Commercial Real-Estate Occupancy Prediction Using Multi-Source ML
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Solution Overview
Problem
Existing methods fail to accurately predict the occupancy of commercial properties for future time periods and revenue potential, relying heavily on intuition rather than integrated data analysis.
Innovation Solution
A computer-implemented method using machine-learning models to collect, derive, and analyze data from multiple sources, including property and tenant features, to predict occupancy and revenue, with features ranked by contribution and actions automatically taken.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If property owners rely on intuition and gut feeling to predict occupancy, then decision-making is simple and quick, but prediction accuracy is poor
Solution Approach 1:
The system segments the complex prediction task into multiple independent components: data collection module, feature extraction module, machine learning model module, and prediction output module. Each module handles a specific aspect of the prediction process, making the overall complex system manageable and maintainable while achieving high prediction accuracy through integrated analysis of multiple property features, tenant characteristics, and market conditions
Solution Approach 2:
The patent introduces a machine learning model as an intermediary between raw property data and occupancy predictions. This intermediary automatically processes and integrates diverse features (property characteristics, tenant financials, market trends) to generate accurate predictions, eliminating the need for owners to manually analyze complex datasets while providing scientifically-based predictions rather than intuitive guesses
2Measurement precision
If comprehensive data from multiple sources is collected and integrated, then prediction accuracy improves, but data processing complexity increases
Solution Approach 1:
The system employs a universal machine learning framework that can process multiple types of data sources (property features, tenant information, market data) through a single integrated model. This multi-functional approach allows the system to handle diverse data formats and sources uniformly, improving prediction accuracy by considering all relevant factors while avoiding the need for separate processing systems for each data type
Solution Approach 2:
The machine learning model automatically performs feature extraction, data cleaning, and integration without requiring manual intervention. The system self-services by autonomously processing comprehensive data from multiple sources, transforming raw data into meaningful predictions while managing its own complexity internally, thus providing accurate predictions without proportionally increasing operational complexity for users
Data Source
AI summary
A computer implemented method for predictive modeling, which includes collecting data relating to properties leased to tenants, including features relating to the properties, the tenants, regional data, national data, or global data. Additional features are derived from the collected data, including additional features relating to the properties or the tenants. Each of the properties of the tenants is labeled to indicate a characteristic of the property or the tenant, where each labeled property or tenant is associated with property-features or tenant-features. A machine-learning based model is trained to predict a parameter a specific property or of a specific tenant, using a first subset of the labeled data. Following the training, and in response to receipt of an indication of a specific property or tenant, a prediction of the parameter for the property or the tenant is obtained from the machine-learning based model, and a report including the prediction is generated.