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

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.

Are there some skills valuers will ‘no longer need to learn’ in the digital technology environment?

“…These technological transformations are changing the way real estate markets operate in terms of data generation, storage, and sharing, thereby creating digital footprints of property data online as many market participants now prefer paperless reports (Valocity, 2022). Since the valuation profession heavily strides on the availability and accessibility of property/market data, these changes in information production and transfer among business clients in the property market enable valuers, with the aid of digital tools, to conduct in-depth investigations speedily as well as accurately collect and process data more easily (Filippova et al, 2022;Fristedt, 2018;Gindelsky et al, n.d.;Saull et al, 2020;Starr et al, 2021;Blackledge, 2009). At present, electronic devices for measuring dimensions, personal digital assistants (PDAs), laptop computers, smartphones, tablets, and drones have greatly enhanced how valuers collect and manage property data (Blackledge, 2009;Scheurwater, 2017).…”