Statistical Methods in Comp Analysis

A hedonic pricing model decomposes property prices into the individual contributions of each characteristic. To build a simple model: collect data on 50+ recent sales in your target market, recording sale price, GLA, lot size, bedrooms, bathrooms, age, condition (rated 1-5), garage stalls, and a binary variable for each neighborhood. Run a multiple regression with sale price as the dependent variable and property characteristics as independent variables. The resulting coefficients tell you the market's implicit price for each feature. For example, if the GLA coefficient is $135/SF with a standard error of $12, you can be 95% confident that each square foot contributes between $111 and $159 to value. This statistical backing is far stronger than a single paired sale.

The Coefficient of Variation (CV) measures the relative dispersion of your value estimate. A CV of 10% on a $300,000 property means your standard error is $30,000—your estimate could reasonably range from $270,000 to $330,000. For residential appraisal, a CV below 10% is considered excellent, 10-15% is acceptable, and above 15% suggests the model or comp set is unreliable. Confidence intervals provide a formal range: a 90% confidence interval of $285,000-$315,000 means you are 90% confident the true value falls within that range. Presenting value conclusions with confidence intervals communicates the precision of your analysis and helps decision-makers understand the risk of over- or underpaying.

Statistical reliability requires adequate sample size. For a simple comp analysis, 3-5 comps provide point estimates but no statistical power. For regression-based adjustment values, a minimum of 30 sales is needed for basic statistical significance, with 50-100+ preferred for reliable multi-variable models. For AVM-level accuracy, thousands of transactions are needed. Appraisal Management Companies (AMCs) use statistical methods to monitor appraisal quality: they track the CV of appraisal values against subsequent sale prices and flag appraisers whose valuations consistently deviate. This statistical oversight creates a feedback loop that improves appraisal accuracy across the industry.