# RECORD 13a61953f01e5f8f119d4fa1872eb72503252526
# TITLE: The Reflection of Income Segregation and Accessibility Cleavages in Sydney's House Prices
# YEAR: 2021.0  VENUE: ISPRS Int. J. Geo Inf.  DOI: 10.31235/osf.io/2psk5
# AUTHORS: M. Ng; J. Roper; C. Lee; C. Pettit
# PAGES: 25  CANDIDATE TABLE PAGES (1-based): [13, 19]
# ABSTRACT: This paper explores the relationships between accessibility, income segregation, and house prices in the Greater Sydney Area. Sydney is found to have reasonably even employment accessibility, reflecting the increasingly polycentric nature of the modern city; however, it also shows considerable income segregation and variance in property prices between different parts of the city. Entropy is used to examine the diversity and mixing of different income groups. Finally, hedonic price models using ordinary least square and geographically-weighted regression techniques to show the differing effects of employment accessibility on house prices in different parts of the city. The results show that accessibility has small to negative effects on prices in the most valuable areas, suggesting that other effects such as recreational access and employment type/quality may be important to investigate further in this context.


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The Reflection of Income Segregation and Accessibility
Cleavages in Sydney’s House Prices
Author:
Ng, MKM; Roper, J; Lee, CL; Pettit, C
Publication details:
ISPRS International Journal of Geo Information
v. 11
Chapter No. 7
2220-9964 (ISSN)
Publication Date:
2022-07-01
Publisher DOI:
https://doi.org/10.3390/ijgi11070413
License:
https://creativecommons.org/licenses/by/4.0/
Link to license to see what you are allowed to do with this resource.
Downloaded from http://hdl.handle.net/1959.4/unsworks_80905 in https://
unsworks.unsw.edu.au on 2026-10-20


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ISPRS Int. J. Geo-Inf. 2022, 11, 413
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being unaccounted for by all other independent variables within the model. Remedial
measures to address the arising issues with multicollinearity include variable exclusions,
principal component analyses, or step-wise regression applications [82]. This paper opted
to exclude collinear variable pairs with an observed VIF scores greater than 10 (VIF ≥10).
6. Results Objective 1: Income Distributions in Greater Sydney
As the first objective, the spatial distribution of income groups within the Greater
Sydney area was calculated. Entropy statistics (H) were used to quantify the spatial
diversity between these income groups. These values ranged from 0.00 to a maximum of
1.10 (Hmax)—meaning, in areas where H = 1.10, an equal distribution of all three income
groups is observed. The global mean entropy value in Greater Sydney was calculated to be
0.862, with a minimum and maximum of 0.00 and 1.09, respectively.
Table 3 quantifies the correlation coefficients between all income groups and their
respective entropy values. What is first noticeable is the negative relationship between
the distribution of low-income groups in Sydney with their middle-(R = −0.22) and high-
(R = −0.56) income counterparts. This relationship is also seen with respect to entropy
values (R = −0.22). These findings come in stark contrast to those values associated
with high- and middle-income earners, with both groups displaying similar correlation
coefficients to the diversity index. These findings indicate that Sydney’s income distribution
appears to be highly asymmetric. Where it was initially thought that both high- and low-
income groups would both display exiguous mixing (i.e., low entropy scores in both high-
and low-income groups), this generalisation can only be extended to the latter. The results
rather show that high-income groups in Sydney appear relatively more integrated at the
metropolitan level, whereas low-income earners look to be concentrated in locations where
those outside this income bracket remain unlikely to reside in.
Table 3. Global correlation coefficients between income groups in Greater Sydney.
Low Income
Middle Income
High Income
Entropy
Low Income
1.000
−0.218
−0.561
−0.219
Middle Income
−0.218
1.000
0.198
0.565
High Income
−0.561
0.198
1.000
0.629
Entropy
−0.219
0.565
0.629
1.000
This more cursory evaluation does hint towards a possible degree of socioeconomic
segregation in Greater Sydney; however, a disaggregated view of income distributions
is needed to investigate whether the prominence of low-income groups is a consistent
mainstay of areas of low diversity. Figure 7 illustrates the spatial distribution of entropy
and all income groups in view of this. It is evident from this visualisation that the spread
of entropy values shows a relatively clear delineation between low- and high-diversity
areas, with lower entropy areas concentrated in the Central and Western suburbs of Greater
Sydney, such as suburbs around Fairfield, Merrylands, Auburn, and Mount Druitt. In these
areas, the diversity index is estimated to range between 0.39 and 0.55; and they also have
the highest proportions of low-income earners in Greater Sydney (Figure 8). Here, low-
income groups constitute between 63 and 77 per cent of the population—often, with less
than 2 per cent belonging to high-income groups (Figure 8). The findings echo back to
previous work by Lee et al. [83], which discussed the divide of the Sydney’s demographic
and socioeconomic distribution along a ‘latte line’.


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Figure 7. Entropy and income group distribution in 2016.
Figure 8. Entropy values against low-income groups in Greater Sydney.
When considered in line with the distribution of high-income groups, it is worth
pondering on the reason these areas do not exhibit similar segregation seen with low-income
areas. From the results displayed in Table 3, the distribution of high-income groups has
already shown to be negatively correlated with the lowest earners (R = −0.57) in Sydney,
which alludes to the spatial division between both groups. However, it is interesting to
note that, despite this disparity, high-income groups also occupy those areas with the
highest entropy values. One explanation is due to the asymmetrical size of the different
groups—as the high income group represents only 12 per cent of Sydney’s population
overall, even areas where they are heavily over-represented will still show considerable


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ISPRS Int. J. Geo-Inf. 2022, 11, 413
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level, there is a relative rise of 0.3 per cent of house prices in areas that have increased
accessibility. It is worth noting that the presence of middle-income groups, as defined in
this study, does not have the same level of predictive potential. This is likely due to their
larger aggregation of income into a single group; and resulting relatively even distribution
throughout all of Greater Sydney.
Table 4. Coefficient values derived from the OLS regression of variables to house prices.
Residuals:
Min
1Q
Median
3Q
Max
−3.08558
−0.14556
−0.01844
0.11633
2.40827
Coefficients:
Estimate
Std. Error
t-Value
Pr (>|t|)
(Intercept)
1.33 × 101
5.69 × 10−2
233.182
<2.00 × 10−6
***
Bedrooms
1.42 × 10−1
1.58 × 10−3
90.152
<2.00 × 10−6
***
Bathrooms
4.89 × 10−2
1.48 × 10−3
33.016
<2.00 × 10−6
***
Distance to Sydney
−1.45 × 10−5
1.69 × 10−7
−85.66
<2.00 × 10−6
***
Distance to Secondary City
7.07 × 10−7
1.74 × 10−7
4.065
4.82 × 10−5
***
Distance to Beach
−2.44 × 10−6
2.24 × 10−7
−10.916
<2.00 × 10−6
***
Distance to Bus Interchanges
2.91 × 10−6
4.83 × 10−7
6.031
1.65 × 10−9
***
Distance to Highway
7.50 × 10−6
4.44 × 10−7
16.883
<2.00 × 10−6
***
Distance to Primary School
1.02 × 10−5
3.26 × 10−6
3.118
0.00182
**
Distance to University
1.12 × 10−6
4.27 × 10−7
2.617
0.00889
**
Distance to Swimming Pool
−4.61 × 10−6
9.32 × 10−7
−4.952
7.40 × 10−7
***
Distance to Shopping Centre
1.11 × 10−5
7.50 × 10−7
14.774
<2.00 × 10−6
***
Distance to Sport Centre
9.00 × 10−6
7.27 × 10−7
12.378
<2.00 × 10−6
***
Population Above 65 (%)
5.08 × 10−3
2.17 × 10−4
23.355
<2.00 × 10−6
***
Crime Rate (%)
−4.35 × 10−2
1.62 × 10−2
−2.681
0.00735
**
Low Income (%)
−1.87 × 10−1
5.75 × 10−2
−3.244
0.00118
**
Middle Income (%)
−1.02 × 10−1
5.80 × 10−2
−1.758
0.0788
.
High Income (%)
2.51
5.97 × 10−2
41.983
<2.00 × 10−6
***
Accessibility Score
2.73 × 10−1
8.68 × 10−2
3.142
0.00168
**
Significance Codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘.’ 0.1; Number of data points: 32,050; Residual standard error: 0.2455 on
32,049 degrees of freedom; Multiple R-squared: 0.8103, Adjusted R-squared: 0.8102; F-statistic: 7604 on 18 and
32,049 DF, p-value: <2.2 × 10−16; Residual sum of squares: 1931.751; AIC: 952.1797; AICc: 952.2059.
8.3. GWR Results
Whilst the OLS model provides a relatively sound representation of house price
variances at the metropolitan level, local effects also need to be considered with respect
to the spatial non-stationarity of property. These local effects are considered within the
subsequent GWR model. The GWR model specified here shows an improvement in fit,
with an adjusted R2 value of 0.844 and a lowered residual sum of squares value of 1569.
The spatial distribution of the local R2 values, along with the coefficient estimates for
accessibility, high- and low-income groups, is illustrated in Figure 13. The GWR model
performance is high everywhere, with at least 67 per cent of house price variances accounted
for across all of Greater Sydney.
A summary of the model’s coefficient estimates is also provided in Table 5. The find-
ings here provide a more granular estimate of the model’s income and accessibility effects—
in which, larger spatial disparities at local levels are more evident than previously indicated
in the OLS model. The model’s estimate coefficients indicate that, generally, improvements
in accessibility garner a 3.3 per cent median increase in house prices. However, disaggre-
gating these results spatially, the models show a clear divide between the East and West
regions of Greater Sydney. The most striking observation from these GWR results is the
difference in direction and magnitude of the effect of accessibility across Greater Sydney.
Generally, there is a negative relationship of prices with accessibility in the far Southeast
and Northeast of Sydney, whilst there is a weak positive relationship in central Sydney.
Further, in Western and outer Sydney, a stronger positive relationship is documented.
These findings suggest that accessibility may not necessarily be an important determinant
of house prices in all areas. There may be several possible explanations for this.


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Figure 13. Contour visualisation of income and accessibility coefficients throughout Greater Sydney.
Table 5. Distribution of coefficient values from the GWR model of variables to house prices.
Summary of GWR Coefficient Estimates
Min
1Q
Median
3Q
Max
Intercept
6.2
1.3 × 101
1.4 × 101
1.4 × 101
1.8 × 101
Bedrooms
1.1 × 10−1
1.2 × 10−1
1.4 × 10−1
1.6 × 10−1
1.9 × 10−1
Bathrooms
2.8 × 10−2
4.0 × 10−2
5.1 × 10−2
5.8 × 10−2
8.1 × 10−2
Distance to Sydney
−3.4 × 10−5
−3.0 × 10−5
−2.3 × 10−5
−1.5 × 10−5
0.0
Distance to Secondary City
−1.6 × 10−5
−4.7 × 10−6
−5.6 × 10−7
4.1 × 10−6
0.0
Distance to Beach
−3.8 × 10−5
−8.7 × 10−6
2.3 × 10−6
8.7 × 10−6
0.0
Distance to Bus Interchanges
−3.8 × 10−5
−9.7 × 10−6
−3.2 × 10−6
4.4 × 10−6
0.0
Distance to Highway
−1.8 × 10−5
3.0 × 10−6
7.6 × 10−6
1.1 × 10−5
0.0
Distance to Primary School
−3.7 × 10−5
1.3 × 10−5
2.7 × 10−5
4.5 × 10−5
1.0 × 10−4
Distance to University
−2.1 × 10−5
2.3 × 10−6
6.5 × 10−6
1.1 × 10−5
0.0
Distance to Swimming Pool
−2.7 × 10−5
−1.3 × 10−5
-4.5 × 10−6
6.8 × 10−6
0.0
Distance to Shopping Centres
−1.5 × 10−5
−4.0 × 10−7
7.2 × 10−6
1.8 × 10−5
0.0
Distance to Sports Centres
−1.2 × 10−5
3.6 × 10−6
6.7 × 10−6
1.4 × 10−5
0.0
Population Above 65
1.2 × 10−3
2.6 × 10−3
3.7 × 10−3
5.8 × 10−3
8.9 × 10−3
Crime Rate
−3.1 × 10−1
−8.0 × 10−2
−2.1 × 10−2
1.0 × 10−1
6.8 × 10−1
Low Income (%)
−7.2
−3.8 × 10−1
−2.8 × 10−1
−1.6 × 10−1
5.0
Middle Income (%)
−6.6
−3.1 × 10−1
−1.8 × 10−1
−6.7 × 10−2
5.4
High Income (%)
−4.5
1.5
1.7
1.8
7.7
Accessibility Score
−7.8 × 10−1
−2.8 × 10−1
3.3 × 10−4
5.7 × 10−1
1.1
Diagnostic information: Number of data points: 32,068; Effective number of parameters: 192.2415; Effective
degrees of freedom: 31,875.76; AICc: −5447.286; AIC: −5594.308; Residual sum of squares: 1569.958; R-square
value: 0.8458043; Adjusted R-square value: 0.8448744.
First, the higher attractiveness of homes in Sydney’s more affluent suburbs (e.g., in
Eastern suburbs) appear to be more sensitive to locational attributes such as views, access
to green-spaces and beaches, good schools, and larger houses compared with employment
accessibility. Importantly, these affluent suburbs are characterised by these better amenities
compared with less prosperous suburbs [11]. In comparison, in more segregated areas in the
outer Sydney suburbs, home choice becomes more utilitarian. In these areas, accessibility
to employment becomes a more important factor in determining house prices. This is
likely related to demographic differences seen between these areas. In wealthier areas of
Sydney, accessibility based on journey to work may not be as important for wealthy retirees
that cluster in these areas (e.g., Sutherland Shire, Hornsby-Warringah, and the Northern
Beaches). Another issue may be the further anisotropic nature of Sydney’s public transport
network, resulting in travel times for subgroups of commuters that differ from the road
network based accessibility used in this model. As shown in Figure 14, the Southeastern and
Northeastern suburbs are relatively less served by rail lines or dedicated right-of-way bus
services than other suburbs (in 2016—prior to the opening of new light rail in the Southeast).