Fingleton, Bernard (2010): Predicting the Geography of House Prices.
Download (501Kb) | Preview
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|
|Original Title:||Predicting the Geography of House Prices|
|Keywords:||new economic geography, real estate prices, spatial econometrics, panel data, prediction.|
|Subjects:||O - Economic Development, 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, Production Analysis, and Firm Location > R31 - Housing Supply and Markets
|Depositing User:||Bernard Fingleton|
|Date Deposited:||06. Mar 2010 04:17|
|Last Modified:||18. Feb 2013 22:31|
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