Database Search Engine Optimization for Real Estate Listings
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Solution Overview
Problem
Users of online real estate search services face the burden of sifting through listings with incomplete information, particularly dealing with missing or stale property values, which hinders accurate pricing insights and efficient search results.
Innovation Solution
The implementation of a method that estimates property values by accessing listing information across multiple communities, defining a cost function based on parameters, and optimizing it to generate optimal parameters for updated estimates, even when data is sparse, thereby providing more accurate and robust data for users and reducing processing load.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If conventional search techniques are used to access property listings, then users can obtain vast amounts of real estate information, but they face the burden of sifting through listings with incomplete or stale information
Solution Approach 1:
The system performs preliminary actions by proactively identifying and flagging missing or stale property values in the database before users query for them. This allows the system to prepare estimated values in advance using cost function optimization, so that when users search, they immediately receive complete and reliable information without having to sift through incomplete listings.
Solution Approach 2:
The patent introduces an intermediary mechanism - a cost function optimization system - that acts as a mediator between the raw property data and the user queries. This intermediary processes the data to identify gaps, generates estimated values using optimization techniques, and integrates these estimates back into the database, thereby improving the overall reliability of property information without changing the fundamental search capability.
2Reliability
If the system provides complete property information for all listings, then user experience is enhanced, but database processing load and network traffic increase
Solution Approach 1:
The system applies local quality by selectively estimating property values only for specific properties that have missing or stale information, rather than processing all property listings uniformly. The cost function optimization is applied locally to individual properties or small groups of properties with incomplete data, thereby improving information completeness while minimizing unnecessary processing load and network traffic.
Solution Approach 2:
The patent changes the parameter state of property values by transitioning them from known to estimated states using cost function optimization. Instead of maintaining all property values at full detail indefinitely, the system dynamically adjusts the precision and completeness of property information based on data availability and recency, optimizing the balance between information quality and processing efficiency.
3Measurement precision
If conventional estimation techniques are used with sparse data, then processing is simpler, but accurate estimates cannot be generated
Solution Approach 1:
The system implements feedback mechanisms where the cost function continuously evaluates the quality of estimated property values and adjusts optimization parameters accordingly. By using feedback from the optimization process itself, the system can generate accurate estimates even with sparse data, as the cost function learns from each estimation iteration and improves its predictions over time, managing complexity through adaptive rather than static methods.
Solution Approach 2:
The patent applies dynamics by making the estimation process adaptive and flexible rather than static. The cost function optimization dynamically adjusts to the available data conditions, changing its approach based on data sparsity, property types, and market conditions. This dynamic methodology allows accurate estimates to be generated across varying data scenarios without requiring overly complex predefined rules for each situation.
Data Source
AI summary
Methods, including computer programs encoded on a computer storage medium, for improving database functionality. In one aspect, a method includes: in response to receiving a query for data associated with one or more properties, accessing listing information for multiple, different communities within which multiple, different types of properties are physically located; selecting, from among the different communities, a particular set of communities; determining that a value of one or more properties within the selected particular set of communities is missing or stale; obtaining an initial estimate of the value of one or more properties within the particular set of communities; defining a cost function that depends on a set of parameters, optimizing the cost function to generate a set of optimal parameters, and obtaining an updated estimate of the value of each property within the particular set of communities based on the generated set of optimal parameters.