Evidence from Regional Analysis

Journal of
Risk and Financial
Management
Article
U.K. House Prices: Bubbles or Market Efficiency?
Evidence from Regional Analysis
Yi Wu and Nicole Lux *
Cass Business School, City University of London, London EC1Y 8TZ, UK; Yi.Wu@city.ac.uk
* Correspondence: Nicole.Lux@city.ac.uk
Received: 24 July 2018; Accepted: 10 September 2018; Published: 13 September 2018

Abstract: This paper studies U.K. regional house prices across nine regions from January 2005 to
December 2017 to identify regional versus national effects on house prices and potential house price
bubbles. It uses a version of the Gordon dividend discount model, modelling house prices as the
present value of imputed rents as a measure of fundamentals. It differentiates between long-term and
short-term effect using pooled mean group (PMG) and mean group estimation (MG) to determine
variations in regional house prices during different periods relating to the most recent financial
crisis. The results confirm that the crisis had differentiating effects in the short term, but there is
reversion back to long-run fundamentals. Regional trend analysis shows that the house price growth
in the regions has been affected differently in the short run and each region has varying long-run
fundamentals. Residential property values in London have shown strongest short-run momentum.
Keywords: U.K. regional house price; housing bubbles; pooled mean group estimation; mean
group estimation
1. Introduction
Ten years after the global financial crisis (GFC) of 2008/2009, the U.K. housing market recovered
and has been experiencing another boom period until 2017. This has resulted in an increasing debate
regarding a new house price bubble and the impact on regional economies in the United Kingdom.
The aim of this paper is to identify the reason for the most recent rises in house prices in the United
Kingdom at the regional level and test whether or not a house price bubble does indeed exist within
the regions. The analysis will identify if the most recent changes are part of a regime shift or the effect
of a new short term residential housing bubble. For the purposes of this research, we define a bubble
as the deviation of expected house price growth based on fundamentals from actual observed house
price growth. Further, we define house price growth as a function of lagged responses to the present
value of expected future cash flows (imputed rents).
First studies on property market bubbles started in the U.S. market. The property market in
the United States during 1998–2008 has been widely perceived as having had a bubble, but with
the interest rate decline during the 1990s and early 2000s, fundamental factors had also changed.
An acceleration of house prices took place from 2000 to 2006. This coincides with the growths of the
subprime mortgage market, securisation, and credit expansion. Credit market liberalization has also
occurred in other countries, including the United Kingdom. Barrell et al. (2004) and the Organisation
for Economic Co-operation and Development (OECD) (2005) suggest that U.K. house prices were
overvalued by 30% or more during the period 2003–2004. However, the effects have not been studied
in depths and the argument is split between those who argue in favour of a bubble and those who
argue there was a fundamental regime shift. Nickell (2005), for example, rejects the bubble hypothesis
based on the argument that a shift in fundamentals has occurred.
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J. Risk Financial Manag. 2018, 11, 54 2 of 16
However, after the global financial crisis (GFC) and the collapse of credit markets and house
prices, several countries have experienced increased house price growth due to excess cash availability
globally. Some research has focused on foreign real estate investment and its impact on house prices
and the economy. This has been a popular topic in different countries including Australia, China, Korea,
Malaysia, Spain, Vietnam, and other countries (Gholipour et al. 2014; Rodríguez and Bustillo 2010;
Rogers et al. 2015). Since the GFC, the United Kingdom in particular has been a safe haven for foreign
investors. A lot of this capital has been focused on real estate investments, hence there is reason to
believe that this has led to a new housing bubble. The assessment of the most recent house prices
changes—including the question, if this represents a fundamental regime shift with regard to the role
of real and nominal interest rates—is thus of wider interest.
Figure 1 shows the development of house price growth and rental growth on national U.K. level
in form of the rent to price ratio. The series shows that in fact, house price growth outpaced rental
growth for most of the period on the national level. A potential house price bubble is visible during
the years leading up to the financial crisis, when house price growth was higher than rental growth
shown in the rent to price ratio below 1.
J. Risk Financial Manag. 2018, 6, x FOR PEER REVIEW 2 of 17
However, after the global financial crisis (GFC) and the collapse of credit markets and house
prices, several countries have experienced increased house price growth due to excess cash
availability globally. Some research has focused on foreign real estate investment and its impact on
house prices and the economy. This has been a popular topic in different countries including
Australia, China, Korea, Malaysia, Spain, Vietnam, and other countries (Gholipour et al. 2014;
Rodríguez and Bustillo 2010; Rogers et al. 2015). Since the GFC, the United Kingdom in particular
has been a safe haven for foreign investors. A lot of this capital has been focused on real estate
investments, hence there is reason to believe that this has led to a new housing bubble. The
assessment of the most recent house prices changes—including the question, if this represents a
fundamental regime shift with regard to the role of real and nominal interest rates—is thus of wider
interest.
Figure 1 shows the development of house price growth and rental growth on national U.K. level
in form of the rent to price ratio. The series shows that in fact, house price growth outpaced rental
growth for most of the period on the national level. A potential house price bubble is visible during
the years leading up to the financial crisis, when house price growth was higher than rental growth
shown in the rent to price ratio below 1.
Figure 1. Monthly rent to price ratio, 2005–2017, U.K. Office for National Statistics (ONS), United
Kingdom.
When house prices and selling activity reached a low point in 2009, rental growth was outpacing
house price growth for a short period. Over the last five years, the graph shows the clear pickup in
the pace of house price growth again, similar to the pre-crisis period. Overall U.K. national house
prices increased by 30% compared with rent, which grew only by 17% between December 2007 and
December 2017.
The second key variable to our model is interest rates. As the effect of inflation would distort
our analysis of real price growth based on fundamentals, our analysis is based on real rates rather
than nominal rates. Figure 2 shows the 10-year government real bond rate as a reference rate for longterm interest rates together with the variable mortgage (real) rate by Nationwide Building Society.
As a result of higher inflation than interest rates observed from 2012 onwards, real interest rates in
the United Kingdom have been negative for some periods and only recently returned to positive
rates.
Figure 1. Monthly rent to price ratio, 2005–2017, U.K. Office for National Statistics (ONS), United Kingdom.
When house prices and selling activity reached a low point in 2009, rental growth was outpacing
house price growth for a short period. Over the last five years, the graph shows the clear pickup in
the pace of house price growth again, similar to the pre-crisis period. Overall U.K. national house
prices increased by 30% compared with rent, which grew only by 17% between December 2007 and
December 2017.
The second key variable to our model is interest rates. As the effect of inflation would distort
our analysis of real price growth based on fundamentals, our analysis is based on real rates rather
than nominal rates. Figure 2 shows the 10-year government real bond rate as a reference rate for
long-term interest rates together with the variable mortgage (real) rate by Nationwide Building Society.
As a result of higher inflation than interest rates observed from 2012 onwards, real interest rates in the
United Kingdom have been negative for some periods and only recently returned to positive rates.
J. Risk Financial Manag. 2018, 11, 54 3 of 16
J. Risk Financial Manag. 2018, 6, x FOR PEER REVIEW 3 of 17
Figure 2. Monthly 10-year U.K. government real bond rate, Bloomberg; and U.K. variable real
mortgage, Nationwide.
Thirdly, the paper also aims to explore the wider issues of house price increases, credit
availability, and the effect on regional economies. The impact of housing market volatility has been
widely discussed in the years following the onset of the banking crisis. Volatility especially affects
certain pockets of the market characterised by chronic under-supply of new homes; stretched
affordability; falling home ownership; and, in some areas, large-scale investment that some have
described as speculative. We thus have tested the relevance of additional fundamental variables
including regional disposable household income, first time buyer affordability, and price to earnings
ratio, and found that price to earnings ratio was significant on national and regional level. Adding
regional price to earnings ratio data as a local demand factor is one of the key contributions in this
research.
Based on the Gordon dividend discount model, we study nine regions from January 2005 to
December 2017. Our hypothesis is that different regions have different levels of “momentum”. Using
mean group (MG) and pooled mean group (PMG) analysis confirms significant differences between
long-term and short-term effects and differences in “momentum” within regions, namely London
and the North East. The variable residential mortgage rate and regional earnings ratios are significant
factors to determine rent to price ratio. Moreover, the regions do not share the same long-run
equation, factors are specific to every region.
More generally, the research also explores the impact of credit availability and low interest rates
and if these can be classified as change away from long-run fundamentals, resulting in short term
effects that potentially lead to house price bubbles. Breaking down the period based on the financial
crisis, we find that the variable residential mortgage rate significantly positively relates to rent to
price ratio during the financial crisis in the long run, but negatively in the short run. At the same time,
the 10-year government real bond rate had no significance during the period of the crisis, but shows
significance again in the post-crisis period.
The following detailed analysis is structured into five sections. The next section provides a
literature review of the previous studies, followed by the third section, which presents the basic
modelling approach for house price growth with a description of the data employed. The fourth
section presents an in-depth analysis of the results, followed by some additional discussion in the
final section.
2. Literature Review
In spite of the widespread interest in real estate bubbles, the analytics of such paths remain
largely unexplored. One main approach is to view asset bubbles as a rapid and unsustainable growth
Figure 2. Monthly 10-year U.K. government real bond rate, Bloomberg; and U.K. variable real
mortgage, Nationwide.
Thirdly, the paper also aims to explore the wider issues of house price increases, credit availability,
and the effect on regional economies. The impact of housing market volatility has been widely
discussed in the years following the onset of the banking crisis. Volatility especially affects certain
pockets of the market characterised by chronic under-supply of new homes; stretched affordability;
falling home ownership; and, in some areas, large-scale investment that some have described as
speculative. We thus have tested the relevance of additional fundamental variables including regional
disposable household income, first time buyer affordability, and price to earnings ratio, and found
that price to earnings ratio was significant on national and regional level. Adding regional price to
earnings ratio data as a local demand factor is one of the key contributions in this research.
Based on the Gordon dividend discount model, we study nine regions from January 2005
to December 2017. Our hypothesis is that different regions have different levels of “momentum”.
Using mean group (MG) and pooled mean group (PMG) analysis confirms significant differences
between long-term and short-term effects and differences in “momentum” within regions, namely
London and the North East. The variable residential mortgage rate and regional earnings ratios
are significant factors to determine rent to price ratio. Moreover, the regions do not share the same
long-run equation, factors are specific to every region.
More generally, the research also explores the impact of credit availability and low interest rates
and if these can be classified as change away from long-run fundamentals, resulting in short term
effects that potentially lead to house price bubbles. Breaking down the period based on the financial
crisis, we find that the variable residential mortgage rate significantly positively relates to rent to price
ratio during the financial crisis in the long run, but negatively in the short run. At the same time,
the 10-year government real bond rate had no significance during the period of the crisis, but shows
significance again in the post-crisis period.
The following detailed analysis is structured into five sections. The next section provides
a literature review of the previous studies, followed by the third section, which presents the basic
modelling approach for house price growth with a description of the data employed. The fourth section
presents an in-depth analysis of the results, followed by some additional discussion in the final section.
2. Literature Review
In spite of the widespread interest in real estate bubbles, the analytics of such paths remain largely
unexplored. One main approach is to view asset bubbles as a rapid and unsustainable growth in asset
prices that cannot be explained by “fundamental” factors. According to Stiglitz, the reason that the
J. Risk Financial Manag. 2018, 11, 54 4 of 16
price is high today is only because investors believe that the selling price will be high tomorrow—when
“fundamental” factors do not seem to justify such a price—then a bubble exists” (Stiglitz 1990).
A number of empirical tests have been developed to exploit the link between asset prices and
various fundamental values. West (1987) proposes an empirical test for the existence of a bubble
using the constant expected return model. His approach relies on comparing two sets of parameters.
One set of estimates is obtained by a projection of stock prices based on past dividends, and the other
is obtained by a set of equations describing the discount rate and the dividend process. This and other
tests to identify bubbles are reviewed in Flood and Hodrick (1990). Meese and Wallace (1994) examine
whether the real expected return on home ownership is close to the real homeowner cost of capital
by studying the relationship between price, rent, and the cost of capital. Abraham and Hendershott
(1993, 1996) study the relationship between house prices, construction costs, real income growth,
and interest rates. They find that these factors explain half of the historical variation in house price
appreciation. The bubble then manifests itself in the “sustained serially correlated deviations”, yet it
remains unclear whether these deviations are because of a “bubble” or a misspecification of the
econometric model. Himmelberg et al. (2005) compare the level of housing prices with local rent and
income for a period of 25 years. They explain that changes in the price-to-rent and price-to-income
ratios might suggest the existence of bubbles even when houses are reasonably priced, because they
fail to account, for example, for differences in risk, property taxes, and maintenance expenses, and
anticipated capital gains from owning a home. Glaeser et al. (2008) present a theoretical model of
housing bubbles, which predicts that areas with more elastic supply will have fewer bubbles with
shorter duration and smaller price run-ups. Their data indicate that the price increases in the 1980s
were almost exclusively experienced in areas with inelastic supply.
Recent tests for speculative bubbles in regional U.S. housing markets typically consider deviations
from market fundamentals. Goodman and Thibodeau (2008) explore to what extent house appreciation
rates over the time period 2000–2005 can be attributed to economic fundamentals and what portion
can be attributed to speculation. According to these authors, much of the appreciation is the result
of inelastic supply and speculative motives are present in less than half of the cities they examined.
Mikhed and Zemˇcík (2009) present a panel test to detect bubbles using price–rent ratios for the period
1975–2006. The bubble indicator they constructed detects bubbles around the decade turn in the
late 1980s and the early 1990s, as well as around the end of the 1990s. Peláez (2012) argues that the
housing bubble in the late 1990s and the early 2000s could have been predicted when considering the
unprecedented growth rate of the house price to per capita income ratio.
Defining the relevant periods in which bubbles grow and collapse opens new avenues for future
research on the impact of fundamentals on housing price movements both in and outside of the bubble
periods. Lai and Van Order (2010, 2017) also find that different periods have to be tested. While they
find that between 1999–2005, house prices are largely explained by fundamentals such as decline of
long-term interest rates, the short period from 2003–2005 shows a price bubble associated with big
changes in markets, such as securitization and sub-prime mortgages.
There is a rapidly growing strand in the recent literature on housing price dynamics that tries
to identify the effects of various fundamental values on prices. Using simulation of the U.S. housing
market, Khandani et al. (2013) find that the declining interest rates and the growth of the refinancing
business contributed significantly to the recent housing boom and the massive defaults during the bust.
Favilukis et al. (2017) argue that much of the housing price appreciation can be explained by relaxation
of credit constraints, and Mayer and Sinai (2009) show that markets with the highest subprime
lending experienced the greatest growth in price-to-rent ratios. In contrast, Glaeser et al. (2010) present
evidence supporting the view that easy credit in the form of low real interest rates and permissive
mortgage approval standards is not a strong contributor to the rising house prices. Differentiating from
the previous studies, we incorporate earnings ratio as an additional regional fundamental variable
to explain the various risk factors represented in the discount rate used in the Gordon dividend
discount model. Further, we test the different levels of “momentum” and use mean group (MG) and
J. Risk Financial Manag. 2018, 11, 54 5 of 16
pooled mean group (PMG) analysis to obtain the properties of short-run momentum and long-run
mean reversion.
3. Methods and Materials
3.1. Basic Modelling of House Price Growth
For the current research, a house price bubble is defined as a change in the properties of deviations
of actual house price growth from its fundamentals, which come from estimates of house price
growth as a function of lagged responses to the present value of expected future rent. This allows for
a parsimonious specification using only lagged property values, interest rates, and rent, which are
summary statistics for all sorts of variables commonly used in modeling real estate to explain residential
property values. Borrowing from Fama and French (1988) and Cutler et al. (1991), the logarithm of the
market price of an asset is divided into (1), a nonstationary part that describes the fundamental price,
and (2), a stationary component that implies the returns are predictable (from previous returns).
We use the Gordon dividend discount model to model house prices as expected present
value of imputed rent, which is similar to the work on housing bubbles by Black et al. (2006),
Hwang and Quigley (2006), Taipalus (2006), and Chan et al. (2001), as well as the pricing model
by Glaeser and Nathanson (2017) for house prices, where traders are “almost” rational. The optimal
forecasting procedure uses past prices to forecast future house prices in a way that allows short-run
momentum to divert from the long-run, with long-run mean reversion and excess volatility. We add
the restriction that the long-run mean is given by the Gordon dividend model.
Following the Lai and Van Order (2010, 2017) framework, we define the “fundamental” value as
the price given an information set, W where the transversality condition means that the second term
approaches zero holds (Giglio et al. 2016). This gives the equilibrium condition for holding property at
time t as follows:
Pt =
¥∑
i=0
E(Rt+i/Dti+ijWt ) (1)
where P is the housing price; R is the net rental income, in this case, imputed services of the property;
and D is the risk-adjusted discount factor. Dividing through by Rt, we have
Pt/Rt =
¥∑
i=0
E((Rt+i/Rt/Dti+ijWt ) (2)
which corresponds to a price earnings ratio for equities. However, for our further analysis, we use the
reciprocal of this Pt/Rt, which is the rent-to price ratio, as the more commonly used variable in real
estate markets.
Based on Equations (1) and (2), it is assumed that expected interest rates and future rents are
correlated and the adjustment process can be expected to vary significantly across regions with different
geography. The next expression (2) is approximated by the Gordon model used for pricing stocks, that
is, if interest rates are constant and rents grow at a constant rate, rearranging the formula results in the
following:
Rt
Pt = aiit – appt∗ + a ≡ giit + grrt + a (3)
where i is the interest rate; pt∗ is the expected rate of rental growth; r = (it – p∗) is the real rate; and
a, ai, ap, gi, and gr are parameters. In general, we expect gr to be close to 1, ai and ap to be positive,
and we are not sure about the sign of gi. Hence, testing for the magnitude of signs, especially for gr,
requires calibrating our assumptions. We develop a model as expression (4) to simulate the long-run
price estimates for every region, but allow long adjustments, which vary across regions. Furthermore,
the Gordon model assumes a constant growth rate in perpetuity, which is not realistic. Adopting the
Gordon model for the estimation of house prices, we added additional risk parameters such as the
J. Risk Financial Manag. 2018, 11, 54 6 of 16
regional earnings ratio to allow for a variable discount rate. Further, to simplify the process, we assume
there is no taxation on capital gains on housing, resulting in the following:
Rt
Pt = (1 – t)it – pt∗ + a ≡ -ti + r + a (4)
where t is marginal tax rate meaning no difference between owning and renting, and a contains risk
adjustments as well as depreciation and long-run expected future rental growth. The next step of
the analysis is to reconstruct the model into the relationship of lagged long-run and short-run effects
among variables using the pooled mean group (PMG) and mean group (MG) estimation models
developed in (Pesaran and Smith 1995; Hasham et al. 1997). This allows regional real estate data to
have the properties of short-run momentum and long-run mean reversion. This means standard asset
pricing theory can be applied in the long-run, while allowing for sluggishness of price adjustment and
variation of adjustment speeds across different U.K. regions. The dynamic panel estimation model can
be represented by the following:
D Rc,t
Pct =
l∑
j=1
l
c,j,D
Rct-j
Pct-j +
q∑
j=0
n∑k=1
dk
c,jxkc,t-j + ac + #c,t (5)
where Rc,t
Pct is residential rent to price ratio in region c at time t, xkc,t-j is the kth of n regressors for
region c, and ac captures region specific fixed effects. #c,t are the regions’ specific errors, c represents
regions, t represents time in month, and j is an indicator of lags. When it is written in error correction
form, this results in the MG estimation, where some of the parameters are restricted inside the brackets
to be zero. Consequently, we obtain the following long-run specification (6).
D Rc,t
Pct = fc(D RPct ct–jj – k∑=n1 bckxck,t) – j∑=q0 k∑=n1 dkc,j 4 xkc,t-j + ac + #c,t (6)
where
fc = -(1 – lc), ac = mc
(1 – lc), bc =
(d1,c,j + d2,c,j)
(1 – lc)
If assuming homogeneous long-run relationships for the PMG estimation model, resulting in
bck = bk for all regions, then Equation (6) can be written as follows:
D Rc,t
Pct = fc(D RPct ct–jj – k∑=n1 bkxck,t) – j∑=q0 k∑=n1 dkc,j 4 xkc,t-j + ac + #c,t (7)
Finally, if PMG in Equation (7) is preferred to MG in Equation (6), then it can be inferred that
all regions of concern share the same long-run effects, while allowing different regions to adjust
at different rates and in different ways. Both Equations (6) and (7) allow us to restrict some of the
parameters inside the brackets to be zero, so that the long-run specification that looks like the Gordon
model, as given in Equation (3), but with fewer restrictions on short-run adjustment parameters across
regions. Our expectation is that that the error correction coefficients fc will be negative and the sums
of the coefficients of lagged changes in R/P (momentum) will be positive but less than 1 (in order that
the model converge), which means that there is no bubble in this region. The residuals #c,t are assumed
to follow the autoregressive process.
#c,t =
T∑
j-1
w
c,t-j#c,t-j + nc,t (8)
J. Risk Financial Manag. 2018, 11, 54 7 of 16
where nc,t is i.i.d. The sum of coefficients of lagged error terms must be greater than one for an explosive
bubble to exist. A positive w would suggest that the shock generates momentum away from the
fundamentals, while a negative w suggests that the shock is followed by a return to trend.
As fundamental indicators, the model uses (a) the variable residential mortgage real rate,
(b) 10-year real government rate, (c) the regional first time buyer affordability rate measured
by mortgage payments as a percentage of disposable income, (d) disposable household income,
and (e) first time buyer earnings ratio. We first need to analyse if all data series are stationary or if the
rental income, housing prices, and fundamental indicators are non-stationary and are integrated of the
same order. Using the PMG model will identify the long-run and short-run effects among variables.
Based on the error correction equation, regional markets with bubbles versus no housing bubbles can
be distinguished as described previously. Comparing the PMG with MG estimation results provides
another robustness check of model results. The latter assumes that the long-run coefficients of each
region can be different, and the estimated long-run parameter is the average of long-run coefficients of
all the individual regions. The Hausman test can be used to check if a common long-run coefficient
exists. This will identify whether the U.K. national market is a bubble or not.
3.2. Data Sources and Sample
This paper studies the evolution of U.K. monthly residential house prices covering nine regions
(East, London, North East, North West, South East, South West, West Midlands, East Midlands,
Yorkshire, and The Humber) from January 2005 to December 2017. We used both the monthly
residential housing price (HPI) and rental price index (RPI) are from U.K. Office for National Statistics
(ONS). Further, the consumer price index (CPI) for house price inflation by the ONS is used as a deflator
for housing prices, rental prices, and interest rates used in the model. Fundamental variables are the
monthly U.K. 10-year government bond real rate from Bloomberg, which is used as a measure of
long term risk-free rate, and the variable residential mortgage real rate from the Nationwide Building
Society, which serves an indicator of credit availability. Further fundamental variables used are the
quarterly series of the regional first time buyer mortgage affordability rate and regional earnings
ratio from Nationwide Building Society, and the regional disposable household income from ONS.
The series has been interpolated into a monthly data series.1
4. Results
4.1. Panel Unit Root Test
If residential markets were efficient in the usual sense, house prices relative to rent would resemble
a random walk series, and thus be non-stationary. The Gordon model implies that house prices are
a function of rental rates that resemble a random walk series. The null hypothesis is defined as
the presence of a unit root. The alternative hypothesis is the series is stationary. Three panel unit
root tests are adopted here, which are the individual augmented Dickey–Fuller test (ADF) based
tests (Pesaran 2007)2; Im, Pesaran, and Shin (IPS) test (Im et al. 2003)3; and Hadri test (Hadri 2000)4.
Except for the Hadri test, all tests assume the null hypotheses as existence of unit root, and alternative
hypotheses as at least one panel to be stationary. The null hypothesis of Hadri test assumes that all
panels are stationary, while the alternative is to have some panels containing unit root.
1 We use the cubic spline interpolation method of plugging the quarterly values for the monthly time intervals.
2 We use the econometric code package “pescadf”, which runs the t-test for unit roots in heterogenous panels with cross-section
dependence, proposed by Pesaran (2007).
3 We use the econometric code package “ipshin”, which estimates the t-test for unit roots in heterogeneous panels developed
by Im, Pesaran, and Shin (Im et al. 2003).
4 We use the econometric code package “hadrilm” performs a test for stationarity in heterogeneous panel data (Hadri 2000).
J. Risk Financial Manag. 2018, 11, 54 8 of 16
Table 1 shows that the rent to price ratio is non-stationary in all the tests, while all the differenced
series are stationary. The same is tested for all other variables. All results show that their differenced
series are stationary. Based on the unit root test results, an estimation procedure is required that allows
for non-stationarity data series and can estimate the long-run relationships between rent to price ratio
and other fundamental variables.
Table 1. Unit Root Tests for Stationarity.
Variables Individual ADF Based Tests IPS Test Hadri Test
Rent to Price Ratio -1.323 -1.452 18.610 ***
D Rent to Price Ratio -4.252 *** -4.097 *** -0.376
10-Year Government real bond rate 15.137 ***
D 10-Year Government real bond rate -2.019
Residential Mortgage Rate 6.705 ***
D Residential Mortgage Rate -0.831
First time buyer affordability Rate -1.069 -1.316 30.413 ***
D First time buyer affordability Rate -4.512 *** -4.208 *** -1.201
Disposable Household Income -2.501 * -1.869 * 9.123 ***
D Disposable Household Income -4.179 *** -4.057 *** 7.150 ***
Earnings ratio -0.860 -0.833 27.699 ***
D Earnings ratio -4.397 *** -4.135 *** 0.955
Note: “***” and “*” denote significance at 1% and 10% level, respectively. All tests use three lags. For the individual
ADF based test, we report the average T statistics for all observations, H0: All panels contain unit roots. For Im,
Pesaran, and Shin (IPS) test, H0: All panels contain unit roots. For Hadri test, we report the standard Z statistics
when controlling for serial dependence in errors, H0: All panels are stationary.
4.2. Cointegration Test
In the next step, we need to verify that there is a long-run relationship between our variables using
the Westerlund (2007) panel cointergration test. The output of the test is provided in four tests. The first
two Gt and Ga represent the “group mean statistics” to accept or reject the null hypothesis for all
regional subsamples, and Pt and Pa present the results for the whole panel. The results in Table 2 show
that the variables do not show very strong relationships, but there is pair-wise cointegration between
the rent to price ratio and the residential mortgage rate and strong contegration between the rent to
price ratio and earnings ratios. On the other hand, rent to price ratios do not seem to be cointegrated
with the 10-year government real bond rate or any of the other variables on an overall panel level
nor the regional group level. Overall, the tests suggest that the study of long term relationships is
reasonable using the variable real residential mortgage rate and the first time buyer earnings ratio.
We thus continue our tests with these two variables.
Table 2. Cointegration tests of rent to price (R/P) and various rates based on Westerlund (2007).
Gø Gff Pø Pff
R/P and 10-Year Government real bond rate -2.187 -7.697 -6.896 -8.090
R/P and Residential Mortgage Rate -2.786 * -11.761 -7.609 * -10.472
R/P and First time buyer affordability Rate -1.142 -2.966 -3.181 -2.159
R/P and Disposable Household Income -1.760 -5.102 -4.145 -3.837
R/P and Earnings ratio -3.101 *** -21.556 *** -9.109 *** -19.898 ***
Note: “***” and “*” denote significance at 1% and 10% level, respectively.
4.3. Test Results
The second analysis uses the pooled mean group (PMG) estimator that allows the short term
coefficients and error variances to be determined on a cross-section specific basis, but this method is
limited by the long-term coefficients being identical. The analysis uses variations of expressions (6)
and (7), taking into account the various lags of the short-term variables. We use the autoregressive
J. Risk Financial Manag. 2018, 11, 54 9 of 16
Distributed-lag (ARDL) bounds test (Paul et al. 2011) to determine the lag length order of the ARDL
model(s). The Akaike Information Criterion (AIC) selection criteria shows that the lag term of rent
to price ratio should be 2, the lag term for residential mortgage ratio should be 3, and earnings
ratio should be 2. This technique developed by Hasham et al. (1997) incorporates non-stationary
variables and utilizes an error-correction (EC) approach that distinguishes between the long-run
(co-integrating) relationships and the short-run adjustment process. The PMG estimation allows the
long-run coefficients to be the same across panels and the short-run coefficients to vary. However, the
MG estimation provides the different long-run (co-integrating) relationships within regions. In the
PMG estimation, a long-run equation is estimated by pooling the data for all regions, and individual
short-run equations are estimated for each region and averaged to determine the short-run coefficients
for the sample. Therefore, the PMG technique makes effective use of the available data. In addition,
the PMG approach is less sensitive to extreme coefficient values at the panel level.
We use several combinations of long-run and short-run variables in the PMG and MG test.
For long-run variables, we have included combinations of the variable real residential mortgage rate
and the regional first time buyer earnings ratio. Short-run variables include the lagged rent to price
ratio to capture the momentum, and the lagged long-run variables to capture the short-run effects.
In some cases, we also added the 10-year government real bond rate in order to capture the effects
from riskless rates in the short-term (Models B1, B2). Finally, we use the Hausman test statistic to
decide which model will be better to test the data sample. PMG is chosen over MG if we can reject the
null hypothesis that there is a common long-run effect, otherwise MG estimation will be chosen.
The MG and PMG results are shown in Table 3. In general, the results across the four models
are similar and the signs of variables are as expected. The Hausman test shows that MG performs
better than PMG, which means that the long-run variables do not have the same parameters across
regions. However, the PMG model is successful in showing that there are long-run versus short-run
relationships in the variables and the coefficients are more significant. The error correction coefficient
is -0.079 from the monthly data in Model A1 and -0.054 Model B1, which means that the deviation
from the long-run is corrected at a rate of about 5–8% each month. The momentum (from lag to rent to
price ratio) is strong and significant with an average sum of coefficients of 0.6 to 0.9. When the 10-year
government real bond rate is included in long-run and short-run equations. As shown in Model B1 and
Model B2, both become significant for all lagged values. Over the long-run, the fundamental variables,
namely the earnings ratio and 10-year government bond real rate, are both negatively related to the
rent to price ratio. Coefficients of these variables are significant at the 1% level with the magnitudes of
-25.828 and -2.388, respectively. This means that the gap between rental growth and house price
growth will become smaller, resulting in a reasonable market.
The constant term, which is about 0.097 in Model B1, shows the average of the fixed effects of all
the regions. The different values of this constant term divided by the adjustment speeds, as shown in
Equation (7), identify which regions are long-run “growth stocks” relative to others.
Table 3. Comparison between pooled mean group (PMG) estimation and mean group (MG) estimation.
Variable Dependent Variable: Rent to
Price Ratio
Model A1 Model A2 Model B1 Model B2
PMG MG PMG MG
Long-Run Equation
Residential Mortgage Rate 3.712 ***
(10.265)
-0.910
(-0.083)
5.800 ***
(7.226)
1.831
(0.223)
Earnings Ratio -25.882 ***
(-22.937)
-18.678
(-0.475)
-25.828 ***
(-16.894)
-20.898
(-0.927)
10-Year Government real bond rate -2.388 ***
(-3.931)
-0.196
(-0.054)
J. Risk Financial Manag. 2018, 11, 54 10 of 16
Table 3. Cont.
Variable Dependent Variable: Rent to
Price Ratio
Model A1 Model A2 Model B1 Model B2
PMG MG PMG MG
Short-Run Equation
Error-correction (EC) -0.079 ***
(-5.560)
-0.007
(-1.046)
-0.054 ***
(-5.451)
0.013
(1.658)
D Rent to Price Ratiot-1 0.938 ***
(28.668)
0.990 ***
(96.368)
0.877 ***
(27.735)
1.005 ***
(123.896)
D Rent to Price Ratiot-2 0.054
(1.675)
-0.310 ***
(-51.879)
0.122 ***
(3.888)
-0.315 ***
(-45.312)
D Residential Mortgage Ratet -1.264 ***
(-4.818)
-1.572 ***
(-9.370)
-1.281 ***
(-6.851)
-2.068 ***
(-11.341)
D Residential Mortgage Ratet-1 0.843 *
(2.083)
0.339
(1.003)
0.286
(0.537)
1.538 ***
(4.153)
D Residential Mortgage Ratet-2 0.817 *
(2.477)
0.218
(0.625)
-0.097
(-0.194)
-0.684
(-1.791)
D Residential Mortgage Ratet-3 -0.359
(-1.823)
-0.102
(-0.954)
0.115
(0.736)
0.14
(1.203)
D Earnings ratiot -3.709 ***
(-4.829)
-7.650 ***
(-7.605)
-5.783 ***
(-4.713)
-6.117 ***
(-7.366)
D Earnings ratiot-1 -1.689
(-1.292)
10.052 ***
(5.935)
7.422 ***
(4.176)
8.953 ***
(6.873)
D Earnings ratiot-2 5.352 ***
(6.911)
-4.685 ***
(-7.068)
-3.711 ***
(-4.058)
-4.129 ***
(-7.762)
D 10-Year Government real bond ratet -0.473 ***
(-4.894)
-0.282 ***
(-3.385)
D 10-Year Government real bond ratet-1 1.264 ***
(7.137)
1.041 ***
(7.202)
D 10-Year Government real bond ratet-2 -0.878 ***
(-6.645)
-0.844 ***
(-8.199)
D 10-Year Government real bond ratet-3 0.207 ***
(4.656)
0.217 ***
(8.180)
Constant 0.007
(1.329)
0.032 **
(2.663)

Observations 1422 1404 1404 1404
Selected Model ARDL(2,3,2) ARDL(2,3,2,3)
Hausman test chi2(2) = -13.02 chi2(2) = -5.89
Note: ***, **, and * represent statistical significance at the 1 percent, 5 percent, and 10 percent level, respectively.
t-statistics are in parentheses.
Table 4 summarizes the sum of short-run coefficients for each region from the PMG estimation for
Model B1. As shown in Table 4, the error correction coefficients of London (-0.002) is the minimum
number among regions followed by the South East with -0.027. This means that there is weak
correction back to the long-run in London. Furthermore, London has the strongest momentum
with a factor of 0.918, which is close to our threshold of 1 to be identified as explosive. In general,
the differences between the maxima and minima of the lagged rent to price ratio coefficients show
a wide range among regions. The North East is the only region that shows a negative coefficient,
indicating a non-bubble or declining region. However, despite the explosive momentum identified for
London, the constant term is smaller than for other regions, which means we cannot classify this as
a bubble based on Equations (7) and (8). Meanwhile, we notice that the East has the biggest coefficient
of error correction and the constant term. This shows that the East has higher potential of housing
price growth in the future.
J. Risk Financial Manag. 2018, 11, 54 11 of 16
Table 4. Sum of short-run coefficients for each region from PMG estimation of Model B1.
Region Error
Correction DR/P Mortgage Rate D Residential D Earnings Ratio Real Bond Rate Government D 10 Year Constant
East -0.095 0.064 -0.739 -1.957 0.171 0.179
East Midlands -0.071 0.134 -0.367 -3.082 0.038 0.128
London -0.002 0.918 -1.565 -1.950 0.084 0.003
North East -0.087 -0.083 -0.861 -1.072 0.282 0.145
North West -0.060 0.083 -1.539 -0.846 0.082 0.099
South East -0.027 0.340 -0.598 -3.200 0.031 0.053
South West -0.041 0.102 -1.687 -2.556 -0.069 0.086
West Midlands -0.059 0.167 -0.979 -0.364 0.031 0.108
Yorkshire and The Humber -0.041 0.007 -0.466 -3.630 0.430 0.069
Maximum -0.002
(London)
0.918
(London)
-0.367
(East Midlands)
-0.364
(West Midlands)
0.171
(East)
0.179
(East)
Minimum -0.095
(East)
-0.083
(North East)
-1.687
(South West)
-3.630
(Yorkshire and
The Humber)
-0.069
(South West)
0.003
(London)
Figure 3 shows the residuals of the PMG results of Model B1. The residuals series fluctuate a lot,
which indicates autocorrelation. The moving average in London shows more clearly a regime shift in
the residuals during the financial crisis period. Moreover, both London and North East generate more
volatile residuals, especially, both fluctuate to a greater extent during the financial crisis.
J. Risk Financial Manag. 2018, 6, x FOR PEER REVIEW 12 of 17
Figure 3. Residuals for regions from Model B1, pooled mean group (PMG) estimation.
4.4. Financial Crisis Test
In order to test the potential regime shift during the financial crisis, we include a dummy
variable “CRISIS” in our Model B1. We define the period for the financial crisis from August 2007 to
August 2011. Figure 4 shows the growing trend of UK monthly housing price index, which is
represented by the dotted line together with the actual HPI. Our dummy variable “CRISIS” equals
one for the time period from August 2007 to August 2011, and zero outside this period.
120
140
Figure 3. Residuals for regions from Model B1, pooled mean group (PMG) estimation.
4.4. Financial Crisis Test
In order to test the potential regime shift during the financial crisis, we include a dummy variable
“CRISIS” in our Model B1. We define the period for the financial crisis from August 2007 to August
2011. Figure 4 shows the growing trend of UK monthly housing price index, which is represented by
J. Risk Financial Manag. 2018, 11, 54 12 of 16
the dotted line together with the actual HPI. Our dummy variable “CRISIS” equals one for the time
period from August 2007 to August 2011, and zero outside this period.
Figure 3. Residuals for regions from Model B1, pooled mean group (PMG) estimation.
4.4. Financial Crisis Test
In order to test the potential regime shift during the financial crisis, we include a dummy
variable “CRISIS” in our Model B1. We define the period for the financial crisis from August 2007 to
August 2011. Figure 4 shows the growing trend of UK monthly housing price index, which is
represented by the dotted line together with the actual HPI. Our dummy variable “CRISIS” equals
one for the time period from August 2007 to August 2011, and zero outside this period.
Figure 4. Montly HPI index for the United Kingdom, January 2005–December 2017, (2005 = 100). ONS,
UK.
0
20
40
60
80
100
120
140
2003-06
2003-12
2004-06
2004-12
2005-06
2005-12
2006-06
2006-12
2007-06
2007-12
2008-06
2008-12
2009-06
2009-12
2010-06
2010-12
2011-06
2011-12
2012-06
2012-12
2013-06
2013-12
2014-06
2014-12
2015-06
2015-12
2016-06
2016-12
2017-06
2017-12
Housing Price Index(HPI)
Date
Financial Crisis
Figure 4. Montly HPI index for the United Kingdom, January 2005–December 2017, (2005 = 100).
ONS, UK.
Table 5 presents the results from the PMG estimations incorporating our dummy variable for the
financial crisis period in the estimated equation.
Table 5. Pooled Mean Group Results with CRISIS dummy based on Model B1.
Variable Dependent Variable: Rent to Price Ratio
Coefficients Standard Errors
Long-Run Equation

Short-Run Equation

1404
Note: ***, **, and * represent statistical significance at the 1 percent, 5 percent, and 10 percent level, respectively.
J. Risk Financial Manag. 2018, 11, 54 13 of 16
The coefficients of “CRISIS” are significant in both the long-term and short-run equation. They are
significantly positive, which means that the crisis was an important factor affecting house price growth
in the long term. The signs and coefficients of both the long-run and short-run fundamental variables
are the same as those in Model B1 in Table 3.
To get a more detailed picture of the period, we simulated the fundamentals to see how much
of the actual change in the rent to price ratio by region was explained by the predictions of the
estimated fundamentals models during the pre-crisis period (January 2005 to July 2007), crisis period
(August 2007 to August 2011), and post-crisis period (September 2011 to December 2017). We can
conclude from Table 6 that the residential mortgage rate is the most important factor during the
financial crisis and after the financial crisis in the long-run. In the short-run, the residential mortgage
rate was the most significant factor during the financial crisis, but the sign of lagged terms changed
after financial crisis. It is noticeable that the 10-year government real bond rate significantly relates to
rent to price ratio in the short-run, except the period during the financial crisis.
Table 6. Pooled mean group results during different time period based on Model B1.
Variable Dependent Variable: Rent to Price Ratio
Pre-Financial Crisis Financial Crisis Post-Financial Crisis
Long-Run Equation
Residential Mortgage Rate 3.6212
(5.012)
2.481 ***
(7.190)
-10.330 ***
(3.450)
Earnings Ratio -16.493 **
(2.241)
-19.076 ***
(-16.963)
-26.824 ***
(4.940)
10-Year Government real bond rate -1.5992
(2.620)
-1.785 ***
(-7.160)
0.744 **
(0.288)
Short-Run Equation
EC 0.013
(0.083)
-0.276 ***
(-11.353)
-0.184 ***
(0.033)
D Rent to Price Ratiot-1 0.034
(0.543)
-0.048
(0.048)
-0.214 ***
(-5.510)
D Rent to Price Ratiot-2 0.037
(0.597)
0.241 ***
(0.048)
0.012
(0.331)
D Residential Mortgage Ratet -0.323
(-0.555)
-2.620 ***
(-6.679)
0.961
(1.750)
D Residential Mortgage Ratet-1 0.986
(1.694)
2.848 ***
(3.541)
0.446
(0.730)
D Residential Mortgage Ratet-2 1.490 *
(2.222)
-2.137 **
(-2.621)
1.755 **
(2.790)
D Residential Mortgage Ratet-3 -2.374 ***
(-3.553)
0.752 **
(2.667)
-0.233
(-0.395)
D Earnings ratiot -5.112 **
(-2.837)
5.323
(1.930)
-3.232 *
(-2.421)
D Earnings ratiot-1 5.041 *
(2.386)
-5.291
(-1.821)
-0.355
(-0.209)
D Earnings ratiot-2 -6.712 ***
(-3.648)
2.585 *
(1.967)
-4.319 **
(-3.223)
D 10-Year Government real bond ratet 0.563 *
(2.292)
0.880 **
(3.265)
0.204
(1.752)
D 10-Year Government real bond ratet-1 -0.863 ***
(-3.451)
-0.593
(-1.385)
-0.002
(-0.015)
D 10-Year Government real bond ratet-2 0.285
(1.225)
0.284
(0.876)
-0.447 ***
(-3.926)
D 10-Year Government real bond ratet-3 -1.121 ***
(-4.408)
-0.063
(-0.756)
-0.375 ***
(-3.352)
Constant -0.003 ***
(-5.039)
0.480 ***
(11.263)
-0.002 ***
(-4.238)
Observations 243 441 684
Note: ***, **, and * represent statistical significance at the 1 percent, 5 percent, and 10 percent level, respectively.
t-statistics are in parentheses.
J. Risk Financial Manag. 2018, 11, 54 14 of 16
5. Discussion
Our results do not confirm a house price bubble at the national level in the United Kingdom;
however, the results show that residential property price growth has different short-run momentum at
the regional versus the national level. While London shows the strongest short-term momentum and
slow return to long-run fundamentals, it cannot strictly be classified as a price bubble, but it clearly
shows a short-run shift during the financial crisis. The stronger momentum in London could also be
caused by regulation, restrictive planning law, and limited provision of building areas resulting in
a temporal mismatch of demand and supply. Housing supply in London tends to be behind demand,
as a result of high demand from international investors looking for a safe haven to invest their money.
The lag of this additional demand factors in other U.K. regions results in more moderate house price
growth. However, low mortgage rates and high first time buyer earnings ratios have also stimulated
house price growth across regions at a different pace.
Overall, price growth is strongly influenced by the first time buyer earnings ratio and the variable
residential mortgage rate, while the 10-year government bond rate shows less relevance in certain
periods. On a regional level prices show varying speeds of reversion to long-run fundamentals,
but each region displays different long-run and short-run patterns. The regional house price series
has been adjusted for inflation using the national CPI index. However, if we take the perspective of
a homeowner in a regional housing market, regional inflation rates would be the right choice because
some of the regional markets are largely influenced by local economics, such as the level of economic
development or the general economic climate (e.g., London and the North). However, there are no CPI
data available on regional level.
The analysis of separating the sample into pre-crisis, crisis, and post crisis period provides further
inside into short-run momentum versus long-run fundamentals. Government policies of quantitative
easing and lowering of interest rates have changed the relationship between government real bond
rates and house price growth. While historically, house prices have been highly correlated with
government bond rates, our model could not identify a co-integrating relationship in the long-run.
As a result, they also show no significance during the crisis period. Hence, the variable mortgage rate
adjusted for inflation seems to be a better predictor for house price growth. We believe that this is
a short term effect and as government real bond rates return to positive levels, this will restore the
long-run relationship with residential housing markets.
Overall, we can conclude that short-term determinants of houses prices have shifted away from
long-term indicators at varying degrees on a regional level, but we cannot confirm that these short-term
effects result in sufficient momentum to be considered a house price bubble in the regions.
Author Contributions: Conceptualization, Y.W. and N.L.; Methodology, Y.W.; Software, Y.W.; Validation,
Y.W.; Formal Analysis, Y.W. and N.L.; Investigation, Y.W. and N.L.; Resources N.L.; Data Curation, N.L.;
Writing—Original Draft Preparation, Y.W. and N.L.; Writing—Review & Editing, N.L.
Acknowledgments: This paper is supported by The PhD Start-up Fund of Natural Science Foundation of
Guangdong Province, China (Grant number: 2017A030310238).
Conflicts of Interest: The authors declare no conflict of interest.
References
Abraham, Jesse M., and Patric H. Hendershott. 1993. Patterns and Determinants of Metropolitan House Prices,
1977–91 (October 1992). NBER Working Paper No. w4196. Available online: https://ssrn.com/abstract=
879241 (accessed on 12 September 2018).
Abraham, Jesse M., and Patric H. Hendershott. 1996. Bubbles in metropolitan housing markets. Journal of Housing
Research 7: 191–207. [CrossRef]
Barrell, Ray, Simon Kirby, and Rebecca Riley. 2004. The Current Position of UK House Prices. National Institute
Economic Review 189: 57–60. [CrossRef]
Black, Angela, Patricia Fraser, and Martin Hoesli. 2006. House Prices, Fundamental and Bubbles. emphJournal of
Business Finance and Accounting 33: 1535–55. [CrossRef]
J. Risk Financial Manag. 2018, 11, 54 15 of 16
Chan, Hing Lin, Shu Kam Lee, and Kai Yin Woo. 2001. Detecting Rational Bubbles in the Residential Housing
Markets in Hong Kong. Economic Modelling 18: 61–73. [CrossRef]
Cutler, David M., James M. Poterba, and Lawrence H. Summers. 1991. Speculative dynamics. The Review of
Economic Studies 58: 529–46. [CrossRef]
Fama, Eugene F., and Kenneth R. French. 1988. Dividend yields and expected stock returns. Journal of financial
economics 22: 3–25. [CrossRef]
Favilukis, Jack, Sydney C. Ludvigson, and Stijn Van Nieuwerburgh. 2017. The macroeconomic effects of housing
wealth, housing finance, and limited risk-sharing in general equilibrium. Journal of Political Economy
125: 140–223. [CrossRef]
Flood, Robert P., and Robert J. Hodrick. 1990. On testing for speculative bubbles. Journal of Economic Perspectives
4: 85–101. [CrossRef]
Gholipour, Hassan Fereidouni, Usama Al-mulali, and Abdul Hakim Mohammed. 2014. Foreign investments in
real estate, economic growth and property prices: Evidence from OECD countries. Journal of Economic Policy
Reform 17: 33–45. [CrossRef]
Giglio, Stefano, Matteo Maggiori, and Johannes Stroebel. 2016. No-bubble condition: Model-free tests in housing
markets. Econometrica 84: 1047–91. [CrossRef]
Glaeser, Edward L., Joseph Gyourko, and Albert Saiz. 2008. Housing supply and housing bubbles. Journal of
Urban Economics 64: 198–217. [CrossRef]
Glaeser, Edward L., Joshua D. Gottlieb, and Joseph Gyourko. 2010. Can Cheap Credit Explain the Housing Boom?
In NBER book Housing and the Financial Crisis (2013). Edited by Edward L. Glaeser and Todd Sinai. Chicago:
University of Chicago Press, pp. 301–59.
Glaeser, Edward L., and Charles G. Nathanson. 2017. An extrapolative model of house price dynamics. Journal of
Financial Economics 126: 147–70. [CrossRef]
Goodman, Allen C., and Thomas G. Thibodeau. 2008. Where are the speculative bubbles in US housing markets?
Journal of Housing Research 17: 117–37. [CrossRef]
Hadri, Kaddour. 2000. Testing for stationarity in heterogeneous panel data. The Econometrics Journal 3: 148–61.
[CrossRef]
Himmelberg, Charles, Christopher Mayer, and Todd Sinai. 2005. Assessing high house prices: Bubbles,
fundamentals, and misperceptions. Journal of Economic Perspectives 19: 67–92. [CrossRef]
Im, Kyung So, M. Hashem Pesaran, and Yongcheol Shin. 2003. Testing for unit roots in heterogeneous panels.
Journal of Econometrics 115: 53–74. [CrossRef]
Khandani, Amir E., Andrew W. Lo, and Robert C. Merton. 2013. Systemic risk and the refinancing ratchet effect.
Journal of Financial Economics 108: 29–45. [CrossRef]
Lai, Rose N., and Robert A. Van Order. 2010. Momentum and house price growth in the United States: Anatomy
of a bubble. Real Estate Economics 38: 753–73. [CrossRef]
Lai, Rose Neng, and Robert Van Order. 2017. US House Prices over the Last 30 Years: Bubbles, Regime Shifts and
Market (In) Efficiency. Real Estate Economics 45: 259–300. [CrossRef]
Mayer, Christopher, and Todd Sinai. 2009. U.S. House Price Dynamics and Behavioral Finance. In Policy Making
Insights from Behavioral Economics. Edited by Christopher L. Foote, Lorenz Goette and Stephan Meier. Boston:
Federal Reserve Bank of Boston, chp. 5.
Meese, Richard, and Nancy Wallace. 1994. Testing the present value relation for housing prices: Should I leave my
house in San Francisco? Journal of Urban Economics 35: 245–66. [CrossRef]
Mikhed, Vyacheslav, and Petr Zemˇcík. 2009. Testing for bubbles in housing markets: A panel data approach.
The Journal of Real Estate Finance and Economics 38: 366–86. [CrossRef]
Nickell, Steve. 2005. Practical Issues in UK Monetary Policy, 2000–2005. Keynes Lecture in Economics. London:
British Academy. [CrossRef]
Paul, Biru Paksha, Md Gazi Salah Uddin, and Abdullah M. Noman. 2011. Remittances and output in Bangladesh:
An ARDL bounds testing approach to cointegration. International Review of Economics 58: 229–42. [CrossRef]
Peláez, Rolando F. 2012. The housing bubble in real-time: The end of innocence. Journal of Economics and Finance
36: 211–25. [CrossRef]
Pesaran, M. Hashem, and Ron Smith. 1995. Estimating long-run relationships from dynamic heterogeneous
panels. Journal of Econometrics 68: 79–113. [CrossRef]
J. Risk Financial Manag. 2018, 11, 54 16 of 16
Hasham, Pesaran M., Shin Yongcheol, and P. Smith Ron. 1997. Pooled Estimation of Long-Run Relationships in
Dynamic Heterogeneous Panels. Cambridge Working Paper in Economics, Cambridge. August. Available
online: https://EconPapers.repec.org/RePEc:cam:camdae:9721 (accessed on 12 September 2018).
Pesaran, M. Hashem. 2007. Simple panel unit root test in the presence of cross-section dependence. Journal of
Applied Econometrics 22: 265–312. [CrossRef]
Hwang, Min, and John M. Quigley. 2006. Economic fundamentals in local housing markets: evidence from US
metropolitan regions. Journal of Regional Science 46: 425–53. [CrossRef]
Rodríguez, Carlos, and Ricardo Bustillo. 2010. Modelling Foreign Real Estate Investment: The Spanish Case.
The Journal of Real Estate Finance and Economics 41: 354. [CrossRef]
Rogers, Dallas, Chyi Lin Lee, and Ding Yan. 2015. The Politics of Foreign Investment in Australian Housing:
Chinese Investors, Translocal Sales Agents and Local Resistance. Housing Studies 30: 730–48. [CrossRef]
Stiglitz, Joseph E. 1990. Symposium on bubbles. Journal of Economic Perspectives 4: 13–18. [CrossRef]
West, Kenneth D. 1987. A specification test for speculative bubbles. The Quarterly Journal of Economics 102: 553–80.
[CrossRef]
Taipalus, Katja. 2006. A Global House Price Bubble? Evaluation Based on a New Rent-Price Approach. Bank of
Finland Research Discussion Paper No. 29/2006. Available online: https://ssrn.com/abstract=1018329
(accessed on 12 September 2018). [CrossRef]
Westerlund, Joakim. 2007. Testing for error correction in panel data. Oxford Bulletin of Economics and Statistics
69: 709–48. [CrossRef]
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article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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