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Understanding whether a property is priced fairly hinders buyers and sellerssince they usually do not have an objective viewpoint of the price distributionfor the overall market of their interest. Drawing from data collected of allpossible available properties for rent in Manhattan as of September 2023, thispaper aims to strengthen our understanding of model residuals; specifically onmachine learning models which generalize for a majority of the distribution ofa well-proportioned dataset. Most models generally perceive deviations frompredicted values as mere inaccuracies, however this paper proposes a differentvantage point: when generalizing to at least 75\% of the data-set, theremaining deviations reveal significant insights. To harness these insights, weintroduce the Price Anomaly Score (PAS), a metric capable of capturingboundaries between irregularly predicted prices. By combining relative pricingdiscrepancies with statistical significance, the Price Anomaly Score (PAS)offers a multifaceted view of rental valuations. This metric allows experts toidentify overpriced or underpriced properties within a dataset by aggregatingPAS values, then fine-tuning upper and lower boundaries to any threshold to setindicators of choice.

Utilizing Model Residuals to Identify Rental Properties of Interest: The Price Anomaly Score (PAS) and Its Application to Real-time Data in Manhattan