Irish Property Price Estimation Using A Flexible Geo-spatial Smoothing Approach: What is the Impact of an Address?

TL;DR

This paper introduces a flexible geo-spatial hedonic regression model for property valuation that effectively handles low property turnover, address mislabelling, and quantifies feature contributions, with broad applicability beyond Ireland.

Contribution

The paper develops a novel spatial hedonic regression model that separates spatial and non-spatial effects, improving property valuation accuracy in markets with low turnover and addressing address mislabelling issues.

Findings

The model outperforms competitors in prediction accuracy and uncertainty estimation.

Address mislabelling impacts valuation accuracy, which the model can mitigate.

Quantifies the contribution of property improvements to value.

Abstract

Accurate and efficient valuation of property is of utmost importance in a variety of settings, such as when securing mortgage finance to purchase a property, or where residential property taxes are set as a percentage of a property's resale value. Internationally, resale based property taxes are most common due to ease of implementation and the difficulty of establishing site values. In an Irish context, property valuations are currently based on comparison to recently sold neighbouring properties, however, this approach is limited by low property turnover. National property taxes based on property value, as opposed to site value, also act as a disincentive to improvement works due to the ensuing increased tax burden. In this article we develop a spatial hedonic regression model to separate the spatial and non-spatial contributions of property features to resale value. We mitigate the issue of low property turnover through geographic correlation, borrowing information across multiple property types and finishes. We investigate the impact of address mislabelling on predictive performance, where vendors erroneously supply a more affluent postcode, and evaluate the contribution of improvement works to increased values. Our flexible geo-spatial model outperforms all competitors across a number of different evaluation metrics, including the accuracy of both price prediction and associated uncertainty intervals. While our models are applied in an Irish context, the ability to accurately value properties in markets with low property turnover and to quantify the value contributions of specific property features has widespread application. The ability to separate spatial and non-spatial contributions to a property's value also provides an avenue to site-value based property taxes.