Enhancing datasets in CRE through AI

April 25, 2024, 11:18 a.m.

AI can be effective when datasets are sporadic and siloed in commercial real estate by using a variety of techniques, including:
Transfer learning: Transfer learning is a technique where a model trained on one task is used as a starting point for training a model on a different task. This can be useful for commercial real estate because it allows us to leverage models that have been trained on large datasets of other types of data, such as images, text, and code.
Data augmentation: Data augmentation is a technique of creating new data points from existing data points. This can be useful for commercial real estate because it can help us to increase the size and diversity of our datasets.
Domain-specific knowledge: We can incorporate domain-specific knowledge into our AI models to help them to learn from sporadic and siloed data. For example, we can use knowledge about the commercial real estate industry to help our models to understand the relationships between different data points.
Here are some specific examples of how AI is being used to overcome the challenges of sporadic and siloed data in commercial real estate:
AI is being used to predict the value of commercial properties. AI models are being trained on historical data of commercial property sales to predict the value of future sales. This information can be used by investors and lenders to make better decisions about commercial real estate investments.
AI is being used to identify investment opportunities. AI models are being trained to identify commercial properties that are undervalued or that have the potential to be redeveloped. This information can be used by investors to find profitable investment opportunities.
AI is being used to improve the efficiency of commercial real estate transactions. AI models are being used to automate tasks such as due diligence and contract review. This can help to reduce the time and cost of commercial real estate transactions.

Using AI to organize CRE data 

In CRE, lack of organized data is probably the biggest factor that’s hampering a complete technology overhaul in the industry.  However, few  things that can be done to make AI more effective when datasets are sporadic and siloed in commercial real estate:
Use a variety of data sources: The more data sources you have, the better your AI model will be able to learn. Try to collect data from a variety of sources, including public records, private databases, and surveys.
Clean and organize your data: AI models are only as good as the data they are trained on. Make sure to clean and organize your data before training your model. This will help to ensure that your model is learning from accurate and up-to-date data.
Use a model that is appropriate for your data: Different AI models are better suited for different types of data. Choose a model that is appropriate for the type of data you have and the task you are trying to accomplish.
Monitor and evaluate your model: Once you have trained your model, it is important to monitor and evaluate its performance. This will help you to identify any areas where your model needs improvement.
By following these tips, we can make AI more effective when datasets are sporadic and siloed in commercial real estate.
 

As AI technology continues to develop, we can expect to see even more innovative ways to use AI to overcome the challenges of sporadic and siloed data in commercial real estate.