How to Support GLA Adjustments With MLS Market Data: A Practical Guide for Residential Appraisers

1. Extract the right comparable closed sales

Start from the market the subject actually competes in, not the whole county. Filter to closed sales (not active or pending listings) of the same property type, within a defined market area and a time window long enough to give you a usable count but short enough that market conditions are reasonably stable. Widen the search only when you must, and note each time you do.

Keep the GLA range relevant. Including homes far larger or smaller than the subject can bend the relationship: buyers rarely pay the same amount for the 400th extra square foot as for the 100th. Export the fields you will need for the analysis, such as sale price, close date, GLA, age, bedrooms, bathrooms, garage and lot size, so they can be checked and held constant later. Our CSV requirements and template list a practical column set.

2. Treat data quality as part of the analysis

MLS data is entered by many people under deadline. Before running any numbers, look for:

• GLA values that are missing, zero, or clearly include basement or garage area.
• Duplicate records for the same sale, or relistings counted twice.
• Non-arm's-length transfers, REO or short sales, and heavy concessions.
• Mixed measurement sources (assessor, builder plans, agent estimates).
• Typos such as an extra zero in price or square footage.

Verify suspicious records where you can, and exclude what you cannot verify, with a note explaining why. Silent deletions are what make an analysis hard to reproduce.

3. Read the regression diagnostics, not just the coefficient

A multiple regression of sale price on GLA and the other characteristics that matter locally estimates the GLA contribution with those factors held constant. The coefficient alone tells you very little. Check:

• Standard error and confidence interval. A wide interval means the data supports a range, not a point.
• p-value. Whether the GLA effect is distinguishable from noise in this sample.
• R² and residuals. How well the model fits, and whether a few sales sit far from the line and drive the result.
• Correlated variables. GLA usually moves with bedrooms and bathrooms, which can make individual coefficients unstable.

For a deeper walkthrough, see regression analysis in appraisal.

4. Be careful with small samples

Many subject markets produce only a dozen or two usable sales. With that few, each additional variable in the model costs real precision, and a single outlier can move the GLA coefficient substantially. Keep the model simple, try removing one sale at a time to see how stable the result is, and test the indication against another method such as paired sales. When the methods disagree and the data is thin, say so.

5. Avoid false precision

A model output of $52.37 per square foot is not more accurate than $50. Reporting cents implies a certainty the data does not have. Round to a level consistent with the confidence interval, present the range the evidence supports, and explain where in that range you landed and why. As our guide to the GLA adjustment per square foot explains, the adjustment rate is also not the same thing as average price per square foot.

A fictional worked example

(1,980 − 2,140) × $45 = -$7,200

6. Appraiser judgment stays at the center

Statistics describe the sales you selected; they do not decide which sales belong in the set, whether the market treats size linearly, or how much weight a thin result deserves. Those are professional decisions. A tool can make the evidence clearer, but the reconciliation is yours.

7. Document the rationale

A reviewer should be able to follow your path. Record:

• The search criteria, market area, date range and number of sales.
• What you excluded and why.
• The model used and its key diagnostics.
• Any supporting method, such as paired sales, and how it compared.
• The rate selected and the reasoning for that choice.