A robust house price index using sparse and frugal data

Abstract

In this article, we describe a house price index algorithm which requires only sparse and frugal data, namely house location, date of sale and sale price, as input data. We aim to show that our algorithm is as effective for predicting price changes as more complex models which require detailed or extensive data. Although various methods are employed for determining house price indexes, such as hedonic regression, mix-adjusted median or repeat sales, there is no consensus on how to determine the robustness of an index, and hence no agreement on which method is the best to use. We formalise an objective criterion for what a house price index should achieve, namely consistency between time periods. Using this criterion, we investigate whether it is possible to achieve strong robustness using frugal data covering only 66 months of transactions on the Irish property market. We develop a simple multi-stage algorithm and show that it is more robust than the complex hedonic regression model currently employed by the Irish Central Statistics Office.