Foreclosure tax will appear on Oakland’s November ballot 17%

By Natalie Orenstein12%

7/9/2026, 10:00:00 PM

BS Summary: This article contains 23 faulty reasoning types, including Representativeness Heuristic, Confirmation Bias, and Self-Serving Bias, with Negativity Bias as the most egregious example at 16.4% saturation with 83 hits. Analysis detected 629 faulty-reasoning hits from 505 analyzed words, generating a BS Score of 32.6% and a BS Rank of 17% (17,640 of 21,169 articles). This article is better (less manipulative) than 83.30% of the article peer group.

Voters will decide in November whether to tax big banks and investors that foreclose on Oakland properties. 
This is not a new tax, exactly. 
Oakland’s real estate transfer tax already applies to buyers and sellers of homes and buildings in the city. 
But foreclosures and similar transactions are currently exempt. 
Extending the tax to corporations that seize homes “closes a loophole,” said Councilmember Charlene Wang at Tuesday’s City Council meeting. 
The council voted almost unanimously (Council President Kevin Jenkins was excused) to place the measure on the Nov. 3 ballot. 
Wang urged her colleagues to fast-track the measure since Oakland is currently experiencing more foreclosures than usual. 
She noted that while most taxes bring in higher amounts of revenue when the economy is hot  for example, when more homes are selling, or people are buying more goods  the foreclosure tax is “counter-cyclical.” 
When there’s an economic downturn, more properties go through foreclosure, and the tax is an opportunity for the city to capitalize on the spike. 
She suggested the tax could dissuade lenders from foreclosing. 
Wang’s proposal still exempts a number of real estate deals and entities. 
Smaller banks, with under $10 billion in assets, wouldn’t have to pay the tax. 
Homeowners who get foreclosed on wouldn’t either. 
Companies of any size that convert office buildings into affordable housing would also be exempt. 
“The intent is not to punish the individuals experiencing foreclosure,” Wang said. 
“These are banks, as well as reinvestment trusts and private equity.” 
The seven votes in support of Wang’s measure represented a surprising switch. 
The item had failed to even make it through a committee of the council last month. 
At the time, Councilmember Janani Ramachandran was especially critical of the proposal, worrying that the small bank exemption could let some predatory lenders off the hook. 
On Tuesday, she offered an amendment that Wang accepted to strengthen the council’s ability to change whom the measure applies to after it passes. 
Councilmember Carroll Fife, generally a vocal proponent of holding corporate housing investors accountable, said she supported the idea behind the measure but worried the process was rushed and risked losing trust from voters. 
On Tuesday, she seemed reassured by the Finance Department’s confirmation of Wang’s revenue projections. 
Wang had initially proposed a second ballot measure advising that the revenue from the foreclosure tax be spent on homeless shelters and addiction treatment. 
But her colleagues and members of the public balked at that idea, in part because it wouldn’t be a binding requirement, and because it would have been costly and more complicated to run two measures. 
Despite the measure proposal struggling in committee, the council agreed to hear the item Tuesday given the looming deadline for placing items on the November ballot. 
In San Francisco, there are multiple ballot measure proposals under consideration that deal with that city’s transfer tax, including one from Mayor Daniel Lurie and a supervisor that’s similar to Wang’s in that it removes an exemption for foreclosures. 
Article reasoning-pattern comparisonThis article: 9.1%Natalie Orenstein: 1.6%Berkeleyside: 2.1%Confirmation Bias9.1%This article: 1.4%Natalie Orenstein: 1.5%Berkeleyside: 0.9%Anchoring Bias1.4%This article: 5.1%Natalie Orenstein: 2.6%Berkeleyside: 2.4%Availability Heuristic5.1%This article: 15.0%Natalie Orenstein: 1.0%Berkeleyside: 0.8%Representativeness Heuristic15.0%This article: 2.4%Natalie Orenstein: 0.6%Berkeleyside: 0.5%Hindsight Bias2.4%This article: 1.8%Natalie Orenstein: 0.1%Berkeleyside: 0.8%Overconfidence Bias1.8%This article: 3.4%Natalie Orenstein: 3.6%Berkeleyside: 3.1%Framing Effect3.4%This article: 0.0%Natalie Orenstein: 0.2%Berkeleyside: 0.4%Loss Aversion0.0%This article: 5.1%Natalie Orenstein: 0.8%Berkeleyside: 0.5%Status Quo Bias5.1%This article: 5.1%Natalie Orenstein: 0.2%Berkeleyside: 0.2%Sunk Cost Effect5.1%This article: 4.8%Natalie Orenstein: 2.1%Berkeleyside: 3.5%Optimism Bias4.8%This article: 0.0%Natalie Orenstein: 0.2%Berkeleyside: 0.7%Pessimism Bias0.0%This article: 16.4%Natalie Orenstein: 6.4%Berkeleyside: 3.8%Negativity Bias16.4%This article: 8.5%Natalie Orenstein: 2.7%Berkeleyside: 1.8%Self-Serving Bias8.5%This article: 0.0%Natalie Orenstein: 0.7%Berkeleyside: 0.6%Fundamental Attribution Error0.0%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: 0.2%Out-Group Homogeneity Bias0.0%This article: 0.0%Natalie Orenstein: 2.7%Berkeleyside: 3.6%Halo Effect0.0%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: 3.4%Natalie Orenstein: 1.1%Berkeleyside: 0.8%Recency Bias3.4%This article: 0.0%Natalie Orenstein: 0.2%Berkeleyside: 0.3%Primacy Effect0.0%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: 2.8%Natalie Orenstein: 4.5%Berkeleyside: 3.2%Appeal to Authority2.8%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: 5.1%Natalie Orenstein: 1.9%Berkeleyside: 2.4%Hasty Generalization5.1%This article: 7.7%Natalie Orenstein: 0.3%Berkeleyside: 0.1%Red Herring7.7%This article: 4.0%Natalie Orenstein: 0.6%Berkeleyside: 0.5%Bandwagon4.0%This article: 2.4%Natalie Orenstein: 2.4%Berkeleyside: 3.3%Appeal to Emotion2.4%This article: 4.0%Natalie Orenstein: 0.3%Berkeleyside: 0.5%Begging the Question4.0%This article: 8.1%Natalie Orenstein: 1.6%Berkeleyside: 2.2%Post Hoc (False Cause)8.1%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: 0.2%Composition/Division0.0%This article: 0.0%Natalie Orenstein: 1.6%Berkeleyside: 3.1%Anecdotal0.0%This article: 0.0%Natalie Orenstein: 0.2%Berkeleyside: 0.0%No True Scotsman0.0%This article: 3.6%Natalie Orenstein: 2.6%Berkeleyside: 1.3%Ambiguity (Equivocation)3.6%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: 1.1%Berkeleyside: 0.2%Special Pleading0.0%This article: 0.0%Natalie Orenstein: 0.0%Berkeleyside: 0.1%Genetic Fallacy0.0%This article: 4.0%Natalie Orenstein: 0.8%Berkeleyside: 0.8%Unattributed Quote4.0%This article: 0.0%Natalie Orenstein: 2.0%Berkeleyside: 0.6%Quote-first Misdirection0.0%This article: 1.4%Natalie Orenstein: 2.5%Berkeleyside: 2.3%Biased Writer Voice1.4%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%

505 words analyzed.

Speakers

3speakers40%attributed speech302writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 8 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 7 words • 100.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageCouncilmember Charlene Wang • 20 words • 100.0% coverageWriter's voice • 20 words • 0.0% coverageCouncilmember Charlene Wang • 17 words • 0.0% coverageCouncilmember Charlene Wang • 37 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageCouncilmember Charlene Wang • 9 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageCouncilmember Charlene Wang • 12 words • 0.0% coverageCouncilmember Charlene Wang • 11 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageCouncilmember Janani Ramachandran • 26 words • 0.0% coverageCouncilmember Janani Ramachandran • 24 words • 0.0% coverageCouncilmember Carroll Fife • 33 words • 0.0% coverageCouncilmember Carroll Fife • 14 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 35 words • 0.0% coverageWriter's voice • 26 words • 0.0% coverageWriter's voice • 39 words • 0.0% coverage
100%flagged-word coverage
50 attributed words25% of attributed speech63% writer coverage
0%2.5%5.0%Biased Writer Voice-2.3 ptsWriter: 2.3%Councilmember Janani Ramachandran: 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.