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Putting MARS into space. Non‐linearities and spatial effects in hedonic models

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  • Fernando López
  • Konstatin Kholodilin

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. Multivariate Adaptive Regression Spline (MARS) es un algoritmo de aprendizaje automático no paramétrico sencillo y potente que automatiza la selección de términos no lineales en modelos de regresión. En este estudio se propone utilizar MARS en un marco de regresión espacial para tener en cuenta las posibles no linealidades y los efectos espaciales en los modelos de regresión espacial. Se examinó el funcionamiento de este algoritmo utilizando un conjunto de datos relativamente grande de 17.000 viviendas en San Petersburgo (Rusia). Las pruebas empíricas muestran que la mayoría de las variables explicativas del modelo de regresión espacial, incluido el desfase espacial de la variable dependiente, tienen un impacto no lineal en los precios de venta de las viviendas. 多変量適応的回帰スプライン(Multivariate Adaptive Regression Spline:MARS)は、回帰モデルにおける非線形項の選択を自動化する単純で強力なノンパラメトリックな機械学習アルゴリズムである。本稿では、空間回帰モデルにおける潜在的な非線形性と空間効果を説明するために、空間回帰フレームワークにおいてMARSを使用することを提案する。サンクトペテルブルク(ロシア)の17,000の住宅の比較的大きなデータセットを用いて、このアルゴリズムがどのように機能するかを検討する。実証的エビデンスから、従属変数の空間ラグを含む空間回帰モデルのほとんどの説明変数が、住宅の希望価格に非線形の影響を与えることが示される。

Suggested Citation

  • Fernando López & Konstatin Kholodilin, 2023. "Putting MARS into space. Non‐linearities and spatial effects in hedonic models," Papers in Regional Science, Wiley Blackwell, vol. 102(4), pages 871-896, August.
  • Handle: RePEc:bla:presci:v:102:y:2023:i:4:p:871-896
    DOI: 10.1111/pirs.12738
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