Sales Comparison: Data Analysis and Adjustments

Chapter: Sales Comparison: Data Analysis and Adjustments

Introduction

The Sales Comparison Approach (SCA) relies on analyzing comparable sales to estimate the market value of a subject property. This chapter focuses on the critical process of data analysis and adjustments, which transforms raw comparable data into a reliable value indication. The accuracy of these adjustments directly impacts the credibility and defensibility of the appraisal. This chapter will explore both quantitative and qualitative techniques for comparative analysis and adjustment in sales comparison.

1. Understanding the Data

Before diving into adjustments, it’s imperative to understand the data being used. This involves:

• 1.1 Data Sources and Reliability:
  • Verifying the accuracy and reliability of the source. Is it a reputable MLS, public records, or a private database? Consider potential biases.
  • Confirming sales details with involved parties (buyer, seller, agent).
  • Analyzing the reported price and sale terms (cash, financing, seller concessions). Unreliable data leads to unreliable conclusions.
• 1.2 Identifying Key Property Characteristics:
  • Comprehensive list of all relevant characteristics affecting value (size, location, condition, amenities, zoning, legal rights).
  • Defining units of comparison (price per square foot, price per acre, price per unit).
  • Documenting any unique features or conditions influencing value (easements, environmental issues, etc.).
• 1.3 Market Research and Analysis:
  • Analyzing the overall market trends and conditions.
  • Identifying the relevant submarket for the property being analyzed.
  • Understanding the local economic indicators that can impact sales prices.

2. Comparative Analysis: Quantitative Adjustments

Quantitative adjustments are market-supported, numerical adjustments applied to the sale price of comparable properties to account for differences between them and the subject property.
* 2.1 The Principle of Contribution:
* The amount an item contributes to the total value of a property is measured by how much it increases or decreases market value.
* This principle guides the adjustment process, ensuring adjustments reflect market perception.

• 2.2 Order of Adjustments: While there’s no universally mandated order, a common and logical sequence is:

  1. Financing Terms: Adjust for differences in financing terms that affect the effective sale price (e.g., seller financing, below-market interest rates).
  2. Conditions of Sale: Address motivations of buyer and seller, such as foreclosure sales, related-party transactions, or estate sales, which might have forced a price deviation from market value.
  3. Market Conditions (Time): Account for changes in market conditions between the comparable sale date and the effective date of the appraisal. This adjustment reflects appreciation or depreciation in the market.
  4. Location: Reflects differences in desirability between the location of the comparables and the location of the subject.
  5. Physical Characteristics: Adjust for differences in physical attributes (size, age, condition, features, amenities).
  6. Legal Characteristics: Refers to adjustment due to difference in zoning, or easements on the properties

• 2.3 Techniques for Extracting Adjustments:

  • 2.3.1 Paired Data Analysis (Matched Pairs Analysis):

    • Definition: Identifies the impact of a single characteristic by comparing two similar properties that differ only in that one characteristic. The price difference is attributed to that characteristic.
    • Formula: Adjustment = Sale Price of Property A - Sale Price of Property B, where Property A and Property B are identical except for the characteristic being analyzed.
    • Example: Two identical houses sold recently. House A has a pool and sold for \$450,000. House B lacks a pool and sold for \$420,000. The implied adjustment for a pool is \$30,000.

    • Limitations: Difficult to find truly “matched pairs.” Assumes all other factors are equal, which is often not the case. Inaccurate or incomplete data weakens the results of paired data analysis.

      • 2.3.2 Statistical Analysis (Regression Analysis):

    • Definition: Uses statistical techniques to identify relationships between property characteristics and sale prices in a large dataset. Linear regression is a common technique.

    • Equation: Y = b0 + b1X1 + b2X2 + … + bnXn + ε
      • Y = Predicted sale price
      • b0 = Intercept (base value)
      • b1, b2, … bn = Regression coefficients (adjustment rates) for each characteristic
      • X1, X2, … Xn = Values of property characteristics
      • ε = Error term
    • Example: A multiple regression model could estimate the value contribution of square footage, number of bedrooms, and lot size, simultaneously. The regression coefficients (b1, b2, b3,…) represent the adjustment rates for each.

    • Experiment: To illustrate regression, collect sale data for a homogenous area (e.g., a condominium complex) with at least 30 recent sales. Include data on square footage, number of bedrooms/bathrooms, floor level, and view. Perform a multiple regression analysis using a statistical software package (e.g., SPSS, R). The output will provide regression coefficients (adjustment rates) for each variable.

    • Cautions: Requires a large, reliable dataset. Model accuracy depends on the quality of data and proper model specification. Be aware of multicollinearity (high correlation between independent variables).

      • 2.3.3 Cost Analysis (Depreciated Cost):

    • Definition: Estimates the adjustment based on the current cost to reproduce the characteristic, less depreciation (physical, functional, and external obsolescence).

    • Formula: Adjustment = Reproduction Cost - Accrued Depreciation.
    • Example: A comparable property has a detached garage that the subject property lacks. The new cost of the garage is \$35,000. Estimated depreciation is 20% (\$7,000). The adjustment would be -\$28,000 (subtracting value from the comparable for the extra garage).
    • Advantages: Easy to understand and often applicable when market data is scarce.
    • Disadvantages: Cost may not always equal market value. Difficult to accurately estimate all forms of depreciation.

  • 2.3.4 Income Capitalization:

    • Definition: Adjusting properties for variations in income, which allows property variations like income generated or capitalization of rent to be quantified.
    • Formula: Adjustment = Income Difference/ Capitalization Rate.
    • Example: The comparables have an additional \$5,000 income from leasing and market capitalization rates stand at 10%. Therefore the adjustment would be $50,000.

• 2.4 Types of Adjustments

  • 2.4.1 Dollar Adjustments:
    • Definition: The adjustment type used is indicated in currency form, which is easily understood by the public. It is most commonly used for residential appraisals and some non-residential assignments.
  • 2.4.2 Percentage Adjustments:
    • Definition: The adjustment type uses percentage for adjustment to maintain ratios. Percentage adjustments can be converted to dollar adjustments and are most helpful when the comparisons are not comparable and large adjustments are necessary.

3. Comparative Analysis: Qualitative Analysis

Qualitative analysis involves subjective assessments based on market knowledge and experience. It’s used when differences cannot be precisely quantified. It is done after Quantitative Analysis.

• 3.1 Relative Comparison Analysis:

  • Ranking: Ranking the comparables relative to the subject property for each characteristic. Properties are ranked as “superior,” “equal,” or “inferior” to the subject.
  • Example: Compare 3 properties for location and conclude that property A has a more favorable location to the subject, property B has an equally favorable location, and property C has a less favorable location.
  • Benefits: Provides a holistic comparison when precise adjustments are difficult. Focuses on the overall impact of combined differences.
  • Application: Can be used where data may be lacking or unclear.

• 3.2 Subjective Judgement:

  • Recognizing subtle differences that are difficult to quantify.
  • Considering intangible factors influencing value (curb appeal, neighborhood character, view quality).

4. Reconciliation

Reconciliation is the final step, where the adjusted prices of the comparables are synthesized into a single value indication. This involves:

• 4.1 Weighing the Comparables:

  • Giving more weight to comparables that are most similar to the subject property in terms of key characteristics, adjustments required, and market conditions.
  • Considering the number and magnitude of adjustments. Logically, the fewer adjustments made to a comparable sale price, the more reliable the final indication of value.

• 4.2 Range of Value:

  • Recognizing that the SCA provides a range of possible values, not a single, precise number.
  • Selecting a point within the range that represents the appraiser’s best estimate of market value, based on the strengths and weaknesses of the comparables.

• 4.3 Considerations During Reconciliation:

  • How much evidence of value is available?
  • Have listings, pending sales, or expired listings been considered?
  • Should the market exposure of the subject be considered?
  • How many comparables are truly comparable?
  • Do the adjustments made reflect the market’s reactions?
  • Are the comparables used legitimate alternatives to the subject property?

5. Potential Problems and Mitigation

• 5.1 Over-Adjusting: Excessive adjustments can lead to inaccurate results. Strive for minimal adjustments supported by market data.

• 5.2 Double-Counting: Avoid adjusting for the same factor twice (e.g., adjusting for both square footage and number of rooms).

• 5.3 Data Scarcity: In limited data situations, expand the search radius or time frame, but be cautious about the impact on comparability.

• 5.4 Market Volatility: In rapidly changing markets, use more recent sales and consider shorter adjustment periods for market conditions.

Conclusion

Data analysis and adjustments are the cornerstones of a reliable Sales Comparison Approach. By understanding the principles, employing appropriate techniques, and exercising sound judgment, appraisers can transform raw data into a credible and defensible estimate of market value.