One of the projects I’m currently developing for my upcoming book, Real Estate Analytics Playbook: Practical AI and Data Strategies for Agents and Lenders, focuses on a question every real estate professional asks:
“Who is most likely to move next?”
The playbook is being developed alongside my doctoral dissertation research on AI-Augmented Exploratory Learning (AAEL), where participants use AI, data, and analytical tools to solve real-world business problems. The goal is not to teach people how to become data scientists. The goal is to help professionals become better decision-makers.
One project in the book asks participants to build a Seller Opportunity Model.
Instead of treating every homeowner as equally likely to sell, participants analyze market indicators such as:
• Neighborhood turnover rates
• Length of ownership
• Local appreciation trends
• Market activity patterns
• Inventory changes
Using Excel, Python, and AI tools, participants create an Opportunity Score that helps identify neighborhoods where future listing activity may be more likely to emerge.
For example:
Neighborhood A
• 9-year average ownership duration
• Strong appreciation
• Increasing turnover
Neighborhood B
• 2-year average ownership duration
• Stable pricing
• Low turnover
Which neighborhood deserves more prospecting attention?
Rather than relying solely on intuition, analytics provides a structured framework for investigation.
This project mirrors the same AI-Augmented Exploratory Learning process used in my dissertation study:
Scope → Define the problem
Explore → Investigate the data
Analyze → Identify patterns
Evaluate → Assess findings
Communicate → Recommend action
Whether you’re in real estate, lending, education, healthcare, or business, the future belongs to professionals who can combine domain expertise with data and AI to make better decisions.
That’s the type of work we’re building at AAEL Labs.
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