Open access peer-reviewed chapter

UK House Prices – Connectedness or Ripple Effect?

Written By

Taufiq Choudhry, Syed S. Hassan and Sarosh Shabi

Submitted: April 9th, 2021 Reviewed: June 12th, 2021 Published: August 9th, 2021

DOI: 10.5772/intechopen.98868

From the Edited Volume

Sustainable Housing

Edited by Amjad Almusaed and Asaad Almssad

Chapter metrics overview

97 Chapter Downloads

View Full Metrics


The performance of the housing market is currently considered a measure of economic activity. This research explores the connectedness vs. the ripple effect hypothesis in the current house pricing literature. Using linear causality and nonlinear causality tests we show significant bidirectional dependence between the London house prices and other UK regions’ house prices except for Northern Ireland and Wales in contrast to the existing literature where more evidence of ripple effect is reported. Furthermore, linear and non-linear forecasting tests back these results. This result has important implications for policymakers and investors.


  • Connectedness
  • House Prices
  • Nonlinearity
  • Ripple Effect

1. Introduction

The housing market is closely associated with consumer spending, implying that an increase in house prices boosts homeowners’ confidence. Similarly, a decline in house prices raises concerns for the homeowners due to the risk of losing the value of their property resulting in a reduction in spending and holding off personal investments. Thus, house prices have become an indicator of the economic performance of a country [1, 2]. Gallin [3] and Costello et al. [4] further show the importance of the role of housing in the economy and the effects of the underlying economic factors on house prices.

Transmission of regional housing prices within one single country has been researched widely over the years [5, 6, 7, 8, 9, 10, 11]. Regarding the UK, most of the literature focuses on the ripple effect – i.e. house prices initially originate from London and the South East and are then transmitted to the rest of the country [5, 9].1 This implies that the housing market in London is the main transmitter of shocks, but developments in other regions have no impact on London. Geographical proximity to London appears to be a decisive factor in relation to the ripple effect [5]. Holly et al. [12] and Cook and Watson [9] report that it takes more time for a shock in the housing market of London to propagate another UK region when this region is relatively distant from London. Further according to Holly et al. [12] any other UK region may have an impact on London prices; however, this impact is relatively short-term.

Connectedness is defined as the inter-linkage or dependence between two or more-time series [13]. The key differences between connectedness and ripple effect relate to implications in terms of Granger causality. Ripple effect only shows unidirectional shock transmission whereas Connecteness implies bidirectional Granger causality among the underlying variables. Zhu et al. [14] show that rising connectedness may be due to the information spillover considering investment aspects of the housing market which may come from geographically adjacent or economically linked regions. Our study contributes to the literature by empirically investigating the connectedness vs the ripple effectbetween the house prices in London and 13 other regions of the UK.

This chapter studies the price transmission mechanism driving the UK regional house prices using linear causality and the nonlinear Granger causality model proposed by Hiemstra and Jones [15], and the impulse response. Application of the non-linear causality test and impulse response on the UK housing market makes this study unique in the literature. Linear and non-linear forecasting tests are further conducted as a robustness check. This chapter, thus investigates the connectedness vs the ripple effectbetween the house prices in London and other regions of the UK. The key focus is to show that the changes in house prices in the UK are transmitted in a bidirectional manner between London and most of the country. According to Antonakakis et al. [5] this may have implications for investors seeking efficient diversification of investment across mortgage backed assets across various regions in the country. Further, identification of regional disparities can help policymakers and investors to achieve more balanced growth across the regions under study. This chapter is motivated by Antonakakis et al. [5] who claim that recent empirical evidence is rather inconclusive about the actual manifestation of the ripple effect and further by Cook and Watson [9] who advocate further research in this field.

Our results show bidirectional dependence between the London house prices and other regions’ prices except for Northern Ireland and Wales. Thus, we provide evidence of connectedness among the house prices in London and other regions of the UK. This result is confirmed by linear causality, the nonlinear causality and impulse response tests. Further empirical examination applying linear and non-linear forecasting tests back the linear and non-linear causality results.

The format of the chapter is the following. Section 2 describes the data and the empirical methodology employed. Section 3 discusses the results, and Section 4 presents the conclusion and implications.


2. Data description and methodology

2.1 Data

This study is based on UK regional housing quarterly prices’ data ranging from Q4–1973 to Q2–2018 obtained from the Nationwide website2 for 13 regions; these are London, East Anglia, East Midlands, North, North West, Northern Ireland, Outer Metropolitan, Outer South East, Scotland, South East, South West, Wales, West Midlands, and Yorkshire and Humberside. The different regions of the UK exhibit certain and unique characteristics. Northern Ireland is unique in the sense it has achieved an increased level of independence from the UK government since the late 1990s when it comes to social security provisions and the taxation of its housing market [16]. Further there is a strong link between the housing markets of the Northern Ireland and the Republic of Ireland [17]. Similarly Wales also achieved increased level of independence from the UK government after the 1997 devolution. East Midlands is a region with a very strong manufacturing in the country and is considered important when it comes to the production sector of the economy [5]. In contrast the West Midlands has a relatively poor economic conditions [18]. According to ONS [19], the South West is one of the UK regions with the highest rates of employment and economic activity. Given the uniqueness, strength and location relative to London of these other regions, it is possible for house prices in these regions to impact London house prices. Figure 1 shows the approximate location of the 13 regions relative to London. Figure 2 presents all the prices normalised to one at the start, including the UK. The close movements of all the indices are clearly visible. The rising prices across all regions during the mid-1980s and falling prices during the financial crisis of the late 2000s are clearly prominent.3

Figure 1.

Regional areas of the UK.

Figure 2.

Regional house prices.

This figure shows the regional house prices of the UK normalised to one at the start of the sample period; i.e. 1973Q3 – 2018Q2.

2.2 Bivariate and multivariate linear causality

In order to examine the linear relationship between various UK regional house prices with the London house prices, we consider the widely accepted vector autoregression (VAR) specification and the corresponding Granger causality test [20]. This approach enables us to assess whether there is a causal relationship between the variables in terms of time precedence and in which direction the causality flows. This will help us to test whether there is a ripple effect or connectedness between the prices UK regional house prices. The specification of the applied bivariate VAR model can be expressed as follows:


where, in our case, xtrepresents house prices in London in first differences, ytis the log-difference of the respective UK regional house prices. φ1and φ2are the constants, whereas αi, βi, γiand δi, i = 1,…,n, are the parameters for linear relationships between the underlying variables. Ripple effect hypothesis can be tested if only London house prices affect other regions, but not vice versa. On the other hand, connectedness can be shown if bidirectional causality exists between London house prices and other regions. In the next sections, we present the nonlinear approach adopted in our study and describe the relevant tests employed.

2.3 Bivariate nonlinear causality

Arrival of new information and dynamics of economic fluctuations cause changes in the security prices. Campbell et al. [21] describe these processes as nonlinear. Furthermore, many other researchers have highlighted the existence of nonlinear features in macroeconomic variables and models [22, 23, 24, 25, 26, 27]. Hiemstra and Jones [15] reported nonlinear causality in financial variables using a correlation integral based approach. Subsequent research papers have provided more evidence on nonlinear modelling of various financial variables [28, 29, 30, 31, 32, 33, 34]. Market frictions such transaction cots and information asymmetries could be associated with the nonlinear dynamics and can cause non-convergence towards the long-term equilibrium. Anderson [35] reports that the transaction costs in the asset pricing literature could be one of the factor for disequilibrium error. He further demonstrates that nonlinear models which consider the transaction costs often outperform the parametric models. Some of the studies have identified heterogenous investors’ beliefs as one of the sources for nonlinearities in macro-financial time series [36]. This heterogeneity exisit mainly due to differences in investor horizonds, risk profiles [37] and herding behaviour [38]. Due to the above, we study the Granger causality in using nonlinear framework.

Correlation integral based nonlinear Granger causality was introduced by Baek and Brock [39] and was further developed by Hiemstra and Jones [15]. This research studies nonlinear causality between the UK regional house prices, using the Hiemstra and Jones [15] test statistic.

Consider two stationary time series Xtand Yt,for t = 1,2,…… An m-length lead vector of Xtis denoted by Xtmwheras XtLxLxand YtLyLyare lag vectors of Xtand Ytas shown below:


Using the Hiemstra and Jones [15] framework, Y does not strictly Granger cause X if:


Porbablity and maximum norm in Eq. (4) are denoted by Pr(.) and ||∙||, respectively. The conditional probability that the deviation between two arbitrary lead vectors of Xtof m-length is less than e, while deviation between the corresponding lag vectors of XtLxLxand YtLyLyis also less then e, is shown on the left hand side of the Eq. (4). The right hand side represent the conditional probability that two arbitrary m-length lead vectors of Xtare with a distance of e of each other, assuming that the corresponding lag vectors i.e. XtLxLxand XsLxLxare also within a distance e of each other. For all regions, Xtrepresents the changes in the London housing prices and Ytrepresents the changes in the housing prices in other regions. Therefore, if Eq. (4) is true, this implies that the changes in the London housing prices do not affect the respective changes in regional housing prices. Nonlinear causality test proposed by Hiemstra and Jones [15] is based on the conditional probabilities using corresponding ratios of joint probabilities:


where joint probabilities are denoted as C1, C2, C3 and C4.4 Assuming XtandYtare strictly stationary and weakly dependent, if Ytdoes not strictly Granger cause Xtthen,


Details on the definition and the estimator of the variance σ2mLxLyeare provided in an appendix of Hiemstra and Jones [15].


3. Results

Table 1 shows the results for linear Granger causality based on Eqs. (1) and (2). The results show that London predominantly affects the regional house prices except for Northern Ireland, Outer Metropolitan and Outer East, and Wales. Similarly, regional house prices affect London house prices in seven out of 13 regions with some of these showing a feedback effect from or connectedness to the changes in the London house prices. No evidence of price feedback is found in any direction for Northern Ireland and Wales. The Northern Ireland and Wales results may be due to the increased independence of these regions from the UK government and the far distance location from London. Scotland prices are affected by London but not vice versa. Surprisingly Outer Metropolitan and Outer East affect the London prices but not vice versa. These results confirming connectedness between the house prices may be due to geographically adjacent or economically linked regions.

RegionsLondon ➔ RegionRegion ➔ London
East Anglia16.85**30.12***
East Midlands27.69***0.91
North West28.53***4.52
Northern Ireland11.309.87
Outer Metropolitan12.3394.37***
Outer East5.2449.02***
South East20.68***56.11***
South West24.33***43.19***
West Midlands50.03***24.85***
Yorkshire and Humberside14.65*33.97***

Table 1.

Linear causality results.

Notes: Table 1 shows linear Granger causality results based on Eqs. (1) and (2). ***, ** and * imply significant causality at the 1%, 5% and 10% levels, respectively.

Table 2 shows results for the nonlinear Granger causality. This test is applied to the standardised residuals obtained from the VAR models after filtering any linear dependence among the underlying variables. The null hypothesis of no nonlinear Granger causality has been rejected in most of the cases except for Northern Ireland and Wales. This shows significant evidence of nonlinear interdependence among the housing prices of London and other regions in the UK. We report bidirectional dependence between London and the other regions except for Northern Ireland and Wales. These results evince the nonlinear feedback effect or connectedness. No evidence of any causality in any direction is found between London and Wales/Northern Ireland.

RegionsLondon ➔ RegionRegion ➔ London
East Anglia12.651***13.598***
East Midlands11.193***12.679***
North West13.492***13.734***
Northern Ireland0.200.926
Outer Metropolitan13.04***12.817***
Outer East11.151***13.617***
South East14.329***14.621***
South West12.397***13.339***
West Midlands12.749***13.915***
Yorkshire and Humberside13.231***13.422***

Table 2.

Nonlinear causality results.

Notes: Table 1 shows test-statistic proposed by Hiemstra and Jones [27] using Eq. (2). ***, ** and * imply significant causality at the 1%, 5% and 10% levels, respectively.

Further evidence is presented by means of the impulse response function. Figure 3 shows the impulse response function to one-standard-deviation innovations to the housing prices originating in London and other regions, respectively. These graphs can be interpreted into two categories – i.e. i) house prices in other regions responding to the shocks to London house prices and ii) London house prices responding to the shocks occurring in other regions in the UK. Firstly, a one per cent shock to the London house prices shows an immediate impact on house prices in most of the regions within a range from 1.5–3% – e.g., East Anglia, East Midlands, West Midlands, Outer Metropolitan, Outer Southeast, South West and Yorkshire. Geographically speaking, with the exception of Yorkshire, these regions are close to London. In other regions, although the shocks are statistically significant, they are smaller in magnitude. Interestingly, in the second category, innovations that originate in regions like East Anglia, Outer Metropolitan, Outer Southeast, South West, West Midlands and Yorkshire affect the London house prices with shocks in the range of 1% to 2.5%. This shows that London remains the central focus in the overall UK housing market and any shocks occurring here transmit to most of the regions. However, local shocks in other regions also show a spillover effect on London house prices. Antonakakis et al. [5] also report that East Anglia, Outer South East and South West are the major transmitters of regional shocks.

Figure 3.

Impulse response functions (response to Cholesky one S.D. innovations).

The evidence of connectedness presented here implies that although London is important from the housing market perspective, other regions also transmit the shocks back to the London market. This may be due to the information spillover (investor expectations) between different regions [14] although this research does not explicitly test the information hypothesis. By taking into consideration the impact of the bidirectional spillover effect of price, appropriate regulations and policies for the UK housing sector should be formulated. The results further imply the importance of house prices in other regions when investing in houses in London, and vice versa.


4. Robustness checks and further empirical evidence

4.1 Linear and nonlinear forecasting regressions

This section provides additional empirical evidence and explores the nature of the relationship between London and other UK regional house prices. Therefore, it complements the results of Granger causality and serves as a useful robustness check. To this end, we initially focus on the following forecasting regression:


where yt + h refers to the changes in the London house prices, yt+h=400h+1lnyt+hYt,with forecast horizon, h > 0 and x represents the changes in the regional house prices. The null hypothesis of β =0 is tested here to observe the predictability of changes in the London house prices using the other regional house prices. The corresponding results for h = 1 are presented in Table 3.

RegionsLondon ➔ RegionRegion ➔ London
Adj. R2Adj. R2
East Anglia13.75**0.4817.32***0.45
East Midlands19.40***0.5110.62*0.43
North West12.91**0.5810.69*0.44
Northern Ireland13.04**0.365.220.45
Outer Metropolitan4.410.6841.91***0.52
Outer East8.790.6324.39***0.47
South East13.79**0.5119.63***0.43
South West14.41**0.5822.91***0.47
West Midlands23.66***0.4113.67**0.44
Yorkshire and Humberside12.32*0.4617.62***0.46

Table 3.

Linear forecasting results.

Notes: This table presents the results from the linear forecasting regressions described in Section 4.1 (Eq. (7)). ***, ** and * imply significant causality at the 1%, 5% and 10% levels, respectively.

We report that the other regional house prices are a significant short-term predictor of the changes in the London house prices in most of the cases, with the exception of Northern Ireland, Scotland and Wales. These forecasting results reaffirm and strengthen the evidence against the ‘ripple effect’ hypothesis in the literature.

We further extend the forecasting model to show more evidence of nolinear relationship between the regional house prices. For this purpose, we use smooth-transition threshold (STR) models [40, 41, 42, 43, 44]. Simple threshold model can trigger an abrupt change in the parameter values, however, STR models are capable to allow smooth transition between different regime states. Following Smooth Transition Threshold model is used:


Fytdis the transition function and ytdis the transition variable, whereas remaining variables are as defined in Eq. (7). Based on the existing literature, we first consider the logistic form of transition function (LSTR) as shown in Eq. (9) [40, 42, 43, 44]:


Where λ, d and c are the smoothing, delay and transition parameters, respectively. This function is monotonically increasing in ytd. Note that when λ+, Fytdbecomes a Heaviside function: Fytd=0when ytdcand Fytd=1when ytd>c.

Monotonic transition may not always be successful in empirical applications. Therefore, we consider exponential transition function (ESTR) [42, 43, 44]:


Here, the transition function is symmetric around c. This model emplies that expansion and contraction have similar dynamics while the these vary for the middle ground [44]. STR module can have some issues involving the smoothing parameter λ, therefore, we follow the literature and using variation of the transition varible λis scaled in both of the models [44].

In this case, the transition function is symmetric around c. The ESTR model implies that contraction and expansion have similar dynamic structures while the dynamics of the middle ground differ [43, 44]. Hence, we have the obtain the following versions of transition functions, respectively:


Table 4 presents the results of the LSTR and the ESTR models testing the changes in London house prices as a predictor for changes in the regional house prices. In the LSTR model results, the estimated transition parameter c, which marks the half-way point between the two regimes, is significantly different from zero in most of the cases. Moreover, we observe that most of the estimated betas are positive and significant (at 1% and 5% levels, depending on the case), suggesting that higher regional house prices boost London house prices in the following quarter. Further, the estimates of φ1 in the upper regime significance are found in nine out of 13 regions, revealing the importance of regional house prices as an explanatory variable for changes in the London house prices. Insignificant results are found for Northern Ireland, Scotland and Wales. Table 5 shows the results based on the LSTR and ESTR models confirming as expected that changes in the London house prices are a significant predictor of house price changes in other regions in the UK.

Countryαßφ0φ1λcAdj. R2
Panel – I:Exponential Smooth Transition Threshold Model (ESTR)
East Anglia32.09***11.53***45.11***−9.21***0.071**−9.23***0.485
East Midlands21.79**3.17**55.17***23.98***0.9721.13***0.467
North West13.31***19.69***−11.49***18.83***9.71***−19.71***0.482
Northern Ireland4.633.51*5.092.450.089−9.28***0.435
Outer Metropolitan51.21**8.6**57.34**8.26*0.046**−49.280.534
Outer East0.040.9911.450.050.00644.630.486
South East40.84−55.72***40.72***55.54***0.10***−60.74**0.447
South West7.315.30**−25.71**−5.41**0.035**13.06**0.491
West Midlands5.674.21***6.523.77**0.068**−7.30**0.469
Yorkshire and Humberside11.319.12***5.174.11**0.533*−14.30**0.478
Panel – II:Logistic Smooth Transition Threshold Model (LSTR)
East Anglia−5.17**6.34***12.03*2.24**0.091**2.38**0.485
East Midlands11.27*8.17***−25.35***−16.520.012***23.52***0.476
North West2.19***0.89**−11.5***−6.07***0.03***3.73***0.489
Northern Ireland0.910.13−2.19−2.670.04915.47***0.462
Outer Metropolitan4.15**8.95**0.376.93*0.048**1.94**0.61
Outer East5.193.82−2.795.140.013−1.0090.548
South East6.848.9**−6.02−5.14**0.064**1.66***0.473
South West−18.64***7.14***16.97*−4.27**0.015***−42.33***0.482
West Midlands4.1**7.88***−1.220.31***0.013***39.89**0.48
Yorkshire and Humberside3.512.14*2.180.77**0.002***11.17***0.479

Table 4.

Nonlinear forecasting results (regional house prices➔London house prices).

Countryαßφ0φ1λcAdj. R2
Panel – I:Exponential Smooth Transition Threshold Model (ESTR)
East Anglia−4.75**34.06*9.30***−5.6*27.17*3.19***0.2559
East Midlands−6.59***−31.49**11.58***41.47***21.57**−3.60**0.397
North West−6.09***−86.09***10.5***10.68***11.65−10.870.411
Northern Ireland−0.064***18.8867.44*11.42***−23.3956.830.397
Outer Metropolitan0.0152***35.54***−0.061***−0.056***85.12−49.280.264
Outer East−0.071***10.4215.0712.83***−14.6524.660.408
South East−8.13***−4.689.72***21.1315.47−5.68***0.4107
South West−6.94***−5.2612.21***11.6622.46−3.4939.70
West Midlands10.40***22.31−11.27***−45.6322.33−8.76***40.67
Yorkshire and Humberside−7.05***13.4173.05*−34.1419.42−0.037**40.35
Panel – II:Logistic Smooth Transition Threshold Model (LSTR)
East Anglia−0.00250.170.0493***−0.2827.17*0.031***0.4028
East Midlands−0.0063−0.0870.054***0.17317.86*0.0299***0.3985
North West−0.0064−0.257*0.0522***0.557*15.70*0.027**0.4123
Northern Ireland−0.00440.07050.0513***−0.116923.905*0.0303***0.3999
Outer Metropolitan−0.00560.05080.056***−0.26320.128**0.0306***0.40
Outer East−0.00340.2950.056***−0.6723.52**0.032***0.408
South East−0.0035−0.01170.072***−0.6127.13*0.059***0.33
South West−0.0061−0.03950.057***−0.04718.62**0.031***0.398
West Midlands−0.0081−0.220.05***0.2215.34*0.0284***0.408
Yorkshire and Humberside−0.006−0.0210.05−0.2017.05*0.0315***0.405

Table 5.

Nonlinear forecasting results (London house prices➔regional house prices).

Results for the estimated ESTR models are very similar to the LSTR results. This reaffirms the significance of the regional house prices as a short-term predictor of future changes in the London house prices in a nonlinear context and complements the previously reported results under the linear and nonlinear frameworks. Thus, ESTR and LSTR results reinforce the idea that the regional house prices have a feedback effect or connectedness to the London house prices. This shows evidence against the ripple effect where a unidirectional impact of changes in the London house prices on other regions is reported.


5. Conclusion and implications

This chapter investigates the transmission mechanism driving the UK regional house prices using the linear causality model, the nonlinear Granger causality model, and the impulse response process. We employ quarterly housing prices data ranging from Q4–1973 to Q2–2018 from 13 regions from the UK. Results show bidirectional dependence between the London prices and other regions’ prices except for Northern Ireland and Wales. This result is confirmed by the linear causality, the nonlinear causality and the impulse response tests. Further empirical examination applying linear and non-linear forecasting tests support the linear and non-linear causality results. Thus, we provide that London is not always important for the other UK regions over time, as well as that London itself may also receive shocks from other regions. Impulse response shows that London remains the central focus in the overall UK housing market and any shocks occurring here transmit to most of the regions. However, local shocks in other regions also show a spill over effect on London house prices. Identification of regional disparities can help policymakers to achieve a more balanced growth across the country. These results underline the importance of establishing appropriate regulations and stabilisation policies in the housing sector of the economy. Further, the interdependence between regional housing prices might provide significant insight regarding efficient diversification of investments across mortgage-backed securities.

JEL Classification: R2, R21, R31


  1. 1. Attanasio, O., A. Leicester, and M. Wakefield,Do house prices drive consumption growth? The coincident cycles of house prices and consumption in the UK.Journal of the European Economic Association, 2011.9(3): p. 399-435.
  2. 2. Mishkin, F.S.,Financial markets, institutions, and money. 2007: HarperCollins Publishers.
  3. 3. Gallin, J.,The long‐run relationship between house prices and income: evidence from local housing markets.Real Estate Economics, 2006.34(3): p. 417-438.
  4. 4. Costello, G., P. Fraser, and N. Groenewold,House prices, non-fundamental components and interstate spillovers: The Australian experience.Journal of Banking & Finance, 2011.35(3): p. 653-669.
  5. 5. Antonakakis, N., et al.,The dynamic connectedness of UK regional property returns.Urban Studies, 2018.55(14): p. 3110-3134.
  6. 6. Churchill, S.A., J. Inekwe, and K. Ivanovski,House price convergence: Evidence from Australian cities.Economics Letters, 2018.170: p. 88-90.
  7. 7. Cook, S.,The convergence of regional house prices in the UK.Urban studies, 2003.40(11): p. 2285-2294.
  8. 8. Cook, S.,Detecting long‐run relationships in regional house prices in the UK.International Review of Applied Economics, 2005.19(1): p. 107-118.
  9. 9. Cook, S. and D. Watson,A new perspective on the ripple effect in the UK housing market: Comovement, cyclical subsamples and alternative indices.Urban Studies, 2016.53(14): p. 3048-3062.
  10. 10. Gupta, R., C. Andre, and L. Gil-Alana,Comovement in Euro area housing prices: A fractional cointegration approach.Urban Studies, 2015.52(16): p. 3123-3143.
  11. 11. Gupta, R. and S.M. Miller,The time-series properties of house prices: A case study of the Southern California market.The Journal of Real Estate Finance and Economics, 2012.44(3): p. 339-361.
  12. 12. Holly, S., M.H. Pesaran, and T. Yamagata,The spatial and temporal diffusion of house prices in the UK.Journal of urban economics, 2011.69(1): p. 2-23.
  13. 13. Billio, M., et al.,Econometric measures of connectedness and systemic risk in the finance and insurance sectors.Journal of financial economics, 2012.104(3): p. 535-559.
  14. 14. Zhu, B., R. Füss, and N.B. Rottke,Spatial linkages in returns and volatilities among US regional housing markets.Real Estate Economics, 2013.41(1): p. 29-64.
  15. 15. Hiemstra, C. and J.D. Jones,Testing for linear and nonlinear Granger causality in the stock price‐volume relation.The Journal of Finance, 1994.49(5): p. 1639-1664.
  16. 16. McKee, K., J. Muir, and T. Moore,Housing policy in the UK: The importance of spatial nuance.Housing Studies, 2017.32(1): p. 60-72.
  17. 17. Stevenson, S.,House price diffusion and inter‐regional and cross‐border house price dynamics.Journal of Property Research, 2004.21(4): p. 301-320.
  18. 18. Bailey, D. and N. Berkeley,Regional responses to recession: The role of the West Midlands Regional Taskforce.Regional Studies, 2014.48(11): p. 1797-1812.
  19. 19. ONS,Regional Labour Market Statistics in the UK.2017, Office for National Statistics (ONS).
  20. 20. Granger, C.W.,Investigating causal relations by econometric models and cross-spectral methods.Econometrica: journal of the Econometric Society, 1969: p. 424-438.
  21. 21. Campbell, J.Y., A.W. Lo, and A.C. MacKinlay,The econometrics of financial markets. 2012: princeton University press.
  22. 22. Barnett, W.A., et al.,A single-blind controlled competition among tests for nonlinearity and chaos.Journal of econometrics, 1997.82(1): p. 157-192.
  23. 23. Hsieh, D.A.,Chaos and nonlinear dynamics: application to financial markets.The journal of finance, 1991.46(5): p. 1839-1877.
  24. 24. Kahneman, D. and A. Tversky,Prospect theory: An analysis of decision under risk.Econometrica, 1979.47(2): p. 363-391.
  25. 25. Keynes, J.M.,The general theory of employment, interest, and money. 1936: Springer.
  26. 26. Shiller, R.J.,Macro markets: creating institutions for managing society’s largest economic risks. 1994: OUP Oxford.
  27. 27. Shiller, R.J.,Irrational Exuberance. 2005: Princeton University Press.
  28. 28. Bekiros, S.D.,Exchange rates and fundamentals: Co-movement, long-run relationships and short-run dynamics.Journal of Banking & Finance, 2014.39: p. 117-134.
  29. 29. Bekiros, S.D. and C.G. Diks,The nonlinear dynamic relationship of exchange rates: Parametric and nonparametric causality testing.Journal of macroeconomics, 2008.30(4): p. 1641-1650.
  30. 30. Bekiros, S.D. and C.G. Diks,The relationship between crude oil spot and futures prices: Cointegration, linear and nonlinear causality.Energy Economics, 2008.30(5): p. 2673-2685.
  31. 31. Chen, A.-S. and J. Wuh Lin,Cointegration and detectable linear and nonlinear causality: analysis using the London Metal Exchange lead contract.Applied Economics, 2004.36(11): p. 1157-1167.
  32. 32. Diks, C. and V. Panchenko,A new statistic and practical guidelines for nonparametric Granger causality testing.Journal of Economic Dynamics and Control, 2006.30(9-10): p. 1647-1669.
  33. 33. Shin, Y., B. Yu, and M. Greenwood-Nimmo,Modelling Asymmetric Cointegration and Dynamic Multipliers in a Nonlinear ARDL Framework, inFestschrift in Honor of Peter Schmidt: Econometric Methods and Applications, R.C. Sickles and W.C. Horrace, Editors. 2014, Springer New York: New York, NY. p. 281-314.
  34. 34. Silvapulle, P. and J.-S. Choi,Testing for linear and nonlinear Granger causality in the stock price-volume relation: Korean evidence.The Quarterly Review of Economics and Finance, 1999.39(1): p. 59-76.
  35. 35. Anderson, H.M.,Transaction costs and nonlinear adjustment towards equilibrium in the US Treasury Bill market.Oxford Bulletin of Economics and Statistics, 1997.59(4): p. 465-484.
  36. 36. Brock, W. and B.D. LeBaron,A dynamic structural model for stock return volatility and trading volume. 1995, National Bureau of Economic Research Cambridge, Mass., USA.
  37. 37. Peter, E.E.,Fractal market analysis: applying chaos theory to investment and economics. Vol. 24. 1994: John Wiley & Sons.
  38. 38. Lux, T.,Herd behaviour, bubbles and crashes.The economic journal, 1995.105(431): p. 881-896.
  39. 39. Baek, E. and W. Brock,A general test for nonlinear Granger causality: Bivariate model.Iowa State University and University of Wisconsin at Madison Working Paper, 1992.
  40. 40. Chan, K.S. and H. Tong,On estimating thresholds in autoregressive models.Journal of time series analysis, 1986.7(3): p. 179-190.
  41. 41. Granger, C.W. and T. Terasvirta,Modelling non-linear economic relationships.OUP Catalogue, 1993.
  42. 42. McMillan, D.G.,Non‐linear predictability of UK stock market returns.Oxford Bulletin of Economics and Statistics, 2003.65(5): p. 557-573.
  43. 43. Teräsvirta, T.,Specification, estimation, and evaluation of smooth transition autoregressive models.Journal of the american Statistical association, 1994.89(425): p. 208-218.
  44. 44. Terasvirta, T. and H.M. Anderson,Characterizing nonlinearities in business cycles using smooth transition autoregressive models.Journal of applied econometrics, 1992.7(S1): p. S119-S136.


  • Antonakakis et al. [5] and Cook and Watson [9] provide an excellent survey of the papers in literature that investigate the ripple effect in the UK housing market. Cook and Watson [9] also provide a good discussion of the different empirical methods applied in these papers.
  • ADF test [20] and KPSS test [21] show that the changes in the house prices (first difference series) are stationary. Results for these tests are available from the authors on request.
  • See Hiemstra and Jones [15] for further details on correlation integrals and joint probabilities.

Written By

Taufiq Choudhry, Syed S. Hassan and Sarosh Shabi

Submitted: April 9th, 2021 Reviewed: June 12th, 2021 Published: August 9th, 2021