Real Estate Appraisal Data: Macro and Micro Sources

Real Estate Appraisal Data: Macro and Micro Sources

Introduction

Real estate appraisal relies heavily on data analysis to determine the value of a property. This chapter explores the diverse sources of data appraisers utilize, categorizing them into macro-level and micro-level data. Macro-level data provides a broad understanding of the market and economic conditions, while micro-level data focuses on specific property characteristics and transactions. The chapter will cover relevant scientific theories and principles, practical applications, and mathematical formulas where applicable, with examples related to real estate appraisal.

Macro-Level Data Sources

Macro-level data is crucial for understanding the overall economic environment influencing property values. This data is often collected and disseminated by government agencies, trade associations, and private entities.

1. Government Sources
   A. Federal Agencies:

i. Council of Economic Advisors:
- Publication: The Economic Report of the President.
- Information: Labor force statistics (employment rate, unemployment rate), industrial production indices, tax policy changes, technology advancements, and government regulation impacts.
- Application: Appraisers use this data to assess the overall health of the economy and its potential impact on real estate demand. For example, an increasing unemployment rate (U) may indicate a softening housing market.
- Equation: Real GDP Growth Rate = [(GDPt – GDPt-1) / GDPt-1] * 100, where GDPt is the Gross Domestic Product in the current period and GDPt-1 is the GDP in the previous period. This growth rate helps assess the economic climate.

ii. Federal Reserve Board:
- Publication: Federal Reserve Bulletin.
- Information: Gross National Product (GNP), Gross Domestic Product (GDP), national income, mortgage market trends (interest rates), consumer credit data, and business activity indices.
- Application: Changes in interest rates directly affect property values and affordability. Higher interest rates (i) typically lead to lower property values (PV), as the cost of financing increases.
- Equation: PV = CF / (1 + i)^n, where CF is the cash flow, i is the interest rate, and n is the number of periods.

iii. Federal Housing Finance Agency (FHFA):
- Information: Residential market conditions and house price indices.
- Application: The FHFA House Price Index (HPI) provides valuable insights into the historical trends in home values. Appraisers use this to compare current market conditions with past performance.

iv. National Vital Statistics System:
- Publication: National Vital Statistics Reports.
- Information: Birth and death rates.
- Application: Demographic shifts influence housing demand. For instance, an increasing population (P) in a region might lead to higher demand for housing (D).

v. US Department of Commerce, Census Bureau:
- Publications: American Community Survey, Census of Agriculture, Census of Population and Housing.
- Information: Population statistics, consumer income levels, housing completions, housing permits, and business performance data.
- Application: Population growth, income levels, and housing permits are critical indicators of future housing supply and demand.
- Experiment: An appraiser could analyze the correlation between population growth and median home prices in a specific area using regression analysis. This will demonstrate the predictive power of census data.

vi. US Department of Commerce, Bureau of Economic Analysis:
- Publication: Survey of Current Business.
- Information: Consumer Price Index (CPI), Wholesale Price Index (WPI), mortgage debt data, and the value of new construction.
- Application: Inflation rates (CPI, WPI) impact construction costs and property values.

vii. US Department of Housing and Urban Development (HUD):
- Information: Reports on FHA building starts, financing programs, and vacancy surveys in metropolitan areas.
- Application: HUD data helps understand the availability of affordable housing and government-backed mortgage programs.

viii. US Department of Labor, Bureau of Labor Statistics:
- Publication: Monthly Labor Review.
- Information: Consumer Price Index (CPI), wholesale prices, employment and earnings figures.
- Application: Employment data reflects economic stability, directly impacting real estate demand.

B. State and Local Agencies:
- Information: Population trends, household data, employment figures, land use plans, utility and transportation systems.
- Application: Local development plans and population projections help forecast future property values.

2. Trade Associations:

   • Examples: American Real Estate Society (ARES), Appraisal Institute, National Association of Realtors (NAR).
   • Information: National and regional economic indicators, existing home sales data, and office vacancy rates.
   • Application: These associations provide industry-specific data and insights, enhancing appraisal accuracy.

3. Private Sources:

   • Examples: Banks, utility companies, university research centers.
   • Information: Bank debt levels, department store sales, employment indicators, land prices, mortgage costs, wage rates, and construction costs.
   • Application: These sources offer granular data that complements government and association data, refining market analysis.

Organizing Macro-Level Data:
Macro-level data should be cataloged and cross-indexed in an appraiser’s office files. Computerized systems can be used to access data such as multiple listing information and census data. Local planning and development agencies often computerize data by geographic area, including:
1. Housing inventory and vacancies.
2. Demolitions and conversions.
3. Commercial construction.
4. Household incomes.
5. New land use by zoning classification.
6. Population and demographics.
7. Housing forecasts.

Micro-Level Data Sources

Micro-level data focuses on specific properties and transactions, providing detailed insights into individual property values.

1. Public Records:

   • Information: Property deeds (ownership details, transaction dates, legal descriptions), property tax assessor’s records (land and building sketches, area measurements, sale prices).
   • Application: Appraisers examine deeds to verify ownership, identify easements, and confirm the property’s legal description. Tax assessor records provide details on property characteristics, which are critical for comparative analysis.
   • Mathematical Example: Gross Living Area (GLA) can be calculated using assessor’s building sketches and area measurements: GLA = Length * Width. This figure is essential for valuation.

2. Listings and Offerings:

   • Information: Data on properties offered for sale (listing prices) and purchase offers received.
   • Application: Listings reflect the upper limit of value, while offers commonly set the lower limit.
     • Mathematical Concept: The concept of a “bracketing” approach, where listings represent the high end of the range, and offers the low end. Value is estimated within this bracket.
   • Multiple Listing Service (MLS):
   • Information: Data on residential properties listed for sale during the calendar year or fiscal quarter, citing their listing prices. The service will contain fairly complete information about these properties, including descriptions and brokers’ names. However, details about a property’s square footage, basement area, or exact age may be inaccurate or excluded. Sale prices of properties that have been sold are sometimes published.

3. Real Estate Professionals:

   • Information: Sales data, improvement costs, income and expense data.
   • Application: Personal contact with developers, builders, brokers, property managers, and other real estate professionals provides qualitative and quantitative insights that cannot be obtained solely from published sources. Communication skills are essential to get this data.

4. Commercial Real Estate Databases

   • Examples: CBRE, Colliers International, CoStar, Cushman & Wakefield, Jones Lang LaSalle (JLL), LoopNet, Marcus & Millichap Real Estate Investment Services, NAI Global, RealtyRates.com, Reis, Inc. (Real Estate Solutions by Moody’s Analytics), Site to Do Business, Transwestern
   • Information: Properties for sale, properties for lease, verified comparable sales transactions, and tenant information
   • Application: Commercial real estate performance information and analysis at the metro (city), submarket (neighborhood), and property level. Includes some sales data, rental data, new construction data, real estate marketinformation, and other data.

5. Property Inspections and Surveys:

   • Information: Detailed property conditions, building materials, and site characteristics.
   • Application: Inspections reveal physical characteristics that influence value, such as structural integrity, functional obsolescence, and potential repairs.

Practical Application: Comparative Market Analysis (CMA)

A common application of both macro and micro data is the Comparative Market Analysis (CMA).

1. Data Collection: Gather micro-level data on comparable properties (recent sales, listings). Also, collect macro-level data on market trends (interest rates, economic growth).

2. Adjustment Process: Adjust the sale prices of comparable properties based on differences in property characteristics (size, location, amenities).

3. Value Estimate: Use the adjusted prices to estimate the value of the subject property.

   • Equation: Adjusted Sale Price = Sale Price ± Adjustments (for differences in features, condition, location).

Experiment: A real estate appraiser can conduct a CMA using different sets of comparable properties and assess how the choice of comparables influences the estimated property value. This experiment helps to understand the sensitivity of the appraisal process to data quality and selection.

Ethical Considerations

It is essential to acknowledge any disclaimers related to the accuracy of data and to accept responsibility for errors resulting from the use of the data. The Uniform Standards of Professional Appraisal Practice (USPAP) and federal legislation such as the Gramm-Leach-Bliley Act of 1999 set forth privacy requirements regarding confidential information.

Future Trends

Advances in artificial intelligence (AI), blockchain databases, and automated valuation models (AVMs) have the potential to transform real estate appraisal. Blockchain databases allow for data sharing in a decentralized “peer-to-peer” network. Advances in AI and AVMs will increase the amount of available data, the speed at which the data can be accessed, and the ways that the data can be sorted and narrowed down by specifications.

Conclusion

Real estate appraisal requires a comprehensive understanding of both macro-level and micro-level data sources. Appraisers must utilize a variety of data sources, apply relevant scientific theories and principles, and perform thorough data analysis to arrive at credible property value estimates. Continuous advancements in technology and data availability are expected to further enhance the appraisal process in the future.