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Practical guide

Building a Comparable Set That Holds Up to Scrutiny

How to assemble comparable evidence that survives a challenge from a client, a committee or a credit file — and what to do when the evidence is thin.

PropertyMetric Research · · 6 min read

A comparable set is only as good as the reasoning behind it. The difference between defensible evidence and a filtered list is whether you can explain every inclusion and every exclusion.

  1. 1

    Define the subject property precisely

    Building, community, property type, size and bedroom count. Everything that follows is a comparison against this definition, so vagueness here propagates through the whole exercise.

  2. 2

    Start at the tightest match and work outward

    Same building, same bedroom count, same size band, last six months. This is your ideal set. If it contains enough records, you are finished and your evidence is strong.

  3. 3

    Widen deliberately, one dimension at a time

    Extend the period first — it preserves the property comparison while accepting older evidence. Then widen the size band. Then, only if necessary, move to the surrounding community. Each step weakens the comparison in a specific, explainable way, which is exactly what you want when defending it.

  4. 4

    Record how far you widened and why

    A note saying "no sales in this building in the last six months, so extended to eighteen months" is the difference between a professional conclusion and an assertion. This is what someone reviewing your work needs to see.

  5. 5

    Normalise before comparing

    Look at both recorded price and AED/sqft. A larger comparable at a lower rate per foot is not a cheaper property, and presenting it as one is the error most likely to be caught.

  6. 6

    Say so when the evidence is insufficient

    Sometimes a defensible comparable set does not exist — a unique property, a new building, a very thin segment. The correct output is then "the available evidence does not support a precise conclusion, and here is why", supported by the sample size. That is a professional answer, not a failure.

Definitions used in this guide: data methodology

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