1. Statistical Toolbox
1. Understanding What Income Quintiles Affects
For the following toolbox, a sample size of 70 was created to follow the Central Limit
Theorem and income quintiles were split into two subsets dependent on its value.
1.1 lowINC vs Commute)
The following sampling distributions are not normal and highlight the effect of outliers
(Figure 3). The point estimator (the average sample mean) was between low (3.393hours) and highincome (3.596 hours) differs by 0.203. There is a 95% confidence that the mean for commute time in
the higher income quintile is within [3.28, 3.50] and there is also a 95% confidence that the mean for
lower-income quintiles is within [3.49, 3.70]. As these confidence intervals overlap, it suggests that
these variables may be weakly correlated.
Figure 1
Figure 2
Figure 3
1.2 lowINC vs Age)
For the low income, the average sample mean for age is an estimated 50.0 years, whereas for higherincomes it was 49.7 years. There is a 95% confidence that the population mean for age (in lower
quintiles) is between [49.6,50.2] and a 95% confidence that the mean in higher quintiles is between
[49.36, 49.99]. Overlapping confidence intervals [Table 2] suggests little correlation between age and
income quintiles within this sample.
Figure 4
Figure 5
1.3 MLR: lowINC vs Age, Commute and lowSEIFA)
𝑙𝑜𝑤𝐼𝑁𝐶𝑖 = 𝛽0 + 𝛽1 (𝑙𝑜𝑤𝑆𝐸𝐼𝐹𝐴)𝑖 + 𝛽2 (𝐶𝑜𝑚𝑚𝑢𝑡𝑒𝑇𝑖𝑚𝑒)𝑖 + 𝛽3 (𝐴𝑔𝑒)𝑖
Null Hypothesis: The coefficients = 0.
Alternative Hypothesis: The coefficients ≠ 0.
This sample is not entirely reflective of the population as the independent variables only
account for 6.9% of total variation in lowINC.
Age had a coefficient of 0.0009, and a 99% confidence that it lay between [-0.0019, 0.0039].
With level of significance equivalent to 0.01, age’s p-value (0.385) (Table 3) indicates no statistical
significance nor economic significance. Similarly, commute time had a coefficient of 0.005 and 99%
confidence that the coefficient was between [-0.0031, 0.1312]. Its p-value (0.112) was above the
significance level and indicates that commute time is not statistically significant. Simply put, there is
not enough evidence in this sample to reject the null hypothesis for commute time and age.
SEIFA, on the other hand, revealed a strong statistically significant result. With a coefficient of 0.262,
and a 99% confidence that the coefficient is between [0.206, 0.318], there is enough evidence to reject
the null hypothesis for SEIFA. Therefore, to avoid confoundment, lowSEIFA must be included in the
regression analysis between HCOST and income.
2. Income vs HCOST
𝐻𝐶𝑂𝑆𝑇𝑖 = 𝛽0 + 𝛽1 (𝑙𝑜𝑤𝐼𝑁𝐶)𝑖 + 𝛽2 (𝑙𝑜𝑤𝑆𝐸𝐼𝐹𝐴)𝑖
Null Hypothesis: 𝛽1 = 0
Alternative Hypothesis: 𝛽1 ≠ 0
3.1.) Justification
As only lowINC and lowSEIFA appeared to be correlated in this sample, all other variables were
omitted to better control for confoundment.
3.2.) Sampling Distribution
The team is 95% confident that the mean HCOST (high-income) is between [0.248, 0.256]. There is
also 95% confidence that the mean for lower-income quintiles is between [0.366, 0.377]. From an
observational stance, there is a significant increase in the HCOST sample mean (0.252 to 0.371) and
95% confidence interval, which suggests that the null hypothesis can be rejected.
Figure 6
Figure 7
3.3 MLR: HCOST vs lowINC and lowSEIFA
The LowINC and lowSEIFA variables explain 15.592% of the variation in HCOST.
Using a significance level of 0.01 both the p-values for lowINC and lowSEIFA are smaller, indicating
that the coefficients are strongly statistically significant. This signifies that for this sample, the null
hypothesis can be rejected. The team is 99% confident that the coefficient for low income is between
[0.09, 0.125] and that the coefficient for income region is between [0.023, 0.058]. Economically, this
signifies that decreases in income may potentially increase HCOST.
4. Limitations
Declaring causality is almost impossible as there are no controlled experiments. Additionally,
there is no guarantee that the survey data is truthful as participants may be embarrassed to reveal
income information.
Additionally, the income data contains binary values rather than discrete values, despite
having five quintiles that each differ by a significant amount. Merging data dissolves the accuracy of
analysis. Ideally, discrete quantitative variables are required to better support causality.
The analysis is also subject to outliers and assumes that all variables contain a strong positive
correlation. There are also the possibility of Type 1 and 2 Errors, as it is unknown if the sample means
and coefficients are a part of the confidence interval, or the margin of error.
2. Ethics Toolbox
Step 1.
Payday loans are a short-term debt finance with high interest rates (IR). High IR such as
407.6% p.a. compensate for increased risk as credit history is ignored [Consumer Action,2019,p6].
For 78% of distressed households, only pay-day loans are accessible and of that, 38% are continual
borrowers [Monash University, 2015, pg15]. Over a five-year period, 15% of borrowers experienced
debt spiral [Consumer Action, 2019, pg1]. It is unethical that the financially stressed cannot safely
access any debt-finance. It can be inferred that this affects other creditors and activists. Other lenders
may struggle with repayments whereas activists – like Stop The Debt Trap Alliance – aim to influence
regulations.
Step 2.
There is the cognitive bias that pay-day lenders are ‘scams’ due to high IR. Confirmational
bias enhances this, with individuals more likely to remember outcomes with bankruptcy. In contrast,
high IR is necessary to compensate for greater administration costs than for large-valued loans
[Corones et al, 2011, pg 10]. Furthermore, there is the potential for algorithmic bias as it is unknown
how lenders calculate interest rates.
Step 3.
Ideally, truthfulness and integrity would be ensured through legal protection acts and unions.
3.1) Borrowers
It is a duty to protecting borrowers through legal entities like ACCC and ASIC. For example,
under the National Consumer Credit (NCC) Protection Act, consumers have the right to clear contact
terms and financial assistance [Corones et al, 2011, pg 32].
3.2) Payday Lenders
Payday lenders can set its own IR, provided that it abides by ASIC Acts. Lenders can also use
direct debit payments for collection, highlighting the power imbalance between lenders and
borrowers.
3.2.1) Conflict Between Duties
Current legislation appears to favour lenders and fails to protect borrowers from being a
means to an end. Enacting justice would be adjusting legislation to protect borrowers from debt-traps.
3.3) Other Creditors and Activists
Other lenders, hold the right to security on assets and repayments as per the loan contract.
Activists have the right to voice concerns, but the government does not have to heed them.
Step 4.
No changes to the current system will encourage financial stress and the continued
exploitation of vulnerable customers.
1) Forcing mandatory provision of current financial history would limit pay-day lenders’
ability to manipulate vulnerable individuals. However, low-income individuals would have no access
to any debt-finance and may struggle with expenses, leading to poverty or homelessness. Therefore,
this is a non-feasible option.
2) A more plausible option is to reduce the administrative costs that drive high IR. By
providing incentives to become more efficient, IR may be reduced, and borrowers would have a better
chance of repaying debt. Other creditors would have an improved chance of receiving debt
repayments and activists would be less fearful about debt-traps.
Step 5.
Integrity is essential as lender must not prioritise profit over wellbeing. Furthermore, lenders and
creditors must be empathetic and place themselves in the borrower’s situation – just as activists have
done. This gives way to compassionate actions that promote less financial stress and create an
organisational identity of trustworthiness.
Step 6.
Protecting financial stability warrants the greatest priority as it can spiral into homelessness, poverty –
ultimately preventing access to necessities and preventing humanity flourishing.
The most fitting position is to somehow limit pay-day lending activities through lowering interest
rates so borrowers still have access to short-term debt finance but do not fall into debt traps and can
retain essential assets.
Step 7.
While placing regulations may initially reduce profitability it allows humanity to experience
financial stability. Ignoring this situation or placing strict regulations would bring greater suffering to
borrowers and stakeholders – as explored in earlier sections. Therefore, reducing lender IR may
reduce financial stress, while allowing lenders to survive.
3. Information Toolbox
Situation
Mortgage is a long-term secured loan used to finance purchases in property and is repaid
through regular installments. Within Sydney, mortgage stress is a growing issue, suggesting that lowincome households are struggling to maintain mortgage. For low-income earners, a house may be
unaffordable should the mortgage-debt servicing ratio exceed 30%. Mortgage stress appears to be the
culmination of actions by the key stakeholders like, mortgagors, investors, the government, and
financial institutions. A next section focusses on how to mitigate the issue.
Recommendations
Demand-side factors such as income are impossible to control as it is a personal and largely
varied attribute. Despite the regression analysis indicating strong statistical significance between
income and housing costs, attempting to solve mortgage stress through adjusting income is not a
feasible option. An alternative is a niche focus on supply with changes to regulation for taxation and
debt-finance.
Alteration to taxation laws involves preventing negative gearing so that investors are less
interested in the housing market. This is achievable as foreign investors face stricter investment laws
and contribute significantly less to housing costs. Reducing demand for housing may be the first step
to creating a less volatile and unnecessarily expensive housing market.
Policy reform is necessary for the entire loan industry. The current system immorally favors
high-income earners and sends lower-income earners into debt-traps and debt-induced poverty. As
mentioned in 2B)’s ethical toolbox, the government should incentivize payday lenders to become
more efficient and reduce administration costs – a component which has been a main driver of the
high interest rates. 2A)’s Ethical Toolbox also suggested that security should be spread out across
several assets to protect borrowers rather than immediately claim large assets.
Focusing government funds on these two areas will create a sustainable solution, where debtfinance is safely accessible to everyone, and housing pricing is stable.
Observations
2A)’s logic tree shows that the drivers of mortgage stress appear to be government policies,
income, financial institutions, home-owners, and investors. This is split into demand and supply.
Investors (demand) increase demand in property due to lax tax regulation and the assumption that
housing is a “source of wealth accumulation”. Following the laws of supply and demand, this raises
housing prices [Morris, 2018, pg65].
Mortgage stress seems to pertain to young individuals (first-home buyers/FHB) or older
Australians. For FHB in 2016, only 10% of properties were affordable given that the property was
50km away from the Sydney center [Morris, 2018, p64]. Additionally, elderly Australians found that
mortgage repayments accounted for their largest expense [Ong et al 2019, p52]. As it is assumed that
FHB and elderly-Australians either have low-accumulated wealth or low-income, it suggests that
housing is inaccessible to low-income earners. Further studies promote this link with Western Sydney
areas that have the lowest household income and income growth rate, is less resilient to changes to
house prices in comparison to eastern and northern regions [Bangura, 2020, p104].
The Statistical Analysis for 2A and 2B revealed strong statistical and economical significance
between income and housing costs. In Table 5 of 2B)’s statistical toolbox, the average sample mean
for housing costs differed significantly between the income quintiles. For low-income, the point
estimator for HCOST is 37.1%, which well exceeds the mortgage-debt servicing ratio. In comparison,
high income quintiles had a significantly smaller point estimate of 25.2%. Therefore, for this sample –
which is susceptible to outliers - the null hypothesis that income does not influence HCOST, was
rejected.
Furthermore, strict loan-requirements (supply-side) means that low-income households
cannot safely access debt finance. 2A)’s Ethics Toolbox revealed that the rigid structure of loan
contracts places unnecessary stress on older Australians or individuals in an unstable financial
situation and possibly forces them into mortgage-induced poverty. Furthermore, the Ethics Toolbox in
2B) examined how these households are then forced to continually withdraw payday loans with
excessively high interest rates as the only way to manage daily expenses [Monash, DFF]. 15% of
these financially stressed households fall into debt spiral [Consumer Action, 2019, pg 4].
Simply put, the combination of current housing prices and loan regulations have most likely
resulted in this gradual increase in mortgage stress.
COMM1110 –
References
Assessment 2A)
Bangura, Mustapha (2020), ‘Housing Affordability and Housing Submarkets: The Case of Greater
Sydney’, PhD Thesis, Western Sydney University.
La Cava G., Leal H., and Zurawski A., Economic Group: RBA, Housing Accessibility for First Home
Buyers, 2017, Australia.
Morris, A. 2018 , ‘The Financialisation of Housing and the Housing Affordability Crisis in Sydney’,
Housing Finance International, VXXXII No. 4, p63-67, accessed 21/6/21, from Google Scholar, ISSN:
2078-6328.
Ong, R., Wood G., Cigdem, M., and Salazar, S., ‘Mortgage Stress and Precarious Home Ownership:
Implications for Older Australians’, Australian Housing Institute and Urban Research Institute, p2-63,
accessed 18/6/21, from Trove, ISSN: 1834-7223, DOI: 10.18408/ahuri-8118901
Assessment 2B)
Corones, S., McGill, D., Durrant, R., 2011, ‘Phase Two of National Credit Reforms Examining the
Regulation of Payday Lenders’, Queensland University of Technology, Australia
Monash University, 2015, ‘The Stressed Finance Landscape Data Analysis’, Report, Monash
University
Consumer Action Law Centre, ‘Stop the Debt Trap’, 2019, Australia.