Machine Learning Applications to Land and Structure Valuation

Michael Mayer, Steven Bourassa, Martin Hoesli and Donato Scognamiglio
Additional contact information
Michael Mayer: Actuarial Department, la Mobilière, 3001 Bern, Switzerland
Martin Hoesli: Geneva School of Economics and Management, University of Geneva, 1211 Geneva, Switzerland
Donato Scognamiglio: IAZI AG, 8050 Zurich, Switzerland

JRFM, 2022, vol. 15, issue 5, 1-24

Abstract: In some applications of supervised machine learning, it is desirable to trade model complexity with greater interpretability for some covariates while letting other covariates remain a “black box”. An important example is hedonic property valuation modeling, where machine learning techniques typically improve predictive accuracy, but are too opaque for some practical applications that require greater interpretability. This problem can be resolved by certain structured additive regression (STAR) models, which are a rich class of regression models that include the generalized linear model (GLM) and the generalized additive model (GAM). Typically, STAR models are fitted by penalized least-squares approaches. We explain how one can benefit from the excellent predictive capabilities of two advanced machine learning techniques: deep learning and gradient boosting. Furthermore, we show how STAR models can be used for supervised dimension reduction and explain under what circumstances their covariate effects can be described in a transparent way. We apply the methodology to residential land and structure valuation, with very encouraging results regarding both interpretability and predictive performance.

Keywords: land and structure valuation; machine learning; structured additive regression; gradient boosting; deep learning; interpretability; transparency; hedonic modeling (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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