Estimating the Seasonal Effects of Residential Property Markets - A Case Study of Adelaide

Abstract

This paper examines the seasonal effects in the detached housing markets in Adelaide, South Australia. Residential markets are generally considered to exhibit slight seasonal effects. Anecdotal evidence suggests that this is often observed in the form of variations in the volume of sales, with less noticeable effects on price levels. It is also suggested that there are significant variations in the seasonal effect in different locations particularly beachside and hill locations. This paper attempts to quantify the seasonal affects in the volume of transactions and the achieved prices of detached dwellings. Hedonic price models and time series models are developed for a range of locations across Adelaide to examine the effects in different locations. The results suggest that there are significant seasonal effects on the volume of detached dwelling transactions in Adelaide particularly in beachside and hills locations where summer and autumn show statistically significant seasonal effects. There is little evidence of any seasonal effect on prices of detached dwellings. If there is any seasonality in these property prices, it is too small to quantify at sub-market level, however there is some evidence that property prices may be around 1% lower in winter than in other seasons. Introduction: This paper examines the seasonal effects of residential property markets in Adelaide, South Australia. Several different techniques are used to quantify the seasonal effects on the volumes of detached houses sold in Adelaide and on their prices. The metropolitan area of Adelaide and 30 suburban submarkets are examined to see if there is locational variation in the seasonal effects. The study uses traditional time series analysis and hedonic price functions to estimate the effect of seasonality. The purpose of this research is to test the usually untested assumption that residential property markets exhibit significant variations in prices and volumes of transactions at different times of the year. Anecdotally, real estate agents often talk about the "spring rush", a period in spring when it is believed that purchasers "emerge" from winter slumber to madly purchase residential properties. It may also be considered as the best time to sell because gardens are at their best during spring. This is when plant growth is at its maximum in Mediterranean climates such as Adelaide's where cool wet winters and hot dry summers, inhibit growth. Any seasonal effect may have regional differences. The Adelaide topography is largely flat but with significant beachside areas, foothills and a small suburban area in the Adelaide Hills. It might be expected that the beachside areas would become more popular in spring and summer because of beach activities. Similarly, locations in the Adelaide Hills and foothills that are generally cooler may be more likely to be attractive during summer and less attractive in winter.

Full text

Estimating the Seasonal Effects of Residential Property Markets – A Case Study of Adelaide – Rossini Page 1
Sixth Annual Pacific-Rim Real Estate Society Conference
Sydney, Australia, 24-27 January 2000
Estimating the Seasonal Effects of Residential Property
Markets – A Case Study of Adelaide
Peter Rossini
Lecturer
School of International Business, University of South Australia
Centre for Land Economics and Real Estate Research (CLEARER)
Phone: 61-8-8302-0649, Facsimile: 61-8-83020512, E-mail: peter.rossini@unisa.edu.au
Keywords: Residential Real Estate, Housing Markets, Seasonality, Forecasting, Regression Analysis, Time
Series Analysis, Hedonic Price Models
Abstract: This paper examines the seasonal effects in the detached housing markets in Adelaide, South
Australia. Residential markets are generally considered to exhibit slight seasonal effects. Anecdotal evidence
suggests that this is often observed in the form of variations in the volume of sales, with less noticeable effects
on price levels. It is also suggested that there are significant variations in the seasonal effect in different
locations particularly beachside and hill locations. This paper attempts to quantify the seasonal affects in the
volume of transactions and the achieved prices of detached dwellings. Hedonic price models and time series
models are developed for a range of locations across Adelaide to examine the effects in different locations. The
results suggest that there are significant seasonal effects on the volume of detached dwelling transactions in
Adelaide particularly in beachside and hills locations where summer and autumn show statistically significant
seasonal effects. There is little evidence of any seasonal effect on prices of detached dwellings. If there is any
seasonality in these property prices, it is too small to quantify at sub-market level, however there is some
evidence that property prices may be around 1% lower in winter than in other seasons.
Introduction: This paper examines the seasonal effects of residential property markets in Adelaide, South
Australia. Several different techniques are used to quantify the seasonal effects on the volumes of detached
houses sold in Adelaide and on their prices. The metropolitan area of Adelaide and 30 suburban submarkets are
examined to see if there is locational variation in the seasonal effects. The study uses traditional time series
analysis and hedonic price functions to estimate the effect of seasonality.
The purpose of this research is to test the usually untested assumption that residential property markets exhibit
significant variations in prices and volumes of transactions at different times of the year. Anecdotally, real estate
agents often talk about the “spring rush”, a period in spring when it is believed that purchasers “emerge” from
winter slumber to madly purchase residential properties. It may also be considered as the best time to sell
because gardens are at their best during spring. This is when plant growth is at its maximum in Mediterranean
climates such as Adelaide’s where cool wet winters and hot dry summers, inhibit growth. Any seasonal effect
may have regional differences. The Adelaide topography is largely flat but with significant beachside areas,
foothills and a small suburban area in the Adelaide Hills. It might be expected that the beachside areas would
become more popular in spring and summer because of beach activities. Similarly, locations in the Adelaide
Hills and foothills that are generally cooler may be more likely to be attractive during summer and less attractive
in winter.
Methodology: There does not appear to be a generally accepted methodology for estimating the seasonal
effect of the volumes of property transactions nor of property prices. Transaction volumes present little
difficulties. Basic methodologies suggested in most major forecasting and econometric texts (for example
Mendenhall & Sincich (1996), Hanke & Reitsch, 1998, Wilson & Keating, 1999) would seem perfectly adequate
for this analysis. Classical methods such as the ratio to moving average estimates used in classical time series
decomposition will provide evidence of the seasonal effects. The disadvantage with this method is the lack of
statistical testing. A more robust method is to use a series of seasonal dummy variables in a linear regression.

Estimating the Seasonal Effects of Residential Property Markets – A Case Study of Adelaide – Rossini Page 2
This enables significance testing of the dummy variable coefficients to determine if the seasonal effect is
statistically different from other seasons.
Estimating the seasonal effects of residential prices is somewhat more difficult. It is possible to use basic time
series methods on a mean or median price series, however while this might produce reasonable estimate for a
very large sample, it is likely that smaller samples will exhibit substantial sample bias for each period. There is
also likely to be a problem over time with changes to housing quality. These are similar issues which were
addressed by Bailey et al. (1963) when discussing a regression method for price indexing and later expanded by
Goodman (1978). The principle methods to overcome these problems ware based on hedonic price functions,
and were the focus of a great deal of literature in the early 1990’s. (Case & Shiller, 1989 and Mankiw & Weil,
1989 are important examples) This involves analysing individual transactions rather than time series data. The
advantage of this methodology is that the effect of time and a variety of property characteristics can be
considered jointly. The effects of time (e.g. seasonality in this case) can then be considered with all other factors
being held constant. This approach as well as a basic time series approach will be used to estimate if the seasons
affect residential property prices. The difficulty with this method is isolating the seasonal influences from the
trend, cyclical and irregular components.
The data for this research is from the South Australian Department of Environment, Heritage and Aboriginal
Affairs. This data set is derived from property transactions at the Lands Titles Office. All property transactions
are recorded with sale price, date and significant legal information. This data is then amalgamated with data
from the Valuer Generals Office, which provides data about valuations, property characteristics and
circumstances of sale. Sales of detached dwellings were extracted for the Adelaide metropolitan area for the
period from January 1982 to the end of June 1999. Probable non-market transactions were excluded, as were
sales that included multiple parcels or titles. The final data set contained 279,103 probable market transactions
of detached residential properties. The data was then analysed at metropolitan area and 30 suburban subsets
were created. The suburbs used in this analysis were those used in former residential market studies in Adelaide.
These suburbs were used by Rossini (1998) as a representative sample of Adelaide suburbs and had been found
to be suitable for analysis at suburban level. The locations of these are shown in Figure 1. Of the 30 suburbs
used, 4 are beachside suburbs, 1 is in the Adelaide Hills and 6 in the foothills. The remaining 19 suburbs are
distributed across the Adelaide plains.
Temporal boundaries for seasons were chosen to reflect the probable time of purchase decision. Sale dates
recorded relate to settlement dates. Residential property transactions generally, settle within 4 weeks of final
contract. For example, sales recorded in December were most likely contracted during November. On this basis
the seasons were allocated as follows. Summer is the January to March quarter, autumn the April to June
quarter, winter the July to September quarter and spring the October to December quarter. Basic statistics for
each quarter were calculated for the metropolitan area and for each suburb. The statistics were the number of
probable market transactions and price indicators including the mean, standard deviation, median, mode,
maximum and minimum values. The time series for the volume of transactions for each suburb and the
metropolitan area are attached as Table 4. The median price series for each suburb and the metropolitan area are
attached as Table 5. These series were used as the base time series for the time series analysis. Cross-sectional
transaction data was also grouped for each suburb to enable hedonic modeling and the creation of a constant
quality price index for each suburb. Because of a lack of data relating the physical characteristics of properties
prior to 1985, only sales after this data were used for this analysis.
Models: Property Transaction Volumes
The models to test for seasonality in the volumes of transactions for detached dwellings are based on the
quarterly time series data. The time series for the metropolitan area and for each suburb were analysed
separately. The starting point was to calculate seasonal indices based on the ratio to moving average method.
This uses basic moving averages to compare each quarterly figure with the annual figure for which the quarter is
the centre. This is a well-established method for calculating seasonal indices. The disadvantage with this
method is that there is no robust statistical testing of the indices and that it will be adversely affected by the
sample size for each suburb that results in highly variable estimates. On this basis it was considered that it might
provide a reasonable indication for the seasonal effects in over the metropolitan area, but might produce some
unusual results for individual suburbs. To provide better statistical testing, a regression-based method was used.
Since the series were basically stationary (due to fixed housing volumes) variations in the volumes would most
probably be the result of seasonality or random error thus
(
)
3,2,1SSSfVt=
Where Vt is the volume of transaction at time t and S1 to S3 are the seasonal dummy variables.
Regression models were used, to test this function using (for each series) the log of the series as the dependent
variable and three dummy variables for the seasons.

Estimating the Seasonal Effects of Residential Property Markets – A Case Study of Adelaide – Rossini Page 3
The models were specified as
3322110 lnlnlnlnln
ββββ
SSSV+++=
Where V = observed volume of transactions
β0 = constant volume over time
S1 = dummy variable if the quarter was in summer
β1 = seasonal index for season 1 (summer)
S2 = dummy variable if the quarter was in autumn
β2 = seasonal index for season 2 (autumn)
S3 = dummy variable if the quarter was in winter
β3 = seasonal index for season 3 (winter)
Models for each suburb and the metropolitan area were estimated twice. In the first estimate, the seasonal
indices were excluded if they did not satisfy a two-tailed test of the coefficients at a 95% confidence interval. In
the second estimate all seasonal indices were jointly estimated regardless of their significance, but were tested at
a 90% level of confidence. The resulting seasonal indices relate the volumes in the season to spring (which was
excluded from the model). To allow comparison with the ratio to moving average figures, all the ratio to moving
average estimates were adjusted to a spring base of 100.
Models: Property Transaction Prices
Seasonal indices for detached dwelling transaction prices were calculated using both time series and cross
sectional data. The time series analysis mirrored the approach used for the transaction volumes. The time series
was based on median prices for each quarter. Unlike the transaction data, the price data displayed significant
trend so first differences were used to remove the underlying trend component. Otherwise the indices were
calculated in the same manner using both ratio to moving averages and regression analysis with dummy
variables. While this approach might be considered adequate, the inherent problems of using a median price
series based on a small sample (suburb level) were considered to justify the use of a hedonic approach similar to
that used by Bailey et al. (1963) and other subsequent authors. The aim of this analysis was to jointly estimate
the effects of physical characteristics, time and seasonality. Cross sectional analysis was used in two ways. In
each case the approach required two stages.
The first approach involved the calculation of a hedonic price index for each suburb and for the metropolitan
area and then the same ratio to moving average and dummy variable regression analysis to test for seasonality in
the index. The hedonic price index relates prices to the properties physical attributes and the quarter in which it
was sold.
(
)
nn ddXXfY...... 1,1
=
Where Y is the transaction price, X1 to Xn is an array of property characteristics and d1 to dn is an array
of dummy variables for each quarter
The models were specified as
3113110 ln.....lnln....lnlnln
θθβββ
nn XXddY++=
Where Y = observed transaction price
β
0 = a constant
d1 = dummy variable for quarter 1
dn = dummy variable for quarter n
β
1 = price index for quarter 1
β
n = price index for quarter n
X1 = 1st physical attribute variable
Xn = nth physical attribute variable
θ
1 = price index for physical attribute 1
θ
n = price index for physical attribute n
The estimates of
β
1 to
β
n are then used to form the price index. The resultant indices are shown in Table 6.
The second approach attempted to jointly estimate the effect of physical characteristics, and the trend and
cyclical components over time as the systematic component of the regression model, leaving the seasonal and
irregular effects in the residual term. The residual term was then modeled for any seasonal component.

Estimating the Seasonal Effects of Residential Property Markets – A Case Study of Adelaide – Rossini Page 4
(
)
3,2,11,1,,...... SSSqyyXXfYnn
=Where Y = the transaction price
X1 to Xn = an array of property characteristics
y1 to yn = an array of dummy variables for each year
q =.the quarter number in which the property sold (1 to 4)
S1 to S3 = seasonal dummy variables.
All seasonal indices were calculated with a base in the spring season.
Results: The results from this research support the hypothesis that there is some seasonal variation in both
transaction volumes and achieved prices within the detached housing market in Adelaide but that the affect on
prices is very small.
Table 1 shows the results of the seasonal indices of property transactions. The most stringent of tests (the
regression indices @95%) show that there is no statistically significant variation in volumes across the
metropolitan area. While the simple moving average indices and the regression analysis show that summer and
autumn have on average around 5 percentage, more sales than spring, and winter has 1% less sales, none of these
figures can be considered to be statistically different from the spring figure. The figures for the individual
suburb markets do show some statistically significant differences. Three of the four beachside suburbs and the
one hills suburb show statistically significant increases in sales during Summer and Autumn. There is no
statistically significant evidence of any seasonal variation in any of the foothills suburbs as regards transaction
volume. There is little evidence of variations in the suburbs on the Adelaide plains with the exception of 4
suburbs which show a considerable decrease in sales in the winter quarter. A further two suburbs show some
increase in sales in autumn while one suburb shows a significant decrease in autumn. Generally there would
appear to be little statistically significant variation in the volumes of transactions except that in beachside and
hills suburbs there is a tendency to higher volumes in summer and autumn. These higher volumes are
considerable, generally in the range of 15% to 45 % above those in spring and winter. Some suburbs on the
Adelaide plains show significant decreases in sales volumes during winter in the range of 15% to 22% below
other seasons.
Table 2 shows the seasonal indices based on median prices of detached dwellings. The expectation of these
indices was that any variation would be small and difficult to measure at a suburban level because of the high
degree of sample bias. The figures for the whole metropolitan area should however be reasonably reflective
given the large sample size. The most rigorous of analysis of these data (the dummy variable models tested at a
95% level of confidence) showed variable results for individual suburbs that probably reflect the sample bias.
The more reliable figure for the Adelaide Metro area shows that there is a statistically significant decrease of just
over 2% in achieved prices in winter compared to all other seasons. This figure is supported by the less rigorous
regression model for the metropolitan area that suggests a decrease of around 1.5% in winter as being the only
statistically significant difference. This is largely supported by the moving average ratio analysis that shows
summer and autumn prices being on average about 1% above spring figures with those in winter being about ½%
below spring.
The figures in Table 3 are the result of seasonal analysis of prices using the cross sectional transaction data. This
table suggests some consistency of results. The moving average ratios show that any variation that might be
expected will be very small. Even with small sample sizes these (statistically untested) indices show that
generally there is little or no seasonal effect with almost no seasonal indices showing values of more that 1 or 2
% difference between seasons. The statistically tested dummy variable regression analysis did not show any
significant differences across seasons in any of the individual suburbs but did show a statistically significant
decrease in achieved prices in winter across the whole metropolitan area. The metropolitan model shows that on
average detached dwelling prices across Adelaide might be expected to be about 1% below prices in other
seasons. This figure is the only statistically significant result from the seasonal analysis of the constant quality
price indices. This analysis is somewhat supported by the results from the analysis of residuals from the trend
analysis of the cross sectional data. Across all 30 suburbs and the metropolitan area, there are few statistically
significant differences in prices that might be attributed to seasonal variations. Significantly, three of these
indexes show reduced values in winter including the index for the metropolitan area. While there are some
inconsistencies in all of these models, this is expected with the relatively low levels of variation that are being
suggested. It might be safe to make two conclusions from the analysis of prices. If there a seasonal variation in
the achieved prices for detached dwellings in Adelaide, then the variation is extremely small. The most probable
seasonal effect is for prices to be about 1 to 2% lower in winter than in the other seasons.

Estimating the Seasonal Effects of Residential Property Markets – A Case Study of Adelaide – Rossini Page 5
Reference Suburb Location
1Brighton Beachside
2Burnside FootHills
3Campbelltown Plains
4Christies Beach Beachside
5Colonel Light Gardens Plains
6Enfield Plains
7Flagstaff Hill FootHills
8Flinders Park Plains
9Gawler East Plains
10 Glen Osmond FootHills
11 Golden Grove Plains
12 Greenwith Plains
13 Henley Beach Beachside
14 Kensington Park Plains
15 Klemzig Plains
16 Magill Plains
17 Morphett Vale Plains
18 Nailsworth Plains
19 Netherby/Springfield FootHills
20 North Adelaide Plains
21 Parkside Plains
22 Plympton Plains
23 Rostrevor FootHills
24 Salisbury Plains
25 St. Peters Plains
26 Stirling Hills
27 Unley Plains
28 Wattle Park FootHills
29 West Lakes Beachside
30 Woodcroft FootHills
Figure 1 - Map of Suburbs Selected for Study

Estimating the Seasonal Effects of Residential Property Markets – A Case Study of Adelaide – Rossini Page 6
Conclusions: The results from this research suggest that there are clear seasonal variations within the
detached housing market in Adelaide. In beachside and hills suburbs there are significantly more sales in
summer and autumn than in winter or spring. This could result in 15 to 40 % more transactions during these
seasons. There are generally a lower number of detached dwelling sales on the Adelaide plains in winter than
during other seasons. This is especially noticeable in some of the suburbs on the plains where about 20% fewer
transactions seem to occur. The number of transactions of detached dwellings in the Adelaide foothills remains
reasonable consistent across all the seasons. While there is a tendency across Adelaide for the greatest number
of transactions to be in summer and autumn, and the smallest number in winter, this trend can not be proven with
a reasonable level of statistical probability.
Prices for detached dwellings show very little seasonal variation. There is reasonable evidence that across the
metropolitan area; detached dwellings sell for approximately 1% less during winter than during the other
seasons. There is no evidence to suggest that this varies with location. While the so-called “spring rush” in the
residential markets may possibly result in more human activity, there is no evidence to suggest compared to
summer and autumn, there is more transactions or higher prices. However the evidence does suggest that on
average, winter is the period with the least sales and very marginally lower prices. On this basis the return to
“normal” during spring may well be interpreted as a spring rush.
Recommendations and Limitations: This research is clearly limited in a number ways. The data
used relates only to property transactions at point of settlement. The research would be greatly enhanced by
considering not only what sales were achieved but also what properties were available for sale during each
season. The summer and autumn increases in volumes in the beachside and hills locations may simply be the
result of a greatly increased volume of listed properties in spring, with sales being achieved at the later time.
Certainly an analysis of listed properties would be very worthwhile. Unfortunately this data is not available over
an extended period of time. The research would also be enhanced by reference to vendors and purchasers
attitudes. A survey of vendor and purchaser motivations and attitudes would assist in the understanding of the
seasonal variation and its causes. Similarly a survey of other market functionaries, particularly real estate agents,
would be useful. The use of a large number of individual tests in the various tables, probably creates a classic
Bonferoni problem and comparison of these tests would need to be corrected for this. The study will be
extended using the data from this research. The analysis of these data at suburb level has proven to be somewhat
difficult. The relatively small sample sizes in each time period, makes statistically significant results difficult.
Further research will involve dividing the metropolitan area into logical regional sectors for analysis. Such
sectors would include all beachside suburbs, all hills suburbs etc. It is anticipated that this small number of
sectors would provide sample sizes that make it easier to identify any seasonality across the whole sector. These
results will be presented in a subsequent paper.

Estimating the Seasonal Effects of Residential Property Markets – A Case Study of Adelaide – Rossini Page 7
Table 1 - Seasonal Indicies - Detached Dwelling Transactions Volume
Suburb
Location
Spring
Summer
Autumn
Winter
Spring
Summer
Autumn
Winter
Spring
Summer
Autumn
Winter
Brighton
Beachside
100%
144%
136%
100%
100%
134%
127%
93%
100%
136%
141%
97%
Christies Beach
Beachside
100%
100%
100%
100%
100%
106%
108%
99%
100%
106%
110%
101%
Henley Beach
Beachside
100%
125%
100%
100%
100%
118%
91%
92%
100%
117%
95%
95%
West Lakes
Beachside
100%
116%
115%
100%
100%
117%
116%
102%
100%
120%
118%
108%
Burnside
Foothills
100%
100%
100%
100%
100%
102%
103%
102%
100%
105%
104%
103%
Flagstaff Hill
Foothills
100%
100%
100%
100%
100%
107%
107%
94%
100%
114%
110%
98%
Glen Osmond
Foothills
100%
100%
100%
100%
100%
130%
140%
115%
100%
128%
146%
121%
Netherby/Springfield
Foothills
100%
100%
100%
100%
100%
98%
85%
86%
100%
105%
90%
88%
Rostrevor
Foothills
100%
100%
100%
100%
100%
101%
105%
92%
100%
103%
108%
95%
Wattle Park
Foothills
100%
100%
100%
100%
100%
96%
93%
99%
100%
100%
98%
104%
Woodcroft
Foothills
100%
100%
100%
100%
100%
81%
100%
93%
100%
121%
103%
108%
Stirling
Hills
100%
136%
130%
100%
100%
136%
130%
101%
100%
142%
136%
106%
Campbelltown
Plains
100%
100%
100%
84%
100%
93%
98%
80%
100%
96%
101%
82%
Colonel Light Gardens
Plains
100%
100%
100%
100%
100%
114%
109%
97%
100%
121%
113%
103%
Enfield
Plains
100%
100%
120%
100%
100%
126%
137%
121%
100%
125%
134%
126%
Flinders Park
Plains
100%
100%
100%
100%
100%
116%
118%
93%
100%
112%
117%
94%
Gawler East
Plains
100%
100%
100%
100%
100%
113%
102%
112%
100%
113%
106%
113%
Golden Grove
Plains
100%
100%
100%
100%
100%
93%
98%
86%
100%
103%
92%
105%
Greenwith
Plains
100%
100%
100%
100%
100%
100%
98%
105%
100%
97%
93%
86%
Kensington Park
Plains
100%
100%
100%
100%
100%
87%
99%
84%
100%
92%
95%
82%
Klemzig
Plains
100%
100%
100%
100%
100%
90%
98%
87%
100%
94%
99%
90%
Magill
Plains
100%
100%
100%
100%
100%
101%
92%
99%
100%
102%
93%
101%
Morphett Vale
Plains
100%
100%
100%
100%
100%
100%
96%
91%
100%
102%
96%
93%
Nailsworth
Plains
100%
100%
100%
78%
100%
89%
91%
73%
100%
92%
92%
74%
North Adelaide
Plains
100%
100%
100%
100%
100%
96%
101%
85%
100%
99%
101%
86%
Parkside
Plains
100%
100%
100%
100%
100%
97%
107%
91%
100%
101%
113%
94%
Plympton
Plains
100%
100%
100%
82%
100%
116%
119%
91%
100%
117%
122%
94%
Salisbury
Plains
100%
100%
100%
100%
100%
99%
100%
91%
100%
98%
101%
91%
St. Peters
Plains
100%
100%
128%
100%
100%
90%
115%
81%
100%
91%
116%
82%
Unley
Plains
100%
100%
82%
85%
100%
100%
82%
85%
100%
103%
81%
86%
Average 100% 101% 102% 97% 100% 103% 105% 94% 100% 107% 106% 96%
Metro Area
100%
100%
100%
100%
100%
104%
106%
98%
100%
105%
106%
99%
Regression Seasonal Indices
Significant @95%
Regression Seasonal Indices
Bold figures
significant @ 90%
Moving Average Seasonal Indices

Estimating the Seasonal Effects of Residential Property Markets – A Case Study of Adelaide – Rossini Page 8
Table 2 - Seasonal Indices - Detached Dwelling Prices – Based on Median Prices
Suburb
Location
Spring
Summer
Autum
Winter
Spring
Summer
Autum
Winter
Spring
Summer
Autum
Winter
Brighton Beachside 100% 106.79% 104.32% 101.06% 100% 100.00% 100.00% 100.00% 100% 100.12% 100.66% 99.14%
Christies Beach Beachside 100% 96.59% 99.65% 95.96% 100% 93.68% 100.00% 92.59% 100% 97.60% 99.28% 96.55%
Henley Beach Beachside 100% 100.67% 98.70% 97.58% 100% 100.00% 100.00% 100.00% 100% 94.86% 98.34% 99.27%
West Lakes Beachside 100% 101.12% 99.58% 103.57% 100% 100.00% 100.00% 100.00% 100% 98.30% 97.04% 98.85%
Burnside FootHills 100% 104.29% 100.84% 95.63% 100% 100.00% 100.00% 100.00% 100% 96.14% 95.15% 95.91%
Flagstaff Hill FootHills 100% 98.66% 98.10% 100.09% 100% 100.00% 100.00% 100.00% 100% 97.19% 96.48% 99.26%
Glen Osmond FootHills 100% 100.59% 93.27% 97.42% 100% 100.00% 100.00% 100.00% 100% 101.53% 99.86% 96.93%
Netherby/Springfield FootHills 100% 113.17% 106.08% 101.85% 100% 100.00% 100.00% 100.00% 100% 96.38% 97.84% 97.49%
Rostrevor FootHills 100% 103.07% 102.03% 105.51% 100% 100.00% 100.00% 100.00% 100% 96.57% 97.78% 100.11%
Wattle Park FootHills 100% 101.91% 101.31% 101.09% 100% 100.00% 100.00% 100.00% 100% 99.42% 100.43% 98.31%
Woodcroft FootHills 100% 97.04% 103.14% 97.20% 100% 100.00% 100.00% 84.34% 100% 97.12% 99.91% 98.95%
Stirling Hills 100% 100.16% 100.35% 99.16% 100% 100.00% 100.00% 100.00% 100% 95.59% 97.62% 97.63%
Campbelltown Plains 100% 96.42% 93.76% 94.19% 100% 100.00% 100.00% 100.00% 100% 95.49% 97.96% 99.12%
Colonel Light Gardens Plains 100% 103.31% 100.53% 100.30% 100% 100.00% 100.00% 100.00% 100% 96.02% 95.38% 97.31%
Enfield Plains 100% 103.81% 102.68% 101.58% 100% 100.00% 100.00% 100.00% 100% 99.70% 100.37% 101.24%
Flinders Park Plains 100% 101.03% 100.65% 99.36% 100% 100.00% 100.00% 100.00% 100% 97.80% 98.99% 99.64%
Gawler East Plains 100% 102.25% 98.05% 101.38% 100% 100.00% 92.76% 100.00% 100% 96.42% 96.06% 96.66%
Golden Grove Plains 100% 97.22% 101.22% 101.42% 100% 100.00% 105.75% 100.00% 100% 99.12% 99.19% 100.89%
Greenwith Plains 100% 99.52% 97.27% 103.26% 100% 100.00% 100.00% 100.00% 100% 98.33% 98.05% 97.44%
Kensington Park Plains 100% 99.02% 97.05% 96.96% 100% 100.00% 100.00% 100.00% 100% 98.48% 98.65% 102.69%
Klemzig Plains 100% 101.95% 100.29% 101.02% 100% 100.00% 100.00% 100.00% 100% 97.64% 97.13% 96.92%
Magill Plains 100% 100.07% 96.26% 95.79% 100% 100.00% 100.00% 100.00% 100% 95.23% 96.81% 97.86%
Morphett Vale Plains 100% 100.48% 100.55% 99.24% 100% 100.00% 100.00% 100.00% 100% 98.56% 99.14% 99.64%
Nailsworth Plains 100% 99.94% 97.78% 98.90% 100% 100.00% 100.00% 100.00% 100% 93.92% 93.07% 96.72%
North Adelaide Plains 100% 106.70% 120.03% 116.92% 100% 117.60% 125.92% 100.00% 100% 95.17% 96.09% 100.05%
Parkside Plains 100% 95.33% 96.67% 98.48% 100% 94.22% 100.00% 100.00% 100% 92.23% 94.33% 98.71%
Plympton Plains 100% 100.52% 102.06% 100.19% 100% 100.00% 100.00% 100.00% 100% 97.16% 97.71% 100.84%
Salisbury Plains 100% 100.74% 101.87% 101.21% 100% 100.00% 100.00% 100.00% 100% 96.08% 97.66% 97.26%
St. Peters Plains 100% 110.43% 104.62% 105.87% 100% 114.23% 100.00% 100.00% 100% 93.25% 97.39% 97.58%
Unley Plains 100% 98.73% 98.42% 98.50% 100% 100.00% 100.00% 100.00% 100% 96.49% 96.68% 100.91%
Average 100% 101.30% 100.46% 100.26% 100% 104.93% 108.15% 88.47% 100% 96.93% 97.70% 98.66%
Metro Area
100%
101.33%
101.21%
99.44%
100%
100%
100%
97.71%
100%
96.86%
98.23%
98.66%
Ratio to Moving Averages
(Median Prices)
Regression Based @95% (Median Prices)
Regression Based (Median Prices)
Bold figures
significant @ 95%
Bold figures
significant @ 90%

Estimating the Seasonal Effects of Residential Property Markets – A Case Study of Adelaide – Rossini Page 9
Table 3 - Seasonal Indicies - Detached Dwelling Prices Based on Constant Quality Indicies
Suburb
Location
Spring
Summer
Autum
Winter
Spring
Summer
Autum
Winter
Spring
Summer
Autum
Winter
Brighton Beachside 100% 97.32% 97.90% 100.62% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
Christies Beach Beachside 100% 95.34% 98.14% 97.23% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 97.47%
Henley Beach Beachside 100% 99.37% 98.94% 98.97% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
West Lakes Beachside 100% 102.42% 102.13% 102.79% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
Burnside FootHills 100% 99.43% 103.31% 101.41% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
Flagstaff Hill FootHills 100% 102.20% 102.24% 102.08% 100% 100.00% 100.00% 100.00% 100% 100.00% 98.01% 100.00%
Glen Osmond FootHills 100% 95.68% 96.86% 101.79% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
Netherby/Springfield FootHills 100% 100.63% 101.90% 101.60% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
Rostrevor FootHills 100% 100.87% 100.46% 99.80% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
Wattle Park FootHills 100% 97.44% 98.82% 100.14% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
Woodcroft FootHills 100% 100.76% 101.14% 101.79% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
Stirling Hills 100% 98.22% 100.56% 99.10% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
Campbelltown Plains 100% 99.68% 99.00% 99.08% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
Colonel Light Gardens Plains 100% 101.25% 102.38% 102.54% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
Enfield Plains 100% 98.25% 98.17% 98.93% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
Flinders Park Plains 100% 100.28% 101.26% 100.78% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
Gawler East Plains 100% 99.04% 103.08% 100.18% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
Golden Grove Plains 100% 97.64% 97.72% 98.13% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
Greenwith Plains 100% 98.26% 97.71% 96.64% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 98.37%
Kensington Park Plains 100% 102.99% 98.11% 99.84% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
Klemzig Plains 100% 98.76% 99.48% 100.71% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
Magill Plains 100% 100.10% 102.44% 100.84% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
Morphett Vale Plains 100% 99.45% 98.84% 99.84% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
Nailsworth Plains 100% 101.93% 105.33% 104.26% 100% 100.00% 100.00% 100.00% 100% 100.00% 97.12% 100.00%
North Adelaide Plains 100% 100.86% 100.69% 100.80% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
Parkside Plains 100% 103.30% 102.38% 99.65% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
Plympton Plains 100% 101.93% 100.38% 100.89% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
Salisbury Plains 100% 99.17% 100.12% 99.01% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
St. Peters Plains 100% 98.12% 98.62% 98.31% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
Unley Plains 100% 102.79% 99.99% 100.49% 100% 100.00% 100.00% 100.00% 100% 100.00% 100.00% 100.00%
Average 100% 99.76% 100.25% 100.26% 100% 100% 100% 100% 100% 100.00% 97.57% 97.92%
Metro Area
100%
99.13%
99.71%
99.73%
100%
100%
100%
98.66%
100%
99.44%
100.00%
99.40%
Ratio to Moving Averages
(Quality Adjusted Price Index)
Regression Based @95% (QA Price Index)
Regression Based @95% (Residuals)
Bold figures
significant @ 95%
Bold figures
significant @ 95%

Estimating the Seasonal Effects of Residential Property Markets – A Case Study of Adelaide – Rossini Page 10
References
Bailey M.J., R.F. Muth & H.O. Nourse.(1963) “A Regression Method for Real Estate Price Index
Construction”, Journal of the American Statistical Association, 58: 933-942, December, 1963.
Case, K.E. and Shiller, R.J. (1989) “The Efficiency of the Market for Single - Family Homes”, The American
Economic Review, Vol79 No 1 pp125.
Goodman A.C.. “Hedonic Prices, Price Indices and Housing Markets” (1978) Journal of Urban Economics, 5:
471-484, 1978.
Hanke, J.E & Reitsch, A.G. (1998) Business Forecasting, 6th Edition, Prentice Hall, USA
Kershaw, P.J. & Rossini, P.A. (1999) "Using Neural Networks to Estimate Constant Quality House Price
Indices", proceedings of the International Real Estate Society Conference, Kuala Lumpur 1998
Mankiw N.G. & D.N. Weil.(1989) “The Baby Boom, The Baby Bust, and the Housing Market” Regional
Science and Urban Economics, 19: 235-258, 1989.
Mendenhall, W. & Sincich, T. (1996), A Second Course in Statistics, 5th Edition, Prentice Hall, USA
Rossini, P.A. (1996a) “Using Constant Quality House Prices to Assess Property Market Performance” The
Valuer and Land Economist , August 1996
Rossini, P.A. (1998) “Assessing Buyer Search Behavior for Residential House Purchasers in Adelaide”,
proceedings of the 4th Pacific Rim Real Estate Society Conference, Perth 1998
Wilson, J.H & Keating, B (1999) Business Forecasting, 3rd Edition, McGraw Hill, USA
Peter Rossini, Lecturer - University of South Australia
School of International Business
North Terrace, Adelaide, Australia, 5000
Phone (61-8) 83020649
Fax (61-8) 83020512
Mobile 041 210 5583
E-mail peter.rossini@unisa.edu.au

Estimating the Seasonal Effects of Residential Property Markets – A Case Study of Adelaide – Rossini Page 11
Table 4 - Detached Dwelling Transaction Volumes
Quarter
Brighton
Burnside
Campbelltown
Christies Beach
Colonel Light
Gardens
Enfield
Flagstaff Hill
Flinders Park
Gawler East
Glen Osmond
Golden Grove
Greenwith
Henley Beach
Kensington Park
Klemzig
Magill
Mar-82 19 12 14 31 16 14 34 17 22 11 25 7 10 28
Jun-82 7 14 11 34 27 12 47 13 8 6 12 14 14 29
Sep-82 15 7 19 20 20 6 24 9 11 3 20 12 12 28
Dec-82
9
7
21
25
14
9
29
15
13
3
25
14
10
20
Mar-83 16 6 19 21 18 19 43 19 11 11 26 8 20 24
Jun-83 23 10 21 34 21 19 46 13 17 8 28 9 10 19
Sep-83 7 12 17 24 12 14 30 14 12 4 15 5 15 21
Dec-83
12
20
30
38
17
15
39
15
17
9
24
13
12
32
Mar-84 13 10 37 43 20 20 44 18 10 12 21 15 23 30
Jun-84 10 13 28 30 16 14 41 14 14 9 19 12 13 32
Sep-84
9
18
22
33
21
18
34
14
13
8
22
7
9
31
Dec-84 5 9 28 29 21 16 35 18 17 4 19 9 14 24
Mar-85 22 12 26 26 22 19 49 12 13 6 25 10 15 26
Jun-85 12 10 28 35 18 13 40 10 15 9 25 6 16 29
Sep-85
13
7
23
20
20
8
31
7
14
6
11
8
13
25
Dec-85 9 11 18 27 11 9 44 12 10 3 16 4 11 25
Mar-86 9 11 20 21 14 8 30 7 7 3 17 6 11 24
Jun-86 16 16 12 14 12 7 29 9 6 4 9 8 11 18
Sep-86 9 9 17 28 21 13 33 8 13 7 12 4 14 23
Dec-86 11 8 10 24 10 5 32 7 5 5 13 8 11 26
Mar-87 20 12 18 19 18 10 47 13 13 6 17 5 13 23
Jun-87
7
8
12
21
15
17
25
14
18
5
12
12
16
24
Sep-87 10 9 21 27 13 7 28 9 12 6 16 9 15 26
Dec-87 10 9 22 31 21 13 52 9 13 8 16 12 23 40
Mar-88 14 12 28 32 23 12 43 12 15 9 21 8 10 31
Jun-88
23
12
32
36
17
14
61
16
19
10
17
13
16
22
Sep-88 12 15 20 36 11 9 55 15 16 14 17 8 14 32
Dec-88 10 12 31 33 18 9 62 12 17 7 18 12 13 30
Mar-89
13
18
28
27
20
14
51
10
21
7
26
15
14
28
Jun-89 13 11 27 25 14 15 47 11 14 5 11 12 16 25
Sep-89 16 12 13 23 24 9 38 10 21 7 17 10 11 23
Dec-89 11 10 17 21 14 9 40 7 12 4 16 16 21 20
Mar-90
11
16
14
31
17
14
57
22
15
5
16
13
15
26
Jun-90 21 5 23 27 8 13 46 19 11 5 14 8 14 20
Sep-90 10 7 12 15 7 6 35 3 15 3 9 11 9 29
Dec-90 8 14 21 21 25 10 40 5 21 9 17 9 11 25
Mar-91 12 8 16 19 13 12 42 13 23 9 19 15 6 28
Jun-91 13 8 18 26 15 10 61 11 21 3 15 11 15 20
Sep-91 5 10 20 21 14 21 43 9 14 8 22 8 10 31
Dec-91
12
12
15
26
15
7
34
9
15
8
10
12
11
25
Mar-92 9 12 9 19 11 10 36 11 11 8 15 10 10 32
Jun-92 17 9 26 28 23 10 44 9 22 9 21 11 10 29
Sep-92 7 16 27 29 15 16 45 8 25 9 20 13 9 32
Dec-92
8
18
26
28
18
18
42
30
14
8
0
0
12
18
18
38
Mar-93 16 11 15 23 12 10 48 8 31 3 21 17 17 6 6 28
Jun-93 12 14 19 24 23 16 43 19 20 14 21 17 12 6 11 31
Sep-93
15
12
17
28
17
20
37
20
18
10
17
21
15
8
15
30
Dec-93 10 6 24 24 11 10 42 13 21 3 26 20 28 12 14 35
Mar-94 15 10 13 31 18 12 61 14 21 7 28 29 17 16 14 30
Jun-94 10 9 18 22 22 13 68 19 16 11 31 25 18 8 14 23
Sep-94
8
18
16
20
19
16
52
21
22
6
37
22
14
6
14
21
Dec-94 15 13 24 17 14 16 50 12 21 6 26 18 14 5 12 35
Mar-95 11 11 15 21 22 10 36 12 27 7 27 29 20 12 12 23
Jun-95
17
16
22
29
12
11
39
19
21
11
34
38
21
13
10
26
Sep-95 7 10 11 23 10 13 42 11 19 3 23 54 26 9 8 28
Dec-95 12 10 12 17 18 7 43 15 17 6 33 34 17 12 14 26
Mar-96 15 11 16 19 24 10 46 13 23 7 38 37 21 5 13 22
Jun-96
14
11
13
18
19
16
45
12
14
8
29
31
9
8
11
17
Sep-96 12 10 7 22 13 15 42 9 25 12 18 35 11 17 8 23
Dec-96 13 12 15 18 16 11 35 18 17 3 29 39 9 13 11 20
Mar-97 17 7 19 27 17 14 45 16 24 7 24 32 20 11 12 21
Jun-97
11
21
14
20
12
21
50
16
24
11
24
40
13
15
16
26
Sep-97 12 14 14 25 10 15 57 20 25 10 25 36 13 10 15 21
Dec-97 12 15 20 15 12 9 52 13 19 9 26 39 15 12 16 23
Mar-98
17
12
13
26
14
11
52
19
23
10
30
59
15
11
12
31
Jun-98 17 12 15 25 18 13 38 19 17 12 36 47 12 10 12 30
Sep-98 9 10 8 14 12 14 53 9 28 3 39 39 12 10 13 27
Dec-98 17 12 21 25 10 12 44 11 19 7 37 49 24 11 17 27
Mar-99
18
16
25
24
12
16
56
10
33
8
29
49
22
4
13
30
Jun-99 15 13 20 21 11 23 54 7 26 8 33 44 22 16 14 29
Transaction Volumes

Estimating the Seasonal Effects of Residential Property Markets – A Case Study of Adelaide – Rossini Page 12
Quarter
Morphett Vale
Nailsworth
Netherby
Springfield
North Adelaide
Parkside
Plympton
Rostrevor
Salisbury
St. Peters
Stirling
Unley
Wattle Park
West Lakes
Woodcroft
Metro Area
Mar-82 91 9 6 19 21 16 27 28 11 17 13 824 3761
Jun-82 91 8 4 25 26 18 15 21 15 12 19 926 3685
Sep-82 45 911 19 28 18 23 23 11 624 818 3271
Dec-82
79
18
7
20
20
11
17
25
17
10
18
8
22
3292
Mar-83 74 14 10 27 24 10 27 21 19 19 17 925 3690
Jun-83 94 12 923 15 13 23 33 16 18 17 13 24 4008
Sep-83
79
5
7
22
28
14
18
16
6
17
18
5
18
3410
Dec-83 99 16 824 26 13 24 23 815 12 814 4168
Mar-84 131 8 8 26 27 20 32 38 20 22 23 822 4704
Jun-84 111 11 621 35 11 23 25 16 23 16 821 4476
Sep-84 134 911 20 25 636 32 17 24 20 723 4116
Dec-84
142
4
9
19
37
9
35
40
11
12
21
10
22
4164
Mar-85 119 12 8 9 26 827 23 12 12 22 824 4114
Jun-85 89 8 9 16 26 12 30 24 13 16 26 12 15 3904
Sep-85
90
9
7
6
24
6
15
21
7
7
14
12
24
3731
Dec-85 102 13 8 7 15 724 28 10 14 16 817 3206
Mar-86 64 6 6 10 13 11 20 19 11 16 13 922 2914
Jun-86 95 15 313 26 11 28 13 12 18 17 11 22 3255
Sep-86 81 5 4 7 21 615 17 15 12 11 11 21 3163
Dec-86
70
6
11
15
20
6
15
25
11
12
18
10
15
2968
Mar-87 88 813 13 20 722 25 925 16 11 18 3597
Jun-87 69 10 9 9 21 10 13 21 20 16 16 717 3252
Sep-87 99 11 910 22 15 16 21 14 12 19 15 19 3500
Dec-87 112 13 719 19 11 33 19 19 10 26 618 4167
Mar-88
109
21
14
18
20
13
28
25
20
14
16
11
23
4317
Jun-88 103 12 816 30 14 24 33 20 24 16 929 4621
Sep-88 114 7 8 20 27 11 23 27 12 17 12 11 25 4634
Dec-88
135
12
10
14
25
17
33
20
17
16
15
10
16
4526
Mar-89 145 912 14 31 13 32 30 22 25 19 416 94599
Jun-89 105 12 917 19 19 28 30 18 17 17 11 17 84212
Sep-89 80 7 6 13 24 827 16 16 13 15 618 53427
Dec-89 91 9 9 13 18 728 32 16 13 17 417 10 3593
Mar-90
139
8
11
10
21
18
28
13
13
14
14
10
23
13
4064
Jun-90 107 3 6 12 18 830 17 16 16 10 922 12 3749
Sep-90 76 3 4 8 12 17 24 15 710 14 5 7 21 2732
Dec-90
112
12
5
12
24
10
18
18
21
15
12
8
23
24
3857
Mar-91 126 9 6 14 19 927 15 12 17 17 720 27 3897
Jun-91 117 7 6 10 23 14 24 15 23 14 11 320 18 4002
Sep-91 105 13 19 10 17 925 29 19 11 14 925 25 3951
Dec-91 114 14 516 17 813 12 8 8 16 11 12 29 3463
Mar-92
86
7
5
8
20
10
11
11
6
19
18
6
26
24
3491
Jun-92 126 7 8 11 22 16 31 15 12 12 18 821 51 4304
Sep-92 121 13 712 10 12 20 15 10 13 16 721 32 4194
Dec-92
118
6
10
12
20
12
34
28
18
10
12
10
22
36
3897
Mar-93 116 7 5 14 17 10 22 20 617 15 316 25 3971
Jun-93
112
16
11
10
17
13
24
30
15
19
8
5
25
36
4362
Sep-93 130 5 7 10 35 12 23 16 12 23 12 916 65 4265
Dec-93 101 10 12 11 22 19 22 19 19 20 16 620 28 3983
Mar-94
138
8
9
11
30
17
27
29
11
28
21
13
28
32
4677
Jun-94 119 12 11 14 25 10 29 30 14 21 10 424 54 4509
Sep-94 132 9 4 14 19 623 24 11 18 13 416 44 4083
Dec-94 104 12 11 15 23 10 19 20 22 718 616 41 3802
Mar-95 87 14 10 10 18 11 16 20 13 12 18 611 28 3636
Jun-95
87
11
5
14
24
11
17
16
9
12
10
6
18
47
3820
Sep-95 84 7 7 11 17 914 18 17 910 12 13 32 3679
Dec-95 100 9 7 11 27 10 25 14 8 9 15 926 38 3494
Mar-96
87
8
9
8
17
13
20
22
13
17
15
11
14
38
3562
Jun-96 75 6 7 12 30 13 25 14 14 16 11 11 13 32 3529
Sep-96 77 5 6 11 16 719 15 916 15 11 19 38 3307
Dec-96 80 9 9 9 16 14 23 21 14 23 15 716 37 3607
Mar-97 92 11 10 19 18 21 17 12 16 17 20 523 43 3792
Jun-97
87
10
4
12
21
13
24
23
19
14
10
9
16
60
4024
Sep-97 99 10 9 9 11 10 26 18 713 10 616 43 3708
Dec-97 94 9 8 13 22 12 26 21 12 21 19 13 15 46 4006
Mar-98
87
7
9
14
21
9
18
17
13
15
14
7
18
45
4000
Jun-98 94 712 11 25 11 26 15 13 25 9 4 19 53 4032
Sep-98 85 6 6 8 16 921 15 713 10 10 14 29 3653
Dec-98 122 612 10 23 13 25 18 14 11 17 15 16 59 4145
Mar-99 102 4 5 6 20 16 30 26 617 10 19 20 43 4109
Jun-99
103
8
10
8
20
15
31
21
19
15
12
9
20
61
4216
Transaction Volumes

Estimating the Seasonal Effects of Residential Property Markets – A Case Study of Adelaide – Rossini Page 13
Table 5 - Detached Dwellings Median Prices
Quarter
Brighton
Burnside
Campbelltown
Christies Beach
Colonel Light
Gardens
Enfield
Flagstaff Hill
Flinders Park
Gawler East
Glen Osmond
Mar-82
$ 50,000
$ 66,375
$ 37,750
$ 32,000
$ 44,500
$ 35,500
$ 62,900
$ 41,500
$ 42,375
$ 105,000
Jun-82
$ 60,000
$ 84,250
$ 36,500
$ 33,250
$ 48,000
$ 32,475
$ 65,000
$ 44,500
$ 36,000
$ 94,750
Sep-82
$ 50,000
$ 68,000
$ 40,500
$ 29,000
$ 47,150
$ 40,250
$ 66,000
$ 42,500
$ 48,500
$ 160,000
Dec-82
$ 52,950
$ 85,500
$ 46,000
$ 37,000
$ 43,250
$ 28,000
$ 66,000
$ 50,000
$ 35,000
$ 73,000
Mar-83
$ 58,750
$ 127,500
$ 42,300
$ 39,000
$ 50,450
$ 32,000
$ 67,000
$ 52,000
$ 43,000
$ 98,000
Jun-83
$ 53,950
$ 107,000
$ 46,500
$ 43,500
$ 47,000
$ 43,000
$ 64,750
$ 49,700
$ 47,000
$ 75,500
Sep-83
$ 75,000
$ 81,125
$ 46,500
$ 40,000
$ 52,250
$ 43,850
$ 69,500
$ 50,750
$ 56,250
$ 89,500
Dec-83
$ 60,750
$ 95,500
$ 57,000
$ 38,500
$ 63,250
$ 43,000
$ 68,000
$ 52,000
$ 49,950
$ 84,000
Mar-84
$ 68,750
$ 92,500
$ 54,000
$ 48,000
$ 65,600
$ 51,000
$ 73,250
$ 60,500
$ 68,500
$ 117,500
Jun-84
$ 85,500
$ 109,000
$ 62,500
$ 51,500
$ 65,725
$ 54,000
$ 90,000
$ 61,600
$ 55,500
$ 96,000
Sep-84
$ 78,000
$ 125,000
$ 61,250
$ 53,500
$ 68,000
$ 54,425
$ 90,000
$ 64,475
$ 68,000
$ 141,000
Dec-84
$ 86,500
$ 113,000
$ 67,875
$ 57,950
$ 74,500
$ 58,000
$ 105,000
$ 73,250
$ 72,000
$ 159,375
Mar-85
$ 95,000
$ 157,500
$ 70,025
$ 61,000
$ 83,250
$ 68,000
$ 102,500
$ 79,075
$ 71,250
$ 136,750
Jun-85
$ 93,750
$ 138,500
$ 71,500
$ 58,000
$ 81,500
$ 65,000
$ 96,250
$ 86,000
$ 75,000
$ 119,500
Sep-85
$ 94,500
$ 129,750
$ 75,000
$ 60,000
$ 76,500
$ 57,000
$ 103,000
$ 88,000
$ 82,000
$ 129,975
Dec-85
$ 85,000
$ 140,000
$ 77,375
$ 66,000
$ 75,000
$ 67,000
$ 104,750
$ 78,750
$ 68,500
$ 135,000
Mar-86
$ 103,000
$ 152,000
$ 69,750
$ 59,000
$ 84,000
$ 62,250
$ 95,750
$ 73,250
$ 99,000
$ 110,000
Jun-86
$ 85,500
$ 176,250
$ 59,500
$ 59,475
$ 79,125
$ 67,750
$ 91,000
$ 74,000
$ 68,250
$ 122,000
Sep-86
$ 90,000
$ 125,000
$ 62,000
$ 58,000
$ 75,500
$ 55,500
$ 104,000
$ 81,500
$ 79,500
$ 150,000
Dec-86
$ 74,000
$ 170,000
$ 77,750
$ 61,000
$ 75,750
$ 62,500
$ 99,750
$ 72,500
$ 74,500
$ 160,000
Mar-87
$ 90,000
$ 179,000
$ 71,750
$ 64,000
$ 79,500
$ 64,750
$ 100,000
$ 76,000
$ 85,000
$ 154,000
Jun-87
$ 95,000
$ 105,750
$ 73,500
$ 57,000
$ 84,500
$ 62,500
$ 105,000
$ 74,500
$ 68,000
$ 117,000
Sep-87
$ 99,475
$ 140,000
$ 71,500
$ 61,000
$ 78,000
$ 70,000
$ 95,500
$ 78,500
$ 73,500
$ 151,000
Dec-87
$ 116,250
$ 160,000
$ 64,000
$ 57,000
$ 84,000
$ 59,500
$ 110,500
$ 82,000
$ 79,500
$ 154,750
Mar-88
$ 119,975
$ 132,500
$ 76,500
$ 61,000
$ 89,000
$ 71,750
$ 103,000
$ 85,000
$ 83,000
$ 126,000
Jun-88
$ 118,000
$ 151,500
$ 77,500
$ 65,000
$ 87,000
$ 70,500
$ 106,500
$ 86,500
$ 78,500
$ 143,000
Sep-88
$ 121,500
$ 180,000
$ 82,250
$ 61,500
$ 95,500
$ 72,000
$ 127,000
$ 91,000
$ 92,000
$ 156,250
Dec-88
$ 97,000
$ 195,000
$ 93,950
$ 66,500
$ 108,500
$ 72,500
$ 121,750
$ 88,375
$ 87,000
$ 226,000
Mar-89
$ 125,000
$ 265,000
$ 87,325
$ 64,950
$ 111,250
$ 75,000
$ 120,000
$ 93,750
$ 86,000
$ 235,000
Jun-89
$ 151,000
$ 178,000
$ 92,000
$ 69,950
$ 123,500
$ 79,000
$ 137,000
$ 98,000
$ 90,500
$ 290,000
Sep-89
$ 132,500
$ 255,000
$ 106,000
$ 76,000
$ 127,500
$ 76,500
$ 140,000
$ 98,000
$ 95,000
$ 274,000
Dec-89
$ 142,000
$ 166,900
$ 100,000
$ 78,000
$ 127,750
$ 78,000
$ 122,000
$ 100,000
$ 90,500
$ 155,000
Mar-90
$ 155,000
$ 265,750
$ 93,750
$ 70,000
$ 130,000
$ 80,000
$ 126,000
$ 108,500
$ 88,000
$ 240,000
Jun-90
$ 130,000
$ 282,000
$ 87,000
$ 75,000
$ 148,500
$ 85,500
$ 121,000
$ 109,600
$ 95,000
$ 255,000
Sep-90
$ 153,000
$ 207,500
$ 93,250
$ 78,000
$ 125,000
$ 81,500
$ 135,000
$ 115,500
$ 95,000
$ 180,000
Dec-90
$ 156,500
$ 252,500
$ 114,000
$ 80,500
$ 139,500
$ 85,500
$ 134,250
$ 120,000
$ 105,000
$ 245,450
Mar-91
$ 153,000
$ 214,000
$ 109,000
$ 71,000
$ 120,000
$ 86,750
$ 148,000
$ 115,000
$ 96,500
$ 305,000
Jun-91
$ 196,000
$ 198,000
$ 103,250
$ 72,250
$ 137,000
$ 80,500
$ 140,000
$ 115,000
$ 93,000
$ 270,000
Sep-91
$ 125,000
$ 220,500
$ 106,500
$ 77,500
$ 132,000
$ 88,500
$ 130,000
$ 116,000
$ 106,500
$ 210,000
Dec-91
$ 150,000
$ 201,000
$ 106,000
$ 72,500
$ 138,500
$ 95,000
$ 156,250
$ 130,000
$ 109,000
$ 375,000
Mar-92
$ 161,500
$ 256,500
$ 110,000
$ 74,000
$ 143,000
$ 97,750
$ 131,000
$ 107,000
$ 100,000
$ 230,000
Jun-92
$ 187,000
$ 252,000
$ 105,250
$ 79,000
$ 142,000
$ 78,750
$ 134,000
$ 131,000
$ 112,250
$ 230,000
Sep-92
$ 182,000
$ 203,750
$ 111,000
$ 70,000
$ 130,000
$ 86,700
$ 141,000
$ 120,750
$ 101,500
$ 275,500
Dec-92
$ 148,875
$ 260,000
$ 105,500
$ 80,500
$ 154,000
$ 85,000
$ 130,000
$ 119,000
$ 126,500
$ 226,250
Mar-93
$ 164,000
$ 178,000
$ 108,000
$ 75,000
$ 157,500
$ 96,500
$ 158,500
$ 123,000
$ 122,000
$ 240,000
Jun-93
$ 177,500
$ 172,300
$ 105,500
$ 85,500
$ 145,000
$ 93,750
$ 152,000
$ 120,000
$ 110,000
$ 207,500
Sep-93
$ 157,000
$ 194,000
$ 102,000
$ 78,500
$ 143,000
$ 95,000
$ 130,000
$ 130,000
$ 107,350
$ 230,000
Dec-93
$ 192,475
$ 167,500
$ 108,000
$ 86,000
$ 154,100
$ 85,750
$ 135,500
$ 115,000
$ 127,000
$ 210,000
Mar-94
$ 184,000
$ 173,500
$ 116,500
$ 85,000
$ 152,000
$ 93,500
$ 138,000
$ 132,600
$ 118,950
$ 185,000
Jun-94
$ 133,375
$ 285,000
$ 105,000
$ 82,750
$ 154,750
$ 101,000
$ 143,000
$ 128,500
$ 119,550
$ 210,000
Sep-94
$ 177,750
$ 220,000
$ 109,250
$ 85,000
$ 157,000
$ 89,250
$ 135,750
$ 119,000
$ 122,750
$ 283,300
Dec-94
$ 152,000
$ 275,000
$ 109,975
$ 70,000
$ 143,750
$ 96,000
$ 138,750
$ 146,000
$ 118,250
$ 177,000
Mar-95
$ 225,850
$ 293,500
$ 114,000
$ 74,000
$ 149,000
$ 87,600
$ 151,250
$ 126,500
$ 120,000
$ 300,000
Jun-95
$ 163,500
$ 246,000
$ 99,500
$ 83,000
$ 153,500
$ 83,000
$ 142,500
$ 117,000
$ 127,000
$ 217,500
Sep-95
$ 127,000
$ 208,000
$ 106,000
$ 72,500
$ 157,250
$ 95,500
$ 127,750
$ 120,000
$ 115,000
$ 220,000
Dec-95
$ 163,000
$ 267,500
$ 98,750
$ 83,000
$ 151,000
$ 88,500
$ 138,000
$ 117,000
$ 115,000
$ 240,000
Mar-96
$ 147,500
$ 265,000
$ 106,500
$ 78,000
$ 151,350
$ 96,500
$ 130,000
$ 110,000
$ 117,000
$ 230,000
Jun-96
$ 153,250
$ 220,000
$ 96,000
$ 81,250
$ 155,000
$ 82,500
$ 134,500
$ 118,000
$ 118,750
$ 232,500
Sep-96
$ 129,750
$ 220,000
$ 89,000
$ 71,250
$ 160,000
$ 91,000
$ 142,500
$ 107,500
$ 129,950
$ 210,500
Dec-96
$ 161,000
$ 225,500
$ 105,000
$ 85,500
$ 140,125
$ 82,500
$ 128,000
$ 116,000
$ 121,000
$ 410,000
Mar-97
$ 150,000
$ 172,000
$ 99,950
$ 71,000
$ 155,000
$ 78,000
$ 140,000
$ 120,700
$ 131,750
$ 196,000
Jun-97
$ 150,000
$ 261,000
$ 104,250
$ 81,000
$ 138,250
$ 92,000
$ 134,250
$ 124,975
$ 131,250
$ 270,000
Sep-97
$ 141,250
$ 230,000
$ 99,000
$ 75,000
$ 186,000
$ 84,000
$ 155,000
$ 125,250
$ 115,000
$ 212,000
Dec-97
$ 202,500
$ 240,000
$ 110,000
$ 85,950
$ 183,250
$ 88,000
$ 149,000
$ 120,050
$ 115,200
$ 266,500
Mar-98
$ 155,000
$ 232,625
$ 99,000
$ 75,425
$ 188,500
$ 91,800
$ 142,250
$ 135,000
$ 124,000
$ 302,500
Jun-98
$ 165,000
$ 244,000
$ 102,000
$ 77,500
$ 166,375
$ 80,000
$ 136,500
$ 130,000
$ 114,000
$ 237,500
Sep-98
$ 205,000
$ 269,500
$ 101,500
$ 77,900
$ 191,000
$ 79,475
$ 155,000
$ 115,000
$ 112,500
$ 172,000
Dec-98
$ 179,000
$ 230,000
$ 115,000
$ 76,500
$ 139,250
$ 86,000
$ 167,000
$ 127,000
$ 130,000
$ 235,000
Mar-99
$ 233,500
$ 239,000
$ 110,000
$ 78,500
$ 187,000
$ 87,875
$ 150,000
$ 130,000
$ 126,000
$ 337,500
Jun-99
$ 195,000
$ 225,000
$ 110,000
$ 80,000
$ 179,500
$ 95,000
$ 159,000
$ 125,500
$ 140,000
$ 288,500
Median Prices

Estimating the Seasonal Effects of Residential Property Markets – A Case Study of Adelaide – Rossini Page 14
Quarter
Golden Grove
Greenwith
Henley Beach
Kensington Park
Klemzig
Magill
Morphett Vale
Nailsworth
Netherby
Springfield
North Adelaide
Mar-82
$ 46,500
$ 69,000
$ 33,000
$ 39,000
$ 36,500
$ 39,950
$ 103,250
$ 75,000
Jun-82
$ 47,750
$ 44,250
$ 45,850
$ 42,750
$ 37,000
$ 37,500
$ 57,250
$ 76,000
Sep-82
$ 45,750
$ 48,800
$ 46,250
$ 45,250
$ 38,000
$ 44,000
$ 89,900
$ 52,000
Dec-82
$ 48,500
$ 51,875
$ 46,000
$ 44,000
$ 39,950
$ 43,250
$ 93,000
$ 63,750
Mar-83
$ 50,000
$ 73,000
$ 41,250
$ 48,475
$ 39,500
$ 43,850
$ 105,250
$ 60,000
Jun-83
$ 53,725
$ 57,500
$ 43,850
$ 50,000
$ 42,625
$ 42,000
$ 68,000
$ 69,000
Sep-83
$ 51,000
$ 49,500
$ 51,500
$ 44,000
$ 43,000
$ 45,700
$ 75,000
$ 70,250
Dec-83
$ 59,000
$ 70,500
$ 51,500
$ 55,250
$ 45,500
$ 52,225
$ 90,550
$ 67,000
Mar-84
$ 60,000
$ 75,000
$ 57,000
$ 63,250
$ 50,000
$ 56,500
$ 134,250
$ 77,900
Jun-84
$ 67,500
$ 75,000
$ 52,500
$ 65,000
$ 52,800
$ 58,250
$ 100,375
$ 89,000
Sep-84
$ 72,000
$ 68,000
$ 70,000
$ 67,500
$ 57,500
$ 81,500
$ 95,000
$ 102,500
Dec-84
$ 77,000
$ 79,500
$ 65,250
$ 73,750
$ 58,500
$ 76,750
$ 88,000
$ 107,000
Mar-85
$ 79,500
$ 94,500
$ 80,000
$ 75,975
$ 61,000
$ 75,000
$ 159,500
$ 135,000
Jun-85
$ 81,000
$ 96,500
$ 75,000
$ 79,000
$ 62,500
$ 78,250
$ 121,000
$ 120,000
Sep-85
$ 84,000
$ 109,975
$ 85,250
$ 75,000
$ 64,250
$ 75,000
$ 120,000
$ 199,250
Dec-85
$ 85,000
$ 152,500
$ 75,000
$ 85,500
$ 63,875
$ 75,000
$ 148,250
$ 145,000
Mar-86
$ 85,000
$ 146,500
$ 79,500
$ 75,000
$ 64,600
$ 77,250
$ 143,750
$ 175,500
Jun-86
$ 84,000
$ 127,000
$ 68,000
$ 73,750
$ 62,000
$ 78,000
$ 149,000
$ 136,000
Sep-86
$ 81,000
$ 108,500
$ 62,225
$ 77,000
$ 60,000
$ 72,000
$ 230,000
$ 134,000
Dec-86
$ 82,000
$ 123,500
$ 64,000
$ 75,500
$ 63,975
$ 72,250
$ 319,500
$ 92,000
Mar-87
$ 78,000
$ 117,250
$ 71,500
$ 82,000
$ 62,250
$ 71,500
$ 93,000
$ 140,000
Jun-87
$ 82,000
$ 105,000
$ 80,750
$ 80,750
$ 64,500
$ 80,000
$ 225,000
$ 194,000
Sep-87
$ 87,500
$ 110,000
$ 70,000
$ 84,000
$ 60,000
$ 78,000
$ 160,500
$ 133,750
Dec-87
$ 85,050
$ 114,000
$ 85,000
$ 79,000
$ 64,000
$ 77,500
$ 163,000
$ 135,000
Mar-88
$ 92,000
$ 137,500
$ 74,000
$ 83,000
$ 64,000
$ 87,500
$ 175,000
$ 150,000
Jun-88
$ 98,500
$ 125,000
$ 73,500
$ 87,750
$ 66,500
$ 87,750
$ 184,000
$ 212,500
Sep-88
$ 92,000
$ 104,600
$ 79,000
$ 91,500
$ 67,975
$ 95,000
$ 236,250
$ 202,500
Dec-88
$ 109,000
$ 110,500
$ 92,000
$ 95,000
$ 68,000
$ 97,450
$ 202,500
$ 166,500
Mar-89
$ 121,375
$ 120,000
$ 90,250
$ 104,500
$ 72,000
$ 110,000
$ 303,500
$ 184,000
Jun-89
$ 109,000
$ 161,250
$ 91,125
$ 97,000
$ 74,000
$ 109,000
$ 196,000
$ 361,000
Sep-89
$ 114,000
$ 146,000
$ 101,000
$ 107,000
$ 73,250
$ 118,000
$ 212,500
$ 350,000
Dec-89
$ 113,500
$ 157,500
$ 100,000
$ 121,000
$ 75,000
$ 124,500
$ 313,000
$ 279,000
Mar-90
$ 119,275
$ 157,500
$ 105,000
$ 129,000
$ 77,800
$ 116,125
$ 306,000
$ 165,000
Jun-90
$ 121,250
$ 170,500
$ 97,000
$ 113,500
$ 79,000
$ 96,000
$ 421,000
$ 300,000
Sep-90
$ 121,000
$ 170,000
$ 121,000
$ 112,000
$ 78,250
$ 85,000
$ 263,750
$ 315,000
Dec-90
$ 123,000
$ 172,500
$ 102,000
$ 118,000
$ 79,950
$ 131,000
$ 240,000
$ 230,000
Mar-91
$ 130,000
$ 177,500
$ 110,925
$ 125,500
$ 81,105
$ 115,000
$ 176,500
$ 236,500
Jun-91
$ 137,000
$ 155,000
$ 125,000
$ 110,000
$ 81,500
$ 137,000
$ 265,000
$ 218,500
Sep-91
$ 133,000
$ 233,500
$ 85,500
$ 120,000
$ 82,500
$ 135,000
$ 300,000
$ 359,250
Dec-91
$ 123,500
$ 186,250
$ 99,000
$ 131,000
$ 81,975
$ 134,250
$ 370,000
$ 200,925
Mar-92
$ 135,100
$ 143,000
$ 103,875
$ 126,250
$ 82,500
$ 155,000
$ 225,000
$ 317,500
Jun-92
$ 124,950
$ 160,000
$ 110,000
$ 119,000
$ 82,000
$ 118,000
$ 252,500
$ 307,500
Sep-92
$ 137,075
$ 200,000
$ 124,000
$ 136,750
$ 84,000
$ 140,000
$ 173,000
$ 308,750
Dec-92
$ 133,750
$ 160,000
$ 115,000
$ 133,000
$ 79,950
$ 151,000
$ 217,000
$ 150,000
Mar-93
$ 126,500
$ 135,000
$ 144,000
$ 163,000
$ 101,900
$ 117,750
$ 84,000
$ 152,500
$ 375,000
$ 248,000
Jun-93
$ 135,000
$ 145,000
$ 127,500
$ 165,000
$ 113,000
$ 121,000
$ 84,500
$ 145,000
$ 315,000
$ 215,000
Sep-93
$ 139,000
$ 139,000
$ 133,000
$ 189,250
$ 110,000
$ 119,000
$ 84,000
$ 129,000
$ 245,000
$ 208,750
Dec-93
$ 137,750
$ 146,475
$ 146,000
$ 172,600
$ 111,000
$ 127,500
$ 84,000
$ 138,500
$ 247,250
$ 268,000
Mar-94
$ 143,500
$ 132,000
$ 147,000
$ 190,500
$ 104,500
$ 135,000
$ 85,000
$ 146,000
$ 290,000
$ 230,000
Jun-94
$ 150,000
$ 135,000
$ 142,000
$ 147,500
$ 115,500
$ 130,000
$ 84,000
$ 146,250
$ 275,000
$ 260,000
Sep-94
$ 142,000
$ 161,000
$ 133,000
$ 218,000
$ 107,475
$ 110,000
$ 86,500
$ 135,000
$ 331,000
$ 250,000
Dec-94
$ 138,250
$ 137,950
$ 150,500
$ 275,000
$ 113,000
$ 138,000
$ 83,250
$ 148,500
$ 272,000
$ 205,000
Mar-95
$ 134,000
$ 144,650
$ 142,000
$ 163,500
$ 119,000
$ 130,000
$ 86,950
$ 161,500
$ 306,500
$ 190,750
Jun-95
$ 142,500
$ 122,250
$ 133,000
$ 187,000
$ 116,000
$ 119,500
$ 83,200
$ 135,500
$ 380,000
$ 261,250
Sep-95
$ 130,000
$ 122,750
$ 126,000
$ 156,000
$ 93,000
$ 126,000
$ 80,500
$ 132,000
$ 390,000
$ 280,000
Dec-95
$ 138,000
$ 124,750
$ 130,000
$ 165,000
$ 103,500
$ 123,500
$ 82,725
$ 140,000
$ 180,000
$ 224,500
Mar-96
$ 129,442
$ 129,500
$ 136,000
$ 178,500
$ 130,000
$ 123,250
$ 77,500
$ 142,000
$ 290,000
$ 227,500
Jun-96
$ 137,000
$ 127,000
$ 120,000
$ 194,250
$ 115,000
$ 135,000
$ 83,000
$ 152,250
$ 200,000
$ 297,500
Sep-96
$ 142,000
$ 136,000
$ 134,000
$ 167,000
$ 101,125
$ 110,000
$ 78,000
$ 140,000
$ 248,500
$ 175,000
Dec-96
$ 135,000
$ 135,000
$ 138,000
$ 175,600
$ 91,000
$ 134,500
$ 82,500
$ 140,000
$ 275,000
$ 220,000
Mar-97
$ 126,300
$ 135,750
$ 129,750
$ 183,000
$ 121,550
$ 134,000
$ 78,500
$ 132,500
$ 400,000
$ 200,000
Jun-97
$ 135,000
$ 134,900
$ 155,000
$ 181,000
$ 107,500
$ 121,500
$ 80,000
$ 146,000
$ 370,000
$ 316,250
Sep-97
$ 140,000
$ 144,000
$ 128,000
$ 202,000
$ 117,000
$ 132,000
$ 79,500
$ 155,000
$ 270,000
$ 360,000
Dec-97
$ 144,000
$ 130,000
$ 170,000
$ 199,500
$ 111,000
$ 135,000
$ 81,975
$ 165,000
$ 276,250
$ 360,000
Mar-98
$ 148,500
$ 131,500
$ 140,000
$ 152,500
$ 99,500
$ 133,000
$ 84,000
$ 136,000
$ 275,000
$ 309,950
Jun-98
$ 149,750
$ 142,000
$ 147,000
$ 197,500
$ 108,500
$ 139,500
$ 80,500
$ 150,000
$ 390,000
$ 300,000
Sep-98
$ 159,000
$ 147,000
$ 165,500
$ 186,000
$ 106,500
$ 140,000
$ 80,000
$ 188,000
$ 352,500
$ 240,000
Dec-98
$ 158,500
$ 146,000
$ 145,750
$ 190,000
$ 120,000
$ 143,500
$ 80,875
$ 150,000
$ 231,000
$ 264,000
Mar-99
$ 155,000
$ 145,000
$ 175,500
$ 185,000
$ 115,000
$ 142,750
$ 84,500
$ 167,000
$ 351,000
$ 356,250
Jun-99
$ 163,000
$ 156,500
$ 164,250
$ 229,750
$ 119,750
$ 150,000
$ 85,500
$ 187,500
$ 195,500
$ 364,500
Median Prices

Estimating the Seasonal Effects of Residential Property Markets – A Case Study of Adelaide – Rossini Page 15
Quarter
Parkside
Plympton
Rostrevor
Salisbury
St. Peters
Stirling
Unley
Wattle Park
West Lakes
Woodcroft
Metro Area
Mar-82 $ 47,000 $ 42,750 $ 49,500 $ 31,000 $ 56,000 $ 64,000 $ 57,000 $ 95,025 $ 79,750 $ 41,500
Jun-82 $ 46,500 $ 43,250 $ 53,000 $ 32,500 $ 50,000 $ 68,500 $ 50,000 $ 103,000 $ 76,250 $ 42,000
Sep-82 $ 48,875 $ 46,625 $ 53,500 $ 38,000 $ 62,000 $ 63,750 $ 49,900 $ 99,250 $ 80,000 $ 43,000
Dec-82 $ 50,100 $ 42,000 $ 54,000 $ 36,250 $ 59,100 $ 59,500 $ 55,125 $ 78,500 $ 81,000 $ 43,525
Mar-83 $ 51,475 $ 54,500 $ 55,000 $ 38,000 $ 79,000 $ 60,000 $ 62,000 $ 92,000 $ 87,000 $ 46,000
Jun-83 $ 59,500 $ 58,000 $ 58,550 $ 42,000 $ 63,500 $ 61,000 $ 58,000 $ 83,500 $ 91,500 $ 46,675
Sep-83 $ 59,975 $ 46,200 $ 63,000 $ 38,000 $ 88,500 $ 75,000 $ 62,750 $ 89,000 $ 100,000 $ 49,000
Dec-83 $ 62,250 $ 59,500 $ 58,545 $ 45,000 $ 62,500 $ 74,000 $ 70,500 $ 108,500 $ 101,500 $ 51,000
Mar-84 $ 65,500 $ 54,875 $ 66,250 $ 49,250 $ 82,500 $ 71,250 $ 71,250 $ 125,000 $ 126,250 $ 56,000
Jun-84 $ 72,250 $ 69,500 $ 73,000 $ 56,000 $ 88,500 $ 80,000 $ 78,550 $ 125,000 $ 111,000 $ 61,175
Sep-84 $ 76,000 $ 67,500 $ 75,500 $ 55,750 $ 78,500 $ 87,500 $ 88,000 $ 140,000 $ 145,000 $ 64,500
Dec-84
$ 80,000
$ 77,000
$ 81,600
$ 57,250
$ 86,600
$ 121,500
$ 100,000
$ 120,000
$ 136,000
$ 68,950
Mar-85
$ 84,000
$ 68,750
$ 88,000
$ 63,500
$ 110,250
$ 88,250
$ 106,625
$ 157,500
$ 146,250
$ 72,000
Jun-85
$ 79,500
$ 85,625
$ 83,250
$ 59,500
$ 128,000
$ 104,250
$ 97,500
$ 144,250
$ 165,000
$ 74,000
Sep-85
$ 90,100
$ 76,000
$ 97,000
$ 65,000
$ 114,000
$ 100,000
$ 93,500
$ 130,000
$ 136,975
$ 73,950
Dec-85 $ 95,000 $ 85,000 $ 87,250 $ 64,475 $ 137,500 $ 88,500 $ 102,250 $ 177,500 $ 142,000 $ 75,000
Mar-86 $ 85,000 $ 70,000 $ 86,000 $ 60,000 $ 134,000 $ 93,250 $ 105,000 $ 152,000 $ 175,500 $ 75,500
Jun-86 $ 86,250 $ 72,000 $ 87,500 $ 65,000 $ 123,750 $ 86,250 $ 129,500 $ 147,000 $ 164,500 $ 73,500
Sep-86 $ 95,000 $ 76,000 $ 101,250 $ 63,000 $ 162,500 $ 101,000 $ 98,750 $ 166,500 $ 164,000 $ 73,000
Dec-86 $ 89,500 $ 72,750 $ 87,000 $ 62,000 $ 112,000 $ 119,500 $ 113,500 $ 137,500 $ 176,000 $ 75,000
Mar-87 $ 84,000 $ 84,500 $ 87,000 $ 60,000 $ 116,000 $ 95,000 $ 107,500 $ 150,000 $ 164,000 $ 74,000
Jun-87 $ 83,400 $ 73,125 $ 92,500 $ 66,000 $ 130,750 $ 117,500 $ 106,625 $ 140,000 $ 149,000 $ 75,000
Sep-87 $ 92,000 $ 87,000 $ 88,250 $ 65,000 $ 145,000 $ 90,750 $ 112,000 $ 161,000 $ 168,000 $ 75,000
Dec-87 $ 98,000 $ 77,500 $ 87,000 $ 66,500 $ 135,000 $ 104,000 $ 121,500 $ 169,125 $ 171,500 $ 76,000
Mar-88 $ 105,500 $ 94,750 $ 92,500 $ 60,000 $ 168,000 $ 108,750 $ 126,000 $ 137,000 $ 176,500 $ 79,000
Jun-88 $ 107,500 $ 85,850 $ 96,500 $ 64,000 $ 151,000 $ 117,000 $ 124,000 $ 195,800 $ 230,000 $ 80,000
Sep-88 $ 106,000 $ 86,000 $ 105,000 $ 65,000 $ 143,250 $ 127,500 $ 147,500 $ 169,500 $ 190,000 $ 82,000
Dec-88
$ 128,500
$ 87,500
$ 109,000
$ 61,500
$ 160,000
$ 112,500
$ 155,000
$ 201,500
$ 211,000
$ 85,000
Mar-89
$ 127,000
$ 95,950
$ 110,000
$ 66,500
$ 180,050
$ 147,500
$ 163,000
$ 196,000
$ 182,250
$ 73,000
$ 89,500
Jun-89
$ 140,000
$ 100,000
$ 111,000
$ 66,500
$ 147,500
$ 128,500
$ 150,000
$ 250,000
$ 213,000
$ 78,975
$ 92,000
Sep-89
$ 133,000
$ 100,000
$ 117,000
$ 72,750
$ 197,500
$ 135,000
$ 148,000
$ 238,500
$ 237,500
$ 81,000
$ 94,000
Dec-89 $ 136,500 $ 136,500 $ 115,500 $ 64,250 $ 213,750 $ 150,000 $ 160,000 $ 329,000 $ 208,000 $ 111,000 $ 95,000
Mar-90 $ 145,500 $ 114,000 $ 126,000 $ 70,500 $ 226,000 $ 148,500 $ 176,500 $ 238,000 $ 220,000 $ 75,000 $ 97,500
Jun-90 $ 150,000 $ 131,000 $ 126,500 $ 80,000 $ 235,100 $ 136,250 $ 158,250 $ 240,000 $ 223,250 $ 103,700 $ 99,000
Sep-90 $ 152,000 $ 106,000 $ 129,000 $ 70,000 $ 233,500 $ 142,500 $ 159,500 $ 194,000 $ 225,000 $ 34,750 $ 98,500
Dec-90 $ 159,500 $ 115,000 $ 116,500 $ 76,500 $ 220,000 $ 135,000 $ 192,375 $ 205,000 $ 240,000 $ 57,000
$ 100,000
Mar-91 $ 135,000 $ 122,000 $ 130,000 $ 82,500 $ 211,000 $ 157,000 $ 200,000 $ 196,000 $ 240,000 $ 85,500
$ 105,000
Jun-91 $ 143,000 $ 121,250 $ 130,500 $ 77,000 $ 200,000 $ 149,000 $ 151,250 $ 205,000 $ 228,000 $ 102,500
$ 106,000
Sep-91 $ 155,000 $ 115,000 $ 135,000 $ 79,000 $ 235,000 $ 148,000 $ 169,000 $ 240,000 $ 235,000 $ 105,500
$ 105,000
Dec-91 $ 152,000 $ 130,000 $ 115,000 $ 72,500 $ 248,750 $ 160,000 $ 157,350 $ 207,500 $ 234,250 $ 91,500
$ 108,500
Mar-92 $ 154,500 $ 124,000 $ 125,000 $ 78,500 $ 255,500 $ 205,000 $ 174,000 $ 172,000 $ 252,000 $ 87,750
$ 108,500
Jun-92 $ 150,750 $ 117,000 $ 125,000 $ 80,000 $ 211,000 $ 179,750 $ 187,250 $ 243,750 $ 250,000 $ 90,000
$ 109,950
Sep-92 $ 176,500 $ 122,500 $ 141,750 $ 78,000 $ 198,000 $ 170,000 $ 213,000 $ 195,000 $ 242,500 $ 103,000
$ 109,000
Dec-92
$ 170,500
$ 117,750
$ 130,000
$ 81,000
$ 170,000
$ 136,500
$ 221,360
$ 195,000
$ 215,000
$ 104,000
$ 110,000
Mar-93
$ 163,000
$ 121,500
$ 148,500
$ 87,000
$ 244,500
$ 160,000
$ 190,000
$ 263,000
$ 206,500
$ 105,000
$ 114,000
Jun-93
$ 157,000
$ 122,500
$ 129,500
$ 79,475
$ 180,000
$ 158,000
$ 213,000
$ 220,000
$ 236,500
$ 105,000
$ 113,000
Sep-93
$ 166,500
$ 119,000
$ 121,800
$ 83,000
$ 200,000
$ 160,000
$ 202,750
$ 200,000
$ 246,275
$ 92,000
$ 110,000
Dec-93 $ 170,250 $ 120,000 $ 135,750 $ 90,000 $ 186,000 $ 148,000 $ 181,750 $ 201,000 $ 230,000 $ 106,500
$ 114,000
Mar-94 $ 150,000 $ 135,000 $ 132,500 $ 79,000 $ 263,000 $ 191,250 $ 205,000 $ 225,000 $ 278,250 $ 97,500
$ 115,000
Jun-94 $ 165,000 $ 135,000 $ 125,000 $ 82,000 $ 210,000 $ 170,000 $ 185,000 $ 186,250 $ 244,250 $ 111,250
$ 116,950
Sep-94 $ 170,000 $ 129,000 $ 130,000 $ 85,500 $ 232,500 $ 161,000 $ 220,000 $ 301,500 $ 268,500 $ 109,500
$ 113,000
Dec-94 $ 180,000 $ 126,000 $ 125,000 $ 90,750 $ 245,000 $ 155,000 $ 180,750 $ 183,750 $ 265,000 $ 115,000
$ 113,000
Mar-95 $ 145,000 $ 125,000 $ 140,500 $ 80,000 $ 281,000 $ 162,500 $ 184,250 $ 227,500 $ 230,000 $ 111,500
$ 115,000
Jun-95 $ 166,275 $ 125,000 $ 128,000 $ 84,500 $ 245,000 $ 177,500 $ 195,000 $ 227,500 $ 202,500 $ 110,000
$ 115,000
Sep-95 $ 180,000 $ 125,000 $ 121,000 $ 78,500 $ 220,000 $ 150,000 $ 172,000 $ 196,900 $ 280,000 $ 100,000
$ 111,000
Dec-95 $ 149,000 $ 122,000 $ 129,000 $ 80,000 $ 220,250 $ 160,000 $ 215,000 $ 255,000 $ 217,000 $ 100,250
$ 110,000
Mar-96 $ 150,000 $ 114,000 $ 117,500 $ 86,900 $ 205,500 $ 145,000 $ 166,000 $ 220,000 $ 216,500 $ 105,000
$ 110,000
Jun-96 $ 165,000 $ 115,000 $ 127,000 $ 76,500 $ 250,000 $ 170,000 $ 195,000 $ 230,000 $ 215,000 $ 105,000
$ 112,000
Sep-96 $ 160,250 $ 125,000 $ 140,000 $ 89,950 $ 245,000 $ 163,900 $ 212,500 $ 225,000 $ 255,000 $ 111,000
$ 110,000
Dec-96
$ 170,500
$ 122,375
$ 125,000
$ 72,500
$ 192,500
$ 196,000
$ 185,500
$ 198,000
$ 246,125
$ 111,950
$ 111,000
Mar-97
$ 194,000
$ 106,000
$ 121,000
$ 72,500
$ 217,500
$ 175,750
$ 221,750
$ 247,000
$ 218,000
$ 105,000
$ 115,000
Jun-97
$ 180,000
$ 113,000
$ 128,000
$ 76,500
$ 271,000
$ 179,250
$ 233,500
$ 210,000
$ 208,750
$ 113,500
$ 116,000
Sep-97
$ 167,000
$ 123,250
$ 132,000
$ 73,500
$ 250,000
$ 209,000
$ 231,750
$ 208,500
$ 220,000
$ 101,000
$ 112,500
Dec-97 $ 191,250 $ 116,000 $ 121,000 $ 74,000 $ 240,250 $ 185,000 $ 228,000 $ 212,000 $ 270,000 $ 110,000
$ 117,000
Mar-98 $ 182,250 $ 139,000 $ 139,000 $ 84,500 $ 250,000 $ 175,000 $ 182,750 $ 244,000 $ 273,000 $ 104,500
$ 118,000
Jun-98 $ 190,000 $ 140,000 $ 139,750 $ 76,000 $ 315,000 $ 179,500 $ 240,000 $ 220,250 $ 258,000 $ 111,000
$ 120,000
Sep-98 $ 192,500 $ 149,000 $ 139,000 $ 73,000 $ 255,000 $ 170,000 $ 232,500 $ 292,500 $ 273,750 $ 116,500
$ 118,000
Dec-98 $ 218,000 $ 125,000 $ 145,000 $ 78,450 $ 313,250 $ 215,000 $ 209,000 $ 259,000 $ 237,500 $ 117,000
$ 121,000
Mar-99 $ 213,000 $ 129,250 $ 142,000 $ 76,000 $ 295,000 $ 191,000 $ 194,500 $ 230,000 $ 262,500 $ 120,000
$ 124,000
Jun-99 $ 208,250 $ 140,000 $ 145,000 $ 85,000 $ 253,000 $ 212,000 $ 266,875 $ 217,000 $ 278,000 $ 123,000
$ 127,000
Median Prices

Estimating the Seasonal Effects of Residential Property Markets – A Case Study of Adelaide – Rossini Page 16
Table 6 - Detached Dwellings - Constant Quality Price Indices
Quarter
Brighton
Burnside
Campbelltown
Christies Beach
Colonel Light Gardens
Enfield
Flagstaff Hill
Flinders Park
Gawler East
Glen Osmond
Mar-85
100.00
100.00
100.00
100.00
100.00
100.00
100.00
100.00
Jun-85 115.76 105.29 91.25 100.08 96.50 98.10 67.06 97.95
Sep-85 100.46 109.96 97.20 95.63 97.19 104.57 87.58 101.12
Dec-85 84.83 102.96 99.62 95.05 91.85 100.60 67.43 80.98
Mar-86 94.56 100.00 102.45 101.39 107.69 98.11 99.35 74.32 96.46
Jun-86 97.98 95.59 92.30 100.49 102.89 103.75 95.09 77.83 93.71
Sep-86 90.77 89.92 106.13 96.63 97.16 90.86 101.12 80.84 96.21 100.00
Dec-86 95.18 97.97 100.61 98.71 105.21 99.85 100.58 83.14 102.15 91.38
Mar-87 95.15 91.78 103.09 100.74 102.09 97.25 101.82 87.33 97.36 105.79
Jun-87 102.52 92.60 109.54 93.18 111.28 97.70 101.86 87.28 92.94 92.96
Sep-87 97.10 87.40 103.20 96.66 101.34 88.83 99.25 91.89 101.79 83.44
Dec-87 99.13 99.53 103.68 94.69 115.40 97.95 103.80 96.48 107.66 106.26
Mar-88 126.53 96.20 107.14 98.85 112.33 102.12 102.65 97.42 94.95 93.03
Jun-88 116.69 102.17 111.25 107.64 115.65 106.60 106.33 102.44 94.29 99.01
Sep-88 112.62 105.08 119.35 96.10 124.14 106.90 115.16 102.24 114.08 110.46
Dec-88 105.12 121.92 122.68 108.72 132.98 111.04 114.53 110.30 109.96 128.08
Mar-89 128.14 135.66 123.13 112.54 138.56 113.26 124.87 107.87 105.68 136.58
Jun-89 119.98 123.16 130.76 120.01 147.02 119.18 126.71 104.93 107.63 176.36
Sep-89 137.49 136.63 137.43 111.30 146.29 115.96 133.52 105.24 115.67 141.22
Dec-89 140.82 130.74 133.28 116.40 158.47 114.18 130.04 108.52 124.24 124.42
Mar-90 137.90 141.93 146.67 119.40 151.07 118.44 131.70 107.30 120.48 131.32
Jun-90 147.05 134.13 134.42 121.42 149.45 125.27 127.58 109.57 120.20 145.41
Sep-90 145.49 135.98 138.73 122.75 159.23 142.32 138.94 112.44 120.41 138.31
Dec-90 151.95 135.36 147.81 123.47 166.40 134.25 134.42 108.14 125.68 133.79
Mar-91 139.10 135.10 146.37 114.82 164.54 128.70 139.25 114.72 121.10 141.32
Jun-91 155.19 123.91 147.85 119.61 158.94 132.97 131.67 109.93 120.28 145.09
Sep-91 144.46 119.33 155.46 125.92 162.34 138.03 135.34 109.72 122.79 129.30
Dec-91 160.03 134.14 157.67 124.40 165.51 148.23 147.48 108.52 131.58 139.69
Mar-92 164.95 126.97 150.97 129.36 175.86 142.98 133.36 114.99 126.62 151.57
Jun-92 191.59 124.44 155.71 129.31 167.54 133.31 131.27 113.62 132.85 138.41
Sep-92 160.41 124.89 154.94 117.16 167.37 138.75 142.03 112.05 121.19 132.33
Dec-92 160.80 137.71 146.05 131.30 187.16 137.01 128.92 120.37 135.84 130.38
Mar-93 160.89 125.65 154.33 123.46 174.92 146.63 138.60 111.39 140.88 166.75
Jun-93 170.08 117.33 161.46 129.75 168.35 139.97 139.53 107.66 130.30 125.20
Sep-93 153.44 123.52 148.60 128.52 172.43 136.47 134.33 111.23 135.91 130.36
Dec-93 171.27 136.00 154.52 131.34 181.11 136.96 140.75 105.64 143.35 127.71
Mar-94 164.48 112.12 163.38 136.01 184.68 143.65 137.94 98.35 135.70 140.51
Jun-94 158.69 138.39 158.56 140.05 188.24 140.36 138.92 106.64 142.19 146.72
Sep-94 173.89 134.98 146.60 137.85 193.17 137.52 133.60 108.78 131.81 142.48
Dec-94 157.82 140.53 152.59 118.22 176.60 137.59 135.11 99.52 135.85 109.85
Mar-95 171.07 148.84 138.43 122.80 177.41 125.65 142.08 105.99 139.87 149.08
Jun-95 170.40 142.22 142.33 129.05 179.02 132.76 136.61 109.82 144.77 140.27
Sep-95 153.65 131.75 149.73 117.51 175.09 141.70 122.78 107.11 137.91 123.98
Dec-95 164.28 131.29 139.26 116.53 177.42 128.64 132.38 111.27 132.97 128.81
Mar-96 157.97 129.27 141.55 116.16 176.35 129.77 128.85 109.53 143.61 131.72
Jun-96 160.61 121.75 148.75 125.44 170.47 123.04 131.92 109.01 136.89 122.62
Sep-96 151.33 129.22 141.15 112.06 188.63 132.44 129.48 108.32 138.66 129.10
Dec-96 156.12 128.14 135.33 124.64 176.16 123.37 129.80 102.94 136.76 125.82
Mar-97 176.88 118.60 143.49 118.63 185.17 118.40 133.92 122.62 135.98 137.43
Jun-97 151.94 136.78 140.35 125.46 173.32 136.25 131.10 119.26 137.10 146.39
Sep-97 176.61 142.60 138.01 116.59 198.10 130.15 142.35 123.77 131.14 128.49
Dec-97 183.20 128.78 145.16 128.40 196.07 123.43 139.50 139.50 135.80 159.09
Mar-98 173.02 129.04 144.36 120.24 211.40 140.87 138.09 138.09 136.75 142.14
Jun-98 165.47 128.35 147.45 127.00 189.41 138.31 132.27 132.27 136.79 130.75
Sep-98 188.68 134.37 147.45 124.28 219.84 121.76 139.69 139.69 132.64 135.76
Dec-98 172.07 139.13 152.07 129.03 186.56 127.81 148.67 148.67 144.48 147.04
Mar-99 200.01 154.94 148.37 129.53 220.92 133.27 138.84 138.84 142.54 154.45
Jun-99 183.92 152.59 158.16 132.93 229.58 140.49 132.48 132.48 152.83 158.15
Sep-99
186.58
139.97
164.47
135.44
234.14
150.19
143.57
143.57
145.11
175.60
Constant Quality Price Indices

Estimating the Seasonal Effects of Residential Property Markets – A Case Study of Adelaide – Rossini Page 17
Quarter
Golden Grove
Greenwith
Henley Beach
Kensington Park
Klemzig
Magill
Morphett Vale
Nailsworth
Netherby/Springfield
North Adelaide
Mar-85
100.00
100.00
100.00
100.00
100.00
100.00
Jun-85 109.07 102.83 100.00 101.10 99.80 103.96 108.68
Sep-85 101.91 116.04 89.01 101.33 99.71 102.15 112.90
Dec-85 106.16 112.49 94.14 99.03 105.53 109.02 98.92
Mar-86 111.87 114.92 85.93 101.02 96.92 102.79 120.06
Jun-86 99.78 93.66 97.20 88.10 102.31 100.05 98.98 128.20
Sep-86 107.05 92.65 96.67 88.83 99.66 100.86 119.14 132.73
Dec-86 104.76 94.24 102.71 86.65 98.54 97.32 116.55 121.67
Mar-87 102.08 95.29 108.23 92.71 99.11 100.15 99.11 132.94
Jun-87 101.76 93.29 109.38 89.35 100.19 93.81 102.52 144.53
Sep-87 115.89 98.84 93.42 93.31 98.64 95.48 116.50 123.28
Dec-87 110.37 95.36 111.91 93.28 102.26 104.28 114.48 130.28
Mar-88 118.88 118.12 113.89 93.75 103.54 111.53 136.44 148.52
Jun-88 116.73 97.81 113.24 106.86 104.98 111.31 127.19 146.29
Sep-88 119.39 114.01 118.03 107.13 109.55 118.80 133.19 183.86
Dec-88 136.42 106.33 126.98 109.38 108.17 138.36 146.60 183.26
Mar-89 140.43 115.88 133.64 118.92 115.54 139.77 166.92 191.99
Jun-89 134.65 132.42 135.55 119.04 120.16 135.73 173.05 224.19
Sep-89 140.08 138.46 140.10 130.12 118.39 137.49 166.76 202.59
Dec-89 100.00 141.09 143.68 143.05 137.99 119.56 160.67 176.64 204.72
Mar-90 115.53 141.56 141.23 145.30 141.52 123.56 158.17 174.88 190.81
Jun-90 112.67 154.31 158.80 143.99 129.73 128.11 142.64 200.26 206.02
Sep-90 115.13 152.07 157.27 146.79 134.24 127.53 158.47 149.56 231.23
Dec-90 107.72 159.96 140.28 142.26 134.87 130.20 171.92 168.46 220.53
Mar-91 96.93 100.00 151.16 148.25 157.60 133.61 131.77 159.38 160.16 210.86
Jun-91 103.44 95.07 185.97 130.30 162.40 133.59 132.26 157.66 169.54 185.06
Sep-91 112.18 94.29 169.68 167.10 148.86 137.14 134.88 174.47 153.27 206.71
Dec-91 101.52 104.73 169.48 141.83 161.33 145.24 132.53 177.92 177.35 212.94
Mar-92 100.52 89.76 158.13 133.67 136.06 139.97 135.14 181.67 181.17 212.12
Jun-92 101.71 86.85 159.40 137.43 162.09 150.90 133.50 180.98 155.39 218.06
Sep-92 105.71 89.25 160.45 146.44 163.35 147.46 136.28 182.45 159.48 176.44
Dec-92 101.04 97.49 161.53 140.88 151.85 150.62 133.79 196.60 177.62 180.28
Mar-93 104.92 95.89 160.93 140.67 144.48 135.60 134.11 196.84 172.36 200.17
Jun-93 107.62 96.77 162.68 139.33 157.15 142.73 134.12 181.85 160.44 177.27
Sep-93 105.02 93.91 150.56 136.46 157.17 138.65 134.78 181.75 153.83 185.28
Dec-93 104.09 97.82 170.42 135.87 163.16 151.34 134.66 191.92 157.56 183.00
Mar-94 109.62 96.02 173.91 156.07 161.97 150.60 134.83 191.20 164.92 210.96
Jun-94 105.86 98.17 180.04 143.37 162.48 148.05 137.30 196.63 157.26 198.11
Sep-94 105.60 94.39 168.90 156.18 155.55 138.77 135.15 181.48 160.76 219.21
Dec-94 105.78 97.76 159.36 148.89 153.73 155.64 133.34 188.05 170.82 209.67
Mar-95 101.38 92.41 160.83 137.55 156.30 136.98 135.18 193.77 182.00 190.17
Jun-95 100.58 88.70 159.53 144.80 159.97 139.31 128.14 180.06 158.14 167.42
Sep-95 100.28 85.94 170.30 141.25 159.03 134.95 129.60 190.66 160.63 180.75
Dec-95 98.85 89.40 154.41 131.73 143.57 146.79 126.70 185.98 149.78 204.11
Mar-96 97.96 88.69 160.77 131.35 156.01 137.53 126.65 186.11 159.81 179.63
Jun-96 97.55 90.31 160.21 145.94 154.31 135.93 129.87 188.69 169.09 200.44
Sep-96 104.17 89.56 158.92 131.65 154.50 132.77 122.46 176.53 173.93 188.55
Dec-96 100.11 88.83 152.77 142.58 145.09 136.40 125.75 181.14 171.59 224.94
Mar-97 97.10 91.23 164.17 150.59 158.38 146.45 123.72 168.33 162.92 195.06
Jun-97 97.43 91.16 181.47 144.26 142.71 146.33 123.84 186.42 141.71 216.76
Sep-97 100.33 93.29 164.45 155.62 156.34 143.34 124.75 200.09 175.77 219.99
Dec-97 101.55 93.21 190.05 151.17 156.73 141.45 126.52 209.65 163.90 228.71
Mar-98 104.45 94.28 175.85 150.88 151.03 148.03 130.13 203.89 162.61 217.88
Jun-98 105.03 94.29 170.01 154.92 154.25 145.66 127.52 187.62 187.20 241.55
Sep-98 107.67 98.06 209.46 165.66 155.16 152.54 129.99 228.50 186.01 243.06
Dec-98 108.22 99.27 180.48 149.30 162.48 161.58 128.21 201.50 169.22 235.04
Mar-99 113.85 101.50 203.44 155.59 167.94 162.43 130.83 232.62 140.87 274.49
Jun-99 114.53 99.75 228.15 182.29 163.70 156.89 134.18 226.11 180.53 216.66
Sep-99
114.21
99.22
226.05
178.63
163.23
169.26
139.04
280.77
191.83
303.78
Constant Quality Price Indices

Estimating the Seasonal Effects of Residential Property Markets – A Case Study of Adelaide – Rossini Page 18
Quarter
Parkside
Plympton
Rostrevor
Salisbury
St. Peters
Stirling
Unley
Wattle Park
West Lakes
Woodcroft
Metro Area
Mar-85
100.00
100.00
100.00
100.00
100.00
100.00
100.00
Jun-85 100.90 97.58 114.86 103.05 100.00 100.00 99.39 100.00 108.92 105.55
Sep-85 106.56 90.07 116.27 102.30 93.18 116.43 115.92 90.80 97.49 107.10
Dec-85 102.77 97.96 108.17 106.75 105.93 117.21 109.81 99.28 98.70 108.41
Mar-86 101.40 93.03 94.98 98.45 98.53 122.70 105.86 107.26 112.12 107.42
Jun-86 98.54 90.17 106.80 106.98 102.14 118.50 112.64 102.27 104.68 107.27
Sep-86 107.60 94.98 110.58 99.99 104.68 115.48 104.89 98.29 104.06 106.67
Dec-86 105.86 94.00 109.12 104.24 95.94 140.76 104.42 99.33 110.54 108.23
Mar-87 100.13 96.64 107.14 95.00 95.15 126.88 111.16 103.72 104.08 107.99
Jun-87 99.96 94.43 103.35 105.95 103.09 131.56 103.79 100.60 107.86 107.68
Sep-87 108.86 102.98 111.50 99.62 118.87 127.08 109.30 100.68 110.52 108.52
Dec-87 119.58 99.52 113.94 101.99 121.45 130.80 117.98 110.49 119.85 111.70
Mar-88 116.96 111.91 120.19 103.99 127.50 141.49 113.51 109.85 116.90 114.36
Jun-88 118.62 106.28 117.93 104.37 133.36 144.83 119.62 114.48 129.59 116.23
Sep-88 125.91 108.18 120.81 109.95 120.65 143.36 150.20 119.06 126.14 119.92
Dec-88 142.23 111.99 125.58 101.64 131.03 136.78 154.75 124.94 133.88 124.05
Mar-89 142.90 122.22 133.80 108.27 141.90 164.46 153.34 133.64 136.54 131.26
Jun-89 162.82 119.75 142.32 109.14 160.50 169.65 153.35 141.21 132.31 134.11
Sep-89 154.26 128.37 140.97 111.57 148.77 187.20 163.37 139.52 143.93 135.74
Dec-89 166.54 155.75 141.83 111.91 177.05 205.64 167.25 176.05 137.67 100.00 137.26
Mar-90 162.25 134.17 152.86 116.92 176.15 176.62 191.29 157.89 139.65 96.17 140.89
Jun-90 164.17 141.36 152.49 120.34 173.20 177.30 171.08 148.79 140.91 105.91 144.31
Sep-90 187.17 137.00 153.83 119.49 169.86 171.86 160.20 145.32 139.78 75.48 143.13
Dec-90 171.04 145.27 148.42 124.40 179.93 170.28 171.00 147.26 155.21 101.21 145.60
Mar-91 161.09 144.54 146.15 125.94 153.63 181.61 177.04 134.45 154.79 106.04 146.20
Jun-91 166.11 143.46 150.93 127.76 166.57 180.40 165.36 125.74 147.84 102.46 149.60
Sep-91 177.11 140.16 153.23 129.99 178.90 182.55 173.32 152.20 159.31 114.77 151.31
Dec-91 171.45 160.66 152.58 126.64 170.13 200.49 155.52 139.48 136.91 104.87 151.83
Mar-92 184.49 152.57 147.41 125.10 182.61 201.68 178.68 132.67 151.09 114.05 151.77
Jun-92 172.50 141.40 157.60 128.79 168.41 221.76 178.01 148.92 145.19 110.52 151.64
Sep-92 185.00 157.58 161.20 128.85 174.57 197.49 182.93 141.74 141.77 111.45 152.29
Dec-92 160.64 138.82 165.45 130.32 162.72 180.36 185.45 141.28 144.49 100.70 154.02
Mar-93 174.03 150.72 159.28 131.44 181.94 187.78 182.19 138.63 162.54 106.53 154.45
Jun-93 176.59 145.59 154.81 126.78 172.24 184.59 182.54 158.08 147.24 108.20 154.91
Sep-93 187.18 148.41 158.75 134.95 177.39 192.31 175.27 138.86 152.05 100.85 153.18
Dec-93 176.60 152.99 162.66 137.38 176.11 196.62 172.99 138.15 148.80 107.09 154.73
Mar-94 176.58 150.33 160.76 129.47 190.22 198.37 188.36 150.09 152.88 104.55 154.71
Jun-94 180.79 158.21 155.25 127.25 184.41 194.08 177.72 136.61 153.99 107.62 156.46
Sep-94 197.65 145.42 169.88 130.10 184.71 198.24 186.80 145.33 169.46 106.99 154.94
Dec-94 196.18 155.12 152.50 129.68 185.38 190.21 178.61 136.91 157.62 104.35 154.30
Mar-95 177.50 162.98 169.10 130.47 180.59 195.18 180.32 152.91 152.10 104.29 152.99
Jun-95 178.14 151.34 153.08 125.85 178.87 211.02 177.34 142.94 137.68 105.74 150.12
Sep-95 179.47 149.43 154.51 117.01 170.06 197.20 180.64 128.61 159.01 98.45 148.70
Dec-95 168.90 148.98 156.78 122.69 169.92 198.72 175.60 137.06 148.39 102.94 148.28
Mar-96 175.42 138.95 147.56 119.80 177.40 199.63 169.11 127.57 146.47 102.45 147.54
Jun-96 183.08 146.89 152.77 120.69 196.54 185.82 181.81 134.51 149.06 101.45 148.11
Sep-96 178.89 163.94 156.54 129.66 188.68 182.82 197.24 131.05 148.90 105.09 147.02
Dec-96 180.29 137.68 158.54 116.04 195.34 195.82 180.81 128.67 153.87 101.47 149.32
Mar-97 178.62 134.37 145.91 113.73 174.63 193.88 186.65 150.46 149.50 101.42 151.68
Jun-97 196.65 137.24 165.79 117.26 188.59 195.92 200.59 165.21 142.02 104.37 152.30
Sep-97 182.26 146.61 153.25 107.90 180.57 219.42 214.35 136.29 144.19 98.66 150.40
Dec-97 202.52 142.09 153.98 119.86 186.53 209.07 211.39 157.74 142.63 106.42 153.26
Mar-98 206.36 150.31 160.49 124.16 179.38 194.06 199.61 139.49 155.70 104.94 155.17
Jun-98 206.84 169.36 163.25 114.91 209.82 206.19 209.19 142.36 148.64 105.58 156.88
Sep-98 218.50 179.70 156.36 110.17 199.66 201.01 229.04 152.71 161.97 110.30 156.65
Dec-98 226.89 154.14 166.83 118.06 212.46 229.61 210.50 139.81 164.88 110.73 159.02
Mar-99 216.88 160.65 173.97 115.16 225.60 205.25 211.18 159.85 165.05 110.93 160.97
Jun-99 225.64 173.23 172.95 121.42 233.68 232.45 253.51 163.63 162.26 113.12 167.03
Sep-99
255.86
186.06
188.46
131.66
241.65
220.91
273.09
171.82
148.79
114.93
168.31
Constant Quality Price Indices