Understanding Stock on Market (SoM) for Real Estate Investment

If you’re serious about property market research it’s crucial to get a grasp on the role of the ‘Stock on Market’ (SoM) metric.

Providing a rapidly accessible view of supply, this metric tallies the number of properties currently up for sale.

However, leaning too heavily on these unadjusted reported numbers can miss out on the wider context that different geographical realities lend to the property market.

After all, suburbs come in varying sizes, leading to a natural disparity in total stock availability.

That’s why adjusted SoM can provide further value to property investors in that they can now perform in-depth assessment of supply levels in any given market.

In this article we explore SoM and SoM% metrics and explain how to perform suburb shortlisting based on pre-established thresholds.

Additionally, we clarify an important nuance regarding how stock is measured at HTAG Analytics.

What is Stock on Market (SoM)?

Stock on Market is probably one of the most straightforward metrics requiring little explanation.

It is simply the number of properties currently listed for sale.

Whilst SoM provides a quick supply snapshot, the reported numbers are not put into the geo-demographical context of relevant property market.

Because some suburbs are larger than others, they will naturally have more total theoretical stock available.

Therefore SoM% is a more precise metric to go by when bench-marking different markets one to another.

HtAG’s Stock on Market Methodology

Key points

• Unsold listings only. HtAG counts properties still on the market at month-end. Methodologies that include sold listings inflate supply figures, especially in fast-moving markets.
• First-period counting. A listing is counted in the month it first appears and never recounted. Counting the same listing every month it stays active overstates ongoing stock.
• 3-month rolling average for current SoM. Tabular current-period values are smoothed across three months to dampen short-term noise. Charts use a rolling month.
• GPS-based suburb matching. Each listing is assigned to its true suburb by coordinates, not by what the agent typed. Critical in regional areas where listings get tagged to a more recognisable neighbour.
• Suburb-level granularity. HtAG reports at suburb/locality level rather than postcode.
• Monthly updates on the last calendar day, covering houses and units.
• Net effect. HtAG measures new supply momentum. Other methods measure total active inventory. The two should not be benchmarked against each other without adjustment.

Why methodology matters

Not all data is created equal. Two providers can publish a Stock on Market figure for the same suburb in the same month and produce different numbers. The difference is not error. It is methodology. Understanding what sits behind a number matters more than the number itself.

HtAG’s approach to SoM differs from other providers in four specific ways. Each choice has a reason.

Unsold listings, not all listings

A listings dataset can be built two ways. One captures everything that was on the market during the reporting period, including properties that sold within the month. The other captures only what remains unsold at the end of the period.

HtAG uses the second. The question a buyer or investor needs answered is what is available now, not what passed through the market in the last 30 days. A market that turned over 200 properties in a month but has 20 left at the end is a different market from one that listed 200 and still has 180 sitting. A combined figure obscures that.

First-period counting

Counting methodology matters as much as the source data.

HtAG counts a property in the month it first appears as a listing. If it remains active into the following month, it is not counted again. Each monthly figure reflects only fresh supply entering the market.

The alternative, counting every active listing in every reporting period, produces a different metric closer to real estate inventory levels, which measures the cumulative standing pool of properties on the market. A property listed for four months appears four times. This inflates the apparent total and dampens month-on-month variation, which makes it harder to read turning points in the market.

The trade-off is that first-period counting under-represents the standing pool of unsold stock. A buyer scrolling listings today sees properties from previous months too. That is real supply they can transact on.

The reason to count first-period anyway: the metric most useful for investment decisions is momentum. New supply entering each month is the leading signal. The standing pool is a lagging one. If new listings collapse for three months running, that is a market signal even if the standing pool looks healthy. The reverse is also true.

For users who want the standing pool view, see the real estate inventory levels metric. HTAG SoM specifically tracks momentum, inventory tracks accumulation. They answer different questions and both have their place.

3-month rolling average for current values

The current-period SoM shown in tabular form is a three-month rolling average of new stock added each month. Charts show rolling monthly values.

The reason is noise. Monthly listing counts at suburb level can swing 20 to 30 percent on small absolute numbers. A three-month window smooths the signal without flattening it. Users comparing suburbs side by side get a more stable comparison.

GPS-based suburb matching

Agents sometimes list a property under a more recognisable suburb than where it actually sits. A house in a quiet locality near Singleton in the Hunter Valley gets listed as Singleton. A property near Leura in the Blue Mountains gets listed as Leura. The motive is search visibility, not deception, but the effect is the same: the listing counts toward the wrong market.

HtAG matches every listing to its true suburb using GPS coordinates from the property record, not the suburb the agent typed. The difference matters most in regional markets and on the edges of metro suburbs, where mismatched listings can distort SoM figures by double-digit percentages in low-volume locations.

Comparison at a glance

So what’s a good SoM and SoM%? In order to answer this question let’s explore the data distributions of these 2 metrics.

Low, Balanced & High Supply Ranges for SoM

It’s important to highlight that the distributions presented in this article are ‘point in time’ and are reflective of the current Australian property market conditions.

These distributions change and shift as markets go through their usual cycles.

HtAG updates the ranges based on the latest data on the Data Dictionary page.

To distil this complex data, we place SoM values into categories (horizontal axis) and plot the volume of suburbs in each category range (vertical axis).

Our data illustrates that numerous suburbs boast no more than 10 active online ‘for sale’ listings for both houses and units.

Conversely, a few suburbs simultaneously host as many as 60 active listings.

We produced the histograms in this article by assigning SoM values to bins (horisontal axis) and plotting the number of suburbs falling into the bin range on the vertical axis.

Looking at the distribution of SoM we can see that a large portion of suburbs have no more than 10 active online ‘for sale’ listings for both houses and units. We can also see that only a few suburbs have as many as 60 active listings.

Generally speaking supply balances out at and around the mid-point i.e. between 25% and 75% quantiles.

Values below 25% or above 75% quantiles (green lines) are more likely to be reflective of low and high supply conditions respectively.

In this instance, SoM values under 5 are reflective of “low supply” markets for houses.

Difference Between SoM and SoM%

Given that SoM of 5 can mean different things in a suburb with 100 dwellings as opposed to a suburb with 1,000 dwellings, we need to benchmark the actual number of listings against total stock.

To provide a universal measure, we present the ‘Stock on Market Percentage’ (SoM%).

For a more accurate insight into the market, this metric takes the SoM and relates it to the number of dwellings in a particular area.

SoM% levels the playing field when comparing dissimilar markets, ensuring your benchmarks are precisely positioned.

Total number of units/houses is calculated based on the Australian address database.

So, how can we identify preferable SoM% values? Let’s cast a light on the data distributions of these two crucial metrics in the contemporary market.

In contrast to SoM, units have a smoother SoM% distribution curve compared to that of houses.

This means that ranges for units can be somewhat wider than for houses. However they are still in the same ballpark.

Both units and houses are more likely to exhibit low supply conditions (relative) at SoM% under 0.5% in this environment.

Tracking our end-month SoM figures can help investors to identify variances and trends over time, offering vital cues on shifts in the market.

Conclusion

SoM and SoM% reflect relative supply conditions in a property market i.e. council area or suburb.

By comparing SoM reported for suburbs within your research scope, you should be able to identify markets with low and high supply based on a predefined range.

You are likely to find better deals in suburbs offering higher SoM/SoM% if you are a buyer.

On the other hand, if you are a seller, lower SoM/SoM% are great indicators for little competition in the market from other vendors.

It will probably be challenging securing a property in markets with high competition from other buyers reporting SoM% under 0.25%.

This is assuming there is a high enough demand to match the supply.

Just remember: the Stock on Market (SoM) metric shouldn’t be employed as a standalone tool.

It should always be interpreted alongside other supply and demand metrics to get a robust overview of the market dynamics.

Staying informed and interpreting these metrics accurately can be the determining factor between secure property investment and risky ventures.