Machine learning systems and methods for determining home value

A system, comprising:

• at least one computer hardware processor;
• and
• at least one non-transitory computer-readable storage medium storing processor executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform:
• obtaining a plurality of images, the plurality of images including a first image of a first room inside a home, the first image having a first resolution;
• determining a type of the first room by:
• generating, from the first image, a second image of the first room having a second resolution lower than the first resolution;
• and
• processing the second image of the first room with a first neural network model comprising:
• a first neural network sub-model comprising a first plurality of layers comprising at least one million parameters, the first plurality of layers comprising at least deep neural network layers, an average pooling layer, a fully connected layer, or a softmax layer;
• and
• a second neural network sub-model comprising a second plurality of layers comprising at least one million parameters, the second plurality of layers comprising at least deep neural network layers, a max pooling layer, a fully connected layer, or a softmax layer;
• identifying at least one first feature in the first image of the first room by processing the first image with a second neural network model different from the first neural network model and trained using images of rooms of a same type as the first room, the second neural network model further having a third plurality of layers comprising at least a convolutional layer, a pooling layer, a fully connected layer, or a softmax layer, the third plurality of layers including at least one million parameters;
• and
• determining a value of the home at least in part by using the at least one first feature as input to a machine learning model different from the first neural network model and the second neural network model.

A method, comprising:

• using at least one computer hardware processor to perform:
• obtaining a plurality of images, the plurality of images including a first image of a first room inside a home and a second image of a second room inside the home;
• generating, from the first image of the first room, a second image of the first room having a lower resolution than the first image of the first room;
• determining a type of the first room by processing the second image of the first room having a lower resolution than the first image of the first room with a first neural network model, the first neural network model having a first plurality of layers comprising at least a convolutional layer, a pooling layer, a fully connected layer, or a softmax layer, the first plurality of layers including at least one million parameters;
• determining a type of the second room by processing the second image of the second room with the first neural network model;
• identifying at least one first feature in the first image of the first room having a higher resolution than the second image of the first room by processing the first image of the first room with a second neural network model different from the first neural network model and trained using a first plurality of training images of rooms of a same type as the first room, the first plurality of training images including training images augmented by one or more transformations, the second neural network model having a second plurality of layers comprising at least first deep neural network layers, a reduction layer, second deep neural network layers, an average pooling layer, a fully connected layer, a dropout layer, or a softmax layer, the second plurality of layers including at least one million parameters;
• identifying at least one second feature in the second image of the second room by processing the second image with a third neural network model different from the first neural network model and second neural network model, the third neural network model trained using a second plurality of training images of rooms of a same type as the second room, the second plurality of training images including training images augmented by one or more transformations, the third neural network model having a third plurality of layers comprising at least first deep neural network layers, a reduction layer, second deep neural network layers, an average pooling layer, a fully connected layer, a dropout layer, or a softmax layer, the third plurality of layers including at least one million parameters;
• and
• determining a value of the home at least in part by using the at least one first feature and the at least one second feature as input to a machine learning model different from the first neural network model, the second neural network model, and the third second neural network model.