Ekonometri 2 Vize Ödevi

This technical report explores the viability of density-based spatial clustering, specifically the Density Based Spatial Clustering of Applications with Noise (DBSCAN), as a practical method for objectively delineating real estate market areas within assessor workflows in St. Tammany Parish, Louisiana. A market area is the geographic region from which demand originates and where competing properties are located (IAAO, 2013). Traditionally, market areas are defined using assessor judgement or fixed administrative boundaries, for convenience many times a subdivision is used, potentially missing true spatial market dynamics. This study addresses the research question: How viable is DBSCAN as a practical method for objectively delineating real estate market areas to improve assessor workflows within a Computer-Assisted Mass Appraisal (CAMA) system? DBSCAN was implemented in the R statistical environment using 2020-2024 parcel real estate transaction data, which included sale prices, geographic coordinates, and relevant property characteristics. Parameters such as epsilon (ε) and minimum points (MinPts) were determined using assessor domain knowledge combined with exploratory analysis via k-distance plots. Preliminary analysis demonstrated DBSCAN effectively identified 85 clusters aligning closely with local market behaviors, capturing meaningful spatial variations and highlighting potential inaccuracies in traditional boundaries. Results also indicated DBSCAN’s sensitivity to parameter selection, evidenced by an average silhouette score of -0.02, underscoring the need for careful tuning to balance meaningful clusters and noise reduction. This report demonstrates that DBSCAN offers assessors a viable, data-driven method for refining market area delineations, potentially increasing valuation accuracy and consistency within existing CAMA workflows.

The data workflow described in this technical report offers a predictive model for the construction of size charts using statistical learning techniques. This is in contrast to previous researchers who have only employed descriptive and exploratory statistics to summarize various body measurements to construct size charts to support garment fit. The garment size charts constructed by this research are designed to support stock management decisions rather then garment comfort. The size chart measurements identified by the statistical model were identified by applying a regression model to 800 female subjects; whose measurements were obtained by computer vision technology. The research discovered that the regression model is sensitive to body shape and that more accurate body measurements are predicted when the body measurement data set of subjects has been separated into body shape/height subgroups.

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