Appraisal Data Landscape: Macro and Micro Foundations

Chapter Title: Appraisal Data Landscape: Macro and Micro Foundations

Introduction

The real estate appraisal profession relies heavily on data analysis to arrive at credible opinions of value. This chapter explores the landscape of appraisal data, differentiating between macro-level and micro-level foundations. We will delve into the theoretical principles underlying data usage in appraisal, practical applications, and relevant data sources.

1. Macro-Level Data: The Broader Economic Context

Macro-level data provides a broad perspective on economic conditions, market trends, and demographic shifts that influence real estate values. This data is typically gathered at a national, regional, or metropolitan level.

1.1. Theoretical Foundations

• Supply and Demand: Macroeconomic indicators directly impact the supply and demand dynamics in real estate markets. For example, interest rates influence borrowing costs, affecting demand, while construction costs impact the supply of new properties. Mathematically, we can represent this relationship as:

  • D = f(I, Y, P) (Demand is a function of interest rates (I), income (Y), and price (P))
  • S = f(C, T, P) (Supply is a function of construction costs (C), technology (T), and price (P))

• Economic Base Theory: This theory suggests that a region’s economy is driven by its primary industries. Identifying the economic base and understanding its performance is crucial for forecasting real estate trends. A simple employment multiplier can be used to estimate the impact of basic sector jobs on the overall economy:

  • Total Employment = Basic Employment * Multiplier

• Regression Analysis: Multiple regression analysis can be applied to macro data to identify variables which are most correlated to value. The formula would be:

  • Y = β0 + β1X1 + β2X2 + … + βnXn + ε, where Y is the dependent variable (e.g. sales prices), Xi are the independent variables (e.g. interest rates, income), βi are the coefficients, and ε is the error term.

1.2. Practical Applications

• Market Analysis: Analyzing employment rates, population growth, and income levels helps appraisers understand the overall health of a real estate market and predict future trends.
• Highest and Best Use Analysis: Macro data informs decisions about the most profitable and legally permissible use of a property. For instance, population trends can influence decisions between residential and commercial development.
• Forecasting: Appraisers use macroeconomic forecasts to project future income streams and property values, especially for income-producing properties.

1.3. Data Sources

• Government Agencies:
  • U.S. Census Bureau: Provides data on population, housing, and demographics. Access publications such as the American Community Survey.
  • Bureau of Economic Analysis (BEA): Offers data on gross domestic product (GDP), consumer price index (CPI), and income.
  • Bureau of Labor Statistics (BLS): Publishes data on employment, unemployment, and wages. The Monthly Labor Review is a key resource.
  • Federal Housing Finance Agency (FHFA): Delivers information on residential market conditions.
  • U.S. Department of Housing and Urban Development (HUD): Provides reports on housing programs.
• Federal Reserve Board: Publications such as the Federal Reserve Bulletin offer extensive data on mortgage markets, interest rates, and economic activity. Whitehouse.gov, council of economic advisors publication the economic report of the president.
• Trade Associations:
  • National Association of Realtors (NAR): Offers data on existing home sales and market trends. www.nar.realtor, www.houselogic.com
  • National Association of Home Builders (NAHB): Provides data on new housing starts, prices, and construction costs. www.nahb.org
  • Appraisal Institute: www.appraisalinstitute.org
  • Building Owners and Managers Association (BOMA) International: www.boma.org Provides office vacancy rates. BOMA Industrial Experience Exchange Report and BOMA Office Experience Exchange Report.
• Private Sources: Banks, utility companies, university research centers, private advisory firms (like Moody’s Analytics, www.reis.com), and multiple listing services (MLS).

1.4. Experiment Example

• Experiment: Assessing the Impact of Interest Rate Hikes on Housing Prices

  • Hypothesis: An increase in mortgage interest rates will lead to a decrease in housing prices.
  • Data Collection: Gather historical data on mortgage interest rates (from the Federal Reserve) and housing prices (e.g., Case-Shiller Home Price Index) over a period of 10-20 years.
  • Analysis: Perform a regression analysis with housing prices as the dependent variable and interest rates as the independent variable, while controlling for other factors like income and population growth.
  • Expected Outcome: A statistically significant negative coefficient on the interest rate variable would support the hypothesis.

2. Micro-Level Data: Property-Specific Information

Micro-level data pertains to the characteristics of individual properties and their immediate surroundings. This data is essential for making direct comparisons and adjustments in the appraisal process.

2.1. Theoretical Foundations

• Principle of Substitution: This fundamental appraisal principle states that a buyer will pay no more for a property than the cost of acquiring an equally desirable substitute. Micro-level data allows appraisers to identify and compare substitute properties.

• Sales Comparison Approach: The sales comparison approach relies heavily on micro-level data to compare the subject property to comparable sales. Adjustments are made for differences in location, size, condition, and other relevant factors. The mathematical representation of the sales comparison approach can be shown as:

  • Indicated Value = Sale Price of Comparable + Adjustments

• Cost Approach: The cost approach requires detailed micro-level data on construction costs, depreciation, and land value.

  • Property Value = Cost of New - Depreciation + Land Value

2.2. Practical Applications

• Comparable Sales Analysis: Micro-level data is used to select comparable sales, verify transaction details, and make necessary adjustments.
• Property Inspection: Gathering data on the physical characteristics, condition, and features of the subject property.
• Rent Surveys: Collecting data on rental rates, occupancy, and operating expenses for comparable properties in the subject’s market area.

2.3. Data Sources

• Public Records:
  • County Assessor’s Office: Provides property tax records, including assessed values, property characteristics, and sales data.
  • County Recorder’s Office: Maintains records of deeds, mortgages, and other legal documents.
• Multiple Listing Services (MLS): Contains data on properties listed for sale, including listing prices, property descriptions, and photographs. However, data quality may vary.
• Commercial Real Estate Services: CBRE (www.cbre.us), Colliers International (www2.colliers.com/en), Cushman & Wakefield (www.cushmanwakefield.com), Jones Lang LaSalle (JLL) (www.us.jll.com/en), Marcus & Millichap Real Estate Investment Services (www.marcusmillichap.com), NAI Global (www.naiglobal.com), Transwestern (www.transwestern.net), RealtyRates.com (www.realtyrates.com)
• Private Data Providers:
  • CoStar: Provides extensive data on commercial properties, including sales data, lease information, and tenant details (subscription required).
  • LoopNet: Contains listings and sales information (some free, some subscription-based). www.loopnet.com
  • Reis, Inc. (Real Estate Solutions by Moody’s Analytics): Provides commercial real estate performance information. Subscriptions for a fee. Appraisal Institute members get a discount. www.reis.com
  • Site to Do Business: www.stdb.com. Demographic data with robust reporting and analytical tools.

2.4 Experiment Example

• Experiment: Adjusting Comparable Sales for Square Footage Differences

  • Objective: To determine the appropriate dollar adjustment per square foot for differences in building size between the subject property and comparable sales.
  • Data Collection: Gather data on recent sales of similar properties in the same market area, including sale prices and square footage. Focus on sales where square footage is the primary difference.
  • Analysis: Calculate the price per square foot for each comparable sale. Compare the prices per square foot and analyze the relationship between size and price. A scatter plot of price per square foot versus square footage can help visualize this relationship. One could also use regression to develop a coefficient for square footage.
  • Application: Apply the derived adjustment factor to comparable sales based on their size difference with the subject property.

3. Data Quality and Validation

Whether macro or micro, the quality of data is paramount. Appraisers must critically evaluate the reliability, accuracy, and relevance of their data sources.

• Data Verification: Cross-reference data from multiple sources to identify discrepancies and ensure accuracy.
• Source Credibility: Evaluate the reputation and expertise of the data provider. Government agencies and reputable private data providers are generally more reliable.
• Outlier Analysis: Identify and investigate outliers in the data. Outliers can indicate errors or unusual circumstances that require further investigation. Statistical methods such as box plots and z-scores can be used for outlier detection.
• Understanding Data Limitations: Be aware of the limitations of each data source, such as sample size, data collection methods, and potential biases.

4. The Impact of Technology

Advances in technology are transforming the appraisal data landscape, making data more accessible, affordable, and easier to analyze.

• Geographic Information Systems (GIS): GIS software allows appraisers to analyze spatial data, such as zoning maps, flood zones, and environmental hazards.
• Automated Valuation Models (AVMs): AVMs use statistical models to estimate property values based on available data. While AVMs can be useful tools, appraisers must understand their limitations and validate their results.
• Blockchain Databases: Blockchain technology offers a secure and transparent way to share real estate data. This technology has the potential to improve data accuracy and reduce fraud.
• Artificial Intelligence (AI): AI algorithms can be used to automate data collection, identify patterns in data, and improve the accuracy of valuation models.

Conclusion

A thorough understanding of the appraisal data landscape, including both macro and micro foundations, is essential for producing credible appraisals. By leveraging appropriate data sources, applying sound theoretical principles, and critically evaluating data quality, appraisers can develop reliable opinions of value that meet the needs of their clients and the requirements of the Uniform Standards of Professional Appraisal Practice (USPAP). The increasing availability of data through technology presents both opportunities and challenges for the appraisal profession.