Pricing office rents in Sydney CBD: testing the water on automated rent reviews

Author:

Filippova OlgaORCID,Gabe JeremyORCID,Rehm MichaelORCID

Abstract

PurposeAutomated valuation models (AVMs) are statistical asset pricing models omnipresent in residential real estate markets, where they inform property tax assessment, mortgage underwriting and marketing. Use of these asset pricing models outside of residential real estate is rare. The purpose of the paper is to explore key characteristics of commercial office lease contracts and test an application in estimating office market rental prices using an AVM.Design/methodology/approachThe authors apply a semi-log ordinary least squares hedonic regression approach to estimate either contract rent or the total costs of occupancy (TOC) (“grossed up” rent). Furthermore, the authors adopt a training/test split in the observed leasing data to evaluate the accuracy of using these pricing models for prediction. In the study, 80% of the samples are randomly selected to train the AVM and 20% was held back to test accuracy out of sample. A naive prediction model is used to establish accuracy prediction benchmarks for the AVM using the out-of-sample test data. To evaluate the performance of the AVM, the authors use a Monte Carlo simulation to run the selection process 100 times and calculate the test dataset's mean error (ME), mean absolute error (MAE), mean absolute percentage error (MAPE), median absolute percentage error (MdAPE), coefficient of dispersion (COD) and the training model's r-squared statistic (R2) for each run.FindingsUsing a sample of office lease transactions in Sydney CBD (Central Business District), Australia, the authors demonstrate accuracy statistics that are comparable to those used in residential valuation and outperform a naive model.Originality/valueAVMs in an office leasing context have significant implications for practice. First, an AVM can act as an impartial arbiter in market rent review disputes. Second, the technology may enable frequent market rent reviews as a lease negotiation strategy that allows tenants and property owners to share market risk by limiting concerns over high costs and adversarial litigation that can emerge in a market rent review dispute.

Publisher

Emerald

Subject

Business, Management and Accounting (miscellaneous),Finance

Reference58 articles.

1. Artificial neural network in property valuation: application framework and research trend;Property Management,2017

2. Property valuation methods in practice: evidence from Australia;Property Management,2019

3. Client influence in residential property valuations: an empirical study;Property Management,2007

4. The coming downsizing of real estate: implications of technology;Journal of Real Estate Portfolio Management,1997

5. Blass, E. (2016), “The future role of ‘the property professional’ – is there a role for a valuer?”, available at: www.api.org.au/sites/default/files/uploaded-content/website-content/20160912_future_the_future_role_of_the_property_professional.pdf.

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