Microeconomic success, macroeconomic failure

When I teach macroeconomics, I use a dog and bone analogy to demonstrate that the macro-economy is not equivalent to just “adding up” the micro.

Let’s see the analogy in action.

In the dog and bone economy, ten dogs repeatedly try to find nine bones buried in the yard. Each round, at least one dog misses out. We think that this outcome is undesirable— we can’t have an economy with over 10% dog “bone poverty” and perpetual “dog unemployment”!

Some astute dog economists notice that dogs that miss out on a bone are usually a little slower, or have some other traits that make them relatively poor performers. They reason that there is a “skills mismatch” that, if corrected, could solve the macro-economic problems in the dog economy.

These economists go the extra mile and conduct some randomised controlled trials on interventions that seem promising.

1. Give the dogs that miss out a head start
2. Provide the dogs that miss out advice about where to find the bones
3. Train the dogs that miss out to sniff out bones better

After trialling each of these interventions, the results come in. They are astounding!

In each policy experiment, dogs that missed out on finding a bone 75% of the time in the control group only missed out 5% of the time in the treatment group.

The researchers responded to media enquiries about their results. “This is the largest effect I’ve ever seen in a social science intervention,” they said.

If it can be replicated at scale, the experimenters may have hit on a powerful new tool for dismantling bone poverty in the dog economy. Policymakers are now looking to invest in expanding these programs in dog parks across the country.

I don’t know about you, but it always helps me to understand what is really going on when we talk in the abstract. In the dog economy, it is clear that regardless of the microeconomic success of these interventions, there is still going to be “dog poverty” and “dog unemployment” because of the macroeconomic conditions. There are always nine bones and ten dogs. At least one dog still misses out and experiences “dog poverty”.

Helping someone jump the queue for access to scarce resources is obviously going to help that individual. But it can’t help everyone in the queue.

And yet, these microeconomic “queue-jumping” policies are politically attractive. Job training is widely thought to be an important tool for solving unemployment. But if the unemployed are competing over scarce jobs, then job training can only change the preferred ordering of candidates.

A recently popular policy in this vein has been “intensive housing counselling”. This involves lobbying landlords on behalf of housing voucher tenants and advising these tenants to move to “high opportunity areas”. Not surprisingly, these tenants took up the professional advice and assistance given to them.

As one tenant noted, after deciding where they would like to move, the housing counsellors “pretty much took care of the rest. I gave them my information, they gave my information to the leasing office, they applied for me, and they helped with the first month’s rent and the renter’s insurance for a year.”

Making renting and finding a home easier is great. I’m not going to argue against that.

But what puzzles me is this. Like the nine dogs and ten bones, not everyone in a “low opportunity area” can move to a “high opportunity area”. And in fact, as people start to move out of these “low opportunity areas” those areas will have even fewer economic opportunities for residents that ultimately move into them! The policy can’t “add up” to the macro, despite its success at the micro-level.

So what sort of policies do work at a macro level?

In the dog economy, the thing that works is to compress the “bone distribution”—take the nine bones, cut off one-tenth of each bone, and let the ten dogs access 9/10ths of a bone each. Alternatively, have a handler keep some bones in reserve to share amongst the dogs that miss out. Macroeconomic success requires a mechanism that changes the nature of the game itself, rather than the individual behaviour within it.

Two problems with opportunity cost

If there is one idea that defines economics, it is opportunity cost. Unfortunately, muddled thinking about this idea means that across the economics discipline it is applied rather inconsistently. Economists often use the word to mean whatever they want it to mean. 

In its most basic form, opportunity cost just means your next best alternative use of resources. What opportunity did you forgo to undertake this action instead of an alternative? But it gets much more difficult to translate this idea consistently into more detailed economic theories.

I want to highlight two big inconsistencies with the use of opportunity cost in economics. To do that I want to start with a question that triggered a mini-controversy in the discipline a few years back when it was revealed that economists did worse than chance in answering a multiple-choice textbook question about opportunity cost.

The question was:

You won a free ticket to see an Eric Clapton concert (which has no resale value). Bob Dylan is performing on the same night and is your next‐best alternative activity. Tickets to see Dylan cost $40. On any given day, you would be willing to pay up to $50 to see Dylan. There are no other costs of seeing either performer.
What is the opportunity cost of seeing Eric Clapton? A. 0, B. 10, C. 40, D. 50.

According to the textbooks, the answer is B.

There are two mistakes here.

Comparison with different costs

First, the two given alternatives have different resource costs. If you see Dylan you have $40 less to spend. Therefore, a clean comparison of opportunity costs requires us to compare these alternatives

A. See Clapton for free.
B. See Dylan that night and have $40 less to spend.

If we don’t account for the full costs of each alternative, we end up with ridiculous scenarios, like comparing the profit from investing $1m with the profit from investing $1k. It makes no sense when we abstract into raw financial terms, and it makes no sense here either.

Strictly speaking, the correct answer is $10 minus the net benefit from my next best use of $40. But then again, maybe I don't know what opportunity cost is either!

Alternative options are not discrete

Another problem with opportunity cost is that, in reality, there is a continuum of alternatives to any action. The next best option to Alternative A is usually doing Alternative A but cutting some corners slightly. In this case, if seeing the Clapton concert is Alternative A, then seeing Clapton and going to the bathroom when your favourite song is played might be a “next best” alternative.

We could, if we want, break out any of the discrete alternative actions into an infinite array of alternatives. Each of those could be broken out again until we have a continuum.

If we zoom in on this continuum, then the opportunity cost is always equal to best Alternative, and even the opportunity cost of the best Alternative is itself.

This point is important. If, for example, we think that supply curves include opportunity costs of resources, then economic profits are always zero or below by definition.

In a topic I study, property markets, this is also important. Many people think that the second-best alternative use of land sets the price. For example, in regard to the price of land for housing:

…in the absence of any restrictions on supply, the price of raw land on the fringes should be tied reasonably closely to its value in alternative uses, such as agriculture.

Why is agriculture the next best alternative to housing? Surely there are multiple residential subdivision options that are alternatives, and some will be better than others.

Property valuers (appraisers) are clear that the value of property rights comes from its highest and best use, but for some reason many economists think they know better. Valuers test out the various legal options for land use to determine which one provides the highest value to land, and it is this use that determines its value.

The opportunity cost logic, in this case, becomes more absurd when we think about the case where there are three possible legal uses of land—say agriculture, industrial, and residential (in order of value). If the second-best alternative sets the price, then you can make the land cheaper by regulating against the second-best use of industrial development, making agricultural use the second-best alternative and decreasing land prices for housing.

And I haven't even considered the case when there is only one allowable use of land. Doesn't this make the second-best use to do nothing, therefore bringing the land price to zero?

Like many seemingly insightful economic ideas opportunity cost is less powerful than it appears and often confuses more than it clarifies. 

NOTE: Here are some recent articles that follow up on the original survey question that show just how varied the interpretations of opportunity cost can be.

The bathtub analogy of housing supply

Many people hold the view that rezoning land to allow higher density residential uses on each plot will accelerate the rate of city housing development.

I think this is wrong.

The main reason I think this is because there are a finite number of new buyers per period, and residential developers are not in the business of competing with themselves on price. No sane developer floods the market with new housing just because the regulations are changed to allow them to build 100, rather than 50, homes on their lot. In fact, they might just build at the same rate on that lot for twice as long before moving to the next location.

A key confusion in housing supply and zoning discussions is that density limits per lot are interpreted incorrectly as a constraint the rate of new housing supply per period. New homes per lot is not the variable of interest in city housing supply. New homes per year across all lots in a city is the critical variable.

Zoning constrains the location of different densities of housing, but not the total rate of supply across all lots in a city. [1]

In the past, I have tried to dispel some of the key problems with the standard static economic models that conflate the allowable density per lot with the rate of supply. This is what I said then about these models 

The only problem is this. When you convert the model to English you realise it has little basis in reality. The only real pattern that is consistent with the model is that higher buildings are near the city centre. But I could come up with a million other models that are consistent with that pattern.

One of the main flaws in the AMM model is that there is no possibility for development of sites within the city into new buildings. Every site is already used at its optimal level. There are no vacant sites or sites with old buildings ready for knock-down and reuse. There is no development industry. There are no landowners.

Also because of the comparative-static nature of how the model is used, every time there is a marginal change in any of the parameters of the model — a new person moves to the city, the rental price of the second best land use increases, or the efficiency of construction methods change — the whole city is wiped clean of homes and buildings. The single social planner who controls everything in the city then dictates that the whole city will be rebuilt with a new optimal allocation of housing and commercial buildings under new conditions, and this whole new stock of buildings rebuilt in an instant to that new specification. 

In that blogpost I introduced some new ideas about how to conceptualise regulatory constraints using this diagram.

I want to now offer a simple “bathtub” analogy that demonstrates why our thinking about housing supply and zoning is often misguided. 

Imagine a city region is like a bathtub. The limit on total development, if every location was used to its highest-value use, is the depth of the tub. This is affected by geographic, regulatory, and economic constraints. The water level is the current total stock of housing across the city. Lastly, the dripping water from the tap is how fast new development is occurring across the total city to increase the total stock of housing. 


The question is, what part of this bathtub situation would you address with policy changes to increase the depth of the water? The depth of the bathtub, or the rate of water flow from the tap?

Changing the depth of the tub is a bit like rezoning the whole city for higher density. It seems intuitively like a good idea, but if the city is nowhere near its bathtub capacity, what mechanism is there for this to affect the rate at which the tub gets filled?

The more effective approach is the look at the tap, and the rate at which new housing is developed. This can involve a few things, like making it more costly for landowners to delay converting land into higher-value residential uses. Or, it can mean redirecting credit flows into new, rather than existing housing, to encourage new supply. Regardless, when you start to look at the tap you see that the key variable that needs to be tweaked by policy are the dynamic incentives of landowners—delaying, or slowing, development needs to be made relatively more costly.

However, when you start to focus on the rate of supply you realise that the challenge of tackling price booms with supply is far from as simple as they seem. To even maintain the current drip feed rate of new housing requires a substantial portion of the workforce, and it doesn’t change the total stock very much (just a couple of per cent per year).

In Australia, for example, our housing tap drips at a rate that is around 2% of the total stock, and it requires something like 5-7% of the workforce to build at this rate (and more in some cities with high rates of housing construction).

To have a meaningful effect on the total stock housing, and therefore the price, requires an economically significant long-term construction boom. For example, increasing the rate of new supply by 50% for a decade—employing more than 7.5% of the total workforce instead of about 5%—will increase the total stock by just 9.8%. By any metric, this will have a price effect in the range of 5-15%. The point being, the large changes in the rate of supply have small effects on the total stock and these require a large share of economic resources shifted away from current uses and towards housing construction over a long period, particularly in boom cities.

Now, I am totally supportive of a sustained effort to build more housing to provide more options for households. But I am against pretending that rezoning means that developers voluntarily, and dramatically, increase the rate that they supply new housing to such a degree that they subsume a substantial portion of the workforce while at the same time reducing the price of the asset that earns them a living.

To change the rate of supply requires changing the dynamic incentives of landowners by making it relatively more costly to delay new housing development. This cost to delay means that bringing forward development, even if a lower price must be accepted, becomes viable. These types of changes will be labelled as punitive by landowners, but that’s how you know they are effective—it forces them to build housing when they prefer not to.

Finally, we can always create non-market housing institutions that build new housing regardless of market conditions, allowing this organisation to actually build at a rate that will depress prices, or offer housing to residents at below market prices.

With a bit of luck I hope that in future conversations about housing supply and zoning that the rate of new housing supply per period across all lots is no longer conflated with the allowable density of housing per lot.

fn. [1] I do note that some cities may very well have planning regulations that are so poorly designed that they do in fact constrain the rate of supply.

Update: Total employment in housing construction reduced to remove the engineering construction workforce.

Rising home prices reduce the willingness to supply homes

TL;DR

There is a major flaw in the idea that high prices stimulate more housing supply. It is that high price growth itself decreases the willingness of landowners to supply land for housing. 

Supply and demand (AKA Static Production Theory, or SPT)

“If we want to create widespread affordability, so that anyone can walk into a real estate office and rent something reasonable, then we must confront the laws of supply and demand.”

So says Ed Glaeser, the highest profile urban economist of this century. He has forged a career from restating this conventional wisdom, and now major national governments and institutions, including the Reserve Bank of Australia, generally prescribe to this view.

Underlying this view is a single-period, asset-free, model of the world where higher prices induce more supply of housing. New housing is a simple production process that combines land and construction inputs. As long as the price of a home exceeds the input costs of land and construction, each of which is determined by their own independent markets, profit maximisation says that these homes will be immediately produced.

In the notation, and in terms of annual flows, the profit for any new home is the rent minus the interest on input costs,

π = rent − (land + construction) x interest rate

or

π = r − (L + c) i

where L and c are the land and construction input costs.

In the equilibrium, all currently profitable development options have been taken up and there is no further new supply. Additional new housing (a change in the equilibrium stock) arises when the demand curve shifts enough for marginally unprofitable development options to become profitable and get taken up. This moves the market to a new equilibrium with more dwellings supplying residential accommodation in the market, and where the new equilibrium price depends on the shape of the supply curve.

As long as r > (L + c) i, new housing will be supplied.

Because of the durable nature of homes, the otherwise standard upward-sloping supply curve is kinked at the current price and stock of housing. The vertical portion of the curve below the kink reflects the willingness to supply existing homes to the market at a price below cost (a negative profit) in order to minimise losses on sunk investments in the event of a negative demand shock. This is shown in the figure below.

The slope of the supply curve above the kink is thought to arise from a combination of regulatory and geographic constraints that mean that the cost of building new homes is higher than it was for existing homes. As Glaeser et al. explain, costs “depend on the city size, reflecting community opposition to development as density levels increase.” If regulatory costs involved with new housing can be reduced, such as by lowering impact fees or relaxing density restrictions that force each dwelling to use more land, then it will create more profitable development options that can be immediately taken up as well as more marginally profitable ones that can be taken up after small price increases, on net flattening this part of the supply curve, or ‘elastifying supply’.

In panel (b) of the above figure we see how the model is used to describe the case of legislative or geographic constraints. Because there are a finite number of locations where homes can be produced below current prices, the stock can only increase when prices rise and these new higher-cost locations become profitable. This can be due to any constraint, such as planning regulations or natural geography.

Implicit in this static production theory (SPT) is that costs are additive. The market price of undeveloped land is set in one distinct market, construction prices are set in another distinct market, and to supply new housing requires simply combining both inputs.

Key problems with the SPT model

Inputs to housing are themselves assets

The first problem is that new housing supply is not a production decision, but a capital allocation decision. The question is not whether the price of housing exceeds the input costs. The question is why should I take my land and financial capital inputs and combine them into a new home? The returns to both the home and the capital inputs must be the key variables in the housing supply decision.

This problem is quite obvious when we think about the housing supply condition for SPT, which is never met in Australia. Housing rents almost never exceed the interest on input costs. Only if we treat housing as an asset can we make sense of the observed market behaviour.

Poor description of the market

The model is pretty bad at describing or predicting housing market cycles. Ed Glaeser himself noted that the model was not so useful — “the fact that highly elastic places had price booms is one of the strange facts about the recent price explosion”.

Land prices are set by magic

Appraisers and valuers view the price of vacant land as being directly determined by the value in its highest and best use, such as a residential subdivision, minus the cost of transforming it into that use. Rather than land and construction costs being additive, the valuer’s view is the reverse; land values are determined as the residual of the highest use value minus construction costs.

But without the additive cost assumption, this static production theory of supply falls apart. Home prices minus construction costs would always equal the value (cost) of land by definition, and the supply curve would lose all meaning. The implied story of how land prices are set in this model is that they are simply set by the magic of some other market that exists, but that is unrelated to the housing market itself.

A better model (Balance Sheet Dynamic Theory - BSDT)

All we need to do to improve our understanding of housing supply is incorporate into our model the fact that housing is an asset, and that building new housing is a capital reallocation away from cash and undeveloped land.

In my balance sheet dynamics theory (BSDT) landowners are rational agents making capital allocation decisions to maximise the rate of growth in the value of the total balance sheet.

Rather than housing being a new product made whenever its price exceeds its production cost, new housing is a balance sheet reallocation of land and financial capital, each of which are already earning a return.

That forgone rate of return is the opportunity cost of housing development. In this perspective, new housing is only produced when the rate of return on housing (its “return price”) exceeds the rate of return on the inputs of undeveloped land and cash (its “return cost”) to generate a “return profit”. If the rate of return on capital inputs is higher than on housing, then reallocating this capital to produce new housing reduces the growth rate of the balance sheet and it is therefore uneconomical.

Notice that price levels do not enter this assessment at all, only returns.

In BSDT the production cost of new homes does not arise from the addition of independently determined land and construction cost inputs. Instead, new housing costs at any point in time are always equal to the home price because, at the time of development, land values are determined by the residual of home prices minus construction costs (as in the appraiser's, or valuer's, view of land price determination).

This residual market-determined value of undeveloped land reflects the nature of land as a perpetual development option. But this real option characteristic of land has another effect.

Because of the flexibility in the scale of development, the price of undeveloped land does not merely track home prices but moves more than proportionally to account for the fact that higher home prices also justify higher density uses, and vice-versa. Thus, committing undeveloped land to an irreversible residential development comes at a “rate of return cost” of this price growth premium of undeveloped land. 

The rate of return problem of housing supply

Three returns are relevant for understanding housing supply incentives in the BSDT; the total returns to housing, land, and cash respectively at a particular time for an amount of each input necessary to supply a single new home.

For housing, the total return is the sum of the value gain and the current rental return such that RH = Ṗ + rH where Ṗ is the change in home prices, and rH is the net rent for a representative home. 

For cash, the total return, RC, is the nominal interest rate, i, multiplied by the construction cost of a home, c.

For land undeveloped, the total return is the sum the value gain plus any rents from lower-value uses, rL. Since land prices are the residual of home price minus construction costs, this means that the value gain for undeveloped land includes the price change of housing, Ṗ (assuming fixed construction costs).

But it also includes the change in the option value because the optimal density of a subdivision also changes with home prices, which we call the option premium and denote ω.

For example, a site that is currently optimally subdivided into 6 lots might be optimally subdivided into 8 slightly smaller lots if home prices in the area increase from $300,000 to $350,000 (such as the example subdivisions in the figure below).

Instead of a return due to this price growth being $300,000 (6 x 50,000), the return now includes the full value of two additional lots. As long as the price effect of subdividing into smaller lots is less than proportional to the size decrease of each lot, the total return will be higher than the home price growth for a fixed number of lots alone. 

In this example, as long as the smaller lots exceed $262,500 each, the total return to land exceeds the home price growth of six lots. If we take the case where the price of the eight smaller lots is $280,000 each, then the return from price growth alone for the previously optimal six lots is $300,000, and the additional return from the option premium is $140,000 (280,000 x 8 — 350,000 x 6) to give a total return from capital gains to this undeveloped land of $440,000. Here ω=0.47.

In the notation, the return to land is RL = Ṗ + ωṖ + rL.

The housing supply problem

We can now express the gains from supplying housing in terms of the effect of on returns from shifting land and cash into housing, or RH− (RL + RC). After substituting our returns for each component we get “return profits”, π, of

π  = Ṗ + rH −􏰀Ṗ + ωṖ + rL +ci􏰁 

= rH −􏰀 ωṖ + rL + ci􏰁.

Therefore, supplying new homes at any point in time increases “return profits” if

rH > ωṖ + rL + ci. 

This is a more difficult hurdle than the one under SPT, where price growth did not enter the housing supply problem at all.

But this also tells us that high home price growth reduces the willingness of landowners to convert their land into new housing. Not building now is valuable because it keeps the option open to build a more dense subdivision in the future (either a vertical subdivision in the form of apartments or horizontal in the form of housing lots).

If you feel the urge to imagine a supply curve of sorts, then put the rate of price growth on the y-axis and the supply curve (willingness to supply) is downward sloping.

Many property researchers are now adopting this type of model. For example, in the model of Alvin Murphy shows that “rising prices make building today more attractive, but also make waiting more attractive, thus reducing the responsiveness to price.”

But if rising prices cause less willingness to supply, why are home price booms associated with massive construction booms? The answer to this is quite simple. Not only are landowners and potential housing developers return-maximising, so are all agents in the economy. A little house price growth will attract everyone in the economy to shift away from cash and into housing, new or existing, as this increases their “return profit”.

Since the current owners of the stock of housing have the same incentives to buy and not sell, new housing becomes a the main available investment option for all those willing buyers. But there will always appear to be a shortage of new (or existing) housing for sale. The weight of money that shifts into housing markets both increases home prices and the volume of new home sales. but also decreases the willingness of landowners to sell enough new or existing housing to significantly reduce this price growth.

This BSDT also shows the effect of binding density constraints, which fix ω at zero. This increases the willingness of landowners to supply homes now rather than delay. Instead of planning constraints reducing the total supply of housing as assumed in SPT, they can instead increase the rate of new housing supply. 

In the economic literature housing supply has been a mystery for a long time because of the attachment to a model that is conceptually misapplied. Putting returns and balance sheets front and centre is going to be a far more productive way to improve our understanding of housing supply in the future.  

Three Economic Myths about Ageing: Participation, Immigration and Infrastructure

Leith van Onselen and I were commissioned by the Sustainable Australia Party’s Victorian branch to examine the causes and implications of population ageing in Australia, and whether maintaining a high immigration program is a worthwhile policy response.

Below is a summary of our report:

Overview

Population ageing due to longevity is one of the greatest successes of the modern era. However, it is widely thought to dramatically reduce workforce participation and overall output resulting in significant economic costs.

This widely held view is wrong. Ageing countries have higher economic growth and the improved health and longevity of older people increases their economic contribution.

High immigration is also thought to combat population ageing and be a remedy for these non-existent costs of ageing.

This is wrong. Low immigration can affect the age structure by helping to stabilise the population, but high immigration has almost no long-run effect besides increasing the total population level. This creates bigger problems in the future.

It is also widely thought that simply investing in infrastructure will accommodate high immigration and population growth at little cost.

This too is wrong.

Diseconomies of scale are a feature of rapid infrastructure expansion due to (1) the need to retrofit built-up cities, (2) the dilution of irreplaceable natural resources, and (3) the scale of investment relative to the stock of infrastructure.

This ageing-immigration-infrastructure story is wrong on all three of its major points. Population ageing should be seen as the successful result of improvements in medical and health practices that have improved longevity and fostered a long-lived and economically productive society.

Key Research Findings

• Population ageing is a successful result of efforts to improve longevity.
• Countries with older populations maintain high workforce participation, are more productive, and grow faster economically.
• Ageing does not lower workforce participation in general. Since 2012 there have been more full-time workers aged over 65 than under 20.
• Low net immigration of between 50-80,000 permanent migrants per year can alter the age structure over the long-term by stabilising the population.
• Low net immigration increases GDP per capita and wage growth.
• High net immigration above this 50-80,000 amount has almost no additional effect on changing the age structure and simply increases the total population.
• Most of the increase in permanent migration since the early 2000s has been through the skilled migration program.
• This program primarily benefits the migrants themselves and increases wage competition for other workers.
• A focus on skilled immigration fosters a “brain drain” from developing countries, reducing human welfare.
• There is a real economic cost to high population growth due to the diseconomies of scale inherent in rapid infrastructure expansion.
• There is a real cost from environmental degradation due to development to accommodate much higher populations.
• The high costs of population growth are often ignored, as immigration policy is a federal matter, while infrastructure provision is predominantly a state and council matter.
• Population growth in general dilutes ownership of our environmental endowments, mineral wealth, fisheries, wildlife, and national parks.
• The political capital and resource devoted to managing high growth have an opportunity cost in terms of solving other social problems such as homelessness, indigenous disadvantage, mental health, and other social services.

Policy Recommendations:

• Reframe ageing as the economic success story that it is.
• Reframe immigration as an environmental and ethical choice, not an economic necessity.
• Lower overall net immigration to the 50-80,000 range by mainly targeting skilled visas. This can largely be achieved by increasing the minimum salary for skilled migrants to 150% of the average full-time salary, or $129,900. This desirable net immigration range can be achieved while having a slightly higher permanent intake of around 80-90,000 per year, as permanent departures will reduce the net effect while still maintaining the optimal target range.
• Adopt systems for infrastructure planning and provision that clarify the expected cost of new public and essential services, and ensure upgrades keep pace with city growth for the benefit of existing and new residents.

Key charts:

The Full report is downloadable here.

High home prices jack up rents

In traditional economic thinking, the interaction an independently determined supply and demand for rental housing set the market rental price.

But that simplification ignores an important part of the story—where does demand, or the willingness to pay for rent, come from?

It might help to start thinking about a different product to clarify my point. Consider that you need some fruit and the prices per kilogram are as follows:
Apples - $5
Pears - $4
Bananas - $3

The demand for apples will be quite low since the close substitute goods have a lower price. Now consider this situation:
Apples - $5
Pears - $7
Bananas - $6

What does the demand for apples look like now? The demand for apples will be higher since the price of substitutes has risen.

This is basic microeconomics, right? The demand for a good rises if the price of substitute goods rise, and vice-versa. High priced substitutes mean that each buyer will have a higher willingness to pay.

So now let’s talk about housing. There are roughly three goods in this market—buying, private renting, and social/public renting. If the price of one of these substitutes rises (or their accessibility diminishes due to queueing) so should the demand for the others.

What this means is that even though rental prices are a better indicator of the supply and demand interaction in the housing market than home prices, the demand curve that determines the rental price itself shifts with home prices. The demand curve in the rental market is not independent of the price (or cost) of home-buying.

We can see a pattern in some markets, like the chart of Seattle below, where rising prices led to rising rents, then falling prices led to falling rents. 

While there are many other important interactions in housing markets, the substitute goods price effect is going to be part of the story. 

It is also a helpful guide for thinking about housing policy.

To dampen housing demand (and therefore rental price) it pays to create a housing system with many substitute ways to access secure housing. A huge investment in social (below-market-priced) housing, for example, will provide a substitute option for many private renters. 

The effect of this investment will be larger than the number of people who take up the option. Many households who don’t end up in social housing will keep their bids for private rentals below the price of the social housing option, reducing prices in the private rental market as well.

I don't know how big this effect is. But even a 5% effect on the willingness to pay for private rental housing still equates to $2.5 billion in annual total rents paid by the 30% of households who rent.

In general, therefore, the more housing alternatives that exist, the more stable and low-priced the total housing system should be. Any substitution effect on demand from price changes in one housing market will have a lower effect on each other market. 

Using economics to justify our fears: The case of ageing and holidays

One of the great myths in economics is that there are substantial negative impacts from population ageing. But there is no economic basis for this fear. In fact, it is a good example of how economic reasoning is often used to provide a back-story for our instinct and emotion, rather than as a basis upon which to form a considered view.

For example, in the economic story we tell to justify our fear of ageing we use outdated assumptions about work patterns (over 15 years old is in the workforce, over 65 is not) and often ignore the counterbalancing effect of fewer young children needing care and schooling.

But when it comes to other discussions about policy, such as more public holidays, a shorter working week, or robots taking jobs, we pick a different economic story to make sense of our insecurities about losing our job.

In reality, the economics behind these two views are contradictory, as I will now show.

When it comes to ageing the economic story involves the calamity that might arise if the ratio of working to non-working people in the economy falls (the age dependency ratio). But imagine shifting your focus from this ratio to another ratio, which is the number of working to non-working days in the economy each year (the workday dependency ratio). Both are equivalent ways to conceptually carve up the total work done in the economy—either by people or by time— since Yearly Output = Working People (WP) x Working Days (WD).

In the absence of changing work norms, ageing may reduce output by reducing the first term in this equation as a share of total people. But so too will any policy change that reduces someone's working years of life, such as introducing additional years of schooling.

The number of working days (or even hours) per year is a function of many things, like norms about overtime, limits on weekly hours, weekends, statutory holidays, and more. Changes in these variables can also reduce output if they reduce yearly work time.

According to the Australian Bureau of Statistics, expected ageing in Australia over the next 15 years is likely to increase the age dependency ratio by 18%. This is the ratio that justifies fears about population ageing.

But where is the fear about shorter working years? We can, for example, look at what would happen if Australia had the same number of annual vacation days at the UK of 37 compared the current 28. Some sources say that leading the charge of fewer work days are countries such as Austria with 38 vacation days, and out in the lead are Brazil and Sweden with 41 days!

If Australia merely adopted the yearly workdays of the UK then we would increase the workday dependency ratio by 11%. If we went crazy and had six-weeks annual leave and hit the world-leading 41 days paid annual leave, then our workday dependency ratio would increase 16%, or nearly the same as the increase in the dependency ratio that is so feared when it comes to ageing.

If a higher age dependency ratio is a major economic problem, then so too is a higher workday dependency ratio. One cannot be an economic problem if the other is not. I'm not the first to say this. Dean Baker has said it all before.

This is just one example to show how our policy debates are not shaped by raw economic reasoning, but are instead shaped emotions, instinct, and more often than not, by interest groups. Economic reasoning is then used to justify opinions already held, and the economic reasoning used to justify one view need not be consistent with the reasoning used to justify any others. That's just the way human minds work. 

Solving the housing supply mystery

Static thinking creates the mystery

A famous 1999 article in the The Journal of Real Estate Finance and Economics was entitled Why don't we know more about housing supply? It starts by observing that in the 1960s and 1970s most scholars had found that the supply of new housing was perfectly elastic. In plain English, this means that new housing construction seemed to mysteriously occur whenever it was required, regardless of the price.

So if the standard economic logic that high prices incentivise more new construction fails, how is one to solve this economic mystery?

The solution is surprisingly simple and was identified as early as 1970. The decision to build a new house on a piece of land is not a production output decision but an irreversible capital allocation decision (i.e. you can’t easily ‘unbuild’ the house next year if it is the wrong decision to build it today).

If you try and understand housing supply in terms of the standard single-period static supply and demand model you will struggle to make sense of housing. In this static model world, every opportunity for housing construction is already taken up, since it compresses all future time into a single period. If it could be profitable to build a house in ten years time, then the model says it is already built!

Once you break free of this static view and add time to your thinking, housing supply makes a lot more sense. Landowners can choose not only how densely to develop housing on their land, but also when to develop. After all, if the price of the land is rising quickly, why would I develop today when I could make more money by holding the land vacant and developing more housing later?

When you start thinking dynamically the price cycle and the supply of housing become tightly linked. I want to demonstrate briefly how dynamic balance sheet allocation decisions make sense of housing supply mystery with a simple example.

An example balance sheet

To set up this example, we need to understand firstly that the economically optimal behaviour in capital allocation decisions is to maximise the rate of growth in the value of the total balance sheet. For the moment I ignore risk and uncertainty to make a simple point that is much more subtle in reality.

Consider a balance sheet as per the Table below. In the beginning, the portfolio is $100 of cash, vacant land (2x lots worth $50 each), and housing (say one house on a lot worth $50 with a building worth $50).

Now consider that over time the total returns to each part of the balance sheet are as follows:

• Cash receives a total return of 2% from interest.
• Vacant land receives a total return of 4% in capital gains which reflect the rising value of the option to develop.
• Housing gets a 3% rental return that comes in the form of cash, and a 2% return from capital growth.

If no capital reallocation decisions are made, then in the next period the balance sheet is as shown in the Base column. The extra $5 cash comes from the 2% interest on the cash balance plus the 3% rental return from the house. Both the vacant land and housing are marked up to the new market prices.

	Today	Base	More	More	More land
			housing	land	& housing
Cash	150	156	108	105	6
Vacant land	100	104	52	156	156
Housing	100	102	204	102	204
Total	350	362	364	363	366
Growth		3.43%	4.00%	3.71%	4.57%

It is only economical for a landowner to increase housing supply on their vacant land by swapping cash for a house if it increases the total rate of growth of their balance sheet. You can alternatively think about the 'cash for new construction' swap as borrowing for new construction.

More housing

Let us now see what happens to the balance sheet growth if a new house is built on one of the vacant lots using $50 cash. The result is in the More housing column.

From the $150 cash, $50 was used to build a new house on one of the two $50 plots of land, and the now $200 worth of housing increased in value to $204. The two houses also returned $6 in rent, which in addition to the $2 interest on the remaining cash balance, took the total cash balance to $108. The remaining vacant land lot increased in value by 4%, from $50 to $52. In total this portfolio reallocation decision increased the rate of growth of the balance sheet to 4%, compared to 3.43% if no reallocation was made. Thus, it was economical to make this reallocation and build a new house.

Importantly, this decision did not depend on the price of housing at all. We have no price level parameter at play here—only the yield and capital growth rate. Thus, the first important insight from this dynamic view is that decisions to invest in new housing do not depend on the price level. This contrasts with conventional static economic models that suggest that the supply curve for new goods is upward sloping with respect to price.

More land

Now consider that instead of reallocating cash to housing structures, it was reallocated to holding more vacant land.

In the More land column I show the balance sheet effect of buying another vacant lot. Here the now three vacant lots grow from $150 to $156 in value over the period. The cash balance is reduced to $100 but increases with $2 of interest and $3 of rental returns to $105. The housing grows in value to $102 for a total balance sheet of $363, or 3.71% total return.

Adding more land to the balance sheet was also desirable in this situation. Under these conditions there is an incentive to both increase the stock of vacant land owned, and the stock of housing built.

This is exactly what we see happening with land developers in Australia. When they develop more lots they also increase the stock of vacant land held in their ‘land bank’. The chart below shows this relationship for the top eight residential developers (for both houses and apartments). When the rate of new construction increases they also source more vacant land to expand the stock kept on their books.

More land and housing

I demonstrate the effect of responding to both incentives in the final More land and housing column. Here, two vacant land lots are bought with cash and one is used to build a new house, expanding both the stock of vacant land and housing on the total balance sheet. Here, the three vacant lots increase in value by 4%, taking the value from $150 to $156. The two houses increase in value from $200 to $204 and deliver $6 in cash from rents. The total balance sheet at the end of the period is $366, or a 4.57% return. During a price boom, it is optimal to increase both new dwelling construction and the stock of developable vacant land.

When the tide turns land and housing materialise

Consider the again the same starting balance sheet, but this time the capital growth is negative, just like after a peak of housing price boom. Interest on cash falls a little to 1.5%, land price growth turns more negative than the price of housing, but yields are roughly constant.

• Cash – 1.5%
• Land – negative 2%
• Housing — negative 1% growth plus 3% rent

In the table below, we can see the balance sheet effect of reallocation decisions under these new conditions. In this case, since owning vacant land offers a pure negative return with no imminent positive cash flow, it is a bad play to have too much on the balance sheet, and either converting it to housing or cash is the way to go.

	Today	Base	More	Less	Less land,
			housing	land	more housing
Cash	150	155.25	107.5	206	158.25
Vacant land	100	98	49	49	0
Housing	100	99	198	99	198
Total	350	352.25	354.5	354	365.25
Growth		0.64%	1.29%	1.14%	1.79%

This scenario is very similar to what is happening now in Australia. Developers are trying to transform their vacant land assets into new housing using a build-to-rent model while at the same time reducing their land banks. All of a sudden there seems to be a flood of land and housing available!

Similar logic applies to owners of under-utilised housing who might be holding a dwelling vacant, or using it only occasionally, because keeping their options open is being paid for by capital gains. When their balance sheet starts shrinking their best option is to start renting the home to improve their overall portfolio return, further flooding the market.

Why prices and supply must track together

Rising prices encourage the conversion of land and cash to housing but also encourage the conversion of cash directly to housing. This mostly occurs by buying up the existing homes and in the process bidding up prices.

The dynamic balance sheet approach shows why these purchases cannot happen without bidding up prices. The simple reason is that current owners of housing also have an incentive not to sell and to themselves expand their balance sheet exposure to housing. An equilibrium in which prices are stable and the stock expands to accommodate demand defies balance sheet logic. 

The feedback cycle of high house price growth attracting further home buyers can continue until all willing participants have maximised their available exposure to housing. Then, as buying stops, growth rates fall, and logic dictates that reducing balance sheet exposure to housing is necessary, fuelling the bust.

This is why credit plays such a crucial role in the cycle. It facilitates large negative cash balances (loans) being converted to housing, greatly expanding the total capacity of the agents in the economy to shift their asset balance towards housing.

One way to align the cycle more closely to expanding supply rather than prices is to differentiate credit availability, making it more accessible for new housing and less accessible for existing housing. The increase in Chinese buying in the 2012-2017 cycle that was biased towards new housing did help expand supply higher than otherwise in the cycle, but in the process further fuelled price growth.

How does zoning fit in?

Rezoning vacant land to allow higher densities fits into this view as a way to increase the rate of growth in the value of vacant land held. In short, it makes keeping land vacant relatively more attractive than converting it to new housing. It is easy to understand the logic here if we think about the reverse scenario of down-zoning land to decrease its development density. The threat of future down-zoning would encourage faster conversion of vacant land to housing. The possibility of future up-zoning, therefore, encourages the holding of vacant land and a slower rate of conversion to housing.

Developers aren't secretive about this behaviour. In 2013  Stockland told its investors that it was delaying some projects to “improve return prior to launch.” Without the option for up-zoning to improve returns, these projects would have been built and sold sooner than otherwise.

Mystery solved?

I have spent more than ten years trying to unravel the mystery of why the observed patterns of housing supply conflict with standard economic reasoning. I now think I have solved it, but it is a radical departure from the standard thinking that most economists, housing analysts, and policy-makers are trained in.

Instead of housing supply responding to prices, it responds to the rate of return of different asset classes. In this world, the notion of a static equilibrium seems to make little sense if the rebalancing of portfolios into new housing feeds back into prices. Both prices and the rate of new supply move together until the system runs out of new buyers. What seemed in the boom to be a shortage of land and housing suddenly becomes a flood as expectations of high returns vanish. Just ask anyone in Ireland or Spain about this.

The only remaining mystery is why this dynamic approach, which has part of the intellectual debate in economics journals for nearly half a century, is not a central part of mainstream economic discourse.

Unpopular Economic Opinions

I was an illogical ideologue

I recall many heated political and economic discussions with Paul Frijters when I started my PhD. In retrospect, these would simply involve me rambling and him pulling me up questioning the logic of the next line of my argument. I realise now that “younger me” was happy to regurgitate arguments I liked no matter the logical coherence.

But now life is much harder. Being absolutely critical and logical means you don’t believe the same myths as your social groups. It makes you an outsider in the purest sense.

It is also career-destroying. Telling people comforting lies is how you make the big bucks, particularly as an economics consultant.

You can tax capital gains due to inflation

I’ve never understood the concern adjusting for inflation in the taxing of capital gains. The gain, inflation-adjusted or not, is simply a profit in the year it is realised. Businesses investing in new capital equipment don’t get to say “Yeah, but if I bought this machine today it would cost heaps more, so let me just deduct today’s price as an expense."

Brexit will not be an economic disaster 

If supply chains are so tightly integrated that the British economy will collapse, then it is in the political interests of countries at the other end of these supply chains to agree to reasonable trade conditions so that their countries don't also suffer. Indeed, most of the arguments for Remain are not economic ones. They look and sound like economics, but there is no economic reasoning.

Take this article, which suggests that the need for exports to conform to EU product standards is a scary big cost of Brexit. But by definition, all products currently exported to the EU conform to EU standards. This is a minor administrative change by any stretch of the imagination.

While it is certainly in the interests of the EU bureaucracy to make a Brexit as economically disruptive as possible in order to send a message to other countries, I think in reality this will be hard to do.

Also, calls for a second referendum seem pretty anti-democratic— "democracy is good, except when the stupid people vote for things I don't like".

These don’t seem like strong economic arguments against Brexit IHMO.

"No deal? Seven reasons why a WTO-only Brexit would be bad for Britain” https://t.co/SVLTaqEaEv

— Cameron Murray (@DrCameronMurray) November 19, 2018

Am I batshit mental?

Or is it batshit mental to pretend the solution to a problem of badly distributed access to resources won't require redistribution of access to those resources?

Yesterday’s ‘bottom-up’ is tomorrow’s ‘top-down’

Russ Roberts is an excellent interviewer and I thoroughly enjoy his Econtalk podcasts. But every time he talks about ‘bottom up’ solutions to organisational problems I immediately think “here comes tomorrow’s top-down rule that you are going to hate"!

Nobody is serious about higher wages

If you aren’t interested in unleashing enough government spending to decrease the unemployment rate to a number starting with a 3, or even 2, then you can't be that interested in increasing wages.

We were interested in this once. And being far richer than we were then, we could do an even better job today. If that’s what we wanted.

Micro-efficiency obsessions are macro-economically inefficient

With such huge macro-level inefficiency from under-utilised resources, we should stop debating the microeconomic efficiency of government spending so much.

• NBN? Just do it. Spend on the best quality stuff now.
• Airport rail? Just do it.
• Clean our city rivers? Plant trees in the catchment and install stormwater filters now.

Just about anything, even digging holes and filling them in, is better than a 5%+ rate of unemployment with hundreds of thousands of underutilised workers.

High population growth is ideology hiding in economic jargon

Arguments from supporters of high population growth in Australia are that:

• Immigrants are job-creators
• Immigration offsets ageing
• Immigration makes the country richer

Sounds like something our stagnant regional towns could do with more of!

Ignoring the practicalities of internal borders, all of these arguments also favour an “immigrants to the regions” policy. After all, Australian cities are a small global region. Why argue for “immigration to the Sydney region” and not “immigration to the Tamworth region”?

For some reason, this is philosophically wrong rather than impractical, and immigration boosters seem happy to turn their economic arguments 180 degrees when the location of immigration changes.

Also, if you want public support for immigration, don't insult everyone who has concerns about the past decade of record high levels. Has insulting someone ever been persuasive?

Bad economics of the stamp duty discourse

This is an expanded version of an article first published at The Conversation. 

Most think-tanks and policy groups in Australia think that a policy to swap ‘stamp duties for land value taxes’, or what I will call SD4LVT, is going to provide some impressive economic and housing benefits.

But layers of bad economics hide beneath the new populism of SD4LVT.

It is extremely frustrating to me that leading minds in Australian policy have smashed their heads together and decided that the best reform they can think of is to replace one good tax on property with another good tax on property that is less politically popular. It's like subbing off the best player on the field for the best player on the team? Why not sub off the worst player instead?

The better minds in this game don’t even think SD4LVT will have any effect on reducing housing prices or making housing at all more affordable, yet still put it forward as the ‘holy grail’ of state tax reform.

Is this some kind of joke?

The bad economics behind SD4LVT arises because four key points are missing or overlooked.

1. Price effects

The economic incidence of stamp duty is on the seller. This means that if you remove stamp duty, all else equal, prices will rise by exactly the amount of the duty. Good analysts know this, even if they rely on poor analysis by others that assumes stamp duty “increases the price that property buyers pay”. If the average home price is $500,000, and stamp duty is 5%, prices will immediately rise to $525,000 when you remove the duty.

But if you want to replace the same tax revenue from stamp duty with revenue from land value taxes, you could wind up inflating prices and inadvertently creating an economic transfer to property owners.

Imagine an example economy with:

• 20 houses 
• Turnover of 1 house each year (a turnover rate of 5%) 
• Average rent of $25,000 per year (5% gross yield) 
• An average home price of $500,000 
• Land price component of $250,000 per house (total land value of $5 million) 
• Total stamp duty revenue of $25,000 per year. 

To replace the $25,000 stamp duty revenue with a land value tax requires taxing all twenty homes at $1,250 per year each.

Whether the market price of homes rises or falls depends on whether buyers think the upfront present value of $1,250 per year is lower or higher than the $25,000 upfront stamp duty they would have paid. At a 5% capitalisation rate in this example, $1,250 per year in perpetuity is equivalent to $25,000 and the taxes replace each other perfectly with no price effect.

But what if turnover is half that, say 2.5%, which in this example would be where one house is sold every two years? Here, the total tax revenue to be replaced is $12,500, which equals just $625 per house in land tax.

If we capitalise the perpetual cost of $625 per year at 5% we get a present value of only $12,500, which is half the stamp duty rate. A new buyer can now pay $512,500 for the house plus $625 per year in land tax and be equally as well off as paying $500,000 for the house plus $25,000 upfront in stamp duty. The net effect in this case is a land price increase from $250,000 to $262,500, or 5%.

If this situation happened nationally in Australia, that would be nearly a $200 billion economic transfer to landowners, which would completely offset the value of their new land tax obligations.

In short, replacing stamp duty revenue with land tax will increase prices if the capitalisation rate is higher than the turnover rate, and decrease prices if the opposite holds. Price effects are ambiguous for SD4LVT.

2. Asset churn

Taxing capital gains only happens when a transaction is made that realises gains. Thus, it is a transaction tax, just like stamp duty, though the size of the tax is related to historical changes to property values rather than current values.

Many in the housing discourse, such as the Grattan Institute, want to increase transaction taxes on housing by increasing capital gains taxes and expanding their scope to apply to owner-occupiers. At the same time, they want to decrease transaction taxes on housing by eliminating stamp duty because apparently transaction taxes stop people relocating.

Somehow abolishing a transaction tax in the form stamp duty is good because it reduces the cost of owner-occupiers relocating, but then putting back a large transaction tax in the form of a capital gains tax specifically on owner-occupiers is also good. Huh?

There are two problems with the ‘transaction costs impede household mobility and are bad’ view. First is the simplest. People who relocate for work generally don’t buy and sell houses to accommodate that move. 

Moving for work is not a common reason for buying a home, regardless of age, which might reflect a view by some households that work is a temporary reason for moving and therefore not sufficient to commit to homeownership.

Thus, the effect of stamp duty on reducing housing turnover is likely to fall mostly on investors, who make up nearly half of housing transactions and can easily time their decisions. Transaction taxes make it more expensive for people to quickly buy and sell and deters speculative buying that seeks only to capitalise on short bursts of capital growth before selling, which itself fuels the capital growth and accentuates the bust. Many investors fall in this category, and many homeowners also make their location and purchase decisions based on expectations of capital gains rather than housing need. Go on, ask new homebuyers about property prices. See if they bought where they did for housing need or capital growth?

The evidence on stamp duty deterring moving shows pretty clearly that moving for work is unaffected, as the below plot shows, with the main effect being from people making 'short moves' by relocating less than 10km. Indeed, the best evidence on turnover effects estimates only the size of the short-term shock as people adjust to the new price equilibrium.

In terms of the claim lower stamp duty helps more efficiently use the stock of dwellings, as it reduces the costs to older people down-sizing, freeing up large homes for families, the evidence here is the opposite. When the ACT scrapped stamp duty for over-65s, the retirees who utilised the exemption bought bigger homes, rather than smaller ones, requiring them to implement a low value-cap on the exemption. After all, the distribution of home ownership is not driven by housing need, but primarily by wealth. Reducing the cost of redistributing housing by wealth will not suddenly make housing ownership less unequal. 

Regardless, turnover as a whole seems relatively unaffected by stamp duties. The early 2000s boom also saw massive stamp duty increases yet turnover shot up dramatically. The role of stamp duty in these patterns of housing turnover is tiny compared to other factors.

Indeed, if you want people to move more frequently for a lower cost then implement a ‘moving house subsidy’, which could also apply to renters. It would also help renters forced to move unnecessarily due to investor turnover (yes, stamp duty also reduces undesirable moves for renters).

Second, taxing capital gains on owner-occupied housing will be a far bigger transaction tax than stamp duty for most homeowners. Stamp duties are typically around 5% of the property price. For a homeowner who bought a home prior to 2012 in Sydney or Melbourne, their home has appreciated in value about 50%, or to put it another way, 33% of the current price is capital gain.

If a capital gains tax applied to their sale, the tax rate as a proportion of the current value would be:

Capital gain (33%) x Marginal tax rate (45%) = 14%

This is triple the stamp duty for most properties. Even if the capital gains tax was discounted 50%, the nearly 80% of homeowners who bought prior to 2012 would still have much higher transaction costs from capital gains taxes than from stamp duties.

In effect, stamp duties are taxing some of the capital gains during a boom that would otherwise accrue to homeowners, particularly owner-occupiers who are exempt from the capital gains tax. This seems like a good thing to me.

So while stamp duties are often claimed to be economically costly because they deter households relocating, their main effect is actually reducing investor churn, which is a good thing for property market stability. Additionally, the costs of relocating are probably less than the costs from other property transaction taxes that opponents of stamp duty support.

3. Tax revenue stability 

Another concern in the discourse is that “stamp duty revenues are much more volatile than other taxes.” During a boom, revenues rise more than proportionally to prices since they depend on prices and turnover. Vice-versa in a bust.

If I was to think in the abstract about what sort of taxes are good macro-economically, I would say those that are pro-cyclical, meaning they automatically increase tax revenues during an economic boom, and decrease them in a bust. On this metric stamp duty is a terrific tax to help stabilise the economy, which is even more useful in Australia as our economic cycles are closely tied to the housing market.

The land value tax that many propose to replace stamp duty would have smaller automatic stabiliser characteristics, meaning the tax system as a whole would be less stabilising.

From what I can tell the call for more stable tax revenues is not driven by economic reasoning at all. At best it appears to rest on some kind of political preference for state governments not to be involved in macroeconomic objectives.

4. Wrong assumptions 

The metrics of economic disaster caused by stamp duties are usually derived from economic analysis using computational general equilibrium (CGE) models of the macroeconomy. Sounds fancy.

You might have even seen a chart similar to the one below, with stamp duties presented as having massive flow-on economy-wide costs compared to other taxes. In this example, stamp duty is apparently forty times more economically costly per dollar of revenue than council rates, which are also levied on property values.

As you might have guessed, this is absolute nonsense. CGE models cannot account for transaction taxes because, wait for it, there are no transactions in these models. It’s true. Look it up.

But that hasn’t stopped many economists. Instead of using a better tool for the job, they simply invent a different thing that they pretend is stamp duty and put that into a CGE model.

Rather than a transaction tax that is incident on the seller, and therefore incident on land values, a key layer of bad economic analysis assumed this instead (p125).

conveyancing stamp duties are modelled as a tax on investment in residential and commercial structures

Against all the evidence they assume that stamp duty is not a tax whose economic incidence is on land. Instead, they assume that stamp duties raise the cost of housing to all buyers and renters. In addition, the model assumption requires that stamp duties also raise the cost of building new houses without affecting land prices, leading to reduced new housing construction in general. Garbage in, garbage out.

It’s hard to explain in plain English what is going on in these models. My best explanation is that the model says that stamp duty forces every new home to be built with an additional extension on the house, where the construction cost of the extension is the same as the stamp duty. But then as soon as it is built, the extension is immediately demolished, yet somehow the buyer is forced to pay for it. Meanwhile, all the other households in the economy are somehow forced to also pay more to occupy their existing houses, as if they were now renting non-existent home extensions. 

If that sounds crazy, that’s because it is. There is no actual economic evidence of broad economy-wide costs of stamp duties. Just garbage inventions that don’t look anything like stamp duties.

In sum

Stamp duties don’t increase the cost of housing. They do reduce asset churn by investors and speculators, which is desirable for price stability, and the apparent economic costs from reducing household mobility are overblown. The ‘revenue instability’ of stamp duties is actually a huge positive for the economy as a whole, and the economic analysis that has underpinned the talk of the high economy-wide costs of stamp duty is simply made up.

All of this is true. Yet SD4LVT is the apparently the holy grail of Australian tax policy. Our best policy wonks want politicians to use up the precious political capital to swap one very good property taxes for another very good but electorally unpopular one, for no net economic gain.