First audit of Oakland’s vacant property tax gives pretty good marks 7%

By Natalie Orenstein12%

7/15/2026, 11:31:34 PM

BS Summary: This article contains 19 faulty reasoning types, including Negativity Bias, Biased Writer Voice, and Availability Heuristic, with Appeal to Authority as the most egregious example at 25.6% saturation with 249 hits. Analysis detected 1,456 faulty-reasoning hits from 972 analyzed words, generating a BS Score of 22.6% and a BS Rank of 7% (20,538 of 21,887 articles). This article is better (less manipulative) than 93.80% of the article peer group.

Oakland’s vacancy tax is generally effective and is bringing in millions every year, a new audit found, but it might not be living up to its goal of addressing the housing crisis head-on. 
In 2018, voters approved Oakland’s first vacant property tax. 
Owners of undeveloped lots and empty buildings have to pay a flat rate of $3,000 to $6,000 per year, with some exemptions for low-income owners, nonprofits, and others. 
The city hoped the tax would push owners to make use of the properties sitting vacant in Oakland while a housing shortage raged around them and blight scarred the city. 
Last week, Auditor Michael Houston published his office’s first report on the tax, which has been in effect for six years. 
The auditor found that Oakland is generally complying with the requirements of the tax and getting better at applying it. 
The city contracts with the company SCI to administer the tax, which involves identifying vacant properties, communicating with taxpayers, and determining exemptions. 
Last year, the city taxed 1,966 properties and made far fewer mistakes  falsely identifying properties as vacant  than it did in the past. 
The tax generates about $6 million per year, a number that’s increasing as more properties are correctly identified. 
On the ballot, Measure W told voters the tax would “fund homeless services and resources to address illegal dumping, and discourage vacant properties.” 
At least 25% of the funds had to go toward illegal dumping, and no more than 15% could be spent on administrative costs. 
Technically, the city seems to be following the law, Houston found. 
But its priorities might be a bit skewed. 
Instead of 25%, Oakland has spent 66% of the revenue from the vacant property tax on combatting illegal dumping. 
“The City appears to underutilize funds to support services for people at risk of experiencing homelessness, which is described as one of the primary purposes of the tax,” the auditor wrote. 
Part of the challenge in evaluating the tax, according to the auditor, is that Oakland has not thoroughly tracked relevant data. 
Houston has three recommendations for the city: 
* Clearly determine what counts as an “administrative cost”. 
* Regularly assess “vacant property trends and causes in Oakland” along with how the tax is performing against its policy goals. 
* Provide annual reports, as required by state law, to the City Council and the tax oversight commission. 
This has generally not happened so far. 
In a formal response, Finance Director Bradley Johnson said the administration agrees with the findings and is prepared to comply with the recommendations. 
But he challenged the idea that it’s possible to deduce from the available data the reasons why properties are vacant. 
“These decisions rest largely with private property owners, such as whether to build, wait for favorable market conditions, or put the property in use,” he wrote. 
Who’s made to pay the vacancy tax in Oakland? 
The city defines a vacant property as one that’s in use for fewer than 50 days of the year. 
This can include everything from weedy vacant lots to abandoned storefronts and empty homes. 
According to the audit, the vast majority of properties subject to the tax are undeveloped land, not empty buildings. 
The largest concentration of taxed properties is in the hills, especially the Claremont, Forestland, and Piedmont Pines neighborhoods, and in West Oakland’s Prescott area. 
While the vacancy tax is designed to encourage housing construction and use, the Oakland hills, where many of the vacant properties are located, present challenges to building. 
These neighborhoods are at high risk of wildfire and other environmental hazards. 
The city doesn’t discourage housing in these zones but cautions, in its overarching housing plan, that development there demands extra considerations and infrastructure improvements. 
Overall, the vacancy tax applies to about 2% of all properties in Oakland, the audit found. 
But that low percentage is a bit misleading. 
Under the tax, an empty apartment building with 50 units would count as one property rather than 50. 
The vacancy rate for housing units in the city is higher than 2%. 
If a property owner believes they’ve been unfairly taxed, they can appeal it. 
We’ve reported on people and groups who bought vacant Oakland properties with the intention of using the land for housing but got hit with the tax for the prior year of vacancy. 
For example, in 2020, a nonprofit that builds housing for homeless people acquired empty land in East Oakland. 
They qualified for an exemption from the vacancy tax. 
But they still got the $6,000 tax bill for the previous year, covering a period when they didn’t yet own the lot. 
The city at the time explained that the tax is on the land, not on the owner. 
At the end of 2025, the City Council created a new exemption from the tax for new property owners, avoiding this sort of surprise. 
Not many other cities have vacancy taxes like Oakland’s, but those that do tend to generate substantially more revenue, the audit found. 
This is partially because other cities don’t use flat tax rates for all properties, but rather increase the bill based on assessed value or size. 
An empty house worth $2 million would be taxed the regular $6,000 in Oakland, but would be hit with a $60,000 bill in Vancouver and anywhere between $100,000 and $200,000 in Washington, D.C., according to the audit. 
San Francisco also passed a tax on empty apartment buildings a few years ago, using a formula based on the square footage of a property. 
That tax has been tied up in the courts, with landlords arguing it violates their private property rights. 
Some property owners in Berkeley appear to be withholding their vacancy tax payments as they await the verdict in the San Francisco case. 
Article reasoning-pattern comparisonThis article: 0.0%Natalie Orenstein: 1.6%Berkeleyside: 2.1%Confirmation Bias0.0%This article: 5.1%Natalie Orenstein: 1.4%Berkeleyside: 0.9%Anchoring Bias5.1%This article: 11.0%Natalie Orenstein: 2.6%Berkeleyside: 2.3%Availability Heuristic11.0%This article: 0.0%Natalie Orenstein: 1.1%Berkeleyside: 0.8%Representativeness Heuristic0.0%This article: 0.0%Natalie Orenstein: 0.6%Berkeleyside: 0.4%Hindsight Bias0.0%This article: 0.0%Natalie Orenstein: 0.1%Berkeleyside: 0.8%Overconfidence Bias0.0%This article: 7.3%Natalie Orenstein: 3.9%Berkeleyside: 3.2%Framing Effect7.3%This article: 0.0%Natalie Orenstein: 0.2%Berkeleyside: 0.4%Loss Aversion0.0%This article: 0.0%Natalie Orenstein: 0.8%Berkeleyside: 0.5%Status Quo Bias0.0%This article: 0.0%Natalie Orenstein: 0.2%Berkeleyside: 0.2%Sunk Cost Effect0.0%This article: 3.6%Natalie Orenstein: 2.2%Berkeleyside: 3.4%Optimism Bias3.6%This article: 0.0%Natalie Orenstein: 0.2%Berkeleyside: 0.6%Pessimism Bias0.0%This article: 15.9%Natalie Orenstein: 5.9%Berkeleyside: 3.8%Negativity Bias15.9%This article: 2.4%Natalie Orenstein: 2.5%Berkeleyside: 1.8%Self-Serving Bias2.4%This article: 2.7%Natalie Orenstein: 0.9%Berkeleyside: 0.6%Fundamental Attribution Error2.7%This article: 0.0%Natalie Orenstein: 0.2%Berkeleyside: 0.2%Actor-Observer Bias0.0%This article: 0.0%Natalie Orenstein: 0.4%Berkeleyside: 0.9%In-Group Bias0.0%This article: 0.0%Natalie Orenstein: 0.1%Berkeleyside: 0.1%Out-Group Homogeneity Bias0.0%This article: 7.4%Natalie Orenstein: 2.9%Berkeleyside: 3.6%Halo Effect7.4%This article: 0.0%Natalie Orenstein: 0.2%Berkeleyside: 0.0%Horn Effect0.0%This article: 0.0%Natalie Orenstein: 0.0%Berkeleyside: 0.0%Dunning-Kruger Effect0.0%This article: 6.8%Natalie Orenstein: 1.0%Berkeleyside: 0.8%Recency Bias6.8%This article: 2.6%Natalie Orenstein: 0.3%Berkeleyside: 0.3%Primacy Effect2.6%This article: 0.0%Natalie Orenstein: 0.0%Berkeleyside: 0.0%Blind-Spot Bias0.0%This article: 0.0%Natalie Orenstein: 0.1%Berkeleyside: 0.2%Ad Hominem0.0%This article: 0.0%Natalie Orenstein: 0.2%Berkeleyside: 0.2%Straw Man0.0%This article: 25.6%Natalie Orenstein: 4.0%Berkeleyside: 3.2%Appeal to Authority25.6%This article: 0.0%Natalie Orenstein: 0.6%Berkeleyside: 0.7%False Dilemma0.0%This article: 0.0%Natalie Orenstein: 0.1%Berkeleyside: 0.3%Slippery Slope0.0%This article: 0.0%Natalie Orenstein: 0.0%Berkeleyside: 0.1%Circular Reasoning0.0%This article: 4.9%Natalie Orenstein: 1.7%Berkeleyside: 2.3%Hasty Generalization4.9%This article: 0.0%Natalie Orenstein: 0.3%Berkeleyside: 0.1%Red Herring0.0%This article: 0.0%Natalie Orenstein: 0.9%Berkeleyside: 0.5%Bandwagon0.0%This article: 4.9%Natalie Orenstein: 3.1%Berkeleyside: 3.3%Appeal to Emotion4.9%This article: 0.0%Natalie Orenstein: 0.2%Berkeleyside: 0.5%Begging the Question0.0%This article: 9.3%Natalie Orenstein: 1.4%Berkeleyside: 2.2%Post Hoc (False Cause)9.3%This article: 0.0%Natalie Orenstein: 0.2%Berkeleyside: 0.1%Tu Quoque0.0%This article: 0.0%Natalie Orenstein: 0.3%Berkeleyside: 0.4%Burden of Proof0.0%This article: 0.0%Natalie Orenstein: 0.6%Berkeleyside: 0.2%Appeal to Nature0.0%This article: 0.0%Natalie Orenstein: 0.0%Berkeleyside: 0.1%Composition/Division0.0%This article: 7.4%Natalie Orenstein: 1.5%Berkeleyside: 3.0%Anecdotal7.4%This article: 0.0%Natalie Orenstein: 0.1%Berkeleyside: 0.0%No True Scotsman0.0%This article: 9.8%Natalie Orenstein: 2.4%Berkeleyside: 1.3%Ambiguity (Equivocation)9.8%This article: 0.0%Natalie Orenstein: 0.0%Berkeleyside: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Natalie Orenstein: 0.0%Berkeleyside: 0.1%Middle Ground0.0%This article: 0.0%Natalie Orenstein: 0.0%Berkeleyside: 0.1%Personal Incredulity0.0%This article: 0.0%Natalie Orenstein: 1.0%Berkeleyside: 0.2%Special Pleading0.0%This article: 0.0%Natalie Orenstein: 0.0%Berkeleyside: 0.1%Genetic Fallacy0.0%This article: 5.6%Natalie Orenstein: 1.4%Berkeleyside: 1.0%Unattributed Quote5.6%This article: 2.4%Natalie Orenstein: 1.8%Berkeleyside: 0.6%Quote-first Misdirection2.4%This article: 15.1%Natalie Orenstein: 3.2%Berkeleyside: 2.3%Biased Writer Voice15.1%This article: 0.0%Natalie Orenstein: 0.4%Berkeleyside: 0.7%Indoctrination0.0%This article: 0.0%Natalie Orenstein: 0.0%Berkeleyside: 0.1%Politically Left Leaning Bias0.0%This article: 0.0%Natalie Orenstein: 0.0%Berkeleyside: 0.1%Politically Right Leaning Bias0.0%This article: 0.0%Natalie Orenstein: 0.0%Berkeleyside: 2.0%Attempt to Sell a Product or S…0.0%

972 words analyzed.

Speakers

3speakers19%attributed speech789writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 11 words • 100.0% coverageWriter's voice • 33 words • 100.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 28 words • 0.0% coverageWriter's voice • 30 words • 100.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 25 words • 100.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 23 words • 100.0% coverageWriter's voice • 23 words • 0.0% coverageMichael Houston • 11 words • 0.0% coverageWriter's voice • 8 words • 100.0% coverageWriter's voice • 19 words • 0.0% coverageMichael Houston • 31 words • 100.0% coverageWriter's voice • 21 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageMichael Houston • 9 words • 0.0% coverageMichael Houston • 21 words • 0.0% coverageMichael Houston • 18 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageBradley Johnson • 23 words • 0.0% coverageBradley Johnson • 20 words • 0.0% coverageBradley Johnson • 26 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 8 words • 100.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 32 words • 100.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageCity Council • 24 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 37 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 23 words • 100.0% coverage
Selected voice

City Council

100%flagged-word coverage
24 attributed words13% of attributed speech74% writer coverage
0%10.0%20.0%Biased Writer Voice-18.6 ptsWriter: 18.6%City Council: 0.0%0.0%Unattributed Quote-2.9 ptsWriter: 2.9%City Council: 0.0%0.0%Quote-first Misdirection-2.9 ptsWriter: 2.9%City Council: 0.0%0.0%

Attribution is sentence-level. Pattern percentages are calculated only from words assigned to that voice.

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Analysis

Hover over highlighted words in the article to view the associated bias or fallacy analysis.