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In this episode of Breaking New Ground I sit down with Lillian Chen, founder of Proptimal and one of the most thoughtful minds working across real estate, financial analysis, and technology. Lillian’s path from grinding through underwriting to learning how to build software offers a rare look at how modern analysts are developed and why capacity and clarity matter as much as technical skill.
We dig into the realities of financial modeling and why analyst work is so difficult to scale. Lillian describes the challenges of training teams, the limits of templates, and the importance of judgment when translating assumptions into sound underwriting. She also shares how her real-world experience shaped the creation of Proptimal and why small and midsize developers need access to better data, better tools, and higher quality analysis.
What stood out most is Lillian’s approach to AI. She explains how AI can expand productivity, improve workflows, and give analysts more mental bandwidth, but only when it is paired with responsible use and a strong foundation in financial principles. For her, technology is a force that strengthens good thinking rather than replacing it.
Whether you are an analyst, developer, operator, or anyone curious about how real estate and technology are converging, this episode offers a practical and forward-looking perspective on how smarter tools and stronger workflows can elevate the entire industry.
Key Takeaways
Proptimal was created to bring better financial analysis to real estate professionals who often don’t have analyst teams behind them.
Becoming a strong analyst requires persistence, real-world reps, and navigating complexity.
Training analysts is difficult because underwriting isn’t plug-and-play—templates alone don’t cut it.
AI can streamline repetitive processes and expand analyst capacity.
Financial modeling tools can democratize knowledge and increase access to institutional-quality insights.
Learning to code helps bridge the gap between analysis and scalable software.
AI can accelerate both underwriting and software development when used intentionally.
Mental bandwidth is one of the most important variables in good decision-making.
Responsible AI use requires understanding limits, risks, and ethical considerations.
Closing the information gap empowers small developers with better financial modeling, clearer assumptions, and stronger deals.
Chapters
00:00 Introduction to Proptimal and Lillian Chen
02:55 Lillian’s Journey in Real Estate
05:57 Challenges in Training Analysts
08:45 The Role of AI in Financial Modeling
11:59 Building a Financial Modeling Tool
14:44 Learning to Code and Building Software
17:46 Using AI for Software Development
20:38 Strategizing Technology Transformation
24:17 Defining Roles in AI Interactions
25:30 Establishing Clear Objectives for AI Engagement
27:53 Utilizing AI for In-Depth Research
30:14 Learning and Adapting to AI in Workflows
32:01 The Dual Nature of AI: Enhancing or Diminishing Intelligence
34:20 Ethics and Personal Growth Through AI
36:04 Proptimal: Bridging the Gap in Real Estate Analysis
39:23 Understanding the Market Needs of Small Developers
Show Notes & Links
Listen now on Spotify, Youtube, or Apple
Proptimal: proptimal.com
Guest: Lilian Chen, Founder & CEO of Proptimal
Connect: Lilian Chen on LinkedIn
Closing Thought
The way real estate companies underwrite and make decisions is evolving fast. AI is giving both large institutions and small developers the ability to analyze more deals, surface better insights, and move with greater clarity. Lillian’s perspective is a reminder that technology doesn’t replace judgment—it strengthens it. Her work with Proptimal shows how better tools and disciplined thinking can expand capacity, sharpen assumptions, and ultimately lead to better outcomes across the market.
Cheers,
John
