What adjustment support does DataMaster provide?

Lenders increasingly expect adjustments to be backed by market evidence rather than judgment alone. DataMaster derives adjustment values from the closed sales using your own data and shows you the math behind each one.

DataMaster analyzes the market comparable sales already in your report and estimates what the market is paying for each property feature — in dollars per unit (eg. /sqft,/sqft,/Bedroom, $/Bathroom).

• It uses your data, not national averages. Every figure comes from the closed sales you import into your own report that pass your search filters. Active, pending, and expired listings are excluded.

• It shows three independent estimates. Each adjustment is computed using three different methods (below) so you can see whether they agree and pick the best one. Agreement is strong support; disagreement tells you to dig deeper.

• It shows the supporting evidence. For every value you can open the underlying sale pairs, the price distribution, the regression scatter and equation, and the summary statistics — so the number is transparent and defensible to your clients, not a closed box.

• It rates confidence. Each estimate carries a Support badge (how much data backed it) and a Stability badge (how consistent that data was), rated High / Medium / Low.

You'll find these under Market Analysis in a UAD 3.6 report, on the Feature Adjustments and Time Adjustments tabs.

Once you've decided on a value, the Power Adjust tool on the Sales Comparison Grid lets you enter an adjustment rule once for a grid row — for example $2.50 per Sq. Ft. — and apply it across every comparable automatically, with per-comp override where you need it.

DataMaster provides support for time and several common feature adjustments.

DataMaster estimates a dollar-per-unit value for:

Yes/no features (pool, basement, heating, cooling, attached) are priced as the market's dollar difference between an otherwise-comparable home with and without that feature.

DataMaster fits a price trend over your market's sales history and measures the movement from each comparable's date to your effective date, returning both a percentage and a dollar amount per comp. You choose whether to trend on contract date or sale date, and on median or average price.

Condition, quality, view, site influence, neighborhood, amenities, design, functional utility, garage and similar ratings aren't statistically derived — the market data doesn't isolate them cleanly. Those rows are yours to judge, and Power Adjust will apply your chosen adjustment consistently across the grid.

DataMaster runs three common statistical approaches on every feature, side-by-side.

The classic textbook method. Two closed sales form a valid pair for a feature only when they differ on that feature alone and match on everything else within a tolerance band. The reported adjustment is the median across all valid pairs.

pair adjustment = (price of higher comp − price of lower comp) ÷ (feature difference)

Tolerances define "close enough to match" and default to 10% on GLA and lot size, 30 days on sale date, 5 years on year built, and 1 on bedrooms and bathrooms. You can override them on the Tolerances tab. Cleanest evidence, but strict rules mean some features find few pairs.

Solves the main weakness of strict pairs: features that always move together (size, baths, luxury) rarely appear in isolation. Grouped Pairs prices one feature at a time and removes its effect before moving on.

1. Price the feature with the most clean pairs — usually GLA.

2. Subtract that feature's contribution from every comp's sale price.

3. Re-run the analysis on the adjusted prices for the next feature.

4. Repeat until no feature can find another clean pair.

Each feature reports the round it was extracted in, so you can follow the sequence. Later features get priced on prices that already have the larger effects stripped out — which is how bedrooms, baths, and pools finally surface enough pairs to value.

Uses every comparable at once instead of two at a time. An ordinary least-squares model estimates each feature's marginal dollar value while holding the others constant; each coefficient is that feature's adjustment.

Reported alongside it: R² and adjusted R² (how much of price the model explains), p-value (whether the feature's effect is statistically meaningful), standard error and t-statistic, and VIF (a warning that two features are too correlated to separate cleanly — GLA and above-grade area, for instance). Best when you have a large comp set; weaker with only a handful of sales.

Time adjustments use a separate curve fit over your market's sales history — a polynomial least-squares trend on price per month and on price per square foot. Your suggested adjustment is the trend's movement between each comp's date and your effective date. At least three months of sales data are required.

Note that time is also controlled for inside the feature analysis: sale date is treated as a feature in its own right, so paired sales must have closed within about 30 days of each other and the regression carries a time coefficient. Feature adjustments are therefore already time-aware.

In short, here's a quick table summarizing the support:

Compare all three, choose the value you can defend, then use Power Adjust to apply it across the grid.