Australia’s Migration Trends: Where Are People Moving To?

Australia’s Migration Trends: Where Are People Moving To?

Data consultancy, Data Army, delved into the Australia Post Movers Statistics dataset to understand where people are migrating to within Australia and predicting where they’re likely to move to next.

We explain the data visualisations created and documented in our previous blog How to Predict Migration Patterns using Auspost Movers Statistics Data and Snowflake’s Cortex ML functions>

By analysing movements and observing trends, we’re able to gather valuable insights to inspire decisions with data-driven intelligence.

How to Interpret the Data Visualisations

The visualisations below show the net migration metric for all Australian states.

Net migration is calculated by forecasting the number of people moving into an area subtracted by the number of people people moving out of an area.

White, grey and lighter blue colours indicate regions with lower net migration, representing regions where a high number of people are leaving the regions and a lower number of people are relocating into these regions.

Mid to darker blue colours represent regions with higher net migration, regions where the number of people moving to those regions outweigh the number of people leaving those regions.

The Results by State

 

New South Wales (NSW)

In NSW, prior to the pandemic, the light blue areas in the inner city areas indicate there was some movement in inner city areas including Chatswood, the Sydney Central Business District (CBD) and areas just west of the city.

However, there was a much higher level of migration into the areas much further west of the city including Penrith and Blaxland, as well as Newcastle.

The trend of moving away from the city has further increased since the COVID pandemic in NSW, where areas very close to the city show the lowest forecasted net migration in the state.

This indicates that people are moving away from the city. Some possible explanations for these movements could be due to rising rents or potentially due to the fact that they no longer need to live within metropolitan areas for work.

In NSW, rural areas south of the city close to Canberra such as Goulburn, and rural areas north of Newcastle such as Taree are the regions with the highest amount of forecasted net migration as shown by the visualisations below.

NSW Pre-COVID
NSW Post-COVID

Figure 1: Pre- and post-COVID migration per SA4 for New South Wales

Victoria

A similar trend can be observed in Victoria. Both pre- and post-pandemic, the areas which had the lowest net migration were the inner city Melbourne suburbs of Brunswick, North Melbourne and Fitzroy.

However, prior to the pandemic, the areas with the highest forecasted net migration was Geelong and the south coast of Melbourne.

Post-COVID, the areas with the highest forecasted migration are even further away, possibly indicating these coastal areas are now also less desirable or unaffordable.

These include rural areas including Warragul and Taree. Greenfield suburbs just north of the city including Sunbury also have high levels of forecasted migration.

VIC Pre-COVID
VIC Post-COVID

Figure 2: Pre- and post-COVID migration per SA4 for Victoria

Queensland

Like Melbourne, the forecasts for net migration in the inner city part of Brisbane is relatively similar both pre- and post-pandemic.

The inner city areas have low levels of forecasted net migration.

Interestingly, the highest amount of forecasted migration in Queensland prior to the pandemic was in the Gold Coast, which is only approximately an hour from Brisbane CBD.

Post-pandemic, areas further west of the Brisbane city including Ipswich, and Harrisville have higher levels of forecasted migration.

This could be indicative of people from Queensland relocating, but could also suggest people from interstate or overseas moving from other locations to places west of the city.

There is also a high level of migration predicted for the Sunshine Coast post-pandemic, further highlighting the trend also observed in Sydney and Melbourne of people moving into more rural areas.

QLD Pre-COVID
QLD Post-COVID

Figure 3: Pre- and post-COVID migration per SA4 for Queensland

South Australia

South Australia, unlike NSW, Victoria and Queensland is one of the few states where the highest forecasted pre-pandemic net migration was in an inner-city area.

However, the trend to relocate to rural areas was very high post-pandemic. Rural areas including Kangaroo Island, Murray Bridge and Clare had much higher forecasted net migration after the pandemic. This supports the trend observed in the other states.

SA Pre-COVID
SA Post-COVID

Figure 4: Pre- and post-COVID migration per SA4 for South Australia

Western Australia

Western Australia is one of the few states where the forecasted net migration into rural areas is not high.

The pre-COVID migration forecasts indicate the highest level of net migration were in the Perth City area and post-COVID the highest amount of net migration was just south of the city.

One possible reason for this could be that while Perth house prices and rents have been rising, they are still much lower than Sydney or Melbourne, and therefore is still affordable for people to be able to live close to the city.

Secondly, as mining is the predominant industry in Western Australia, it is possible that it is not feasible for many of these workers to move and work remotely.

WA Pre-COVID
WA Post-COVID

Figure 5: Pre- and post-COVID migration per SA4 for Western Australia

Tasmania

Tasmania is the only Australian state where the amount of net migration into the inner city forecasts are higher post-covid as compared to pre-COVID.

Prior to COVID , Hobart had the lowest net migration compared to all other regions in Tasmania. However, post-COVID the amount of met migration in the CBD is higher, indicating people are moving into Hobart.

Similarly, the amount of forecasted migration into Launceston, Tasmania’s second biggest city, is higher post-COVID as compared to pre-COVID.

The reason that the same rural migration has not been seen in Tasmania, unlike other states, could be because of Tasmania’s population.

Hobart’s population is only approximately 250,000 which is smaller than rural areas that people were migrating to including the Sunshine Coast.

Thus, the high rental and accommodation costs that are evident in highly populated cities, including Sydney or Melbourne may not be evident in Tasmania.

TAS Pre-COVID
TAS Post-COVID

Figure 6: Pre- and post-COVID migration per SA4 for Tasmania

​Summary of Findings

In the period post-COVID there is high evidence of people migrating to rural areas, especially in states with larger CBDs such as New South Wales, Victoria and Brisbane.

Interestingly, in these states, people seemed to be migrating to outer-city areas even prior to the pandemic.

This may suggest that there were factors encouraging people to move out of the city. This trend seems to have increased further since COVID.

Overall, there is a clear trend in the two most populated states, New South Wales and Victoria for net migration into rural areas.

These were the two states that were most affected by COVID lockdowns in Australia and have the highest house prices in the country which may be one of the key the drivers behind the high level of relocation to rural areas.

Less populated states including South Australia and Queensland have experienced a similar trend with high levels of net migration to rural areas including Kangaroo Island, Clare and the Sunshine Coast.

The only states that haven’t experienced net migration to rural areas are Western Australia and Tasmania.

A Reflection of Australia’s Housing Situation

Australia is in the midst of a housing crisis where steep house prices prevent many first-home buyers from entering the market, especially in inner-city areas.

Driven by the low supply of rentals and high post-pandemic migration, rents continue to skyrocket in many metropolitan cites.

In Australia’s most populous metropolitan areas Sydney and Melbourne, rents rose by 10.2% and 11.1%1 from December 2022 to December 2023 respectively.

Since 2020, the COVID pandemic has transformed the workplace environment, by forcing some office workers to do their job remotely due to lockdowns and government restrictions.

To this day, some office workers continue to work remotely full or part time, meaning that when picking a place to live, they may not need to prioritise being within a reasonable commuting distance from their usual physical office.

The combination of unaffordable rents and mortgages in inner city areas and increase in work from home trends may have contributed to many Australians migrating to outer-city and rural locations.

Strategic Insights

The findings hold significant strategic value for both the private and public sectors.

Incorporating these insights alongside additional data points, such as overseas migration into Australia, enriches the analysis, providing a more comprehensive understanding of migration patterns.

This broader perspective can enhance strategic planning and decision-making processes across various industries and governmental levels.

Examples include real estate development, investment, business expansion, transportation and infrastructure decisions, as well as urban, land use, policy or even healthcare and public services planning.

These findings can offer a foundation for both private and public sectors to adapt to changing demographic patterns in a way that maximises economic opportunities while ensuring community well-being and sustainability.

About The Analysis

Data Army used the Australian Post Movers Statistics dataset to base the forecasts in migration patterns during and after the COVID pandemic in each Australian state.

The primary dataset used in this study is the Australia Post Movers Statistics. It contains de-identified and aggregated data on moves across Australia based on mail redirection requests from the previous 5 years.

For this exercise, data from February 2019 to January 2024 was used.

Each entry in the data includes

  • the postcode the household relocated from,
  • the postcode the household relocated to,
  • the month of relocation, and
  • the number of the people that relocated.

This analysis shows forecasted migrated trends for the next year when pre-pandemic data is used (Feb 2019 – Jan 2020) compared to forecasts based on mail redirection requests in the post-pandemic era (2022-2024).

The analysis was conducted on a Statistical Area Level 4 level which are Australian Bureau of Statistics (ABS) defined regions that clearly distinguish inner-city areas, outer-city areas and rural areas.

For a step-by-step guide, see our blog on How to Predict Migration Patterns using Auspost Movers Statistics Data and Snowflake’s Cortex ML functions>

Australia Post Movers Statistics Data

This dataset contains five years of de-identified, aggregated information on past moves captured by Australia Post’s Mail Redirection service.

Access Australia Post mail redirect statistics now to help you develop competitive data-driven strategies.

The intellectual property rights for all content in this blog are exclusively held by Data Army and The Proptech Cloud. All rights are reserved, and no content may be republished or reproduced without express written permission from us. All content provided is for informational purposes only. While we strive to ensure that the information provided here is both factual and accurate, we make no representations or warranties of any kind about the completeness, accuracy, reliability, suitability, or availability with respect to the blog or the information, products, services, or related graphics contained on the blog for any purpose.

Subscribe to our newsletter

Subscribe to receive the latest blogs and data listings direct to your inbox.

Data consultancy, Data Army dives into the Australia Post Movers Statistics dataset to understand where people are migrating to within Australia and predicting where they’re likely to move to next.

We explain the data visualisations created and documented in our previous blog How to Predict Migration Patterns using Auspost Movers Statistics Data and Snowflake’s Cortex ML functions>

By analysing movements and observing trends, we’re able to gather valuable insights to inspire decisions with data-driven intelligence.

How to Interpret the Data Visualisations

The visualisations below show the net migration metric for all Australian states.

Net migration is calculated by forecasting the number of people moving into an area subtracted by the number of people people moving out of an area.

White, grey and lighter blue colours indicate regions with lower net migration, representing regions where a high number of people are leaving the regions and a lower number of people are relocating into these regions.

Mid to darker blue colours represent regions with higher net migration, regions where the number of people moving to those regions outweigh the number of people leaving those regions.

The Results by State

 

New South Wales (NSW)

In NSW, prior to the pandemic, the light blue areas in the inner city areas indicate there was some movement in inner city areas including Chatswood, the Sydney Central Business District (CBD) and areas just west of the city.

However, there was a much higher level of migration into the areas much further west of the city including Penrith and Blaxland, as well as Newcastle.

The trend of moving away from the city has further increased since the COVID pandemic in NSW, where areas very close to the city show the lowest forecasted net migration in the state.

This indicates that people are moving away from the city. Some possible explanations for these movements could be due to rising rents or potentially due to the fact that they no longer need to live within metropolitan areas for work.

In NSW, rural areas south of the city close to Canberra such as Goulburn, and rural areas north of Newcastle such as Taree are the regions with the highest amount of forecasted net migration as shown by the visualisations below.

NSW Pre-COVID
NSW Post-COVID

Figure 1: Pre- and post-COVID migration per SA4 for New South Wales

Victoria

A similar trend can be observed in Victoria. Both pre- and post-pandemic, the areas which had the lowest net migration were the inner city Melbourne suburbs of Brunswick, North Melbourne and Fitzroy.

However, prior to the pandemic, the areas with the highest forecasted net migration was Geelong and the south coast of Melbourne.

Post-COVID, the areas with the highest forecasted migration are even further away, possibly indicating these coastal areas are now also less desirable or unaffordable.

These include rural areas including Warragul and Taree. Greenfield suburbs just north of the city including Sunbury also have high levels of forecasted migration.

VIC Pre-COVID
VIC Post-COVID

Figure 2: Pre- and post-COVID migration per SA4 for Victoria

Queensland

Like Melbourne, the forecasts for net migration in the inner city part of Brisbane is relatively similar both pre- and post-pandemic.

The inner city areas have low levels of forecasted net migration.

Interestingly, the highest amount of forecasted migration in Queensland prior to the pandemic was in the Gold Coast, which is only approximately an hour from Brisbane CBD.

Post-pandemic, areas further west of the Brisbane city including Ipswich, and Harrisville have higher levels of forecasted migration.

This could be indicative of people from Queensland relocating, but could also suggest people from interstate or overseas moving from other locations to places west of the city.

There is also a high level of migration predicted for the Sunshine Coast post-pandemic, further highlighting the trend also observed in Sydney and Melbourne of people moving into more rural areas.

QLD Pre-COVID
QLD Post-COVID

Figure 3: Pre- and post-COVID migration per SA4 for Queensland

South Australia

South Australia, unlike NSW, Victoria and Queensland is one of the few states where the highest forecasted pre-pandemic net migration was in an inner-city area.

However, the trend to relocate to rural areas was very high post-pandemic. Rural areas including Kangaroo Island, Murray Bridge and Clare had much higher forecasted net migration after the pandemic. This supports the trend observed in the other states.

SA Pre-COVID
SA Post-COVID

Figure 4: Pre- and post-COVID migration per SA4 for South Australia

Western Australia

Western Australia is one of the few states where the forecasted net migration into rural areas is not high.

The pre-COVID migration forecasts indicate the highest level of net migration were in the Perth City area and post-COVID the highest amount of net migration was just south of the city.

One possible reason for this could be that while Perth house prices and rents have been rising, they are still much lower than Sydney or Melbourne, and therefore is still affordable for people to be able to live close to the city.

Secondly, as mining is the predominant industry in Western Australia, it is possible that it is not feasible for many of these workers to move and work remotely.

WA Pre-COVID
WA Post-COVID

Figure 5: Pre- and post-COVID migration per SA4 for Western Australia

Tasmania

Tasmania is the only Australian state where the amount of net migration into the inner city forecasts are higher post-covid as compared to pre-COVID.

Prior to COVID , Hobart had the lowest net migration compared to all other regions in Tasmania. However, post-COVID the amount of met migration in the CBD is higher, indicating people are moving into Hobart.

Similarly, the amount of forecasted migration into Launceston, Tasmania’s second biggest city, is higher post-COVID as compared to pre-COVID.

The reason that the same rural migration has not been seen in Tasmania, unlike other states, could be because of Tasmania’s population.

Hobart’s population is only approximately 250,000 which is smaller than rural areas that people were migrating to including the Sunshine Coast.

Thus, the high rental and accommodation costs that are evident in highly populated cities, including Sydney or Melbourne may not be evident in Tasmania.

TAS Pre-COVID
TAS Post-COVID

Figure 6: Pre- and post-COVID migration per SA4 for Tasmania

​Summary of Findings

In the period post-COVID there is high evidence of people migrating to rural areas, especially in states with larger CBDs such as New South Wales, Victoria and Brisbane.

Interestingly, in these states, people seemed to be migrating to outer-city areas even prior to the pandemic.

This may suggest that there were factors encouraging people to move out of the city. This trend seems to have increased further since COVID.

Overall, there is a clear trend in the two most populated states, New South Wales and Victoria for net migration into rural areas.

These were the two states that were most affected by COVID lockdowns in Australia and have the highest house prices in the country which may be one of the key the drivers behind the high level of relocation to rural areas.

Less populated states including South Australia and Queensland have experienced a similar trend with high levels of net migration to rural areas including Kangaroo Island, Clare and the Sunshine Coast.

The only states that haven’t experienced net migration to rural areas are Western Australia and Tasmania.

A Reflection of Australia’s Housing Situation

Australia is in the midst of a housing crisis where steep house prices prevent many first-home buyers from entering the market, especially in inner-city areas.

Driven by the low supply of rentals and high post-pandemic migration, rents continue to skyrocket in many metropolitan cites.

In Australia’s most populous metropolitan areas Sydney and Melbourne, rents rose by 10.2% and 11.1%1 from December 2022 to December 2023 respectively.

Since 2020, the COVID pandemic has transformed the workplace environment, by forcing some office workers to do their job remotely due to lockdowns and government restrictions.

To this day, some office workers continue to work remotely full or part time, meaning that when picking a place to live, they may not need to prioritise being within a reasonable commuting distance from their usual physical office.

The combination of unaffordable rents and mortgages in inner city areas and increase in work from home trends may have contributed to many Australians migrating to outer-city and rural locations.

Strategic Insights

The findings hold significant strategic value for both the private and public sectors.

Incorporating these insights alongside additional data points, such as overseas migration into Australia, enriches the analysis, providing a more comprehensive understanding of migration patterns.

This broader perspective can enhance strategic planning and decision-making processes across various industries and governmental levels.

Examples include real estate development, investment, business expansion, transportation and infrastructure decisions, as well as urban, land use, policy or even healthcare and public services planning.

These findings can offer a foundation for both private and public sectors to adapt to changing demographic patterns in a way that maximises economic opportunities while ensuring community well-being and sustainability.

About The Analysis

Data Army used the Australian Post Movers Statistics dataset to base the forecasts in migration patterns during and after the COVID pandemic in each Australian state.

The primary dataset used in this study is the Australia Post Movers Statistics. It contains de-identified and aggregated data on moves across Australia based on mail redirection requests from the previous 5 years.

For this exercise, data from February 2019 to January 2024 was used.

Each entry in the data includes

  • the postcode the household relocated from,
  • the postcode the household relocated to,
  • the month of relocation, and
  • the number of the people that relocated.

This analysis shows forecasted migrated trends for the next year when pre-pandemic data is used (Feb 2019 – Jan 2020) compared to forecasts based on mail redirection requests in the post-pandemic era (2022-2024).

The analysis was conducted on a Statistical Area Level 4 level which are Australian Bureau of Statistics (ABS) defined regions that clearly distinguish inner-city areas, outer-city areas and rural areas.

For a step-by-step guide, see our blog on How to Predict Migration Patterns using Auspost Movers Statistics Data and Snowflake’s Cortex ML functions>

Australia Post Movers Statistics Data

This dataset contains five years of de-identified, aggregated information on past moves captured by Australia Post’s Mail Redirection service.

Access Australia Post mail redirect statistics now to help you develop competitive data-driven strategies.

The intellectual property rights for all content in this blog are exclusively held by Data Army and The Proptech Cloud. All rights are reserved, and no content may be republished or reproduced without express written permission from us. All content provided is for informational purposes only. While we strive to ensure that the information provided here is both factual and accurate, we make no representations or warranties of any kind about the completeness, accuracy, reliability, suitability, or availability with respect to the blog or the information, products, services, or related graphics contained on the blog for any purpose.

Subscribe to our newsletter

Subscribe to receive the latest blogs and data listings direct to your inbox.

Data consultancy, Data Army dives into the Australia Post Movers Statistics dataset to understand where people are migrating to within Australia and predicting where they’re likely to move to next.

We explain the data visualisations created and documented in our previous blog How to Predict Migration Patterns using Auspost Movers Statistics Data and Snowflake’s Cortex ML functions>

By analysing movements and observing trends, we’re able to gather valuable insights to inspire decisions with data-driven intelligence.

How to Interpret the Data Visualisations

The visualisations below show the net migration metric for all Australian states.

Net migration is calculated by forecasting the number of people moving into an area subtracted by the number of people people moving out of an area.

White, grey and lighter blue colours indicate regions with lower net migration, representing regions where a high number of people are leaving the regions and a lower number of people are relocating into these regions.

Mid to darker blue colours represent regions with higher net migration, regions where the number of people moving to those regions outweigh the number of people leaving those regions.

The Results by State

 

New South Wales (NSW)

In NSW, prior to the pandemic, the light blue areas in the inner city areas indicate there was some movement in inner city areas including Chatswood, the Sydney Central Business District (CBD) and areas just west of the city.

However, there was a much higher level of migration into the areas much further west of the city including Penrith and Blaxland, as well as Newcastle.

The trend of moving away from the city has further increased since the COVID pandemic in NSW, where areas very close to the city show the lowest forecasted net migration in the state.

This indicates that people are moving away from the city. Some possible explanations for these movements could be due to rising rents or potentially due to the fact that they no longer need to live within metropolitan areas for work.

In NSW, rural areas south of the city close to Canberra such as Goulburn, and rural areas north of Newcastle such as Taree are the regions with the highest amount of forecasted net migration as shown by the visualisations below.

NSW Pre-COVID
NSW Post-COVID

Figure 1: Pre- and post-COVID migration per SA4 for New South Wales

Victoria

A similar trend can be observed in Victoria. Both pre- and post-pandemic, the areas which had the lowest net migration were the inner city Melbourne suburbs of Brunswick, North Melbourne and Fitzroy.

However, prior to the pandemic, the areas with the highest forecasted net migration was Geelong and the south coast of Melbourne.

Post-COVID, the areas with the highest forecasted migration are even further away, possibly indicating these coastal areas are now also less desirable or unaffordable.

These include rural areas including Warragul and Taree. Greenfield suburbs just north of the city including Sunbury also have high levels of forecasted migration.

VIC Pre-COVID
VIC Post-COVID

Figure 2: Pre- and post-COVID migration per SA4 for Victoria

Queensland

Like Melbourne, the forecasts for net migration in the inner city part of Brisbane is relatively similar both pre- and post-pandemic.

The inner city areas have low levels of forecasted net migration.

Interestingly, the highest amount of forecasted migration in Queensland prior to the pandemic was in the Gold Coast, which is only approximately an hour from Brisbane CBD.

Post-pandemic, areas further west of the Brisbane city including Ipswich, and Harrisville have higher levels of forecasted migration.

This could be indicative of people from Queensland relocating, but could also suggest people from interstate or overseas moving from other locations to places west of the city.

There is also a high level of migration predicted for the Sunshine Coast post-pandemic, further highlighting the trend also observed in Sydney and Melbourne of people moving into more rural areas.

QLD Pre-COVID
QLD Post-COVID

Figure 3: Pre- and post-COVID migration per SA4 for Queensland

South Australia

South Australia, unlike NSW, Victoria and Queensland is one of the few states where the highest forecasted pre-pandemic net migration was in an inner-city area.

However, the trend to relocate to rural areas was very high post-pandemic. Rural areas including Kangaroo Island, Murray Bridge and Clare had much higher forecasted net migration after the pandemic. This supports the trend observed in the other states.

SA Pre-COVID
SA Post-COVID

Figure 4: Pre- and post-COVID migration per SA4 for South Australia

Western Australia

Western Australia is one of the few states where the forecasted net migration into rural areas is not high.

The pre-COVID migration forecasts indicate the highest level of net migration were in the Perth City area and post-COVID the highest amount of net migration was just south of the city.

One possible reason for this could be that while Perth house prices and rents have been rising, they are still much lower than Sydney or Melbourne, and therefore is still affordable for people to be able to live close to the city.

Secondly, as mining is the predominant industry in Western Australia, it is possible that it is not feasible for many of these workers to move and work remotely.

WA Pre-COVID
WA Post-COVID

Figure 5: Pre- and post-COVID migration per SA4 for Western Australia

Tasmania

Tasmania is the only Australian state where the amount of net migration into the inner city forecasts are higher post-covid as compared to pre-COVID.

Prior to COVID , Hobart had the lowest net migration compared to all other regions in Tasmania. However, post-COVID the amount of met migration in the CBD is higher, indicating people are moving into Hobart.

Similarly, the amount of forecasted migration into Launceston, Tasmania’s second biggest city, is higher post-COVID as compared to pre-COVID.

The reason that the same rural migration has not been seen in Tasmania, unlike other states, could be because of Tasmania’s population.

Hobart’s population is only approximately 250,000 which is smaller than rural areas that people were migrating to including the Sunshine Coast.

Thus, the high rental and accommodation costs that are evident in highly populated cities, including Sydney or Melbourne may not be evident in Tasmania.

TAS Pre-COVID
TAS Post-COVID

Figure 6: Pre- and post-COVID migration per SA4 for Tasmania

​Summary of Findings

In the period post-COVID there is high evidence of people migrating to rural areas, especially in states with larger CBDs such as New South Wales, Victoria and Brisbane.

Interestingly, in these states, people seemed to be migrating to outer-city areas even prior to the pandemic.

This may suggest that there were factors encouraging people to move out of the city. This trend seems to have increased further since COVID.

Overall, there is a clear trend in the two most populated states, New South Wales and Victoria for net migration into rural areas.

These were the two states that were most affected by COVID lockdowns in Australia and have the highest house prices in the country which may be one of the key the drivers behind the high level of relocation to rural areas.

Less populated states including South Australia and Queensland have experienced a similar trend with high levels of net migration to rural areas including Kangaroo Island, Clare and the Sunshine Coast.

The only states that haven’t experienced net migration to rural areas are Western Australia and Tasmania.

A Reflection of Australia’s Housing Situation

Australia is in the midst of a housing crisis where steep house prices prevent many first-home buyers from entering the market, especially in inner-city areas.

Driven by the low supply of rentals and high post-pandemic migration, rents continue to skyrocket in many metropolitan cites.

In Australia’s most populous metropolitan areas Sydney and Melbourne, rents rose by 10.2% and 11.1%1 from December 2022 to December 2023 respectively.

Since 2020, the COVID pandemic has transformed the workplace environment, by forcing some office workers to do their job remotely due to lockdowns and government restrictions.

To this day, some office workers continue to work remotely full or part time, meaning that when picking a place to live, they may not need to prioritise being within a reasonable commuting distance from their usual physical office.

The combination of unaffordable rents and mortgages in inner city areas and increase in work from home trends may have contributed to many Australians migrating to outer-city and rural locations.

Strategic Insights

The findings hold significant strategic value for both the private and public sectors.

Incorporating these insights alongside additional data points, such as overseas migration into Australia, enriches the analysis, providing a more comprehensive understanding of migration patterns.

This broader perspective can enhance strategic planning and decision-making processes across various industries and governmental levels.

Examples include real estate development, investment, business expansion, transportation and infrastructure decisions, as well as urban, land use, policy or even healthcare and public services planning.

These findings can offer a foundation for both private and public sectors to adapt to changing demographic patterns in a way that maximises economic opportunities while ensuring community well-being and sustainability.

About The Analysis

Data Army used the Australian Post Movers Statistics dataset to base the forecasts in migration patterns during and after the COVID pandemic in each Australian state.

The primary dataset used in this study is the Australia Post Movers Statistics. It contains de-identified and aggregated data on moves across Australia based on mail redirection requests from the previous 5 years.

For this exercise, data from February 2019 to January 2024 was used.

Each entry in the data includes

  • the postcode the household relocated from,
  • the postcode the household relocated to,
  • the month of relocation, and
  • the number of the people that relocated.

This analysis shows forecasted migrated trends for the next year when pre-pandemic data is used (Feb 2019 – Jan 2020) compared to forecasts based on mail redirection requests in the post-pandemic era (2022-2024).

The analysis was conducted on a Statistical Area Level 4 level which are Australian Bureau of Statistics (ABS) defined regions that clearly distinguish inner-city areas, outer-city areas and rural areas.

For a step-by-step guide, see our blog on How to Predict Migration Patterns using Auspost Movers Statistics Data and Snowflake’s Cortex ML functions>

Australia Post Movers Statistics Data

This dataset contains five years of de-identified, aggregated information on past moves captured by Australia Post’s Mail Redirection service.

Access Australia Post mail redirect statistics now to help you develop competitive data-driven strategies.

The intellectual property rights for all content in this blog are exclusively held by Data Army and The Proptech Cloud. All rights are reserved, and no content may be republished or reproduced without express written permission from us. All content provided is for informational purposes only. While we strive to ensure that the information provided here is both factual and accurate, we make no representations or warranties of any kind about the completeness, accuracy, reliability, suitability, or availability with respect to the blog or the information, products, services, or related graphics contained on the blog for any purpose.

Subscribe to our newsletter

Subscribe to receive the latest blogs and data listings direct to your inbox.

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How to Predict Migration Patterns using Auspost Movers Statistics Data and Snowflake’s Cortex ML functions

How to Predict Migration Patterns using Auspost Movers Statistics Data and Snowflake’s Cortex ML functions

Data consultancy, Data Army taps into the hidden insights of the Australia Post Movers Statistics dataset to forecast the future hotspots in Australia—predicting where people are likely to move next.

By analysing trends in mail redirection requests through the Time Series Forecasting function, we provide valuable insights with precision.

This advanced analysis is a feature of Snowflake Cortex, Snowflake’s comprehensive Artificial Intelligence and Machine Learning service, designed to empower your decisions with data-driven intelligence.

Overview of Data Used

  • The primary dataset is Australia Post’s Movers Statistics currently available from The Proptech Cloud with a limited free trial. Updated monthly, it contains de-identified and aggregated data on moves across Australia based on mail redirection requests for the previous 5 years. For this exercise, we used the data from February 2019 to January 2024. Each entry in the data includes the postcode the household relocated from, the postcode the household relocated to, the month of relocation and the number of the people that relocated.
  • The secondary dataset used is the Geography Boundaries & Insights – Australia, specifically Australian Bureau of Statistics (ABS) 2021 Postcode Boundary Data (ABS_POA_2021_AUST_GDA2020) to conduct geospatial visualisations. This dataset is free from The Proptech Cloud.

Australia Post Movers Statistics Data

This dataset contains five years of de-identified, aggregated information on past moves captured by Australia Post’s Mail Redirection service.

Access Australia Post mail redirect statistics now to help you develop competitive data-driven strategies.

Introduction to Snowflake Functionality & Technology Stack

  • The Snowflake Forecasting Model is part of the Snowflake Cortex ML- Powered Functions. This model uses a Machine Learning algorithm to predict future trends from historical data.
  • SnowPark which is the set of libraries in Snowflake which will allow us to deploy and process the data pipeline using Python
  • Streamlit which is used to visualise the forecasts created using interactive Python apps. This functionality is fully integrated within the Snowflake Platform

Introduction to Snowflake’s Forecasting Model

Snowflake Cortex is a service by Snowflake which offers Machine Learning (ML) and Artificial Intelligence (AI) solutions.

This blog will focus on using the Time-Series Forecasting Model which is part of this service. The ML forecasting model used in this algorithm is Gradient Boosting (GB).

The intuition behind the GB algorithm is that the combination of multiple models that learn and improve on each other will perform better than one model. To implement this approach, the GB algorithm will firstly implement the best possible model on the dataset.

The second model will assess where the first model performs poorly and try to improve on these areas. This process continues and the models continue to learn and iterate on one another until the model iteration process no longer improves the model outcomes and therefore the optimal model combination is found and used for predictions.

The GB algorithm is very popular due to its ability to learn from itself and it performs very strongly in many different ML problems. One of the typical challenges in using the GB model is that it is usually very computationally expensive and time consuming to iterate and find the optimal model. The advantage of using the Snowflake Cortex Machine Learning Powered Forecasting Model is that it is extremely quick to compute on even very large datasets as it leverages Snowflake’s existing highly scalable infrastructure.

Technical How-To Guide

The forecasting model example shown will use Snowpark to create the data pipeline and use Streamlit for the data visualisations.

Step 1: Accessing the data

  1. Go to the link on the listing to access the Australia Post – Movers Statistics Data.
  2. Click the “Access Data Listing on Snowflake Marketplace” button to access the data listing on the Snowflake Marketplace
  3. Click the ‘Get’ button and the top right of the page to access the Australian Post Movers Statistics dataset. This will then redirect to a link to either create a new Snowflake account or sign in if one already exists.
  4. Once your Snowflake account is set up and running , the Australia post Mover Statistics dataset listing is located within Data Products and then Marketplace as shown by the link below:
Snowflake Marketplace screen

5. Click on the Open button to explore the sample data within the Snowflake Environment. This will redirect to a Snowflake worksheet which will show some sample queries.

6. The full product can also be requested from the Marketplace page with the button ‘Request Full Product’ if access to the entire dataset is needed.

Step 2: Setting Up the Example Data

The forecasting model created is a multi-time-series model. The following types of variables were needed to create this:

  • The series variable to create multiple time series forecasting
  • A timestamp column
  • A target column which includes a quantity of interest at each timestamp

The cleaning and transformation of the dataset to prep the data for forecasted was completed by running SQL queries using Snowpark. A sample of this data is shown below:

Snowflake Marketplace - tables
This data is then saved as a view named migration_people_moving_to to use in the forecasting model.

Step 3: Creating the Forecasting Model

Each row in the migration_people_moving_to view corresponds to the three types of columns needed to create a multi-series forecasting model; the postcode (series column), month (timestamp column) and the number of people who moved into the postcode that month (the target column)

The code to create a forecasting model is as follows:

CREATE OR REPLACE SNOWFLAKE.ML.FORECAST forecasting_model_people_to (

INPUT_DATA => SYSTEM$REFERENCE(‘VIEW’, ‘migration_people_moving_to’),

SERIES_COLNAME => ‘to_postcode’,

TIMESTAMP_COLNAME => ‘timestamp_month’,

TARGET_COLNAME => ‘number_of_people_to_postcode’

)

This will create the forecasting model forecasting_model_people_to

Step 4: Calling and Interpreting the Forecasting Model

The model can then be used to forecast for any number of periods in the future. The model will perform better for forecasting periods that are closer training dataset, and are less reliable when it is used to forecast for periods further into the future.

The code used to forecast the number of people moving into a postcode every month for 12 months and save it to a table is shown below.

BEGIN

CALL forecasting_model_people_to!FORECAST(FORECASTING_PERIODS => 12);

LET x := SQLID;

CREATE OR REPLACE TABLE forecast_12periods_move_to AS

SELECT * FROM TABLE(RESULT_SCAN(:x));

END;

An example of the forecasting model output results are shown below.

Snowflake Marketplace - forecasting model output results

The way to interpret the above output would be to say that the number of people forecasted to move into Postcode 5096 (which covers Para Hills, Para Hills West, Gulfview Heights, Adelaide) in April 2024 is approximately 26. The lower bound and upper bound of 12.6 and 38.7 represent the prediction interval. For the April 2024 forecast into Para Hills, the model is 95% confident that the number of people who will move into postcode 5096 in April 2024 is between 12.6 and 38.7. A smaller prediction interval indicates that there is less error in the model and the estimated forecast is more likely to be accurate and vice versa.

The default prediction interval when calling a model in Snowflake is 95%. However, this can be configured when calling the model by adding a prediction interval. The code below shows how to call a model with an 80% prediction interval:

CALL forecasting_model_people_to!FORECAST(FORECASTING_PERIODS => 12, CONFIG_OBJECT => {'prediction_interval': 0.8})

Step 5: Visualising the forecasts using Snowpark and Streamlit

The 12 months results of the forecasting model were then aggregated to produce the total of number people forecasted to move into each postcode across Australia.

The data was then also joined with the Australian Postcode boundaries from the Geography Boundaries & Insights – Australia to allow for geospatial visualisations.

The visualisations were hosted using Streamlit within the Snowflake User Interface.

Streamlit is an open source python library which allows for the creation and sharing of data web applications. Using Streamlit within the Snowflake console allows for the flexibility to securely clean, transform and visualise the data in one place, without the need for any external environments.

Data Visualisation – Greater Melbourne Region

The visualisation shows the postcodes that people are moving to in the Greater Melbourne region.

The green and yellow regions show the places where high numbers of people are forecasted to move into in the next year, while the purple and blue regions show the regions that are forecasted to have a lower amount of relocation in the next year.

Interestingly, the visualisation shows that places in the outer East including Cranbourne, Clyde North and the outer west including Point Cook and Werribee South. The inner city postcodes which include suburbs such as Fitzroy, Brunswick and North Melbourne are forecasted to have much less migration in the next year.

Streamlit - Data Visualisation of migrations
Streamlit - Data Visualisation of migrations SYD

Data Visualisation – Greater Sydney Region

A similar visualisation was done in the Greater Sydney area, where a similar trend was observed.

High levels of migration are forecasted for outer-city areas including Kellyville and North Kellyville and outer-city south west south west areas including Camden and Oran Park.

Like Melbourne, there seems to be less migration forecasted for inner city suburbs including Chippendale, Ultimo and Redfern.

Steps to Create the Visualisations

The following steps were performed to create the geospatial visualisations.

Firstly, the base steps to create a Streamlit App were completed. This includes creating an app and selecting a warehouse to run the queries. This will then create a Snowpark worksheet which allows the creation of a Streamlit app using Python. The Streamlit environment also needs to be set up to allow for the ingestion of packages which requires the CREATE STREAMLIT permission.

The third-party packages were then ingested using the Packages tab at the top of the worksheet. Only packages which are supported by Snowflake are able to be ingested to ensure that the Snowflake platform remains secure. Both Matplotlib and Pydeck were ingested to create these visualisations.

The required packages were then imported to create the Streamlit visualisation

# Import python packages

import streamlit as st

from snowflake.snowpark.context import get_active_session

import json

import pydeck as pdk

import matplotlib.pyplot as plt

import matplotlib as mpl

The Snowpark package was used to connect the worksheet to the table containing the 12 month forecasting data in Snowflake. The postcode geospatial boundaries were also obtained, joined to the forecasting data and converted into a geojson format. This was achieved using the code below:

session = get_active_session()


session.sql ("""SELECT

POA_CODE_2021 as POSTCODE_NAME,

NUMBER_OF_PEOPLE,

ST_ASGEOJSON(geometry) AS geojson

FROM

forecast_12periods_move_to --forecasting model table created in Step 3


INNER JOIN ABS_POA_2021_AUST_GDA2020

ON POA_CODE_2021 = TO_POSTCODE

""").collect()

Each row in the query represents the postcode, the number of people forecasted to move into the postcode in the next year and a geojson representing to geometry of the postcode boundary. Further transformations were done on the result so that each row in the query result was transformed into a dictionary. A key aspect of this transformation was assigning RGB colour code to each postcode depending on the number of people forecasted to migrate to that postcode. A sample of the geojson format is shown below:

map_geojson = {

"type": "FeatureCollection",

"features":[

{"type": "Feature",

"properties":

  {"postcode": 2000,

'colour':(68, 1, 84)

  },

"geometry":{'geometry in geosjon format'}

},

{"type": "Feature",

"properties":

   {"postcode": 2000,

'colour':(38, 130, 142)

 },

"geometry":{'geometry in geosjon format'}

},

]

}

The base chloropeth map was then set up by assigning the centre and zoom point for the map to render.

pydeck_chloropeth = pdk.Deck(

map_style=None,
initial_view_state=pdk.ViewState(

latitude={insert centre latitude},
longitude={insert centre longitude},
zoom={insert zoom level},

),

The geojson created in the step above was then added to the base map to create the chloropeth layer using the code below.

layers=[

pdk.Layer(

"GeoJsonLayer",

map_geojson, #name of the goejson created earlier

opacity=0.8,

stroked=False,

filled=True,

extruded=True,

wireframe=True,

get_fill_color='properties.colour',

get_line_color=[255, 255, 255],

)

],

)

The legend on the map was created using the colorbar function in the matplotlib library.

fig, ax = plt.subplots(figsize=(6, 1), layout='constrained')

cmap = mpl.cm.viridis
norm = mpl.colors.Normalize(vmin=0, vmax=1)

fig.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=cmap),

cax=ax, orientation='horizontal', label='Map legend', ticks=[])

Finally, the following lines of code were used to render both the chloropeth map and the legend on the Streamlit app.

st.pydeck_chart(pydeck_chloropeth)

st.pyplot(fig)

Summary

In this blog, the Australia Post Movers Statistics Marketplace listing was used along with Snowflake's Cortex ML Forecasting function to forecast the postcodes within Australia that have high levels of population movement.

The Streamlit data visualisations revealed that the postcodes that the highest amount of people were forecasted to move into were predominantly located in the outer-city area. Read our following blog to understand where people are moving to.

The rundown above highlights how the Snowflake Data Platform makes it straightforward for businesses to access quality data and market-leading compute, AI and visualisations all on one neat platform.

 

Australia Post Movers Statistics Data

This dataset contains five years of de-identified, aggregated information on past moves captured by Australia Post's Mail Redirection service.

Access Australia Post mail redirect statistics now to help you develop competitive data-driven strategies.

The intellectual property rights for all content in this blog are exclusively held by Data Army and The Proptech Cloud. All rights are reserved, and no content may be republished or reproduced without express written permission from us. All content provided is for informational purposes only. While we strive to ensure that the information provided here is both factual and accurate, we make no representations or warranties of any kind about the completeness, accuracy, reliability, suitability, or availability with respect to the blog or the information, products, services, or related graphics contained on the blog for any purpose.

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