TL;DR: I’m conducting an independent data audit of my county’s (Harris County in Houston) CAD property tax appraisal algorithm. What started as a personal project to protest my home's wildly elevated appraisal has turned into what looks like hard-coded algorithmic bias designed to maximize tax revenue. I initially was just looking microscopically and the data seemed “BAD” like wrong bad. As I got into wider and wider fields of view, I recognized it was a pattern. Looking for someone to confirm this finding, which if true seems to be in the millions of dollars across the county. But also I'm new to this forensic accounting of property tax data, so if I'm wrong there's that too.
I won, but I'm still fighting. I ended up getting my value down from about 500k to 350k at the ARB hearing. This was primarily due to field notes, codes, and ratings that did not accurately represent my home (though that didn’t stop the district from trying to defend these wild numbers with + 5 year-old listing photos).
The discrepancy I noticed was that boundary homes (homes on larger arterial streets) with listing pictures still online within my Neighborhood code 7476 were graded lower on average than homes on interior streets that looked remarkably identical. What I expected to see was that the total values of these properties would be slightly lower due to a land value discount because of traffic. What was unexpected was that homes on boundary streets were consistently graded as "Average" condition, where on an interior street that exact same home would be rated "Good."
Because of this, I downloaded the backend data (https://hcad.org/pdata/pdata-property-downloads.html) and ran a report for my 7476 Neighborhood. I parsed it in Google Colab using prompts I wrote with Gemini.
The relevant columns are:
- Accrued_depr_pct - The hidden decimal for the Average, Good, Very Good, Excellent CDU category. This adds a multiplier to the improvement value.
- date_erected - Year the home was built.
- Yr_remodel - Year the home was renovated. (Note: Most left with a '0' show obvious evidence of remodels, but I removed those from contention because the data is so bad you can't rely on the 0. These homes were built from 1955-1963. The likelihood of zero improvements is extremely low).
- Neighborhood_code - The grouping HCAD uses to define comparables.
- Str - The street, which I used to determine boundary vs interior.
- land_ar & land_val - Used to calculate Land Price per SqFt.
The District's argument is usually that homes on boundary streets are less desirable and less marketable, so they are not updated as frequently, the updates aren't as nice, etc. What I found in my 725-home neighborhood was almost exactly the opposite.
Property records confirm that the 114 boundary parcels fronting major arterial thoroughfares (W. Bellfort, Hillcroft, Willowbend) are actually physically superior to the 611 interior parcels:
- Boundary homes are chronologically newer (1961.5 vs. 1960.1 mean build year)
- Remodeled at a higher rate (65.8% vs. 56.8%)
- Renovated more recently (2013.1 vs. 2011.8 average)
The district does officially recognize the commercial traffic penalty along these corridors by applying a negative Land Influence discount (making it ~$16.59 vs. $23.25 median land PSF).
But here is the smoking gun: Instead of confining this discount strictly to the land schedule, the district systematically suppresses the physical improvement ratings of these boundary homes down to a median condition of 46.40% Good (versus 51.75% interior). This docks the remodeled boundary homes by an unearned 5.35% physical condition gap. By stripping condition points from newer, renovated structures just because they sit on a busy road, the district launders external traffic noise directly into the building's structural value.
The Motive The functional objective of this mechanism is to weaponize the automated sales grids against interior property owners (which make up the vast majority). Recognizing boundary sales as physically comparable would pull down the baseline appraised value per square foot across the entire neighborhood.
To prevent a lower baseline, HCAD artificially forces boundary improvements into suppressed condition tiers. When their automated CAMA system compares these arterial sales against an interior property, it double-counts the identical traffic obsolescence across both the land and building components.
I believe this financial engineering violates the Equal and Uniform mandate. By my estimation, this quirk protects over $130k in tax revenue in my 7476 neighborhood alone. With the over 8,500 residential neighborhood codes and 1M residential properties HCAD assesses annually, I believe they could be pocketing well over $100M with this scheme.
My Ask I would love for some data engineers to look at this completely blindly to make sure I haven't fallen victim to confirmation bias.
I intentionally don't want to share my Colab script or my cleaned files because I want someone to run this from scratch and see if they hit the exact same numbers. The raw data is public at the link above.
I would love for someone to:
- Download the raw files, write your own parsing logic, and see if you hit the exact same condition gap I did for neighborhood 7476.
- Check my methodology for flagging boundary streets versus interior streets.
- Test this against other neighborhoods across the county (especially outside of SW Houston) to see if this algorithm bias is county-wide.
Is anyone willing to pull the data and tell me if I'm crazy?
