How Time Adjustments are derived

DataMaster is able to calculate and apply time adjustments to help model the impact of market timing on your sales comparable properties.

DataMaster fits a price trend through your report's market data set, reads that trend on two dates — the date your comparable transacted (Contract Date), and the report's effective date — and expresses the difference between them as a percentage. That percentage, applied to the comparable's sale price, is the suggested date-of-sale adjustment or, commonly referred to as, time adjustment.

The market trend is built from the market data set attached to the report — the sold properties you pulled in under Market Analysis. A record is used only if it has both a closing price and a closing date, and it closed on or before the effective date.

Active, pending and expired listings drop out automatically because they have no closing price. Your Search Filters apply here exactly as they do on the Properties and Map tabs, so narrowing the filters re-fits the trend.

Your comparables come from the sales grid. A comp appears in the adjustment list only if it has a sale price and a settlement date on or before the effective date.

The effective date must be set on the report; without it the page cannot compute.

The page fits two independent models and reports an adjustment from each.

Both are ordinary least-squares fits: the line is the best fit through the points, not a connect-the-dots path, so no single sale can pull it far. With only a few data points the fit automatically simplifies toward a straight line. At least three monthly data points are required.

For each comparable:

Adjustment %  = ( Trend at effective date − Trend at comp's date ) ÷ | Trend at effective date |
Adjustment $  = comp's sale price × Adjustment %

Example. The $/sq ft trend reads $250 at the effective date and $240 on the date your comp went under contract. The market moved (250 − 240) ÷ 250 = +4.00%. On a comp that sold for $500,000, the suggested adjustment is +$20,000.

A positive figure means the market rose between the comp's date and the effective date, so the comp is adjusted upward; a negative figure means the market declined.

Each chart carries its own Market Adjustment list beneath it, showing the percentage and dollar amount that model produces for every comparable.

Date Used — Contract / Sale. Chooses which of the comp's dates is read off the trend. Contract date is the default: it reflects when price was actually negotiated, typically one to two months ahead of closing, so it usually produces the more defensible adjustment. Note that the market data points themselves are always plotted by their closing date — this toggle moves your comps along the trend, not the trend itself.

Metric — Median / Average. Applies to the per-month chart only. Median resists outliers and unusual sales; average reflects the full dollar mix, including the high end.

Blue dots are the market sales, the green line is the fitted trend, and the red diamonds are your comparables plotted at their own sale price (per-month chart) or their own $/sq ft (per-sq-ft chart).

The comps are plotted for context only. A comp sitting above or below the trend line is telling you it sold above or below market for its date — that difference is a matter for your other adjustments, not this one. Only the comp's date affects its time adjustment.

The per-month model tracks what typical homes sold for; the per-sq-ft model normalizes for size. When they diverge noticeably, it usually means the mix of what sold has shifted — larger or smaller homes trading in different months — rather than prices themselves moving that much. The per-sq-ft trend is generally the steadier of the two in that situation. Pick the model that best fits your market and note the reasoning in your report.

• A comp with no contract date shows "—" under the Contract setting. Switch to Sale for that comp, or add the contract date.

• Comps that settled after the effective date are excluded entirely, as are market sales that closed after it.

• Curve fits can bend at the edges. If your effective date falls well past the most recent closed sale in the pool, the trend is being extended beyond the data — sanity-check the suggested rate before using it.

• Thin data produces unstable rates. A handful of sales per month, or a market pool narrowed by aggressive Search Filters, will move the trend around. Widening the date range usually steadies it.

• Changing Search Filters changes these numbers, because it changes which sales the trend is fitted to.

• These are supported suggestions, not conclusions. The appraiser remains responsible for the rate applied and its reconciliation in the report.