Donald Trump has suggested that the Fed should cut interest rates by 1 percentage point and engage in a further round of quantitative easing. He wants to see monetary policy used to give a substantial boost to US economic growth at a time when inflation is below target. In a pair of tweets just before the meeting of the Fed to decide on interest rates, he said:
China is adding great stimulus to its economy while at the same time keeping interest rates low. Our Federal Reserve has incessantly lifted interest rates, even though inflation is very low, and instituted a very big dose of quantitative tightening. We have the potential to go up like a rocket if we did some lowering of rates, like one point, and some quantitative easing. Yes, we are doing very well at 3.2% GDP, but with our wonderfully low inflation, we could be setting major records &, at the same time, make our National Debt start to look small!
But would this be an appropriate policy? The first issue concerns the independence of the Fed.
It is supposed to take decisions removed from the political arena. This means sticking to its inflation target of 2 per cent over the medium term – the target it has officially had since January 2012. To do this, it adjusts the federal funds interest rate and the magnitude of any bond buying programme (quantitative easing) or bond selling programme (quantitative tightening).
The Fed is supposed to assess the evidence concerning the pressures on inflation (e.g. changes in aggregate demand) and what inflation is likely to be over the medium term in the absence of any changes in monetary policy. If the Federal Open Market Committee (FOMC) expects inflation to exceed 2 per cent over the medium term, it will probably raise the federal funds rate; if it expects inflation to be below the target it will probably lower the federal funds rate.
In the case of the economy being in recession, and thus probably considerably undershooting the target, it may also engage in quantitative easing (QE). If the economy is growing strongly, it may sell some of its portfolio of bonds and thus engage in quantitative tightening (QT).
Since December 2015 the Fed has been raising interest rates by 0.25 percentage points at a time in a series of steps, so that the federal funds rate stands at between 2.25% and 2.5% (see chart). And since October 2017, it has also been engaged in quantitative tightening. In recent months it has been selling up to $50 billion of assets per month from its holdings of around $4000 billion and so far has reduced them by around £500 billion. It has, however, announced that the programme of QT will end in the second half of 2019.
This does raise the question of whether the FOMC is succumbing to political pressure to cease QT and put interest rate rises on hold. If so, it is going against its remit to base its policy purely on evidence. The Fed, however, maintains that its caution reflects uncertainty about the global economy.
The second issue is whether Trump’s proposed policy is a wise one.
Caution about further rises in interest rates and further QT is very different from the strongly expansionary monetary policy that President Trump proposes. The economy is already growing at 3.2%, which is above the rate of growth in potential output, of around 1.8% to 2.0%. The output gap (the percentage amount that actual GDP exceeds potential GDP) is positive. The IMF forecasts that the gap will be 1.4% in 2019 and 1.3% in 2020 and 2021. This means that the economy is operating at above normal capacity working and this will eventually start to drive up inflation. Any further stimulus will exacerbate the problem of excess demand. And a large stimulus, as proposed by Donald Trump, will cause serious overheating in the medium term, even if it does stimulate growth in the short term.
For these reasons, the Fed resisted calls for a large cut in interest rates and a return to quantitative easing. Instead it chose to keep interest rates on hold at its meeting on 1 May 2019.
But if the Fed had done as Donald Trump would have liked, the economy would probably be growing very strongly at the time of the next US election in November next year. It would be a good example of the start of a political business cycle – something that is rarer nowadays with the independence of central banks.
Articles
FOMC meeting
Questions
- What are the arguments for central bank independence?
- Are there any arguments against central bank independence?
- Explain what is meant by an ‘output gap’? Why is it important to be clear on what is meant by ‘potential output’?
- Would there be any supply-side effects of a strong monetary stimulus to the US economy at the current time? If so, what are they?
- Explain what is meant by the ‘political business’ cycle? Are governments in the UK, USA or the eurozone using macroeconomic policy to take advantage of the electoral cycle?
- The Fed seems to be ending its programme of quantitative tightening (QT). Why might that be so and is it a good idea?
- If inflation is caused by cost-push pressures, should central banks stick rigidly to inflation targets? Explain.
- How are expectations likely to affect the success of a monetary stimulus?
Latest resesarch from the independent American think tank The Conference Board paints a worrying picture about the growth of UK labour productivity. While global growth in labour productivity has weakened following the financial crisis, its weakness in the UK is singled out in the Board’s 2019 Productivity Brief. It finds that amongst large mature economies the decline in labour productivity growth rates has been greatest in the UK. This has important implications for the country’s longer-term well-being and, specifically, it peoples’ living standards.
The UK saw the growth in real GDP (national output) fall from 1.8 per cent in 2017 to 1.4 per cent in 2018. The Conference Board predicts that this will fall further to 0.8 per cent in 2019. In the context of living standards, the growth in real GDP per capita is particularly important. An increase in the population will, other things being equal, lower living standards because more people will be sharing a given amount of real national income. The growth in real GDP per capita fell from 1.1 per cent in 2017 to 0.7 per cent in 2018 and is predicted to fall to just 0.1 per cent in 2019.
Chart 1 shows the annual rates of growth in real GDP and real GDP per capita from the 1950s. The average growth rates are 2.4 and 1.9 per cent respectively. The other series shown is the annual growth in real GDP per person employed. This is a measure of the growth in labour productivity. Its average annual growth rate is also 1.9 per cent. This illustrates the intrinsic long-run relationship between labour productivity growth and the growth rate of GDP per capita and hence in general living stanadards. (Click here to download a PowerPoint copy of the chart.)
In the short term, rates of growth in output per worker (labour productivity) and GDP per capita (general living standards) can be less similar. For example, when unemployment rates rise labour productivity rates may be little affected despite GDP per capita falling. Nonetheless, the important point here is the close long-run relationship between the growth in labour productivity and GDP per capita. This then raises an important question: what factors contribute to the growth in output and labour productivity?
An approach known as growth accounting helps to identify four key contributors to the growth of total output. The first is the quantity of labour, commonly measured in labour hours. The second is the quality of labour, also known as labour composition. Third is capital services which are physical inputs into production and include machinery, structures and IT capital. Capital services are affected by quantity and quality, but, unlike labour, it is practically more difficult to separate out these dimensions. Fourth, is Total Factor Productivity (TFP).
TFP it is essentially the residual contribution to output growth that cannot be explained by changes in the quantity and quality of the individual inputs. Hence, in principle, it is capturing changes in how effectively the labour and capital inputs are being employed and combined in production. The Conference Board’s Productivity Brief describes the growth in TFP as providing ‘a more accurate picture of the overall efficiency by which capital, labour and skills are combined in the production process’.
Chart 2 shows Conference Board estimates of the percentage point contribution of these four sources of growth since 1990. Over this period, output growth averaged 2 per cent per year. The contribution of capital services and, hence, what is known as capital accumulation is particularly significant at 1.5 percentage points per year. This has been significantly larger than the contribution of labour hours which averaged only 0.3 percentage points per year since 1990. This evidences the importance played by capital deepening for output growth in the UK. (Click here to download a PowerPoint copy of the chart.)
Capital deepening captures the growth in capital services relative to the growth in the labour input. It takes on even greater significance when we think about the growth in labour productivity since, after all, this is the growth in output relative to the quantity of labour. It is significant though that since 2015 the growth of capital services has contributed only 1 percentage point to output growth while the growth of labour hours has contributed an average of 0.7 percentage points. This points to a slowdown in capital deepening and hence in the growth of labour productivity.
Chart 2 also illustrates the importance of TFP growth to overall output growth. It is also important (along with capital deepening and the growth in labour quality) for the growth in labour productivity. Interestingly, we observe significant fluctuations in the growth of TFP. This is thought to reflect fluctuations in the utilisation of inputs. For example, if the utilisation of inputs falls (rises) when output falls (increases) this will be mirrored by a disproportionately large fall (increase) in TFP. In the longer-term, however, changes in TFP capture aspects of technological progress and advancement that enable more effective production methods and techniques to be deployed. In other words, the growth of TFP captures the ability of production to benefit from the advancement in ideas, products, processes and know-how.
A decline in the growth in TFP growth following the financial crisis is found quite widely in mature economies. The annual rate of growth of TFP across mature economies fell from 0.5 per cent year in 2000-2007 to 0.2 per cent in 2010-2017. In the UK this fall was from 0.5 per cent to -0.1 per cent. Hence, the decline in TFP growth of 0.6 percentage points between 2010 and 2017 was double the 0.3 percentage point fall across all mature economies. In 2018 the Conference Board estimate that TFP in the UK fell by 0.1 percent further exacerbating the downward pressure on labour productivity.
As our final chart shows, it is the magnitude to which labour productivity has eased following the financial crisis that sets the UK apart. While across all mature economies the growth of output per labour hour (another measure of labour productivity growth) fell from an average of 2.3 per cent per year in 2000-2007 to 1.2 per cent in 2010-2017, in the UK the fall was from 2.2 per cent to 0.5 per cent per year. (Click here to download a PowerPoint copy of the chart.)
While the productivity problem facing the UK is not new, the latest figures comes as a very timely reminder of the extent of the problem. To some extent the uncertainty around Brexit and the negative impact on capital accumulation has only helped to exacerbate the problem. But, this may mask a more systemic problem facing the UK. Getting to the root of this problem matters. It matters most significantly for our long-term wellbeing and prosperity. The productivity gap with our major industrial competitors is a gap that policymakers need not only to be mindful of but one that needs closing.
Articles
Questions
- What do you understand by the term labour productivity. How could we measure it?
- Why is it important to look at the growth of output per capita when assessing the benefits of long-term growth?
- Why is labour productivity important for the long-term well-being of a country?
- What do you understand by the method of growth accounting?
- What is the distinction between capital accumulation and capital deepening?
- What might explain why the growth of labour productivity has been lower in the years following the post-financial crisis?
- What do you understand by Total Factor Productivity (TFP)?
- What does the long-term growth of TFP attempt to capture?
- If you were an economic advisor to the government, what types of policy initiatives might you recommend for a government concerned about low rates of growth of labour productivity?
It is perhaps timely given the ongoing uncertainty around Brexit to revisit and update our blog Desperately seeking confidence written back in January. Consumer and business confidence reflects the sentiment, emotion, or anxiety of consumers and businesses. Confidence surveys therefore try to capture these feelings of optimism or pessimism. They may then provide us with timely information for the short-term prospects for private-sector spending. For example, declining levels of confidence might be expected to play a part in weakening the growth of consumption and investment spending.
Attempts are made to measure confidence through the use of surveys. One long-standing survey is that conducted for the European Commission. Each month consumers and firms across the European Union are asked a series of questions, the answers to which are used to compile indicators of consumer and business confidence. For instance, consumers are asked about how they expect their financial position to change. They are offered various options such as ‘get a lot better, ‘get a lot worse’ and balances are then calculated on the basis of positive and negative replies.
The chart plots confidence in the UK for consumers and different sectors of business since the mid 1990s. The chart captures the volatility of confidence. This volatility is generally greater amongst businesses than consumers, and especially so in the construction sector. (Click here to download a PowerPoint copy of the chart.)
Confidence measures rebounded across all sectors during the 2010s, with positive balances being recorded consistently from 2013 to 2016 in services, retail and industry. Subsequently, confidence indicators became more erratic though often remaining at above-average levels. However, confidence indicators have eased across the board in recent months. In some cases the easing has been stark. For example, the confidence balance in the service sector, which contributes about 80 per cent of the economy’s national income, fell from +10.9 in February 2018 to -16.2 in February 2019, though recovering slightly to -9.2 in March 2019.
Chart 2 shows how the recent easing of consumer confidence has seen the confidence balance fall below its long-term (median) average of -7. In March 2019 the balance stood at -11.7 the lowest figure since November 2013. To put the easing into further perspective, the consumer confidence balance had been as high as +8.2 in September 2015. (Click here to download a PowerPoint copy of the chart.)
Changes in confidence are used frequently as an example of a demand shock. In reality changes in consumer confidence are often likely to be an amplifier of shocks rather than the source. For example, the collapse in aggregate demand in 2007/8 that followed the ‘credit crunch’, the severe tightening of credit conditions and financial distress of many sectors of the economy is likely to have been amplified by the collapse in consumer confidence. The weakening of confidence since 2016 is perhaps a purer example of a ‘confidence shock’. Nonetheless, falls in confidence, whether they amplify existing shocks or are the source of shocks, are often a signal of greater economic uncertainty.
Greater uncertainty is likely to go and hand in hand with lower confidence and is likely to reflect greater uncertainty about future income streams. The result is that people and businesses become more prudent. In the context of households this implies a greater willingness to engage in self-insurance through increased saving. This is known as buffer stock or precautionary saving. Alternatively, people may reducing levels of borrowing. In uncertain times prudence can dominate our impatience that encourages us to spend.
Chart 3 plots the paths of the UK household-sector saving ratio and consumer confidence. The saving ratio approximates the proportion of disposable income saved by the household sector. What we might expect to see, if greater uncertainty induces buffer-stock saving, is for falls in confidence to lead to a rise in the saving ratio. Conversely, less uncertainty as proxied by a rise in confidence would lead to a fall in the saving ratio. (Click here to download a PowerPoint of the chart.)
The chart provides some evidence of this. The early 1990s and late 2000s coincided with both waning confidence and a rising saving ratio, whilst the rising confidence seen in the late 1990s coincided with a fall in the saving ratio. However, the easing of confidence since 2016 has coincided with a period where the saving ratio has been historically low. In the first quarter of 2017 the saving ratio was just 3.3 per cent. Although the saving ratio has ticked up a little, in the final quarter of 2018 it remained historically low at just 4.9 per cent. Hence, the available data on the saving ratio does not provide clear evidence of the more cautious behaviour we might expect with waning confidence.
Consider now patterns in the consumer confidence balance alongside the annual rate of growth of consumer credit (net of repayments) to individuals by banks and building societies. Consumer credit is borrowing by individuals to finance current expenditure on goods and services.
Data on consumer credit is more timely than that for the saving ratio. Therefore, Chart 4 shows the relationship between consumer confidence and consumer credit into 2019. We observe a reasonably close association consumer credit growth and consumer confidence. Certainty, the recent easing in confidence is mirrored by an easing in the annual growth of net consumer credit. (Click here to download a PowerPoint of the chart.)
The year-to-year growth in net consumer credit has eased considerably since the peak of 10.9 per cent in November 2016. In February 2019 the annual growth rate of net consumer credit had fallen back to 6.3 per cent, its lowest rate since September 2014. As we noted in our recent blog Riding the consumer credit cycle (again) it is hard to look much past the effect of Brexit in acting as a lid on the growth in consumer credit. Therefore, while the recent falls in consumer confidence have yet to markedly affect the saving ratio they may instead be driving the slowdown in consumer credit. The effect will be to weaken the growth of consumer spending.
Articles
Questions
- Draw up a series of factors that you think might affect both consumer and business confidence. How similar are both these lists?
- Which of the following statements is likely to be more accurate: (a) Confidence drives economic activity or (b) Economic activity drives confidence?
- What macroeconomic indicators would those compiling the consumer and business confidence indicators expect each indicator to predict?
- What is meant by the concept of ‘prudence’ in the context of spending? What factors might determine the level of prudence
- How might prudence be expected to affect spending behaviour?
- How might we distinguish between confidence ‘shocks’ and confidence as a ‘propagator’ of shocks?
- What is meant by buffer stock or precautionary saving? Draw up a list of factors that are likely to affect levels of buffer stock saving.
- If economic uncertainty is perceived to have increased how could this affect the consumption, saving and borrowing decisions of people?
Spring has already made its appearance here in Norfolk. Our garden is in full bloom and I am in a particularly spring-philosophical mood today – especially so as I should soon be hearing news from the editorial office of a coveted economics journal. This concerns a paper that I submitted for publication what feels like months ago.
And just as I was reflecting on this thought, a paper by Firmuc and Paphawasit (2018) landed on my desk, evaluating the impact of physical attractiveness on academic research productivity in the field of economics. More specifically, the authors pull together information about the research productivity of about 2000 published economics researchers. They then find photos of them and rate their attractiveness (yes, seriously!) using an online survey. In particular:
Besides collecting some basic information on the authors, we also rated their attractiveness. To this effect, we circulated a number of online survey links to potential participants at Brunel University and elsewhere, using direct communication, email and social networks. Each online survey collected basic background information on the assessor (gender, age, ethnicity, highest education, and whether they are currently enrolled as a student) followed by 30 randomly-chosen and randomly-ordered photos, with each picture placed on a separate page.
…Each rater was asked to rate the attractiveness of the person in the photo on an 11-point scale, from 0 (unattractive) to 10 (very attractive). No information on the photographed individuals was provided and the raters were told that the survey studies the formation of perceptions of beauty. The raters were also asked whether they recognised the person in the picture, or whether the picture did not load properly: in such instances, their scores were excluded from the analysis.
The average beauty score was 3.9, with the most attractive academic scoring 7.6
They even attach photographs of the three most attractive male authors in their sample in an appendix (thankfully the other end of the distribution was left out – I had to check to make sure, as I was worried for a few minutes I would find my photo posted there!).
Their results show that there is a link between authors’ attractiveness and quality of journals where their papers are published, as well as number of citations that they receive. According to their findings, this association matters most for more productive authors (‘of intermediate and high productivity’), whereas there seems to be very small or no effect for less productive authors. Some of these effects disappear once controlling for journal quality:
…attractive authors tend to publish their research in better journals, but once their work is published, it does not attract more citations than other papers published in the same journal by less good-looking authors.
Although there are many methodological parts of this paper that I do not quite understand (probably because it is not my area of specialisation), it does remind us that looks do matter in labour markets. There is a well-established literature in labour economics discussing the association between appearance/beauty and wages and the so-called ‘halo effect’ (referring to the physical attractiveness premium that more attractive workers are likely to command in labour markets – see also Langlois et al., 2000; Zebrowitz et al., 2002; Kanazawa and Kovar, 2004; for a detailed discussion on this).
I was also surprised to read that this beauty bias can be also gender specific. For instance, Cash et al (1977) and Johnson et al. (2010) find that the effect goes the other way (negative impact) when considering female candidates applying for jobs traditionally perceived as ‘masculine’ ones. By contrast, male candidates are more likely to experience a positive return on good looks, irrespective of the type of job that they do (see also Johnson et al., 2010).
No surprise then that ‘guyliners’, ‘make up for men’ and other male beauty products are becoming increasingly popular amongst younger workers – in Europe it is not as common yet as it is in parts of Asia (Japan comes to mind), but I imagine it is a matter of time, as more workers realise that there are positive returns to be made!
References
- Beautiful Minds: Physical Attractiveness and Research Productivity in Economics
Institute of Labor Economics conference paper, Jan Fidrmucand Boontarika Paphawasit (July 2018)
- Maxims or myths of beauty? A meta-analytic and theoretical review
Psychological Bulletin, Vol 126(3), pp.390–423, Judith H Langlois, Lisa Kalakanis, Adam J Rubenstein, Andrea Larson, Monica Hallam and Monica Smoot (May 2000)
- Looking Smart and Looking Good: Facial Cues to Intelligence and their Origins
Personality and Social Psychology Bulletin, Leslie A Zebrowitz, Judith A Hall, Nora A Murphy, Gillian Rhodes (February 2002)
- Why beautiful people are more intelligent
Intelligence, Vol 32, pp.227–243, Satoshi Kanazawa and Jody L Kovar (2004)
- Sexism and beautyism in personnel consultant decision making
Journal of Applied Psychology, Vol 62(3), pp.301–10, Thomas Cash, Barry Gillen and D S Burns (January 1977)
- Physical Attractiveness Biases in Ratings of Employment Suitability: Tracking Down the “Beauty is Beastly” Effect
The Journal of Social Psychology, 150(3), pp.301–18, Stefanie K Johnson, Kenneth E Podratz, Robert L Dipboye and Ellie Gibbons (April 2010)
Article
Questions
- Read some of the papers posted above and explain the main argument about the link between physical attraction and wages. What does the empirical evidence show on this?
- Using examples and anecdotal evidence, do you agree with these findings?
- If these findings are representative of the real world, what do they suggest about the functioning of modern labour markets?
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.
Articles
Questions
- What is meant by the annual rate of house price inflation?
- How is a rise in the rate of house price inflation different from a rise in the level of house prices?
- What factors are likely to determine housing demand (instructions to buy)?
- What factors are likely to affect housing supply (instructions to sell)?
- Explain the difference between nominal and real house prices.
- What does a decrease in real house prices mean? Can this occur even if actual house prices have risen?
- How might we explain the recent differences between house price inflation rates in London relative to other parts of the UK, like the Midlands and Wales?
- Why were house prices so affected by the financial crisis?
- Assume that you asked to measure the affordability of housing. What data might you collect?
Trump Urges Steep Fed Rate Cut as Central Bankers Hold Meeting