Predicting the Geography of House Prices

Fingleton, Bernard (2010): Predicting the Geography of House Prices.

Abstract

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: new economic geography, real estate prices, spatial econometrics, panel data, prediction.
• Subjects: O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development > O18 - Urban, Rural, Regional, and Transportation Analysis ; Housing ; Infrastructure; R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R1 - General Regional Economics > R12 - Size and Spatial Distributions of Regional Economic Activity; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C31 - Cross-Sectional Models ; Spatial Models ; Treatment Effect Models ; Quantile Regressions ; Social Interaction Models; C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C21 - Cross-Sectional Models ; Spatial Models ; Treatment Effect Models ; Quantile Regressions; R - Urban, Rural, Regional, Real Estate, and Transportation Economics > R3 - Real Estate Markets, Spatial Production Analysis, and Firm Location > R31 - Housing Supply and Markets

References

Anselin L (1988) Spatial Econometrics: Methods and Models Dordrecht Kluwer.

Anselin L, Le Gallo J, and Jayet J (2007) Spatial Panel Econometrics, Chapter 19 in Matyas L. and Sevestre P. (Eds.), The Econometrics of Panel Data, Fundamentals and Recent Developments in Theory and Practice (3rd Edition). Dordrecht Kluwer.

Baltagi B H (2005) Econometric Analysis of Panel Data 3rd Edition Chichester Wiley.

Baltagi, B H, Song S H and Koh W (2003) Testing panel data regression models with spatial error correlation, Journal of Econometrics, 117 123-150

Baltagi B H and Li D ( 2006) Prediction in the Panel Data Model with Spatial Correlation: The Case of Liquor, Spatial Economic Analysis 1 175-185

Baltagi B H, Bresson G and Pirotte A (2007), Forecasting with Spatial Panel Data, ERMES Working Paper number 07-10, University of Paris II

Baltagi, B H, Egger P and Pfaffermayr M (2008) A Monte Carlo Study for pure and pretest estimators of a panel data model with spatially autocorrelated disturbances, Annales d’Économie et de Statistique 97-98 11-38

Behrens K, Robert-Nicoud F (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 88 467-489

Bowden R J and Turkington D A (1984) Instrumental variables Cambridge Cambridge University Press.

Brakman S, Garretsen H, and Schramm M (2004) The Spatial Distribution Of Wages: Estimating The Helpman-Hanson Model For Germany, Journal Of Regional Science 44 437–466

Elhorst J P (2003) Specification and Estimation of Spatial Panel Data Models International Regional Science Review 26 244 – 268

Elhorst J P(2010) Spatial Panel Data Models, Chapter C2 pp. 377-405 in Fischer M M and Getis A (eds.) Handbook of Applied Spatial Analysis, Berlin Springer-Verlag.

Fingleton B (1999) Spurious spatial regression: some Monte-Carlo results with a spatial unit root and spatial cointegration, Journal of Regional Science, 39, 1-19

Fingleton B (2005) Towards applied geographical economics: modelling relative wage rates, incomes and prices for the regions of Great Britain, Applied Economics 37 2417-2428

Fingleton B (2008a) Housing supply, housing demand, and affordability, Urban Studies 45 1545-1563

Fingleton B (2008b) A Generalized Method of Moments estimator for a spatial panel model with an endogenous spatial lag and spatial moving average errors Spatial Economic Analysis 3 27-44

Fingleton B (2009) Prediction using panel data regression with spatial random effects International Regional Science Review 32 173-194

Fujita M, Krugman P R and Venables A (1999) The Spatial Economy : Cities, Regions, and International Trade Cambridge Massachusetts MIT press

Glaeser E L (2008) Cities, Agglomeration, and Spatial Equilibrium Oxford Oxford University Press

Goldberger A S (1962) Best linear unbiased prediction in the generalized linear regression model. Journal of the American Statistical Association 57 369-375.

Greene W H (2003) Econometric Analysis 5th Edition New Jersey Prentice Hall

Hanson G H (2001) Scale Economies and the Geographic Concentration of Industry, Journal of Economic Geography 1 255–276

Hanson G H (2005) Market potential, increasing returns and geographic concentration, Journal of International Economics 67 1 –24

Helpman E (1998) The Size of Regions, pp. 33–54 in D. Pines, Sadka E. and Zilcha I. (eds), Topics in Public Economics. Cambridge Cambridge University Press

Larch M and Walde J (2009) Finite sample properties of alternative GMM estimators for random effects models with spatially correlated errors Annals of Regional Science, 43 473 – 490

Kapoor M, Kelejian H H and Prucha I (2007) Panel Data Models with Spatially Correlated Error Components Journal of Econometrics, 140 97-130

Kelejian H H and Prucha I (1998) A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances Journal of Real Estate Finance and Economics, 17 99-121

LeSage J and Pace K R (2009) Introduction to Spatial Econometrics New York CRC Press

Mutl J and Pfaffermayr M (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, Vienna

Partridge M (2005) Does Income Distribution Affect U.S. State Economic Growth? Journal of Regional Science 45 363-394