Case study · Real estate
Transparent AI property valuation for a multi-city marketplace
A growing real estate marketplace wanted buyers to trust its prices. We built a valuation engine and property pages that compare the estimated value with the asking price.
Context
The client and the situation.
The client connects buyers, sellers and agents in several cities. It wanted to become a data-driven property platform rather than a listing site.
Challenge
What was holding them back.
- Property prices were inconsistent and often inaccurate.
- Buyers could not tell whether a listing was fairly priced.
- Engagement on property detail pages was low.
- Few visitors became serious buyers.
Approach
How we framed the problem.
We treated trust and data as one problem. The platform shows its estimate, how confident it is and how the listing compares, so buyers can decide faster.
What we built
The product, module by module.
Valuation engine
Predicts prices from historical transactions, location trends and property features, with confidence scores.
Market intelligence dashboard
Price trends by area, investment insights and comparable property analysis.
Smart property page
Estimated value next to the listed price, a good deal / fair / overpriced indicator and a pricing breakdown.
Mobile app
Instant valuation on listings, saved properties with price alerts and recommendations.
Data visualization
Interactive charts for price trends, market growth and demand.
Results
What changed.
Results reported for the project after launch.
Technology
Stack used.
- React
- Next.js
- Node.js
- Python
- Regression models
- Geospatial analytics
- Market data APIs
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