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.

This technical report evaluates the feasibility and effectiveness of density-based spatial clustering methods, specifically DBSCAN, for objectively delineating real estate market areas in St. Tammany Parish, Louisiana. According to the International Association of Assessing Officers (IAAO), a market area is the geographic region from which demand originates and where competing properties are located. The primary research question is: How effectively can DBSCAN identify localized real estate market areas to enhance assessor workflows? Using real estate transaction data from 2024: including sale price, geographic coordinates, property characteristics, and transaction dates, this study demonstrates clustering applications exclusively using R. The effectiveness of DBSCAN will be evaluated primarily for spatial coherence and alignment with known market behaviors through qualitative assessment and descriptive statistics. This report outlines a practical framework for integrating density-based clustering techniques into assessor workflows.

In this lab, I explore Louisiana’s socio-economic landscape by looking at two important factors: education levels and income. By analyzing data at the parish level, I aim to understand how these factors vary across the state. Part A: The percentage of residents holding graduate degrees. Part B: The median household income. Using data from the American Community Survey (ACS) and visualization tools in R, we will explore county-level trends related to graduate degrees and median household income.

This report summarizes the total billed property tax by parcel for the precincts and wards in St. Tammany Parish. A 3% deduction has been applied to account for the Louisiana State Pension. Note: These total amounts are estimates. Since many parcels do not align perfectly with precinct or ward boundaries, values are derived through spatial analysis.

Lesson 4 Assignment - R Exploring Tornado Data in Oklahoma 2016-2021

An exploration of RStudio using following the R-Ladies Sydney Tutorial using parcel data from the St. Tammany Parish Assessor