Purpose
– The purpose of this paper is to exam the financial impact on the owner/lessor who is considering a partial energy upgrade to an existing medical office building. The owner who leases the building using a triple net lease does the upgrade prior to leasing the building, with the expectation of earning higher rents. How much should the owner who leases the property spend for a given rent per square foot increase?
Design/methodology/approach
– The empirical study highlights the impact of key financial variables on the dependent variable medical office construction spending put in place in the USA. The independent variables prime interest rate, cost of natural gas per therm and electricity cost per KWH, resale building prices are significant variables when predicting medical office construction spending. A case study using a cost-benefit model is developed. It inputs corporate income tax rates, incorporates a debt service coverage ratio, prime interest rate, analyzes investment tax credit (ITC) and rebate scenarios and varies the level of rental income and energy savings. The case study results provide insight into which factors are enabling higher net construction spending when considering a green energy retrofit project. Both the regression model and the case study model focussed on the owner of a building who rents medical office space to tenants using a triple net lease. The owner/lessor paradigm analyzes revenue enhancements, the tax implications of having these savings and benefits associated with borrowing when financing the green retrofit. The availability of low cost borrowing, increases in the ITC percent and rebates and increases in rent per square foot have an impact on potential energy upgrade spending.
Findings
– The empirical model finds the independent variables to be significant. Utility cost, resale value of office buildings, the prime interest rate, business bankruptcy court filings and unemployment rate fluctuations adequately explain movements in medical office building spending for the years 2000 through 2015 yielding a R2 of 73.8 percent. The feasibility case study indicates that the energy saving levels and ITCs not income tax rates are the primary drivers for a partial energy retrofit.
Research limitations/implications
– Market incentives are a function of the cost of energy. If the cost of energy drops, then the profit incentive to conserve energy becomes less important. The role of tax credits, rebates, property tax reductions and government directives, then become primary incentives for installing energy upgrades. The owner of an empty building assumes all of the operating costs normally paid by a tenant under a triple net lease. This possibility was not included in the replacement cost-benefit model used in this paper.
Practical implications
– The feasibility of doing an energy upgrade to an existing building requires that a cost-benefit analysis be undertaken. The independent variables that are significant when doing a regression model or proxies for these variables are incorporated into a present value model. The results in Table V can be used as an initial template for determining how much to spend per square foot when doing an energy upgrade. The square foot amounts can be applied to different size office buildings. The corporate income tax rate or a personal income tax rate has minimal impact on energy construction upgrade spending.
Social implications
– More energy efficient office buildings reduce the amount of greenhouse gases released into the atmosphere. Energy efficient buildings also conserve on scarce fuel reserves. ITCs and rebates limit the role of government in directing decisions to do energy upgrades. The market mechanism to some degree can help encourage energy conservation through asset upgrades.
Originality/value
– The paper incorporates an empirical model which is a form of technical analysis to examine independent variables that explain medical office building spending with a case study structured on expected revenues and costs which takes a fundamental approach to understanding the relationship between the dependent variable and its independent variables. The regression model combines factors that impact the demand for energy efficient medical buildings from an owner/lessor perspective which includes resale values of existing buildings, business bankruptcy filings and unemployment rates. Supply independent variables include the prime interest rate and electricity per KWH and natural gas per therm. The regression model found these variables to be significant. The case study uses the same independent variables or close proxy variables to determine the maximum financially feasible per square foot spending that can be invested in energy upgrades.
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