Sales Comparison: Adjustment Methodologies & Reconciliation

Chapter: Sales Comparison: Adjustment Methodologies & Reconciliation

Introduction

The Sales Comparison Approach (SCA) is a cornerstone of real estate valuation, relying on the principle that a property’s value is directly related to the prices of comparable properties in the market. However, rarely are two properties identical. Therefore, adjustments must be made to the sale prices of comparables to account for differences between them and the subject property. This chapter delves into the scientific methodologies for making these adjustments and reconciling the resulting value indications.

I. Adjustment Methodologies: Quantitative Analysis

Quantitative adjustments are data-driven and seek to quantify the impact of specific differences on property value. The goal is to isolate the contribution of each characteristic to the overall property price.

A. Paired Data Analysis (Matched Pairs Analysis)

1. Principle: Paired data analysis, also known as matched pairs analysis, isolates the impact of a single variable by comparing the sale prices of two otherwise identical properties, differing only in that one variable. The difference in sale price is then attributed to that specific feature.
2. Theoretical Basis: This method is based on the economic principle of marginal utility. The change in price reflects the market’s valuation of the marginal utility derived from that specific feature.
3. Formula:
   • Adjustment Value = Sale Price of Property with Feature - Sale Price of Property without Feature
4. Example: Two identical houses sold recently. House A has a garage and sold for $350,000. House B does not have a garage and sold for $330,000. The adjustment for a garage is $350,000 - $330,000 = $20,000.
5. Limitations and Challenges:
   • Data Availability: Finding truly matched pairs in real estate is rare. Unique property characteristics and limited recent transfers make ideal matches difficult to locate.
   • Unidentified Variables: It assumes all other factors are equal. However, subtle differences, not readily apparent, may influence the prices.
   • Market Dynamics: Market conditions can shift between the sale dates of the properties, distorting the results.
   • “One Sale Doesn’t Make a Market”: A single paired sale is not definitive. Multiple pairs should be analyzed to identify a consistent pattern.

B. Statistical Analysis

1. Principle: Statistical analysis leverages large datasets of comparable sales to extract adjustment rates. Regression models are particularly useful.
2. Theoretical Basis: Regression analysis employs statistical methods to model the relationship between a dependent variable (sale price) and one or more independent variables (property characteristics). It aims to find the best-fitting line or surface that represents the correlation.
3. Linear Regression Model (Simple Example):
   • Y = a + bX + ε
     • Where:
       • Y = Sale Price (dependent variable)
       • X = Size of the house in square feet (independent variable)
       • a = Y-intercept (constant)
       • b = Slope (coefficient representing the value per square foot)
       • ε = Error term (accounts for unexplained variance)
4. Multiple Regression Analysis: A more sophisticated approach that considers multiple independent variables (e.g., lot size, number of bedrooms, age of the property). This can isolate the contribution of each variable while controlling for the influence of others.
5. Application The coefficient b represents the estimated market value of each independent variable, which can be used as an adjustment factor. For example, an increase of 1 sq ft is associated with an average increase of \$b to the sales price.
6. Mean and Median Analysis: Calculating the mean (average) and median sale prices of comparable properties provides a general range of value. However, these methods lack the precision to support specific adjustments as they don’t explain the variance within the dataset.
7. Limitations:
   • Data Quality: The accuracy of the statistical results depends heavily on the quality and completeness of the data.
   • Collinearity: High correlation between independent variables (e.g., lot size and house size) can distort the results of regression analysis.
   • Model Specification: Choosing the correct regression model (linear, non-linear) is crucial.
   • Misinterpretation: As shown in the student handbook, it is easy to misinterpret or misuse statistical logic to support misleading conclusions.
   • Sample Size: A sufficiently large sample size is required to produce reliable statistical results.

C. Cost Analysis/Cost-Related Adjustments

1. Principle: Estimates adjustments based on the depreciated cost of the feature.
2. Theoretical basis: The cost approach to value implies that a rational buyer would not pay more for a property than the cost to build a similar property, subject to depreciation. This principal is reflected in adjustments to the comparable.
3. Formula:
   • Adjustment Value = New Cost - Accumulated Depreciation
4. Depreciation Considerations: Depreciation includes:
   • Physical Depreciation: Wear and tear, age-related deterioration.
   • Functional Obsolescence: Loss of value due to outdated design or features.
   • External Obsolescence: Loss of value due to factors outside the property itself (e.g., neighborhood decline).
5. Application: Cost per area, age, and depreciation are calculated for characteristics such as basements or screened porches to determine the adjustment rate.
6. Example: A comparable property has a screened porch that would cost $8,000 to build new. It’s 5 years old, and the appraiser estimates total depreciation at 10%. The adjustment would be $8,000 - ($8,000 * 0.10) = $7,200.
7. Limitations:
   • Cost vs. Market Value: Cost does not always equal market value. If a feature is not desired by buyers, its depreciated cost may exceed its actual contribution to the property’s value.
   • Difficult to Estimate Depreciation: Accurately estimating depreciation, particularly functional and external obsolescence, can be challenging.

D. Capitalization of Income Differences

1. Principle: This applies primarily to income-producing properties. If a feature generates additional income, the adjustment is based on the capitalized value of that income difference.
2. Theoretical Basis: This method derives from the income capitalization approach to valuation, where the value of a property is determined by the present worth of its anticipated future income stream.
3. Formula:
   • Adjustment Value = Change in Net Operating Income (NOI) / Capitalization Rate
4. Application: By finding properties with and without the subject feature, the difference in rents is calculated. Then, using a cap rate, an adjustment factor is derived.
5. Example: A comparable property has a small office attached that generates an additional $5,000 in annual rental income. The appropriate capitalization rate for this type of property is 8%. The adjustment would be $5,000 / 0.08 = $62,500.
6. Limitations:
   • Income Data: Requires accurate and reliable income data for both the subject and comparable properties.
   • Capitalization Rate: Selecting an appropriate capitalization rate is critical and can significantly impact the adjustment.

II. Adjustment Types: Expressing the Changes

A. Dollar Adjustments

1. Description: Adjustments are expressed as absolute dollar amounts.
2. Advantages: Easy to understand and apply.
3. Disadvantages: May not accurately reflect the relative importance of a feature, particularly for properties with significantly different overall values.
4. Best Use: Generally preferred for residential appraisals where the price range is relatively narrow.

B. Percentage Adjustments

1. Description: Adjustments are expressed as percentages of the comparable sale price.
2. Advantages: Maintains ratios and can be more appropriate when comparisons are not highly comparable.
3. Disadvantages: Can be less intuitive to understand.
4. Best Use: Often used in commercial appraisals or when making large adjustments. Percentage adjustments can be converted to dollar amounts for clarity.

III. Qualitative Analysis: Relative Comparison Analysis

Quantitative adjustments address quantifiable differences, but qualitative analysis addresses subjective features.

A. Relative Comparison Analysis

1. Principle: Compares comparables based on the sum of all differences, ranking them from most similar to least similar.
2. Application: Once quantitative adjustments are complete, comparables are ranked. This can be done subjectively, or by assigning numerical scores to qualitative attributes. This process creates a narrowed range of values for the final reconciliation.
3. Types:
   * Ranking Analysis: Arranging comparables in order from best to worst based on overall comparability.
   * Relative Comparison Analysis: Comparing comparables to the subject property based on individual characteristics (e.g., superior, inferior, similar).
4. Example:
   | Comparable | Location | Condition | Amenities | Overall Score |
   |------------|----------|-----------|-----------|---------------|
   | Comp A | Good | Excellent | Few | 7/10 |
   | Comp B | Excellent| Good | Many | 9/10 |
   | Comp C | Average | Good | Average | 5/10 |
   * Comp B is determined the most similar to the subject property
5. Limitations:
   • Subjectivity: Highly dependent on the appraiser’s judgment.
   • Lack of Precision: Does not provide specific adjustment amounts.

B. Additional Qualitative Analyses

1. Scenario Analysis:
   * Examines the subject property under different potential future conditions to gauge value sensitivity.
2. Trend Analysis:
   * Identifies significant market trends that may influence value over time, accounting for time-related adjustments.
3. Descriptive statistics:
   * Descriptive statistics help summarize and present data related to comparable properties. These statistics can include measures of central tendency (e.g., mean, median, mode) and measures of dispersion (e.g., range, standard deviation).
4. Inferential Statistics:
   * Inferential statistics involve using sample data to make inferences or generalizations about a larger population. In real estate, inferential statistics may be used to make predictions about future property values based on historical data and market trends.

IV. Reconciliation: Synthesizing Value Indications

Reconciliation is the final step in the SCA, where the appraiser weighs the value indications derived from the adjusted comparables to arrive at a single, supportable value estimate for the subject property.

A. Weighing the Indications

1. Not Averaging: Reconciliation is not a simple mathematical average of the adjusted sale prices.
2. Criteria for Weighing:
   • Number of Adjustments: Fewer adjustments generally indicate a more reliable comparable. More adjustments do not always make the value more accurate.
   • Size of Adjustments: Smaller adjustments suggest greater similarity to the subject.
   • Data Source Reliability: The credibility of the data source is crucial.
   • Market Relevance: The comparable’s relevance to the current market conditions.
   • Property Rights Conveyed: Important when unusual conditions are present.
   • Conditions of Sale: Sales must be arms-length transactions.
   • Physical Property Attributes: Significant attributes may require an “across-the-board” adjustment.
3. Questions to Ask During Reconciliation
   • How much evidence of value is available and how much is included?
   • Should comparable listings, pending sales, or even expired listings be considered?
   • Should a history of market exposure of the subject property be considered?
   • How many of the available comparable sales are truly comparable?
   • Do the adjustments to the sales or listings represent the market’s reactions?
   • Are the comparable properties used legitimate alternatives to the subject property?

B. Final Value Estimate

1. Supporting the Conclusion: The final value estimate must be clearly supported by the data and analysis presented in the appraisal report.
2. Range of Value: The appraiser may conclude on a specific point estimate or within a well-supported range.
3. Transparency: The reconciliation process should be transparent, explaining the rationale for the weighting of each comparable.
4. Narrative Report: The entire reasoning and data used to derive a property value should be written in a well-written report so others can review and follow the logic.

V. Conclusion

Mastering the Sales Comparison Approach requires a thorough understanding of adjustment methodologies and the reconciliation process. By applying the theoretical and practical principles outlined in this chapter, appraisers can develop credible and supportable value conclusions, providing valuable insights to clients and stakeholders in the real estate market. Remember that thorough market research is important, as is being transparent in the logic used for derivation.