Transforming Commercial Real Estate Market Reporting

Background & Client Overview

A leading commercial real estate (CRE) firm wanted to enhance the quality and efficiency of its market and submarket reports, which are critical for supporting its broker teams. These reports provide vital insights to clients on trends, pricing, occupancy, and overall market conditions. Historically, the production of these reports was labor-intensive, requiring analysts to manually collect and analyze both structured data (from databases) and unstructured data (from PDFs, text documents, and external sources).

Malleable.AI was engaged to design and implement an AI-driven solution that would reduce manual effort, deliver deeper insights, and help scale the client’s market reporting capabilities to more regions and submarkets.

Business Challenge

1. High Labor Intensity: Analysts spent significant time each quarter compiling and cleaning data from disparate sources.
2. Data Silos: Valuable information existed in multiple SQL databases and across unstructured files (PDFs, text documents).
3. Time-to-Market: With growing broker demands, the existing process struggled to produce timely, high-quality reports.
4. Limited Insight Generation: Analysts had little time to generate deeper intelligence or expanded coverage of smaller, emerging submarkets.

The CRE firm recognized that an AI-powered approach could dramatically streamline operations, but needed guidance and technology to effectively deploy it.

Malleable.AI’s Solution

1. Automated Data Ingestion & Analysis

• Structured Data: Deployed data extraction pipelines to pull property metrics, transaction details, and occupancy data from existing SQL databases.
• Unstructured Data: Employed tools to parse text from PDFs, broker memos, and market briefs, extracting relevant real estate metrics and commentary.

2. Dual AI Agents for Insights & Commentary

• Insight Generation Model: An AI model built to identify noteworthy trends (e.g., price fluctuations, inventory changes, notable transactions).
• Commentary Generation Agent: A second AI agent synthesizes high-level themes and overarching narratives suitable for report summaries or executive briefs.

3. Web Application for Analyst Oversight

• Built a user-friendly web portal that allows analysts to:
  
  • Visualize hierarchical metro data, from high level markets to granular submarkets
  • Review AI-generated insights.
  • Curate or remove items deemed irrelevant or low-quality.
  • Review AI generated commentary and insights and provide feedback to AI if necessary.
  • Add their own expert commentary or market color to improve the final output.
• This “human-in-the-loop” approach ensures both efficiency (automated drafting) and quality (expert review).

4. One-Click Deliverables

• Once approved, the system automatically generates PowerPoint and PDF files:
  
  • Charts & Graphs: Visuals reflecting transactional data, pricing, and occupancy rates using PowerBI
  • Professionally Structured Reports: Formatted market or submarket summaries with consistent branding and layout.

5. Modular AI Architecture

• All AI models are designed to be easily replaceable as more advanced solutions become available.
• This future-proof design ensures the system can evolve with the rapid pace of AI innovations.

Architecture and Engineering

The software engineering work for the Commercial real estate client comprises:

Frontend: Built with Next.js and React. Provides responsive interface for property management and data access

Backend: The processing layer including the orchestrator, commentary generation, revision and prompt composing is written in Python. This includes the core logic, data processing, and frontend requests.

Database: CosmosDB and SQL.

Both the front and backend were containerized with Docker for consistent deployment and scalability. This architecture delivers a modern, efficient platform for the client's real estate operations, balancing functionality with maintainability.

Results & Impact

1. 50% Reduction in Reporting Time
   
   • By automating the data gathering and preliminary analysis, analysts now spend far less time on routine tasks, accelerating the creation of market reports.
2. Seven-Figure Time Savings
   
   • The efficiency gains translate into substantial cost savings, allowing the CRE firm to redirect resources towards higher-value analytics and strategic research initiatives.
3. Expanded Research Coverage
   
   • Freed from repetitive tasks, analysts can now explore smaller or emerging submarkets and niche segments, providing brokers with more comprehensive market coverage.
4. Framework for Future AI Investments
   
   • The newly established pipeline and architecture create a solid foundation for integrating future AI improvements, including sentiment analysis, predictive analytics, and dynamic data visualizations.
5. Improved Quality & Consistency
   
   • Analyst oversight combined with AI insight ensures both accuracy and consistency across all deliverables, bolstering the firm’s reputation for reliable market intelligence.

Client Testimonial

“Malleable has been key in leading our AI engineering efforts, rapidly iterating to deliver impactful results” 

- Client’s SVP of Technology

Key Takeaways

• Seamless Data Integration: By unifying structured and unstructured sources, AI solutions can provide a more holistic view of complex datasets—whether for real estate markets or other industries—leading to richer insights and faster decision-making.
• Human-in-the-Loop for Quality: Automation accelerates workflows but expert oversight remains crucial. Including analysts or subject matter experts in the review process ensures accuracy, relevance, and trust in AI-generated outputs.
• Scalable, Future-Proof Architecture: Designing AI systems with modular components and clear data pipelines enables organizations to swap or upgrade models as technology evolves, protecting investments over the long term.
• Significant ROI & Strategic Flexibility: Time saved through AI-driven efficiencies can translate into major cost savings and free up resources for higher-value activities—like deeper analysis, broader market coverage, or developing innovative new services.