What’s the Difference Between GDA94 and GDA2020?

What’s the Difference Between GDA94 and GDA2020?

Feb 11, 2025 | Data Domain

Geodetic datums, or geodetic systems, are often used by proptechs for mapping  or analysing spatial data.

Here is a rundown of everything you need to know about the different geodetic datums we use and reference in Australia.

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What is a Geodetic Datum?

A geodetic datum is a reference framework used to define the Earth’s shape and orientation, providing a coordinate system that allows for accurate mapping, surveying, and pinpointing exact locations on the Earth’s surface.

In Australia, we use Geodetic Datum of Australia 1994 (DA94) and Geodetic Datum of Australia 2020 (GDA2020).

The History of Australia’s Geodetic Datums

Prior to GDA94, Australian surveyors primarily used the Australian Geodetic Datum 1966 (AGD66), which was based on a network of ground-based survey points and astronomical observations.

AGD66 was the standard datum used for mapping and surveying in Australia for several decades until it was superseded by GDA94 in the 1990s.

The decision to switch to GDA94 was driven by the need for a more accurate and up-to-date geodetic datum that could take advantage of advances in geospatial technology such as GPS. AGD66 was also affected by tectonic movements and other changes in the Earth’s surface, which made it increasingly difficult to use for accurate positioning and navigation.

GDA94 (Geocentric Datum of Australia 1994) was the geodetic datum used in Australia from 1994. Based on a mathematical model of the Earth’s surface defined using measurements from a network of ground-based survey points, and used as the standard datum for mapping and surveying in Australia.

Now, GDA2020 (Geocentric Datum of Australia 2020) is the current geodetic datum used in Australia. It was introduced in 2017 to replace GDA94 and is based on more recent measurements of the Earth’s surface using advanced satellite and ground-based technology.

GDA2020 provides a more accurate representation of the Earth’s surface than GDA94, and is designed to be compatible with global positioning systems (GPS) and other modern geospatial technologies.

Even though AGD66, and to some extent GDA94, are no longer the primary datums used in Australia, it’s still important to maintain historical data that was referenced to this datum. And it is possible to transform data from AGD66 to GDA94 or GDA2020 using appropriate transformation parameters to ensure compatibility and accuracy when comparing or integrating data from different sources.

Conversions between Geodetic Datums

Conversions between AGD66 and GDA94 are not 100% accurate, because the two datums are based on different mathematical models of the Earth’s surface with different reference points and parameters.

To convert data from AGD66 to GDA94 (or vice versa), a mathematical transformation must be applied that takes the differences between the two datums into account.

This transformation involves adjusting the latitude, longitude and height values of the data to align with the new datum.

However, there are many factors that can affect the accuracy of this transformation, such as:

  1. The quality and accuracy of the original data: If the original data was collected using imprecise or inaccurate methods, the transformation may introduce additional errors or inaccuracies.
  2. The complexity of the transformation: Some transformations may require more complex mathematical models or additional parameters to be specified, which can increase the likelihood of errors.
  3. The location and terrain of the data: The accuracy of the transformation can vary depending on the location and terrain of the data. Some areas may be more affected by tectonic movements or other changes in the Earth’s surface, which can make the transformation more challenging.
  4. The type of data being transformed: Different types of data (e.g. points, lines, polygons) may require different transformation methods or parameters, which can affect the accuracy of the transformation.

While conversions between AGD66 and GDA94 can be relatively precise, they’re not 100% accurate.

This is due to the inherent differences between the two datums, and the potential for errors or inaccuracies in the transformation process. It’s important to use appropriate transformation methods and understand the limitations and potential sources of error when converting data between different geodetic datums.

The Difference Between GDA94 and GDA2020

The key differences

The main difference between GDA94 and GDA2020 is their accuracy and the methods used to define them.

GDA2020 is a more accurate and up-to-date datum, with improvements in the modeling of the Earth’s surface that take into account changes in its shape over time. This means that positions and distances measured using GDA2020 are more accurate than those measured using GDA94. Additionally, GDA2020 is designed to be compatible with modern geospatial technologies and is expected to be used for many years to come.

It’s worth noting that the difference between GDA94 and GDA2020 may not be significant for many applications, particularly those that don’t require high levels of accuracy. However, for applications that require precise positioning or measurement, such as surveying or mapping, selecting the correct geodetic datum is important to ensure accurate results.

Differences in distance and direction

The average distance and direction difference between GDA94 and GDA2020 depends on the location on the Earth’s surface.

In general, the differences between the two datums are greatest in areas with high tectonic activity or areas where the Earth’s surface is undergoing significant changes, such as due to land subsidence or sea level rise.

According to Geoscience Australia, the organisation responsible for geodetic information and services in Australia, the average difference between GDA94 and GDA2020 in Australia is around 1.5 meters. However, this value can vary significantly depending on the location, with some areas showing differences of several meters or more.

The direction of the difference between the two datums also varies depending on the location, as it is related to the direction and magnitude of any tectonic movements or changes in the Earth’s surface. In general, the direction of the difference is determined by the vector between the two datums at a given location.

It’s important to note that the difference between GDA94 and GDA2020 is not constant over time and may continue to change in the future. This is because the Earth’s surface is constantly changing due to tectonic activity, sea level rise, and other factors. As such, it’s important to regularly update geodetic data and use the most up-to-date geodetic datum for accurate positioning and navigation.

Migrating from GDA94 to GDA2020

The differences between the two means that migrating from GDA94 to GDA2020 can present several challenges and issues, particularly for organisations or projects that rely heavily on geospatial data.

Some of the key issues with migrating to GDA2020 include: 

  1. Data compatibility: Data that was created using GDA94 may not be compatible with GDA2020. This can cause issues when trying to integrate or compare datasets that use different datums.
  2. Application compatibility: Applications that were designed to work with GDA94 may not be compatible with GDA2020. This can require updates or modifications to existing software or the adoption of new tools.
  3. Training and expertise: Staff who work with geospatial data may need to be trained on the new GDA2020 datum and its associated tools and workflows. This can take time and resources.
  4. Time and cost: Migrating to GDA2020 can be a complex and time-consuming process, particularly for large organisations or projects. There may be costs associated with updating software, purchasing new equipment, or retraining staff.
  5. Accuracy: While GDA2020 is a more accurate datum than GDA94, some existing data may still be more accurate when referenced to GDA94. This can make it difficult to compare or integrate data from different sources.
  6. Data transformation: In some cases, it may be necessary to transform data from GDA94 to GDA2020, which can introduce errors or inaccuracies. The accuracy of the transformation depends on the quality of the original data and the transformation method used.

Migrating from GDA94 to GDA2020 requires careful planning and consideration of the potential issues and challenges. It’s crucial to work closely with geospatial experts and stakeholders to ensure a smooth and successful transition.

What is WGS84 and Why is it Used by Software?

WGS84 (World Geodetic System 1984) is a geodetic datum used for positioning and navigation purposes. It defines a reference system for the Earth’s surface that allows locations to be specified in latitude and longitude coordinates.

The WGS84 datum was developed by the United States Department of Defense for use by the military and intelligence agencies, but it has since become the standard geodetic datum used by many organisations and applications around the world, including GPS (Global Positioning System) devices and mapping software.

The WGS84 datum is based on a mathematical model of the Earth’s surface that takes into account its shape, size, and rotation. It defines a set of reference points and parameters that allow positions on the Earth’s surface to be accurately calculated and communicated.

The WGS84 datum is widely used because it is compatible with GPS and other global navigation systems, allowing precise positioning and navigation in real-time. However, while there may be regional differences in the Earth’s surface that are not fully captured by the WGS84 model, that other geodetic datums may be more appropriate for certain applications or regions.

How to Convert Between GDA2020 and WGS84

To convert between GDA2020 (Geocentric Datum of Australia 2020) and WGS84 (World Geodetic System 1984), you can use coordinate transformation parameters provided by geodetic authorities. The transformation process involves converting coordinates from one datum to another using a mathematical model.

In the case of GDA2020 and WGS84, the transformation parameters provided by the Intergovernmental Committee on Surveying and Mapping (ICSM) in Australia are known as the National Transformation Version 2 (NTv2) grid files. These grid files contain the necessary information for accurate transformations.

The accuracy of the transformation depends on the specific region and the quality of the NTv2 grid files used. Always use the most up-to-date and accurate transformation parameters available from reputable sources.

To convert coordinates between the GDA2020 (Geocentric Datum of Australia 2020) and WGS84 (World Geodetic System 1984) datums using Python, you can utilise the pyproj library. pyproj provides a convenient interface to the PROJ library, which is a widely used cartographic projection and coordinate transformation library.

Geodetic Datum Usage in Australia

In Australia, a lot of data providers offer data sets in both GDA94 and GDA2020 geodetic datums because the uptake of GDA2020 is not universal. It’s common practice for these providers to specify which datum was used to create each dataset.

When combining geospatial datasets, it’s important for data professionals to ensure consistency in the geodetic datums employed.

Using different datums without proper alignment can lead to inaccuracies, such as misaligning spatial features. For this reason, careful attention to datum consistency is essential to maintain the integrity and accuracy of integrated geospatial data.

 

Originally published: 5 August, 2023

Last updated: 11 February, 2025

 

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Alternative Data: What Is It, Who Uses It And Why It Matters

Alternative Data: What Is It, Who Uses It And Why It Matters

Dec 12, 2024 | Data Domain

Often in business, staying ahead means looking beyond the obvious.

Alternative data, derived from non-traditional sources, offers a depth and uniqueness that traditional data sources can lack.

In this blog, we’ll cover what alternative data is, how it differs from traditional data, who uses it, its role in business, and the pros and cons of integrating it into decision-making.

Table of Contents

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What Is Alternative Data?

Alternative data (or Alt data) refers to non-traditional data sources that provide unique insights and information not typically captured by conventional data sets. The depth, detail, variety and uniqueness of alternative data are what make it such a powerful form of intel.

Examples include:

  • Social media sentiment analysis
  • Geolocation data tracking foot traffic
  • Credit card transaction volumes
  • Satellite imagery
  • Drone imagery
  • Wearable tech data
  • Web traffic data
  • Mobile app usage statistics
  • IoT sensor data
  • Product reviews
  • Weather data
  • Flight data
  • App usage
  • ESG (environmental, social and corporate governance) data
  • Market prices
  • Company filings
  • Jet tracking

Alternative data is far from a new concept. For centuries, astute business people have sought to understand their trading environments by observing the world through different lenses. These observations often generated insights—data that, while not traditional, provided a valuable edge in navigating markets effectively.

What has changed in recent years is the remarkable accessibility of alternative data, driven by technological advancements across industries. This progress has expanded the volume and variety of data, making it easier for businesses to derive insights and make more informed decisions.

Source: Casting the Net

What is the Difference Between Alternative and Traditional Data?

Traditional data comes from established, conventional sources like financial statements, government reports and structured databases. It is typically well-organised and standardised.

Alternative data comes from unconventional, non-traditional sources. It is often unstructured or semi-structured, and thus less readily accessible or usable, and not easily searchable.

It generally requires advanced processing techniques, substantial computational power and storage, as well as the ability to link seemingly unrelated pieces of information to develop a holistic understanding or derive meaningful insights.

Who Uses Alternative Data?

Alternative data is often used by hedge funds, private equity firms, investment banks and retail investors to gain insights and identify opportunities beyond traditional data sources.

It is commonly used to:

  • Uncover potential risks not evident from traditional financial data.
  • Conduct stress tests and scenario analyses.
  • Identify and manage tail risks.
  • Discover new investment opportunities.
  • Reduce information asymmetry.
  • Optimise portfolio construction.

Other specific examples include:

  • Environmental, Social and Governance (ESG)
    Alternative data like social media sentiment and news can be used to assess companies’ environmental, social, and governance (ESG) performance which can help investors incorporate ESG factors into decision-making.
  • Fintech
    Alternative data can also be used by fintech firms to offer tailored financial advice, identify potential customers and develop innovative investment products accessible to retail investors.

Pros & Cons of Using Alternative Data

Pros

By using unconventional insights, businesses and investors can uncover opportunities and sharpen their strategies using alternative data. Here’s how it can help deliver an edge:

More Detailed and Accurate Analysis

Alt data provides additional data points of a business’s performance beyond traditional sources. When analysed holistically, it can potentially offer a more complete understanding of business performance and customer loyalty.

Decisions Backed by Historical Trends

The access to historical data enables predictive analysis, helping anticipate outcomes, identify opportunities and avoid potential risks.

Improved Investments and Partnerships

A broader range of data points allows businesses to assess compatibility with potential partners and make more strategic, mutually beneficial investment decisions.

Enhanced Customer Relationships

Data on customer feedback, web traffic and audience demographics helps businesses refine strategies to improve customer satisfaction and loyalty.

Competitive Advantage

Real-time insights allow quicker, informed decisions, helping businesses stay ahead of competitors reliant on traditional data.

Cons

While alternative data offers exciting possibilities, it’s not without its hurdles. Effectively leveraging these unconventional insights requires addressing some critical challenges. Here are the key drawbacks businesses might consider:

Inconsistent Data Quality

Due to the wide variety and the varied applications, alt data is hard to regulate. It can come aggregated or as a straight data feed. The lack of standardisation and rules around alt data means these types of data sets may contain errors, which can lead to misinterpretations which, in turn, affects decision-making.

Transparency and Ethical Concerns

Data collection methods, such as tracking GPS or online activity, may erode consumer trust if it is seen as invasive or unethical.

Privacy and Security Risks

Sensitive data usage exposes businesses to breaches, legal violations and potential harm to individuals. This can pose serious risks to a brand’s reputation and business compliance.

Discrimination and Bias Risks

Using personal demographics in decision-making can lead to unintentional discrimination, flawed data sets and long-term reputational damage.

Manipulated Data Variables

Publicly available data can sometimes be intentionally skewed to serve specific interests—businesses might highlight only positive reviews, while individuals may curate information to boost their credibility. This highlights the importance of adopting a comprehensive approach, drawing from a diverse range of data channels rather than relying on a single source.

The True Value of Alternative Data

Alternative or alt data offers deeper insights, empowering businesses and investors to make more informed decisions and gain a competitive edge.

As accessibility to alt data continues to grow, so do the opportunities to harness its potential in creative and transformative ways.

But challenges like data quality, transparency and privacy means that alt data requires careful management.

By adopting a thoughtful and ethical approach, progressive businesses can tap into alt data’s inherent value to inspire smarter investments, spark innovations and deliver groundbreaking solutions.

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How to Incorporate Mesh Blocks into Datasets

How to Incorporate Mesh Blocks into Datasets

May 28, 2024 | Technology Application

Mesh Blocks in real estate and proptech applications

Mesh Blocks are useful for geospatial and proptech applications, providing granularity and accuracy for understanding local real estate markets, demographics and land use.

The integration of Mesh Blocks into datasets can enhance the precision and relevance of analyses within the proptech and real estate sectors.

Useful in geospatial data and census analyses, embedding Mesh Blocks into digital boundaries enhances their usability in various applications.

We will cover the steps to incorporate mesh blocks into data sets below.

What are Mesh Blocks and how are they used in real estate?

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

Learn more

How to incorporate Mesh Blocks into datasets

Incorporating Mesh Block into datasets involves several steps to ensure seamless integration and effective utilisation of geographical information. Here’s a guide on how to incorporate Mesh Blocks into datasets:

Step 1: Data Collection

Gather relevant data that aligns with Mesh Blocks.

This may include demographic information, property values, land use details, or any other dataset that can be associated with specific geographical areas.

 

Step 2: Download Mesh Block Boundaries

Mesh Block boundary files can be downloaded from authoritative sources, such as the Australian Bureau of Statistics (ABS) or relevant statistical agencies.

For ease, The Proptech Cloud has a free comprehensive dataset Geography – Boundaries & Insights – Australia ready for access and immediate use.

Geography – Boundaries & Insights – Australia

This free dataset from The Proptech Cloud is available for seamless access from Snowflake Marketplace.

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Step 3: Geospatial Data Processing

Use Geographic Information System (GIS) software or programming libraries (e.g., Python with geospatial libraries like GeoPandas) to process and manipulate the mesh block boundaries.

Tip:

Geographical boundaries can be imported using Python libraries including Geopandas and shapely.

Many data warehouses including Snowflake, BigQuery and PostgreSQL have in-built geospatial functionality to allow for the processing of geospatial data.

QGIS – Loading in Geospatial files in QGIS

1. From the toolbar at the top of the page click Layer > Add Layer > Add Vector Layer

2. Make sure the Source Type is clicked to File

3. Load in the Source Data by using the three dots button at the side of the Vector Dataset(s) toolbar

QGIS - Loading in Geospatial files in QGIS

Geospatial Formats

The two most common ways geospatial data are represented in files are Well-Known-Text (WKT) which is a textual representation of a polygon and the geojson format which shows the coordinates and type of geojson format.

Both Python and Snowflake have capabilities to work with these 3 formats and parse them so they can be used in geography functions

WKT Format

#Example 2 using WKT format

from shapely import wkt

brisbane_bbox = “POLYGON ((153.012021 -27.471741, 153.012021 -27.462598, 153.032931 -27.462598, 153.032931 -27.471741, 153.012021 -27.471741))”

brisbane_poly = wkt.loads(brisbane_bbox)

Python – Loading in GeoJSON

The libraries geojson, shapely and json need to be installed.

#EXAMPLE 1 working with a geojson format

import json

import geojson

from shapely.geometry import shape

geojson_example = {

"coordinates": [[[153.01202116, -27.47174129], [153.01202116, -27.46259798], [153.03293092, -27.46259798], [153.03293092, -27.47174129], [153.01202116, -27.47174129]]],

"type": "Polygon"

}

geojson_json = json.dumps(geojson_example)

# Convert to geojson.geometry.Polygon

geojson_poly = geojson.loads(geojson_json)

poly = shape(geojson_poly ))

Snowflake

GeoJSON and WKT format can also be loaded into snowflake and converted to a geometry using the following commands:

#converting Well-Known-Text into geography format

SELECT ST_GEOGRAPHYFROMWKT('POLYGON ((153.012021 -27.471741, 153.012021 -27.462598, 153.032931 -27.462598, 153.032931 -27.471741, 153.012021 -27.471741))');

#Converting Geojson to geography format

SELECT TO_GEOGRAPHY('{

"coordinates": [[[153.01202116, -27.47174129], [153.01202116, -27.46259798], [153.03293092, -27.46259798], [153.03293092, -27.47174129], [153.01202116, -27.47174129]]],

"type": "Polygon"

}

')

Step 4: Data Matching

Match the dataset records with the appropriate mesh blocks based on their geographical coordinates. This involves linking each data point to the corresponding mesh block within which it is located.

Tip:

Geospatial functions which are supported in big data warehouses and Python can be used to match geospatial data.

A common way to match two geographical objects is to see if the coordinates of the two objects intersect. An example of how to do this in Python and Snowflake is shown below.

In Python

Data matching can be done using the shapely library intersects function.

from shapely import wkt, intersects

shape1 = wkt.loads("POLYGON ((153.012021 -27.471741, 153.012021 -27.462598, 153.032931 -27.462598, 153.032931 -27.471741, 153.012021 -27.471741))")

shape2 = wkt.loads("POLYGON ((153.012021 -27.471741, 153.032931 -27.462598, 153.012021 -27.471741))")

shape_int = intersects(shape1, shape2)

print(shape_int)

 

In Snowflake

Data matching can be done using the ST_Intersects function. One of the advantages of using big data warehouses including Snowflake to geospatially match data is that it leverages its highly scalable infrastructure to quickly complete geospatial processing.

WITH geog_1 as (

SELECT ST_GEOGRAPHYFROMWKT('POLYGON ((153.012021 -27.471741, 153.012021 -27.462598, 153.032931 -27.462598, 153.032931 -27.471741, 153.012021 -27.471741))') as poly

),

geog_2 as (

SELECT ST_GEOGRAPHYFROMWKT('POLYGON ((153.012021 -27.471741, 153.022021 -27.465, 153.032931 -27.462598, 153.012021 -27.471741))') as poly

)

SELECT

g1.poly, g2.poly

FROM geog_1 as g1

INNER JOIN geog_2 as g2

on ST_INTERSECTS(g1.poly, g2.poly)

Step 5: Attribute Joining

If your dataset and mesh blocks data have common attributes (e.g., unique identifiers), perform attribute joins to combine information from both datasets. This allows you to enrich your dataset with additional details associated with mesh blocks.

Step 6: Quality Assurance

Verify the accuracy of the spatial integration by checking for any discrepancies or errors. Ensure that each data point is correctly associated with the corresponding mesh block.

Tip:

geojson.io is a handy website that can help with visualising geojson data and ensure it is correct.

If you’re using Snowflake, the ST_AsGeojson command can be used to convert geography into a geojson which allows you to quickly visualise the shapes created.

Step 7: Data Analysis and Visualisation

Leverage the integrated dataset for analysis and visualisation. Explore trends, patterns, and relationships within the data at the mesh block level. Utilise geospatial tools to create maps and visual representations of the information.

Tip:

It’s worth mentioning that Snowflake has the option to create a Streamlit app within the Snowflake UI which allows for the cleaning and processing of data using Python and SQL and the interactive visualisation of data through the Streamlit App.

Read our blog which demonstrates how to predict migration patterns and create forecasts using Snowpark and Streamlit>

Snowflake also integrates really well with local Python development environments so all the initial data processing and cleaning can be done through a Snowflake API, then geography can be converted to a GeoJson or Text formal. Thereafter, libraries like plotly, folium, pydeck can be used to do complex geospatial visualisations.

Step 8: Data Storage and Management

Establish a system for storing and managing the integrated dataset, ensuring that it remains up-to-date as new data becomes available.

Consider using databases or platforms that support geospatial data.

Tip:

Geospatial datasets are usually very large and complex datasets due to the number of attributes included in a geospatial dataset, the resolution of the data and the number of records.

Cloud-based big data platforms can be an excellent option for storing geospatial data due to the low-cost of storage. Many of these platforms including also have spatial clustering options so that geospatial data in a similar location are grouped together, meaning queries for data in certain areas run more efficiently.

Snowflake (Enterprise Edition or Higher) also has an option to add a search optimisation to geospatial data tables to optimise the performance of queries that use geospatial functions.

Step 9: Documentation

Document the integration process, including the source of mesh block boundaries, any transformations applied, and the methods used for data matching. This documentation is essential for transparency and replicability.

By following these above steps, you can effectively incorporate mesh blocks into your datasets, enabling a more detailed and location-specific analysis of the information at the mesh block level.

 

Geography – Boundaries & Insights – Australia

This free dataset from The Proptech Cloud is available for seamless access from Snowflake Marketplace.

Access data listing

All rights are reserved, and no content may be republished or reproduced without express written permission from Data Army and The Proptech Cloud. 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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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?

Apr 1, 2024 | Inference and Analytics

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.

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Environmental Risks in Real Estate: Essential Metrics for Assessment

Environmental Risks in Real Estate: Essential Metrics for Assessment

Jan 3, 2024 | Data Domain, Inference and Analytics

Driven by greater awareness and demand, environmental considerations are increasingly at the forefront of property development and investment.

Environmental factors can significantly impact site selection, regulatory compliance, property values, safety, infrastructure, sustainability, insurance costs, and the overall desirability of real estate.

To adequately build resilient properties for our future, understanding and assessing environmental risks in real estate is crucial.

Examining the key metrics and statistics used to measure these risks can also provide valuable insights for property stakeholders who are building climate intelligence as a means of value creation and strategic differentiation in the real estate industry.

On the other hand, the real estate industry contributes approximately 39% of total global emissions. This highlights how factors as broad as the choice of building materials, construction methods, real estate planning and development can influence and help to mitigate global climate change and environmental risk.

Environmental assessment metrics provide important intel to stakeholders and businesses in and around the real estate industry. 

Here we examine the top 16 key metrics used in assessing environmental risk in real estate.

1. Flood Risk Assessment

Managing flood risk is an important aspect of adapting to global climate change and flood risk assessments have become an important part of risk management practices. The estimation of risk is somewhat challenging and involves careful consideration of a number of varying factors such as location, historical flood data, and elevation.

Flood risk is a concern, especially for properties near water bodies.

Flood

2. Earthquake Risk Score

In earthquake-prone areas, earthquake risk score is vital. It evaluates the probability of earthquakes and their potential impact, factoring in seismic activity and building standards.

Earthquake risk metrics can support stakeholders in developing risk reduction measures such as emergency response plans, building design codes, or insurance-related decisions.

3. Wildfire Risk Rating

Properties in or near wilderness areas must consider the risk of wildfires. This rating looks at location, vegetation, and climate conditions. There are a number of data and solutions in the market that help decision makers with deeper location intelligence insights, such as CoreLogic with their climate risk solutions and Precisely with their wildfire risk data.

Then there are those protech innovators who actively incorporate environmental risks into their solutions, such as Nearmap who’ve recently acquired BetterView, will also be integrating risk ratings for data decisions given the ever-changing nature of bush fires, floods, and other disasters.

4. Storm Surge and Tsunami Risk

While storm surges and tsunamis are caused by different events, they both have the potential to cause significant harm and damage, such as substantial erosion of beaches and coastal highways, and waves pose a threat to boats and buildings along the shoreline. As the surging waters move inland, rivers and lakes may experience adverse effects, contributing to the escalation of flood levels.

Coastal properties are evaluated for their vulnerability to storm surge and tsunamis, crucial in today’s changing climate.

Sea levels

5. Sea Level Rise Projections

With climate change, assessing the long-term risk of sea level rise is essential for coastal real estate investments.

Climate Central’s Coastal Risk Screening Tool is a handy tool for quick future projections.

6. Air Quality Index (AQI)

Air pollution stands as the most significant environmental threat to global public health, resulting in an approximate annual total of 7 million premature deaths.

AQI impacts property desirability and occupant health, making it a significant factor in urban and industrial areas.

7. Soil Contamination Levels

Soil contamination can limit property use and affect value, necessitating thorough assessments.

Australian soil information is collected by government and held by the states and territories. Soil Science Australia, the national soil science body, shares a handy list of Soils Data, Maps and Information Sources for reference.

Soil

8. Water Quality Assessments

Our water systems, including surface and groundwater, catchments, as well as estuarine and marine bodies, constitute intricate ecological networks that we engage with daily. These waterways and wetlands play an important role in:

  • Providing drinking water
  • Supporting irrigation and agriculture
  • Receiving and purifying effluent and stormwater
  • Facilitating recreational and commercial activities such as fishing and boating.

The quality of local water sources is a key consideration, as it affects usability and desirability

9. Heat Island Effect

Urban areas influence the surrounding atmosphere and engage with climate processes, resulting in distinct microclimates within cities.

This heat island phenomenon leads to urban areas experiencing notably higher temperatures compared to their surroundings, particularly in areas with limited green cover and increased hard surfaces that absorb, retain, and emit heat.

Urban heat islands can increase energy costs and affect living conditions, and is an important factor in urban planning.

10. Noise Pollution Levels

The World Health Organization (WHO) recognises noise pollution—defined as unfavorable noise caused by human activity—as an underestimated threat that can cause a number of short- and long-term health problems.

As well as contributing to health, noise pollution is often seen as a less-than-desirable attribute contributing to liveability.

Properties exposed to high noise levels from traffic or industry can affect their value.

11. Environmental Regulation Compliance

In Australia, compliance with legislation including protection of threatened plants, animals and ecosystems, wildlife trade, hazardous waste, air quality and monitoring compliance with the conditions of approvals granted under the Environment Protection and Biodiversity Conservation Act 1999 (EPBC Act) is conducted by The Department of Climate Change, Energy, the Environment and Water.

Compliance with environmental regulations is critical to avoid legal issues and maintain property value.

Soil erosion

12. Land Stability and Erosion Rates

Land stability and erosion rates is affected by soils, surface cover, topography, and climate; all of which are interrelated.

Particularly in areas with unstable soils or steep terrain, assessing the risk of landslides or erosion is essential for many aspects of real estate such as site selection, development planning and insurance, while these risks can also have an influence on property valuations.

13. Proximity to Hazardous Facilities

Numerous research studies have indicated a correlation between living in close proximity to sites with hazardous wastes, industrial facilities, pesticide-treated cropland, busy roadways, nuclear power plants, and gas stations or repair shops, result in an increased likelihood of detrimental health effects.

Government may form regulations and implement procedures for permits and enforcement to limit pollution.

As such, properties in close proximity may face increased regulations, risks or insurance costs.

14. Biodiversity and Wildlife Protection Areas

With a sustained need for housing, urban planners and conservation managers are consistently exploring alternative strategies for residential development that aim to reduce adverse effects on biodiversity and ecosystem functioning.

Thus, proximity to protected areas can limit development options and affect property value.

Biodiversity

15. Carbon Footprint Analysis

Climate change is already underway, with an escalating impact that is increasingly affecting all of us around the globe.

Without immediate and systemic action to address its destructive consequences, the impact is expected to be substantial.

For greater visibility into the environmental footprint of transactions and impacts of our consumption and production activities, organisations such as FootprintLab provide current, credible and commercially ready carbon data. This information can aid consumers, producers and governments in decision-making that aligns with their sustainability goals.

Managing carbon emissions from the construction industry is one crucial step in limiting these effects on climate change and a property’s carbon footprint is becoming a significant factor in light of global climate concerns.

16. Sustainability Certifications

A green building certification is a verification process ensuring that a building is designed and constructed to enhance energy efficiency, decrease water usage, foster a healthier indoor environment, manage resources and waste effectively, and limit environmental impact.

The process generally requires adherence to specific guidelines and criteria, often assessed by an accredited third-party organisation, leading to the certification of the building.

There are different green building certifications around the globe, with LEED in the United States, BREEAM in the United Kingdom and NABERS in Australia, each with its own set of criteria and scoring systems.

Properties with these certifications are often seen as less risky and more desirable.

The Proptech Cloud’s Environment and Energy Efficiency Data provides energy supply data and NABERS energy rating data to guide decisions on energy sourcing as part of a robust sustainability strategy.

Managing Environmental Risks

Understanding and mitigating environmental risks is important in the real estate sector. By using these metrics, stakeholders can make informed decisions, adapt to environmental challenges, uncover business opportunities and invest in sustainable and resilient properties.

As the world continues to focus on environmental sustainability, these considerations will become increasingly integral to real estate assessment and development.

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