Do Spatial Effects Drive House Prices Away from the Long-run Equilibrium?

Author/s: Le Ma, Chunlu Liu

Date Published: 1/01/2014

Published in: Volume 20 - 2014 Issue 1 (pages 13 - 29)

Abstract

Long-run equilibrium of house prices has been investigated by researchers in multiple countries. The identification of this equilibrium not only provides references against contemporary house price levels, but also contributes to creation of stable-development policies and healthy investment strategies. However, there is little research investigating the factors that drive house prices away from the long-run equilibrium. Based on a framework of the conventional stationarity test process, this research develops a panel regression model and a spatial regression model to investigate the roles of spatial heterogeneity and correlations on house prices preceding the long-run equilibrium, respectively. Housing data generated from the capital cities in Australia are used to illustrate the models. Spatial effects can have a strong influence in the long-run performance of house prices, while the short-run performance of house prices is not influenced by the spatial effects.

Download Full Article

Download the Full Article PDF

14445921.2014.11104384.pdf 14445921.2014.11104384.pdf (435kB)

Keywords

House Price Indices - Long-Run Equilibrium - Panel Dynamic Regression - Spatial Panel Regression

References

  • Australian Bureau of Statistics 2005, Renovating the established house price index, Cat. No. 6417.0, ABS, Canberra
  • Australian Bureau of Statistics 2013, House price indexes: eight capital cities, Cat. no. 6416.0, ABS, Canberra
  • Anselin, L and Lozano-Gracia, N 2008, ‘Errors in variables and spatial effects in hedonic house price models of ambient air quality’, Empirical Economics, Vol. 34, No. 1, pp. 5-34
  • Arellano, M and Bover, O 1995, ‘Another look at the instrumental variable estimation of error-components models’, Journal of Econometrics, Vol. 68, No. 1, pp. 29 - 51
  • Beenstock, M and Felsenstein, D 2007, ‘Spatial vector autoregressions’, Spatial Economic Analysis, Vol. 2, No. 2, pp. 167 - 96
  • Bourassa, SC, Hoesli, M and Peng, VS 2003, ‘Do housing submarkets really matter?’, Journal of Housing Economics, Vol. 12, No. 1, pp. 12-28
  • Cook, S and Thomas, C 2003, ‘An alternative approach to examining the ripple effect in UK house prices’, Applied Economics Letters, Vol. 10, No. 13, pp. 849 – 51
  • Dicky, DA and Fuller, WA 1979, ‘Distribution of the estimators for autoregressive time series with a unit root’, Journal of the American Statistical Association, Vol. 74, No. 336, pp. 427-31
  • Drake, L 1995, ‘Testing for convergence between UK regional house prices’, Regional Studies, Vol. 29, No. 4, pp. 357-66
  • Fingleton, B 2008, ‘A generalized method of moments estimator for a spatial panel model with an endogenous spatial lag and spatial moving average errors’, Spatial Economic Analysis, Vol. 3, No. 1, pp. 27 – 44
  • Greene, WH 2002, Econometric Analysis, Pearson Education, Inc., New Jersey
  • Holly, S, Hashem Pesaran, M and Yamagata, T 2011, ‘The spatial and temporal diffusion of house prices in the UK’, Journal of Urban Economics, Vol. 69, No. 1, pp. 2-23
  • Holmes, MJ 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, Vol. 9, No. 1, pp. 1 - 17
  • Holmes, MJ and Grimes, A 2008, ‘Is there long-run convergence among regional house prices in the UK?’, Urban Studies, Vol. 45, No. 8, pp. 1531 -44
  • Hsiao, C 2007, ‘Panel data analysis - advantages and challenges’, TEST, Vol. 16, No. 2, pp. 1 - 22
  • Im, KS, Pesaran, MH and Shin, Y 2003, ‘Testing for unit roots in heterogeneous panels’, Journal of Econometrics, Vol. 115, No. 1, pp. 53-74
  • Judson, RA and Owen, AL 1999, ‘Estimating dynamic panel data models: a guide for macroeconomists’, Economics Letters, Vol. 65, No. 1, pp. 9-15
  • Levin, A, Lin, C-F and James Chu, C-S 2002, ‘Unit root tests in panel data: asymptotic and finite-sample properties’, Journal of Econometrics, Vol. 108, No. 1, pp. 1-24
  • Liu, C, Ma, L, Luo, Z and Picken, D 2009, ‘An interdependence analysis of Australian house prices using variance decomposition’, International journal of Housing Markets and Analysis, Vol. 2, No. 3, pp. 218 - 32
  • Ma, L and Liu, C 2010, ‘The decomposition of housing market variations: a panel data approach’, International journal of Housing Markets and Analysis, Vol. 3, No. 1, pp. 6 - 16
  • Ma, L and Liu, C 2013, ‘Ripple effects of house prices: considering spatial correlations in geography and demography’, International Journal of Housing Markets and Analysis, Vol. 6, No. 3, pp. 284 - 299
  • MacDonald, R and Taylor, MP 1993a, ‘Regional house prices in Britain: long-run relationships and short-run dynamics’, Scottish Journal of Political Economy, Vol. 40, No. 1, pp. 43-55
  • MacDonald, R and Taylor, MP 1993b, ‘Regional house prices in Britian: long-run relationships and short-run dynamics’, Scottish Journal of Political Economy, Vol. 40, No. 1, pp. 43-55
  • Meen, G 1996, ‘Spatial aggregation, spatial dependence and predictability in the UK housing market’, Housing Studies, Vol. 11, No. 3, pp. 345-72
  • Meen, G 1999, ‘Regional house prices and ripple effects: a new interpretation’, Housing Studies, Vol. 14, No. 6, pp. 335-46
  • Pace, RK, Barry, R, Clapp, JM and Rodriquez, M 1998a, ‘Spatiotemporal autoregressive models of neighborhood effects’, The Journal of Real Estate Finance and Economics, Vol. 17, No. 1, pp. 15 - 33
  • Pace, RK., Barry, R. and Sirmans, C. F. 1998b, ‘Spatial statisitics and real estate’, Journal of Real Estate Fianance and Economics, Vol. 17, No. 1, pp. 5-13
  • Pesaran, MH and Shin, Y 1998, ‘Generalized impulse response analysis in linear multivariate models’, Eonomics Letters, Vol. 58, No. 1, pp. 17-29
  • Stevenson, S 2004, ‘House price diffusion and inter-regional and cross-border house price dynamics’, Journal of Property Research, Vol. 21, No. 4, pp. 301 – 20
  • Tu, Y 2000, ‘Segmentation of Australia housing market: 1989-98’, Journal of Property Research, Vol. 17, No. 4, pp. 311-27
  • Zellner, A 1962, ‘An efficient method of estimating seemingly unrelated regressions and tests for aggregation bias’, Journal of American Statistics Association, Vol. 57, No. 298, pp. 348-68
  • Zhu, B, Fuss, R and Rottke, NB 2011, ‘The predictive power of anisotropic spatial correlation modeling in housing prices’, Journal of Real Estate Finance and Economics, Vol. 42, No. 4, pp. 542-65