Toptal helps real estate technology firm lay groundwork for property analytics platform.
A real estate technology firm struggling to extract meaningful insights from fragmented property data partnered with Toptal to build an analytical foundation and validate its product direction before full-scale development.
Client
A Dubai-based R&D and software firm offering custom development, data analytics, and R&D consulting.
Industry
TechnologyDelivered Services
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Raw transaction, listing, and property data lacked consistent structures, making it difficult to compare pricing trends, property performance, and market activity. Inconsistent geographic information further complicated community-level analysis, leaving the company without the insights needed to confidently define its planned application.
Solution
Structured Data Architecture
Toptal used Python and PostgreSQL to organize data from multiple sources into analysis-ready datasets, applying a layered architecture and standardized metrics for pricing trends and property performance.
Geospatial Analytics Development
The data engineer normalized geographic boundaries, mapped property transactions to Dubai communities, and built Metabase dashboards to visualize market trends and test analytical concepts with stakeholders.
Outcome
Validated Product Direction
Clean datasets, consistent KPIs, and proof-of-concept dashboards helped the company identify which insights its future application should prioritize, reducing uncertainty around product scope and development decisions.
Improved Market Visibility
Interactive community maps made transaction volumes, pricing trends, and absorption patterns easier to explore, giving stakeholders a clearer understanding of local market performance and faster access to insights.
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