Instant suburb statistics and forecast for houses, units and rental markets.
House median asking price (12m ago). As above, this helps give you a snapshot of the market as of a year back. For many smaller suburbs with fewer sales, it is common to see price volatility. It is also common to see many suburbs with multiple price distributions, especially long beachside locations. This often creates confusion about the suburb median, especially when using it as a reference for changes in house values. More often than not, you should focus on the Sa3 index to get a better understanding of price trends and use the suburb median as a quick reference only.
House listings. This is the average count of listings we would see on a typical day within a given month. You can often find the current listings count might be higher or lower than this average. We measure the counts several times throughout the month to arrive at our average, which is used in many key metrics including inventory. For buyers, this can also help you quickly understand the size of the market within a given suburb.
House inventory. Inventory calculates how many months of stock exist in a given market. We take a) the total number of average listings and b) the total average sales per month and create a ratio: a/b. Based on our approach, 0 to 2.99 months is a strong sellers market, 3 to 4.99 is a sellers market, 5 to 6.99 is a balanced market, 7 to 8.99 is a buyers market and over 9-months is a strong buyers market. For convenience, we display a maximum value of 10. Suburbs with very few sales are hard to validate and measure, so as a general rule if your suburb has fewer than 3 listings you are best to look at surrounding suburbs or the Sa3 region.
House inventory (3m ago | 6m ago | 12m ago). This helps you see the short terms trend. What is often the case is a suburb that has remained below 3-months (strong sellers market) for 3 months or more is the most likely to see a price increase. For example, our analysis shows us it is both the inventory level at the current time and the total number of months that is stays low for is what drives an increase in prices.
House inventory forecast (12m). Here we use hundreds of time-series data points as well as Census and economic data within a machine learning model. The predictor is inventory (rather than price) as we find this is a much more robust model. Obviously models like this have limitations, and like most machine learning models suffer from ‘over-fitting’. As new data is collected that might have a large variation from prior months these models can often create large swings. As such, we seek to focus on data that is least prone to random and large variations so our predictions don’t sway too much between months.
House & unit vacancy now. Vacancy rates are calculated by taking the total number of rental properties (all residential property types) that have been advertised for 21-days or longer. This we classify as a ‘vacant property’. Next we compare this to the total number of properties rented within that suburb by a real-estate agent. We know this by using the Census data (ref 2016 Census G33). As a general guide, anything below 0.99% is critically low, between 1 and 1.99% is very low, 2 to 2.99% is relatively balanced and over 4% is high.
House & unit vacancy (3m ago | 6m ago | 12m ago). Where the vacancy rate has changed significantly within 3, 6 or 12-months we will often see the rental prices shift with a very high level of correlation. For example, in select markets like Inner Melbourne (as of April 2021) we have seen vacancy rates of over 15% result in Sa3 median price adjustments of -15%. We have also seen many suburbs falling below 1% result in price increases of 10% or more as renters seek to bid up prices and secure scarce rentals.
House rent median. This is the suburb median rent. Just like sales data, suburbs with smaller samples are prone to volatility. However in the case of rental prices they are often less of a problem than sales prices when it comes to medians.
House rent median (12m ago). This helps guide us as to market changes between rental reviews. However for formal rental reviews and index calculations, we strongly recommend using the Sa3 rental data.