How Do Counties Respond to Revenue-Neutral Tax Rates?
The revenue-neutral tax rate (RNTR) exists to allow taxpayers to determine if their county has increased or decreased its total property tax levy following a county-wide reappraisal of real property. The RNTR is the property tax rate that would generate the same tax levy on the county’s new, post-reappraisal tax base next year as the county received this year on its existing tax base. In other words, the RNTR is the rate that would keep the county-wide tax bill the same next year as it was this year, despite the change in the tax base.
[Note that the RNTR requirement applies to municipalities as well as counties. I’ll refer to counties in this blog post for the sake of simplicity.]If real estate prices are generally rising in a county, a reappraisal will increase the tax base. The county’s RNTR will be less than its existing tax rate, because the county could decrease its tax rate but still produce the same tax levy given the larger tax base next year.
If real estate prices are declining in a county, a reappraisal will decrease the tax base. The county’s RNTR will be greater than its existing tax rate, because the county would need to increase its tax rate to produce the same tax levy given the smaller tax base next year.
While a county must calculate and publish the RNTR, it is not required to adopt the RNTR as the actual tax rate for the coming fiscal year. If the county chooses not to adopt the RNTR for the coming year, taxpayers can compare the adopted rate to the RNTR to determine if the county has increased or decreased its total tax levy for the coming year.
If the county adopts a new property tax rate that is greater than the RNTR, then the county has increased its total tax levy for the coming fiscal year–even if the adopted rate is less than this year’s tax rate. Conversely, if the county adopts a new tax rate that is less than the RNTR, then the county has decreased its total tax levy–even if the adopted rate is greater than this year’s tax rate.
I’ve written about the basic RNTR requirement and calculation here, offered guidance for municipalities that lie in multiple counties here, and gotten really into the computational weeds here.
But I have never had the data needed to answer the most important practical questions about RNTRs. How do local governments respond to their RNTRs? How often do they adopt their RNTRs and keep tax levies neutral? How often do they adopt rates above or below their RNTRs and thereby increase or decrease their tax levies?
To answer these questions, I collected RNTRs and adopted tax rates from all 100 North Carolina counties relating to their two most recent reappraisals. If a county could not produce the requested data, I estimated its missing RNTR based on county tax base and tax rate information available from the North Carolina State Treasurer’s Office. The result was a data set covering 200 reappraisals between the years 2008 and 2023. I then analyzed that data by county-specific details such as population, tax rate, and reappraisal year.
Here are the key conclusions from my analysis:
- Most counties increased their tax levies after reappraisals.
- 67% of reappraisals resulted in adopted tax rates greater than RNTRs (134 adopted rates > RNTRs out of 200 reappraisals).
- Adopted tax rates were on average 4.4% greater than RNTRs.
- 16% of reappraisals resulted in adopted tax rates equal to RNTRs (33 of 200).
- 16% of reappraisals resulted in adopted tax rates less than RNTRs (33 of 200).
- When property values rose, counties almost always increased their tax levies.
- 92% of reappraisals which increased tax bases resulted in adopted taxed rates greater than RNTRs (130 adopted tax rates > RNTRs out of 141 reappraisals in which tax bases increased)
- The adopted tax rates were on average nearly 6% greater than RNTRs following those 141 reappraisals.
- Larger increases in tax bases from reappraisals were directly related to larger increases in tax levies for the next fiscal year. This point is illustrated by the chart below, which plots the percentage difference between adopted tax rates and RNTRs (y-axis) versus percentage changes in the county’s tax base following a reappraisal (x-axis). As the trend line demonstrates, larger tax base increases (further to the right on x-axis) generally produced larger increases in adopted tax rates relative to RNTRs (higher on the y-axis).
- When property values dropped, counties were less likely to increase their tax levies.
- Only 57% of reappraisals that reduced tax bases resulted in adopted tax rates greater than RNTRs (22 adopted tax rates > RNTRs in 38 reappraisals that reduced tax bases).
- Adopted tax rates were on average .24% less than RNTRs following those 38 reappraisals.
- Reduced tax bases occurred most frequently after reappraisals conducted from 2012 to 2016, following the “Great Recession” of 2008-2010.
- Large and small counties made similar tax rate decisions following reappraisals.
- 71% of reappraisals for small counties (populations under 25,000) resulted in adopted tax rates greater than RNTRs.
- 69% of reappraisals for large counties (populations over 100,000) resulted in adopted tax rates greater than RNTRs.
- Counties with low property tax rates and those with high property tax rates made similar tax rate decisions following reappraisals.
- 84% of reappraisals in counties with the lowest property tax rates (the 25 counties with rates equal to or below $.0565) resulted in adopted tax rates greater than RNTRs.
- 78% of reappraisals in counties with the highest property tax rates (the 25 counties with rates equal to or above $0.77) resulted in adopted tax rates greater than RNTRs.
One way to interpret these findings: most counties use increased tax bases following reappraisals to “hide” tax increases. They adopt a new tax rate that is greater than their RNTR (which increases the overall county tax bill) but less than their current rate (which looks like a tax decrease to casual observers).
I will soon publish a bulletin that dives more deeply in my RNTR study. For now, you can check out my data here. Many thanks to Emily Roscoe for her research assistance with this study.
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