IAAO Course 300 Exam 2023 with complete solution
Mass appraisal
Systematic appraisal of groups of properties as of a given date using standardized
procedures and statistical testing.
Purpose of mass appraisal
Equitable and efficient appraisal of all property in a jurisdiction for ad valorem tax
purposes.
When did first generation computers emerge?
The 1950s
Mass appraisal models may be:
Physical, conceptual or mathematical.
Property valuation models seek to explain or predict what?
The market value of properties from real estate data.
Supply factors:
Building costs, interest rates and economic conditions.
Demand factors:
Location, condition and construction quality
Cost approach models require estimates of:
Land values, is if vacant; reproduction or replacement cost new of all improvements and
all accrued depreciation.
Sales comparison approach models require:
A verified sales database and contributory value components.
Income approach models require:
Net operating incomes and capitalization rates for similar use properties.
Basic steps in model building:
Specification and calibration.
Model specification
Defining the form of the model.
Model calibration
Determining the rates used in the model.
Model structures:
Additive, multiplicative or hybrid (generic).
Components of a mass appraisal system:
Data management system, sales analysis system, valuation system and administrative
system.
Data management system:
Collection, entry and editing, conversion, storage and security.
Sales analysis system:
Data collection, screening and processing, sales ratio studies and sales reports.
Valuation system:
Sales comparison approach, cost approach, income approach, review and
reconciliation.
Administrative system:
Budgeting, scheduling, planning, inquiry, analysis, reporting, assessment roll / notices,
forms, wordprocessing and record cards.
, Requirements of a mass appraisal system:
Three primary files: (Parcel, property characteristics and sales file) and methods of
updating files: (Batch data entry and online data entry)
Batch data entry:
Hold file
Online data entry:
Live file
Steps in reappraisal:
Performance analysis (ratio study) reappraisal decision, analysis of available resources,
planning and organizing, system development procedures, data collection, system
implementation, valuation, preparation of assessment roll and final performance
analysis (ratio study).
Data accuracy is determined by:
The quality and quantity of the data.
Quality
Relates to how accurately recorded information, used to estimate values, reflects the
current status of the property.
Quantity
Relates to the completeness of the available data or what must be collected to capture
local market influences.
Methods of evaluating data adequacy:
Field study, ratio study and pilot study.
Qualitative data:
Subjective
Quantitative data:
Objective
Continuous data:
Quantitative (objective)
Discrete or categorical data:
Qualitative (subjective)
Binary data:
Qualitative (subjective); yes or no
Objective data:
Quantitative
Subjective data
Qualitative
Types of data edits:
Range edits and consistency (logic) edits
Range data edits:
Hard edits, data does not fit
Consistency (logic) data edits:
Soft edits, count is off
Array:
A listing of data in order of magnitude (either ascending or descending)
Frequency distribution:
Mass appraisal
Systematic appraisal of groups of properties as of a given date using standardized
procedures and statistical testing.
Purpose of mass appraisal
Equitable and efficient appraisal of all property in a jurisdiction for ad valorem tax
purposes.
When did first generation computers emerge?
The 1950s
Mass appraisal models may be:
Physical, conceptual or mathematical.
Property valuation models seek to explain or predict what?
The market value of properties from real estate data.
Supply factors:
Building costs, interest rates and economic conditions.
Demand factors:
Location, condition and construction quality
Cost approach models require estimates of:
Land values, is if vacant; reproduction or replacement cost new of all improvements and
all accrued depreciation.
Sales comparison approach models require:
A verified sales database and contributory value components.
Income approach models require:
Net operating incomes and capitalization rates for similar use properties.
Basic steps in model building:
Specification and calibration.
Model specification
Defining the form of the model.
Model calibration
Determining the rates used in the model.
Model structures:
Additive, multiplicative or hybrid (generic).
Components of a mass appraisal system:
Data management system, sales analysis system, valuation system and administrative
system.
Data management system:
Collection, entry and editing, conversion, storage and security.
Sales analysis system:
Data collection, screening and processing, sales ratio studies and sales reports.
Valuation system:
Sales comparison approach, cost approach, income approach, review and
reconciliation.
Administrative system:
Budgeting, scheduling, planning, inquiry, analysis, reporting, assessment roll / notices,
forms, wordprocessing and record cards.
, Requirements of a mass appraisal system:
Three primary files: (Parcel, property characteristics and sales file) and methods of
updating files: (Batch data entry and online data entry)
Batch data entry:
Hold file
Online data entry:
Live file
Steps in reappraisal:
Performance analysis (ratio study) reappraisal decision, analysis of available resources,
planning and organizing, system development procedures, data collection, system
implementation, valuation, preparation of assessment roll and final performance
analysis (ratio study).
Data accuracy is determined by:
The quality and quantity of the data.
Quality
Relates to how accurately recorded information, used to estimate values, reflects the
current status of the property.
Quantity
Relates to the completeness of the available data or what must be collected to capture
local market influences.
Methods of evaluating data adequacy:
Field study, ratio study and pilot study.
Qualitative data:
Subjective
Quantitative data:
Objective
Continuous data:
Quantitative (objective)
Discrete or categorical data:
Qualitative (subjective)
Binary data:
Qualitative (subjective); yes or no
Objective data:
Quantitative
Subjective data
Qualitative
Types of data edits:
Range edits and consistency (logic) edits
Range data edits:
Hard edits, data does not fit
Consistency (logic) data edits:
Soft edits, count is off
Array:
A listing of data in order of magnitude (either ascending or descending)
Frequency distribution:
