Artificial Intelligence (AI) is transforming the way we live and work, with many of us knowingly or unknowingly using some form of AI daily. Businesses are also adopting AI in increasingly innovative ways. One example of this is the use of pricing algorithms, which use large datasets on market conditions to set prices.
While these tools can drive innovation and efficiency, they can also raise significant competition concerns. Subsequently, competition authorities around the world are dedicating efforts to understanding how businesses are using AI and, importantly, the potential risks its use may pose to competition.
How AI pricing tools can enhance competition
The use of AI pricing tools offers some clear potential efficiencies for firms, with the potential to reduce costs that can potentially translate into lower prices for consumers.
Take, for instance, industries with highly fluctuating demand, such as airlines or hotels. Algorithms can enable businesses to monitor demand and supply in real time and respond more quickly, which could help firms to respond more effectively to changing consumer preferences. Similarly, in industries which have extensive product ranges, like supermarkets, algorithms can significantly reduce costs and save resources that are usually required to manage pricing strategies across a large range of products.
Furthermore, as pricing algorithms can monitor competitors’ prices, firms can more quickly respond to their rivals. This could promote competition by helping prices to reach the competitive level more quickly, to the benefit of consumers.
How AI pricing tools can undermine competition
However, some of the very features that make algorithms effective can also facilitate anti-competitive behaviour that can harm consumers. In economic terms, collusion occurs when firms co-ordinate their actions to reduce competition, often leading to higher prices. This can happen both explicitly or implicitly. Explicit collusion, commonly referred to as illegal cartels, involves firms agreeing to co-ordinate their prices instead of competing. On the other hand, tacit collusion occurs when firms’ pricing strategies are aligned without a formal agreement.
The ability for these algorithms to monitor competitors’ prices and react to changes quickly could work to facilitate collusion, by learning to avoid price wars to maximise long-term profits. This could result in harm to consumers through sustained higher prices.
Furthermore, there may be additional risks if competitors use the same algorithmic software to set prices. This can facilitate the sharing of confidential information (such as pricing strategies) and, as the algorithms may be able to predict the response of their competitors, can facilitate co-ordination to achieve higher prices to the detriment of consumers.
This situation may resemble what is known as a ‘hub and spoke’ cartel, in which competing firms (the ‘spokes’) use the assistance of another firm at a different level of the supply chain (e.g. a buyer or supplier that acts as a ‘hub’) to help them co-ordinate their actions. In this case, a shared artificial pricing tool can act as the ‘hub’ to enable co-ordination amongst the firms, even without any direct communication between the firms.
In 2015 the CMA investigated a cartel involving two companies, Trod Limited and GB Eye Limited, which were selling posters and frames through Amazon (see linked CMA Press release below). These firms used pricing algorithms, similar to those described above, to monitor and adjust their prices, ensuring that neither undercut the other. In this case, there was also an explicit agreement between the two firms to carry out this strategy.
What does this mean for competition policy?
Detecting collusion has always been a significant challenge for the competition authorities, especially when no formal agreement exists between firms. The adoption of algorithmic pricing adds another layer of complexity to detection of cartels and could raise questions about accountability when algorithms inadvertently facilitate collusion.
In the posters and frames case, the CMA was able to act because one of the firms involved reported the cartel itself. Authorities like the CMA depend heavily on the firms involved to ‘whistle blow’ and report cartel involvement. They incentivise firms to do this through leniency policies that can offer firms reduced penalties or even complete immunity if they provide evidence and co-operate with the investigation. For example, GB eye reported the cartel to the CMA and therefore, under the CMA’s leniency policy, was not fined.
But it’s not all doom and gloom for competition authorities. Developments in Artificial Intelligence could also open doors to improved detection tools, which may have come a long way since the discussion in a blog on this topic several years ago. Competition Authorities around the world are working diligently to expand their understanding of AI and develop effective regulations for these rapidly evolving markets.
Articles
Questions
- In what types of markets might it be more likely that artificial intelligence can facilitate collusion?
- How could AI pricing tools impact the factors that make collusion more or less sustainable in a market?
- What can competition authorities do to prevent AI-assisted collusion taking place?
We continue to live through incredibly turbulent times. In the past decade or so we have experienced a global financial crisis, a global health emergency, seen the UK’s departure from the European Union, and witnessed increasing levels of geopolitical tension and conflict. Add to this the effects from the climate emergency and it easy to see why the issue of economic uncertainty is so important when thinking about a country’s economic prospects.
In this blog we consider how we can capture this uncertainty through a World Uncertainty Index and the ways by which economic uncertainty impacts on the macroeconomic environment.
World Uncertainty Index
Hites Ahir, Nicholas Bloom and Davide Furceri have constructed a measure of uncertainty known as the World Uncertainty Index (WUI). This tracks uncertainty around the world using the process of ‘text mining’ the country reports produced by the Economist Intelligence Unit. The words searched for are ‘uncertain’, ‘uncertainty’ and ‘uncertainties’ and a tally is recorded based on the number of times they occur per 1000 words of text. To produce the index this figure is then multiplied up by 100 000. A higher number therefore indicates a greater level of uncertainty. For more information on the construction of the index see the 2022 article by Ahir, Bloom and Furceri linked below.
Figure 1 (click here for a PowerPoint) shows the WUI both globally and in the UK quarterly since 1991. The global index covers 143 countries and is presented as both a simple average and a GDP weighted average. The UK WUI is also shown. This is a three-quarter weighted average, the authors’ preferred measure for individual countries, where increasing weights of 0.1, 0.3 and 0.6 are used for the three most recent quarters.
From Figure 1 we can see how the level of uncertainty has been particularly volatile over the past decade or more. Events such as the sovereign debt crisis in parts of Europe in the early 2010s, the Brexit referendum in 2016, the COVID-pandemic in 2020–21 and the invasion of Ukraine in 2022 all played their part in affecting uncertainty domestically and internationally.
Uncertainty, risk-aversion and aggregate demand
Now the question turns to how uncertainty affects economies. One way of addressing this is to think about ways in which uncertainty affects the choices that people and businesses make. In doing so, we could think about the impact of uncertainty on components of aggregate demand, such as household consumption and investment, or capital expenditures by firms.
As Figure 2 shows (click here for a PowerPoint), investment is particularly volatile, and much more so than household spending. Some of this can be attributed to the ‘lumpiness’ of investment decisions since these expenditures tend to be characterised by indivisibility and irreversibility. This means that they are often relatively costly to finance and are ‘all or nothing’ decisions. In the context of uncertainty, it can make sense therefore for firms to wait for news that makes the future clearer. In this sense, we can think of uncertainty rather like a fog that firms are peering through. The thicker the fog, the more uncertain the future and the more cautious firms are likely to be.
The greater caution that many firms are likely to adopt in more uncertain times is consistent with the property of risk-aversion that we often attribute to a range of economic agents. When applied to household spending decisions, risk-aversion is often used to explain why households are willing to hold a buffer stock of savings to self-insure against unforeseen events and their future financial outcomes being worse than expected. Hence, in more uncertain times households are likely to want to increase this buffer further.
The theory of buffer-stock saving was popularised by Christopher Carroll in 1992 (see link below). It implies that in the presence of uncertainty, people are prepared to consume less today in order to increase levels of saving, pay off existing debts, or borrow less relative to that in the absence of uncertainty. The extent of the buffer of financial wealth that people want to hold will depend on their own appetite for risk, the level of uncertainty, and the moderating effect from their own impatience and, hence, present bias for consuming today.
Risk aversion is consistent with the property of diminishing marginal utility of income or consumption. In other words, as people’s total spending volumes increase, their levels of utility or satisfaction increase but at an increasingly slower rate. It is this which explains why individuals are willing to engage with the financial system to reallocate their expected life-time earnings and have a smoother consumption profile than would otherwise be the case from their fluctuating incomes.
Yet diminishing marginal utility not only explains consumption smoothing, but also why people are willing to engage with the financial system to have financial buffers as self-insurance. It explains why people save more or borrow less today than suggested by our base-line consumption smoothing model. It is the result of people’s greater dislike (and loss of utility) from their financial affairs being worse than expected than their like (and additional utility) from them being better than expected. This tendency is only likely to increase the more uncertain times are. The result is that uncertainty tends to lower household consumption with perhaps ‘big-ticket items’, such as cars, furniture, and expensive electronic goods, being particularly sensitive to uncertainty.
Uncertainty and confidence
Uncertainty does not just affect risk; it also affects confidence. Risk and confidence are often considered together, not least because their effects in generating and transmitting shocks can be difficult to disentangle.
We can think of confidence as capturing our mood or sentiment, particularly with respect to future economic developments. Figure 3 plots the Uncertainty Index for the UK alongside the OECD’s composite consumer and business confidence indicators. Values above 100 for the confidence indicators indicate greater confidence about the future economic situation and near-term business environment, while values below 100 indicate pessimism towards the future economic and business environments.
Figure 3 suggests that the relationship between confidence and uncertainty is rather more complex than perhaps is generally understood (click here for a PowerPoint). Haddow, Hare, Hooley and Shakir (see link below) argue that the evidence tends to point to changes in uncertainty affecting confidence, but with less evidence that changes in confidence affect uncertainty.
To illustrate this, consider the global financial crisis of the late 2000s. The argument can be made that the heightened uncertainty about future prospects for households and businesses helped to erode their confidence in the future. The result was that people and businesses revised down their expectations of the future (pessimism). However, although people were more pessimistic about the future, this was more likely to have been the result of uncertainty rather than the cause of further uncertainty.
Conclusion
For economists and policymakers alike, indicators of uncertainty, such as the Ahir, Bloom and Furceri World Uncertainty Index, are invaluable tools in understanding and forecasting behaviour and the likely economic outcomes that follow. Some uncertainty is inevitable, but the persistence of greater uncertainty since the global financial crisis of the late 2000s compares quite starkly with the relatively lower and more stable levels of uncertainty seen from the mid-1990s up to the crisis. Hence the recent frequency and size of changes in uncertainty show how important it to understand how uncertainty effects transmit through economies.
Academic papers
- The World Uncertainty Index
National Bureau of Economic Research, Working Paper 29763, Hites Ahir, Nicholas Bloom and Davide Furceri (February 2022)
- The Buffer-Stock Theory of Saving: Some Macroeconomic Evidence
Brookings Papers on Economic Activity, Christopher D Carroll (Vol 2, 1992)
- Macroeconomic uncertainty: what is it, how can we measure it and why does it matter?
Bank of England Quarterly Bulletin, 2013 Q2, Abigail Haddow, Chris Hare, John Hooley and Tamarah Shakir (13/6/13)
Articles
Data
Questions
- (a) Explain what is meant by the concept of diminishing marginal utility of consumption.
(b) Explain how this concept helps us to understand both consumption smoothing and the motivation to engage in buffer-stock saving.
- Explain the distinction between confidence and uncertainty when analysing macroeconomic shocks.
- Discuss which types of expenditures you think are likely to be most susceptible to uncertainty shocks.
- Discuss how economic uncertainty might affect productivity and the growth of potential output.
- How might the interconnectedness of economies affect the transmission of uncertainty effects through economies?
Sustainability has become one of the most pressing issues facing society. Patterns of human production and consumption have become unsustainable. On the environmental front, climate change, land-use change, biodiversity loss and depletion of natural resource are destabilising the Earth’s eco-system.
Furthermore, data on poverty, hunger and lack of healthcare show that many people live below minimum social standards. This has led to greater emphasis being placed on sustainable development: ‘development that meets the needs of the present without compromising the ability of future generations to meet their own needs’ (The Brundtland Report, 1987: Ch.2, para. 1).
The financial system has an important role to play in channelling capital in a more sustainable way. Since current models of finance do not consider the welfare of future generations in investment decisions, sustainable finance has been developed to analyse how investment and lending decisions can manage the trade-off inherent in sustainable development: sacrificing return today to enhance the welfare of future generations.
However, some commentators argue that such trade-offs are not required. They suggest that investors can ‘do well by doing good’. In this blog, I will use ‘green’ bonds (debt instruments which finance projects or activities with positive environmental and social impacts) to explain the economics underpinning sustainable finance and show that doing good has a price that sustainable investors need to be prepared to pay.
I will analyse why investors might not be doing so and point to changes which may be required to ensure financial markets channel capital in a way consistent with sustainable development.
The growth of sustainable finance
Sustainable finance has grown rapidly over the past decade as concerns about climate change have intensified. A significant element of this growth has been in global debt markets.
Figure 1 illustrates the rapid growth in the issuance of sustainability-linked debt instruments since 2012. While issuance fell in 2022 due to concerns about rising inflation and interest rates reducing the real return of fixed-income debt securities, it rebounded in 2023 and is on course for record levels in 2024. (Click here for a PowerPoint.)
Green bonds are an asset class within sustainability-linked debt. Such bonds focus on financing projects or activities with positive environmental and social impacts. They are typically classified as ‘use-of-proceeds’ or asset-linked bonds, meaning that the proceeds raised from their issuance are earmarked for green projects, such as renewable energy, clean transportation, and sustainable agriculture. Such bonds should be attractive to investors who want a financial return but also want to finance investments with a positive environmental and/or social impact.
One common complaint from commentators and investors is the ‘greenium’ – the price premium investors pay for green bonds over conventional ones. This premium reduces the borrowing costs of the issuers (the ‘counterparties’) compared to those of conventional counterparties. This produces a yield advantage for issuers of green bonds (price and yield have a negative relationship), reducing their borrowing costs compared to issuers of conventional bonds.
An analysis by Amundi in 2023 using data from Bloomberg estimated that the average difference in yield in developed markets was –2.2 basis points (–0.022 percentage points) and the average in emerging markets was –5.6 basis points (–0.056 percentage points). Commentators and investors suggest that the premium is a scarcity issue and once there are sufficient green bonds, the premium over non-sustainable bonds should disappear.
However, from an economics perspective, such interpretations of the greenium ignore some fundamentals of economic valuation and the incentives and penalties through which financial markets will help facilitate more sustainable development. Without the price premium, investors could buy sustainable debt at the same price as unsustainable debt, earn the same financial return (yield) but also achieve environmental and social benefits for future generations too. Re-read that sentence and if it sounds too good to be true, it’s because it is too good to be true.
‘There is no such thing as a free lunch’
In theory, markets are institutional arrangements where demand and supply decisions produce price signals which show where resources are used most productively. Financial markets involve the allocation of financial capital. Traditional economic models of finance ignore sustainability when appraising investment decisions around the allocation of capital. Consequently, such allocations do not tend to be consistent with sustainable development.
In contrast, economic models of sustainable finance do incorporate such impacts of investment decisions and they will be reflected in the valuation, and hence pricing, of financial instruments. Investors, responding to the pricing signals will reallocate capital in a more sustainable manner.
Let’s trace the process. In models of sustainable finance, financial instruments such as green bonds funding investments with positive environmental impacts (such as renewable energy) should be valued more, while instruments funding investments with negative environmental impacts (such as fossil fuels) should be valued less. The prices of the green bonds financing renewable energy projects should rise while the prices of conventional bonds financing fossil-fuel companies should fall.
As this happens, the yield on the green bonds falls, lowering the cost of capital for renewable-energy projects, while yields on the bonds financing fossil-fuel projects rise, ceteris paribus. As with any market, these differential prices act as signals as to where resources should be allocated. In this case, the signals should result in an allocation consistent with sustainable development.
The fundamental point in this economic valuation is that sustainable investors should accept a trade-off. They should pay a premium and receive a lower rate of financial return (yield) for green bonds compared to conventional ones. The difference in price (the greenium), and hence yield, represents the return investors are prepared to sacrifice to improve future generations’ welfare. Investors cannot expect to have the additional welfare benefit for future generations reflected in the return they receive today. That would be double counting. The benefit will accrue to future generations.
A neat way to trace the sacrifice sustainable investors are prepared to make in order to enhance the welfare of future generations is to plot the differences in yields between green bonds and their comparable conventional counterparts. The German government has issued a series of ‘twin’ bonds in recent years. These twins are identical in every respect (coupon, face value, credit risk) except that the proceeds from one will be used for ‘green’ projects only.
Figure 2 shows the difference in yields on a ‘green bond’ and its conventional counterpart, both maturing on 15/8/2050, between June 2021 and July 2024. The yield on the green bond is lower – on average about 2.2 basis points (0.022 percentage points) over the period. This represents the sacrifice in financial return that investors are prepared to trade off for higher environmental and social welfare in the future. (Click here for a PowerPoint.)
The yield spread fluctuates through time, reflecting changing perceptions of environmental concerns and hence the changing value that sustainable investors attach to future generations. The spread tends to widen when there are heightened environmental concerns and to narrow when such concerns are not in the news. For example, the spread on the twin German bonds reached a maximum of 0.045 percentage points in November 2021. This coincided with the 26th UN Climate Change Conference of the Parties (COP26) in Glasgow. The spread has narrowed significantly since early 2022 as rising interest rates and falling real rates of return on bonds in the near-term seem to have dominated investors’ concerns.
These data suggest that, rather than being too large, the greeniums are too small. The spreads suggest that markets in debt instruments do not seem to attach much value to future generations. The valuation, price and yield of green bonds are not significantly different from their conventional counterparts. This narrow gap indicates insufficient reward for better sustainability impact and little penalty for worse sustainability impact.
This pattern is repeated across financial markets and does not seem to be stimulating the necessary investment to achieve sustainable development. An estimate of the scale of the deficit in green financing is provided by Bloomberg NEF (2024). While global spending on the green energy transition reached $1.8 trillion in 2023, Bloomberg estimates that $4.8 trillion needs to be invested every year for the remainder of this decade if the world is to remain on track under the ‘net zero’ scenario. Investors do not seem to be prepared to accept the trade-off needed to provide the necessary funds.
Can financial markets deliver sustainable development?
Ultimately, the hope is that all financial instruments will be sustainable. In order to achieve that, access to finance would require all investors to incorporate the welfare of future generations in their investment decisions and accept sacrificing sufficient short-term financial return to ensure long-term sustainable development. Unfortunately, the pricing of green bonds suggests that investors are not prepared to accept the trade-off. This restricts the ability of financial markets to deliver an allocation of resources consistent with sustainable development.
There are several reasons why financial markets may not be valuing the welfare of future generations fully.
- Bounded rationality means that it is difficult for sustainable investors to assign precise values to future and distant benefits.
- There are no standardised sustainability metrics available. This produces great uncertainty in the valuation of future welfare.
- Investors also exhibit cognitive biases, which means they may not value the welfare of future generations properly. These include present bias (favouring immediate rewards) and hyperbolic discounting (valuing the near future more than the distant future).
- Economic models of financial valuation use discount rates to assess the value of future benefits. Higher discount rates reduce the perceived value of benefits occurring in the distant future. As a result, long-term impacts (such as environmental conservation) may be undervalued.
- There may be large numbers of investors who are only interested in financial returns and so do not consider the welfare of future generations in their investment decisions.
Consequently, investors need to be educated about the extent of trade-offs required to achieve the necessary investments in sustainable development. Furthermore, practical models which better reflect the welfare of future generations in investment decisions need to be employed. However, challenges persist in fully accounting for future generations and it may need regulatory frameworks to provide appropriate incentives for effective sustainable investment.
Articles
- The fallacy of ESG investing
Financial Times, Robert Armstrong (23/10/20)
- Energy Transition Investment Trends 2024: Executive Summary
BloombergNEF (30/1/24)
- ESG metrics trip up factor investors
Financial Times, Emma Boyde (1/11/21)
- Our Common Future: Report of the World Commission on Environment and Development
United Nations, Gro Harlem Brundtland (chair) (20/3/87)
Who killed the ESG party?FT Film, Daniel Garrahan (17/7/24)
- Green bond issuance surges as investors hunt for yield
Financial Times, Lee Harris (19/6/24)
- Investing for long-term value creation
Journal of Sustainable Finance & Investment, 9(4), Dirk Schoenmaker and Willem Schramade (19/6/19)
- Facts and Fantasies about the Green Bond Premium
Amundi working paper 102-2020, Mohamed Ben Slimane, Dany Da Fonseca and Vivek Mahtani (December 2020)
- Climate change and growth
Industrial and Corporate Change, 32 (2), 2023, Nicholas Stern and Joseph E Stiglitz (30/7/24)
Report
Data
Questions
- Using demand and supply analysis, illustrate and explain the impact of sustainable investing on the markets for (i) green bonds and (ii) conventional bonds. Highlight how this should produce an allocation of finance capital consistent with sustainable development.
- Research the yields on the twin bonds issued by Germany since this blog was published. Can you identify any association between heightened environmental concerns and the spread between the ‘green’ and conventional bond?
- Analyse the issues which prevent financial markets from producing the pricing signals which produce an allocation of resources consistent with sustainable development.
- Research some potential regulatory policies which may provide appropriate incentives for sustainable investment.
When I worked as a professional economist at HM Treasury and later the Council of Mortgage Lenders (now part of UK Finance), I would regularly brief on the state of the affordability of housing, with a particular focus on the owner-occupied market. That was back in the late 1990s. Fast forward a quarter of a century and I recognise not only how much I have aged but also how deep-rooted and long-standing the affordability problem is.
It is perhaps not surprising that in her first speech as the new Chancellor of the Exchequer, Rachel Reeves, referenced directly the housing market and the need to address supply-side issues. She has set a target of one and a half million new homes built over the next five years.
It is therefore timely to revisit the trends in house prices across the UK. By applying the distinction between nominal and real values we get a very clear sense of the deteriorating affordability of housing.
Nominal house price patterns
The average UK actual or nominal house price in April 2024 was £281 000. As Chart 1 shows, this masks considerable differences across the UK. In England the average price was £298 000 (105 per cent of the UK average), though this is heavily skewed by London where the average price was £502 000 (178 per cent of the UK average). Meanwhile, in Scotland it was £190 000 (68 per cent of the UK average), in Wales £208 000 (74 per cent of the UK average) and in Northern Ireland it was £178 000 (74 per cent of the UK average). (Click here to download a PowerPoint copy of the chart.)
A simple comparison of the average house price in April 2024 with January 1970 reveals a 72-fold increase in the UK, an 80-fold increase in England, including a 101-fold increase in London, a 65-fold increase in Wales, a 59-fold increase in Scotland and a 45-fold increase in Northern Ireland. Whilst these figures are sensitive to the particular period over which we choose to measure, there is little doubting that upward long-term trend in house prices.
Whilst nominal prices trend upwards over time, the short-term rates of increase are highly volatile. This can be seen from an inspection of Chart 2, which shows the annual rates of increase across the four nations of the UK, as well as for London. This is evidence of frequent imbalances between the flows of property on to the market to sell (instructions to sell) and the number of people looking to buy (instructions to buy). An increase in instructions to buy (housing demand) relative to those to sell (housing supply) puts upward pressure on prices; an increase in the number of instructions to sell (housing supply) relative to those to buy (housing demand) puts downward pressure on prices. (Click here to download a PowerPoint copy of the chart.)
Chart 2 nicely captures the recent slowdown in the housing market. The inflationary shock that began to take hold in 2021 led the Bank of England to raise Bank Rate on 15 occasions – from 0.25 per cent in December 2021 to 5.25 per cent in August 2023 (which remains the rate at the time of writing, but could be cut at the next Bank of England meeting on 1 August 2024). Higher Bank Rate has pushed up mortgage rates, which has contributed to an easing of housing demand. Demand has also been dampened by weak growth in the economy, higher costs of living and fragile consumer confidence. The result has been a sharp fall in the rate of house price inflation, with many parts of the UK experiencing house price deflation. As the chart shows, the rate of deflation has been particularly pronounced and protracted in London, with house prices in January 2024 falling at an annual rate of 5.1 per cent.
Real house price patterns
Despite the volatility in house prices, such as those of recent times, the longer-term trend in house prices is nonetheless upwards. To understand just how rapidly UK house prices have grown over time, we now consider their growth relative to consumer prices. This allows us to analyse the degree to which there has been an increase in real house prices.
To calculate real or inflation-adjusted house prices, we deflate nominal house prices by the Consumer Prices Index (CPI). Chart 3 shows the resulting real house prices series across the UK as if consumer prices were fixed at 2015 levels.
The key message here is that over the longer-term we cannot fully explain the growth in actual (nominal) house prices by the growth in consumer prices. Rather, we see real increases in house prices. Inflation-adjusted UK house prices were 5.3 times higher in April 2024 compared to January 1970. For England the figure was 5.9 times, Wales 4.8 times, Scotland 4.3 times and for Northern Ireland 3.3 times. In London, inflation-adjusted house prices were 7.4 times higher. (Click here to download a PowerPoint copy of the chart.)
As we saw with nominal house prices, the estimated long-term increase in real house prices is naturally sensitive to the period over which we measure. For example, the average real UK house price in August 2022 was 5.8 times higher than in January 1970, while in London they were 8.7 times higher. But the message is clear – the long-term increase is not merely nominal, reflecting increasing prices generally, but is real, reflecting pressures that are increasing house prices relative to general price levels.
Chart 4 shows how the volatility in house prices continues to be evident when house prices are adjusted for changes in consumer prices. The UK’s annual rate of real house price inflation was as high as 40 per in January 1973; on the other hand, in June 1975 inflation-adjusted house prices were 15 per cent lower than a year earlier. (Click here to download a PowerPoint copy of the chart.)
Over the period from January 1970 to April 2024, the average annual rate of real house price inflation in the UK was 3.2 per cent. Hence house prices have, on average, grown at an annual rate of consumer price inflation plus 3.2 per cent. For the four nations, real house price inflation has averaged 3.8 per cent in England, 3.4 per cent in Wales, 3.0 per cent in Scotland and 2.9 per cent in Northern Ireland. Further, the average rate of real house price inflation in London since January 1970 has been 4.5 per cent. By contrast, that for the East and West Midlands has been 3.7 and 3.5 per cent respectively. The important point here is that the pace with which inflation-adjusted house prices have risen helps to contextualise the extent of the problem of housing affordability – a problem that only worsens over time when real incomes do not keep pace.
House building
The newly elected Labour government has made the argument that it needs to prioritise planning reform as an engine for economic growth. While this ambition extends beyond housing, the scale of the supply-side problem facing the housing market can be seen in Chart 5. The chart shows the number of housing completions in the UK since 1950 by type of tenure. (Click here to download a PowerPoint copy of the chart.)
The chart shows the extent of the growth in house building in the UK that occurred from the 1950s and into the 1970s. Over these three decades the typical number of new properties completed each year was around 320 000 or 6 per thousand of the population. The peak of house building was in the late 1960s when completions exceeded 400 000 per year or over 7.5 per thousand of the population. It is also noticeable how new local authority housing (‘council houses’) played a much larger role in the overall housing mix.
Since 1980, the average number of housing completions each year has dropped to 191 000 or 3.2 per thousand of the population. If we consider the period since 2000, the number of completions has averaged only 181 000 per year or 2.9 per thousand of the population. While it is important to understand the pressures on housing demand in any assessment of the growth in real house prices, the lack of growth in supply is also a key factor. The fact that less than half the number of properties per thousand people are now being built compared with half a century or so ago is an incredibly stark statistic. It is a major determinant of the deterioration of housing affordability.
However, there are important considerations around the protection of the natural environment that need to be considered too. It will therefore be interesting to see how the reforms to planning develop and what their impact will be on house prices and their affordability.
Articles
- Rachel Reeves requests urgent assessment of spending inheritance
The Guardian, Larry Elliott (8/7/24)
- Reeves to bring back housebuilding targets
BBC News, Faisal Islam and Daniel Thomas (8/7/24)
- UK Chancellor Reeves Vows to Fix Broken Planning System for Housebuilding
Bloomberg UK, Tom Rees, Damian Shepherd, and Joe Mayes (8/7/24)
- What to expect for house prices for the rest of 2024
i News, Callum Mason (10/7/24)
- UK house prices still unaffordable for many people, says Nationwide
The Guardian, Richard Partington (1/7/24)
- House prices still unaffordable for the average earner despite wage rises – Nationwide
Sky News, Sarah Taaffe-Maguire (1/7/24)
- Labour cannot build 1.5m homes without cash for affordable housing, providers say
The Guardian, Jack Simpson (12/7/24)
Statistics
Questions
- Explain the difference between a rise in the rate of house price inflation a rise in the level of house prices.
- Explain the difference between nominal and real house prices.
- If nominal house prices rise can real house price fall? Explain your answer.
- What do you understand by the terms instructions to buy and instructions to sell?
- What factors are likely to affect the levels of instructions to buy and instructions to sell?
- How does the balance between instructions to buy and instructions to sell affect house prices?
- How can we differentiate between different housing markets? Illustrate your answer with examples.
- What metrics could be used to measure the affordability of housing?
- Discuss the argument that the deterioration of housing affordability is the result of market failure.
The Competition and Markets Authority (CMA) is proposing to launch a formal Market Investigation into anti-competitive practices in the UK’s £2bn veterinary industry (for pets rather than farm animals or horses). This follows a preliminary investigation which received 56 000 responses from pet owners and vet professionals. These responses reported huge rises in bills for treatment and medicines and corresponding rises in the cost of pet insurance.
At the same time there has been a large increase in concentration in the industry. In 2013, independent vet practices accounted for 89% of the market; today, they account for only around 40%. Over the past 10 years, some 1500 of the UK’s 5000 vet practices had been acquired by six of the largest corporate groups. In many parts of the country, competition is weak; in others, it is non-existent, with just one of these large companies having a monopoly of veterinary services.
This market power has given rise to a number of issues. The CMA identifies the following:
- Of those practices checked, over 80% had no pricing information online, even for the most basic services. This makes is hard for pet owners to make decisions on treatment.
- Pet owners potentially overpay for medicines, many of which can be bought online or over the counter in pharmacies at much lower prices, with the pet owners merely needing to know the correct dosage. When medicines require a prescription, often it is not made clear to the owners that they can take a prescription elsewhere, and owners end up paying high prices to buy medicines directly from the vet practice.
- Even when there are several vet practices in a local area, they are often owned by the same company and hence there is no price competition. The corporate group often retains the original independent name when it acquires the practice and thus is is not clear to pet owners that ownership has changed. They may think there is local competition when there is not.
Often the corporate group provides the out-of-hours service, which tends to charge very high prices for emergency services. If there is initially an independent out-of-hours service provider, it may be driven out of business by the corporate owner of day-time services only referring pet owners to its own out-of-hours service.- The corporate owners may similarly provide other services, such as specialist referral centres, diagnostic labs, animal hospitals and crematoria. By referring pets only to those services owned by itself, this crowds out independents and provides a barrier to the entry of new independents into these parts of the industry.
- Large corporate groups have the incentive to act in ways which may further reduce competition and choice and drive up their profits. They may, for example, invest in advanced equipment, allowing them to provide more sophisticated but high-cost treatment. Simpler, lower-cost treatments may not be offered to pet owners.
- The higher prices in the industry have led to large rises in the cost of pet insurance. These higher insurance costs are made worse by vets steering owners with pet insurance to choosing more expensive treatments for their pets than those without insurance. The Association of British Insurers notes that there has been a large rise in claims attributable to an increasing provision of higher-cost treatments.
- The industry suffers from acute staff shortages, which cuts down on the availability of services and allows practices to push up prices.
- Regulation by the Royal College of Veterinary Surgeons (RCVS) is weak in the area of competition and pricing.
The CMA’s formal investigation will examine the structure of the veterinary industry and the behaviour of the firms in the industry. As the CMA states:
In a well-functioning market, we would expect a range of suppliers to be able to inform consumers of their services and, in turn, consumers would act on the information they receive.
Market failures in the veterinary industry
The CMA’s concerns suggest that the market is not sufficiently competitive, with vet companies holding significant market power. This leads to higher prices for a range of vet services. However, the CMA’s analysis suggests that market failures in the industry extend beyond the simple question of market power and lack of competition.
A crucial market failure is asymmetry of information. The veterinary companies have much better information than pet owners. This is a classic principal–agent problem. The agent, in this case the vet (or vet company), has much better information than the principal, in this case the pet owner. This information can be used to the interests of the vet company, with pet owners being persuaded to purchase more extensive and expensive treatments than they might otherwise choose if they were better informed.
The principal–agent problem also arises in the context of the dependant nature of pets. They are the ones receiving the treatment and, in this context, are the principals. Their owners are the ones acquiring the treatment for them and hence are the pets’ agents. The question is whether the owners will always do the best thing for their pets. This raises philosophical questions of animal rights and whether owners should be required to protect the interests of their pets.
Another information issue is the short-term perspective of many pet owners. They may purchase a young and healthy pet and assume that it will remain so. However, as the pet gets older, it is likely to face increasing health issues, with correspondingly increasing vet bills. But many owners do not consider such future bills when they purchase the pet. They suffer from what behavioural economists call ‘irrational exuberance’. Such exuberance may also occur when the owner of a sick pet is offered expensive treatment. They may over-optimistically assume that the treatment will be totally successful and that their pet will not need further treatment.
Vets cite another information asymmetry. This concerns the costs they face in providing treatment. Many owners are unaware of these costs – costs that include rent, business rates, heating and lighting, staff costs, equipment costs, consumables (such as syringes, dressings, surgical gowns, antiseptic and gloves), VAT, and so on. Many of these costs have risen substantially in recent months and are reflected in the prices pet owners are charged. With people experiencing free health care for themselves from the NHS (or other national provider), this may make them feel that the price of pet health care is excessive.
Then there is the issue of inequality. Pets provide great benefits to many owners and contribute to owners’ well-being. If people on low incomes cannot afford high vet bills, they may either have to forgo having a pet, with the benefits it brings, or incur high vet bills that they ill afford or simply go without treatment for their pets.
Finally, there are the external costs that arise when people abandon their pets with various health conditions. This has been a growing problem, with many people buying pets during lockdown when they worked from home, only to abandon them later when they have had to go back to the office or other workplace. The costs of treating or putting down such pets are born by charities or local authorities.
The CMA is consulting on its proposal to begin a formal Market Investigation. This closes on 11 April. If, in the light of its consultation, the Market Investigation goes ahead, the CMA will later report on its findings and may require the veterinary industry to adopt various measures. These could require vet groups to provide better information to owners, including what lower-cost treatments are available. But given the oligopolistic nature of the industry, it is unlikely to lead to significant reductions in vets bills.
Articles
- UK competition watchdog plans probe into veterinary market
Financial Times, Suzi Ring and Oliver Ralph (12/3/24)
- Vet prices: Investigation over concerns pet owners are being overcharged
Sky News (12/3/24)
- UK watchdog plans formal investigation into vet pricing
The Guardian, Kalyeena Makortoff (12/3/24)
- ‘Eye-watering’ vet bills at chain-owned surgeries prompt UK watchdog review
The Guardian, Kalyeena Makortoff (7/9/23)
- Warning pet owners could be overpaying for medicine
BBC News, Lora Jones & Jim Connolly (12/3/24)
- I own a vet practice, owners complain about the spiralling costs of treatments, but I only make 5 -10% profit – here’s our expenditure breakdown
Mail Online, Alanah Khosla (14/3/24)
- Vets bills around the world: As big-name veterinary practices come under pressure for charging pet owners ‘eyewatering’ care costs, how do fees in Britain compare to other countries?
Mail Online, Rory Tingle, Dan Grennan and Katherine Lawton (13/3/24)
CMA documents
Questions
- How would you establish whether there is an abuse of market power in the veterinary industry?
- Explain what is meant by the principal–agent problem. Give some other examples both in economic and non-economic relationships.
- What market advantages do large vet companies have over independent vet practices?
- How might pet insurance lead to (a) adverse selection; (b) moral hazard? Explain. How might (i) insurance companies and (ii) vets help to tackle adverse selection and moral hazard?
- Find out what powers the CMA has to enforce its rulings.
- Search for vet prices and compare the prices charged by at least three vet practices. How would you account for the differences or similarities in prices?