Adjusted Weighted Repeat Sales Index for Disaggregated Real Estate Valuation
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Solution Overview
Problem
The weighted repeat sales index (WRSI) struggles to accurately estimate real estate growth rates at disaggregated geographic levels, such as zip codes or census tracts, due to fewer transactions, leading to lagged and non-seasonal property value assessments.
Innovation Solution
An adjusted WRSI method that calculates deviations from aggregated levels to disaggregated levels using a repeat sales house price index function, incorporating data from aggregated levels to improve forecasting accuracy and detect seasonal trends at lower geographic levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the WRSI uses data from disaggregated geographic levels (zip codes, census tracts), then the localization precision is improved, but the quantity of transactions is reduced leading to lagged and non-seasonal property value assessments
Solution Approach 1:
The patent combines data from multiple geographic levels (aggregated and disaggregated) to create a hybrid index. The adjusted WRSI merges the localized precision of disaggregated data with the statistical reliability of aggregated data, allowing the system to maintain high localization precision while compensating for insufficient transaction quantities at the disaggregated level through borrowing strength from higher aggregation levels.
2Measurement precision
If the WRSI focuses on disaggregated levels to improve market condition analysis, then the measurement precision is improved, but the reliability is reduced due to fewer transactions
Solution Approach 1:
The patent applies local quality by allowing different geographic levels to have different weights and characteristics in the index calculation. Disaggregated levels receive higher weight for localization precision where sufficient transactions exist, while aggregated levels provide reliability support where transaction data is sparse. This creates a spatially varying quality structure that optimizes both precision and reliability locally.
3Measurement precision
If the WRSI uses only disaggregated level data to detect seasonal trends, then the measurement precision is improved, but the loss of time increases due to lagged assessments
Solution Approach 1:
The patent performs preliminary action by using aggregated level data to establish early trends and seasonal patterns that can be detected sooner due to larger transaction volumes. These preliminary findings from aggregated levels serve as leading indicators that prepare the system to quickly interpret and validate patterns when disaggregated data becomes available, reducing the overall time loss in trend detection.
Data Source
AI summary
Systems, methods, and computer-readable storage media are described for estimating real estate property values based on an adjusted repeat sales model. In one exemplary embodiment, a computer-implemented method comprises calculating data for estimating the adjustments from aggregated levels to disaggregated levels by marking a first transaction to a second transaction using a repeat sales house price index function at an aggregated level; determining, using the calculated data, an estimate of the deviation between the repeat sales house price index at the aggregated level and a repeat sales house price index at a disaggregated level; and calculating the repeat sales house price index at the disaggregated level based on the determined estimate of the deviation from the aggregated level.