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Housing Finance: Testing for Significant Unobserved Components

Dr. Harry M. Karamujic. Housing Finance: Testing for Significant Unobserved Components. Objectives.

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Housing Finance: Testing for Significant Unobserved Components

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  1. Dr. Harry M. Karamujic Housing Finance: Testing for Significant Unobserved Components

  2. Objectives • The paper employs a structural time series modelling approach to investigate the presence and significance of unobserved components within the Australian housing finance data., i.e. the objective of the analysis is to shed light on those factors that explain the high degree of variability in Australian housing finance data. • More precisely, the objectives of the paper are to: • establish whether or not the cyclical and seasonal variations exist; and • (ii) test whether the observed variations, if any, are random (stochastic) or deterministic (predictable). • The paper also points to some suggestions for future research.

  3. Mortgage Loans • The paper models mortgage loans to investors and compares the results with mortgage loans to owner-occupiers. A mortgage loan can be defined as a loan secured by real property through the use of a mortgage (a legal instrument). • This analysis utilises housing finance data (collected by the Australian Prudential Regulatory Authority - APRA) represented by monthly home loan drawdowns of the five biggest banks in Australia. • The authors specify a model for each bank and by aggregating the data model the total drawdowns referred to in this paper as the All banks case. The five banks include the major four banks plus St. George bank. The major four banks are: Aust. and New Zealand Bank. Group (ANZ), Commonwealth Bank of Aust. (CBA), Westpac Bank. Corporation (Westpac) and National Aust. Bank (NAB).

  4. Home Loan Drawdowns • Home loan drawdowns represent an actual withdrawal (on the instruction of a house purchaser) of previously approved funds. A home loan drawdown (HLD) can be represented as: • HLD = HFC - DHFC (1) • where HFC represents housing finance commitments and DHFC denotes declined housingfinance commitments. A HFC can be defined as a firm offer of housing finance, by lender, to a perspective borrower. • HFCs include secured finance commitments for: (i) the construction or purchase of owner occupied dwellings, and (ii) finance commitments for the construction or purchase of dwellings for rent or resale (investment).

  5. Methodology • The focus of this study is not on modelling the behaviour of time series in terms of explanatory variables (the conventional modelling approach). Instead, the aim in this study is to use a univariate structural time series modelling approach (allows modelling both stochastic and deterministic trend and seasonality) and as such show that conventional assumptions of deterministic trend and seasonality are not always applicable. • The conventional modelling approach assumes that the behaviour of the trend and seasonality can be effectively captured by a conventional regression equation that assumes deterministic trend and seasonality. • Evidently, a problem with the conventional procedure is that deterministic seasonality is imposed as a constraint, when in fact it should be a testable hypothesis .

  6. Methodology • Within a structural time series approach, the term ”structural” implies that a time series is observed as a set of components not observable directly. The approach allows the selected time series, including intervention variables, to be modeled simultaneously with the unobserved components . The intention is to decompose the selected time series in terms of its respective components and to understand how these components relate to the underlying forces that shape its evolution. • The empirical analysis uses the model as presented in Harvey (1985, 1990), whereby time series are modeled in terms of their components. The model can be written as: yt = µt + фt + γt + εt (2) • where yt represents the actual value of the series at time t, µt is the trend component of the series, фt is the corresponding cyclical component, γt is the seasonal component and εt is the irregular component (assumed to be ‘white noise’).

  7. Results and Discussion • The structural time series model represented by (2) is applied to seasonally unadjusted monthly HLD data for the specified banks, between 2004:7 and 2009:8. • The data have been sourced from the APRA. For consistency, the sample for each variable is standardised to start with the first available July observation and end with the latest available June observation. • The final state vector in all cases includes a stochastic trend (composed of the level and slope components), seasonal, cyclical and irregular components. • As shown in Table 1-4, the paper considers the following four modelling specifications: • - Model 1 (Loans to Owner-occupiers - deterministic seasonal specification) • - Model 2 (Loans to Investors - deterministic seasonal specification) • - Model 3 (Loans to Owner-occupiers - stochastic seasonal specification) • - Model 4 (Loans to Investors - stochastic seasonal specification)

  8. Table 1: Est. Coef. of Fin. State Vector (Loans to Own.-occup). deterministic seasonal specification

  9. Table 2: Est. Coef. of Fin. State Vector (Loans to Investors). deterministic seasonal specification

  10. Table 3: Est. Coef. of Fin. State Vector (Loans to Owner-occupiers) stochastic seasonal specification

  11. Table 4: Est. Coef. of Fin. State Vector (Loans to Investors)stochastic seasonal specification

  12. Results and Discussion • In most cases we conclude that cyclicality and seasonality is present but that, solely based on the diagnostic tests and goodness of fit measures, the nature of seasonality (i.e. whether it is deterministic or stochastic) cannot be determined. We find that there is very little difference in the reported goodness of fit measures and associated diagnostic tests. • The measure of goodness of fit includes the coefficient of determination ( ) and the modified version ( ), which captures the differences in the seasonal means, and the Akaike’s information criterion (AIC), which implies inferior goodness of fit the higher the value. The diagnostic test for serial correlation is based on the Ljung-Box (1978) test for serial correlation (Q). • The deterministic nature of the seasonal components becomes apparent when we consider the Figures 1 – 12. Consequently, we focus our attention on interpreting the results of deterministic models i.e. models outlined in Table 1 and 2.

  13. Results and Discussion • As shown in Table 1, in the cases of ANZ, St George, Westpac and All Banks we find that the cycle is insignificant. • Table 2 implies that all cycle factors are insignificant. To a large extent this may be a result of the limited sample (i.e., 2004:7 – 2009:8) under consideration. • In each case the cycles manifest (from peak to trough) over a short period between 2-3 years and indicate that other factors are influential in explaining the variability in housing finance data. These factors may include growth in income levels and/or fluctuations in other economic variables including interest rates and output as approximated by gross domestic product and its associated components. • It is reasonable to expect that as more data becomes available, short, medium and in fact long terms trends may manifest in the data.

  14. Results and Discussion • With respect to the modelling results relating to seasonal variations of owner occupiers’ HLDs, out the eleven seasonal factors presented in Table 1, only those corresponding to NAB (August (γ1), July (γ2), June (γ3) and April (γ5)), CBA (June (γ3)) and St. George (June (γ3) and January (γ8)) were found to be statistically significant. • Similarly, with respect to the modelling results relating to seasonal variations of investors’ HLDs, out the eleven seasonal factors presented in Table 2, only those corresponding to NAB (June (γ3), January (γ8), December (γ9) and October (γ11)), CBA (June (γ3)) and St. George (June (γ3) and January (γ8)) were found to be statistically significant.

  15. Results and Discussion • The results for both investors and owner occupiers HLDs provide evidence that HLDs experience seasonal increase during the ‘end of the financial year’ season. The primary cause of this seasonal effect is that the Australian Taxation Office (ATO) accepts prepayment of the interest payable on investment properties by the end of the first week in July. Consequently, towards the end of the financial year, investors seeking to minimise their tax exposure, and, as such, optimise their wealth creation opportunities, increase the demand for this kind of funding. • The end of the financial year season generally starts by the end of April or the beginning of May, and finishes at the end of the first week in July. More precisely, the ATO allows interest rate prepayments made by the end of the first week in July to be claimed as costs in the current financial year.

  16. Results and Discussion • As shown in Table 1, five out of the seven statistically significant factors (those corresponding to April and June) may be attributed to the end of the financial year season. The two exceptions refer to the modelling results obtained for NAB’s August (γ1) and St. George’s January (γ8). • With respect to the modelling results relating to investors, shown in Table 2, only those corresponding to NAB (June (γ3), January (γ8), December (γ9) and October (γ11)), CBA (June (γ3)) and St. George (June (γ3) and January (γ8)) were found to be statistically significant. Not surprisingly, the end of the financial year season effect is also present with statistically significant seasonal factors corresponding to June, observed for CBA, NAB and St. George. • To reach a conclusive explanation for the other (not related to the ‘end of the financial year’ season) observed statistically significant seasonal factors we would need to conduct a more detailed analysis of (amongst other things) associated changes in the yield curve and pricing strategies used by the affected banks during the observed period.

  17. Results and Discussion Figure 1: ANZ – deterministic seasonal model (Loans to Homeowners) Figure 2: CBA – deterministic seasonal model (Loans to Homeowners) Figure 3: NAB – deterministic seasonal model (Loans to Homeowners) Figure 4: St George – deterministic seasonal model (Loans to Homeowners)

  18. Results and Discussion Figure 5: Westpac – deterministic seasonal model (Loans to Homeowners) Figure 6: All Banks – deterministic seasonal model (Loans to Homeowners) Figure 7: ANZ – deterministic seasonal model (Loans to Investors) Figure 8: CBA – deterministic seasonal model (Loans to Investors)

  19. Results and Discussion Figure 9: NAB – deterministic seasonal model (Loans to Investors) Figure 10: St George – deterministic seasonal model (Loans to Investors) Figure 11: St George – deterministic seasonal model (Loans to Investors) Figure 12: All Banks – deterministic seasonal model (Loans to Investors)

  20. Questions

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