Gathering & Analyzing Appraisal Data

Chapter 5: Gathering & Analyzing Appraisal Data

I. Understanding the Nature of Appraisal Data

A. Definition and Scope
Appraisal data comprises all information relevant to determining the market value of a property. It encompasses a wide range of information, from broad economic indicators to specific details about the subject property and comparable sales. Accurate and comprehensive data collection and analysis are critical for developing credible appraisal opinions.

B. Data Classification

1. General vs. Specific Data
    a. General Data: Pertains to the overall real estate market and economic conditions, influencing property values broadly. Examples include interest rates, employment rates, inflation rates, zoning regulations, and demographic trends.
    b. Specific Data: Relates directly to the subject property and comparable properties. This includes property characteristics (size, condition, features), sales prices, dates of sale, financing terms, and location-specific information.

2. Primary vs. Secondary Data
    a. Primary Data: Collected directly by the appraiser through personal inspection, surveys, interviews, or direct observation. Considered the most reliable data source.
    b. Secondary Data: Obtained from existing sources, such as public records, real estate databases, multiple listing services (MLS), government publications, and commercial data providers. Requires careful verification for accuracy and reliability.

3. Quantitative vs. Qualitative Data
    a. Quantitative Data: Measurable and expressed numerically. Examples include square footage, lot size, number of bedrooms, sales prices, and interest rates.
    b. Qualitative Data: Descriptive and non-numerical, representing characteristics or attributes. Examples include property condition, quality of construction, neighborhood desirability, and architectural style.

C. The Four Forces Influencing Value
Real estate values are influenced by the interplay of four fundamental forces:

1. Social Forces: Demographic trends, population growth, household size, lifestyle preferences, and community values.
2. Economic Forces: Employment rates, income levels, interest rates, inflation, availability of credit, and economic cycles.
3. Governmental Forces: Zoning regulations, building codes, property taxes, government subsidies, and environmental regulations.
4. Environmental Forces: Natural features, climate, proximity to amenities, pollution, and environmental hazards.

II. Gathering Regional and Community Data

A. Purpose and Application
Regional and community data provides the context for understanding the local real estate market. It helps appraisers identify market trends, assess economic stability, and evaluate the overall investment climate.

B. Key Data Points

1. Economic Base Analysis
    a. Definition: Examines the primary industries and employers that drive the local economy. A diverse and stable economic base supports healthy real estate values.
    b. Location Quotient (LQ): A statistical measure used to determine if a region has a greater concentration of a particular industry compared to the nation. The formula is:
        LQ = (Regional Employment in Industry / Total Regional Employment) / (National Employment in Industry / Total National Employment)
        An LQ greater than 1 indicates a higher concentration of that industry in the region.
    c. Example: A city with a strong technology sector may have a higher concentration of software engineers than the national average, resulting in a higher LQ for the software industry.
2. Demographic Trends
    a. Population Growth: Indicates the overall demand for ho<a data-bs-toggle="modal" data-bs-target="#questionModal-326228" role="button" aria-label="Open Question" class="keyword-wrapper question-trigger"><span class="keyword-container">using</span><span class="flag-trigger">❓</span></a> and services.
    b. Age Distribution: Influences housing preferences and demand for specific types of properties (e.g., senior housing).
    c. Household Income: Affects affordability and purchasing power.
    d. Education Levels: Correlate with employment opportunities and economic growth.
3. Infrastructure and Public Services
    a. Transportation Networks: Accessibility to highways, public transportation, and airports.
    b. Utilities: Availability of water, sewer, electricity, and internet services.
    c. Schools: Quality and reputation of local schools.
    d. Public Safety: Crime rates and emergency services.
    e. Amenities: Parks, recreational facilities, shopping centers, and cultural attractions.

C. Data Sources

1. Government Agencies: U.S. Census Bureau, Bureau of Labor Statistics, local planning departments, economic development agencies.
2. Trade Associations: National Association of REALTORS®, local real estate boards, chambers of commerce.
3. Commercial Data Providers: Real estate data companies, market research firms.
4. Online Resources: Websites of government agencies, trade associations, and commercial data providers.

III. Gathering Neighborhood Data

A. Defining Neighborhood Boundaries
A neighborhood is a geographically defined area with similar characteristics that influence property values. Boundaries can be physical (e.g., rivers, highways), political (e.g., city limits), or social (e.g., school districts).

B. Key Neighborhood Characteristics

1. Location and Accessibility: Proximity to employment centers, transportation, amenities, and major roadways.
2. Physical Characteristics: Topography, lot sizes, street patterns, landscaping, and overall appearance.
3. Land Use: Mix of residential, commercial, and industrial properties. Compatibility of land uses.
4. Property Values: Average sales prices, price trends, and vacancy rates.
5. Quality of Housing Stock: Age, condition, architectural style, and construction quality of homes.
6. Neighborhood Amenities: Parks, schools, shopping centers, and recreational facilities.
7. Community Services: Police protection, fire protection, and sanitation services.
8. School District: Reputation and performance of local schools.
9. Environmental Factors: Noise levels, air quality, and presence of environmental hazards.

C. Data Sources

1. On-Site Inspection: Direct observation of neighborhood characteristics and conditions.
2. Local Real Estate Agents: Knowledge of local market conditions and neighborhood trends.
3. Residents: Insights into neighborhood dynamics and local issues.
4. City Planning Departments: Zoning maps, land use plans, and development projects.
5. Property Records: Sales data, property tax assessments, and building permits.
6. Online Mapping Tools: Satellite imagery and street-level views of the neighborhood.

D. Neighborhood Analysis Techniques

1. Market Segmentation: Identifying sub-markets within the neighborhood based on property types, price ranges, or buyer profiles.
2. Trend Analysis: Examining changes in property values, vacancy rates, and other market indicators over time.
3. Competitive Analysis: Evaluating the strengths and weaknesses of the neighborhood compared to other competing areas.
4. SWOT Analysis: Strengths, Weaknesses, Opportunities, and Threats – a strategic planning tool to evaluate the overall attractiveness of the neighborhood.

IV. Gathering Site Data

A. Site Description

1. Location: Address, legal description, and parcel identification number (PIN).
2. Size and Shape: Lot dimensions, acreage, and shape (e.g., rectangular, irregular).
3. Topography: Slope, elevation, and drainage.
4. Soil Conditions: Stability, drainage capacity, and suitability for construction.
5. Utilities: Availability of water, sewer, electricity, gas, and internet services.
6. Easements and Restrictions: Rights of way, utility easements, and deed restrictions.
7. Zoning: Permitted land uses, building height restrictions, and setback requirements.
8. Environmental Issues: Presence of wetlands, floodplains, or hazardous materials.
    Example: Formula for area of a triangle: Area = 0.5 * base * height

B. Data Sources

1. Property Deeds: Legal description, easements, and restrictions.
2. Survey Maps: Lot dimensions, topography, and easements.
3. Zoning Maps: Zoning classifications and regulations.
4. Soil Maps: Soil types and characteristics.
5. Environmental Reports: Presence of wetlands, floodplains, or hazardous materials.
6. Utility Companies: Availability and capacity of utilities.
7. On-Site Inspection: Direct observation of site characteristics and conditions.

V. Gathering Building Data

A. Building Description
Comprehensive data on the subject improvement is essential for appraisal accuracy.

B. Key Data Points

1. General Information
    a. Property type: Single-family, multi-family, commercial, etc.
    b. Age: Actual and effective age of the building.
    c. Condition: Overall physical condition of the building (e.g., excellent, good, fair, poor).
    d. Quality of Construction: Level of craftsmanship and materials used (e.g., high, average, low).
    e. Architectural Style: Design and characteristics of the building (e.g., colonial, ranch, modern).

2. Dimensions
    a. <a data-bs-toggle="modal" data-bs-target="#questionModal-86639" role="button" aria-label="Open Question" class="keyword-wrapper question-trigger"><span class="keyword-container"><a data-bs-toggle="modal" data-bs-target="#questionModal-326219" role="button" aria-label="Open Question" class="keyword-wrapper question-trigger"><span class="keyword-container">gross living area</span><span class="flag-trigger">❓</span></a></span><span class="flag-trigger">❓</span></a> (GLA): Total area of finished, above-grade living space.
    b. Basement Area: Finished and unfinished areas of the basement.
    c. Other Areas: Garage, porch, deck, and storage areas.
        *Formula for calculating area of a rectangular room: Area = Length * Width*

3. Substructure
    a. Foundation Type: Basement, crawl space, or slab-on-grade.
    b. Foundation Condition: Cracks, leaks, or other structural issues.

4. Exterior
    a. Exterior Walls: Materials used (e.g., brick, siding, stucco).
    b. Roof: Type of roofing material (e.g., asphalt shingles, tile, metal) and condition.
    c. Windows: Type and condition of windows.

5. Interior
    a. Number of Rooms: Bedrooms, bathrooms, living rooms, kitchens, etc.
    b. Floor Coverings: Materials used (e.g., hardwood, carpet, tile).
    c. Wall Finishes: Paint, wallpaper, or other wall coverings.
    d. Interior Fixtures: Lighting, plumbing, and electrical fixtures.

6. Equipment/Appliances
    a. Heating System: Type (e.g., forced air, radiant) and efficiency.
    b. Air Conditioning System: Type (e.g., central air, window units) and efficiency.
    c. Appliances: Refrigerator, stove, dishwasher, washer, and dryer.

7. Energy Efficiency
    a. Insulation: Type and thickness of insulation in walls, ceilings, and floors.
    b. Windows: Energy-efficient windows (e.g., double-pane, low-E).
    c. Solar Panels: Presence and capacity of solar panels.
    d. Energy Efficiency Ratio (EER): A measure of the cooling efficiency of air conditioners and heat pumps.
        EER = Cooling Output (BTU/hour) / Electrical Input (Watts)
    Higher EER values indicate greater energy efficiency.

8. Special Features
    a. Fireplace: Type (e.g., wood-burning, gas) and location.
    b. Swimming Pool: Size, shape, and features.
    c. Landscaping: Type and quality of landscaping.
    d. Outbuildings: Sheds, barns, and guest houses.

C. Data Sources

1. On-Site Inspection: Thorough examination of the building's characteristics and condition.
2. Building Plans: Architectural drawings and specifications.
3. Contractor Estimates: Costs of repairs or renovations.
4. Homeowner Disclosures: Information provided by the seller about the property's condition and features.

VI. Gathering Specific Market Data

A. Purpose and Application
Specific market data pertains to comparable sales and listings, cost data, and income/expense data for income-producing properties. It is used to develop adjustments in the sales comparison approach, estimate replacement cost in the cost approach, and determine capitalization rates in the income approach.

B. Key Data Points

1. Prices and Terms of Sale: Sales prices, financing terms, concessions, and other conditions of sale.
2. Date of Sale: Time elapsed since the sale date. Market conditions may change over time.
3. Financing: Type of financing, interest rate, loan terms, and loan-to-value ratio.
4. Sale Conditions: Arm's length transactions, foreclosures, or short sales.
5. Cost Data: Construction costs, material costs, and labor costs.
6. Income and Expense Data: Rental income, operating expenses, and net operating income (NOI) for income-producing properties.

C. Data Sources

1. Multiple Listing Services (MLS): Sales data on listed properties.
2. Public Records: Property deeds, sales records, and tax assessments.
3. Real Estate Agents: Knowledge of local market conditions and recent sales.
4. Appraisers: Data from previous appraisal assignments.
5. Cost Estimating Services: Companies that provide construction cost data.
6. Property Owners: Income and expense information for income-producing properties.

VII. Analyzing Appraisal Data

A. Data Verification

1. Accuracy: Verify the accuracy of all data points from multiple sources.
2. Reliability: Assess the reliability of data sources and methods of collection.
3. Consistency: Check for consistency across different data points and sources.

B. Data Interpretation

1. Market Trends: Identify patterns and trends in the data.
2. Comparability: Assess the comparability of properties based on key characteristics.
3. Adjustments: Make appropriate adjustments to comparable sales prices based on differences from the subject property.
4. Weighting: Assign weights to different data points based on their relevance and reliability.

C. Statistical Analysis

1. Measures of Central Tendency: Mean, median, and mode.
    a. Mean: The arithmetic average of a set of numbers.
        Mean = (Sum of Values) / (Number of Values)
    b. Median: The middle value in a sorted set of numbers.
    c. Mode: The value that appears most frequently in a set of numbers.
2. Measures of Dispersion: Range and standard deviation.
    a. Range: The difference between the highest and lowest values in a set of numbers.
        Range = (Highest Value) - (Lowest Value)
    b. Standard Deviation: A measure of how spread out the values are from the mean.
        *Formula for Sample Standard Deviation:
        s = sqrt[ Σ (xi - x̄)^2 / (n-1) ]*
            where:
            s = sample standard deviation
            xi = each individual value in the dataset
            x̄ = the sample mean
            n = the number of values in the sample
            Σ = summation (the sum of)

D. Regression Analysis
Statistical technique that can be used to model the relationship between a dependent variable (e.g., sales price) and one or more independent variables (e.g., square footage, number of bedrooms). It can help to quantify the impact of different property characteristics on value.

E. Sensitivity Analysis
A technique used to assess the impact of changes in key assumptions on the final value conclusion. For example, an appraiser might perform a sensitivity analysis to determine how the value would change if the capitalization rate were to increase or decrease.

VIII. Practical Applications and Experiments

A. Sales Comparison Grid

1. Purpose: To organize and analyze data on comparable sales and identify adjustments for differences from the subject property.
2. Elements: Property characteristics, sales prices, dates of sale, adjustments, and adjusted sales prices.
3. Experiment: Create a sales comparison grid for a subject property and three comparable sales. Identify the key differences between the properties and make appropriate adjustments to the sales prices.

B. Cost Approach

1. Purpose: To estimate the replacement cost of the improvements and subtract accrued depreciation.
2. Steps: Estimate the replacement cost of the improvements, estimate accrued depreciation (physical deterioration, functional obsolescence, and external obsolescence), and add the value of the land.
3. Experiment: Estimate the cost of constructing a new house similar to the subject property. Use cost estimating services or contractor estimates to determine the costs of materials and labor.

C. Income Approach

1. Purpose: To estimate the value of an income-producing property based on its net operating income (NOI) and capitalization rate.
2. Formula: Value = NOI / Capitalization Rate
    (Capitalization Rate = Income / Value)
3. Experiment: Analyze the income and expenses of a commercial property and calculate the NOI. Research comparable sales of similar properties to determine the appropriate capitalization rate.

IX. Conclusion

Gathering and analyzing appraisal data is a critical step in the appraisal process. It requires a thorough understanding of market dynamics, property characteristics, and appraisal techniques. By following a systematic approach and using reliable data sources, appraisers can develop credible and defensible value opinions.