2SLS OLS Regression Results using first differences – Equation 1.
R-squared 0.4748
𝐼𝑛𝑐𝑜𝑚𝑒 𝑡 .000017 1.44
𝑆𝑢𝑏𝑠𝑖𝑑𝑦𝑡 6.06e-06 0.418
𝑀𝑒𝑑𝑖𝑎 𝑡 .2992383 1.54
𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡𝑡 -.0335719 -1.46
𝐻𝐵𝐼𝑡−1 .8955135 4.43***

Table 10. 2SLS OLS Regression Results using first differences – Equation 2.
R-squared 0.0010
Adjusted R-squared -0.0170
𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟𝑡 .0012041 0.29
𝐻𝐵𝐼𝑡 .2128104 0.16
As expected, using differences lowered the t-statistics of all the variables in the model. In
addition, the adjusted R-squared also diminished. It might be because “using first differences
tends to throw away information that economic theory can provide in the form of equilibrium
relationships between the variables when they are expressed in their original units” (Studenmunt
and Cassidy, 2011, p. 424). As can be seen from Table 9, the lagged dependent variable was the
only significant variable in the model.

VI. Limitations
First, since this research is conducted using data from 1986 to 2014, it cannot account for
all kinds of government legislation, which have contributed to the weakening of lending
standards. Two of the most important ones are the Depository Institutions Deregulation and
Monetary Control Act in 1980 and the Garn-St. Germain Depository Institution Act in 1982. As
a result, the findings of this study might not reflect the true role of the government in creating the
housing bubble. A study that goes back further in time is required to properly understand the
impacts of government regulations/deregulations on the housing bubble.
Second, there is a concern with the way the Owners’ Equivalent Rent of residences is
computed, which affects the validity of my housing bubble index. Drake and Silver (2014)
pointed out in their article that the Bureau of Labor Statistics determines the Owners’ Equivalent
Rent by asking ordinary American homeowners the following question: “If someone were to rent
your home today, how much do you think it would rent for monthly, unfurnished, and without
utilities?” They believe this question is imprecise and subjective. Someone without proper
knowledge of economics would not know the answer to this question and would typically give
an estimation that is highly questionable. Therefore, future researchers should look for a more
reliable rent index or better still, find the actual levels of house prices and rents instead of using
indexes.
Third, the search string used to find the inflate/deflate ratio is not perfect. Although
Glynn et al. (2008) indicates that it has a precision rate at above or close to 80 percent,
sometimes the search returns results that should belong to the inflate variable instead of deflate
variable or vice versa. Future researchers should try to find a better mechanism to quantify the
influence of the media on the housing bubble.

Fourth, there might be a misspecification problem. As noted in Section III, some of the
variables used in the model might not be the best proxies. For example, housing subsidies are
used as a proxy for regulatory changes assuming that there is an indirect relationship, operating
through the political framework, between housing subsidies and changes in regulation. Hence,
future research should attempt to test the robustness of this study by using better proxies for
many of the variables in this paper and comparing the results.
Finally, there are some econometrics problems that have not been satisfactorily addressed
in the study. The issues with serial autocorrelation and nonstationarity are two of those. As a
result, future researchers should attempt to find better methods to address these issues.

VII. Policy Implications
As can be seen from Section V, the lagged variable 𝐻𝐵𝐼𝑡−1 is statistically significant with
a relatively high coefficient, indicating the virtuous cycle inherent in a housing bubble. As a
result, in the presence of an increase in the housing bubble index, policy makers should make
smart decisions to impose preemptive countercyclical policies before the housing bubble gets
worse. It is recommended that when the bubble is still in its early stages, government should
impose more regulations. In addition, since the interest rates might have an impact on the media
coverage, which directly influence the housing bubble, the Fed should also consider raising
interest rates in a timely manner to help deal with the bubble.

Appendix A
In the 1970s, most states still had usury laws from earlier years, which placed limits on
the interest rates banks could offer on deposits. This became a constraint as inflation rose. In
1978, the Supreme Court made a decision that the usury laws of a bank’s home state, instead of
the borrower’s home state, applied to bank lending across states. This provided an incentive for
banks to relocate to states that were less regulated to utilize the favorable interest rate regulations
in their new home state across their operations around the country. As a consequence, some
states, such as South Dakota and Delaware, eliminated their usury ceilings to attract investors.
The result was that even though usury laws were still effective in nearly every other state, banks
were able to charge any interest rate they wanted nationwide. Moreover, in the 1970s, inflation
caused market interest rates to rise beyond the ceilings mandated by Regulation Q6. As a result,
investors had to look for alternatives to traditional deposit accounts. Money market mutual
funds, which had no restrictions on rates of return, were born. Realizing the higher potential
profits, investors began to relocate their investments from regulated accounts in depositary
institutions to these mutual funds. In 1980, in an attempt to help banks and savings and loans
compete with money market mutual funds, Congress passed the Depository Institutions
Deregulation and Monetary Control Act of 1980. It allowed depository institutions to charge
rates comparable to those in the market. Furthermore, in 1982, the Garn-St. Germain Act of 1982
was introduced. It allowed thrifts to engage in commercial loans up to 10 percent of assets. It
also gave thrifts powers to act more like a bank, not just a specialized mortgage lending
institution (Sherman, 2009).
The financial deregulation of the 1980s caused the competition for deposits to go out of
control. Many institutions offered large brokered deposits at above-market rates to attract capital.
6 Regulation Q is a Federal Reserve Board regulation that imposed interest rate ceilings on bank deposits.

The savings and loans industry saw a rapid expansion with a great inflow of deposits. With the
expanded powers of thrifts, savings and loan associations started to invest in condominiums and
other commercial real estate. This meant that the investment portfolios of these associations
consisted of a greater proportion of higher-risk loans and a smaller proportion of traditional
home mortgage loans. The increase in investments in the real estate market caused house prices
to increase. The boom in real estate finally burst in the mid-1980s (Sherman, 2009).
The financial deregulation continued after the burst of the housing boom.7 In 1986, the Federal
Reserve reinterpreted the Glass-Steagall restrictions, which had authorized banks to obtain up to
5 percent of gross revenues in their investment banking business. In 1996, the Federal Reserve
raised the limit further to 25 percent, effectively abolishing the Glass-Steagall Act. In addition,
the banking industry also moved towards greater consolidation. The Riegl-Neal Interstate
Banking and Branching Efficiency Act of 1994 eliminated restrictions on interstate banking and
branching. Later, in 1999, the passage of the Gramm-Leach-Bliley Act removed regulations that
prevented the merger of banks, stock brokerage companies, and insurance companies. As a
result, mega-banks were able to form. It is argued that the consolidation in banking made it
harder for regulators to not only oversee different business lines of the same institution but also
keep pace with the innovations in financial markets. The growth of new derivatives instruments8
posed problems for regulators. Derivatives trading increased from a total outstanding nominal
value of $106 trillion in 2001 to $531 trillion in 2008 (Goodman, 2008). This rapid growth in
derivatives trading overwhelmed the legal infrastructure of the industry. Regulators could not
keep track of the actual contracts made by commercial banks and often had to rely on the self-
regulation of firms to avoid potential risks (Sherman, 2009).
7 This refers to the housing boom in the 1980s (Shiller, 2008).
8 A derivative is a contract that derives its value from the price of an underlying asset.

Appendix B
To prove that it is not appropriate to apply the OLS method for each equation separately, I will
first simplify my model by discarding all the lag terms (The result would still hold for the
original model). Therefore, the simplified model is now:
𝐻𝐵𝐼𝑡 = 𝛼0 + 𝛼1 𝐼𝑛𝑐𝑜𝑚𝑒𝑡 + 𝛼2 𝑆𝑢𝑏𝑠𝑖𝑑𝑦𝑡 + 𝛼3 𝑀𝑜𝑟𝑡𝑔𝑎𝑔𝑒𝑠 𝑡 + 𝛼4 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑡 + 𝛼5 𝑀𝑒𝑑𝑖𝑎 𝑡 + 𝜀1𝑡
𝑀𝑒𝑑𝑖𝑎𝑡 = 𝛽0 + 𝛽1 𝐻𝐵𝐼𝑡 + 𝛽2 𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟𝑡 + 𝜀2𝑡
Then I will prove that 𝐻𝐵𝐼𝑡 and 𝜀2𝑡 in equation (4) are correlated.
First, notice that the classical linear regression model requires that 𝐸(𝜀1𝑡) = 𝐸(𝜀2𝑡 ) = 0,
𝐸(𝜀1𝑡
2
) = 𝐸(𝜀2𝑡
2
) = 𝜎2
, 𝐸(𝜀1𝑡 𝜀2𝑡) = 0, and 𝑐𝑜𝑣(𝐼𝑛𝑐𝑜𝑚𝑒 𝑡, 𝜀1𝑡) = 𝑐𝑜𝑣( 𝑆𝑢𝑏𝑠𝑖𝑑𝑦𝑡
, 𝜀1𝑡 ) =
𝑐𝑜𝑣( 𝑀𝑜𝑟𝑡𝑔𝑎𝑔𝑒𝑠 𝑡
, 𝜀1𝑡) = 𝑐𝑜𝑣( 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑡, 𝜀1𝑡) = 𝑐𝑜𝑣( 𝑀𝑒𝑑𝑖𝑎𝑡, 𝜀1𝑡) = 0 where 𝐸(𝐴) refers to the
expected value of 𝐴.
Now I will substitute (4) into (3) to obtain
𝐻𝐵𝐼𝑡 = 𝛼0 + 𝛼1 𝐼𝑛𝑐𝑜𝑚𝑒 𝑡 + 𝛼2 𝑆𝑢𝑏𝑠𝑖𝑑𝑦𝑡 + 𝛼3 𝑀𝑜𝑟𝑡𝑔𝑎𝑔𝑒𝑠𝑡 + 𝛼4 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡𝑡 + 𝛼5( 𝛽0 + 𝛽1 𝐻𝐵𝐼𝑡 + 𝛽2 𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟𝑡 + 𝜀2𝑡) + 𝜀1𝑡.
𝐻𝐵𝐼𝑡 = 𝛼0 + 𝛼1 𝐼𝑛𝑐𝑜𝑚𝑒 𝑡 + 𝛼2 𝑆𝑢𝑏𝑠𝑖𝑑𝑦𝑡 + 𝛼3 𝑀𝑜𝑟𝑡𝑔𝑎𝑔𝑒𝑠𝑡 + 𝛼4 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑡 + 𝛼5 𝛽0 + 𝛼5 𝛽1 𝐻𝐵𝐼𝑡 + 𝛼5 𝛽2 𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟𝑡 + 𝛼5 𝜀2𝑡
+ 𝜀1𝑡.
𝐻𝐵𝐼𝑡 − 𝛼5 𝛽1 𝐻𝐵𝐼𝑡 = 𝛼0 + 𝛼1 𝐼𝑛𝑐𝑜𝑚𝑒 𝑡 + 𝛼2 𝑆𝑢𝑏𝑠𝑖𝑑𝑦𝑡 + 𝛼3 𝑀𝑜𝑟𝑡𝑔𝑎𝑔𝑒𝑠𝑡 + 𝛼4 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑡 + 𝛼5 𝛽0 + 𝛼5 𝛽2 𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟𝑡 + 𝛼5 𝜀2𝑡
+ 𝜀1𝑡.
(1 − 𝛼5 𝛽1) 𝐻𝐵𝐼𝑡 = 𝛼0 + 𝛼1 𝐼𝑛𝑐𝑜𝑚𝑒 𝑡 + 𝛼2 𝑆𝑢𝑏𝑠𝑖𝑑𝑦𝑡 + 𝛼3 𝑀𝑜𝑟𝑡𝑔𝑎𝑔𝑒𝑠𝑡 + 𝛼4 𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡𝑡 + 𝛼5 𝛽0 + 𝛼5 𝛽2 𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟𝑡 + 𝛼5 𝜀2𝑡
+ 𝜀1𝑡.
Let 1 − 𝛼5 𝛽1 = 𝑝, we have:
𝐻𝐵𝐼𝑡 =
𝛼0
𝑝
+
𝛼1
𝑝
𝛼2
𝑝
𝑆𝑢𝑏𝑠𝑖𝑑𝑦𝑡 +
𝛼3
𝑝
𝑀𝑜𝑟𝑡𝑔𝑎𝑔𝑒𝑠𝑡 +
𝛼4
𝑝
𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑡 +
𝛼5 𝛽0
𝑝
+
𝛼5 𝛽2 𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟𝑡
𝑝
+
𝛼5 𝜀2𝑡
𝑝
+
𝜀1𝑡
𝑝
.
In addition,

𝐸( 𝐻𝐵𝐼 𝑡
) =
𝛼0
𝑝
+
𝛼1
𝑝
𝛼2
𝑝
𝑆𝑢𝑏𝑠𝑖𝑑𝑦𝑡 +
𝛼3
𝑝
𝑀𝑜𝑟𝑡𝑔𝑎𝑔𝑒𝑠 𝑡 +
𝛼4
𝑝
𝐼𝑛𝑡𝑒𝑟𝑒𝑠𝑡 𝑡 +
𝛼5 𝛽2
𝐶𝑜𝑛𝑠𝑢𝑚𝑒𝑟 𝑡
𝑝
+
𝛼5 𝛽0
𝑝
.
because 𝐸(𝜀1𝑡) = 𝐸(𝜀2𝑡) = 0 and the expectation value of all the exogenous and constant terms
do not change.
Subtracting 𝐸(𝐻𝐵𝐼𝑡) from 𝐻𝐵𝐼𝑡 results in
𝐻𝐵𝐼𝑡 − 𝐸( 𝐻𝐵𝐼𝑡)=
1
1−𝛼5 𝛽1
𝜀1𝑡 +
α5
1−𝛼5 𝛽1
𝜀2𝑡.
Moreover,
𝜀2𝑡 − 𝐸(𝜀2𝑡) = 𝜀2𝑡 − 0 = 𝜀2𝑡
Therefore,
𝑐𝑜𝑣( 𝐻𝐵𝐼𝑡, 𝜀2𝑡) = 𝐸[ 𝐻𝐵𝐼𝑡 − 𝐸( 𝐻𝐵𝐼𝑡)][𝜀2𝑡 − 𝐸( 𝜀2𝑡)]
= 𝐸(
1
1 − 𝛼5 𝛽1
𝜀1𝑡 𝜀2𝑡 +
α5
1 − 𝛼5 𝛽1
𝜀2𝑡
2
)
=
1
1 − 𝛼5 𝛽1
𝐸( 𝜀1𝑡 𝜀2𝑡 ) +
α5
1 − 𝛼5 𝛽1
𝐸(𝜀2𝑡
2
)
= 0 +
α5
1−𝛼5 𝛽1
𝜎2
=
α5
1 − 𝛼5 𝛽1
𝜎2
,
which is different from zero. Hence, 𝐻𝐵𝐼𝑡 and 𝜀2𝑡 are correlated, violating the assumption of the
classical linear regression model.

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