Maguire, Phil and Miller, Robert and Moser, Philippe and Maguire, Rebecca (2016) A robust house price index using sparse and frugal data. Journal of Property Research, 33 (4). pp. 293-308. ISSN 0959-9916

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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.

Item Type:	Article
Additional Information:	Cite as: Phil Maguire, Robert Miller, Philippe Moser & Rebecca Maguire (2016) A robust house price index using sparse and frugal data, Journal of Property Research, 33:4, 293-308, DOI: 10.1080/09599916.2016.1258718
Keywords:	House price index; sparse data mining; frugal heuristics; index robustness; central price tendency model;
Academic Unit:	Faculty of Science and Engineering > Computer Science Faculty of Science and Engineering > Psychology
Item ID:	10325
Identification Number:	https://doi.org/10.1080/09599916.2016.1258718
Depositing User:	Phil Maguire
Date Deposited:	13 Dec 2018 16:30
Journal or Publication Title:	Journal of Property Research
Publisher:	Taylor & Francis
Refereed:	Yes
URI:	Publisher
Use Licence:	This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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