Refining Value: Quantitative & Qualitative Analysis

Refining Value: Quantitative & Qualitative Analysis

This chapter delves into the core of the Sales Comparison Approach: comparative analysis. We will explore how appraisers utilize both quantitative and qualitative techniques to analyze comparable sales data and arrive at a credible value indication for the subject property.

1. Understanding Comparative Analysis

Comparative analysis is the process of scrutinizing comparable sales to identify and quantify differences that affect value. This involves a dual approach:

• Quantitative Analysis: Applies numerical adjustments (dollars, percentages) to the sale prices of comparable properties to account for specific differences relative to the subject property.
• Qualitative Analysis: Provides descriptive assessments of the relative position (inferior, superior, similar) of each comparable property compared to the subject, particularly when quantitative data is lacking or insufficient.

The goal is to refine the raw sales data❓❓ of comparable properties into a value indication that reflects the unique characteristics and market position of the subject property.

2. Quantitative Adjustments: Tools and Techniques

Several methods exist for quantifying adjustments to comparable sale prices. It’s vital to remember that these adjustments should reflect the reactions of market participants, not simply mathematical exercises. The final value indication derived from these adjustments should be reconciled with the results of the Cost and Income Capitalization approaches.

• 2.1 Data Analysis Techniques

  • Paired Data Analysis:

    • Principle: Isolates the value of a single difference between two properties that are otherwise equivalent. The price difference is attributed to that isolated variable.

    • Formula: Let P1 be the price of property 1 and P2 be the price of property 2. Assume property 1 and property 2 are identical except for feature X. Then the value of feature X = |P1 - P2|.

    • Example: Two identical houses sell at the same time. One has a two-car garage; the other has a one-car garage. If the house with the two-car garage sells for $10,000 more, the adjustment for the two-car garage is +$10,000.

    • Caveats: Requires careful verification that the properties are truly comparable except for the single identified difference. Reliance on a single pairing can be misleading due to unknown factors. Appraisers should use several paired sales to support an adjustment.

    • Experiment: Conducting paired data analysis with a large dataset of residential sales. Filter properties to ensure high similarity (e.g., same size, age, condition, neighborhood). Then, analyze the impact of a single difference (e.g., presence of a swimming pool) on sales prices, using statistical tests (t-test) to confirm the significance of the price difference.

  • Grouped Data Analysis:

    • Principle: Extends paired data analysis by grouping comparable sales based on an independent variable (e.g., date of sale, lot size) and comparing the average values of the groups.

    • Formula: Let S1 be the set of sales prices for group 1, and S2 be the set of sales prices for group 2. The adjustment for the independent variable distinguishing the groups is approximated by: (Average(S2) - Average(S1)).

    • Example: Comparing average sale prices of houses on corner lots to average sale prices of houses on interior lots within the same neighborhood.

    • Application: Instead of comparing a single house on a corner lot to a single house on an interior lot, a group of houses on interior lots is compared to a different group of houses on corner lots.

    • Relationship to Sensitivity Analysis: Paired and Grouped data analysis are variants of sensitivity analysis that help isolate the impact of individual variables on value.

  • Secondary Data Analysis:

    • Principle: Utilizes data from sources unrelated to the subject or comparable properties to support adjustments.

    • Example: Using data on general market trends from a research firm to develop a market conditions adjustment. County assessor data could be useful, after verification.

• 2.2 Statistical Analysis

  • Principle: Uses statistical methods (regression analysis, correlation) to quantify adjustments.

  • Caution: Statistical precision must be balanced with logical meaning and market relevance. Results should reflect market participant behavior.

  • Simple Linear Regression: Can be used to develop adjustment factors for variables like tract size.

    • Formula: Y = a + bX, where Y is the sale price, X is the independent variable (e.g., land size), ‘a’ is the intercept, and ‘b’ is the slope (the adjustment factor per unit of X).

    • Experiment: Collect sales data for similar properties with varying land sizes. Perform a linear regression analysis with sale price as the dependent variable and land size as the independent variable. Analyze the regression coefficients (slope) to determine the appropriate adjustment factor for land size differences. Test the model’s accuracy by applying it to another set of sales data.

  • Descriptive vs. Inferential Statistics: Appraisers must distinguish between these two. Descriptive statistics summarize data, while inferential statistics draw conclusions about a population based on a sample.

  • Scenario Analysis: A form of modeling that forecasts different outcomes based on changing conditions to test the probability or correlation of alternate outcomes.

• 2.3 Graphic Analysis

  • Principle: Visual representation of data to identify trends and patterns.

  • Curve Fit Analysis: Employs formulas to determine the best-fitting curve for the market data.

• 2.4 Cost Analysis and Cost-Related Adjustments

  • Principle: Adjustments based on cost indicators such as depreciated building cost or cost to cure.

  • Application: Used in markets with limited sales activity or for features difficult to isolate (e.g., a porch).

  • Example: Adjusting for the cost to cure a deferred maintenance item. However, the cost may not equal the increase in value.

• 2.5 Capitalization of Income Differences

  • Principle: Capitalizing differences in net operating income (NOI) to derive an adjustment.

  • Formula: Adjustment = ΔNOI / Capitalization Rate

    • Where ΔNOI is the difference in net operating income between the comparable and the subject property.

    • The Capitalization Rate is the rate used to convert income into value.

  • Example: A comparable property has an elevator, while the subject does not. Calculate the income loss due to the lack of an elevator and capitalize that loss to determine the adjustment.

  • Caution: Can diminish the independence of the Sales Comparison and Income Capitalization Approaches, so the analyst must avoid double-counting.

  • Application: Eminent domain cases (loss in value due to a taking) or for residential properties using a Gross Rent Multiplier (GRM).

3. Qualitative Analysis: Judgment and Interpretation

Qualitative analysis acknowledges that real estate markets are not perfectly efficient and that numerical data is not always sufficient to capture all value differences. This approach relies on the appraiser’s judgment and experience to assess the relative position (superior, inferior, or similar) of the comparable properties compared to the subject.

When quantitative data is unavailable for a specific element of comparison, qualitative analysis determines which sales are inferior, similar, or superior to the subject property regarding that element.

Even with quantitative adjustments, qualitative analysis is crucial to ensure that the adjustments are reasonable and reflect market perceptions. For example, a 0% net adjustment is not necessarily the best result if there are large positive and negative adjustments that simply cancel each other out. The adjusted value should reflect the market’s perception of the property.

Appraisers should reexamine elements of comparison for which no adjustments were made and explain why they did not require adjustment.