Appraisal Data: Gathering & Analysis

Chapter Title: Appraisal Data: Gathering & Analysis

Introduction

Real estate appraisal hinges on the systematic collection and rigorous analysis of relevant data. This chapter delves into the types of data essential for accurate valuation, exploring the methods used to gather and interpret this information. We will examine various data categories and the analytical techniques employed to derive meaningful insights for appraisal purposes.

I. Data Classification and Categories

Appraisal data can be classified in various ways. One useful method, as outlined in the provided material, is to categorize data based on its scope and relevance to the subject property. These categories include:

1. Market Trend Data (General Data):

   • Definition: Information about the broad social, economic, governmental, and environmental forces that influence property values. This data is not specific to a single property but rather reflects trends across a larger market area.
   • Examples: Interest rates, employment rates, inflation, population growth, zoning regulations, environmental regulations.
   • Relevance: Helps to understand the overall market climate and identify potential drivers of value change.
   • Analysis: Statistical analysis of historical trends, forecasting models (e.g., time series analysis), and econometric modeling to assess the impact of macroeconomic factors on real estate values.

2. Competitive Supply and Demand Data:

   • Definition: Data specific to the local real estate market, focusing on the balance between the supply of properties available for sale or rent and the demand from potential buyers or renters.
   • Supply Data: Number of existing and proposed properties, vacancy rates, construction activity, land availability for development, conversion potential.
   • Demand Data: Wage and employment levels, population shifts, household formation rates, consumer confidence indices, mortgage availability.
   • Relevance: Provides insights into the current market conditions and potential future imbalances that may affect property values.
   • Analysis: Market surveys, absorption rate analysis, supply and demand modeling to project future market trends.
     *Absorption Rate (AR): AR = (Number of units sold or leased) / (Total number of units available). This metric helps determine how quickly properties are being absorbed into the market.

3. Subject Property Data:

   • Definition: Specific information about the property being appraised, including its physical characteristics, legal attributes, and any unique features that may influence its value.
   • Physical Characteristics: Size, number of rooms, floor plan, architectural style, construction quality, landscaping, amenities (e.g., pool, garage).
   • Legal Attributes: Title, easements, zoning restrictions, property taxes, special assessments.
   • Relevance: Forms the foundation for the appraisal process and is used to compare the subject property to comparable properties.
   • Analysis: Detailed property inspection, review of property records, and analysis of potential functional or external obsolescence.

4. Comparable Property Data:

   • Definition: Information about properties that are similar to the subject property and have recently sold or leased in the same market area.
   • Key Characteristics: Similarity in physical features, location, market appeal, and time of sale.
   • Data Sources: Multiple Listing Service (MLS), public records, real estate brokers, appraisers.
   • Relevance: Provides a basis for estimating the market value of the subject property using the sales comparison approach.
   • Analysis: Adjustment grids to account for differences between the comparable properties and the subject property (e.g., size, location, condition). Statistical techniques like regression analysis can be used to determine the impact of different property characteristics on sales price.

Another method to classify appraisal data, also highlighted in the text, involves categorizing by influence factors:

1. Regional and Community Data: Reflects the effect of local social, economic, governmental, and physical forces on value. This includes factors such as: scarcity of similar neighborhoods, regional economic slumps, climate, and trends in defense spending.
2. Neighborhood Data: Provides context for the analysis of local influences on value. Examines how larger regional aspects influence the desirability of a particular neighborhood.
3. Site Data:
4. Building (Improvement) Data:
5. Specific Market Data:

II. Data Gathering Techniques

Effective data gathering is crucial for a credible appraisal. Common techniques include:

1. Physical Inspection:

   • Purpose: To directly observe the subject property and gather first-hand information about its condition, features, and potential deficiencies.
   • Process: A thorough walk-through of the property, noting details about the interior and exterior, taking measurements, and documenting any relevant observations with photographs.

2. Record Review:

   • Purpose: To verify information about the subject property and gather data from reliable sources.
   • Sources: Public records (e.g., county assessor’s office, land registry), title companies, zoning departments, building permit offices, MLS, and commercial data providers.

3. Interviews:

   • Purpose: To gather information from knowledgeable sources about the property, the market, and relevant trends.
   • Sources: Property owners, real estate agents, local developers, property managers, and government officials.

4. Online Resources:

   • Purpose: To access a wide range of data quickly and efficiently.
   • Sources: MLS, real estate websites, government websites, financial publications, and social media.

III. Data Analysis Techniques

Once the data has been gathered, it must be analyzed to extract meaningful insights and support the appraisal opinion. Common techniques include:

1. Sales Comparison Analysis:

   • Principle: The value of a property is related to the prices of comparable properties in the market.
   • Process: Identify comparable properties, gather data on their sales prices and characteristics, and make adjustments for differences between the comparables and the subject property.
   • Mathematical Formulation: Adjusted Sales Price = Sales Price +/- Adjustments. The goal is to arrive at an indicated value for the subject property based on each comparable sale.
   • Statistical Enhancements: Regression analysis can be used to quantify the relationship between property characteristics and sales prices, providing a more objective basis for adjustments. For example:
     P = b0 + b1X1 + b2X2 + … + bnXn + e
     Where:
     * P = Predicted sales price
     * b0 = Intercept
     * b1, b2, …, bn = Regression coefficients for each variable
     * X1, X2, …, Xn = Independent variables (e.g., square footage, number of bedrooms)
     * e = Error term

2. Cost Approach:

   • Principle: The value of a property is equal to the cost of constructing a new building with similar utility, less depreciation.
   • Process: Estimate the cost of land, estimate the cost of constructing the improvements, and deduct any depreciation due to physical deterioration, functional obsolescence, or external obsolescence.
   • Mathematical Formulation: Value = Land Value + (Reproduction Cost - Depreciation).
     *Depreciation (D) can be calculated using various methods, such as the straight-line method: D = (Cost - Salvage Value) / Useful Life.

3. Income Capitalization Approach:

   • Principle: The value of a property is related to the income it generates.
   • Process: Estimate the potential gross income (PGI) of the property, deduct vacancy and collection losses to arrive at effective gross income (EGI), deduct operating expenses to arrive at net operating income (NOI), and divide the NOI by a capitalization rate to estimate the value.
   • Mathematical Formulation: Value = NOI / Capitalization Rate (Cap Rate).
     *Cap Rate (R) can be derived from market data using the formula: R = NOI / Sales Price.

4. Trend Analysis:

   • Purpose: To identify patterns and trends in market data over time.
   • Techniques: Time series analysis, graphical analysis, and statistical analysis of market indicators.
     *For example, a simple moving average (SMA) can be used to smooth out fluctuations in sales prices: SMA = (Sum of prices over n periods) / n.

5. Sensitivity Analysis:

   • Purpose: To assess the impact of changes in key assumptions on the appraisal opinion.
   • Process: Varying the assumptions used in the valuation models and observing the effect on the estimated value.

IV. Data Reliability and Verification

The accuracy and reliability of appraisal data are paramount. Appraisers must:

1. Verify Data Sources: Assess the credibility of the sources used to gather data and cross-reference information from multiple sources whenever possible.
2. Identify and Resolve Inconsistencies: Reconcile conflicting data points and investigate any discrepancies.
3. Document Data Sources and Assumptions: Clearly document the sources of all data used in the appraisal report and state any assumptions made during the analysis.

V. Practical Applications and Experiments

1. Case Study: Impact of Interest Rate Changes on Housing Prices:

   • Objective: To analyze the relationship between interest rates and housing prices in a specific market area.
   • Data: Historical data on interest rates, housing prices, and other relevant economic indicators.
   • Analysis: Regression analysis to quantify the impact of interest rate changes on housing prices, controlling for other factors.

2. Experiment: Sales Comparison Adjustment Analysis:

   • Objective: To determine the appropriate adjustment for a specific property characteristic (e.g., square footage).
   • Data: Sales data on comparable properties with varying square footage.
   • Analysis: Paired sales analysis to isolate the impact of square footage on sales price.

Conclusion

Data gathering and analysis are the cornerstones of the real estate appraisal process. By understanding the different types of data, employing appropriate gathering techniques, and applying rigorous analytical methods, appraisers can develop credible and reliable opinions of value.