When Gavin Newsom was running for California governor in 2017, he famously vowed to “lead the effort to build the 3.5 million new housing units we need by 2025.” Newsom conceded that the goal was “audacious” but argued that “our solutions must be as bold as the problem is big.”1 Everyone agreed that California’s housing problem was big. What drew skepticism was the prospect of building 3.5 million homes by 2025.
Indeed, the target has proved elusive. Replying to a dubious reporter in January 2020, a year after his gubernatorial inauguration, Newsom said that the figure had been “established by a report McKinsey put out,” adding that his office would publish “a more nuanced estimate.”2 It had yet to do so on September 14, 2021, the day he beat a recall threat. New York Times reporter Conor Dougherty noted that Newsom’s leading opponent, talk show host Larry Elder, had cited the incumbent’s failure to make good on his campaign vow as “an example of broken promises.” Piling on, Dougherty commented that Elder “did not need sophisticated research to find fault with the number: In a state that permits around 100,000 housing units a year, delivering 3.5 million—35 years of housing at the current pace—is close to a physical impossibility.”3
In March 2022, the Newsom administration released a new housing plan that pegged California’s housing need for the next eight years at “more than 2.5 million homes.”4 Eliciting no comment from the governor, the shrunken target was flagged in a sardonic tweet by Los Angeles Times reporter Liam Dillon: “Some goalposts officially moved today.”5
The Politics of Possibility
In fact, the goalposts were portable from the start. When it comes to housing, Newsom practices what Louise Amoore calls the “politics of possibility,” a type of governance that deals in “mobile norms.”6 Its unstable criteria originates in the logic that underlies derivatives: algorithms establish “associations between x, y, z, and p, not as definitive norms but as contingent and mutable, modulating over space and time.” Uncertainty also inheres in statistical calculation, but whereas probabilistic logic filters out low probability occurrences at the edges of a standard distribution (think bell curve), possibilistic reasoning infers and accredits “unpredictable and potentially catastrophic” events that have not occurred and indeed may never take place. Accordingly, “algorithmic judgments” authorize measures taken in response to future crises, “however unlikely and improbable.”
Political authorities relying on such logic mask its contingency and claim to be proceeding on the basis of objective evidence. And, Amoore notes, their decisions may well take into account “past frequencies,” as well as future unknowns and personal conviction. In any case, “what matters is not the accuracy gleaned from large volumes of data, analyzed and statistically assessed, but the intelligibility of the derivative as an instrument, its precision as a basis for decision.” Common parlance associates derivatives with the securitized mortgages whose collapse precipitated the 2008 global recession. Now, thanks in large part to its dissemination by management consultants like McKinsey, “[t]he business knowledge that dominated early twenty-first century models for embracing risk … has become a resource” for political decisionmakers. Amoore considers how such knowledge has shaped security policy and practice.
Brandishing possibilistic data tagged as “actionable evidence,” private consultants, academic researchers, and official analysts contend that the key factor in California’s housing crisis is local resistance to development. The state cites that data to justify a sweeping centralization of land use governance that has shifted the risk of real estate investment from the private real estate industry onto the public. The twinned financialization of housing policy and centralization of land use authority was underway a good decade before Newsom became governor, but his administration has given those projects new legitimacy and legal force. In so doing, he and his allies haven’t mitigated, much less resolved, the state’s housing crisis7—and for good reason: the remedies they’re pursuing are unproven and arguably unprovable. Rather, their success lies in having exploited that predicament in behalf of a California-style authoritarian capitalism whose rollout has set the pace for other states.8
McKinsey Benchmarks California’s Housing Need
The McKinsey report that Newsom referenced, “A Tool Kit to Close California’s Housing Gap: 3.5 Million Homes by 2025”9 provides a case study of the operation of consulting-agency doubletalk. Published in 2016 by the management consultancy’s in-house think tank, the McKinsey Global Institute (MGI), the Tool Kit exemplifies the instrumentalism that informs Amoore’s possibilistic politics. The evidence MGI offers in support of its claims—insofar as it offers evidence—has a questionable relationship to observable reality and, in many cases, to the claims themselves. What’s unquestionable is the report’s intent: to gin up California’s housing crisis, justifying the Tool Kit’s draconian remedies and facilitating their adoption by California policymakers. On that score, the report was a success.10 Newsom is only one of many state officials who have invoked its claims. The California Department of Housing and Community Development, staff to the Legislature, and state legislators have all cited its findings. Its goal of 3.5 million new homes by 2025 was embedded in state law by SB 330, the Housing Crisis Act of 2019.11
A shortage of three and a half million homes is both shocking and unfathomable. Its calculation is thus the Tool Kit’s first order of business. MGI arrived at the number by benchmarking California’s housing need. McKinsey defines a benchmark as a quantified standard against which the performances of “comparable peers” are judged.12 Benchmarkers, however, do far more than compare the performances of peers. They constitute the performers as peers and as competitors.
To effect this transformation, they deploy the logic of the derivative. First they decompose things—in the case at hand, California, New Jersey, and New York—into selected attributes linked by correlation (the x, y, and z of Amoore’s algorithms). Next they make the things commensurable by quantifying the attributes. They then establish numerical goals or benchmarks—here, numbers of housing units—and record the things’ success in meeting those goals; place their performances on a hierarchical public scale; pressure them to emulate their superiors; and imbue the whole contest with an ethical cast. The contest is unending. As Isabelle Bruno observes, “the benchmark, i.e. the reference point identified as a goal, is a moving target which cannot be reached once and for all.”13 It’s “set only to be caught up and replaced by the latest ‘best performer.’” What’s at stake, Bruno emphasizes, is “not merely sharing knowledge, but reallocating power and legitimacy.” With its “harmonizing statistics and quantifying indicators,” managerial benchmarking makes it possible to subject “completely disparate public services, territories, and populations to the same competitive regime.” To McKinsey, that’s all to the good: “Greater competition means stronger productivity growth, which in turn means a faster-growing economy and more wealth to share.”14
Guided by that credo, MGI benchmarked California’s housing supply, calculated as housing units per capita, against corresponding figures, for New York and New Jersey. The consultants deemed the three states “a useful peer set, given their demographic profiles, economic output per capita, and land economics.” Compared with the other two states, laggard California had “a shortage of some two million units,” the “base case.” If California were to accommodate its population growth through 2025, as estimated by Moody’s Analytics, at the same per capita rate as New York and New Jersey, it would need 1.5 million more housing units. Ergo, the “gap” or benchmark of 3.5 million new homes.15
In 2018 demographers at the University of Southern California charged that McKinsey had grossly inflated its estimate of California’s housing need by using “a per capita new housing rate,” which “counts all people equally, including children, working age adults and retirees.”16 In other words, McKinsey had proceeded as if children and retirees were as likely to form a household as working age adults. By contrast, “the most accepted method among experts for linking population and housing” is the headship rate, which “measures the rate of household formation of each specified” segment in the population. The demographers expressed “shock and exasperation” at the “exorbitant size” of McKinsey’s estimate; their own calculations estimated a need for 2.5 million new homes. They also objected to MGI’s reliance “on a combination of New York and New Jersey experience.” Observing that headship rates “vary between locales and also over time,” they argued that the data should be limited “to our own state.”
The USC critics apparently didn’t realize that McKinsey—according to the New York Times, “the most respected voice in consulting”—was following the protocol of management consultants. That approach, writes William Davies, “is animated by a desire to energize decision makers, rather than provide a neutral or objective view of the world that withstands scrutiny….Empirical data are used to the extent that they support a policy narrative, or open up valuable questions about future decisions, but are never analyzed for the sake of it.”17 Assimilating political leadership to business leadership, competitive experts “reinvent executive authority in ways that are compatible with market logic.” Their ideal is the “heroic” corporate executive whose summary decision-making enables her firm to surmount the existential threats presented by the globalized economy. “The injunction is to obey, or else possibly perish.” The consultants themselves affect this “charismatic-managerial” style. “[I]t is their personality, self belief and powers of convincing representation that offer reason to believe and follow.”
Davies’s profile of management consultants’ expertise as crisis-driven, peremptory, and empirically unmoored illuminates McKinsey’s cynicism. A long footnote to the Tool Kit’s benchmarking exercise states: “A limitation of [our] methodology is that it does not control for the various demographic factors that contribute to rates of household formation, including age, income and ethnicity, which vary at the local level and across states.”18 This acknowledgment may seem to anticipate objections of the USC demographers. But MGI wasn't admitting to an analytic oversight; it was flagging an occupational hazard. The Tool Kit goes on to note that “[o]ther analysts who have estimated California’s housing gap using different methodologies have also found a very sizeable housing shortage.” Researchers hewing to academic convention would have specified the differences and justified their own procedure. Instead, McKinsey moved on, leaving the impression that calculations of California’s housing need are indeterminate.
Housing Hot Spots
The Tool Kit is a how-to guide, a self-described “blueprint to help communities close the housing gap” and thereby “improve social equality, quality of life, and economic competitiveness in the state of California.” Estimating California’s housing need was only the start. The next step was to demonstrate the feasibility of the envisioned growth scenario. To that end, McKinsey next inventories “housing hot spots,” places in California where, it claims, up to 5.6 million new homes “could be built with attractive returns.” According to MGI, the state has the
capacity to build as many as 225,000 housing units on vacant urban land that is already zoned for multifamily housing; 1.2 million to three million housing units within a half mile of major transit hubs; nearly 800,000 units by allowing homeowners to add units to their homes; nearly one million units on land zoned for multifamily development but underutilized; and more than 600,000 affordable single-family units on “adjacent” land currently dedicated to nonresidential uses.
These figures are illustrated by maps speckled with “high-potential” parcels. The accompanying discussion is footnoted with another notice of methodological latitude: “Our estimate of five million potential units represents the physical capacity for new housing in California; we have not attempted to address the economic feasibility of building this new supply in various communities across the state.” This time, there are no references to other analyses. That figures: nobody else had undertaken such an inquiry.
The decision not to assess the economic feasibility of the new housing may seem perplexing; after all, the Tool Kit teems with financial calculations. Viewed through Davies’s lens, it makes sense: McKinsey was relying on its “powers of convincing representation.” Like the firm’s other major reports, the Tool Kit is packaged in both the accoutrements of academic scholarship—acknowledgments, footnotes, charts, tables, maps, a technical appendix, and a bibliography—and the trappings of a highly produced corporate business prospectus—color photos, infographics, and ample white space—a presentation that emanates authoritativeness and accessibility.
Tools to Change the Rules
MGI advises that to “unlock” the millions of envisioned housing units, it would be necessary to “change the rules of the game.” To that end, it offered dozens of “tools” whose use would facilitate the requisite “public and private sector innovations.” Unlike the benchmarking and mapping operations, these instruments were sourced from an agenda that the Bay Area Council, the lobby shop of the region’s biggest employers, had been circulating in various forms since 1980, and an ideologically aligned report published by the San Diego Planning Commission in 2015.19 In 2016 the state growth coalition, California Forward, engaged MGI to scale up the city and regional programs to statewide dimensions. The result was the Tool Kit.
The report’s narrative goes like this: California’s housing crisis is rooted in the failure to accommodate the state’s growing population. Inadequate supply has caused prices to soar, resulting in $140 billion in lost economic output per year and a $50 billion annual “housing affordability gap.” That gap can only be filled, however, by providing homes for households of all income levels. Making such provisions is the proper work of “market-driven real estate development.” Given the opportunity—and in the case of housing affordable to the least affluent, the needful subsidies and incentives—private builders will build. Unfortunately, they’re hampered from doing so, in part by the low productivity of the construction sector, which “has not merely stagnated, but has in fact declined.” That’s no small matter, given that “labor accounts for roughly half of all construction costs.”
But the far greater problem, at least to judge from the attention it gets in the Tool Kit, is a political structure that creates risks for developers. In McKinsey’s succinct formulation:
California’s land-use approval process is largely discretionary, with power resting with local government bodies. This reality of decentralized decision making, coupled with community-based politics and the state’s environmental review requirements, leads to a significantly longer and riskier entitlement process than in other jurisdictions.
Local land use processes can be complex and unpredictable, especially when they involve public engagement. For builders, who typically finance their projects on credit, time is money. In California, “[m]ost developers and individual property owners confront not only long and uncertain timelines, but also high land and additional expenses for engineers, architects, lawyers, and staff overhead while awaiting approval.” They also face the challenges of zoning. Customarily delegated by states to local governments, zoning specifies allowable uses on particular sites; limits building heights and densities; and requires new housing to provide expensive off-street parking. Local impact fees require builders to mitigate the demands that their developments place on infrastructure and public service. To complicate matters further, the rules vary by jurisdiction, adding time and cost to the approval process.
A major source of these constraints is local electeds’ deference to reactionary homeowning constituents. Residents who bought their homes when the city looked a certain way want it to stay that way and may oppose development because of its impact on parking, traffic, schools, sight lines, or community character. City council members who make land-use decisions respond to homeowner voices, creating an environment where it is easier to say “no” to housing than “yes.” Above all, homeowners who live in neighborhoods zoned for single-family homes want their council members to say “No” to multifamily housing—the sort of development whose proliferation anchors the Tool Kit’s vision of massive growth.
For evidence that local resistance has blocked “housing creation,” McKinsey points to California cities’ low compliance rates with their state-mandated housing allocations, “especially for the very-low-income, low-income and moderate-income segments.” As the Tool Kit has it,
Every eight years, the government conducts a Regional Housing Needs Assessment and assigns a certain number of target units to each region, which then determines production goals for each jurisdiction. Localities are required to zone and plan for the units but do not necessarily give developers approval to build them.
Because “state funds for local public projects are usually provided regardless of housing performance,” delinquent cities have “little reason to raise low compliance rates.”
Growth-averse local jurisdictions aren’t the only impediment to housing production. MGI maintains that “[a] primary reason entitlement is so complex is the California Environmental Quality Act (CEQA)…” Passed in 1970, California’s premier environmental statute authorizes the decentralized political action that the Tool Kit deplores. As Natural Resources Defense Council attorney David Pettit wrote in 2013, the law “is typically only enforced by citizens going to court.”20 But government agencies also file CEQA challenges. MGI claims that CEQA lawsuits delay housing projects “by months or years.”
In short, local political actors are largely responsible for California’s housing crisis. It follows that the state’s housing need can only be met by insulating the housing market from bottom-up democracy. Decision-making must be centralized, local prerogatives curbed, and environmental review trimmed. At the same time, the public must be exposed to—indeed, assume responsibility for—private developers’ risks. To boost cities’ compliance with the Regional Housing Need Allocations, the Tool Kit suggests incentives such as “increas[ing] tax revenue allocations to cities that approve more housing,” tying “state and regional dollars to local housing performance,” and “Grant[ing] greater land-use autonomy to cities that meet their RHNA targets.” For intransigent jurisdictions, McKinsey recommends punitive measures such as the creation of a regional-level appeals board to which developers may turn if a municipality has rejected their “zoning-compliant” housing project. In place of impact fees, it proposes that the costs of new infrastructure could be absorbed by “the full pool of homeowners” in a city, or that “fees could be distributed among a broad base of users” via regressive measures such as “utility billing assessments, vehicle license fees, parking permits, road tolls or sales taxes.” To “encourage high-density and mixed use growth” while “reduc[ing] timelines and risk for developers,” minimize parking requirements or eliminate them altogether. To check CEQA “abuse,” the land-use approval process “for projects that meet critical housing needs,” such as “multifamily housing in specific locations that contains an affordable component,” should be “streamlined.” That’s code for making the approval of such projects “ministerial” or “by right,” thereby eliminating environmental review and any other discretionary public process.
McKinsey’s recommendations conflict with basic realities of the housing market. Cities do not build housing; developers do. And contrary to the Tool Kit’s core assumptions, whether developers build housing depends mainly on factors over which cities have no control: the vagaries of the construction cycle and the cost and availability of land, labor, materials, and credit. If a project won’t pencil out—that is, yield the returns that developers, their investors, and their creditors require—it won’t get built. Witness the Bay Area in Winter 2022–2023. In San Francisco, entitled housing projects comprising tens of thousands of units had yet to break ground.21 A study commissioned by the city’s Board of Supervisors showed that under the most favorable conditions, developers still wouldn’t build below-market-rate housing. In San Jose, city staff told the council that the cost of construction made even new market-rate housing “infeasible” in their town. Rising interest rates, inflation, and supply chain snarls had slowed construction nationwide, while the growth of remote work “has some developers questioning whether it makes sense to pursue projects in city centers.”22
The Tool Kit’s attack on the California Environmental Quality Act is similarly baseless. The report’s allegation that CEQA lawsuits frequently delay housing projects by months or years is not corroborated by its only footnoted source, a 2000 study by the California Department of Housing and Community Development. The Tool Kit’s bibliography lists two standard citations for CEQA opponents, a pair of studies by Jennifer Hernandez et al. that considered CEQA lawsuits filed during two periods, 2010–2012 and 2013–2015, respectively. Both studies have been conclusively debunked on methodological grounds.23 The Tool Kit states that “Shortening the land-use approval process for housing” by eliminating CEQA review “could save Californians $422 million a year”; it does not explain how it calculated those figures.
Smart Growth’s Travails
Despite its assault on CEQA, McKinsey claimed environmentalist credentials for the Tool Kit’s agenda: “Locating housing on public transit lines increases connectivity and convenience while reducing sprawl, highway gridlock, and greenhouse emissions.” This claim has no footnote, but the report’s bibliography includes a handful of relevant studies. The most assiduous of the lot, Jed Kolko’s 2011 report for the Public Policy Institute of California shows that the capacity of transit-oriented development (TOD) to get people out of their private automobiles and onto public transit is unclear, and that the program faces daunting obstacles, especially in California.
The financial viability of mass transit depends on mass patronage. A key factor in ridership is proximity to transit. “Transit ridership diminishes rapidly as distances from transit stations increase: one quarter mile is the limit that most people will walk for most trips.” Moreover, although “employment density is more closely tied to transit ridership than residential density … policy studies and recommendations have focused primarily or exclusively on residential density and residential growth near transit stations.”
While, despite popular opinion, residential density in California is 49 percent above the national average, but the state’s employment density is 15 percent lower “and—like the national trend—is falling.”
Kolko analyzed employment and residential growth and density around the 217 transit stations in California that became operational between 1992 and 2006. He focused on fixed-line rail, subway, streetcar, and bus-rapid transit routes, modes that had greater ridership capacity and, in the case of fixed-line transit, the permanence that “also adds to their lure for associated land use development.”
His “main finding”: “there was no increase in employment growth associated with transit station opening.” Worse, “TOD strategies have been unsuccessful, on average, in promoting residential development, which is generally the focus of these strategies: in fact, residential growth appears to have been significantly slower in the areas around new transit stations than in comparison areas. Kolko urged the state to promote “intense” commercial development relative to residential development near stations. That recommendation came with more caveats:
Even if land use policies and demand for space near transit were successful in raising densities near transit, the effect on regional VMT [Vehicle Miles Traveled] would likely be small ….[T]hree-quarters of workers within one-half mile of a transit station drive to work, most of them driving alone. Even within one-quarter-mile of a transit station—just a five-minute walk—only 10 percent of workers commute via fixed-line transit. Past transit investments in California have not gotten commuters out of their cars. Furthermore, commute trips account for only 27 percent of VMT, and trips for other purposes—school, social, personal business—are much less likely to occur on transit.
Consider, then, that California law defines a “major transit stop” as “a site containing an existing rail or bus rapid transit station, a ferry terminal served by either a bus or rail transit service, or the intersection of two or more major bus routes with a frequency of service interval of 15 minutes or less during the morning and afternoon peak commute periods.” That leaves out midday, nighttime, and weekend service. In support of its recommendation to “[i]ntensify housing around transit hubs,” the Tool Kit cites that definition and its source, California’s Sustainable Communities and Climate Protection Act of 2008, SB 375. Kolko, for his part, took SB 375 to task for “discourag[ing] commercial development relative to residential development around transit stations.”
He put a brave face on the results of his research. Despite his findings of TOD’s negligible effects on public transit use, he ventured that. “[i]f the planning encouraged by SB 375 succeeds in raising densities in California, emissions at the regional level could fall because higher-density residential units tend to be smaller and consume less energy.”
Kolko’s hopeful surmise resonates with the findings of another item in the Tool Kit’s bibliography, the much-cited 2014 report by climate scientists Christopher Jones and Daniel Kammen, which suggested that “at population densities above a threshold of about 3,000 persons per square mile, household carbon footprints [HCF] tend to be lower, primarily due to smaller homes, shorter driving distances, and also somewhat lower incomes.” That seems to support McKinsey’s categorical homage to TOD and the densification at the heart of smart growth. But Jones and Kammen found that “more population-dense suburbs actually have noticeable higher HCF, largely because of income effects,” and that cities themselves display a huge range of household carbon foot prints. More importantly, they warned that their
results should be understood in the context of uncertainty and the methods used to derive the estimates. We have used national survey data to predict consumption at fine geographic scales and have used average GHG emission factors to estimate emissions. This approach hides important regional differences.
As Salim Furth observes, it also hides local differences. Jones and Kammen, writes Furth, “rely on non-localized surveys to create estimates which they then project on localities … [U]sers really want a causal estimate.” Instead, we’re getting “a descriptive one.”24
The Affordability Con
The Tool Kit’s most significant bluff is its devious treatment of housing affordability. The report’s introduction features the plight of California’s least affluent residents:
As California real estate prices rise three times faster than household incomes, more than 50 percent of the state’s households cannot afford the cost of housing…. The state now has a $50 billion to $60 billion annual housing affordability gap. Virtually none of California’s low-income and very-low-income households can afford the local cost of housing. Nearly 70 percent of these households would have to spend more than half of their income to afford the local cost of housing.
The report emphasizes that “California’s poorest households are affected the most by rising housing costs.” A reader might expect the construction of housing affordable to such households would be the Tool Kit’s top priority. That it’s not could be inferred from McKinsey’s failure to break down its estimate of California’s housing need in terms of affordability. Indeed, on page 36 of the 44-page report the authors state that while “the tools we have discussed—including identifying housing hot spots, unlocking supply by shifting incentives and cutting the cost and risk of producing housing—could unlock millions of new market-rate housing units”; they “will not solve the problem for California’s most vulnerable residents. Low-income, special needs and homeless individuals will require support to access housing.” What’s more, any measure that would provide such support—for example, requiring new multi-family, market rate-housing to include an affordable component—comes with a warning: “Regulations must be designed with developers’ risks and financial returns in mind to ensure that affordable housing policies do not stifle new market-driven supply.” Accordingly, tax increment financing that would allow local agencies to designate a portion of future tax revenue for affordable housing is deemed “powerful, because it does not operate as an up-front tax on developers.”
No such provisos accompany the Tool Kit’s recommendation of accessory dwelling units (ADUs) as “inherently affordable because they use existing land, buildings, and infrastructure.” According to McKinsey, “One unit can be created for less than $25,000.” Unsurprisingly, the report offers no evidence in support of that preposterous claim. Besides ADUs’ questionable affordability, other factors limit their potential capacity. The typical secondary unit is very small—either a studio or tiny one-bedroom. And, unlike most landlords, ADU owners live in close proximity to their tenants. Jealous of their privacy, many ADU landlords choose to rent their secondary units to friends or family at below-market rate rents or not to rent them at all. Good for social solidarity and aging in place, such arrangements hold little promise for widespread affordability.
McKinsey uses the hardship of California’s most vulnerable residents to evoke the enormity of the state’s housing crisis but subordinates their needs to the demands of finance capital. The primary reason that cities miss their targets for low-income housing is that market-rate developers won’t build such housing because it can’t yield the profits they demand. Worse yet, contrary to the Tool Kit’s claim that increasing supply lowers prices, the market-rate housing that developers do build in a hot market raises real estate values, widening the affordability gap and threatening to force low-income residents out of their homes. These effects are tacitly acknowledged by the Tool Kit’s recommendation to “[m]itigate displacement risk” by “[a]llocating resources and developing policies to enable large-scale redevelopment without displacing current residents, such as preferential or discounted tenancy in new buildings.” Such mitigation would be unnecessary if, as McKinsey contends, market-driven growth led to broadly shared prosperity. Doubling down on that fallacy, the consultants blame the failures of the private real estate industry on local political actors, whose powers they exaggerate so as to warrant their state-imposed disfranchisement and assumption of market-driven risk.
Judged by social scientific standards, an inquiry as beset with specious analyses, airy caveats, and implausible calculations as the Tool Kit is a debacle. But the Tool Kit is not a work of social science. It’s a call to political arms—propaganda, if you like—aimed at policymakers disinclined to question its credibility. Their receptiveness was predictable; bolstering the rhetoric of housing crisis, McKinsey gave new impetus to the marketized, authoritarian course of action on which the state of California had already embarked.
- Gavin Newsom, “The California Dream Starts at Home,” Medium, October 20, 2017. https://medium.com/@GavinNewsom/the-california-dream-starts-at-home-9dbb38c51cae.
“California Gov. Gavin Newsom presents his budget proposal for 2020-21,” ABC10 on YouTube, https://www.youtube.com/watch?v=VESqJEEGIUQ.
Conor Dougherty, “California Housing is a Crisis Newsom Can Take Into His Own Hands,” The New York Times, September 16, 2021.
California Department of Housing and Community Development, “A Home for Every Californian: 2022 Statewide Housing Plan,” April 2022, 23.
Liam Dillon, Twitter post, March 2, 2022, 3:42 pm. https://twitter.com/dillonliam/status/1499168236025245702.
Louise Amoore, The Politics of Possibility: Risk and Security Beyond Probability (Durham: Duke University Press, 2013).
Marisa Kendall, “Housing still lags in spite of laws,” East Bay Times, March 5, 2023.
See, for example, the New York Housing Compact announced by Governor Kathy Hochul in January: https://www.governor.ny.gov/news/governor-hochul-announces-statewide-strategy-address-new-yorks-housing-crisis-and-build-800000. California’s preeminence is documented in Shazia Manji et al., “Incentivizing Housing Production: State Laws from Across the Country to Encourage or Require Municipal Action,” Terner Center for Housing Innovation at UC Berkeley and the Urban Institute, February 2023.
Jonathan Woetzel, Jan Mischke, Shannon Peloquin, and Daniel Weisfield, “A Tool Kit to Close California’s Housing Gap: 3.5 Million Homes by 2025,” October 2016. McKinsey Global Institute Report.
Southern California Association of Governments, “A Rundown of Federal and State Legislation,” November 1, 2021, 9. https://scag.ca.gov/sites/main/files/file-attachments/scag-gpla_forum_1_presentation.pdf?163604777. This survey ignores fifty years of policy formulation.
California Department of Housing and Community Development, “California’s Housing Future: Challenges and Opportunities,” February 2018: 15, 49; Steve Wertheim, Analysis of SB 330 for the Senate Third Reading, August 12, 2019; and Alison Hughes, Analysis of SB 50 for the Senate Committee on Housing, April 2, 2019. Scott Wiener, the state senator who has most frequently cited the McKinsey report, in particular the goal of 3.5 million new homes, is also the Legislature’s most aggressive promoter of California’s housing crisis regime. See, for example, Senate District 11, “Senator Scott Wiener (D-San Francisco) Introduces Housing First Legislative Package to Continue Work to Fix California’s Housing Shortage,” January 4, 2018; Scott Wiener and Daniel Kammen, “Why Housing Policy is Climate Policy, The New York Times, March 25, 2019; and Scott Wiener, “Complete report on California’s housing shortage & strategies to address it,” with link to the McKinsey Tool Kit. April 20, 2019. Tweet.
Tom Danker, Thomas Dorhman, Nancy Killefer, and Lenny Mendonca, “How can American government meet its productivity challenge?” McKinsey & Company, July 2006, 15.
Isabelle Bruno, “The ‘Indefinite Discipline’ of Competitiveness Benchmarking as a Neoliberal Technology of Government,” Minerva 47.
Woetzel et al., “A Tool Kit,” 2-4.
Dowell Myers, JungHo Park, and Janet Li, ‘How Much Added Housing is Really Needed in California?” Report, Sol Price School of Public Policy, University of Southern California, May 2018. For another in-depth critique of the Tool Kit, see the Embarcadero Institute Board, “California’s 3.5M Housing Shortage Number Raises Questions,” Embarcadero Institute, July 2019.
Walt Bogdanich and Michael Forsythe, “How McKinsey Got Into the Business of Addiction,” The New York Times, September 29, 2022.
William Davies, The Limits of Neoliberalism: Authority, Sovereignty, and the Logic of Competition (London: Sage Publishing, 2014).
Woetzel et al., “A Tool Kit,” 2, fn2.
For an early iteration, see Bay Area Council, “Housing: The Bay Area’s Challenge of the ‘80s,” San Francisco: Bay Area Council, 1980. For a more recent version, see Bay Area Council Economic Institute, “A Roadmap for Economic Resilience: The Bay Area Regional Economic Strategy,” San Francisco: Bay Area Council, November 12, 2015. Neither of these reports appears in the Tool Kit’s bibliography. Also absent is California Forward’s restatement of the BAC housing agenda, initially laid out in the “California Economic Summit Playbook 2013.” https://cafwd.org/resources/summit-playbook-2013/. McKinsey only cites the report prepared by LeSar Development Associates for the San Diego Housing Commission, “Addressing the Housing Affordability Crisis in San Diego and Beyond: An Action Plan for San Diego Civic Leaders,” November 25, 2015.
David Pettit, “CEQA—the Litigation Myth,” National Resources Defense Council, January 2013.
J.K. Dineen, “With Bay Area housing construction stalled, developers look to squeeze more units into existing projects,” San Francisco Chronicle, November 5, 2022; Tim Redmond, “New study says market conditions, not city requirements, prevent housing construction, 48 hills, January 21, 2023.
California Department of Housing and Community Development, “Raising the roof: California housing development projections and constraints, 1997-2020,” 2000.
The Housing Workshop, “CEQA: California’s Living Environmental Law: CEQA’s Role in Housing, Environmental Justice & Climate Change,” The Rose Foundation for Communities and the Environment, October 2021, 26.
Salim Furth, “Are the new carbon footprint maps accurate?” Market Urbanism, December 14, 2022.