A New Dynamic House-Price Index

Abstract

Using a newly available panel data set containing property-specific, time-varying hedonic characteristics and sales prices, we develop a new dynamic house-price model that is suitable for out-of-sample forecasting applications such as mortgage valuation and bank stress-testing. The model is set up in a classical state-space framework and includes common factors that are univariate structural time series models scaled to form linear combinations specific to locations. Our common factors include trend, seasonal, and autoregressive components. The equations are linear and errors are Gaussian; however, the unbalanced nature of our panel data means that standard Kalman-filter smoothing algorithms are not suitable. Instead, we apply an alternative three-block Markov Chain Monte Carlo algorithm Strickland, Turner, Denham, and Mengersen (2009). We find significant in- and out-of-sample forecasting differences between our model and standard repeat-sales and hedonic regressions. We also test, and reject, two assumptions of repeat-sales estimators: a single common trend for all regions and a unit root in the index.