Comparative Market Analysis:
A Home Feature-Level Approach to
Property Valuation

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. Preliminary results show this approach identifies more genuinely comparable properties and supports more defensible price estimates. The methodology has since been applied to every home in Australia, producing a current-day comparable-sales estimate for all 15.1 million residential properties nationwide (Section 8). Critically, Microburbs does not take an agent's listing at face value. Every feature is cross-checked against independent sources, so the comparison rests on verified attributes rather than marketing copy.

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.

Industry participants confirm this gap. A buyer's agent who reviewed the Microburbs CMA noted: "It doesn't take into account public housing, flood zones, bushfire zones, all that sort of stuff, whereas people just sort of rely on those things." Another property professional 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 starting point is trust, not the agent's word. A listing description is marketing. It can overstate, omit, or quietly age. Before any property enters a comparison, Microburbs verifies what it can actually confirm, cross-checking the claimed features against satellite and aerial imagery, planning and cadastral records, and the full sales history. The comparison runs on what survives that check, not on what an agent wrote. This is the single biggest difference between a Microburbs comparison and a portal estimate, and it is why two homes that look identical on paper are not treated as identical here.

On that verified base, the Microburbs CMA extends the comparison across structural features, location and amenity, area-level investment context, and environmental risk, each contributing to how genuinely comparable two properties are.

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. Tags include:

When comparing a subject property to a potential comparable, the system computes three tag sets: matching tags (features both properties share), extra tags (features the comparable has that the subject does not), and missing tags (features the subject has that the comparable lacks). This gives buyers and agents immediate insight into exactly why two properties command different prices.

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:

Each comparison includes the actual measured distance in metres, providing concrete data rather than subjective impressions.

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 between the subject property's pocket and each comparable's pocket:

This means a comparable that looks identical on paper but sits in a pocket with lower capital growth forecast or higher crime will be flagged accordingly.

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:

A 4-bedroom house in a bushfire prone area is not genuinely comparable to an identical house outside that zone, yet traditional CMAs treat them identically.

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 the median has since climbed is not worth $1.2 million today.

The Microburbs CMA applies a growth-adjusted valuation to each comparable sale. The adjustment uses suburb median price data (updated weekly) to compute:

This is displayed alongside the original sale price and date, giving users both the historical fact and the growth-adjusted estimate.

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:

1. Identifies the subject property from its GNAF address identifier
2. Queries a pre-computed similarity database ordered by composite distance score
3. Retrieves property tags, area metrics, and risk overlays for all candidates
4. Computes matching, extra, and missing features for each comparable
5. Fetches Google Street View images for visual context
6. Applies the price adjustment model to historical sales
7. 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

The objection to feature-level matching is complexity. Why compare 30+ attributes when four or five will do?

The answer is straightforward. 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

Property tags are derived from listing descriptions and third-party data sources. Listing descriptions are written by agents and contain promotional language. This introduces noise.

Microburbs addresses this through three mechanisms:

• Multiple data sources: Tags are cross-referenced against satellite imagery (for solar panels, pool presence), planning data (for zoning), and census data (for area characteristics).
• Weekly updates: Data is refreshed weekly, not monthly as with most competitors. This reduces staleness.
• Human validation: The tagging system has been tested against manual property assessments by buyer's agents and investment analysts in over 200 consultation sessions.

6.3 Assumptions

The methodology rests on the following assumptions:

• Property features identified from listing descriptions are materially accurate. This holds for objective features (pool, garage, bedrooms) but is weaker for subjective claims ("beautifully renovated").
• 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 but not yet validated against a holdout dataset of repeat sales.

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. This improvement is attributable primarily to better comparable selection through feature-level matching.

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. The feature comparison highlights specific reasons to negotiate: "Comparable A sold for $50,000 more but had a pool and solar panels that this property lacks."

7.2 Negotiation Support

The CMA produces what one investor described as "discount points." 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.

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. National Coverage: A Comparable-Sales Estimate for Every Home

The methodology above was built for properties on the market. It has now been extended to every home in Australia, all 15.1 million of them, across every state and territory. Each home is matched to recent nearby sales of genuinely similar homes, and those sales are growth-adjusted to a current-day figure using the price adjustment model in Section 4.

The estimate is built for normal residential homes. Large rural holdings, acreage, lifestyle blocks and farms (any site above a quarter acre, about 1,012 square metres) are deliberately left out of both the homes valued and the sales used as comparables. A 50 hectare cropping farm is not a comparable for a suburban house, and including one distorts the estimate. This scope decision is about property size, not location. Small towns and country hamlets are fully covered.

For about 9 in 10 homes, enough genuine local comparable sales exist to form an estimate. Coverage is strong in every state, from 86% of homes in Western Australia to 97% in Tasmania, with the rest of the country in between.

To illustrate how the estimate tracks the market, here are recent comparable-sales estimates for typical houses across the price range, from a starter suburb to blue-chip Sydney, next to what houses there have recently been selling for:

The estimate is most reliable in the mid-market where most homes trade, and a little rougher at the very top end and in thin markets where few similar homes change hands. It is best read as a current-day starting point and a sanity check against an asking price, not a formal valuation. The tuned automated valuation in Section 6.4 builds on these comparable selections.

Effect on capital growth. The comparable-sales estimate does not forecast growth. Its investor value is avoiding overpaying: buying in line with, or below, genuinely comparable sales protects the return on the purchase.

9. 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. The system is fully automated, delivered within the standard property report, and available via API for enterprise users.

For the property investor, buyer's agent, or mortgage professional, this means faster, more accurate, and more defensible pricing decisions.