A Spatial Model Averaging Approach to Measuring House Prices

Abstract

We present a novel approach to the estimation of hedonic imputation (HI) price indices for real estate markets using a new Mallows model averaging (MMA) estimator that is robust to spatial dependence. The spatial MMA (SMMA) method is explicitly designed to minimize the quadratic forecast loss of the imputed sales transactions that comprise the HI index when sales transactions are spatially correlated. We apply the SMMA HI approach to a sales transaction dataset for three geographic real estate suburbs of Auckland, New Zealand. The SMMA HI price indices outperform conventional OLS HI methods, exhibiting more accurate out-of-sample prediction and tighter in-sample confidence intervals. The SMMA HI method is expected to offer practitioners enhancements in constant-quality price index accuracy in data sparse environments where model overfitting is a concern, such as high frequency price measurement or highly localized geographies. Full paper available at http://doi.org/10.1007/s43071-021-00013-4

Keywords: Hedonic Imputation Price Index, House prices, Model averaging, Mallows model averaging