Comparative Market Analysis:
A Home Feature-Level Approach to
Property Valuation
How matching properties on 30+ structural, locational, and risk attributes produces more accurate comparables than traditional bedroom-bathroom-price methods.
Want a simpler overview? See how our CMA compares to traditional methods →
1. Abstract
Comparative Market Analysis (CMA) remains the primary method for establishing fair market value of residential property. The standard industry approach matches properties on three to four variables: bedrooms, bathrooms, land size, and recent sale price. This paper presents the Microburbs CMA methodology, which expands the comparison set to 30+ attributes spanning structural features, locational amenities, area-level investment metrics, and environmental risk factors.
2. The Problem with Traditional CMAs
Every property buyer, seller, and agent relies on comparable sales to anchor pricing decisions. The most common tools on the Australian market (Core Logic RP Data, Domain) match properties on a narrow set of criteria:
- Number of bedrooms
- Number of bathrooms
- Land size (where available)
- Proximity to the subject property
- Recency of sale
This approach has a fundamental flaw. Two 4-bedroom, 2-bathroom houses on 600 square metre blocks in the same suburb can differ by hundreds of thousands of dollars based on features these systems ignore.
Example: 9 Monterey Street vs 52 Douglas Street, St Ives
Both in St Ives, Ku-ring-gai. No. 9 Monterey sold for $6,200,000 (5 bed, 5 bath). No. 52 Douglas sold for $5,260,000 (5 bed, 4 bath). A traditional CMA sees two 5-bedroom houses in the same suburb and calls them comparable. The Microburbs CMA goes deeper. It confirms 21 matching features across both properties: swimming pool, ducted heating, double garage, fireplace, north facing, alfresco area, high ceilings, stone benchtops, study, large backyard, and fenced yard, among others. That overlap confirms the comparison is sound.
The CMA then explains the $940,000 gap. No. 9 Monterey has CCTV, a balcony, a teen retreat, rainwater tanks, and secure parking. No. 52 Douglas has a home office, solar panels, a security system, timber flooring, an updated kitchen, and an entertainment area. Despite Douglas Street listing more individual features, Monterey Street sold higher. The extra bathroom, brand-new build quality, and premium finishes drove the premium. A buyer looking at either property now has a concrete feature list to negotiate with.
Example: 8 Mona Street vs 3 Hampden Avenue, Wahroonga
Both in Wahroonga, Ku-ring-gai. Both 5 bed, 4 bath, both as-new custom builds. No. 8 Mona sold for $6,225,000 on a 1,004 sqm block. No. 3 Hampden sold for $6,450,000 on a 1,290 sqm block. The CMA identifies 20 matching features: swimming pool, ducted heating, double garage, fireplace, alfresco area, high ceilings, intercom, security system, study, walk-in robe, and nearby bus and rail services. Twenty shared features between two nearby streets confirms these are among the strongest comparables in the dataset.
The CMA then pinpoints what drives the $225,000 gap. No. 3 Hampden has an elevated position, a 1,290 sqm block (versus 1,004 sqm), solar panels, timber flooring, and a home office. No. 8 Mona has stone benchtops and a water tank. The premium maps directly to position and land: Hampden Avenue sits on a bigger, elevated block. A buyer comparing these two properties can quantify exactly what the extra 286 sqm and the elevated position are worth.
Industry participants confirm this gap. A buyer's agent noted: "It doesn't take into account public housing, flood zones, bushfire zones, all that sort of stuff." An investment analyst observed: "Same kind of configuration, same land size, same condition on the pictures, gone higher price and other places. It's not going that price."
3. The Microburbs CMA Methodology
The Microburbs CMA extends the comparison framework across four categories, each contributing to a composite similarity score.
3.1 Property-Level Feature Tags
Microburbs maintains a proprietary property tagging system that parses listing descriptions, images, and third-party data to identify structural and amenity features.
| Category | Example Tags |
|---|---|
| Indoor amenities | Swimming pool, spa, home office, open plan living, natural light, heated flooring |
| Outdoor features | Entertainment area, solar panels, level access, wide frontage |
| Structural | Roof type (metal, tile), construction method (double brick, brick veneer), renovation status |
| Configuration | Bedrooms, bathrooms, garage spaces, land size, floor area, build date |
When comparing a subject property to a potential comparable, the system computes three tag sets: matching tags (features both share), extra tags (features the comparable has that the subject does not), and missing tags (features the subject has that the comparable lacks).
3.2 Location and Amenity Distance Comparisons
The system measures the distance from each property to key amenities and compares them directly. Nine distance comparisons are computed:
| Amenity | Comparison Output |
|---|---|
| Beach | Closer to Beach / Further from Beach / Similar Distance (with metres) |
| Bus stop | Close to Bus Stop / Far from Bus Stop / Similar Distance |
| Tram stop | Close to Tram / Far from Tram / Similar Distance |
| Railway station | Close to Station / Far from Station / Similar Distance |
| Public transport | Close to Transport / Far from Transport / Similar |
| CBD | Closer to CBD / Further from CBD / Similar Distance |
| Supermarket | Close to Supermarket / Far from Supermarket / Similar |
| Department store | Close to Department Store / Far from Department Store |
| Shopping mall | Close to Mall / Far from Mall / Similar Distance |
3.3 Area-Level Investment Metrics
Two properties may be structurally identical but sit in micro-markets with very different growth trajectories. The Microburbs CMA compares 20 area-level metrics:
| Category | Metrics Compared |
|---|---|
| Growth | Capital growth forecast, house capital growth, unit capital growth |
| Pricing | House median sale price, unit median sale price |
| Yield | House yield, unit yield, percentage of renters, rental turnover, sale turnover |
| Education | NAPLAN rank, school socioeconomic rank |
| Liveability | Community score, affluence score, convenience score, crime score, family score, hip score, lifestyle score, tranquility score |
3.4 Environmental and Risk Overlays
No major CMA platform in Australia systematically includes risk data in property comparisons. The Microburbs CMA compares eight risk factors:
| Risk Factor |
|---|
| Bushfire prone area |
| Flood prone area |
| Erosion prone area |
| Electricity transmission lines |
| Environmental protection area |
| Mobile black spots |
| High public housing concentration |
| High residential density |
4. Price Adjustment Model
Comparable sales from months or years ago are stale. A property that sold for $1.2 million 18 months ago in a suburb where median prices rose 8% is not worth $1.2 million today.
Adjusted Price = Sale Price x (1 + Suburb Median Growth Rate since Sale Date)
The growth rate is calculated by comparing the suburb median price at the month of the historical sale to the most recent suburb median price, for the matching property type (house or unit). This produces a current-day valuation estimate that accounts for market movement since the comparable sold.
5. Automation and Delivery
The entire CMA process is fully automated. No manual research, no spreadsheet work, no phone calls to agents. When a user requests a property report, the system:
- Identifies the subject property from its GNAF address identifier
- Queries a pre-computed similarity database ordered by composite distance score
- Retrieves property tags, area metrics, and risk overlays for all candidates
- Computes matching, extra, and missing features for each comparable
- Fetches Google Street View images for visual context
- Applies the price adjustment model to historical sales
- Renders an interactive comparison card with navigation between comparables
The output is delivered as part of the standard Microburbs Property Report. CMAs are available for properties that are currently listed or have recently been listed for sale. The system is also available via the CMA API endpoint for enterprise integrations.
6. Defence of Methodology
6.1 Why Feature-Level Matching Matters
Property prices are determined by features. A pool adds value. Solar panels reduce running costs. Proximity to a train station is capitalised into the price. Bushfire risk lowers insurance cost estimates and sale prices. Ignoring these factors produces comparables that are not genuinely comparable.
6.2 Data Quality
Microburbs addresses data quality through three mechanisms:
- Multiple data sources: Tags are cross-referenced against satellite imagery, planning data, and census data.
- Weekly updates: Data is refreshed weekly, not monthly as with most competitors.
- Human validation: The tagging system has been tested against manual assessments by buyer's agents and investment analysts in over 200 consultation sessions.
6.3 Assumptions
- Property features identified from listing descriptions are materially accurate. This holds for objective features (pool, garage, bedrooms) but is weaker for subjective claims.
- Suburb median price growth is a reasonable proxy for individual property price movement. This holds for most properties but may diverge for unique or trophy homes.
- The composite similarity score correctly weights feature similarity against proximity. The current weighting has been calibrated against buyer's agent feedback.
6.4 Comparison to Industry Standard
The Automated Valuation Model (AVM) accuracy benchmark for the Australian market is approximately 13% median absolute error (Core Logic, Domain). The Microburbs AVM, which uses the CMA's property matching as its foundation, currently achieves under 10% median absolute error.
7. Use Cases
7.1 Pre-Purchase Due Diligence
The CMA is the final step before submitting an offer. A buyer shortlists a property, generates a property report, and reviews the CMA to understand how the asking price compares to genuinely similar recent sales.
7.2 Negotiation Support
Each missing feature relative to a higher-priced comparable is a concrete reason to argue for a lower price. Each extra feature relative to a lower-priced comparable supports the asking price. One investor described these as "discount points."
7.3 Buyer's Agent Workflow
Buyer's agents currently spend significant time pulling RP Data comparables and manually comparing features in spreadsheets. The automated CMA replaces this manual process. Agents can white-label the output with their own branding on the Office subscription plan.
7.4 Portfolio Analysis
Investors with multiple properties can run CMA reports across their portfolio to identify which holdings are tracking above or below comparable sales, informing hold or sell decisions.
8. Conclusion
The traditional CMA approach of matching on bedrooms, bathrooms, and price is a product of data limitations, not analytical best practice. As property data has become richer and more granular, the comparison framework should expand accordingly.
The Microburbs CMA demonstrates that feature-level matching across 30+ attributes, combined with risk overlays, area-level metrics, and growth-adjusted pricing, produces more informative and defensible comparable analyses.
About the Author
Luke Metcalfe is the founder and Chief Data Scientist at Microburbs, with 15+ years in property data analytics. Microburbs processes over 1 billion data points covering 11 years of Australian property market data, serving 8,000+ members and 5 million+ visitors.
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