The first icon displays a summary of key suburb data. If you click on an adjacent suburb on the map, you will see the same data panel display allowing you to compare instantly.

Unit Median Asking Price.

Taking all current listings and sorting them from the highest to the lowest price, we select and display the middle value. The median asking price is a good indication of what you would expect to see when you start researching properties for sale in this suburb.

Unit Median Asking Price 12-Months Ago.

These helps you determine if the market has changed significantly in the last 12-months. For larger suburbs with higher volumes of listings and house prices that are considered more ‘normally distributed’, a change in price over the last 12-months can often indicate a genuine change in values. For other suburbs with lower volumes of sale or less reliable price distributions, a change in price here can often be a reflection of the price differences in the types of properties listed between then and now.

Unit Listings.

This is an average of how many listings you should expect to count on an average day. You will often find that when you search for listings on one of the portals that this figure may be higher or lower than what you count on any given day. However this is what we have measured as the average when we do our counts over several days spread throughout the month. The number of listing sis a handy measure to tell you how tight the market is and also indicate how reliable some the key measures used may be, including some of the heat map results.

For many suburbs across Australia you will find very few unit listings, especially in regional Australia. In suburbs with few listings and sales they are often statistically unreliable.

Unit Inventory.

Unit inventory score is calculated using (a) the average number of unit listings and (b) the average number of sales. Using (a)/(b), this provides a demand-supply-ratio for units. The lower the score the better. As a general guide;

When using the heat map or viewing an inventory score be careful not to jump to a decision too quickly before doing a few checks. Always look at the number of average listings. If the count of listings is very low, yet the inventory level is high, then it is often better to look at the surrounding suburbs and overall Statistical Area 3 (Sa3) to get a more robust measure.

Throughout 2020 and 2021 we have seen a number of suburbs with a very low inventory score and what we would classify as strong sellers markets. One obersvation has been that the longer a suburb remains classified as a strong sellers market the higher the price growth. Generally speaking, it does not matter if the suburb inventory score ranges from 1 to 2 (etc), as along as it stays under 3 for a prolonged period. We don’t expect too much easing of upward price pressure until we see suburb inventory levels increase and become more balanced.

Unit Inventory (3,6 And 12 Months Earlier).

Unit inventory trends a an important metric when it comes to price growth. You will not only find the current inventory score driving prices – it is also the trend. Many suburbs have current inventory levels of 7 or higher but have been experiencing price growth, primarily due to the inventory level trending down over the last 6 to 12-months. So when looking at the inventory score, always look at the changes at a suburb level and at a Statistical Area 3 (Sa3) level and even look at the surrounding suburbs.

Unit Inventory Forecast.

Over 150,000 data points and used within machine learning to help us forecast inventory levels 6-months into the future. This is done using data as of 6-months ago and trained on the current inventory level for the Statistical Area 3 (Sa3). Using an inventory score in the models has been selected over prices, as this gives us a more robust result in terms of accuracy and less volatility. Like any model, they all have limitations and should always be treated with caution. We recommend looking closely at the current inventory score and the trend over the last 12-months, using the forecast to help determine where this trend might likely be in the near future.