Traditional appraisals mirror how buyers behave in the market - that is their strength ( not their weakness)
It;'s simple - buyers pay more for positive features (such as upgrades or a lake view ) and buyers expect a discount for adverse conditions ( such as repair, noise influence on a site)
Not every buyer will pay the exact same $ amount, but the MV definition references the typically motivated buyer ( not the outlier buyer ). The adjustment reflects the decisions buyers make with their wallets, and that means the most probable amount the typically motivated buyer for that property tends to pay.
The reason we adjusted is to make the sale properties more equivalent to the subject, and that narrowed the adjusted range. The appraiser should have sound reasoning for why they reconcile within that range to X $ point value and the reason should not be rote or random. No two buyers may pay exactly the same amount for a feature, but they tend to show a pattern of the more probable and typical amount they pay. The adjustments are right there on the sales comps on the grid. The SCA relies primarily on lending assignments, and additional support for the adjustments can be profited from depreciate costs, RE agent surveys, or other methods relevant to the assignment.
You have some of the dots, but you need to do a. better job of connecting them.
1. Understanding Market Value
"Market value" is the
expected value of a property under typical market conditions, as specified by standard definitions. This concept has important implications:
It's a theoretical construct, not an exact point value.
- It's derived from imperfect real-world data (actual sale prices).
- It accounts for typical market conditions and motivations.
2. The Nature of Sale Prices and Errors
2.1 Variability in Sale Prices
Sale prices are not perfect representations of market value. They contain various types of errors:
- Random Errors
- Examples: Rounding to nearest $5K, minor miscalculations
- Characteristics: Unpredictable, tend to balance out over large samples
- Statistical Assumption: Expected to average out to zero
- Systematic Errors
- Examples: Consistent GLA (Gross Living Area) measurement biases in certain neighborhoods. E.g. contractor removel of 2nd floor areas for vaulted ceilings, result in significant reductions of GLA that do not get reported back to the assessor's office in some towns - at certain perious during the past. If the appraiser is aware of this, then he knows to check the data, especially for the comparables going into the sales grid.
- Characteristics: Consistently skew data in one direction
- Appraiser's Role: Identify and compensate for these errors
- Adjustable Factors
- Examples: Sales concessions, atypical financing terms
- Appraiser's Role: Adjust sale prices to account for these factors
2.2 Sources of Errors in Real Estate Data
- Rounding of sale prices (e.g., to nearest $5K)
- Communication issues between parties
- Inaccuracies in property data records
- Variations in measurement methods (e.g., GLA calculations)
3. Statistical Approach to Market Value
3.1 Sale Prices as Sample Data Points
- Each sale price is viewed as an imperfect sample from the true market value distribution.
- The market value is estimated by analyzing the central tendency of these samples.
3.2 Regression Analysis in Appraisal
- Technique: Fitting a line (or curve) through the midpoint of sample values
- Goal: Estimate the expected value (market value) from imperfect sample data
3.3 Law of Large Numbers in Appraisal
- With a sufficient number of data points, unbiased random errors tend to cancel out.
- This principle underlies the statistical validity of market value estimates.
4. Appraiser's Role in Error Mitigation
- Data Verification
- Cross-reference multiple data sources (e.g., assessor records, MLS, satellite imagery)
- Example: Compare GLA figures from different sources to identify discrepancies
- Adjustment for Known Biases
- Identify and correct for systematic errors in the local market
- Statistical Analysis
- Use regression techniques to estimate market value from imperfect sale data
- Apply appropriate statistical tests to validate the reliability of estimates
- Professional Judgment
- Interpret statistical results in the context of market knowledge
- Make informed decisions on which data points to include or exclude
--->> Market value in real estate appraisal is a statistical concept derived from imperfect real-world data. By understanding the nature of errors in sale prices and applying appropriate statistical techniques, appraisers can provide reliable estimates of market value despite the inherent variability in real estate transactions.
5. Therefore
The Net Sale Pridce is the Holy Grail, It Is The Major Constraint In Sales Adjustments
- Because adjustments implicitly come from feature Value Contributions, and all Value Contributions must add up to the Net Sale Price. This constraint ripples down to those precious subjective adjustments through the Residual, which is the Residual = (Net Sale Price - Regression Model Estimated Sale Price Based On Measued Variables).
- Total of all Subjective Adustments = Subject Residual - Comparable Residual. Is The Critical Constraint.
But to reiterate, to do things this way, the correct way, requires being able to develop a very good price model via a tool like MARS Regression. - And that requires training and experience, plus a good knowledge of the subject Market Area.