Automated Real Estate Analysis Using Sensor Data
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Solution Overview
Problem
The real estate industry faces challenges in efficiently gathering consumer preferences due to labor-intensive and time-consuming traditional methods of property evaluation, which lack quantitative data, leading to inefficient matching of prospects with properties and inefficient selection of furniture, fixtures, and remodeling tasks.
Innovation Solution
A computerized system utilizing large language models and AI-based image methods for self-guided property tours, where users can explore properties alone while their preferences and interactions are monitored, providing data on user behavior and preferences, and using augmented reality to visualize changes, facilitating data aggregation and analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional in-person property tours and evaluations are conducted, then consumer preference data can be gathered, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces the mechanical system of in-person property tours with an automated computerized system that uses sensors, image recognition, and large language models to monitor and analyze prospect behavior. This substitution eliminates the need for manual data collection while capturing detailed consumer preferences through automated tracking of movements, dwell times, and interactions with property features.
Solution Approach 2:
The system enables self-guided property tours where prospects independently explore properties while the automated system monitors their behavior. This self-service approach allows multiple prospects to tour properties simultaneously without requiring broker intervention, thereby reducing labor intensity and time consumption while still gathering comprehensive preference data.
2Loss of information
If in-person tours with multiple prospects are conducted, then consumer preferences can be observed, but travel and resource waste increase
Solution Approach 1:
The patent replaces the mechanical process of brokers physically accompanying prospects on tours with an automated monitoring system using sensors and cameras. This eliminates the energy consumption associated with broker travel while maintaining the ability to observe and record consumer preferences through automated detection of prospect behaviors, dwell times, and interactions.
Solution Approach 2:
The system creates a digital copy of the prospect-tour experience by using sensors and cameras to capture and analyze prospect behavior. This digital replication allows multiple prospects to be monitored simultaneously without requiring physical broker involvement, thereby eliminating travel waste while preserving detailed preference data collection.
3Loss of information
If traditional property evaluation methods are used, then consumer preferences can be assessed, but the matching process becomes inefficient
Solution Approach 1:
The patent replaces manual analysis of consumer preferences with large language models and AI algorithms that automatically process sensor data, image recognition results, and behavioral patterns. This substitution enables rapid analysis of multiple prospects' preferences and generates optimized property matching recommendations, dramatically improving matching efficiency while maintaining comprehensive data collection.
Solution Approach 2:
The system implements a feedback loop where sensor data and image recognition results are continuously analyzed by large language models to refine property matching recommendations. The system learns from prospect behaviors and preferences, adjusting and improving matching accuracy over time, thereby enhancing productivity in the property evaluation and matching process.
4Productivity
If automated monitoring systems are deployed, then data collection efficiency improves, but system complexity increases
Solution Approach 1:
The patent employs sensors and imaging devices that serve multiple functions: they monitor prospect locations, track movements, capture images for analysis, and detect interactions with property features. This multi-functionality reduces the need for separate specialized devices, thereby improving data collection efficiency while limiting the increase in system complexity through consolidated hardware design.
Data Source
AI summary
Computerized system and method of obtaining and analyzing data on how large numbers of real estate visitors view and interact with real estate property. The system, which optimally will operate during real-world real estate tours, may utilize data from either property associated sensors or user mobile device sensors (e.g., smartphone sensors) to obtain and aggregate visitor position and/or orientation data with respect to various designated locations of interest on the property. This can be used to produce statistics on visitor positions and/or orientations with respect to such locations. The resulting data can be used for various purposes, including as input to LLM AI systems for automated real estate recommendations and information about various real estate features associated with below or above-average visitor interest or approval. Various methods to encourage use, such as self-guided real-world tours, virtual staging, and virtual goods and services, are also discussed.