Frequently Asked Questions

Section that answers general questions about our company and services. Click on the question titles below to see the answers.

HtAG (Higher than Average Growth) is an innovative real estate analytics platform tailored to assist home buyers, investors, and real estate professionals in making informed property decisions. Capitalising on machine learning algorithms, HtAG Analytics assesses and ranks the ROI potential of more than 5,000 property markets throughout Australia.

Drawing on the capabilities of Data Science and Big Data, HtAG Analytics adeptly processes vast amounts of data to establish the long-term capital value & cash flow potential as well as risk factors associated with investing in a specific geographical location.

We suggest the Professional Tier for buyers’ agents and property investors, as it offers discounted access to Market Insights reports. These reports feature extensive metrics designed to facilitate shortlisting markets based on advanced supply and demand factors.

For home buyers, our Personal Tier is a popular choice, providing a selection of demand, supply, and ROI metrics that help pinpoint capital growth locations. Unlike the nationwide reporting available in the Professional Tier, the Personal Tier focuses on metrics for individual Local Government Areas (LGAs).

Section that addresses common questions related to HtAG Analytics data. Click on the question titles below to expand the tiles and see the answers.

Our data is updated on a monthly basis, typically on the last day of the month at 11:30PM Sydney time.

We curate our own dataset of online and offline listings, which is used to calculate suburb typical price, median rent, indicative gross yield as well as over 80 other metrics.

HtAG reports are produced for the house and unit segments. Some suburbs only have reports for houses due to no data in the unit segment and vice versa.

Houses are free standing houses (excl. townhouses & villas).

Units are apartments, studios, flats, units (excl. unit blocks).

Australia boasts approximately 15,000 suburbs and localities, yet only roughly a third of these are home to active property markets. This equates to 5,000 dynamic real estate arenas, with each providing distinct opportunities for both houses and units.

These active markets are categorised into three key segments:

1. A significant portion of these markets teem with a blend of house and unit sales /rentals, showcasing a diverse offering.
2. Some markets are specifically regional or located on the outskirts of cities, specialising in house sales/rentals largely due to the geographical context.
3. Conversely, certain inner-city markets primarily focus on unit sales/rentals, responding to urban living preferences.

Further to this, in some low-activity markets, property sales are more prevalent than rentals, often leading to a lack of definitive rental pricing due to the restrained volume of transactions.

Our ranking table adeptly navigates these complexities, curating insightful data tailored to empower real estate professionals and investors. It concentrates on suburbs and localities with active markets, with the option to segment by houses or units. Furthermore, the table features markets that offer both sales and rentals, deliberately disregarding markets devoid of rental activity.

Finally, it should be noted that certain regional markets may not provide an ample amount of data on sales and rentals at a suburb or locality level. In these situations, it becomes valuable to consider the data at the Local Government Area (LGA) level (default view in the ranking table) in order to get a more comprehensive view of the property landscape in that region. As an alternative you may also choose to rely on data from a neighbouring suburb where available.

Please bear in mind that even though some suburbs may not appear in our ranking table, they are by no means absent from our platform. You can readily locate over 8,000 of them by utilising the search bar situated in the top right corner of the site menu.

“Any” bedroom filter enables a more granular view of the data i.e. it shows typical price, rent and yield data per number of bedrooms (where available) as well as the overall figures under the default “All” bedrooms view.

You’ll notice that clicking on this filter enables an additional column in the ranking table (Bdrms) that shows the number of bedrooms pertaining to the data in each row.

This filter is helpful on low budgets as it enables you to capture markets where the overall typical price is above the defined threshold. For example, 2-bedroom houses at 400K with an overall typical price at 500K. If your budget is 450K you would capture 2-bedroom rows of data by setting the Max Price filter at 450K if you also enabled the “Any” bedrooms filter. 

Note that data per number of bedrooms is not available for all suburbs due to the way typical price is calculated (explained in the next question).

HtAG uses a distinct method to calculate property values, differing from other data providers. Instead of relying on unbalanced median values, it uses a data fit process and averages across series based on the number of bedrooms. The end result is a more reliable and insightful evaluation of property prices within any given area.

The process to arrive at the typical price of properties within an area is twofold. The method applied depends largely on the data composition:

If there is a sufficient number of sales for different categories based on the number of bedrooms (such as 2-bedroom, 3-bedroom, 4-bedroom, and 5-bedroom houses) within an area, the model first computes the typical values of relevant bedroom series through a data fit process. The overall value is then arrived at by averaging all bedroom series.

When there isn’t adequate sales data to form a bedroom series, the model turns to drawing an overall price through the data fit process of all recorded sales, deriving a representative property value for the suburb.

It’s important to note that the resulting value is different to median prices reported by other providers. Our process gives a clearer and more informative impression of the typical property value in a specific suburb or council area.

We understand that price values hold the most importance when potential buyers can correlate them with the number of bedrooms of the properties in their search area. Therefore, the model’s focus is on generating typical values for each bedroom category.

Furthermore, averaging the values of each ‘number of bedrooms’ to represent the general value of a suburb ensures that each category gets equal importance, leading to a fairer representation. This approach aids in eliminating any bias in markets that may be dominated by a particular type of property, such as a 3-bedroom house.

Where there is not enough historical sales data to produce typical values, the last known typical value is used to impute the data in the preceding quarters. For example if the sales activity within an area accumulates a significant number of sales in Q1 2018 with no prior sales data recorded, the typical values between Q1 2008 – Q4 2017 are imputed using the Q1 2018 data.

Imputed historical values are common in new/developing markets with new land releases or recent surges in developer activity. Some rural locations with low sales volumes can also exhibit this pattern.

Have a question about a property market metric we report on? Visit the Data Dictionary page to learn more.

Section that answers common questions related to HtAG Analytics forecasts. Click on the question titles below to expand the tiles and see the answers.

Our predictive model leverages a machine learning algorithm to create long-term price trend. This model is regularly back-tested to determine the error rate, which helps identify market volatility and potential growth channels for future price increases. To access the error rate, simply enable the Expert View feature within the table sections on our dashboards.

Please be aware that long-term trend projections should not be solely relied upon; while they are significant, they constitute just one of the many metrics that our ranking algorithm, the Relative Composite Score, incorporates for effectively classifying and comparing markets.

This question is best answered with an example. A suburb’s typical price was $500K a year ago with a forecast of $520K. The actual current typical price is $530K. Use this formula to calculate the error: abs(actual-forecast))/actual *100 or (530K-520K)/530K * 100 equal to 1.9% rounded error.

We have developed this metric in the interest of transparency regarding the accuracy of reported data. High confidence is closely associated with the volume of sales recorded in a particular area. In essence, the greater the number of sales, the higher the confidence level.

Projections can have a variance of several percent between data releases and should be interpreted as a range value. Slight short-term variations occur as new data is recorded and inputted into the model, resulting in value fluidity between data releases.

Our growth rate cycle (GRC) graphs plot the YoY rate of change and not the actual typical price. The decreasing values on the graph that are above the 0 X-Axis (thick red line) indicate that the rate of growth will remain positive but significantly lower than the growth in the preceding years.

In contrast to that, an increasing trend that is below the 0 line, indicates that the prices are declining but at a slower rate.

First scenario highlights that the market has reached its’ peak and the growth rate is beginning to slow down. Second scenario is representative of a declining market that has the potential to reach its’ bottom in the near future.

Section that answers questions related to the website search feature. Click on the question titles below to expand the tiles and see the answers.

Jump to our advanced search page and give it another try. We did our best to recognise typos and phonetic searches. In a rare case when you can’t see your suburb in search results, try searching for a shortened version of the name i.e. “Druitt” instead of “Mount Druitt”.

Property market data is available for approximately 8,000 suburbs in Australia. If a suburb is not listed, it indicates that there were not enough statistically significant sales recorded in that area. To accurately calculate a suburb’s typical price, we require a minimum of 2-3 sales per quarter. Suburbs that do not meet this minimum threshold are excluded from our ranking tables.

This section answers miscellaneous questions by our subscribers and prospect customers. Click on the questions below to expand the tiles and see the answers.

You can cancel your subscription any time from the Account page. Cancelled subscriptions remain active for the duration of the billing cycle. For example if you signed up on the monthly plan on the 1st of January and cancelled your subscription on the 15th of January, it will remain active until the 1st of February.

Not at this stage. However, if you are assessing an actual property, feel free to examine the metrics reported for the relevant suburb and number of bedrooms.

Yes, click on the Generate PDF button on any LGA or suburb page (Professional subscription required).

Yes. All data available on the platform is accessible via our Snowflake Data Listing. You will need a Snowflake account to access the data directly on Snowflake or via an API. Contact us for details.