What Makes Real Estate Data Accurate?

In general, real estate data is accurate when it meets four key criteria:

Currency refers to the recency with which data was collected. Accurate data is current data; the fresher real estate data is, the more accurately it reflects reality.

Precision refers to exactness. For example, measuring to the nearest inch is an example of precision. Identifying the exact material that composes an exterior wall is an example of precision.

Consistency means the data collected is uniform in the way it is collected. Said another way, the same standards apply to every data point in the data set. For example, for measurement data the IAAO recommends that data collectors round their measurements to the nearest foot so that every parcel is measured consistently.

Completeness involves capturing all necessary valuation details to ensure that data collectors obtain every piece of data required by the assessor's valuation model.

In data collection, accurate data is the goal. You're probably saying to yourself, "Well thank you, Captain Obvious. As an assessor, I know accurate data is the goal and is essential." And yet, how often have you reviewed data from the field and realized that it was poor? Perhaps there are too many fireplaces. Perhaps the data collector inaccurately labeled the sketch. Perhaps it's obvious the data collector didn't actually visit any of the properties. Whatever the instance, minor or major, all of us in the assessing business have come face-to-face with inaccurate property data and suffered the consequences in the form of crazy property valuations (that we hopefully caught before tax bills were printed) or something as simple as a property owner calling to dispute their property record and the need to correct the record is obvious.

That's why, to ensure that all data collected is accurate, a simple, clearly written tool is helpful: published standards in the form of a data collection handbook.

Standards - A Necessary Evil

Standards are the rules or principles that help codify what makes a particular kind of data accurate. As the IAAO discusses in “3.1 Methods for Data Quality Control and Assurance” of the Standard on Data Quality, assessors need specifications and definitions of each data element as part of their standard. They also need acceptable methods of uniform collection, regular, ongoing data reviews, and procedural reviews to ensure accuracy.

It’s not always easy to develop the standards necessary for accurate data, as data types can vary. In general, assessors must establish standards for three broad types of data: construction data, space usage data, and evaluative data. Each category plays a pivotal role in property valuations.

Defining Construction Data

Construction data includes information like the building materials used in construction, mechanical systems used to heat and cool a building, story height, and physical building envelope measurements.

Clear definitions and examples are essential when forming construction data standards. Data collectors must be able to accurately identify systems and materials. For example, while central and heat-pump air conditioning systems use vents, their condensers and heat exchangers differ in location and shape. Data collectors must be able to recognize these differences and they can't do that if definitions are unclear and there are no examples. 

For story heights, it's essential to specify what constitutes different variations of a story, from a full story to a ¾  story and everything in between.

When defining measurements, data collectors should know how precise the measurements must be, how to round them, and which tools to use. When direct measurement isn’t possible, data collectors must know how to estimate measurements.

Gathering Information on Layout and Living Space

Space usage data captures the property's layout and encompasses how much living space a property has and how the owners use it.

The number of rooms, bedrooms, bathrooms, kitchens, and the finished state of a basement are examples of data that tell data collectors how the owners use the space. Data collectors need definitions of each kind of room to identify them accurately. 

Sketches show the type of finished space and reinforce the story height. Definitions and examples of each kind of space are essential for the correct identification and labeling of the space on the sketch.

Determining Evaluative Characteristics

Evaluative characteristics are qualitative and consist of architectural style, building grade, building condition, kitchen grade, or bathroom grade. Because these characteristics are qualitative, clear definitions and examples are key to understanding the assessor's criteria. Clear definitions provide a solid foundation for determining these characteristics and create common ground in discussions surrounding them.

Building grade, for example, captures a plethora of information, including the quality of a building’s materials, construction, workmanship, and architectural complexity. To determine a building grade, data collectors must decide the quality of the building material, what constitutes quality workmanship, and how the assessor factors architectural design into the grade.

Consistency across multiple parcels can’t happen without clear definitions and examples. A data collector needs carefully laid out evaluation criteria with examples to evaluate a parcel’s building grade completely.

The Value of Clear Standards

Clear standards provide a practical framework for observing, evaluating, and verifying property data. They ensure that each data point—from objective data points like building measurements and property layouts to subjective assessments of a building's condition or quality—is precise, consistently applied, and completely accounted for.

Standards evolve, along with expectations for precision, consistency, and completeness. Accurate property data collection is not simply an administrative task; it is the cornerstone of a reliable ad valorem system. When property data meets this definition, it is indeed trustworthy.