There are countless professionals behind the scenes working hard to ground-truth home values and ensure that property values are fair, equitable, and uniform.  These are the property assessors, tax assessors, property appraisers, and auditors working for your county, city, town, or even state.  Tax assessors’ offices are tasked with, well assessing property.  These assessment professionals work to put a fair market value on real estate. For many people it is their single largest asset.

Every local government interprets state statues and has developed their own process and way of calculating assessments. But at the end of the day they’re ultimately all trying to answer the same difficult question: How do we fairly assess property within the statutes, standards, and industry practices? These property assessments determine the tax a property owner will pay, and it is used to finance education, infrastructure, public safety, and other services that benefit the community and make our life nice.

Even though the thought process on how to properly value property might differ from community to community, there are several key common analyses: the sales ratio study, outlier analysis, tax roll closure, neighborhood realignment, and others.  The list goes on, but we’ll focus on just a few for this article.

Incorporating the Spatial Component

Often assessors and appraisers work with spreadsheet upon spreadsheet of data, comparing property characteristics to guide them neighborhood by neighborhood. It is a long and arduous process with countless hours spent pouring over the data before assessments can even be made.  Even then, a site visit is often required to make sure a property in question is aligned well with the data. If property characteristic data is inaccurate or incomplete, inaccurate assessments result. Similarly, a lack of a location data component during the analysis can make an already complex process take more time, effort, and resources.

ArcGIS Insights is an analytics tool that allows you to explore your property valuation data with the location considered up front during the analysis.  Insights can help you work through these questions. Think about how valuation with a spatial component can help improve and speed the process, and understand how a property fits into a particular neighborhood. More importantly, it improves the quality of property valuations and provides property appraisers access to current property characteristics and neighborhood trends, ensuring that property is valued fairly and equitably while helping reduce appeals.

So how do tax assessors determine your home’s value?  And how do they do it for the thousands of other homes in your town?  After working with a few of them, here are some take-aways that we’ve discovered.

In the following sections let’s explore some tools for making sense of the spreadsheet upon spreadsheet of data used to assign property valuations. Let’s look at this data in new ways.

Sales Ratios

Assessors work to value property at the most likely selling price, known as the assessed value. When this assessed value is compared to the actual sale price, it’s a strong indicator of the assessor’s estimation quality as well as market trends.

A Sales Ratio study formally compares assessed values to sales values. Specifically it analyzes the relationship of assessed value to market value expressed as a percentage. The goal is to investigate how sales ratios range across an assessment neighborhood as a quality and compliance check. Year-over-year measurements are compared for overall accuracy, uniformity, and fairness. The ratio is calculated by dividing the assessed value by the sale price (or market value) of a property.

Sales Ratio = (Assessed value/Market value)* 100

Once the ratio has been calculated, it can be easily grouped with similar values (quartiles) to perform what is known as an interquartile analysis. The interquartile analysis allows you to identify which sales fall within the desirable range (as outlined by state statute) and which fall outside the desirable range. The goal is to do this quickly and accurately, focus on potentially problematic property sales, and identify data quality issues.

In the video example below, sales are grouped into assessment neighborhoods (more on that below) and sized by their sales ratio.

First, you choose the assessment neighborhood(s) you would like to investigate.

Second, you choose the sales ratio quartile(s) that you are interested in. In our example the percentages are broken into three categories, the two inner quartiles are lumped into one (65-130).

Next the histogram shows a frequency of all sales, and where the sales ratios fall. You can use the map to explore specific areas, and to understand if sales and neighborhoods are near each other.

Neighborhoods & Outliers

Neighborhoods are core to what makes a community and provide a personal sense of belonging. Assessment neighborhoods are similar, but are used to group like properties together. Assessment neighborhoods are made up of like-valued properties, and there is logic and criteria (and of course spatial analysis) that goes into how they are determined.

Part of this process uses outlier analysis, or simply finding those that are different than others in the neighborhood. To do this, property characteristics must be understood – how they compare, and which are edge cases.  For those edge cases properties, next validate that they are assessed accurately, given their uniqueness.

For example, because a property has a finished basement and a pool, it could have a higher value than its neighbors, and therefore is an outlier compared to the other similar properties in the assessment neighborhood. However, this property would be a valid outlier – there’s a good reason it’s assessed with a higher value compared to its neighbors. There are many “adjustments” that are used for property amenities that help keep neighborhoods intact.

Several visualization and analysis techniques can assist appraisers in determining these outliers. Let’s return to the example used above during the sales ratio study.  Because sales ratios are grouped based on their quartile, you can then “flip the switch” to visualize the sales that fall outside the inner quartiles. In the video example below, this would mean viewing properties with a sales ratio that is less than 65 or greater than 130.

Once these filters are set, scroll down and view descriptive statistics like average property value and median sales price. You can also use the box plots to investigate and identify other outliers such as year built or living area. Clicking on an outlier whisker in the box plot will further filter the data in the above cards.

Below the box plot is a summary able that allows you to view property characteristics details in a tabular format. Viewers can focus the selection based on the interactivity with the charts and filters set above.

This is just one example. Note that these “cards” can be configured many ways to show many types of graphs, charts, maps, and visualizations to help you see what the data is telling you.

Get Started with Your Data

Light up your data and get it on the map. Improve your analysis using location, visualization, and new analysis. It’s not difficult to do this today with your own data, and even configure something like this with your own variables, statistics, and values. In this next section you’ll see how to do this yourself. Follow along in the video, and for additional context use the help links embedded within these steps below.

1. 1. First let’s start with two datasets – one of parcel polygons with CAMA data, and a separate table of sales. Both have a common parcel ID. Bring these together with a join, using Insights’ create relationships.
   2. Next create a new field (sales ratio) and calculate it with a simple expression.
   3. I bet you can’t wait to get your sales ratios onto a map, right? Let’s put these on the map card and symbolize the data by quartiles.
   4. A histogram allows you to see the distribution of ratios that you have selected or filtered.
   5. After that, use the predefined filter to create two ways to filter the report – one by neighborhood, and another by sales ratio value.
   6. With a drag-and-drop of numeric fields, summary statistics are created, and they update with the selections and filters you make.
   7. In the same fashion, box plots help to quickly identify outliers.
   8. Lastly, a summary table is available for getting into the granular details.
   9. Optionally share your interactive report with others.

What’s Next?

There are vast resources, including training and detailed information to help you get started. Below are some links you will find helpful. We would like to hear from you. Please let us know how it’s going, either leave a reply in the comments below, or post a question to the ArcGIS Insights Community.

• 21-day trial
• Discovery path: Try Insights
• Discovery path: Perform Analysis with Insights
• Instructor-led training

There’s more to come. Stay tuned for a new Insights Assessor Learn Lesson, and the next release of Insights is coming in late March.