Making sense of property as a monopoly

Let us start by assuming that the property system is competitive and see how far that gets us in making sense of property pricing. After all, there are many different owners of different locations. That seems like what the textbooks describe.

The competitive market of economic theory has a few quirks. At the firm level, the demand curve is flat. Varying your own supply has no price effect. Yet, at the market level, varying supply does have a price effect. The problem of adding up a bunch of zero effects from firm quantity variation to a positive effect at the market level is an issue. It is resolved by assuming free entry at the market price; if a firm decreases its output while all others retain the same output, a new firm will enter the market and sell exactly that amount necessary to get back to the market equilibrium.

Neat, huh?

So let’s get back to it. We have a bunch of property-owning firms that can redevelop their space into housing. Each property owner sees a flat demand curve at the market price (because of our neat assumption) and has a cost curve that looks like...

Well, what does it look like exactly?

In strictly economic terms, the only input to the property right over a location is the existence of a property system. There are no input costs. To develop housing there are of course costs involved, such as fees, construction, and selling costs. But you can net these costs out of both demand and supply to look at the market for “empty” locations, or property rights to locations. After all, new housing supply is merely the subdivision of space—the subdivision of property rights into smaller pieces. The demand curve is still downward-sloping for that property rights market, but the supply curve must sit at zero.

Here we hit our first problem. If we assume perfect competition, we can only have land (location) prices of zero.

This “zero cost of location” view is what Ed Glaeser argues is the right way to think about housing.

…housing is expensive because of artificial limits on construction created by the regulation of new housing. It argues that there is plenty of land in high-cost areas, and in principle, new construction might be able to push the cost of houses down to physical construction costs.

In other words, land prices should be zero everywhere. Any deviation from this is due to regulatory intrusions in the market. Indeed, this implies that the very existence of a land market with trades at non-zero prices indicates it is not competitive. This is a bizarre conclusion in my view.

Maybe we will have more luck making sense of the property system by assuming it is a monopoly despite the many different owners. This has some intuitive appeal. First, you can’t choose to have your locations supplied by a competing property titles system. You can’t run more than one property system in parallel. Can you imagine the conflicts over who owns what space with multiple property systems? Second, the ownership of monopolies is often carved up (subdivided) and owned by many different people who each own constituent parts. Though we usually call these company shares or stocks. 

So what then of the economic theory of monopoly?

As a starting point, a monopoly model avoids the assumption of free entry as the reason individual firms cannot observe their own-supply effect on price. This matches the reality of the private property system as it does not allow free entry. You can only compete in the property market by first buying property from the property market. This seems sensible.

The property market for any use, like housing, therefore looks this at a point in time.

Since costs are all sunk, we can simplify a bit by ignoring the stock of existing housing and look forward in time only. Think about redrawing the axes with the origin at the equilibrium current price and stock.

A change in the housing stock is the supply of new housing. The question of interest is how the stock of housing evolves over time in a property monopoly to determine an equilibrium rate of new supply?

In this model, if demand stays fixed and existing housing does not depreciate, then no new housing is built.

As demand shifts, new supply is added at a rate that maximises the revenue gain from selling those new properties rather than keeping them undeveloped in your balance sheet. It is the same monopoly maximisation principle applied at the margin. In other words, you sell new housing lots at a rate that maximises the present value of that flow of sales.

This logic is shown below. From the t=0 equilibrium, demand shift upwards. The green line shows the marginal revenue from the new supply (i.e. the change in the housing stock, Q, over that period). The new equilibrium is where the revenue from that flow of new property put to housing uses is maximised (i.e. where marginal revenue from that flow is zero).

This equates to a rate of supply that I explain in my absorption rate theory of housing supply (assuming a zero interest rate to simplify the inter-temporal trade-off).

The steeper (more price sensitive) the demand, the less supply responds to the same vertical demand shift. That’s because each property owner is sensitive to their bigger own-supply effect on the market price. Thin markets mean less supply.

Under this monopoly logic, the supply curve for property is not an independently-determined cost curve, but a derived curve based on the slope of the market demand curve.

But how do you get to the monopoly outcome? Is a conspiracy needed?

Not at all. All that is needed is trial and error. Remember, unlike the competitive market assumption of free entry, in the monopoly model, changes in firm output affect market output. If firms start near the competitive rate of supply per period in the face of a sloping (and rising) market demand curve, they will quickly learn to get to the monopoly rate of supply independently.

All that is needed is a learning rule of “win-stay, lose-shift”. This rule says that if the increasing the rate of supply last period increased your marginal revenue, then increase next period, otherwise decrease the rate of supply. If decreasing the rate of supply last period increased your marginal revenue, continue to decrease, otherwise increase.

When I simulate this learning rule with three firms, they quickly converge to the monopoly rate of supply, and hence the monopoly price. This convergence will occur even with 1,000 firms in the market. Property markets have been around for a while now. It seems likely that the current set of property owners has learnt this maximising equilibrium.

It seems logical to conclude that the monopoly model of property makes more sense than a competitive model. Indeed, monopoly was the traditional economic way of understanding property markets. In terms of understanding housing markets and effective policies to reduce prices, I think the following points are key.

1. That the property system is a monopoly shows that the rate of new housing development is mostly a product of demand. Property owners don’t want to build faster. This is because supply is not independent of demand. Supply is merely a reflection of demand.
2. Rezoning and changing planning rules might change where development happens (as it should—they are location regulations after all) but probably won’t change the rate of new supply. The rate is the one the market wants. Developers don’t want to flood the housing market.
3. Making housing cheaper should be understood the same way we understand other monopolies. We regulate prices. We create public options. Pretending that we can somehow capitalise on competitive market forces that don’t exist won’t change things.

Luxury beliefs of YIMBYs and free markets

Yes In My Back Yard (YIMBY) is the belief that more housing density in your neighbourhood is good. But I argue that almost nobody who makes these arguments truly believes them. Mostly, it’s a case of Yes In Your Back Yard (YIYBY). I say that because even property developers who make a living building high-rise apartments hate it when these same types of apartments are built in their street. What about the politicians who impose massive density increases in the suburbs? They hate it when that policy arrives in their street. Sometimes YIMBYs will change their story when density comes to their neighbourhood to one about "missing middle density" or "urban design". Yet if their belief in the economics of more supply was genuine, it should trump these minor concerns.

Former pro-development Brisbane Mayor turns NIMBY when not-for-profit aged care comes to his neighbourhood. https://t.co/pHz65YD4dy

— Cameron Murray (@DrCameronMurray) March 12, 2021

Such hypocrisy also happens with immigration policy. Those who support turbo-charged immigration policies do so because they personally are insulated from the effects. But when those effects come to their own leafy town, they turn their beliefs 180 degrees to be against it. All those apparent benefits used to justify the belief exist only when others bear the cost.

These are classic examples of what Rob Henderson has called luxury beliefs. Things you don’t really believe, but use to signal group loyalty and status. Luxury beliefs are “ideas and opinions that confer status on the rich at very little cost, while the main price is paid by those less fortunate.” 

In economics, I think the efficiency of markets is a classic luxury belief. Even the most pro-free-market economist, like Milton Friedman, are out there speaking for status, rather than reflecting true beliefs. Friedman might even have himself fooled most of the time.

For example, when speaking to a group of Chicago economics colleagues, he “was bemoaning the fact that Chicago price theory was dying out.” One colleague responded “Milton, I thought you believed in markets. It sounds to me like price theory is losing.” At least according to Steven Levitt who said that “even Milton Friedman, in the end, didn’t really believe in markets when markets moved against him” (from 52.00 here).

In fact, markets are the last resort for resource allocation. As a society we really hate markets. We avoid them as much as possible by creating firms to bring market transactions within a hierarchy, and when we have markets, we regulate them heavy-handedly to ensure they function in a very un-market-like way. Only those expressing luxury beliefs who are insulated from the costs pretend otherwise.

I would even go so far as to say COVID policy has been fuelled by these luxury beliefs. Pro-lockdown politicians repeatedly violated their apparently strong beliefs when it came to their personal lives. Perhaps they were expressing status and loyalty with their lockdown comments, not their genuinely held beliefs.

The fact that people say things that contradict their actions is one reason why many economists are sceptical of survey responses. You can ask people “is it a good idea to build more dense housing in this neighbourhood” but then those who agree will end up spending thousands on lawyers defending the status quo situation in court. Their response was not genuine. It was a luxury belief. Better to ask survey questions that are more personal, like "it is a good idea to build high-density housing next door to your house?"

What can be done to weed out luxury beliefs?

One thing is to align decision-makers with the costs of policy change. Put them personally on the receiving end of the costs of the policy and you will see their true beliefs emerge. High immigration and housing density in the suburbs where politicians live needs to be a prerequisite for the same policy elsewhere. Alternatively, we could randomise the decision-makers through sortition—a type of random jury system to become a politician—ensuring that those who bear the costs of a policy are part of the political process. 

Evil rent control revisited

1/8 One year ago, the left-leaning government of Berlin introduced unprecedented rent controls.

Lots of economists warned against it.

So what do the numbers say now?

A thread. https://t.co/wIDm9awhre#Mietendeckel @bopinion

— Andreas Kluth (@andreaskluth) March 2, 2021

A new report is out looking at the Berlin experience with rent control (original German report here). The basic idea is that dwellings built prior to 2014 now have regulated rents and caps on rental increases. All very standard price-cap regulation. New dwellings are not regulated. 

What I find funny are the contortions that economists make to explain good things as evil things because of their cultural taboo about rent control. With the help of another recent paper on rent control, I want to straighten out some of these contorted arguments.

Rents in the unregulated market don’t fall. This is evil. 

High rents are bad. This is the foundation of the claim that rent control is bad. Yet the enormous benefit of rent growth at a rate 60% less for roughly half of all households in Berlin is ignored and downplayed.

There aren’t many other policy interventions that get this size outcome. Even doubling home building for a decade is at most going to see a 10% relative rent reduction in a decade's time, which in terms of growth rates would be a tiny effect. This rent control case study appears to be one of the most successful affordable housing policies I've ever seen.

Even if you assume that market rental increases of the past year are related to rent-control policy (rather than being a continuation of previous market trends, which my eyeballs tell me is the case, especially in the original report chart with error bars marked) the welfare calculation is hugely in favour of rent control.

The basic welfare calculation is:

With a 0.5 share of households renting pre-2014 buildings with rent rising 2% per year instead of, say 5%, saves each household about half of the current year’s rent over 5 yrs. That’s 0.25 “city rent years” of benefit to residents.

With a 0.05 share of households relocating each year paying at worse 0.08 more for their rent, we get 0.027 of “city rent years” of cost to residents over 5 yrs.

On pure "high rent is bad" benefit-cost terms the Berlin experience got a 10x economic return! This even assumes that all private market rental growth is due to rent control, which is implausible (more on new supply later).

You can’t just pretend that these orders of magnitude are similar.

Additionally, the macro-economic effects of additional non-housing renter spending in the local economy must be quite large. This is a diversion of half the rents in the city from landlords to tenants, with the downwards income distribution likely having large spending effects.  

Landlords sell to homeowners. This is evil.

“Landlords treated by rent control reduce rental housing supplies by 15 percent by selling to owner-occupants and redeveloping buildings.”



I find this statement hilarious. 

Landlords sold to owner-occupants. This is a good thing, not an evil thing. 

Many policies are aimed at getting more homeownership and the best way to do that is for landlords to sell to owners (even the tenant perhaps). 

If they do this, there is no effect on the supply of dwellings. This is because the former renter who buys the home is now also no longer a renter. It’s a minus one from the supply AND a minus one from the demand. 

Redeveloping buildings increases new housing supply. This is evil.

The whole economic schtick about rent-control is that it decreases new housing supply. But both the Berlin report and the Diamond et. al. paper show this is not the case. 


Diamond shows that rent-controlled housing is roughly 5% more likely to get additions and alterations, and 7% more likely to be redeveloped. But I thought rent control meant that landlords won’t invest in their buildings anymore? The charts below show these effects over time.

In fact, there is a whole movement of economists and urbanists who are itching for ways to get more housing supply because they believe it helps reduce the cost of renting in prime areas. Maybe try rent control?

Turning now to the supply of unregulated rental housing advertised in the market, it looks from the Berlin study that new apartment listings are outpacing peer cities. But this is labelled evil. They are "only slightly outpacing" other cities. Terrible!

Rent control stops poor people from being displaced. This is evil.

The final strange contortion happens in the Diamond paper when concerns over low supply (now invalidated) turn into concerns over gentrification and the rapid new supply of luxury market-priced housing. That rent-control works to stop the displacement of poor people during this process is then labelled as evil by saying that it widens income inequality. 

…it appears rent control has actually contributed to the gentrification of San Francisco, the exact opposite of the policy’s intended goal. Indeed, by simultaneously bringing in higher income residents and preventing displacement of minorities, rent control has contributed to widening income inequality of the city. 

The problem with rent control is that it works. This is evil.

The real issue is not that rent-control is an economic failure. The zeal with which the theoretical economic case against it is made, regardless of the evidence, is necessary because rent-control works. Without an economic story for why rents should not be regulated, all those landlords would constantly face the political risk of losing billions in rents. 



One argument that I’ve heard is that economists have tricked themselves with their own supply and demand theories. But I think that’s wrong. I argue that the theory is used to back-fill a convenient political story. I say that because supply and demand logic can just as easily be used to show why rent-control should be a huge success with no changes to investment incentives around new housing. 

Let me show you. In the first supply and demand diagram below I include total housing supply and demand, with a kinked supply curve to represent the existing stock of dwellings that don’t get demolished because prices might fall. This the way Ed Glaeser describes housing supply curves.  

All we then do is split the demand curve into existing and new demand. We subtract out the tenants of existing houses from new demand, and their existing homes from new supply, and are left with the following, where the dashed blue line is the demand curve for existing housing and the solid blue line is the demand curve for new housing. It’s the same equilibrium. We merely cut off all demand and supply to the left of the market equilibrium and put it in the regulated system.

The economics of rent control is evil.

The taboo topic of rent control is so screwed up that the evidence it works must be contorted into something evil.

And that's all folks. 

Merit doesn't cause income

In a podcast interview with the terrific Joseph Walker, geneticist Robert Plomin said something that stuck in my mind—"in a meritocracy there will still be large deviations of income due to genetic variation in abilities."

I also stumbled onto an economics blog about capital taxation that relied on the idea that high capital incomes come from previous savers who therefore deserve that higher income (I can't seem to find it again, but I'm sure you've heard the idea before). To bring Plomin's comment into the mix, we could say that if genetics determines time-preferences or risk-taking attitudes, then capital incomes are also determined genetically. Taxing those capital incomes is therefore taxing the "merit" of saving.

Like much of the economic and political debate about poverty and inequality, these ideas rely on a flawed understanding of how income is distributed in human societies. That flaw is to pretend that there is something called "merit" or "value" (or "productivity" for that matter) that is independent of the ability to be rewarded via payments.

Think about it like this. We often look at the characteristics of people who earn more money—their education, their patience, their leadership skills, their attractiveness—and then label those attributes that correlate with incomes as "merit", or "value", or "productivity". This approach merely defines "merit" based on current income. If "merit" causes incomes, there must be a way to define and measure it independently before looking at any correlations.

Imagine LeBron James was born in 1784. His genetic gifts are identical—his athleticism, work ethic, and his overall intelligence on every dimension. His genetic "value" or "merit" assessed without reference to his income is identical to what it is now.

But society at that time would be structured in a way that his genetic gifts would make him a high-value slave. His personal reward would be the pleasure of working hard manual agricultural tasks for longer, and the slave owner would get the income for having the "merit", "value", and "skills" of cultivating high productivity slaves. (Do you see the parallel with capital owners generally? Their "skill" and "value" is choosing investments. In practice, that means choosing what tasks other people do when they work for their income.)

Here's another example. In-breeding of royal families historically led to genetic variation that made many of them useless in the productive activities of the day. Despite this, their incomes and power remained nearly identical to their genetically superior siblings. If our social structures mean that "merit" determines income, and "merit" is heavily genetically determined, this should not happen.

In fact, if you follow the logic that genetic variation causes merit variation, which causes income variation, then genetically diverse places should naturally have more inequality. That "homogenous Nordics" argument rears its head again. Compression of genetic variability or some other attribute we have labelled "merit", like education, is therefore seen as the best way to compress the income distribution.

But in reality, the Nordics compress their income distribution through welfare state policies. Their market income distribution is much the same as peer nations. They merely choose to create a social structure that keeps the income distribution more compressed regardless of any underlying variation in individual attributes, genetic or otherwise.

We also know that income and wealth inequality has grown tremendously in the past four decades. This has nothing to do with changing genetic variation. It has nothing to do with a changing distribution of an independently-assessable concept of "merit". School teachers make lower relative salaries today because we chose not to raise their salaries faster. Some countries didn't do this, and their school teachers make more money. 

Some of you might be thinking that places with high-income school teachers have better teachers. But you are again failing at basic logic by reversing out "merit" from income. 

We know that increasing incomes actually causes changes in personal attributes that are usually labelled as "merit" and assumed to be determined independently from income. Basic income experiments show that higher incomes cause people to become more confident, trusting, healthy, less depressed and more able to concentrate, for example.

It seems logical to me that if there is no such thing as an independent concept of "merit" or "value", then we should simply choose a more compressed income distribution in society rather than the unequal one we have chosen. That more compressed distribution will help avoid the pitfalls of high inequality by ensuring that regardless of variation in genetics or "merit", people can live comfortable lives and take opportunities that low incomes often prevent them from doing. 

In practice, this comes about by setting an income range via the tax and transfer system. Very high marginal tax rates on high incomes, coupled with generous unconditional payments for low-income households, will compress incomes.

Perhaps the minimum income could be roughly $20,000 per person (with variation depending on household size and type), with a maximum of $2,000,000 per person. That leaves a 100x income range within which market-based incentives can operate. I see no reason why the range of variation should be any higher than that. Can individual merit, however defined, really explain a 1000x variation in income? Will some people not go to work because they earn $8,000 per day instead of $20,000 per day, even if there are no $20,000/day options because of the tax system? I doubt it. People won't even see a change in their rank within the distribution.

Income compression should be the focus of policies for dealing with poverty and inequality. Ideas that rely on "merit", "value", or "productivity" are going to be failures as they implicitly (or explicitly) define merit by income. 

The Henry George logic of wages as an economic rent

George was concerned that property owners gained all the economic rents. But his logic relies on a quite arbitrary and limited concept of property rights.

We know, for example, that human slaves could historically be the exclusive property of another person.

By George’s logic, the economic gains to human-type property owners would be an economic rent, just as the gains to location-type (or land-type) property owners are an economic rent.

From the viewpoint of property and economic rents, freeing slaves transferred a property right, in the form of the right to choose labour tasks and receive payment for them, from the slave owner to the slave.

Thus, the wages of freed slaves are the economic rent from the property right they now own. The owner of human-type property still gets the rent. It is just that each person became an own-slave-owner.

Hence, when modern Georgists say things like “all taxes come out of rents” they are just saying “all taxes come from property rights owners who can capture the economic surplus.” Or simply, “all taxes come from someone.” 

But because the understanding of property rights is limited mainly to location-type resources, not human and other resources, they miss a big part of the economic picture. While it is certainly the case that owners of location-type property need not labour for their share of economic gain, changes in the distribution of the ownership of location-type property can spread those gains more widely, just as a change in the distribution of slave-type property owners spread the gains from human labour. 

George's idea of a tax on the value of location-type property is a good way to socialise the gains from concentrated land ownership. In the modern era of high top labour incomes, taxes on the high value of these human-type property rights might also be a good way to socialise some of these economic rents.

The balance of wages, profits and rents, are not a product of physical aspects of production, but rather than property rights distributions. George somewhat indirectly makes this point when he writes:

Where land is free and labour is unassisted by capital, the whole produce will go to labour as wages.

Where land is free and labour is assisted by capital, wages will consist of the whole produce, less that part necessary to induce the storing up of labour as capital.

Where land is subject to ownership and rent arises, wages will be fixed by what labour could secure from the highest natural opportunities open to it without the payment of rent.

Where property rights to land are limited (i.e. land is free), wages and capital owners get all the surplus. As one would expect, only those with property rights can get a share of the surplus. If then property rights to capital were removed, or limited, then all the gains would go to labour. We can get any economic distribution we like with different sets of rights (aka rules). 

The relationship between George's insights on rents and the concept of property rights need more careful consideration in the modern world where the evolution of property rights tends to be invisible to many economists and policymakers. 

Downs-Thomson housing paradox

Anthony Downs and various others made the observation that “the equilibrium speed of car traffic on a road network is determined by the average door-to-door speed of equivalent journeys taken by public transport.”

Though known as the Downs-Thomson (DT) paradox, it is not a paradox at all. It is merely an observation that people adapt their behaviours to road network changes that affect the cost of driving.

The basic idea is that there are three margins from which substitution towards peak hour use of new freeway capacity occurs.

1. many drivers who formerly used alternative routes during peak hours switch to the improved expressway (spatial convergence), and
2. many drivers who formerly travelled just before or after the peak hours start travelling during those hours (time convergence), and
3. some commuters who used to take public transportation during peak hours now switch to driving, since it has become faster (modal convergence).

This is a simple application of the logic of substitution in microeconomic consumer theory. If an alternative has a lower economic cost for the same value gained, people will substitute towards the lower-cost alternative. Consumer choices are what enables markets to select for the more efficient producers and mix of goods and services over time as they respond to relative prices. 

In the DT example, the three alternatives and the peak hour freeway use start at the initial equilibrium. Prior to the new freeway, all the transport alternatives are taken up to the degree that the total cost (time/money/convenience) is equalised across them.

The cost of travelling on an alternative route, or at an alternative time, or using an alternative mode, is roughly the same as the cost of peak hour freeway transport. It must be because if it wasn’t there would be a substitution towards the lower-cost option.


If you reduce the cost of one of these alternatives what happens? That option begins to look relatively attractive, and people substitute from the other alternatives until there are no remaining gains from substitution.

Afterwards, the new equilibrium looks like this. I’ve put a dashed line at the old equilibrium to show that there can be overall gains from expanding transport capacity, even though they are not observed in the option that received the investment.[1]

How much below the previous equilibrium the cost profile of the transport alternatives remains depends on how much these costs can adjust downwards and the preference for substituting towards non-transport uses of time and money.

For example, if alternative transport modes have a fixed cost (e.g. taking the train in peak hour has a fixed price and time cost that does not change based on usage), then the new cost equilibrium will occur at the same level as previously. That fixed cost of the alternative mode will anchor the equilibrium time and cost level at which these options equilibrate.

This is what is meant by the observation that “the equilibrium speed of car traffic on a road network is determined by the average door-to-door speed of equivalent journeys taken by public transport.”[2]

Similar margins of substitution happen in housing. Housing is another spatial allocation problem where the cost involves a trade-off between price and time costs amongst alternatives.

Location alternatives are one such margin. Total cost equilibrates, via rent adjustment, between comparable dwellings at alternative locations based on relative transport/accessibility costs.

When the transport/accessibility cost of a location falls, the rent in that area rises so that there are no gains from substitution between locations.



Another margin of substitution in housing is between modes, like renting and buying. When the cost of some buying goes up, it can drag up the cost of renting as market participants close the gap between buying and renting. Alternatively, if the cost of renting falls, it tends to pull down the cost of buying.

What if there is another alternative housing mode? Say, a social or public housing option that has a fixed price. In this case, the point at which there is no substitution between them is where they all have the same economic cost.

If the cost of public housing falls, this puts pressure on the housing equilibrium. Homebuyers and renters begin substituting to this cheaper alternative, just like the case of the new freeway in transport equilibrium.

This substitution process continues until there are no gains from substituting between housing modes. Public (non-market) housing alternatives can create an anchor for prices, just as public transport alternatives anchor congestion levels.

One could argue that “the equilibrium price of housing in a private market is determined by the average price of equivalent public housing.”

Our policy choice not to extensively provide cheap housing alternatives has allowed housing prices to be anchored by the maximum willingness to pay in the market rather than the cost of public housing.

The DT paradox is why places with low traffic congestion are usually those that have good alternatives. It is also why places with cheaper private housing markets have cheaper and more widely available non-market alternatives.

[1] This is actually how monetary policy works. The overnight cash rate is manipulated, and then all the alternative interest rates shift because there is a substitution between the overnight rate and long-dated assets.

[2] The D-T paradox has implications for pricing choices on transport networks. For example, peak-hour congestion charging will raise the cost fo driving, creating substitution to alternatives, including the alternative of not travelling. When a new rail line is built to relieve pressure on roads, tickets must be priced low enough to attract people towards that alternative and away from road travel. If new rail lines are privately owned and earn a return from ticket sales only, they have an incentive to maximise their own revenue, but this may involve a ticket price that is too high to maximise overall transport gains by attracting more substitution away from road travel. New toll roads also are likely to over-price compared to the optimum for the network as a whole that would see more substitution to these new roads.

The COVID story is ideal political cover, regardless of the truth, which never mattered

NOTE: This post was written in November 2020 for a magazine that later decided it was too controversial. Some notes to reflect new information are included. 

What will I say to Mum?

When news of the first dozen COVID cases was broadcast my Mum panicked. Facts could not persuade her about the true scale of the disease. It didn’t matter what proportion of deaths COVID was responsible for, nor the chance of even more deaths from our policy reaction. It was “bad news” and “something must be done”.

So when we gather for Christmas the conversation will not drift towards this elephant in the room. The implications and outcomes of the biggest world event for decades will go unspoken when we normally reflect on the major events of the year.

COVID has introduced another tear in an already torn social fabric. No longer can we freely discuss the merits of our policy reaction, the cost of it, the human toll. You are a COVID denier or a COVID believer. There is nothing else.

I can predict that you, dear reader, are already judging me based upon the previous 140 words. That is the power of a story in human society; especially a scary one. The COVID story has changed your perception. It has changed my own conversations with my own family. The biggest effect of all—one that will be felt for years—is that this story will cover up the exorbitant exercise of political power for the benefit of political mates. It is through this mechanism that the story of COVID, not the reality of it, will cost the world dearly.

Stories and power

One of the crucial ingredients to exercising political power in favour of one’s own network is having a good cover story. One of my big mistakes in understanding the global response to COVID early on was to underestimate the power of the story. I predicted that the huge economic and health cost of locking down human society would be obvious within the first months of it occurring. I thought the story would change so that we would avoid the huge health costs of lockdowns.

What is truly scary is that the enormous costs of lockdowns are now obvious, but the lockdowns continue. Most frightening of all, most people want them!

Lockdowns are now a politically expedient response to a story that has been more infectious than COVID itself.

Partly, the use of lockdowns continues because it mostly affects the working class. The elites are insulated from this policy, at least to a degree. The main countervailing force is the small degree to which lockdowns apply to the political elite. Although politicians gain power with lockdowns and aggressive strong-man responses to COVID, like border closures, they must also personally suffer some of the consequences. That is why many elites flaunt their own rules, revealing that they are not creating and enforcing lockdown rules because they believe them to be effective, but because they understand the politics.

Harnessing the power of the scariest story yet invented

The obvious next move for the political elites is to capitalise on the virulent COVID story by transforming it into a justification for more substantial political power plays.

Normal oversight of major economic decisions is now almost non-existent, meaning favours for mates can be slipped into COVID-response policy decisions such as cash grants and subsidies to businesses who are unaffected, or rezoning and dodgy land deals. Indeed, the usual financial engineering of the central banks in response to economic shocks is mostly a giveaway in terms of insuring the risk of financial corporations.

Since first writing the above sentences the US passed the COVID stimulus bill, a monster of a document bundled together with various appropriations that seems to be written by lobbyists and contains the exact giveaways and law changes that further consolidate power.

Those who fuelled the fear should be ashamed. This was the inevitable result. Does fear ever provide the environment for the dilution of power and the sharing of wealth and prosperity? No.

2021 predictions

Note: These were first written on 21st November 2020. Some updated comments are added.

1. News media and big tech need to sell advertising, and fear sells. They will publish any story that is bad. Even vaccine news will have headlines like “The vaccine is 90% effective, but there’s a hidden catch.” Other routine medical and health data will be linked to COVID and made newsworthy to capitalise on the fear story.
2. Many useful idiots in cushy jobs desk jobs have found life to be better with COVID. Because of this, they will be subconsciously motivated to find reasons to keep the new economic order of extensive control over the lives of others. Keep an eye out for that.
3. New unrelated reasons to continue to be scared will emerge. The virus will mutate, and when the next strain arrives, the media will unleash fear-mongering headlines. Those in the game will promote it. When there are vaccine side effects, these too will be excessively reported. (Note that I wrote the previous sentences a month BEFORE the UK mutation and any vaccination approvals.)
4. Rebuilding the economy will be the cover story for huge giveaways to mates. It already has been in many ways, with untargeted cash programs, and fast-tracked planning approvals, and more. These will continue into 2021.
5. The result of this will be a rapid economic recovery. Lockdowns will mutate into sheer political theatre and behaviour policing, with limited effects on most economic production. As these lockdowns pass, macroeconomic performance will be supercharged by the huge fiscal response seen in many countries. The housing bubbles of the UK, US, Canada and Australia will continue for a few more years.
6. The battle of writing the history of 2020 will be fierce. Mainstream censorship of voices that reveal how irrational and costly the policy panic was in terms of human lives and livelihoods will continue. Sensible analysis will be shut out of social media distribution channels and even out of universities where debates and analysis are expected to take place. Technology companies have a huge incentive to police this official story because of benefits to them from the world now relying on them to an unimaginable degree. 

What are your thoughts? Just remember, the truth doesn’t matter. All major institutions will have to conform to the narrative that gets written by the powerful. As I have said before, no one in power will ever want a cost-benefit analysis of their policy choices—what is the incentive to prove yourself an idiot to the world? If anything, there will be some reports written that have obviously implausible counterfactuals and these will become the standard reference point for the official writing of the history of 2020. The story and the fear will live on through these. It will be decades before the true lessons are learnt. Such is the power of the story.

2020 was an economic lesson about insurance

My report on superannuation came out at the beginning of 2020. One of the main arguments in that report is that Australia’s superannuation system is not a retirement income system because it cannot perform an insurance function.

I didn’t know then that how we handle insurance at a society-wide level would be the most important economic policy lesson of 2020.

To explain what I mean, I must first differentiate between insurance in the financial sense and insurance in the economic sense.

Financial insurance smooths balance sheet variation when specific low-probability events occur. It works because any one person or group faces idiosyncratic risks so that pooling many people in an insurance system allows it to pay out for those few events from the funds collected from others.

But economic insurance is much different. Economic insurance deals with real resources, not financial ones.

A financial insurance system is an economic insurance system for any individual. As long as an individual’s loss of real economic resources—buildings, vehicles, equipment, stock—from an insured event is small relative to the system as a whole, these real economic losses can be easily replaced from the production of others without widespread economic disruption.

But when losses from an event are large relative to the productive economic system as a whole, simply repairing the balance sheets of individuals with cash payments will not overcome the resource loss that will affect many individuals across the system as a whole.

Consider a small island community who eat 100% of the food they grow to survive—there is not one bit of waste. If a cyclone destroys 50% of their food crops for a season, it won’t matter what sort of financial insurance systems they have in place. Their ability to feed themselves this season has declined by 50% and the loss will be felt across society, not just by the farmers whose crops were destroyed. Financial insurance cannot provide economic insurance for this type of event. The island community must suffer the loss of food and nutrition regardless of whether they can repair their financial balance sheets.

The reality of economic insurance is why I have argued in the past that a food production system that wastes a large portion of the food grown and cultivated is a type of economic insurance that provides a real resource buffer against large unforeseen shocks to the food supply system.

The military also has the features of economic insurance. Why employ tens of thousands of troops when your country is not at war? Why build and maintain ships, tanks, submarines, and jet fighters during periods they are not needed?

The answer is that financial insurance cannot stop the real resource losses from war. You must have some form of economic insurance in place with a real resource buffer being produced just in case of war. Often that requires spending years or decades devoting substantial materials and manpower to maintain a military with nothing to do.

So why is economic insurance the big lesson of 2020?

Because the health system in most countries is not run like an economic insurance system against large health shocks, even though it is clear that financial insurance will not help deal with the next pandemic. Hospitals are incentivised to run at, or near, full capacity at all times, even though health needs fluctuate, sometimes substantially, just as we have seen this year.

If we ran the health system like we do our military, or our food production systems, we would maintain a large buffer of real health resources. That would mean hospitals with surge capacity and staff maintaining them. It might involve, for example, international joint health operations to practice developing and distributing drugs and medical equipment in case of emergency.

If we ran our health system as an economic insurance system, like we do our military, that would mean an enormous expansion of health services generally. As well as being maintained as a buffer in case of emergency, these additional healthcare resources could be used for training and treating less serious health problems.

What has surprised me in 2020 is that few people are looking at the way we manage health resources and why the healthcare system is not designed as an economic insurance system like our military.

Australia's out-sized COVID stimulus

After the global financial crisis of 2008 the Australian government responded with a large fiscal stimulus.

Initially worth about $10 billion, it soon grew by $42 billion by February 2009. Out of this total about $21 billion came in the form of direct cash payments to households. 

At the same time the RBA reduced the cash rate, which flowed through a decline in variable rate mortgages from nearly 9% to around 6%. That 3% interest saving on the balance of mortgages at the time was worth around $30 billion per year. 

In total, we are looking at about $70 billion in total economic stimulus.

This scale of this stimulus effort was widely regarded to be appropriate for the circumstances of a large global macrocycle shock. 

So how big has the 2020 stimulus spending for the global COVID shock to the economy been compared to this reference point?

Early estimates of the combined fiscal and monetary stimulus were around $180 billion, or about 2.5x larger than the 2008-09 Rudd stimulus. 

On the fiscal side we have around $150 billion. 

• Initial welfare boost = $18 billion
• JobSeeker and JobKeeper and second welfare boost = $66 billion
• Business cashflow boost = $32 billion
• Early super release = $35 billion

We also have state governments increase spending and subsidies across the board, easily totalling $12 billion.

On the monetary side, we have again around $30 billion in annual interest savings to mortgage holders.  We also have the RBA intervening to lower all sorts of interest rates across the board. 

Just these items get us to nearly $200 billion, most of which came in the form of cash payments. In cash terms, rather than total value of government spending terms, the COVID stimulus measures are enormous. 

Instead of $21 billion in cash payments we are looking at nearly $130 billion in cash payments. 

Since there have been few spending options for all that extra income, the household savings rate has boomed. Households have spent $121 billion less than they earned so far this year. We can see just how much the income to households from stimulus spending has exceeded declines in incomes in the chart below. The orange bars are how much non-labour income, which includes government payments, contributed to growth in total household income. In short, disposable incomes are up massively and the main reason is the stimulus cash given to households. 

This out-sized stimulus package will have lasting effects throughout the next cycle. We have loaded up households with spending money. We have decreased the interest costs on their mortgages and encouraged them to borrow more money. We have turned off the immigration tap, meaning labour market demands can be more effectively channelled towards wage and salary growth.

To me, it seems obvious that the only thing that can happen is a massive economic surge coupled with rising asset prices. There is even a chance of a "surprise" rise in inflation. 

All of this could be seen in advance. Back in May I argued that house prices are more likely to rise than fall, and that most people had under-estimated the scale of the stimulus. 

Here's an interview were I explained my reasoning. 

The sale price of housing is not its economic price

The sale price of homes is not the economic price of housing. Prices, in the economic sense, are the relative cost of consuming newly-produced goods and services.

The economic price of housing is therefore the rental price. It represents the price of being housed today. The rental price is what goes into consumption price indexes. It goes into the national accounts to represent housing service production. It is what standard economic theory says is the price of housing.

The sale price of a house represents the purchase of a perpetual stream of housing services. In essence, you are buying the right not to have to rent a home from someone else.

Why is it important that the sale price of a house is not its economic price?

Because sale prices of housing can rise while their economic price falls. The economic price of housing translates into a sale price via other factors.

So how is the sale price of housing determined?

The asset pricing model 

The sale price of homes is determined in much the same way that the price of other rights to future income streams; the asset pricing model.

Converting the value of a flow of uncertain future income flows into a capital value today is an imperfect procedure, relying on estimates of risk, uncertainty and growth prospects. But we can for now ignore these judgements to demonstrate how the ingredients that create an asset price from the value of an income stream fit together.

Housing asset prices can be reflected by the following formula.

Asset price = (gross rent - ongoing costs) / (risk-adjusted return + property tax rate - growth expectations)

There are hence five ingredients in asset prices, of which only one is the economic price in the form of the annual rent. This is why housing asset prices can diverge so significantly from rental prices.

Let us go through each ingredient.

• Gross rent is the total amount a renter would have to pay to occupy the home for a period. It is the economic price of housing. 
• Ongoing costs include upkeep of the house—the maintenance required to sustain its current quality over time (depreciation)—as well as other ownership costs such as taxes and fees that are not levied as a fixed rate on the property value. 
• The risk-adjusted return is the rate of return buyers are will to pay for that property based on their assessment of market alternative investment returns, and the relative rate of risk of buying this house. For example, if you can get a 5% return on you money in the bank from interest, and buying this house is seen as much risker than bank deposits, then this return will be something above 5%, say 8%. 
• The property tax rate is the rate per dollar on the total property value that is required to be paid in taxes. In most US cities and states, property taxes are levied on the full asset value of the house and land, whereas in other places like Australia, these taxes are levied only on the land value component. I have adopted the US approach here for simplicity. 
• Growth expectations reflect the rate at which much buyers expect rents or prices to rise in the near future. For example, you may buy a house to avoid paying $20,000 per year rent, but rents might be rising at 5% per year. Next year, your current house purchase saves you $21,000, and it saves you $22,000 the following year. 
• We are going to simplify this model for now by pretending that growth expectations are zero. This avoids the part of the recipe that leads to big swings in the value of housing that usually self-correct over time. We will also pretend that the risk-adjusted return is best captured by the prevailing mortgage interest rate. Once you see the logic if the model, you can easily tweak these assumptions yourself to see their effect on asset prices. 

The cost of buying vs the cost of renting

Here is an example of the asset pricing model in action. A house has the following characteristics.

Rent is $20,000 per year
Ongoing costs are $5,000 per year
Mortgage rates (risk-adjusted return) are 6%
The property tax rate is 0.5%
Growth expectations are zero.

The sale price of this home will be roughly $231,000 according to the asset pricing formula [(20,000-5,000)/(0.06+0.005) = $231,000].

What this means is that you will be equally well off economically—i.e. you will spend the same each year—if you borrow the full house price amount and buy it as you would be renting it. We can add up the annual costs of buying to seeing that it is the same as renting.

Interest = $13,850 ($231,000 x 0.06)
Ongoing costs = $5,000
Taxes = $1,150 ($231,000 x 0.005)
Total = $20,000

You might notice that I have only considered the interest cost of a mortgage, not the total repayment. But paying the principle of the house is not an economic cost. It is an asset investment just like paying listed equities is an investment, not an economic cost.

Our homebuyer in this situation is paying a $20,000 economic cost of housing. But if they pay the principle of their loan over 30 years, the mortgage repayment is $16,780 per year, or $2,930 more than the interest alone. Their out-of-pocket expenses are $22,930, but that include the purchase of $2,930 of housing equity each year.

The effect of low interest rates 

Most of the change in housing sale prices over the past decade are not due to the economic price of housing as the chart below shows. Rents have been flat in Australia, and in many cities globally, while sale prices have grown enormously. 

If recent sale price changes are not due to rents, then they must be due to one of the other ingredients in the asset pricing model.

We can eliminate the ongoing costs of upkeep. Construction costs have been steady, and fixed fees and charges also have seen little variation. Property tax rates are also relatively unchanged, at least in Australia. Note also that higher property taxes reduce asset prices.

We are now down to two factors—the risk-adjusted return and growth expectations.

It could be that buyers of housing have increased their expectations of rental growth. Though after a decade of flat or falling rents, rental growth expectations should certainly have been curtailed, rather than increased.

This leaves only the interest rate. To me, this is the big story for housing prices over the past two decades globally.

We can take our previous example dwelling where we calculated its value to be $231,000 when mortgage interest rates were 6%. Now, mortgage interest rates are closer to 2%. At what price does buying cost the same as renting at this much lower rate?

Again assuming zero growth expectations, our asset pricing formula is (20,000 - 5,000) / (0.02 + 0.005) = $600,000. That’s a 160% increase in sale price, while the economic price of house remains unchanged.

We can check this calculation to ensure that the economic cost of buying at this price remains the same as before.

Interest = $12,000 ($600,000 x 0.02)
Ongoing costs = $5,000
Taxes = $3,000 ($600,000 x 0.005)
Total = $20,000

Notice that the interest paid on a mortgage is actually lower in this case, though the annual taxes are higher due to the higher sale price.

What about property taxes? 

At lower interest rates the effect of property taxes on asset prices increases. Let us compare the sale price of our example house in two different jurisdictions—one with a property tax rate of 0.5%, and one with a property tax rate of 2%.

First, we can see the effect of property taxes in our high 6% interest rate scenario. Recall that the asset price with a 0.5% property tax rate was $231,000. If we now put a 2% property tax rate in our asset pricing equation, the house is worth $187,500. Compared to this high-tax region, the asset price of the same house in a low-tax region will be 23% higher. That is a huge difference. Yet the economic price of housing is the same.

In the low interest rate scenario our house was worth $600,000 in the 0.5% low property tax area. If we plug in a 2% property tax rate and a 2% interest rate we get a value of $375,000 in the high-tax region. The sale price is now 60% higher in the low tax area for the same economic price of housing. The low interest rate environment amplifies the price effect of different property tax rates.

We can see the components of the economic price in the high property tax area in the low interest rate scenario below. 

 
Interest = $7,500 ($375,000 x 0.02)

Ongoing costs = $5,000
Taxes = $7,500($375,000 x 0.02)
Total = $20,000

We can therefore use the asset pricing model to predict that the recently-announced reductions in property tax rates in Harris County (Houston) will fuel price growth in the current low interest rate environment. We can also predict that states like Texas, with comparably high property tax rates (often 2% and above) will have even greater housing asset price divergence compared to regions with low (sub 1%) property tax rates.

Predicting strange price differences

Strange house price differences make more sense through the lens of asset pricing.

Here is an example of the types of strange price differences commonly noted. Here are two houses where the asset price seems unrelated to the quality or size of the dwelling.

House 1

House 2

Can asset pricing make sense of the fact that the much smaller home is worth nearly double the much larger one?

It can.

We can even acknowledge that the economic price of the larger home is much higher, despite location difference. But this does not have to translate into a higher asset price.

The large home, House 1, might have the following characteristics.

Gross rental per year: $40,000
Property tax rate: 3%
Ongoing/upkeep per year: $10,000

The high upkeep costs (aka depreciation) come from its size and features, such as the pool. Notice that property taxes are around $15,000 per year for this dwelling, which is over 3%.

The smaller home might have the following characteristics.

Gross rental per year: $30,000
Property tax rate: 0.8%
Ongoing/upkeep per year: $5,000

Notice the much lower property taxes and upkeep costs. This has a big effect when converting the economic price into an asset price.

I’m going to apply a 2% mortgage interest rate to the asset pricing formula to show what price to expect for these two homes.

House 1
Price = $40,000 - $10,000 / (0.02 + 0.03) = $600,000

House 2
Price = $30,000 - $5,000 /(0.02 + 0.008) = $893,000 

Although the economic price, the rent, is 33% higher for House 1, this doesn’t translate to asset prices. In fact, House 2 is worth nearly 50% more than House 1 under these conditions (my price estimate is a bit on the high side for both as I’m using a round 2% and ignoring any difference in other asset pricing factors). 

Remember, at these prices, each house has the same economic price for buying as it does for renting. House 1 also has an economic price 33% higher than House 2. 

House 1
Interest = $12,000 ($600,000 x 0.02)
Ongoing costs = $10,000
Taxes = $18,000($600,000 x 0.03)
Total = $40,000

House 2
Interest = $17,900 ($893,000 x 0.02)
Ongoing costs = $5,000
Taxes = $7,100($893,000 x 0.008)
Total = $30,000

If you are not looking at housing sale prices through an asset pricing lens, these strange house price differences will only seem to get worse as we enter a super-low interest rate period. Many people will mistakenly attribute the difference in asset price to physical and regulatory factors, like zoning. But if these factors do affect housing, the must do it through their effect on the economic price, the rent, not the asset price.
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