Understanding Auction Clearance Rates: Why Do Calculations Differ?

Understanding Auction Clearance Rates: Why Do Calculations Differ?

Auction clearance rates can serve as a barometer of Australia’s real estate market strength, particularly across its major cities.

These rates are generally a superficial gauge of market strength, because private treaty is still the most common means of property sale in some cities. Nevertheless, property predictions can be drawn when auction clearance rates are analysed alongside other factors and data points.

Clearance rates for Australia’s major real estate markets can be helpful for proptechs who leverage data, analytics, and technology to advance various aspects of the real estate industry.

What Is An Auction Clearance Rate?

The auction clearance rate typically represents the percentage of properties that sold on its advertised auction date in a specific market versus the number of properties that didn’t sell during a particular time frame (typically a week or month).

How Are Auction Clearance Rates Calculated?

There are variations in how clearance rates are calculated and reported, so it’s important to consider this and understand your data provider’s calculations. The variance in calculations means these metrics offer different views and are not interchangeable.

 

Variations to the Calculation

Calculation

Calculation (%)

Basic calculationPercentage of properties sold on auction date during a particular period (week or month)Basic calculation of auction clearance rates
Includes properties sold prior to and during auctionPercentage of properties sold prior to plus on auction date during a particular period (week or month)Basic Calculation + Properties sold prior to auction
Includes properties sold prior to, during and after auctionPercentage of properties sold prior to auction plus on auction date plus after auction date during a particular period (week or month)Sold prior + at + after auction Calculation 

 

What Do Auction Clearance Rates Tell Us?

Auction clearance rates are a crucial market indicator of real estate activity by gauging the numbers of buyers and sellers in a specific market during a certain time frame.

Generally, higher auction clearance rates indicate a higher buyer demand for property in that market, limited supply of available properties and/or with an increased likelihood of rising price, i.e. a hot market for sellers.

Conversely, low auction clearance rates indicates weak buyer demand, possible over-supply of properties and chance of reduced prices which is more favourable to buyers.

In Sydney and Melbourne, a clearance rate above 70% signals a seller’s market, below 60% suggests a buyer’s market, and 60-70% indicates balance.

But the true significance of auction clearance rates lies in its contextual analysis alongside factors such as listing numbers, days on market, withdrawn auctions, fluctuations and regional disparities.

By tracking these rates alongside additional metrics, analysts can anticipate market direction, and measure buyer and seller confidence.

Where Can I Find Clearance Rates For Australia’s Capital Cities?

Auction clearance rates in Australia are reported on a weekly basis.

Some organisations collect data from sales agents and aggregate the data by city and region, such as:

Some news outlets, auction houses and real estate agencies may also publish auction clearance rates for specific regions. Industry reports and analyses related to real estate may also compile this data to provide a comprehensive view into trends.

How Important Are Auction Clearance Rates?

For anyone involved in or impacted by the real estate market, auction clearance rates are an important indicator of demand levels, market sentiment, and potential shifts in property values. But when comparing available data, its crucial to understand the methodology behind the calculations of auction clearance rates.

Auction clearance rates should be used as part of a comprehensive analysis alongside other property data, localised research, and broader market factors. While auction clearance rates offer valuable insights into the direction of the property market, they are just one of many factors to consider, and a holistic approach incorporating various data points is recommended for a thorough understanding of market conditions.

How Might Auction Clearance Rates Be Used By Proptechs?

While not exhaustive, these are a few examples of how auction clearance rates might be used by proptechs and businesses working with real estate data.

  • By analysing clearance rates and buyer demand, price trends can be used to gauge competitiveness of the market. These could all be incorporated into tool development or software development, it could be used to optimise platform features, or to guide content creation to engage users.
  • Combining this information with localised data for property investment, integrating clearance rate data into risk assessment models could allow for more informed investment decisions.
  • Property valuation models could be enhanced with the use of real-time clearance rate data which provides more accurate and dynamic property valuations in areas of high auction activity.

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Understanding Housing Affordability: Key Metrics and Statistics

Understanding Housing Affordability: Key Metrics and Statistics

Housing affordability is a significant concern in many parts of the world, affecting the quality of life and economic wellbeing of individuals and families.

Professor Nicole Gurran from the School of Architecture, Design and Planning says governments around the world are searching for solutions to fix housing affordability, with two opposing schools of thought seeing the solutions as:

  1. Increasing supply. Those in support of this point of view see housing as more expensive because there’s not enough new supply. They see land use regulation and planning processes as restrictive to new construction, adding costly delays and uncertainty to the development process.
  2. On the flipside, others argue that ‘demand side’ factors underlying global house price inflation, such as low cost credit under financial deregulation, or government incentives to encourage property investment are being ignored. They highlight the political influence of property industry groups sustaining housing demand while advocating for reduced regulations. Some even suggest that extensive rezoning reforms may trigger surges in redevelopment and gentrification, potentially displacing individuals with lower incomes.

To truly understand the dynamics of housing affordability we need to take a detailed look at a range of different metrics and statistics to gain a full picture.

Shedding light on these crucial measures can offer insights for homebuyers, policymakers, real estate professionals, and urban planners.

1. Median and Average Home Prices

These figures provide a baseline for understanding the cost of purchasing a home in a particular area, with the median providing a middle point and the average presenting an overall trend.

2. Price-to-Income Ratio

This critical ratio compares home prices to average household incomes. A higher ratio suggests that homes are less affordable relative to income.

3. Housing Affordability Measures

A Housing Affordability Index (HAI) assesses whether a typical family can afford the mortgage on a median-priced home, based on their income. An index above 100 indicates greater affordability.

The issue with the HAI is that it primarily focuses on purchase affordability.

The Australian Institute of Health and Welfare (AIHW) broadens what they classify as housing costs in measuring housing affordability.

AIHW defines housing costs as

the sum of rent payments, rate payments (water and general), and housing–related mortgage payments”,

AIHW expresses housing affordability as

“the ratio of housing costs to gross household income”,

While housing stress is typically described as

lower-income households that spend more than 30% of gross income on housing costs“.

The second measure is a more comprehensive approach which considers a range of housing costs, the complexity of housing affordability and its impact on households.

4. Rent-to-Income Ratio

Rent-to-Income Ratio compares a tenant’s monthly rent to their gross monthly income expressed as a ratio. For those in the rental market, this ratio measures how much of a household’s income is spent on rent, with higher values indicating less affordability.

Rental property

5. Mortgage Interest Rates

Interest rates directly affect the cost of borrowing money for home purchases.

An increase in mortgage interest rates typically mean an increase in mortgage repayments, which can negatively impact affordability.

While a reduction in rates typically means reduced mortgage repayments, which may improve affordability.

6. Mortgage Payment as a Percentage of Income

Mortgage payment as a percentage of income is an important measure of affordability by demonstrating the burden of mortgage payments relative to a household’s income.

This percentage is calculated by dividing monthly mortgage repayments by gross monthly wages. 

The recommended figure is 28% of pre-tax income. Or in other words, no more than 28% of gross monthly income should go towards monthly mortgage repayments.

7. Homeownership Rates

Broad changes in homeownership rates can signal shifts in affordability, and the overall health of the housing market. 

To gain an idea of homeownership rates in Australia, AIHW shares a view of Home ownership and housing tenure in Australia.

8. Cost of Living

Several measures are published to calculate and help gauge changes in the cost of living. Changes in cost of living impacts our household purchasing power and has implications for housing demand. 

The main ways we measure cost of living is the Consumer Price Index.

Consumer Price Index (CPI)

According to the Australian Bureau of Statistics, CPI is a measure of the average change over time in the prices paid by households for a fixed basket of goods and services (which is grouped into 11 categories: Food and non-alcoholic beverages, Alcohol and tobacco, Clothing and footwear, Housing, Furnishings, household equipment and services, Health, Transport, Communication, Recreation and culture, Education, and Insurance and financial services).

 It’s important to note that the calculation of CPI does not include the cost of buying established dwellings, nor mortgage repayments. However, it does include rents, the cost of new dwellings (excluding value of land) and major alterations and additions to dwellings. 

Included in CPI

Not included in CPI

  • Rent
  • Cost of new dwellings (excluding value of land)
  • Major alterations and additions to dwellings
  • Rates and charges
  • Utilities
  • The cost of buying established dwellings
  • The cost of purchasing land
  • Mortgage repayments
  • Costs associated with servicing a mortgage
Consumer Price Index

9. Gini Coefficient of Home Prices

The Gini Coefficient statistical measure is typically used as a measure of income inequality, although it can be used to assess inequality in various other contexts, including home values in a real estate market.

A Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.

This measure can indicate the inequality in home values within a market, with higher values suggesting greater disparity.

10. Building Permits and Housing Starts

Building permits and housing starts are indicators of building activity and housing supply.  They can signal future market changes which may impact affordability.

11. Vacancy Rates

A vacancy rate is a measure of the percentage of all rental properties that are currently vacant and available for rent.

Fluctuations in vacancy rates can impact rental prices, as elevated rates often correlate with decreased rents, and conversely, lower vacancy rates may lead to higher rental prices.

12. Debt-to-Income Ratio

An individual’s Debt-to-Income Ratio is calculated by taking their total debt and dividing it by their annual income.

This ratio reflects a person’s capacity to afford housing in light of their existing debts.

14. Population Growth and Urbanisation

Rapid population increases or urbanisation can heighten housing demand, affecting affordability.

A Multifaceted View of Housing Affordability

These range of metrics offer a multifaceted and broader view of housing affordability, reflecting the many factors that impact pricing, while implicitly highlighting the complexities of the housing market.

They’re essential for making informed decisions, shaping policies, and understanding market trends.

By keeping a close eye on these indicators, stakeholders can better navigate the challenges and opportunities within the housing sector.

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The Role of Data in Real Estate Decisions

The Role of Data in Real Estate Decisions

Businesses use a wide range of data to confidently analyse trends, forecast changes, and identify opportunities. In today’s competitive real estate industry, the data behind informed business decisions can be the difference between success and failure.

Why is data-driven decision-making so important in real estate?

With the advent of big data and accessibility of data, companies are now able to make more accurate and strategic decisions by analysing information, key trends and metrics. Data-driven decision-making has become critical in real estate because it allows businesses to identify opportunities, reduce risks and maximise returns on investments.

Using data in real-estate decisions

Some examples may include:

  • Real estate investment decisions

    Real estate and related data as diverse as historical sales data, demographics, market demand, property valuations can be used to identify profitable investment and development opportunities, determine a value of a property and assess potential returns on property investments.

  • Retail analytics

    Property data together with foot traffic data, demographics, sales data, can play a role in location selections and market expansion decisions. It will also influence merchandising decisions such as retail promotions and campaigns, product placement, store layout and inventory management.

  • Real estate sales decisions

    By analysing current and historical market trends, businesses can determine optimal pricing strategies for properties. Similarly demographic data can be used to identify the target audience for a particular property and inform advertising and marketing efforts.

  • Mortgage and financing decisions

    For risk assessments, lenders use property data to evaluate the value and condition of collateral for mortgage loans, determining loan eligibility and interest rates.
    On the flipside, credit scoring models may incorporate property data to evaluate borrower creditworthiness.

  • Government and urban planning

    Urban planners use property data to identify areas in need of infrastructure development, such as roads, schools, and utilities. Governments may use property data to enforce zoning regulations and property tax assessments.

  • Building and construction planning

    Property data aids in estimating construction costs, project timelines, and feasibility studies.

  • Insurance underwriting and claims

    Insurers consider property data when determining premiums and coverage for homeowners and property insurance policies. Property data may play a role in processing claims by verifying property details and assessing damage.

  • Environmental impact assessment

    Property, environmental, regulatory and geospatial data may all factor into decisions made concerning property projects.

Tips for interpreting and analysing property data

While data-driven decision-making is a valuable tool in real estate, it is important to understand how to properly analyse and interpret property data.

Here are some tips to keep in mind:

  • Data Accuracy

    Ensure the data you’re using is accurate and up-to-date. Rely on reputable sources and verify the information where possible.

  • Compare and Contrast

    Don’t make decisions based on a single data point. Compare property data from different sources and periods to identify trends and outliers.

  • Consider Context

    Understand the broader economic and market context in which the data exists. External factors like interest rates, local regulations, and economic conditions can significantly impact real estate data.

  • Data Visualisation

    Utilise data visualisation tools to transform complex data sets into easy-to-understand graphs and charts. Visual representations can highlight patterns and trends.

  • Consult Experts

    When in doubt, seek advice from experienced proptech professionals or data scientists and analysts. They can provide valuable insights and guidance in interpreting property data effectively.

  • Human Judgement

    Don’t ignore intuition and personal experience entirely. Data is important, but it should be used to inform decisions, not replace human judgement.

As we’ve explored, various types of data play a pivotal role in shaping business decisions. In an era of big data and accessible information, real estate professionals have the tools at their disposal to analyse trends, forecast changes, and seize opportunities like never before.

Businesses can navigate the intricate terrain of real estate with greater precision, with data illuminating the path.

Human judgment and expertise are still indispensable. Seek out experts, draw from your intuition, and let data guide your decisions, not dictate them.

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    What is a Cadastre?

    What is a Cadastre?

    Cadastres are used extensively in real estate and beyond. We break down what they are, how they’re stored, used and maintained in Australia.

    What is a cadastre?

    A cadastre is a comprehensive register or database that captures detailed information about real estate or land within a specific jurisdiction; with each cadastral record defining its respective boundary, as determined by cadastral surveying. Important attributes such as property location, characteristics, and value are also included in a cadastre.

    Essentially, it serves as a vital legal and administrative tool for managing and regulating land ownership and usage, while also used for collecting property taxes, assessing land values, and resolving any disputes related to properties. It typically contains property boundaries, ownership details, and physical descriptions, such as size, shape, and topography. It may even encompass additional information pertaining to land use, zoning regulations, building permits, and environmental regulations.

    Which file types are typically used to store a cadastre?

    These file types are commonly used to store a cadastre:

    • Shape file (.shp)
    • GDB (.gdb)
    • Geojson (.geojson)

    File sizes of cadastre files can become quite large, depending on the extent of the coverage.

    For example, the cadastre for New South Wales (NSW) in Australia is approximately 1.4 GB when compressed. Working with large files can be more manageable in cloud environments like Amazon Web Services (AWS). These cloud platforms provide substantial computing power that can be accessed when needed, then switched off in a pay-per-use model to more efficiently handle the processing requirements of large cadastre files.

    Primarily spatial files, they contain geometry data that represents the boundaries of each land parcel within the cadastre. The geometry information can be stored and represented in various text formats, which are universally understood by spatial data software applications.

    The most common approaches for storing and representing the geometries are

    which ensure compatibility and ease of interpretation across different software tools and platforms.

    Additional attributes relating to the cadastre can also be served within the same spatial file, such as through the properties key within a cadastre’s GeoJSON document. Other formats such as WKT or WKB do not support the direct inclusion of additional attributes to the geometry, but can be associated with in different ways such as in an accompanying csv file containing any additional attributes.

    What does a cadastre look like on a map?

    The way a cadastre is represented on a map can vary significantly depending on the source of the data and the configuration settings used in the mapping software. Different factors like styling, symbols, and labeling options can influence how the cadastre appears visually on the map.

    The following image is a typical representation of cadastre on a map, showing boundary lines that delineate the various land parcels or lots. These boundary lines help visually separate one property from another. While lot numbers are used as an identifying label to provide a quick reference to specific parcels within the cadastre.

    Cadastre represented on a map

    How does a cadastre look in a Snowflake Marketplace listing?

    To incorporate a cadastre into Snowflake, it needs to be transformed into a table structure. The process involves loading the cadastre data in the form of GeoJson as a VARIANT data type in Snowflake. Then the GeoJson features are flattened and converted into individual rows within the table.

    Alternatively, the cadastre file can be converted to a flat file outside of Snowflake, then loaded into Snowflake as you would with any other flat file.

    This flattening process makes it easier to query and analyse the cadastre data using standard SQL operations within Snowflake, allowing for efficient storage, retrieval, and analysis of the information.

    Attribute {A}Attribute {B}Attribute {C}Geometry
    123POLYGON((30 10, 40 40, 20 40, 10 20, 30 10))

    *The actual columns (feature attributes) available for each piece of land registered on a cadastre is dependent on the maintainer/publisher of the cadastre.

    Australian cadastres

    In Australia, individual state and territory governments are responsible for the maintenance of cadastres, rather than the federal government.

    Each state and territory has its own land administration agency responsible for maintaining cadastres within their jurisdiction.

    The following organisations maintain cadastres:

    These agencies are responsible for updating and managing the cadastre, including recording changes to property ownership, boundaries, and other relevant information. They also provide access to the cadastre and related services to the public, including title searches, property reports, and other land-related information.

    Who provides cadastres on Snowflake Marketplace?

    • The Proptech Cloud
    • Geoscape
    • Precisely

    What would a cadastre be used for?

    Cadastres can be used to:

    • Identify the unique number of properties in a country,
    • Identify changes to properties (merges, subdivisions, title registrations),
    • Spatially link other spatial information to a property,
    • Spatially lookup a property based, i.e. lookup properties based on latitude and longitude coordinates, or based on geospatial shape (think drawing a circle on map to search for properties on the map),
    • Represent property boundaries on a map.

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    What’s the Difference Between GDA94 and GDA2020?

    What’s the Difference Between GDA94 and GDA2020?

    Geodetic datums, or geodetic systems, are often used by proptechs. Here is a rundown of everything you need to know about the different geodetic datums we use and reference in Australia.

    What is a geodetic datum?

    A geodetic datum is a set of reference points and parameters used to define the shape and orientation of the Earth’s surface for mapping and surveying purposes. It provides a coordinate system that allows locations on the Earth’s surface to be accurately described and located. In Australia, we use Geodetic Datum of Australia 1994(DA94) and Geodetic Datum of Australia 2020 (GDA2020).

    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. 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, using the correct geodetic datum is imperative 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.

    Usage in Australia

    In Australia, a lot of data providers are providing data sets in both GDA94 and GDA2020 geodetic datums because the uptake of GDA2020 is not universal. Most data providers of spatial data sets will reference the geodetic datum used to build the data set.

    When combining geospatial data sets, ensure you are using a consistent geodetic datum to prevent incorrectly linking two or more shapes.

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