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Putting MARS into Space: Non Linearities and Spatial Effects in Hedonic Models

Referierte Aufsätze Web of Science

Fernando A. López, Konstantin A. Kholodilin

In: Papers in Regional Science 102 (2023), 4, S. 871-896

Abstract

Multivariate Adaptive Regression Spline (MARS) is a simple and powerful non-parametric machine learning algorithm that automatizes the selection of non-linear terms in regression models. In this study, we propose using MARS in a spatial regression framework to account for potential non-linearities and spatial effects in spatial regression models. Using a relatively large data set of 17,000 dwellings in St. Petersburg (Russia), we examine how this algorithm works. The empirical evidence shows that most explanatory variables in the spatial regression model—including the spatial lag of the dependent variable—have a non-linear impact on the asking prices of dwellings.

Konstantin A. Kholodilin

Research Associate in the Macroeconomics Department



Keywords: Hedonic models, multivariate adaptive regression spline, non-linearity, spatial regression models, St. Petersburg
DOI:
https://doi.org/10.1111/pirs.12738

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