Building a Comparable Sales Sheet With ChatGPT

A comparable sheet organizes properties similar enough to inform a pricing conversation. It does not turn portal advertisements into completed sales, and ChatGPT cannot certify market value. The value of the sheet is the visible inclusion logic, source trail, and arithmetic.

After this lesson, you can build a comparable-listing sheet, normalize units, calculate asking price per area, and explain why each record is included or excluded.

Define the Subject Before Collecting Comparables

Create a subject card:

valuation_date | city | exact locality/block | property_type |
source_area | verified_area_basis | beds/baths/floor if relevant |
construction_age/condition | possession/document state |
orientation/access/features | known defects | unknowns

Write inclusion rules before seeing prices. For a flat, they might be: same block or defensible adjacent micro-market, same property type, area within a stated band, similar bed count, comparable floor/lift condition, and current listing observed within a chosen date window.

The misconception is that more rows make an estimate stronger. Ten mixed rows can be worse than four clearly comparable records. Exclusion reasons are evidence, not wasted work.

Build the Sheet and Preserve Source Values

Use these columns:

comp_id | source_url | observed_at | listing_status | city | locality |
property_type | source_area | source_unit | conversion_basis |
normalized_sq_ft | asking_price_pkr | asking_price_per_sq_ft |
beds | baths | floor | condition | possession_state | key_differences |
duplicate_group | verification_state | include | exclusion_reason

Formula:

asking_price_per_sq_ft = asking_price_pkr / normalized_sq_ft

Keep the source area and conversion basis beside the normalized value. If the scheme-specific area basis is uncertain, do not normalize it silently. Use Zameen’s current converter as a convenience check, then verify the property’s actual dimensions or scheme convention.

Detect duplicates using contact, photos, exact wording, location, size, and price—but do not publish private contact details in the sheet you share. Keep one representative record and note the duplicate group.

Let AI Classify, Not Invent Adjustments

AI can compare categorical fields and draft questions:

Compare each candidate row with SUBJECT and INCLUSION RULES. Return comp_id,
include (yes/no/review), exact matched fields, material differences, missing
facts, and exclusion reason. Do not estimate value, sale price, appreciation,
or adjustment percentages. Treat every portal price as advertised asking price.
Preserve row IDs.

Review the classification manually. Never ask the model to decide “corner adds 10%” or “new construction adds 15%” without a defensible, current local evidence method. Record qualitative differences and obtain a qualified appraisal when the decision requires one.

Summarize Without False Precision

For included advertised comparables, calculate count, minimum, maximum, median asking price, and median asking price per normalized area. Median is the middle value after sorting and is less distorted by one extreme listing than the average. It is still only a statistic about your selected advertised sample.

Always display:

• observation window and source;
• exact inclusion/exclusion rules;
• count after duplicate removal;
• missing facts;
• subject differences;
• no completed-sale evidence unless a legitimate source actually provides it;
• no legal/title conclusion.

Worked Example

Sample only: subject is a 1,200 sq ft, two-bedroom, lift-served flat in one Karachi block. Five candidate asking listings remain after duplicate review:

Arithmetic check: 23,600,000 / 1,180 = 20,000; 25,010,000 / 1,220 = 20,500; 27,600,000 / 1,200 = 23,000.

Using C1 and C2 only, the midpoint/median for two values is (20,000 + 20,500) / 2 = 20,250 PKR/sq ft. The report does not multiply that by subject area and call the result “market value.” It states that two non-duplicate asking listings matched the strict rules, which is too thin for a confident valuation. C3 is shown separately because renovation evidence needs review.

Failure Cases to Diagnose

• Completed-sale language is used for portal ads: relabel all values as advertised asking prices.
• A duplicate survives because the URL differs: compare photos, wording, property facts, and restricted contact data.
• Area conversion has no basis: preserve source units and mark normalization unresolved.
• Mean is reported without distribution: show count, range, median, and every included row.
• AI creates adjustment percentages: remove them and retain qualitative differences.
• Localities or property types are mixed: enforce the prewritten inclusion rules.
• A thin sample becomes a precise valuation: state insufficiency and seek stronger evidence or a qualified professional.

🇵🇰 Pakistan Angle

Pakistani portal prices may be written as lakh/crore, while calculations need integer PKR. Convert carefully: one crore is 10,000,000 PKR and one lakh is 100,000 PKR. Check every conversion twice and keep the original string. Also preserve square-yard, square-foot, marla, or kanal source units; local conventions can make a silent marla conversion materially wrong.

A comparable sheet does not verify ownership, society approval, dues, transfer status, possession, or tax. Those questions belong to the relevant authority, society, land-record route, and qualified legal/valuation professionals. Redact seller phone numbers, CNICs, and ownership files before using AI or sharing the sheet.

Hands-On Exercise

1. Create one detailed subject card and written inclusion rules.
2. Collect eight labelled sample advertised listings with URLs and dates.
3. Normalize only values with a documented basis.
4. Detect duplicates and record exclusion reasons.
5. Run the classification prompt and review every decision.
6. Calculate price per area by hand for two rows and compare with spreadsheet formulas.
7. Write a one-page summary with sample limits and unresolved checks.

Done means: a reviewer can reproduce every inclusion, conversion, formula, and limitation and cannot confuse the output with certified market value.

Completion Rubric

• Subject card and inclusion rules were written before price selection.
• Source URL, date, unit, conversion basis, and verification state remain visible.
• Duplicate and exclusion decisions are documented by comp ID.
• Price-per-area arithmetic has been independently checked.
• Summary reports count, range, median, differences, and missing facts.
• Asking observations are never represented as completed sales or an appraisal.

Sources

• Zameen — Property search
• Zameen — Area unit converter
• Google Sheets — MEDIAN
• Google Sheets — Data validation

Key takeaway: a comparable sheet is credible when its market cell, exclusions, units, duplicates, and arithmetic are inspectable—and its advertised data is labelled honestly.