Econometric Modeling of Residential Property Valuations: Ordinary Least Squares Regression Analysis of the South Atlantic Housing Market

This project conducts an applied statistical and econometric evaluation for D. M. Pan National Real Estate Company to determine the predictive validity of residential square footage on property listing prices. Focusing on the South Atlantic real estate market—a geographically diverse and high-growth region encompassing Florida, North Carolina, South Carolina, and Georgia—the analysis evaluates whether a single-variable ordinary least squares (OLS) linear regression model provides a reliable, data-driven benchmark for real estate pricing strategies and valuation advisory.Using simple random sampling generated via Excel, a representative sample of $n = 50$ property transactions was drawn from a 2019 regional housing dataset. Exploratory data analysis revealed that both listing price ($y$) and square footage ($x$) exhibit right-skewed distributions, with sample metrics elevated above national baselines ($\$414,340$ sample mean vs. $\$342,365$ national mean for price; $2,417$ sq ft vs. $2,111$ sq ft for home footprint). Bivariate scatterplot inspection confirmed a strong, positive linear association without non-linear curvature. High-leverage coastal Florida listings exceeding $4,000$ square feet were audited for influence and deliberately retained to maintain authentic regional market variance.The fitted OLS linear regression model yielded the prediction equation $\hat{y} = 94,000.80 + 132.5271x$. Statistical evaluation produced a correlation coefficient of $r = 0.9448$ and a coefficient of determination of $R^2 = 0.8926$, proving that home size alone accounts for $89.26\%$ of the total variance in property listing prices across the sample. The model establishes a marginal value increment of $\$132.53$ per additional square foot ($\$13,252.71$ per $100$ sq ft). For a standard $1,500$ square-foot property, the regression equation estimates a baseline listing price of $\$292,791.45$.The empirical findings confirm that square footage serves as a dominant quantitative anchor for residential property pricing in the South Atlantic region. While the uncaptured $10.74\%$ of price variance reflects micro-location quality, property age, and interior condition, the model provides real estate professionals with an objective, empirical framework for setting competitive listing prices, reducing subjective valuation bias, and evaluating market comparables.