To read this content please select one of the options below:

Forecasting office rents with ensemble models – the case for European real estate markets

Benedict von Ahlefeldt-Dehn (International Real Estate Business School, University of Regensburg, Regensburg, Germany)
Marcelo Cajias (Investment Strategie and Research, PATRIZIA SE, Augsburg, Germany)
Wolfgang Schäfers (International Real Estate Business School, University of Regensburg, Regensburg, Germany)

Journal of Property Investment & Finance

ISSN: 1463-578X

Article publication date: 6 September 2022

Issue publication date: 27 March 2023

352

Abstract

Purpose

Commercial real estate and office rental values, in particular, have long been the focus of research. Several forecasting frameworks for office rental values in multivariate and univariate fashions have been proposed. Recent developments in time series forecasting using machine learning and deep learning methods offer an opportunity to update traditional univariate forecasting frameworks.

Design/methodology/approach

With the aim to extend research on univariate rent forecasting a hybrid methodology combining both ARIMA and a neural network model is proposed to exploit the unique strengths of both methods in linear and nonlinear modelling. N-BEATS, a deep learning algorithm that has demonstrated state-of-the-art forecasting performance in major forecasting competitions, are explained. With the ARIMA model, it is jointly applied to the office rental dataset to produce forecasts for four-quarters ahead.

Findings

When the approach is applied to a dataset of 21 major European office cities, the results show that the ensemble model can be an effective approach to improve the prediction accuracy achieved by each of the models used separately.

Practical implications

Real estate forecasting is essential for assessing the value of managing portfolios and for evaluating investment strategies. The approach applied in this paper confirms the heterogeneity of real estate markets. The application of mixed modelling via linear and nonlinear methods decreases the uncertainty of abrupt changes in rents.

Originality/value

To the best of the authors' knowledge, no such application of a hybrid model updating classical statistical forecasting with a deep learning neural network approach in the field of commercial real estate rent forecasting has been undertaken.

Keywords

Acknowledgements

The authors especially thank PATRIZIA SE for contributing to this study. All statements of opinion reflect the current estimations of the authors and do not necessarily reflect the opinion of PATRIZIA SE or its associated companies.

Citation

von Ahlefeldt-Dehn, B., Cajias, M. and Schäfers, W. (2023), "Forecasting office rents with ensemble models – the case for European real estate markets", Journal of Property Investment & Finance, Vol. 41 No. 2, pp. 182-207. https://doi.org/10.1108/JPIF-11-2021-0094

Publisher

:

Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

Related articles