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Modelling regional house prices

Author

Listed:
  • Bram van Dijk
  • Philip Hans Franses
  • Richard Paap
  • Dick van Dijk

Abstract

We develop a panel model for regional house prices, for which both the cross-section and the time series dimension is large. The model allows for stochastic trends, cointegration, cross-equation correlations and, most importantly, latent-class clustering of regions. Class membership is fully data-driven and based on the average growth rates of house prices, and the relationship of house prices with economic growth. We apply the model to quarterly data for the Netherlands. The results suggest that there is convincing evidence for the existence of two distinct clusters of regions with pronounced differences in house price dynamics.

Suggested Citation

  • Bram van Dijk & Philip Hans Franses & Richard Paap & Dick van Dijk, 2011. "Modelling regional house prices," Applied Economics, Taylor & Francis Journals, vol. 43(17), pages 2097-2110.
    • Handle: RePEc:taf:applec:v:43:y:2011:i:17:p:2097-2110
    DOI: 10.1080/00036840903085089
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    1. Moon, H.R.Hyungsik Roger & Perron, Benoit, 2004. "Testing for a unit root in panels with dynamic factors," Journal of Econometrics, Elsevier, vol. 122(1), pages 81-126, September.
    2. M. Hashem Pesaran, 2021. "General diagnostic tests for cross-sectional dependence in panels," Empirical Economics, Springer, vol. 60(1), pages 13-50, January.
    3. Im, Kyung So & Pesaran, M. Hashem & Shin, Yongcheol, 2003. "Testing for unit roots in heterogeneous panels," Journal of Econometrics, Elsevier, vol. 115(1), pages 53-74, July.
    4. Peter Boswijk, H., 1994. "Testing for an unstable root in conditional and structural error correction models," Journal of Econometrics, Elsevier, vol. 63(1), pages 37-60, July.
    5. M. Hashem Pesaran, 2007. "A simple panel unit root test in the presence of cross-section dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(2), pages 265-312.
    6. Paap, Richard & Franses, Philip Hans & van Dijk, Dick, 2005. "Does Africa grow slower than Asia, Latin America and the Middle East? Evidence from a new data-based classification method," Journal of Development Economics, Elsevier, vol. 77(2), pages 553-570, August.
    7. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    8. Malpezzi, Stephen, 1999. "A Simple Error Correction Model of House Prices," Journal of Housing Economics, Elsevier, vol. 8(1), pages 27-62, March.
    9. Robert B. Davies, 2002. "Hypothesis testing when a nuisance parameter is present only under the alternative: Linear model case," Biometrika, Biometrika Trust, vol. 89(2), pages 484-489, June.
    10. Levin, Andrew & Lin, Chien-Fu & James Chu, Chia-Shang, 2002. "Unit root tests in panel data: asymptotic and finite-sample properties," Journal of Econometrics, Elsevier, vol. 108(1), pages 1-24, May.
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    2. Ling Zhang & He Wang & Yan Song & Haizhen Wen, 2019. "Spatial Spillover of House Prices: An Empirical Study of the Yangtze Delta Urban Agglomeration in China," Sustainability, MDPI, vol. 11(2), pages 1-17, January.
    3. Dominik Blatt & Kausik Chaudhuri & Hans Manner, 2021. "Spillover in the UK Housing Market," Graz Economics Papers 2021-13, University of Graz, Department of Economics.
    4. James D. Hamilton & Michael T. Owyang, 2012. "The Propagation of Regional Recessions," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 935-947, November.
    5. Katharina Pijnenburg, 2013. "The Spatial Dimension of US House Price Developments," Discussion Papers of DIW Berlin 1270, DIW Berlin, German Institute for Economic Research.
    6. Roel Helgers & Erik Buyst, 2016. "Spatial and Temporal Diffusion of Housing Prices in the Presence of a Linguistic Border: Evidence from Belgium," Spatial Economic Analysis, Taylor & Francis Journals, vol. 11(1), pages 92-122, March.
    7. Ceren Ozgen & Thomas de Graff, 2013. "Sorting out the impact of cultural diversity on innovative firms. An empirical analysis of Dutch micro-data," Norface Discussion Paper Series 2013012, Norface Research Programme on Migration, Department of Economics, University College London.
    8. Cipollini, Andrea & Parla, Fabio, 2020. "Housing market shocks in italy: A GVAR approach," Journal of Housing Economics, Elsevier, vol. 50(C).
    9. Katharina Pijnenburg, 2017. "The spatial dimension of US house prices," Urban Studies, Urban Studies Journal Limited, vol. 54(2), pages 466-481, February.
    10. Katharina Pijnenburg, 2014. "The Spatial Dimension of US House Price Developments," ERSA conference papers ersa14p127, European Regional Science Association.
    11. Maureen Lankhuizen & Thomas De Graaff & Henri De Groot, 2012. "Product Heterogeneity, Intangible Barriers & Distance Decay: The effect of multiple dimensions of distance on trade across different product categories," ERSA conference papers ersa12p151, European Regional Science Association.
    12. Sergei S. Shibaev, 2016. "Recession Propagation In Small Regional Economies: Spatial Spillovers And Endogenous Clustering," Working Paper 1369, Economics Department, Queen's University.
    13. Zhu, Bing & van Dijk, Dorinth & Lizieri, Colin, 2024. "Price diffusion across international private commercial real estate markets," Journal of International Money and Finance, Elsevier, vol. 140(C).
    14. Maureen B. M. Lankhuizen & Thomas De Graaff & Henri L. F. de Groot, 2015. "Product Heterogeneity, Intangible Barriers and Distance Decay: The Effect of Multiple Dimensions of Distance on Trade across Different Product Categories," Spatial Economic Analysis, Taylor & Francis Journals, vol. 10(2), pages 137-159, June.

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    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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