Neural Network Real Estate Forecasting for Cost Optimization
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Solution Overview
Problem
Existing methods for forecasting future real estate space needs for businesses are imprecise, relying on estimates from real estate professionals and failing to consider various business and market variables, leading to suboptimal real estate solutions.
Innovation Solution
A computer-implemented method using a neural network system to analyze historical business data and objectives, forecasting headcounts, generating scenarios, constructing discrete option trees, and performing stress tests to identify an optimal real estate solution with minimized costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional real estate professionals provide lease options based on business inquiries, then real estate solutions can be provided quickly, but the precision and accuracy of forecasting future space needs deteriorates
Solution Approach 1:
A neural network system acts as an intermediary between business data and real estate forecasting, processing historical headcount data, business objectives, and market variables to generate accurate future space need predictions without requiring complex manual analysis by real estate professionals
Solution Approach 2:
The patent replaces the mechanical system of manual real estate professional assessment with an automated neural network-based forecasting system that processes multiple variables simultaneously, achieving higher precision through computational algorithms rather than human estimation
2Adaptability or versatility
If businesses lease large spaces to accommodate future growth, then space availability is ensured, but cost increases and flexibility decreases
Solution Approach 1:
The system generates multiple dynamic scenarios representing different future business conditions (growth, contraction, stability) and evaluates real estate options under each scenario, enabling businesses to choose flexible lease structures that can adapt to actual future conditions rather than assuming worst-case growth
Solution Approach 2:
The neural network performs preliminary stress testing of real estate options against multiple forecasted scenarios before commitment, identifying lease structures that pre-adapt to various future conditions, allowing businesses to secure space availability while maintaining flexibility through pre-evaluated optimal solutions
3Measurement precision
If multiple business variables and market forces are considered in forecasting, then forecasting accuracy improves, but the complexity of analysis increases
Solution Approach 1:
The neural network system serves as a universal platform that simultaneously processes multiple types of input data (historical headcount, business objectives, market variables) and performs multiple functions (forecasting, scenario generation, stress testing, optimization) through a single integrated system rather than separate analysis tools
Solution Approach 2:
The system transforms complex qualitative business variables and market forces into quantifiable parameters that the neural network can process, changing the state of input data from unstructured descriptions to standardized numerical inputs that maintain forecasting accuracy while simplifying computational handling
Data Source
AI summary
A computer-implemented system and method of analyzing historical business data and business objectives for real estate solution prediction is provided. The method includes receiving a user dataset associated with a business entity by a server computing device. The server may determine a set of forecasted headcounts at different points of future time based on a historical headcount, generate a plurality of scenarios associated with the business entity based on the forecasted headcount and the commercial objective dataset, and construct option trees with respective real estate solutions corresponding to respective scenarios. A neural network system may be configured to perform stress tests against respective scenarios to evaluate respective real estate solutions and determine respective costs and actions associated with respective real estate solutions. The computing device may determine an optimal real estate solution with a minimized cost and a corresponding action that the business entity takes to minimize the costs.