Leasing analytics platform for residential property operations

• Client type: Residential leasing and property operations team working across HubSpot, Yardi, and Power BI
• Scope: Build one controlled reporting foundation for summary dashboards, renter-enquiry flow visibility, and rent-trend KPI review across multiple source systems
• Core result: Better management visibility and more dependable reporting instead of disconnected source-level analysis

Reporting for the leasing process depended on data spread across several systems rather than one controlled reporting foundation.

The source material shows three main inputs: HubSpot data for viewing-ticket pipelines, Yardi exports across multiple property-management databases, and Excel-based incentives data provided through SharePoint. That made it harder to build a trusted view of the enquiry funnel, rent trends, and operational KPIs without repeated data handling, reconciliation, and reporting work.

The reporting need was practical rather than abstract. Teams wanted dashboards that could show:

• a summary page bringing together the key charts from other views
• current status volumes through the renter enquiry process
• rent trends comparing actual performance against KPI targets

From a buyer perspective, the risk was not only reporting inconvenience. When management review depends on fragmented inputs, decision-making slows down, KPI discussions become less confident, and every reporting change carries more delivery friction than it should.

iQberry approached the work as a reporting-trust and decision-support problem rather than a dashboard exercise in isolation.

The core objective was to give the client one controlled route from source data to management reporting. That meant reducing the amount of reporting logic scattered across source-level extracts, creating a more reliable refresh model, and making the reporting stack easier to govern and evolve.

The delivery architecture was built around a Databricks medallion model with bronze, silver, and gold layers on Azure storage. That created a cleaner path from raw source ingestion into conformed business entities and finally into Power BI reporting tables and semantic models.

The delivery approach centered on:

• connecting HubSpot, Yardi, and Excel-based inputs into one governed pipeline
• using Databricks workflows for scheduled ingestion and transformation
• standardizing business entities such as property, unit, lease, and tenant in the conformed layer
• separating development, test, and production environments for safer change control
• applying access control through Unity Catalog, Azure AD, and Key Vault-backed secret handling

This kept the work focused on reliability, governance, and repeatable management reporting rather than only on the front-end dashboard views.

iQberry designed a governed reporting platform for residential leasing analytics, using Databricks and Power BI as the delivery stack.

According to the source material, the solution includes:

• Azure Data Lake Storage as the shared data foundation
• Databricks workflows for orchestration and scheduled processing
• ingestion of HubSpot pipeline data through a Python-based API connector
• ingestion of Yardi exports from Azure Blob storage
• a medallion structure for raw, conformed, and presentation-ready data
• Power BI datasets connected to gold-layer reporting tables
• Unity Catalog governance and Azure AD-based access control

On top of that foundation, the reporting layer supports:

• summary reporting for management review
• viewings-overview analysis for renter-enquiry status volumes
• rent-trend dashboards comparing actual performance against KPI targets

At the point captured in the architecture document, the incentives file still involved a manual update path. The broader design, however, moved the core reporting stack into a more structured and maintainable platform.

The clearest outcome in the source material is a stronger reporting operating model rather than a published commercial KPI set.

Instead of relying on disconnected source exports, the client gained a structured platform that could bring leasing and property data into one governed analytical flow and refresh Power BI reporting on a scheduled basis. For a senior buyer, that matters because it reduces reporting drag and makes routine KPI review easier to trust.

In practical terms, the platform created:

• one shared foundation for leasing and property reporting
• less dependence on manually stitched source-level analysis
• clearer visibility into enquiry-stage volumes for prospective renters
• a more consistent way to compare rent actuals against KPI targets
• stronger governance over data access, transformation logic, and environment separation
• a cleaner base for future reporting changes than manually stitched reporting flows

No approved before-and-after business metrics were provided in the source files, so the case study keeps the outcome framing operational and factual.

In residential property operations, reporting only helps when teams trust the numbers enough to act on them.

This case matters because it shows how iQberry turned fragmented reporting inputs into a more dependable management reporting foundation. With enquiry-funnel data, rent trends, and property reporting brought into one governed model, KPI review became easier to trust and less dependent on manual reporting workarounds.

For operators dealing with fragmented systems and recurring performance review, that matters because better reporting is not just about visibility. It is about making decisions faster, with more confidence, and without adding more reporting complexity.