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英文代写-COMM1110
时间：2022-03-17
COMM1110: Assessment 2a Student SID
Report Overview:
This briefing pack provides input into the Report on Mortgage Stress (MS) in Sydney, via three (3)
key aspects of analytical focus:
x The Information Toolbox (IT) discusses recent problem-related research (both academic &
grey), then builds a visual (logic tree) representation of the problem and its causes,
x The Statistical Toolbox (ST) describes key variables in the provided dataset and begins the
process of statistical analysis, and
x The Ethical Toolbox (ET) highlights an ethical dilemma for Lenders (personal circumstances),
and uses ethical frameworks to explore the moral aspects of effective decision-making.
Information Toolbox:
This toolbox considers key management aspects of effective problem-solving (PS). This PS typically
involves 3 sequential steps, ie:
x Scoping: defining (via research) the problem, then disaggregating it into causes to help flag
the complexity of problem, then
x Analysis: exploring of this research and related statistical evidence as key problem inputs,
and starting the process of drawing analytical conclusions, then
x Decision: synthesising and presenting the analysis, then communicating proposed decision
options to problem stakeholders.
This report focusses mostly on Steps 1 & 2, and the subsequent report on Step 3.
In order to scope the problem, we require quality research inputs. Below is a summary of three (3)
inputs based on both Academic, (ie peer-reviewed) and Grey (ie Business / Government) papers.
Yates and Berry (2011) provide a long view exploration of the flow of ƵƐƚƌĂůŝĂ͛ƐŚŽƵƐŝŶŐŵĂƌŬĞƚ,
firstly from a historical perspective ;ǁŚĂƚ͛ƐƵŶŝƋƵĞĂďŽƵƚƵƐƚƌĂůŝĂ͛ƐŚŽƵƐŝŶŐŵĂƌŬĞƚ͍Ϳ, then posits
two contrasting scenarios going forward (continued growth vs a sharp downward correction). The
paper discusses the decline on the stock of social housing, the growth of the private rental market
and broader economic issues including ongoing economic growth challenges (eg the role of China),
increasing household consumption levels, and declining wage growth. Some of the key policy-related
conclusions it draws are around the need for policy interventions to increase affordability, the need
for more innovation around related infrastructure development, a more flexible labour supply, the
ŶĞĞĚƚŽĚĞĂůďĞƚƚĞƌǁŝƚŚ͚ƚƌĂƉƉĞĚƚĞŶĂŶƚƐ͟, and the need (or desire) to redistribute populations
beyond key urban centres ʹ like Sydney.
Bullock (2018), a Reserve Bank Assistant Governor, links mortgage stress to broader financial (debt)
stress challenges. She notes that whilst the number of households in mortgage stress has actually
fallen over the last decade (2006-2016) there is still a significant proportion still in mortgage stress.
She contrasts owner-occupier vs investor-related stress issues, noting increasing indebtedness in
States with a more Mining-exposed economy (eg WA) and the ongoing importance of managing (or
at least influencing) such factors as interest rate movements, and the need for more prudent lending
standards by Banks and other Lenders, and interestingly for us the need for more timely data on the
problem as it changes over time.
Finally, Roy Morgan Research (2021) notes that 15.8% of mortgage holders (in NSW, Victoria and
the dͿǁĞƌĞ͚ĂƚƌŝƐŬ͟ŽĨŵŽƌƚŐĂŐĞƐƚƌĞƐƐin the three months to September 2021, ie during the
COMM1110: Assessment 2a Student SID
current pandemic. This contrast with a rate of 35.6% during the GFC (2008). The report notes such
ƌĞůĂƚĞĚĨĂĐƚŽƌƐĂƐƚŚĞŐŽǀĞƌŶŵĞŶƚƉĂLJŵĞŶƚŽĨ͞Žǀid-ϭϵŝƐĂƐƚĞƌWĂLJŵĞŶƚƐ͟ (and its provision
timing links to levels of household indebtedness), the changes in available working hours, job
opportunities (especially in some retail-related sectors of the economy) and increasing level of
redundancies across the Australian economy during the pandemic. Importantly from a problem
definition and data analysis perspective, the study contrasts ͞ƚZŝƐŬ͟(between 25% and 45% of
after-tax income spent on loan repayments depending on income and spending) - ĂŶĚ͞džƚƌĞŵĞůLJat
ZŝƐŬ͟ (based on interest only repayment).
Figure 1: Mortgage Stress by Owner-Occupied Mortgage Holders (2021)
The problem of Mortgage Stress is therefore defined as households who are spending more than
25% of after-tax income on loan repayments. This definition depends on and thus makes a number
of related and important assumptions - including individual consumption patterns, household
income levels and growth potential, government financial support, prevailing interest rates and
movements, and the broader economic conditions and potential shocks (eg the pandemic).
Using the above inputs, the problem is presented below in visual form is via a logic tree. It is framed
firstly around the key stakeholder͛Ɛas identified above, then the many causal variables explored in
the research (like interest rates, job security, and lending standards), and a number of additional
variables identified as part of the author͛Ɛ wider related research (like health and well-being, and
risk appetite).
COMM1110: Assessment 2a Student SID
Figure 2: Logic Tree, incorporating research-related plus other causal factors.
COMM1110: Assessment 2a Student SID
Statistical Toolbox:
To analyse mortgage stress, we will need to understand the patterns and variables in the sample
that has been provided to us. As part of this analysis, we will assume that the data was collected
appropriately this includes assumptions such as the fact that we have a random sample and thus
there are no selection issues associated with the sample. Furthermore, we assume that there are no
mistakes in the data and has been appropriated cleaned. In addition to the above, all statements
made here are all in relation to the sample. Thus, at no point should the patterns observed be
extrapolated to the population, our purpose here is to describe what is observed in the sample.
Finally, when describing any relationship, we do not imply causality as this is purely an exploratory
piece and we have not examined any possible theories behind any relationship that may arise.
Before we begin this analysis, we will describe the data that has been presented to us. The dataset
consists of 4,866 observations and five variables including, hcost, lowinc, lowSEIFA, age and
comtime. Definitions of these variables can be found in the ͞ƐƐŝŐŶŵĞŶƚϮ͟ĚŽĐƵŵĞŶƚ͘We note
that hcost, age and comtime are continuous, while lowinc and lowSEIFA are binary.
To examine mortgage stress, we need to define mortgage stress, according to the RBA (2018),
͞owner-occupier debt had mortgage payments of 30 per cent or less of income, which is often used
as a rough indicator of the limit for a sustainable level of mortgage repayments͘͟1. Using this
definition, we will use the variable hcost as proxy for this as it measures ͞/ŶĚŝǀŝĚƵĂůŚŽƵƐŝŶŐĐŽƐƚ-
income ratios calculated as the ratio of weekly mortgage repayments and weekly gross household
ŝŶĐŽŵĞ͘͟ Whilst this is not a perfect fit as we do not know whether it is owner occupied or otherwise
e.g. rent vesting we will use this as an indicative measure. Thus, for the purposes of this report we
will define mortgage stress as hcost>0.3. We do acknowledge that some relevant data are not
provided which would better capture mortgage stress for example, whether the household head is
working or not and household size. One of these factors have been controlled which is household
size but we are unsure of others. We also note that quite a few households have hcost ratios that
are very high, say >80%.
It is also worth noting that our client is also interested in examining the question of whether
mortgage stress is more prevalent amongst older individuals and low income households.
Looking at the provided data we can summarise hcost, age and lowinc as follows:
Table 1: Summary stats of key variables
hcost age lowinc
Mean 0.287 49.626 0.382
Median 0.250 50 0
Mode 0.222 49 0
Standard Deviation 0.145 9.198 0.486
Sample Variance 0.021 84.598 0.236
Minimum 0.0291 17 0
Maximum 0.889 85 1
Count 4866 4866 4866
1 https://www.rba.gov.au/speeches/2018/sp-ag-2018-02-20.html#fn2
COMM1110: Assessment 2a Student SID
From the above, we note that the mean is larger than the median which suggests that there is a
positive skew to this data. We also note that the range is quite wide and the standard deviation
ƌĞůĂƚŝǀĞƚŽŝƚ͛ƐŵĞĂŶŝƐĂƌŽƵŶĚ50.6%. We also note that since the median is approximately at 0.25
that most of the people in this sample would not be experiencing mortgage stress. In terms of age,
we note that the average age in the sample, is 50 and with the youngest being 17 and oldest being
85. The median age is 49. According to the ABS, the median age in June 2020 was 38 2 which means
that this sample has a much higher than the median Australian. In addition to this we would also like
to note that in a recent survey from money.co.uk the average age of home ownership is 36 years
old, which is quite different to what we find in this sample.3 Also because of the range of the data we
may some retired individuals the survey. Thus, it is worth investigating how this data was collected.
Finally, for low income we found that 38% of the sample is low-income earners which is consistent
with the definition of ͞ůŽǁĞƐƚƚǁŽƋƵŝŶƚŝůĞƐ͘͟
Following from this we will analyse the relationships between housing costs, age and low income.
We will first examine the relationship between low income and housing costs by comparing the
distribution of housing costs for all households to that of low income and for completeness those
categorised as low SEIFA. Investigating the lowSEIFA and hcost association yields another useful
insight as it measures the spatial/neighbourhood element of housing affordability, albeit crudely
given you are aggregating different types of neighbourhoods based on SEIFA.
Immediately, we make the following observations about the sample:
(1) The proportion of low income earners experiencing mortgage stress is higher than all other
households, 53.9% compared to 35.9%.
(2) The proportion of low SEIFA individuals experiencing mortgage stress is higher than all other
households, 49.4% compared to 35.9%.
These findings are summarised in the histogram below which shows a right shift in the histogram
from low income and low SEIFA when compared to all households, reflecting the above two points.
2 https://www.abs.gov.au/articles/twenty-years-population-change
3 https://www.realestate.com.au/news/average-age-of-aussie-first-home-buyers-closer-to-40-than-20-
research-reveals/
COMM1110: Assessment 2a Student SID
Figure 1: Histogram of Housing Costs
The implication of this is not clear from the perspective of the population. ƚƚŚŝƐƉŽŝŶƚ͕ǁĞŚĂǀĞŶ͛ƚ
proven whether these two distributions are distinctly different. To this point we have only suggested
that they may be different, statistical tests would need to be undertaken to make this a more
concrete claim e.g. a Chi-squared test. If we assume that indeed this is statistically different then we
can say that within this sample that a larger proportion of lower income earners do experience
mortgage stress when compared to all households. However, we cannot necessarily say that this is
also true of the population as we have not performed any inference on this data. Thus, our
statement is restricted to this sample and it suggests that mortgage stress may be more prevalent
amongst low income earners. However, we would like to note some of the interesting anomalies
with this dataset as the average age is higher than what other surveys suggest.
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
Histogram - All household Histogram - Low Seifa Histogram - Low Income
COMM1110: Assessment 2a Student SID
Our next analysis is to examine the relationship between housing costs and age. To do this we
construct a scatterplot of age vs hcost and yield the following plot.
Figure 2: Scatterplot and regression of housing cost and age
It is very difficult to identify the relationship between these two variables so ůĞƚ͛Ɛ construct a
correlation matrix to see if we can better characterise the relationship of these two variables.
Table 2: Correlation matrix of housing costs, age and comtime
age comtime hcost
age 1
comtime -0.03418 1
hcost 0.029928 -0.01928 1
The above suggests that in the sample there may be a positive linear association between age and
housing costs and a negative linear association between comtime and hcost. This is also reinforced
by the simple linear regression results below where the coefficient for age is positive and statistically
significant at the 95% level of confidence.
y = 0.0005x + 0.2638
R² = 0.0009
0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
0 10 20 30 40 50 60 70 80 90
COMM1110: Assessment 2a Student SID
Table 3: Regression output of hcost vs age
Regression Statistics
Multiple R 0.029928
R Square 0.000896
Adjusted R
Square 0.00069
Standard Error 0.145336
Observations 4866
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 0.263803 0.011434 23.0722 7.3E-112 0.241388 0.286219
age 0.000473 0.000227 2.088155 0.036836 2.89E-05 0.000917
The above regression suggests that there is a small positive linear association which these two
variables. However, it is worth noting that we have used this regression purely for descriptive
purposes, we have not identified causality nor can we assert that this is the relationship which exists
in the population. This is merely just a more specific description of the relationship we observe in
this sample. It is also worth noting from the scatterplot that there is a lot of variation in the data and
thus the fit of the model is poor as suggested by the R2 of 0.08%. Which suggests that more
investigation is required to understand the relationship of these two variables and a linear model
may not be appropriate.
Following from the above, we can say that we observe a positive linear association between
between age and hcost. What is more difficult to ascertain is causality e.g. does an increase in age
lead to an increase in hcost. We cannot answer this question presently as there may be confounding
factors which are not captured e.g. gender as females tend to earn less and are on average are
older4.
To control for some of these confounding effects we run a multiple linear regression which include
all the other variables. The results are presented below.
Table 4: Multiple linear regression of housing costs
Regression Statistics
Multiple R 0.368027
R Square 0.135444
Adjusted R
Square 0.134732
Standard Error 0.135238
Observations 4866
Coefficients
Standard
Error t Stat P-value Lower 95%
Upper
95%
Intercept 0.222297 0.011041 20.13394 1.17E-86 0.200652 0.243942
lowinc 0.0989 0.004095 24.152 8.3E-122 0.090872 0.106928
lowSEIFA 0.03026 0.004129 7.328088 2.72E-13 0.022165 0.038356
age 0.000401 0.000211 1.901563 0.057287 -1.2E-05 0.000815
4 https://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/3101.0Jun%202019?OpenDocument
Population by Age and Sex Tables
COMM1110: Assessment 2a Student SID
comtime -0.0011 0.000574 -1.9166 0.055348 -0.00223 2.52E-05
From the above we note that the coefficient of age remains qualitatively similar to the simple linear
regression. However, it is no longer statistically significant at the 95% level of confidence. Which
suggests that it could potentially be zero as a coefficient as indicated by the confidence intervals. It is
also pleasing to see that the adjusted R-Square has increased to 13.4% which suggests a better fit of
a linear model to the sample data. In totality, this provides more evidence that there may be a
positive association between age and housing costs. Again we cannot assert causality on what we
have found in this sample.
Another interesting insight we found was that when we investigated the relationship between
comtime and hcost. Thought this was not specifically flagged but is a useful insight that is relevant in
considering whether households are potentially willing to pay more in rent to live closer to work.
Negative correlations are supportive of this although these correlations are small as per the above
regression results and correlation matrix.
All in all, respecting the assumptions made above we can report the following with respect to this
sample:
x Compared to all households, there is a larger proportion of low income individuals
experiencing mortgage stress.
x Compared to all households, there is a larger proportion of low SEIFA individuals
experiencing mortgage stress.
x There does seem to be a positive association between housing costs and age, but we cannot
identify causality.
We also would like to acknowledge that there are limitations associated with the data presented to
us. The first is the fact that the hcost variable is not a perfect fit to what the RBA defines as a
ǀĂƌŝĂďůĞĨŽƌŵŽƌƚŐĂŐĞƐƚƌĞƐƐ͘&ŽůůŽǁŝŶŐĨƌŽŵƚŚŝƐǁĞĚŽŶ͛ƚŬŶŽǁŚŽǁƚŚŝƐǀĂƌŝĂďůĞǁĂƐĐĂůĐƵůĂƚĞĚ͘
The second point is that the average age of home ownership is 50 in this sample whereas in other
surveys this is significantly lower i.e. 36. From a data perspective, these two points alone should lead
to more questions as to how the variables are calculated and how the data was collected.
Finally, we would like to acknowledge that all that is written in this section relates to what we
observe in the sample which we have and to date we cannot extrapolate this to the population.
Furthermore, we cannot ascertain causality as this is currently outside the scope of the analysis, the
purpose here is to observe and describe the patterns we observe in this sample.
Ethical Toolbox:
Ethics is about decision-making with moral perspectives included. Morality involves the
consideration of a range of context-related perspectives to think about what would be the ͞right͟
(or an acceptable) decision to make given the many contextual and causal factors and thus
reasonable decision criteria.
These perspectives might include the economic complexity, stakeholder needs, principles or duties
of each stakeholder, social benefits and/or ƚŚĞǀĂůƵĞƐĂŶĚ͞ĐŚĂƌĂĐƚĞƌ͟ŽĨkey decision-makers.
COMM1110: Assessment 2a Student SID
For this analysis I will explore a specific ethical dilemma from the perspective of Banks and Other
Lenders (B&OL). One ethical dilemma for them would be to what extent they should consider the
personal circumstances of borrowers in their lending decisions - beyond their financial ability to pay.
This is an ethical dilemma because these personal circumstances can change quickly and over time
(and for reasons beyond borrowers reasonable control), that home ownership is a social good which
has value beyond the economic, and that B&OL lenders have a duty of care to help and support the
Australian community and a key player in their ongoing prosperity and well-being.
Considering this dilemma via a range of theoretical frameworks, we can see that:
x Deontologically the rules and duties which govern effective decisions by B&OL need to
change over time given economic circumstances, may not always be clear to lenders (who
many not be highly financially - or even language literate) and are sometimes influenced (or
even mandated) by government policy.
x Utilitarianism ŵĞĂŶƐƚŚĂƚƚŚĞƌĞĂƌĞŵĂŶLJƐƚĂŬĞŚŽůĚĞƌƐ;ŝŶĐůƵĚŝŶŐƚŚĞΘK>͛ƐŝŶǀĞƐƚŽƌƐĂŶĚ
owners) who may have conflicting interests and needs for their role the Lending/Mortgage
Industry,
x Virtue involved the reflection on important values and intended character, and raises a
number of critical thinking questions like ͞ǁŚĂƚƐŽƌƚŽĨĐŽƌƉŽƌĂƚĞĐŝƚŝnjĞŶĚŽǁĞǁĂŶƚƚŽ
aspire to ďĞ͍͟ĂŶĚ͞how do we want to impact and influence social outcomes in our
community͍͟
x Care means each B&OL exploring the problem from ĂŵŽƌĞ͞ĨĞŵŝŶŝŶĞ͟Žƌ͞socially-aware͟
perspective, and thus carefully considering the impact of their decisions on each ongoing
relationship rather than just the effective application of rules or lending principles.
Bringing together the above discussion, the issues associated with having a Mortgage in Sydney
include the need to:
1) Identify key stakeholders to the mortgage stress problem and exploring their needs and
wants in good depth,
2) Research and document the current rules and principles which govern the decisions made in
relation to the approval of mortgages,
3) Have a clear goal (or objective) of harm minimisation as it relates to the level of stress which
mortgage holders can or should bear, and
4) Continue to gather and analyse the data about the size and complexity of the problem so
that more and better informed decisions can be made over time.
COMM1110: Assessment 2a Student SID
References:
ƵůůŽĐŬ͕D͕ϮϬϭϴ͕͞,ŽƵƐĞŚŽůĚ/ŶĚĞďƚĞĚŶĞƐƐĂŶĚDŽƌƚŐĂŐĞ^ƚƌĞƐƐ͕͟ĚĚƌĞƐƐƚŽƚŚĞZĞƐƉŽŶƐŝďůĞ
Lending and Borrowing Summit. Reserve Bank of Australia, 20 February, 2018.
Roy Morgan, 2021, ͞Mortgage stress at record lows during the 2021 lockdowns in NSW, Victoria and
the ACT͟. November 22 2021, Finding No. 8843.
Yates. J and ĞƌƌLJ͘D͕ϮϬϭϭ͕͞,ŽƵƐŝŶŐĂŶĚDŽƌƚŐĂŐĞDĂƌŬĞƚƐŝŶdƵƌďƵůĞŶƚdŝŵĞƐ͗/ƐƵƐƚƌĂůŝĂ
ŝĨĨĞƌĞŶƚ͍͕͟Housing Studies, Vol 26, EŽ͛Ɛϳ-8, 1133-1156.
Report Overview:
This briefing pack provides input into the Report on Mortgage Stress (MS) in Sydney, via three (3)
key aspects of analytical focus:
x The Information Toolbox (IT) discusses recent problem-related research (both academic &
grey), then builds a visual (logic tree) representation of the problem and its causes,
x The Statistical Toolbox (ST) describes key variables in the provided dataset and begins the
process of statistical analysis, and
x The Ethical Toolbox (ET) highlights an ethical dilemma for Lenders (personal circumstances),
and uses ethical frameworks to explore the moral aspects of effective decision-making.
Information Toolbox:
This toolbox considers key management aspects of effective problem-solving (PS). This PS typically
involves 3 sequential steps, ie:
x Scoping: defining (via research) the problem, then disaggregating it into causes to help flag
the complexity of problem, then
x Analysis: exploring of this research and related statistical evidence as key problem inputs,
and starting the process of drawing analytical conclusions, then
x Decision: synthesising and presenting the analysis, then communicating proposed decision
options to problem stakeholders.
This report focusses mostly on Steps 1 & 2, and the subsequent report on Step 3.
In order to scope the problem, we require quality research inputs. Below is a summary of three (3)
inputs based on both Academic, (ie peer-reviewed) and Grey (ie Business / Government) papers.
Yates and Berry (2011) provide a long view exploration of the flow of ƵƐƚƌĂůŝĂ͛ƐŚŽƵƐŝŶŐŵĂƌŬĞƚ,
firstly from a historical perspective ;ǁŚĂƚ͛ƐƵŶŝƋƵĞĂďŽƵƚƵƐƚƌĂůŝĂ͛ƐŚŽƵƐŝŶŐŵĂƌŬĞƚ͍Ϳ, then posits
two contrasting scenarios going forward (continued growth vs a sharp downward correction). The
paper discusses the decline on the stock of social housing, the growth of the private rental market
and broader economic issues including ongoing economic growth challenges (eg the role of China),
increasing household consumption levels, and declining wage growth. Some of the key policy-related
conclusions it draws are around the need for policy interventions to increase affordability, the need
for more innovation around related infrastructure development, a more flexible labour supply, the
ŶĞĞĚƚŽĚĞĂůďĞƚƚĞƌǁŝƚŚ͚ƚƌĂƉƉĞĚƚĞŶĂŶƚƐ͟, and the need (or desire) to redistribute populations
beyond key urban centres ʹ like Sydney.
Bullock (2018), a Reserve Bank Assistant Governor, links mortgage stress to broader financial (debt)
stress challenges. She notes that whilst the number of households in mortgage stress has actually
fallen over the last decade (2006-2016) there is still a significant proportion still in mortgage stress.
She contrasts owner-occupier vs investor-related stress issues, noting increasing indebtedness in
States with a more Mining-exposed economy (eg WA) and the ongoing importance of managing (or
at least influencing) such factors as interest rate movements, and the need for more prudent lending
standards by Banks and other Lenders, and interestingly for us the need for more timely data on the
problem as it changes over time.
Finally, Roy Morgan Research (2021) notes that 15.8% of mortgage holders (in NSW, Victoria and
the dͿǁĞƌĞ͚ĂƚƌŝƐŬ͟ŽĨŵŽƌƚŐĂŐĞƐƚƌĞƐƐin the three months to September 2021, ie during the
COMM1110: Assessment 2a Student SID
current pandemic. This contrast with a rate of 35.6% during the GFC (2008). The report notes such
ƌĞůĂƚĞĚĨĂĐƚŽƌƐĂƐƚŚĞŐŽǀĞƌŶŵĞŶƚƉĂLJŵĞŶƚŽĨ͞Žǀid-ϭϵŝƐĂƐƚĞƌWĂLJŵĞŶƚƐ͟ (and its provision
timing links to levels of household indebtedness), the changes in available working hours, job
opportunities (especially in some retail-related sectors of the economy) and increasing level of
redundancies across the Australian economy during the pandemic. Importantly from a problem
definition and data analysis perspective, the study contrasts ͞ƚZŝƐŬ͟(between 25% and 45% of
after-tax income spent on loan repayments depending on income and spending) - ĂŶĚ͞džƚƌĞŵĞůLJat
ZŝƐŬ͟ (based on interest only repayment).
Figure 1: Mortgage Stress by Owner-Occupied Mortgage Holders (2021)
The problem of Mortgage Stress is therefore defined as households who are spending more than
25% of after-tax income on loan repayments. This definition depends on and thus makes a number
of related and important assumptions - including individual consumption patterns, household
income levels and growth potential, government financial support, prevailing interest rates and
movements, and the broader economic conditions and potential shocks (eg the pandemic).
Using the above inputs, the problem is presented below in visual form is via a logic tree. It is framed
firstly around the key stakeholder͛Ɛas identified above, then the many causal variables explored in
the research (like interest rates, job security, and lending standards), and a number of additional
variables identified as part of the author͛Ɛ wider related research (like health and well-being, and
risk appetite).
COMM1110: Assessment 2a Student SID
Figure 2: Logic Tree, incorporating research-related plus other causal factors.
COMM1110: Assessment 2a Student SID
Statistical Toolbox:
To analyse mortgage stress, we will need to understand the patterns and variables in the sample
that has been provided to us. As part of this analysis, we will assume that the data was collected
appropriately this includes assumptions such as the fact that we have a random sample and thus
there are no selection issues associated with the sample. Furthermore, we assume that there are no
mistakes in the data and has been appropriated cleaned. In addition to the above, all statements
made here are all in relation to the sample. Thus, at no point should the patterns observed be
extrapolated to the population, our purpose here is to describe what is observed in the sample.
Finally, when describing any relationship, we do not imply causality as this is purely an exploratory
piece and we have not examined any possible theories behind any relationship that may arise.
Before we begin this analysis, we will describe the data that has been presented to us. The dataset
consists of 4,866 observations and five variables including, hcost, lowinc, lowSEIFA, age and
comtime. Definitions of these variables can be found in the ͞ƐƐŝŐŶŵĞŶƚϮ͟ĚŽĐƵŵĞŶƚ͘We note
that hcost, age and comtime are continuous, while lowinc and lowSEIFA are binary.
To examine mortgage stress, we need to define mortgage stress, according to the RBA (2018),
͞owner-occupier debt had mortgage payments of 30 per cent or less of income, which is often used
as a rough indicator of the limit for a sustainable level of mortgage repayments͘͟1. Using this
definition, we will use the variable hcost as proxy for this as it measures ͞/ŶĚŝǀŝĚƵĂůŚŽƵƐŝŶŐĐŽƐƚ-
income ratios calculated as the ratio of weekly mortgage repayments and weekly gross household
ŝŶĐŽŵĞ͘͟ Whilst this is not a perfect fit as we do not know whether it is owner occupied or otherwise
e.g. rent vesting we will use this as an indicative measure. Thus, for the purposes of this report we
will define mortgage stress as hcost>0.3. We do acknowledge that some relevant data are not
provided which would better capture mortgage stress for example, whether the household head is
working or not and household size. One of these factors have been controlled which is household
size but we are unsure of others. We also note that quite a few households have hcost ratios that
are very high, say >80%.
It is also worth noting that our client is also interested in examining the question of whether
mortgage stress is more prevalent amongst older individuals and low income households.
Looking at the provided data we can summarise hcost, age and lowinc as follows:
Table 1: Summary stats of key variables
hcost age lowinc
Mean 0.287 49.626 0.382
Median 0.250 50 0
Mode 0.222 49 0
Standard Deviation 0.145 9.198 0.486
Sample Variance 0.021 84.598 0.236
Minimum 0.0291 17 0
Maximum 0.889 85 1
Count 4866 4866 4866
1 https://www.rba.gov.au/speeches/2018/sp-ag-2018-02-20.html#fn2
COMM1110: Assessment 2a Student SID
From the above, we note that the mean is larger than the median which suggests that there is a
positive skew to this data. We also note that the range is quite wide and the standard deviation
ƌĞůĂƚŝǀĞƚŽŝƚ͛ƐŵĞĂŶŝƐĂƌŽƵŶĚ50.6%. We also note that since the median is approximately at 0.25
that most of the people in this sample would not be experiencing mortgage stress. In terms of age,
we note that the average age in the sample, is 50 and with the youngest being 17 and oldest being
85. The median age is 49. According to the ABS, the median age in June 2020 was 38 2 which means
that this sample has a much higher than the median Australian. In addition to this we would also like
to note that in a recent survey from money.co.uk the average age of home ownership is 36 years
old, which is quite different to what we find in this sample.3 Also because of the range of the data we
may some retired individuals the survey. Thus, it is worth investigating how this data was collected.
Finally, for low income we found that 38% of the sample is low-income earners which is consistent
with the definition of ͞ůŽǁĞƐƚƚǁŽƋƵŝŶƚŝůĞƐ͘͟
Following from this we will analyse the relationships between housing costs, age and low income.
We will first examine the relationship between low income and housing costs by comparing the
distribution of housing costs for all households to that of low income and for completeness those
categorised as low SEIFA. Investigating the lowSEIFA and hcost association yields another useful
insight as it measures the spatial/neighbourhood element of housing affordability, albeit crudely
given you are aggregating different types of neighbourhoods based on SEIFA.
Immediately, we make the following observations about the sample:
(1) The proportion of low income earners experiencing mortgage stress is higher than all other
households, 53.9% compared to 35.9%.
(2) The proportion of low SEIFA individuals experiencing mortgage stress is higher than all other
households, 49.4% compared to 35.9%.
These findings are summarised in the histogram below which shows a right shift in the histogram
from low income and low SEIFA when compared to all households, reflecting the above two points.
2 https://www.abs.gov.au/articles/twenty-years-population-change
3 https://www.realestate.com.au/news/average-age-of-aussie-first-home-buyers-closer-to-40-than-20-
research-reveals/
COMM1110: Assessment 2a Student SID
Figure 1: Histogram of Housing Costs
The implication of this is not clear from the perspective of the population. ƚƚŚŝƐƉŽŝŶƚ͕ǁĞŚĂǀĞŶ͛ƚ
proven whether these two distributions are distinctly different. To this point we have only suggested
that they may be different, statistical tests would need to be undertaken to make this a more
concrete claim e.g. a Chi-squared test. If we assume that indeed this is statistically different then we
can say that within this sample that a larger proportion of lower income earners do experience
mortgage stress when compared to all households. However, we cannot necessarily say that this is
also true of the population as we have not performed any inference on this data. Thus, our
statement is restricted to this sample and it suggests that mortgage stress may be more prevalent
amongst low income earners. However, we would like to note some of the interesting anomalies
with this dataset as the average age is higher than what other surveys suggest.
0.0%
5.0%
10.0%
15.0%
20.0%
25.0%
Histogram - All household Histogram - Low Seifa Histogram - Low Income
COMM1110: Assessment 2a Student SID
Our next analysis is to examine the relationship between housing costs and age. To do this we
construct a scatterplot of age vs hcost and yield the following plot.
Figure 2: Scatterplot and regression of housing cost and age
It is very difficult to identify the relationship between these two variables so ůĞƚ͛Ɛ construct a
correlation matrix to see if we can better characterise the relationship of these two variables.
Table 2: Correlation matrix of housing costs, age and comtime
age comtime hcost
age 1
comtime -0.03418 1
hcost 0.029928 -0.01928 1
The above suggests that in the sample there may be a positive linear association between age and
housing costs and a negative linear association between comtime and hcost. This is also reinforced
by the simple linear regression results below where the coefficient for age is positive and statistically
significant at the 95% level of confidence.
y = 0.0005x + 0.2638
R² = 0.0009
0.000
0.100
0.200
0.300
0.400
0.500
0.600
0.700
0.800
0.900
1.000
0 10 20 30 40 50 60 70 80 90
COMM1110: Assessment 2a Student SID
Table 3: Regression output of hcost vs age
Regression Statistics
Multiple R 0.029928
R Square 0.000896
Adjusted R
Square 0.00069
Standard Error 0.145336
Observations 4866
Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 0.263803 0.011434 23.0722 7.3E-112 0.241388 0.286219
age 0.000473 0.000227 2.088155 0.036836 2.89E-05 0.000917
The above regression suggests that there is a small positive linear association which these two
variables. However, it is worth noting that we have used this regression purely for descriptive
purposes, we have not identified causality nor can we assert that this is the relationship which exists
in the population. This is merely just a more specific description of the relationship we observe in
this sample. It is also worth noting from the scatterplot that there is a lot of variation in the data and
thus the fit of the model is poor as suggested by the R2 of 0.08%. Which suggests that more
investigation is required to understand the relationship of these two variables and a linear model
may not be appropriate.
Following from the above, we can say that we observe a positive linear association between
between age and hcost. What is more difficult to ascertain is causality e.g. does an increase in age
lead to an increase in hcost. We cannot answer this question presently as there may be confounding
factors which are not captured e.g. gender as females tend to earn less and are on average are
older4.
To control for some of these confounding effects we run a multiple linear regression which include
all the other variables. The results are presented below.
Table 4: Multiple linear regression of housing costs
Regression Statistics
Multiple R 0.368027
R Square 0.135444
Adjusted R
Square 0.134732
Standard Error 0.135238
Observations 4866
Coefficients
Standard
Error t Stat P-value Lower 95%
Upper
95%
Intercept 0.222297 0.011041 20.13394 1.17E-86 0.200652 0.243942
lowinc 0.0989 0.004095 24.152 8.3E-122 0.090872 0.106928
lowSEIFA 0.03026 0.004129 7.328088 2.72E-13 0.022165 0.038356
age 0.000401 0.000211 1.901563 0.057287 -1.2E-05 0.000815
4 https://www.abs.gov.au/AUSSTATS/abs@.nsf/DetailsPage/3101.0Jun%202019?OpenDocument
Population by Age and Sex Tables
COMM1110: Assessment 2a Student SID
comtime -0.0011 0.000574 -1.9166 0.055348 -0.00223 2.52E-05
From the above we note that the coefficient of age remains qualitatively similar to the simple linear
regression. However, it is no longer statistically significant at the 95% level of confidence. Which
suggests that it could potentially be zero as a coefficient as indicated by the confidence intervals. It is
also pleasing to see that the adjusted R-Square has increased to 13.4% which suggests a better fit of
a linear model to the sample data. In totality, this provides more evidence that there may be a
positive association between age and housing costs. Again we cannot assert causality on what we
have found in this sample.
Another interesting insight we found was that when we investigated the relationship between
comtime and hcost. Thought this was not specifically flagged but is a useful insight that is relevant in
considering whether households are potentially willing to pay more in rent to live closer to work.
Negative correlations are supportive of this although these correlations are small as per the above
regression results and correlation matrix.
All in all, respecting the assumptions made above we can report the following with respect to this
sample:
x Compared to all households, there is a larger proportion of low income individuals
experiencing mortgage stress.
x Compared to all households, there is a larger proportion of low SEIFA individuals
experiencing mortgage stress.
x There does seem to be a positive association between housing costs and age, but we cannot
identify causality.
We also would like to acknowledge that there are limitations associated with the data presented to
us. The first is the fact that the hcost variable is not a perfect fit to what the RBA defines as a
ǀĂƌŝĂďůĞĨŽƌŵŽƌƚŐĂŐĞƐƚƌĞƐƐ͘&ŽůůŽǁŝŶŐĨƌŽŵƚŚŝƐǁĞĚŽŶ͛ƚŬŶŽǁŚŽǁƚŚŝƐǀĂƌŝĂďůĞǁĂƐĐĂůĐƵůĂƚĞĚ͘
The second point is that the average age of home ownership is 50 in this sample whereas in other
surveys this is significantly lower i.e. 36. From a data perspective, these two points alone should lead
to more questions as to how the variables are calculated and how the data was collected.
Finally, we would like to acknowledge that all that is written in this section relates to what we
observe in the sample which we have and to date we cannot extrapolate this to the population.
Furthermore, we cannot ascertain causality as this is currently outside the scope of the analysis, the
purpose here is to observe and describe the patterns we observe in this sample.
Ethical Toolbox:
Ethics is about decision-making with moral perspectives included. Morality involves the
consideration of a range of context-related perspectives to think about what would be the ͞right͟
(or an acceptable) decision to make given the many contextual and causal factors and thus
reasonable decision criteria.
These perspectives might include the economic complexity, stakeholder needs, principles or duties
of each stakeholder, social benefits and/or ƚŚĞǀĂůƵĞƐĂŶĚ͞ĐŚĂƌĂĐƚĞƌ͟ŽĨkey decision-makers.
COMM1110: Assessment 2a Student SID
For this analysis I will explore a specific ethical dilemma from the perspective of Banks and Other
Lenders (B&OL). One ethical dilemma for them would be to what extent they should consider the
personal circumstances of borrowers in their lending decisions - beyond their financial ability to pay.
This is an ethical dilemma because these personal circumstances can change quickly and over time
(and for reasons beyond borrowers reasonable control), that home ownership is a social good which
has value beyond the economic, and that B&OL lenders have a duty of care to help and support the
Australian community and a key player in their ongoing prosperity and well-being.
Considering this dilemma via a range of theoretical frameworks, we can see that:
x Deontologically the rules and duties which govern effective decisions by B&OL need to
change over time given economic circumstances, may not always be clear to lenders (who
many not be highly financially - or even language literate) and are sometimes influenced (or
even mandated) by government policy.
x Utilitarianism ŵĞĂŶƐƚŚĂƚƚŚĞƌĞĂƌĞŵĂŶLJƐƚĂŬĞŚŽůĚĞƌƐ;ŝŶĐůƵĚŝŶŐƚŚĞΘK>͛ƐŝŶǀĞƐƚŽƌƐĂŶĚ
owners) who may have conflicting interests and needs for their role the Lending/Mortgage
Industry,
x Virtue involved the reflection on important values and intended character, and raises a
number of critical thinking questions like ͞ǁŚĂƚƐŽƌƚŽĨĐŽƌƉŽƌĂƚĞĐŝƚŝnjĞŶĚŽǁĞǁĂŶƚƚŽ
aspire to ďĞ͍͟ĂŶĚ͞how do we want to impact and influence social outcomes in our
community͍͟
x Care means each B&OL exploring the problem from ĂŵŽƌĞ͞ĨĞŵŝŶŝŶĞ͟Žƌ͞socially-aware͟
perspective, and thus carefully considering the impact of their decisions on each ongoing
relationship rather than just the effective application of rules or lending principles.
Bringing together the above discussion, the issues associated with having a Mortgage in Sydney
include the need to:
1) Identify key stakeholders to the mortgage stress problem and exploring their needs and
wants in good depth,
2) Research and document the current rules and principles which govern the decisions made in
relation to the approval of mortgages,
3) Have a clear goal (or objective) of harm minimisation as it relates to the level of stress which
mortgage holders can or should bear, and
4) Continue to gather and analyse the data about the size and complexity of the problem so
that more and better informed decisions can be made over time.
COMM1110: Assessment 2a Student SID
References:
ƵůůŽĐŬ͕D͕ϮϬϭϴ͕͞,ŽƵƐĞŚŽůĚ/ŶĚĞďƚĞĚŶĞƐƐĂŶĚDŽƌƚŐĂŐĞ^ƚƌĞƐƐ͕͟ĚĚƌĞƐƐƚŽƚŚĞZĞƐƉŽŶƐŝďůĞ
Lending and Borrowing Summit. Reserve Bank of Australia, 20 February, 2018.
Roy Morgan, 2021, ͞Mortgage stress at record lows during the 2021 lockdowns in NSW, Victoria and
the ACT͟. November 22 2021, Finding No. 8843.
Yates. J and ĞƌƌLJ͘D͕ϮϬϭϭ͕͞,ŽƵƐŝŶŐĂŶĚDŽƌƚŐĂŐĞDĂƌŬĞƚƐŝŶdƵƌďƵůĞŶƚdŝŵĞƐ͗/ƐƵƐƚƌĂůŝĂ
ŝĨĨĞƌĞŶƚ͍͕͟Housing Studies, Vol 26, EŽ͛Ɛϳ-8, 1133-1156.
