Monday, May 20, 2019

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.  

Monday, April 22, 2019

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 are the Overview, Executive Summary and Key Findings from 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.
Executive Summary

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.

Sunday, March 24, 2019

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. 

Monday, February 4, 2019

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.