The latest UK house price index continues to show an easing in the rate of house price inflation. In the year to January 2019 the average UK house price rose by 1.7 per cent, the lowest rate since June 2013 when it was 1.5 per cent. This is significantly below the recent peak in house price inflation when in May 2016 house prices were growing at 8.2 per cent year-on-year. In this blog we consider how recent patterns in UK house prices compare with those over the past 50 years and also how the growth of house prices compares to that in consumer prices.
The UK and its nations
The average UK house price in January 2019 was £228,000. As Chart 1 shows, this masks considerable differences across the UK. In England the average price was £245,000 (an annual increase of 1.5 per cent), while in Scotland it was £149,000 (an increase of 1.3 per cent), Wales £160,000 (an increase of 4.6 per cent) and £137,000 in Northern Ireland (an increase of 5.5 per cent). (Click here to download a PowerPoint copy of the chart.)
Within England there too are considerable differences in house prices, with London massively distorting the English average. In January 2019 the average house price in inner London was recorded at £568,000, a fall of 1.9 per cent on January 2018. In Outer London the average price was £426,000, a fall of 0.2 per cent. Across London as a whole the average price was £472,000, a fall of 1.6 per cent. House prices were lowest in the North East at £125,000, having experienced an annual increase of 0.9 per cent.
The Midlands can be used as a reference point for English house prices outside of the capital. In January 2019 the average house price in the West Midlands was £195,000 while in the East Midlands it was £193,000. While the annual rate of house price inflation in London is now negative, the annual rate of increase in the Midlands was the highest in England. In the West Midlands the annual increase was 4 per cent while in the East Midlands it was 4.4 per cent. These rates of increase are currently on par with those across Wales.
Long-term UK house price trends
Chart 2 shows the average house price for the UK since 1969 alongside the annual rate of house price inflation, i.e. the annual percentage change in the level of house prices. The average UK house price in January 1969 was £3,750. By January 2019, as we have seen, it had risen to around £228,000. This is an increase of nearly 6,000 per cent. Over this period, the average annual rate of house price inflation was 9 per cent. However, if we measure it to the end of 2007 it was 11 per cent. (Click here to download a PowerPoint copy of the chart.)
The significant effect of the financial crisis on UK house prices is evident from Charts 1 and 2. In February 2009 house prices nationally were 16 per cent lower than a year earlier. Furthermore, it was not until August 2014 that the average UK house rose above the level of September 2007. Indeed, some parts of the UK, such as Northern Ireland and the North East of England, remain below their pre-financial crisis level even today.
Nominal and real UK house prices
But how do house price patterns compare to those in consumer prices? In other words, what has happened to inflation-adjusted or real house prices? One index of general prices is the Retail Prices Index (RPI). This index measures the cost of a representative basket of consumer goods and services. Since January 1969 the RPI has increased by nearly 1,600 per cent. While substantial in its own right, it does mean that house prices have increased considerably more rapidly than consumer prices.
If we eliminate the increase in consumer prices from the actual (nominal) house price figures what is left is the increase in house prices relative to consumer prices. To do this we estimate house prices as if consumer prices had remained at their January 1987 level. This creates a series of average UK house prices at constant January 1987 consumer prices.
Chart 3 shows the average nominal and real UK house price since 1969. It shows that in real terms the average UK house price increased by around 266 per cent between January 1969 and January 2019. Therefore, the average real UK house price was 3.7 times more expensive in 2019 compared with 1969. This is important because it means that general price inflation cannot explain all the long-term growth seen in average house prices. (Click here to download a PowerPoint copy of the chart.)
Real UK house price cycles
Chart 4 shows that annual rates of nominal and real house price inflation. As we saw earlier, the average nominal house price inflation rate since 1969 has been 9 per cent. The average real rate of increase in house prices has been 3.1 per cent per annum. In other words, house prices have on average each each year increased by the annual rate of RPI inflation plus 3.1 percentage points. (Click here to download a PowerPoint copy of the chart.)
Chart 4 shows how, in addition to the long-term relative increase in house prices, there are also cycles in the relative price of houses. This is evidence of a volatility in house prices that cannot be explained by general prices. This volatility reflects frequent imbalances between the demand and supply of housing, i.e. between instructions to buy and sell property. Increasing levels of housing demand (instructions to buy) relative to housing supply (instructions to supply) will put upward pressure on house prices and vice versa.
In January 2019 the annual real house price inflation across the UK was -0.9 per cent. While the rate was slightly lower in Scotland at -1.2 per cent, the biggest drag on UK house price inflation was the London market where the real house price inflation rate was -4.0 per cent. In contrast, January saw annual real house price inflation rates of 2 per cent in Wales, 2.3 per cent in Northern Ireland and 1.8 per cent in the East Midlands.
Inflation-adjusted inflation rates in London have been negative consistently since June 2017. From their July 2016 peak, following the result of the referendum on UK membership of the EU, to January 2019 inflation-adjusted house prices fell by 7.6 per cent. This reflects, in part, the fact that the London housing market, like that of other European capitals, is a more international market than other parts of the country. Therefore, the current patterns in UK house prices are rather distinctive in that the easing is being led by London and southern England.
Today’s title is inspired from the British Special Air Service (SAS) famous catchphrase, ‘Who Dares Wins’ – similar variations of which have been adopted by several elite army units around the world. The motto is often credited to the founder of the SAS, Sir David Stirling (although similar phrases can be traced back to ancient Rome – including ‘qui audet adipiscitur’, which is Latin for ‘who dares wins’). The motto was used to inspire and remind soldiers that to successfully accomplish difficult missions, one has to take risks (Geraghty, 1980).
In the world of economics and finance, the concept of risk is endemic to investments and to making decisions in an uncertain world. The ‘no free lunch’ principle in finance, for instance, asserts that it is not possible to achieve exceptional returns over the long term without accepting substantial risk (Schachermayer, 2008).
Undoubtedly, one of the riskiest investment instruments you can currently get your hands on is cryptocurrencies. The most well-known of them is Bitcoin (BTC), and its price has varied spectacularly over the past ten years – more than any other asset I have laid my eyes on in my lifetime.
The first published exchange rate of BTC against the US dollar dates back to 5 October 2009 and it shows $1 to be exchangeable for 1309.03 BTC. On 15 December 2017, 1 BTC was traded for $17,900. But then, a year later the exchange rate was down to just over $1 = $3,500. Now, if this is not volatility I don’t know what is!
In such a market, wouldn’t it be wonderful if you could somehow predict changes in market sentiment and volatility trends? In a hot-off-the press article, Shen et al (2019) assert that it may be possible to predict changes in trading volumes and realised volatility of BTC by using the number of BTC-related tweets as a measure of attention. The authors source Twitter data on Bitcoin from BitInfoCharts.com and tick data from Bitstamp, one of the most popular and liquid BTC exchanges, over the period 4/9/2014 to 31/8/2018.
According to the authors:
This measure of investor attention should be more informed than that of Google Trends and therefore may reflect the attention Bitcoin is receiving from more informed investors. We find that the volume of tweets are significant drivers of realised [price] volatility (RV) and trading volume, which is supported by linear and nonlinear Granger causality tests.
They find that, according to Granger causality tests, for the period from 4/9/2014 to 8/10/2017, past days’ tweeting activity influences (or at least forecasts) trading volume. While from 9/10/2017 to 31/8/2018, previous tweets are significant drivers/forecasters of not only trading volume but also realised price volatility.
And before you reach out for your smartphone, let me clarify that, although previous days’ tweets are found in this paper to be good predictors of realised price volatility and trading volume, they have no significant effect on the returns of Bitcoin.
Back in October, we examined the rise in oil prices. We said that, ‘With Brent crude currently at around $85 per barrel, some commentators are predicting the price could reach $100. At the beginning of the year, the price was $67 per barrel; in June last year it was $44. In January 2016, it reached a low of $26.’ In that blog we looked at the causes on both the demand and supply sides of the oil market. On the demand side, the world economy had been growing relatively strongly. On the supply side there had been increasing constraints, such as sanctions on Iran, the turmoil in Venezuela and the failure of shale oil output to expand as much as had been anticipated.
But what a difference a few weeks can make!
Brent crude prices have fallen from $86 per barrel in early October to just over $50 by the end of the year – a fall of 41 per cent. (Click here for a PowerPoint of the chart.) Explanations can again be found on both the demand and supply sides.
On the demand side, global growth is falling and there is concern about a possible recession (see the blog: Is the USA heading for recession?). The Bloomberg article below reports that all three main agencies concerned with the oil market – the U.S. Energy Information Administration, the Paris-based International Energy Agency and OPEC – have trimmed their oil demand growth forecasts for 2019. With lower expected demand, oil companies are beginning to run down stocks and thus require to purchase less crude oil.
On the supply side, US shale output has grown rapidly in recent weeks and US output has now reached a record level of 11.7 million barrels per day (mbpd), up from 10.0 mbpd in January 2018, 8.8 mbpd in January 2017 and 5.4 mbpd in January 2010. The USA is now the world’s biggest oil producer, with Russia producing around 11.4 mpbd and Saudi Arabia around 11.1 mpbd.
Total world supply by the end of 2018 of around 102 mbpd is some 2.5 mbpd higher than expected at the beginning of 2018 and around 0.5 mbpd greater than consumption at current prices (the remainder going into storage).
So will oil prices continue to fall? Most analysts expect them to rise somewhat in the near future. Markets may have overcorrected to the gloomy news about global growth. On the supply side, global oil production fell in December by 0.53 mbpd. In addition OPEC and Russia have signed an accord to reduce their joint production by 1.2 mbpd starting this month (January). What is more, US sanctions on Iran have continued to curb its oil exports.
But whatever happens to global growth and oil production, the future price will continue to reflect demand and supply. The difficulty for forecasters is in predicting just what the levels of demand and supply will be in these uncertain times.
One of the key developments in economics in recent years has been the growing influence of behavioural economics. We considered some of the insights of behavioural economics in a blog in 2016 (A nudge in the right direction?). As the post stated, ‘Behavioural economists study how people’s buying, selling and other behaviour responds to various incentives and social situations. They don’t accept the simplistic notion that people are always rational maximisers.’ The post quoted from a Livemint article (see first linked article below):
According to behavioural economists, the human brain neither has the time nor the ability to process all the information involved in decision making, as assumed by the rational model.
Instead, people use heuristics. A heuristic technique is any approach to problem-solving, such as deciding what to buy, which is practical and sufficient for the purpose, but not necessarily optimal. For example, people may resort to making the best guess, or to drawing on past experiences of similar choices that turned out to be good or bad. On other accasions, when people are likely to face similar choices in the future, they resort to trial and error. They try a product. If they like it, they buy it again; if not, they don’t.
On other occasions, they may use various rules of thumb: buying what their friends do, or buying products on offer or buying trusted brands. These rules of thumb can lead to estimates that are reasonably close to the utility people will actually get and can save on time and effort. However, they sometimes lead to systematic and predictable misjudgements about the likelihood of certain events occurring.
In traditional models of consumer choice, individuals aim to maximise their utility when choosing between goods, or bundles of goods. The context in which the choices are offered is not considered.
Yet, in real life, we see that context is important; people will often make different choices when they are presented, or framed, in different ways. For example, people will buy more of a good when it is flagged up as a special offer than they would if there is no mention of an offer, even though the price is the same.
The recognition that framing is important to choices has led to the development of nudge theory. Indeed, it underpins many marketing techniques. These seek to persuade people to make a particular choice by framing it in an optimistic way or presenting it in a way that makes it easy to decide.
Governments too use nudge theory. In the UK, the Coalition government (2010–15) established the Behavioural Insights Team (BIT) (also unofficially known as the Nudge Unit) in the Cabinet Office in 2010. A major objective of this team is to use ideas from behavioural economics to design policies that enable people to make better choices for themselves.
The podcast linked below, looks at the use of nudge theory. The presenter, Mary Ann Sieghart looks at how we are being encouraged to change our behaviour. She also looks at the work of UCL’s Love Lab which researches the way we make decisions. As the programme notes state:
Mary Ann is grilled in UCL’s Love Lab to find out how she makes decisions; she finds taking the pound signs off the menu in a restaurant encourages her spend more and adding adjectives to the food really makes it taste better.
Walking through the Nudge Unit, she hears how powerful a tiny tweak on a form or text can get be, from getting people back to work to creating a more diverse police force. Popular with the political left and right, it has been embraced around the world; from Guatemala to Rwanda, Singapore to India it is used to reduce energy consumption, encourage organ donation, combat corruption and even stop civil wars.
But the podcast also looks at some of the darker sides of nudging. Just as we can be nudged into doing things in our interests, so too we can be nudged to do things that are not so. Politicians and businesses may seek to manipulate people to get them to behave in ways that suit the government or the business, rather than the electorate or the consumer. The dark arts of persuasion are also something that behavioural economists study.
The articles below explore some of the areas where nudge theory is used to devise policy to influence our behaviour – for good or bad.
Coffee chain Starbucks announced last week that it is trialling the introduction a 5p charge for takeaway cups. The proceeds will be donated to environmental charity Hubbub. Starbucks is the first UK coffee chain to make such a move and it hopes that the charge will reduce the use of disposable cups.
Perhaps unwittingly, Starbucks appears to have based its trial on important insights from behavioural economics and this may significantly increase the likelihood that it is successful.
Behavioural economics was thrown into the spotlight last year when one of its leading advocates, Richard Thaler, was awarded the Nobel prize in Economics. However, two of Thaler’s mentors, Amos Tversky and Daniel Kahneman, sowed the seeds for the field of behavioural economics. Most notably, in one of the most cited papers in economics, in 1979 they published a ground breaking alternative to the standard model of consumer choice.
One of the key insights from their model, known as prospect theory, is that rather than simply being concerned with their overall level of wealth, individuals care about gains and losses in wealth relative to a reference point. Furthermore, individuals are loss averse – a loss hurts about twice as much as an equivalent gain makes them feel good.
So how does this help to predict how consumers will react to Starbucks’ trial? Well, crucially, Starbucks is increasing the price of coffee in a takeaway cup. Prospect theory predicts that consumers will see this as a loss relative to the pre-trial price, which serves as a reference point. Since this hurts them a lot, they will be likely to take measures to avoid the levy. In support of this, research undertaken by Starbucks shows that 48% of consumers asked said that they would definitely carry a reusable cup to avoid paying the extra 5p.
As the company’s vice-president of communications, Simon Redfern, made clear, this would be in stark contrast to Starbucks’ previous attempts to reduce waste:
We’ve offered a reusable cup discount for 20 years, with only 1.8% of customers currently taking up this offer.
Furthermore, in 2016 they even experimented with increasing the discount from 25p to 50p. However, the impact on consumer behaviour remained low. Again, this evidence is entirely consistent with prospect theory. If consumers view the discounted price as a gain relative to their reference point, while they would feel some benefit from saving money, this would be felt much less than the equivalent loss would be.
Therefore, it seems likely that introducing a charge for takeaway cups will prove a much better way to reduce waste. More generally, this example demonstrates that the significant insights which prospect and other behavioural theories provide should be taken into account when trying to intervene to influence consumer behaviour in markets.