In high-volume valuation environments, from mass appraisal to mortgage lending, the comparable sales process is often bogged down by subjective, manual methods. "Enhancing High-Volume Comparable Sales Processing with Regression Models" presents a solution that bridges the gap between traditional valuation and sound statistical modeling.
This book provides a hands-on, two-pass regression modeling framework that streamlines and standardizes your workflow. You'll learn how to build robust baseline models using foundational variables such as location, time, and living area, and then refine them by adding conditional variables that meet a high standard for statistical significance.
We demonstrate how to utilize real-world data from diverse markets and property types, explaining powerful, yet often-misunderstood techniques such as dummy, effects, one-hot, and custom deviation coding to handle non-hierarchical categorical variables. You'll learn how to accurately account for time, identify and remove outliers, and evaluate your models' performance using both statistical and sales ratio metrics. You will also learn to use ordinal encoding to manage hierarchical categorical variables, such as property condition or quality ratings, as these can be ranked in a logical sequence.
The result is a transparent and defensible valuation grid that converts model coefficients into precise, data-driven adjustments. By making your models more statistically significant, you can produce consistent, credible valuations at scale, making your work more efficient and professional. In essence, this book is a must-have for anyone looking to replace manual guesswork with a rigorous, repeatable process. It will equip you with the knowledge and skills needed to enhance the efficiency, transparency, and scalability of tour comparable sales processes.
