The Apartment Shortage
Keaton Jenner and Peter Tulip
Research Discussion Paper
RDP 2020-04
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The Apartment Shortage
Keaton Jenner and Peter Tulip
Research Discussion Paper
2020-04
August 2020
Economic Research Department
Reserve Bank of Australia
For helpful comments and suggestions, we thank Brendan Coates, Patrick D’Arcy, Andrew Doualetas,
Luci Ellis, Tom Forrest, Ryan Fox, Hugh Hartigan, Ross Kendall, Kirdan Lees, Sean Macken,
Phil Manners, Cameron Murray, Jonathan Nolan, Max Oss-Emer, Glenn Otto, Peter Phibbs,
Alicia Rambaldi, George Revay, Tom Rosewall, John Simon, Tim Sneesby, Nigel Stapledon,
David Tanevski and seminar participants at the Grattan Institute, Productivity Commission,
NSW Treasury, Reserve Bank of Australia and University of Sydney. We would like to thank the many
builders and developers that guided us through their financial statements. We would especially like
to thank Bill Becker and Daniel Rossi for help with data. The views expressed in this paper are those
of the authors and do not necessarily reflect the views of the Reserve Bank of Australia. The authors
are solely responsible for any errors. Our programs and publicly available data are available at
<https://www.rba.gov.au/publications/rdp/2020/2020-04/supplementary-information.html>.
Authors: jennerk at domain rba.gov.au and peterjtulip at domain gmail.com
Media Office: rbainfo@rba.gov.au
Abstract
This paper measures the excess demand for apartments in Australia’s largest cities. We estimate
that home buyers will pay an average of $873,000 for a new apartment in Sydney though it only
costs $519,000 to supply, a gap of $355,000 (68 per cent of costs). There are smaller gaps of
$97,000 (20 per cent of costs) in Melbourne and $10,000 (2 per cent of costs) in Brisbane. The large
gaps are sustained by planning restrictions. The shortage of apartments is most severe in the inner
suburbs of Sydney, where height limits prevent more construction. Elsewhere, restrictions on
converting low-density housing to apartments are important. High-rise apartments are a much less
costly means of supplying extra housing than the medium-density housing that some planners
favour.
JEL Classification Numbers: R31, R38, R52
Keywords: housing prices, apartments, zoning, land use
Table of Contents
1. Introduction 1
2. Relationship to Other Research 2
2.1 Objections 2
3. The Effect of Planning Restrictions 4
4. The Effect of Height Restrictions 6
4.1 Major Data Sources 6
4.2 The Role of Building Height 8
4.3 Financing Costs and Developer’s Profit 12
5. Variations in Apartment Shortages 13
5.1 Where is the Shortage Most Severe? 13
5.2 Changes over Time 17
6. The Cost of Building Out 18
6.1 Land Purchase Costs 18
6.2 Finance and Margins 19
6.3 The Cost of Building Out 20
7. What is the Cost-efficient Height for Apartment Buildings? 20
8. How Far Can Housing Prices be Lowered? 25
9. Directions for Further Research 26
Appendix A : Data 28
Appendix B : Comparison to Residual Land Valuations 32
Appendix C : Equations and Parameters 34
Appendix D : Hedonic Regressions 36
Appendix E : Sensitivity Analysis 39
References 41
Copyright and Disclaimer Notice 45
1. Introduction
Australian cities face a shortage of apartments. The severity of this shortage can be gauged by the
difference between what home buyers will pay for an apartment and what it costs to supply. For
example, we estimate that the average new apartment in Sydney sells for $873,000 but only costs
$519,000 to supply, a difference of $355,000 or 68 per cent of costs. The wedge is 20 per cent of
costs in Melbourne and 2 per cent in Brisbane. Why don’t builders and developers exploit these
profitable opportunities? The standard answer is that planning regulations stop them.
There are frequent media reports of people willing to pay hundreds of thousands of dollars more for
the legal right to add an extra apartment to their building. For example, in 2014 a property at
661 Chapel St, South Yarra in Melbourne was sold for $20 million when it was zoned for 13 storeys.
It was then rezoned for 31 storeys and sold later that year for $56 million (Lucas 2017). Loosening
restrictions added $36 million in value. For more examples see Kendall and Tulip (2018, Appendix A).
These anecdotes suggest that, but for planning restrictions, apartments would be readily supplied
for much less than current market prices.
While observations like these are common, it is not clear how representative they are. This paper
quantifies the shortage by comparing city-wide estimates of costs and prices. Kendall and
Tulip (2018) conducted a similar exercise, focusing on houses, with some simple estimates for
apartments. This paper takes a closer look at apartments, given that they are the focal point in
planning debates. Whereas estimates of the zoning effect for houses answer the question: why is
housing so expensive?; estimates for apartments are more relevant to the question: what do we do
about it?
The first objective of this paper is to examine the effect of building restrictions on apartment prices
in more detail, using data that Kendall and Tulip did not have. We use unpublished construction cost
data from the ABS, filter CoreLogic sale price data more finely, add an extra two years of data and
review other assumptions based on further consultation with the industry. This results in smaller but
qualitatively similar estimates of the zoning effect in Sydney and Melbourne and significantly smaller
estimates in Brisbane. A closely related objective is to examine the applicability of similar overseas
research, discussed in the following section, in an Australian context. We confirm the key qualitative
result of this previous research: building restrictions substantially increase the cost of housing.
Consistent with this, a key contribution of this paper is to assemble and compare representative data
for Australia’s largest cities.
Our second objective is to examine how the shortage of apartments varies across time, location and
building types. We find that, over the past decade, the excess demand for apartments has increased
substantially in Sydney, fluctuated without trend in Melbourne and declined in Brisbane. In Sydney
the excess demand is most severe in inner suburbs. In Melbourne and Brisbane excess demand is
more dispersed. Tall buildings are a substantially less costly way of increasing supply than the
‘missing middle’ of medium density in middle-ring suburbs promoted by some planners.
Our paper only looks at the costs of land use restrictions. Policy decisions need to weigh these
against benefits, which would be desirable to quantify. Pending that, we note that the literature
surveyed by Ahlfeldt and Pietrostefani (2019) suggests that net externalities of urban density may
be positive. Higher wages, more patent applications, less energy use and other benefits of density
2
are found to more than offset traffic congestion, shadows, noise and other costs. Local studies,
specifically Travers Morgan and Applied Economics (1991), Trubka, Newman and Bilsborough (2008)
and CIE (2010), are less comprehensive, but do not point to overall results being very different in
Australia. Glaeser, Gyourko and Saks (2005) specifically examine height restrictions and estimate
their external costs to be small. Moreover, many observers have a sense of whether the benefits of
lower density are ‘large’ or ‘small’ and these judgements can be compared with our estimates of
costs.
Our results have obvious implications for housing policy and town planning. They also contribute to
understanding the responsiveness of residential construction, a key parameter in the transmission
of monetary policy. And they help to explain the determinants and sustainability of housing prices,
which are important in financial stability policy.
2. Relationship to Other Research
Numerous studies have attributed the high cost of Australian housing to land use restrictions. These
include: OECD (2010); Kulish, Richards and Gillitzer (2011); Productivity Commission (2011, 2017);
Housing Supply and Affordability Reform Working Party (2012); RBA (2014); Senate Economics
References Committee (2015); CEDA (2017); Stevens (2017); and Daley, Coates and
Wiltshire (2018) among others. These papers’ views on land use restrictions seem to be strongly
influenced by anecdotal evidence like that mentioned in the previous section.
Clearly then, our paper cannot make any claim to originality for our main finding – planning
restrictions cause large increases in apartment prices. Rather, our contribution is to quantify this
effect, assess how it varies, and discuss some of the implications that follow.
Our method of measurement follows the widely cited approach of Glaeser
et al
(2005) for apartments
in Manhattan. Similar studies include Lees (2019) for apartments in Auckland, Wälty (2020) for
condominiums in Zurich and Geneva, and Cheshire and Hilber (2008) for commercial property in
Britain and Europe. Like us, these papers find that planning restrictions have large effects on prices.
Our paper differs by looking at Australian cities. Specifically, we examine Sydney, Melbourne and
Brisbane, which account for 72 per cent of all apartments in Australia (according to the
2016 Census).
1
2.1 Objections
Although reports like those cited above repeatedly argue that planning restrictions have large effects
on the cost of housing, this idea is controversial in public discussions. Some objections are worth
addressing.
It is sometimes argued that there is not a significant shortage of apartments because supply is
growing quickly. Phillips and Joseph (2017) and Murray (2020) subtract changes in household
formation from high levels of new construction and conclude there is an ‘oversupply’ of dwellings.
This approach was earlier popularised by the National Housing Supply Council (2014), though with
different results. Rowley, Gurran and Phibbs (2017) point to similar data and conclude that ‘Australia
1
Throughout the paper we use city names as abbreviations for Greater Capital City Statistical Areas (GCCSA). The next
largest concentration of apartments is Perth with 4 per cent of the national total.
3
is almost a world leader in rates of new housing production’ and that ‘supply seems pretty healthy’.
Pawson, Milligan and Yates (2020, Section 9.6) emphasise findings like these in explaining their
scepticism of the importance of planning restrictions. However, it is important to distinguish levels
from changes. Rapid growth in supply, relative to changes in population or the number of
households, implies the shortage is
decreasing
– it does not imply that the supply is adequate or
that housing is affordable. Similarly, whether prices and rents are rising or falling does not indicate
whether they are excessive. That can be judged by whether price is close to marginal cost. Looking
forward, population growth is forecast to temporarily fall following the COVID-19 pandemic, reducing
the demand for housing.
2
This would reduce the shortage, as we define it, but would not necessarily
create an oversupply. We discuss definitions further in the following section.
A very similar argument is that housing shortages can instead be identified by market frictions, such
as the rental vacancy rate, auction clearance rate or time on market. These measures are informative
for some purposes. However, in contrast to the difference between price and marginal cost, they do
not provide guidance on whether we need more housing.
A second objection is that high housing prices reflect high demand rather than limited supply. Factors
boosting demand include interest rates, taxes, financialisation and immigration at a national level
(Mulheirn 2017; Pawson
et al
2020, Sections 3.4.1 and 9.6) and nearby amenities at a local level.
High and rising demand is undoubtedly important but it does not mean that supply restrictions are
unimportant. On the contrary, high demand only results in very high prices when supply is inelastic.
For example, apartments in the inner suburbs of Sydney attract a ‘location premium’. As we show
in Section 5.1, this premium has been sustained because relatively few apartments have been built
in inner Sydney recently. In contrast, central Melbourne and Brisbane, where building has been
strong, do not exhibit a premium.
A third objection is that the correlation between prices and the severity of building restrictions is
weak, or even positive (Michael Buxton, as quoted in Ross (2019)). For example, the most expensive
housing is often found near the city centre, where the highest density is permitted. However, market-
level effects cannot be inferred from neighbourhood-level variations. Housing in nearby locations is
highly substitutable, so restrictions in one location increase demand and prices elsewhere. Planning
regulations increase the
average
price by restricting
total
supply. To see this, suppose odd-numbered
addresses were limited to four storeys and even-numbered addresses to eight storeys. Apartments
in adjacent buildings would still sell for approximately the same price, despite the variation in
restrictions. This argument has important implications for research design: spillovers in demand
mean that the effect of local restrictions cannot be inferred just from variations in local prices.
Researchers often complain about the difficulty in quantifying and standardising local planning
regulations, but it is not clear how disaggregated measures could be used to gauge market-level
effects on prices.
A positive correlation can also be seen in time series data: the effects of building restrictions are
estimated to have increased over time despite denser development being permitted. As with
cross-section comparisons, the perverse correlation arises because the restrictions are
partially
responsive to market needs. However, the wedge between cost and price we find shows restrictions
2
CoreLogic’s unit price index has ceased growing following the pandemic, with little change from March to June.
4
do not fully accommodate changes in demand, so they become more binding as demand rises over
time.
A fourth objection is that binding supply constraints are inconsistent with the pronounced sensitivity
of high-density building approvals to interest rates, sale prices and other demand conditions
(Sneesby 2020). For evidence of this sensitivity, see Saunders and Tulip (2019, Figure 4). The
problem with this argument is that it ignores that land prices, and hence developer’s equity, depend
on supply constraints in the short run. As discussed in the next section, construction is a highly
competitive industry. So land prices are bid up to levels at which developments become marginal.
This is often when collateral constraints start to bind. At this point, any downward revision to demand
prospects can make a project unviable in the short run; for example, by reducing developer’s equity.
In time, lower sale prices would flow through, almost one-for-one, to lower land prices. The original
developer would suffer a capital loss and be unable to proceed, at least for a while, but other
developers would find the project viable. Until that process is complete, the availability of finance
and construction activity will be highly responsive to demand. So planning restrictions bind in the
medium run while financing (in turn, a function of planning) binds in the short run.
3. The Effect of Planning Restrictions
A ‘shortage’ can be defined in different ways. We do not discuss the merits of alternative definitions
but we do want to be clear about what we measure. As shown in Figure 1, planning restrictions can
reduce the quantity of housing and thereby raise the price. The difference between P
Restricted
and
P
Supply
provides a measure of the severity of these restrictions and the shortage they cause. The
primary goal of this paper is to estimate this difference. This difference can be described as an ‘effect
of planning regulations’, ‘zoning tax’, ‘excess demand’ or ‘apartment shortage’, among other terms.
Figure 1: Stylised Apartment Market with Binding Quantitative Constraint
Price
Number of
dwellings
Supply
Demand
Q
Max
Effect of
building
restrictions
Q
E
P
Supply
P
Restricted
5
For many purposes, it does not matter why the gap arises or is sustained. If price exceeds marginal
social cost, then welfare is improved by increasing supply, regardless of the reason for the difference.
That said, we agree with the literature cited in Section 2 that the gap can be attributed to planning
restrictions. This approach may seem like labelling a residual. However, as discussed above, there
is abundant evidence of supply being restricted by planning regulations and this having large effects
on prices. In contrast, non-regulatory factors seem unlikely to be important.
The most obvious alternative explanation of the gap between price and marginal cost is imperfect
competition. However, Grattan Institute analysis of IBISWorld industry reports indicates that
apartment construction has low barriers to entry and low levels of concentration. The four largest
firms in the ‘apartment and townhouse construction’ industry account for only 19 per cent of industry
revenue (Minifie, Chisholm and Percival (2017), supporting data for Figure 1.3). According to ABS
Cat No 8165.0 (Counts of Australian Businesses, including Entries and Exits), 24,641 firms were
primarily engaged in other residential building construction in 2018/19. Of these, 822 businesses
reported annual turnover in excess of $10 million. A small number of firms build the very tallest
buildings and hence have market power over some specialised inputs, such as cranes or land in the
CBD. However, by their nature, these account for a small fraction of the industry-wide costs that
affect our estimates. More importantly, these firms sell their output in the broader market for
apartments (including sales from the existing stock), for which their market power is negligible.
Other non-regulatory explanations are simpler to dismiss. The persistence of excess demand, shown
in Section 5.2, makes the gap between price and cost difficult to attribute to transitory supply
adjustments. The severity of height restrictions makes it difficult to attribute to a shortage of land.
The size of the gap makes it difficult to attribute to momentary misperceptions, frictions or
measurement error. As we argue in the previous section, difficulties in obtaining access to finance
and the high cost of land should be interpreted as effects of planning restrictions, not as alternative
explanations. Similarly, it is sometimes suggested that speculators are withholding properties from
the market. But they would only do this if they expect higher prices in the future – that is, they
expect planning constraints to bind even more tightly. We acknowledge there are important times
and places where regulatory constraints are not binding. Section 5 identifies some of these and
shows that they are consistent with planning restrictions having a large effect on
average
apartment
prices.
Apartments can be supplied by either allowing builders to increase building heights (‘building up’) or
by increasing the number of apartment buildings (‘building out’). We provide estimates of the costs
of both these margins of adjustment. We use the term ‘height restrictions’ to encompass various
regulations, including floor space ratios (FSRs), that discourage building up, whereas a wider range
of land use restrictions discourage building out. Our main estimates are shown in Table 1 and
discussed in detail in following sections.
In Sydney, for example, the average new apartment sells for $873,000 but can be supplied for
$519,000, a gap of 68 per cent of costs. The gap is 20 per cent in Melbourne and 2 per cent in
Brisbane. We think it is fair to describe these effects as huge in Sydney, moderate in Melbourne and
unimportant in Brisbane. We calculate these gaps using the less costly method of supply, building
up. However, the difference in costs between building up and building out is often quite small. The
differences between cities largely reflect differences in apartment prices, with variations in costs
being secondary.
6
Table 1: Apartment Prices, Costs and the Effect of Building Restrictions
Per apartment, $’000, 2018
Sydney
Melbourne
Brisbane
Average new sale price
873
588
470
Cost of building up
519
491
460
Cost of building out
610
505
471
Effect of building restrictions
355
97
10
Effect as per cent of price
41
16
2
Effect as per cent of cost
68
20
2
Note: Data sources and estimates are explained in Section 4 and Appendix A
Our estimates of the effect of building restrictions in Sydney and Melbourne are a bit smaller than
those of Kendall and Tulip (2018). Our estimate for Brisbane is substantially smaller, being revised
down from $110,000 to $10,000 per apartment. The revisions to prices reflect the use of building
characteristics to filter townhouses and updating of data to 2018. Revisions to cost estimates are
discussed in Appendix A.
4. The Effect of Height Restrictions
4.1 Major Data Sources
In the following paragraphs we give a brief summary of our major data sources, then turn to some
of the more interesting and difficult assumptions. We discuss details of data construction in
Appendix A. We view these details as important – perhaps as the main contribution of the paper –
but no conceptual issues are involved and we recognise that data technicalities are primarily of
interest to specialists. For both prices and costs we use the ABS definition of an apartment: a unit
in a multi-dwelling structure that shares a common entrance and does not have private grounds.
Contrary to some usage, an apartment need not be in a tall building.
Our estimates for apartment prices are based on transaction-level data from CoreLogic for 2016.
The raw data provides prices from a very large sample of unit sales in 2016. These estimates,
comparable to those widely discussed in the media, are shown in row 1 of Table 2. Conceptually,
we are more interested in apartments than units (which include townhouses) and in new sales than
the average price. In practice, data anomalies are of comparable, if not more, importance. We adjust
the data to provide estimates of the average price of new apartment sales in 2018, shown in row 2.
3
The multiple filters and adjustments are explained in Appendix A.1. Several of the individual
adjustments raise or lower prices by a few per cent, and hence affect overall conclusions for Brisbane
but not Sydney or Melbourne. In net terms they tend to be offsetting. As can be seen in Table 2,
our final estimates are quite similar to the original data.
3
2018 is the most recent period for which we have many disaggregated data series.
7
Table 2: Apartment Prices
$’000
Sydney
Melbourne
Brisbane
Unfiltered average unit price (2016)
884
578
475
New apartment prices (2018)
873
588
470
Note: Data sources, details and estimates are explained in Appendix A
Our main data source for costs is the ABS Building Activity Survey. The ABS has published estimates
of average construction costs for apartments by state in 2017/18 in ABS (2019a), reproduced in
row 1 of Table 3. We use unpublished estimates for major cities, adjusted to be in 2018 prices,
shown in row 2.
4
These data are somewhat volatile, in part because building height varies from year
to year. As it is
expected
costs that affect building decisions, we smooth through the data as
discussed in Appendix A.2 and focus on predicted average cost, shown in row 3.
Table 3: Apartment Supply Costs
$’000
NSW/
Sydney
Victoria/
Melbourne
Queensland/
Brisbane
Average state construction cost (published, 2017/18)
342
310
312
Average capital city construction cost (2018)
323
295
285
Predicted average construction cost (2018)
340
312
287
Marginal construction cost
(a)
364
350
316
Professional fees (3 per cent of total costs)
12
12
11
Marketing and sales (5 per cent)
20
20
18
Finance (7 per cent)
29
28
26
Developer’s margin (17 per cent)
(b)
74
71
64
Infrastructure charges
(c)
18
10
26
Total cost of building up
(d)
519
491
460
Notes: Sources for most entries are discussed in the accompanying text, with further details in Appendix A
(a) Explained in Section 4.2
(b) Explained in Section 4.3
(c) Includes development levies and Voluntary Planning Agreements
(d) Rows do not sum to totals due to rounding
The average cost estimates include the cost of building the primary structure, GST, the cost of
constructing internal parking, foyers and other common areas, architect fees and builder’s margins.
They exclude costs of land acquisition and preparation, demolition and moveable furnishings. We
suspect that some costs are not included by survey respondents such as legal and management
fees, marketing costs and infrastructure contributions. We add these to the totals presented in
Table 3, based on the estimates in Urbis (2011) and CIE (2011). These are reports by industry
consultants commissioned to examine the cost of supplying housing. We have crosschecked these
estimates with financial statements from developers and – with a few qualifications discussed in
later sections – they line up. Two more important adjustments, the costs arising from increased
4
We are very grateful to Bill Becker and Daniel Rossi of the ABS for their assistance in providing this data and helping
us with its interpretation. The ABS data we use – both published and unpublished – are available in the supplementary
information published with this paper.
8
height and developer’s margins, are discussed in the following subsections. The final row of Table 3,
the total cost of building up, is also shown in Table 1.
4.2 The Role of Building Height
Extra apartments can be supplied by raising the height of future buildings. This increases average
costs due to a need for stronger reinforcing, more space for lift wells and extra safety requirements.
Partially offsetting these, larger construction projects benefit from economies of scale such as
specialisation in labour and machinery and the sharing of utility connections, walls and other fixed
costs. Some of these factors might be expected to give rise to discontinuities – for example,
sprinklers are required in buildings above three storeys (FPAA 2018) – but these are not evident in
our data. The relationship between height and costs is a major determinant of housing density, and
the data surprise some readers, so we discuss this in some detail.
Figure 2 shows average construction cost per apartment for different building heights for our three
cities from 2013 to 2018. The data are an unpublished disaggregation of the average capital city
construction cost estimates in Table 3, discussed in ABS (2019b). The size of each circle reflects the
number of building completions at each height. The horizontal axis is on a log scale, to focus on
shorter buildings, which are more numerous. Three important relationships are clear:
1. Average construction cost does not change much with building height. So large increases in
housing are possible without a substantial increase in the cost of supply.
2. Nevertheless, there is a small positive correlation. Apartments do tend to become a bit
more
expensive to supply as building heights increase.
3. It costs slightly more to build apartments in Sydney than in Melbourne, followed by Brisbane.
We summarise these relationships with the following rule of thumb:
( $/ ) $2,291Average cost in dwelling Base cost number of storeys
(1)
where the base cost is $316,337 for Sydney, $273,450 for Melbourne and $258,470 for Brisbane.
These estimates are from a regression of the 85 observations (representing 3,732 buildings) plotted
in Figure 2.
5
The regression sample is 2013–18; we scale the coefficients to 2018 prices using
changes in the other residential producer price index (PPI). We have relatively few tall buildings in
Sydney or Brisbane. For example, in Sydney fewer than 1 per cent of apartment building completions
in our sample are above 30 storeys. Accordingly, we assume that costs in Melbourne provide a guide
to what tall buildings would cost in Sydney or Brisbane. Specifically, we constrain the slope
coefficients to be the same, though intercepts are allowed to vary. This constraint is significantly
rejected, but extrapolating unrestricted coefficients would imply large differences in costs of very
tall buildings across cities which would be inconsistent with other data sources, such as
Rider Levett Bucknell (2017). Moreover, similar slope coefficients would be expected given that
construction techniques, architectural design and the cost of labour and materials are similar across
cities. We weight the regression observations by the number of buildings, on the assumption that
5
The ABS data are generally available at an individual storey level up to 20 storeys. Beyond this height, buildings are
grouped into larger categories to preserve confidentiality and we use the midpoint of the range.
9
each building provides an independent observation on the relationship. We show some alternative
specifications in Appendix E. A regression that is unweighted or weighted by the number of
apartments would have a flatter slope. Excluding the tallest buildings would increase the slope
estimate slightly, but this approach seems like ignoring relevant information.
Figure 2: Average Apartment Construction Costs
Circle size represents number of buildings, 2013–18
Source: ABS (unpublished)
A more complicated model could allow for the possibility that apartment characteristics vary with
building height. If these characteristics also varied with costs then our slope coefficient would be
biased. Apartment size, as measured by gross floor space per apartment, is weakly correlated with
building height and is not statistically significant when included in our regression (
p
-value = 0.99).
We do not have good data on how costs might vary with location or other dimensions of quality. We
expect that future research using information on building characteristics could develop a more
detailed model.
Figure 3 compares our estimates with others. The thicker black line labelled ABS is an unweighted
average of our estimates for Sydney, Melbourne and Brisbane. Alternative estimates come from a
range of countries and are estimated with different methods. The ABS figures appear to be broadly
in line with most of these other data sources. Most importantly, they are close to the estimates from
10
Rider Levett Bucknell (the orange line, labelled RLB), the other data source we have for Australia.
The RLB estimates, like several others, hold quality constant.
6
The flatness of the empirical cost profiles shown in Figure 3 contrasts with the steep profiles assumed
in calibrated models of urban structure. For example, in an Alonso-Muth-Mills model of Australian
cities, Kulish
et al
(2011) assume, following the international literature, that the elasticity of housing
production with respect to the capital-to-land ratio is 0.6. Given some simple assumptions, that
implies the average cost of supplying an apartment increases by 6.7 per cent with every 10 per cent
increase in height, which would be steeper than any of the empirical estimates in Figure 3. These
models may be attributing the flat, sprawling nature of our cities to unrealistically high costs of
building up instead of to planning restrictions. (Though Kulish
et al
also find planning restrictions to
have large effects).
The cost schedules in Figure 3, or marginal costs derived from them, can be interpreted as
representing a relatively flat short-run supply curve for apartments in the absence of planning
restrictions. We discuss this interpretation further in Section 8. In contrast, empirical estimates of
the actual supply of apartments find it to be highly price inelastic. For example, Saunders and
Tulip (2019, Figures 4 and 7) estimate that a sustained 10 per cent increase in price would
temporarily boost construction of high-density housing by 30 per cent. However, this response is
short-lived and the housing stock only increases by 0.7 per cent. So the estimated medium and long-
run price elasticity of supply is only 0.07 (not a typo). Planning restrictions do not prevent all building,
but they do make it much less responsive to relative prices than it would be otherwise.
6
Some other comments. Picken and Ilozor (2003) for Hong Kong and Blackman and Picken (2010) for Shanghai contain
substantial literature reviews, including discussion of papers we do not show. We estimate costs increase slightly faster
than estimates for Manhattan by R.S. Means and Marshall & Swift discussed in detail by Glaeser
et al
(2005). However,
their estimates of marginal cost are much higher, reflecting the lower height of Australian buildings. Ahlfeldt and
McMillen (2018, Table 5) is high profile and thorough, however, their focus is on super-tall skyscrapers. Their estimates
for small and moderate buildings are from a large international survey that we suspect is heterogenous: taller buildings
within a country are more likely to be built in relatively expensive cities. The Department of Environment (Seeley 1976)
rule of thumb that costs increase by 2 per cent per floor is widely cited, but old. Warszawski’s (2003) engineering-
based estimates assume that buildings above 10 storeys need to provide undercover parking whereas shorter buildings
do not. In contrast, our ABS estimates reflect actual expenditure on undercover parking.
11
Figure 3: Average Apartment Costs
By number of storeys, ratio to lowest height
Notes: (a) As cited in Glaeser
et al
(2005)
(b) As cited in Seeley (1976)
(c) As cited in Arnott and MacKinnon (1977)
(d) Simple average of Sydney, Melbourne and Brisbane construction costs
Sources: ABS; Ahlfeldt and McMillen (2018); Arnott and MacKinnon (1977); Authors’ calculations; Blackman and Picken (2010); Glaeser
et al
(2005); Picken and Ilozor (2003); Rider Levett Bucknall; Seeley (1976); Warszawski (2003)
Multiplying Equation (1) by the number of apartments then differentiating gives marginal
construction cost, the cost of supplying an extra apartment by adding a storey:
2 $2,291
Total costs
Marginal cost Base cost number of storeys
number
(2)
We evaluate Equation (2) at the trend building height of the average apartment.
7
In 2018 this was
10 storeys in Sydney, 17 in Melbourne and 13 in Brisbane. Evaluation at this point gives consistent
comparisons with the price of the average apartment and the cost of building out in Section 6.
Estimates are shown in row 4 of Table 3.
We allow construction costs to increase with height and assume that most other costs (with the
exception of infrastructure charges) increase in proportion. Finance and equity costs should arguably
increase more than proportionately, reflecting the longer construction time and complexity of taller
buildings. Offsetting this, sale prices also increase with height. We suspect the net effect of these
complications is small and we ignore them.
7
This admittedly awkward expression represents the average building height when weighted by the number of
apartments. It is substantially higher than the unweighted average building height because more apartments are in
taller buildings.
1
10
20
30
40
50
60
0.5
1.0
1.5
2.0
2.5
ratio
0.5
1.0
1.5
2.0
2.5
ratio
Number of storeys
Ahlfeldt and
McMillen
ABS
(d)
RLB
(d)
Marshall Valuation Service
(c)
Warszawski
Department of
the Environment
(b)
Blackman
and Picken
Picken
and Ilizor
R.S. Means
(a)
Marshall & Swift
(a)
12
4.3 Financing Costs and Developer’s Profit
Construction cost estimates above are for the ‘tender price’ and include
builder’s
margins. Table 3
makes additional allowances for interest and
developer’s
margins. These returns reflect
compensation for the risks taken by creditors and equity holders respectively. (It is often convenient
to combine returns to equity and debt because there are large variations in leverage among
developers). The risks (and hence profit) are greater for spending on land than for spending on the
structure. Land is often purchased when planning approval, demand conditions and so on are
uncertain, so is highly speculative. In contrast, construction spending occurs after legal permission
to build has been granted and apartments have been pre-sold, so is less risky. Accordingly, we
assume developer’s margins are greater for land and hence building out than for building up.
Following industry discussions and the estimates in Urbis (2011, pp 42–44) and CIE (2011, p 40),
we assume finance costs are 8 per cent of structure costs for ‘building up’ while developer’s margins
are 17 per cent.
8
These are larger estimates than the 15 per cent (covering both finance and equity)
in Kendall and Tulip (2018), 10 per cent for developer margins in Kelly, Weidmann and Walsh (2011)
or 10 to 14 per cent for developer margins in Hsieh, Norman and Orsmond (2012, Table 2). Based
on industry discussions, we assume finance adds 10 per cent and equity 25 per cent to the cost of
land acquisition. Several industry participants use a rule of thumb of 20 or 25 per cent of total costs
(both land and construction) for developer’s margins, which fall between our estimates for land and
structures. This rule of thumb is often used in residual land valuation, discussed in Appendix B.
Returns to finance and equity is perhaps the element of costs with the greatest uncertainty. Part of
the difficulty is quantification, given the absence of broad-based evidence and the variety of industry
estimates. This is especially difficult as the relevant measure for our purposes is
ex ant
e or planned
returns, not the
ex post
or actual returns that are often documented. The greater difficulty is
conceptual. To what extent are these costs separate from the effect of planning restrictions?
Many developers argue that the risks in housing supply (and, by implication, compensation for those
risks) should be attributed to the planning system. A major source of losses is rejection of
development proposals after property has been purchased at prices that reflected a positive
probability of approval. Profits need to be high on completed projects to compensate for these
losses. CIE (2011, p 46) suggest that, based on estimates for the United States, a less risky planning
environment could reduce margins by about 5 percentage points. Moreover, the delays in gaining
approval substantially increase financing costs.
Glaeser
et al
(2005) assume that developer’s margins should not be counted as a cost of supply.
They argue that the planning system generates large rents which are dissipated in efforts to get
around them. It is not clear that losses incurred on lobbying or on rejected rezoning applications
represent social costs or resources requiring compensation. Rather, rent-seeking expenses represent
part of the effect of planning restrictions on housing prices.
In principle, developers also require compensation for bearing the risks of variations in demand and
costs. Unexpected variations in demand are typically small relative to uncertainty about planning.
Most apartments are pre-sold before construction, with buyers putting down deposits of around
8
Taking unweighted averages across the three cities, Urbis estimates that finance and profit comprise 8 per cent and
17 per cent respectively of total costs, while CIE estimates 7 per cent and 17 per cent.
13
10 per cent. A small share of these fail to settle (RBA 2019). The cost of these failures is initially
borne by buyers forfeiting their deposit. Developers make losses when prices fall by more than
deposits, but this is infrequent. Likewise, cost overruns are a smaller risk than they may appear. The
ABS estimates of construction costs are for actual – not planned – expenditures so include the
average overrun. While uncertainty about overruns creates a risk that requires compensation, this
is a primary role of the builder’s margin, which is also in the ABS estimate.
In short, we consider our assumptions, especially the 17 per cent developer’s margin for building
up, to be generous. Some industry contacts suggest a lower margin for construction costs and a
higher margin for land costs might be realistic. That would further strengthen our main conclusions,
so our results may be conservative. We are also told, but are unable to quantify, that margins are
substantially higher in Sydney than in Brisbane. More research and data on this topic would be
useful.
5. Variations in Apartment Shortages
5.1 Where is the Shortage Most Severe?
Where new housing should be located will be determined by site-specific factors such as the price
of land, alternative uses and so on. Nevertheless, the gap between price and cost at a regional level
should be important in determining the broad contours of development. Figure 4 shows the effects
of building restrictions at the ABS’s Statistical Area 3 (SA3) level for Sydney, Melbourne and
Brisbane.
9
The effect is calculated by taking the difference between the average sale price of new
apartments and the cost of supply within each region. We focus on the cost of building up, which is
typically lower than the cost of building out.
To reliably estimate at a local level, we average prices and costs over a longer period of time, from
July 2011 to December 2016. As discussed in Section 5.2, the average effects of building restrictions
were somewhat different over this period than in 2018, especially for Brisbane. Sale prices are SA3
averages from CoreLogic, with the same filters used as in Section 4.1 and Appendix A.1. To estimate
marginal costs, we first calculate average cost per dwelling from the ABS Building Approvals
collection for each SA3. In measuring construction costs at a local level we are allowing for
apartments in expensive areas being larger and of higher quality. We then make a series of
adjustments to convert these raw average construction costs to marginal costs. Specifically, we
make a 5 per cent allowance for cost overruns (our estimate of the average difference in costs in
the ABS Building Activity Survey and its Approvals collection) and an adjustment for the difference
between marginal and average costs. The adjustment from average to marginal costs varies by
region within each city, and depends on the average building height of recently constructed
apartments. For instance, we scale up costs, in accordance with Equation (2), in Sydney Inner City
by 9 per cent (where the building height of the average apartment is 13 storeys) and in Hornsby by
4 per cent (6 storeys).
9
We cannot disaggregate further – for example, to the suburb level – without disaggregated estimates of construction
costs.
14
We make additional adjustments for margins, financing costs and legal and marketing fees, which
collectively add another 37 per cent to our estimate of marginal cost. Finally, we make an allowance
for infrastructure charges, which adds between $10,000 and $20,000 per dwelling, depending on
the city. SA3s with fewer than 200 sales or apartments approved (such as Manly in Sydney or Keilor
in Melbourne) are not shown.
Figure 4: Apartment Shortage by SA3
July 2011–December 2016
(
continued next page
)
Sydney
15
Figure 4: Apartment Shortage by SA3
July 2011–December 2016
(
continued
)
Melbourne
SA3s ranked by distance
to CBD
1 Melbourne City
2 Yarra
3 Stonnington – West
4 Port Phillip
5 Brunswick–Coburg
6 Darebin – South
7 Essendon
8 Maribyrnong
9 Boroondara
10 Stonnington – East
11 Darebin – North
12 Hobsons Bay
13 Glen Eira
14 Moreland – North
15 Banyule
16 Bayside
17 Whitehorse – West
18 Manningham – West
19 Brimbank
20 Monash
21 Whitehorse – East
22 Tullamarine–
Broadmeadows
23 Whittlesea–Wallan
24 Kingston
25 Wyndham
26 Dandenong
27 Maroondah
28 Knox
29 Melton–Bacchus Marsh
30 Frankston
31 Mornington Peninsula
Brisbane
Sources: ABS; Authors’ calculations; Centre for International Economics; CoreLogic data; Industry consultation; Urbis
16
The map of Sydney shows the effect of restrictions to be small on the outskirts, moderate in the
middle ring and large near the centre. The largest gaps between demand and cost occur in inner
areas of Sydney, such as the Eastern Suburbs, Leichardt and North Sydney. In contrast, prices near
the centre of Melbourne and Brisbane are close to costs – even though relative travel times and
amenities are comparable to inner Sydney. These differences seem to reflect differences in building
patterns. As Figure 5 shows, apartment building in Brisbane and Melbourne has been concentrated
in the centre, whereas most of Sydney’s apartments have been built in middle-ring suburbs. As
noted in the introduction, a large location premium, as in inner Sydney, can only be sustained with
supply restrictions.
Figure 5: Apartment Completions by Distance to CBD
Cumulative share of city total, 2013–18
Sources: ABS (unpublished); Authors’ calculations; CoreLogic data
The dispersal of apartment building in Sydney is sometimes supported on the grounds that it is less
costly to build in outlying suburbs, where land is cheaper. However, home buyers place a lower
value on apartments that are far from the city centre and they will readily pay the higher costs of
central locations. Recent development in Melbourne and Brisbane accommodates these preferences.
Housing on the outskirts is worth providing, but it is an imperfect substitute for the housing that
home buyers are most willing to pay for.
Regional disparities within Sydney may get more severe. The NSW Planning Department’s ‘Sydney
Housing Supply Forecast’ projects that a ring of six local government areas some 40 to 65 kilometres
from the city centre (Blacktown, Camden, Campbelltown, Liverpool, Penrith and The Hills) will
0
10
20
30
40
50
0
20
40
60
80
100
%
Distance to city centre – kms
Sydney
Brisbane
Melbourne
17
account for 36 per cent of new housing built over the next five years, although these areas only
account for 24 per cent of the Greater Sydney population.
10
Each of the three maps shows areas in which the effect of building restrictions is small or negative.
Although measurement error and other noise may be a factor, we would expect construction activity
to vary due to non-regulatory factors in these areas. Perhaps more importantly, these observations
show that our overall results are consistent with planning restrictions being important at the
metropolitan level while not binding in some areas. Moreover, they demonstrate that there is nothing
in our estimation technique that forces the effect of building restrictions to be large or positive.
5.2 Changes over Time
Figure 6 extends the estimated effect of building restrictions from Table 1 back in time.
Figure 6: Prices, Costs and Effect of Height Restrictions
Per apartment
Note: (a) The effect of building restrictions for each city is the average sale price of new apartments less the estimated cost of
building up
Sources: ABS; Authors’ calculations; Centre for International Economics; CoreLogic data; Rider Levett Bucknall; Urbis
We use sales data from CoreLogic from 1997 to 2016 and apply the same filters as discussed in
Appendix A.1. That means the price series represents the average sale price of new apartments. We
do not control for changes in characteristics. After 2016 we assume prices grow at the same rate as
CoreLogic’s hedonic unit price index for the relevant city.
Our marginal cost estimates, discussed in Section 4.1 and Appendix A, are calculated using building
completions data from 2013–18. We extend these estimates back to 1997 (the earliest period for
10
The 2019 ‘Sydney Housing Supply Forecast’ data can be downloaded from the NSW Department of Planning, Industry
and Environment website at <https://www.planning.nsw.gov.au/Research-and-Demography/Sydney-Housing-Supply-
Forecast/Forecast-data>.
Sydney
300
600
900
$’000
Sale price
Cost of building up
Melbourne
300
600
900
$’000
Brisbane
2009
2000
0
300
600
900
$’000
Effect of
building restrictions
(a)
2009
2000
2018
-200
0
200
400
$’000
Brisbane
Melbourne
Sydney
18
which data are available) using the other residential PPI for each state. We evaluate marginal costs
at the trend building height of the average apartment for each city, as discussed in Appendix A.2.
We make the same proportionate adjustments for developer’s margins, financing costs, marketing
and legal fees.
11
Over the past decade, we find that the effect of height restrictions has increased substantially in
Sydney while remaining moderate in Melbourne. The small estimate for Brisbane in 2018 is unusual
relative to previous experience – for most of the past two decades apartment prices in Brisbane
have substantially exceeded costs. The differences in recent price movements seem to partly reflect
differences in supply. Shoory (2016, Table 1) shows that the apartment stock has been growing
relatively slowly in Sydney, moderately in Melbourne and quickly in Brisbane.
6. The Cost of Building Out
6.1 Land Purchase Costs
Whereas Sections 4 and 5 assumed that extra apartments could be supplied by increasing building
height, in this section we consider increasing the number of buildings. That saves on construction
costs but requires extra land on which to build the structure. Valuing land is sensitive to assumptions
about where extra construction might occur. For example, land tends to be expensive near the city
centre and inexpensive on the outskirts. Some illustrative data are in Table 4.
Table 4: Apartment Land Requirements and Costs
2018
Sydney
Melbourne
Brisbane
Average number of apartments per building
(a)
117
175
112
Average land per building (m
2
)
(a)
2,397
1,924
2,136
Average land area per apartment (m
2
)
(a)
20
11
19
Average land area of detached houses (m
2
)
(b)
625
629
803
Average price of detached houses
(c),(d)
$1.23m
$0.90m
$0.56m
Cost of land per m
2
(unweighted)
(e)
$1,965
$1,438
$700
Cost of land per m
2
(weighted)
(e)
$4,033
$4,045
$1,763
Cost of land per apartment (weighted)
$82,664
$44,475
$33,581
Notes: (a) Data for these variables are only available aggregated over the 2013–18 period; for the sake of comparability with our
other estimates, we assume that this period overall provides a good representation of the nature of apartment development
in 2018
(b) Average detached house lot areas are for 2016, the latest year for which data are available
(c) House sale prices are trimmed at the top and bottom 1 per cent each year, first at the city level and then within each SA3;
all properties with a land area greater than 2 acres (8,094 m
2
) have also been excluded
(d) The CoreLogic unit record data we use extends to 2016; estimates for 2018 are made by extrapolating forward using
CoreLogic’s city-level hedonic unit price index
(e) Unweighted land costs are averaged over all detached house sales in a city within our CoreLogic database; weighted land
costs take SA3-level detached house sale prices and weight them by each region’s 2013–18 share of new apartment
completions within each city
Sources: ABS (unpublished); Authors’ calculations; CoreLogic data
11
We assume that the GST raised costs relative to the PPI by 10 per cent in 2000. The CoreLogic price data include GST
(in principle) and do not require adjustment.
19
Unpublished data from the Building Activity Survey covering the 2013–18 period indicate that the
average new Sydney apartment is in a building comprised of 117 apartments (row 1, Table 4) and
which occupies 2,397 square metres of land (row 2). That implies the average apartment uses
20 square metres of land (row 3). For reasons discussed below we do not value this land at its
market price but at its opportunity cost under an alternative policy: its value if reserved for detached
houses. The average Sydney house occupies 625 square metres of land and costs $1.2 million,
including structure (Kendall and Tulip (2018), updated) at an (unweighted) average cost of
$1,965 per square metre (rows 4, 5 and 6). This represents a simple benchmark to which we refer
later. A more realistic assumption, and one consistent with estimating effects of marginal changes,
is to assume that new building occurs in similar locations to recent construction. In particular, more
apartments are built on relatively expensive land closer to the city centre. If we weight by apartment
completions in each SA3 from 2013 to 2018, the average price of land used for detached housing
increases to $4,033 per square metre.
12
Multiplying this by the land requirement of the average
apartment implies that the land for extra apartments would cost about $82,700 per apartment (final
row), or $9.7 million for the representative apartment building. Similar calculations in columns 2 and
3 imply that the cost of land for replacing nearby houses with apartments of their current
configuration is about $44,000 per apartment in Melbourne and $34,000 per apartment in Brisbane.
In comparison, Urbis assumes land acquisition costs of $105,000, $41,000 and $53,000 per
apartment in Sydney, Melbourne and Brisbane in 2011, based on a 50-apartment building requiring
5,000 to 10,000 square metres of land and the average price of urban development land at chosen
locations. In 2018 prices, this is substantially more expensive than our estimates. This partly reflects
larger land area spread over fewer apartments. CIE (2011, p 36) assumes costs of $85,000, $55,000
and $72,000 in Sydney, Melbourne and Brisbane for the median apartment in 2011, but does not
provide underlying details.
To value land at the average cost of detached housing would be an unrealistic description of how
apartments are built under existing policy. The most likely sites for development include a large
premium above other land, because their development potential is capitalised into the property
value. Nevertheless, valuing land as though it were used for average detached housing is appropriate
for comparing different policies. An alternative to the current policy of reserving most of our urban
land for detached housing is that we build some apartments on that land. The opportunity cost of
permitting more apartment buildings is the value of land when it is used for detached housing.
13
6.2 Finance and Margins
We assume finance and developer’s margins add 10 per cent and 25 per cent respectively to the
cost of land, as discussed in Section 4.3. These assumptions are larger than those of Urbis and the
CIE. As previously discussed, it seems appropriate to assume risks are substantially bigger at the
beginning of a project than at the end.
12
For the sake of computational simplicity, this estimate ignores some small costs such as stamp duty, conveyancing
and other transaction costs (about 4.5 per cent of the property value, according to Fox and Tulip (2014, Section A.5));
land tax, rates and other holding costs (about 5 per cent of property costs according to CIE (2011, p 42)) and
demolition costs (about $15,000 for the average-sized house according to industry contacts and Rider Levett
Bucknall (2017, p 40)).
13
This is perhaps the most important difference between our estimate of the effect of planning restrictions and the CIE’s
(2010) estimate of ‘transformation benefits’ from infill development.
20
6.3 The Cost of Building Out
Table 5 shows the cost of building out; that is, supplying extra apartment buildings of the current
size and design in nearby locations. Average construction cost estimates are discussed in
Appendix A.2. We then add land acquisition costs and higher finance and margin estimates, as
discussed in the previous two subsections.
Table 5: Costs of Building Out
Per apartment, $’000, 2018
Sydney
Melbourne
Brisbane
Average construction cost
340
312
287
Land
(a)
83
44
34
Professional fees (3 per cent of total costs)
14
12
11
Marketing and sales (5 per cent)
24
20
18
Finance (10 per cent land, 7 per cent structure)
36
30
27
Developer’s margin (25 per cent land, 17 per cent structure)
94
76
68
Infrastructure charges
18
10
26
Total average cost
(b)
610
505
471
Notes: Sources for most entries are the same as for Table 3 or discussed in the accompanying text
(a) From Table 4
(b) Rows do not sum to total due to rounding
Total average cost estimates, the final row, are also presented in Table 1, which shows that the cost
of building out is similar but somewhat higher than the cost of building up. Average costs are larger
in Sydney than in Melbourne or Brisbane. The differences between cities arise partly because land
per square metre is more expensive in Sydney (Table 4). Moreover, apartment buildings tend to be
shorter in Sydney, making land per apartment even more expensive.
7. What is the Cost-efficient Height for Apartment Buildings?
Figure 7 compares the cost of building up with the cost of building out for different building heights.
For illustration, estimates reflect average values for Sydney, where there is the most scope for
changing building heights. The black line shows the marginal cost of adding an apartment on top of
a building as a function of height, as calculated in Section 4. The orange line shows the average cost
of replacing detached houses with apartment buildings of different heights, as calculated in
Section 6. Average cost initially declines with height as the fixed land cost is spread among more
apartments. However, this is partially offset by the increase in average cost with height discussed in
Section 4.2. Parameter values and equations for the orange and black lines are given in Appendix C.
Cheshire and Hilber (2008) discuss several variations on this figure. Chau
et al
(2007) discuss optimal
building height in more detail.
The cost curves in Figure 6 vary height while holding other factors constant. This includes holding
the value of land constant at $4,033 per square metre, even though, in practice, land zoned for
high-rise apartments is much more expensive than land zoned for low rises. This is because a
developer (or town planner examining the project in isolation) must decide where and how high to
build, taking the cost of land as given. The average cost curve illustrates the consequences of
21
different decisions. We also hold building and land area constant at 1,173 square metres and
2,397 square metres respectively. In practice, taller buildings tend to occupy more land, so a more
intuitive description of the curve may be that it varies density (as measured by FSR).
Figure 7: How Cost Estimates Vary with Height
Sydney apartments, 2018
Sources: ABS; Authors’ calculations; Centre for International Economics; CoreLogic data; Urbis
The vertical line represents the building height of the average apartment, 10.5 storeys. Point A is
the marginal cost of building up from Table 3, $519,000. Point B is the average cost of building out
from Table 5, $610,000. It is less expensive to go up (point A) than out (point B) until buildings
reach 20 storeys, labelled point C, when building out becomes the less costly option. We call point C
the ‘efficient’ building height, acknowledging that this is a narrow definition of the term – our
estimate ignores externalities and the tendency of price to increase with height, considerations we
briefly discuss in Section 9. At this height, it costs $581,000 to supply extra apartments by either
approach.
In Melbourne and Brisbane, the average cost curve would intersect with marginal costs much closer
to the building height of the average apartment in those cities. The lowest cost at which apartments
could be supplied would be $504,000 in Melbourne and $468,000 in Brisbane.
Point C in Figure 7 represents the density of development that a builder or planner would choose if
they were free to purchase detached houses at their weighted average price and replace them with
apartment buildings of any height. Economists will wonder if profits would be maximized by building
up until marginal cost equals the price (not shown). Using the average Sydney sale price in 2018 of
$873,000, this would be taller than any building in our database. However, this only applies to a
builder or planner with access to a fixed amount of land (for instance, due to land use restrictions
that prevent new building). If more land can be bought then costs are reduced by supplying more
buildings at the efficient height.
0
10
20
30
40
0
200
400
600
800
1,000
$’000
Number of storeys
A
C
B
Building height of average apartment
Marginal cost (building higher)
Average cost (building out)
22
Figure 8 reproduces the solid orange and black lines from Figure 7. The dotted orange line shows
an increase in the price of land to $10,703 per square metre, the cost of single-dwelling properties
in the Inner Sydney SA3. It would then become economic to build up to 33 storeys (point D). The
blue line represents the average variable cost of building up or the limiting case of building out when
land is free, as is approximately the case in agricultural areas. Then the least costly apartment
buildings would be a single storey.
Figure 8: How Alternative Estimates of Cost Vary with Height
Sydney apartments, 2018
Sources: ABS; Authors’ calculations; Centre for International Economics; CoreLogic data; Urbis
Figure 9 extends these results for a wide range of locations and land prices. Specifically, we take
the average price of land being used for houses in each SA3 from CoreLogic. This tends to vary
inversely with distance from the city centre, and the horizontal axis ranks regions on this dimension.
14
The dark blue squares show implied cost-minimising building heights, given these land prices, as
discussed above. The leftmost observation, for Inner Sydney, represents point D in Figure 8. Orange
circles show the building height of the average apartment built between 2013 and 2018. We only
show estimates within 30 or 40 kilometres of the city centre. As shown in Figure 5, very few
apartments are built further out than this. Moreover, the efficient height estimates are for infill. In
outlying suburbs development tends to be of greenfield sites where the opportunity cost is less
expensive vacant land.
14
SA3 proximity to the CBD is calculated by averaging the mean distance to the CBD of all properties sold within that
SA3 during 2016, rather than the geographic centre of SA3 boundaries.
Average land price ($4,033/sqm)
Inner city land price ($10,703/sqm)
0
10
20
30
40
50
0
200
400
600
800
1,000
$’000
Number of storeys
Average variable cost
C
D
Marginal cost
(building higher)
Average cost
(building out)
23
Figure 9: Efficient and Actual Building Heights – By SA3
Note: Efficient heights are for current land values
Sources: ABS; Authors’ calculations; Centre for International Economics; CoreLogic data; Urbis
Efficient height
Actual building height
0
15
30
45
0
15
30
45
Number
of
storeys
Grea ter Sydney
0–5 km
5–10 km
10–20 km
20–30 km
30–40 km
Efficient height
Actual building height
0
15
30
45
0
15
30
45
Number
of
storeys
Grea ter Melbourne
20–30 km
30–40 km
>40 km
0–5 km
5–10 km
10–20 km
Efficient height
Actual building height
0
15
30
45
0
15
30
45
Number
of
storeys
Grea ter Brisbane
20–30 km
0–5 km
5–10 km
10–20 km
24
Strikingly, newly completed apartment buildings have been shorter than the lowest cost height in
almost every area. The gap is most pronounced in central regions of Sydney: the Inner City,
North Sydney, Eastern Suburbs and Leichhardt would reduce average apartment costs by increasing
building heights by about 20 storeys. In contrast, buildings have been built up to their efficient
height in inner areas of Brisbane and even higher in central Melbourne. Taken at face value, the
result for central Melbourne would imply that developers would increase profits by building more but
shorter buildings. We think this is unlikely and illustrates a limitation of our approach. We estimate
building heights based on the average value of detached housing in each SA3. However, tall buildings
are more likely to be located on the most expensive land, rather than the average. We suspect that
a finer level of disaggregation would result in a higher efficient height for inner Melbourne. Other
pockets of high-density building that may be interesting to note include Auburn, Parramatta and
Liverpool in Sydney and Knox in Melbourne.
Although Figures 4 and 9 both show results disaggregated by SA3, they address different questions.
Figure 4 compares costs with prices to ask:
whether
to build apartments in different locations?
Figure 9 compares the cost of building up with the cost of building out to address the question: if
apartments replace houses, how high should they be?
A striking feature of Figures 7 and 8 is how costly it is to supply medium-density housing. As shown
by the solid orange line, it costs about $894,000 per apartment to replace detached houses with a
three-storey building in Sydney.
15
Two-storey apartments would cost much more.
16
This is
considerably more costly than providing high density. The reason is that land costs represent a large
component of overall costs for low-rise apartments.
The extra cost of low-rise buildings can be compared to the extra amount that home buyers are
prepared to pay to live in them. Real estate advertisements rarely mention being in a low-rise
building as a selling feature, suggesting the value of this is small. To gauge this more precisely we
regress Sydney apartment prices on a wide range of hedonic controls, including suburb dummies
and the number of bedrooms and bathrooms. We include the number of dwellings at an address,
constructed from the PSMA’s Geocoded National Address File (G-NAF), as a measure of density. The
most attractive density, as determined by willingness to pay, is buildings with fewer than ten
dwellings, for which buyers pay a premium of 6.3 per cent (
p
-value < 0.01) or about $55,000.
However, the cost of supplying housing at this density is hundreds of thousands of dollars more than
at average building heights. We note that our regression has some puzzling features. For example,
we expected proximity to train stations and light rail stops to significantly boost values but they do
not. We also did not expect proximity to education facilities (e.g. TAFEs) and swimming pools to
significantly reduce values but they do. Moreover, we are not aware that the G-NAF data have been
used like this before. So our estimates should be treated cautiously. Regression output and further
details are in Appendix D.
15
This assumes new buildings are located near where other apartments have recently been built. If we instead assumed
the new housing was located randomly in the Sydney metropolitan area (and hence the land was valued at the
unweighted average price of detached housing), the cost would be $673,000 per apartment.
16
It is difficult to be more precise about low-rise apartments because the ABS aggregate buildings of one and two
storeys.
25
These results have important implications for debates over urban planning. The Grattan Institute
(Daley
et al
2018, pp 53, 56) suggests that planners should prioritise medium-density housing in the
middle ring of our cities, which they say is ‘under-supplied’. Many planners and policymakers call for
developing the ‘missing middle’ with terraces, townhouses and low-rise apartments. According to
then NSW Minister for Planning Rob Stokes (2016),
Medium density homes such as terraces are highly sought after, efficient and versatile forms of housing,
but are in short supply compared with traditional quarter-acre blocks and high-rise apartments.
However, as noted above, expensive land makes medium-density housing considerably more costly
than high density. And home buyers are largely indifferent between these options. So, on these
narrow criteria, high rises would be more efficient. A free market would provide infill in the form of
high density rather than medium density. Though, of course, policy decisions should also take
externalities into account.
Medium-density housing is sometimes supported by reference to Kelly
et al
((2011); updated by
Daley
et al
(2018, Table 3.2)). This study surveyed home buyers about their preferences for different
levels of density. For equivalent costs, survey respondents expressed a strong preference for more
medium-density housing relative to detached housing. However, an under-emphasised finding of
this survey is that respondents also expressed a strong preference for more high-density housing.
8. How Far Can Housing Prices be Lowered?
Some readers are especially interested in the amount that prices would fall in the absence of planning
restrictions. A full answer would require estimation of general equilibrium effects (some of which are
modelled in Kulish
et al
(2011)) and is beyond our scope. Nevertheless, as discussed in this section,
our analysis suggests important elements of the answer and may provide a reasonable first
approximation.
For context, 98,000 higher-density dwelling units were completed in 2018, representing about 1 per
cent of the Australian housing stock. A mid-range estimate of the price elasticity of demand for
housing is that a 1 per cent increase in dwellings would reduce housing prices by about 2½ per cent
(Saunders and Tulip 2019, Section 5.3). So were the annual supply of new higher-density dwellings
to double, the cost of housing would decline by an extra 2½ per cent per year. Costs of supply,
shown in Table 6, provide a limit to this.
Table 6: Costs of Supply
Per apartment, $’000, 2018
Sydney
Melbourne
Brisbane
Marginal cost of building up
(a)
519
491
460
Minimum cost of building out
(b)
581
504
468
Minimum cost if building is dispersed
(c)
542
456
443
Notes: (a) From Tables 1 and 3
(b) Minimum cost estimates correspond to point C in Figure 7
(c) As in (b), except using the unweighted cost of land from Table 4
Sources: ABS; Authors’ calculations; CoreLogic data
26
The estimates in row 1 represent the cost of supply by increasing building heights, reproduced from
Tables 1 and 3. These estimates apply to a small increase in supply. For a large increase, after
heights reach their efficient level, the lower-cost approach would then be to construct more
apartment buildings. This point, which might be termed a ‘long-run cost of supply’ is represented by
point C in Figure 7 and row 2 of Table 6. These estimates assume that new apartment buildings are
built in the same areas as recently completed apartments. For a very large increase in construction,
it seems possible that apartment buildings would spread throughout the metropolitan area. The final
row of Table 6, which might be termed a ‘very long-run cost of supply’ assumes land is valued at
the unweighted average price of detached housing.
The estimates in Table 6 provide benchmarks that are relatively straightforward to quantify.
However, they are partial equilibrium, holding the price of inputs constant. In reality, costs would
change if construction increased. For example, extra building would increase the demand for scarce
inputs to the construction industry, such as materials and skilled labour. This would bid up their cost
in the short run, until extra supply is forthcoming. However, a more important effect is on the price
of land used for detached housing. Land constitutes a large proportion of housing costs and is
supplied quite inelastically, so its price moves more than other factors. If new construction replaces
each detached house with about 17 apartments, as the average values given in Tables 4 and A2
imply, then the net demand for detached housing will fall. This would alleviate both the physical and
administrative scarcity of land used for detached housing and hence lower its price. By how far
would depend on the elasticity of substitution between houses and apartments. Lower land prices
would reduce the cost of building out. In terms of Figure 7, increases in the supply of apartments
would lower the average (orange) cost curve and the equilibrium would move back along the black
curve towards the origin. It could be possible to reduce housing costs further if, as discussed in
Section 4.3, the risks in the planning process are reduced.
There are other considerations that a comprehensive assessment would take into account. For
example, Kulish
et al
(2011, Section 3.2) argue that, while a relaxation of planning restrictions would
reduce overall housing costs, the price of land near the centre and apartment sizes would both be
expected to increase. Complications like these would affect quantification, however, they may matter
more for the composition and density of housing than its overall price. It is not clear that they would
outweigh the changes in costs noted above, of which the factors lowering prices seem to be more
important than those raising prices. So apartment prices could fall well below the cost estimates in
Tables 1 or 6.
9. Directions for Further Research
Our main conclusions were stated in the introduction and we do not repeat them here. Instead we
offer a few comments on key uncertainties and where further work would be beneficial.
The greatest risk to our results is the possibility that our data do not capture all the costs of supplying
new apartments. This may be because we interpret the data incorrectly or because we omit
important costs. Developers have given us detailed valuation reports and ARGUS EstateMaster
(common industry software) projections that they and their lenders use for financial planning, and
we have attempted to align our estimates with these. However, individual reports vary and
synthesising this information is difficult.
27
Within the components of costs we do measure, perhaps the greatest uncertainty is the threshold
profit at which developers would be prepared to increase supply, as discussed in Section 4.3. We do
not have good data on
ex post
margins and even less information on what might be needed
ex ante
in the absence of planning uncertainty.
With respect to prices, there are three uncertainties we would like to emphasise. The first is the
difference between new and average apartment sales. We assume that sales within five years of
construction are indicative of the returns developers might expect. However, as discussed in
Appendix E, there are uncertainties about these estimates and new sale prices might be substantially
larger than total sale prices.
Second is the Goods and Services Tax (GST). This is explicitly included in costs, so should also be
included in prices. GST is payable on new properties but not on old. CoreLogic’s policy is to quote
prices including GST; however, it is not clear that their source data are always consistent with this.
So some of our prices may be 10 per cent too low.
Third is the tendency of prices to increase with height. Glaeser
et al
(2005, p 362) estimate that
each extra storey of height raises the price of Manhattan condominiums by about 0.08 per cent. A
difficulty with estimates like these is that a view is more valuable if you can see over nearby buildings.
So values increase more if other heights are constant than if all buildings were taller. We ignore this
effect for reasons of simplicity and data availability. In doing so we underestimate the benefits of
higher buildings.
We expect that these and other uncertainties could be narrowed with further effort. That said, our
estimates seem qualitatively consistent with independent industry estimates of site values, discussed
in Appendix B. Moreover, they are in line with international research, a large body of anecdotal
evidence and expert judgement, discussed in Sections 1 and 2. So the uncertainty concerns precise
quantification rather than the overall results.
With respect to future work, the top priority is to quantify the external costs and benefits of supply
restrictions. Our paper estimates private costs. This provides a benchmark against which external
benefits, such as those surveyed by Ahlfeldt and Pietrostefani (2019), can be compared.
28
Appendix A: Data
A.1 Prices
Table A1 shows details of the price estimates shown in Table 2. Specifically, it shows the effect
various adjustments have on the number of sales and average prices.
Table A1: Apartment Prices and Sales
Effect of data filters, 2016
Sydney
Melbourne
Brisbane
Price per
dwelling
($)
No of
sales
Price per
dwelling
($)
No of
sales
Price per
dwelling
($)
No of sales
Unfiltered average unit price
884,261
28,540
578,162
25,319
475,413
10,472
Excluding townhouses, etc
899,529
26,476
578,467
16,773
521,192
7,070
Excluding misc outliers
(a)
856,588
26,298
571,213
16,468
492,537
6,825
Restrict to new apartment sales
860,876
2,855
550,742
3,373
513,356
902
Trim top and bottom 1 per cent of sales
829,523
2,799
536,398
3,307
489,704
884
Memo: in 2018 prices
873,315
587,582
470,118
Note: (a) Drops buildings with duplicate sales, blank or ‘0’ unit numbers; drops sales more than three years before construction
date
Sources: Authors’ calculations; CoreLogic data
CoreLogic data on sale prices are often reported separately for houses and units. Within the latter
category we make an effort to exclude townhouses, villas, estates and other types of strata dwellings
that have a substantial land component. Since data on these characteristics are not always available,
we exclude buildings where at least 10 per cent of sales are labelled as ‘townhouse’, ‘triplex’,
‘quadraplex’ or ‘boarding house’. This is done for comparability with the ABS construction estimates
which are for apartments. We additionally exclude some outliers and other implausible data entries,
such as duplicate sales or sales occurring more than three years before the date of construction. We
spot check these rules against photographs on real estate websites and they generally seem to rule
in and rule out the right properties.
The profitability of supplying extra apartments is the difference between the cost and price of
new
dwellings. Accordingly, we exclude properties sold more than five years after construction. This filter
is perhaps the most important step in Table A1 and we discuss its implications in Appendix E.
A small proportion of sales are anomalously high – in the tens or hundreds of millions of dollars –
even though the building characteristics and location are little different from nearby apartments. We
expect this occurs when an entire building is sold and its price is entered for individual apartments.
To protect against data entry mistakes like this we exclude the top and bottom 1 per cent from our
sample. In comparison, CoreLogic winsorise the top and bottom 5 per cent from many of the
variables entering their indices.
Even after trimming, sale prices are heavily skewed. The median new sale price in Sydney or
Melbourne is 11 per cent lower than the trimmed mean. Although some other research focuses on
median housing prices, the mean is appropriate for calculating excess profits. Moreover, our cost
29
estimates are only available on an average basis and presumably reflect the same skew, so
consistency requires taking the difference between averages, not medians.
Finally, we multiply these estimates by the change in CoreLogic’s unit sales price index for each city,
to express in 2018 prices. This increases prices in Sydney and Melbourne and decreases them in
Brisbane.
A troubling feature of our data is that the number of new apartment sales sometimes differs
substantially from the number of apartment completions, especially at the end of our sample. We
assume that the discrepancies between sales and completions are not systematically related to prices
but were not able to verify this.
A.2 Unpublished Cost Estimates
Given that our estimates of average construction costs from the ABS Building Activity Survey are
unpublished, Table A2 presents some summary statistics for the data, which may be of interest.
Multiplication of units per storey by gross floor area per unit, and assuming that floor area per storey
is constant, provides an estimate of building footprint, used in Section 7.
Table A2: Apartment Completions
By Greater Capital City Statistical Area, 2013–18
Sydney
Melbourne
Brisbane
Average cost per unit ($’000)
318
297
278
Number of buildings
1,562
1,364
806
Number of apartments
98,929
80,421
38,116
Units per building
63
59
47
Gross floor area per unit (m
2
)
105
105
118
Average units per storey
11.4
9.8
8.9
Average cost per gross m
2
($)
3,040
2,839
2,359
Sources: ABS (unpublished); Authors’ calculations
Figure A1 shows unpublished ABS estimates of building heights by year. As can be seen, these have
fluctuated about rising trends. Measuring cost at actual building heights would result in transient
movements in our cost estimates. This volatility does not seem relevant to building decisions, which
are based on expected, rather than historic costs. Instead, we value both average and marginal cost
at the trend building height. For Section 5.2, we extrapolate the estimated trends from 2003, when
the height data begins, back to 1997. An alternative approach of holding each city’s average
apartment height constant at its estimated 2003 level makes little difference.
30
Figure A1: Building Height of Average Apartment
Number of storeys
Note: Dashed lines are raw building heights, solid lines are linear trends
Sources: ABS (unpublished); Authors’ calculations
A.3 Government Charges
Charges for infrastructure and public goods are a private cost, but it is debateable whether they
should be counted as a social cost. Assuming that planning regulations do not change overall
population, an increase in infrastructure use in one area will mean a reduction in infrastructure use
in the areas from where the new residents come. It seems inappropriate to include the extra use as
a cost without also allowing for the offsetting savings elsewhere.
Our estimates come from Urbis (2011) and CIE (2011), which are the most recent estimates of which
we are aware. Developers tell us that government charges have increased substantially since these
estimates were published. In particular, Voluntary Planning Agreements (including for parks and
affordable housing) often increase costs by more than the charges we allow for. Moreover,
developers also suggest that current charges are much greater than needed to fund marginal
increases in infrastructure and that they represent a large element of value capture.
Government charges are not a major cost, so a simple approach is to use the available published
estimates. This judgement recognises that the considerations mentioned above are difficult to
quantify and that some would imply higher estimates and some smaller.
Sydney
8
12
no
8
12
no
Melbourne
10
20
no
10
20
no
Brisbane
2014
2010
2006
2002
1998
2018
0
8
16
no
0
8
16
no
31
A.4 Comparison to Other Cost Estimates
Table A3 compares our estimates of construction costs with estimates from Kendall and Tulip (2018),
Urbis (2011) and CIE (2011). Each of these provide an estimate of the average construction cost of
a typical Sydney apartment, shown in the first row. Definitions for these estimates differ and the
subsequent rows attempt to include various components so that the alternative estimates represent
the same concept. These estimates, shown in the final row, are of the average supply cost excluding
the cost of land, finance and developer’s profit.
Table A3: Estimates of Average Apartment Construction Cost Excluding Cost of Land,
Finance and Developer’s Profit
Sydney, 2018
ABS
Kendall and Tulip
Urbis
CIE
Base estimate
340
244
283
257
Building efficiency
included
25%
included
included
Builder’s margin
included
included
included
14
Architect fees
included
25
included
Legal and management fees
3%
10%
8
10
Marketing and sales
5%
14
14
Infrastructure contribution
18
0
14
16
GST
included
10%
10%
10%
Timing adjustment
0
6%
20%
20%
Average cost on consistent basis
388
391
455
411
Note: All estimates are in $’000, except percentage adjustments denoted with %
Sources: ABS; Authors’ calculations; CIE (2011); Kendall and Tulip (2018); Urbis (2011)
Allowing for conceptual and timing differences, the ABS construction cost estimates are slightly
smaller than the RLB-based estimates used by Kendall and Tulip (2018), offset by inclusion of
infrastructure contributions, discussed in Appendix A.3. The ABS-based estimates are noticeably
lower than those of Urbis (2011) and CIE (2011), perhaps reflecting the more representative ABS
sample.
32
Appendix B: Comparison to Residual Land Valuations
Our estimates of the effect of height restrictions closely correspond to ‘site values’ or ‘residual land
valuations’ that are part of everyday conversation among real estate developers. Indeed,
components of our estimates are derived from the detailed valuations that are prepared for
decision-making and sales within the industry. The typical site valuation represents what a plot of
land would sell for prior to building. It is calculated as expected sales minus construction and related
costs like those shown in Table 3. Site values are routinely quoted and compared on a ‘per-
apartment’ basis, reflecting that the overall value tends to increase proportionately to the number
of apartments allowed to be built. Like our estimate of the effect of building restrictions, it can be
interpreted as the scarcity rent that accrues to landowners.
Our estimate of the effect of building restrictions is conceptually different in that it represents the
increase in value that would arise if an extra apartment were allowed to be built. This is calculated
in the same way as site valuations except we use marginal cost instead of average cost. As indicated
by Table 3, this difference lowers our estimates of the effect of building restrictions relative to site
valuations by $24,000 (Sydney), $38,000 (Melbourne) and $29,000 (Brisbane).
The main practical difference is that site valuations are often quoted at an earlier stage of
development than our estimates. Our estimates implicitly assume that a development approval and
building permit have been granted and ask what would be the change in value if a slightly larger
project had been approved. In contrast, site valuations often precede development approval, when
considerable uncertainty and delays are in prospect. At this earlier stage, higher margins and lower
site values are appropriate.
Knight Frank is one of Australia’s largest property consultancies. Their ‘Australian Residential
Development Review’
regularly reports representative valuations for high-density sites, defined as
sites with more than 4 storeys and 25 apartments.
17
These estimates are based on industry
consultation and expert judgement. Table B1 shows their indicative estimates as of June 2019
(Ciesielski 2019). The site valuations for Sydney are lower than our estimates of the effect of building
restrictions, whereas for Melbourne and Brisbane they are higher. Some of these differences can be
attributed to differences in geographic scope (Knight Frank exclude the CBD) and the difference
between average and marginal cost. Most of the differences seem to reflect the earlier stage of
valuation noted above. This is especially so for Sydney, where delays and uncertainties about gaining
development approval seem to be highest (NSW Productivity Commission 2019, p 126). The
estimates are partly of interest for providing an independent crosscheck on our data. Differences of
timing and definition make precise comparisons difficult, but the qualitative message is the same.
The estimates are also of interest as providing an indication that building restrictions may be binding
in other cities. Note, however, that high-density apartments (more than four storeys) represent a
very small share of the housing stock in most of these other cities.
17
We are indebted to Michelle Ciesielski of Knight Frank for discussions about these data.
33
Table B1: Indicative Site Sale Values and the Effect of Building Restrictions
Per apartment, $’000
Site values
(June 2019)
Effect of building restrictions
(2018)
Sydney
184
355
Melbourne
120
97
Brisbane
40
10
Perth
50
Adelaide
40
Canberra
92
Hobart
89
Gold Coast
72
Darwin
58
Average across cities
84
Note: Indicative values of sites based on potential high density development, excluding CBD
Sources: Authors’ calculations; Ciesielski (2019)
34
Appendix C: Equations and Parameters
This appendix explains the equations and parameter values for the costs of building up and out in
Figures 7 and 8 and the efficient heights in Figure 9.
We start by rewriting Equation (1) for average construction cost, ACC, in abbreviated but hopefully
obvious notation:
ACC Base Slope Height
(C1)
As discussed in Section 4.2, for Sydney Base = $316,337 and Slope = $2,291. We compound
developer’s margins, finance, managerial and professional fees. Values for these terms (as
percentages) are in Table 3. Their product as a ratio, 1.37, is represented by
. We enter
Infrastructure charges additively. This gives average variable costs, AVC, the blue line in Figure 8.
AVC Base Slope Height Infrastructure charges
(C2)
We multiply by number of apartments (= Units per storey × Height) to get total cost. Differentiating
with respect to height gives the marginal cost of supplying apartments by raising height. We then
divide by Units per storey (assumed to be constant) to express on a per apartment basis.
2MC Base Slope Height Infrastructure charges
(C3)
which is the black line in Figures 7 and 8. The average total cost of building out per apartment is:
ATC Land cost per apartment AVC
(C4)
per sqm
Land cost Land area required
ATC Base Slope Height
Units per storey Height
Infrastructure charges
(C5)
In Figure 7, Land cost = $4,033 per square metre and Land area required = 2,397 square metres
are from Table 4. Units per storey = 11.2 is apartments per building = 117, from Table 4, divided
by predicted height in 2018 = 10.5, from Figure A1, after rounding. (Note that units per building in
Table 8, 11.4, is for 2013 to 2018.) The land component of average costs is scaled by
, equal to
1.5. This represents similar factors as
but is larger, reflecting the greater uncertainty (and
therefore larger margins and cost of debt) that exists at the beginning of a project.
The ‘efficient’ or lowest-cost building height is where marginal cost (Equation (C1)) equals average
cost (Equation (C2)). That is
35
2
per sqm
Land cost
Land area required
Base Slope Height Infrastructure charges
Units per storey Height
Base Slope Height
Infrastructure charges
(C6)
Re-arranging for height yields the expression:
per sqm sqm
Land cost Land area required
Height
Units per storey Slope
(C7)
In Figure 8 we hold all the right-hand side variables in Equation (C7) constant except the land cost,
which we calculate from the CoreLogic data as the average value of houses sold for a given SA3,
divided by the average land area of those properties.
36
Appendix D: Hedonic Regressions
As discussed in Section 7, we regress Sydney apartment prices on a wide range of hedonic controls
and find that households do not have a strong preference for low-rise apartments (the ‘missing
middle’) relative to high rises. Regression output is shown in Table D1. Most explanatory variables
are discrete, with omitted categories denoted ‘--’. Coefficients are multiplied by 100 to be
interpretable as approximate per cent changes. The first section of the table shows the value of
architectural features (bedrooms, age, etc). The second section of the table shows the value of
distance to nearby amenities.
The top rows show our key results. Being in a building with 10 or fewer dwellings adds 6.3 per cent
to the value of an apartment, relative to being in a building with more than 100 dwellings, after
controlling for apartment quality and spatial characteristics. Being in a building with 11 to
20 dwellings adds 0.3 per cent.
We do not include in our regression spatial variables whose coefficients are jointly insignificant. This
includes train stations and large shopping centres. That surprised us given that real estate
advertising and past research (Murray 2016; Pettit
et al
forthcoming) suggest that these locations
are highly valued. We suspect that these variables are strongly correlated with other features (noise,
parking, apartment quality) that are difficult to control for. Many other results from the regression
are as expected. The number of bedrooms, number of bathrooms and proximity of the apartment
to water were all associated with large and clear increases in price. Apartment age has large and
clear effects. One interesting implication is that housing ‘filters down’ to lower price ranges as it
ages. The large coefficients contrast with the small unconditional effects of age discussed in
Appendices A and E. The difference may arise because new apartments are less expensive for other
reasons – for example, if they are further from the city centre. For purposes of comparisons with
the unconditional mean of supply costs the unconditional effect of age is relevant. For reasons of
space we do not show coefficients on the approximately 650 suburb dummies, though these are
collectively the most important influence on apartment prices. However, the general contour of
suburb effects can be seen in Figure 4. Apartments near the city centre sell for several hundred
thousand dollars more than those on the outskirts, other things equal.
37
Table D1: Hedonic Regression – Sydney
Dependent variable: log sale price; includes month and suburb fixed effects
(
continued next page
)
Coefficients (multiplied by 100)
Building density (baseline > 100)
≤10 dwellings
6.3***
11–20 dwellings
0.3
21–50 dwellings
–0.5
51–100 dwellings
–0.9
Bedrooms (baseline = 1)
Two beds
24.7***
Three beds
41.5***
Four beds
46.9***
Five+ beds
48.1***
Bathrooms (baseline = 1)
Two baths
11.5***
Three baths
24.8***
Four baths
34.0***
Five+ baths
34.3***
Bedroom/bathroom ratio
1.2
Parking spaces (baseline = 1)
Two spaces
8.2***
Three spaces
17.8***
Four spaces
15.9***
Five+ spaces
33.1***
Extra features
Swimming pool
0.7***
Air conditioned
2.5***
Ducted heating
1.8***
Scenic view
5.4***
Apartment age at sale
2–5 years
–10.4***
5–10 years
–17.1***
10–15 years
–21.8***
15–20 years
–22.4***
20–30 years
–28.1***
30–40 years
–30.8***
40–50 years
–32.7***
50–60 years
–32.9***
60+ years
–15.8***
Arterial road/motorway (yes = 1)
–4.0***
Log distance to CBD
–10.7***
38
Table D1: Hedonic Regression – Sydney
Dependent variable: log sale price; includes month and suburb fixed effects
(
continued
)
Spatial feature
Distance from property
≤ 0.5 km
0.5–1 km
1–3 km
> 3 km
Beach
13.8***
8.3***
3.9**
--
Cemetery
–5.5***
–3.2***
–2.7***
--
Club
–2.4*
–3.0**
–3.2**
--
Community facility
9.9*
9.5*
8.5
--
Education facility (TAFEs etc)
–8.9***
–4.2**
–0.3
--
Fire station
–3.3**
–0.7
1.2
--
Headland
20.3***
7.3***
0.5
--
Library
2.9**
1.9
1.9
--
Mountain
1.7
1.3
–2.3***
--
Light rail stop
–6.1*
–3.2
2.9**
--
Sports centre
5.5*
5.2*
6.1*
--
Swimming pool
–3.5***
–3.1***
–2.5***
--
University
6.0***
1.4
1.1
--
Combined school
–3.1***
–1.9**
0.1
--
High school
–0.8
–1.0
0.3
--
Sewage works
–4.2
–3.5**
1.4
--
General hospital
1.7*
1.5*
–0.6
--
Number of observations
553,275
R-squared
0.81
Root mean squared error
0.25
Notes: ***, ** and * denote statistically significantly different from zero at the 1, 5 and 10 per cent levels, respectively; -- denotes
omitted category; standard errors (not shown) are clustered at buildings, there are 42,411 clusters; spatial categories are
from Spatial Services (2019)
Sources: Authors’ calculations; CoreLogic data; PSMA Australia; Spatial Services
39
Appendix E: Sensitivity Analysis
Some changes to our assumptions would change the results in obvious ways. For example, were we
to assume that infrastructure charges or developer’s margins were not a necessary cost of supply
our estimates of the effect of building restrictions would increase substantially, other things equal.
Were we to measure lower prices, the effect would be smaller. The following two subsections discuss
variations that are less straightforward.
E.1 New versus Average Sale Prices
Glaeser
et al
(2005) note that construction costs for newly completed buildings should be compared
with sale prices for
new
apartments, but consider adjusting for the depreciation of older units to be
too difficult, so use prices for all dwellings. Kendall and Tulip (2018) followed this approach. We also
face problems with data on building age, but believe these are surmountable. In particular, many
sales are missing values for year of construction. However, there is very often an observation
recorded for at least one apartment in a building. We assume that all apartments in a building are
built at the same time and this can be estimated by the modal construction year of dwellings within
that building. For Sydney, this increases the proportion of sales with year of construction data from
75 to 92 per cent.
We then exclude all sales more than five years after the estimated construction date. As shown in
Table A1 this reduces our sample by 80 to 90 per cent. However, it has small effects on prices in
2016, raising them by 1 per cent in Sydney and 4 per cent in Brisbane, while lowering them 4 per
cent in Melbourne. These effects are much smaller than earlier in the sample or the conditional
estimates of depreciation from the hedonic regression in Appendix D. As noted earlier, our dataset
contains substantially fewer new sales than completions at the end of our sample, raising concerns
about the representativeness of the latest estimates. While our approach seems conceptually
superior to others’ assumption of zero depreciation, there is a chance that it may understate prices
of new properties at the end of our sample.
An alternative approach would be to exclude sales of an apartment after the first sale, as is done by
CoreLogic (reported in UDIA (2019, p 16)). However, for our dataset, which begins in 1997, this is
impractical. It would involve assuming that almost all sales near the beginning of our sample are
new sales.
Sales in the early part of our sample are more likely to be missing observations on building age. If
we recalculate our historical estimates using average, rather than new, sale prices we find that the
effect of building restrictions is still positive in all three cities over the past decade, and especially
large for Sydney. However, when calculated this way, the effect of building restrictions in Melbourne
and Brisbane is often negative prior to 2009. The effect remains positive in Sydney, but was relatively
small in the late 1990s.
40
E.2 The Effect of Height
In Section 4.2 we estimate the effect of building up by regressing per unit construction costs on
building height. In doing so, we weight observations by number of buildings, on the assumption that
each building provides an independent observation on the cost–height relationship. Alternative
approaches include using unweighted estimates, or weighting observations by the number of
dwellings or gross floor area. Table E1 compares these alternative estimates with our baseline. In
all cases, using a different weighting scheme would imply a flatter average cost curve than presented
in the body of the paper. We argue that weighting by buildings both makes sense conceptually and
provides a more conservative estimate of the slope coefficient and hence the ratio of marginal to
average cost.
We also check the sensitivity of these results to outliers. A handful of observations – corresponding
to very tall buildings – exert relatively high leverage. As a crosscheck, we exclude buildings above
50 storeys. This increases the slope coefficient by about 15 per cent, relative to the baseline. Base
costs and the overall fit of the model are largely unchanged. We prefer to include the full sample
since these differences are relatively modest and there is no obvious reason for disregarding the
excluded observations. This exclusion result suggests that the slope of our cost curve might decline
with height. However, most studies shown in Figure 3 show the opposite nonlinearity.
Table E1: Construction Cost Regression Comparison
By weighting method, 2013–18
Unweighted
Number of
buildings
(baseline)
(a)
Number of
dwellings
Baseline
Excl buildings
> 50 storeys
Slope coefficient
1,405
2,163
1,396
2,470
Base cost
Sydney
310,848
290,211
304,333
287,039
Melbourne
268,185
270,366
272,102
267,812
Brisbane
274,387
240,019
261,994
236,196
Adjusted R-squared
0.24
0.60
0.40
0.66
Note: (a) Differs from the numbers in the text which have been rescaled to 2018 prices; rounded to the nearest 10
Sources: ABS; Authors’ calculations
41
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