Hedonic Imputed Housing Price Indices from a Model with Dynamic Shadow Prices Incorporating Nearest Neighbour Information Authors

The main objective of this paper is to propose an improvement to existing methods of house price index construction by addressing three important oversights in the literature. Firstly, it is plausible that the shadow prices of property attributes evolve slowly over time, in line with the nature of consumer preferences. Existing methods either assume (explicitly or implicitly) that shadow prices are constant or change in a haphazard fashion from one period to the next. Secondly, the price of a house is spatially correlated with the price of houses that are in close proximity. Thirdly, there has been little research into the accurate specification of index number formulae particular to the housing case, which is partly due to existing methods implicitly defining an index within their formulation. However, improvements in the comprehensiveness of housing data allows for methodologies that consider a wider spectrum of index formulae. This paper specifies a hedonic model with smoothly time-varying parameters which is estimated using a state-space formulation with spatially autocorrelated errors. Subsequently, the estimated model is used in constructing a housing price index using hedonic imputation (HI) methods. The formulated HI indexes are also applied to the traditional spatial errors model, which is new to the literature. The methods proposed in this paper are illustrated using housing price data for the Brisbane metropolitan area, compiled from the property information service, RP Data.