AUTOMATED RENTAL AMOUNT MODELING AND PREDICTION

摘要

Disclosed systems and methods can determine predicted rental income, estimated error of the prediction, and a set of comparable rental real estate properties for use in the valuation of a subject real estate property rental value. In one embodiment, the rent prediction system receives rental information about real-estate properties, determines feature characteristics, trains a rent amount prediction model using the feature characteristics, determines a second set of feature characteristics based on the output of the rent amount prediction model, and trains an error prediction model using the determined second set of feature characteristics. Using the trained models, the systems and method may predict a rental value and prediction error for one or more subject properties.

主权项

1. A system for measuring accuracy of an estimate for a rental amount for a real estate property, the system comprising:
non-transitory data storage configured to store rental data associated with a plurality of real estate properties, wherein the rental data comprises at least a location, a rental amount, and a property characteristic associated with each real estate property in the plurality of real estate properties; a computing system comprising computing hardware configured to communicate with the non-transitory data storage, the computing system configured to store one or more code modules in a memory, the code modules comprising:
a rental amount prediction module configured to predict a rental amount for each real estate property in the plurality of real estate properties based at least in part on the rental data; andan error prediction module configured to:
receive the predicted rental amounts for each of the real estate properties in the plurality of real estate properties;determine deviations between predicted rental amounts and actual rental amounts for each of the properties in the plurality of properties;develop an error model for measuring the accuracy of the rental amount predictions, the error model based at least in part on the stored rental data, the predicted rental amounts, and the deviations between predicted rental amounts and actual rental amounts for each of the properties in the plurality of properties; anddetermine, based at least in part on the error model, an error range for rental amount predictions made by the rental amount prediction module.