Segnon, Mawuli K.Gupta, RanganLesame, KeagileWohar, Mark E.2020-05-142021-02Segnon, M., Gupta, R., Lesame, K. et al. High-Frequency Volatility Forecasting of US Housing Markets. Journal of Real Estate Finance and Economics 62, 283–317 (2021). https://doi.org/10.1007/s11146-020-09745-w.0895-5638 (print)1573-045X (online)10.1007/s11146-020-09745-whttp://hdl.handle.net/2263/74590We propose a logistic smooth transition autoregressive fractionally integrated [STARFI (p, d)] process for modeling and forecasting US housing price volatility. We discuss the statistical properties of the model and investigate its forecasting performance by assuming various specifications for the dynamics underlying the variance process in the model. Using a unique database of daily data on price indices from ten major US cities, and the corresponding daily Composite 10 Housing Price Index, and also a housing futures price index, we find that using the Markov-switching multifractal (MSM) and FIGARCH frameworks for modeling the variance process helps improving the gains in forecast accuracy.en© Springer Science+Business Media, LLC, part of Springer Nature 2020. The original publication is available at : http://link.springer.comjournal/11146.Model confidence setMarkov-switching multi-fractal (MSM)US housing pricesUnited States of America (USA)GARCH processesPostprint Article