Could We Have Predicted the Recent Downturn in Home Sales of the Four US Census Regions?
AbstractThis paper analyzes the ability of a random walk and, classical and Bayesian versions of autoregressive, vector autoregressive and vector error correction models in forecasting home sales for the four US census regions (Northeast, Middlewest, South, West), using quarterly data over the period of 2001:Q1 to 2004:Q3, based on an in-sample of 1976:Q1 till 2000:Q4. In addition, we also use our models to predict the downturn in the home sales of the four census regions over the period of 2004:Q4 to 2009:Q2, given that the home sales in all the four census regions peaked in 2005:Q3. Based on our analysis, we draw the following conclusions: (i) Barring the South, there always exists a Bayesian model which tends to outperform all other models in forecasting home sales over the out-of-sample horizon; (ii) When we expose our classical and ‘optimal’ Bayesian forecast models to predicting the peaks and declines in home sales, we find that barring the South again, our models did reasonably well in predicting the turning point exactly at 2005:Q3 or with a lead. In general, the fact that different models produce the best forecasting performance for different regions, highlights the fact that economic conditions prevailing at the start of the out-of-sample horizon are not necessarily the same across the regions, and, hence, vindicates our decision to look at regions rather than the economy as a whole. In addition, we also point out that there is no guarantee that the best performing model over the out-of-sample horizon is also well-suited in predicting the downturn in home sales.
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Bibliographic InfoPaper provided by University of Pretoria, Department of Economics in its series Working Papers with number 200926.
Length: 21 pages
Date of creation: Dec 2009
Date of revision:
Forecast Accuracy; Home Sales; Vector Autoregressive Models;
Find related papers by JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
- R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets
This paper has been announced in the following NEP Reports:
- NEP-ALL-2009-12-19 (All new papers)
- NEP-FOR-2009-12-19 (Forecasting)
- NEP-URE-2009-12-19 (Urban & Real Estate Economics)
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- Vasilios Plakandaras & Rangan Gupta & Periklis Gogas & Theophilos Papadimitriou, 2014.
"Forecasting the U.S. Real House Price Index,"
Working Papers, University of Pretoria, Department of Economics
201418, University of Pretoria, Department of Economics.
- Plakandaras, Vasilios & Gupta, Rangan & Papadimitriou, Theophilos & Gogas, Periklis, 2014. "Forecasting the U.S. Real House Price Index," DUTH Research Papers in Economics, Democritus University of Thrace, Department of Economics 10-2014, Democritus University of Thrace, Department of Economics.
- Vasilios Plakandaras & Rangan Gupta & Periklis Gogas & Theophilos Papadimitriou, 2014. "Forecasting the U.S. Real House Price Index," Working Papers, Department of Research, Ipag Business School 2014-473, Department of Research, Ipag Business School.
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