Property characteristics data for CAMA - collection and maintenance.

###Introduction to CAMA

Property values are affected by a number of qualitative and quantitative factors. Land size, topography, and improvement data such as square foot of living area, year built, quality of construction, structure’s condition and several other objective and subjective features contribute to it.

But while each property has unique characteristics, individual analyses and appraisals of properties are not practical for ad valorem tax purposes.

Local appraisal districts needed a property value system that offered uniformity, accuracy, effectiveness, cost and time efficiency, but also mass use capabilities, which led to the development of mass appraisals.

Mass appraisal values a group of properties as of a given date by using common data, standardized methods, and statistical testing. A parcel's value is determined by relying upon valuation equations, tables, and schedules developed through mathematical analysis of market data. Sufficient property characteristics data should be collected and maintained by the assessor for classification, valuation, and for other purposes. An accurate appraisal of real property requires descriptions of the land and building characteristics.

Computer Assisted Mass Appraisal systems (CAMAs) or automated valuation models are automated systems used for information management related to properties, valuations, owners’ notifications and the security of taxation credibility through uniform valuation procedures. CAMAs are used to streamline the usage of limited resources and complete the valuation process in time.

###Data for CAMA

CAMA systems use valuation models driven by correct, complete and up-to-date property data.
Determining what data on property characteristics to collect and maintain for a CAMA system is a crucial decision with long-term consequences.

Property characteristics data should be based on the following:

• Factors that influence the market in the locale.
• Requirements of the employed valuation methods.
• Requirements of classification and property tax policy.
• Requirements of other governmental and private users.
• Marginal benefits and costs of collecting and maintaining each property characteristic.

The following characteristics are considered to predict residential property values:

1. Improvement Data:

• Living area.
• Construction quality or key components thereof. (foundation, exterior wall type, and the like)
• Effective age or condition.
• Building design or style.
• Secondary areas including basements, garages, covered porches, and balconies.
• Building features such as bathrooms and central air-conditioning.
• Significant detached structures including guest houses, boathouses, and barns.

1. Land Data:

• Lot size.
• Available utilities. (sewer, water, electricity)

1. Location Data:

• Market area.
• Submarket area or neighborhood.
• Site amenities. (view and golf course or water frontage)
• External nuisances, (heavy traffic, airport noise, or proximity to commercial uses)

Data collection

Collecting property characteristics data is an essential and expensive part of reappraisal. The process must be complete and accurate. A successful data collection program requires clear and standard coding and careful monitoring through a quality control program.

Here are the stages involved:

1. Initial data collection

• Physical inspection to obtain initial property characteristics data.
• Performed either by appraisers or by specially trained data collectors.
• Key subjective decisions, such as the assignment of construction quality class or grade made by appraisers.
• After a minimum comprehensive exterior inspection, an internal inspection may be necessary depending on the data requirement.

1. Data Collection Format

• Data should be collected in a prescribed format that facilitates both data collection on-site and data entry into the computer system.
• Logical grouping and coding of data as objectively as possible, with measurements, counts, and check-off items used in preference to items requiring subjective evaluations makes it easier.
• Clarity, consistency, ease of use and adaptability to virtually all types of construction are characteristics of a good data collection format.
• Specialized data collection formats may be necessary for agricultural property, timberland, commercial and industrial parcels, and other property types.

1. Data Collection Manuals

Clear, thorough and updated data collection manual maintenance is necessary to explain collection and recording of data items. It should include guidelines for personal conduct during field inspections and dealing with risky situations. The manual must also layout the guidelines for situations when property owners deny access to conduct interior inspections.

1. Data accuracy standard

• Continuous or area measurement data should be accurate within 1 foot (rounded to the nearest foot) of the true dimensions or within 5% of the area.
• For each objective, categorical, or binary data field to be collected or verified, at least 95% of the coded entries should be accurate. (exterior wall material, number of full bathrooms, waterfront view etc)
• For each subjective categorical data field to be collected or verified, data should be coded correctly at least 90% of the time. (quality grade, physical condition, architectural style)

1. Data collection quality control

Quality control programs ensure data accuracy.
• Independent quality control inspections by jurisdiction staff, project consultants, auditing firms, or oversight agencies.
• Inspections must review random samples for completeness and accuracy, and keep tabulations of items coded correctly or incorrectly.
• Stratification by geographic area, property type, or individual data collector can help detect patterns of data error.
• The accuracy of subjective data should be judged primarily by conformity with written specifications and examples in the data collection manual.

Data that fail to meet quality control standards should be re-collected.

Data entry

Data entry should be routinely audited to ensure accuracy. A data form serving as the data entry form reduces effort.

Data should be supported by a full set of range and consistency edits. Invalid or unusual data items must be marked with warning messages (ex: missing data codes and invalid characters, data values exceed normal ranges). Warnings should be addressed during data entry.

Maintaining data

• Property characteristics data should be continually updated in response to changes

brought about by new construction, new parcels, remodeling, demolition, and destruction.

• Tracking building permits is the most efficient way to update this data.
• Aerial photography also helps record construction and land use.
• Self-reporting is where property owners review the assessor’s records and submit

additions or corrections.

• Third-party vendors, hearings, data mailers, newspapers, other publications or other

such secondary sources can also validate property records.

In the interest of maintaining accurate data, a physical review including an on-site verification of property characteristics should be conducted at least every 4 to 6 year.

Learn more about other pest of data collected and how it is used in Computer Generated Mass Appraisal of real property.

If you are interested to know all about valuation models, approaches and usage of data for the same, you can learn more here.

What type of property do you own? Know all about the valuation approach used to mass appraise your property here.