A new paper by Schuyler Louie, John Mondragon, Rami Najjar, and Johannes Wieland (LMNW for short) makes a point I have made many times before—housing is not a one-dimensional product where extra demand translates into extra quantity of homogeneous units of things called dwellings.

Or, as they describe:

The standard housing framework in this literature has a single margin of adjustment: all changes to housing demand and supply operate through a single price and quantity of housing, with quantity typically (although not exclusively) measured as units or floor space.

This matters.

Too often, analysts compare the change in the number of homes in a region with the change in its rent or price, and infer that they have learnt something about housing production (supply) and the effect of planning regulations.

But LMNW make clear that this approach tells us nothing at all about supply conditions.

They explain it like this:

We trace out the implications of introducing three realistic features of housing markets: first, some households are rich and some are poor; second, rich households purchase higher-quality housing than poor households; third, high-quality housing is harder to produce, and so more expensive, than low-quality housing. In this setting, there are many unit-supply functions, each indexed by unit “quality”, where quality reflects both physical margins (size, finishes, and so on) and location-specific amenity quality. Thus, the standard single-margin supply and demand functions that relate changes in prices to changes in the number of units alone are misspecified as prices also encode information about the supply and demand of unit quality.

That is a bit much jargon for me.

So let’s look at the scenarios LMNW use to illustrate the point — a lottery and an influx of poor migrants to a region. These scenarios reveal how a lot of what is blamed on regulation and supply-side issues can emerge for sensible demand-side reasons, which also explain many of the patterns we see in housing markets that get ignored in most economic analysis (e.g. gentrification).

Let’s dig in.

Lottery scenario

Here’s their first scenario.

Consider a situation where all households win the lottery. We have assumed that there is no migration, so all this additional income will be spent on more housing quality and non-housing consumption in the city. Quality demanded will go up, which means the price p(q) will increase. There is no change in the number of households or housing units by assumption.

The reason that no extra homes are produced in this lottery scenario is that all the extra income households get goes to demand for the quality of housing units, not the demand for the quantity of them.

Everyone already lives in the number of homes they want to live in. Every household has the number of people they want in it regardless of how rich they get—so they spend more on improving housing quality, and on non-housing consumption goods, not more housing units. This is true even if more housing units of the same quality can be infinitely supplied at the same price (i.e. supply of housing units is perfectly elastic).

LMNW illustrate this lottery scenario as the red line in the figure below, which plots home prices or rents on the vertical axis (in log form) and the number of housing units on the horizontal axis (in log form). Note that the dashed blue line is for the next scenario.

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In this scenario, more housing is supplied in the form of quality, either in the size and design of the buildings themselves, or in the form of the relative value of the location (a location quality benefit).

I try to visualise how this confusion between more housing supply on the quality dimension and lack of supply on the quantity of housing units dimension arises in the diagram below.

The top row shows the baseline pre-lottery market equilibrium of quantity and quality.

The second row shows an increase in demand for quality only. Homes get better, but if you are looking only at the quantity of housing units (the final column, blue charts), you will observe the price increase due to more supply of quality per unit, but no increase in the quantity of housing units. You might inadvertently infer that supply is inelastic because prices went up but the quantity of housing units didn’t. But this is a result only of the fact that demand increased in the quality but not quantity dimension.

If you correctly look at the quality dimension (or the quantity of quality attributes), then you will see that housing quality is supplied perfectly elastically (the second column, second row, red chart).

Or, in their words

From the view of the single-margin model, these data incorrectly imply a perfectly inelastic supply curve, but in truth there has simply been no change in the demand for units at the same time that quality demand has increased.

This distinction really matters. As we get richer, we of course want to buy more housing. But housing is a whole category of expenditure, not a single uniform product.

The illustration below shows how housing is a category made up of a variety of different attributes that we value. Total market demand for housing as a category is merely the sum of demand for all the attributes. As LMNW’s lottery example shows, sometimes demand will increase for many of the attributes of housing that are not related to the quantity of housing units.

Poor migrant scenario

LMNW’s second scenario is an influx of poor migrants to a region.

They plot this scenario as the nearly horizontal dashed blue line in their above chart. All quantity demand, no quality demand. I plot it in the above FET chart in the bottom row as a pure quantity demand shock.

The reason that new migrants must be relatively poor in this scenario is to ensure that any change in quality demand from their migration choice is negligible.

What this scenario shows is that for us to observe perfectly elastic housing unit supply when it exists, demand must shift only for more housing units and not more housing quality at all. This is likely to be an uncommon scenario in reality.

Spillover scenarios

What LMNW then look at is an even more subtle point — that migration into and out of a region is itself determined by the incomes on offer and the rents or prices of homes, such that migration happens when total quality of life improves (a concept known as spatial equilibrium, which is briefly explained in this FET article).

Changes to the income distribution change migration choices. That’s how we get things like gentrification, where high-income people move into a region, and this change leads to low-income people leaving. The basic lottery and poor migration scenarios did not allow any such feedback, or spillovers, to migration choices.

LMNW expand their lottery experiment in three ways to allow it to affect the wealth and income distribution and therefore lead to spillovers in terms of migration choices.

The first way is this.

Reconsider our lottery experiment, but now assume that only rich households are eligible to win. This prospect draws in rich households, driving up house prices. Higher prices reduce real incomes for poor households, who migrate out of the city. It can easily be the case that the outflow of poor households is larger than the inflow of rich households so that prices increase but population and units actually decline.

They show this in the red Gentrification line in the chart below, which reflects an increase in price and a decline in the quantity of housing units in a region. In this scenario, the Gentrification outcome is due to a positive demand shock (a rightward shift of demand) to the upper portion of the income distribution. But because of spillovers to the migration decisions of others lower down the income/wealth distribution, who migrate away, a very unexpected relationship between the price and quantity of housing units emerges, even though housing units are perfectly elastically supplied.

This process of gentrification, whereby fewer larger, higher-quality homes emerge in areas where demand for housing has increased, has been an observable part of the recent housing boom in Australia.

In high-value Sydney suburbs, cheaper housing units are being replaced by fewer, larger, higher-quality ones, forcing out low-income households as a response to relatively higher demand for quality than quantity.

The quote below from a 2023 news report illustrates this exact dynamic.

Two examples of potential net dwelling loss in the Sydney council area include a development application (DA) by property developer Fortis to knock down a 1970s residential complex with 28 apartments in Elizabeth Bay and replace it with only 22 apartments. Another developer plans to demolish 20 dwellings in Potts Point for a new block housing five apartments.

However, Fortis has defended its Elizabeth Bay DA, with associate director Patrick Baldock saying the proposal would increase accommodation on the site by providing 64 bedrooms plus studies compared to the current building’s 48 bedrooms.

The second way LMNW expand their lottery scenario to incorporate spillovers is this.

Alternatively, if rich households consume services provided by poor households so that poor incomes are increasing in rich incomes, then the gains from the lottery will be shared and can cause in-migration for both types of households.

This is labelled as Positive Income Spillovers in their chart. You might think of this scenario as capturing the normal dynamics of a regional economic boom. Think of the Gold Coast since the COVID lockdowns.

Finally, the third way LMNW look at how changes to the income and wealth distribution can result in spillovers is this.

Now imagine that only poor households are eligible for the lottery, that rich households prefer living with other rich households and that poor households are indifferent. Higher incomes draw in poorer households, but this causes out-migration from richer households. If inequality is high and the rich really dislike living with the poor, then house prices may actually fall at the same time that there is an increase in housing units.

This is labelled Capital Flight in their chart. Another way this scenario might be described is as a negative amenity effect — there are many extra homes, but the area has become relatively less desirable because of the changes associated with those extra homes. More homes in a region, but lower rents and prices.

Time also matters

LMNW show that the shape of housing demand and the spillovers it generates lead to an enormous variety of relationships between housing quantity and price. In addition, the choice of time frame over which price and quantity observations are made matters, too.

Housing markets move slowly. A home bought off-the-plan today because of today’s market conditions might get built and available to occupy in 3 or 4 years.

Recent analysis by e61, for example, found that the peak of housing pre-sales for apartments in the 2010s Sydney boom was in 2014, but the peak of construction completion of those dwellings was in 2018. The chart below is from that e61 analysis and shows how the pre-sales peak coincides with price growth, and hence rising demand, but the actual completion of those dwellings coincides with a price downturn.

Whenever you look at the relationship between change in price and change in the stock of homes or rate of new housing produced, you must do so over a certain time period—maybe over a year, maybe over a few years or a decade. And each choice of time involves selecting one part of a slow-moving market cycle.

has shown, in the chart below, how the correlation between new housing permits and price growth is not fixed over time and varies across the market cycle, inverting during downturns. If you observe quantity and price during a boom, it slopes up (like the Positive Income Spillovers line of LMNW). If you observe during a bust, it slopes down (like the Capital Flight line of LMNW).

This doesn’t imply that regulation or supply conditions have changed. Downturns are not supply shocks, but changes to demand. This is essentially the same point made by LMNW— the shape of demand, in this case over time, will affect the observed relationship between price and quantity.

So what?

The lesson from LMNW’s paper is this.

Even without any regulatory constraint on the rate of housing production, its density, quality or any other attribute, when you look at housing unit price or rent and quantity changes in a region, the relationship can be just about any shape for normal demand-side reasons.

What looks like inelastic supply in a region might simply reflect that there is relatively higher demand for quality than quantity, for example.

Or, in their own words.

How can exogenous increases in “demand” for certain types of housing sometimes lead to increases in prices and declines in units and other times lead to increases in units and declines in prices? These seemingly perverse outcomes arise naturally from the facts that demand is not monolithic and that poor and rich households are connected to each other through local housing and labor markets.

The PDF below is their full working paper.

Wp2026 18

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It is all in their report called “Home truths: The case for rebalancing toward better means testing”.

Here’s a revealing example from the report that illustrates some of the concerning realities of how Australia’s welfare system operates:

…a retired couple with a $5 million home in Sydney can qualify for the full Age Pension worth almost $50,000 a year, and up to $150,000 for one spouse in Aged Care. Their neighbours who rent a modest home and hold $1.5 million in super receive no pension at all and roughly half the Aged Care support.

This seems unfair—at least according to my internal moral compass.

Here is another example from the report

…[a] family with two kids earning $400,000 a year qualifying for up to $20,583 in Child Care Subsidy support and about $20,000 in Parental Leave Pay…

I think some listeners would be surprised by these welfare settings. In my imagination, these subsidies and welfare payments go to those in need, not wealthy and high-income households.

In the interview, we get quite nerdy discussing how relatively minor tweaks to just four parts of the welfare system—the Age Pension, Aged Care, the Child Care Subsidy, and Parental Leave Pay—which now cost over $126 billion per year, could save $21 billion per year.

And it would all be achieved in what I perceive as a very fair way, by targeting what amounts to upper-class welfare.

Here’s Policy Institute Australia’s website, and here’s the report.

*Note that we didn’t cover the more radical policy shift of making cash payments universal and letting the tax system do the means testing.

Timestamps

0:00 Introduction
4:01 A family with a $400,000 annual income gets $20,000 of benefits
5:58 What does some of the jargon mean
12:07 The scale of the payments
15:30 Age pension reforms
28:00 Age care payments
39:08 What about welfare for younger people and families
44:59 Is it good to pay people for babies
56:04 How big are the budgetary savings

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If that sounds like you, and you get value from the research and analysis published here, I’d appreciate your financial support.

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A fresh economic consultancy

Most economic consultancies begin with the conclusion a client wants. They sell institutional credibility and a report. I sell economic thinking.

If your organisation has a difficult economic question, a policy argument that needs testing, or a problem where you want to get beyond the usual consultant consensus, I’d like to hear from you.

FET consulting enquiry

FET also provides custom economics training, presentations and workshops for organisations that want their people to understand the economics behind the issues they work on.

If there are economic concepts, debates, or blind spots your team needs a genuine handle on, then please reach out.

FET custom training

Ground Rules: Land and Housing Economics for Professionals

Put that professional development budget to work with an upcoming FET Land and Housing Economics workshop in Sydney or Melbourne.

This two-day workshop is crafted for smart researchers, policy analysts, economists and planners who want to truly understand how land and housing markets work.

Sydney tickets here.

Melbourne tickets here

More details here.

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A lesson from the past few weeks of public debate in Australia is that even those who are most passionate about immigration don’t seem to understand how the design of the visa system affects Net Overseas Migration (NOM).

This Explainer is to help those who want to learn.

UPDATE: Try the FET Aussie NOM simulator spreadsheet

Fet Aussie Nom Sim

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Migration’s big picture

Net Overseas Migration (NOM) is a term that describes the change in the number of residents in Australia due to international movements in and out of the country. A resident is someone who resides in the country for more than 12 months out of a 16-month period.1

This 12/16 rule is why the NOM data has a long delay—it takes a while to work out if a person stayed 12 out of 16 months to be a resident rather than a visitor. Only preliminary data using modelled estimates is released six months after the reference quarter, then final data arrives at least 16 months after the reference quarter (as explained here).

NOM includes: the change in the number of Australian citizens, as some return from living abroad and some depart; the change in the number of NZ citizens residing in Australia, who have free labour mobility; and the change in the stock of resident temporary and permanent visa holders.

NOM excludes short-term holiday-making and business travel, which is the bulk of the 1.6 million international arrivals (and departures) each month, as these do not change where a person is a resident.

The diagram below shows the buckets of the Australian resident population, to scale, and each bucket’s labelled flows in and out of the country (A to H) from international resident movements (i.e. excluding short-term visitors and holiday-makers). Also shown and labelled are paths I and J, which are temporary residents becoming permanent, and permanent becoming citizens, neither of which directly affect the NOM because these people are already residing in the country and merely swapping buckets.

What sharp eyes might notice from this diagram is that we can’t directly regulate NOM to a fixed number if we want many of the potential international movements of residents (B, D, E, F, G, and H) to be freely chosen and unregulated by visa requirements. Few would argue that Australians leaving to live abroad and return to live at home should be restricted.

Instead, the rules of the temporary and permanent visa system can influence key resident flows like A, C and I, which can have the largest effects on NOM.

Another important point is that NOM depends on net flows of residents to each bucket rather than gross flows. For example, you can have very high flows of A and B into and out of the temporary visa stock of residents, but if these flows are equal, then they do not affect NOM. Only if there is a gap between the inflows and outflows, and hence a rising or falling stock of temporary residents, is there an effect on NOM.

It is underappreciated that the resident flows in and out of the country are much larger than the change in the stock of residents.

For example, 234,000 temporary student visas were issued to those outside Australia in calendar year 2025 (out of 371,000 altogether, including those issued onshore). Still, the stock of student visa holders residing in Australia fell from 522,000 to 477,000, or 45,000 during that calendar year because departures of student visa holders (or conversion onto another visa) were higher than the large in-flow of new temporary student visa holders.

Over the past decade, net flows into the temporary migrant bucket have increased NOM by around 100,000 per year out of a 250,000 per year average total NOM.2

The net flows of Australian and New Zealand citizens (E, F, G and H) are usually small, with an effect on NOM of less than 20,000 per year and sometimes a negative effect.

In total, the migrant inflows (A, C, E, and G) of those who stayed more than 12 out of 16 months over the past decade averaged 512,000 per year, while the migrant outflows (B, D, F and H) of those who remained abroad more than 12 out of 16 months averaged 262,000. This meant that NOM averaged 250,000 per year for the last decade (or 291,000 per year if we exclude the two COVID years of 2020 and 2021).

All of this is important context to help make clear the key message, which is that NOM can only be indirectly regulated using the temporary and permanent visa system to affect the flows A, B, C, and I, via

1. conditions and caps on the number of permanent visas issued annually (flows C and I), and

2. temporary visa conditions that affect the stock of temporary residents (flows B and A).

We can see how these four resident flows affect NOM mathematically:

For a long time, visa settings have been such that the temporary stock has increased because A has been higher than (B+I), while the permanent gross flow has been capped by annual permanent visa rules, setting an annual maximum of (C+I).

Let’s take a closer look.

Temporary resident stock

Data on temporary visa holders is here and downloadable below.

Bp0019l Number Of Temporary Visa Holders In Australia At 2026 07 31 V100

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Most temporary visas are uncapped and issued based on compliance with each visa rule. This is called a demand-driven system. The number granted is only limited by compliance with the eligibility requirements of the available temporary visas and the administrative capacity of the Department of Home Affairs.

The largest increases in the stock of temporary visa holder residents in the past three years have been in bridging visa holders (up by 235,000), who were typically holders of other visas wishing to extend their stay rather than leave, and temporary graduates (up by 70,000), who have similarly switched from other temporary visas, in this case student visas.3 Over 100,000 per year of the NOM experienced in Australia from 2023-2026 was from a growing stock of residents in these two temporary visa categories alone.

The temporary visa system allows for switching to other visas, which is a policy setting that can be changed to affect NOM via the net flow of temporary residents. Removing the options to stay on a different temporary visa will decrease NOM by increasing flow B, the temporary resident departures. There may also be a deterrent to flow A as future seekers of temporary visas realise that getting a temporary visa in Australia is far less likely to lead to long-term residency.

These dynamics are well known, which is why analysts from across the political spectrum propose tightening up these temporary settings to reduce the stock of temporary residents and reduce the visa swapping that has fostered a “permanently temporary” cohort of people.

Policy changes that would reduce the stock of temporary residents, thereby reducing NOM, might involve tightening student language standards for student visas, increasing income thresholds for temporary skilled workers, limiting secondary family visas for many temporary visa categories, and making bridging visas harder to get.

If the stock of temporary residents began falling because of tighter temporary visa settings, it would have a large temporary effect in reducing NOM. If, say, the stock of temporary residents fell by 500,000 over 5 years (from roughly 2 million today to 1.5 million in 2031), that would have a temporary negative effect of 100,000 per year on NOM.

This is a scenario playing out in Canada right now.

Visa rules were tightened there in 2025 with the intention of reducing the stock of temporary residents. As a result, Canada has seen negative population growth for three quarters in a row, with a negative change of 55,000 in population in the most recent quarter.

Their total population decline has been 234,000 in 9 months. This effect will be temporary, until such time as the stock of temporary residents stabilises at a lower number.

Permanent resident flow

The second major policy lever is the permanent visa program that predominantly issues family and skilled visas and has an annual cap, called a planning level. It is a long-standing policy to limit flows C and I via this planning level. For the 2026-27 financial year, that annual cap is 185,000 permanent visas.

Typically, most permanent visa holders come via flow I from temporary visa holders. As noted by the Department of Home Affairs, “In 2024–25, 61 per cent of permanent skilled visas and 39 per cent of permanent family visas were issued to people already onshore.”

Although these flows from temporary to permanent (flow I) do not directly affect NOM, they allow for temporary visa flows A to be above B by I before affecting NOM.

If the stock of temporary residents remains fixed, the permanent visa planning level is the main way that the visa system affects NOM.

There are a variety of sub-categories within the overarching skills and family streams of the permanent visa system. For example, a strange one that has been the subject of great debate is the 7,000 places (down from 8,000 last year) for parental visas, which allow citizens and permanent residents to bring their parents to Australia permanently.4 The demand is high for them, and the waiting period is now decades long. Should they even exist?

To use the permanent visa system to reduce NOM simply means reducing the planning level of some of these permanent visa categories, preferably starting with those known to be issued to fairly low-performing households, such as regional skills visas. Making income hurdles higher for some classes of skilled permanent visa might also better target those visas and reduce demand for them.

Such changes would reduce NOM long-term by reducing flows C and I, while immediately deterring some temporary inflows, A, because of the lower chance of permanency for some potential temporary migrants.

Back in 2004, for example, before the 2006 ramp-up to structurally higher immigration levels in Australia, the temporary resident stock was relatively stable, and the planning level for permanent migration was 120,000. That type of outcome could be achieved again without much difficulty.

Yes, the visa system is complex. But the right conceptual understanding can greatly simplify it and clarify how temporary and permanent visa policy changes will affect which new residents, and how many, come to Australia.

As always, please like, share, comment, and subscribe. Thanks for your support. You can find Fresh Economic Thinking on YouTube, Spotify, and Apple Podcasts.

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1

This 12/16 rule was established in the measurement of NOM in 2006, so comparability is not perfect before and after this date, though the close tracking of NOM using the 12/16 rule and the net arrivals data, shown in the chart below (dashed black NOM and grey net arrivals), suggests that there is not a major ongoing change as a result of this methodological change.

After the successful Ground Rules: Land and Housing Economics for Professionals workshop in Brisbane in June, I have opened up two more workshop events in Sydney and Melbourne in November 2026.

The workshop is an intensive event with activities, class simulations, and plenty of participation. Held over two days. Catered. Workbook and materials supplied. It is challenging but fun.

It took me over 20 years to learn and deeply appreciate the concepts I try to pass on in two days—so save yourself some time and put that professional development budget to work.

Melbourne: 10-11th November 2026 (book here)

Sydney: 17-18th November 2026 (book here)

Who is the workshop for?

Here are the types of professionals that would get enormous value from the workshop:

• Town planners and policymakers who want to understand the market and how policy changes affect market outcomes, whether planning, taxation, or value capture mechanisms.

• Economists who study housing and property and want to deepen their knowledge.

• Property professionals, such as investors and developers, who want to understand emerging patterns in the broader market.

• Social and affordable housing advocates and businesses looking to understand policy settings and how they fit into the housing market.

• Researchers in housing and property who want to ensure they have a solid understanding of the economics of land and housing.

The pitch

Have you noticed that home prices usually rise even when supply increases, or that zoning reforms often fail to reduce prices? Are you still uncertain whether land banking is rational or conspiratorial?

Dr Murray is the author of The Great Housing Hijack and publishes extensively in academic and media outlets. He teaches economics to industry leaders in Australia and the Middle East and has designed this workshop to upskill property professionals involved in investment and management, valuation, planning, research and public policy.

Participants will walk away able to answer the above questions and:

1. Appreciate how property rights can be crafted in a variety of ways to incentivise resource allocation to high-value uses via scarcity and trade.

2. Identify how five powerful economic forces constrain market outcomes in terms of prices, rents, density, supply, and the spatial patterns of our cities.

3. Have the ability to incorporate the logic of cycles into their analysis of property markets and policy.

4. Be equipped to assess how policy settings interact with market forces beyond the simple first-order effects commonly debated in the media.

This is an experiential workshop promoting active discussion and debate. A variety of topical examples and case studies are integrated into the workshop activities to help participants apply these lessons and guide their future decision-making.

Here’s what Paula said about her experience at a previous workshop:

Thank you for a brilliant two days. I love economics but it’s been a while so I was worried I would struggle to keep up. Thanks to you I didn’t struggle and was not bored at any point. The interactive nature of the event was perfect.

Attendees are provided with a workbook (mini textbook) to keep for future reference and a copy of the book The Great Housing Hijack.

If you have any questions about these events, or would like a customised workshop or presentation for your organisation, click the button below.

Enquire Now

Finally, below is the topic list and schedule for the events.

And here are some printable flyers.

Fetworkshopsydney2026

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For those looking for the weekly FET content, I have a couple of recommendations.

Check out the FET YouTube channel

Occasionally, some videos make it to the Fresh Economic Thinking YouTube channel that don’t make it to the podcast feed.

Here is a recent conversation where I try to explain that the housing market is having a normal cycle. Yes, it is expensive now. No, it won’t always be.

Back in January 2026, I wrote about the likely peak of the housing cycle and examined the different features of this cycle in terms of relative prices between cities, between houses and apartments, and the yield on housing versus other assets.

Below is a fun video with where we explore the housing supply debates and his new housing data mapping tool called ProMap (for US data only).

And for a change of pace, below is a video with about how hard it is these days in our culture see the positive side of masculine behaviour (I am the father of two teenage boys, so this topic is a personal interest).

Read the recent Housing Supply 4-part series

Part 1:

Part 2:

Part 3:

Part 4:

As always, please like, share, comment, and subscribe. Thanks for your support. You can find Fresh Economic Thinking on YouTube, Spotify, and Apple Podcasts.

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This is Part 4 of a four-part series coming to FET that I hope will become a reference point for understanding key concepts related to housing production.

1. Why a feasible change of use is different from commercial feasibility

2. How prices determine the cost of building new homes via choices of density and quality

3. Housing supply is not new housing production

4. Why landbanking is a normal market outcome of balancing present and future housing

Interested in learning more? New workshop dates for Sydney and Melbourne have just been released for mid-November 2026. Click the images below to book your tickets at Eventbrite.

If you value this content, please consider signing up as a paid FET subscriber to support this work and get access to all articles and audiobooks.

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In Part 1, we looked at change-of-use feasibility versus commercial feasibility.

In Part 2, we looked at how higher home prices lead to higher construction spending on each new home via the choice of density and quality.

In Part 3, we looked at housing supply as a concept of trade, not production.

Now, in this final Part 4, we look at the housing production rate (i.e. new homes produced per period of time). This is also known as the absorption rate, or build-out rate, and is often wrongly called supply.

As a primer, you can take a look at this FET article.

Here, I want to try to explain the economics of the emergent market production rate of housing by doing something a little different.

Instead of starting with the question “Why are homes built?”, I will start with the question “Why are homes not built?”

It is common to own a site with a feasible change of use and not develop new housing (or any other type of feasible use, like retail or commercial buildings) on it today. This is why the distinction between change-of-use feasibility and commercial feasibility in Part 1 of this FET article series was so important.

Outside of a handful of academics who take the questions of timing and irreversibility of investment in the real options literature seriously, there is almost no attention given to the optimal timing of new housing. It is merely assumed that every landowner is in a hurry to build any feasible change of use instantly, and that any waiting creates a huge cost for them.

This has led to a contested debate about the concept of landbanking.

This real, observable phenomenon gets denied or redefined rather than grappled with. Below is a video of a property investor describing clearly the economic logic of delay. He notes (in his terminology) how sites that are change-of-use feasible are generally not going to be commercially feasible for many decades until population growth patterns change the waiting calculus. In another video, the same property investor explains how the highest and best use can be to hold change-of-use feasible sites rather than develop today.

Before outlining the limited academic and policy debate about landbanking, I will be clear that I define landbanking as the existence of a large pool of sites with a feasible change of use that are not currently developed for that use.

Landbanking is just the name we give to the fact that not every feasible change of use of property to housing is developed the moment it becomes a feasible change of use.

It is true that most landbanked sites are not commercially feasible. But commercial infeasibility is an emergent pricing outcome that arises for the sensible economic reason that building in the future is what traders in the market expect to generate the largest economic gain. And it is this definition that is clearly a real phenomenon that needs a coherent economic explanation.

Implicit acknowledgments of landbanking by those who deny it

In a 2015 inquiry into land use, the New Zealand Productivity Commission grappled with the issue. Many submitters raised the topic. The New Zealand Transport Agency (NZTA), for example, submitted that “land banking is an issue where developers may drip-feed zoned land on to the market to maximise the value of new sections”.

But since then, many individuals and organisations have denied that landbanking of change-of-use-feasible sites exists. This is what Australia’s Productivity Commission wrote in 2022 (pp. 468- 469):

Landbanking is a theory that the supply of housing is constrained by developers withholding land from the market to drive up prices.

Okay. And then.

Landbanking also requires developers to believe that they can ‘time the market’ — in other words, sell properties when prices are high and hold onto them when prices are low (which would be easier if theyhad the ability to influence market price). And it requires a general belief that property prices will perpetually rise — if the market price is constant or declining over time, there is no benefit from withholding land from the market.

A big claim here is this one.

Landbanking theories also cannot explain why the examples of zoning reform listed above led to greater housing supply.

A critique of an FET article about the methods of analysis of Auckland’s upzoning laid out the denial case by noting that “developers aim to maximize profits, not maintain prices” and that “[m]ost economists tend to dismiss such ideas”. They note that:

The small number of existing studies are limited by a lack of data on developers’ cost structures, which prevents them from ruling out that delayed construction or large “land banks” are driven by factors like managing dynamic production costs (e.g., labor constraints, material price fluctuations) or attempts to smooth costs over time.

It has become a very strange debate.

The craziest part is that those who deny that landbanking is real also imply that it is a necessary part of a normal property market when they seek to upzone to create enormous amounts of change-of-use feasible new housing, knowing that only a tiny fraction will be built on even after decades.

For example, here is an article reporting an analysis of how many new townhouses could be built under changes to zoning in Australia’s capital cities. The key part is in the image below.

The claim is that 9 million new homes would be unlocked in cities with a current housing stock of less than 8 million homes, and that 3 million will be immediately profitable (i.e. change-of-use feasible). If you think landbanking is not real, then all 3 million townhouses should be built immediately.

Of course we all know that won’t happen.

Less than 200,000 new homes of all types are built each year nationwide. No one truly thinks that the rate of housing production will increase by a factor of ten because of a rush to build these homes instantly. Almost all of them will be landbanked instead and held for development at a future time.

It is now the norm to deny landbanking, then assume that most feasible sites are landbanked.

A recent NSW Productivity Commission report also does this dance of contradictions. First, they argue how important it is for the planning system to create feasible capacity to remove this constraint, ignoring that markets will price in these new development rights such that commercially feasible capacity can never be achieved.

They then proceed to acknowledge that they have no idea how much of that feasible capacity they think they can create will get built in any given period, so they present this table. In it, they throw their hands in the air and admit they have no idea what portion of feasible capacity will get built in any period of time. Could be 2%, could be 5%. Who knows?

There is a complete absence of a theory of why change-of-use feasible housing is built or not. To address the strange story they are telling that feasible capacity is the only binding constraint today, but after upzoning some other invisible constraint will bind, they write this gobbledygook.

Notice that they are explicitly acknowledging that there are landbanked sites in Auckland today that could produce nearly 2 million homes, and if their proposed zoning changes happen, that will create 300,000 extra landbanked homes.

In short, if you believe there exists change-of-use feasible capacity, you believe in landbanking. If landbanking is not real, there should be no change-of-use feasible capacity—every change of use has already happened.

You can’t have it both ways.

A closer look at landbanking

The reality is that landbankers are holding land for future homes. By my definition, it is a necessary part of a well-functioning housing market that trades off building new homes faster or slower. It is not a behavioural quirk of naughty property owners, whereas the nice property owners build as fast as possible.

It is an emergent outcome across the market such that, on average, waiting earns the same return on investment as developing now. While developers love to argue that waiting is costly (which it is after construction is committed, but not before) this is not generally the case.

We see this with the staging decisions of larger housing projects.

If it generated a higher return to go faster, developers would not stage large projects. If they faced cash flow constraints, they would sell future stages to others to develop in parallel rather than in sequence. Indeed, if landbanking were not real and waiting was costly, it should actually be easier to attract investors to fund the cash needed to develop large projects more quickly because that would produce a higher return than developing more slowly in stages.

But why wait?

Remember the supply curve. It is the benefit forgone by sellers. The forgone benefit of developing homes today is the benefit of waiting and maybe developing something different in the future instead.

Since change-of-use feasible sites are assets that retain their value into the future, there is a timing choice.

I find a useful way to think of housing development is as an asset portfolio allocation decision. An undeveloped change-of-use feasible site is an asset that offers future returns. Owners are waiting for a future time to convert it into cash by combining that asset with cash to pay for development. This is just like owners of BHP or CommBank shares—they are earning the gain from waiting for a future time to sell and transform those assets into a different form of asset, be it cash, bonds, or other shares.

This insight alone should make us wary of the idea that waiting is extremely costly for landowners. Because land is tradeable, the most patient and optimistic buyers end up owning change-of-use feasible sites. If two people were bidding for a development site, and one was in a hurry to build as quickly as possible, and another was patient and thought there would be even better opportunities in the future, the second bidder would win the auction and own the land.

The trade-off that generates the emergent market equilibrium rate of new housing production is the benefit from waiting versus the benefit from developing. These competing benefits regulate the pace of development so that the market delivers today’s homes today, but saves tomorrow’s homes for tomorrow.

Waiting provides these returns:

• The increase in the value of the site, which comes from some combination of

  • Rising home prices

  • Falling like-for-like construction costs

  • Possible changes to the density and quality choices of future projects

• Net rental income from current site use

Developing now provides these returns:

• The increase in the value of housing from rising home prices only

• Higher net rental income from new housing use

In a rate of new housing production equilibrium, the return to waiting and developing will be the same in risk-adjusted terms. If they weren’t the same, there would be money to be made from speeding up or slowing down new housing production.1

There are two important refinements to this overall economic principle that are necessary to enhance our understanding of landbanking, and hence the rate of housing production.

1. There is an equilibrium rate of production (how fast homes are produced)

2. That rate comes out of an ordering of potential projects (in which order homes are produced)

There has been a fair bit of confusion about these two concepts. In fact, that there are two distinct concepts is somewhat of a new discovery of mine. It puzzled me for years that most change-of-use feasible sites were not developed, and very few economists seemed at all concerned by this observation.

My first academic attempt at explaining the economic logic is in this paper.

Absorptionrate Wp 5

802KB ∙ PDF file

Download

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Like most academic papers, I now realise what I got wrong there. But it was a start.

In that paper, I was also confused about these two distinct concepts: the rate of new housing production and the ordering of sites. Because the same economic force that increases the rate of production pushes some sites back in the ordering.2

Let’s dig in.

The rate of production

The rate of new housing production emerges across all locations and types of dwellings. Often, our conversations about the rate of housing production are actually about the locations of these homes. Best be clear that the rate emerges across many sites, locations, and housing types (i.e. new housing filters across locations, too).

What economic forces regulate this rate? In other words, why does a market of landbankers produce new homes faster or slower?

The best way I know how to begin to explain the emergent market phenomenon of a rate of housing production equilibrium is by analogy. And I have two to choose from.

Equilibrium speed in a highway traffic jam

The first analogy is to imagine a two-lane highway in a traffic jam.

Drivers have the choice to trade lanes when they think the other lane will be faster. Because of this, any time one lane speeds up, drivers switch from the slower lane to the faster lane, and in the process, they make the slow lane speed up when they depart and slow down the fast lane when they arrive.

The only equilibrium outcome is for both lanes to have the same average speed.

The lanes in this analogy are the waiting-to-develop lane and the develop-now lane. The equilibrium rate of housing production is the amount of lane changes from wait to develop that ensures the rate of return to each is the same in risk-adjusted terms (i.e. the average speed is the same for both lanes).

Notice here that every driver is competing to go faster (make higher returns). The outcome is a competitive one, not a monopolistic one. Many more drivers have the opportunity to change from the waiting lane to the developing now lane, but choose not to and are hence landbanking, as necessary in a housing market equilibrium.

Equilibrium rate of food consumption

The second analogy is about food. Imagine you have a house full of hungry teenagers. Their appetites are high relative to others, but still finite. You keep the fridge and pantry well stocked at all times.

Do teenagers eat every food item the moment it enters the kitchen?

Even if they are competing for the best food from the choices available, they eat at a finite rate to match their appetites. When their collective appetites fall, the rate of food consumption falls, and vice versa.

This analogy is useful because it also captures the idea that there is an ordering to the food that gets eaten out of the available food—the rate of calories eaten and the order of food chosen by the teenagers in the “kitchen food market” are different.

The rate of housing production equilibrium is such that you can’t arbitrage by developing homes faster or slower. If you slowed development, others would accelerate their development because that provides them a higher rate of return on their non-housing development investment compared to waiting.

It is a competitive outcome.3

The rate of return on investment rises when prices are rising, or construction costs on a like-for-like basis for the same project are falling. This is like the other lane speeding up and offering a higher return to switching.

The diagram below helps illustrate and visualise this idea.

The top shows the sequence (pipeline) of yellow change-of-use feasible sites, moving into developed housing at a particular rate. The ordering in this sequence is discussed below.

The bottom of the diagram shows the scenarios that make changing into the develop-now lane more or less economically attractive. When the gap between market price and development cost is rising, it means a rising payoff to development. When the gap is falling, it means a lower payoff.4

If we look at this price-development cost gap in recent years, we can see a pretty clear relationship with the rate of new housing commencements. In the next chart, I take this gap, remove the long-run trend, and plot it alongside new housing commencements (both scaled).

As a proximate cause of whether homes are built or not, the trade-off between waiting and developing is the answer.

But if you zoom out to look for deeper causes, things get weird because the cycle in new housing development is part of broader macroeconomic trends.

Here is a chart from Twitter analyst Mark the Graph (Bryan Palmer) showing that after a variety of tests, the best predictor of building approvals for new houses was the approvals last quarter plus the change in the unemployment rate.

Why would rising unemployment predict stronger approvals? As he says:

That’s because approvals lead the economic cycle and are driven by interest rates, and the RBA cuts rates precisely when unemployment is climbing. So “unemployment rose over the past year” really stands in for “the RBA has been easing,” which lifts approvals. The unemployment rate is just a cleaner, less noisy way to capture that monetary cycle than the cash rate itself.

Rising unemployment usually goes hand-in-hand with lower construction costs, and falling interest rates make current prices more justifiable to buyers. This fits well with the micro-level story of a rising price-development cost gap leading to faster home building.

The ordering (sequencing) of housing sites

Back in 1931, Harold Hotelling noted that mineral and coal resources, like locations for new homes, get sorted into a sequence by market forces.

They will be removed and used in order of accessibility, the most cheaply available first. If interest rates or degrees of impatience vary among the mine-owners, this fact will also affect the order of extraction.

So too do a variety of factors affect the ordering of sites into the housing production rate. Another market equilibrium emerges where you can’t arbitrage and make excess returns by changing the order of sites in the housing production line.

The most interesting part is that where density and quality are flexible, rising prices lead to some sites being pushed back in the sequence. The rate of return from waiting at that site increases relative to other sites, even though the equilibrium rate of production also increases. This reorders the emergent market sequence of sites to be developed (illustrated in the above image).

To use our teenager’s appetite analogy, when they are really hungry, and the “rate of eating” equilibrium is high, it will also be the case that this involves a reordering of what they choose to eat out of the kitchen—the pasta moves to the front of the line and gets eaten first, but the fruit goes to the back of the line and gets eaten later.

As we saw in the Part 1 FET article in this series on change-of-use feasibility versus commercial feasibility, a pushback in the order (or sequence) is associated with a rising land value of that site. The market assessment of this sequence is expressed in the pricing of change-of-use feasible sites.

As we saw in the Part 2 FET article in this series, rising prices usually mean there is a payoff to changing to a higher-density housing project.

The equilibrium arises because you shouldn’t be able to arbitrage by changing the sequence of sites into housing development—if you could, then the sequence would already be changed.5

Rising prices, therefore, have the effect of

1. increasing the equilibrium rate of production, and

2. re-sorting the order of sites and, in doing so, changing the delay premium pricing of some sites more than others.

Both the rate of production and the value of delay are emergent market outcomes.

In our teenage food consumption analogy, this sorting is equivalent to them choosing which foods from the kitchen to eat first. There is an order to this. Even if you are really hungry and eating quickly (high demand, fast equilibrium rate of production), those kids might still leave the unripe fruits for later.

There is an interesting tension between the production rate and the ordering of sites.

Some projects, when prices are rising, will move backwards in the order. These are ones where, for example, there is extra gain to be made from increasing the density of a project in response to higher prices. The market pushes forward other projects, and these projects move back in the order.

The fact that there are really two elements to the absorption rate—the rate of development and the sequence—helps explain some of the confusing results of real options theory. There are unresolved contradictions in that theory, since a higher future payoff provides an incentive to delay new housing at a site, but rising prices (which are necessary for higher future payoffs) bring forward development.

A resolution comes from identifying the two separate effects of rising prices, where one effect is on the rate of production, and one effect is on the ordering of the sequence of sites into production.

So what?

Landbanking is a normal and necessary part of the property market.

No one truly wants the homes we might desire in the next few decades to be built today. Market traders learn how quickly to develop housing via trial and error as they try to develop homes faster or slower in response to return incentives.

It seems to me that an obvious step one before assessing the effects of regulation on housing markets is to understand how the market works. That means understanding why landbanking is real.

I think confusion often arises because when planning rules change and new housing types are developed where they were not previously allowed, this is interpreted as extra housing, rather than a different location, and hence ordering of the overall rate of production.

In terms of our food analogy, it is as if you buy a new food for your teenagers and put it in the kitchen, and suddenly they start eating some of it. But you don’t notice that they eat less of other things, and the net result is simply a change in the ordering to bring forward the new food and push back the other foods within the same overall rate of food consumption.

If landbanking is a normal part of the market, just like keeping food in the kitchen until your appetite grows, then we have already refined what are sensible questions about housing policy.

Instead of assuming all change-of-use feasible housing is ready to be immediately built, due to regulations, we see that the normal situation will be to have an enormous pool of change-of-use feasible housing landbanked for the future.

We can ask more refined questions like “How could we know if the market isn’t optimally delaying housing for the future?”

Using our food analogy to argue that planning rules reduce the amount of housing might involve arguing that planning rules stock the kitchen with brussels sprouts and other undesirable foods. If only the planning system would stock the kitchen with doughnuts and mangoes, then the rate of food consumption by these teenagers would be higher.

But at least we are now getting the economics right.

Regardless, the economics of the rate of housing production is often ignored, but when you dig in, it explains why landbanking is a necessary part of any property market, and changes completely the underlying assumptions about how markets work that motivate many people to deny its existence.

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I have written many times about why fears about ageing are misplaced.

For starters, we have panicked about it for a century.

Also, the reality is that we aren’t adding extra old years at the end of life. Instead, we are stretching out our entire lives. We work until older ages, but start work later, too. Women work a larger share of their lives.

Here’s my take on this important but misunderstood pattern of stretched-out lives.

It is a little strange that few people notice this. It should be obvious. It is extremely clear in the data. I can only surmise that ageing is something that can be transformed into a crisis and promoted to justify self-interested policy positions.

Which is weird when you think about it.

We have spent the past century devoting a huge amount of social resources to the science of medicine with the intention of making people live longer. That is what ageing is, after all. And I never hear that we should curtail medical investment and innovation because of the cost of ageing it creates.

Another part of ageing is the baby-boomer bulge.

The odd thing about the boomers is that we know their demographic bulge is temporary and their ageing inevitable, and there is essentially nothing that can be done about it. Many people like to argue that immigration will solve it, but this is simply not true. Immigrants age at the same rate as the rest of us, and now we have a bulge in the age profile where most immigrants arrive, creating the same “pig in the python” bulge that will age just like the baby boomers.

That is clear from the image below.

A substantial population bulge in Australia at ages 25 to 45 is all due to migration over the past 10-20 years, which is a demographic bulge that will also age into retirement, like the baby boomers, doing nothing for the overall age profile long term.

The mistaken ageing crisis script goes like this.

Look at changes to the population age structure and simply assume that it is bad and creates more non-working dependents who rely on others for their income, either from others in their household, or the state via pensions.

Take a look at the chart below.

In it, I compare the trends for the past 35 years of two measures.

First, age dependency, which is a characteristic of the age profile of the nation. Nothing more. Nothing about who is working or how much, nor who is dependent on whom.

There are many ways to measure it, but here, age dependency is the number of people aged 14 and below plus those aged 65 and above, divided by the 15-64 population. So when the measure is 50%, it means there are two people aged 15-64 for every one person outside that age range. When it is 70%, like it has been in Japan recently, it means there are only 1.42 people in the 15-64 range for every one person outside that range. At 100%, it would mean one person aged 15-64 for every one person aged outside this range.

The second measure is economic dependency. This is the actual workforce divided by the population (including both full- and part-time workers). There are no age limits on working, so why not look at who is actually working?

Here, I have measured economic dependency as the total number of non-workers (the population minus the labour force) divided by the population. So when the measure is 50%, it means that there is one worker for every one non-worker. When it is 40%, it means that there is one worker for every 0.67 non-workers.

Notice that in all of these countries, age dependency has been rising, especially in the past 10-20 years.

But in all of them, economic dependency is falling. Often quite substantially.

This is true even in countries like Japan, which have both an ageing and declining population.

This pattern of declining economic dependency despite rising age dependency is because of our stretched-out lives. We work later but longer. Looking at ratios of people in fixed age brackets was always silly.

The working later but longer pattern is evident in Australia in the charts below, which include a breakdown of the full- and part-time share of the population for key age groups.

Across the top are the 15-19 and 20-24 age groups. Notice that although total workforce participation remains stable, there has been a major switch from full-time to part-time work. This is generally associated with people studying longer rather than commencing full-time work in their late teens. So overall, less work from the young.

But look at the bottom two charts.

Since the 1990s, the share of 60-64 year olds working has doubled, from 30% to 60%. Meanwhile, the share of the population at this age has increased from 4.2% to 5.6% since 2000. This older age group is working much more and growing as a proportion of the population.

Back in the 1980s, people aged 20-24 worked full-time at triple the rate of those aged 60-64 (66% compared to 22%). Now, both age groups see about 40% of people working full-time, while the younger group has higher overall participation via more casual and part-time work.

Looking older still, the share of those over 65 working has tripled from 5% to 15% over the same period. Their share of the population has grown from 11% to 14% since 2000.

We are the oldest we have ever been on average.

Has the sky fallen?

Nope.

Ageing has been a huge success. We live longer, healthier lives, and we spend them becoming more educated and working later but longer.

Any claim that is backed by the need to “do something” about the ageing crisis should be carefully scrutinised, because there is no such thing.

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This is Part 3 of a four-part series coming to FET that I hope will become a reference point for understanding key concepts related to housing production.

1. Why a feasible change of use is different from commercial feasibility

2. How prices determine the cost of building new homes via choices of density and quality

3. Housing supply is not new housing production

4. Why landbanking is a normal market outcome of balancing present and future housing

Interested in learning more? Fresh Economic Thinking runs in-person and online workshops to help your organisation dig into the economic issues you face and learn powerful insights.

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Many non-economists, and frankly, many economists, use the term housing supply incorrectly.

That term supply has a particular meaning in economics.

Supply is a concept representing the willingness of all potential sellers to sell in a period of time. We usually order each seller, or sale, from the lowest price they will accept to the highest price in a list, or schedule.

Or, in other words, supply is an ordered representation of the subjective benefit forgone by all potential sellers. It is always a relative concept—relative to the ordered representation of the subjective benefit of potential buyers.

In asset markets with centralised trading systems, we call the small part of the supply curve that we can see the offer curve. If you want to see supply and demand in action, just look at a depth chart in finance and rotate it 90 degrees, then flip it vertically.

When someone says that the price of CommBank shares, or SpaceX, is determined by supply and demand, they are simply restating the fact that the price is determined by sellers and buyers trading.

The idea that the supply of homes is an important determinant of price is a tautology—after all, what else can determine prices except buyers and sellers interacting in the market?

Supply is an economic concept related to an ordering of potential sellers by how much they will accept to trade and forgo the benefit of owning the thing until the next opportunity to trade (i.e. their benefit to waiting).

For newly-produced goods and services, it is usually because of specialisation and capital investment that producers are willing to accept a lower price than buyers are willing to pay—the benefit of them holding on to a good until the next period is low because they will produce more goods next period too.

Things that aren’t housing supply, but are often called supply, include:

• The stock of homes

• The cost of housing construction

• The amount of development-feasible capacity

• The number of new homes built in a period of time

These are not supply. But they are an emergent market outcome of the interaction of supply and demand (i.e. buyers and sellers) on a variety of trading dimensions.

For example, the quantity of homes traded within a period of time is the emergent outcome of the interaction of willing sellers who will accept a price lower than what willing buyers are willing to pay, for both new and existing homes.

Why does all this matter?

Because the idea of reducing the price of homes by engineering some kind of policy effect on supply dominates our housing policy debates.

It is both true and tautological that if sellers were suddenly willing to sell for lower prices, holding demand fixed, more trades would happen and prices would fall.

Unfortunately, little attention is given to the practicalities of housing markets, where most potential sellers are owners of the existing housing stock, and where the willingness to sell and buy do not move independently, but jointly based on the expectations of all market participants, like in other asset markets.

Common “supply” errors

Before we dig further, I want to note a few errors I see when discussing housing supply. The first is thinking about supply in physical rather than economic terms.

Our brains excel at navigating the physical world but are quite poor at conceptualising deeper emergent concepts like market interactions.1 When we are in the physical thinking realm, we start looking for spaces to put homes, such as air rights above buildings, and physical ways to build houses differently, like pre-fab.

“If only we had more physical things”, we think.

But we have a value problem much more than a physical problem—a value of investing in new homes over alternatives. It reminds me of solar roads, which became a thing because of reasoning physically about needing more space for solar, rather than economically about the trade-off between the benefits of alternative uses of space and the benefit of solar power versus alternatives.

Another common error is to deny that supply exists or to claim that “demand and supply don’t apply to housing and land”.

They do. They must.

Sure, these concepts are often applied inconsistently, without recognising the full opportunity cost of potential sellers (the benefit they get from waiting) or the relevant trading dimensions. In a previous three-part FET series, I explained much of this nuance.

Indeed, in housing markets the supply and demand of trading opportunities lead to emergent outcomes across many different dimensions.

Back to supply

Who are the willing sellers of homes?

In Australia, they are the owners of the 11 million existing homes, plus the owners of development-feasible sites who can sell new homes.

Most housing trades are for existing home sales, soaking up the supply from these owners, with relatively little of the overall market supply being from new homes.

Below is a plot of the total property transactions versus the number of new dwellings in Australia. Less than a third of housing transactions are new homes, meaning that most of the market supply is existing homes.

In the United States, the stock of existing dwellings is much larger and growing more slowly, so almost all housing trades are existing homes.

Here’s the data for Canada, showing a similar share of new home trades compared to existing trades as in Australia.

The lesson here is that a change in housing supply (i.e. willingness to sell) that has a substantial effect on the market price of homes will predominantly come from the much larger pool of potential sellers of existing homes than new homes.

If the 11 million owners of existing homes decide they prefer owning non-housing assets instead (including cash), they reduce the price they require to sell now. Another way to describe this is that potential home sellers increase their demand for money relative to their demand for homes.

This increases housing supply (i.e. a sudden rightward shift in the supply curve).

We have a name for this—a market crash or correction. This is what increasing housing supply usually looks like in reality.

And generally we think this is bad.

One reason it is bad, beyond the price change, which will be bad for sellers and good for future buyers, is that demand and supply are both determined by common factors in asset markets rather than being independent.2 If sellers suddenly decide that money is better to hold now rather than homes, many potential buyers will also think that.

If you lived through the financial crisis in Ireland, Spain or the United States, you would recall the housing market feedback process of a greater willingness to sell across all sellers, but falling willingness to buy. The net result was that prices fell substantially, but with much fewer trades. Turnover fell from 6.5% per year before the financial crisis in the United States to 3.5% per year during and after it.

This non-independence is also why emergent pricing in share markets is rarely analysed in terms of supply and demand, and more in terms of overall return expectations of all market participants, buyers and sellers alike.

Supply and demand are also tied together by common factors when prices are rising.

When potential buyers increase their willingness to buy, shifting demand rightward, some potential sellers will also change their willingness to sell for the same reasons. If buying a home suddenly becomes a good investment and buyers notice, potential sellers will recognise that too and no longer accept the previous lower prices they were willing to.

Combining the ideas that 1) supply is the willingness to sell now rather than hold, 2) most potential housing suppliers are owners of the existing stock, and 3) that demand and supply in asset markets are not independent, leaves us in a predicament when translating the policy intent of “supply more new homes to bring down prices” into practice.

Is it even possible to orchestrate a policy shift such that only sellers of new homes become impatient and willing to accept lower prices, while buyers and sellers of existing homes stay patient and are willing to pay and accept higher prices?

It seems a stretch to me. And only thinking clearly about supply helps identify this predicament.

As always, please like, share, comment, and subscribe. Thanks for your support. You can find Fresh Economic Thinking on YouTube, Spotify, and Apple Podcasts.

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Subscribe to Aziz now. You won’t regret it.

Home Economics

Deeply researched, beautiful charts on American housing, demographics, and happiness

By Aziz Sunderji

With a background in banking and finance, Aziz comes to the question of housing with a focus on asset pricing and investment incentives. This means he diverges from much of the public policy conversation that merely assumes home prices should always be lower, but planning regulations and zoning are in the way.

But he also comes with a lot of technical and design know-how.

He has built a website summarising the Great Debate on housing supply and price, and a mapping tool called ProMap. Aziz was able to show me live on screen how ProMap can reveal patterns in the data that are usually hard to see, such as the migration to the suburbs and the price gains in suburbs relative to city centres since COVID.

You could say that his ProMap tool has made housing analysis Great Again.

It would be a dream to have a tool like that for Australian property data.

I hope you enjoy our conversation.

Table of contents

0:00 Intro

2:51 Backgrounds in finance and property

12:43 Writing books about housing

20:54 The Great Debate compendium

31:56 Where AI helps and where it fails

36:15 The ProMap housing data tool

43:36 Urban cores versus suburbs

58:00 Doubting the zoning explanation

1:02:47 Housing as an asset with a yield

1:11:38 Monopoly, land, and patient owners

1:15:49 An elevator pitch for the housing crisis

1:19:57 What the housing crisis really is

1:33:44 Austin, Tokyo, and closing thoughts

References:

Aziz’s Substack, Home Economics

Aziz’s mapping tool, thepromap.com

Aziz’s microsite, The Great Debate

As always, please like, share, comment, and subscribe. Thanks for your support. You can find Fresh Economic Thinking on YouTube, Spotify, and Apple Podcasts.

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This is Part 2 of a four-part series coming to FET that I hope will become a reference point for understanding key concepts related to housing production.

1. Why a feasible change of use is different from commercial feasibility

2. How prices determine the construction cost of building new homes via choices of density and quality

3. Housing supply is not new housing production

4. Why landbanking is a normal market outcome of balancing present and future housing

Interested in learning more? Fresh Economic Thinking runs in-person and online workshops to help your organisation dig into the economic issues you face and learn powerful insights.

Enquire about FET workshops

If you value this content, please consider signing up as a paid FET subscriber to support this work and get access to all articles and audiobooks.

Subscribe now

Another way to help is to share these articles. I am aiming to get to 10,000 subscribers in 2027.

Share

In Part 1 of this FET article series, we defined the terms commercial feasibility and change-of-use feasibility. While doing that, we took as given the density and quality of a housing project to be the one that provides the highest residual value for the property owner today.

But as noted, the highest-value use might be future development, and the project's density and quality might look very different at that future time.

The purpose of this Part 2 is to explain how the choice of density and quality is caused by the market price, and hence, so too is the construction and development cost of homes caused by the market price.

Too often, we get this causality backwards.

We think that construction costs drive home prices; hence, reducing construction costs is a way to make homes cheaper. It will certainly make homes bigger, denser, and better. But not cheaper.

Property industry lobbyists claim that causality runs from cost to price, starting with land costs paid, and adding on all other costs (e.g. p10 here). If home prices are caused by the sum of costs, then adding costs makes housing more expensive.

This sounds intuitive.

But it’s wrong.

Indeed, one of the puzzles in recent years has been the shift towards lower-density, but more luxurious, high-quality apartments. But if construction costs are rising, you build smaller dwellings, not bigger ones, right?

Here’s the thing.

Average home prices are determined by overall asset markets, rent levels by renters' incomes, and rental variation across locations by differences in quality of life.

As we will see, when the price of homes rises, it changes the choice of density and quality of homes, justifying spending more on construction costs per dwelling to maximise returns.

The fact that the total land and development costs of a housing project are roughly equal to the price is just an equilibrium outcome about the choice of project and the way land value is derived.

Causality runs from market prices to the choice of density and quality, then to the cost (i.e. how much is spent per dwelling) of housing construction and development.

Developers choose what density and quality of homes to build on a site, and this choice determines both revenue and costs.

Home prices can’t be determined by cost, since the cost of producing a $1 million home is very different depending on the density of that home. A small budget house that costs $200,000 to build could sell for $1 million at the right location, or a fancy apartment in a CBD tower that costs $900,000 to build might sell for $1 million.

Which of these costs determines the price?

Abstracting away from the choice of density and quality makes it impossible to understand housing markets and housing development. There is no such thing as the cost of developing a new home. But there is such a thing as a massive range of costs to develop new housing of different densities and qualities.

Let’s take a look.

Adding density and quality choice to our analysis

The trick to understanding the choice of density and quality of a housing project is to recognise that there are construction diseconomies of density. It costs a lot more to build a dwelling with the same market value in a denser project.

Because costs rise with density, high-density housing is only the optimal choice in high-priced locations and at high-priced periods of time. High prices themselves lead to the choice of building higher-cost housing density and quality.

The table below illustrates this point.

I had put the paywall here, but decided it was more important that these ideas were widely read. If you get value from the rest of this article, please consider becoming a paid subscriber.

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The numbers are somewhat arbitrary, but they demonstrate clearly the economic relationships.

The top section of the table shows the density alternatives, the development cost per dwelling (for a dwelling with an equal market price in each project), and the number of dwellings in each alternative project design. The detached home has a construction cost of $500,000, but a dwelling in a high-rise tower with 120 apartments that sells for the same price would cost $950,000 per dwelling to construct.

The bottom part of the table shows the residual economic gain, which is the revenue minus the cost of each density project under different market price scenarios ranging from $600,000 for this dwelling to $1,100,000.1

At a market price of $600,000, only the single house is a change-of-use feasible development, with a price above its development cost.2 Building four townhouses is not, as they cost $600,000 each to receive the market price of $600,000. You are better off building one house instead. In fact, none of the higher density options provides a positive residual gain at this market price, as the cost per dwelling is higher than the price.

Notice that as the market price rises, the situation changes. The biggest gain for each market price scenario is highlighted in yellow.

At a market price of $700,000, building one detached house for $500,000 leaves you with $200,000. But building four townhouses for $600,000 each leaves you with $400,000. At this slightly higher market price, you are better off choosing the higher-density townhouses, even though they have a higher cost per dwelling to develop.

As the market price rises further to $800,000 per dwelling, you can still make money with a single house. Spend $500,000, and you have a $300,000 residual gain. You can also make even more money with townhouses, with a $200,000 margin on four of them for an $800,000 residual gain,

But now, 12 apartments leave you in the best situation.

The price is high enough to justify spending $700,000 per dwelling on development costs, and because there are now 12 dwellings with a margin on each of $100,000, you get a $1,200,000 residual gain.

We can see the pattern. Rising prices justify higher densities, even though they also increase the economic gain from developing at the same density.

The same is true for quality choices. Rising prices relative to cost justify improving the quality of housing in all aspects, from kitchens and bathrooms, appliances, to the quality of building common areas, to carparks and storage, to balconies and more.

Rising prices relative to construction and development costs change the choice of density and quality of homes more than the overall quantity. This is why thinking that some new, cheaper construction technology will mean more homes is mostly wrong—it will mean building bigger, better and denser homes. This is good and desirable. But it is a different outcome.

For those who love an economic chart, here is an animation of the choice of density at different market prices. The slope of the blue line is the market price of a dwelling, which hence determines the total project revenue at different densities. The yellow line is the total project cost at different densities, with the curve arising due to diseconomies of density.

As the market price rises (i.e. the blue line gets steeper), the optimal density choice increases, dragging up the total cost (and hence per dwelling cost too). Spending higher costs at higher prices is what provides the highest total project residual gain (the shaded area).

So what?

This might seem straightforward, but understanding how market prices relative to construction costs change density choices really matters for understanding the housing market.

In our example, rising prices led to the choice to build dwellings at a density that was more expensive to build. At a market price of $600,000, it was best to choose to spend $500,000 per dwelling on construction. But a 67% market price increase (relative to construction costs) meant a mid-rise apartment project was the best choice, even though the cost per dwelling was 76% higher than the cost of the detached home (at $880,000 per dwelling instead of $500,000 per detached house).

Along the highlighted diagonal of optimal density choices, development cost was always between 83% and 88% of the market price. It never fell.

So construction costs can’t determine prices. They can’t. Construction costs were 83% to 88% of the market price at all market prices because costs follow prices.

This example also helps us see what happens when prices fall relative to construction costs (from either rising costs or falling prices).

For example, instead of making $7.2 million on a mid-rise apartment project when the market price per dwelling is $1 million, a price decline of 20% to $800,000 means that the best possible project is now a small walk-up apartment block, which makes only $1.2 million, or an 83% decline in residual gains.

This enormous volatility arises because the residual gain to the property owner is a leveraged exposure to housing prices, as I explain here.

Notice that at higher market prices, the often-desirable middle densities are less economically advantageous than the higher densities. So to get those middle densities requires a market price high enough to justify them, but not too high that even higher densities offer a better residual gain. Or, they require preventing even higher densities in high-priced locations.

This relationship between market price and optimal density choice has long been known.

Here’s an image from Roy Wenzlick’s 1942 thesis on the Fluctuations of Urban Rents, Their Causes, and Implications. He clearly notes how rising office rents justify higher density. But when rents fall, the optimal density choice falls too. Even though offices are still development-feasible at these new lower prices, the same density is no longer the highest value use.

It was also the case that construction technology changed substantially during the 1920s, which allowed for much higher density without as much added cost. The combination of higher prices and falling construction costs (i.e. much higher market prices relative to construction costs) is why, in the highest-value locations of the period, the world’s tallest (densest) buildings were developed—the Chrysler and Empire State Buildings.

As always, please like, share, comment, and subscribe. Thanks for your support. You can find Fresh Economic Thinking on YouTube, Spotify, and Apple Podcasts.

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This is Part 1 of a four-part series coming to FET that I hope will become a reference point for understanding key concepts related to housing production.

1. Why a feasible change of use is different from commercial feasibility

2. How prices determine the construction cost of building new homes via choices of density and quality

3. Housing supply is not new housing production

4. Why landbanking is a normal market outcome of balancing present and future housing

Interested in learning more? Fresh Economic Thinking runs in-person and online workshops to help your organisation dig into the economic issues you face and learn powerful insights.

Enquire about FET workshops

If you value this content, please consider signing up as a paid FET subscriber to support this work and get access to all articles and audiobooks.

Subscribe now

Another way to help is to share these articles. I am aiming to get to 10,000 subscribers in 2027.

Share

What do we mean by feasible new housing?

The word feasible gets used in two ways. We should have two words.

First, the word feasible gets used to mean that the development of new housing today would provide a larger residual income stream to a property owner than the current use. Let’s call that change-of-use feasibility.

Second, the word feasible gets used to mean that buying a site today at the market price and building new homes today can be done in a way that provides a return on costs that exceeds the risk taken. Let’s call that commercial feasibility.

In a recent FET article, I explained how commercial feasibility can never be found—the price of land will ensure that, at best, new development profits reflect the risk that developers in the market would be willing to take. Commercial feasibility gets bid all the way down to zero via rising land values, such that development returns only reflect risk.

And yet, this concept is widely thought to be very important for town planners and policymakers to try to measure and target via policy.

Let’s use the diagram below to illustrate the difference between these two feasibility concepts.

The first column in light red represents the present value of the income stream of the current use of a site. This could be zero in the case of a vacant or empty site.

The next column is the total revenue from the development project of a design and density that, if undertaken today, would lead to the highest residual value after development costs and a margin that reflects the risks taken.

The third column shows the breakdown of how that revenue generates the maximum residual (yellow) after subtracting development costs (blue) and a margin that reflects the risk taken to complete the project (grey).

That also happens to be the minimum value of that land, or site, today.1

At that land value, the project is just barely commercially feasible for the most optimistic developer who thinks they can best manage costs and risks and design the project with the highest residual income.

Note that the land value (yellow) is higher than the present value of the income of the site in its current use (the light red first column). This scenario meets change-of-use feasibility, meaning that the value from development today is higher than the value of never developing and retaining the current use.

But the fourth column shows another scenario.

Here, the land is valued higher than what is needed to make the site barely commercially feasible today, and hence the project is commercially infeasible today. I have labelled the extra land value a delay premium.

This situation arises because property owners have more than two options: never develop or develop today. They can also develop at some point in the future. And since all expected future economic returns are priced in by traders today, buyers will often pay more for a site than it is worth for development today only. They pay a premium for the possibility of even higher gains from developing in the future.

The market of buyers and sellers of sites will sort out via their trades which sites are expected to be best to develop now, and which are best to develop later, and this will be reflected in the pricing of those sites. And indeed, the return from waiting to develop will, even at this higher starting price, reflect the risk taken and the rate of return in a market equilibrium. Waiting also pays. As it must.

For completeness, the final column shows the scenario of a clearly commercially feasible site, where the land, development cost and risk sum up to less than the revenue from the best project today. But since markets price in all risks and returns, land simply cannot trade at this low price and leave excess returns on the table, as I previously explained here.

These conceptual distinctions matter for policy design and analysis.

Many policy changes in recent years have been motivated by the idea of creating more feasible housing development sites. But the feasibility concept often being inadvertently applied is one of commercial feasibility, which we know cannot exist on average in a market.

We can’t create excess returns. They get immediately priced in via higher land prices.

Where the more appropriate change-of-use feasibility concept has been considered, it has usually been done without recognition that the market will sort sites into those best to develop today or in the future, and it will do this via pricing expected future gains. Adding more sites to a large pool of sites with feasible changes of use will simply cause them to be priced in a way that reflects that the highest-value use is development in the future, meaning that they remain commercially infeasible, but at a much higher land value.

Using two terms to ensure the two distinct meanings of feasible housing are used consistently is a first step toward building a sensible understanding of why and when new homes are built.

As always, please like, share, comment, and subscribe. Thanks for your support. You can find Fresh Economic Thinking on YouTube, Spotify, and Apple Podcasts.

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Also, we comment on the emergence of culture within subreddits and respond to some of the comments on Reddit about Cameron’s work. The rise of Pauline Hanson and One Nation becomes somehow relevant to the whole conversation too!

Enjoy this chat and leave a comment with your thoughts.

Starting next week at FET is a four-part analysis that I hope will become a reference point for understanding key concepts related to housing production.

1. Why a feasible change of use is different from commercial feasibility

2. How prices determine the cost of building new homes via choices of density and quality

3. Housing supply is not new housing production

4. Why landbanking is a normal market outcome of balancing present and future housing

As always, please like, share, comment, and subscribe. Thanks for your support. You can find Fresh Economic Thinking on YouTube, Spotify, and Apple Podcasts.

Theme: Happy Swing by Serge Quadrado Music—Creative Commons Licence CC BY-NC 4.0

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Australia’s Productivity Commission (PC) is an in-house economic research group for the federal government. They run inquiries into matters referred to them by politicians, and respond with sensible policy comments and recommendations.

At the end of May, Treasurer Jim Chalmers asked them to look into Housing Supply Regulation. Those wishing to make a submission to the inquiry were given three weeks to do so, and the PC is expected to report their findings a month after that.

Two months to solve housing. Again.

I’ve attached below a submission I made with focussing on the economics of why new homes are built. You can read all the submissions to the PC here.

Submission To Pc Housing Supply Regulation Inquiry Helm And Murray June 2026

625KB ∙ PDF file

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One more time with feeling

When you step back a little, giving the PC two months to solve housing with yet another inquiry is comical political theatrics. Unequal access to housing has been an issue for centuries, and it reflects broader changes to economic inequality during both booms and busts.

The “coexistence of progress and poverty” emerges through uneven distribution of housing and land ownership during periods of rapid economic change, including inflationary periods.

Back in 1911, for example, there was the NSW Inquiry into Fair Rents that led to the 1915 Fair Rents Act. For the century since, we have constantly inquired, reviewed, and investigated housing. If you spend some time in the archives of Trove, you will notice this pattern. Below is a headline from 1963 about the housing shortage, for example.

The 1970s inflationary period most closely mirrors the 2020s in terms of the political debates about housing. For example, home asset prices in Brisbane doubled in the first four years of that decade, while inflation was about 40% cumulatively. In the past four years, Brisbane prices have increased 50%, and cumulative inflation has been 20%.

But we never seem able to zoom out and see these patterns.

Governments have responded to the cycle of public concerns with many reports and reviews in recent decades that focus on the micro level, seeking out a regulatory tweak here or there to leverage for large housing market effects, which never arrive. I noted as such a few years back in my submission to that year’s rental inquiry.

The table below is an AI summary of these many housing reports and inquiries (so please send me any corrections). As you can imagine, many more industry and think-tank reports on the same topics have been written as well.

Australia’s 2026 housing market in context

The strange part about all these inquiries is how they confuse the cyclical for the structural.

For example, the 2017 to 2021 period was one of the cheapest times to buy Australian homes in over two decades in terms of the cost of renting money to buy a home or the cost of renting a home directly. Falling interest rates, rising incomes, and flat nominal prices had resulted in a substantial market adjustment that was hard to see when looking at the asset price of homes alone.

During COVID, we dropped interest rates further and stimulated the economy, as did many nations, leading to a global housing price boom. In recent years, we tried to unwind this with higher interest rates, intentionally making buying a home more expensive to reduce home buying and, in the process, reduce home prices and new housing construction (to dampen inflation).

Yes, it is a difficult time to buy a home in 2026. No, it won’t always be like this, and it did not get like this because of long-term structural issues.

If we look at the more stable part of the housing market, the rental market, we actually see nearly four decades of flat rent-to-income ratios for renter households, who are generally occupying bigger and better homes.

The terms of reference for the new PC inquiry start with the claim that “Australia has a housing shortage.”

Does it really?

I’ve looked into housing shortage claims and analysis before and found they are generally very poor and often confuse the housing outcomes that emerge from both inequality and consumer choice to be a problem of having the wrong number of homes.

In fact, the stock of homes relative to the Australian population was at a record high just a few years ago, and this housing stock was bigger and better in quality than at any point in history. Since the borders reopened, this has come back a tiny fraction, which I suspect is a temporary adjustment—I predict that in 5 or 6 years we will hit a new record for dwellings per capita.

The strange part about the PC’s shortage claim is that even their own 2022 report showed a high level of disagreement about the idea of a housing shortage, as seen in this chart (p472).

The same housing shortage concerns are happening in the United States, which has totally different housing regulations and planning rules. Many people think there is a shortage, but no one knows the right number—maybe somewhere between 1 million and 8 million in a country with 150 million dwellings (or between 0.7% and 5% of the total stock).

So what?

Yes, buying a home is not easy in 2026 compared to the 2017 to 2021 period. Yes, there has been a rental squeeze as borders have reopened and incomes have risen unevenly.

But these are cyclical phenomena.

Despite all these inquiries, I don’t think there is general agreement on what is wrong with the housing market. I explain in my book, The Great Housing Hijack, why that is, but to briefly summarise, there is an inherent market symmetry that can’t be ignored. Landlords, developers, investors, and homeowners want prices to rise. New buyers and renters want prices to fall (at least until the day they buy). Renters want lower rents and landlords want higher rents. At every point in the market cycle, one or the other group is upset, and we must respond politically to that with an inquiry.

We first need to acknowledge as a nation how property markets work. Then we can decide how and when we might want to intervene, and for whose benefit. Despite all the inquiries for the past century, I don’t think we are there yet.

If you are interested in issues of construction productivity and its measurement, I can recommend this two-part FET series from last year:

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In the 1940s, there was a heated academic debate in economics, now referred to as the marginal cost controversy.

It is still relevant today.

Just last week, I read a paper that had to make assumptions about marginal cost and price, which showed me that the controversy is far from resolved. And indeed, the controversy is very much alive when it comes to the price of land and housing (as I explained here).

The basic controversy rested on the assumption, which still prevails today, that prices being equal to marginal cost is the welfare-maximising point of economic activity.

Marginal cost is the cost of producing an additional unit of a product in a given period, taking as given the stock of capital and other historical sunk costs.

It is not the average cost of each unit, taking into account all input costs.

The image below helps differentiate the two cost concepts. It shows the output per period on the x-axis and the cost per unit on the y-axis. The average cost is the blue line, which includes the per-unit share of the capital costs. The more you produce per period, the lower the average unit cost because you are spreading the capital cost among more units.

The yellow line is the marginal cost of extra inputs needed to produce one more unit per period (for example, material inputs), ignoring the cost of capital.

Industries with any capital requirement and economies of scale will have a higher average cost than marginal cost. So, pricing at marginal cost means that the cost of capital investment can never be recouped.

Economies of scale are especially important in the case of city-wide utilities and large manufactories, meaning average cost and marginal cost will be vastly different, and this is why this was a major topic of economic interest in the 1940s.

The standard economic theory, then and now, says that marginal cost equal to price is socially optimal. But reality is that this can’t be true unless all capital investment is subsidised.

Hence the controversy.

A simple modern example could be software. Here, the cost of production is only in creating the code. The marginal cost of someone downloading and using an extra unit of that code is basically zero. But if the price were zero, no one would produce software because they could never cover the cost of producing it in the first place.

Standard economic reasoning of the time, and frankly, of today, is that a price of zero is the socially optimal price of this software.

So something is wrong here.

Where they went wrong

The wrong part is that marginal cost, being the opportunity cost forgone from selling in this period, and the input cost of production based on what was actually paid for capital and variable inputs, are being conflated in economic theory.

Many students of economics notice this.

They finish their first lessons on opportunity cost and sunk costs, then they get to the idea of firms and production, and the concept of cost is switched to input costs. Opportunity cost is waved away with the assumption that all input costs include a normal rate of return.

What is that normal return?

Oh, just what you could earn elsewhere on your dollar invested. But of course, you can only get a normal return elsewhere if other industries are pricing above marginal cost, and indeed above average cost!

This also presents problems because we know that market-clearing in all markets happens when rising supply curves (i.e. the willingness to sell different quantities at different prices) intersect with falling demand curves (i.e. the willingness to buy different quantities at different prices).

So how do we reconcile this? Subscribe to read on and indulge your inner econ nerd.

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Mike has a deep understanding of bond markets, macro policy, and money. It was a good chance to ask him about Modern Monetary Theory (MMT), which is a school of thought in economics that focuses on monetary operations and both sides of the monetary balance, which includes the debt (the liability to the government) and the asset (the Treasury bond held by the public or other organisation).

Is the MMT view really that different from the standard macro view? And where does a lot of the fear about public debt come from? Who really bears the cost of interest on public debt?

We dig into these questions. Enjoy the conversation.

Follow Mike’s terrific Housing Hell Substack here.

As always, please like, share, comment, and subscribe. Thanks for your support. You can find Fresh Economic Thinking on YouTube, Spotify, and Apple Podcasts.

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Post-budget, there has been a lot of fuss in Australia’s media about taxes.

Today, I want to experiment with simply sharing a few concepts that help me think coherently about taxation as an economist, and where the confusion and blind spots are in the taxation discourse.

The main question I ask to help make sense of it all is, “Where do I send the guy with the gun?”

Tax bases versus people taxed

The important thing is that a tax base is not a person, except in the case of a head or poll tax (which is what we call a per-person tax on simply being alive for a period).

People have claims to things after tax rules have been applied to tax bases.

The value you own is the value after the rules of society are applied, not before. Your home is your home after the rules of society about property rights apply, not before. Your income is your income after the rules of society about taxation apply.

We all know this. People are incentivised by their after-tax incomes, not their “before tax” personal income, or what I will call the tax base for personal income taxes.

One of the biggest conceptual confusions is that taxes take your income. No. Your income is determined after the rules have been applied and all the price and output adjustments have taken place.

Tax bases are not people—they are places we can send a guy with a gun.

This has a few implications.

First, it means that taxing big, observable things that cannot be hidden are the best tax bases. That’s why historically taxes were raised from trade—on imports and exports that went on ships, and on land and its trade.

The tax base could be the tonnage of the ship, and you could charge a fixed tax per commodity, or it could be the value of the cargo, and you charge a percentage on that (called a tariff). On land, the tax base could be the area of land, on which a per-area tax would be applied, or the value, which would be a percentage land value tax, or the value of all fixed improvements, a property tax, or it can also include mobile property like motor vehicles that are big and easy to track.

As the economy became more sophisticated, we began taxing tax bases that were previously unobservable and therefore hard to enforce, such as personal income. In a world with minimal record-keeping and few large-scale businesses, this is hard to do. So we created systems like withholding, whereby businesses that pay employees don’t even give the employees the before-tax income (the tax base), but give them the after-tax income and apply the tax to the tax base on behalf of the tax office.

We can easily send the guy with a gun to big organisations that own major facilities.

This also means that the complexity of the tax base is a killer. The Petroleum Resources Rent Tax (PRRT) applies to a tax base that is a special convoluted accounting construct that leaves many opportunities to account differently to minimise it.

Taxing unrealised capital gains (change in value of assets owned), for example, fails not because of any economic theory about the nature of this gain versus an income payment, but because there is no tax base there to be taxed. Without the sale, there is no value to tax. It fails because we don’t know where to send the guy with the gun.

In fact, taxing income from capital (dividends and capital gains from owning businesses, interest payments on loans, etc) is tricky in general because ownership and lending can occur across national borders for not much more cost within them. Why not relocate ownership to a lower-taxing jurisdiction? Ireland, for example, has one of the lower corporate taxes in Europe, so attracts a lot of ownership into the jurisdiction, most famously aircraft leasing services.

People who gain from minimising the tax base will always do it.

So make sure tax bases are an easy place to send a guy with a gun and won’t simply disappear or be relocated because of a tax.

Want to find out more about how property rights create predictable incentives and price patterns?

Come to my two-day intensive Land and Housing Economics Workshop in Brisbane tomorrow.

Will the workshop help you invest? Probably. Will it help you understand policy? Definitely. Will it help you cut through the media nonsense about housing? Certainly.

There are a few spots left. Last chance to book here.

Tax incidence

When we say tax incidence, what we mean is the net effect on the after-tax wealth and incomes of different people after new tax rules have been applied.

Adam Smith notably described how difficult it is to really work out which people are worse off or better off after a new tax is applied because behaviour and prices adjust (see the Wealth of Nations, page 690 of the version here).

Economists call the net effect on the income and wealth of different people due to a tax the tax incidence.

For example, stamp duty is a tax that applies to the tax base of the transaction price of a home. It is the responsibility of the buyer in the transaction to pay this tax to the state, not the seller. But the tax incidence is not on the buyer, but the seller. We know that the price of homes adjusts upwards by a dollar for every dollar a stamp duty is reduced, and vice versa. Imposing a stamp duty reduces the seller’s wealth and income on net, not the buyer’s, even though the buyer sends the money to the revenue office.

If you conflated the administrative payment of a tax with the “people it taxes”, you would be wrong. It can often be the case that this is true, but usually the tax incidence is less than 100% of the person or company with the responsibility of tax payments on a tax base.

Any given tax setting is just a system of rules operating, and there are no “before-rules” outcomes, except insofar as there are measurable things called tax bases.

For example, the stamp duty described above decreases the value of property trades, and in doing so, decreases its own tax base. The property value is already different because of the tax, so that value is not the “before tax” outcome.

Tax equivalences

Often, analysis of taxation will revert to what I call ad hoc equivalences. This means that people impose a model of how they think a fair world works and compare a tax to that imagined world.

A popular example of this is to assume that taxes on the tax base of personal income are actually meant to be taxes on personal consumption. Here’s an example of such an equivalence claim.

This is important - we have a background principle for an income tax of equal sacrifice where we are thinking about people in similar situations sacrificing similar amounts of consumption in order to fund government services. In all of this discussion this is a central principle.

Why should income taxes try to reflect an underlying principle involving consumption? The Goods and Services Tax (GST) is already a tax on the tax base of the value of consumer goods.

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Where does this central principle come from?

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Since then, I have kept track of his views on evolving macro-economic and demographic trends.

This conversation was a chance for me to see how his independent analysis had led him to some similar views to mine on housing markets, but also test where we differ on the likely social, economic and political effects of long-term demographic trends.

It was a surprise to hear that we have both arrived at a similar view on the application of monetary policy being too broad for many of the industry-specific inflation concerns we have today.

Enjoy this conversation and find Moses on Twitter here and at his Random Walk Substack below.

Random Walk

"A multidisciplinary cast of mind . . . with a yen for synthesis." - Charlie Munger (about himself, not Random Walk)

By Moses Sternstein

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Prosper Australia released a report last week explaining how and why to price development rights.

Download a copy here.

The basic idea is that property rights are created and defined by the state. So when high-value rights are added to the bundle of an existing owner, they should be priced rather than given for free.

As the Prosper report explains:

The legal right to use land in specified ways is how society regulates the use of a common resource for the public good. There are no natural rights to landownership, and no rights inherent in land title – rather, the bundle of rights that makes up landownership is always and everywhere a creation of the state, and relies on active enforcement by the state.

…

The right to develop or intensify land use is not inherently part of this private bundle. In modern planning systems, development rights are retained by the state to be granted through planning approvals.

The limited nature of property rights makes total sense when we talk about other rights. The property rights to undersea mining, radio spectrum, or intellectual property are all clearly invented by the state and limited, and changes to those rights are a negotiation with the community that creates and enforces them.

The same is true with property rights to spaces (i.e. land).

Before I share an extract of the report with more explanation, I want to push back on a common argument that I expect in response.

That argument is that if you upzone small areas, the value of the extra property right is high, but if you upzone a whole city, the extra value is low, and hence there won’t be much value to capture from pricing the extra rights.

As one recent article noted:

Development rights are only valuable because our planning systems have choked the supply of new housing in the most desirable parts of our cities, driving up prices for everyone.

This is wrong.

Development rights are valuable because asset markets price them such that there is no arbitrage. That’s it. Just like all property rights.

For example, say you owned the intellectual property rights to Medicine A, where the rights bundle expires in ten years. Your competitors own the intellectual property rights to Medicine B, but their rights bundle lasts in perpetuity.

Even if the same profits could be generated from each medicine each year, one property would be worth more than the other. As it must. The market price today of any property rights, intellectual or real, accounts for all future flows of profits and their risk.

If we changed the property rights for Medicine A through “intellectual property upzoning” so that they now last in perpetuity rather than ten years, would that mean there is more competition for longer-lasting intellectual property, and less competition for shorter-lasting intellectual property, so that the price of Medicine B and its intellectual property value will fall?

No.

The pricing of property in any form, whether real or intellectual, always reflects the most profitable way to exploit that bundle of rights.

We, as a society that creates, defines and enforces property, could sell the longer-term intellectual property rights for Medicine A. Only a ten-year period of rights has been granted. So from year eleven onwards, the state holds in reserve the intellectual property rights.

These longer-term rights could be auctioned off, whether in the form of a bundle that lasts another ten years or a bundle that lasts from the end of the current ten years in perpetuity.

To give them away to the owner of Medicine A’s ten-year intellectual property rights bundle would be a massive unpriced transfer. Yet that is how we do zoning in the cities.

Zoning rules describe what new property rights the state is willing to grant in different locations, and the planning system, through the approval process, hands over those valuable new property rights for free to those who apply.

Scroll down for a report extract and PDF.

Want to find out more about how property rights create predictable incentives and price patterns? Come to my two-day intensive Land and Housing Economics Workshop in Brisbane.

Will the workshop help you invest? Probably. Will it help you understand policy? Definitely. Will it help you cut through the media nonsense about housing? Certainly.

Book here.

Pricing development rights

There is a deep unfairness at the heart of Australian housing policy. State governments, by way of their planning systems, are routinely giving away public assets worth billions of dollars each year.

Legal permission to develop land in new and more profitable ways is a valuable property right. Development rights are created by and belong to the public. When transferred to private landowners, they should be priced at their private value. Right now, most states are handing them over for free.

The result is a system of enormous windfall gains. By way of rezoning and planning permission, development rights worth an estimated $11 billion are granted to private beneficiaries each year. This silent, legalised giveaway delivers vast sums to some of Australia’s wealthiest landowners, with the public receiving nothing in return. This is plainly unjust.

Development rights are public property, held in reserve for public purposes. To transfer these to private landowners is a healthy and necessary part of economic growth and change. But the value of these rights should accrue to the public, not the private owners who happen to hold title.

Such a giveaway would be plainly unacceptable if rights of the same value were transferred in the form of a separate, horizontally-defined land title. When public land is given away, or sold at ‘mates rates’, the injustice is obvious to all. And when private landowners sell land with a planning permit attached, they expect the price received to reflect the value of the permit. By contrast, the equivalent giveaway of public airspace remains the status quo within Australian planning systems. This is clearly unacceptable, and as the next section shows, the value being given away is enormous

The estimated $8 billion raised each year could pay for the abolition of stamp duty for all first home buyers, benefiting 90,000 buyers each year, while also funding an additional 195,000 social housing dwellings, a 43% increase on the current stock.

Pricing Development Rights 2026

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Are they the fairest tax that helps everyone start life on a more even footing? Or are they a nightmare of rules that require intrusive monitoring of gifts and that destroy family businesses?

We take some initial steps towards understanding how much tax revenue is possible, the problems involved, and the intergenerational issues at play.

An article of mine from 2014 on inheritance taxes is here.

And a link to The Australia Institute report we mentioned on inheritance taxes and gift duties is here.

Don’t forget that I am hosting a Land and Housing Economics workshop in Brisbane on 9-10th June 2026. Find out more here and come along to get into the weeds on property economics.

As always, please like, share, comment, and subscribe. Thanks for your support. You can find Fresh Economic Thinking on YouTube, Spotify, and Apple Podcasts.

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Here is a housing chart that looks like a banana.

In this post, I will show you some more banana charts and explain the economic reasons why such patterns are common but often misinterpreted.

Here’s another banana chart that was recently popular in housing circles. It was interpreted to mean that faster housing permit rates cause lower monthly rents.

Here’s one plotting the gross yield and purchase price of rental properties. This was interpreted to be the result of a trade-off between risk and return, notably of the lower long-term returns due to the poor condition of the housing amongst low-price, high-yield homes.

Here’s one with a rotated banana plotting lot land size (x-axis) and per square metre price (y-axis) in a major new subdivision. This banana shape has been interpreted by the popular analytical method of Ed Glaeser to be the result of planning regulations. He thinks that in the absence of zoning regulations, we would see a straight horizontal line.

Here’s another. The author interpreted it this way: “There’s no reason we can’t have wealthy and affordable cities. We just have to increase the denominator of aggregate income per household.” Regulations are implied to be the cause of the lack of data in the top-right corner of the chart.

Finally, here’s a banana chart that emerges when you plot financial returns and volatility (risk). It’s not housing, but it shows a similar pattern, with a rotated banana. The interpretation of this relationship is simple—markets price in risk.

Each banana chart usually has an ad hoc interpretation. You will notice many more charts and many more ad hoc interpretations as you dig deeper into housing or any other market.

Don’t forget about the upcoming Land & Housing Economics Workshop I am hosting in Brisbane in June. Tickets are here, and the event flyer with full details is below.

Ground Rules Workshop Flyer

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Also, check out this recent conversation I had with Tim Williams on the Gimme Shelter podcast.

Is there an economic reason for these charts?

The reason for these banana-shaped relationships is simple but subtle.

Markets price in all trade-offs.

What does this mean?

Read on and subscribe to find out (you can also unlock this article with a free trial)

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This article was first published in 2017, but is relevant again in the context of the recent Artemis II mission and Blue Origin’s successful rocket landings.

Don’t forget about the upcoming Land & Housing Economics Workshop I am hosting in Brisbane in June. Tickets are here, and the event flyer with full details is below.

Groundrulesworkshopflyer

2MB ∙ PDF file

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Elon Musk has lost billions of dollars for nearly a decade trying to make electric vehicles at Tesla in a way that has never been done before.

In the new tech-billionaire space-race, where Musk has also been active with his SpaceX company, his competitor Blue Origin, run by Amazon CEO Jeff Bezos, has been running at an enormous financial loss, requiring Bezos to sell over a billion dollars of Amazon stock a year to fund his space venture.

In both of these sectors, electric vehicles and space transport, there is no guarantee of any long-term profit - there are threats from multiple new entrants as well as incumbents in both sectors. Yet billions of dollars are being poured into these experimental investments.

Facebook burned through $80 billion on its Metaverse, which is as real a loss for the economy as if the government wasted $80 billion on a failed technology misadventure.

But there is nothing “market-like” about long-term, long-shot gambles like these. This behaviour falls outside any core economic theory about the efficiency of markets in allocating resources. After all, most billionaires don’t spend their fortunes on privately-funded industrial policy with only a fleeting chance of any future payoff.

If a country wishes to expand its production capabilities, it seems foolish to rely on the type of luck that the United States finds itself with. Australia’s billionaires, for example, are busy fighting to keep their generous tax loopholes and government-protected monopolies, innovating more in their lobbying and corruption than in production.

Australia’s rich list is peppered with billionaires who made their fortune on the back of favourable government deals that handed them economic windfalls. There is barely a single innovator amongst them.

It should be no surprise then that Australia’s multi-decade de-industrialisation has finally become a mainstream political topic. According to research from Harvard, in the 20 years prior to 2015, Australia dropped from 49 to 77 in the country rankings of the complexity of their production capacity - that is, how advanced and diverse the country’s manufacturing capabilities are.

And our lazy billionaires were nowhere in sight.

Despite the benefit of billionaire industrial policy and a stable position in the country's ranking of economic complexity, Donald Trump made it to the White House, selling the message that manufacturing still matters in the United States.

So what does effective industrial policy look like when it doesn’t rely on lucky billionaires whose ambitions may not always match those of the rest of us?

The answers are simple.

If society accepts that losses from some experimental investments to make the impossible possible are worth the risk, it doesn’t matter who takes the losses—the public via government, or billionaires. After all, the macroeconomic implications are the same; real resources, the land, materials, human labour and its brainpower, are all diverted from productive uses to high-risk high-stakes experiments, be it in space, or the electrification of transport.

Typically, governments have been one of the few large organisations able and willing to make large long-term losses for a small chance of a broad long-term gain. There have often also been military motivations lurking in the background of such investments, but this need not always be the case.

NASA’s spending on the original space race, for example, was itself an extensive piece of industrial policy. NASA’s spending peaked at $5.9 billion in 1966, which was 4.4% of the federal budget, with limited direct commercial return. The graph below shows just how abnormally large the spending program was in the 1960s, and a back-of-the-envelope calculation shows it to be about 100 times larger than losses being made in the current billionaire space race.

In effect, the 1960s spending on the space race was a decade-long loss-making venture undertaken by the US government that radically expanded the production capabilities of the country. Indeed, if not for this earlier period of enormously costly industrial policy, the current private space race would have little reason to exist.

What happens with these types of experimental investments is that any future benefits often emerge indirectly. The materials and computer technology developed in the 1960s space race have dispersed through the economy, creating broad and indirect benefits that are very hard to see, but that nevertheless have expanded global production capabilities.

The other thing that competent governments have traditionally been good at is overcoming coordination problems that would stifle innovation happening in any one particular area. What good is a boom in private flight, for example, without air traffic control and a system to coordinate it? What good is developing the capability to build railroads without the ability to acquire the land to put them on?

Here again, we see billionaire industrial policy playing this coordinating role. Tesla has been investing in both new electric vehicles and the large infrastructure network of high-speed charging stations necessary for them to become a fully useful replacement for the petroleum-fuelled vehicle fleet. And again, like all vehicle manufacturers, they are relying on earlier public investments in the coordinating system that is the road network itself.

The trick to understanding the logic of industrial policy is this. Markets won’t automatically experiment with high-risk new investments with a fleeting chance of any future direct return. Somewhere, someone must make losses for a long time while experimenting with new types of production, knowing that there may never be a direct future return. Somewhere, someone must coordinate to avoid external conflicts that can arise and stifle new production.

With renewed interest in manufacturing, renewable energy, and space, governments can take on these traditional industrial policy roles while also supporting billionaire industrial policy if it emerges as well. There is no conflict. But if we want to build things that have never been built before, someone has to take on the short-term economic losses to get there.

As always, please like, share, comment, and subscribe. Thanks for your support. You can find Fresh Economic Thinking on YouTube, Spotify, and Apple Podcasts.

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