The Future of Commercial Real Estate Market Research: A Case for Applying Machine Learning

Benedict von Ahlefeldt-Dehn, Marcelo Cajias and Wolfgang Schäfers

ERES from European Real Estate Society (ERES)

Abstract: The commercial real estate market is opaque and build upon complex relationships of countless property market and macroeconomic factors. Yet, office markets are due to its sheer volume and importance for numerous market players such as investors, developers, mortgage underwriters and valuation firms broadly researched. Especially, the prediction of property market indicators found strong interest among researchers and practitioners in the field of commercial real estate. Thus, the literature proposes three main frameworks for predicting office rents, among other. The estimation via multiple equation models such as error correction mechanism models (e.g. Hendershott et al., 2002; Ke and White, 2009; McCartnery, 2012) or interlinked demand and supply models (e.g. Rosen, 1984; Hendershott et al., 1999; Kim, 2012), reduced form single equation models (e.g. Matysiak and Tsolacos, 2003; Voigtländer, 2010; Kiehelä and Falkenbach, 2014) or autoregressive models (e.g. McGough and Tsolacos, 1995; Brooks and Tsolacos, 2000; Stevenson and McGarth, 2003). However, the limitations of the applied methods lay within the econometric methods itself. “Traditional” statistical modeling as an approximation of causality will only understand trends and relationships in the underlying market to the degree the employed econometric methods themselves can mirror. In contrast, more recent methodological attempts such as machine learning can be seen as a process of selecting the relevant features leading to a trade-off between precision and stability of a predictive model (Conway, 2018). This however, creates opportunities to expand and enhance existing efforts – in a way that complex and non-linear relationships within the data are captured. Many studies (e.g Dabrowski and Adamczyk, 2010; Rafatirad, 2017, Cajias and Ertl, 2018; Mayer et al., 2019) apply advanced machine learning methods to residential markets and demonstrate that “traditional” linear hedonic models can be outperformed. While linear models are found to produce less volatile predictions advanced machine learning methods yield more accurate results. Promising results can also be shown in commercial real estate markets. In particular, the aim of research is the performance assessment of the forecasting of office rents in European markets with advanced machine learning methods. A dataset of European markets with office prime rents and market as well as macroeconomic indicators is analysed and advanced machine learning models are estimated. A “traditional” linear regression model (ordinary least squares) functions as a benchmark for the evaluation of the employed methods: random forest and extreme gradient boosting. In particular, the prediction power and forecasting ability is assessed in- and out-of-sample, respectively. The tree-based advanced machine learning methods yield promising estimations in the observed markets. It becomes clear that in commercial real estate markets complex and non-linear relationships are present and can effectively be estimated by non-parametric econometric models. By the application of these methods the estimation error (out-of-sample) can be reduced by up to 60 percent. To the best of the authors knowledge such applications of machine learning methods in commercial real estate markets has not been considered in prior research. However, in the area of textual analysis results show that commercial real estate markets can be forecasted on the basis of market sentiment (e.g. Beracha et al., 2019). The capability of improving the forecasting power with advanced machine learning methods creates value and transparency for numerous market players and authorities.

Keywords: commercial real estate; Forecast; Machine Learning; Office Rent (search for similar items in EconPapers)
JEL-codes: R3 (search for similar items in EconPapers)
Date: 2021-01-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-isf and nep-ure
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