The Market Velocity Index: How to Measure Property Demand Honestly

Ask any property professional how hot a market is, and you will get an answer. Ask them how they know — and the answer dissolves.

Auction clearance rates are a feeling, not a measurement. Hotspot lists are narrative dressed up as data. Price growth is a lagging indicator by definition: by the time it shows up in the median, the move has already happened and you've missed it. Sales volume on its own rewards size, not heat. Days on market on its own can be flattered by a single freak result.

The deeper problem isn't that any of these indicators is wrong. It's that there has never been a single, honest, universal way to standardise demand — to put a number on how hot a market is that actually behaves like a unit of measurement. Without that standard, the property industry has been doing something it would never tolerate in any other asset class: making multi-million-dollar capital decisions based on the gut feel of whoever happens to be talking loudest in the room.

The Market Velocity Index is our attempt to fix that. It is a single number, calculated at the level of one specific property type and bedroom count in one specific suburb, designed from first principles to measure real demand the same way everywhere — and it's built to support the two questions professionals actually ask:

• The like-for-like question. Across all 3-bed houses in Greater Melbourne, which suburbs are showing the strongest demand right now? This is the question a buyer's agent screens with, a developer compares sites with, an investor builds a yield-equivalent shortlist with. Same asset class, ranked across geography. The MVI was built for exactly this, and it's the most common way the score gets used in practice. You can see this view across both our benchmarking tables and our choropleth maps.
• The cross-market question. Should I be looking at 2-bed units in Dee Why or 4-bed houses in Pakenham? Different asset classes, different geographies, ranked against each other on a common scale. This is harder — it's why the formula is engineered the way it is, and why we can only show this comparison on table-based benchmarking views, not on choropleths (a single map can't honestly render two different asset classes at once).

Both questions need the same underlying property: a score that behaves the same way in a tiny coastal pocket and a major urban corridor. That's the standardisation problem the MVI exists to solve.

This is how it works, why the math is built the way it is, and why we had to rebuild the whole thing once already to make it work properly.

The Monday Morning Problem

Two micromarkets land on your desk.

The first sold its last three properties in fourteen days each. Blistering speed. Headline-worthy.

The second is sitting at twenty-three days on market — a noticeable beat slower — with fifty-two sales behind it last quarter.

Without a universal way to score them, the choice between these two markets becomes a vibes contest. The faster one sounds better. The bigger one feels safer. Most property reports will quietly favour whichever one fits the article's pre-existing thesis. None of them will give you a defensible reason why.

The honest answer requires looking at two things at once — and understanding what each one is actually telling you about the humans on either side of the transaction. So before we get to the formula, we need to spend some time on the psychology of the two raw inputs. Because the MVI isn't a clever piece of math sitting on top of arbitrary numbers. It's a clever piece of math sitting on top of two of the most behaviourally rich signals the property market produces.

What Days on Market Is Really Telling You

Days on Market is, on its surface, the most boring statistic in property: the median number of days between for sale and sold.

Look harder, and DOM is one of the closest things the property market produces to a live readout of buyer-seller psychology. To see why, compare what actually happens during a fast sale and a slow one.

In a market with 9-day DOM, here is the typical sequence: the property hits the portals on Wednesday. The first inspection is Saturday — by which point three or four buyers have already requested the contract and run their finance pre-approvals. By the end of that weekend, multiple offers are on the table, often above the asking price. The agent isn't negotiating in any meaningful sense; they're running an auction by another name. The property is unconditional inside two weeks because every buyer in the queue knows the next person behind them will move faster if they hesitate.

In a market with 60-day DOM, the same property hits the portals on Wednesday and nothing happens. Inspections are sparse. The first offer, if it arrives, comes in three weeks later and is well below the asking price. The agent goes back to the seller and asks for a price reduction. The seller refuses for a fortnight, then capitulates. A buyer who walked through six weeks ago re-emerges, makes a low offer, and successfully negotiates conditions that would have been unthinkable in the faster market.

These are not just two different speeds of the same process. They are two different processes, driven by two different psychological conditions on each side of the transaction.

What a low DOM is actually capturing

When properties move fast, two specific things are true at the same time — and both are rarer than most analysts assume.

The first is on the buyer side: buyers are afraid of losing the property more than they are afraid of overpaying. In most markets, buyers are the more cautious side of any transaction. They walk through twice. They wait for the building inspection. They negotiate. When DOM collapses, that pattern breaks: buyers skip second inspections, offer above asking, waive conditions, do whatever is necessary to not lose this one. That behavioural shift — from regret-aversion to loss-aversion — is the buyer-side fingerprint of a hot market.

The second is on the seller side: seller price expectations are anchored close to what the market is actually willing to pay. This sounds trivial but isn't. In most markets — particularly cooling ones, or recently-peaked ones — sellers carry expectations from twelve months ago that the market has long since left behind. Those properties sit, because no buyer will validate the seller's number. A low DOM means sellers are pricing in step with current demand, not nostalgic demand. The two sides have arrived at the same answer, and the deal closes.

A low DOM, in other words, isn't just "speed." It's the data fingerprint of a moment when buyer urgency and seller realism happen to coincide — and that moment is what professionals call a transactional market.

What a high DOM is actually capturing

A property sitting on the market for sixty days is not a neutral statistic. It is recorded evidence of disagreement. Either:

• The seller's price is anchored above what buyers will pay, and the seller is waiting for a buyer who never arrives.
• The buyers in this market have lost their urgency — interest rates have shifted, sentiment has cooled, alternatives have emerged — and they're choosing to wait.
• The product itself isn't matching what the market currently wants in this location at this time.

Whichever of these it is, friction is the signal. The deal is harder to close. And friction in property markets compounds against you — sellers who don't sell quickly start lowering prices, which signals weakness to other buyers, which lowers prices further.

This is the part most analysts miss: DOM moves before price does. By the time a median price has rolled over, DOM has been telling you the story for months. It is not a coincidence indicator. Read correctly, it is a forward-looking one.

Why the median, not the average

One technical point worth pausing on. We use the median DOM, not the average. Property markets produce nasty outliers — the deceased estate that sat for 400 days, the development site that took two years to settle. Averages get yanked around by these outliers; medians don't. The median tells you what the typical transaction in this micromarket looked like, and the typical transaction is what you're trying to understand.

What Sales Volume Is Really Telling You

If DOM is about the psychology of the individual transaction, Sales Volume is about the psychology of the crowd. It answers a different question entirely: not "is this deal closing cleanly?" but "is this market actually a market, or are we looking at a handful of disconnected transactions pretending to be one?"

Volume is the proof that demand is shared

A single buyer paying a high price for a single property tells you nothing about the market. It might be an emotional decision, an off-market negotiation, an inheritance situation, a developer assembly play. One transaction is one story.

Fifty buyers paying fast prices for fifty properties tells you something fundamentally different. Fifty buyers cannot be coordinating. They cannot be making the same emotional mistake. They cannot all be paying inheritance money or running off-market deals. When fifty separate buyers, each making their own decision, all arrive at similar conclusions about value and similar urgency about closing — that is the closest thing the property market produces to a vote.

Sales volume is that vote count. It is how the market converts individual decisions into a collective signal. A high volume number means demand is broad — many independent decisions, all pointing the same direction. A low volume number means demand is narrow — possibly real, possibly not, but in either case impossible to verify because the sample is too small.

Volume is also a measure of liquidity

There's a second dimension here that matters enormously to anyone making a real decision. Volume is a proxy for how easily you'll be able to transact in this market when you need to.

A 4-bed house corridor doing 400 sales a year is liquid. If you buy in, you can sell out — there are buyers, the price discovery is robust, and the next transaction is a few weeks away. A 2-bed unit pocket doing 8 sales a year is not liquid. Even if every one of those 8 sales is fast and at a strong price, your ability to exit when you need to is constrained by the rate at which buyers naturally appear. That's a different kind of risk, and it doesn't show up in price data.

Professional investors price liquidity. Retail investors usually don't. The MVI forces it into the conversation by making volume a structural part of the score.

But volume on its own is a trap

Here is the catch — and it is the catch that most "top suburbs" lists fall directly into. Volume is highly correlated with size. Big suburbs with big housing stocks produce big volume numbers, almost regardless of how hot they actually are. Sydney's middle ring will out-volume any growth corridor in regional Australia not because it's hotter, but because it's bigger.

Use volume on its own as a heat indicator and you'll spend your career re-discovering that established urban suburbs have a lot of houses in them. Which is true, but not interesting, and definitely not actionable.

So volume alone — much like DOM alone — is a partial signal that misleads as often as it informs. Which brings us to the only honest answer: read them together.

Reading DOM and Volume Together: The Four Signatures of a Market

This is where the analysis stops being a description of two metrics and starts being a diagnostic framework. Because DOM and Volume don't just combine arithmetically inside the MVI formula — they combine qualitatively into four distinct market signatures, each with a different story about underlying fundamentals.

If you internalise one section of this article, make it this one. The four signatures are how you read what kind of market you're actually looking at.

Signature 1 — High Volume + Low DOM: a fundamentally sound market

This is the textbook hot market, but the textbook description undersells what it's actually telling you. When a micromarket is producing both high volume and low DOM at the same time, you are looking at a place where the underlying fundamentals are genuinely working.

Specifically, four things are usually true:

• The price point is accessible to a deep pool of buyers. A market can only do high volume if there are many qualified buyers within reach of its prices. If the price point has run too far ahead of the local buyer pool, volume contracts — even if the suburb is desirable. Low-DOM-high-volume markets are almost always sitting in a price band that the broader market can transact at.
• The amenity case is real. Schools, transport, employment, lifestyle infrastructure — something is genuinely pulling buyers toward this micromarket on its merits, not just its price tag. Pure affordability without amenity produces volume but not speed; pure amenity without affordability produces speed but not volume. You need both halves of the value proposition firing for both halves of the signal to fire.
• Supply is constrained relative to that demand. There aren't enough properties to satisfy all the qualified, interested buyers, which is what produces the competitive bidding behaviour that collapses DOM in the first place.
• The product type matches what the market wants. A 3-bed house corridor doing high-volume-low-DOM is selling 3-bed houses to people who actually want 3-bed houses. The product-market fit is right.

When all four conditions hold, you get the signature. It's the rarest of the four signatures and the most valuable to identify, because it represents broad-based market endorsement of underlying value rather than speculation, hype, or a thin sample.

Signature 2 — High Volume + High DOM: a desirable market that's currently mispriced

This is the most misunderstood signature, and the one professional analysts most often misread.

A market that's producing real volume but laboured DOM is telling you something specific: the fundamentals are pulling buyers in, but something about the current offering is creating friction. The volume proves the demand is genuinely there; the DOM proves that demand isn't converting cleanly.

There are three usual culprits:

• Seller expectations have outrun the market. Vendors are anchored to recent peak prices — often informed by the 12-month-ago narrative — while buyers, looking at current conditions, are unwilling to validate those numbers. The properties eventually sell because the underlying location is desirable, but only after extended marketing periods and price reductions. This is what a market looks like in the early stages of cooling.
• The product mix is a poor fit for current demand. A suburb might be flooded with older 3-bed units when the buyer pool wants 2-bed apartments or 4-bed family homes. Volume is healthy because something is selling, but each individual transaction is laboured because the average property isn't quite what the average buyer is hunting for.
• The market is in transition. Gentrifying suburbs often produce this signature for a year or two before tipping into Signature 1. The new buyer profile (younger, professional, willing to pay more) is showing up in volume, but the existing stock and existing seller expectations are still calibrated to the old buyer profile. The mismatch resolves over time as the market re-prices, but during the transition itself, DOM stays elevated.

In all three cases the signature is telling you the suburb is fundamentally desirable — that part is settled — but that the current pricing or product mix is the problem. For a developer, this can be an extraordinary signal: real demand for the right product is right there, waiting to be served. For an investor, it's a yellow flag pending diagnosis: which of the three causes is in play, and is it resolving in the right direction?

Signature 3 — Low Volume + Low DOM: usually a mirage, occasionally a leading indicator

Most of the time, this signature is the trap the MVI's reliability gate is built to catch. A handful of fast sales in a thin micromarket can produce alarming-looking DOM numbers that mean nothing — three deceased-estate buyers happening to move at the same time, a developer quietly assembling, a lifestyle migration cluster of friends moving in together.

But occasionally — and this is the genuinely interesting case — a persistent pattern of low-volume, low-DOM in a market that has historically been quiet is the early signature of a coming breakout. The first wave of a thesis typically arrives quietly: a small number of well-informed buyers spotting something the broader market hasn't priced in yet. New infrastructure announcement. Major employer relocating. Demographic shift. Zoning change.

If you see this signature persist across two or three quarters in a market that previously sat dormant, the question to ask isn't "is the MVI score reliable?" — the formula's reliability gate handles that — but "what do these early buyers know that the broader market doesn't yet?" The signature itself is the question worth investigating.

Signature 4 — Low Volume + High DOM: a cold market, but not a meaningless one

A market with thin volume and extended DOM is, on the surface, simply uninteresting. But the combination still tells you something diagnostic about why.

• Price-point misalignment. The market is priced too high relative to comparable surrounding areas, given its actual amenity. Buyers exist, but they're choosing alternatives.
• Declining fundamentals. A major employer leaving, a school catchment change, a perceived safety or quality-of-life shift. These show up here before they show up in price.
• Structural product mismatch. Oversupply of a product type the local buyer pool doesn't actually want — most commonly older units in markets that are increasingly family-oriented, or oversized houses in markets that have shifted toward downsizing demand.

A persistent Signature 4 reading is often a sell signal for anyone holding existing exposure — and a do not enter signal for anyone considering it.

A Note on Absolute Levels: Each Market Has a Native Profile

Beyond the four combinatorial signatures, there's one more layer of insight worth holding in mind. Every market has a native profile — a typical range of DOM and Volume that reflects its character — and elite MVI scores look different in different contexts.

• Blue-chip established suburbs (premium amenity, stable demographics, mature housing stock) tend to operate at moderate volumes with consistently moderate DOM. Their elite-MVI signature isn't extreme on either axis — it's consistency. The score earns its place through year-after-year stability rather than spectacular numbers in any single metric.
• Growth corridors (affordable, new-build, attracting first-home buyers and relocators) tend to operate at very high volumes with very low DOM. Their elite-MVI signature is exuberant — high numbers across both axes — driven by new-buyer-cohort demand meeting newly-released supply.
• Luxury and prestige markets typically operate at low volumes with moderately higher DOM. Their elite-MVI signature is rare to see at the top of rankings, because the formula is calibrated to reward depth of demand — and luxury markets, by definition, have shallow demand pools. This is correct behaviour, not a flaw: a luxury market is a fundamentally different asset class from a transactional residential one, and the MVI is honest about not flattering it.
• Recovering or post-cycle markets (Perth and parts of QLD over the last few years) often produce the most spectacular MVI readings, because they combine the volume of a mature urban market with the DOM compression of a growth corridor. The elite signature here is the confluence of scale plus speed in a market that recently had neither.

Understanding which native profile a micromarket sits in is what separates a literal MVI reading from an interpreted one. A 12M MVI of 350 in a luxury micromarket means something different than a 12M MVI of 350 in a growth corridor — same number, different stories.

The Formula

Here it is, in full — the calculation that runs against every viable micromarket on the platform every month.

Three terms. Each one is doing specific work, and the reasoning behind each is worth understanding because it explains exactly what the score rewards — and, just as importantly, what it refuses to reward.

Term 1 — Volume^0.65: depth, with diminishing returns

Volume is raised to the power of 0.65 — deliberately less than 1. This is the most important design decision in the whole formula, so it's worth being precise about what it does.

A linear treatment of volume (just Volume itself) would say a market with 200 sales is twice as good as one with 100. A quadratic treatment (Volume²) would say it's four times as good. The 0.65 exponent says it's about 2.4 times as good. The bigger market still wins, but it doesn't crush the smaller one geometrically.

Why does that matter? Because of the size-bias trap we covered earlier. A 4-bed house micromarket in middle-ring Sydney might do 400 sales a year. A 2-bed unit micromarket in regional Queensland might do 60. If you reward volume too aggressively, the Sydney market wins every comparison on size alone — and you stop being able to see the regional market's quality of velocity at all.

The 0.65 exponent is the mathematical way of saying: depth is real, depth matters, but past a certain point, additional depth is gravy. We care more about how cleanly a market is moving than how big it happens to be. This single parameter is what makes both like-for-like ranking and cross-market comparison possible — and what makes the MVI a standardised unit of demand rather than just another size-weighted hotspot list.

Term 2 — (DOM + 2)^2.2: friction, punished sharply

Days on market sits in the denominator, raised to the power 2.2. Friction compounds against the score fast: a 9-day DOM scores roughly 2.7x higher than an 18-day DOM with everything else held constant — not 2x, because slow markets aren't proportionally less hot, they're disproportionately less hot.

This reflects the psychology we covered earlier. DOM doesn't move linearly with market temperature. The difference between 9 days and 18 days isn't just "twice as slow" — it's the difference between a market where buyers are competing in a frenzy and one where they're calmly considering options. Two qualitatively different worlds.

The "+2" inside the bracket is a small floor that prevents the score from blowing up on freak edge cases (a market with a 1-day median) and reflects the reality that even the most efficient markets carry some minimum friction.

Term 3 — exp(−(DOM + 2) / 40): the conviction filter

The final term is an exponential decay. It does something the polynomial penalty in Term 2 cannot: it captures the qualitative difference between a market that's merely fast and one that's moving with overwhelming, almost frictionless conviction.

Past about 30-day DOM, this term starts driving the score toward zero regardless of how much volume the market has. It's the part of the formula that says: at a certain point, a market either has true momentum or it doesn't, and no amount of transaction count can fake the difference. A 60-day DOM market with 1000 sales is not a hot market with a lot of transactions. It's a busy market with a problem, and the score should reflect that.

One last piece: the reliability gate

Before any of the above runs, the DOM input itself is filtered for honesty. The formula uses the median DOM from actual transactions only when there are at least 10 of them in the relevant window. Below that threshold, it falls back to a broader adjusted figure that draws on neighbouring data.

This stops the obvious failure mode — three or four freak quick sales in a sleepy micromarket producing a misleading "fast market" reading. Statistical credibility is built into the input, not bolted on at the end. It's the boring kind of engineering that nobody notices when it's done right, and that everyone notices when it isn't.

A Note on Evolution: Why the MVI You're Reading About Is the Second Version

The MVI didn't always look like the formula above. The first version we shipped was sound in spirit but flawed in geometry — and once we noticed the distortion, it was hard to unsee.

Here's what we used to run:

Three things were different, and each created a specific bias.

Volume was squared. A market with 400 sales a year scored 16x higher than one with 100, before any other factor entered the calculation. The result was depressingly predictable: large urban suburbs dominated the rankings on sheer scale. Small, fast, high-conviction micromarkets — exactly the ones a working professional most needs to find — were buried beneath them. The fix was the move to Volume^0.65: same direction of travel, far gentler slope, universal comparability restored.

DOM was cubed. Raising the DOM penalty to the power 3 was, in hindsight, overkill. Even a perfectly healthy 25-day DOM became almost impossible to recover from, regardless of the underlying volume. The score collapsed too aggressively on the kinds of established, proven markets where most professional decisions actually get made. Lowering the exponent to 2.2 preserved the principle (friction punishes you) without becoming punitive about ordinary market behaviour.

The decay was twice as impatient. The old exponential used a divisor of 20 instead of 40. It pushed scores toward zero at DOM levels that are completely normal in established suburbs, again favouring only the very fastest markets. Relaxing it widened the band of markets the MVI can resolve meaningfully without losing the conviction-filter behaviour that made the term valuable.

The cumulative effect: the old MVI was answering the question "which markets are largest and fastest?" — and giving the same handful of answers most months, because size dominated. The current MVI answers "which markets have the highest quality of velocity for their size?" — which is the question that actually leads somewhere investable.

The old formula is still computed in our database as market_velocity_index_old, retained for historical comparison and audit. Running both side-by-side on any given month is one of the more illuminating things you can do as a user: the divergence on small-but-fast micromarkets is striking, and it's where the new formula earns its keep.

Reading the Score: Two Independent Lenses

Here is something important to understand before you start using the MVI in practice. The platform calculates the score over two different time windows — twelve months and three months — and these two scores are not directly comparable as numbers.

This is a subtle point that catches people out, so it's worth being explicit about. The 12-month MVI is built from a full year of sales data. The 3-month MVI is built from a single quarter. The volume input alone differs by roughly 4x in scale, and because Volume^0.65 is non-linear, the resulting scores live in different numerical ranges. A 12M MVI of 400 and a 3M MVI of 150 are not measuring the same thing in the same units. Doing arithmetic between them — subtracting one from the other, taking ratios — produces nonsense.

What you do instead is treat them as two independent readings, each interpreted on its own terms, with the narrative between them being the diagnostic.

The 12-Month MVI — structural conviction

A full year of transactions filters out seasonality, hype cycles, and any single hot quarter masquerading as a trend. What remains is structural — markets where high velocity is a persistent characteristic, proven across enough turnover to be trusted.

For anyone underwriting decisions that have to survive a few quarters — site acquisitions, fund allocations, multi-year holds — this is the lens that matters. It's also the lens to start with: if a market doesn't show up well on the 12M MVI, almost nothing the 3M MVI can say about it will be enough on its own.

It answers: where has deep, widespread conviction proven durable over time?

The 3-Month MVI — momentum in motion

Three months is a smaller, more volatile sample by design. Read in isolation it's a noisy signal. But its purpose isn't to be read in isolation — it's to give you a fresher read on what's happening right now, against the structural backdrop the 12M MVI provides.

The right way to use the 3M MVI is to rank it against itself — compare a micromarket's 3M score to other 3M scores, never to its own 12M score — and then ask the narrative question:

• A market that ranks highly on both is a blue-chip performer: durable structural strength, and the most recent quarter is consistent with that strength. The market is doing what it's supposed to be doing.
• A market that ranks highly on the 3M but more modestly on the 12M is a breakout candidate: something has changed in the most recent quarter, lifting velocity above where the longer-term baseline would predict. This is where new investment theses are born.
• A market that ranks highly on the 12M but has dropped on the 3M is a decay signal: structurally strong, but recently losing pace. Often visible in the 3M weeks or months before it shows up in headline price data — which makes it a leading risk indicator for anyone holding existing exposure.
• A market that ranks modestly on both is exactly that: not currently interesting on either timeframe.

Notice that none of those reads requires comparing the numerical values of the two scores against each other. The information is in the ranking of each score against its peers, and in the relative position of the same market across the two views.

It answers: how is this market's momentum evolving, and where is it pointing?

A worked example

Suppose two micromarkets both show up in the top 5% of the 12M MVI rankings. By that lens alone they look equivalent. Now you check the 3M view:

• Market A also ranks in the top 5% of 3M scores. Conclusion: blue-chip. Structural and recent strength agree. Lower-risk allocation.
• Market B has slipped to roughly the 60th percentile on the 3M. Conclusion: decay signal. The structural story is still there in the long-window data, but the freshest quarter is telling you the engine is losing pace. Investigate before committing capital.

Same starting point, completely different decisions — and the MVI surfaced the difference cleanly. That's the value of holding both lenses at once, properly understood.

Where Elite MVI Scores Come From

A high MVI doesn't appear at random. It is the statistical fingerprint of real-world forces converging on a specific micromarket — and across Australia today, three forces are doing most of the work.

The Great Rebalancing

Years of price growth in Sydney and Melbourne, combined with permanent post-pandemic work flexibility, have produced a wave of demand radiating outward — interstate to WA and QLD, and to the urban fringes of every major capital.

The MVI footprint is unmistakable: a flood of motivated buyers (first-home buyers, relocating families, downsizers cashing out of expensive metros) hitting markets that were previously overlooked. DOM collapses because these buyers are price-insensitive relative to where they're coming from. Volume swells because the demand is genuinely broad. Growth corridors and affordable regional centres are where this trend prints its highest scores — and the signature is almost always Signature 1: high volume, low DOM, broad-based endorsement of underlying value.

The Post-Cycle Rebound

Many of today's elite-MVI markets — Perth, key Queensland mining centres — endured prolonged downturns after the last resources cycle. Years of stagnant prices absorbed historical oversupply. Then state economies diversified, population grew, and rentals tightened to sub-1% vacancies. The narrative flipped from risk to opportunity.

Investor demand floods in, and investor buying tends to be more decisive than owner-occupier buying — which compresses DOM hard. Strong yields keep the volume side of the equation high. The MVI surges accordingly. These markets often look "expensive" on price-growth charts and "cheap" on yield charts at the same time, which is exactly the contradiction the MVI is designed to resolve.

The Missing Middle

Inner and middle-ring suburbs of Perth and Brisbane especially. As detached houses in well-connected, lifestyle-rich locations price out a large segment of the buyer pool, demand cascades to the next-best alternative: high-quality units, villas, townhouses.

Constrained supply of that specific product type, meeting fierce competition for it, collapses DOM in a way the headline house-price story tends to miss entirely. Elite MVI scores hide in this stock for exactly that reason — they're invisible to anyone scanning at the suburb level, and only emerge when you slice down to the property-type-and-bedroom level the MVI operates on.

How Different Professionals Use It

The MVI is a different tool depending on the seat you're sitting in.

Developers use it for site acquisition and feasibility. A high 12M MVI in your target product type is hard evidence that absorption assumptions are realistic — and a low MVI in a market your competitors are entering is one of the cheapest red flags you'll ever get. The 3M overlay tells you whether the market is still accelerating into your delivery window or already softening. Signature 2 markets (high volume, high DOM) are particularly worth investigating: they often signal real demand for the right product, waiting to be served.

Buyer's agents and advisers use it to find growth corridors ahead of consensus, and to defend recommendations with transparent data. "This micromarket has a 12M MVI in the 95th percentile and a rising 3M reading" is a different kind of conversation than "the agent thinks it's hot." The like-for-like ranking — all 3-bed houses in this region, sorted by demand — is the most common workflow, and it makes the rare-but-critical "don't buy here" recommendation defensible in a way that pure narrative never can be.

Investors and fund managers use it on both sides of the book — for alpha (screening for breakout candidates where the 3M is well-ranked off a strong 12M base) and for risk (catching decay signals in existing holdings before they show up in headline price data). Used systematically, the 3M lens is one of the few ways to get a forward-looking read on portfolio momentum without waiting for price prints to arrive.

Beyond the Buy Market: The Rental MVI

Everything we've covered so far applies to the buy market — properties trading hands between owners. But the same logic, with a slightly different formulation, applies to the rental market — and we calculate a Rental Market Velocity Index that runs alongside the buy-side score.

The rental MVI matters because rental markets behave fundamentally differently to sales markets, and conflating the two is one of the more common analytical errors.

Rental markets cycle on much shorter horizons — leases turn over in weeks, not months. The decision-making is faster (most renters can move within four weeks; most buyers can't move within four months). The psychological asymmetry is also flipped: when rental DOM collapses, what's being measured isn't buyer FOMO meeting seller realism — it's tenant urgency meeting landlord pricing power, in a market where the landlord is the structurally advantaged party. A low rental DOM in a market with sub-1% vacancy isn't just "fast" — it's a market where tenants are actively losing the negotiation, and it tends to precede sharp rental yield expansion.

The rental MVI uses the same formula structure as the buy-side version, applied to leased volumes and rental DOM. Read together, the buy MVI and rental MVI tell you something neither can tell you alone: whether the demand pressure in a market is showing up first as buyer urgency or tenant urgency. The two often diverge — Perth's rental MVI was firing for two years before its buy MVI caught up — and that divergence is itself a powerful leading indicator.

We also publish a rental market velocity density index, which adjusts the rental MVI by suburb area to surface the markets where rental absorption per square kilometre is highest — useful for identifying genuinely supply-constrained zones at a more granular spatial level.

A Companion Metric: The Pure Demand Index

The MVI tells you how fast and deep a market is — how cleanly it's processing transactions. But there's a second question it deliberately doesn't answer: how much of that activity is being driven by genuine supply scarcity?

A market can be moving briskly with comfortably-replenished listings, or it can be moving briskly while the available pool of properties drains faster than new ones appear. Both look similar on the MVI. They are not similar at all in terms of what comes next — particularly in terms of price-growth pressure.

We publish a separate score, the Pure Demand Index (PDI), specifically to surface this dimension. The PDI is structurally similar to the MVI but replaces raw volume with the ratio of volume to available listings — making it a measure of demand intensity relative to supply rather than demand in absolute terms. Where the MVI tells you a market is moving, the PDI tells you whether the supply-demand balance is the thing forcing it to move.

If you're using the MVI to find momentum, the PDI is the natural companion to confirm whether that momentum has the kind of scarcity foundation that translates into price growth. We've written a full guide to it.

Where to Find the MVI on the Platform

The Market Velocity Index — including both the 12-month and 3-month timeframes, the rental MVI, and the supporting four-signature framework on the choropleth and benchmarking views — is available as part of our Plus+ add-on, which can be enabled on any plan tier. That means whether you're on a single-state Access subscription, a national Professional subscription, or an Executive plan, the MVI is the same metric, calculated the same way, available the moment you turn Plus+ on.

This was a deliberate decision. The MVI is not a tier-gated upsell — it's the spine of how we measure demand on the platform, and the standardisation argument we've made throughout this article only holds if the same score is available to anyone who wants to use it, regardless of geographic scope. Plus+ also unlocks the near-real-time 3-month rolling data window the 3M MVI runs against, the 40+ dual-metric heatmaps, and five years of historical depth — all of which compound the value of the MVI itself.

Final Thought

The property industry has spent decades trying to answer one of the simplest-sounding questions in finance — how hot is this market right now? — without an honest, standardised, universal way to actually do it.

The Market Velocity Index is our answer. Not a perfect one. Not a final one. But the first one we know of that is built from first principles to be directly comparable across micromarkets of wildly different sizes — whether you're ranking 3-bed houses against each other across a region, or testing a unit-versus-house thesis across asset classes — that fuses the psychology of individual transactions with the proof-by-numbers of the crowd, and that tells you where momentum is forming, not just where prices have already moved.

You started this article without a defensible way to choose between the 14-day-with-3-sales market and the 23-day-with-52-sales market. You now have one — and you have the framework that turns DOM and Volume from two numbers on a report into four signatures of underlying market fundamentals.

Stop reading about market shifts. Start seeing them before they happen.