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

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

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

In this discussion, we’ll break down 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.

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.

Australia’s Housing Situation

Australia is currently 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 no longer 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 have contributed to many Australians migrating to outer-city and rural locations.

The Analysis

In this blog, Data Army uses 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 will be 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.

Data Visualisations

The metric that is forecasted in the visualisations below is ‘net migration’ which is calculated by forecasting the number of people moving into an area subtracted by the number of people people moving out of an area.

The visualisations below show net migration for all Australian states.

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.

Interpreting the Results

In the period post-COVID there is high evidence of people migrating to rural areas, especially in states with larger Central Business Districts (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.

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 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 a 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

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 have not experienced net migration to rural areas are Western Australia and Tasmania.

Strategic Insights

The findings which reveal a trend of internal net migration holds 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.

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.

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.

Subscribe to our newsletter

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

Read more blogs from The Proptech Cloud

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

This detailed visual analysis for Australia’s major capital cities breaks down how net migration trends are evolving across different regions.

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

How to predict the Australia postcodes people are most likely to relocate to using the Australian Post Movers Statistics dataset and Snowflake Time Series Forecasting function.

Could a Revamp of Australian Property Planning Rules Solve Some of Australia’s Housing Issues?

Rising property prices and high costs of living means the Australian dream of home ownership is slipping further away for many. Could the answer lie in a revamp of property planning rules?

Top Real Estate Technology Global Events

Staying abreast of the latest trends, technologies, and innovations is crucial for professionals seeking to leverage the full potential of real estate technology and proptech.

What Are Mesh Blocks & How Are They Used in Real Estate

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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 the Australian 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 Account. 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.

Subscribe to our newsletter

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

Read more blogs from The Proptech Cloud

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

This detailed visual analysis for Australia’s major capital cities breaks down how net migration trends are evolving across different regions.

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

How to predict the Australia postcodes people are most likely to relocate to using the Australian Post Movers Statistics dataset and Snowflake Time Series Forecasting function.

Could a Revamp of Australian Property Planning Rules Solve Some of Australia’s Housing Issues?

Rising property prices and high costs of living means the Australian dream of home ownership is slipping further away for many. Could the answer lie in a revamp of property planning rules?

Top Real Estate Technology Global Events

Staying abreast of the latest trends, technologies, and innovations is crucial for professionals seeking to leverage the full potential of real estate technology and proptech.

What Are Mesh Blocks & How Are They Used in Real Estate

What are Mesh Blocks? As defined by Australian Bureau of Statistics (ABS), mesh blocks are the smallest geographical area of the Australian Statistical Geography Standard (ASGS) and ABS’s classification of Australia into a hierarchy of statistical areas. Mesh Blocks...

Could a Revamp of Australian Property Planning Rules Solve Some of Australia’s Housing Issues?

Could a Revamp of Australian Property Planning Rules Solve Some of Australia’s Housing Issues?

Rising property prices and high costs of living means the Australian dream of home ownership is slipping further and further away for many. Could an overhaul of Australian property planning rules offer a solution?

In recent discussions during a heated ABC Q+A debate on Homeownership, Homelessness & Housing supply, the Australian dream of homeownership has taken centre stage again, unveiling a crisis that grips not just potential homeowners but extends its grasp towards the homeless and vulnerable communities across the nation.

Even those “fortunate” enough to have purchased property are feeling significant interest rate stress as cost-of-living soars in recent times. Renters are experiencing rent hikes and dealing with historically low vacancy rates.

Australia’s housing issues in the spotlight

“Fundamentally, the problem is that we’re not building enough homes,” Mr Leigh, the Assistant Minister for Competition, Charities and Treasury, told Q+A.

It’s clear that Australia requires millions more homes to meet current demand but also accommodate future population growth.

However, as it stands, governments are finding it challenging to meet their own targets.

The debate, and followed up by The Sydney Morning Herald article Do planning rules really affect house prices? The answer is clear, has cast a spotlight on a host of interconnected factors contributing to this issue.

At the heart of the matter are planning and zoning rules, which, contrary to some beliefs, significantly influence housing prices and supply. This is a contentious point, highlighted by the disagreement between Max Chandler-Mather, Greens Spokesperson on Housing & Homelessness, and Dan McKenna, CEO of Nightingale Housing, pointing to a deeper complexity within the debate.

While Shadow Assistant Minister for Home Ownership, Senator Andrew Bragg’s remarks on construction industry, skills shortage and migration underscores the multifaceted approach needed to address the crisis.

This crisis reflects broader societal issues—including a shortage in construction and trades to debates on policy, immigration, and infrastructure development.

The challenges extend to financial mechanisms of owning a home, with strategies like tapping into superannuation funds or adopting shared equity schemes considered as possible solutions (which have their own implications).

As housing prices in some states soar to record levels and impact housing affordability, the dream slips further away for many, with rising homelessness a sign of a deepening emergency.

The conversation also touched on regulatory measures like controlling rent increases and revisiting the impacts of capital gains tax and tax concessions, such as negative gearing, which has been identified as contributing factors in the price hikes over the last few decades.

A possible solution to the housing crisis

Looking beyond our shores for solutions, it’s clear that this is not an issue unique to Australia.

International examples offer alternative paths forward and suggest a re-evaluation of property planning rules.

But first, we need to understand our current property planning rules.

Captured and represented by Archistar, Australian Property Planning Rules for Land Use could provide crucial insights into land use and, potentially, relief to the crisis. The data, available via the Snowflake Marketplace, details current land use zoning applied across the nation with geospatial representation. The use of that data can help us to understand where we currently stand and offer possible solutions when variables are tweaked, such as housing density.

Another challenge in solving the housing affordability problem in Australia, and globally for that matter, is the accessibility of data. 

Archistar is helping to break down these barriers by collating national datasets for planning rules that can be easily accessed and analysed using Snowflake’s Data Platform.

The way forward

As we negotiate this national emergency, it becomes increasingly evident that a multifaceted and inclusive approach is essential.

Engaging in open discussions, exploring innovative housing policies, and reconsidering the frameworks which our housing market operates could pave the way towards a more equitable future.

The dream of homeownership, safeguarding against homelessness, and the creation of sustainable communities demand it.

Australian Property Planning Rules for Land Use

Access Archistar’s Australian Property Planning Rules and understand zoning designations and regulations across the nation.

Subscribe to our newsletter

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

Read more blogs from The Proptech Cloud

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

This detailed visual analysis for Australia’s major capital cities breaks down how net migration trends are evolving across different regions.

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

How to predict the Australia postcodes people are most likely to relocate to using the Australian Post Movers Statistics dataset and Snowflake Time Series Forecasting function.

Could a Revamp of Australian Property Planning Rules Solve Some of Australia’s Housing Issues?

Rising property prices and high costs of living means the Australian dream of home ownership is slipping further away for many. Could the answer lie in a revamp of property planning rules?

Top Real Estate Technology Global Events

Staying abreast of the latest trends, technologies, and innovations is crucial for professionals seeking to leverage the full potential of real estate technology and proptech.

What Are Mesh Blocks & How Are They Used in Real Estate

What are Mesh Blocks? As defined by Australian Bureau of Statistics (ABS), mesh blocks are the smallest geographical area of the Australian Statistical Geography Standard (ASGS) and ABS’s classification of Australia into a hierarchy of statistical areas. Mesh Blocks...

Top Real Estate Technology Global Events

Top Real Estate Technology Global Events

As the real estate industry continues to evolve at a rapid pace, staying abreast of the latest trends, technologies, and innovations is crucial for professionals seeking to leverage the full potential of real estate technology and proptech.

For anyone in property and real estate, attending global proptech events offers a chance to integrate into the wider real estate technology industry. 

Top 3 reasons why these events are valuable

1. Networking, learnings and potential partnerships

A diverse mix of professionals allow for broader networking opportunities than what your local market may offer. Particularly in overseas markets where the proptech industry may be more mature or advanced compared with your local region, these events allow you to connect with leaders, potential customers, and collaborators who might offer valuable learnings, insights to trends and new technology, alternative business models or a global perspective into the market and competition. These events could offer a platform for gaining feedback and validation, or even to identify and recruit talent.

2. Brand visibility and marketing

Participation at these events may significantly increase a business’ visibility within the global industry; even more so if you attend as a speaker or exhibitor. An effective platform to showcase innovations, solutions, and success stories to a targeted audience, high visibility can enhance brand recognition, attract leads or other marketing opportunities.

3. Investor engagement

Often attended by venture capitalists, angel investors, and other financial backers actively seeking to invest in promising startups, these events can offer potential pitching sessions, one-on-one meetings, and informal networking opportunities. 

Global Real Estate Technology and Proptech events

2024 delivers a range of global events dedicated to real estate technology and proptech, offering plenty of opportunities for learning, networking, and growth. Varying from conferences to interactive expos, these events bring together thought leaders, innovators, and practitioners from around the world. Here is our round-up of top global events:

Event

Description

Location

Dates

RETCON 2024Real Estate’s Leading Technology & Innovation ConferenceNew York, USA1-3 April
PropTech SummitPropTech Summit is the new trade fair and conference for the PropTech industryHamburg, Germany10-11 April
CRETech LondonThe World’s Leading Built World Innovation and
Sustainability Conference Series
London, UK8-9 May
PropTech Symposium DenmarkExciting debates, startup showcases, and insights from international thought leadersCopenhagen, Denmark13 May
Future of Construction SummitThe annual gathering for the people and companies redefining Australia’s construction industryBrisbane, Australia14-15 May
Property Technology ConfexA Digital & Sustainable Built Environment –
Investing, Selling, Managing and Operating
Dubai, UAE3-4 June
Proptech ViennaThe networking conference on innovation, technology and sustainability in the international real estate industry, connecting tech and real estate experts with startups, scaleups, investors, business angels, VCs and industry associations.Vienna, Austria13 June
Australian Proptech SummitProptech solutions for Australia’s new commercial and residential realitySydney, Australia30 July – 1 August
Inman ConnectThrough immersive experiences, innovative formats, and an unparalleled lineup of speakers, this gathering becomes more than a conference — it becomes a collaborative force shaping the future of our industrLas Vegas, USA30 July – 1 August
iOi Summit by NARPropTech leaders and futurists, investors, and forward-thinking real estate professionals come together to fuel the future of the industry.Chicago, USA28-29 August
PropTech ConnectEurope’s largest proptech  eventLondon, UK4-5 September
REAL PropTech ConferenceGermany’s most important conference on PropTech, digitalisation and transformation of the construction and real estate industryFrankfurt am Main, Germany4-5 September
BlueprintThe premier event for industry executives, real estate & construction tech startups, and VCs.Las Vegas, USA17-19 September
Expo Real 2024Expo Real is the most important trade fair for the real estate industry and offers a comprehensive overview of developments, topics, innovations and solutions in the real estate industry.Dubai, UAE7-9 October
Proptech Forum 2024An event to unite proptech founders and teams collaborating on the future of the industry.Sydney, Australia17 October
Urban Tech ForwardUrban Tech Forward aims to accelerate the development of net-zero cities. Designed to rethink spaces where people live and work – through the prism of decarbonisation and resilience – it brings together urban tech innovators, VCs, real estate developers, policy-makers and most prominent entrepreneurs to shift the way we build and make real progress on achieving a zero-carbon future.Warsaw, Poland25-26 October
CRETech New YorkThe World’s Leading Built World Innovation and
Sustainability Conference Series
New York, USA13-14 November
Home apti AwardThe apti award recognises the most innovative Tech provider and startups in the international Real Estate sector. Both national and international PropTechs have the opportunity to apply in the categories.Palais Berg, Austria14 November

Other Considerations

Other than factoring in the cost and time associated with traveling and attending these global events, consider the relevance and quality, because not all events are created equal.

Check for alignment and relevance with your niche and strategic focus.

And lastly, with the growing range of digital alternatives, consider whether virtual events can offer you similar benefits.

 

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What Are Mesh Blocks & How Are They Used in Real Estate

What are Mesh Blocks? As defined by Australian Bureau of Statistics (ABS), mesh blocks are the smallest geographical area of the Australian Statistical Geography Standard (ASGS) and ABS’s classification of Australia into a hierarchy of statistical areas. Mesh Blocks...

What Are Mesh Blocks & How Are They Used in Real Estate

What Are Mesh Blocks & How Are They Used in Real Estate

What are Mesh Blocks?

As defined by Australian Bureau of Statistics (ABS), mesh blocks are the smallest geographical area of the Australian Statistical Geography Standard (ASGS) and ABS’s classification of Australia into a hierarchy of statistical areas.

Mesh Blocks are essentially a set of geographic boundaries designed to segment Australia into very small areas. These boundaries are used to apply a systematic grid over the entire country, dividing it into tiny sections called Mesh Blocks. In 2021, the ABS reported 368,286 Mesh Blocks covering the whole of Australia without gaps or overlaps.

Each Mesh Block is a polygon that outlines a specific piece of land, which can range from a single block in a city to a vast, sparsely populated area in the countryside. Most Mesh Blocks contain 30 to 60 dwellings.

How are Mesh Blocks used?

The ABS does not and cannot provide detailed segmentation data (Census data) that can be directly connected to individuals or businesses. Instead, they provide anonymised and aggregated data against geographic areas. Mesh Blocks are the smallest geographic area that the ABS provide statistics against, so offer population and dwelling counts at a hyper-local level – this is particularly useful for census analysis.

These geographic boundaries allow for the aggregation of data from individual Mesh Blocks into larger geographic units, such as suburbs, towns, cities, and regions. This hierarchical structuring makes it possible to analyse data at various levels, from very detailed local information to broader regional or national trends.

Most businesses, including Proptechs, looking to augment their analysis with population segmentation data will adopt Mesh Blocks as their default level geographic unit to gain the highest level of accuracy. The popularity of Mesh Blocks mean many businesses will use Mesh Blocks for geographic statistics regardless of whether or not the Census data is being leveraged.

What role do Mesh Blocks play in proptech?

Mesh Blocks play a vital role in Proptech, geospatial data, and the real estate industry in Australia. Some example uses include:

  • Granular geographical data

Since Mesh Blocks are the smallest geographical units, providing a granular level of detail in geographic data, its precision is valuable for analysing real estate trends at a hyper-local level.

  • Accurate small area statistics

Mesh Blocks are designed to fulfill the need for accurate small area statistics. In Proptech, having precise data at this level is instrumental for understanding localised property markets, demographics, and trends.

  • Spatial mapping and analysis

Geospatial data, including Mesh Blocks, facilitates spatial mapping and analysis. Proptech platforms can leverage this data to visualise and analyse property-related information, helping users make more informed decisions based on geographical insights.

  • Enhanced property valuation

Proptech applications can utilise Mesh Blocks to refine property valuation models. The data on dwellings and residents at this level allows for a more nuanced understanding of property values, considering localised factors.

  • Land use identification

Mesh Blocks broadly identify land use, such as residential, commercial, industrial, parkland, and so forth. Land use information is valuable for proptechs involved in property development, urban planning, and investment strategies.

  • Targeted marketing and outreach

Proptech businesses can use Mesh Blocks data to tailor marketing and outreach strategies to specific geographical areas. Understanding the demographics and dwelling counts at this level allows for targeted and effective location-based campaigns.

  • Census-driven insights

The inclusion of Census data within Mesh Blocks, such as the count of usual residents and dwelling types, provides proptech platforms with up-to-date demographic information. This can aid market analysis, customer profiling, and investment strategies.

  • Integration with digital boundary files

The availability of Mesh Block boundaries in digital boundary files enhances their usability in Proptech applications. These files can be readily integrated into geospatial systems, making it easier for developers and analysts to work with this geographical data.

The foundational building blocks in real estate

Mesh blocks are foundational building blocks for geospatial and proptech applications, providing the necessary granularity and accuracy for understanding local real estate markets, demographics, and land use.

To aid proptechs, The Proptech Cloud offers its Geography – Boundaries & Insights dataset which includes all mesh blocks and their spatial areas for analysis and location-based visualisation of statistics.

The integration of this important information can enhance the precision and relevance of analyses within the proptech and real estate sectors.

Subscribe to our newsletter

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

Read more blogs from The Proptech Cloud

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

This detailed visual analysis for Australia’s major capital cities breaks down how net migration trends are evolving across different regions.

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