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Generative AI > Google Gemini API > Property Valuation Agent

Property Valuation Agent

Author: Venkata Sudhakar

Property valuation requires synthesising location desirability, property attributes, recent comparable sales, and market trends into a price estimate. A Gemini-powered agent can automate this analysis - ingesting structured property data, reasoning over comparables, and producing a valuation range with supporting justification.

ShopMax India's corporate real estate team evaluates office and warehouse spaces across Indian cities for expansion. Manual valuation requires a registered valuer and takes 5-7 days. An AI valuation agent provides a rapid preliminary estimate to prioritise which properties warrant a formal valuation, saving time and fees.

The below example shows a property valuation agent that analyses a commercial property and generates a market value estimate with key value drivers.

import google.generativeai as genai
import json

genai.configure(api_key="your-api-key")
model = genai.GenerativeModel("gemini-2.0-flash")

# Comparable sales data (would come from a property database in production)
COMPARABLES = [
    {"location": "Whitefield, Bangalore", "type": "warehouse", "area_sqft": 8000,
     "sale_price_rs": 9600000, "age_years": 5, "sale_date": "2024-11"},
    {"location": "Whitefield, Bangalore", "type": "warehouse", "area_sqft": 12000,
     "sale_price_rs": 13200000, "age_years": 3, "sale_date": "2024-09"},
    {"location": "Electronic City, Bangalore", "type": "warehouse", "area_sqft": 10000,
     "sale_price_rs": 10500000, "age_years": 7, "sale_date": "2024-10"},
]

def value_property(property_details):
    # Filter relevant comparables by type
    relevant = [c for c in COMPARABLES if c["type"] == property_details["type"]]

prompt = f"""You are a commercial property valuation expert for the Indian real estate market.
Property to value: {json.dumps(property_details, indent=2)}
Comparable sales: {json.dumps(relevant, indent=2)}

Perform a valuation using the sales comparison approach:
1. Calculate price per sqft from each comparable
2. Adjust for age, location quality, and property condition
3. Apply adjustments to the subject property
4. Estimate market value range (low, mid, high) in Rs
5. Identify top 3 value drivers and 2 risk factors
Respond in JSON with keys: price_per_sqft_range, estimated_value_low, estimated_value_mid,
estimated_value_high, value_drivers, risk_factors, methodology_notes"""

response = model.generate_content(prompt)
    text = response.text.strip()
    start = text.find("{")
    end = text.rfind("}") + 1
    return json.loads(text[start:end])

# Subject property
subject = {
    "location": "Whitefield, Bangalore",
    "type": "warehouse",
    "area_sqft": 9500,
    "age_years": 4,
    "condition": "good",
    "features": ["loading dock", "3-phase power", "CCTV", "fire suppression"]
}

result = value_property(subject)
print(f"Estimated Value Range: Rs {result['estimated_value_low']:,} - Rs {result['estimated_value_high']:,}")
print(f"Mid-point Estimate: Rs {result['estimated_value_mid']:,}")
print(f"Value Drivers: {result['value_drivers']}")
print(f"Risk Factors: {result['risk_factors']}")

It gives the following output,

Estimated Value Range: Rs 10,450,000 - Rs 11,875,000
Mid-point Estimate: Rs 11,162,500
Value Drivers: ['Prime Whitefield location with tech park proximity',
               'Modern age (4 years) commanding premium over older stock',
               'Loading dock and 3-phase power increase industrial utility']
Risk Factors: ['Market liquidity risk - warehouse transactions thin in micro-market',
              'Rental yield compression if tech sector slowdown reduces demand']

The agent applies the sales comparison approach - calculating price-per-sqft from comparables and adjusting for property-specific factors. In production, connect to a live property transaction database, add a discounted cash flow tool for income-producing properties, and layer in zoning data and infrastructure project pipelines that affect future value. Route the agent output to a formal valuation report template for presentation to stakeholders.

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