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Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection

Lasse Bork and Stig V. Møller

International Journal of Forecasting, 2015, vol. 31, issue 1, 63-78

Abstract: We examine house price forecastability across the 50 states using Dynamic Model Averaging and Dynamic Model Selection, which allow for model change and parameter shifts. By allowing the entire forecasting model to change over time and across locations, the forecasting accuracy improves substantially. The states in which housing markets have been the most volatile are the states in which model change and parameter shifts have been needed the most.

Keywords: Forecasting housing markets; Kalman filtering methods; Model change; Parameter shifts; Boom-bust cycle (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1016/j.ijforecast.2014.05.005

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Handle: RePEc:eee:intfor:v:31:y:2015:i:1:p:63-78