This study unfurls the non-linear behaviour of regional house prices in the United Kingdom by employing quarterly observations spanning the period 1992Q1–2017Q4. Our enquiry aims at examining UK house prices within a multivariable framework and, more specifically, by employing panel quantile regression with fixed effect. In brief, the empirical findings obtained from these methodologies indicate that the UK house prices are influenced at lower and upper quantiles, and that precisely they are influenced by variables such as income, private sector housing starts and employment. We highly support that there is a strong heterogeneity among UK regions and that asymmetry may be one of the keys of the ripple effect. Particularly, the income shows a positively significant performance at lower and higher regional house prices. Moreover, the variables private sector housing starts and employment rate are statistically significant for house prices. Leveraging for first-time panel quantile regression for the case of regional house prices in the UK, policymakers will have a profound understanding of regional house prices.
AbbottA., & De VitaG. (2013). Testing for long-run convergence across regional house prices in the UK: A pairwise approach. Applied Economics, 45(10), 1227–1238.
2.
AlexanderC., & BarrowM. (1994). Seasonality and cointegration of regional house prices in the UK. Urban Studies, 31(10), 1667–1689.
3.
AlexanderM., HardingM., & LamarcheC. (2011). Quantile regression for time-series-cross-section data. International Journal of Statistics and Management System, 6(1–2), 47–72.
4.
AshworthJ., & ParkerS. C. (1997). Modelling regional house prices in the UK. Scottish Journal of Political Economy, 44(3), 225–246.
5.
CanayI. A. (2011). A simple approach to quantile regression for panel data. Econometrics Journal, 14(3), 368–386.
6.
CookS. (2003). The convergence of regional house prices in the UK. Urban Studies, 40(11), 2285–2294.
7.
CookS. (2005). Detecting long-run relationships in regional house prices in the UK. International Review of Applied Economics, 19(1), 107–118.
8.
CookS. (2012). β-convergence and the cyclical dynamics of UK regional house prices. Urban Studies, 49(1), 203–218.
9.
CookS., & HollyS. (2000). Statistical properties of UK house prices: An analysis of disaggregated vintages. Urban Studies, 37(11), 2045–2051.
10.
CookS., & VougasD. (2009). Unit root testing against an ST–MTAR alternative: Finite-sample properties and an application to the UK housing market. Applied Economics, 41(11), 1397–1404.
11.
CookS., & WatsonD. (2016). A new perspective on the ripple effect in the UK housing market: Comovement, cyclical subsamples and alternative indices. Urban Studies, 53(14), 3048–3062.
12.
DametteO., & DelacoteP. (2012). On the economic factors of deforestation: What can we learn from quantile analysis? Economic Modelling,29(6), 2427–2434.
13.
DrakeL. (1993). Modelling UK house prices using cointegration: An application of the Johansen technique. Applied Economics, 25(9), 1225–1228.
14.
DrakeL. (1995). Testing for convergence between UK regional house prices. Regional Studies, 29(4), 357–366.
15.
GalvaoA. F. (2011). Quantile regression for dynamic panel data with fixed effects. Journal of Econometrics, 164(1), 142–157.
16.
GalvaoA. F., & KatoK. (2016). Smoothed quantile regression for panel data. Journal of econometrics, 193(1), 92–112.
17.
GiussaniB., & HadjimatheouG. (1990). Econometric model of regional house prices in the UK. Kingston Polytechnic.
HardingM., & LamarcheC. (2009). A quantile regression approach for estimating panel data models using instrumental variables. Economics Letters, 104(3), 133–135.
20.
HardingM., & LamarcheC. (2017). Penalized quantile regression with semiparametric correlated effects: An application with heterogeneous preferences. Journal of Applied Econometrics, 32(2), 342–358.
21.
HollyS., & JonesN. (1997). House prices since the 1940s: Cointegration, demography and asymmetries. Economic Modelling, 14(4), 549–565.
22.
HollyS.PesaranM. H. & YamagataT. (2011). The spatial and temporal diffusion of house prices in the UK. Journal of Urban Economics, 69(1), 2–23.
23.
HolmesM. J. (2007). How convergent are regional house prices in the United Kingdom? Some new evidence from panel data unit root testing. Journal of Economic and Social Research,9(1), 1–17.
24.
HolmesM. J., & GrimesA. (2008). Is there long-run convergence among regional house prices in the UK? UrbanStudies, 45(8), 1531–1544.
25.
HudsonC., HudsonJ., & MorleyB. (2018). Differing house price linkages across UK regions: A multi-dimensional recursive ripple model. Urban Studies, 55(8), 1636–1654.
26.
ImK. S., PesaranM. H., & ShinY. (2003). Testing for unit roots in heterogeneous panels. Journal of Econometrics, 115(1), 53–74.
27.
KatoK., Galvao JrA. F., & Montes-RojasG. V. (2012). Asymptotics for panel quantile regression models with individual effects. Journal of Econometrics, 170(1), 76–91.
28.
KoenkerR. (2004). Quantile regression for longitudinal data. Journal of Multivariate Analysis, 91(1), 74–89.
29.
KoenkerR., & BassettG. (1978). Regression quantiles. Econometrica: Journal of the Econometric Society, 46(1), 33–50.
30.
KoenkerR., & BassettG. (1982). Tests of linear hypotheses and l" 1 estimation. Econometrica: Journal of the Econometric Society, 50(6), 1577–1583.
31.
LamarcheC. (2010). Robust penalized quantile regression estimation for panel data. Journal of Econometrics, 157(2), 396–408.
32.
LamarcheC. (2011). Measuring the incentives to learn in Colombia using new quantile regression approaches. Journal of Development Economics, 96(2), 278–288.
33.
LancasterT. (2000). The incidental parameter problem since 1948. Journal of Econometrics, 95(2), 391–413.
34.
MacDonaldR., & TaylorM. P. (1993). Regional house prices in Britain: Long-run relationships and short-run dynamics. Scottish Journal of Political Economy, 40(1), 43–55.
35.
MeenG. (1996). Spatial aggregation, spatial dependence and predictability in the UK housing market. Housing Studies, 11(3), 345–372.
36.
MeenG. (1999). Regional house prices and the ripple effect: A new interpretation. Housing Studies, 14(6), 733–753.
37.
MeenG. (2011). A long-run model of housing affordability. Housing Studies, 26(7–8), 1081–1103.
38.
ParamatiS. R., & EduardoR. (2019). Does financial inclusion drive house prices in the major economies?Evidence from augmented mean group estimator (Evidence). Augmented Mean Group Estimator.
39.
PesaranM. H., & TimmermannA. (1992). A simple nonparametric test of predictive performance. Journal of Business & Economic Statistics, 10(4), 461–465.
40.
PetersonW., HollyS., & GaudoinP. (2002). Further work on an economic model of the demand for social housing. Department for the Environment, Transport and the Regions.
41.
SáF. (2015). Immigration and house prices in the UK. The Economic Journal, 125(587), 1393–1424.
42.
SaizA., & WachterS. (2011). Immigration and the neighborhood. American Economic Journal: Economic Policy, 3(2), 169–188.
43.
ShenghuaY. U., & PanZ. H. A. O. (2018). Analysis of influencing factors of housing price based on panel quantile regression. The Theory and Practice of Finance and Economics, (5), 19.
44.
StaikosD., & XueW. (2017). What drives housing prices, rent and new construction in China. International Journal of Housing Markets and Analysis, 10(1).
45.
ZhuH., LiZ., & GuoP. (2018). The impact of income, economic openness and interest rates on housing prices in China: Evidence from dynamic panel quantile regression. Applied Economics, 50(38), 4086–4098.
