Using machine learning to identify spatial market segments. A reproducible study of major Spanish markets

David Rey-Blanco, Pelayo Arbués, Fernando A. López and Antonio Páez

Environment and Planning B, 2024, vol. 51, issue 1, 89-108

Abstract: Identifying market segments can improve the fit and performance of hedonic price models. In this paper, we present a novel approach to market segmentation based on the use of machine learning techniques. Concretely, we propose a two-stage process. In the first stage, classification trees with interactive basis functions are used to identify non-orthogonal and non-linear submarket boundaries. The market segments that result are then introduced in a spatial econometric model to obtain hedonic estimates of the implicit prices of interest. The proposed approach is illustrated with a reproducible example of three major Spanish real estate markets. We conclude that identifying market sub-segments using the approach proposed is a relatively simple and demonstrate the potential of the proposed modelling strategy to produce better models and more accurate predictions.

Keywords: Hedonic prices; market segments; decision trees; spatial econometrics; reproducible research (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:51:y:2024:i:1:p:89-108

DOI: 10.1177/23998083231166952

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