Taxation on Real Estate Conference - Virtual-6 - Andreasen

6 - Andreasen - AI_in_Real_Estate

Pdf Summary

This document is a presentation on how AI is changing real estate, with a strong focus on practical implementation rather than hype. It begins by framing AI as a system that creates value only when supported by strong data engineering, governance, legal/ethical safeguards, security, scaling, monitoring, and retraining. It emphasizes that models are delicate and that data governance is increasingly critical.<br /><br />The presentation distinguishes between older analytical AI and newer generative AI. Analytical AI is used for prediction, forecasting, anomaly detection, optimization, segmentation, and similar tasks. Generative AI is used to create or transform text, code, images, audio, and video. It highlights the rapid growth of frontier models and compute, and suggests that AI capabilities are advancing quickly across many domains.<br /><br />A major theme is the evolution of AI skills into four disciplines: prompt craft, context engineering, intent engineering, and specification engineering. These represent increasingly sophisticated ways of communicating with AI systems, with specification engineering being especially important for creating self-contained, executable instructions that agents can follow with minimal human intervention.<br /><br />The deck also explains the difference between AI and machine learning, and notes that different problem statements require different types of AI. It stresses computational thinking and the need to translate real-world problems into precise, machine-readable instructions.<br /><br />For real estate specifically, the presentation covers system design, causal loops, and data strategy, with responsible AI and trustworthy data as foundational priorities. It lists many use cases across property operations, asset management, underwriting, development, accounting, leasing, procurement, maintenance, and tenant management. Examples include lease abstraction, invoice processing, utility monitoring, predictive forecasting, market rent anomaly detection, and land value analysis.<br /><br />The closing message asks what AI can unlock that is currently impossible, and urges leaders to think about how AI can compress the distance between raw data and decisions in real estate.

Keywords

artificial intelligence

real estate

data governance

generative AI

analytical AI

prompt engineering

specification engineering

machine learning

predictive forecasting

responsible AI