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IJHMA
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442
Received 27 June 2018
Revised 18 July 2018
Accepted 19 July 2018
Asymmetric sensitivities of house prices to housing fundamentals: evidence from UK regions
Huthaifa Alqaralleh
Department of Economics, Business and Finance, Mutah University,
Karak, Jordan
Abstract
Purpose
–
This paper aims to examine asymmetries in the house price cycle and to understand the dynamic of housing prices, incorporating macroeconomic variables at regional and country level, namely, housing affordability, the unemployment rate, mortgage rate and in fl ation rate.
Design/methodology/approach
–
To highlight signi fi cant differences in the asymmetric patterns of house prices between regions, the STAR model is adopted.
Findings
–
The authors highlight signi fi cant differences in the asymmetric patterns of house prices between regions, in which some areas showed asymmetric response over the housing cycle; here the
LSTAR model outperforms other models. In contrast, some regions (the South West and the North West) showed symmetric properties in the tails of the cycle; therefore, the ESTAR model was adopted in their case.
Practical implications
–
Being limited to a few fundamentals, this study opens an avenue for further research to investigate this dynamic using in addition such demand-supply factors as land supply, construction cost and loans made for housing. These fi ndings can also be used to examine whether other models such as ARIMA, exponential smoothing or arti fi cial neural networks can more accurately forecast housing prices.
Originality/value
–
The present paper aims to highlight housing affordability as a cause of asymmetric behaviour in house prices. Put differently, the authors seek to understand the dynamics of housing prices with other fundamentals incorporating macroeconomic variables in regions and country level data as a means of achieving a more concise result.
Keywords Housing prices, Housing market analysis, Housing affordability,
House prices asymmetric, SATR model, UK regions
Paper type Research paper
International Journal of Housing
Markets and Analysis
Vol. 12 No. 3, 2019 pp. 442-455
© Emerald Publishing Limited
1753-8270
DOI 10.1108/IJHMA-06-2018-0047
1. Introduction
There is a growing body of literature that recognizes the importance of studying the diffusion of trends in house price to the whole economy. Such a relationship stems from the fact that housing falls into two categories of assets, as both a consumer good and a long-
term investment ( Drelichman and Agudo, 2014 ; Glaeser et al.
, 2008 ). Traditionally, empirical
writers have believed that the uncertainty generated by house price swings is a vital factor in macroeconomic fl uctuations (
;
in the literature (
;
Shiller, 2007 ; Drelichman and Agudo, 2014
), the fl uctuations in such economic fundamentals as mortgage rates and consumer income also play a crucial role in determining the nature of the housing market.
Recent developments in examining these fundamentals have heightened affordability as a source of volatility among house prices. In fact, housing affordability has become a central issue in determining these prices, as it includes the capacity to get credit for a house
purchase, for example, the capacity to incur great obligations in the form of long-term debt
(
From the Economics point of view, the affordability level is used to re fl ect the unbalanced status in which housing supply cannot meet the demand, thus indicating the overall state of the housing market. In this manner, purchasing a house becomes dif fi cult when the ratio of housing cost to incomes rises steeply. In a well-functioning market, this imbalance of demand and supply would eventually self-correct, and reduced demand should result in downward pressure on house prices (
Glaeser and Nathanson, 2017 ). However,
housing markets have some intrinsic qualities that make them complicated places and thus produce a situation where supply and demand cannot fi
nd equilibrium ( Alqaralleh, 2017
).
Still, increased constraints on affordability may lead to the accumulation of unsustainable mortgage debt and could pose a threat to a nation ’ s economic stability.
A considerable number of empirical studies has been published on this relationship between housing affordability, as well as on house prices and other macro fundamentals (see among others
Campbell and Cocco, 2007 ). However, these studies suggest that
the costs of housing respond symmetrically to the fundamentals. One question that should fi rst be asked, however, is whether the house prices respond to the fundamentals symmetrically or asymmetrically over the housing cycle[
1 ]. Another drawback of these
studies is that the previous literature considers the properties of house prices in relation to the whole country. However, it is likely that housing affordability presents a particular and growing crisis to urban areas with constrained housing markets because of the high persistence of house prices in metro areas with higher population growth. In the faster-
growing cities, in contrast, mean reversion is smaller ( Baranoff, 2016
;
).
Thus, house price behaviour in large metropolitan areas may follow a different pattern from that in smaller urban areas.
This analysis may merit particular attention in the UK regions, fi rst, because of signi fi cant swings in housing prices and, second, because the UK housing market, as widely accepted, is suffering from unaffordability. In this regard, according to the UK House Price
Index (HPI), the North West showed the highest annual growth, with prices increasing by
6.5 per cent in the year to August 2017. This was followed by the East of England, the East
Midlands and South West, in each of which prices increased by 6.4 per cent. The lowest annual growth was in London, where prices increased by 2.6 per cent over the year, followed by the North East at 3.7 per cent. These trends have boosted affordability for fi rst-time
buyers in the later areas, in particular[ 2 ].
With this in mind, the present paper aims to highlight housing affordability as a cause of asymmetric behaviour in house prices. Put differently, we seek to understand the dynamics of housing prices with other fundamentals to investigate how varying housing market fundamentals explain the housing asymmetry by incorporating macroeconomic variables in regions and country level data as a means of achieving a more concise result.
The next section, Section 2, brie fl y reviews the literature, while Section 3 describes the methodology used in this investigation. In Section 4, the empirical results are discussed. We conclude in Section 5.
Housing fundamentals
443
2. Literature review
There is a growing body of literature that recognizes the close relationship between housing prices and economic fl uctuation. Theoretically, the research suggests that the housing prices variations stem from several demand-supply factors. In this regard, as the supply side is supposed to be rigid, improving the economic conditions tends, on the one hand, to increase household incomes and therefore to boost housing demand. On the other, once property
IJHMA
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444 prices rise above the cost of replacement, property developers initiate the construction process by regulating current property prices. However, supplying new properties is, by de fi nition, a slow process. By the time a new feature is delivered, economic conditions may have changed for the worse and prices start to decline. This delay in supply responsiveness
causes asymmetries in the cycle of house prices ( Davis and Zhu, 2011
).
The literature on house price fl uctuations has highlighted several determinants that cause asymmetries in the properties of house prices (see among others;
;
Meen, 2002 ). Most of the earlier attempts, based on linear
models, concentrate on the linkage between house prices and macroeconomic variables.
investigated the presence of a relationship between house prices and several demand-supply factors, namely, total household income, short-run interest rates, the stock price index, construction costs and housing completions. Operating the vector error correction mechanism, they showed that the demand side factors (including short-term interest rates) are the primary cause of the variability in house prices. They also highlighted a constant long-run equilibrium relationship between house prices and income, which other authors have used as a measure of affordability.
examines also the role of national incomes, interest rates and stock prices in determining house prices. He highlights that the housing market is positively affected by shocks in the gross domestic product and equity prices, whereas an expanded fi nancial policy and a low interest rate encourage an increase in house prices.
point out that house prices have proved in the long run to be a stable hedge against in fl ation.
Similarly, in their seminal article,
demonstrated that the affordability issue of housing in Hong Kong plays a signi fi cant role in determining actual house prices.
In contrast, another strand of the literature argues that the fl uctuating housing prices cannot altogether be attributed to the factor of economic fundamentals. According to
, the extent of the sharp increase in house prices, in this case, the boom in house prices and the discrepancies between them in several economies can be better explained by a housing bubble or social behaviour.
found no cointegration between house prices and other demand and supply factors. However,
show in previous episodes of possible bubbles the slow adjustment of house prices toward the equilibrium. Moreover, house prices and the asymmetric cycle decrease gradually when the time-frame is extended.
A few studies have assessed whether nonlinear models can better explain the asymmetric nature of the house price cycle. In this category, the seminal work of
suggests that the likelihood of transition depends on the extent of disequilibrium in the system. Moreover, a boom in real house prices is related to an unstable regime, and the probability of an unpredictable regime is less when the equilibrium adjustment value of
Markov switching increases.
test the unit root of the UK housing market using a smooth transition momentum threshold autoregressive model. They provide evidence that models that take into account nonlinear, asymmetric house price cycles perform better than their linear counterparts.
adopt the threshold cointegration model to explore asymmetric relationships in the US housing and stock markets. They conclude that, although cointegration exists between markets, the adjustments toward its long-run equilibrium are asymmetric.
use a sample of 13 European countries to examine the long-run relationship between real house prices and macroeconomic fundamentals, namely, disposable income, long-term interest rates and the unemployment rate. The results indicate that high growth phases may be associated with booms in the housing market or with an uncertain increase in house prices. In addition, the results suggest that a stable
regime leads to high phases in real house prices. Adopting the same technique,
focus on the impact of positive and negative shocks using an asymmetric cointegration approach. The outcomes of the MTAR model show the existence of an asymmetric long-run relationship between house prices and the fundamentals. Moreover, the positive shocks lead to a divergence between the variables used while adverse shocks cause convergence. One study conducted by
Bahmani-Oskooee and Ghodsi (2016)
used
ARDL model to examine whether the shocks in the housing fundamentals (especially the
Household income) have asymmetric effects on house prices. The authors highlight that the adopted nonlinear model provides more support for cointegration between house prices and household income.
capture asymmetries in real estate cycles through a novel nonlinear model. They indicate that the dynamic symmetry in house price cycles is to be strongly rejected. Moreover, they suggest that the duration of an expansion phase is shorter than that in a contraction phase. In their in fl uential work,
investigated the role of UK house prices in business cycles using a Markov switching model, and they found that house prices signi fi cantly affect business cycles.
Housing fundamentals
445
3. Economic framework
The studies presented thus far provide several explicitly used demand and supply fundamentals in exploring the dynamics of house prices, together with some that opt to manipulate the variables according to the speci fi c hypothesis being tested. However, the interest rate and in fl ation rate are standard when investigating any aspect of the housing market (
;
Following the same argument, our model defends the view that the house price affordability plays a crucial role in house price fl uctuations. This model includes a set of variables proxying an equilibrium price, which relate to house prices in the UK regions. In the case of cyclical behaviour, however, the house price to income ratio and the affordability measure variables would deviate at the same time, which could cause an affordability crisis.
Therefore, house prices will show no mean reversion to previous long-term value ( Chen et al.
;
). Thus, the fundamental determinants of the neo-classical approach can be better explained by such cyclical movement. These fundamentals are built around the house price fl uctuations in the short run rather than the long term; in these fl uctuations, the house prices are developed as a function of the demographic factors, interest rate and in fl ation (as shown in
HP ¼ F AFF
þ ; INF ; UN ; MR (1)
represents our model where HP stands for house price index; AFF is the affordability variable, UN denotes the demographic factor measured by the unemployment rate, MR denotes the short run housing mortgage rates and INF signi fi es in fl ation. Below is detailed explanation for the expected sign of these variables.
3.1 A ff ordability
Historically, affordability of housing is regarded as one of the fundamental issues for players in the housing market, in which, change in housing affordability implies dif fi culties in affording necessities, which undermines borrowers ’ ability to pay their obligations, resulting in a higher increasing in housing prices. In general, therefore, it seems that housing prices positively affect the housing prices.
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3.2 The short run housing mortgage rates
Interest rate in fl
uences the investment decisions in the housing market ( Chen and Patel,
;
). In the in fl uential work of
, the author suggests six direct and indirect ways in which the mortgage rate affects the housing market: directly affecting the user cost of capital, the housing supply and the expectations for the future movement of prices; and indirectly affecting housing wealth changes and the in fl uence of the credit channel on consumption. One can, therefore, argue that such relationships between house prices and the mortgage rate is a negative correlation, which depends on the degree of competition in the banking sector.
3.3 In fl ation
The coincidences of how changes in the in fl ation rate in fl uence the housing prices are distinct and have been widely recognized in the literature, and several evidences have discussed possible theoretical links. In this vein, buying homes can be more affordable and increase the demand for homes when interest rates are low. However, supplying new properties is, by de fi nition, a slow process. By the time an increasing in demand results in an increase in housing prices especially in large cities where land availability is often limited, we can see a more pronounced effect of in fl
;
An implication of this is the possibility that there is a negative relationship between the housing prices and the in fl ation.
3.4 Unemployment
The relationship between unemployment and house prices can be a mixture between the direct and indirect relationships, as housing is an asset with a complicated relationship with the macro economy. In such case, when house prices fell caused by unemployment, then supplying new housing becomes less pro fi table, reducing supplier activity, which would then feedback via the construction industry creating more unemployment (Miles, 1997;
). The evidence, therefore, suggests that growing unemployment rate reduces house price changes.
4. Smooth transition regression model
Consider a smooth transition regression model for a univariate stationary house prices series at the time t = 1 – p , 1 – (1 – p ), . . .
, – 1,0, 1, . . .
, T – 1, T expressed as:
Y t
¼ U
1 x t
1 ð t
; g ; c Þ þ U
2 x t t
; g ; c Þ þ « t
; « t nid 0 ; s 2 u
(2) where x t t
U i1
¼ ð y t 1
, . . .
, U
¼
; y ip
)
T
ð 1 t 2
;
;
^ t
Þ T
. . .
a vector that represents all the explanatory variables such that
; y t p
Þ T
, and the parameters need to be estimated as a set are
U
. The transition function G (s determined by the transition variable s t i
= (
U i0
,
; g
, c) is a continuous transition function parameter g
. This model is de fi ned as a two-regime switching model, in which the transition function G (s t t
, the vector of location parameter c and the slope
; g , c), which is bounded by 0 and 1, allows the dynamics of a model to switch
smoothly between regimes ( Franses and Van Dijk, 2000 ).
According to
, the regime occurring at time t depends on the type of transition variable s t
, which is de fi ned in numerous techniques. For example, the transition variables s t is supposed to be a lagged endogenous variable y t – d for a particular
integer delay parameter d , exogenous variable s t
= z t or sometimes linear trend s t
= t , in which the changing parameter of the model rises smoothly over the period.
A common speci fi cation of the generalized version of smooth transition functions is given by:
ð t
; g ; c Þ ¼
1 þ exp g = s
1 k s t
Y k
ð s t
C k
Þ k
(3) where s s t is the standard deviation of the transition variable. In
, the speed of adjustment parameter g controls for the smoothness change from one regime to the other, while the threshold between the two regimes is expressed by parameter c .
To explain the regime switching pattern in house prices, two transition function G (s model, which is given by setting k = 1 in
. That is: t
; g
, c) can be derived: fi rst, the fi rst-order Logistic Smooth Transition Autoregressive (LSTAR)
ð t
; g ; c Þ ¼ ð
1 þ e g ð s t c Þ Þ 1
; g > 0 ; ð t
; g ; c Þ e ½ 0 ; 1 (4)
The change in sign of parameters g and c are made to determine the increase ( g > 0) or decrease ( g < 0) in the logistic function value. Conversely, the faster transition function is explained by c , in which a greater value of c indicates a steeper transition.
The second type of transition function is known as the exponential smooth transition autoregressive (ESTAR) model in which the transition function is given by: t
; g ; c Þ ¼ ð
1 þ e g ð s t c
1
Þ ð s t c
2
Þ Þ 1
; c
1
# c
2
; g > 0 ; ð t
; g ; c Þ e ½ 0 ; 1 (5)
The ESTAR model in
concentrates only on the size of the transition variable s t and the parameter c .
The method of
starts by modelling linear Autoregressive (AR) and tests it against the STAR model. Next, the Lagrange Multiplier is used to choose a suitable delay parameter (d) for selecting the best transition variable. Finally, sequences of the nested hypothesis are tested to determine the appropriate type of STAR model. In the following subsections, these steps are brie fl y discussed in turn.
Following standard practice in the nonlinear modelling literature ( Teräsvirta et al.
fi rst step in the model speci fi cation procedure is to test whether a linear AR(p) representation is adequate for the data in question. If the answer is negative, then the second step involves the selection of a nonlinear symmetric model. This can be done as follows:
(1) estimate a suitable linear autoregressive model;
(2) test for the linearity of the model; and
(3) if the linearity hypothesis is rejected, select the functional form of the transition function.
In Step 1, the correct order autoregressive order p is selected using, for example, the
Bayesian information criterion and portmanteau test for serial correlation. In Step 2, linearity can be tested using the inference procedure suggested in
(1988) . As far as Step 3 is concerned, the type of model can be selected by using an LM-
type test. Finally, in Step 4, the choice of the transition function can be guided by
Housing fundamentals
447
IJHMA
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448 economic theory. Alternatively, a procedure suggested in
and
can be used.
5. Data and descriptive statistics
The considered data consist of a monthly time series of the house price index, affordability, the short run housing mortgage rates, unemployment and in fl ation over the period 1998:1 to
2017:12 for a sample of six main UK regions that includes East Midlands, Yorkshire, West
Midlands, South West, North West, London. Because of the limited data availability, the sample was carefully chosen to re fl ect the importance of particular cities (with the highest average prices) as an early warning for the boom (bust) in house prices for the whole economy. The reason behind limiting the data to this interval is to concentrate on the troubled period in which the UK is suffering from housing unaffordability, rather than on stable periods.
The descriptive statistics for the above-mentioned variables are reported in
. The statistics show that there is a considerable variability among the observations over the time as supported by the standard deviation value. The Jarque – Bera test for normality reveals that the series are not normally distributed, as this test is signi fi cant at the 5 per cent level.
This is in line with the skewness and kurtosis values. Moreover, the skewness statistics show that the distribution has a long right tail and deviation from normality. Accordingly, this analysis gives more support to the suitability of addressing the feedback between the house price and the selected variables within nonlinear framework.
Furthermore, it is crucial to test the stationarity of the series before testing the linearity
as the unit root in the series might result in wrongly rejecting the linear model ( Kilic, 2004
;
). The calculated test statistics refer to the series in fi rst difference, that is, D y t
= y t
– y t – 1
. The house price series do not have a unit root, as indicated in the ADF test.
6. Empirical results
As described in the previous section, the modelling STAR fi rst chooses the optimal lag (p) of the AR model and tests this order against any misspeci fi cation. As presented in
, there is a signi fi cant difference in terms of the levels of persistence among the regions, perhaps re fl ecting different market conditions and local housing affordability. For example, the table indicates that the AR (2) model is suitable for modelling house prices for both
Yorkshire and the South West. By contrast, the West Midlands and North West need the highest number of lags to make the residuals white noise, thus revealing a higher level of persistence in these regions. As far as the other regions are concerned, the lag order is included in these two boundaries. It is worth noting that the adopted AR(p) is tested against any misspeci fi cation problem and no autocorrelation was found in the supposed lag.
From
, we can also see that the p-values of the test for linearity and the test for dynamic symmetry are less than 5 per cent; thus, the null hypothesis of linearity is rejected for all regions. Once the delay parameter is speci fi ed, the appropriate transition variable can be determined. Following the seminal work of Kapetanios et al.
(2003) and
this parameter can be chosen according to the LM-test for different values of the transition variables such that the optimal choice of this delay parameter d corresponds to the greatest test statistics (where p -value is the smallest). The justi fi cation of this operative procedure is
to maximize the test power alongside the appropriate transition function[ 3 ].
provides an optimal choice of transition variables.
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449
Table I.
Descriptive statistics
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Having rejected linearity, the next step is to discriminate between the LSTAR(P) model and the ESTAR(P) model. Following
(2002) , the choice can be made by sequences
of the F – test, such that:
H
01
:
H
H
02
03
U
4i
¼ 0 i ;
:
: U
2i
U
¼
3i
¼ 0 and
0 and U
3i
U
4i
¼ U
¼ 0
4i ii
¼ 0 iii :
; 8 integer i e ½ 1 ; p
Such a choice of model may depend on the p -value of the F-test. When it does, the ESTAR(P) will be chosen if the p -value of (ii) is smaller than (i) and (iii). Otherwise, LSTAR(P) is the best choice. Kapetanios (2001) also concludes that such standard information criteria as AIC and BIC have a crucial role in this model selection.
The result in the last column of
shows the speci fi cation of an appropriate nonlinearity STAR-type model through testing a sequence of the hypothesis. In the cases of both the South West and the North West, nonlinearity appears to be exponential form, which supports the use of the ESTAR model. A possible explanation for this is that these regions follow symmetrical adjustment behaviour. Put differently, the housing market in these two regions responds similarly from high to low levels towards the middle ground.
However, the other regions showed an asymmetrical distribution and thus the LSTAR model is adopted.
6.1 Estimated parameter of the STAR model
Turning now to the experimental evidence on the STAR model,
provides an overview of the dynamics of the house prices ’ response to the fundamentals. The most exciting aspect of these results is that the speed of transition between regimes g is positive and signi fi cant. Moreover, the relatively small value of the speed parameters g indicates a slower transition between regimes, which endorses the choice of the STAR model instead of the other nonlinear model. There was also a signi fi cant difference among the regions in terms of the threshold between the two regimes. These results are in accord with recent
studies ( Bahmani-Oskooee and Ghodsi, 2016
;
) indicating that house prices follow an asymmetric pattern in which the periods of expansion are different from those of contraction as measured by the speed of switching between regimes. In detail, the South
West region has the highest rate of transition ( g = 12.2), while the East Midlands area assumes a slow transition ( g = 1.9).
It is interesting also to note that in all cases examined in this study, the threshold between two regimes, parameter c, is signi fi cant. A possible explanation for this is that the housing market is susceptible to any change in the market fundamentals. In detail, the cases
Table II.
Testing linearity against a nonlinear model
UK regions AR(P) Transition variable
East midlands
Yorkshire
AR (3) INF(t 1)
AR (2) INF(t 1)
West midlands AR (4) INF(t 1)
South west AR (2) INF(t 1)
North west
London
AR (4) INF(t 1)
AR (3) INF(t 1)
F F4 F3 F2 Suggested model
0.001
0.116
0.054
0.002
LSTR1
0.026
0.017
0.104
0.595
LSTR1
0.000
0.131
0.001
0.008
LSTR1
0.050
0.190
0.017
0.593
LSTR2
0.008
0.213
0.034
0.038
LSTR2
0.003
0.010
0.303
0.021
LSTR1
of the South West and the North West (where the ESTAR model outperform the LSTAR) suggest that observations are equally distributed between the right-hand and left-hand tails of their respective exponential functions. By contrast, the effect in other regions seems to be skewed to the right as the location parameter c is positive, except the case of Yorkshire, where the location parameter is negative.
As house prices and local economic variables are often cointegrated, we assess the role of affordability as well as that of other variables in capturing the effect of lagged house price changes on house price dynamics. The results reveal several interesting dissimilarities between regimes. In line with our expectation, the estimated parameters for the variables in question signi fi cantly affect the house prices dynamic in both the expansion and contraction phases. Although the coef fi cients corresponding to the estimate of the STAR model are different for each region, the interesting feature of the results (reported in
) is that affordability is the main factor affecting the house price dynamic. This is the case everywhere except in the North West and the
West Midlands, where affordability was seen to negatively impact on house prices in the nonlinear part.
To evaluate the goodness of the estimated model, misspeci fi cation tests were applied.
The results shown in panel A of
verify that the null hypothesis of no autocorrelation was accepted against the q-order autoregressive for all estimated models.
This applies to panel B of
also. Similarly, panel C provides the LM tests based on a third-order Taylor approximation to test for parameter constancy. Again, the null hypothesis was accepted for all the estimated models. Finally, the results of the ARCH-LM tests (see Panel D), which validate the null hypothesis that t no ARCH effect is present, are accepted.
Housing fundamentals
451
7. Conclusion
The present study was designed to examine asymmetries in the house price cycle and to understand the dynamic of housing prices, incorporating macroeconomic variables at regional and country level, namely, housing affordability, the unemployment rate, mortgage
Coefficient South west North west West midlands London East midlands Yorkshire
Linear part
Aff
INF
MR
Un
0.196** (0.088) 0.211** (0.090)
0.024* (0.008) 0.014 (0.004)
0.118* (0.022)
0.013** (0.005)
0.163** (0.062)
0.007* (0.004)
0.079 (0.064) 0.650* (0.146)
0.002 (0.005) 0.035** (0.009)
0.064*** (0.044) 0.096** (0.042) 0.111** (0.043) 0.100** (0.057) 0.097** (0.043) 0.402** (0.124)
0.029*** (0.018) 0.070** (0.029) 0.012 (0.024) 0.014* (0.002) 0.036 (0.031) 0.143** (0.074)
Nonlinear part
Aff.
INF
MR
Un
0.229** (0.128) 0.210* (0.025) 0.023** (0.011) 0.324** (0.127) 2.942** (1.257) 0.867** (0.411)
0.036** (0.005) 0.068** (0.018) 0.012** (0.006) 0.142** (0.049) 0.088** (0.048) 0.627** (0.291)
0.097** (0.048) 0.166** (0.020) 0.244** (0.085) 0.171** (0.032) 0.513** (0.216) 0.121** (0.041)
0.078** (0.032) 0.102** (0.010) 0.090** (0.048) 2.236* (0.896) 0.486** (0.196) 0.307** (0.132)
Transition parameters
Gamma
C1
12.214* (3.684) 5.646** (1.300) 9.491* (2.807) 5.370** (2.678)
0.494** (0.234) 0.581** (0.120) 0.143** (0.059) 0.564* (0.229)
C2 0.200** (0.015) 0.656** (0.230) ** **
1.849* (0.215)
1.292* (0.259)
**
8.928* (3.479)
0.416** (0.079)
**
Notes: Sig. Codes: *: 1%, **:5%, ***: 10%; Standard error between parentheses
Table III.
STAR models results
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452
Diagnostic tests South west North west West midlands London East midlands Yorkshire
5
6
7
8
1
2
3
4
Test of no error autocorrelation lag
0.547
0.133
0.278
0.290
0.581
0.099
0.068
0.359
Test of no remaining nonlinearity
Transition variable
HPI_log_d1(t 1)
HPI_log_d1(t 2)
HPI_log_d1(t 3)
0.662
0.819
0.731
Aff
IR
Un
0.964
0.501
0.821
Parameter constancy test transition function
H1
H2
H3
0.683
0.546
0.558
ARCH-LM test
Chi
2
F
0.579
0.557
0.757
0.145
0.232
0.212
0.419
0.070
0.076
0.134
0.889
0.595
0.204
0.428
0.017
0.253
0.784
0.919
0.965
0.501
0.478
0.571
0.843
0.227
0.056
0.115
0.045
0.066
0.068
0.359
0.676
0.647
0.430
0.655
0.461
0.116
0.362
0.344
0.424
0.399
0.131
0.339
0.162
0.089
0.160
0.256
0.358
0.158
0.625
0.453
0.775
0.218
0.324
0.966
0.782
0.887
0.952
0.672
0.654
p
0.462
0.742
0.834
0.287
0.127
0.151
0.086
0.060
0.418
0.102
0.145
0.104
0.939
0.347
-value
0.121
0.406
0.716
0.984
0.983
0.748
0.544
0.202
0.323
0.254
0.257
0.358
0.331
0.152
0.946
0.290
0.399
0.531
0.188
0.929
0.993
0.999
0.235
0.210
Table IV.
Misspeci fi cations test
Notes: Panel A: The presented values are the p -values of the Ljung and Box test. The test is valid only for lags larger than the AR lag order. Panel B: the test is explained the Chi-Square for lag 3. RESID (i): indicates the LM statistics for the residuals for each lag rate and in fl ation rate. UK central regions were considered containing monthly data over the period 1998:1 to 2017:12.
This study has identi fi ed that the house prices incorporate nonlinear properties and, thus, using a linear model may lead to inaccurate estimates. The second signi fi cant fi nding was that the observations are equally distributed between the right-hand and left-hand tails of their respective exponential functions in both the South West and the North West regions of the UK and, hence, the ESTAR model outperforms the LSTAR. Other regions, however, showed asymmetric distribution on the tails; thus, the LSTAR model is used. One of the more signi fi cant fi ndings to emerge from this study is that housing affordability plays a crucial role in determining the house price dynamic in these regions. Finally, the misspeci fi cation tests indicated that the estimated model has no misspeci fi cation problems.
The fi ndings of this research provide insights for policy implications for policymakers and fi nancial regulators: instead of lowering the mortgage rate only, they should think of applying the loan to value policy to control the affordability issues and, thus, the fl uctuation of house prices.
Being limited to a few fundamentals, this study opens an avenue for further research to investigate this dynamic using in addition such demand-supply factors as land supply,
construction cost and loans made for housing. These fi ndings can also be used to examine whether other models such as ARIMA, exponential smoothing or arti fi cial neural networks can more accurately forecast housing prices.
Housing fundamentals
Notes
1. Although the same question has been addressed by
Bahmani-Oskooee and Ghodsi (2016)
, the authors set out the di ff erent ways to address such asymmetry. Moreover, they tend to examine such asymmetry between house prices and the Household income.
2. More details can be found in the UK House Price Index (HPI) Quality and Methodology
Information reports.
3. For more details, refer;
.
453
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Corresponding author
Huthaifa Alqaralleh can be contacted at: huthaifa89@mutah.edu.jo
Housing fundamentals
455
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