Parcels, Titles, Addresses and Properties. What’s The Difference?

Parcels, Titles, Addresses and Properties. What’s The Difference?

Oct 14, 2024 | Data Domain

“Parcels”, “titles”, “addresses” and “properties” are often used interchangeably in real estate, but they refer to distinct concepts.

Understanding the differences between these terms is essential, particularly when dealing with land transactions, property ownership, or legal documentation.

We discuss it here.

The Definitions and Differences Between Parcels, Titles, Addresses and Properties

We define each of these terms in the context of Australian real estate. Additionally, we’ll explore how these terms are interlinked, while highlighting the key differences that set them apart.

What is a Parcel?

A Parcel refers to a specific section of land within a larger property. Think of it as a defined portion of land with clear boundaries, often identified by a unique number for legal or administrative purposes. Parcels are essential for land registration and property management.

In some Australian states, such as New South Wales (NSW), a parcel may also be referred to as a Lot and Plan, which serves as its identification number.

Properties can include multiple parcels. For example, a farm might consist of several individual parcels, but all these parcels are sold together when the property is transferred to a new owner.

What is a Title Reference or Volume Folio?

The Volume Folio or Title Reference is a unique identifier assigned to a property title (a legal document that records specific information about a property) in the Australian land registration system. It represents the registered ownership and legal description of a property.

  • The Volume refers to the specific book or register where the property title is recorded.
  • The Folio refers to the page or entry in that volume where the property information is found.

This unique identifier plays a crucial role in land transactions and legal documentation, ensuring properties are accurately identified.

When you hear someone refer to a “Volume Folio,” they are pointing to the formal documentation that confirms a property’s legal ownership.

What is an Address?

An Address is what we commonly use to describe the physical location of a property.

It consists of separate elements such as:

  • Street number
  • Street name
  • Suburb
  • State
  • Postcode

Addresses serve practical purposes, such as mail delivery, and are a key element in identifying a property’s physical location.

However, a property can sometimes have more than one address. For instance, corner lots or properties with access from back lanes may have multiple addresses.

What is a Property?

The term Property can have different meanings depending on the context. Within state government titling systems, a property refers to a piece of land that can be bought and sold.

In this context:

  • A property may consist of one or more parcels.
  • It may have one or more addresses.
  • It is recorded with a Volume Folio.

Understanding this broader definition helps clarify why “property” can be a complex term to define. For more details, check out our blog, What is a Property, where we dive deeper into the nuances of defining property in various contexts.

Common Questions

Can a parcel appear on more than one volume folio?

No, a parcel typically cannot appear on more than one Volume Folio. In the Australian torrens title system maintained by each state, each parcel of land is associated with a single Volume Folio. This folio contains the registered ownership and legal details of that specific property. However, there are exceptions where some states have titles that share a common parcel, like the Northern Territory and Unit Titles.

Are addresses unique?

In theory, addresses should be unique, but in practice, there can be exceptions.

Several factors can lead to address duplication or confusion, such as:

  • Unit addresses: Sometimes unit numbers are left out, making it difficult to distinguish between multiple units in the same complex.
  • Rural addresses: Properties without a public road frontage or properties identified by a name rather than a street number may cause complications.
  • Multi-building complexes: In some cases, the same unit number can appear in different buildings within the same complex, especially if building names are omitted.

Why Understanding the Differences Between These Terms Matters

Understanding the distinctions between properties, parcels, titles, and addresses is essential for ensuring accuracy and clarity, particularly when it comes to land transactions and property ownership.

For further research on Australian properties, parcels, volumes, and folios, check out our recommended resources in the article, Useful Websites for Australian Properties, Parcels and Addresses.

Originally published: 17 July 2023

Last updated: 15 October 2024

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What Is A Property?

What Is A Property?

Sep 23, 2024 | Data Domain

The concept of “property” seems simple at first glance. However, depending on the application or context, defining “property” can become surprisingly tricky. This article explores why the definition is often debated and how various industries view “property” differently.

What Is a Property? The Basic Definition

In the most straightforward sense, we might define property as:

“A property is a piece of land or building that is owned or possessed by someone or something.”

Seems like a solid definition, right?

But as we dive deeper, we start encountering exceptions.

For instance, does a leasehold count as property? What about a shared office space or a mobile home in a trailer park?

The more we think about it, the more complicated it gets.

Eventually the conversation changes to just simply define everything as a property. That is definitely an option, but it has its own drawbacks. We explain why.

Why Defining Property Is Complex

When everything is labeled as a “property,” things can get muddled.

The challenge with a broad definition is that it makes understanding specific attributes harder, which can cause issues when integrating property data across platforms or use cases.

Attempting to obtain a deep understanding of a property when everything is a property would be difficult.

Let’s break down a few of these challenges:

  • Data Integration Issues
    Integrations to other data sets is extremely difficult taking this approach, as the definition is quite fluid and can lead to inconsistent results.
    For example, one data set may contain data on a granny flat and another on a main dwelling on the same parcel of land.
    An address match used to link the two data sets will mean attributes from the granny flat will be added to the main dwelling, which isn’t correct.
  • Over-classification
    Imagine you’re dealing with multiple addresses for the same building or property, such as an apartment building with different unit numbers—should each be considered a separate property, even if there’s only one main building? You could accidentally treat each address as a separate property, even though they all belong to the same main building.
    A broad definition could lead to over-counting properties, skewing your data.

Context Matters: Defining Property Across Industries

The way we define property depends heavily on the context. Here are a few examples:

  • In banking
    In mortgage applications, “property” refers to the real estate that is used as security for the loan. Banks focus on its value, legal ownership, and physical condition because these factors determine how much they can lend. This contrasts with other banking activities, where property may be viewed more generally as an asset without the same level of detailed scrutiny or long-term interest in its specifics.
  • In the legal context
    The definition of “property” often extends beyond just physical assets like land or buildings. According to the Australian Law Reform Commission (ALRC), property can be described as a bundle of rights—a legal construct that gives someone control over the use, enjoyment, and disposition of a certain asset. This means property doesn’t just refer to physical ownership but includes rights to lease, sell, or inherit that asset.
  • In vacation rentals
    On a website like Airbnb, a property could be anything from a private room to an entire villa, or even a yurt!
  • In hotels
    For accommodation platforms, each hotel itself may be considered one property, even if it contains multiple rooms for rent.
  • In government data
    Government data systems define property in different ways through a combination of spatial, legal, economic, and usage-based attributes to support various administrative, planning, and statistical functions.

There’s no universal definition of property, but aligning the term to the needs of your business and customers is critical.

Tips for Defining Property in Your Data

When defining what constitutes a property in your business, here are a few key things to keep in mind:

  • Avoid overly broad definitions
    The wider your definition of property, the harder it will be to capture detailed information. Try to be specific.
  • Ensure integration flexibility
    If you plan to use third-party property data, ensure your definition allows for easy data integration.
  • Align with common property concepts
    Typically, property data revolves around parcels, titles, addresses, or buildings. Ensure your definition aligns with one or more of these. For more in-depth guidance on property data, read our post on the differences between parcels, titles, addresses and addresses.
  • Carefully handle third-party data
    When integrating external property data, verify that the definitions are compatible or identify any differences early to avoid data issues.
  • Consistency is key
    Ultimately, the definition of a property will vary depending on your industry, business needs, and customer expectations. What matters most is consistency—once you define what “property” means to you, apply it consistently across all of your data handling processes. Inconsistencies can lead to misinterpretations and poor business decisions.

Originally published: 26 July 2023

Last updated: 23 September 2024

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Why Is It So Difficult To Parse Addresses?

Why Is It So Difficult To Parse Addresses?

Aug 5, 2024 | Data Domain

Precise address data is fundamental to a multitude of services.

The ability to accurately dissect and interpret address components is important for the accurate delivery of mail, managing customer databases, integrating geographic information systems and more.

This blog explores what address parsing is and why it presents such unique challenges.

Discover the intricacies behind making sense of seemingly simple address data and why getting it right is more complicated than it first appears.

TL;DR

Address parsing involves breaking down addresses into their individual components (like street name, city, state, and postcode/ZIP code) to make them understandable for computers.

It’s challenging due to variations in address formats, international differences, ambiguous elements, complex building details, and lack of standardisation.

Despite these difficulties, commercial address parsers achieve high accuracy, and emerging machine learning techniques offer potential for developing custom solutions.

What is Address Parsing?

In essence, address parsing is breaking down and identifying the individual components of an address to make it more understandable and usable for computers. This process ensures that each part of the address is correctly identified, interpreted and standardised for greater accuracy in subsequent applications.

Let’s take a letter that you receive in the mailbox.

On the front, there’s a block of text with your name, street address, city (or suburb or town), state, and postcode (or ZIP code). All these combined tell the postman where to deliver the letter.

Now, let’s say you have a robot assistant, and you want to teach it to understand and organise this information.

You’d instruct the robot to recognise the different parts of the address: This part is the person’s name. This is the street they live on. This part tells us the city, and so on.

Address parsing is like teaching the robot to recognise and separate these individual parts of the address. So, instead of seeing one big block of text, the robot (or computer program) sees the address as different pieces of information:

  • name,
  • street,
  • city,
  • state, and
  • ZIP code/postcode.

This helps computers and software understand and manage addresses more efficiently, just like how you can easily tell apart the street name from the city when you look at the address on a letter.

Why is Address Parsing Difficult?

Address parsing is difficult because addresses vary greatly in format and structure, both within and across countries. Ambiguous elements (e.g., “St.” for “Street” or “Saint”), complex building details, misspellings and multiple languages add to the challenge.

Additionally, addresses often change due to renaming or updates, and there is very little standardisation in how people enter addresses.

These factors make it hard to create a parser that can accurately interpret all possible address variations.

These are some examples that demonstrate the complexity involved. 

Example 1

Address = 64 YORK STREET SYDNEY NSW 2000.

  • 64 = Street number,
  • YORK = Street name,
  • STREET = Street type,
  • SYDNEY = Suburb,
  • NSW = State,
  • 2000 = Postcode

Done, why do people tell me it is difficult….?

 

Example 2

Address = 6/64 THE BOULEVARDE STRATHFIELD NSW 2135

  • 6 = Unit number
  • 64 = Street number
  • THE = Street name
  • BOULEVARDE = Street type
  • STRATHFIELD = Suburb
  • NSW = State
  • 2135= Postcode

Wait, the street name is “THE”?
It should be the “THE BOULEVARDE”!
Boulevarde is a street type as well, but not in this instance! We need a rule for that!

 

Example 3

Address = WTC BLDG A / TWR 4 MATTHEW FL LEVEL 1 18-38A SIDDELEY ST, DOCKLANDS VIC 3008

This address is significantly more difficult to parse than previous examples, however the address still includes many prefixes that can assist with parsing.

It is not uncommon for many of these prefixes to removed to look more like this address:

Address = WTC A / TWR 4 MATTHEW 1 18-38A SIDDELEY ST, DOCKLANDS VIC 3008

Without the BLDG and LEVEL prefixes, we now have additional complexity to deal with.

Challenges with Address Parsing

  • Variability in formats
    Addresses can be written in numerous formats.
    For instance, “123 Maple St. Apt 4B” and “Apt 4B, 123 Maple Street” represent the same location but are formatted differently.
  • International differences
    Different countries have different address structures. What’s common and straightforward in one country might be unusual in another. For instance, some countries might include districts or regions in their addresses, while others don’t.
  • Ambiguous elements
    Some parts of an address can be confused for others.
    For instance, “St.” could be short for “Street” or “Saint.”
    Without context, determining the correct interpretation can be tough.
  • Complex building details
    Addresses can have complex unit numbers, building names, floor numbers, and so forth.
    Parsing these details correctly, especially when they’re in non-standard formats, can be difficult.
  • Misspellings and typos
    People often make mistakes when entering addresses. A parser needs to be robust enough to handle and possibly correct common misspellings or recognise when an address might be invalid.
  • Multiple languages and scripts
    In multilingual countries or regions, addresses might be written in different languages or scripts. Parsing these requires the program to be aware of multiple linguistic structures.
  • Historical changes and inconsistencies
    Cities change, streets get renamed, postal codes get updated. An address parser needs to be updated regularly to account for these changes, or it should be robust enough to recognise and possibly map outdated addresses to their current counterparts.
  • Abbreviations and Synonyms
    There are multiple ways to refer to the same thing in addresses. For example, “Avenue” might be written as “Ave,” “Av,” or “Avnue.” A parser must recognise all these variations as referring to the same concept.
  • Lack of standardisation
    Unlike some data types where a strict format can be enforced, addresses are often entered by users who have no idea about the backend system’s preferred format.
  • Embedded information
    Sometimes, addresses can contain extra information that’s not strictly part of the address but is crucial for delivery, like instructions or landmarks.
Address block on a letter

Is Accurate Address Parsing Possible?

Most commercial address parses achieve parsing accuracy at a rate of 97/98%+.

They achieve this through constant development, testing and refinement of their software over many years.

Is it possible to build your own address parsing solution and achieve similar results?

Maybe.

New capabilities and accessibility of machine learning algorithms mean self-developed address parsing solutions may be able to produce results that are acceptable for your use case. But it is worth noting, the solution won’t be easy to develop and there will be inaccuracies. You should carefully weigh up the effort to develop an address parsing solution vs buying a solution off the shelf.

 

Address Parsing Software Providers

Australia:

  • Geoscape Australia: Provides geospatial data solutions, including address parsing and geocoding for Australian addresses.
  • Precisely: They offer global solutions, including for Australia, in the realm of data quality and address management.
  • Equifax Australia: Offer address cleansing and geocoding solutions.

USA:

  • SmartyStreets: Offers address validation, geocoding, and parsing primarily for the U.S. but also internationally.
  • Melissa Data: Provides data quality solutions, including address validation, correction, and parsing for the USA and other countries.
  • Pitney Bowes: Global solutions, including for the U.S., in data quality and address management.

Canada:

  • Canada Post: Their AddressComplete solution provides parsing, validation, and autocomplete for Canadian addresses.
  • DMTI Spatial: Offers Canadian geospatial data solutions, which include address parsing and validation.

UK:

  • PCA Predict (Loqate): Provides address lookup, validation, and parsing solutions predominantly for the UK but also globally.
  • Allies Computing: Their PostCoder web service offers address lookup and validation for the UK and other countries.
  • Royal Mail: They have solutions for address validation and parsing for UK addresses.

It’s worth noting that many of these providers offer services for multiple countries, not just the ones listed under their respective headers. For example, a company that provides services in the USA might also cater to UK or Australian addresses.

When considering an address parsing provider, it’s essential to check if they cover the specific regions and countries you need, and if they offer the depth of functionality (e.g., address validation, geocoding, etc.) that your project requires.

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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.

Access data listing

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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Understanding Auction Clearance Rates: Why Do Calculations Differ?

Understanding Auction Clearance Rates: Why Do Calculations Differ?

Jan 24, 2024 | Data Domain

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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