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MACHINE LEARNING

Case Study: Property Price Prediction

An ML model analyzing Bangladesh property market data to accurately predict real estate prices, enabling data-driven investment and valuation decisions.

Team Size 4 Specialists
Prediction Accuracy 89% R² Score
Project Duration 4 Months

The Challenge

The Bangladesh real estate market lacked transparent, data-driven valuation tools. Property prices varied wildly based on subjective assessments, making it difficult for buyers, investors, and developers to make informed decisions. Existing methods relied on agent estimates with no systematic analysis of the factors that truly drive property values in different neighborhoods.

The Solution

We built a comprehensive ML pipeline that collected and cleaned thousands of property transaction records across Dhaka and major cities. Feature engineering captured location-specific factors, property characteristics, proximity to amenities, and market trend indicators. Multiple algorithms were evaluated before selecting XGBoost as the optimal model, achieving an 89% R² score on holdout data.

The trained model was deployed as a FastAPI service, with a React-based web interface allowing users to input property details and receive instant valuations with confidence intervals and key value drivers.

Case Study: Property Price Prediction

The Results

89% Prediction accuracy
10K+ Properties analyzed
3s Average valuation time

Real Results from Real Partners

Don't just take our word for it. Our clients have launched successful products, scaled their businesses, and built software that actually works.

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Lisa Oda

Head of Content, Upwork

“Nahid and his team helped build our WordPress site under tight deadlines with many moving parts. He was extremely gracious and helpful throughout.”
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Kenelm Tonkin

CEO, Tonkin Corporation

“Worked more than 5 years with them on different web development Projects & I must say, I always got 100% of what I wanted. Highly recommended.”
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Mukit Ur Rahman

Senior Product Manager, Sheba xyz

“We had to outsource some of our projects at Sheba Platform Ltd. Nahidul Islam and his teams prompt support and pro-activeness made sure that all the projects were delivered on time”
John Francis Verified

John Francis

CEO, Sixfold Digital

“Nahid and the teams work goes much deeper than purely executional. They have an acute understanding of the why behind what they are building.”

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FAQs

Questions, answered.

Common questions about our machine learning solutions for real estate.

How accurate is the property price prediction model?

Our model achieves an 89% R² score on holdout data, meaning it explains 89% of the variance in property prices. We also provide confidence intervals for each prediction to help users understand the reliability of the estimate.

Can the model be customized for specific regions or property types?

Yes, our machine learning models can be fine-tuned based on specific regional data and property types. The model learns from historical transaction data, so more data leads to better predictions.

How long does implementation take?

Implementation timelines vary based on data availability and customization requirements. For a typical deployment with clean historical data, we can have a working model within 4-6 weeks.

What data sources does the model use?

Our model can integrate multiple data sources including historical property transactions, location features, property characteristics, proximity to amenities, market trends, and economic indicators.