What are the top ways to use machine learning for property value prediction?

I'm uncertain that a top-down approach, similar to Zillow's, would be effective in the commercial real estate sector. This is primarily due to the highly fragmented information dispersed across various local and state-level digital platforms. However, if such an approach were feasible, I believe the following data points would be invaluable: - Traffic patterns and volume information - Demographic details such as age and income distribution from census data - Proximity to points of interest, including cafes, restaurants, grocery stores like Trader Joe's, and perhaps even popular spots in augmented reality games like Pokémon Go stations.

Based on my experience, while internal data are crucial, they are often insufficient on their own. To create a more comprehensive database, it is necessary to enhance internal datasets with additional features. For instance, by integrating credit bureau data into resident or tenant profiles, you can gain a deeper understanding of your target audience. Similarly, for labor-intensive asset classes such as senior living facilities, applying this method to caregiver information can significantly improve the effectiveness of recruitment strategies. This approach ensures a richer and more detailed profile for both potential customers and employees.

Even after the data is prepared and the necessary pipeline is developed, it’s always recommended to ensure that the model is accurately capturing the expected features. Conducting feature sufficiency tests can help determine whether the prediction aligns closely with the target by adding or removing certain feature components. This allows for a clearer understanding of what the model has learned. It’s also advised to choose the metric with care, ensuring it’s suitable for the use case.

I think it's vital to remember that despite advances in technology, the cornerstone of property valuation remains the comparative approach, benchmarking against other transactions. At REDD, we've embarked on a project where we trained a machine learning model to estimate values based on inputs like past appraisals, current rent rates, vacancy ratios, macroeconomic parameters, and currency exchange rates. Our model achieves over 80% accuracy, but it's still a provisional assessment. Real estate is tangible; it exists in the physical world. As one appraiser told me, "I can't imagine valuing a property without seeing it with my own eyes." This blend of tech and touch is key in our field.

I'm open to further discussion, but I remain skeptical that machine learning would markedly enhance the current valuation process in commercial real estate. Nonetheless, the latest developments with OpenAI's GPTs (custom agents) may offer a way to deconstruct the intricate due diligence process into smaller, more manageable tasks that could be efficiently addressed by artificial intelligence. This potential to streamline complex processes suggests that while AI may not revolutionize valuation as a whole, it could optimize certain aspects of it.