Supporting Time Adjustments in Changing Markets

‍ Article 8 of 9  |  The Adjustment Series ‍

Time adjustments are among the most commonly applied and least carefully supported adjustments in the sales comparison approach. In a stable market — one where values are moving slowly and predictably — thin time adjustment support may not materially affect the credibility of an appraisal. In a rapidly appreciating or declining market, inadequate time adjustment support can be the difference between a credible value conclusion and one that is fundamentally flawed.

The markets of the past several years have made this point forcefully. The rapid appreciation cycle of 2020 through 2022, followed by the moderation and recalibration of 2023 and beyond as interest rates climbed sharply, created an environment where time adjustments were both more consequential and more difficult to support than in normal market conditions. Appraisers who had reliable methods for deriving and documenting time adjustments navigated that period more credibly than those who did not.

This article examines what good time adjustment support looks like, what methods are available, and what the most common errors are in how time adjustments are developed and applied.

‍Why Time Adjustments Matter

‍When comparable sales occurred months or years before the effective date of appraisal, market conditions at the time of those sales may differ from conditions at the effective date. A time adjustment — also called a market conditions adjustment — accounts for that difference, bringing the comparable's sale price forward or backward to reflect what the market would have paid for that property at the effective date.

The challenge is that market conditions are not always moving in a single direction, and they rarely move at a uniform rate. A market that appreciated 15% over 24 months did not necessarily appreciate at 0.625% per month throughout that period. There may have been periods of rapid appreciation, periods of stability, and periods of modest decline — all within the same general upward trend. An adjustment methodology that assumes uniform movement will misrepresent what actually happened in specific sub-periods.

‍Time adjustments also interact with other adjustments in the sales comparison grid. A comparable sale that is significantly dated may require a substantial time adjustment — which, in turn, affects the weight that sale should receive in the final reconciliation. A comparable sale with a large time adjustment is, all else equal, a less reliable indicator of current market value than a recent sale requiring no time adjustment. This interaction needs to be acknowledged in the reconciliation, not just in the adjustment grid.

‍Methods for Deriving Time Adjustments

‍Paired Sales Over Time

The most direct method for supporting a time adjustment is analyzing repeat sales of the same property or matched pairs of similar properties at different points in time. If the same property sold twice within the relevant period, the price change directly reflects market movement for that property type and location. If closely matched properties sold at different times, the price difference can be attributed to market conditions, provided the properties are sufficiently similar and properly adjusted for any non-temporal differences.

The same data adequacy caveats that apply to paired sales for feature adjustments apply here. A single repeat sale is an observation, not a trend. Several consistent repeat sales or matched pairs begin to establish a pattern. The more consistent the pattern across multiple transactions, the more reliable the implied time adjustment.

Index-Based Support

Indexed data sources provide market-level support for time adjustments based on aggregated transaction data. The Federal Housing Finance Agency house price index, CoreLogic, Black Knight, and local MLS market reports are commonly used sources for residential work. These indices measure aggregate price changes across a defined market area and can be used to support time adjustments when the subject property is reasonably representative of the market the index measures.

‍The critical caveat with index-based support is specificity. A national or regional index may not accurately reflect conditions in the specific local market where the subject property is located. If the subject is in a rural county that experienced different market dynamics than the broader metropolitan area captured by the index, applying the metropolitan index to the rural property will produce a misleading time adjustment.

The best practice is to use the most geographically specific index available that still provides a statistically meaningful sample. A neighborhood-level or zip code-level analysis is preferable to a county-level analysis, which is preferable to a metropolitan-level analysis. When no adequately specific index is available, paired sales or regression analysis on local transaction data is preferable to applying a broad index with limited local relevance.

Regression Analysis on Time-Stratified Data

Regression analysis can isolate the time variable when sufficient sales data exists across the relevant time period. By including a time variable — sale date expressed as months before or after the effective date — in a regression model alongside other property characteristics, the appraiser can estimate the monthly rate of price change while controlling for other sources of variation.

The data requirements for this approach are the same as for regression generally: sufficient observations to produce statistically significant results, a reasonably homogeneous data set, and proper model specification. In markets with adequate transaction volume, time-based regression can produce well-supported, market-specific time adjustment rates that are more reliable than broad indices.

The Linear Trend Mistake

One of the most frequent and consequential errors in time adjustment support is applying a uniform monthly appreciation or depreciation rate across a period when the market was not actually moving uniformly. This error is sometimes called the linear trend assumption, and it was particularly visible during the 2021 through 2023 market cycle.

During that period, many markets experienced rapid appreciation in 2020 and 2021, continued strong appreciation into early 2022, then a significant shift as interest rates rose sharply beginning in mid-2022. By 2023, many markets were experiencing price moderation or modest declines even as they remained well above 2019 price levels. A single blended monthly appreciation rate applied across that entire period would misrepresent what was happening in the market during any specific sub-period.

An appraiser valuing a property with an effective date in late 2022 who uses comparable sales from early 2021 and applies a uniform 18-month appreciation rate is applying a rate that was appropriate for neither the early 2021 period nor the late 2022 period. The methodology obscures the market dynamics that were actually relevant to the assignment.

Applying a blended appreciation rate across a period of uneven market movement is not support for a time adjustment. It is a simplification that may produce a plausible-sounding number while misrepresenting what actually happened in the market.

Segmenting the Analysis

The solution to the linear trend problem is to segment the time adjustment analysis to reflect actual market behavior. If the market moved at different rates in different sub-periods, the analysis should identify those sub-periods and support the adjustment rate for each one separately.

This approach requires more analytical work than applying a single blended rate, but it produces results that actually reflect market dynamics. In a rapidly changing market, that additional work is not optional — it is the difference between a credible time adjustment and a misleading one.

‍Segmentation does not need to be overly precise. Identifying three or four meaningful sub-periods within a longer span of time — and supporting a different adjustment rate for each — is typically more defensible than either applying a single blended rate or attempting to model market movement at a monthly level of precision the data cannot support.

‍Documentation Requirements

Whatever method is used to derive a time adjustment, the support should appear in the report — not just the conclusion. Show the data, identify the source, explain the trend, and connect it to the adjustment applied.

For index-based support, identify the specific index used, its geographic scope, and why it is appropriate for the subject market. For paired sales or regression-based support, present the transactions analyzed, explain the methodology, and summarize the results. For segmented analysis, explain how the sub-periods were identified and what evidence supports the rate applied in each one.

A reviewer should be able to evaluate your time adjustment support independently, without simply accepting your conclusion. In volatile markets especially, a well-supported time adjustment is one of the clearest demonstrations of market competency an appraiser can offer.

Time Adjustments in Yellow Book and Litigation Work

In federal acquisition and eminent domain work, time adjustments face the same adversarial scrutiny as all other adjustments. The before-and-after structure of a partial taking appraisal — and the potential for a significant time gap between comparable sales and the valuation date — makes time adjustment support particularly important in this context.

‍Opposing experts in condemnation proceedings will examine the market conditions evidence carefully. A time adjustment supported by a single data source, applied as a uniform rate across an uneven market period, is a target for challenge. A time adjustment supported by multiple converging sources, segmented to reflect actual market dynamics, is substantially more defensible.

‍The Bottom Line

Time adjustments deserve the same analytical rigor as any other adjustment in the sales comparison approach. The market does not stand still, and neither should the methodology for measuring how it moves. The tools are available — repeat sales, matched pairs, market indices, regression analysis — and the documentation requirements are the same as for any other market-supported adjustment.

The most important single habit to develop is resistance to the linear trend assumption. Real markets move unevenly. Adjustment methodology that acknowledges that unevenness and supports the specific rates applied in specific sub-periods is more credible, more accurate, and more defensible than methodology that smooths over it with a convenient average.

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Next in the series: Article 9 — Adjustments in Partial Takings & Complex Assignments