The Sales Comparison Approach

Chapter 9: The Sales Comparison Approach

I. Introduction to the Sales Comparison Approach

The Sales Comparison Approach (SCA), also known as the market approach or market data approach, is a widely used and often preferred method for estimating the value of real estate. Its core principle relies on the substitution principle: a rational buyer will pay no more for a property than the cost of acquiring an equally desirable substitute. In essence, the SCA analyzes recent sales of similar properties (“comparables”) to infer the market value of the subject property.

II. Scientific Theories and Principles Underlying SCA

• Substitution Principle: As mentioned above, the core principle.
• Supply and Demand: SCA implicitly reflects the forces of supply and demand in the market. Comparable sales prices are influenced by the availability of similar properties (supply) and the desires/purchasing power of potential buyers (demand).
• Market Efficiency: SCA assumes that markets are reasonably efficient, meaning that information is reflected in transaction prices. However, it acknowledges that market inefficiencies exist, hence the need for adjustments.
• Regression Analysis (Implicit): While adjustments are often made judgmentally, the underlying logic mimics a regression model where property characteristics are independent variables, and sales price is the dependent variable. The goal is to isolate the contribution of each characteristic to overall price.
• Theory of Attributes: This theory suggests that value is not inherent in the whole property but is derived from the various attributes it possesses (e.g., size, location, condition). SCA involves quantifying the contribution of these attributes to the final value.

III. Steps in Applying the Sales Comparison Approach

1. Identify and Select Comparable Properties:
   • Similarity Criteria: Comparables should be similar to the subject property in terms of location, physical characteristics (size, style, age, condition), property rights conveyed, financing terms, and market conditions.
   • Data Sources: MLS, public records, assessors’ offices, real estate professionals, commercial data providers (like CoreLogic or FNC), etc.
   • Number of Comparables: Typically, 3-5 comparables are used, but this depends on data availability and the complexity of the appraisal. More is generally better, up to a point of diminishing returns.
   • Data Verification: Crucially important! Verify sales data❓ with a reliable source (e.g., both buyer and seller agents, reviewed sales contract).
2. Adjustments to Comparable Sales Prices:
   • Purpose: Adjustments are made to the sale prices of the comparables to account for differences between them and the subject property. The goal is to estimate what the comparables would have sold for if they were identical to the subject.
   • Adjustment Categories:
     • Property Rights Conveyed: Adjust for differences in fee simple ownership, leaseholds, easements, etc.
     • Financing Terms: Adjust for non-market financing, such as seller financing with below-market interest rates.
     • Conditions of Sale: Account for sales under duress, related-party transactions, or other unusual circumstances. Arm’s length transactions are preferred.
     • Market Conditions (Time Adjustment): Adjust for changes in market values between the date of the comparable sale and the date of the appraisal. This is often done using paired sales analysis (see below).
     • Location: Adjust for differences in desirability between the subject property’s location and the comparables’ locations. Factors include neighborhood quality, accessibility, amenities, and nuisances.
     • Physical Characteristics: Adjust for differences in size, style, age, condition, quality of construction, amenities (e.g., number of bedrooms/bathrooms, presence of a garage or pool), and site characteristics (e.g., lot size, views).
     • Economic Characteristics (for income properties): Adjust for differences in lease terms, operating expenses, tenant mix, and other factors that affect income.
   • Types of Adjustments:
     • Dollar Adjustments: A specific dollar amount is added to or subtracted from the comparable’s sales price.
     • Percentage Adjustments: A percentage of the comparable’s sales price is added to or subtracted from the sales price.
   • Paired Sales Analysis: A powerful technique for quantifying adjustments. It involves finding two otherwise very similar properties that differed only in one specific attribute (e.g., one property has a garage, the other doesn’t). The price difference between the two sales directly indicates the market value of that single attribute (in this case, the garage). This is an experimental approach in a real-world context.
   • Regression Analysis (Statistical): More sophisticated SCA might use statistical regression models to estimate the size of adjustments. This provides a more objective, data-driven approach than purely judgmental adjustments. Requires a larger dataset of comparable sales.
   • Adjustment Order: While not universally mandated, it’s common to adjust in this order: 1) property rights, 2) financing terms, 3) conditions of sale, 4) market conditions, 5) location, 6) physical characteristics. This helps avoid compounding errors.
3. Reconciliation and Value Indication:
   • Analyze Adjusted Sales Prices: Examine the range and distribution of the adjusted sales prices of the comparables.
   • Weighting: Not all comparables are created equal. Assign weights to the comparables based on their similarity to the subject property and the reliability of the data. A closer comparable will get more weight.
   • Value Indication: Based on the adjusted sales prices and weighting, arrive at a single point estimate (or a narrow range) of value for the subject property. This isn’t a simple average. It’s a reasoned conclusion.
   • Reconciliation: If other appraisal approaches (cost, income) are used, reconcile the value indications from each approach to arrive at a final value estimate. The SCA is often given the most weight, particularly for residential properties, if good comparables are available.

IV. Mathematical Formulas and Equations

• Adjusted Sales Price = Comparable Sales Price ± Adjustments

  • ASP = CSP ± A1 ± A2 ± A3 ... ± An
    • Where:
      • ASP = Adjusted Sales Price
      • CSP = Comparable Sales Price
      • A1, A2, A3 ... An = Individual adjustments (either positive or negative) for various factors.

  • Paired Sales Analysis (Implicit):

  • Value of Attribute = SP_with_Attribute - SP_without_Attribute

    • Where:
      • SP_with_Attribute = Sales Price of comparable with the specific attribute.
      • SP_without_Attribute = Sales Price of comparable without the specific attribute (but otherwise very similar).

  • Percentage Adjustment:

  • Dollar Adjustment = CSP * Percentage Adjustment

  • Regression Equation (Simplified): While adjustments are subjective, thinking in terms of a multiple regression framework helps.

  • SP = β0 + β1X1 + β2X2 + ... + βnXn + ε

    • Where:
      • SP = Sales Price
      • β0 = Intercept (base value)
      • β1, β2 ... βn = Regression coefficients (adjustments) representing the value contribution of each attribute.
      • X1, X2 ... Xn = Independent variables representing the attributes of the property (e.g., square footage, location, number of bedrooms).
      • ε = Error term.

  • Weighted Average (Value Indication):

  • Indicated Value = (W1 * ASP1) + (W2 * ASP2) + ... + (Wn * ASPn)

    • Where:
      • Indicated Value = The final estimated value
      • W1, W2 ... Wn = Weights assigned to each comparable (summing to 1)
      • ASP1, ASP2 ... ASPn = Adjusted Sales Prices of each comparable.

V. Examples of Practical Applications and Related “Experiments”

1. Residential Appraisal Example:

   • Subject Property: 3-bedroom, 2-bath, 1500 sq ft house in a suburban neighborhood.
   • Comparables:
     • Comp 1: 3-bed, 2-bath, 1400 sq ft, sold 1 month ago for $300,000.
     • Comp 2: 3-bed, 2-bath, 1600 sq ft, sold 2 months ago for $320,000.
     • Comp 3: 2-bed, 1-bath, 1500 sq ft, sold 3 months ago for $280,000.
   • Adjustments (simplified):
     • Comp 1: +$10,000 (for size difference)
     • Comp 2: -$10,000 (for size difference)
     • Comp 3: +$20,000 (for bedroom/bathroom difference)
   • Adjusted Sales Prices:
     • Comp 1: $310,000
     • Comp 2: $310,000
     • Comp 3: $300,000
   • Value Indication: Based on these comparables, an appraiser might estimate the value of the subject property at $310,000 (with comps 1 and 2 weighted slightly higher).

2. Paired Sales Experiment (Hypothetical):

   • Objective: To determine the value of a swimming pool in a specific market.
   • Process: Identify two similar homes in the same neighborhood that sold within a similar timeframe. One has a pool, the other does not.
   • Results:
     • House A (with pool): Sold for $400,000
     • House B (without pool): Sold for $380,000
   • Conclusion: Based on this “experiment,” the market is placing a value of approximately $20,000 on swimming pools in this particular area.

3. Regression Analysis in Commercial Valuation:

• Objective: Value a multi-tenant office building
• Process: Collect data on a large number of comparable office buildings, including net operating income (NOI), vacancy rates, square footage, age, and quality of construction.
• Model: Develop a multiple regression model to predict sales price based on these factors.
  • Regression Equation * SP = a + b1(NOI) + b2(Square Footage) + b3(Vacancy Rate) + …. + error
    • By looking at the regression weights (b1, b2, b3) that are created by the model, an appraiser can see what effect these characteristics have on value.

VI. Limitations and Considerations

• Data Availability: The SCA relies on the availability of sufficient and reliable comparable sales data. In some markets or for unique properties, finding good comparables can be challenging.
• Subjectivity: Adjustments are often judgmental, which introduces subjectivity into the appraisal process.
• Market Volatility: In rapidly changing markets, historical sales data may quickly become outdated.
• Complexity: Applying the SCA can become complex when dealing with properties that have many differences or when adjusting for numerous factors.
• Sales Concessions: Sellers pay credits or pay for some of the buyers closing costs in order to entice them to purchase a property. Those concessions need to be accounted for when calculating the value.

VII. Conclusion

The Sales Comparison Approach is a fundamental and versatile appraisal method. By carefully selecting and analyzing comparable sales, and by making appropriate adjustments for differences, appraisers can arrive at a credible and well-supported estimate of market value. However, it’s crucial to understand the underlying principles, the limitations of the approach, and the importance of sound judgment and thorough data verification. Integrating statistical techniques and paired sales analysis can enhance the objectivity and reliability of the SCA. Ultimately, the skill of the appraiser in applying these techniques is what determines the accuracy and defensibility of the value estimate.