Property Revaluation Update: 202 out of 206 Homes on Neptune Ave in Greenville #JerseyCity were Over-Taxed.

I've got another Revaluation update, this one focused on Neptune Avenue which is an East/West street that stretches pretty much across Jersey City's south Greenville neighborhood.  Similar to other Greenville Revaluation-related updates, this is a story of systemic overtaxation that is being fixed with Revaluation.

First, some high level details about the data.

  • Appraisal Systems' most recent assessment file is from March 23, 2018 and there are 206 properties on Neptune Ave. in this dataset.
  • Of the 206 properties:
    • Total net change in tax expense: - $511,773.
    • 202 properties will see property tax expense DECREASES
    • 4 properties will see property tax expense INCREASE
  • Of the 202 properties that will see their property taxes go DOWN:
    • Highest tax expense decrease is $8,483 on a home assessed (in 2018) at $289,100.  This homeowner was SEVERELY overtaxed.
    • The average tax change is -$2,484. The median tax change is -$2,202.

So to recap. One street in Jersey City's Greenville neighborhood will see over $500,000 in tax expense REDUCTION. Or, in other words, Neptune Ave residents have been over-paying by over $500,000 per year in property tax expense. It would be interesting and informing to total the over-payments since 2013, when the last Revaluation was halted. This is doable. We'd need to estimate market values for these homes each year, and then compare to assessed values, but we could do this. More to come on this.

I mapped this data in Google Maps and sharing below. RED pin drops will see tax expense DECREASES.  Green pin drops will see tax expense INCREASES.

How I made this map:

  1. I downloaded Appraisal Systems March 23rd file which is available, as of today, here.
  2. I did a wildcard (*) search in MS Excel for Neptune Avenue.
  3. I  copied those records into a new sheet, and did a quick tax expense change calculation (2018 Estimated Tax Expense - 2017 Tax Expense).
  4. I added a column to identify tax expense change as "Increase" or "Decrease" based on positive or negative change in tax expense.
  5. I added columns for City and State.
  6. I uploaded the dataset to Google maps and wrote up this post.
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