UK Property Investors Head For The Exit

The UK Property Investment Market could be a leading indicator of what is ahead for our market. But in the UK just 15% of all mortgages are for investment purposes (Buy-to-let), compared with ~35% in Australia.  Yet, in a down turn, the Bank of England says investment property owners are four times more likely to default than owner occupied owners when prices slide and they are more likely to hold interest only loans. Sounds familiar?

According to a report in The Economist,  “one in every 30 adults—and one in four MPs—is a landlord; rent from buy-to-let properties is estimated at up to £65bn ($87bn) a year. But yields on rental properties are falling and government policy has made life tougher for landlords. The age of the amateur landlord may be”.

Investing in the housing market has seemed like a one-way bet, with prices trending upwards in real terms for four decades, mainly because government after government has failed to loosen planning restrictions on building new houses. Now, however, there are signs that regulatory changes have begun to send the buy-to-let boom into reverse.

Yields on rental properties have fallen. House prices have risen faster than rents, in part because buy-to-letters have reduced the supply of housing available to prospective owner-occupiers while simultaneously increasing the supply of places to rent. Britain’s ratio of house prices to rents is now 50% above its long-run average. All this makes buy-to-let investment less lucrative. Data from the Bank of England suggest that yields in September were below 5%, their joint-lowest rate since records began in 2001, when they were above 7.5%.

One consequence of this could be a more stable financial system. Roughly 15% of mortgage debt is on buy-to-let properties. The Bank of England has warned that there are risks associated with this. One problem is that property investors buy when house prices are rising but sell when they are falling, making house prices more volatile. Buy-to-let landlords are also more likely to default than owner-occupiers. One reason is that doing so does not force them out of their home. Another is that buy-to-let mortgages are more likely to be interest-only (ie, where the principal is not repaid). That can be tax-efficient but it means that monthly repayments can jump sharply if interest rates rise. The Bank of England’s stress tests last month showed that the rate at which landlords’ loans turn sour could be four times greater than the rate for owner-occupiers’ loans. All things considered, the shrinking of the buy-to-let sector may come as a relief to regulators.

The future for buy-to-letters will not get much brighter. In January a tweak to the rules on capital-gains tax will increase the liabilities of landlords who register as businesses. Large institutional investors are moving on to buy-to-letters’ turf, hoping to benefit from their economies of scale to offer better-quality housing to tenants. It was good while it lasted, but the golden age for the amateur landlord may be over.

UK Lifts Counter Cyclical Buffer

The Bank of England release their Financial Stability Report today, which includes the results of recent stress tests.  Though the stress tests show that UK Banks could handle the potential losses in the extreme scenarios, the FPC is raising the UK counter cyclical buffer rate from 0.5% to 1% with binding effect from 28 November 2018. In addition buffers for individual banks will be reviewed in January 2018, to take account of the probability of a disorderly Brexit, and other risk factors hitting at the same time.

They highlighted risks from higher LTI mortgage and consumer lending, and the potential impact of rising interest rates. They still have their 15% limit on higher LTI income mortgages (above 4.5 times). They are concerned about property investors in particular  – defaults are estimated at 4 times owner occupied borrowers under stressed conditions! Impairment losses are estimated at 1.5% of portfolio.

Beyond this, they discussed the impact of Brexit, and potential impact of a disorderly exit.

Finally, from a longer term strategic perspective, they identified potential pressures on the banks (relevant also we think to banks in other locations). There were three identified , first competitive pressures enabled by FinTech may cause a greater and faster disruption to banks’ business models than they currently expect; next the cost of maintaining and acquiring customers in a more competitive environment could reduce the scope for cost reductions or result in greater loss of market share and third the future costs of equity for banks could be higher than the 8% level that banks expect either because of higher economic uncertainty or greater perceived downside risks.

Here is the speech and press conference.

The FPC’s job is to ensure that UK households and businesses can rely on their financial system through thick and thin. To that end, today’s FSR and accompanying stress tests address a wide range of risks to UK financial stability. And they will catalyse action to keep the system well‐prepared for potential vulnerabilities in the short, medium and long terms.

In particular, this year’s cyclical stress test incorporates risks that could arise from global debt vulnerabilities and elevated asset prices; from the UK’s large current account deficit; and from the rapid build‐up of consumer credit. Despite the severity of the test, for the first time since the Bank  began stress testing in 2014 no bank needs to strengthen its capital position as a result.

Informed by the stress test and our risk analysis, the FPC also judges that the banking system can continue to support the real economy even in the unlikely event of a disorderly Brexit. At the same time, the FPC has identified a series of actions that public authorities and private financial institutions need to take to mitigate some major cross cutting financial risks associated with leaving the EU.

The Bank’s first exploratory scenario assesses major UK banks’ strategic responses to longer term risks to banks from an extended low growth, low interest rate environment and increasing competitive pressures enabled by new financial technologies. The results suggest that banks may need to give more thought to such strategic challenges.

The Annual Cyclical Stress Test

Today’s stress test results show that the banking system would be well placed to provide credit to households and businesses even during simultaneous deep recessions in the UK and global economies, large falls in asset prices, and a very large stressed misconduct costs. The economic scenario in the 2017 stress test is more severe than the deep recession that followed the global financial crisis. Vulnerabilities in the global economy trigger a 2.4% fall in world GDP and a 4.7% fall in UK GDP falls.

In the stress scenario, there is a sudden reduction in investor appetite for UK assets and sterling falls sharply, as vulnerabilities associated with the UK’s large current account deficit crystallise. Bank Rate rises sharply to 4.0% and unemployment more than doubles to 9.5%. UK residential and commercial real estate prices fall by 33% and 40%, respectively. In line with the Bank’s concerns over consumer credit, the stress test incorporated a severe consumer credit impairment rate of 20% over the three years across the banking system as a whole. The resulting sector‐wide loss of £30bn is £10bn higher than implied by the 2016 stress test.

The stress leads to total losses for banks of around £50 billion during the first two years ‐ losses that would have wiped out the entire equity capital base of the banking system ten years ago. Today, such losses can be fully absorbed within the capital buffers that banks must carry on top of their minimum capital requirements. This means that even after a severe stress, major UK banks would still have a Tier 1 capital base of over £275 billion or more than 10% of risk weighted assets to support lending to the real economy.

This resilience reflects the fact that major UK banks have tripled their aggregate Tier 1 capital ratio over the past decade to 16.7%.

Countercyclical Capital Buffer

Informed by the stress test results for losses on UK exposures, the FPC’s judgement that the domestic risk environment—apart from Brexit—is standard; and consistent with the FPC’s guidance in June; the FPC is raising the UK countercyclical buffer rate from 0.5% to 1% with binding effect from 28 November 2018. In addition, as previously announced, capital buffers for individual banks will be reviewed by the PRC in January. These will reflect the firm‐specific results of the stress test, including the judgement made by the FPC and PRC in September. These buffers can be drawn on as necessary during a downturn to allow banks to support the real economy.

Brexit

There are a range of possible outcomes for the future UK‐EU relationship. Consistent with its remit, the FPC is focused on scenarios that, even if the least likely to occur, could have the greatest impact on UK financial stability. These include scenarios in which there is no agreement or transition period in place at exit. The 2017 stress test scenario encompasses the many possible combinations of macroeconomic risks and associated losses to banks that could arise in this event. As a consequence, the FPC judges that, given their current levels of resilience, UK banks could continue to support the real economy even in the event of a disorderly exit from the EU.

That said, in the extreme event in which the UK faced a disorderly Brexit combined with a severe global recession and stressed misconduct costs, losses to the banking system would likely be more severe than in this year’s annual stress test. In this case where a series of highly unfortunate events happen simultaneously, capital buffers would be drawn down substantially more than in the stress test and, as a result, banks would be more likely to restrict lending to the real economy, worsening macroeconomic outcomes. The FPC will therefore reconsider the adequacy of a 1% UK countercyclical capital buffer rate during the first half of 2018, in light of the evolution of the overall risk environment. Of course, Brexit could affect the financial system more broadly. Consistent with the Bank’s statutory responsibilities, the FPC is publishing a checklist of steps that would promote financial stability in the UK in a no deal outcome.

It has four important elements:

– First, ensuring that a UK legal and regulatory framework for financial services is in place at the point of leaving the EU. The Government plans to achieve this through the EU Withdrawal Bill and related secondary legislation.
– Second, recognising that it will be difficult, ahead of March 2019, for all financial institutions to have completed all the necessary steps to avoid disruption in some financial services. Timely agreement on an implementation period would significantly reduce such risks, which could materially disrupt the provision of financial services in Europe and the UK.
– Third, preserving the continuity of existing cross‐border insurance and derivatives contracts. Domestic legislation will be required to achieve this in both cases, and for derivatives, corresponding EU legislation will also be necessary. Otherwise, six million UK insurance policy holders with £20 billion of insurance coverage, and thirty million EU policy holders with £40 billion in insurance coverage, could be left without effective cover; and around £26 trillion of derivatives contracts could be affected. HM Treasury is considering all options for mitigating these risks.
– Fourth, deciding on the authorisations of EEA banks that currently operate in the UK as branches. Conditions for authorisation, particularly for systemic firms, will depend on the degree of cooperation between regulatory authorities. As previously indicated, the PRA plans to set out its approach before the end of the year. Irrespective of the particular form of the United Kingdom’s future relationship with the EU, and consistent with its statutory responsibility, the FPC will remain committed to the implementation of robust prudential standards. This will require maintaining a level of resilience that is at least as great as that currently planned, which itself exceeds that required by international baseline standards.

Biennial Exploratory Scenario

Over the longer term, the resilience of UK banks could also be tested by gradual but significant changes to business fundamentals. For the first time, the FPC and PRC have examined the strategic responses of major UK banks to an extended low growth, low interest rate environment combined with increasing competitive pressures in retail banking from increased use of new financial technologies. FinTech is creating opportunities for consumers and businesses, and has the potential to increase the resilience and competitiveness of the UK financial system as a whole. In the process, however, it could also have profound consequences for the business models of incumbent banks. This exploratory exercise is designed to encourage banks to consider such strategic challenges. It will influence future work by banks and regulators about longer‐term issues rather than informing the FPC and PRC about the immediate capital adequacy of participants.

Major UK banks believe they could, by reducing costs, adapt to such an environment without major changes to strategy change or by taking more risk. The Bank of England has identified clear risks to these projections:
– Competitive pressures enabled by FinTech may cause a greater and faster disruption to banks’ business models than they currently expect.
– The cost of maintaining and acquiring customers in a more competitive environment could reduce the scope for cost reductions or result in greater loss of market share.
– The future costs of equity for banks could be higher than the 8% level that banks expected in this scenario either because of higher economic uncertainty or greater perceived downside risks.

Conclusion

The FPC is taking action to address the major risks to UK financial stability. Given the tripling of their capital base and marked improvement in their funding profiles over the past decade, the UK banking system is resilient to the potential risks associated with a disorderly Brexit.

In addition, the FPC has identified the key actions to mitigate the impact of the other major cross cutting issues associated with a disorderly Brexit that could create risks elsewhere in the financial sector.

And on top of its existing measures to guard against a significant build‐up of debt, the FPC has taken action to ensure banks are capitalised against pockets of risk that have been building elsewhere in the economy, such as in consumer credit.

As a consequence, the people of the United Kingdom can remain confident they can access the financial services they need to seize the opportunities ahead.

The Investment Web That Is Fintech

From The Bank Underground.

Investment in the Financial Technology (FinTech) industry has increased rapidly post crisis and globalisation is apparent with many investors funding companies far from their own physical locations.  From Crunchbase data we gathered all the venture capital investments in FinTech start-up firms from 2010 to 2014 and created network diagrams for each year.

The animation below depicts FinTech investments by year from 11 hub countries (coloured pink) to a broader set of recipient countries (coloured blue) from 2010 to 2014.

Source: Crunchbase data and our own calculations.

The arrows indicate the direction of flow, the thickness of each line is proportional to the number of investments, and the node area denotes the total number of foreign FinTech investments into that particular country. To avoid the issue of missing data and purchasing power parity across the globe, we did not use the monetary value of these investments. Unsurprisingly, the USA and the UK attract the largest number of foreign FinTech investments; although the number of outgoing investments from the UK is relatively small (demonstrated by the lack of thick outgoing arrows).

FinTech investment is also revolutionising developing financial sectors in China along with other African and Asian countries. However, it is interesting that China is not one of the fastest growing nodes. An explanation for this is that the majority of venture capital investment in Chinese FinTech firms comes from domestic investors, which we have not captured in this animation.

Nevertheless, one can see the expansion and the growing interconnectedness of global FinTech investments from these network diagrams.

Note: Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

Machine Learning, Analytics And Central Banking

The latest Bank Underground blog post “New Machines for The Old Lady”,  explores the power of machine learning and advanced analytics.

Rapid advances in analytical modelling and information processing capabilities, particularly in machine learning (ML) and artificial intelligence (AI), combined with ever more granular data are currently transforming many aspects of everyday life and work. In this blog post we give a brief overview of basic concepts of ML and potential applications at central banks based on our research. We demonstrate how an artificial neural network (NN) can be used for inflation forecasting which lies at the heart of modern central banking.   We show how its structure can help to understand model reactions. The NN generally outperforms more conventional models. However, it struggles to cope with the unseen post-crises situation which highlights the care needed when considering new modelling approaches.

Similarly to the victory of DeepBlue over chess world champion Garry Kasparov in 1997, the 2017 victory of AlphaGo over Go world champion Ke Jie is seen as a hallmark of the advancements of machine intelligence. Both victories were made possible by rapid advancements of information technologies, however in different ways. For DeepBlue, it was improvements in computer memory and processing speed. But for AlphaGo, it was the ability to learn from and make decisions based on rich data sources, flexible models and clever algorithms.

Recent years have seen an explosion in the amount and variety of digitally available data (“big data”). Examples are online activities, such as online retail and social media or from the usage of smartphone apps. Another novel source is the interaction of the gadgets themselves, e.g. data from a multitude of sensors and the connections of everyday devices to the internet (the “internet of things”).

Monetary policy decisions, the supervision of financial institutions and the gauging of financial market conditions – the common tasks of the Bank of England and many other central banks – are certainly data-driven activities. However, these have traditionally been fuelled by relatively “small data”, often in the form of monthly or quarterly time series. This also changed in recent years, partly driven by reforms following the Global Financial Crisis 2008 (GFC), which handed central banks and regulators with additional powers, responsibilities and more data. These novel data sources and analytical techniques provide central banks, and also the economics profession more widely, with new opportunities to gain insights and ultimately promote the public good.

What is machine learning?

ML is a branch of applied statistics largely originating from computer science. It combines elements of statistical modelling, pattern recognitions and algorithm design. Its name can be interpreted as designing systems for automated or assisted decision making, but not (yet) autonomous robots in most cases. Hence, ML is not a fixed model or technique, but rather an analytical toolbox for data analysis, which can be used to tailor solutions for particular problems.

The main difference between ML and conventional statistical analysis used in economic and financial studies (often summarised under the umbrella of econometrics) is its larger focus on prediction compared to causal inference. Because of this, machine learning models are not evaluated on the basis of statistical tests, but on their out-of-sample prediction performance, i.e., how the model describes situations it hasn’t seen before. A drawback of this approach is that one may struggle to explain why a model is doing what it does, commonly known as the black box criticism.

The general data-driven problem consists of a question and a related dataset. For example, “What best describes inflation given a set of macroeconomic time series?” This can be framed as a so-called supervised problem in ML terminology. Here, we are trying to model a concrete output or target variable Y (inflation), given some input variables X.  These supervised problems can be further segmented into regression and classification problems. The regression problem involves a continuous target variable, such as the value of inflation over a certain period of time. Classification, in the other hand, involves discrete targets, e.g. if inflation is below or above target at certain point in time, or if a bank is in distress or not. Alongside this, there is also unsupervised machine learning where no such labelled target variable Y exists. In this case, any ML approach would try to uncover an underlying clustering structure or relationships within data. These main categories of machine learning problems are shown in Figure 1. We discuss a case study for all three problem types in SWP 674: Machine learning at central banks. Case study 3 on analysis tech start-ups with a focus on financial technology (fintech) is also reviewed in this post.

Figure 1: Machine learning taxonomy. Case studies refer to SWP 674: Machine learning at central banks.

Case study: UK CPI inflation forecasting

As a simple example, we feed a set of macroeconomic time series (e.g. the unemployment rate or rate of money creation) into an artificial neural network to forecast UK CPI inflation over a medium-term horizon of two years and compare its performance to a vector autoregressive model with one lag (VAR). It is worth noting that this is not how central banks typically forecast inflation but it works well to see how ML techniques can be used.

An important aspect to consider here is that many ML approaches do not take time into account, meaning that they mostly focus on so-called cross sectional analyses. ML approaches which do take time into account are, among others, online learning or reinforcement learning. These approaches would need considerably more data than are available for our coarse-grained time series. We therefore take a different approach building temporal dependencies implicitly into our models. Namely, we match pattern in a lead-lag setting where changes in consumer prices lead changes or levels of other aggregates by two years. The contemporaneous 2-year changes of input variables and CPI target are shown in Figure 2, with the exception of the Bank rate, implied inflation from indexed Gilts and the unemployment level which are in levels. One can see that the crisis in the end of 2008 (vertical dashed line) represents a break for may series.

Figure 2: Selection of macroeconomic time series used as inputs and target of NN.

A key element of machine learning is training, i.e. fitting, and testing a model on different parts of a dataset. The reason for this is the mentioned absence of general statistical tests in many situations. The difference between training and test performance indicates then how well a model generalises to unseen situations. In the current setting this is performed within an expanding window setting where we successively fit the model on past data, evaluate its performance based on an unconditional forecast and then expand the training dataset by a quarter.

The result of this exercise is given in Figure 3, which shows the model output of a neural network (NN) with two hidden layers, technically a deep multi-layered perceptron.  This is a multi-stage model which combines weighted input data in successive layers and map these to a target variable (supervised learning). They are also at the forefront of recent AI developments. The NN model (green) in this unconditional forecast has an average annualised absolute error below half a percentage point over a two-year horizon during the pre-GFC period. This is already more than twice as accurate as the simple vector-autoregressive (VAR) benchmark model with one lag (grey line in Figure 3). The NN also shows relatively low volatility in its output, contrasted to the VAR.

Figure 3: ML model performance of combination of a deep neural network and support vector machine (green) relative to UK CPI inflation (blue). Red prediction intervals (PI) are constructed from sampled input data. The GFC only impacts the models in 2010 because of 2-year lead-lag relation of all models. Source: SWP 674.

Looking into the black box

ML models, particularly deep neural networks, are often criticised for being hard to understand in terms of their input-output relations. We can, however, get a basic understanding of the model in the current case as it is relatively simple. The model performance in Figure 3 drops markedly as soon as the effects from the GFC enter the model (vertical red dashed line), forecasting inflation persistently too low.

This behaviour can be understood when looking at the NN input structure before and after the GFC. Figure 4 depicts the relative weights stemming from different variables entering the first hidden layer of the neural network for pre and post-crisis data. This part of the NN has been identified to contribute the leading signal of the model’s output.  We see that changes in private sector debt and gross disposable household income (GDHI) provided the strongest signal in the pre-crisis period, as seen by the darker shades of normalised inputs. Particularly, the former saw a sharp drop at the onset of the crisis. Post-crisis, model weights gradually gave more importance to the increased level of unemployment. Both factors can explain why the neural network – wrongly in this case – predicted a sharp drop in inflation (see Figure 2).

The above discussion can best be thought of as a statistical decomposition. Artificial neural networks, like other machine learning approaches, are non-structural models focusing on correlations in the data. Therefore, care has to be given when interpreting the results of such an analysis. A strong correlation may or may not point to a causal relationship. Further analyses may be needed to pinpoint such a relation.

Figure 4: Pre and post crisis input weight structure to first (hidden) layer of neural network from macroeconomic time series inputs. Darker values indicate a stronger signal. Source: SWP 674.

Conclusion

We have given a very brief introduction of machine learning techniques and demonstrated how they might be used for tasks which central banks have been trusted with. Many of these tasks are linked to the availability of ever more granular data. Here, their particular strength lies in the modelling of non-linearities and accurate prediction.

However, care is needed when interpreting the outputs from ML models. For example, they do not necessary identify economic causation. The fact that a correlation between two variables has been observed in the past does not mean it will hold in the future, as we have seen in the case of the artificial neural network when it is faced with a situation not previously seen in the data, resulting in forecasts wide of the mark.

Chiranjit Chakraborty and Andreas Joseph both work in the Bank’s Advanced Analytics Division.

Note: Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

700 Years Of Data Suggests The Reversal In Rates Will Be Rapid

From Bank Underground.

A GUEST post on the Bank of England’s “Bank Underground” blog makes the point that most reversals after periods of interest rate declines are rapid. When rate cycles turn, real rates can relatively swiftly accelerate. The current cycle of rate decline is one of the longest in history, but if the analysis is right, the rate of correction to more normal levels may be quicker than people are expecting – and a slow rate of increases designed to allow the economy to acclimatize may not be possible.  Not pretty if you are a sovereign or household sitting on a pile of currently cheap debt!

With core inflation rates remaining low in many advanced economies, proponents of the “secular stagnation” narrative –that markets are trapped in a period of permanently lower equilibrium real rates- have recently doubled down on their pessimistic outlook. Building on an earlier post on nominal rates this post takes a much longer-term view on real rates using a dataset going back over the past 7 centuries, and finds evidence that the trend decline in real rates since the 1980s fits into a pattern of a much deeper trend stretching back 5 centuries. Looking at cyclical dynamics, however, the evidence from eight previous “real rate depressions” is that turnarounds from such environments, when they occur, have typically been both quick and sizeable.

Despite much research into the causes of real rate distortions in recent years, the discussion has arguably suffered from a lack of long-term context. Key additions – such as the  influential BoE staff working paper confirming the role of excess savings and lower investment preferences – typically trace back their observations to the late Bretton Woods period, or at best to Alvin Hansen’s time in the interwar period. Hamilton et al. and Eichengreen are rare exceptions in their inclusion of 19th century data.

Therefore, the majority of work on secular stagnation– and with it the debate regarding bond market valuations  – fails to consider the deeper historical rate trends. In contrast, a  multi-century dataset  offers the opportunity to look at cyclical behavior and the dynamics of reversals from earlier real rate depressions.

Seven centuries of real risk free rates

In this spirit, this post (based on a new Staff Working Paper) provides a real rate dataset for the last 700 years, and identifies a total of nine “real rate depressions” sharing similar traits to the trend observed since the 1980s.

This chart further expands risk-free nominal bond data introduced in a previous post, and adjusts for historical ex-post inflation data provided by Bob Allen, Bank, Bundesbank, archival, and FRED data. We trace the use of the dominant risk-free asset over time, starting with sovereign rates in the Italian city states in the 14th and 15th centuries, later switching to long-term rates in Spain, followed by the Province of Holland, since 1703 the UK, subsequently Germany, and finally the US.

The all-time real rate average stands at 4.78% and the 200-year real rate average stands at 2.6%. Relative to both historical benchmarks, the current market environment thus remains severely depressed.

Upon closer inspection, it can be shown that trend real rates have been following a downward path for close to five hundred years, on a variety of measures. The development since the 1980s does not constitute a fundamental break with these tendencies.

Regressing the 7-year average on a constant time trend (the red line) indicates an average fall of 1.6 basis points per annum. Simple averages tell a similar story of decline. In many ways, therefore, the “secular decline” in real rates since the days of Paul Volcker is but a part of a deeper “millennial decline” tracing back its roots to the days of the late Quattrocento.

The all-time peaks in real yields in the mid-1400s coincide with the geopolitical escalations amid the fall of Constantinople, the seizure of silver mines by the Ottomans, on the Balkans, and evidence of increasing European BoP deficits to the Levant – factors consistent with the narrative of a “Great Bullion Famine”. The “real rate turning point” on our basis thus somewhat precedes the classical dating of the “financial revolution” and the sharp inflows of New World treasure. The falling trend continues unabatedly after other political inflections, such as the Reformation, the Thirty Years War, and into the modern days of Globalization.

The breakdown of real risk-free rates: nominal and inflation components

The real rate, by definition, represents the difference between nominal rates and inflation.

The 700-year average annual ex-post headline inflation for the risk-free issuer stands at 1.09%,, the 200-year average, since 1817, stands at 1.55%, with a further pickup in the 1900s. Three observations stand out: First, the past 60 years, in which the US has been the benchmark bond issuer has been the most inflationary in our whole sample period; second, current inflation rates of slightly below 2% remain fully in-line with the ex-post performance witnessed in modern times, with today’s typical inflation targets already being accommodative if measured against (very) long run trends. Third, never before has a longer period without deflation existed than the ongoing 70-year spell since World War Two.

Economists often view the real-nominal-inflation nexus through the lens of the Fisher equation– where long run nominal rates are the sum of two “structural” variables: real rates, and (expected) inflation.  The chart below presents the real rates and ex post inflation rates in terms of century averages:

The green bars show the fall in real rates, blue bars the contribution of inflation. Evidently, the fall in real rates over successive centuries has been partially muted by the higher inflation in the 20th and 21st centuries. As a result the decline in nominal rates over time has been somewhat less than the underlying fall in real rates.

“Real rate depression cycles”

Focusing on our cyclical precedents, on several previous occasions, rates have exhibited a sustained divergence from long-term averages. Over the seven centuries, nine historical “real rate depression cycles” can be identified, which saw a secular decline of real interest rates, followed by reversals.  The chart below plots the size and duration of these compression episodes:

Our current “secular stagnation” of real rates, at 34 years, is the second longest thus far recorded. Only the years immediately surrounding the discovery of America outstrip the current cycle by length. Measured by total rate compression from peak to trough, the period from 1325 to 1353 – at 1700 basis points in real rates – is the most notable. In our 7-year moving average dataset, the all-time trough within depression episodes is recorded in 1948, at -5.3%.  Turning to how these depressions end, the chart below plots the path of real rates in each reversal period following the trough.

Most reversals to “real rate stagnation” periods have been rapid, non-linear, and took place on average after 26 years. Within 24-months after hitting their troughs in the rate depression cycle, rates gained on average 315 basis points, with two reversals showing real rate appreciations of more than 600 basis points within 2 years. Generally, there is solid historical evidence, therefore, for Alan Greenspan’s recent assertion that real rates will rise “reasonably fast”, once having turned.

Fundamental Stagnations: A closer look at the “Long Depression”

Most of the eight previous cyclical “real rate depressions” were eventually disrupted by geopolitical events or catastrophes, with several – such as the Black Death, the Thirty Years War, or World War Two – combining both demographic, and geopolitical inflections. Most cyclical real rate depressions equally coincided with inflation outperformances. But for a minority of cycles, economic fundamentals were decisive, and exhibited both excess savings and subdued inflation. The prime example – and likely the closest historical analogy to today’s “secular stagnation” – is represented by the global “Long Depression” of the 1880s and 1890s.

Following years of a global railroad investment frenzy, and global overcapacity indicators inflecting in the mid-1860s, the infamous “Panic of 1873” heralded the advent of two decades of low productivity growth, deflationary price dynamics, and a rise in global populism and protectionism.

Following the crash, the UK’s 10-year moving average TFP growth declined from 1.7% in the early 1870s, to flat, and even at times negative levels in the following two decades (Chart below). Labor productivity in particular shrunk, plummeting by around two-thirds in the same timeframe, after reaching new all-time records at the dawn of the crisis. Though recent research has emphasized nominal factors for the period, most contemporaries including Joseph Schumpeter stressed real drivers. Indeed, real GDP trend growth in the UK reached century lows by the 1890s. Despite alleged money scarcity, borrowing rates declined.

What ended the Long Depression? Labor productivity bottomed out in 1892-3, prior to the discovery of gold at the Klondike, and the associated monetary expansion. Wage inflation started outstripping productivity increases as early as 1885, leading the recovery in general inflation. And US equities finally bounced back from their 15-year lows with the Presidential election of William McKinley – a Republican pro-business protectionist – in November 1896. In other words, there is strong evidence suggesting that the last “secular stagnation cycle” started fading relatively autonomously after just over two decades following the key financial shock, not requiring the aid of decisive fiscal or monetary stimulus.

Conclusion

On aggregate, then, the past 30-odd years more than hold their own in the ranks of historically significant rate depressions. But the trend fall seen over this period is a but a part of a much longer ”millennial trend”. It is thus unlikely that current dynamics can be fully rationalized in a “secular stagnation framework”. Meanwhile, looking at past cyclical patterns, the evidence suggests that when rate cycles turn, real rates can relatively swiftly accelerate.

Paul Schmelzing is an academic visitor to the Bank from Harvard University’s History Department.

Note: Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England or its policy committees.

Why the Bank of England is raising interest rates – and the risks involved

From The Conversation.

The Bank of England is poised to raise interest rates for the first time since July 2007. Its monetary policy committee (MPC) will meet to decide on November 2. The MPC’s last vote on the issue was a 7-2 majority for maintaining current rates, but it’s only a matter of time before rates rise.

Initially, the rise will likely be from 0.25% to 0.5%. This may not sound like much, but it could have significant implications for the UK economy. Mark Carney, the bank’s governor, is facing an uncomfortable trade-off, mulling priorities of curbing inflation versus financial stability.

The reason for the rate rise is inflation, which has risen to its highest level since April 2012 (3%) – beyond the government’s target figure of 2%. This is a result of the Brexit vote in June 2016, which saw a precipitous drop in the pound, making imports more expensive and pushing up prices of everyday items.

A rise in interest rates should help stem this by boosting the value of the pound. For example, expectations of a rate increase last month prompted a temporary jump in the value of the pound of 1.5%. Plus, the central bank will be hoping that higher interest rates will encourage people to save – another method of curbing inflation – although any increases on savers’ rates will be negligible.

But, despite hints of a rise from Carney, the situation is not that simple. The long period of low interest rates has been accompanied by a worrying surge in consumer borrowing. Household debt exceeds 100% of household income and house prices are on an upward trajectory, climbing back to 2007 crisis levels.

Clearly, an interest rate increase would harm borrowers and may even harm financial stability if monthly repayments are no longer manageable and defaults rife.

UK household debt to income ratio. ONS and Bank of England calculations

With the average outstanding balance on a mortgage in the UK estimated to be close to £120,000 and, assuming repayment will take 15 years, the next graph shows estimated annual repayments for a variety of possible interest rate hikes.

What is worrying is that the Taylor rule (a popular interest rate forecasting tool) suggests that interest rates should be approximately 2% higher than they currently are. This would further squeeze household budgets and push the average repayments above £11,000 per year.

Stopping a car crash

Another area the Bank of England is keeping a close eye on is the way cars are financed. Many new cars are purchased on personal contract plans (PCPs) whereby they are paid for via monthly repayments – usually with zero or low upfront payments. A rise in interest rates could result in an awkward situation for car financiers if owners are unable to keep up with payments and decide to return their keys early.

With a glut of cars returning to the forecourt, as people reallocate resources to increasing mortgage and debt payments, the estimated residual values of the cars may prove inaccurate, leaving financiers (banks and car firms) with heavy losses. And it doesn’t stop there. The loans have been syndicated so there could be ripple effects through the financial system.

Headed for a crash? John Stillwell/PA Archive/PA Images

All in all, this sounds rather reminiscent of 2007, with the difference being that the asset here is a car that is depreciating in value as opposed to a house. Not surprisingly, the Bank of England is trying to reign in car financing to engineer a soft landing.

Slow and steady

On top of all this, there is the notably gloomy outlook for the UK economy to contend with. To keep growth on an upward trajectory, keeping interest rates low is still seen by some as a necessity, and some economists (notably Danny Blanchflower, a former MPC member) still advocate for this.

Unemployment is now at low levels not seen since the mid-1970s, which is music to the ears of cautious central bankers. Yet wage growth – a measure of longer term inflation – remains subdued, and “underemployment” (such as the part-time worker who really wants to work full time) still has some way to fall to get back to pre-crisis levels not withstanding recent drops.

This will ensure that any hikes in interest rates will be a drawn out process – to avoid frightening financial markets, which have grown accustomed to cheap central bank funds and loose monetary policy over the past decade.

Author: Johan Rewilak, Lecturer, Aston University

UK Rate Rise Has Little Growth Impact, Shows Global Shift

The Bank of England’s (BoE) decision to increase UK interest rates by 25 bp partly unwinds the monetary stimulus it provided last summer, and is unlikely to have a large economic impact, Fitch Ratings says. The BoE looks set to tighten policy slowly, but the first UK rate hike in over decade highlights how shrinking output gaps and tighter labour markets are pushing central banks towards interest rate normalisation.

The BoE said Thursday that its Monetary Policy Committee (MPC) voted by 7:2 to increase the Bank Rate to 0.5%, reversing the cut it made last August in the aftermath of the Brexit referendum. It left the stock of bonds purchased under its quantitative easing (QE) scheme unchanged. Prior to last August, the Bank Rate had been unchanged for over seven years. The BoE’s last rate hike was in July 2007.

Fitch has for some time been expecting the post-referendum interest rate cut to be reversed, although in our most recent Global Economic Outlook (September 2017), we expected this to happen in early 2018. The MPC summary said that all members agreed that future increases “would be expected to be at a gradual pace and to a limited extent,” and that monetary policy “continues to provide significant support to jobs and activity.”

We think another increase is unlikely in the next 12 months, given the impact of Brexit uncertainty on the outlook for investment. Today’s decision does not alter our UK growth forecasts , which see a net trade boost partially offsetting slower domestic demand this year, enabling real GDP to rise by 1.5%, before slowing to 1.3% next year. But it remains to be seen how firms and households adjust to a shift in the monetary policy stance after such a long period without a rate rise.

While the BoE has no intention of slowing the economy down, its decision highlights how tighter labour market conditions (UK unemployment is at a 42-year low) and concerns about adverse supply-side impacts from Brexit have reduced tolerance for above-target inflation. Inflation rose to 3% in September partly in response to the weakening of sterling. We forecast inflation to slow next year, averaging 2.5%, but this would still be above the BoE’s 2% target.

As output gaps close, central banks around the world are generally refocusing on policy normalisation. The BoE said it was “ready to respond to changes in the economic outlook as they unfold” to ensure a sustainable return to target, while supporting the UK economy through its Brexit adjustment. Meanwhile the ECB has announced smaller monthly QE purchases from January, while this week’s Fed statement emphasised solid growth and did little to suggest that it felt that recent low US inflation readings were becoming more persistent.

Bank of England Lifts Cash Rate

From the UK Office For National Statistics.

Concerns about “inflation over target” and “limited” slack in the economy have prompted the Bank of England to raise interest rates for the first time in more than a decade.

On 2 November 2017, the Bank’s Monetary Policy Committee (MPC) voted 7-2 in favour of raising the interest rate to 0.5%, from a record low of 0.25%.

The majority of the MPC agreed that now was the right time to raise interest rates “to return inflation sustainably to the target”.

But the Committee acknowledged that “uncertainties associated with Brexit are weighing on domestic activity”. Some economists, including former MPC member David Blanchflower, had warned against an interest rate rise.

After rising to 5.75% in July 2007, the Bank of England base rate was subsequently cut nine times in the next two years as the financial crisis took effect.

It reached a record low of 0.5% in March 2009 and remained at that level, partly because of the long-lasting impact of the crisis, before dropping again to 0.25% in August 2016.

What’s changed since the last interest rate rise?

The last time we saw a rise in the interest rate was 5 July 2007. To put that into context, Tony Blair had recently resigned as Prime Minister and the first iPhone had just been released.

On that day, the Bank voted for a higher interest rate against a backdrop of a “strong global economy”. The rate had risen twice that year already and there was little sign of the impending financial crisis.

But between the last interest rate rise and today, the MPC had met 118 times and decided against raising interest rates on every occasion.

When setting interest rates, the MPC considers many factors including debt, savings, inflation, economic growth, employment and wages. They’ll also look at conditions in economies and financial markets worldwide.

Consumer credit rose by 4.6% compared with the previous year in May 2007, less than half as fast as September 2017 (9.9%). People are increasingly borrowing to finance their purchase of a new car. The Bank estimates that growth of dealership car finance accounts for three-quarters of growth in the total stock of consumer credit since 2012.

Raising the interest rate is expected to increase the size of repayments on loans, and could therefore lead to a reduction in the amount of borrowing.

Households saved 5.4% of their disposable income in April to June this year, compared with 8.8% in the first three months of 2007.
The interest rate dictates your earnings on money saved. Savers, who outnumber borrowers, could get a higher return thanks to today’s rise.

The economy took much longer to recover from this recession compared with previous ones, which kept interest rates low

Previous recessions, number of quarters taken for GDP to reach pre-recession level

But the general downward trend in interest rates goes back further than the last decade. Less than 30 years ago, the base rate was close to 15%.

The interest rate has fallen substantially in the last 30 years

Bank of England base rate, 1975 to 2017

Despite today’s rise, we’re unlikely to see a substantial reversal to that trend – Mr Carney has said that any increases to the rate will be “gradual” and “limited”.

Will Global Interest Rates Fall Further?

Mark Carney, Governor of the Bank of England gave a speech “[De]Globalisation and inflation“.  One passage in particular is highly significant. He discussed the impact of globalisation on inflation, and suggests that there are likely to be further downward pressure on real world interest rates, partly thanks to changing demographics and the relative pools of global investment and global savings. The net result is more investors looking for returns, compared with investment pools – which explains the bidding up of asset prices (including property and shares) while returns to investors continue to fall. The point is, this is structural – and wont change anytime soon. Indeed, he suggests real world interest rates could go lower, with the flow on to inflation.

For the past thirty years, a number of profound forces in the world economy has pushed down on the level of world real interest rates by as much as 450 basis points. These forces include the lower relative price of capital (in part as a consequence of the de-materialising of investment), higher costs of financial intermediation (due to financial reforms), lower public investment and greater private deleveraging. Two other factors – demographics and the distribution of income – merit particular attention.

Bank research estimates that the increased retirement savings as a result of global population ageing and longer life expectancy have lowered the global real interest rate by around 140 basis points since 1990 and they could lead to a further 35 basis point fall by 2025. The crucial point is that these effects should persist after the demographic trends have stabilised because the stock, not the flow, of savings is what matters.

 

By changing the distribution of income, the global integration of labour markets may also lower global R*. The changes in relative wages in advanced economies have shifted income towards skilled workers, who have a relatively higher propensity to save. Rising incomes in emerging market economies may be reinforcing that effect as saving rates are structurally higher in emerging market economies, reflecting a variety of factors including different social safety regimes.

The high mobility of capital across borders means that returns to capital will move closely together across countries, with any marked divergences arbitraged.

As a consequence, global factors are the main drivers of domestic long-run real rates at both high and low frequencies. For example, Bank of England analysis suggests that about 75% of the movement in UK long-run equilibrium rates is driven by global factors. Estimates by economists at the Federal Reserve deliver similar results.

 

Global factors also influence domestic financial conditions and therefore the effective stance relative to the shorter-term equilibrium rate of monetary policy, r*.

The presence of borrowers and lenders operating in multiple currencies and in multiple countries creates multiple channels through which developments in financial conditions can be transmitted across countries. For example, changes in sentiment and risk aversion can lead to international co-movement in term premia, affecting collateral valuations and so borrowing conditions.

Work by researchers at the Bank of England, building on analysis by the IMF, shows that a single global factor accounts for more than 40% of the variation in domestic financial conditions across advanced economies. For the UK, which hosts the world’s leading global financial centre, the relationship is much tighter, at 70%.

Highlighting the openness of the UK economy and financial system, a third of the business-cycle variation in the UK policy rate can be attributed to shocks that originate abroad.

One important channel of global spillovers is of course monetary policy. In coming years, it is reasonable to expect global term premia to rise as net asset purchases could shift significantly from the situation during the past four years when all net issuance within the G4 was effectively absorbed.

Is the global financial system any safer than before?

The latest Bank of England KnowledgeBank discusses the impact of regulation since 2007.  They discuss, capital, shadow banking and banker remuneration.

The financial crisis that struck in 2007 was among the worst on record. And it was global in nature. Financial markets seized up, world trade plummeted and the global economy went into recession. The cost of supporting banking sectors around the world reached $15 trillion. And the impact on people’s lives was severe. Many lost their jobs or saw a fall in their wages.

What’s been done to fix the global financial system?

A decade on since the start of the crisis, what’s changed?

After the crisis hit, the G20 – made up of the leaders and central bank governors from 20 major economies – set up the Financial Stability Board, which is tasked with monitoring the global financial system and making recommendations to make it serve society better.

Since then, various reforms have taken place. The video below summarises these changes: from the amount of ‘capital’ banks need to have, to new rules on bankers’ pay. While there is more work to be done, the video argues that the global financial system is today safer, simpler and fairer than it was a decade ago: