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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Purpose – The purpose of this paper is to address the apparent slow acceptance on the part of developers located in the USA to seek green certifications. If green-certified construction costs more than non-green construction, then is there a financial reason for not seeking a green rating. Do green buildings perform better than non-green buildings financially? The paper develops and presents a discounted present value model for doing a cost-benefit analysis for building green. This model enables an investor to determine the feasibility of constructing a new green-certified building instead of a conventional non-green building. Non-green buildings are not certified by a rating agency such as Leadership in Energy and Environmental Design (LEED), Energy Star or Building Research Establishment Environmental Assessment Method (BREEAM). Real estate permits are granted by local municipalities in the USA. This means that local government mandates requiring green construction that significantly adds to the initial cost of a project could have the unintended result of encouraging new non-green construction just outside their municipal boundaries. Design/methodology/approach – The paper collects publically available research data for office buildings located in the USA, and inputs this information into an income statement. It tests the hypothesis: is green-certified construction a financially feasible choice for an investor? An incremental approach using a 15-year holding period is presented. This time period takes into account equipment wear and tear. Heating/cooling systems and other green-technologically based operating systems have a limited life and do not last for 30 or 40 years. They are likely to need replacement after 15 years have lapsed. Findings – The negative net present value (NPV) results and high payback periods indicate that increased rents for green construction, a tax credit for the present value loss and/or property-tax reduction covering the shortfall is needed as an incentive to commercially build green. The implication of a negative NPV is that green office buildings will be built by government agencies where green is mandated, corporations that want a green image and benefit from this image, where local ordinances mandate green construction features and where local and federal tax incentives are available increasing a construction project's feasibility. Research limitations/implications – The limitation of any cost-benefit study is that analytical models and/or data used to forecast energy and water consumption savings in green-certified buildings compared to conventional buildings can be inaccurate. Forecasting models can understate or overstate the actual savings realized from green construction especially in the long-term given the difficulty of predicting equipment wear and tear, net rents and energy costs. The modeled percentage cost associated with green new construction features could remain constant or grow through time. Tables I and II results assume energy and water expenses remain a constant percentage over the 15-year period. The agency costs associated with obtaining a LEED or BREEAM certification was not calculated as an upfront cost. Certification by LEED or BREEAM increases the upfront cost associated with building a green building. Practical implications – The length of the payback period estimates coupled with negative NPV for green certified compared to non-green construction suggests that developers do not have an incentive to build green. Higher WACC rates would result in green-certified projects being less feasible to build. Social implications – The LEED certification point system may need to be reviewed. Points are assigned for features that improve occupant satisfaction, but may have little impact on reducing energy usage. Originality/value – A model is presented for determining whether green-certified construction is financially feasible. The model enables the investor to determine the size of a tax incentive that is needed to enable new green construction to be economically feasible to build. The higher the negative NPV the larger the income or property tax incentive or other financial incentives needed. Prior research studies compared green and non-green buildings, but did not compare the energy savings generated to the additional construction and upfront costs incurred using a discount rate. They assumed the energy savings justified the additional initial cost associated with building a new green certified.

Purpose This study aims to examine the permit changes enacted by the city of Portland, Oregon, USA, on the construction and subsequent short-term rental of tiny homes. The permitting process was eased by the city in 2014. The city’s enforcement of occupancy and rental ordinances, sometimes called Airbnb laws, were tightened in 2019. The new code restrictions are tighter than the rental codes that existed previously. Design/methodology/approach This paper uses time-series data to first consider the thesis that relaxing building permit requirements for tiny homes has encouraged legal construction and increased the number of applications filed with the city planning office. The number of permits was the dependent variable and time-sensitive dummy variable was the independent variable. An adjusted T-statistic was calculated using a least-squares regression model with a moving average autocorrelation adjustment. The second regression model considers the financial relationship between active listings on Airbnb and HomeAway to a housing price coverage ratio and the aggregated dynamic-factor model used to calculate the economic activity index for Portland. Findings There were two reported case study findings. The first regression used a dummy variable measuring the application response to permit easing. It was positive and significant. The second finding measures active host listings on Airbnb whether they are directly associated with the calculated multiple of the changes in the S&P/Case–Shiller housing price index low tier divided by weekly employee income. Higher numbers for this coverage ratio suggest that listings on short-term rental platforms are increasing directly with the ratio. The economic activity index is insignificant when predicting the level of listings. Regression results indicate that property owners are financially motivated to list dwellings as visitor rentals and possibly motivated to install tiny homes behind their primary residences as short-term rental units. Local economic conditions do not seem to influence the number of properties listed on short-term rental websites. Research limitations/implications Higher coverage ratios encourage property owners to list dwellings on short-term rental websites in the absence of enforceable rental restrictions. Without a method to quickly and feasible identify owners violating short-term rental restriction legislation and enforce fines there is a tendency for active listings to grow in a locale. San Francisco, California, under its new short-term rental ordinance requires online websites such as Airbnb to enforce permit requirements. San Francisco’s ordinance change seems to have resulted in a dramatic drop in active listings available for visitor rentals. Practical implications Information published by Inside Airbnb and Airdna does not separate entire dwelling information into categories such as single-family detached houses; tiny homes; apartments; or condominiums ownership types. Even public housing units are sometimes listed as short-term rentals. The aggregate data makes the relationship between active listings and the coverage ratio difficult to interpret. Listing information is limited and only available for a three-year rolling cycle on a quarterly basis for the city of Portland, Oregon. Social implications Future research studies could consider how tiny homes might play a role in providing permanent housing to local residents or for providing a shelter for the homeless in cities experiencing acute long-term rental shortages. Does limiting the number of homes available as short-term visitor rentals noticeably increase the quantity of housing and lower the monthly rental rates available to permanent residents of the city? Cities have passed short-term rental codes with the objective of increasing the availability of rental housing available to residents at affordable prices. Originality/value Prior research studies focused on who purchases tiny homes; tiny homes used as housing for the homeless; communities composed of tiny homes; and the connection between tiny home living and political activism. The study herein links permit changes to tiny-home building applications. It uses the home price index low tier and the economic condition index for the Portland metropolitan area to predict the number of active listings on Airbnb and HomeAway websites pre-regulation enforcement.

The Game of Thrones television program was widely seen throughout the world. The show acted as an advertisement for travel and home purchases in the Republic of Croatia. A hedonic least squares regression model adjusted for autocorrelation is used to consider the impact of the television show, tourist visits to the country and domestic personal income on the housing price index. The descriptive statistics and regression results suggest that the television show and tourism impact existing housing prices. Visitors to the country purchased or rented enough housing to cause demand to increase for residential properties which results in a higher housing price index. Per capita domestic income is not a significant factor influencing the housing price index for existing dwellings.