A spatial semiparametric M-quantile regression for hedonic price modelling

Francesco Schirripa Spagnolo (), Riccardo Borgoni (), Antonella Carcagnì (), Alessandra Michelangeli and Nicola Salvati ()
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Francesco Schirripa Spagnolo: Università di Pisa
Riccardo Borgoni: Università Degli Studi di Milano-Bicocca
Antonella Carcagnì: Università Degli Studi di Milano-Bicocca
Nicola Salvati: Università di Pisa

AStA Advances in Statistical Analysis, 2024, vol. 108, issue 1, No 6, 159-183

Abstract: Abstract This paper proposes an M-quantile regression approach to address the heterogeneity of the housing market in a modern European city. We show how M-quantile modelling is a rich and flexible tool for empirical market price data analysis, allowing us to obtain a robust estimation of the hedonic price function whilst accounting for different sources of heterogeneity in market prices. The suggested methodology can generally be used to analyse nonlinear interactions between prices and predictors. In particular, we develop a spatial semiparametric M-quantile model to capture both the potential nonlinear effects of the cultural environment on pricing and spatial trends. In both cases, nonlinearity is introduced into the model using appropriate bases functions. We show how the implicit price associated with the variable that measures cultural amenities can be determined in this semiparametric framework. Our findings show that the effect of several housing attributes and urban amenities differs significantly across the response distribution, suggesting that buyers of lower-priced properties behave differently than buyers of higher-priced properties.

Keywords: Robust method; Hedonic regression; Penalised splines; Spatial data (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10182-023-00476-w

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