November 2016 – Page 3 – Digital Finance Analytics (DFA) Blog

Australia is discriminating against investors

Many Australians dream of starting their own businesses. But they face restrictions on where they can access startup capital. In Australia you must be certified as a “sophisticated investor” to invest in risky, early stage ventures that cannot yet comply with costly disclosure requirements.

A “sophisticated investor” is someone with an income of at least A$250,000 per annum or assets worth A$2.5 million. But this qualification not only discriminates against some investors, it is a very limited view of what it means to be “sophisticated”. It also ignores recent changes in how companies interact with an important group of early investors – their customers. Even more, it robs startups of valuable capital.

The argument against “sophistication”

The argument for this restriction is that investing in private companies with unregulated disclosures is risky. They are not subject to the same requirements of a public company and are potentially more difficult for a layman to evaluate. “Unsophisticated investors” should just stick to publicly listed investments because they are less risky and more transparent.

But there’s nothing particular about having money that makes you a good investor and investors get shortchanged in public markets as well.

In particular, it is well documented that, on average, shares sold to the public through an IPO significantly underperform other investments in the long-run. Even when a high quality IPO does come to the market, unsophisticated investors will struggle to get a meaningful allocation, while wealthy, well-connected investors end up with most of what they ask for.

The academic literature refers to this as the “winner’s curse”, whereby unsophisticated investors only receive shares in an IPO when sophisticated investors think it’s a lemon.

Many startups have a unique relationship with customers

But companies also have greater intimacy with their customers than ever before. Micro-investing startup Acorns recently sought to raise A$6 million in a private share issue, at least partially from its estimated 160,000 Australian users. Acorns’ users are reported to have already pledged more than A$1 million to help the startup replenish its cash and pursue further growth opportunities.

Acorns may be slightly unusual in being able to raise this money, as it is itself an investing app. It helps its users build wealth by saving “spare change” and investing this money for them. So its client base is at least familiar with the tenets of investing.

But Acorns’ ability to tap its user base as a source of capital also challenges the notion that only “sophisticated” investors are suitably qualified to participate in early stage deals. Acorns’ users are typically young tech savvy millennials who are unlikely to pass the sophisticated investor test (which is probably why they are using the app). Yet, because of their interaction with the app, these users have unique insights in evaluating Acorns’ prospects.

It raises questions as to whether the distinction between “sophisticated” and “unsophisticated” investors remains relevant in the world of app based tech startups. These startups often have aggressive go-to-market business models that attempt to capture as many users as possible relatively early in their life. Would someone that is cash rich have a better understanding of this business than a customer or user of it?

In making an early stage investment decision a “sophisticated” investor could try to determine whether an app solves a significant problem in its user’s life and thus how deeply a user will engage with it. But predicting the behaviour of app users is inherently difficult. So who better to predict it than the users themselves?

Discriminating against certain investors costs everyone

Under the current rules, a lot of “unsophisticated” users are denied access to such investment opportunities because they are simply not wealthy enough. This robs investors of an opportunity and startups of a potential source of capital. Even more, we all could lose as companies that create incredible products struggle or die for lack of funds.

For startups, drawing on customer support, as Acorns has done, would provide a source of capital that does not carry the costs and conditions that are typically attached to angel and venture capital funding. For small investors it gives them direct access to some potentially very lucrative (but very high-risk) investments that otherwise would be impossible or very costly to access.

Democratising the way startups are financed could create an environment whereby entrepreneurs, small investors and the economy as a whole all benefit from financing new and interesting endeavours. But it all starts with re-conceptualising the current arbitrary notion of “sophistication”.

Author: Jason Zein, Associate Professor, UNSW Australia

Umbrellas don’t cause rain

In a speech at Sheffield University, external MPC member Gertjan Vlieghe discusses the how the UK economic outlook has evolved recently and what this implies for the stance of monetary policy. He also examines several arguments that have been used to support the idea that low interest rates are counterproductive, either for the economy as a whole, or for a particular sector of the economy.

The UK economy in Q2 and Q3 has held up better than he expected: “Judging by headline GDP at least, the economy has continued in a “business as usual” manner”. However, financial markets seem to be expecting “some economic underperformance relative to pre-referendum expectations.” And investment and employment intentions by UK businesses “are generally below their pre-referendum levels”.

“Sentiment among UK households, on the other hand, appears rather buoyant”.

Jan argues that “the tension between the fairly pessimistic assessment by financial markets, the cautious assessment by businesses and the rather optimistic response by households so far, cannot last”. It might turn out that financial markets are too pessimistic and the economy continues to grow in a “business as usual” way, in which case I would expect the exchange rate to move higher over time. Or it might mean that households are too optimistic”.

Turning to the stance of monetary policy, the MPC has decided that the current policy rate of 0.25% remains appropriate. “Having an inflation projection that is ½ of a percentage point above the target at the end of the forecast period is uncomfortable for an inflation-targeting MPC. The reason we are willing to tolerate this particular inflation path, is that if we tried to bring inflation down faster, with tighter monetary policy, we would create more slack in the economy – lower real income growth, and higher unemployment”.

The reason why the current policy rate is so low relative to history in the UK as well as in many other advanced economies, is that monetary policy is responding to persistent global disinflationary forces. “It has been raining, so we have all opened our umbrellas”.

Not everyone agrees that this is the appropriate response for monetary policy. “Some argue that low rates themselves are the problem”. Jan argues that “is just a case of erroneously reversing the causality”. “Umbrellas together with rainfall are observed in many countries. Nobody actually believes that umbrellas cause rainfall”. “We have had several low interest rate, low inflation countries that have raised interest rates over the past decade. This was not followed by an escape from the alleged confidence trap”. “Higher interest rates, far from boosting demand and inflation, have caused growth to slow and inflation to fall”. “Some of these countries now have even lower short term and long term interest rates than the UK, as inflation expectations have drifted lower”.

Next he considers the argument that “low rates are a problem because they hurt savers”. This ignores the fact that savers hold significant amounts of non-deposit assets, which benefit from low interest rates and asset purchases. Moreover, many savers also work, and low interest rates “have helped lower the unemployment rate” and “have boosted wage inflation”. He presents evidence that the additional income to savers from the improvement in the economy due to lower interest rates is similar to the additional income earned by borrowers.

Jan considers the impact of low interest rates on defined benefit (DB) pension schemes: “the conclusion is not to deny that lower long term rates make life more difficult for pension funds. But, first to acknowledge low long term interest rates are not primarily caused by monetary policy, they are caused by other factors that monetary policy is reacting to. Second, that there is no evidence yet that large pension deficits are weighing on business investment. Third, that arguing for tighter monetary policy to help pension funds ignores the big offsetting effect accommodative policy has on corporate profitability through higher demand and lower cost of investment, and ultimately higher long-term interest rates.”

As well as considering the impact on DB pension schemes, Jan considers the plight of pensioners themselves. He shows that “Since the financial crisis, retired households have experienced faster income growth than non-retired households”.

After looking at these different groups within the economy, he concludes that “there is no evidence that monetary stimulus has hurt them, once the broader effects of monetary policy on employment, wages, profits and the prices of widely held assets are also taken into account”.

He asks whether “Even if, in further research, we were to find a specific group that had unambiguously suffered from very low interest rates, what would be the right policy prescription? Should monetary policy try to help them by tightening?”

He argues that “The suffering group would get a larger share of a shrinking pie. As monetary policy makers, we are trying to meet the inflation target by growing the pie in line with potential, and letting the government decide how to divide it up, using its fiscal and structural policy tools. Monetary policy cannot solve distributional issues, and should not be asked to try.”

The good, the bad, and the ugly of algorithmic trading

From The Conversation.

Algorithms are taking a lot of flak from those in financial circles. They’ve been blamed for a recent flash crash in the British pound and the greatest fall in the Dow in decades. They’ve been called a cancer and linked to insider trading.

Government agencies are taking notice and are investigating ways to regulate algorithms. But the story is not simple, and telling the “good” algorithms from the “bad” isn’t either. Before we start regulating we need a clearer picture of what’s going on.

The ins and outs of trading algorithms

Taken in the widest sense, algorithms are responsible for the vast majority of activity on modern stock markets. Apart from the “mum and dad” investors, whose transactions account for about 15 to 20% of Australian share trades, almost every trade on the stock markets is initiated or managed by an algorithm.

There are many different types of algorithms at play, with different intentions and impacts.

Institutional investors such as super funds and insurance companies rely on execution algorithms to transact their orders. These slice up a large order into many small pieces, gradually and strategically submitting them to the market. The intention is to minimise transaction costs and to receive a good price – if a large order were submitted in one go it might adversely move the entire market.

Human market makers used to provide quotes to buy or sell a given stock and were responsible for maintaining an orderly market. They have been replaced by algorithms that automatically post and adjust quotes in response to changing market conditions.

Algorithms drove the human market makers out of business by being smarter and faster. Most market-making algorithms, however, don’t have an obligation to maintain an orderly market. When the market gets shaky, algorithms can (and do) pull out, which is where the potential for “flash crashes” starts to appear – a sudden drop and then recovery of a securities market.

Further concerns about algorithmic trading are focused on another kind – proprietary trading algorithms. Hedge funds, investment banks and trading firms use these to profit from momentary price differentials, by trading on statistical patterns or exploiting speed advantages.

Rather than merely optimising a buy or sell decision of a human trader to minimise transaction costs, proprietary algorithms themselves are responsible for the choice of what to buy or sell, seeking to profit from their decisions. These algorithms have the potential to trigger flash crashes.

Fast vs. slow algorithms

Proprietary algorithmic traders are often further divided, between “slow” and “fast” (the latter also referred to as “high-frequency” or “low-latency”).

Many traditional portfolio managers use mathematical models to inform their trading. Nowadays such strategies are often implemented using algorithms, drawing on large datasets. Although these algorithms are often faster than human portfolio managers, they are “slow” in comparison to other algorithmic traders.

High-frequency algorithmic trading (HFT) is on the other end of the spectrum, where speed is fundamental to the strategy. These algorithms operate at the microsecond scale, making decisions and racing each other to the market using an array of different strategies. Winning this race can be highly profitable – fast traders can exploit slower traders that are yet to receive, digest or act on new information.
Proponents of HFT argue that they increase efficiency and liquidity because market prices are faster to reflect new information and fast market makers are better at managing risks. Many institutional investors, on the other hand, argue that HFTs are predatory and parasitic in nature. According to these detractors, HFTs actually reduce the effective liquidity of the stock market and increase transaction costs, profiting at the expense of institutional investors such as superannuation funds.

The effects of algorithms are complicated

A recent study by Talis Putnins from UTS and Joseph Barbara from the Australian Securities and Exchange Commission (ASIC) investigated some of these concerns. Using ASIC’s unique regulatory data to analyse institutional investor transaction costs and quantify the impacts of proprietary algorithmic traders on these, the study found considerable diversity across algorithmic traders.

While some algorithms are harmful to institutional investors, causing higher transaction costs, others have the opposite effect. Algorithms that are harmful, as a group, increase the cost of executing large institutional orders by around 0.1%. This ends up costing around A$437 million per year for all large institutional orders in the S&P/ASX 200 stocks.

But these effects are offset by a group of traders that significantly decrease those costs by approximately the same amount. The beneficial algorithms provide liquidity to institutional investors by taking the other side of their trades.

They do so not out of the goodness of their little algorithmic hearts, but rather because they earn a “fee” for this service (for example, the difference between the prices at which they buy and sell). What makes these algorithms beneficial to institutions, is that “fee” they charge is lower than the “fee” institutions would face if these algorithmic traders were not present and instead had to trade with less competitive or less efficient liquidity providers, such as humans. The ability for algorithms to provide liquidity more cheaply comes from the use of technology, as well as increased competition.

What distinguishes the algorithms is that the beneficial ones trade against institutional investors (serving as their counterparties), whereas the harmful ones trade with the institutions, competing with them to buy or sell. In doing so, the beneficial algorithms reduce the market impact of institutional trading. This allows institutions to get into or out of positions at more favourable prices.

The study also found that high-frequency algorithms are not more likely to harm institutional investors than slower algorithms. This suggests institutional investor concerns about HFT may be misdirected.

We shouldn’t stamp out the ‘good’ algorithms

ASIC is now using the tools developed in the Putnins and Barbara study to detect harmful algorithms in its surveillance activities. These are identified by looking for statistical patterns in the trading activity of individual algorithmic traders and the variation in institutional transaction costs. The result is an estimated “toxicity” score for every algorithmic trader, with the highest-scoring traders attracting the spotlight.

So, we know the affect of algorithms is complicated and we can start to tell the harmful apart from the beneficial. Regulators need to be mindful of this diversity and avoid blanket regulations that impact all algorithmic traders, including the good guys. Instead, they should opt for more targeted measures and sharper surveillance tools that place true misconduct in the cross-hairs.

Authors: Marco Navon, Senior Lecturer in Finance, University of Technology Sydney; Talis Putnin, Professor of Finance, University of Technology Sydney

Blockchain – A Touch Of Reaility

Separating the hype of Blockchain from reality is important. So, some interesting remarks from Carl-Ludwig Thiele, Member of the Executive Board of the Deutsche Bundesbank on the subject. The debate has largely shifted from open blockchain applications, such as bitcoin, to closed networks with a limited circle of participants. But doubts will also increase as to whether this technology can meet the expectations being placed on it.

Many enterprises and institutions currently working on blockchain-based solutions expect to reap great benefits from them. Blockchain technology holds out the promise of cost savings, de-risking potential and efficiency gains. This includes, among other things, the automation of work-sharing processes as well as faster processing and the fulfilment of contractual obligations via smart contract solutions.

One positive effect that can already be seen is industry-wide cooperation. Dialogue between various market participants on future market developments can foster mutual understanding and facilitate the harmonisation of processes. This makes it possible to adequately react to the challenges posed by new technologies. This is of importance in the financial industry, in particular, which is characterised by network effects.

That said, one should not simply gloss over the challenges and weaknesses posed by the technology.

The requirements imposed on regulated providers cannot currently be met by blockchain technology, or can only be met with difficulty. This concerns, for example, the question of how to engineer absolute finality. Furthermore, the know-your-customer requirements need to be observed and the confidentiality of transaction data must be ensured. This is also a reason why the regulatory status of blockchain technology in many countries is still unclear.

Furthermore, despite the supposedly greater resilience of its decentralised structure, blockchain still has high obstacles to surmount before it can be applied across the board, owing to its susceptibility to manipulation. Recent hacker attacks are a case in point.

This is another reason why the debate has largely shifted from open blockchain applications, such as bitcoin, to closed networks with a limited circle of participants.

Inefficiencies are often perpetuated not by a lack of technology, but by (historical) structures. Blockchain technology is therefore not a patent solution for change, but it does provide an opportunity to make change.

Disruptive technologies require time to develop, mature and unfurl their full potential. Not every innovation succeeds, though, and it remains to be seen how the application of blockchain technology will develop.

Following the revolutionary beginnings with bitcoin, the prevailing view now seems to be that blockchain applications will spread rather more gradually. One might therefore speak of evolution rather than revolution. Before we can even ask questions about the broader use of this technology, we must first be sure that using this new technology is at least as secure, efficient and cost-effective in financial transactions as conventional technology.

Blockchain technology could become a game changer, in the financial industry and, perhaps in particular, beyond. The potential of blockchain technology is often compared to that of the internet. It should be remembered that it took some time before the truly beneficial applications of the internet emerged. With blockchain, we are only at the very beginning of a potential development of this kind.

Innovations are the lifeblood of a continually developing economy. Moreover, evolution processes are never linear. The first great wave of euphoria, which was also seen in the media, is being followed by a phase of checking, weighing-up and consolidation, before new offers and technologies are rolled out on a broad scale.

Ladies and gentlemen, Goethe once said: “We know accurately only when we know little; with knowledge doubt increases.”

My impression is that with the increasing efforts being devoted to blockchain technology, doubts will also increase as to whether this technology can meet the expectations being placed on it, which in some cases are extremely high.

ASIC bans former Westpac financial planner for eight years

ASIC has banned a former employee representative of Westpac Financial Consultants Ltd (which is a part of the Westpac Banking Corporation), from providing financial services for eight years.

ASIC found that during the period between July 2010 to April 2014 he was was involved in the provision of inappropriate advice to clients and also involved in the failure to provide one client with a written statement of advice.

ASIC found that he implemented a “one size fits all” advice strategy that

did not tailor advice to clients’ personal and financial circumstances; and

led to clients being over insured with inappropriate level of premiums.

ASIC also found he had

made one misrepresentation concerning tax savings; and

not be competent to provide financial services.

ASIC Deputy Chair Peter Kell said, ‘Advice needs to be tailored to the client’s needs and circumstances, and an advice provider must not lose sight of the needs of their client.’

His behavior was reported to ASIC in May 2014.

A customer remediation process was undertaken and 29 former clients were paid a total of $1,127,543 made up of advice fees, refunds of premiums for inappropriate advice and market loss relating to investments.

Background

This outcome is a result of ASIC’s Wealth Management Project. The Wealth Management Project was established in October 2014 with the objective of lifting standards by major financial advice providers. The Wealth Management Project focuses on the conduct of the largest financial advice firms (NAB, Westpac, CBA, ANZ, AMP and Macquarie).

ASIC’s work in the Wealth Management Project covers a number of areas including:

Working with the largest financial advice firms to address the identification and remediation of non-compliant advice; and

Seeking regulatory outcomes, when appropriate, against licensees and advisers.

ASIC listed more than 20 named advisors who have been banned.

ASIC launches new digital toolkit to help Australians navigate financial advice

A new online toolkit developed by ASIC’s MoneySmart will enable Australians to better understand and navigate the financial advice process.

ASIC’s MoneySmart Financial Advice Toolkit is available on ASIC’s MoneySmart website.

ASIC’s MoneySmart Financial Advice Toolkit is a free educational tool that breaks down the complexity around the financial advice process. It will assist consumers with their research and help them evaluate the financial advice they receive.

ASIC’s MoneySmart Financial Advice Toolkit provides an overview of the financial advice process and gives impartial guidance on:

Identifying financial goals and advice needs;

Tips on choosing an adviser;

Preparing to meet a financial adviser;

Understanding your Statement of Advice; and

Reviewing your financial situation.

Consumers can use the toolkit to create a customised ‘to do’ list which they can modify to suit their personal financial needs. The toolkit also includes links to ASIC’s Financial Advisers Register where consumers can check a financial adviser’s credentials – their licence, authorisations, experience and qualifications, and whether they have ever been banned or disqualified from providing financial services.

‘Australians face major financial decisions throughout their lifetime, many of which can be complex and confusing. Yet only about one in five Australians obtain financial advice. ASIC recognises the value that quality advice can deliver and wants to see this increase,’ said Mr Peter Kell, ASIC Deputy Chairman.

‘ASIC’s new toolkit is a practical resource to help Australians assess the quality of the advice they receive and make better financial decisions.’

The resource is a new digital tool that complements and supports ASIC’s regulatory and enforcement work in the financial advice sector and is designed to improve demand-side capability at critical financial moments.

Background

ASIC is the Australian Government agency responsible for financial literacy, consistent with its strategic priority to promote consumer confidence and trust in the financial system. Financial literacy is about having the knowledge, skills, attitudes and behaviours to make good financial decisions.

ASIC leads and coordinates the National Financial Literacy Strategy, which sets out a national framework for financial literacy work in Australia. The Strategy highlights the importance of providing people with tailored resources and tools, and of responding to the financial issues facing vulnerable sectors of the community. People experiencing high financial stress and crisis are identified as one of a number of priority audiences in the National Strategy.

ASIC’s MoneySmart website provides impartial and trusted financial guidance and tools to support informed financial decision-making for all Australians.

Banks will displace fintech lenders, predicts PwC

The fintech lending sector will eventually be cannibalised by the activities of the major banks, argues PwC as reported in Fintech Business.

Speaking on a panel at the Australian Securitisation Forum in Sydney this week, PwC Asia head of digital financial services, Thomas Achhorner, said incumbents will most likely muscle in on any innovation from disruptors and “mimic” their offerings more successfully.

“It is fair to say that an online digital mortgage is something that every bank in this country is currently working on,” Mr Achhorner said.

“Not only the mortgage itself, but a broader, ecosystem-based experience starting from the real estate purchase all the way to insurance and everything that happens downstream from the mortgage.”

Asked about the impact of fintech disruptors, the PwC partner said banks typically react to new fintech players in three ways.

“One is they might collaborate with them and establish partnerships and build them into their own platforms,” Mr Achhorner said.

“The second one is the incumbents create the capabilities themselves – they mimic what fintechs do. Or, in the third case, sometimes fintechs are ignored,” he said.

“In the long term many fintechs will disappear because the incumbents have better cards in this game. They have the means, the funding, the brand, to some extent the trust although that is eroding a little bit, and they have the customer base.

“If there is a new offering from a peer-to-peer lender, for example, the incumbent who is able to mimic what the fintech does very quickly will probably win.”

Mr Achhorner said this is already happening in other sectors, such as financial advice, where banks are dominating robo-advice.

“In the long term most fintechs disappear, with a few exceptions. About 98 per cent will disappear,” he said.

However, as banks continue to work on a digital mortgage product, one of the biggest hurdles they face is codifying credit rules.

“The first challenge is to codify those rules in a systematic fashion,” Mr Achhorner said. “Before you can even think about putting the decisioning into a machine. That will be critical. It will be critical for every financial institution to put its own rules, and its own credit rules, into this machine.”

If successful, this has the potential to increase the consistency of decision making and reduce operational risk, Mr Achhorner said. However, he warned that if everyone has the same rules or similar rules we might see fluctuations in the market very quickly.

“Which is what happened when algorithmic trading was introduced into financial markets,” he said. “Everyone did the same thing. This could happen in the credit market as well where everyone tries to lend to the same ‘good’ risks and nobody lends to the poorer risks anymore.

“So we could see a huge imbalance. A reasonable or more prudent model is something more hybrid.”

US Mortgage Rates Sharply Higher

Mortgage rates in the US have risen by more than 50 basis points since the election in November.

A 30 year fixed is now 4.19%, compared with 3.59% immediately prior to the poll. The dark line shows the Freddie Mac 30 Year rate, the lighter line MND 30 Year fixed.

Further confirmation of a significant reversal in mortgage rates, thanks to the changed yield curve. Such rises will create pressure on households whose income has been static for years. Because most households are on a long-term fix, however, they will have some protection, but any new loan will be set at the higher, less affordable rate.

Of course in Australia, most households are on a variable rate – so any upward movement in rates will translate to immediate pain.

UK’s Increased and More Flexible Grant for English Housing Associations

Moody’s says “last Wednesday, the UK Chancellor of the Exchequer Phillip Hammond pledged in the Autumn Statement £1.4 billion in an extra capital grant for the affordable housing budget and announced relaxed restrictions on the grant programme for English Housing Associations (HAs). We view the increased grant as credit positive for HAs because it provides increased funding flexibility, and relaxed restrictions on the pre-existing grant programme, which will incentivize HAs to moderate increasingly aggressive plans to build for market sale”.

The government’s £1.4 billion increase in the Affordable Homes Programme is intended to fund 40,000 new homes for sub-market rent. The increase adds to the existing £4.7 billion capital grant programme over 2016-21, which was previously restricted to home ownership tenures. HAs have historically relied on government grants to fund the majority of social housing development, but the grants have diminished in recent years, as shown in Exhibit 1. In 2015, HAs primarily funded growth in housing properties with debt and by reinvesting operating surpluses, while grants comprised £0.5 billion and total debt of social housing registered providers grew by £4.1 billion to £63.4 billion, according to the Homes and Communities Agency. In Moody’s-rated HAs, grants funded 10% of housing capex, a level below historical average. Planned increases in HA capital expenditure should continue to raise debt; however, the increased availability of grants should moderate the incline.

The relaxed restrictions on capital grants, combined with the new grant for affordable rented homes will incentivize HAs to moderate plans for more aggressive commercial tenure mixes. In an environment of minimal grants for social housing, a growing proportion of HAs have cross-subsidized themselves by building units for sale to generate revenue to fund building in their core social rental business. We think these policy changes will mitigate a shift towards this more risky business model. New and increased grants available for affordable rented housing are likely to encourage HAs to reconsider tenure mixes, decreasing exposure to the pro-cyclical housing market, which we forecast to expand (see Exhibit 2). By 2019, about 38% of Moody’s-rated HAs will generate about one-fifth or a greater proportion of their turnover from sales, up from 20% in 2013.

Other credit-supportive announcements in the Exchequer’s Autumn Statement include the confirmation of a £3.15 billion affordable homes settlement for London, and a £2.3 billion infrastructure fund for local authorities to support housing infrastructure, which should also indirectly benefit HAs. The additional grant and relaxed restrictions in particular suggest a softening of the government’s attitude towards HAs and a reversal from the previous government’s focus on home ownership. Despite this positive policy shift, HAs’ capital grant remains low relative to the historical average, and near-term downside risks remain because of HAs’ high planned capital expenditures, increased reliance on revenue from sales, and policy uncertainty in the rental regime.

Auction Markets Results – Second Busiest Week So Far This Year

CoreLogic says last weekend’s auction markets continue their strong run of high clearance rates after the second busiest week for auctions so far this year.

Auction volumes increased with 3,367 properties taken to auction this week. This was the second highest number of reported auctions this year for the combined capital cities, up from 2,987 over the previous week. Despite the surge in auction numbers, market volume is still significantly lower than the corresponding week last year (3,729). The preliminary auction clearance rate, despite the increase in volume, remains strong (76.0 per cent), up from last week’s final of 74.4 per cent and also higher than equivalent week last year (60.1 per cent). Every capital city except Perth and Canberra are showing auction numbers to be lower than a year ago, while every capital has recorded a higher clearance rate compared with last year.

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