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The purpose of this paper is to investigate whether energy-efficient green buildings tend to provide net lease structures over gross lease ones. It then considers whether owners benefit by trading away operational savings in a net lease structure.

Empirical models of office leasing transactions in Sydney, Australia, with wider transferability supported by analysis of office rent data in the USA.

Labeled green buildings are approximately four to five times more likely than non-labeled buildings to use a net lease structure. However, despite receiving operational savings, tenants in net leases pay higher total occupancy costs (TOC), benefiting owners. On average, the increase in TOC paid by tenants in a net lease is equal to or greater than savings attributed to an eco-labeled building.

A full accounting of TOC in eco-labeled buildings suggests that net lease structures provide numerous benefits to owners that offset the loss of trading away operational savings.

The principal-agent market inefficiency, or “split incentive,” is a widely cited barrier to private investment in energy-efficient building technology. Here, a uniquely broad look at rental cash flows suggests its role as a barrier is exaggerated.

Automated 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.

The 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.

Using 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.

AVMs 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.

Using a unique data set, the purpose of this paper is to test the hypothesis that tenants pay increased accommodation costs for space in energy efficient office property.

The authors obtain lease contracts for office space in central Sydney, Australia. Empirical data on annual gross face rent and contract terms from each lease are combined with building characteristics and measured energy performance at the time of lease. Hedonic regression isolates the effect of energy performance on gross face rent.

No significant price differentials emerged as a function of energy performance, leading to a conclusion that tenants are not willing to pay for energy efficiency. Six factors – tenancy floor level, submarket location, proximity to transit, market fixed effects, building quality specification and, surprisingly, outgoings liability – consistently explain over 85 per cent of gross face rent prices in Sydney.

Rent premiums from an asset owner's perspective could emerge as a result of occupancy premiums, market timing or agent bias combined with statistically insignificant rental price differentials.

Tenants are likely indifferent to energy costs because the paper demonstrates that energy efficiency lacks financial salience and legal obligation in Sydney. This means that split incentives between owner and tenant are not a substantial barrier to energy efficiency investment in this market.

This study is the first to thoroughly examine energy efficiency rent price premiums at the tenancy scale in response to disclosure of measured performance. It also presents evidence against the common assumption that rent premiums at the asset scale reflect tenant willingness to pay for energy efficiency.