Market Rental Rate Index Prediction Using Random Forest Models
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Solution Overview
Problem
Conventional methods for determining market rental rates are labor-intensive and lack accuracy, requiring manual identification and aggregation of comparable properties' rental rates.
Innovation Solution
A software facility that uses rental listings to train models, such as random classification tree forests, to predict market rental rates for properties, including those not currently for rent, by analyzing property and rental attributes, and displays these rates on web pages, maps, and lists, allowing for filtering and aggregation to generate market rental rate indices for geographic areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification and aggregation of comparable properties' rental rates is used, then market rental rates can be determined, but the process becomes labor-intensive and lacks accuracy
Solution Approach 1:
The patent replaces the manual mechanical process of identifying and aggregating comparable properties with an automated computer-based system. The system uses algorithms to automatically retrieve rental listings, identify comparable properties, and calculate market rental rates, eliminating human labor while improving accuracy through consistent application of computational methods.
Solution Approach 2:
The patent introduces computer-readable storage media containing algorithms as an intermediary between raw rental listing data and market rental rate determination. These algorithms serve as the mediating mechanism that processes unstructured rental data, applies comparison criteria, and produces standardized market rate outputs, replacing direct manual analysis.
2Productivity
If automated models are used to predict market rental rates, then efficiency and accuracy improve, but system complexity increases
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models on historical rental data before actual market rate predictions are needed. The system pre-processes and stores rental listings, establishes comparison algorithms, and prepares predictive models in advance, so that when market rates are needed, the computationally intensive work has already been completed, improving real-time efficiency.
Data Source
AI summary
A facility for determining a market rental rate index for homes located in a distinguished geographic area is described. The facility accesses a current market rental rate attributed to substantially every home in the named geographic area based on automatically comparing the attributes of each home to attributes of homes recently listed for rental in the named geographic area. The facility then applies an aggregation function to the accessed current market rental rates to obtain an aggregation result. The facility causes to be displayed a characterization of the current market rental rate of homes in the named geographic area that is based on the aggregation result.