Martin North – Page 1028 – Digital Finance Analytics (DFA) Blog

First Time Loans Now 25% Higher – ABS

The ABS published revised First Time Buyer data to try and iron out some data issues. As a result in November 2014 an extra 1,566 loans (25.8%) were found. This means First Time Buyer Loans were 14.6% of new loans in November, as opposed to 11.6% reported previously. Still a low number, compared with the peak of 30.6% in April 2009.

This does not count First Time Buyers going direct to the investment sector, which we have highlighted before. The ABS explanation follows.

The First Home Owner Grant (FHOG), introduced on 1 July 2000, is a national scheme funded and administered by the states and territories http://www.firsthome.gov.au. Under the scheme, a one-off grant is payable to eligible first home owners. Until October 2012, all first home buyers were eligible for the grant regardless of whether they bought a new or an established home.

Gradually, States and Territories restricted grants to new homes only so that first home buyers who were buying established homes were no longer eligible for the grant. APRA reporting instructions state that a First Home Buyer is a borrower entering the home ownership market for the first time as an owner-occupier. The instructions do not make any distinction between first home buyers who are eligible for a First Home Owner Grant and those who are not. Nonetheless, some lenders’ reporting systems only record first home buyers if they are eligible for a grant which may cause under-reporting of first home buyers.

This under-reporting has progressively impacted on first home buyer statistics from October 2012 as individual States and Territories have changed the eligibility of their First Home Owner Grants, generally to cover only the purchase of newly constructed homes.

States and Territories restricted grants to new homes from different dates – New South Wales and Queensland from October 2012; Victoria from July 2013; the Australian Capital Territory from September 2013; South Australia and Tasmania from July 2014. Loans to first home buyers were therefore underestimated in these States from the dates specified due to some lenders under-reporting. Other lenders have reported correctly throughout. Originally, the drop in loans to first home buyers from October 2012 had been attributed to the change in grant eligibility reducing the affordability for first home buyers and economic conditions, such as rising house prices and the increase in investment loans for housing. However, subsequent analysis and follow-up with lenders has confirmed that the drop was due, at least in part, to under-reporting by some lenders.

CHANGES TO THE ESTIMATION METHOD

The ABS estimates that the number of loans to first home buyers which are currently being reported are approximately 80% of the total number of loans to first home buyers. Total reported monthly home loan commitments are not affected by this under-reporting.

For lenders who are under-reporting loans to first home buyers, the ABS has developed a model to adjust the proportion of first home buyers to total loans for each period of incorrect reporting. The model uses the following components:

a) proportion of first home buyers to total loans for those lenders reporting correctly this period;

b) the proportion of first home buyers to total loans for those lenders reporting incorrectly in the previous period;

c) the proportion of first home buyers to total loans for those lenders reporting correctly in the previous period; and

d) coefficients which determine the relative contribution of the above components to the incorrectly reported proportion.

The coefficients (d) of this model were estimated using data from January 2002 to the month prior to the First Home Owner Grant policy being changed (for example, in NSW the data were from January 2002 to September 2012). All the affected states were analysed separately. When more lenders are able to report correctly, the coefficients and estimates will be updated accordingly.

Application of the adjusted proportion:

The following table is an excerpt from the Housing Finance form (ARF392.0) and will be used to demonstrate the application of the adjusted proportion.

There are no known issues in reporting the total number and value of Fixed rate home loans (9t and 9vt), Secured revolving credit home loans (10t and 10vt) and Other home loans (11t and 11vt). The estimated proportion of first home buyers is applied to the totals for Question 9, Question 10 and Question 11 (9t, 9vt; 10t, 10vt; and 11t, 11vt) respectively to determine the number of first home buyers of the particular loan type. The values for non-first home buyers (i.e. All other loans) are then derived by subtracting the values for first home buyers from the respective totals.

Each lender reports the data by State and Territory, and the proportion for each period is applied to the relevant lenders at the state level. The adjustment is made at the lowest level collected, and is applied to the affected lenders and affected States only. The data are then aggregated to the published States and the national level.

Revisions have been made to the previously published data for the Number, Percentage (%) of all dwellings financed, and Average loan size of First Home Buyers and Non-first home buyers at the national level (columns B to G of Table 560909a). Relevant States’ previously published data have also been revised (Table 560909b) back to when the First Home Owner Grant was first restricted in that State or Territory.

RBA Cuts Rate to 2.25%

The RBA has cut the cash rate by 25 basis points to 2.25 per cent, effective 4 February 2015.

Growth in the global economy continued at a moderate pace in 2014. China’s growth was in line with policymakers’ objectives. The US economy continued to strengthen, but the euro area and Japanese economies were both weaker than expected. Forecasts for global growth in 2015 envisage continued moderate growth.

Commodity prices have continued to decline, in some cases sharply. The price of oil in particular has fallen significantly over the past few months. These trends appear to reflect a combination of lower growth in demand and, more importantly, significant increases in supply. The much lower levels of energy prices will act to strengthen global output and temporarily to lower CPI inflation rates.

Financial conditions are very accommodative globally, with long-term borrowing rates for several major sovereigns reaching new all-time lows over recent months. Some risk spreads have widened a little but overall financing costs for creditworthy borrowers remain remarkably low.

In Australia the available information suggests that growth is continuing at a below-trend pace, with domestic demand growth overall quite weak. As a result, the unemployment rate has gradually moved higher over the past year. The fall in energy prices can be expected to offer significant support to consumer spending, but at the same time the decline in the terms of trade is reducing income growth. Overall, the Bank’s assessment is that output growth will probably remain a little below trend for somewhat longer, and the rate of unemployment peak a little higher, than earlier expected. The economy is likely to be operating with a degree of spare capacity for some time yet.

The CPI recorded the lowest increase for several years in 2014. This was affected by the sharp decline in oil prices at the end of the year and the removal of the price on carbon. Measures of underlying inflation also declined a little, to around 2¼ per cent over the year. With growth in labour costs subdued, it appears likely that inflation will remain consistent with the target over the next one to two years, even with a lower exchange rate.

Credit growth picked up to moderate rates in 2014, with stronger growth in lending to investors in housing assets. Dwelling prices have continued to rise strongly in Sydney, though trends have been more varied in a number of other cities over recent months. The Bank is working with other regulators to assess and contain economic risks that may arise from the housing market.

The Australian dollar has declined noticeably against a rising US dollar over recent months, though less so against a basket of currencies. It remains above most estimates of its fundamental value, particularly given the significant declines in key commodity prices. A lower exchange rate is likely to be needed to achieve balanced growth in the economy.

For the past year and a half, the cash rate has been stable, as the Board has taken time to assess the effects of the substantial easing in policy that had already been put in place and monitored developments in Australia and abroad. At today’s meeting, taking into account the flow of recent information and updated forecasts, the Board judged that, on balance, a further reduction in the cash rate was appropriate. This action is expected to add some further support to demand, so as to foster sustainable growth and inflation outcomes consistent with the target.

New CEO Announces New Structure At Westpac

On his first day as the Westpac Group CEO, Brian Hartzer today confirmed his Executive team and their responsibilities, and detailed some of his immediate priorities for the Group.

There are no changes to the individuals within the Executive team. However, some responsibilities and reporting lines have changed. These include:

As a division, Australian Financial Services (AFS) will no longer exist, with the CEOs of Westpac Retail & Business Banking, St.George Banking Group and BT Financial Group will now report directly to Mr Hartzer.
The Technology function will now report directly to the Group CEO (previously reported to the Chief Operating Officer
All retail product development, marketing and analytics functions previously within AFS, along with Group-wide operations, will now be the responsibility of the Chief Operating Officer
David McLean has been appointed as the CEO of Westpac New Zealand. Mr McLean has been acting CEO of Westpac New Zealand since August 2014

Mr Hartzer also today outlined his key priorities for the Group.

“We have a clear customer-centric strategy, which has consistently delivered. We are building good momentum and have a number of growth opportunities that over time will help us to continue to increase the value of our franchise. These include digitally transforming our business, increasing our investment in wealth, business banking and Asia, and working more closely with innovation industries and disruptive technologies that are transforming our economy. At the same time, we will continue to improve productivity, and make sure that our risk and business practices continue to set the standard for sustainability, in line with changing regulatory and community expectations. Above all we will continue to invest in our people and the distinctive strength of Westpac’s culture, delivering a service revolution for our customers and strong, consistent, returns for shareholders.”

DFA’s New Household Finance Confidence Index Falls

DFA has just launched a New Household Finance Confidence Index, and so we are going to explain how the index works, and also discuss the initial results. This brief video covers both.

DFA has been surveying households on aspects of their finances for many years. We have 26,000 households in our sample at any one time. We include detailed questions covering various aspects of a household’s financial footprint. We are using this data to create a monthly index – THE HOUSEHOLD FINANCE CONFIDENCE INDEX. The index measures how households are feeling about their financial health.

To calculate the index we ask questions which cover a number of different dimensions. We start by asking households how confident they are feeling about their job security, whether their real income has risen or fallen in the past year, their view on their costs of living over the same period, whether they have increased their loans and other outstanding debts including credit cards and whether they are saving more than last year. Finally we ask about their overall change in net worth over the past 12 months – by net worth we mean net assets less outstanding debts.

The overall result for Australian households shows a gradual but significant fall in confidence over the past few months. Whilst a score of 100 would be a neutral result, the latest data to January 2015 came in at only 92.4. So on average, Household Finance Confidence Is Falling.

This initially seems quite surprising, because nearly 58 percent of households said their net worth had improved over the past year, thanks to significant rises in house prices. For example in Sydney prices rose on average 12 per cent. In addition, superannuation is growing, and the stock market has been performing quite well. However, a quarter of households were less comfortable with their level of debt compared with last year, reflecting larger mortgages and higher levels of credit card debt.

One third said their real incomes have dropped, partly because overtime is being cut, and partly because average pay increases are lower than inflation has been. Many households have had no increases for several years.

In addition, more than 35 percent said their costs of living had risen. Despite recent falls in fuel prices at the bowser, the costs of child care, school fees, electricity and gas, and food more than offset any gains.

Looking at savings, about 30 per cent of households were less comfortable with their level of savings compared with last year.Many are dipping into savings to make ends meet, and others are seeing overall income dropping because of falling interest rates.

Finally, nearly one fifth were less confident of their job security than a year ago.

Now, these are national results. Whilst the survey is completed at a segment level, and by each state, this more granular data is not available in this post.

We will be updating the confidence index each month, and will post the results on the DFA blog. You can of course subscribe to receive updates.

ECB’s QE Unlikely to Kick-Start Bank Loan Growth – Fitch

The ECB’s quantitative easing programme is unlikely to materially boost eurozone banks’ earnings or kick-start lending in the bloc, Fitch Ratings says. But it does reduce downside risk from prolonged deflation. Any positive impact on banks is likely to be temporary unless their balance sheets are freed up for more lending or structural reforms raise real and sustainable economic growth.

We think QE is unlikely to stimulate lending in the eurozone’s crisis-hit economies, despite the start of rebalancing and recovery in some countries. The economic outlook is still fragile, so demand for credit is likely to remain subdued, and tighter regulatory requirements are making loan growth more difficult for banks.

Banks have to hold an increasing amount of regulatory capital against the loans they extend as Basel III rules are phased in. The bar is also being raised by the ECB in its role as the new single supervisor – it recently communicated its Pillar 2 expectations for buffer capital to each of the banks. They will also have to build debt and capital buffers to meet new total loss-absorbing capacity (TLAC) and minimum requirement for own funds and eligible liabilities (MREL) rules. Gearing up bank balance sheets through loan expansion runs contrary to these regulatory pressures.

Some banks may be able to generate more trading income, as the ECB’s accommodative policy should push down bond yields and encourage trading flow. Gains on any sales of sovereign bond holdings by banks are likely to be limited because yields have continued to fall in recent months. Any revenue benefits from sovereign bond sales and trading will probably be offset by lower margins from a flatter yield curve, so the balance is likely to be neutral or even slightly negative for profitability.

Many banks in northern Europe are already awash with liquidity, so lower bond yields would only distort credit pricing there even more. Weaknesses in some of the economies, such as Germany and France, which grew 1.2% and 0.4%, respectively, yoy in 3Q14, are likely to keep investment appetite, and therefore loan demand, muted, especially for good-quality corporates, despite even cheaper potential funding rates.

Southern European banks could benefit more from QE, but the impact would depend on the pricing of their sovereign debt portfolios and the extent to which these are booked as held-to-maturity assets. But there could be some rebalancing of sovereign debt portfolios and banks are likely to look to lock in some gains. Loan expansion is even less likely in southern Europe, as most banks there are still strengthening balance sheets, gradually reducing impaired loans and dealing with legacy assets.

If the ECB’s actions do not ward off deflation, eurozone banks would come under more pressure from dampening earnings, increasing non-performing loans and weakening collateral values.

Metadata – A Marketeer’s Dream Comes True

Next time you shop, and pay by credit card, consider this. According to a recent report “Unique in the shopping mall: On the reidentifiability of credit card metadata”, it is possible to take anonymous transaction data and by applying analytics to the data, uncover considerable information about the card holder, especially, when cross-matched with other data sources. More broadly, anyone who thinks retaining meta-data has no consequences, should read this report.

Metadata contain sensitive information. Understanding the privacy of these data sets is key to their broad use and, ultimately, their impact. We study 3 months of credit card records for 1.1 million people and show that four spatiotemporal points are enough to uniquely reidentify 90% of individuals. We show that knowing the price of a transaction increases the risk of reidentification by 22%, on average. Finally, we show that even data sets that provide coarse information at any or all of the dimensions provide little anonymity and that women are more reidentifiable than men in credit card metadata.

Large-scale data sets of human behavior have the potential to fundamentally transform the way we fight diseases, design cities, or perform research. Ubiquitous technologies create personal metadata on a very large scale. Our smartphones, browsers, cars, or credit cards generate information about where we are, whom we call, or how much we spend. Scientists have compared this recent availability of large-scale behavioral data sets to the invention of the microscope. New fields such as computational social science rely on metadata to address crucial questions such as fighting malaria, studying the spread of information, or monitoring poverty. The same metadata data sets are also used by organizations and governments. For example, Netflix uses viewing patterns to recommend movies, whereas Google uses location data to provide real-time traffic information, allowing drivers to reduce fuel consumption and time spent traveling.

The transformational potential of metadata data sets is, however, conditional on their wide availability. In science, it is essential for the data to be available and shareable. Sharing data allows scientists to build on previous work, replicate results, or propose alternative hypotheses and models. Several publishers and funding agencies now require experimental data to be publicly available. Governments and businesses are similarly realizing the benefits of open data. For example, Boston’s transportation authority makes the real-time position of all public rail vehicles available through a public interface, whereas Orange Group and its subsidiaries make large samples of mobile phone data from Côte d’Ivoire and Senegal available to selected researchers through their Data for Development challenges.

These metadata are generated by our use of technology and, hence, may reveal a lot about an individual. Making these data sets broadly available, therefore, requires solid quantitative guarantees on the risk of reidentification. A data set’s lack of names, home addresses, phone numbers, or other obvious identifiers [such as required, for instance, under the U.S. personally identifiable information (PII) “specific-types” approach, does not make it anonymous nor safe to release to the public and to third parties. The privacy of such simply anonymized data sets has been compromised before.

Unicity quantifies the intrinsic reidentification risk of a data set. It was recently used to show that individuals in a simply anonymized mobile phone data set are reidentifiable from only four pieces of outside information. Outside information could be a tweet that positions a user at an approximate time for a mobility data set or a publicly available movie review for the Netflix data set. Unicity quantifies how much outside information one would need, on average, to reidentify a specific and known user in a simply anonymized data set. The higher a data set’s unicity is, the more reidentifiable it is. It consequently also quantifies the ease with which a simply anonymized data set could be merged with another.

Financial data that include noncash and digital payments contain rich metadata on individuals’ behavior. About 60% of payments in the United States are made using credit cards, and mobile payments are estimated to soon top $1 billion in the United States. A recent survey shows that financial and credit card data sets are considered the most sensitive personal data worldwide. Among Americans, 87% consider credit card data as moderately or extremely private, whereas only 68% consider health and genetic information private, and 62% consider location data private. At the same time, financial data sets have been used extensively for credit scoring, fraud detection, and understanding the predictability of shopping patterns. Financial metadata have great potential, but they are also personal and highly sensitive. There are obvious benefits to having metadata data sets broadly available, but this first requires a solid understanding of their privacy.

To provide a quantitative assessment of the likelihood of identification from financial data, we used a data set D of 3 months of credit card transactions for 1.1 million users in 10,000 shops in an Organisation for Economic Co-operation and Development country. The data set was simply anonymized, which means that it did not contain any names, account numbers, or obvious identifiers. Each transaction was time-stamped with a resolution of 1 day and associated with one shop. Shops are distributed throughout the country, and the number of shops in a district scales with population density.

For example, let’s say that we are searching for Scott in a simply anonymized credit card data set. We know two points about Scott: he went to the bakery on 23 September and to the restaurant on 24 September. Searching through the data set reveals that there is one and only one person in the entire data set who went to these two places on these two days. Scott is reidentified, and we now know all of his other transactions, such as the fact that he went shopping for shoes and groceries on 23 September, and how much he spent.

Furthermore, financial traces contain one additional column that can be used to reidentify an individual: the price of a transaction. A piece of outside information, a spatiotemporal tuple can become a triple: space, time, and the approximate price of the transaction. The data set contains the exact price of each transaction, but we assume that we only observe an approximation of this price with a precision a we call price resolution. Prices are approximated by bins whose size is increasing; that is, the size of a bin containing low prices is smaller than the size of a bin containing high prices.

Despite technological and behavioral differences, we showed credit card records to be as reidentifiable as mobile phone data and their unicity to be robust to coarsening or noise. Like credit card and mobile phone metadata, Web browsing or transportation data sets are generated as side effects of human interaction with technology, are subjected to the same idiosyncrasies of human behavior, and are also sparse and high-dimensional (for example, in the number of Web sites one can visit or the number of possible entry-exit combinations of metro stations). This means that these data can probably be relatively easily reidentified if released in a simply anonymized form and that they can probably not be anonymized by simply coarsening of the data.

Our results render the concept of PII, on which the applicability of U.S. and European Union (EU) privacy laws depend, inadequate for metadata data sets. On the one hand, the U.S. specific-types approach—for which the lack of names, home addresses, phone numbers, or other listed PII is enough to not be subject to privacy laws—is obviously not sufficient to protect the privacy of individuals in high-unicity metadata data sets. On the other hand, open-ended definitions expanding privacy laws to “any information concerning an identified or identifiable person” in the EU proposed data regulation or “[when the] re-identification to a particular person is not possible” for Deutsche Telekom are probably impossible to prove and could very strongly limit any sharing of the data.

From a technical perspective, our results emphasize the need to move, when possible, to more advanced and probably interactive individual or group privacy-conscientious technologies, as well as the need for more research in computational privacy. From a policy perspective, our findings highlight the need to reform our data protection mechanisms beyond PII and anonymity and toward a more quantitative assessment of the likelihood of reidentification. Finding the right balance between privacy and utility is absolutely crucial to realizing the great potential of metadata.

Urgent Action Required To Boost Business Lending

Today the data from RBA and APRA showed the strength of lending in the housing sector, and only 33% of lending is business related. This over-emphasis on property investment in particular is a systemic problem, and must be addressed if we are to drive growth in the right direction. Business needs more focus.

To illustrate the point, the mix between business lending and housing (both owner occupied and investment) is worth examining from 1990 onwards. Back then, business lending accounted for about 65% of all lending. Today it is 33%. As a result we know from our SME surveys that many businesses are finding it difficult to get funding on reasonable terms. On the other hand, we see lending for housing, and especially investment lending inflating the banks balance sheets and house prices, but this is not truly productive.

Its time to impose additional controls on investment lending, and to re-balance lending towards businesses who will be able to generate real economic growth for the country. The current situation, where household net worth grows…

… thanks to over-high house prices is not sustainable when it is powered by ever more household debt.

HouseholdRatios2

From our surveys, a quarter of households are less comfortable with the amount of debt they have compared with 12 months ago.

Any further interest rate cuts without these measures would be irresponsible.