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Using machine learning to identify spatial market segments. A reproducible study of major Spanish markets
- Source :
- Environment and Planning B: Urban Analytics and City Science; January 2024, Vol. 51 Issue: 1 p89-108, 20p
- Publication Year :
- 2024
-
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.
Details
- Language :
- English
- ISSN :
- 23998083 and 23998091
- Volume :
- 51
- Issue :
- 1
- Database :
- Supplemental Index
- Journal :
- Environment and Planning B: Urban Analytics and City Science
- Publication Type :
- Periodical
- Accession number :
- ejs65057495
- Full Text :
- https://doi.org/10.1177/23998083231166952