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The advantage of machine learning modeling is that a model does not have to be constrained to the 10 to 15 variables that the researcher deems important for property value. Instead, an unlimited number of variables could be used by the model, computation power being the only constraint. To predict value, NOI per unit is obviously the most important attribute. In addition, the year of construction and average size of the unit are important. Furthermore, locational variables like “green space within 30 minutes” and “music events within 30 minutes” are considered to be the variables with the highest weight. These variables should be measured at a very detailed level of granularity, which would make them useful proxies for indicators such as income, employment, and neighborhood vibrancy.