EquitySight Methodology

1. Why I publish my methodology

This page documents how EquitySight produces the numbers you see on the site — and, just as importantly, which of those numbers are real measured data and which are estimates. I think you deserve to know the difference before you rely on anything here.

I publish the methodology because:

• Investors should never rely on opaque "scores" without understanding what they measure and where the inputs come from.
• Australia's property industry has a long history of undisclosed conflicts and paid placements — I want mine out in the open.
• Being upfront about what is estimated is more useful than a confident-sounding number with no honest basis.

EquitySight is built and maintained by one person — Jacoby, a solo operator. There is no in-house valuation team and no certified valuer on staff. I do not collect any paid data from agents or developers, and I do not accept sponsored content.

2. What is real data vs. an estimate

Two fields on each suburb profile are sourced from published, official datasets:

Everything else currently shown on a suburb profile is an estimate or a placeholder, not a measured value:

• Median household income — currently a pseudo-random figure derived from the suburb name. It is not an ABS Census income figure and should not be read as one. I intend to replace or remove it once I have a verified source, and in the meantime the suburb pages are kept out of search (see below).
• Transport score and amenity score — currently pseudo-random placeholders. They are not derived from OpenStreetMap, transit data, or any external amenity dataset.
• School count and park count — currently rough population-ratio estimates (roughly one school per ~4,000 residents, one park per ~2,500 residents). They are not counts of real, named facilities and are not sourced from OpenStreetMap.
• Investment Score — because it is computed partly from the estimated fields above, the score should be treated as illustrative only, not as a measured assessment.

Fields that are not populated at all in production today: median weekly rent, median monthly mortgage, dwelling-type percentages, distance to CBD, and suburb centroid coordinates (lat/lng). Live rental and sales market data (Domain or similar listing feeds) and OpenStreetMap amenity data are not integrated.

Because of all this, suburb profile pages are currently not indexed for search while I upgrade the underlying data. I would rather hold these pages back than have them rank on numbers I cannot stand behind.

3. The EquitySight Investment Score (0–100)

The investment score is a weighted combination of several factors. I am documenting it here for transparency, but please read it alongside section 2: several of its inputs are estimates, so the score itself is illustrative, not measured.

The factors and their weights are:

The sum is capped at 100. Suburb-type points are lookup values: inner-city 18, middle-ring 16, coastal 14, outer-metro 12, regional 8.

Scores fall into four labels:

• Strong — 81 to 100
• Good — 61 to 80
• Moderate — 41 to 60
• Weak — 0 to 40

4. Suburb prose and strategy text

The strategy verdicts, risk factors, and outlook text on suburb pages are generated by rule-based logic over the available fields, with deterministic phrasing. Where that logic depends on the estimated fields described in section 2, the resulting text inherits the same caveat: it reflects estimates, not measured market conditions. I am reworking this content as verified data becomes available.

5. Known limitations

• The most important limitation is the one in section 2: most suburb figures are estimates or placeholders, and several richer fields are not populated at all. I am working to replace these with verified sources before re-indexing the pages.
• ABS 2021 Census data (the population field) is four years old at time of writing. Census 2026 data will be incorporated as soon as the ABS releases it.
• I do not collect any paid data from agents or developers, and I do not accept sponsored content.
• EquitySight is run by a solo, non-credentialed operator. Nothing on the site is personal financial advice. Always seek licensed, independent advice before making a property decision.

6. Mortgage repayment formulas (main calculator)

The calculators are where my methodology is on the firmest ground: they apply standard, published formulas to the numbers you enter, and the RBA cash rate they reference is real.

The Purchase Calculator (/app) uses the standard monthly amortisation formula:

`` monthly = P × r / (1 − (1 + r)^−n) ``

where P is the loan amount, r is the monthly rate (annual rate ÷ 12 ÷ 100), and n is the number of monthly periods (term × 12). Total interest is monthly × n − P.

Fortnightly-benefit figure

The "switching to fortnightly saves X years and $Y in interest" number is computed by full amortisation, not a closed form. Specifically:

• Payment: monthly / 2 paid every 14 days. Because there are 26 fortnights per year (not 24), this is the equivalent of 13 monthly payments per year — an extra monthly payment vs. the strict 12-per-year schedule.
• Periodic rate: annual rate ÷ 26 (applied once per fortnight).
• Loop: I iterate fortnight-by-fortnight, applying interest then reducing the balance, until the balance hits zero. yearsLess = (term × 12 − monthsEquivalent) ÷ 12 and interestSaved = monthlyInterestTotal − fortnightlyInterestTotal.

This matches the "pay half the monthly amount every fortnight" offer most Australian lenders advertise. It is not the same as paying annual / 26 every fortnight (which would just be monthly repayments relabelled and would save nothing).

A note on the written guides

The explanatory guides that accompany my calculators are drafted with AI assistance and then reviewed and edited by a human for accuracy before publishing. I mention this so the AI-assistance is disclosed rather than implied to be otherwise.

7. Questions?

If anything on this page is unclear, or you find a calculation error, email me at support@equitysight.app. I would genuinely rather hear about it.