Auction Results 18 Nov 2017

The preliminary auction results from Domain are in. The trend looks like it is continuing to ease compared with last year, though a little up from last week so far. Melbourne is sitting on 72.1% stronger than last weekend.

Brisbane cleared 33% of 136 listed, Adelaide 62% of 98 listed and Canberra 69% of 96 listed.

Walking The Tightrope – The Property Imperative Weekly 18 Nov 2017

A really mixed bag of news this week, with stronger business and employment data, lower mortgage defaults and yet weak wage growth, and more evidence of the pressure on households. We pick over the coals and try to make sense of what’s going on.

 Welcome to the Property Imperative Weekly to 18 November 2017. Watch the video or read the transcript.

We start with some good news.

The latest National Australia Business (NAB) survey — a composite indicator that measures trading activity, profitability and employment — surged by a massive 7 points to +21, leaving it at the highest level since the survey began in 1997. On this measure, Australian businesses have not had it so good in at least two decades. There were enormous increases recorded in trading and profitability, suggesting that demand was rampant during October. However, beware, this included a massive unexplained jump in manufacturing and the survey’s lead indicators softened over the month, which, along with an unchanged reading on business confidence, raises questions as to whether the bounce in the conditions index can be sustained.

Deputy Governor Guy Debelle spoke at the UBS Australasia Conference on “Business Investment in Australia“. He argued that investment has been strong over the last decade, thanks to the mining sector. This is now easing back, and the question is will the non-mining sector start firing or not? Even if it does, they have huge boots to fill!

Luci Ellis RBA Assistant Governor (Economic) delivered the Stan Kelly Lecture on “Where is the Growth Going to Come From?“. An excellent question given the fading mining boom, and geared up households! But we really got few answers. Australia’s population is growing faster than in almost any other OECD economy. That has remained true over the past couple of years. The rate of natural increase is higher than many other countries, but most of the difference is the large contribution from immigration. Of course, just adding more people and growing the economy to keep pace wouldn’t boost our living standards. Next, employment participation has been rising recently. The increase has been concentrated amongst women and older workers and is linked with the increase in health and education employment. Finally, productivity can improve, especially if innovation can be leveraged, although she noted the rate of technology adoption has slowed down since the turn of the century. We wonder if this has something to do with the sluggish and underpowered NBN rollout currently underway.

The monthly trend unemployment rate remained at 5.5 per cent in October 2017, according to figures released by the Australian Bureau of Statistics. While the trend is down, it was not as strong as some analysts were expecting.  The seasonally adjusted unemployment rate decreased by 0.1 percentage points to 5.4 per cent and the labour force participation rate decreased to 65.1 per cent.

The ABS released their analysis of individual state accounts to Jun 2017. This includes an estimate of average gross household disposable income per capita. The variations across states are significant and interesting. Of note is the astronomical value, and trajectory of individuals in the ACT, at more than $90,000. We saw a decline in gross incomes in WA (one reason why mortgage defaults are rising there) at around $50,000. NSW was also around $50,000 while VIC was around $45,000 and TAS was $40,000.

Wages rose 0.5 per cent in the September quarter 2017 and 2.0 per cent over the year, according to the ABS. This was below consensus expectation, and continues the slow grind in household income, for many falling below the costs of living.  Those in the public sector continue to do better than those in the private sector. In original terms, wage growth to the September quarter 2017 ranged from 1.2 per cent for the Mining industry to 2.7 per cent for Health Care and Arts and recreation services. Western Australia recorded the lowest growth through the year of 1.3 per cent and Victoria, Queensland and Tasmania the highest of 2.2 per cent.

The legislation to tighten some aspects of investment property, and levy a charge on vacant foreign owned property has been passed in the Senate. The legislation prevents property investors from claiming travel expenses when travelling between properties, as well as tightening depreciation on plant and equipment tax deductions. Foreign owners will be charged a fee if they leave their properties vacant for at least six months in a 12-month period, in an attempt to release more property to ease supply. The latest Census showed that there are 200,000 more vacant homes across Australia than there were ten years ago.

Turning to the mortgage industry, Fitch Ratings says Australia’s RBMS mortgage arrears fell to 1.02% in 3Q17, a 15bp decrease from the previous quarter; consistent with the nine-year long seasonal trend where 30+ days arrears have eased in the third quarter. They say the curing of third-quarter arrears was helped by borrowers using tax return receipts to make repayments. The 30+ days arrears were 4bp lower than in 3Q16, reflecting Australia’s improved economic environment and lower standard variable interest rates for owner-occupied lending. They said the gap between investor lending and owner-occupied rates has widened, as banks respond to regulatory investment and interest-only limits on new loan origination. Historically, investors paid a 25bp-30bp premium over owner-occupied loans, but this widened to 60bp in September 2017.

S&P Global Ratings said RMBS Mortgage arrears fell to 1.08% in September across Australian down from 1.10% in August 2017. They say mortgage arrears rose in both the Northern Territory and the ACT during September but fell elsewhere. The ACT mortgage arrears it is only at a low 0.64%, compared with Western Australia who has the highest arrears of 2.21%. However, while outstanding loan repayments on 30-to-60-day arrears also declined in most states between January and September, 90-day+ arrears rose in Western Australia and Queensland. This is the same as we saw recently in the bank reporting season. S&P expects arrears to rise over the coming months, as they “traditionally start to increase in November and continue through to March.”

There was more evidence of poor mortgage lending practice this week, following the recent UBS “Liar Loans” research study. A liar loan is a loan that is approved on the basis of unverified and possibly false information about income, assets or capacity to repay. This is important because mortgage delinquency and default may rise due to excessive risk taking in mortgage lending combined with deteriorating economic conditions; or due to falling income and rising unemployment during a housing downturn.

Connective remained brokers of their obligations, and pointed to findings from the 2016 Veda Cybercrime and Fraud Report, which recorded a 27 per cent year-on-year increase in falsifying personal information. “Falsified documentation — particularly documents that verify a customer’s income — is the most common type of fraud that a mortgage broker is likely to encounter,” the aggregator said. Back in June, Equifax informed brokers at a Pepper Money roadshow that 13 per cent of frauds reported were targeting home loans and there has been a 25 per cent year-on-year increase in frauds originating from the broker channel.

In the same vein, NAB has said it has commenced a remediation program for some of its customers, after a review identified their home loan may not have been established in accordance with NAB’s policies. NAB identified around 2,300 home loans since 2013 that may have been submitted without accurate customer information and/or documentation, or correct information in relation to NAB’s Introducer Program. As a result of NAB’s review, 20 bankers in New South Wales and Victoria had their employments terminated, or are no longer employed by NAB, and an additional 32 bankers had consequences applied including the reduction of remuneration. NAB has commenced writing to these customers – many of whom live overseas – asking them to participate in a detailed review of their loan, which may include verification of documents submitted at the time of their home loan application. Affected customers may be offered compensation as appropriate.

More evidence of the risks in the system came when The Reserve Bank in New Zealand said that Westpac New Zealand has had its minimum regulatory capital requirements increased after it failed to comply with regulatory obligations relating to its status as an internal models bank. Internal models banks are accredited by the Reserve Bank to use approved risk models to calculate how much regulatory capital they need to hold. Westpac used a number of models that had not been approved by the Reserve Bank, and materially failed to meet requirements around model governance, processes and documentation.

Still talking of risks, there was an interesting paper from the Federal Reserve Bank of Cleveland “Three Myths about Peer-to-Peer Loans” which suggested these platforms, which have experienced phenomenal growth in the past decade, resemble predatory loans in terms of the segment of the consumer market they serve and their impact on consumers’ finances and have a negative effect on individual borrowers’ financial stability. This is of course what triggered the 2007 financial crisis. There is no specific regulation in the US on the borrower side.  Given that P2P lenders are not regulated or supervised for antipredatory laws, lawmakers and regulators may need to revisit their position on online lending marketplaces.

We published two research reports this week. First our Quiet Revolution Banking Channel and Innovation Report, which is available for free download. And second the impact of rising interest rates on households.

It seems that eventually mortgage rates will rise in Australia, as global forces exert external pressure on the RBA, and as the RBA tries to normalise rates (at say 2% higher than today). Timing is, of course, not certain. But it is worth considering the potential impact. While our mortgage stress analysis takes a cash flow view of household finances, our modelling can look at the problem another way. One algorithm we have developed is a rate sensitivity calculation, which takes a household’s mortgage outstanding, at current rates, and increments the interest rate to the point where household affordability “breaks”.  We use data from our household survey to drive the analysis.

So we start with the average across the country. We find that around 10% of households would run into affordability issues with less a 0.5% hike in mortgage rates, and around another 8% would be hit if rates rose 0.5%, and a larger number would be added to the “in pain” pile, giving us a total of around 25% of households across the country in difficulty if rates went 1% higher. [Note that the calculation does not phase the rate increases in]. Around 40% of households would be fine even if rates when more than 7% higher. At a state level we found that around 40% of households in NSW would have a problem, compared with 27% in VIC and 24% in WA. We can also take the analysis further, to a regional view across the states. This reveals that the worst impacted areas would be, in order, Greater Sydney, Central Coast, Curtain and Greater Melbourne. These are all areas where home prices relative to income are significantly extended, thus households are highly leveraged.

CoreLogic said Mortgage clearance rates have continued to track below 70 per cent since June the year; this is a considerably softer trend than what was seen over the same period last year when clearance rates were tracking around the mid 70 per cent range for most of the second half 2016.  Results across each of the individual markets were varied this week, with Canberra recording the highest preliminary auction clearance rate of 72.9 per cent, while in Brisbane only 45.7 per cent of auctions cleared.

So to, two important reports.

According to the eighth edition of the Credit Suisse Research Institute’s Global Wealth Report, in the year to mid-2017, total global wealth rose at a rate of 6.4%, the fastest pace since 2012 and reached USD 280 trillion. But wealth distribution has become more uneven. This reflected widespread gains in equity markets matched by similar rises in non-financial assets (home prices), which moved above the pre-crisis year 2007’s level. Household wealth in Australia grew at an average annual growth rate of  12%, with about half the rise due to exchange-rate appreciation against the US dollar. Australia’s wealth per adult in 2017 is USD 402,600, the second highest in the world after Switzerland.

However, the composition of household wealth in Australia is heavily skewed towards non-financial assets, which average USD 303,200, and form 60% of gross assets. The high level of real assets partly reflects a large endowment of land and natural resources relative to population, but also results from high property prices in the largest cities.

Finally, Industry Super Australia, published an excellent discussion paper on “Assisting Housing Affordability” which endeavours to identify the underlying causes of affordability issues, and  considers some useful policy responses in the current and historical context. They rightly consider both supply and demand related issues.

They call out specifically the impact of incoming migration, especially around university suburbs in the major centres as one major factor.

More broadly, they articulate the problem facing many, in that access to affordable housing – a basic need – is now more difficult than ever and the issue is affecting household spending decisions:

  • Key workers like police officers, teachers and nurses can’t afford to live near the communities they serve.
  • Children are staying at home for longer, marrying later and taking longer to save for a home deposit.
  • Many older Australians are locked into big houses that no longer suit their needs while a greater number of near retirees are renting or paying off a mortgage.
  • Commuters spend too much time on congested roads and trains which are now the norm in certain Australian cities.
  • More Australians are renting.

The report is worth reading because it knits together the complex web of issues, and confirms the complexity which is housing affordability, and that there are no simple single point solutions.

And that’s the point. Sure, employment looks strong, but the nature of that employment is favouring lower wage occupations. Business confidence is strong, because business profits are up, but this is not translating into higher wages. As a result, wealth distribution is becoming more skewed, as home prices and stock prices rise. But the risks remain. Property is overvalued, and we lack joined up thinking to address the fundamental structural issues which exist. So meantime we muddle on, hoping that wage growth will start to rise before home prices fall too far and mortgage rates rise. Don’t look down, we are walking a tightrope!

So that’s the Property Imperative weekly to 18th November 2017. If you found this useful, do leave a comment below, subscribe to receive future updates, and check back next time.  Thanks for watching.

Housing Affordability, A Complex Equation

Industry Super Australia, a research and advocacy body for Industry super funds, has published an excellent discussion paper on “Assisting Housing Affordability” which endeavors to identify the underlying causes of affordability issues, and to consider some useful policy responses in the current and historical context. They rightly consider both supply and demand related issues.

They call out specifically the impact of incoming migration, especially around university suburbs in the major centres as one major factor.

More broadly, they articulate the problem facing many, in that access to affordable housing – a basic need – is now more difficult than ever and the issue is affecting household spending decisions:

  • Key workers like police officers, teachers and nurses can’t afford to live near the communities they serve.
  • Children are staying at home for longer, marrying later and taking longer to save for a home deposit.
  • Many older Australians are locked into big houses that no longer suit their needs while a greater number of near retirees are renting or paying off a mortgage.
  • Commuters spend too much time on congested roads and trains which are now the norm in certain Australian cities.
  • More Australians are renting.

This has been a long standing issue, but they say from 2013 the problem of housing affordability became more serious.

Many property developers (small and large) entered the market, chasing short-term speculative capital gains. This coincided with a ramping up of student arrivals who drew on their parents’ savings (a safe haven strategy) to acquire bricks and mortar, usually near centres of education. Alarm bells did not ring for Australian governments, even though most new arrivals were settling in a limited number of localities. These factors and market dynamics combined to drive record house prices in key centres. The key drivers of low housing affordability are due to imbalances in demand and supply in certain key markets.

  • On the demand side, key factors include the extent of unanticipated or uncoordinated immigration flows to growth centres; the relationship between international student intake and the dynamics of foreign investment in established dwellings; the interaction between record low interest rates and investors chasing future capital gains via gearing-oriented tax concessions; and lax lending practices.
  • On the supply side, key factors include poorly coordinated land release and infills approvals and the outright restriction of supply by state governments; private land developers stockpiling tracks of land around the urban fringe, and restrictive town planning and zoning rules by local governments that have produced very long lead-times for the construction of new, denser housing stock in areas where affordability is worsening.

There are significant risks attached to ignoring affordability issues.

The lack of coordination in housing policy across all levels of Australian government has generated hotspots in property markets that have undermined macroeconomic stability. Destabilising wealth effects and the continuing expansion of household debt are feeding an unsustainable cycle of property price inflation. Net foreign indebtedness has risen to concerning levels for a small open economy that lacks a diversified economic structure and runs persistent current account deficits. Australia is far too dependent on property and pits (extraction of iron ore, coal and now liquefied natural gas) as the launch pad of its economic advance. This is very risky and may end in tears.

Booming house prices are good news for existing owners and bad news for those entering the market for the first time. Prospective buyers paying 2017 prices must have faith, at a time when even investment professionals believe a purchase now is, over the short to medium term, ill-advised. They must also have faith in their capacity to maintain an adequate income to service their debt, or hope that prices will just keep rising. In Sydney, where prices have risen 87 per cent over five years, whilst incomes have risen around 15 per cent on average, that is a tough call. Yet so many people (mostly Australians below age 35) have been prepared to take out home loans valued at over six times their income, facilitated by the relatively lax lending standards of banks.

The paper confirms the complexity which is housing affordability, and that there are no simple single point solutions.

The key findings of the paper are:

  • Australia’s housing affordability problem has developed over several decades and will require a long-term commitment by all levels of government to resolve.
  • Destabilising wealth effects and the continuing expansion of household debt are feeding a cycle of property price inflation which looks unsustainable.
  • Policy responses that increase the buying power of households (for example, through grants, or reduced taxes) will only increase demand, and therefore prices.
  • Ignoring the emerging crisis in assisted housing (affordable, public and community) now risks major future social and productivity costs.
  • Simply increasing overall housing stock will not ensure that more assisted housing becomes available. Instead, increasing the supply of assisted housing specifically is required.
  • Waitlists for social housing remain intractable and this system no longer serves as a safety net.
  • Achieving the necessary growth in assisted supply is beyond the capacity of Australian governments, and private investment is required.

To resolve the issues in assisted housing, Federal, state and local governments need to coordinate their activity without duplication or political interference. The core elements of any strategy will require:

  • A central body to provide rigorous housing supply forecasting, which will assist with planning.
  • Developing appropriate incentives (for example, tax policy) to encourage institutional investment in a new assisted housing asset class.
  • Expanding the capacity and professionalism of the community housing sector to deal with larger scale developments and tenant administration.

Additionally, some general policy suggestions to address broader housing affordability issues are as follows:

  • Explicitly linking state and local government planning and housing approvals to estimates of regional housing supply gaps.
  • Encouraging more work and student visa holders to reside outside of property market hot-spots.
  • Directing all foreign investment in residential property to new buildings.
  • Streamlining town planning procedures by mandating the removal of unreasonable height restrictions within urban infill development zones (including ‘inner’ and ‘middle-ring’ suburbs).
  • Discouraging land hoarding by identifying underutilised assets for redevelopment (including assisted housing), and providing recycling bonuses to incentivise the release of public and private sites.
  • Reorienting some current tax concessions for existing property towards investment in new housing and institutional investment in new assisted housing.
  • Reforming land taxes in Australia via the abolition of stamp duties and replacing them with a mix of land and betterment taxes.
  • Promoting stability around property – the largest asset class held by ordinary Australians.

Australian 3Q17 Mortgage Arrears See Seasonal Falls

Australia’s RBMS mortgage arrears fell to 1.02% in 3Q17, a 15bp decrease from the previous quarter; consistent with the nine-year long seasonal trend where 30+ days arrears have eased in the third quarter. Fitch Ratings believes the curing of third-quarter arrears was helped by borrowers using tax return receipts to make repayments.

The 30+ days arrears were 4bp lower than in 3Q16, reflecting Australia’s improved economic environment and lower standard variable interest rates for owner-occupied lending. Unemployment improved by 10bp and real wage growth, although low, was positive. Underemployment has continued to improve, reflecting an increase in available work for underemployed workers.

Prepayment rates remained low during 2017, with the conditional prepayment rate (CPR) staying below 20% for three consecutive quarters; the longest period this rate has remained below 20% since 2011. The CPR increased slightly qoq to 19.6%, from 19.1%, while the Dinkum RMBS Index borrower payment rate increased to 21.6% qoq, from 21.2%.

The gap between investor lending and owner-occupied rates has widened, as authorised deposit-taking institutions respond to regulatory investment and interest-only limits on new loan origination. Historically, investors paid a 25bp-30bp premium over owner-occupied loans, but this widened to 60bp in September 2017.

Losses experienced after the sale of collateral property remained extremely low, with lenders’ mortgage insurance payments and excess spread sufficient to cover principal shortfalls in all transactions during the quarter.

Fitch’s Dinkum RMBS Index tracks arrears and the performance of mortgages underlying Australian residential mortgage-backed securities.

Is The Global Banking Network Really De-globalising?

An IMF working paper “The Global Banking Network in the Aftermath of the Crisis: Is There Evidence of De-globalization?” released today, shows that contrary to popular belief, the Global Banking Network has not shrunk since the GFC in the simple way often thought. Using complex and innovative modelling, they conclude that the banking world in some ways is connected more deeply, and with greater complexity than before. This means that players in one location could be impacted more severely by events in other geographies. They conclude that the hidden dynamics of the global banking network after the crisis suggest that the assertion that cross-border lending has shrunk globally seems to miss out significant details. They refrain from assessing the risk impact of this observation.

However, we conclude, like our digital world, global banking is more financially networked than ever, suggesting that risks could be propagated widely and in unexpected directions.

The global financial crisis in 2008-09 underscores the unique role of financial interconnectedness in transmitting and propagating adverse shocks. Previous literature stresses the significance of network structure in generating contagion,  lays out detailed mechanisms of contagion through balance-sheet effects, is followed by a large body of theoretical and empirical research on interbank markets, mostly within a single country or region, that focuses on modeling banks’ behavior in response to shocks in the financial system. Cross-border implications of the banking network, however, are mostly ignored due to scarcity of data and rich country-level heterogeneity that may lower the explanatory power of a unified framework.

The sharp fall in global cross-border banking claims after the crisis has been persistent, either measured in Bank for International Settlements (BIS) Locational Banking Statistics (LBS) or BIS Consolidated Banking Statistics (CBS). This persistent aggregate decline in cross-border banking claims has been considered evidence of financial deglobalization. In this paper, we consider the validity of the financial de-globalization argument by studying the evolution of the global banking network before, during and after the crisis, with a particular focus on the aftermath of the crisis. Instead of trying to establish the role of the network in propagating the crisis at a global level, we take the role of the global banking network as given and seek to investigate the impact of the crisis on the network. In this context, our key contributions to the literature are twofold: (i) we measure and map the global banking network using a model-free and data driven approach; and (ii) we analyze the evolution of the network using network analysis tools, including some novel applications, that are relevant given the  characteristics of the global banking network and the available data.

The foremost challenge in constructing the global banking network is to map and identify an accurate and comprehensive network structure using the available data on cross-border banking flows. Researchers face a tradeoff between data coverage and frequency. High frequency data, such as banks’ daily transactions, often contain a limited number of banks within a country, while datasets with a good coverage of global lending mainly report country-level aggregate statistics, and are updated infrequently. This challenge is further complicated by the difficulty in identifying the composition, sources and destinations of bank flows, primarily due to the use of offshore financial centers as important financial intermediaries. Not only are global banks able to conduct cross-border lending via entities in their headquarters and offshore financial centers, but also they can lend domestically through subsidiaries and/or branches within the border of the borrower countries. BIS International Banking Statistics (IBS), through its two datasets (LBS and CBS), offer the best available data to map the international bank lending activity across countries. This is especially the case of the CBS dataset, which consolidates gross claims of each international banking group on borrowers in a particular country, aggregating those claims following the nationality of the parent banks. This nationality-based nature of CBS is an advantage over LBS, which follows a residency-based principle, and thus obscures the linkages between the borrower country and the parent bank institution, when lending originates in affiliates located in third countries (e.g., off-shores financial centers). A disadvantage of using CBS is that it registers the full claims of the affiliates, independent of how those assets were funded (e.g., a claim of a foreign affiliate that is fully funded with local domestic depositors is still counted as a claim from the country of the parent bank on the borrower country where the affiliate is located). In order to avoid this overstatement of financial linkages, which are large in the case of emerging countries as shown in the next section, we combine BIS CBS data with bank level data, taking into account the claims of foreign affiliates and the local deposit funding used by subsidiaries and branches.

We use the improved measure of cross-border banking linkages to  onstruct a sequence of global banking networks, and apply tools from network theory to analyze the evolution of economic and structural properties of the network. We take a step further to incorporate this important discussion into our choice of metrics to identify important players and trace the structural evolution of the global banking network. We provide an in-depth discussion of network measure choice based on the structural context of a core-periphery, asymmetric and unbalanced network structure and in the economic context of characterizing banking flows at the country level.

We introduce measures of node importance that capture  distinct aspects of global banking linkages. In particular, we use recursively defined Katz-Bonacich centrality and authority/hub measure to characterize country importance based on its connection to and dependence on other important countries, as well as a novel application of modularity in order to capture the regional fragmentation of the network. The flexibility of our network configuration allows us to use a small number of network metrics to reveal distinct aspects of network structure and node importance.

We find that the overall shrinkage of cross-border bank lending after the crisis, which has been the key argument behind the claims on financial de-globalization, is also reflected in the average number of links and their strength in the global banking network.

However, rich details on the evolution of the network suggest that this argument is overly simplified.

While connections within traditional major global lenders (banks in France, Germany, Japan, UK, and US) became sparser, many non-reporting countries located at the periphery of the network are more connected, mainly due to the rise of non-major global lenders out of Europe. Measured in metrics of node importance, these lenders have been steadily climbing up the rank, resulting in a corresponding decline of European lenders in status and borrowers’ decreasing dependence on traditional lending countries. Moreover, we find substantial evidence indicating increasing level of regionalization of the global banking network. Even though post-crisis retrenchment of major global and non-major European banks’ operation in the aggregate was just partially offset by the rest of the BIS reporting countries’ regional expansion, their targeted expansions have increased regional interlinkages through both direct cross-border and affiliates’ lending. More formally, using network modularity as a novel application to assess the quality of network cluster structure based on region divisions, we find that this measure increases after the crisis, thus indicating, from the perspective of network theory, that some form of regionalization characterizes the post-crisis dynamics of the global banking network. Finally, we also confirm this regionalization process through a regression analysis of the evolution of cross-border lending. After controlling by geographical distance and trade relationships as well as lender and borrower characteristics, we find a statistically significant increase in cross-border lending when both borrower and lender belong to the same region, especially in the case of peripheral lenders during the post-crisis period.

We show that without proper adjustment, country-level banking statistics suffer from multiple data issues that distort the actual role of each country in cross-border lending, and increase the difficulty of accurately detecting key players in the network. We find evidence confirming the overall shrinkage in the scale of cross-border bank lending using a variety of network analysis tools. Moreover, these methods capture rich dynamics that occur inside the global banking network and are not captured by traditional aggregate indicators.

Using a set of centrality measures with meaningful economic interpretations, we delve substantially deeper to capture the interconnectedness faced by each country. While the structural stability of the highly concentrated global banking network is mainly due to the stability of major global lenders, we observe decline in importance for non-major global European lenders and a corresponding rise in the ranks for lenders from other region, comprised of mostly emerging market lenders. The hidden dynamics of the global banking network after the crisis suggest that the assertion that cross-border lending has shrunk globally seems to miss out significant details.

NOTE: IMF Working Papers describe research in progress by the authors and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the authors and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

The ACT Is Another Country

The ABS has released their analysis of individual state accounts to Jun 2017.

This includes an estimate of average gross household disposable income per capita. The variations across states are significant and interesting. Of note is the astronomical value, and trajectory of individuals in the ACT, relative to everywhere else.  In addition, we see a decline in gross incomes in WA (one reason why mortgage defaults are rising there).

Households in TAS and SA are, on average on the lower rungs. The slowdown in income growth is also visible.

This goes a long way to explaining the high current levels of mortgage stress we observe, because home prices, mortgages and credit growth are all rising faster than income. NSW and VIC, then QLD are worse hit.

 

RMBS Mortgage Arrears Lower Again, But…

S&P Global Ratings said RMBS Mortgage arrears fell to 1.08% in September across Australian down from 1.10% in August 2017.

They say mortgage arrears rose in both the Northern Territory and the ACT during September but fell elsewhere. While the ACT tops the list with a rise mortgage arrears it is only at a low 0.64%, compared with Western Australia who has the highest arrears of 2.21%.

However, while outstanding loan repayments on 30-to-60-day arrears also declined in most states between January and September, 90-day+ arrears  rose in Western Australia and Queensland. This is the same as we saw recently in the bank reporting season.

S&P said the growth in full-time jobs is positive for mortgage arrears. In addition, the rises rates on in more risky investor loans have minimal impact on RMBS.

This is a myopic view of mortgage portfolios as securitised loans are selected, and seasoned to manage risks. To that extent, it is not necessarily a good indicator of the wider market – including investor loans.

S&P expects arrears to rise over the coming months, as they “traditionally start to increase in November and continue through to March.”

This from Macquarie shows the trends.

 

Assessing China’s Residential Real Estate Market

The IMF just published a working paper examining real estate in China.

After a temporary slowdown in 2014-2015 China’s real estate market rebounded sharply in 2016. As signs of overheating emerged, the government turned to tighten real estate markets through a range of macroprudential and administrative measures. Many empirical studies point out that the house price surge is driven by fundamentals, while others consider the pickup of real estate activity is unsustainable. This paper uses city-level real estate data to estimate the range of overvaluation of real estate markets across city-tiers, and assesses the main risks of a real estate slowdown and its impact on economic growth and financial stability.

Real estate has been a key engine of China’s rapid growth in the past decades. Real estate investment grew rapidly from about 4 percent of GDP in 1997 to the peak of 15 percent of GDP in 2014, with residential investment accounting for over two thirds of the total real estate investment.

Bank lending to the sector makes up 25 percent of total bank loans, about half of all new loans in 2016, and banks’ increasing exposures to real estate, including through property developers and household mortgages, may pose financial stability concerns. Real estate also has strong linkages to upstream and downstream industries (about a quarter of GDP is real-estate related).2 In addition, land sales are a key source of local public finance, accounting for about 30 percent of local government revenue in 2016, while general government net spending financed by land sales is about 9 percent of the headline revenue in 2016. There has been a rapid expansion of government subsidies on social housing, consisting of nearly 6 million apartment units in 2015-2017.

Real estate markets vary significantly in China because of its large economic size, economic and social diversity, and fragmented local government policies. The real estate cycles tend to be more pronounced in top-tier cities in terms of price volatility, but they account for a small fraction of real estate inventory and investment.  Smaller cities constitute over half of residential real estate investment, but the price increase on average was much lower during 2013-16.

Distortions render China’s property market susceptible to both price misalignment and overbuilding. On the supply side, the market is distorted by local governments’ control over land supply and their reliance on land sales to finance spending. On the demand side, the market is prone to overvaluation—housing is attractive as an investment instrument given a history of robust capital gains, high savings, low real deposit interest rates, a lack of alternative financial assets, as well as capital account restrictions.

The government has closely monitored real estate activity given its importance in the economy. Policies are highly decentralized, with local governments (often with local branches of the financial regulators) deciding land sale and infrastructure development, granting construction and sales permits to developers, and setting purchases restrictions. The central government and financial regulators can also affect the housing market through financing conditions and macro-prudential tools for mortgage lending.

If house prices rise further beyond “fundamental” levels and the bubble expands to smaller cities, it would increase the likelihood and costs of a sharp correction, which would weaken growth, undermine financial stability, reduce local government spending room, and spur capital outflows. Empirical analysis suggests that the increasing intensity of macroprudential policies tailored to local conditions is appropriate. The government should expand its toolkit to include additional macroprudential measures and push forward reforms to address the fundamental imbalances in the residential housing market.

Note: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Where Do Consumers Fit in the Fintech Stack?

An excellent speech from Federal Reserve Governor Lael Brainard on the opportunities for innovation in customer facing services enabled by the digital revolution and the risks arising – specifically looking at “financial autopilots”.

As we have been highlighting, the evolutionary path is changing fast, see, our “Quiet Revolution” report, published just this week.  We track this path using our innovation life cycle mapping, updated below.

Here is the Governor’s speech:

The new generation of fintech tools offers the potential to help consumers manage their increasingly complicated financial lives, but also poses risks that will need to be managed as the marketplace matures.

In many ways, the new generation of fintech tools can be seen as the financial equivalent of an autopilot. The powerful new fintech tools represent the convergence of numerous advances in research and technology–ranging from new insights into consumer decisionmaking to a revolution in available data, cloud computing, and artificial intelligence (AI). They operate by guiding consumers through complex decisions by offering new ways of looking at a consumer’s overall financial picture or simplifying choices, for example with behavioral nudges.

As consumers start to rely on financial autopilots, however, it is important that they remain in the driver’s seat and have a good handle on what is happening under the hood. Consumers need to know and decide who they are contracting with, what data of theirs is being used by whom and for what purpose, how to revoke data access and delete stored data, and how to seek relief if things go wrong. In short, consumers should remain in control of the data they provide. In addition, consumers should receive clear disclosure of the factors that are reflected in the recommendations they receive. If these issues can be appropriately addressed, the new fintech capabilities have enormous potential to deliver analytically grounded financial services and simplified choices, tailored to the consumers’ needs and preferences, and accessible via their smartphones.

Consumers Face Complex Financial Choices
When the first major “credit card,” the Diner’s Club Card, was introduced in 1949, consumers could only use the cardboard card at restaurants and, importantly, only if they paid the entire amount due each month. Today, the average cardholder has about four credit cards, and the Federal Reserve Bank of New York estimates that American consumers collectively carry $785 billion in credit card debt.

When signing up for a credit card, consumers face a bewildering array of choices. Half of consumers report that they select new cards based on reward programs, weighing “cash back” offers against “points” with their credit card provider that may convert into airline or hotel “miles,” which may have varying values depending on how they are redeemed. In some cases, rewards may apply to specific spending categories that rotate by quarter and require that consumers re-register each term, and the rewards may expire or be forfeited under complicated terms.

In some cases, the choices may be confusing. Let’s take the example of zero percent interest credit card promotions. A consumer may choose a zero percent interest credit card promotion and expect to pay no interest on balances during a promotional period, after which any balances are assessed at a higher rate of interest going forward. But if a consumer instead chooses a zero percent interest private-label credit card with deferred interest and has a positive balance when the promotional period expires, interest could be retroactively assessed for the full time they held a balance during the promotional period. Even sophisticated consumers could be excused for confusing these products.

As it turns out, it is often the most vulnerable consumers who have to navigate the most complicated products. For instance, one recent study of the credit card market found that the average length of agreements for products offered to subprime consumers was 70 percent longer than agreements for other products.

The complexity multiplies when we go beyond credit cards and consider other dimensions of consumers’ financial lives. The Federal Deposit Insurance Corporation has found that nearly a quarter of the Americans that don’t maintain bank accounts are concerned that bank fees are too unpredictable. Even though mortgage debt is over two-thirds of household debt, nearly half of consumers don’t comparison shop before taking out a mortgage. Student loans now make up 11 percent of total household debt, more than twice its share in 2008. Over 11 percent of student debt is more than 90 days delinquent or in default–and researchers at the Federal Reserve Bank of New York estimate that this figure may understate the problem by as much as half.

Today, consumers navigate numerous weighty financial responsibilities for themselves and their dependents.  It seems fair to assume they could use some help managing this complexity. In the Federal Reserve Board’s annual Survey of Household Economics and Decisionmaking (SHED), more than half of respondents reported that their spending exceeded their income in the prior year.  Indeed, 44 percent of SHED respondents reported that they could not cover an emergency expense costing $400 without selling something or borrowing money.

New Tools to Help Consumers Manage Their Finances
Given the complexity and importance of these decisions, it is encouraging to see the fast-growing development of advanced, technology-enabled tools to help consumers navigate the complex issues in their financial lives. These tools build on important advances in our understanding of consumer financial behavior and the applications, or “app,” ecosystem.

Researchers have invested decades of work exploring how consumers actually make decisions. We all tend to use shortcuts to simplify financial decisions, and it turns out many of these can prove faulty, particularly when dealing with complex problems.  For example, empirical evidence consistently shows that consumers overvalue the present and undervalue the future.  Researchers have documented that consumers make better savings decisions when they are presented with fewer options.  They have shown the importance of “anchoring” bias–the tendency to place disproportionate weight on the first piece of information presented. This bias can lead consumers either to make poor financial choices or instead to tip the scales in favor of beneficial choices, as with automatic savings defaults.  Similarly, “nudges” can help consumers in the right circumstances or instead backfire in surprising ways.

These behavioral insights are especially powerful when paired with the remarkable advances we have seen in the technological tools available to the average consumer, especially through their smartphones. Smartphones are ubiquitous. The 2016 Federal Reserve Survey of Consumer and Mobile Financial Services (SCMF) found that 87 percent of the U.S. adult population had a mobile phone, the vast majority of which were smartphones. Smartphone use is prevalent even among the unbanked and underbanked populations. Survey evidence suggests we are three times more likely to reach for our phone than our significant other when we first wake up in the morning.

Some evidence suggests that smartphones are already helping consumers make better financial decisions. The 2016 SCMF found that 62 percent of mobile banking users checked their account balances on their phones before making a large purchase, and half of those that did so decided not to purchase an item as a result.  In addition, 41 percent of smartphone owners checked product reviews or searched product information online while shopping in a retail store, and 79 percent of those respondents reported changing their purchase decision based on the information they accessed on their smartphone.

And those use cases just scratch the surface of what is possible. First of all, the smartphone platform has become a launch pad for a whole ecosystem of apps created by outside developers for a wide variety of services, including helping consumers manage their financial lives.

Second, the smartphone ecosystem puts the enormous computing power of the cloud at the fingertips of consumers. Interfacing with smartphone platforms and other apps, outside developers can tap the computing power of the leading cloud computing providers in building their apps. Importantly, cloud computing offers not only the power to process and store data, but also powerful algorithms to make sense of it. Due to early commitment to open-source principles, app developers have open access to many of the same machine-learning and artificial intelligence tools that power the world’s largest internet companies.  Further, the major cloud computing providers have now taken these free building blocks and created different machine-learning and artificial intelligence stacks on their cloud platforms. A developer that wants to incorporate artificial intelligence into their financial management app can access off-the-shelf models of cloud computing providers, potentially getting to market faster than by taking the traditional route of finding training data and building out models in-house from scratch.

Third, fintech developers can also draw from enormous pools of data that were previously unavailable outside of banking institutions. Consumer financial data are increasingly available to developers via a new breed of business-to-business suppliers, called data aggregators. These companies enable outside developers to access consumer account and transactional information typically stored by banks. But aggregators do more than just provide access to raw data. They facilitate its use by developers, by cleaning the data, standardizing it across institutions, and offering their own application programming interfaces for easy integration. Further, similar to cloud computing providers, data aggregators are also beginning to provide off-the-shelf product stacks on their own platforms. This means that developers can quickly and easily incorporate product features, such as predicting creditworthiness, determining how much a consumer can save each month, or creating alerts for potential overdraft charges.

Researchers have documented the benefits of tailored one-on-one financial coaching. Until recently, though, it has been hard to deliver that kind of service affordably and at scale, due to differences in consumers’ circumstances. Let’s again consider the example of deferred interest credit cards. It turns out only a small minority of consumers miss the deadlines for repaying promotional balances and are charged retroactive interest payments, and they typically have deep subprime scores.  Similarly, for consumers that opt into overdraft products on their checking accounts, 8 percent of consumers pay 75 percent of the fees.  Up until now, it has been hard for consumers to understand those odds and objectively assess whether they are likely to be in the group of customers that will face challenges with a particular financial product. The convergence of smartphone ubiquity, cloud computing, data aggregation, and off-the-shelf AI products offer the potential to make tailored financial advice scalable. For instance, a fintech developer could pair historical data about how different types of consumers fare with a specific product, on the one hand, with a consumer’s particular financial profile, on the other hand, to make a prediction about how that consumer is likely to fare with the product.

The Evolution of Financial Autopilots
Since the early days of internet commerce, developers have tried to move beyond simple price comparison tools to offer tailored “agents” for consumers that can recommend products based on analyses of individual behavior and preferences.  Today, a new generation of personal financial management tools seems poised to make that leap. When a consumer wishes to select a new financial product, he or she can now solicit options from a number of websites and mobile apps. These new comparison sites can walk the consumer through a wide array of financial products, offering to compare features like rewards, fees, and rates, or tailoring to a consumer’s stated goals. Some fintech advisors ask consumers to provide access to their bank accounts, retirement accounts, college savings accounts, and other investment platforms in order to enable a fintech advisor to offer a consumer a single, near complete picture of his balances and cash flows across different institutions.

In reviewing the advertising, terms and conditions, and apps of an array of fintech advisors, it appears that many of these tools offer advanced data analysis, machine learning, and even artificial intelligence to help consumers cut down on unnecessary spending, set aside money for savings, and use healthy nudges to improve their financial decisions. For instance, a fintech advisor may help a consumer automate savings “rules,” like rounding up charges and putting the difference into savings, enabling these small balances to accumulate over time or setting a small amount of money aside every time a consumer spends money on little splurges.

The early stages of innovation inevitably feature a lot of learning from trial and error. Fortunately, as the fintech ecosystem advances, there are useful experiences and good practices to draw upon from the evolution of the commercial internet. To begin with, one internet adage is that if a product is free, “you are the product.”  In this vein, fintech advisors frequently offer free services to consumers and earn their revenue from the credit cards and other financial products that they recommend through lead generation.

Of course, many fintech advisors are not lead generators. Some companies offer fee-for-service models, with consumers paying a monthly fee for the product. Other companies are paid by employers, who then provide the products free of charge to their employees as an employee benefit. In these cases, they likely have quite different business models.

But for those services that do act as lead generators, there are important considerations about whether and how best to communicate information to the consumer about the nature of the recommendations being made. For instance, according to some reports, fintech advisors can make between $100 and $700 in lead generation fees for every customer that signs up for a credit card they recommend.

In many cases, a fintech advisor may describe their service as providing tailored advice or making recommendations as they would to friends and family. In such cases, a consumer might not know whether the order in which products are presented by a fintech assistant is based on the product’s alignment with his or her needs or different considerations. Different fintech advisors may order the lists they show consumers using different criteria. A product may be at the top of the advisor’s recommendations because the sponsoring company has paid the advisor to list it at the top, or the sponsoring company may pay the fintech assistant a high fee, contingent upon the consumer signing up for the product. Alternatively, a fintech advisor may change the order of the loan offers or credit cards based on the likelihood that the consumer will be approved. Moreover, in some cases, the absence of lead generation fees for a particular product may impact whether that product is on the list shown to consumers at all.

There appears to be a wide variety of practices regarding the prominence and placement of advertising and other disclosures relative to the advice and recommendations such firms provide. Overall, fintech assistants have increasingly improved the disclosures that explain to consumers how they get paid, but this is still a work in progress.

The good news is that these challenges are not new. The experience with internet search engines outside of financial products, such as Google, Bing, and Yahoo!, as well as with other product comparison sites, such as Travelocity and Yelp, may provide useful guidance. As consumers and businesses have adapted to the internet, we have, collectively, adopted norms and standards for how we can expect search and recommendation engines to operate. In particular, we generally expect that search results will be included and ranked based on what’s organically most responsive to the search–unless it is clearly labeled otherwise.  Accordingly, when we search for a product, we now know to look for visual cues that identify paid search results, usually in the form of a text label like “Sponsored” or “Ad”, different formatting, and visually separating advertising from natural search results.  Even when an endorsement is made in a brief Twitter update, we now expect disclosures to be clear and conspicuous.

As fintech advisors evolve to engage consumers in new ways, disclosure methodologies will no doubt be expected to adapt as well. For instance, some personal financial management tools now interact with consumers via text message. If consumers move to a world in which most of their interactions with their advisors occur via text-messaging “chatbots”–or voice communication–I am hopeful that industry, regulators, consumers, and other stakeholders will work together to adapt the norms to distinguish between advice and sponsored recommendations.

The Data Relationship
While the lead generation revenue model presents some familiar issues that are readily apparent, under the hood, fintech relationships raise even more complex issues for consumers in knowing who they are providing their data to, how their data will be used, for how long, and what to expect in the case of a breach or fraud. Let me briefly touch on each issue in turn.

Often, when a consumer signs up with a fintech advisor or other fintech app, they are asked to log into their bank account in order to link the fintech app with their bank account data. In reviewing apps’ enrollment processes, it appears that consumers are often shown log-in screens featuring bank logos and branding, prompting consumers to enter their online banking logins and passwords. In many cases, the apps note that they do not store the consumers’ banking credentials.

When the consumer logs on, he or she is often not interfacing with a banks’ computer systems, but rather, providing the bank account login and password to a data aggregator that provides services to the fintech app. In many cases, the data aggregator may store the password and login and then use those credentials to periodically log into the consumer’s bank account and copy available data, ranging from transaction data, to account numbers, to personally identifiable information. In other cases, things work differently under the hood. Some banks and data aggregators have agreed to work together to facilitate the ability to share data with outside developers in authorized ways. These agreements may delineate what types of data will be shared, and authorization credentials may be tokenized so that passwords are never stored by the aggregator.

It is often hard for the consumer to know what is actually happening under the hood of the financial app they are accessing. In most cases, the log in process does not do much to educate the consumer on the precise nature of the data relationship. Screen scraping usually invokes the bank’s logo and branding but infrequently shows the logo or name of the data aggregator. In reviewing many apps, it appears that the name of the data aggregator is frequently not disclosed in the fintech app’s terms and conditions, and a consumer generally would not easily see what data is held by a data aggregator or how it is used. The apps, websites, and terms and conditions of fintech advisors and data aggregators often do not explain how frequently data aggregators will access a consumer’s data or how long they will store that data.

Recognizing this is a relatively young field, but one that is growing fast, there are a myriad of questions about the consumer’s ability to opt out and control over data that will need to be addressed appropriately. In examining the terms and conditions for a number of fintech apps, it appears that consumers are rarely provided information explaining how they can terminate the collection and storage of their data. For instance, when a consumer deletes a fintech app from his or her phone, it is not clear this would guarantee that a data aggregator would delete the consumer’s bank login and password, nor discontinue accessing transaction information. If a consumer severs the data access, for instance by changing banks or bank account passwords, it is also not clear how he or she can instruct the data aggregator to delete the information that has already been collected. Given that data aggregators often don’t have consumer interfaces, consumers may be left to find an email address for the data aggregator, send in a deletion request, and hope for the best.

If things go wrong, consumers may have limited remedies. In reviewing terms, it appears that many fintech advisors include contractual waivers that purport to limit consumers’ ability to seek redress from the advisor or an underlying data aggregator. In some cases, the terms and conditions assert that the fintech developer and its third-party service providers will not be liable to consumers for the performance of or inability to use the services. It is not uncommon to see terms and conditions that limit the fintech adviser’s liability to the consumer to $100.

Traditionally, under the Electronic Funds Transfer Act and its implementing Regulation E, consumers have had protections to mitigate their losses in the event of erroneous or fraudulent transactions that would otherwise impact their credit and debit cards, such as data breaches. Those protections are not absolute, however.  In particular, if a consumer gives another person an “access device” to their account and grants them authority to make transfers, then the consumer is “fully liable” for transfers made by that person, even if that person exceeds his or her authority, until the consumer notifies the bank.  As the industry matures, the various stakeholders will need to develop a shared understanding of who bears responsibility in the event of a breach.

Shared Responsibility and Shared Benefit Moving Forward
So what can be done to make sure consumers have the requisite information and control to remain squarely in the driver’s seat? Establishing and implementing new norms is in the shared interest of all of the participants in the fintech stack. For instance, in the case of credit cards, mortgages, and many other products, it is often banks or parties closely affiliated with banks that pay fees to fintech advisors to generate leads for their products, pursuant to a contract. Through these contractual relationships with fintech advisors, banks have considerable influence in the lead generation relationship, including through provisions describing how a sponsored product should be described or displayed. Banks have a stake in ensuring that their vendors and third-party service providers act appropriately, that consumers are protected and treated fairly, and that the banks’ reputations aren’t exposed to unnecessary risk.  Likewise, some of the leading speech-only financial products are currently credit card and bank products. Accordingly, banks have incentives to invest in innovating the way they disclose information to consumers, as they also invest in new ways of interacting with them.

As for consumers’ relationships with data aggregators, there’s an increasing recognition that consumers need better information about the terms of their relationships with aggregators, more control over what is shared, and the ability to terminate the relationship.  We have spoken to data aggregators who recognize the importance of finding solutions to many of the complex issues involved with the important work of unlocking the potential of the banking stack to developers. And while there are some difficult issues in this space, other issues seem relatively straightforward. It shouldn’t be hard for a consumer to be informed who they are providing their credentials to. Consumers should have relatively simple means of being able to consent to what data are being shared and at what frequency. And consumers should be able to stop data sharing and request the deletion of data that have been stored.

Responsibility for establishing appropriate norms in the data aggregation space should be shared, with banks, data aggregators, fintech developers, consumers, and regulators all having a role.  Banks and data aggregators are negotiating new relationships to determine how they can work together to provide consumers access to their data, while also ensuring that the process is secure and leaves consumers in the driver’s seat.  In many cases, banks themselves were often the original customers of data aggregators, and many continue to use these services. According to public filings, more than half of the 20 largest banks are customers of data aggregators.  The banks have an opportunity as customers of data aggregation services to ensure that the terms of data provision protect consumers’ data and handle it appropriately.

Regulators also recognize that there may be opportunities to provide more clarity about how the expectations about third-party risk management would work in this sector, as well as other areas experiencing significant technological change. Through external outreach and internal analysis, we are working to determine how best to encourage socially beneficial innovation in the marketplace, while ensuring that consumers’ interests are protected. We recognize the importance of working together and the potential to draw upon existing policies, norms, and principles from other spaces. Consumers may not fully understand the differences in regulations across financial products or types of financial institutions, or whether the rules change when they move from familiar search and e-commerce platforms to the fintech stack. Consumers, as well as the market as a whole, will benefit if regulators coordinate to provide more unified messages and support the development of standards that serve as a natural extension of the common-sense norms that consumers have come to expect in other areas of the commercial internet.

Conclusion
The combination of technologies that put vast computing power, rich data sets, and artificial intelligence onto simple smartphone apps together with important research into consumer financial behaviors has great potential to help consumers navigate their complex financial lives more effectively, but there are also important risks. I am hopeful that fintech developers, data aggregators, bank partners, consumers, and regulators will work together to keep consumers in the driver’s seat as we move forward with these new technologies. If we work together effectively toward this goal, the fintech stack may be able to offer enormous benefits to the consumers they aim to serve, while appropriately identifying and managing the risks.

 

How ‘liar loans’ undermine sound lending practices

From The Conversation.

How truthful are we when it comes to negotiating loans in Australia?

With increasing pressure on the housing market, some of us might be tempted to stretch the truth to secure a mortgage on our dream property – but research shows that this practice can have serious repercussions.

Recent news reports have alerted borrowers to the dangers of “liar loans”, based on the findings of a new UBS research study. A liar loan is a no-documentation loan that is approved on the basis of unverified and possibly false information about income, assets or capacity to repay.

In the United States, where many loan applications have been approved without any information on the borrower’s income and assets – these liar loans have been implicated as one of the reasons for the global financial crisis.

Should we be worried in Australia?

The UBS study found that a third of Australian mortgage borrowers reported being “not factual or accurate” in their mortgage applications. Being “not accurate” is not the same thing as being a “liar”. However, we need to be aware and pro-active to avoid poor standards and practices.

They further estimate that there is roughly US$500 billion (A$657.95 billion) worth of factually inaccurate mortgages on banks’ books in Australia. This is worrying, because it could mean that borrowers are taking on bigger debts than they can actually afford, falling into financial stress or even losing their homes.

The Australian situation

In Australia, when borrowers apply for a mortgage they need to provide documentation that verifies their employment history, creditworthiness, and overall financial situation. Borrowers are required to provide a payslip or most recent tax returns, and show that they have been employed in the same job for at least 12 months.

Other documentation may include: credit card and bank statements; sales contract; confirmation of rental income if purchasing an investment property; and more. The mortgage originator may perform credit checks and bankruptcy or default searches.

Some mortgage borrowers may not be required to provide much documentation if they are existing clients of the bank and already have a strong credit history.

In my research I found that 88.8% of mortgage applicants were existing customers of the bank where they apply for a mortgage, and had been so for 9.3 years on average. But low documentation loans exist for self-employed borrowers.

Where accuracy of mortgage applications becomes difficult to determine is when estimating the expenses of the household. Mortgage applicants are asked about their monthly expenses to assess whether they can service the debt without major stress. Here, applicants may be “mostly factual and accurate” or even “partially accurate” when trying to calculate their monthly expenses. After all, how many people actually keep accurate and up-to-date spreadsheets of all their expenses?

Debts outstanding with other financial institutions or family and friends may also be misreported. In addition, mortgage lenders who receive commissions linked to loan size have incentives to overestimate borrower’s incomes and underestimate expenses.

Australian version of liar loans?

These arguments do not suggest that there is no “lying” or “truth hiding” in mortgage applications in Australia, but that it may not be comparable to the trend of “liar loans” seen in the US.

More importantly, banks do not rely only on their clients’ word. Banks estimate monthly expenses and uncommitted income for their clients based on borrower characteristics and solid financial records.

Data from previous research reveals that banks estimate on average A$1,637 more on monthly expenses than applicants report. Based on the bank’s calculations, housing investors underestimate their monthly expenses by A$1,932 on average, while owner-occupiers underestimate by A$1,560 on average.

Similarly, mortgage applicants report their monthly uncommitted income to be on average A$702.5 more than what the bank estimates it to be. Housing investors only overestimate their monthly uncommitted income by A$174 on average, while owner-occupiers overestimate by A$840 on average.

Due diligence required from both banks and borrowers

Research finds that mortgage features (for example fixed or adjustable rates, maturity, loan-to-value ratio, and so on) help borrowers select mortgage products that are affordable and safer for them, with the guidance of mortgage lenders and brokers.

The research further finds that lenders should make sure that borrowers have the financial capacity to repay their loans out of income or by selling assets under plausible conditions, and not by relying on the value of the collateral.

Mortgage delinquency and default may rise due to excessive risk taking in mortgage lending combined with deteriorating economic conditions; or due to falling income and rising unemployment during a housing downturn. This later case is more likely the potential threat in the Australian current environment.

The Australian Prudential Regulatory Authority (APRA) and the Reserve Bank of Australia (RBA) perform stress tests to check the financial system’s resilience. Along with APRA’s macro- and micro-prudential regulations, some lenders are introducing higher requirements and credit restrictions on potential borrowers.

These include obtaining more information on the clients, which helps assess credit and default risks and helps design and target financial products to specific type of borrowers. There is however risk of mortgage discrimination.

More careful monitoring needed

Mortgage risks often relate to mismatches between the products used by households and their financial capabilities and ability to bear risks. For that reason, mortgage product characteristics should be monitored carefully both by banks and borrowers.

The Organisation for Economic Co-operation and Development (OECD) suggests that financial authorities should make sure lending standards are sound, both in the banking and non-banking sectors. It is important that banks do not face incentives encouraging excessive risk taking.

Requiring more transparency, reinforcing consumer protection and financial education encourages sound lending and borrowing practices.

Author: Maria Yanotti, Lecturer of Economics and Finance Tasmanian School of Business & Economics, University of Tasmania