From Data to Decisions: How AI Is TransformingProperty Valuation

HOW AI IS BRINGING PRECISION, FAIRNESS,

AND EFFICIENCY TO PROPERTY VALUATION!

Introduction: The Problem with Traditional Valuation

In 2025, pricing a home still relies heavily on human intuition - a slow and

subjective process often riddled with inconsistencies.What if artificial intelligence

could deliver faster, more objective estimates?

At Amzil AI, we’ve developed a machine learning model that predicts property

values in Lille, France using real transaction data - challenging traditional real

estate valuation methods.

This article walks through our project’s journey—from raw data to actionable

insights—highlighting how AI can empower buyers, sellers, and agencies with

transparent, data-backed price estimates.

The Data: Laying the Foundation

We began with France’s Demandes de Valeurs Foncières (DVF) dataset, a treasure

trove of property transactions from 2020 to 2024. The raw dataset included over

44,000 property records in Lille, but not all were usable. After rigorous cleaning and

validation, we narrowed it down to 26,000 high-quality transactions filtering out:

Redundant fields: Columns like ancien_code_commune (100% missing) and

outdated lot subdivisions.

Outliers: Properties priced above €1.2 million and unrealistic surface areas (e.g.,

300m² apartments).

Incomplete entries: Focused on key features like surface area, number of

rooms, location (postal code, latitude/longitude), and property type.

Key Insight: The cleaned data revealed strong correlations. For example, property

prices in central Lille (Cluster 6) were, on average, 20% higher than in peripheral

neighbourhoods, and each additional room added approximately €15,000 to a

property’s value.

The AI Engine: How It Works

At the heart of our project is a structured, data-driven approach based on the

CRISP-DM framework, ensuring a seamless transition from raw data to actionable

insights.

Why It Matters: While not perfect, the model uncovers hidden trends—like

the premium for properties near Lille’s metro lines—that even experts might miss.

The process began with business understanding, where we identified the critical

need for a reliable tool to eliminate pricing guesswork in real estate. Next, data

preparation played a pivotal role—multi-lot transactions were aggregated, and

numerical features such as surface area were normalised using min-max scaling,

and neighborhoods were intelligently clustered via K-Means into 10 distinct

geographic zones, such as Vieux-Lille and Hellemmes, to capture location-based

trends.

The modelling phase rigorously tested three powerful algorithms: Linear

Regression as a baseline, Random Forest for its robustness against outliers, and

XGBoost, which excelled in handling complex, non-linear patterns. The final model

was then seamlessly deployed into user-friendly dashboards for real estate

agencies and integrated via APIs for partner platforms, ensuring both accessibility

and scalability for a wide range of users.

This end-to-end pipeline—meticulously designed and executed—demonstrates

how structured methodology and cutting-edge machine learning can transform real

estate valuation into a precise, efficient, and scalable solution. All of this was achieved

through the disciplined application of the CRISP-DM framework, ensuring clarity,

reproducibility, and real-world impact.

Conclusion: The Future of Real Estate

This project isn’t about replacing agents; it’s about equipping them with better

tools. By combining AI’s speed with human expertise, we can create a fairer, more

efficient market.

What’s Next? We’re exploring integrations with seasonal trends and hyperlocal

data.

We’d love to hear your thoughts — [Join the conversation].