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Quantifying Commercial Building Learning Rates in Australia

Final Report

Prepared for:

Date:

Department of the Environment & Energy

June 2017

Revision History

Rev No. Description Prepared

byReviewed by

Authorised by Date

00 Draft Report PH ET PH 12/6/2017

01 Final Report PH ET PH 27/6/2017

02 Final Report revised

PH ET PH 29/6/2017

03 Web-accessible version

PH ET PH 10/10/2017

© 2017 Strategy. Policy. Research.This document is and shall remain the property of Strategy. Policy. Research. Pty Ltd. The document may only be used for the purposes for which it was commissioned and in accordance with the Terms of Engagement for the commission. Unauthorised use of this document in any form is prohibited.

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Table of ContentsExecutive Summary.........................................................................................................................................

1. Background......................................................................................................................................1.1 Objective......................................................................................................................................1.2 Scope and Limitations..................................................................................................................1.3 Context.........................................................................................................................................1.4 Overview of Approach and Methodology....................................................................................

2. Macro Analysis.................................................................................................................................2.1 Background..................................................................................................................................2.2 Data Sources and Key Definitions.................................................................................................2.3 Methodology..............................................................................................................................2.4 Results........................................................................................................................................

3. Micro Analysis................................................................................................................................3.1 Scope of Analysis........................................................................................................................3.2 Methodology..............................................................................................................................3.3 Conclusions................................................................................................................................

4. Incremental Cost Analysis – NCC2019 vs NCC2016..........................................................................4.1 Scope of Costs............................................................................................................................4.2 Building Forms............................................................................................................................4.3 Impact of Real Elemental Cost Changes.....................................................................................4.4 Design and Elemental Cost Changes...........................................................................................4.5 Conclusions................................................................................................................................

5. Future Cost Directions....................................................................................................................5.1 Lighting.......................................................................................................................................5.2 High-Performance Glazing..........................................................................................................5.3 High Performance Chillers and Heat Pumps...............................................................................5.4 The Market Transformation Opportunity...................................................................................

Index of FiguresFigure 1: Real Price Trends, Unweighted Basket of 150 Energy-Related Building Products, 2005 –

2016 (Annual % Change).................................................................................................................Figure 2: Macro Analysis of Per-Unit Commercial Building Construction Costs, Australia, 2004 - 2015

.....................................................................................................................................................Figure 3: Real Price Trends, Weighted Basket of 150 Energy-Related Building Products, 2005 – 2016

(Annual % Change) and Australian Dollar Trade Weighted Index..................................................Figure 4 Commercial Stock Turnover Data,’000 m2 – Australia.......................................................................Figure 5 Value of building work done (private and public commercial sectors) – current prices, $

million...........................................................................................................................................Figure 6 Total Change in Price Indices Over time, %.......................................................................................Figure 7 Value of construction work done vs Commercial Stock Turnover Data............................................Figure 8 Comparison of normalised and un-normalised total value of building work done (2004-2016)

......................................................................................................................................................Figure 9 Average Cost of Construction in the Commercial Sector (2004-2016)..............................................Figure 10: Underlying Inflation, Australia, 1993 – 2011.................................................................................Figure 11: Wage Price Index, CP and RBA Core Inflation Rate, 2005 - 2016..................................................Figure 12: Australian Dollar Trade Weighted Index, 1984 - 2014..................................................................

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Figure 13: Quarterly GDP Growth, Australia, 2006 - 2016.............................................................................Figure 14: Overview of Real Cost Changes by Product Type, Composite Price Deflator................................Figure 15: Overview of Real Cost Changes by Product Class, Composite Price Deflator................................Figure 16: Unweighted Average Change in Real Costs, Building Product Basket, 2004 – 2016,

Composite Price Deflator..............................................................................................................Figure 17: Weighted Average Change in Real Costs, Building Product Basket, 2004 – 2016, Composite

Price Deflator................................................................................................................................Figure 18: Weighted Basket of Building Products Real Cost Movements cf Australian Dollar Trade

Weighted Index............................................................................................................................Figure 19: LED Lighting Energy Savings Project, US Department of Energy...................................................Figure 20: LED Lamp Price Projections...........................................................................................................Figure 21: US DOE Ranking of Emerging Non-Vapour Heat Pump Technologies...........................................

Index of TablesTable 1: Change in Construction Cost (%) by Building Form, Climate Zone and Window-to-Wall Ratio

.......................................................................................................................................................Table 2: Average Change in Construction Costs, All Building Forms, With/Without Change in Cost of

Building Elements..........................................................................................................................Table 3: Change in Benefit Cost Ratio due to Reductions in Elemental Costs, by Building Form and

Climate Zone, 45% Window-to-Wall Ratio....................................................................................Table 4 Commercial Stock Turnover Data,’000 m2 – Australia.........................................................................Table 5 Value of Building Work Done (private and public commercial sectors), $ million (current

dollars)..........................................................................................................................................Table 6 Annual Change in the Price Indices, %...............................................................................................Table 7 Results of the Regression Analysis.....................................................................................................Table 8 Correlation between the Value of Construction Work Done and Consumer and Producer Price

Indices...........................................................................................................................................Table 8 Relative Shares of the Components of the Total Value of Construction Work Done.........................Table 9 Annual Cost of Construction ($ per m2) in the Commercial Sector and the Change in Cost

Over Time.....................................................................................................................................Table 10: Product Classes and Types Covered...............................................................................................Table 11: Average Annual Change in Real Cost by Product Type, 2004 - 2016..............................................Table 12: Product Class Weightings...............................................................................................................Table 13: Energy Action Building Forms........................................................................................................Table 14: Change in Annual Energy Use (%), NCC2019 vs NCC2016 Stringency............................................Table 15: Change in Construction Cost (%) by Building Form, Climate Zone and Window-to-Wall Ratio

......................................................................................................................................................Table 16: Benefit Cost Ratios by Climate Zone, Building Form and Window-to-Wall Ratio...........................Table 17: Reduction in Total Construction Costs due to Reductions in Elemental Costs, by Building

Form and Climate Zone, 30% Window-to-Wall Ratio...................................................................Table 18: Reduction in Total Construction Costs due to Reductions in Elemental Costs, by Building

Form and Climate Zone, 45% Window-to-Wall Ratio...................................................................Table 19: Change in Benefit Cost Ratio due to Reductions in Elemental Costs, by Building Form and

Climate Zone, 45% Window-to-Wall Ratio....................................................................................Table 20: Average Change in Construction Costs, All Building Forms, by Climate Zone, With/Without

Change in Cost of Building Elements............................................................................................Table 21: Glass Cost Observations: Australia................................................................................................Table 22: US Department of Energy HVAC Cost Targets................................................................................

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Executive SummaryThis report aims to quantify the learning rate, or rate of change over time in the incremental costs of compliance, in the context of changes in the energy performance requirements in the National Construction Code for commercial buildings. Second, it addresses the question of whether there is any evidence that building construction costs rose as a result of energy performance requirements being introduced in the past (specifically, in 2006 and 2010) for commercial buildings.The relevance of these questions is that faster learning – for example, more rapid evolution in building designs, economies of scale for high-performance building elements, and innovation in construction techniques and materials – would reduce compliance costs, justifying higher energy performance standards and resulting in less energy consumption and lower greenhouse gas emissions. If learning does not occur, and the incremental costs of compliance with energy performance requirements remain static through time, then only lower performance standards would be able to be justified on economic grounds.The primary challenge in addressing this issue is that the costs involved in building projects are commercially confidential and not revealed in any published form. Second, key inputs to the analysis, such as the quantity of commercial building work done in Australia, are not captured in national statistical collections. Finally, question involves a counter-factual consideration – what would construction have cost (for a particular building or representative set of buildings) in the absence of (new) energy performance standards? In a commercial context, this question is never answered – what gets calculated (but not reported) is the costs of buildings in the presence of whatever standards are applicable at the time. To address these questions, we employ a range of methods that are feasible given the challenges noted.

Quantification of the Learning Rate

In Chapter 3 we examine real price trends over the 2004 – 2016 period for a basket of over 150 energy-related building elements, including insulation products, glazing, and different kinds of mechanical and electrical plant, including lighting, which were priced by quantity surveyors, Donald Cant Watts Corke.

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This analysis firstly indicates that real price movements for these products are highly volatile from year to year, although the causes of this volatility are beyond the scope of the current analysis. The majority of the price volatility occurs in time periods where Code changes did not occur, and from this we can deduce that factors other than Code changes are the causative ones.We find that, for the most part, the real cost of this basket of building products has declined over time, albeit at varying rates, and some insulation and fluorescent lighting products, and also chillers, appear to have experienced rising real costs over this period. Taken together, an unweighted basket of real cost changes indicates an average real cost reduction of 0.4% per year over this period, or around 0.2% per year on a weighted basis (see Figure 1). Some products, such as LED lighting, are experiencing much faster rates of cost reduction than the average, while others – such as high-performance glazing – have the potential to do so in future, but only if market conditions or policy incentives were to change – as described in Chapter 5.

Figure 1: Real Price Trends, Unweighted Basket of 150 Energy-Related Building Products, 2005 – 2016 (Annual % Change)

However, this observation is one input into a learning rate, not a learning rate in itself, as it simply measures the average rate of change in the real cost of a basket of products: it does not take into account design and specification changes required for compliance with new energy performance standards. Energy Action (EA) examines the latter issues in the context of its recent work for the Australian Building Codes Board. In SPR1702

short, EA finds that the incremental cost of compliance with proposed NCC2019 energy performance requirements is, on average, negative, and it is also negative for most building forms and climate zones (see Table 1 below).One key explanation for this result is that design parameters including the window-to-wall ratio (WWR) are carefully defined for NCC2019, which was not the case for NCC2016 and earlier. Because it is much cheaper to meet given thermal performance standard with conventional wall, rather than

glazed, construction, a lower WWR means lower construction costs. Overall, EA shows that construction costs for NCC2019 could be up to 52% lower than similar buildings compliant with NCC2016 – although, for some building forms and climate zones, construction costs are expected to rise – at the same time as 30% - 50% energy efficiency improvements are realised.

Table 1: Change in Construction Cost (%) by Building Form, Climate Zone and Window-to-Wall Ratio

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Source: Energy Action 2017

By contrast, the contribution of falling real building element costs to incremental compliance costs (noted above) is, on average, much smaller (with notable exceptions, such as LED lighting). Despite this, the falling cost of key building elements over time would be sufficient to reduce total construction costs, for the building forms modelled by Energy Action (EA) for NCC2019, by between 1.5% - 3.5% over a six-year period (depending upon the building and climate zone), a construction cost saving of between $120,000 and $130,000 per building, on average, for the building forms modelled.Adding this reduction effect due to element cost changes to those associated with design and specification changes almost doubles the savings in construction costs, modelled by EA, from 1.5% to 2.8%, on average for all the building forms and climate zones studied (see Table 2).

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Table 2: Average Change in Construction Costs, All Building Forms, With/Without Change in Cost of Building Elements

Average cost reductions - all climate zones

Change in average cost, design only (NCC2019)

-$49,461.36

% of base case (NCC2016)

-1.53%

Change in average cost, design + elements (NCC2019)

-$80,543.09


-2.83%

Difference attributable to elements only

-$31,081.73-1.30%

Perhaps more importantly, the expected change in building element costs lifts benefit cost ratios for the move to NCC2019, including in at least one case moving from a BCR less than 1 to greater than one, significantly increasing BCRs for some building form/climate zone combinations, or even turning incremental construction costs negative for some combinations where a positive incremental cost existed.

Table 3: Change in Benefit Cost Ratio due to Reductions in Elemental Costs, by Building Form and Climate Zone, 45% Window-to-Wall Ratio

CZ1 CZ2 CZ3 CZ4 CZ5 CZ6

CZ7 CZ8

Hotel (3A)

BCR – w/out cost reductions

-ve -ve -ve -ve -ve -ve

-ve -ve

BCR - with cost reductions

-ve -ve -ve -ve -ve -ve

-ve -ve

Office (5A)


-ve 3.16

1.00

8.22

1.08

1.23

-ve -ve


-ve 3.56

1.05

14.20

1.13

1.30

-ve -ve

Retail (6B)


3.40

3.46

21.23

2.68

9.97

1.58

12.83

1.76


4.09

4.42

-ve 3.26

21.11

1.75

-ve 2.23

Healthcare (9Ac)


1.11

1.65

12.03

0.88

3.79

0.62

3.92

0.31


1.34

2.27

-ve 1.13

18.19

0.73

-ve 0.38

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Evidence of Code-Related Construction Cost Increases?

Chapter 2 examines national statistical information over the 2004 – 2016 period to discover what evidence exists about per-unit construction costs at this macro level. In particular, we looked for any evidence of additional costs being incurred following the introduction of energy performance standards for commercial buildings in 2006 and 2010. Any such effects would tend to be lagged by at least a year due to transition arrangements – buildings that have already received building permits prior to a new standard being introduced are generally able to complete their design and construction at the old standard. This process could take 1 to 3 years. Further, the national statistical record in this area is highly incomplete. The Australian Bureau of Statistics publishes the value of construction work undertaken but not the volume, meaning that we have no record of output associated with this activity, and no top-down observation of unit cost. Also, the value indicators published confound new building work, demolitions, renovations and major refurbishment. In the absence of good statistical information about the volume of construction work done, models of the building stock turnover have to be constructed, but the paucity of statistical information means there is considerable uncertainty about this turnover, or even the total size of the commercial building stock in Australia.With these cautions, we find in Chapter 2 that there is evidence that per-unit construction costs rose in the two years following 2006 and 2010 (Figure 1).

Figure 2: Macro Analysis of Per-Unit Commercial Building Construction Costs, Australia, 2004 - 2015

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Multi-variate analysis suggests that the trend in this data is best explained by changes in construction input costs. These in turn are driven by concurrent movements in macroeconomic indicators such as real GDP, underlying inflation (including key elements such as real wage growth and energy/utility costs) and the exchange rate – for example as shown in Figure 3 below. While the data resolution is limited, we consider that the rising cost trend in these time periods (post 2006 and post 2010) is well explained by these macroeconomic factors and is not likely to reflect regulatory changes.

Figure 3: Real Price Trends, Weighted Basket of 150 Energy-Related Building Products, 2005 – 2016 (Annual % Change) and Australian Dollar Trade Weighted Index

Overall, we find no evidence in this data that the real cost of a large basket of energy-related building products was affected by the introduction of energy performance requirements into the National Construction Code in 2006 and 2010.

Future Cost Savings?

Finally, we note in Chapter 5 that building technologies such as LED lighting, high-performance glazing, and high-performance heat pumps and chillers have very substantial potential for further performance gains and/or unit cost reductions in coming years. While some of these changes are likely to flow through to the Australian building market without government intervention –

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such as LED lighting, which is already offering cost and longevity advantages over other technologies – certain key products, like high-performance glazing, are not likely to realise their full potential while they remain in high-cost niche markets. We recommend that the government examines the potential for cost reductions in high-performance glazing, and other critical, high-performance building products, via carefully designed market transformation initiatives, such as those used very successfully in many OECD countries.

Conclusions

No single learning rate number can adequately reflect the complex mix of design, specification and elemental cost changes that occur in reality. The range of outcomes varies in sign and magnitude depending upon the particulars of the building form and climate zone modelled. It is clear that the reduction in the real cost of building-related elements contributes around 0.2% (weighted) to incremental construction cost savings per year, but there is also evidence (from simulation modelling) that much larger cost reductions are attributable to the design and specification changes, reaching as high as 52% in some cases.Thus, a key insight from this work is that, when considering the incremental costs of compliance of buildings in response to a proposed change in energy performance requirements, it is critical that designs are optimised for the proposed new performance standard, in addition to taking expected changes in building element costs into account. Both effects – but in particular design changes – can more than offset any cost-increasing elements of standards leading, in this case, to a total reduction in construction costs at the same time as realising very significant (30% - 50%) increases in energy performance.Second, setting aside the design element, it appears that a basket of energy-related building costs has tended to fall over time at around 0.4% per year unweighted, or 0.2% per year weighted (by their average contribution to construction costs), over 2004 – 2016. While these appear modest values, they have been shown to almost double to value of construction cost savings in the specific case of the proposed change from NCC2016 – NCC2019 for commercial buildings, and also to enhance the benefit cost ratios materially for some building forms and climate zones. We propose that these rates be adopted a minimum assumption for future benefit cost analysis, noting that the contribution of design and specification changes to incremental cost reductions over time will be an order of magnitude larger, but highly specific to building solutions and climate zones, and will need to be simulated on a case-by-case basis.

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1. Background

1.1 ObjectiveThis project aims to:

1. quantify the rate or rates of change over time in commercial building construction costs that relate to the energy performance of the buildings (the ‘learning rate’);

2. project learning rates that are expected to apply over the period of application of NCC2019 – which may be to 2025.

It also examines the extent to which there is evidence that industry compliance with past energy performance requirements led to increased construction costs and, if so, to what degree and over what time period.

1.2 Scope and LimitationsThe primary limitation has been the availability of current cost data for key building elements on a consistent basis over the 2004 – 2016 period. As described in more detail in Chapter 4 in particular, there are important gaps in the cost record that we have been able to compile over time. Also, we reduced the number of cost elements covered, in response to feedback from quantity surveyors DCWC that certain cost data was not available (for the whole time period). This means that not all cost elements that may be affected by a change to the energy performance requirements of the National Construction Code fall within the scope of this analysis. In principle, these data gaps could be filled, for example by seeking alternative cost data sources. At the same time, qualitative changes in the energy services and performance of building elements also complicate longitudinal studies such as this. In Chapter 4 we note, for example, that typical sizes for packaged air conditioner (PAC) units has tended to increase over this period. While we have sought to limit the impact of such changes by measuring costs over time on the basis of $/kiloWatt of cooling capacity, changes in typical sizes can also affect unit costs due to changing specific energy consumption efficiency with changing size (not always, but typically, specific energy consumption per unit capacity will fall with increasing capacity). These effects are discussed further in Chapter 4.

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1.3 Context

1.3.1 What are learning rates? Why do they matter?In technology and innovation literature, learning rates are generally defined as the rate of change in the cost of a technology for every doubling in production volumes.

The ... learning curve, or experience curve, is a log-linear equation relating the unit cost of a technology to its cumulative installed capacity... A characteristic parameter is the “learning rate,” defined as the fractional reduction in cost for each doubling of cumulative production (Rubin, et al., 2015).1

Terms such as learning rate, learning curve, progress ratio, experience curve and others are used largely interchangeably, or with subtle variations of meaning. For a detailed treatment, see pitt&sherry (2016).2

In the context of regulation impact assessment and benefit cost analysis, we use the term learning rate to refer to the rate or rates at which incremental costs of compliance with regulation – in this case, energy performance requirements in the National Construction Code (NCC) – change over time. Learning rates have been highlighted in recent years as a key uncertainty that is likely to impact on the process of determining optimal performance requirements for future Codes. A RIS for Section J for NCC2019 is already in the process of being commissioned, for example.In Australia, the issue first become controversial in 2008 - 2009, during the process of determining the last energy performance requirements for commercial buildings, which were finalised by May 2009 and took effect from May 2010. The controversy arose during consultation on the relevant Regulation Impact Assessment, as that RIS assumed that the then-estimated incremental costs of achieving higher energy performance buildings would continue on indefinitely into the future – that is, that the learning rate would be zero. Many stakeholders at that time considered that such as assumption was not consistent with observable reality, as new technologies, new designs, economies of scale and the application of new know-how – innovation, by another name - mean that construction firms find lower cost ways of achieving required energy performance levels over time. Since, then and now, governments use benefit cost analysis to determine optimal energy performance requirements, assumptions about whether costs are static or falling over time directly affect the energy performance targets 1 Rubin, E, Azevedo, I, Jaramillo P, Yeh, S. (2015). ‘A review of learning rates for electricity supply technologies’. Energy Policy 86 (2015) 198-218.2 Pitt&sherry, Commercial Building Learning Rates – Final Report, August 2016, Chapter 2.

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that are indicated to be optimal. Many consider that an absence of learning rates was one factor that contributed to commercial building energy performance targets in 2010 being ‘soft’, even by standards of the day – and, as it has happened, those same standards remain in force today.

1.3.2 AECOM (2012)The concerns that were expressed in 2009 led, inter alia, to the then Department of Climate Change and Energy Efficiency (DCCEE) commissioning AECOM to prepare a report which was published as Understanding how the Building Industry Responds to Energy Efficiency Standards: Final Report, June 2012. Key findings of this report included:

Where design changes are made, costs of responding to energy performance regulation can be negative, but where building specifications are increased, costs can increase – the actual outcome in any given case will depend on decisions made by the construction industry

Different sectors of the building industry respond differently – generally it was argued that commercial buildings are more likely to be design-optimised, while small-volume home-builders are less likely to do so, and may therefore add cost to achieve compliance, while larger-volume home-builders may fall somewhere in between these two

The existing literature on learning rates is not easy to apply to buildings, as it generally focuses on individual technologies (building elements), yet design solutions for buildings vary widely, meaning that different elements are combined in different ways and designs, and so the overall cost impact is hard to quantify. The report did not that elements such as insulation and glazing to tend to have learning rates of around 4% per year – but these were measured (in line with the broader technology literature) as the rate of change in costs for every doubling of production levels

Traditional approaches to costing energy efficiency are likely to over-estimate actual cost increases – to the extent that learning effects and design changes are ignored.

The report recommended (p iii) that, “Going forward, assessments might adopt a more considered approach to estimating the costs of increasing energy efficiency standards that is more reflective of how industry responds to energy efficiency standards.“ For the commercial sector in particular it recommended (pp iii and iv) that the government: “Provide a long term plan for energy efficiency in the commercial buildings sector that makes clear commitments but allows flexibility for industry to achieve improved energy efficiency through

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a variety of ways” and “Provide training and information aimed at improving awareness of new approaches to energy efficiency for the commercial sector.” While the report contained limited quantitative analysis, it did find – as here – that observations of baskets of construction costs over time a) are highly volatile and b) may be considerably affected by factors unrelated to energy performance, such as changing in underlying commodity costs (steel, glass, aluminium, etc) and the supply/demand balance in specific markets (the building cycle). Distinguishing the incremental cost of compliance signal from the background noise is particularly challenging. It notes (p. 26) that “…industry learning from the design component…is likely to be high”, but this effect is not quantified.

1.3.3 Pathway to 2020Beginning in 2010, and guided by Measure 3.1.1 of the National Strategy on Energy Efficiency, the then Department of Climate Change & Energy Efficiency undertook a series of steps that was designed to put in place the kind of ‘long term plan for energy efficiency in the commercial buildings sector’ that AECOM called for in its 2012 Report. The Pathway to 2020 for Low-Energy, Low-Carbon Buildings in Australia: Indicative Stringency Study was published in July 2010, and recommended (p. 8), inter alia, that “the potential for industry learning to lead to reduced compliance costs over time” was taken into account in future regulation impact assessment of potential Code changes.This project was followed by a benefit cost analysis, published January 2012, in which a range of learning rate assumptions formed part of the scenarios modelled.3 This report noted that optimal energy performance standards were highly sensitive to learning rate assumptions. However, there was a lack of evidence regarding appropriate learning rate values.

1.3.4 pitt&sherry (2016a, b)In December 2015, the Australian Government published the National Energy Productivity Plan, which includes Item #31: Advancing the National Construction Code.4 pitt&sherry undertook a study entitled Initial Scoping Work for Implementation of NEPP #31 – Advancing the National Construction Code (June 2016), to determine short term research priorities to support future Code development, in particular giving weight to the use of ‘real world data’ upon which to base regulatory settings. The report recommended (p. 3), inter alia:

3 pitt&sherry, Pathway to 2020 for Increased Stringency in New Building Energy Efficiency Standards: Benefit Cost Analysis, January 2012.4 http://www.environment.gov.au/energy/national-energy-productivity-plan

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Working with industry to identify how designs, specifications and costs changed over time in response to past energy performance requirements.

As a next step, the Department of Industry, Innovation & Science commissioned a literature review and methodology study on commercial building learning rates, which was published in August 2016 as Commercial Building Learning Rates.5 This report found that while the phenomenon of learning is well-established and recognised around the world, no quantitative analyses or attempts to estimate learning rates for Australia’s commercial building industry appear to have ever been undertaken. Assumptions from overseas markets would risk to be questioned by stakeholders as not necessarily reflective of Australian market practices and prices. It therefore recommended a number of possible methodologies for quantifying commercial building learning rates for the Australian market, and two of these methodologies (here denoted ‘macro analysis’ and ‘micro analysis’) are embodied in this project.

1.4 Overview of Approach and Methodology

1.4.1 Macro AnalysisThe first method – which is detailed in Chapter 2 below – represents a ‘top down’ or macro analysis of national level data, to determine if that reveals any correlation between the introduction of building energy performance standards and increases in unit construction costs. Data from the ABS 8755.0 Construction Work Done, Australia, Preliminary, and potentially other sources, is compiled with stock turnover data, from sources including the Commercial Building Baseline Study6 and past models constructed for the analysis of regulatory proposals in the commercial building sector, to examine movements in unit costs over time. The analytical period begins prior to the introduction of performance requirements in 2006 to the present. Construction value data from ABS is examined for correlations with other causative factors, including inflation, exchange rate movements, commodity prices, construction activity cycles and potentially others, and normalised where appropriate. As noted in pitt&sherry (2016), this macro methodology has limited resolution and should not be expected to reveal strong relationships. However, it is useful to examine whether the limited available statistical information in this area reveals any movement in average construction costs around the time of the introduction of new standards (in 2006 and 2010).

5 pitt&sherry, Commercial Building Learning Rates – Final Report, August 2016.6 http://www.environment.gov.au/energy/efficiency/non-residential-buildings/commercial-buildings-baseline-study

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1.4.2 Micro AnalysisTo complement the top-down approach above, a second method is used to examine the evidence for real cost movements at the level of individual building elements or products, selecting a basket of those most relevant for energy performance. Working with quantity surveyors Donald Cant Watts Corke (DCWC), we document trends in elemental building costs over the period, as far as possible, from 2004 – 2016. Inherent challenges with this methodology include the imperfect record of cost information, and also qualitative changes in the nature and energy performance of building elements over this period of time. To the extent possible, we have controlled for this effect by tracking cost changes for a range of product sizes/specifications, or for ratios such as cost/kW of chiller capacity. DCWC has examined their own historical records, contacted suppliers and also drawn on reference sources such as Rawlinsons to quantify how the unit costs of relevant building elements has evolved over time.Second, we have aggregated the rate of change in real elemental costs up to the whole building level by using cost weightings from typical commercial buildings. This approach approximates the average rate of real cost change for the basket of building products that most impact on energy performance. However, this weighted average value is not exactly the same as the learning rate, as defined above. For this, it would be necessary to isolate just the elements that actually change, or that need to change, to move a given design from, in this context, compliance with NCC2016 to compliance with the proposed performance requirements for NCC2019 – and then examine the rate of change in the weighted average cost of these elements. This approach still overlooks design changes, which the above literature – and current work for the Australian Building Codes Board by Energy Action for NCC20197 – shows can be negative cost; that is, realise net cost reductions.Further methodological details are provided in Chapters 2 and 3 below.

7 Energy Action, NCC 2019 DTS Final Report – NCC Section J Revision, May 2017 (forthcoming).

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2. Macro Analysis

2.1 BackgroundThe overall aim of this analysis to quantify the rate of change over time in commercial building construction costs that related to the energy performance of buildings (the ‘learning rate’).A ‘top-down’ analysis of the national level data has been undertaken in order to determine if that revealed any correlation between the introduction of building energy performance standards and increase in unit construction costs. It seeks to establish historical cost trends, relevant to energy performance, over the 2004 to 2016 period covering Class 3 and 5 – 9 buildings.Data from the Australian Bureau of Statistics has been compiled along with stock turnover data, from sources including the Commercial Building Baseline Study and past models constructed for the analysis of regulatory proposals in the commercial building sector. A combinatorial analysis of these data was then conducted.What follows is a description of the data sources used in the analysis along with definitions of the key terms. This is followed by an outline of the top-down methodological approach; further reporting on the analysis of the results and a summary of the outcomes concludes this report.

2.2 Data Sources and Key DefinitionsUnless otherwise stated, all historical data on building activity in the tables and charts of this report are sourced from the Australian Bureau of Statistics. Data for value of construction work done and multiple price indices have been sourced from regular ABS quarterly releases. Data for stock building turnover is based on an internal analysis carried out by SPR and is the most recent and complete database of the commercial buildings available to date. The quarterly ABS data has been aggregated annually to align with the stock turnover data, which is reported annually. More detail on the data sources can be found later in this section along with a summary of the key definitions of terms used in the current analysis.

2.2.1 Commercial Building DefinitionCurrent analysis has been carried out for the commercial buildings nationally over the period from 2004 to 2016. ABS classifies commercial buildings as buildings primarily occupied with or engaged in commercial trade or work intended for commercial trade, including buildings used primarily in wholesale

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and retail trades, office and transport activities. This classification includes: offices, retail and wholesale trade buildings, education buildings, aged care facilities, religion buildings, health buildings, entertainment and recreation buildings, short term accommodation and other commercial buildings not elsewhere classified.8 As also reported by BIS Shrapnel,9 this classification includes office buildings primarily used in the provision of professional services or public administration (e.g. offices, insurance or finance buildings). Accommodation Buildings primarily providing short-term or temporary accommodation, including self-contained short-term apartments (e.g. serviced apartments), hotels (predominantly accommodation), motels, boarding houses, cabins, and other short-term accommodation not elsewhere classified (e.g. migrant hostels, youth hostels, lodges). Other Commercial buildings not elsewhere classified, such as garage - smash repair, marina, mail sorting centre, metering station and petrol stations. Warehouse Buildings primarily used for storage of goods, excluding produce storage. It should be noted that whilst SPR’s stock turnover database covers the total national commercial stock turnover, its categories are not directly comparable to the ABS’s as it aggregates the stock into somewhat broader categories: offices, public buildings (museums, libraries, galleries), aged care, education, healthcare and retail. To maximise consistency of the estimates, it was therefore deemed appropriate to limit the analysis to total commercial buildings.

2.2.2 Stock Turnover (Volume of Work Done)Building stock turnover10 is estimated using a stock model developed by SPR over many years, drawing inputs from diverse sources including GeoSciences Australia’s NEXUS database and the Commercial Buildings Baseline Study.11 Key values from this model are reported in Table 4 below. As noted in the Baseline Study (p. 17), there is significant disagreement between NEXUS and other sources of data on the absolute size of the commercial building stock. This issue will be researched further in the context of the forthcoming ASBEC Industry Vision/Code Trajectory project. For this project, is important to note that this uncertainty is unlikely to affect the signal we are looking for, in that different assumptions about the absolute size of the stock would, when matched with ABS value of work done data, simply change the implied average

8 http://www.abs.gov.au/AUSSTATS/[email protected]/Latestproducts/8752.0Glossary1Sep%202016?opendocument&tabname=Notes&prodno=8752.0&issue=Sep%202016&num=&view=9 https://www.bis.com.au/verve/_resources/BIA_June_2016_Extract.pdf 10 Turnover is estimated as the net change in stock by building class annually, plus an estimate of the portion of the total stock that undergoes knockdown/rebuild or major refurbishment each year, which is assumed to be 1%.11 COAG National Strategy on Energy Efficiency, Baseline Energy Consumption and Greenhouse Gas Emissions in Commercial Buildings in Australia: Part 1 Report, November 2012.

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value per unit of that work, without changing the underlying variability in that value. Commercial stock turnover data – which can be thought of as the volume of new work done annually – is reported in square meters.

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Table 4 Commercial Stock Turnover Data,’000 m2 – Australia

Year

Total stock - commercial '000 m2

Offices, '000 m2

Retail and wholesale

Healthcare

Education

Aged care

Public buildings (galleries, etc)

Hotels

2004

3665 1,609 1,046 184 501 244 4 77

2005

2710 642 1,022 324 369 234 -1 119

2006

3879 1,347 1,404 40 962 182 6 -62

2007

3993 1,354 1,488 -6 893 172 29 61

2008

4458 1,649 1,452 133 941 230 -13 66

2009

5248 1,831 1,508 -56 1,650 168 19 128

2010

4810 1,747 1,354 54 1,373 206 8 69

2011

3264 843 1,072 49 1,297 93 10 -100

2012

3155 881 1,098 283 600 119 10 165

2013

3399 956 1,028 297 773 167 0 178

2014

3901 924 1,231 422 905 216 0 204

2015

3910 1,052 1,263 242 886 246 0 219

2016

4229 1,454 1,156 231 929 274 0 184

Figure 4 below shows that the total volume of new construction work is highly variable from year to year, in a phenomenon that is known as the building cycle. It is notable for this project that construction activity in Australia peaked in 2009, at the end of the long boom that ended, with a lag, after the global financial crisis in 2008. Construction activity – a major economic activity in its own right – is correlated with a range of macroeconomic indicators, and it is

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very likely that these macroeconomic trends overwhelm any incremental cost and learning effects attributable to the National Construction Code, as is discussed further in Section 2.4.4 below.

Figure 4 Commercial Stock Turnover Data,’000 m2 – Australia

2.2.3 Value of Building Work DoneTo estimate the value of building work done, data from the 8752.0 - Building Activity, Australia, Sep 2016 (the Non-residential Building Work Done, by Sector, Australia)12 was compiled on a quarterly basis over the period from 2004 to 2016. ABS states that this data relates to ‘building activity which includes construction of new buildings and alterations and additions to existing buildings and does not include construction activity not defined as building (e.g. construction of roads, bridges, railways, earthworks, etc.)’. Statistics on the value of building work show non-residential building on a GST exclusive basis.13 This dataset contains the data on the value of the building work done for the commercial sector according to the functional classification of buildings (categories described above) and is split by private and public ownership. ABS data is reported in current prices, unadjusted for inflation. It should also be 12 http://www.abs.gov.au/AUSSTATS/[email protected]/Lookup/8752.0Main+Features1Sep%202016?OpenDocument 13 ABS states that this approach is ‘consistent with that adopted in the Australian National Accounts which is based on the conceptual framework described in the 2008 edition of the international statistical standard System of National Accounts (SNA08)’.

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noted that the data for the quarter ending in December 2016 was not available at the time of analysis and as such 2016 was excluded from the analysis.

2.2.4 Current Prices‘Current price’ estimates are valued at the prices of the period to which the observation relates – otherwise known as ‘dollars of the day’. For example, estimates for 2015-16 are valued using 2015-16 prices and estimates for 2006-07 are valued using 2006-07 prices. Current price time series therefore include the effect of inflation. This effect is later removed using a range of different price deflators.

Table 5 Value of Building Work Done (private and public commercial sectors), $ million (current dollars)

Offices

Retail and wholesale trade buildings

Education buildings

Aged care facilities

Health buildings

Entertain-ment and recreation buildings

Short term accommo-dation buildings

$3,120

$2,859 $1,673 $719 $734 $1,011 $780

$4,360

$4,563 $2,530 $928 $932 $1,407 $1,133

$5,395

$4,971 $3,227 $991 $1,236 $1,655 $1,309

$6,930

$5,381 $3,300 $1,365 $1,555 $1,887 $1,335

$8,373

$6,522 $3,614 $1,366 $1,859 $2,206 $1,495

$7,099

$5,063 $5,902 $1,152 $2,464 $2,037 $1,293

$5,591

$4,787 $13,892 $792 $3,348 $2,171 $833

$5,702

$5,616 $7,032 $761 $3,877 $2,120 $1,071

$6,131

$5,136 $4,806 $790 $4,384 $2,271 $1,114

$5,75 $5,732 $4,891 $978 $4,944 $2,222 $1,086

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6$6,17

5$6,368 $4,767 $1,135 $5,130 $2,116 $1,351

$6,676

$6,416 $4,254 $1,615 $4,839 $2,434 $1,968

Figure 5 Value of building work done (private and public commercial sectors) – current prices, $ million

Given that the data is presented in the current prices, it is therefore necessary to deflate the data by an appropriate index to eliminate the effects of inflation. There are multiple factors that may have had an impact on the total value of building work done over time. We have explored and attempted to eliminate the potential impacts of the key ones, including inflation, exchange rates movements, commodity prices, price of the construction output and the labour costs. More detail on each are given in the following section.

2.2.5 Price IndicesAs mentioned above, the total value of building work done is reported in current prices and the overall value therefore includes the impact of such factors as wage rates, cost of materials, price of construction output and

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potentially movements in exchange rates all affecting the shift in value. Given that the key goal of this work is to identify the trends associated with learning in construction, it is necessary to adjust the total value for impacts of these factors. ABS reports the data on a number of price indices that reflect both changes in price and changes in the composition of the aggregate for which the deflator is calculated. These indices can then be used to adjust for the effects of factors for which these indices have been developed.We have identified the following indices specific to the construction industry, that are relevant to the current analysis. These are:

Consumer Price Index (CPI) – changes in inflation over time; Wage price index (Construction) – changes in the cost of labour over

time; Trade Weighted Price Index – changes in exchange rates over time; Producer Price Indices:

o The Input to the House Construction Industry – changes in the cost of materials over time;

o The Output of the Construction Industries – changes in the price of the final construction output.

Each of these is described in detail below.

Consumer price index14

There is currently no single, universally accepted definition of a consumer price index. The ABS quotes description of a CPI in accordance with the following statement from the Resolution concerning consumer price indices released in 2003 by the Seventeenth International Conference of Labour Statisticians convened by the International Labour Organization (ILO):

"The CPI is a current social and economic indicator that is constructed to measure changes over time in the general level of prices of consumer goods and services that households acquire, use or pay for consumption. The index aims to measure the change in consumer prices over time. This may be done by measuring the cost of purchasing a fixed basket of consumer goods and services of constant quality and similar characteristics, with the products in the basket being selected to be representative of households’ expenditure during a year or other specified period." 15

14 http://www.ausstats.abs.gov.au/Ausstats/subscriber.nsf/0/4197C333BA60DD99CA2580CD00173744/$File/64610_2016.pdf15 ibid

14

http://www.ausstats.abs.gov.au/Ausstats/subscriber.nsf/0/4197C333BA60DD99CA2580CD00173744/$File/64610_2016.pdf

CPI tends to be universally used to identify the value of a unit of the output in today’s prices. It can be argued that when trying to determine a change in a construction cost over time, this deflator is too generic as it incorporates effects of factors not applicable to the construction industry. Moreover, the Australian CPI is specifically designed to provide a general measure of price inflation for households residing in the capital cities16. Therefore, several other indices, more specific to construction industry, have been explored.

Wage Price Index (WPI)17

As defined by ABS, the WPIs measure changes over time in the price of wages and salaries unaffected by changes in the quality or quantity of work performed. ABS publishes the quarterly WPI specific to construction industry as part of 6345.0 - Wage Price Index, Australia. It measures the total hourly rates of pay (excluding bonuses) for private and public sectors in the construction industry. This index can appropriately be used to normalise for the effect of changes in the labour costs not impacted by the changes in the performance standards.

Trade-Weighted Price Index18

Trade-Weighted Price index is published as part of the quarterly dataset: 5368.0 - International Trade in Goods and Services, Australia. It measures changes in the units of foreign currency per Australian dollar (AUD). In this analysis, we have used the aggregated weighted average index that combines changes in the following currencies: European (EURO), Japanese (YEN), United States Dollar (USD), United Kingdom Pound Sterling (GBP) and Special Drawing Right (SDR)19.

Input to the House Construction Industry Price Indices

The Input to the House construction industry price indices measure changes in prices of products used in house construction, where a house is defined as a detached building predominantly used for long-term residential purposes and consisting of only one dwelling unit. The scope of the index approximates the ANZSIC 2006 Class 3011 - House construction. Whilst primarily designed for a residential sector, this was the closest indicator available that measures a change in the cost of construction materials over time20. We used the weighted 16 ibid17 http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/6345.0Dec%202016?OpenDocument18 http://www.abs.gov.au/AUSSTATS/[email protected]/DetailsPage/5368.0Jan%202017?OpenDocument19 ”SDR is used as an international reserve asset to settle transactions between countries and help balance international liquidity. The value of the SDR is calculated by the International Monetary Fund (IMF) on the basis of a weighted basket of five currencies: US dollar; European euro; Chinese renminbi; Japanese yen; and UK pound. The IMF publishes the value of the SDR each day in terms of US dollars and the Reserve Bank of Australia provides an equivalent value in Australian Dollars” – as defined by RBA. http://www.rba.gov.au/glossary/20 ABS used to publish similar index for non-residential sector, but these series have been discontinued in 2004.

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average of 49 separate input price indices including iron and steel, concrete, cement and sand etc. where the weighting values are based on the quantities of various materials used in house building.

Output of the Construction industries price indices

The Output of the Construction industries price indices measure changes in prices of the outputs from construction industries, namely ANZSIC 2006 Subdivision 30 - Building construction industries, which consists of three classes: Class 3011 - House construction, Class 3019 - Other residential building construction and Class 3020 - Non-residential building construction. This analysis applies Index Number 3020 of this dataset derived for non-residential building construction in Australia. This index has been explored for its potential to eliminate effects of changes in the prices of construction outputs in the total value of the construction building work done. The chart and the table below shows the summary of the annual and total changes in the described above price indices over the period of 2004 to 2016.

Table 6 Annual Change in the Price Indices, %

Year Construction Output Index

Construction Input Index

Consumer Price Index

Trade-Weighted Price Index

Wage Price Index

2004 5.7% 2.3% 1.3% -1.8% 3.8%2005 4.3% 1.9% 2.3% 1.0% 3.3%2006 5.1% 3.0% 2.8% 0.0% 4.9%2007 5.2% 2.5% 2.5% 5.9% 4.0%2008 4.9% 6.2% 3.3% -14.7% 4.3%2009 -5.2% 0.9% 1.9% 15.1% 3.4%2010 2.7% 2.3% 2.6% 3.9% 4.1%2011 1.5% 1.5% 2.9% 0.8% 4.3%2012 -0.7% 1.2% 2.2% 2.0% 3.9%2013 0.5% 0.7% 2.8% -5.8% 3.2%2014 2.3% 2.9% 1.8% -2.7% 2.9%2015 1.8% 3.8% 1.8% -6.8% 1.9%2016 2% 2.4% 1.6% 3.3% 2.1%

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Figure 6 Total Change in Price Indices Over time, %

2.3 MethodologyThe macro analysis has been carried out in the following key stages, as described below.

2.3.1 Raw Data Analysis and Aggregation First, a quarterly database of the total value of work done in the commercial sector has been compiled for each sub-sector (see section 2.1 above) over the period from 2004 to 2016. The ABS categories were then aligned with the SPR’s commercial stock turnover data to investigate consistency in data categorisation. As a result of significant differences in sub-sectors categorisation, it was more appropriate to carry out the analysis on an aggregate rather than sub-sectoral basis.

2.3.2 Data Normalisation At the next stage, data normalisation requirements were assessed noting that the building work data was reported in “current prices” as defined by the ABS and therefore includes effects of changes in prices and other factors over time

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all affecting the total value of building work done. In order to normalise for these effects, a number of relevant consumer and producer price indices specific to non-residential construction industry has been identified (see section 2.4 for a detailed description). These indices are reported by ABS on a quarterly basis and measure a change in such factors as inflation, exchange rates, labour costs and change in the cost of material and construction output. To determine the presence of any underlying trends in the total value of construction work done, the above price indices were analysed for the presence of significant correlation between the data series.

Table 7 Correlation between the Value of Construction Work Done and Consumer and Producer Price Indices

Correlation The Output of the Construction Industries

The Input to the Construction industry

Trade Weighted Index (exchange rate movements)

Wage price index - construction

Consumer Price Index

Value of construction work done -commercial buildings

80% 61% 4% 58% 58%

Once the correlation patterns have been established, those price indices that did not display any significant correlation were eliminated from the analysis and the remaining ones were applied to the data (either wholly or partially) in order to normalise the data.As can be seen from the above table, Trade-weighted index is extremely weakly correlated to the value of construction work done suggesting a very weak or limited impact on the overall value. It was therefore deemed appropriate to exclude this index from the further analysis. This is perhaps an unsurprising result, as whilst some inputs to the construction industry are internationally traded, however, outputs considered are domestic construction and as such an untraded commodity. Further, one of the key impacts of the Trade-weighted index over recent years relates to the global financial crisis which is the source of a significant skew within the index itself.

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To avoid any double-counting and in the absence of the significant correlation, CPI has also been excluded from the analysis on the assumption that the effect of the key price changes has been captured by the remaining price indices. As each of the remaining indices has only a partial impact on the total value of construction work done, it is extremely challenging to separate an impact of each of these factors. An attempt has been made nonetheless. It has been assumed that on average, the total cost of construction consists of the three key components: profit margin, labour costs and cost of materials. The table below provides an indicative breakdown of the composite shares:21

Table 8 Relative Shares of the Components of the Total Value of Construction Work Done

1. Total Value of Construction Work

2. % share of the total

3. Price index Applied

4. Labour costs 5. 30% 6. Wage Price index7. Cost of Materials 8. 55% 9. Construction Input

Price Index10. Profit Margin 11. 15% 12. Construction

Output Price Index

Each of the indices has been applied to its respective share of the total value.

2.3.3 Calculating the Cost of ConstructionAt the final stage of the analysis the cost of construction in $ per square meter has been calculating by dividing the normalised total value of the building work done by the total commercial stock annually over the period from 2004 to 2016. It is expected that any changes in the total cost of construction will be reflective of the ‘learning rate’ over this period.It should, however, be noted that due to the limited data resolution and the lack of consistency across classification of subclasses between ABS and SPR’s stock turnover data, the results should only be interpreted with caution and any changes in values are only suggestive of the general trends rather than absolute values.

21 Note that the composite shares are indicative only and represent the industry average base on the aggregate of experts opinions. http://www.a4architect.com/2013/04/percentage-of-cost-breakdown-between-labour-materials-and-contractor-profit-in-construction/

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2.4 Results

2.4.1 Raw Data Analysis Figure 7 below shows the trends between the total value of construction work done in current prices (as reported by ABS) and commercial stock turnover data. This appears to show a good correlation between the two data series, but with the growth in building stock lagged by up to 2 years relative to the peak of expenditure. This is consistent with the length of major commercial construction projects and suggests that much of the project expenditure occurs early in the project life.

Figure 7 Value of construction work done vs Commercial Stock Turnover Data

2.4.2 Data NormalisationFigure 8 below shows the impact of normalisation on the total value of building work done. It can be see that after adjusting for the changes in labour costs (wage rates only), costs of materials and price of construction output, the trend of the increase in the total value of work can still be observed. More specifically, there appears to be an increase in the total value post 2006 peaking two years later. While this trend could be an indication of the change in the construction cost following the change in the energy performance standards, further explored in the next section where the total costs of construction are derived. Another important explanation for the data trends could be the business cycle, which peaked in 2008 just prior to the global financial crisis, and fell significantly thereafter.

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Figure 8 Comparison of normalised and un-normalised total value of building work done (2004-2016)

2.4.3 Unit Costs of Construction The final stage of the analysis attempts to calculate the average unit cost of construction by dividing the normalised value of building work done by the annual commercial stock turnover. The table below shows the results of this calculation.

Table 9 Annual Cost of Construction ($ per m2) in the Commercial Sector and the Change in Cost Over Time

Year

Total normalised construction cost - commercial buildings ($ per m2)

Change in the annual construction cost (%)

20 1,737

21

Year

Total normalised construction cost - commercial buildings ($ per m2)

Change in the annual construction cost (%)

042005

3,445 98%

2006

2,628 -24%

2007

2,905 11%

2008

2,962 2%

2009

2,087 -30%

2010

1,866 -11%

2011

2,890 55%

2012

2,959 2%

2013

2,778 -6%

2014

2,556 -8%

2015

2,508 -2%

Whilst the estimates of unit costs of construction are shown to be extremely volatile and due to data limitations cannot be interpreted in absolute terms,

22

there appears to be an increase in the normalised cost of construction peaking in 2005, with a modest rebound in 2007 and 2008, a decline in the period to 2010, with growth again in 2011 and 2012. These effects can be seen more clearly in the following chart and is reflected by the polynomial trendline (Figure 9).

Figure 9 Average Cost of Construction in the Commercial Sector (2004-2016)

2.4.4 Multivariate Analysis The following section reports on the results of the multivariate regression analysis, which attempts to determine what proportion (if any) of construction cost changes could be accounted for by the explanatory variables. The results of the regression are reported in the Table 10.

Table 10 Results of the Regression Analysis

Regression Statistics

Multiple R 0.973

R Square 0.948

Adjusted R Square 0.707

Standard Error 0.257

Observations 10

ANOVA df SS MS F Significance F

Regression 5 6.070 1.214 18.355 0.007

Residual 5 0.330 0.066

Total 10 6.401

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Coefficients Standard Error t Stat P-value Lower 95% Upper 95%

Intercept 0 - - - - -

CPI 59.763 33.784 1.768 0.137 -27.081 146.609

Wage Price Index -29.423 23.359 -1.259 0.263 -89.470 30.624

Construction Input Index 17.120 7.0207 2.438 0.058 -0.926 35.167

Trade-weighted Price Index 3.823 1.905 2.006 0.101 -1.074 8.721

Construction Output Index -3.937 3.940 -0.999 0.363 -14.066 6.191

As can be seen from the table above, despite a relatively good overall model fit (Adjusted R Square of 70%), none of the explanatory variables in the model are statistically significant, i.e. P-value <0.05. The variable, closest to being statistically significant, however, appears to be Construction Input Index, which may indicate that changes in construction costs have been mostly explained by the changes in the cost of construction materials, but strictly this result is not statistically significant.

2.4.5 InterpretationIn terms of measuring the change in the construction cost over time, once again the data resolution appears to be too coarse to allow for the precise measurement. Nonetheless, the overall trend undoubtedly shows the increase in the construction cost post 2006 and slowly declining to 2010 when it starts going up again before flattening off. Due to the Global Financial Crisis in 2008 (and accepting the principle of a 2-year lag) the decline in costs between 2008 and 2010 may in part be explained by the crisis, that is high capital investment projects being mothballed as the uncertainties in the markets required a risk premium to be paid on the costs of capital. As such lower cost projects may have made up a larger proportion of the overall mix of construction activities. So the anomaly of an increase in normalised cost of construction between 2010 and 2011 may simply indicate a rebound from the effects of the GFC. While the data resolution is insufficient to describe causation, the overall trend is consistent with a pattern of rising costs following the introduction of Code energy performance requirements in 2006 and 2010, followed by a decline in normalised costs thereafter. This is consistent with the pattern that would be expected with learning effects - however, as noted below, this is most likely to be chance rather than evidence of causation. At the same time, however, the period 2007 – 2008 was a period of exceptional growth in underlying inflation – see Figure 10 below.22 The Reserve Bank attributes this to rising commodity prices, sharp growth in labour costs (wages)

22 http://www.rba.gov.au/speeches/2011/sp-ag-240611.html

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http://www.rba.gov.au/speeches/2011/sp-ag-240611.html

associated with an exhaustion of spare capacity in the labour market at that time, and also sharp rises in energy cost and housing costs.23

Figure 10: Underlying Inflation, Australia, 1993 – 2011

Source: http://www.rba.gov.au/speeches/2011/sp-ag-240611.html

Figure 11 below confirms that both the 2007 – 2008 and the 2010 – 2011 period had strong growth in wages and at least headline inflation, as the economy picked up from the GFC.

23 See also https://www.rba.gov.au/publications/bulletin/2014/sep/pdf/bu-0914-4.pdf

25

http://www.rba.gov.au/speeches/2011/sp-ag-240611.html

https://www.rba.gov.au/publications/bulletin/2014/sep/pdf/bu-0914-4.pdf

Figure 11: Wage Price Index, CP and RBA Core Inflation Rate, 2005 - 2016

Source: bilbo.economicoutlook.net

Further, Figure 12 indicates that the value of the Australian dollar crashed sharply – albeit relatively briefly – in 2008, as part of the Global Financial Crisis. This is very likely to have exacerbated building construction costs at that time, as major items of mechanical and electrical plant in particular are imported.

Figure 12: Australian Dollar Trade Weighted Index, 1984 - 2014

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Also, if we examine the overall rate of growth in economic activity over this period, it can be noted that there was very strong economic growth in most of the quarters in the 2006 – 2008 period, and again in 2011 – 12 (see Figure 13).

Figure 13: Quarterly GDP Growth, Australia, 2006 - 2016

Source: https://edge.alluremedia.com.au/uploads/businessinsider/2016/12/AU-Q3-GDP-2016.jpg

Overall, we consider it most likely that these macroeconomic effects – changes in the key components of underlying inflation, in exchange rates and in GDP, largely account for the observed rise in the unit costs of construction during these years – particularly noting that the multivariate analysis indicates changes in construction materials input costs had the greatest statistical significance. While the data has a pattern that is broadly consistent with learning effects, this appears to be by chance rather than causation. This top-down methodology has insufficient resolution to determine possible small changes in construction costs due to Code compliance requirements from other much larger effects. For this reason, we also examine how (energy-related) construction costs have evolved over time from a bottom-up or micro perspective in the next chapter.

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https://edge.alluremedia.com.au/uploads/businessinsider/2016/12/AU-Q3-GDP-2016.jpg

3. Micro Analysis

3.1 Scope of AnalysisThis Chapter examines evidence available on how the cost of certain building elements – those that are relevant to building energy performance – have evolved over the 2004 – 2016 period, in nominal and inflation-adjusted terms. We stress that this data is not the same as the learning rate, as it describes the rate of change in a basket of absolute costs, and not incremental costs associated with meeting energy performance requirements. Nevertheless, the data is valid in its own right and forms an input into wider assessments of the learning rate.As noted in Section 1.4.2, data on the current cost of key building elements (ie, cost in dollars of the day) was sourced from quantity surveyors, Donald Cant Watts Corke (DCWC). DCWC in turn drew on their own data records, made contacts with equipment suppliers, and – where necessary, due to unavailability of data from these methods – referred to the industry-standard cost guides. In total, 153 individual equipment types were costed, covering the following product classes and types – see Table 11 below.Data was sought in current dollars, or dollars of the day, in order to enable different deflators to be applied, as discussed in Chapter 2 above. The primary results presented below use a composite price index as a deflator, as described in Section 2.3.2 above.Data limitations included limited coverage of LED lighting types. DCWC relied on Rawlinsons for this data, and that source has only tracked prices for LED products since 2014 – reflecting the recent commercialisation of this technology. Relatedly, the product offering in this area has been evolving rapidly. As a result, it is difficult to track cost changes over time on a basis that is consistent in terms of the energy service that the product delivers, and it is likely that this analysis undervalues the recent trend to higher performance and lower cost LEDS. Similarly, the T8 fluorescent tube technology is heavily represented in the data over this period, but would never form part of a contemporary lighting solution.In the glazing area, DCWC reported considerable difficulty in persuading suppliers to specify pricing for high performance products in particular, such as low-e coated sealed units. We speculate that this is because these are premium, high-margin products in Australia, and suppliers are unlikely to welcome transparency about their pricing practices…at least in the absence of an order for supply.

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Glazing costs are in any case highly variable as a function of glazing and framing specifications, which can move independently. Our time-series analysis has therefore been confined to some types of single and two lower-performance doubling glazing types. This is another area where colloquial evidence suggests that costs have fallen very significantly in recent years – primarily due to rising imports of sealed units from China in particular – but this is not reflected in the data available to this study.

Table 11: Product Classes and Types Covered

Products TypesInsulation K10 Kingspan Reflective Soffit (fibreglass/glasswool) or equiv.

Expanded Polystyrene Sheets (EPS)Extruded Polystyrene Sheets (XPS)Bradford Anticon foil faced insulation (fibreglass/glasswool) or equiv.Bradford Anticon foil faced building blankets (fibreglass/glasswool) or equiv.K10 Kingspan framing board or equiv.Cold Store Roofing PanelPipe insulation - PVC nitrile rubber (9mm - 19mm wall thick)

Glazing Single GlazingLow performance double glazing

Mechanical

Packaged air conditioner (PAC) Units

Air Cooled Chillers < 350 kWWater Cooled Chillers < 350 kWHot Water BoilersSteam Boilers (860 kPa)Condensing BoilersCompressible Fabric Flexible DuctingAir-to-Air Heat Exchangers

Electrical Electric Water Heaters Car Park FansLED light source tubeT8 / T26 lamp without diffuserT8 / T26 lamp with moulded prismatic diffuserT5 / T16 lamp without diffuserT5 / T16 lamp with moulded prismatic diffuser

Cost data on condensing boilers was not available for years earlier than 2016, with the primary reason being the very low sales of these high-efficiency units in Australia, which many European countries have long since required as a minimum standard.

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Other limitations include coverage of only one class of car park ventilation fan unit (2.2 kW), albeit that this is a common size.In a few cases only, linear interpolation was used to cover a one- or two-year gap in the cost data for a particular product. Where data was available only for a limited time period – as with many of the insulation products – no attempt has been made to estimate values in other years.

3.2 MethodologyThe broad approach to analysing this data is described in Section 1.4 above. In addition, we note that the primary treatments of the current dollar cost information were:

1. Converting absolute price changes to annual percentage changes, including converting unit costs (eg, for packaged air conditioners) into dollars per kW of cooling capacity, in order to overcome changes (increases) in the typical sizes of units supplied to the market over the 2004 – 2016 period

2. Adjusting current price data for cost inflation over time, using a range of deflators (see further below)

3. Conducting regression analyses for each product to identify the direction and magnitude of average annual real cost changes over time.

The overall results are first indicated in the composite Figure 14 below, which provide a sense of the overall shape of the data. The data shown are the simple averages of the change in real cost each year for the products within each class shown. Linear trend lines are added to provide an overall sense of the direction of change.

Figure 14: Overview of Real Cost Changes by Product Type, Composite Price Deflator

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Some conclusions are immediately available from inspecting these figures: the change in the real cost of most building products examined is highly

variable from year to year – this ‘noise’ is generally larger than the signal we are searching for, which is the average change year on year

the majority of products show a downward trend in real costs over time, particularly over the period since 2009 – some exceptions, like T8 fluorescent tubes, are likely to be being affected by diseconomies of scale at the end of the technology’s life24, while T5 costs appear to have been declining in real terms since around 2012

a number of products, including chillers and glazing, appear to show a price spike around 2009 – these results may be affected by exchange rate movements associated with the global financial crisis, as discussed in Chapter 2.

To gain an overall sense of the direction and magnitude of the real cost movements, we have compiled data for each broad product type, as a simple average of the annual values for each of the products costed. No weightings applied to this data. In the case of mechnical plant, however, we have left out 24 This effect is noted in Energy Action, ABCB Section J Revision: Artificial Lighting sub-report, March 2017, p. 15.

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the rising cost trend associated with air-to-air heat exchangers, and these are rarely used on Australian commercial buildlings. The results are presented as the composite Figure 15 below.

Figure 15: Overview of Real Cost Changes by Product Class, Composite Price Deflator

This series of figures confirms that, on average, the real cost of key energy-related building components has tended to fall over the 2004 – 2016 period. As also noted in the Chapter 2 macro analysis, there is evidence of a significant (in percentage terms) spike in the cost of many products around 2008 – 2009. This is most likely attributable to the sharp rise in inflation ahead of the GFC, as discussed in the previous Chapter. More broadly, movements in real prices for many products could be split into pre- and post-2009, with a generally rising trend to 2009, and often strongly falling thereafter – although lighting products

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in appear to have experienced a real price spike in recent years – which could be attributable to the increasing use of LED technologies which have been more expensive than their fluorescent equivalents. Notably this latter trend cannot be attributed to any Code effects, as there have been no stringency changes since 2010.More generally, we see no evidence in this data of real cost increases following the introduction of commercial building energy performance standards in 2006 and 2010. This appears to support the view in Chapter 2 that the apparent increase in unit construction costs post 2006 and 2010 is best explained by macroeconomic factors, and not explained by changes in the cost of energy-related building elements. Figure 15 also indicates that the rate of real cost reduction for these product classes is modest, generally less than 1% per year. The data is presented by product class in Table 12 below.

Table 12: Average Annual Change in Real Cost by Product Type, 2004 - 2016

Products Types Average annual change in real cost, 2004 - 201625

Insulation K10 Kingspan Reflective Soffit (fibreglass/glasswool) or equiv. +1.5%Expanded Polystyrene Sheets (EPS) +0.8%Extruded Polystyrene Sheets (XPS) -0.2%Bradford Anticon foil faced insulation (fibreglass/glasswool) or equiv. +1.9%Bradford Anticon foil faced building blankets (fibreglass/glasswool) or equiv. +0.4%K10 Kingspan framing board or equiv. +1.9%Cold Store Roofing Panel -1.7%Pipe insulation - PVC nitrile rubber (9mm - 19mm wall thick) +3.7%

Unweighted Average

All insulation. NB: full data set only available over 2006 – 2011, while some products above measured over a longer period – hence averages don’t agree

-0.3%

Glazing Single Glazing -0.5%Low performance double glazing -0.6%

Unweighted Average

All glazing -0.6%

Mechanical Packaged air conditioner (PAC) Units +1.8%Air Cooled Chillers < 350 kW -0.7%Water Cooled Chillers < 350 kW -0.6%Hot Water/Steam Boilers (non-condensing) -1.1%Compressible Fabric Flexible Ducting -0.3%Air-to-Air Heat Exchangers (large) +3.8%

Unweighted Average

All mechanical -0.7%

Electrical/gas Electric Water Heaters -3.2%Gas Water Heaters -0.7%Car Park Fans -1.8%LED light source tube -14.4% (3 years only)

25 Composite price deflator; some products measured over a shorter time period.

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Products Types Average annual change in real cost, 2004 - 2016

T8 / T26 lamp/luminaire +1.2T5 / T16 lamp/luminaire +0.5%

Unweighted Average

All electrical -0.4%

All Products - Unweighted

-0.4%

All Products - weighted

-0.2%

Overall, on an unweighted basis, the real cost of the above basket of building products has fallen by around 0.4% per year over the 2004 – 2016 period (Figure 16). It can be seen that costs were falling at a significantly faster rate until 2008. Also, the average results over this time period appear to be affected by an apparent jump in the real cost of many products in 2016.

Figure 16: Unweighted Average Change in Real Costs, Building Product Basket, 2005 – 2016, Composite Price Deflator

A similar, but slightly slower, rate of real cost reduction is evident when the above product list is weighted by their approximate contributions to the cost of a typical commercial building. For the weighting process, inspection of actual cost sheets for recent buildings indicated that the overal basket of products considered in this report would typically account for around 12% of total building costs, and around 15% of total construction costs. The share of each product within the 15% was grossed up to 100% to weight the relative contributions of each product class to the average cost of this basket of building products, as indicated in Table 13 below.

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Table 13: Product Class Weightings

Product Class WeightingInsulation (all products) 6.7%Glazing 4.3%PAC 20.2%Chiller 20.2%Boilers 6.7%Insulated flex duct 0.7%Lighting 27.0%Heat exchange 1.3%Water heaters 6.7%Car park fans 6.1%Total 100.0%

The weighted basket shows an average annual real cost reduction over the period of around 0.2% per year. This slow reduction on a weighted basis reflects the relatively high weighting given to packaged air conditioners and chillers, and these products showed very little change in real unit costs over time.

Figure 17: Weighted Average Change in Real Costs, Building Product Basket, 2004 – 2016, Composite Price Deflator

3.3 ConclusionsOverall there appears to have been a modest rate of real cost reduction, on average over the 2004 – 2016 period, in a basket of over 150 energy-related building products in Australia, averaging around 0.4% period year over the

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whole period on an unweighted basis, or 0.2% per year weighted by the products’ relative contributions to typical construction costs. Some products appear to have experienced a price spike around 2008 – 2009, which appears to have been related to the sharp drop in the value of the Australian dollar following the global financial crisis. The data does not appear to support the thesis that real costs rose in the period following the initial introduction of commercial building standards in 2006. While there is some evidence of a modest rise in average real costs around 2011 – 2012, which is the period following the first iteration of commercial building standards in 2010, the annual variability in costs is so large that no conclusions could be drawn from this short-term trend. More generally, it appears that real building component cost movements are strongly reflective on exchange rate movements over time. The correlation is consistent with Australia’s reliance on imports for key products within this basket.

Figure 18: Weighted Basket of Building Products Real Cost Movements cf Australian Dollar Trade Weighted Index

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4. Incremental Cost Analysis – NCC2019 vs NCC2016This chapter draws on costing data supplied by Energy Action Pty Ltd (EA), which is the detailed costing data drawn on in its report for the Australia Building Codes Board, NCC 2019 DTS Final Report: NCC Section J Revision, May 2017. The purpose of this section is to analyse the extent to which applying the findings from Chapter 3 above (regarding the rate of change in the real cost of certain building products) would alter EA’s findings.

4.1 Scope of CostsBuildings are almost in their entirety, including construction costs for the basic building shell. However, from one scenario to the next, the element where costs change include:

Wall construction Glazing HVAC, including air handing units (AHUs) and fan coil units (FCUs),

cooling tower, chiller, boiler, economy cycle Lighting

For reference, building type 9AC has 5 AHUs, 5A has 5 AHUs, 6B has 24 FCUs, 3A has 80 FCUs.Excluded from the cost analysis is:

Roof lights Components that did not have any incremental price difference between

NCC 2016 and 2019 – i.e. roof, ceiling, floor insulation26

Pumping systems Pipe, Vessel and Heat Exchanger Insulation Minimum outside air requirements – modulating CO2 control costs

included but no heat recovery costs considered as it wasn’t in the HVAC simulation models

Perimeter lighting Artificial lighting (interior) – we’ve only considered the change in

illumination power density (IPD W/m2) but not other provisions e.g. lighting control

Vertical transport.

26 As noted, we alter this assumption below.

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4.2 Building FormsEA reports findings for four building forms, each modelled in the eight NCC climate zones. The four forms (denoted 3A, 5A, 6B and 9aC) represent a hotel, office, retail centre and health care building respectively. Building geometry details are as shown in Table 14:

Table 14: Energy Action Building Forms


The simulation parameters for each building are described in detail in the above report, along with a description of the approach to the benefit cost analysis of the changes proposed for NCC2019 deemed-to-satisfy solution compared to NCC2016.

4.2.1 Key ResultsTable 15 below shows that the recommended stringencies for reference buildings in NCC2019 would reduce annual energy consumption by between 30% - 50% for most combinations of building forms and climate zones, albeit with a few exceptions (in the more extreme climates). Further,

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Table 15: Change in Annual Energy Use (%), NCC2019 vs NCC2016 Stringency


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Table 16: Change in Construction Cost (%) by Building Form, Climate Zone and Window-to-Wall Ratio


Noting that the proposed tighter NCC2019 stringencies reduce operating cost, while construction costs also fell in many cases, it is not surprising that EA reports that most benefit cost ratios came out negative. For those combinations of building from and climate zones where incremental construction costs were not negative, most showed a benefit cost ratio above – and in many cases well above – 1. Only three instances of BCRs less than 1 are noted in Table 17 below.

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Table 17: Benefit Cost Ratios by Climate Zone, Building Form and Window-to-Wall Ratio


4.2.2 AnalysisThe result that new commercial buildings could improve their energy performance, relative to current Code requirements, by 30% - 50%, while reducing construction cost, seems surprising at first glance. However, we note that quantum of recommended energy savings is less than that modelled as cost effective in the 2012 Pathway to 2020 benefit cost analysis by pitt&sherry. That report found that reductions of up to 68% could be cost effective by 2020.27 Second, a key reason why construction costs generally fall for EA’s NCC2019 solutions is that they embody design changes – and specifically, lower window-to-wall (WWR) ratios. WWRs one very important factor determining the incremental cost of compliance with new energy performance standards, because it is much more expensive for glazed areas to achieve the recommended thermal performance requirements (total u-value) than it is for wall areas. Therefore, the higher the WWR, the higher the cost of achieving a given performance standard, and vice versa.

27 pitt&sherry, Pathway to 2020 for Increased Stringency in New Building Energy Efficiency Standards: Benefit Cost Analysis, January 2012, p. 13.

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EA reports two scenarios for WWR in the NCC2019 versions of the buildings – one with 30% WWR28 and one with 45% WWR. The WWR used in NCC2016 models varies from about 75% to 15%, depending upon the results generated by the NCC2016 glazing calculator. This design factor helps to explain why construction costs fell in many cases, at least in the 30% WWR NCC2019 scenario, despite energy efficiency improvements generally between 30% and 50%, depending upon the climate zone (refer to Table 16 and Table 17 above).Even in the 45% WWR case, construction costs generally fell for most combinations of climate zone and building form, although with some exceptions. The only cost class where EA applied a learning rate was lighting, reflecting the rapid reduction in the effective cost of LEDs, as noted in Chapter 3. The finding of generally negative incremental costs is therefore all the more remarkable, in that potential cost reductions in other areas are not modelled. This highlights the critical role of design choices in determining construction costs.

4.3 Impact of Real Elemental Cost ChangesThis section examines the impact of applying the changes in the real costs of a basket of building elements to EA’s incremental cost analysis in the specific case of the proposed move from NCC2016 to NCC2019 energy performance standards. In particular, we examine the change in whole building costs, and also the change benefit cost ratios (BCRs). As noted above, since all of the BCRs for the buildings that feature 30% WWRs are negative, we focus primarily on the 45% WWR exemplars.First, applying the changes in real costs of building elements leads to modest, but still significant, changes in the total costs of construction of between 1% - 3%, for the 30% WWR buildings - see Table 18. This applies the cost reduction rates observed for insulation costs to wall construction costs; glazing cost reduction rates to glazing; and so on, while other building elements – and lighting – are not changed. Lighting is already modelled by EA to experience a 30% real cost reduction by 2021, so we avoid double-counting this effect. As with EA, we assume 6 years of cost reductions occur between FY2016 and FY2021, which EA take as around the mid-point of the first regulatory period to which the NCC2019 standards are expected to apply. Note that the impact of the real elemental cost reductions is larger for the smaller buildings, reflecting the higher share of total costs accounted for by HVAC plant in particular in smaller buildings.

28 Noting, as EA’s report does, that because the buildings include a plenum space, a 30% WWR is equivalent to a WWR of 40% to the occupied space for three out of four models.

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Table 19 shows the same results but for the 45% WWR versions of buildings. The reductions in total construction costs, due to elemental cost reductions over time, are a little higher, at between 1.5% and 3.5%. This reflects the higher costs of glazing for these buildings.

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Table 18: Reduction in Total Construction Costs due to Reductions in Elemental Costs, by Building Form and Climate Zone, 30% Window-to-Wall Ratio

Building Climate Zone 1 Climate Zone 2 Climate Zone 3 Climate Zone 4 Climate Zone 5 Climate Zone 6 Climate Zone 7 Climate Zone 8Hotel (3A) 1.74% 1.59% 1.68% 1.65% 1.64% 1.64% 1.57% 1.66%

Office (5A) 1.41% 1.28% 1.36% 1.27% 1.24% 1.22% 1.45% 1.58% Retail (6B) 2.25% 2.19% 2.26% 2.28% 2.22% 2.27% 2.32% 2.48%

Healthcare (9aC) 3.10% 3.08% 3.13% 3.14% 3.08% 3.14% 3.20% 3.16%

Table 19: Reduction in Total Construction Costs due to Reductions in Elemental Costs, by Building Form and Climate Zone, 45% Window-to-Wall Ratio

Climate Zone 1 Climate Zone 2 Climate Zone 3 Climate Zone 4 Climate Zone 5 Climate Zone 6 Climate Zone 7 Climate Zone 8 Hotel (3A) 1.22% 1.13% 1.20% 1.17% 1.17% 1.17% 1.12% 1.11% Office (5A) 1.00% 0.92% 0.98% 0.92% 0.90% 0.89% 1.04% 1.06% Retail (6B) 1.89% 1.86% 1.92% 1.93% 1.88% 1.92% 1.96% 1.97%

Healthcare (9aC) 3.33% 3.32% 3.34% 3.35% 3.32% 3.35% 3.38% 3.39%

Table 20: Change in Benefit Cost Ratio due to Reductions in Elemental Costs, by Building Form and Climate Zone, 45% Window-to-Wall Ratio

Climate Zone 1 Climate Zone 2 Climate Zone 3 Climate Zone 4 Climate Zone 5 Climate Zone 6 Climate Zone 7 Climate Zone 83A BCR – w/out cost reductions -ve -ve -ve -ve -ve -ve -ve -ve

BCR - with cost reductions -ve -ve -ve -ve -ve -ve -ve -ve5A BCR – w/out cost reductions -ve 3.16 1.00 8.22 1.08 1.23 -ve -ve

BCR - with cost reductions -ve 3.56 1.05 14.20 1.13 1.30 -ve -ve6B BCR – w/out cost reductions 3.40 3.46 21.23 2.68 9.97 1.58 12.83 1.76

BCR - with cost reductions 4.09 4.42 -ve 3.26 21.11 1.75 -ve 2.239aC

BCR – w/out cost reductions 1.11 1.65 12.03 0.88 3.79 0.62 3.92 0.31

BCR - with cost reductions 1.34 2.27 -ve 1.13 18.19 0.73 -ve 0.38

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Table 21: Average Change in Construction Costs, All Building Forms, by Climate Zone, With/Without Change in Cost of Building Elements

Average - all climate zones

Climate Zone 1 Climate Zone 2 Climate Zone 3 Climate Zone 4 Climate Zone 5 Climate Zone 6 Climate Zone 7 Climate Zone 8

Change in average cost, design only (NCC2019)

-$49,461.36 -$222,860.19 $176,798.22 $97,758.75 -$133,587.19 -$33,616.21 $131,992.58 -$352,887.77 -$59,289.11


-1.53% -8.58% 8.26% 4.32% -5.41% -1.43% 6.01% -12.98% -2.44%

Change in average cost, design + elements (NCC2019)

-$80,543.09 -$255,023.13 $147,055.52 $66,032.37 -$164,277.12 -$63,635.72 $101,679.04 -$384,836.50 -$91,339.17


-2.83% -9.81% 6.87% 2.92% -6.65% -2.70% 4.63% -14.15% -3.77%

Difference attributable to elements only -$31,081.73 -$32,162.94 -$29,742.70 -$31,726.38 -$30,689.93 -$30,019.51 -$30,313.54 -$31,948.73 -$32,050.07Difference attributable to elements only -1.30% -1.24% -1.39% -1.40% -1.24% -1.27% -1.38% -1.17% -1.32%

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While the cost savings may appear modest, their impact on the overall benefit cost ratio of moving from NCC2016 to NCC2019 is larger – and in some cases, very significantly so. Table 20 shows that, for example, building 9aC in Climate Zone 4 would move from not being cost effective, to being cost effective, due to these cost reductions. Building 5A in Climate Zone 3 becomes less marginal, while in many cases where the BCR is already high, the value of total construction costs in 2019 falls below the level in 2019, leading to a negative BCR. On average, costs fall by between $120,000 and $130,000 per building.

4.4 Design and Elemental Cost ChangesOur final analysis examines the expected change in total costs of construction, associated with the projected move from NCC2016 to NCC2019 energy performance requirements, with the design and specification changes modelled by EA (for the 45% WWR version of the buildings) only, and then with the additional effect of the real cost reduction rates for key building elements as calculated in Chapter 3, and projected forward to FY2022, as noted above. This analysis averages costs for all four buildings discussed above, with the aim of describing the overall trend.On average, the change in cost is negative – that is, a net saving – of 2.8% of total construction costs over six years, or a gross average of over $80,000 per building across the building forms and climate zones modelled. However, it may be noted from Table 21 above that increased construction costs occur in some climate zones. Without the expected reductions in the real cost of building elements, the average cost saving expected for NCC2019, versus NCC2016, is around 1.5% of total construction costs. Thus, the change in elemental costs over time almost double the expected construction cost savings in moving to NCC2019. The difference in the incremental cost of construction that is accounted for by the change in the cost of energy-performance-related building elements is -1.3% on average, over 6 years.

4.5 ConclusionsWhat is the learning rate for commercial buildings? Conceptually it is the set of design, construction and specification changes that firms make in response to changed energy performance standards. This project has found that, on average, a basket of energy-performance-related building products has reduced in real cost by around 0.4% per year unweighted, from 2004 – 2016, or around 0.2% per year weighted. Some products, such as LED lighting, are experiencing much faster rates of cost reduction than the average, while others – such as high performance glazing – have the potential to do so in

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future, but only if market conditions or policy incentives were to change – see Chapter 5.While the real cost decline appears modest, they are modelled to reduce total construction costs, for the building forms modelled by EA for NCC2019, by between 1.5% - 3.5% over a six-year period, even for the higher 45% WWR versions of the buildings. EA’s analysis is that the incremental cost of compliance with proposed NCC2019 energy performance requirements is, on average, negative, and it is also negative for most building forms and climate zones. A key driver of this result is design changes including lower WWRs – although a full explanation of the results is provided in EA’s Report, as referenced above. Combining these design changes with projected reductions in energy-performance-related building element costs almost doubles the average savings in construction costs, from 1.5% to 2.8%, on average for the building forms and climate zones studied. Perhaps more importantly, the expected change in building element costs lifts benefit cost ratios for the move to NCC2019, including in at least one case moving from a BCR less than 1 to greater than one, significantly increasing BCRs for some building form/climate zone combinations, or even turning incremental construction costs negative for some combinations where a positive incremental cost existed.Of the two effects – design changes and elemental cost changes – design changes are at least an order of magnitude more important than the elemental cost changes, leading to (modelled) reductions in construction costs of up to -52% (see Table 16).Overall, we conclude that no single learning rate number can adequately reflect the complex mix of design, specification and elemental cost changes that occur in reality. The range of outcomes varies in sign and magnitude depending upon the particulars of the building form and climate zone modelled. It is clear that the reduction in the real cost of building-related elements contributes around 0.2% to incremental construction cost savings per year, but there is also evidence (from simulation modelling) that much larger cost reductions are attributable to the design and specification changes that Energy Action model for the move to NCC2019, which more than offset the incremental costs for almost all climate zones and building forms modelled.Thus, a key insight from this work is that, when considering the incremental costs of compliance of buildings in response to a proposed change in energy performance requirements, it is critical that designs are optimised for the proposed new performance standard, in addition to taking expected changes in building element costs into account. Both effects – but in particular design changes – can more than offset any cost-increasing elements of standards

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leading, in this case, to a total reduction in construction costs at the same time as realising very significant (30% - 50%) increases in energy performance.Second, setting aside the design element, it appears that a basket of energy-related building costs has tended to fall over time at around 0.4% per year unweighted, or 0.2% per year weighted (by their average contribution to construction costs), over 2004 – 2016. While these appear modest values, they have been shown to almost double to value of construction cost savings in the specific case of the proposed change from NCC2016 – NCC2019 for commercial buildings, and also to enhance the benefit cost ratios materially for some building forms and climate zones. We propose that these rates be adopted a minimum assumption for future benefit cost analysis, noting that the contribution of design and specification changes to incremental cost reductions over time will be an order of magnitude larger, but highly specific to building solutions and climate zones, and will need to be simulated on a case-by-case basis.

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5. Future Cost DirectionsProjecting future costs is always fraught with risk, particularly as the above analysis has clearly demonstrated that the real cost movements of this basket of energy-related building products are highly volatile from year to year, with exchange rate movements offering at least some explanation of the causes of this volatility. This section examines the price outlook for key product segments, particularly those where significant cost changes appear likely or at least possible.

5.1 LightingLED lighting is very rapidly replacing fluorescent lamp technology in mainstream building applications. As noted in the Artificial Lighting Sub-Report by Energy Action (prepared in the context of the proposed Section J Revision for NCC2019), the price of linear LED recessed troffers and diffused battens has been falling over the 2012 – 2017 period at around 15% per year, which is consistent with the findings above, albeit over a shorter time period (p. 12). This analysis takes into account the technology performance change (increasing lumens per Watt), in addition to the technology cost change (reducing $/lumen). Energy Action notes that this would lead to around a 50% cost price reduction by 2021, although it adopts a more conservative 30% assumption for analytical purposes. Similarly, EA expect LED downlights to experience around a 45% price reduction over the period to 2021, or around 14% per year, again taking into account the expected increase in output efficiency in lumens/Watt (p. 15).US literature (noting the LED lamp development is being led globally by the US) suggests that LED household lamps are experiencing a learning rate (defined as price reduction per doubling of production) of around 18%.29 This paper also notes that the per-lumen price of LEDs has fallen by a factor of 10 in each decade since their invention in the 1960s (corresponding to a decline of roughly 25% per year) – although this observation relates to the LED lamp itself and not to commercial products, which include components that are not experiencing similar cost reductions. Overall, the US Department of Energy is currently projecting 75% total lighting energy savings in the US by 2035 due to the uptake of LEDs, along with near complete replacement of other lamp types by LEDs in most applications by that time.30

29 B.F. Gerke et al, Ernest Orlando Lawrence Berkeley National Laboratory, Recent price trends and learning curves for household LED lamps from a regression analysis of internet retail data, June 2015, p. 1.30 US Department of Energy, Energy Efficiency and Renewable Energy, Solid State Lighting, September 2016 (file:///C:/Users/spr99/Documents/StrategyPolicyResearch/Projects/SPR17XX/SPR1702%20DEE%20Learning%20Rates/energysavingsforecast16_summary_0.pdf)

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Figure 19: LED Lighting Energy Savings Project, US Department of Energy

Source: US Dept of Energy

This projection is based on an exponential learning rate that is forecast to lead to a 90%+ price reduction in the next 10 – 15 years, which is equivalent to an annual learning rate of around 12%. With LEDs being an internationally traded product, and with increasing production from low-cost regions including China, it is very likely that similar cost reductions should be expected in Australia over the same period.

Figure 20: LED Lamp Price Projections

Source: US DOE (ibid, p. 74)

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5.2 High-Performance GlazingBy contrast with LEDs, there is much less information available regarding expected future costs of high-performance glazing – in Australia or elsewhere. Amongst the very few references available, hipages31 suggests a premium of at least 25% over single glazed windows, and up to $800/sqm for timber-framed double-glazed windows. The same source offers the following cost observations for glass (glass only, not framed windows):

Table 22: Glass Cost Observations: Australia

Glass Type Cost ($/sqm)

Float glass $38Low iron glass $145Low-e (or emissivity) glass

$220

Double glazed glass $200Laminated glass $320

Source: https://www.homeimprovementpages.com.au

Bunnings offer an aluminium-framed double-glazed window (Polar Eco-View) for the equivalent of $260/sqm, although the technical performance of this window is not specified.32 There is colloquial evidence of imports of Chinese double glazing leading to lower costs in the Australian market, but hard evidence of this was not able to be located.The Beyond Zero Emissions Buildings Plan notes that uptake of double glazing in Australia is very low, estimated at 5%, compared with 70% or more in major European countries.33 This low share is primarily attributed to the relatively high costs of high performance glazing, due in turn to poor economies of scale. This report noted that, at the time, costs for double glazing in the UK were around half of those in Australia, with double glazing in the UK having a similar

31 https://www.homeimprovementpages.com.au/article/how_much_does_double_glazing_cost, viewed 10/6/2017.32 https://www.bunnings.com.au/polar-eco-view-600-x-1545mm-black-double-glazed-openable-window_p1040314 33 Beyond Zero Emissions, Buildings Plan, p. 72.

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https://www.bunnings.com.au/polar-eco-view-600-x-1545mm-black-double-glazed-openable-window_p1040314

https://www.bunnings.com.au/polar-eco-view-600-x-1545mm-black-double-glazed-openable-window_p1040314

https://www.homeimprovementpages.com.au/

https://www.homeimprovementpages.com.au/article/how_much_does_double_glazing_cost

cost to single glazing in Australia.34 This suggests prima facie that, with appropriate market conditions, high performance glazing costs could potentially be halved. However, this may require an active market transformation initiative to overcome existing market barriers, including limited competition in glass manufacture and limited demand for higher-performance product.

5.3 High Performance Chillers and Heat PumpsWhile the analysis above suggests that there has been no decline, on average, in the cost of chillers per kW over the 2004 – 2016 period, it is likely that the effective cost of chillers will fall due to the rising co-efficient of performance of water-cooled chillers in particular. A recent article by Alan Pears and Geoff Andrews notes that co-efficients of performance (COPs) of over 11 are now being achieved by the most efficient product.35 Internationally, the Clean Energy Ministerial Forum launched an Advanced Cooling Challenge in 2016, which aims to improve average air conditioner system efficiency by 30% by 2030, while also promoting climate-friendly and natural refrigerants. This initiative is supported by major manufacturers.36 One source suggest that heat pumps have experienced learning rates of 24% in Switzerland and 30% Germany, although the underlying sources are not stated.37 US DOE is promoting non-vapour-compression HVAC technologies, designed to achieve high efficiencies with natural refrigerants. A 2014 report ranked 17 new heat pump technologies as shown in Figure 21 below.

34 Ibid.35 See, for example, http://www.powerpax.com.au/products/id/21/cid/11/parent/0/t/products/title/WA260.6H+-+2750kWr+Water+Cooled+Chiller 36 http://www.cleanenergyministerial.org/News/cems-advanced-cooling-challenge-recruited-14-billion-in-investments-to-promote-efficient-cooling-technology-84021 37 http://www.climatetechwiki.org/technology/heat-pumps

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http://www.climatetechwiki.org/technology/heat-pumps

http://www.cleanenergyministerial.org/News/cems-advanced-cooling-challenge-recruited-14-billion-in-investments-to-promote-efficient-cooling-technology-84021

http://www.cleanenergyministerial.org/News/cems-advanced-cooling-challenge-recruited-14-billion-in-investments-to-promote-efficient-cooling-technology-84021

http://www.powerpax.com.au/products/id/21/cid/11/parent/0/t/products/title/WA260.6H+-+2750kWr+Water+Cooled+Chiller

http://www.powerpax.com.au/products/id/21/cid/11/parent/0/t/products/title/WA260.6H+-+2750kWr+Water+Cooled+Chiller

Figure 21: US DOE Ranking of Emerging Non-Vapour Heat Pump Technologies

Source: https://energy.gov/sites/prod/files/2014/03/f12/Non-Vapor%20Compression%20HVAC%20Report.pdf

The Clean Energy Ministerial target was based on earlier work by the International Energy Agency (IEA). In its 2011 Technology Roadmap for Heating and Cooling Equipment, the IEA set a goal of 30% improvement in heat pump energy efficiency by 2030, along with a goal of reducing system costs by 5% - 20% for high-efficiency technologies.38 US Building Technologies Office (a Division of the Department of Energy) then described the following cost reduction target potentials for 2020, noting that reductions of between 41% and up to 64% were expected in just the 2015 – 2020 period. This indicates the potential for an annualised learning rate of up to 15% per year.39

38 International Energy Agency, Technology Roadmap: Energy Efficient Buildings: Heating and Cooling Equipment, 2011.39 US DOE, Energy Efficiency & Renewable Energy, Building Technologies Office, The Future of Air Conditioning for Buildings, July 2016, p. 34.

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https://energy.gov/sites/prod/files/2014/03/f12/Non-Vapor%20Compression%20HVAC%20Report.pdf

https://energy.gov/sites/prod/files/2014/03/f12/Non-Vapor%20Compression%20HVAC%20Report.pdf

Table 23: US Department of Energy HVAC Cost Targets

While we have been unable to find estimates of future learning rates for heat pump based technologies, it would appear that the primary focus of international research is the move away from climate-damaging refrigerants while achieving a 30% improvement in energy efficiency by 2030. This may not lead to direct cost savings, but would facilitate significant operational energy cost savings in buildings.

5.4 The Market Transformation OpportunityMany countries have recognised that the costs of high-performance building elements, such as high-efficiency glazing, lighting and HVAC components, can be reduced over time through the judicious use of a mix of policy instruments, in an approach known as ‘market transformation’. The American Council for an Energy Efficient Economy (ACEEE) defines market transformation as “...the strategic process of intervening in a market to create lasting change in market behaviour by removing identified barriers or exploiting opportunities to accelerate the adoption of all cost-effective energy efficiency as a matter of standard practice.”40 It describes market transformation as the process of getting new, high performance products or

40 http://aceee.org/portal/market-transformation, viewed 27/6/2017.

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http://aceee.org/portal/market-transformation

designs to be taken up in the mainstream, without the need for ongoing support or cost. Successful market transformation is based on a detailed understanding of market dynamics – with, if anything, a leaning towards a deep understanding of the supply side of the market, although demand considerations are important. On the demand side, we need to inquire with potential users of low carbon products/buildings what is it that they require, what is it that they do and don’t like about these products, and how could these demand side barriers be overcome. On the supply side – and particularly where price is a major barrier – we need to ask why prices are high. Is it an inevitable consequence of the use of high-priced materials, or elaborate transformation techniques, or is it because demand and supply volumes are low, unit costs of production are failing to benefit from economies scale, market competition is weak, barriers to energy of new players high, or a combination of these and other factors? Similarly we can ask, is this product similarly expensive in other markets around the world, or is the relative price of the product higher here than elsewhere? Again, if so, why? Finally we can ask, what is the potential for cost and/or price reductions for this product in Australia? One technique is to examine the cost of inputs into a product, as compared to the price of the product itself – an approximation of gross profit margin. Is there evidence of ‘super’ pricing – ie, pricing well above the marginal costs of production? Similarly, what are the price trends over time in other markets? Is there an established learning rate from this or related product markets?From these and similar analyses, it is possible to identify suitable candidates for market transformation. Clearly, the potential for energy and emissions savings, as a function of reducing product price, would also be a selection criterion.While it is beyond the scope of this study to examine market transformation opportunities in Australia’s building market more closely, it is highly likely – given the analysis above – that many suitable candidates, and cost-effective market transformation strategies, would be identified. Give that cost benefit analysis is a, if not the, key tool relied upon by governments when making regulatory decisions – and specifically including Code energy performance standards – then actively intervening in markets to ensure that they are delivering efficient product pricing represents a significant opportunity to achieve higher energy performance standards – and better public policy outcomes – at lower societal cost. We suggest that a scoping study of potential candidate products in the building industry could be a valuable first step.

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