ChatGPT and Bard Weigh in on Futuristic Technical Analyses of the Housing Market – Part 3 of 3

In our previous two technical analyses of the housing market, we explored using game theory to analyze Case Shiller's MSA-level market metrics, explained the advantages of using dynamic programming over static regression analysis for predictive modeling, discussed the need for transparent and dynamic AI-powered credit-scoring models, and looked into the pricing of new STEM jobs in relation to local housing costs. In this third and final part, we'll focus on comparable sales analysis and how AI can assist in selecting the best comps, reducing subjectivity in the appraisal and assessment process.

AI-derived Comparables: CMA and Appraisal

Author: Choosing comparable sales objectively is crucial in the valuation process of real estate. Real estate salespeople use recent sales and listings to determine the value of incoming subjects but often fail to make necessary adjustments. Even when adjustments are made, the process is often heuristic and can result in inflated subject valuations. Salespeople must present a price-level analysis to statistically back up and justify the comps, with the median value of the sub-market in which the subject resides. This would lead to more transparent valuations and easier home-buying contract negotiations. Do you agree that salespeople should produce a better analysis of the sub-market and unbiased comps for more accurate valuations? How can AI assist in market analysis, adjustment matrix development, and selecting unbiased comps for subject values?

ChatGPT: Yes, I completely agree that salespeople should produce a better analysis of the sub-market and unbiased comps for more accurate valuations. Artificial intelligence (AI) can help in the market analysis, adjustment matrix development, and selection of unbiased comps for subject values in a few ways. For example...

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