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Predicting the Geography of House Prices


  • Fingleton, Bernard


Prediction is difficult. In this paper we use panel data methods to make reasonably accurate short term ex-post predictions of house prices across 353 local authority areas in England. The issue of prediction over the longer term is also addressed, and a simple method that makes use of the dynamics embodied in New Economic geography theory is suggested as a possible way to approach the problem.

Suggested Citation

  • Fingleton, Bernard, 2010. "Predicting the Geography of House Prices," MPRA Paper 21113, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:21113

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    References listed on IDEAS

    1. Göran Therborn & K.C. Ho, 2009. "Introduction," City, Taylor & Francis Journals, vol. 13(1), pages 53-62, March.
    2. Bernard Fingleton, 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, Taylor & Francis Journals, vol. 3(1), pages 27-44.
    3. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    4. Mario Larch & Janette Walde, 2009. "Finite sample properties of alternative GMM estimators for random effects models with spatially correlated errors," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 43(2), pages 473-490, June.
    5. Baltagi, Badi H. & Song, Seuck Heun & Koh, Won, 2003. "Testing panel data regression models with spatial error correlation," Journal of Econometrics, Elsevier, vol. 117(1), pages 123-150, November.
    6. Badi H. Baltagi & Peter Egger & Michael Pfaffermayr, 2007. "A Monte Carlo Study for Pure and Pretest Estimators of a Panel Data Model with Spatially Autocorrelated Disturbances," Annals of Economics and Statistics, GENES, issue 87-88, pages 11-38.
    7. Steven Brakman & Harry Garretsen & Marc Schramm, 2004. "The Spatial Distribution of Wages: Estimating the Helpman-Hanson Model for Germany," Journal of Regional Science, Wiley Blackwell, vol. 44(3), pages 437-466.
    8. Bernard Fingleton, 2005. "Towards applied geographical economics: modelling relative wage rates, incomes and prices for the regions of Great Britain," Applied Economics, Taylor & Francis Journals, vol. 37(21), pages 2417-2428.
    9. Bernard Fingleton, 2009. "Prediction Using Panel Data Regression with Spatial Random Effects," International Regional Science Review, , vol. 32(2), pages 195-220, April.
    10. H. Hanson, Gordon, 2005. "Market potential, increasing returns and geographic concentration," Journal of International Economics, Elsevier, vol. 67(1), pages 1-24, September.
    11. Badi Baltagi & Dong Li, 2006. "Prediction in the Panel Data Model with Spatial Correlation: the Case of Liquor," Spatial Economic Analysis, Taylor & Francis Journals, vol. 1(2), pages 175-185.
    12. Mark D. Partridge, 2005. "Does Income Distribution Affect U.S. State Economic Growth?," Journal of Regional Science, Wiley Blackwell, vol. 45(2), pages 363-394.
    13. Kapoor, Mudit & Kelejian, Harry H. & Prucha, Ingmar R., 2007. "Panel data models with spatially correlated error components," Journal of Econometrics, Elsevier, vol. 140(1), pages 97-130, September.
    14. Kristian Behrens & Frédéric Robert-Nicoud, 2009. "Krugman's "Papers in Regional Science": The 100 dollar bill on the sidewalk is gone and the 2008 Nobel Prize well-deserved," Papers in Regional Science, Wiley Blackwell, vol. 88(2), pages 467-489, June.
    15. J. Barkley Rosser, 2009. "Introduction," Chapters,in: Handbook of Research on Complexity, chapter 1 Edward Elgar Publishing.
    16. Mutl, Jan & Pfaffermayr, Michael, 2008. "The Spatial Random Effects and the Spatial Fixed Effects Model. The Hausman Test in a Cliff and Ord Panel Model," Economics Series 229, Institute for Advanced Studies.
    17. S. Brakman & H Garretsen & M. Schramm, 2003. "The Strategic Bombing of German Cities during World War II and its Impact for Germany," Working Papers 03-08, Utrecht School of Economics.
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    Cited by:

    1. Badi H. Baltagi & Bernard Fingleton & Alain Pirotte, 2014. "Estimating and Forecasting with a Dynamic Spatial Panel Data Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(1), pages 112-138, February.
    2. Silvia Palombi & Roger Perman & Christophe Tavéra, 2017. "Commuting effects in Okun's Law among British areas: Evidence from spatial panel econometrics," Papers in Regional Science, Wiley Blackwell, vol. 96(1), pages 191-209, March.
    3. Fingleton, Bernard & Palombi, Silvia, 2013. "Spatial panel data estimation, counterfactual predictions, and local economic resilience among British towns in the Victorian era," Regional Science and Urban Economics, Elsevier, vol. 43(4), pages 649-660.

    More about this item


    new economic geography; real estate prices; spatial econometrics; panel data; prediction.;

    JEL classification:

    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets

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