Systems and methods to predict resource availability

A non-transitory computer storage media storing instructions configured to instruct a computing apparatus to perform the method, the method comprising:

• storing, in a database, data for a plurality of retail spaces physically located within one or more building structures and leased or leasable by a plurality of users, wherein the data includes trends for foot traffic within each retail space and sales trends for each retail space
• evaluating, by the computing apparatus, a plurality of pre-defined features, including the trends for foot traffic and the sales trends, for each of the retail spaces based on the data stored in the database to form a dataset of the features for the retail spaces, wherein the dataset is comprised of a training dataset and a test dataset;
• applying, by the computing apparatus, a machine learning technique to the training dataset to derive a predictive model that correlates the features for each retail space with a status indicator of whether the retail space was made available during a first predetermined period of time in the past;
• applying, by the computing apparatus, the predictive model to the testing dataset to evaluate accuracy of predictions from the predictive model for the testing dataset;
• and
• in response to a determination that the accuracy of predictions from the predictive model for the testing dataset is above a threshold, applying, by the computing apparatus, the predictive model to the dataset to generate predictions of whether the retail spaces of the dataset will be made available during a second predetermined period of time in the future.

A computing apparatus, comprising:

• at least one microprocessor;
• memory storing instructions configured to instruct the at least one microprocessor to at least:
• store, in a database, data for a plurality of retail spaces physically located within one or more building structures and leased or leasable by a plurality of users, wherein the data includes trends for foot traffic within each retail space and sales trends for each retail space;
• evaluate a plurality of pre-defined features, including the trends for foot traffic and the sales trends, for each of the retail spaces based on the data stored in the database to form a dataset of the features for the retail spaces, wherein the dataset is comprised of a training dataset and a test dataset;
• apply a machine learning technique to the training dataset to derive a predictive model that correlates the features for each retail space with a status indicator of whether the retail space was made available during a first predetermined period of time in the past;
• apply the predictive model to the testing dataset to evaluate accuracy of predictions from the predictive model for the testing dataset;
• and
• in response to a determination that the accuracy of predictions from the predictive model for the testing dataset is above a threshold, applying, by the computing apparatus, the predictive model to the dataset to generate predictions of whether the retail spaces of the dataset will be made available during a second predetermined period of time in the future.