Detección de las Necesidades de Capacitación

Technological changes, greater economic and monetary stability, and financial innovation have driven the evolution of the Australian financial market in the last twenty-five years. The financial sector experienced development and growth, which resulted in considerable growth in household credit.

A number of factors contributed to the increase in household debt in Australia. Dereg- ulation in the early 1980s resulted in an increase in the number of financing institutions and was accompanied by new financial products. Deregulation increased competition and innovation in the financial sector. The price of housing rose, consistent with the experience of other developed countries around the world. Furthermore, the expecta- tion of growth in prices, together with the Australian tax system, promoted household investment and indebtedness. These factors are all accompanied by a transition to a lower inflation environment in the early 1990s, and therefore, lower interest rates, which enabled larger household debt.

This scenario changed with the advent of the GFC in 2007. Financial uncertainty led to credit tightening and a deceleration in household balance sheet accumulation. Australia was ‘lucky’62in avoiding a recession during the GFC. Currently, the slow recovery of the U.S. and European economies together with a slowdown in China’s growth has created concerns.

These concerns translate directly into the market for housing credit. Most borrowers in Australia hold variable-rate mortgages – which allow a direct flow of cash rate policy into mortgage interest rates, however since 2008 banks have faced higher funding costs and are increasingly divorcing their standard variable rates from the policy cash rate. Furthermore, credit growth and house price appreciation have decelerated. The financial sector is more concentrated than it was before the financial turmoil, due in part to regulator responses such as deposit insurance.

Australian regulators have been aware of, and rapidly responsive to, international uncer- tainty and potential flaws in the financial system. The RBA and the federal government reacted promptly to the global crisis in 2008. New measures are being put in place to

62

See RBA Governor’s, Glenn Stevens, address to The Anika Foundation Luncheon, ‘The Lucky Country’, 24 July 2012, Sydney – RBA Bulletin, September quarter 2012, 75-83.

Chapter 2. The Australian Mortgage Market 39

face external threats – such as the European sovereign debt crisis and the slowdown in China – and potential internal flaws – such as the banking sector’s potential systemic risk due to its high concentration around four pillar banks.

Overall, although major banks increased their share of financial system assets, they have changed the composition of their balance sheets and appear to hold a strong capital po- sition and healthy funding. The household sector has shifted from being net borrowers to net lenders toward the end of the last decade with offsetting changes for the govern- ment sector. Australia maintains a sound financial system, with AAA-rated government debt.

Chapter 3

Data Description

3.1

Introduction

This chapter describes the data used in this thesis, and validates it as representative by comparison with market data. We investigate mortgage product choice observing loan- level, individual-level data for a rich sample of Australian borrowers during January 2003 to May 2009. The proprietary data, originated by one of the major banks with national representation in Australia,1 collects borrower information in the process of a mortgage application made directly to the bank. The raw dataset includes 1.2 million mortgage applications.

Importantly, the data in this study do not rely on survey data and interpolated data. Many empirical studies on mortgage choice have been severely limited in the data avail- able; Dhillon et al. [73] have only 78 observations, Brueckner and Follain [34] have 475 observations, Brueckner [35] has 418 observations and Sa-Aadu and Sirmans [162] have 345 observations. Moreover, each of them draw from relatively constrained geographic areas.

More recently, larger datasets have emerged, however they are compiled from represen- tative surveys. Coulibaly and Li [54] have 2,887 observations for U.S., while Paiella and Pozzolo [150] have 28,000 observations for Italy. Cocco [50] has 3,608 mortgages for the

1

The major bank remains anonymous, and covers mortgage applications made in bank branches around all of Australia, except for the Northern Territory – however some applicants report residence in the Northern Territory.

Chapter 3. Data Description 41

British Household Panel Survey, while Ehrmann and Ziegelmeyer [82] have around 8,500 observations in a Euro Area wealth survey.

The literature on mortgage choice is currently undergoing a strong resurgence, partly due to interest in the role of securitized mortgages in the propagation of the global financial crisis, and partly due to the origination and exploration of greatly improved data resources. The most convincing new evidence is emerging from datasets compiled from financial institutions or regulatory authorities’ collections of data. Fortowsky et al. [88] have over 780,000 observations by combining databases from financial institutions and GSEs. Berndt et al. [26] work with over 300,000 loans generated by one of the largest sub-prime loan originators in the U.S. Amromin et al. [10] manage information on 10 million mortgages obtained from large U.S. mortgage providers. All these studies are based on the U.S. mortgage market. Individual or loan-level data are sought in many other countries, however we are not aware of any study on mortgage product choice that applies administrative loan-level data outside of the U.S.2

Exploiting the richness of the data is one of the contributions in this work. The informa- tion collected for a mortgage application covers not only costs and terms of the mortgage contract, but also provides bank-validated information on borrower demographics, in- come and financial position.

This chapter proceeds to describe in detail the raw administrative loan-level dataset in Section3.2. Section3.3explains the data cleaning process. Sections3.4and 3.5concen- trate on the cleaned sub-samples of owner-occupier and investment loans respectively. Section 3.6 describes complementary data used to obtain indicators of the economy. Section3.7 presents concluding remarks.