CoreLogic Solutions, LLC recently secured a major technological breakthrough with the issuance of U.S. Patent No. 12743487. This patent, titled ‘Computer processes for clustering properties into neighborhoods and generating neighborhood-specific models’, introduces an advanced machine learning approach to real estate valuation. Analysts and industry professionals can now assess local property markets with the same precision previously reserved for manual, localized appraisals.
Advanced Machine Learning for Real Estate Valuation
The innovation focuses on using artificial intelligence to group similar real estate properties into contiguous neighborhoods. This system uses a model generator to identify correlations between property attributes and values for specific localized areas. Rather than relying on broad regional data, the computer processes dynamically generate neighborhood-specific models. Developed by inventors Wei Geng, Duncan Chen, Bin He, Jon Wierks, David Stiff, and Howard Botts, this method allows for accurate estimations of property values based on nuanced local conditions. This setup allows for high accuracy during comparative market analysis.
October 2026 Patent of the Month in Real Estate Development
This technology was recognized as the Patent of the Month for the real estate development industry in October 2026 due to its capacity to modernize how neighborhoods are defined and evaluated. Traditionally, property valuation relies on static geographical boundaries like zip codes, which often fail to reflect the true economic realities of a localized area. This invention is highly innovative because it leverages data driven processes to dynamically draw boundaries based on actual property attributes and current market correlations. By systematically calculating these specific models, real estate developers and investors can make highly informed decisions regarding land acquisition, pricing strategies, and development feasibility, which reduces financial risk.
Eligibility for the U.S. R&D Tax Credit
The practical applications of this patent present strong opportunities for claiming the Research and Development tax credit in the United States. Software development activities related to building, testing, and refining the machine learning algorithms used to cluster properties and generate these predictive models directly align with the requirements of the four part test under Section 41 of the Internal Revenue Code. Specifically, companies investing resources into eliminating technical uncertainty surrounding the integration of massive real estate datasets, or developing new computational processes for neighborhood boundary generation, are engaging in qualified research activities. The systematic trial and error required to train these artificial intelligence models and improve the accuracy of property value estimations constitutes technical experimentation, making the associated payroll and cloud computing expenses potentially eligible for tax incentives.
