Thousands are leaving Los Angeles year after year. This is why we still have a housing crunch 38%

By Jack Flemming0%

5/29/2026, 10:00:00 AM

BS Summary: This article contains 28 faulty reasoning types, including Negativity Bias, Hasty Generalization, and Appeal to Emotion, with Post Hoc (False Cause) as the most egregious example at 22.4% saturation with 230 hits. Analysis detected 1,609 faulty-reasoning hits from 1,025 analyzed words, generating a BS Score of 43.9% and a BS Rank of 38% (13,676 of 21,886 articles). This article is better (less manipulative) than 62.50% of the article peer group.

The City of Angels lost nearly 10,000 residents last year. 
L.A. 
County lost 62,000. 
It’s not exactly a mass exodus, but L.A.’s shrinking population is enough to leave a few economists shuddering at the long-term implications of so many people skipping town: less political influence, a smaller tax base, etc. 
But amid the Southland’s sea of change, one thing seems to always stay the same: Housing is still expensive. 
Typically, population declines and aging housing stock conjure visions of Rust Belt decay, where land loses value and abandoned homes sell for next to nothing. 
But in L.A.  despite vanishing Hollywood jobs and immigration crackdowns  housing costs remain sky-high. 
This year, the region has seen a slight dip in home values and a slight uptick in vacancy rates. 
But L.A. 
County’s population has been whittling down for the last decade, with 400,000 people leaving since 2016. 
During that stretch, home prices and rents have nearly doubled. 
What gives? 
Experts say a few factors are at play, but there’s one sneaky trend that’s often overlooked: household composition. 
“People are moving out of L.A., but households are becoming smaller, so the number of households that require housing is actually rising,” said Stephanie Hawke, associate research director of land use and supply at the Terner Center. 
Hawke said the number of one- to two-person households is rising while three-plus-person households are falling. 
A single-family home that once held a family of four now holds a couple with no kids. 
An apartment that once held a couple with no kids now holds a single person. 
In 2010, the average household size in L.A. 
County was 2.98, according to U.S. 
Census Data. 
In 2024, it was 2.81. 
That might seem a marginal difference, but it actually represents hundreds of thousands of people. 
“A growing housing stock is accommodating fewer people,” said Hans Johnson, a senior fellow at the Public Policy Institute of California. 
Johnson said the shifting demographics are coming from both the young and the old. 
Declining marriage rates among people in their 20s and 30s is leading to lower birth rates, while older people tend to live alone after their spouse dies. 
Both lead to smaller average households. 
Another element keeping housing prices high is the classic law of supply and demand. 
“We build less than we need,” Hawke said. 
State housing goals say L.A. 
County needs to add more than 800,000 housing units by 2029 to keep up with demand, including more than 450,000 in L.A. 
But despite laws meant to boost housing production, including duplex-focused Senate Bill 9 and density-focused SB 79, the city appears well short of that goal. 
“California has long had a housing shortage,” Johnson said. 
“So even though we’ve been building new housing over the last five years at a time of negative population growth, there’s an outstanding stock of undersupply that means markets are stressed, and prices remain higher in L.A. than the rest of the country.” 
Real estate agent Bret Parsons said in many cases, it doesn’t make sense for homeowners to sell even if they want to, which exacerbates the supply shortage. 
“People sit on homes because they don’t want to pay capital gains taxes or the ‘Mansion tax,’” he said. 
“They refuse to sell because they don’t want to give that money to the government, so there are few houses available.” 
Another factor? 
The types of people leaving vs. the ones replacing them. 
“Remember who’s moving out and moving in,” Hawke said. 
“Lower-income folks are being forced out, and higher-income folks who can afford higher rents are moving in.” 
A report from the Public Policy Institute of California showed that housing costs have become the biggest reason people leave the state. 
Lower-income workers without college degrees are often moving out, and they’re generally replaced by higher-income workers who can pay more for housing. 
As a result, the market doesn’t adjust, and rents stay high. 
Parsons said the dynamic is widening the wealth gap in L.A. 
Homeowners stay put, enjoying ever-rising property values, while renters are forced to leave the city and start over elsewhere with no equity built up. 
The mini exodus also brings a more existential question: Is it such a bad thing if people leave? 
In Southern California  a car-jammed, smog-choked metropolis that serves as the face of suburban sprawl, where rush hour is actually four hours and the hip new bagel joint always has a line down the block  is it the worst thing in the world to get a little breathing room? 
From a political perspective, perhaps. 
The American Redistricting Project forecasts California losing four congressional seats in the 2030 apportionment due to population loss, the most of any state by far. 
But Hawke sees the issue through a different lens. 
“You have to think about what, and who, makes L.A.” she said. 
“Are you forcing those folks out? 
And will it be the same city if everybody leaves?” 
Johnson said historically, population growth is a reflection of how desirable a place is, but declines aren’t necessarily negative. 
He said L.A. 
County, and California as a whole, have embraced a slow-growth mindset for a few years now, and the state already lost a congressional seat as a result of the 2020 census. 
“It’s about following the right examples,” he said. 
“The Northeast has embraced slow growth while still doing well economically, but there are other places in the world struggling with stagnant economies, poverty, and cheap-but-low-quality housing. 
That’s a model you won’t want to repeat.” 
Should the departure continue, or even increase, experts say it’s still not likely L.A. will ever be overbuilt like some of its Rust Belt counterparts due to pent-up demand that has never quite been met. 
Johnson said there is market elasticity tied to migration; as we build more housing and it impacts prices and rents, then fewer people will leave, and the ones who were priced out could return. 
“California is a desirable place to live. 
Weather, oceans, mountains,” he said. 
“It’s hard to envision it ever being overbuilt.” 
Article reasoning-pattern comparisonThis article: 1.8%Jack Flemming: 1.0%Daily Pilot: 2.9%Confirmation Bias1.8%This article: 0.0%Jack Flemming: 0.4%Daily Pilot: 1.2%Anchoring Bias0.0%This article: 11.5%Jack Flemming: 2.0%Daily Pilot: 3.5%Availability Heuristic11.5%This article: 0.0%Jack Flemming: 1.0%Daily Pilot: 1.0%Representativeness Heuristic0.0%This article: 0.0%Jack Flemming: 0.4%Daily Pilot: 0.9%Hindsight Bias0.0%This article: 0.0%Jack Flemming: 0.8%Daily Pilot: 1.9%Overconfidence Bias0.0%This article: 3.5%Jack Flemming: 6.9%Daily Pilot: 7.5%Framing Effect3.5%This article: 0.0%Jack Flemming: 1.5%Daily Pilot: 0.9%Loss Aversion0.0%This article: 4.4%Jack Flemming: 1.8%Daily Pilot: 0.9%Status Quo Bias4.4%This article: 0.0%Jack Flemming: 0.5%Daily Pilot: 0.2%Sunk Cost Effect0.0%This article: 3.4%Jack Flemming: 2.8%Daily Pilot: 3.6%Optimism Bias3.4%This article: 4.2%Jack Flemming: 1.3%Daily Pilot: 1.4%Pessimism Bias4.2%This article: 21.5%Jack Flemming: 7.6%Daily Pilot: 7.8%Negativity Bias21.5%This article: 0.0%Jack Flemming: 0.7%Daily Pilot: 2.2%Self-Serving Bias0.0%This article: 2.6%Jack Flemming: 0.9%Daily Pilot: 0.9%Fundamental Attribution Error2.6%This article: 0.0%Jack Flemming: 0.0%Daily Pilot: 0.2%Actor-Observer Bias0.0%This article: 0.0%Jack Flemming: 0.9%Daily Pilot: 1.6%In-Group Bias0.0%This article: 0.0%Jack Flemming: 0.6%Daily Pilot: 0.6%Out-Group Homogeneity Bias0.0%This article: 0.0%Jack Flemming: 2.0%Daily Pilot: 5.1%Halo Effect0.0%This article: 0.0%Jack Flemming: 0.1%Daily Pilot: 0.2%Horn Effect0.0%This article: 0.0%Jack Flemming: 0.0%Daily Pilot: 0.0%Dunning-Kruger Effect0.0%This article: 0.0%Jack Flemming: 0.2%Daily Pilot: 1.3%Recency Bias0.0%This article: 0.0%Jack Flemming: 0.3%Daily Pilot: 0.4%Primacy Effect0.0%This article: 0.0%Jack Flemming: 0.0%Daily Pilot: 0.1%Blind-Spot Bias0.0%This article: 0.0%Jack Flemming: 0.3%Daily Pilot: 0.9%Ad Hominem0.0%This article: 0.0%Jack Flemming: 0.3%Daily Pilot: 0.3%Straw Man0.0%This article: 5.5%Jack Flemming: 4.5%Daily Pilot: 4.9%Appeal to Authority5.5%This article: 2.7%Jack Flemming: 1.5%Daily Pilot: 1.2%False Dilemma2.7%This article: 2.6%Jack Flemming: 0.3%Daily Pilot: 0.7%Slippery Slope2.6%This article: 0.6%Jack Flemming: 0.0%Daily Pilot: 0.1%Circular Reasoning0.6%This article: 19.0%Jack Flemming: 5.3%Daily Pilot: 4.4%Hasty Generalization19.0%This article: 2.0%Jack Flemming: 0.1%Daily Pilot: 0.3%Red Herring2.0%This article: 0.0%Jack Flemming: 0.4%Daily Pilot: 1.0%Bandwagon0.0%This article: 11.6%Jack Flemming: 5.8%Daily Pilot: 5.7%Appeal to Emotion11.6%This article: 1.6%Jack Flemming: 0.2%Daily Pilot: 0.7%Begging the Question1.6%This article: 22.4%Jack Flemming: 4.0%Daily Pilot: 2.6%Post Hoc (False Cause)22.4%This article: 0.0%Jack Flemming: 0.0%Daily Pilot: 0.1%Tu Quoque0.0%This article: 1.2%Jack Flemming: 0.0%Daily Pilot: 0.4%Burden of Proof1.2%This article: 5.3%Jack Flemming: 0.2%Daily Pilot: 0.2%Appeal to Nature5.3%This article: 0.0%Jack Flemming: 0.2%Daily Pilot: 0.3%Composition/Division0.0%This article: 8.1%Jack Flemming: 5.4%Daily Pilot: 2.8%Anecdotal8.1%This article: 0.5%Jack Flemming: 0.1%Daily Pilot: 0.1%No True Scotsman0.5%This article: 2.1%Jack Flemming: 0.9%Daily Pilot: 1.6%Ambiguity (Equivocation)2.1%This article: 0.0%Jack Flemming: 0.0%Daily Pilot: 0.0%Gambler’s Fallacy0.0%This article: 1.9%Jack Flemming: 0.3%Daily Pilot: 0.1%Middle Ground1.9%This article: 1.0%Jack Flemming: 0.2%Daily Pilot: 0.1%Personal Incredulity1.0%This article: 0.8%Jack Flemming: 0.0%Daily Pilot: 0.2%Special Pleading0.8%This article: 0.0%Jack Flemming: 0.0%Daily Pilot: 0.3%Genetic Fallacy0.0%This article: 3.9%Jack Flemming: 0.5%Daily Pilot: 1.3%Unattributed Quote3.9%This article: 0.0%Jack Flemming: 0.3%Daily Pilot: 0.8%Quote-first Misdirection0.0%This article: 10.2%Jack Flemming: 6.3%Daily Pilot: 6.0%Biased Writer Voice10.2%This article: 1.2%Jack Flemming: 1.1%Daily Pilot: 1.5%Indoctrination1.2%This article: 0.0%Jack Flemming: 0.1%Daily Pilot: 0.9%Politically Left Leaning Bias0.0%This article: 0.0%Jack Flemming: 0.0%Daily Pilot: 0.3%Politically Right Leaning Bias0.0%This article: 0.0%Jack Flemming: 0.8%Daily Pilot: 4.0%Attempt to Sell a Product or S…0.0%

1025 words analyzed.

Speakers

5speakers50%attributed speech515writer words
Voice mapSelect a segment to jump to its words
Writer's voice • 8 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 1 words • 0.0% coverageWriter's voice • 3 words • 0.0% coverageWriter's voice • 36 words • 100.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 19 words • 0.0% coverageWriter's voice • 2 words • 0.0% coverageWriter's voice • 16 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageWriter's voice • 2 words • 0.0% coverageWriter's voice • 18 words • 100.0% coverageStephanie Hawke • 37 words • 0.0% coverageStephanie Hawke • 16 words • 0.0% coverageWriter's voice • 17 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageWriter's voice • 8 words • 0.0% coverageWriter's voice • 6 words • 0.0% coverageWriter's voice • 2 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 15 words • 0.0% coverageHans Johnson • 21 words • 0.0% coverageHans Johnson • 14 words • 0.0% coverageHans Johnson • 27 words • 0.0% coverageHans Johnson • 6 words • 0.0% coverageWriter's voice • 14 words • 0.0% coverageStephanie Hawke • 8 words • 0.0% coverageWriter's voice • 5 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 25 words • 0.0% coverageHans Johnson • 9 words • 0.0% coverageHans Johnson • 43 words • 0.0% coverageBret Parsons • 27 words • 0.0% coverageBret Parsons • 19 words • 0.0% coverageBret Parsons • 21 words • 0.0% coverageWriter's voice • 2 words • 0.0% coverageWriter's voice • 10 words • 0.0% coverageStephanie Hawke • 9 words • 0.0% coverageStephanie Hawke • 17 words • 0.0% coveragePublic Policy Institute of California • 22 words • 0.0% coverageWriter's voice • 22 words • 0.0% coverageWriter's voice • 11 words • 0.0% coverageBret Parsons • 11 words • 0.0% coverageWriter's voice • 24 words • 0.0% coverageWriter's voice • 18 words • 0.0% coverageWriter's voice • 51 words • 100.0% coverageWriter's voice • 5 words • 0.0% coverageAmerican Redistricting Project • 25 words • 0.0% coverageWriter's voice • 9 words • 0.0% coverageStephanie Hawke • 12 words • 100.0% coverageStephanie Hawke • 6 words • 100.0% coverageStephanie Hawke • 10 words • 0.0% coverageHans Johnson • 19 words • 0.0% coverageHans Johnson • 3 words • 0.0% coverageHans Johnson • 31 words • 0.0% coverageHans Johnson • 8 words • 0.0% coverageHans Johnson • 27 words • 100.0% coverageHans Johnson • 8 words • 0.0% coverageWriter's voice • 35 words • 0.0% coverageHans Johnson • 34 words • 0.0% coverageHans Johnson • 7 words • 100.0% coverageHans Johnson • 5 words • 0.0% coverageHans Johnson • 8 words • 0.0% coverage
100%flagged-word coverage
25 attributed words4.9% of attributed speech82% writer coverage
0%12.5%25.0%Biased Writer Voice-20.4 ptsWriter: 20.4%American Redistricting Project: 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.