Not only that-machine learning has the potential to take a certain degree of human subjectivity and error out of mapping and valuation efforts.18Īccuracy: While machine learning-based parcel mapping is a big step forward, its outputs are not yet 100 percent conclusive. By automating otherwise labor-intensive processes, machine learning can save time and money, and achieve new levels of efficiency throughout the property rights cycle. Perhaps the biggest strength of machine learning is its potential for scale. Similar to other emerging technologies, machine learning possesses both strengths and limitations for property rights: The Strengths and Limitations of Machine Learning And within the land use and zoning use case, machine learning can use Google Street View and other street-level imagery sets to map gentrification and identify vulnerable housing. Machine learning can help automate registration processes by using natural language processing to scan documents for key terms or identify red flags. Not only can machine learning be used to predict a property’s value, but it can predict market demand based on the type of property. This capability has already been applied in slum mapping.17Īs another example: machine learning has been used to assist with property valuation, both in established markets and in thin real estate markets where comparable sales data is hard to come by. Until recently, this sort of machine learning application was impossible however, the recent proliferation of high-resolution satellite imagery puts it within reach. Machine learning replicates existing knowledge at scale, driving down the cost and time associated with labor intensive tasks like surveying, filing biographical information, and conducting background and financial checks.įor example: machine learning promises to lower the cost and time associated with mapping by predicting the boundaries of land parcels based on common property boundary features (for example, a lack of vegetation, the existence of a fence or a path, etc.) detected from a training set. Why Machine Learning is Important for Property Rightsīy automating multiple components of the property mapping, documentation, and transaction process, machine learning can vastly increase the scale and speed of property rights delivery, resource management, and land use planning. Finally, the trained model is used to predict classes or to segment the input to different classes using new imagery and scale the prediction to larger areas.16.The output of this phase is a trained model. If the model fails on the new data, the training phase is repeated. This process enables the developer to assess the model’s ability to extend beyond its training data. These images are similar to the ones used in the training phase, but the model has not been exposed to them before.
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