Gains:
- Ability to understand how the automatic valuation model (AVM) works, what data it depends on, why it is an indicative estimate, and to distinguish that it is not a substitute for a sworn appraisal report.
- Ability to prepare the precedent (comparable sales) method with artificial intelligence and verify the price range with your own field knowledge and official data
- Ability to detect pitfalls in price estimation (old precedent, different floor/facade/license status, expertise-value difference) before commenting
Determining the price of a property is the most delicate moment in the real estate business. If you set the price high, the ad will be back for months, the property will "burn out" and the buyer will lose confidence. If you put it low, the owner's money will remain on the table and his trust in you will be shaken. That's why pricing is both an art and a matter of data. Artificial intelligence is a powerful assistant at this point: in seconds it can outline a price range, a list of peers, and an breakdown of the factors affecting the price. But the main message of this unit is this: the price the AI produces is a starting point, not an outcome. The responsibility for the final price lies with the consultant, while the responsibility for the official/exact value lies with the sworn appraiser.
What is AVM, what is it based on, where does it go wrong?
AVM (Automated Valuation Model) is a system that mathematically estimates the price of a property based on past sales data, area characteristics and property attributes. It is used in banks' loan pre-evaluation, portals and some AI tools. The strength of the shopping mall is its speed; Its weakness is that it is only "as good as the data it is fed." When you ask a price for a general language model (like ChatGPT), the situation becomes even more careful: the model does not depend on live market data, its information is up to a certain date and it can fit a number fluently.
Factors typically overlooked by shopping mall and language models are: the real floor/facade/view status of the property, its internal condition (renovated or natural), license and occupancy status, condominium problems, dues burden, building age and earthquake regulation status, supply density and bargaining climate in the region at that moment. A human advisor knows all of this; The model doesn't know most of them. It is your job to fill this gap.
Precedent (comparable sales) method
The most reliable method of pricing is comparable analysis: finding properties that are similar to the property being valued, sold recently and in close proximity, and correcting for the differences to derive a range. AI saves you time here — it puts the precedents into an organized table, calculates the differences, suggests a range — but the accuracy and timeliness of the precedent is your responsibility.
Three rules for a good precedent: (1) Recent — preferably within the last 3-6 months, because the market changes quickly; (2) Proximate location — same neighborhood/site, similar street; (3) Similar quality — number of rooms, m², floor, age, heating. If these three don't add up, you need to fix the differences. For example, if the peer is on the 3rd floor and yours is on the 8th floor with a view, plus a correction; If yours is natural with precedent modification, you make a minus correction.
Tip: Listing prices are the "asking" price; The sold price is the "realized" price. There is often a 5-15% difference between the two. Use the actual sales price whenever possible in the precedent; If you only have the advertised price, tell the AI clearly and leave room for correction.
Step by step: peer table and price range with AI
- Gather your data: Write down the features and prices of 4-8 comparables you have (without ID/address). Note your source.
- Have AI set up the table: Have a table that divides the precedents into rows, the features into columns, and calculate the m² unit price.
- Correct differences: Have AI calculate the differences (floor, elevation, age, condition) between the target property and each comparable with percentage correction; But you check the logic.
- Derive a range: Ask for a limited range, not a single number (e.g. "$8.9M – $9.6M, possible 9.2M").
- Put it through your own filter: Narrow or shift the range with your regional climate, urgency, owner expectations and field knowledge.
Template 1 — Comparable table and m² unit price:
Role: You are a meticulous real estate value analyst. Plot the following precedent data into a table: columns = room, gross m², floor, age, status, sales type (realized/advertised), price, m² unit price. Then give the average of m² unit prices and the min-max range. Do not make up; just use the lines I gave. Precedents: [paste precedents]
Template 2 — Difference correction:
Target property: 3+1, 125 m², 8th floor, sea view, 6 years old, unrenovated. Correct the following equivalents according to the target: suggest percentage correction for floor, view, age and condition differences and write the justification. Produce an adjusted unit price range, not an exact price. Precedents: [paste table]
Template 3 — Pricing summary (owner presentation):
Role: Advisor. Write a brief price range rationale (150 words) to present to the owner from the adjusted precedent analysis below. Using the words "guarantee" and "sure"; State that this is a market estimate and an appraisal is required for the official value. Data: [paste analysis]
Template 4 — Querying AVM output:
A shopping mall/vehicle said [X] TL to this property. Question this estimate in terms of the following and list the control questions: currentness of the precedents, floor/facade/view, license-occupancy, dues, building age/earthquake status, regional supply density. Which information should I confirm from which source?
Comparison table: AVM/AI and sworn expertise
feature
AVM/AI forecast
Sworn expert report
speed
seconds
days
field survey
None
Yes (in place)
Official validity
None (indicator)
Yes (credit, official transaction)
Floor/facade/interior situation
Can't see most of the time
sees
Responsibility
Not your vehicle, but yours.
Licensed expert
Place of use
Preliminary idea, rapid interval
final decision, official action
three mini cases
Case 1 — December is correct, update saved. The consultant gives 6 comparables to AI for a 2+1 flat. YZ puts the average m² unit price at 62,000 TL and recommends a range of 7.1M – 7.8M TL. The consultant notices that two of the comparables were sales from 14 months ago and removes them; With the current four remaining, the range shifts to 7.6M – 8.2M. Property sold in 5 weeks for 7.9M. Lesson: peer recency can shift the range by 8-10%.
Case 2 — Hallucination trap. A consultant responded to a general conversation pattern: "How much is 3+1 in Ataşehir?" he asks; The model says "average 6.5M TL". The consultant tells the owner this. However, current precedents are over 9M; The model information is outdated. Malik goes to another office. Lesson: the language model is not a source of live prices.
Case 3 — Expertise difference. Buyer will buy on credit. The market range the consultant found with AI is 10.2M; But the bank expert appraises the value at 9.4M and the loan is issued accordingly. It would not be a surprise if the consultant had foreseen this difference from the beginning and prepared the buyer. Lesson: Mall/market value and appraised value may be different; Expertise is decisive in credit transactions.
Weak prompt / Strong prompt
Weak prompt:
How much is this apartment worth? 3+1, Istanbul.
The model makes predictions without unprecedented, up-to-date data; The city is very large, there is no quality.
Powerful prompt:
Do a price range analysis with VERIFIED peers below. Adjusting the new price; calculate only from precedents. Correct floor/age/condition differences, give m² unit price range and justification. The result is an estimate, not the exact value.Target property: [attributes]. Comparables (realized sales): [list].
Common mistakes
- Asking live prices for the general language model. The model does not know the current market; can make up numbers.
- Working with old precedent. 12+ month precedent does not reflect today's market.
- Mistaking the advertised price for the sales price. Neglecting the difference between what is desired and what actually happens.
- Using the precedent without correcting the floor/frontage/condition difference.
- Presenting the AVM range to the owner as "exact value" and saying "guarantee" — risk of misrepresentation.
Attention: In credit sales, the buyer's bank carries out its own appraisal. Regardless of your market estimate, the bank expert determines the loan amount. Explain this difference to the customer up front.
In summary
AVM and AI give you a quick starting range and regular peer analysis on pricing. But the price is your decision based on current and verified comparables, your field knowledge, and the actual condition of the property. When the exact/official value is required, sworn expertise comes into play. Have the AI produce a justified range, not a single number; filter each precedent for timeliness and quality; Always present it to the owner as "estimate".
Application task
Collect 5-6 current comparables for a property you hold (without address/ID). Derive a YZ-corrected m² unit price range from Templates 1 and 2. Then: (1) mark the date of each precedent and whether it is “announced or sold”; (2) check the logic of floor/facade/condition corrections yourself; (3) Write an interval justification that does not include “warranty” to be presented to the owner using Template 3.
checklist
- [ ] Comparables current (preferably last 3-6 months) and actual sales if possible.
- [ ] Floor, facade, view, age and internal condition differences have been corrected.
- [ ] I didn't make the AI match live prices; I only had it calculated from the examples I gave.
- [ ] I prepared the result as a range, not a single number, by calling it "estimate".
- [ ] I explained to the customer that the bank's expertise will be decisive in the credit transaction.