What the big new housing bill means for California 19%

By Ben Christopher21%

7/13/2026, 1:00:00 PM

BS Summary: This article contains 15 faulty reasoning types, including Framing Effect, Appeal to Authority, and Attempt to Sell a Product or Service, with Negativity Bias as the most egregious example at 11.7% saturation with 113 hits. Analysis detected 698 faulty-reasoning hits from 963 analyzed words, generating a BS Score of 33.9% and a BS Rank of 19% (17,787 of 21,887 articles). This article is better (less manipulative) than 81.30% of the article peer group.

New housing construction in a neighborhood in Elk Grove on July 8, 2022. 
Photo by Rahul Lal, CalMatters 
The largest single piece of housing legislation to come out of Congress since at least 1990 is now law , despite President Donald Trump’s refusal to sign it. 
The law is meant to kick start more housing construction across the country and features a grab bag of regulatory tweaks, pilot programs and targeted loans and grants. 
Though the figures vary, most experts estimate that the U.S. is short millions of units needed to bring housing costs down to affordable levels. 
A disproportionate number are needed in California’s urban areas. 
Median home prices nationally reached a new all-time high of $440,600 in June, according to new data released by the National Association of Realtors. 
That would be a steal in California, which also saw a record high a month earlier: $930,260. 
Supporters are hoping the new housing law will add up to more than the sum of its parts and get the country building. 
Stephen Russell , president of the San Diego Housing Federation: “I think the last time Congress passed anything of this magnitude, many of you were not even alive  it is almost a once-in-a-lifetime event.” 
Among the new provisions that could have a significant effect on California: 
Federal grants awarded to cities that build more and taken away from those that don’t 
A regulatory change aimed at making pre-built housing cheaper 
A program to speed up long-term recovery after natural disasters, such as wildfires 
This, and more, is all becoming law even though Trump refused to sign the bill. 
That isn’t because he didn’t support it, but because he wanted to “protest” the Senate’s refusal to pass a controversial national voter ID bill. 
Trump’s move deprived congressional Republicans the opportunity to celebrate what is among the biggest legislative achievements of Trump’s second term. 
It comes at a time when the GOP is facing electoral headwinds going into November due to, in part, public concerns about affordability. 
Even so, without an official veto from the president, the law went into effect quietly and automatically on Saturday. 
Read more . 
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Other Stories You Should Know 
A beach town where the beach is off limits 
Campers from YMCA Camp Surf at Mission Beach in San Diego on July 2, 2026. 
Photo by Adriana Heldiz, CalMatters 
Sewage pollution from Tijuana has plagued parts of South San Diego for decades, including Imperial Beach where much of its shoreline has been almost continually closed since 2023. 
These closures have prompted YMCA Camp Surf  which has introduced thousands of kids to water sports since its founding in 1969  to find workarounds so that younger San Diegians can still enjoy the water . 
As CalMatters’ Deborah Brennan explains, Camp Surf provides kids a chance to unplug and spend time outdoors. 
Besides hiking, skating and archery, kids can learn how to surf. 
But because airborne pollutants from the Tijuana River can make swimmers and surfers sick, the camp buses kids to neighboring Coronado, 15 minutes away. 
Recently Coronado has also faced beach closures, so campers must travel even farther north  40 minutes to Mission Beach. 
Though the camp has found an alternative for now, beach closures still create gaps in outdoor education, physical activity and water safety for South San Diego youth. 
Taylor Case , a recent graduate of Mar Vista High School in Imperial Beach: “Without having that access to a free, fun activity, the community isn’t as connected as it was when I was a kid.” 
Read more . 
How much did CalPERS make? 
The California Public Employees Retirement System’s building in Sacramento on Sept. 
6, 2022. 
Photo by Rich Pedroncelli, AP Photo 
It’s report card day for CalPERS with the state’s largest public pension fund scheduled to release its annual investment return. 
That’s a critical number to California government agencies, which have to cough up more money when the California Public Employees’ Retirement System comes up short of its 6.8% annual target. 
That won’t be the case this year. 
As of last week, its assets were worth $634.5 billion  about $80 billion more than a year ago. 
We’ll find out the details this morning. 
Today’s earnings report comes at a moment when public safety unions are urging lawmakers to boost retirement benefits for police and firefighters. 
A big number today could make legislators more confident in saying yes. 
California Voices 
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Article reasoning-pattern comparisonThis article: 4.7%Ben Christopher: 1.2%CalMatters: 2.5%Confirmation Bias4.7%This article: 4.5%Ben Christopher: 1.0%CalMatters: 0.9%Anchoring Bias4.5%This article: 0.0%Ben Christopher: 1.5%CalMatters: 2.4%Availability Heuristic0.0%This article: 0.0%Ben Christopher: 0.7%CalMatters: 1.0%Representativeness Heuristic0.0%This article: 0.0%Ben Christopher: 0.2%CalMatters: 0.4%Hindsight Bias0.0%This article: 0.0%Ben Christopher: 1.0%CalMatters: 0.8%Overconfidence Bias0.0%This article: 11.5%Ben Christopher: 5.0%CalMatters: 5.7%Framing Effect11.5%This article: 0.0%Ben Christopher: 0.1%CalMatters: 0.8%Loss Aversion0.0%This article: 0.0%Ben Christopher: 0.4%CalMatters: 0.6%Status Quo Bias0.0%This article: 0.0%Ben Christopher: 0.0%CalMatters: 0.2%Sunk Cost Effect0.0%This article: 4.4%Ben Christopher: 3.8%CalMatters: 1.9%Optimism Bias4.4%This article: 0.0%Ben Christopher: 1.7%CalMatters: 2.0%Pessimism Bias0.0%This article: 11.7%Ben Christopher: 3.6%CalMatters: 8.0%Negativity Bias11.7%This article: 0.0%Ben Christopher: 1.1%CalMatters: 1.6%Self-Serving Bias0.0%This article: 0.0%Ben Christopher: 1.0%CalMatters: 1.0%Fundamental Attribution Error0.0%This article: 0.0%Ben Christopher: 0.3%CalMatters: 0.2%Actor-Observer Bias0.0%This article: 1.9%Ben Christopher: 1.6%CalMatters: 0.8%In-Group Bias1.9%This article: 0.0%Ben Christopher: 0.1%CalMatters: 0.3%Out-Group Homogeneity Bias0.0%This article: 3.8%Ben Christopher: 0.7%CalMatters: 1.4%Halo Effect3.8%This article: 0.0%Ben Christopher: 0.0%CalMatters: 0.0%Horn Effect0.0%This article: 0.0%Ben Christopher: 0.0%CalMatters: 0.0%Dunning-Kruger Effect0.0%This article: 2.1%Ben Christopher: 0.8%CalMatters: 1.1%Recency Bias2.1%This article: 0.0%Ben Christopher: 0.2%CalMatters: 0.2%Primacy Effect0.0%This article: 0.0%Ben Christopher: 0.0%CalMatters: 0.0%Blind-Spot Bias0.0%This article: 0.0%Ben Christopher: 0.1%CalMatters: 0.6%Ad Hominem0.0%This article: 0.0%Ben Christopher: 0.1%CalMatters: 0.3%Straw Man0.0%This article: 10.4%Ben Christopher: 4.0%CalMatters: 3.2%Appeal to Authority10.4%This article: 0.0%Ben Christopher: 0.1%CalMatters: 1.0%False Dilemma0.0%This article: 0.0%Ben Christopher: 0.3%CalMatters: 1.3%Slippery Slope0.0%This article: 0.0%Ben Christopher: 0.0%CalMatters: 0.1%Circular Reasoning0.0%This article: 0.0%Ben Christopher: 1.6%CalMatters: 3.7%Hasty Generalization0.0%This article: 0.0%Ben Christopher: 0.0%CalMatters: 0.1%Red Herring0.0%This article: 0.0%Ben Christopher: 0.6%CalMatters: 0.4%Bandwagon0.0%This article: 2.9%Ben Christopher: 1.3%CalMatters: 5.3%Appeal to Emotion2.9%This article: 0.0%Ben Christopher: 0.1%CalMatters: 0.8%Begging the Question0.0%This article: 1.2%Ben Christopher: 1.2%CalMatters: 2.7%Post Hoc (False Cause)1.2%This article: 0.0%Ben Christopher: 0.0%CalMatters: 0.0%Tu Quoque0.0%This article: 0.0%Ben Christopher: 0.1%CalMatters: 0.4%Burden of Proof0.0%This article: 0.0%Ben Christopher: 0.2%CalMatters: 0.2%Appeal to Nature0.0%This article: 0.0%Ben Christopher: 0.1%CalMatters: 0.2%Composition/Division0.0%This article: 3.7%Ben Christopher: 0.9%CalMatters: 1.8%Anecdotal3.7%This article: 0.0%Ben Christopher: 0.0%CalMatters: 0.0%No True Scotsman0.0%This article: 0.0%Ben Christopher: 0.8%CalMatters: 1.2%Ambiguity (Equivocation)0.0%This article: 0.0%Ben Christopher: 0.0%CalMatters: 0.0%Gambler’s Fallacy0.0%This article: 0.0%Ben Christopher: 0.0%CalMatters: 0.1%Middle Ground0.0%This article: 0.0%Ben Christopher: 0.0%CalMatters: 0.0%Personal Incredulity0.0%This article: 0.0%Ben Christopher: 0.2%CalMatters: 0.3%Special Pleading0.0%This article: 0.0%Ben Christopher: 0.1%CalMatters: 0.1%Genetic Fallacy0.0%This article: 0.0%Ben Christopher: 0.3%CalMatters: 0.9%Unattributed Quote0.0%This article: 2.5%Ben Christopher: 0.3%CalMatters: 0.7%Quote-first Misdirection2.5%This article: 0.0%Ben Christopher: 2.9%CalMatters: 3.3%Biased Writer Voice0.0%This article: 1.9%Ben Christopher: 0.6%CalMatters: 1.0%Indoctrination1.9%This article: 0.0%Ben Christopher: 0.4%CalMatters: 0.6%Politically Left Leaning Bias0.0%This article: 0.0%Ben Christopher: 0.1%CalMatters: 0.2%Politically Right Leaning Bias0.0%This article: 5.3%Ben Christopher: 1.4%CalMatters: 1.0%Attempt to Sell a Product or S…5.3%

963 words analyzed.

Speakers

3speakers12%attributed speech851writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 9 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 28 words • 0.0% coverageWriter's voice • 28 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageStephen Russell • 35 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 24 words • 100.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 23 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 3 words • 0.0% coverageWriter's voice • 10 words • 100.0% coverageWriter's voice • 10 words • 100.0% coverageWriter's voice • 18 words • 100.0% coverageWriter's voice • 9 words • 100.0% coverageWriter's voice • 4 words • 100.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 28 words • 0.0% coverageWriter's voice • 37 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 27 words • 0.0% coverageTaylor Case • 36 words • 0.0% coverageWriter's voice • 3 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 2 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 20 words • 0.0% coverageWriter's voice • 30 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 7 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 2 words • 0.0% coverageRobert Kaplan • 41 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 12 words • 0.0% coverageWriter's voice • 2 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageWriter's voice • 13 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverage
Selected voice

Robert Kaplan

100%flagged-word coverage
41 attributed words37% of attributed speech60% writer coverage
0%5.0%10.0%Attempt to Sell a Product -6.0 ptsWriter: 6.0%Robert Kaplan: 0.0%0.0%Quote-first Misdirection-2.8 ptsWriter: 2.8%Robert Kaplan: 0.0%0.0%Indoctrination-2.1 ptsWriter: 2.1%Robert Kaplan: 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.