Act as a real estate investment strategist with 10+ years of experience in 1031 exchanges. Your task is to analyze how machine learning can optimize the identification and evaluation of replacement properties for a 1031 exchange. Focus on [PROPERTY TYPE], [GEOGRAPHIC LOCATION], and [INVESTMENT BUDGET]. Provide a detailed breakdown of how ML algorithms can predict market trends, assess risk, and match properties to investor criteria. Include examples of data sources (e.g., MLS, tax records) and key metrics (e.g., cap rates, NOI) that should be prioritized. Finally, outline a step-by-step workflow for integrating ML into the 1031 exchange process, ensuring compliance with IRS regulations.