Methodology

1. What goes into the model

PropRadar combines multiple categories of Australian property-market information:

• Listings: active for-sale listings across Australia, refreshed daily.
• Valuations: multiple independent Australian valuation estimates, combined into a single weighted estimate.
• Sold data: historical sold-price data, matched back to original listings where possible.
• Suburb statistics: median prices, yield, growth, vacancy, clearance rates, and demand metrics, refreshed on a rolling schedule.
• Socioeconomic data: Australian Bureau of Statistics (ABS) published indices.
• Walkability & transit: location and accessibility indicators used to compare everyday convenience.

The combination, calibration, and weighting is proprietary to PropRadar.

2. How we combine valuations

Each property can have multiple valuation estimates. Individual estimates disagree, sometimes by 15% or more on the same property. Rather than picking one, we combine them into a single blended estimate, with each estimate weighted by its historical accuracy against actual sold prices in our matched archive.

Weightings are recalibrated periodically against fresh sold-price evidence.

Every valuation also carries a confidence rating (High, Medium, Low, Unknown), taken from the valuation feed's own assessment. Low and Unknown confidence valuations are excluded from derived metrics like yield-based sorting to avoid misleading results.

3. Potential sell price

Our potential sell price estimates what a property is likely to transact for. The model blends several signals:

• The asking price - historically the single best predictor of what a property sells for, especially when the vendor and their agent have priced confidently.
• Independent valuations - combined across multiple estimates (see Section 2), used as a cross-check to the asking price.
• Sale type - auctions, private sales, and expressions of interest behave differently. The model adjusts for each.
• Price tier & property type - premium-priced homes, entry-level units, townhouses, and vacant land all negotiate differently.
• Local comparables - recent sold properties in the same suburb that match on type, bedrooms, and size.

Combined accuracy against actual sold prices: Mean Absolute Error ~4.3% - better than a typical bank valuation. Systematic bias is close to zero (the model is neither optimistic nor pessimistic on average).

4. Investment Score (0-100)

The Investment Score combines multiple weighted components into a single number that helps rank properties quickly. The score is calibrated so that most real properties fall in the 45-80 range, keeping the differences meaningful. The components we consider:

• Value Buy - how the asking price compares to our estimated value and recent local comparables.
• Market Dynamics - current vacancy rates, days on market, and auction clearance rates for the suburb.
• Capital Growth - historical suburb growth across 1-, 3-, 5-, and 10-year windows.
• Location - walkability, school catchments, CBD distance, and socioeconomic indicators.
• Yield - rental yield benchmarked against the suburb median.
• Price Efficiency - price per square metre compared to similar stock in the area.
• Risk deductions - poor data quality, high-yield anomalies, stale listings, or suburb-level red flags all reduce the score.

The exact weighting on each component is tuned against our matched sold-property archive and re-tested periodically. Validation on historical sales shows a strong, monotonic relationship: properties scoring 70+ sell roughly 8% above asking on average, while those scoring 0-30 sell about 5% below asking.

5. PropRadar Suburb Index, change over period

The PropRadar Suburb Index measures cumulative suburb-level price movement over 1, 3, 5, and 10 years. For each suburb and property type we combine available observed market movement with smoothed suburb-level index signals. Higher-volume markets rely more heavily on observed transaction evidence; lower-volume markets are smoothed, or marked as limited data where the sample is too thin.

All values are cumulative percentage change over the selected period (not annualised). Display values are clipped at sensible per-period bounds so a single low-volume sales-mix shock cannot produce extreme readings.

The PropRadar Suburb Index is not an official price series, valuation product, or real-time market measure. It is designed to help investors compare suburbs consistently using confidence labels that show where the evidence is thin.

Why our number may differ from others. Suburb-level "growth" has no single canonical value. Sold-median indices, asking-price indices, hedonic models, and automated-valuation indices each answer a different question and can disagree by 5-15 percentage points for the same suburb in fast-moving or thin-sample markets. We anchor on observed sold prices because that's what investors actually transact at; other published "capital growth" figures may report higher or lower numbers depending on their methodology.

6. Confidence tiers

Every PropRadar Suburb Index value includes a confidence tier so the metric can be interpreted honestly:

• High - strong transaction evidence; the value can be used as the headline figure.
• Medium - moderate transaction evidence; values are usable but display alongside a confidence note.
• Low - thin transaction evidence; values are smoothed and shown with a limited-confidence indicator.
• Limited - insufficient transaction evidence; we display an em-dash rather than risk a misleading number.
• Unknown - sample-size data not available; values are presented without a strong confidence claim.

Lower-confidence values are visually indicated and may be suppressed entirely when the underlying signal is too thin to support a number.

7. PropRadar Market Cycle

The PropRadar Market Cycle classifies each suburb into a phase (boom, rising, peaking, balanced, cooling, recovery, or trough) using a 2-D matrix of demand- pressure and momentum signals computed from PropRadar's own indices.

Phases are model outputs, not predictions. They require sufficient transaction evidence to be displayed. Suburbs with thin sample sizes are suppressed rather than classified incorrectly. The classification draws on vacancy pressure, rent and price momentum, days on market, vendor discount, auction clearance (where available), and comparable demand-side indicators.

8. Yield

Gross rental yield is calculated as:

yield % = (weekly rent x 52) / potential sell price x 100

Net yield (shown on property detail pages) subtracts estimated operating expenses (rates, insurance, maintenance, property management, utilities) but excludes mortgage costs, which are modelled separately in cashflow analysis.

9. Data quality flags

Properties with critical data issues are hidden from default views. You can toggle "Show data warnings" to see them. The kinds of issues we screen for include:

• Asking prices that look unusually low compared to the suburb, often parsing errors or incomplete listings.
• Implausibly high rental yields, usually caused by valuation errors on micro or student apartments, not genuine opportunities.
• Large mismatches between asking price and independent valuations, a signal that one of the inputs is unreliable.
• Retirement villages and similar stock with restricted resale terms, not a standard investment profile.
• Listings with incomplete or malformed data, which we'd rather hide than risk misleading numbers.

10. Limitations you should know

• Our valuations are statistical estimates, not professional appraisals, so individual properties can land outside the typical error range.
• Suburb statistics use public data that lags real-time conditions by weeks or months.
• Investment Score is a relative comparison tool, not a guaranteed outcome. Past returns don't predict future performance.
• We don't inspect properties. Our data can't detect condition issues, structural defects, or local context that affects value.
• New or thinly-traded suburbs have fewer comparables, which increases estimation error.

The data behind the methodology

We publish the back-tests behind the headline numbers. Each of these studies uses the same matched sold-price and valuation archive that drives PropRadar's rankings.

• Sold vs asking price across 19,764 properties
• Australia's highest-DSCR suburbs: DSCR across 12,000+ tracked suburbs
• Capital growth across 12,000+ tracked suburbs