Machine Learning Rental Address Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Short-term vacation rental properties cause noise and traffic burdens on neighbors and local hotels, leading to city restrictions and Transient Occupancy Taxes, but individual owners often evade taxes by not listing property addresses, and current machine learning methods struggle to accurately identify rental property addresses from diverse data sources.
Innovation Solution
A system employing machine learning algorithms, including neural networks, to identify rental property addresses by analyzing multiple public sources such as rental listings, sale listings, maps, and county records, using a combination of image processing and natural language processing techniques, with iterative training to improve detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rental property addresses are not listed to avoid Transient Occupancy Tax, then tax evasion occurs, but the ability to audit and verify rental properties is compromised
Solution Approach 1:
The patent uses machine learning algorithms as an intermediary to connect rental listings with property records. The system automatically matches rental property descriptions, images, and listings with corresponding property records from county assessor databases, enabling address identification without direct disclosure by rental owners. This intermediary process recovers lost address information through automated data correlation.
Solution Approach 2:
The patent replaces manual auditing mechanisms with automated machine learning systems. Instead of relying on direct address disclosure or manual verification, the system uses computer vision, natural language processing, and pattern recognition algorithms to automatically identify and verify property addresses from multiple data sources, substituting mechanical auditing processes with intelligent automated systems.
2Measurement precision
If machine learning algorithms are used to identify rental property addresses from diverse data sources, then address detection accuracy improves, but false positives increase
Solution Approach 1:
The patent implements feedback mechanisms where the machine learning system continuously learns from verification results. When property addresses are identified, the system cross-references multiple data sources (rental listings, sale listings, county records, maps) and uses the consistency of results as feedback to refine future detections. Verified addresses are fed back into the training data to improve algorithm accuracy and reduce false positives over time.
Solution Approach 2:
The patent combines multiple independent detection methods and data sources to verify property addresses. Instead of relying on a single algorithm or data source, the system merges information from rental listings, sale listings, county property records, and map data, requiring consistency across multiple sources to confirm an address match. This combining approach reduces false positives by requiring corroboration from independent sources.
3Measurement precision
If multiple public sources are analyzed to determine rental property addresses, then address identification accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments the complex task of address identification into distinct functional modules: data ingestion from multiple sources, property record matching, address extraction, and verification. Each module handles a specific aspect of the process, making the overall system more manageable despite processing multiple data sources. The segmentation allows independent optimization and testing of each component.
Solution Approach 2:
The patent creates a universal machine learning framework that can process multiple types of data sources (rental listings, sale listings, county records, maps) using the same core algorithms. The system is designed to be multi-functional, handling different data formats and sources through a unified architecture, which reduces overall system complexity compared to having separate specialized systems for each data source.
Data Source
AI summary
Provided herein are media, systems, and methods that identify the address of a rental property from public sources (e.g., rental listings, maps, and county property records). A data mining task process may be performed, by each of a plurality of first data ingestion interfaces to a unique external property data source, to determine at least one property record depiction, each property record depiction associated with a property record. A first machine learning algorithm may be applied to the at least one rental property depiction and the at least one property record depiction from each data source to identify one or more common property records, wherein each common property record comprises a property record that refers to the rental property. A data mining task process may be performed, by a second data ingestion interface to the one or more common property records to determine the street address of the rental property.