How California’s sixth-cycle RHNA was rigged


Here is the unedited version of my RHNA op-ed that appeared in Berkeleyside on Oct. 25, 2021. The op-ed got a nice mention in San Francisco’s 48 Hills online magazine. We no longer have the luxury of thinking Albany can be insulated from the venality and dysfunction of the state legislature in Sacramento. However, that story will have to wait for another post, along with some information on an interesting ballot initiative that can help solve the problem. For now this 4,000+ word op-ed is enough.

Slouching towards Berkeley: The coming of RHNA/Housing Element disaster and how the city can limit the damage

“That zoning is a tool to create housing production is a widely held and completely fallacious idea. Just because something is permitted doesn’t mean it happens.”

architect Daniel Solomon, quoted in the San Francisco Chronicle

Berkeley residents are about to discover how dysfunctional the latest California Regional Housing Assessment (RHNA) has become. The RHNA (pronounced REE-na) process, and the housing elements based on it, have always been bureaucratic, expensive and ineffective. But thanks to the intervention of State Senator Scott Wiener, RHNA has been twisted into a profit-making tool for his corporate allies. With the City of Berkeley starting to work on its housing element, which will force the city to make drastic revisions to its zoning, residents must struggle to limit the damage.

What Wiener and his allies have managed to accomplish is remarkable. If the state senator had proposed a bill to his fellow legislators that would force many of the state’s cities to rezone for bigger buildings and then would restrict cities to rubber-stamping the new building permits, I don’t think he would have found the votes. Yet he has managed to accomplish the same thing in a piecemeal fashion.

In what follows, I’ll provide my perspective on RHNA and the housing element process, explain how Sacramento has corrupted them, and explore what Berkeley residents can do to protect themselves.

The website of the California Department of Housing and Community Development (HCD) states:

“Since 1969, California has required that all local governments (cities and counties) adequately plan to meet the housing needs of everyone in the community. This process starts with the state determining how much housing at a variety of affordability levels is needed for each region in the state, and then regional governments develop a methodology to allocate that housing need to local governments. California’s local governments then adopt housing plans (called housing elements) as part of their “general plan” (also required by the state) to show how the jurisdiction will meet local housing needs.”

Bay Area cities are currently planning for the sixth RHNA cycle, which will start on June 20, 2022, and end on December 2, 2030. Here’s how it works:

Step 1: The California Department of Finance provides detailed forecasts of population and households that span the eight-year RHNA period. These forecasts are passed along to the Department of Housing and Community Development (HCD).

Step 2: HCD starts with the household projections and estimates the number of housing units required to house them. HCD adds housing units to cover older units that will be torn down and to provide enough vacant units to allow housing turnover to take place smoothly. It may make additional adjustments. HCD then allocates the statewide estimates to different regions and passes them along to regional councils of governments.

Step 3: The regional councils next allocate their allotment to cities and unincorporated areas in their region. For the Bay Area, the council of government is the Association of Bay Area Governments (ABAG). ABAG allocates household units to all nine counties in the region and 101 municipalities (note that San Francisco is both a city and a county).

Step: 4: In their housing elements, cities and counties then plan for where the additional housing can be built. They must modify zoning if there is insufficient space for the new housing.

Creating a housing element is labor-intensive and requires specialized expertise. Many cities hire planning consultants to assist them. Recently I spoke with one highly regarded consultant who confided that their office was limiting housing element work because the “process was broken.” Although the criticism was heartfelt, it is more accurate to say that the process never worked well. This had become obvious by 2003, 18 years ago. The introduction to a 2003 report from the Public Policy Institute of California on the state’s housing element policy stated:

“During the 1990s, noncompliant communities were just as likely to expand their housing stock as communities that complied with the law. Furthermore, when other factors were held constant, noncompliance was not a significant predictor of the rate of multifamily development.”

Senate Bill 35

In past decades, the housing element process didn’t make much difference. For market-rate housing, most cities met their numerical targets, but often not in ways the house elements emphasized. When it came to subsidized affordable housing, the targets were seldom met because the required subsidies were beyond the means of most cities, and state and federal funding was scarce.

In 2017, things changed. The state legislature passed a housing bill package of 15 bills (links here and here), and Governor Jerry Brown signed them. Probably the most influential and controversial was Senate Bill 35, authored by San Francisco Sen. Scott Wiener. The bill streamlined multifamily housing projects approvals ministerially (a euphemism for rubber-stamping applications without public hearings) in cities that failed to issue building permits for their share of the RHNA housing allocations.

The bill had a serious defect, as the League of California Cities pointed out in its request for a veto to Governor Brown. The League’s letter stated that the bill should:

 “Require the trigger for ministerial approval of housing projects to be based on the number of entitled and approved applications, a process that a local agency actually controls, rather than building permits, which a developer controls and will not pull until they are ready to construct a project.”

Although this may seem like a trivial point, it has major consequences. To understand why, two things need to be explained: First, cities do not build housing, developers build housing. Cities approve project applications but cannot require developers to build the approved projects. Since approvals typically have a long shelf life of one to three years, developers can bank their approvals and be picky about when they convert them to building permits.

Second, the goal of developers is not to build housing. The goal of developers is to make money. Building housing and making money are not the same thing. Developers are portfolio managers. They hold a range of assets including undeveloped land, project approvals, unfinished projects, market-ready completed projects, and cash and other financial assets.

Developers reallocate their assets to maximize the value of their holdings. Developers are loath to dump so much of their product on the market at one time that they drive their prices down against themselves. Developers are also captive to their lenders and suppliers. If banks don’t want to lend, or if labor and building materials are too expensive, they may have to put their plans on hold until they “pencil out.” According to this recent article:

The builders do not care anything about the existing-home-sales market, and they don’t care about the housing shortage. They’ll always go slow and steady … People want an oversupplied market, and we just don’t do that in America.

For all these reasons it makes no sense for SB 35 streamlining to be triggered by developers failing to pull building permits. But in the aftermath of SB 35 becoming law, this is the reality cities face. Typically a city is not evaluated until the mid-point of the RHNA cycle, when the city reports to the Dept. of Housing and Community Development the number of building permits issued since the start of the RHNA cycle. At that point, a city is required to have issued at least 50 percent of its share of the RHNA goal for building permits in the region. This determination is made separately for each income category of households — above moderate, moderate, low income, and very low income.

If there are an insufficient number of building permits in any income category, for the rest of the RNHA cycle the city must issue building permits ministerially, or by right, for projects in that income category. The city can only require the developer to meet “objective standards,” those that involve “no personal or subjective judgement by a public official.” If these standards are met, then the building permit is issued without any public meetings or any other review. It would be almost impossible to write a set of objective standards that would cover every possible contingency. As an experienced land use attorney told me, due to a lack of review by planning boards and the public, SB 35 can lead to “unpleasant surprises.”

Cities are often blamed for dragging their feet and creating bottlenecks by issuing approvals too slowly, but the data show otherwise. In their report “New Development in California 2018,” the Construction Industry Research Board (CIRB) stated:

Considering only the projects that are under construction or approved awaiting building permits, there are currently 451,000 new homes and 308 million square feet of non-residential space that will likely be built over the next five years.”

Under SB 35, whether cities approve enough housing to meet the RHNA targets makes no difference. And even if developers and trade unions agree they couldn’t possibly build that much new housing, it doesn’t matter. If building permits are not pulled, cities are blamed. The passage of SB 35 left cities vulnerable to schemes that set up cities to fail. All that was needed was a bill that politicized the RNHA process and inflated the numbers. That bill was SB 828.

Senate Bill 828

The Bay Area Council is the leadership organization of the Bay Area’s corporate elites. It lies in the middle of an ecosystem of other pro-development organizations including the Metropolitan Transportation Commission, the Silicon Valley Leadership Group and SPUR. Among legislators in Sacramento, Sen. Wiener, who is notorious for his ties to big real estate (links here and here),  is their main water carrier.

The council usually tries to wield its influence from the shadows, but regarding SB 828, the council openly bragged about its success. In the council’s Jan. 29, 2021, Weekly Flash online newsletter, in an article titled (appropriately enough), “Playing the Housing Numbers Game,” The council made this statement:

“In the fall of 2017 the Bay Area Council’s housing team met with state Senator Scott Wiener to discuss the ongoing housing crisis, its root causes and what needed to be done to fix things…Out of that meeting came SB 828 (Wiener), a law that makes the calculation process more scientific and accurate. Lo and behold, this year the Bay Area’s RHNA allocation jumped from 188,000 units to 441,000 units.”

The Bay Area Council is not interested in making the RHNA calculations more scientific and accurate. The council’s task is to see that its corporate members make more money. Sen. Wiener helped them do that by inflating the target number of housing units in the new RHNA process and by changing the wording of the relevant government code to emphasize production. As with SB 35, the League of California Cities unsuccessfully urged Governor Brown to veto SB 828, and for much the same reason — cities plan and zone for housing, but they do not build housing, and RHNA was created to be a planning tool, not a production tool.

The inflated housing goals in SB 828 set cities up to fail. This was no surprise to Sen. Wiener or other senators when they first heard the bill in the Senate Housing and Transportation Committee on May 24, 2018. A speaker from the California Chapter of American Planning Association expressed this concern, which you can see in the video recording. Go to this link, select the April 24, 2018 hearing and hit “watch.” At 1:30:00 in the video the presentation on SB 828 starts. The opposition speaker from the planning association starts at 1:43:45 and at 1:45:45 states that SB 828 “sets us up for failure.” The American Planning Association continued to oppose this bill throughout the committee amendment process.

SB 828 empowered the Department of Housing and Community Development (HCD) to set outrageous RHNA targets for cities, sometimes more than doubling or tripling the goals from the previous fifth cycle RHNA. The sixth cycle target for the state’s four main planning regions is 2,108,200 housing units. The outside review by the Embarcadero Institute corrected for HCD’s overcounting and placed the target at 1,168,000. That number is still 29 percent larger than HCD’s fifth cycle target of 905,850 housing units, although the state’s population only grew between three to four percent during the fifth cycle. For Berkeley, the goals have more than tripled, from 2,959 total housing units in the fifth RHNA cycle, to 8,934 in the sixth cycle.

To comply with these new targets, in their housing elements cities must first “upzone,” or rezone for higher densities by raising height limits to allow apartment buildings to become taller, and in some cases reducing setbacks from property lines to allow buildings to become wider and deeper. This allows apartments to contain more units.

For example, in an urban residential neighborhood, there might be a height limit of 35 feet, with setbacks of four feet from the property boundaries. These constraints determine the size of a large imaginary box inside which you are allowed to build a house. In areas that allow apartments, there are much larger boxes of developable space inside which developers can build.

The allowable size of this box is a feature of the property, at least until another rezoning, and helps determine the value of the property. With upzoning, the developable box gets taller (as height limits are relaxed), and sometimes wider and longer (if setbacks are relaxed). The larger developable box makes the property more valuable — without any effort from the property owner. It’s a windfall increase in the property value, especially for undeveloped commercial properties. According to land-use economist Cameron Murray, connected landowners capture the benefits of land rezoning:

Land rezoning involves two distinct decisions: the choice to re-zone more land for higher-density development, and the choice of the precise area to be rezoned. Political pressure to expand higher value zoning areas is usually argued to come from owners of undeveloped land who may directly benefit, in concert with a wide range of secondary beneficiaries such as banks and construction companies, in a type of ‘growth coalition.’ The secondary decision, where exactly to rezone, involves the allocation of property rights from the community to the owners of the land within the rezoning boundary at the moment of rezoning.”

But there’s another bonus for developers. If developers don’t pull enough building permits early in the RNHA cycle, the city and its residents are penalized by being forced to accept streamlined ministerial approval processes. This is true even if cities issue a generous number of approvals early in the RHNA cycle. Note that this creates perverse incentives for developers to delay construction (perhaps an unintended consequence of SB 828). On the other hand, if cities fail to comply with RNHA rules, there are painful consequences. The combination of SB 35 and SB 828 has led to what cities call “carrots for developers and sticks for cities.”

Housing and Community Development’s role

SB 828 went through many versions as it passed through committees in the state senate and assembly, but it was never a popular bill. In the concurrence process for the bill, when the senate approved the assembly amendments, SB 828 passed with 22 yes votes, only one more than necessary. According to the rules of the state legislature, a bill must have a majority of the voting body to pass. Since there are 40 senators, passage requires 21 votes.  

Page three of the 8/30/18 Senate Floor Analyses on SB 828 list these three items that had been refined during the lengthy amendment process:

1) Revises the data COGs must provide to HCD as follows:

a) Adds, to the existing requirement to provide overcrowding rates, the overcrowding rate for a comparable housing market, as defined.

b) Adds, to the existing requirement to provide vacancy rates for the existing housing stock and for a healthy housing market, a definition of a healthy housing market vacancy rate as no less than 5%.

c) Adds a requirement to provide data on the percentage of cost burdened households and the rate of housing cost burden for a healthy housing market, as specified.

These three requirements were written vaguely enough to leave a wide latitude for their interpretation. Somewhere is the bowels of the Dept. of Housing and Community Development, unelected staffers implemented these directives in a way to produce absurdly high sixth cycle RHNA numbers. The lack of professionalism was not accidental. As one ABAG official told me, “These weren’t mistakes, they did it on purpose.”

In September 2019, the Southern California Association of Governments (SCAG) filed a formal objection to the sixth cycle RHNA allocations from the Dept. of Housing and Community Development, which, compared to the fifth cycle, more than tripled the number of housing units required in the six-county Southern California region to 1,341,827. The SCAG objections worked within the framework provided by the Dept. of Housing and Community Development (HCD) but did a thoughtful, professional job to correct the problems with HCD’s approach. HCD’s response was to ignore the suggestions in a cynical and self-serving letter.

Having seen how little success their Southern California colleagues had achieved, the Association of Bay Area Government (ABAG) executive board decided not to file an objection to the Bay Area numbers from HCD. Only one member of the ABAG leadership, Novato Mayor Pat Eklund, voted against accepting the figure of 441,176 housing units in the nine-county Bay Area, which more than doubled the fifth cycle targets.

But there may be another reason for ABAG’s lack of will — it no longer exists as a separate entity. The name ABAG is still used in some planning circles, but it is a polite fiction. ABAG was absorbed in a hostile takeover by the Metropolitan Transportation Commission (here and here). ABAG/MTC, as it is often called, is now a subsidiary of the commission, which itself was formed at the behest of the Bay Area Council, the organization that sponsored Sen. Wiener’s SB 828.

In September 2020, the Embarcadero Institute produced a report, “Double Counting in the Latest Housing Needs Assessment.” This report covered the RHNA allocations to all four major regions in California — Southern California (six counties), the Bay Area (nine counties), the Sacramento region (six counties), and San Diego County. Along with the formal complaint by the Southern California Association of Governments, the two reports uncovered similar problems with HCD’s analysis. Although these two reports are well worth reading, I won’t delve into them here. Instead, I’ll provide three common-sense examples of what’s wrong with the latest RNHA targets.

1) In February 2018, HCD published an excellent comprehensive report, California’s Housing Future: Challenges and Opportunities. In the executive summary the report noted that California needed 1.8 million new housing units statewide over a 10-year period, or 180,000 new units annually. During the 8.5 years of the sixth RNHA cycle, that would amount to 1,530,000 new housing units, half a million less than the RHNA totals for the four main planning regions alone. HCD’s RHNA figures aren’t even consistent with their best previous work. Given the problems with some of Sacramento’s other agencies, it is sad to see the Dept. of Housing and Community Development becoming more politicized and less trustworthy.

2) Affordable housing requires major up-front subsidies in the range of $600,000 to $750,000 per unit for low-and very low-income affordable housing. The RNHA goals call for 870,398 of these units in the four major RHNA planning regions. That would require subsidies in the range of $377 to $489 billion. It will not be possible to find these levels of subsidies.  

3) The sixth cycle RHNA benchmark of 2,108,200 housing units will require the production of 248,024 units annually for 8.5 years — just in the four main planning regions, which comprise 82 percent of the state’s population. Since 1967, housing production at this level occurred in only a few years in the 1970s and ’80s (“Mission Impossible? Meeting California’s Housing Challenge,” SCAG, p. 15).

The building industry was decimated in California by the Great Recession of 2008. Building more than 248,000 housing units annually for 8.5 years has never occurred in California, and it’s extremely unlikely it could be achieved in the sixth RHNA cycle. Post-Covid, the construction industry labor shortage is predicted to get worse:

“As homebuying reaches a fevered pitch across the U.S., contractors in New York and several other states are facing the most severe shortage of skilled construction labor since the Great Recession.”

Back in the fall of 2017, when Sen. Wiener planned with the Bay Area Council to inflate the RNHA targets, it appears they were a little too successful. It’s physically impossible to achieve the overall RHNA targets. It is financially impossible to achieve the affordable housing targets. Even the market-rate (above moderate) targets will be difficult. Developers will not oversupply and flood the state with market-rate housing to the point they drive down their profits.

Part of the problem may be that Sen. Wiener and his staff did not meet with the Department of Finance demographers to understand their methodology, which underpinned the household forecasts used in the RHNA process. This would explain the double-counting detected by the Embarcadero Institute. I had an opportunity to ask Sen. Wiener about this, and his non-response left me with the impression that his staff did not meet with the demographers or understand their methodology. But judge for yourselves. Here is a video interview with Sen. Wiener. My question begins at 57:25.

However the goals were reached, the post-SB 828 RHNA targets are exaggerated to the point of absurdity. Cities and counties are being set up to fail. Yet hundreds of cities and counties across the state will spend millions of dollars in staff time and consultant fees to pursue a housing element fantasy.

What Can Berkeley Do?

There are three important things that Berkeley’s residents can do:

1) Have realistic expectations about the amount of affordable housing that can be built, and where it can be built. Berkeley’s RNHA allocation calls for 3,854 housing units for low- and very low-income residents. The subsidies for that much affordable housing would be in the range of $1.6 to $2.2 billion dollars. It’s highly unlikely that non-profit affordable housing developers will find the required amounts.

In addition, some city council members may have the mistaken notion that the RNHA process can be an effective tool for integrating high-income, mostly White neighborhoods. That also is unlikely to happen. I would encourage the residents of Berkeley not to waste time and energy chasing naïve city council members down rabbit holes. Learn about the process, keep your expectations reasonable, and choose your battles carefully.

 2) As the current President of ABAG and the head of the ABAG Executive Board, Mayor Jesse Arreguín accepted the Bay Area’s RHNA target of 441,176 new housing units. He was also in charge of allocating these new units to the Bay Area’s nine counties and 101 cities, including the figure of 8,937 housing units for Berkeley, a 17 percent increase over existing units. He has a history of making deals that may not be in the public interest (here and here).

At an October 15 ABAG administrative committee chaired by Mayor Arreguín, four Marin Counties had their RHNA allocation appeals rejected. You can review the video here. At 0:54:16, the following statement appears on a slide:

 “Areas at risk of natural hazards are not identified in housing element law as a constraint to housing development.”

Mayor Arreguín makes additional comments at 1:06:45 that are worth watching. Berkeley’s hillside fire risks are well known. But there are also earthquake liquefaction risks in parts of the western flatlands. It is imperative to ask Arreguín how the city can plan for almost 9,000 new housing units without creating new fire or liquefaction risks.

3) Get up to speed on land value recapture, as many local governments are doing (including Berkeley). In the words of land-use economist Cameron Murray (quoted earlier), the decision where exactly to rezone:  

Involves the allocation of property rights from the community to the owners of the land within the rezoning boundary at the moment of rezoning. In the absence of mechanisms such as land value taxes or betterment taxes that recoup the value of the resulting price-differential, there is scope for bargaining between politicians and landowners of different areas, including the potential for corruption and bribery during the final determination of rezoning boundaries.”

The residents of Berkeley will have to be on the lookout for precisely this sort of “potential for corruption and bribery,” in addition to learning more about land value and betterment taxes.

I am including a link to the Murray article and to two other academic papers that give you a broad view of land value recapture. The City of Berkeley has been exploring land value recapture since at least 2017. Also see this.

I wish the residents of Berkeley the best of luck in confronting the RHNA monstrosity that is slouching toward their city.

(Postscript, April 15, 2022: The timestamps on the RHNA appeals process video have been updated because MTC/ABAG changed them. On March 9, 2023 some links were updated.) 


Concerning the elimination of parking minimums in Albany

Submitted to the Albany Planning Commission for their June 22, 2023 meeting.

See links here:

The June 22 agenda of the Transportation Commission features a discussion of eliminating parking minimums in Albany. This action is in response to a recently passed state bill, AB 2097, that bans local governments from requiring a minimum amount of parking in new buildings within a one-half-mile radius of major transportation facilities—including major bus routes.

AB 2097 is typical of the incompetent and cynical overreach of the state government into local government affairs, a course of action that has mainly benefitted private-sector developers. The justification for AB 2097 and many other bills has been that removing the impediment of local decision-making would dramatically increase the growth of housing production in California. But the promised boom in new housing development, especially of multi-family buildings (apartments), has not occurred and is unlikely to occur in the next several years.

In this note, I focus on the memo provided to the commission in the June 22 agenda package. There is much there that I disagree with, and I think the record needs to include alternative opinions. However, I will not attempt a point-by-point rebuttal. Rather I will try to address the broader issues raised by the memo and AB 2097.

But first, here are some facts I took from the Transportation Element of Albany’s General Plan, which was also included in the agenda package for the June 22 meeting:

(p. 3) According to US Census (American Community Survey) data for 2009-2013, only 15 percent of Albany’s employed residents worked within the Albany city limits, while 85 percent commuted to a workplace in another city.

(p.4) Albany households have fewer vehicles on average than households in Alameda County as a whole. Although 96 percent of the city’s households own at least one vehicle, which is the same as the countywide average, only 15 percent have 3 vehicles, compared to 32 percent countywide. Moreover, 38 percent of the city’s households have only one vehicle, compared to 23 percent countywide. About 43 percent have two vehicles, compared to the county average of 40 percent.

(p. 27) Although the transportation planning focus is shifting to other modes of travel, it is still likely that most trips in the city will continue to be made by motorized vehicles. (Note: the table on p.27 shows that between 2014 and 2040 driving trips will fall from 68 to 59 percent, transit trips will rise from 7 to 8 percent, and bicycle and pedestrian trips will rise from 25 to 33 percent of all trips.)

(p. 29) As noted earlier, 58 percent of the city’s households have two or more cars—many households park at least one car on the street. Most lots in the city are not large enough to add off-street parking spaces, resulting in high on-street parking demand.

I am writing as an Albany resident since 1995, a homeowner since 2000, a single parent, an avid cyclist and bike commuter, and now, after a recent accident, a temporarily disabled person with a handicapped parking permit. All these experiences give me a broad perspective on living in Albany.

Before coming to the Bay Area, I spent more than two years living in the badly overparked neighborhood of Capital Hill in Seattle. Although I took a vanpool to my job as a state analyst in Olympia, I still had to commute to the vanpool pick-up point. After work, there were many days when I spent several minutes looking for a parking spot in my neighborhood, and sometimes I had to park a quarter mile from my apartment. Here in the Bay Area, I have good friends who live in the Inner Richmond District of San Francisco, another badly overparked neighborhood. Visiting them can be frustrating. Often I can park in their driveway, but if not, I can spend 10 or 15 minutes circling the neighborhood.

I don’t want to see Albany become overparked, but I am afraid that is the direction we are heading. Street parking is a public good. If a developer builds an apartment building with inadequate parking, the tenants will park on the street. The building owner does not have the correct incentives to produce parking for the tenants. Instead, owners can cost-shift by letting the public provide parking while pocketing the savings from not having to build parking spots inside the building.

The fight against air pollution is a useful analogy. Air pollution is an example of what economists call a negative externality. In the past, many industries polluted the air because it was free. The problem of air pollution was not solved until the polluters were forced to internalize the costs they had previously imposed on society. The notion that the public sector should be banned from regulating pollution, and that polluters would voluntarily produce a socially optimal level of pollution, would be an absurd fantasy.

And yet with parking, this is the fantasy the state wants us to believe. If developers build apartments with inadequate parking, tenants will park on the street, creating a negative externality. Without local government’s ability to force developers to internalize this problem, cities will be required to allow overparking on their streets. Just like in Capitol Hill and the Inner Richmond.

The notion that many apartment dwellers in new buildings in Albany will be content to live without cars is difficult to believe, given that our Transportation Element shows that 96 percent of Albany households own at least one vehicle, and 58 percent own two or more. While it is possible that new residents might be more willing to go carless, that is a poor bet given the state of public transportation in the Bay Area, a situation that has been called a “death spiral.” Meanwhile, car- and bicycle-sharing programs have not yet proven to be feasible in Albany. Nor does the city’s history of code enforcement provide much optimism for complicated parking regulation schemes.

In addition, eliminating parking is a form of discrimination. As a single parent who worked full-time, I could not have given my son the quality of life that I did if I didn’t own a car—and one that I could reliably park nearby. If I had been a single parent in an apartment without parking, the quality of life for my son and I would have been drastically reduced.

The memo also assumes that low-income residents tend not to own cars and trucks. This is not my experience in Albany. The men and women who clean our houses, remodel our kitchens, maintain our yards, and provide care for our elderly and disabled do not have fixed workplaces and need vehicles to transport their equipment. It would be difficult for them to transport a lawnmower or a vacuum cleaner on BART. Given the amount of tool theft blue-collar workers are now facing, enclosed parking has become even more important.

But we really don’t have to provide apartments and parking spots for these disproportionately Latina and Latino workers. Why not? Because the nearby city of Richmond does it for us. According to the American Community Survey, two-thirds of the housing units in Richmond are single-family homes, while 43 percent of the population is Hispanic, 20 percent is Black, and 18 percent is White non-Hispanic.

But it’s not just blue-collar workers who cannot work remotely. Hospital medical staff must provide care to their patients 24 hours a day. Evening and night shift nurses and other medical staff typically can’t rely on public transportation late and night and early in the morning. Teachers often must carry textbooks and student papers with them, stay late for after-school meetings and events, and therefore find it difficult to rely on public transit, since many schools are not near bus routes.

As an avid cyclist, I have enjoyed cruising down to Solano Ave. for espresso and light shopping at CVS and Safeway. But due to my cycling accident, I cannot ride a bike for three or four more months, and my ability to walk long distances is limited. I am grateful that I have a temporary handicap permit and for the handicapped parking spots around Solano. This is why I am puzzled that the memo in the June 22 agenda indicates that disabled people don’t need vehicles. Now that I am temporarily disabled, I need my car more than ever.

The memo to the Transportation Commission has one glaring omission. It neglects to mention that like people everywhere, Albany residents have guests. If new apartment buildings have no parking and the streets are overparked, how will out-of-town guests be able to visit? Elderly parents will have a hard time visiting their adult children if they must park thousands of feet away and walk to the building.

In Albany, this is not a hypothetical situation. The condominium towers at 555 Pierce St. were built with about one parking space per unit. The newer mid-rise condominiums at 545 and 535 Pierce St. were built with two parking spaces per unit. That is because those two condominium complexes were built after the Albany voter-approved Measure D, which required two off-street parking spots for each housing unit.

I have suggested more than once that this is an interesting natural experiment, and that the city should study the occupancy rates of the parking lots in all three locations. I know from personal experience that it is easy to visit friends in the two new complexes, especially if the occupant only has one car. In that case, each unit effectively has one guest parking spot. I do not know how easy it is to visit the occupants of the older towers at 555 Piece, where there are minimal guest and street parking spots. It would be good to know the answers to these questions since the rest of Albany might be facing similar parking conditions in the future.

In closing, I want to return to the subject of the cynicism and dishonesty of the state’s invention in local housing policy. The real basis of the intervention, regardless of the stated intention, is to pad the profit margins of developers, who lobby the legislators in Sacramento and help fund their campaigns.

For example, SB 35, carried by Sen. Scott Wiener, was billed as a way to streamline and encourage affordable housing. Yet with the subsequent passage SB 828, also carried by Wiener but sponsored by the Bay Area Council and the Silicon Valley Leadership Group, ridiculously large targets were required by the state for market-rate housing, which allowed the streamlining provisions of SB 35 to be applied to market-rate housing as well. This was classic bait-and-switch. (I have written about this at my city council blog,

AB 2097, the bill that removes cities’ authority to enforce parking minimums near transit, is another bait-and-switch. The stated intention of the bill was to encourage the use of transit. The real intention, once again, was to pad the profit margins of developers by allowing them to cost-shift parking to the public sector.

In a June 19 article in California Policy and Development Review titled “Will TOD Survive the Transit Downturn?” author Josh Stephens writes: 

The fundamental question is whether transit-oriented development actually needs transit to succeed.

Some experts and advocates say TOD will still work because transit stations are located close to other amenities people find attractive such as supermarkets. Meanwhile, Michael Manville, professor of urban planning at UCLA, suggested that the “doom loop” scenario presents the state with an opportunity to potentially drop the pretense of transit ridership and instead extend TOD-style incentives to a wider range of infill locations.

“If you like these programs, it may be time to untether them from transit, and just say we’re just going to have the program along major commercial corridors, or something like that,” said Manville. Whatever the benefits of TOD are to residents, the benefits to developers all but ensure that state and local programs to promote TOD will stick around.

Manville noted that the average resident of TODs, especially those that include large numbers of market-rate units, are not likely to use transit very often under the best of circumstances. Nonetheless, transit orientation gives developers a host of benefits — including density bonuses and, following the 2021 passage of SB 2097, the opportunity for significant parking reductions, all of which can make projects pencil out for developers.

Correction: A resident of the 535 Pierce St. condominiums tells me that most of the units in that complex have only one parking spot, while some have two. I am puzzled by this because this complex was built after the passage of Measure D in 1978. I will need to explore this further.

Michael Barnes

Member, Albany Transportation Commission


Albany 2022 campaign wrap-up

Campaign expenditures

For the last several years, I have posted campaign spending information at the end of each Albany election. The final campaign spending numbers became available on January 31, 2023, so please excuse my tardiness. The records can be found on the city website here.

As in the past, the data are taken from the final FPPC 460 forms, line 11, total expenditures. The numbers were fairly simple this year. In the city council race, the Albany Forward slate of Robin Lopez, John Miki, and Nick Pilch spent $18,325, while Jennifer Hansen Romero spent $15,709.

In addition, an independent expenditure committee, The National Association of Realtors, indicated they spent $13,712 to support Hansen Romero’s campaign. Since that committee did not coordinate with Hansen-Romero’s campaign, it is difficult to know exactly how they spent this money, or whether all of it was spent on this campaign. The realtors did mail out a flyer. There were reports that they paid for online ads, but no one I queried saw them.

The committee that supported Measure K, the Emergency Medical Services, Advanced Life Support, and Fire Protection Special Tax, spent $9,717. Because the tax was dedicated to a specific purpose it required a two-thirds majority vote. The measure passed easily with more than 76 percent yes votes. No campaign expenditures were listed for the school board candidates Lucy Baird, Becky Hopwood, Sadia Khan and Ron Rosenbaum. Candidates are not required to list campaign expenditures unless they spend more than $2,000. Since one candidate had to drop out, this was basically a walk-on election with three candidates for three open positions, so there was no need to campaign.

Ranked choice voting

These were the first city council and school board elections under Albany’s new at-large ranked choice voting system (RCV). I opposed this form of RCV in a previous long post here. However, new council members John Miki, Jennifer Hansen-Romero, and Robin Lopez are working well together, along with continuing council members Preston Jordan and Aaron Tiedemann. The results turned out well enough this time, but only because we got lucky. I don’t think we will always be so lucky.

For me a big concern is how much it costs now to run for city council. When I last ran for city council in 2016 I joked that my goal was to spend as little money as possible to come in third. That’s what I accomplished while spending slightly less than $2,000. There are many competent citizens in Albany who would make fine council members. But the hours spent in meetings are long and the pay — $300 per month — isn’t great. Now that a campaign can cost more than ten thousand dollars, many people quite rationally say no thanks.

I have several problems with our new RCV system:

First: of the 8,133 ballots cast in the election, 875 voters didn’t vote at all for city council members, and 99 voters filled out their ballots incorrectly. Only 88 percent of the voters successfully voted in the RCV council elections. To be fair, I don’t know how this compares to previous elections because I couldn’t find the data. But it’s concerning that almost one-eighth of the city’s voters in the Nov. 2022 election either didn’t attempt to vote or didn’t understand the rules well enough to vote in the city council election.

Second: Hansen-Romero won the second highest total of first round votes, which would have elected her in previous Albany elections. But under RCV rules, Pinguelo and Pilch were eliminated and their votes rolled up the remaining three candidates. Lopez’s total number of votes grew larger than Hansen-Romero’s total, so Lopez was elected to the council instead of Hansen-Romero. However, the council voted to have Hansen-Romero take the place of Ge’Nell Gary, who has resigned from the council just before the election.

Many people, including me, think it is not fair that a voter’s second or third choice should carry the same weight as another’s first place vote. In the Olympics we don’t have the first, second and third place winners stand together while each is awarded the gold medal. You can imagine a voting system based on points, where each voter awards a certain number of points for their first choice, fewer pointer for their second choice, and so on. The points are tallied for all voters and the candidate with the most points wins. Many multiple-event athletic competitions are scored this way. But that is not how RCV works.

Third: The proponents of our new RVC system figured out very quickly how it game it, making it difficult for even a highly qualified individual to win a seat on the council. Under the arcane rules of at-large RCV, if there are two open seats, a candidate must get more than one-third of the vote to guarantee they have won. This can make it almost almost impossible for a strong individual candidate unless they get over the one-third hurdle in the first round.

Our at-large RCV system encourages what I call a pseudo-slate strategy. This strategy encourages up to five candidates to declare themselves a slate, regardless of their politics or positions. The slate then encourages voters to vote only for members of their slate, in whatever order the voters choose.

Here’s an example: In a two-person at-large RCV election, A popular individual runs against a slate of five candidates (five is the maximum number of candidates voters can rank with Alameda County’s software). In the first round of voting, the popular candidate wins 30 percent the vote. That candidate is close but not over the one-third (33.3 percent) threshold to win. The other five candidates receive only 14 percent of the vote each. Note that the single popular candidate earns more than twice the number of votes of any of the other five.

Yet if all the slate voters voted only for the five slate candidates (in any order), then 70 percent of the votes remain within the slate. During the RCV vote counting procedure the two favorites among the slate candidates will win with approximately 35 percent of the vote each. The most popular candidate will lose. Although this seems like an unlikely hypothetical, this is a simplified version of what happened last November in Albany.

The obvious strategy is for even a strong candidate to pick four other candidates to run with, and to encourage the voters only to vote for members of their slate. What will emerge from this is a partisan process where two groups form their own competing slates and urge voters to be loyal only to their slate. Sound familiar? Here in Albany, our at-large RCV system might encourage something that looks a lot like the partisan two-party political system of national elections.


Another problem with Albany’s RCV system is that not only is it complicated, it is arbitrary. At the moment when a voter casts a ballot, it is not clear just how and where their vote will land. During the vote tallying process votes can get sliced up and distributed in a way that is unknowable at the moment of voting. Although in our last election there were two openings on the council, in our new voting system there is no way to vote for two people and give them equal weight.

For example, assume a voter wanted John Miki and Jennifer Hansen-Romero, the top two vote earners, to be elected to the council. The voter might have assumed that it didn’t make much difference which of the two candidates they ranked first, as long as they ranked the other second. That would have been a big mistake.

To see why, you need to look at the RCV tallies on the Alameda County voter registrar’s website here. You can also find a printed report on the city’s website here. At the end of round three of the RCV election, candidates Pinguelo and Pilch had been eliminated and their votes transferred to Miki, Hansen-Romero and Lopez. At that point, the one-third threshold to win was 2355 votes. Miki had 2876 votes, a surplus of 521 above the threshold. Hansen-Romero had 2075, a deficit of 279 votes.

If there were at least 279 voters who voted for Miki first and Hansen-Romero second, and if these voters had switched their voting order to Hansen-Romero first and Miki second, it is likely that both candidates would have won during the round three of the RCV voter tabulation. Of course, many other scenarios are possible–but that is precisely my point. The complexity of possible outcomes makes it almost impossible for voters to grasp how to express their preferences at the time they vote.


The majority of Albany voters are unaware of the complicated settlement agreement reached between the City of Albany, Voter Choice Albany (VCA) — the organization that supported Albany’s at-large RCV plan, and the Southwest Voter Registration Education Project (SVREP) — an organization that supports district-based elections to overcome the voting struggles of Latino voters in the United States. That settlement agreement is here. SVREP works to enforce the California Voting Rights Act, which makes it easier for minority groups in California to prove that their votes are being diluted in “at-large” elections by expanding on the federal Voting Rights Act of 1965.

In the Bay Area, Oakland, Berkeley and San Francisco voluntarily adopted district elections many years ago. A list of cities that have already adopted district elections is here. Other nearby cities that have recently adopted district elections include Novato, Petaluma, Santa Cruz, Pleasanton, San Rafael, Union City, Davis, Vallejo, and Belmont. Statewide, hundreds of local agencies, including school districts have moved to district elections (here and here).

Because of the conflict between the California Voting Rights Act and Albany’s at-large RCV system, the settlement agreement will allow Albany to continue to use their at-large RCV system for a few election cycles, most likely through the 2024 general election. At that point if SVREP wants to contest the issue, a mutually agreed upon neutral arbitrator will determine whether Albany’s at-large RCV system satisfies the requirements of the California Voting Rights Act. If the arbitrator finds against Albany’s at-large RCV system, then most likely the city will be required to switch to district elections.


Now that we are familiar with the nature of the settlement agreement, the rationale of the Albany Forward slate of John Miki, Nick Pilch and Robin Lopez comes into focus. The slate was lucky to find a Latino candidate from Albany Village (and the city was fortunate he was elected). I strongly suspect that Lopez was intended to be a sacrificial lamb. Given the slate ran three candidates for only two open seats, at least one of the candidates had to lose. Miki was a very strong candidate, so I expected him to win. But given Pilch was a former mayor and councilmember, like many others, I believed his name recognition would allow him to win. But it was Lopez won.

But the beauty (if you will) of Albany’s at-large RCV system is that even if Lopez had lost, his votes would carry forward to other members of the slate. Assume a disproportionate number of Latino voters and Asian voters in Albany Village and the condos liked Lopez and voted for him. Then even if he was eliminated those votes would have transferred to Pilch and Miki, creating the appearance that they had support from the western part of town. When it came time for the arbitrator to evaluate how well at-large RCV was meeting the requirements of the California Voting Rights Act, it would appear that, as if by magic, that at-large RCV appealed to the disproportionately Latino and Asian voters in those census districts. Clever, huh? (For my discussion of segregation in Albany, with lots of charts, go here.)

Ironically enough, it was Pilch who lost, while Lopez won. But perhaps this is not surprising. When candidates of color run for office in Albany, they typically get elected. Ge’Nell Gary won in 2020, while former mayor Jewel Okawachi was considered Albany’s First Lady. There have been several members of color on the Albany school board in recent years.

I’ve never been a fan of Albany’s at-large RCV system. It reminds me of those clever, mathematically tractable, abstract models in academic economics journals that serve no useful purpose other than getting tenure for their authors.

Albany would be better served by some version of district elections. This was brought home for me recently by the development project at the old bowling alley site on San Pablo. The neighbors behind the planned project felt that no one on the council was responsive to their concerns, and at least one of the neighbors commented that they wished there were district elections in Albany so that they could elect someone who did represent them.


After I published this blog post, Tod Abbott, local web developer, Parks and Rec. Commissioner and stalwart member of the Albany Chamber of Commerce, reminded me of conversation we had several months ago.

Tod took a slightly different look at the RCV election data. I had only looked at the first-ranked votes for each candidate. Tod looked at both the first-ranked and second-ranked choices for each candidate. When he added together the first- and second-ranked votes for each candidate, it turns out Lopez had more of both than Hansen-Romero.

Tod points out that if we held the Nov. 2022 election under our old at-large rules, each voter would have had two votes, not just one. So Tod’s way of looking at the data might get us closer to what the results would have been under our old system.


A review of Albany campaign spending in the 2020 election

Dear Albany residents,

Although I was termed-out from the Albany City council at the end of 2020, I said I would post the campaign spending results for the 2020 elections, as I have for previous elections. To keep this consistent with the previous postings, the expenditure dollar amounts here are from candidates’ and organizations’ last posted California Fair Political Practices Commission (FPPC) Form 460 page 3, line 11. These are available on the city website here and here.

Albany Unified School District

Measure B was a parcel tax for the Albany Unified School District. It passed in March 2020. I wrote about it here. The total expenditures of the Yes on Albany Schools Measure B committee were $17,575. The majority of that amount came in the form of three $5,000 donations from major school architectural or construction firms. At least one of these firms also contributed to the campaigns to pass the 2016 bond measures B and E to fund the rebuilding of Ocean View and Marin schools. I wrote about that here (you have to scroll down a bit).

Of the three firms that contributed $5,000 to the 2020 Measure B campaign, Dervi Castellanos Architects was the firm that also contributed $5,000 to the 2016 bond campaign. Another, Overaa Construction, is currently working on the Ocean View project. At least I have seen their sign at the Ocean View construction site, so I assume they have been working on that project. I am not sure what other firms are working on the project except for those whose signs are there.

Technically a school construction bond campaign committee must be completely legally separate from the school district itself. However, it still makes me a little nervous when a firm makes a $5,000 contribution to a bond campaign committee to fund a project and is later hired to work on the project. It doesn’t quite pass the “avoid even the appearance of a conflict of interest” test. But that is an issue for the school district to consider.

School board candidates

I’ll simply list the candidates in declining order of how much they spent:

Brian Beall, $4,849

Melissa Boyd, $2,968

Veronica Davidson, no reporting, expenditures below the reporting limit of $2,000.

Albany Teachers Association, $611, mostly for campaign materials in support of state Proposition 15, the “split roll” initiative that would have modified the way commercial property taxes are determined under Proposition 13 (1978).

Albany City Measures

Voter Choice Albany $54,685

Albany Care About Climate $9,808

These two committees supported Measures BB and DD respectively. Both passed.

Albany City Council

Albany has a voluntary campaign spending limit of $6,000. This does not include the Alameda County fee of approximately $1,000 for the 250-word statement that appears in both the sample ballot and the actual ballot. Two of the candidates agreed to the accept the voluntary spending limit–Tod Abbott and Preston Jordan. However, since all but $50 of Jordan’s campaign spending was funneled through a separate committee, Albany Forward, Abbott was the only candidate who spent less than the $6,000 limit.

Ge’Nell Gary $15,644

Albany Forward $12,136

Tod Abbott $5,125

Preston Jordan $50

Aaron Tiedemann $0

Albany Forward was a committee formed to support the election campaigns of candidates Preston Jordan and Aaron Tiedemann.


Albany’s geographic segregation

First, A quick survey of election results

With the elections over, it’s time to take a look at the results. In Albany, the good news is that both the well-qualified African-American female candidates won their elections—Ge’Nell Gary to the city council and Melissa Boyd to the school board. The new city council will consist of three women (two White, one African-American), and two White men. The school board will consist of four women (one African-American, one Latina, and two White), and one African-American man. This may be a first for AUSD—no White males on the school board.

The bad news is that Tod Abbott was not elected to the council. This is a real loss for the city. Tod was among the most qualified candidates to run for council in my memory. Fortunately for both Tod and the city, he is already very involved in civic activities, and I hope for our sake he stays engaged.

The tax measures were a mixed bag. The city passed two out of the three that were on the ballot. Measure CC will increase the transfer tax when a property is sold, and Measure DD adds a user utility tax to our water bills. Measure EE, the special tax for paramedic and ambulance services, failed to achieve the required super-majority and was defeated. The polling for the measures was conducted pre-Covid, so I was prepared for some disappointing results.

Measure BB, the ranked choice voting initiative, won with 73 percent of the votes. Meanwhile at the polls, RCV continued to have a minor effect. There were 23 RCV elections in the Bay area–six in San Francisco, three in San Leandro, nine in Oakland and five in Berkeley. In only one, the District 7 supervisor race in San Francisco, was the eventual winner the not the first-round winner. This is consistent with past experience on RCV–nationally it has only made a difference in about one election in 20. (For my take on RCV, please see my previous post.)

At the federal level, the role of RCV was odder still. RCV is used to elect senators in Maine, but it didn’t matter this time because Republican Susan Collins won a majority in the first round of voting. In Georgia, it’s a good thing that RCV was not used to pick that state’s two senators. If RCV had been used, it’s very likely we would now have two Republican senators from Georgia. Because runoff elections are required, the Democrats get another chance. According to the New York Times, runoff elections were created in the 1960s as racist barriers. But that’s not how it turned out this time.

Exploring Albany through American Community Survey data

When I first came to Albany I lived in University Village (1995-2000). Back then, what remained of the original WWII shipyard housing was still being used to house UC graduate students. Many of those shipyard workers had been African-American. With the help of a UC Berkeley reference librarian who was an expert on census data, I explored the history of University Village. My article still exists in a somewhat garbled form on the Albany Patch website.

A few months ago I began to wonder what census data could tell us about Albany today, and from an unexpected source I found a motherload of information. The United States Census Bureau conducts the national census every decade. It also collects data in between decades through the American Community Survey (ACS). A user-friendly version of the data comes in the form of the Narrative Profiles, 22-page reports which allow you to select national, state, county, city and census tract data. The 22 pages are consistently formatted, making it easy to compare different regions. If you already know what you are looking for, there are more direct ways to search the data. But the narrative profiles allow you to explore the data and make connections that you otherwise might miss.

If you are interested, I have created a download link that includes the narrative profiles for all six Albany census tracts, the cities of Albany and Berkeley, Alameda County, the state of California, the USA, and my spreadsheet summary of what I found. You can download the zip file here. The map below displays the six census tracts that make up the City of Albany, which is shown in pink. The six census tracts are numbered 4201-4206 as you move counter-clockwise from the NE corner.

Map 1: The six Albany census tracts in pink.

Here’s an easy way conceptualize how the census tracts are defined: Draw two vertical lines through the map, one along San Pablo Ave and one along the BART tracks. Next draw a horizontal line along Solano Ave from San Pablo Ave to the eastern city border. Finally, draw a curving line that follows Buchanan Ave from San Pablo Ave to the western border of Albany. These lines divide Albany into the six census tracts. In the graphs that follow, the census tracts will be ordered from left to right by their average household income, as in the table below:

Figure 2: The six Albany census tracts and descriptions, ordered from low- to high-income.

Albany population, households and K-12 enrollment

The first three graphs below display some general population statistics about Albany’s six census tracts. Keep in mind this is data from 2014-18 survey. The 2015-19 survey will be released on Dec. 10. The 2020 census data will become available in the Spring of 2021. The subtitles show the citywide totals:

Graph 1: Population of Albany by six census tracts. City total is 19,758.

In Graph 1 above, note that the most populous census tract is NW-Condos. It contains 24.6 percent of Albany’s population. In addition to the three Pierce St. condo complexes, this tract also includes apartments and a mix of single family homes, from small bungalows to larger houses on Albany Hill.

Graph 2: Number of Albany households by census tract. City total is 7,391.

The largest number of households in Albany is also in the NW-Condos census tract. Graphs 1 and 2 have similar shapes, which indicate the number of persons per household is similar across census tracts. The average number of persons per household in Albany is 2.67. This figure varies from a low of 2.49 in NW-Condos to a high of 2.87 in SE-St. Mary’s.

Graph 3: K-12 enrollment. City total is 3,905.

In Graph 3 above, K-12 enrollment can include students in private schools, although the majority of students are enrolled in Albany public schools. (The 2019-20 enrollment of AUSD is 3,586, according Ed-Data.) Note that the percentage of students from SW-UC Village is only 9.3 percent of the total Albany enrollment. The families of these students are sometimes criticized because UC is exempt from local property taxes. This is not quite true. First of all, UC Village residents pay sales taxes just like any other Albany residents. In addition, the per-student state funding for Village kids is the same as other AUSD students. Finally, the new mixed-use development there does pay local taxes because the land is being used for commercial purposes.

The ethnic composition of Albany’s census tracts

I have ordered the following four graphs, graphs 4-7, in declining order by population of the racial and ethnic groups. Note that In census data the term Hispanic is an ethnic concept, not a racial one. Hispanics can be of any race. Of course, race itself is no longer considered a scientific concept.

Graph 4: White non-Hispanic population. City total is 9,098.

The SE-St. Mary’s census tract is home to the largest White Non-Hispanic population in Albany. In Albany as a whole, 46.0 percent of the population is White Non-Hispanic. This is much higher than the figure for Alameda County (31.8 percent) or California (37.5 percent). But Albany’s percentage is much lower than the United States as a whole (61.1 percent).

Graph 5: Asian/Pacific islander population. City total is 5,994.

In Albany, the Asian/Pacific Islander population is concentrated in the NW-Condos census tract and the two other lower-income tracts (72.8 percent). In Albany as a whole, the percentage of Asian/Pacific Islanders is 30.3 percent, about the same as Alameda County (30.4), but much higher than the state as a whole (14.7 percent) or the nation (5.6 percent).

Graph 6: Hispanic population. City total is 2,501.

Albany has a relatively low Hispanic population (12.7 percent), lower than Alameda County (22.5 percent), California (38.9 percent) or the nation (17.8). There is a fairly large Hispanic population in the SE-St. Mary’s census tract. Otherwise Hispanics are concentrated in the two census tracts west of San Pablo Ave (57.0 percent).

There is a mixture of housing types in all our census tracts. The SW-UC Village tract contains a disproportionate share of younger student families and their children, but it also contains our assisted living center for seniors. All our census tracts border on either San Pablo Ave, Solano Ave, or both. Many apartment buildings are located on our commercial corridors. There are a few large apartments along Solano in the SE-St. Mary’s census tract, but fewer in the NE-AHS tract. This might explain the difference in the Hispanic population of our two highest-income districts. But this is speculation–it is difficult to tell from the data we have.

Graph 7: African-American population. City total is 490.

Albany has a low percentage of African-American residents, at 2.5 percent of the population. This percentage is lower than Alameda County (10.8 percent), California (5.8 percent) or the nation (12.7 percent). Of Albany’s 490 African-American residents, 41.7 percent live in one census tract, N-Plaza to Solano. Albany’s African-American population peaked in WWII when housing was built for shipyard workers at the current location of University Village. This population declined steadily after WWII and continues to fall in the sustained aftermath of the 2008 financial panic, and with the rise of the tech industry and high housing prices in the inner Bay Area.

The racial and ethnic percentage composition of Albany’s census tracts compared to the citywide averages

In the following four graphs, graphs 8-11, I use the same data used in the four graphs above, graphs 4-7, but instead of counting people, I calculate the percentage breakdowns in each census tract and compare them to the citywide average (orange line). This controls for the different sizes of the census tracts, allowing trends to become more apparent. In the graphs below, note how many trends are monotonic or nearly so. That is, they tend to trend either up or down from one side of the city to the other.

Graph 8: The percentage of White non-Hispanic residents in each census tract (blue bars) compared to the citywide average (orange line).

As we move from the census tract that contains University Village to the one that contains St. Mary’s high school, Albany gets progressively more White non-Hispanic. The percentage rises from about one-third of the population west of San Pablo Ave to two-thirds of the population east of the BART tracks.

Graph 9: The percentage of Asian/Pacific Islander residents in each census tract (blue bars) compared to the citywide average (orange line).

As we travel across our census tracts, the percentage of Asian/Pacific Islanders tends to drop, from about 43 percent in NW-Condos to about 16 percent in SE-St. Mary’s.

Graph 10: The percentage of Hispanic residents in each census tract (blue bars) compared to the citywide average (orange line).

As we travel across our census tracts, the percentage of Hispanics declines, but does increase in the highest-income census tract, SE-St. Mary’s. However, it still remains below the citywide average there.

Graph 11: The percentage of Black or African-American residents in each census tract (blue bars) compared to the citywide average (orange line).

The two census tracts between San Pablo Ave and the BART tracts contain above- average percentages of African-American residents, while the other four to the east and west have below-average percentages.

Other related census tract trends

The final four graphs, graphs 12-15, show related information about Albany’s census tracts that is also mostly monotonic as we travel across the city.

Graph 12: Percent foreign-born residents by census tract.

The number of foreign-born residents declines as we move from west to east in Albany, from almost 47 percent west of San Pablo Ave to a little more than 13 percent in the SE-St. Mary’s census tract.

Graph 13: Percent of households in single-family homes by census tract.

The percentage of households living in single-family homes rises as we move from west to east in Albany. The citywide average is 53.8 percent, but it is almost 90 percent east of the BART tracks.

Graph 14: Median household income by census tract.

Graph 14 above shows a monotonic increase in median income. In Albany, as in nearby cities in the East Bay, incomes tend to rise as you travel from west to east, and as altitude increases.

Graph 15: Percent of households with annual incomes greater than $200,000 by census tract.

Graph 15 takes another look at income, this time as the percentage of households with income great than $200,000 annually. The bars tend to rise more steeply than in Graph 14. This is mostly likely because with respect to medians, averages are more affected by outliers–in this case very high incomes. Note that in the SE-St. Mary’s census tract, 35 percent of the households have incomes above $200,000 annually.

Albany is geographically segregated

Many Albany residents like to think of our city as being diverse, and it is–if you only look at the city as a whole. If you zoom down the census tract level, Albany is a geographically segregated city. As we move from west to east, Albany becomes whiter and higher-income, with a higher proportion of native-born residents living in single-family homes. There is nothing unusual about this. You’ll find a similar pattern in Berkeley and Oakland, and in Contra Costa County cities of El Cerrito and Richmond.

To see why I am concerned, see the map below. Currently, All five members of the city council, and three of the five school board members, reside in the two highest-income census tracts in Albany:

Map 2: The approximate location of Albany’s city council and school board members.

Note that five new city council and school board members have been elected and will be seated in December, 2020. I don’t know the exact addresses of the new members, but the net result should be a shift of one or two dots from east of the BART tracks to the census tracts between BART and San Pablo Ave.

Slightly less than 30 percent of Albany residents live in its two highest-income census tracts east of the BART line, yet the majority of city council and school board members live there. This is a problem, especially since the income and ethnic/racial characteristics of the east side and west side of Albany are very different. Albany’s elected officials should be more representative of the city as a whole. I think Albany’s citizens should be concerned about this.

Will our switch to ranked choice voting somehow solve this problem? I doubt it. The reality is that a significant portion of the voters do not follow local government issues closely and the easiest way to get their attention and win their votes is through campaign spending. Campaign expenditures for city council races (and school district parcel taxes) have been growing in recent years, and this puts lower-income candidates and their neighborhood donors at a disadvantage.

The final campaign expenditure reports for the last election are due on February 1, 2021, and can be viewed on the city’s website soon after that date. Although I’ll no longer be on the council then, I’ll review the expenditure reports and post what I discover here. We will have to wait and see how these issues play out over the coming months.


Ranked choice voting in Albany?

Dear Readers, Below you will find a long discussion of ranked choice voting in Albany. Be warned, this piece is about 4,400 words. But I think it’s important to look at some examples of ranked choice voting, and how it would work in our city. For a shorter take, please see my ballot statements here and here. If you get bored or run out of time, you can scan the subheadings or skip down to the closing thoughts at the end. All words in purple are hyperlinks.

This November in Albany, in the form of Measure BB, we are being asked to consider changing to a different set of voting rules known as ranked choice voting (RCV). The proposed RCV system we are being asked to consider will certainly be more expensive than our current system, adding an extra $26,000 cost for each election, but there is no guarantee is will be any better. All voting systems are imperfect, and a system that is suitable for one city may not be the best choice for another city.

Social Choice Theory

The imperfection of all voting systems was established by Nobel Laureate economist Kenneth Arrow with his famous Impossibility Theorem. In his 1951 book, Social Choice and Individual Values, Arrow jump-started the field of social choice theory. This body of thought attempts to provide a systematic framework to explore how well individual votes and other preferences can be aggregated to the societal level. Arrow’s Impossibility Theorem showed that all voting systems have fundamental flaws, although these might not always be serious. Arrow famously stated, “Most systems are not going to work badly all of the time. All I proved is that all can work badly at times.”

Later another Nobel Laureate, the Indian economist Amartya Sen, extended social choice theory to examine problems in developing countries like the failures in institutions that lead to poverty and famine. You can read more about Arrow (here and here) and Sen (here and here).

There are dozens of alternative voting models. RCV is just one. Before we jump down the alternative voting rabbit hole, it’s a good idea to read about Arrow’s Impossibility Theorem to better understand the complexities of social choice theory. Don’t believe me? Read this. But I’ll leave it up to you to browse the literature. You can also start here or here, or search for “ranked choice voting” in Google Scholar.

Ranked choice voting in Albany

For now, let’s focus this conversation on Albany. The Albany City Council and the Charter Review Committee have looked at the issue of RCV several times over the years. Here is a recent example from the March 19, 2018 city council meeting, item 8.1. The staff report is a useful overview.

Albany is a charter city — like many older cities, Albany has its own set of bylaws, or charter. Most newer cities are general law cities that rely on the State of California’s rules for cities. Currently, general law cities are not allowed to use RCV, and most recent governors, including Jerry Brown and Gavin Newsom, have vetoed RCV bills for general law cities because of their concerns that RCV is overly complicated and will lead to voter confusion.

Because Albany is a charter city, we have the option of using RCV. However, this requires study by our charter review commission, consultation with our city attorney, and a modification of the city charter — which requires a vote of the citizens of Albany. Both the city council and the charter review committee have looked at the issue of RCV repeatedly, and in general have not been interested in adopting it.

This year a group of advocates calling themselves Voter Choice Albany began collecting signatures for a citizen’s initiative to put RCV on the Nov. 2020 ballot. A citizen’s initiative requires signatures from ten percent of Albany’s registered voters, plus some extras as a buffer. In Albany, that’s about 1,200 signatures. The group claims it had collected about 600 signatures before it had to stop because of the Covid pandemic. 

Soon after their signature-gathering moratorium, the members of Voter Choice Albany approached the city council and asked it to place their initiative on the ballot as a council-initiated proposal. When the council did not express much interested in doing so, Voter Choice Albany threatened to sue the city if it did not place the measure on the ballot.

Given that lawsuits are expensive regardless of the outcome and given that Albany’s citizens will sign just about any petition, the council concluded that Voter Choice Albany would have probably eventually found their 1,200 signatures. Therefore, the city decided this was not a fight worth having and agreed to put the issue to the voters. That is how we find ourselves where we are today.

I have many issues with RCV that I’d like to discuss. As I mentioned above, for brief explanations, please see my ballot statements here and here. I’ll go into more detail below:

The human problem that RCV doesn’t solve

Albany’s adults are usually focused on their families and careers. Serving in a volunteer capacity on a city commission and gaining the experience necessary to serve on the city council can be a thankless task. But it is an important one. Getting residents involved in the workings of city governance is part of that long process.

If we don’t do this work, we are often left in the few months before elections seeking people who are willing to run. By the time election day rolls around, this problem is either solved or it’s not. At that point, no fancy voting algorithm is going to solve the problem for us. It’s too late. RCV is not a solution to our fundamental problem of getting citizens involved.

Single-seat ranked choice voting (instant runoff voting)

RCV as proposed for Albany comes in two flavors — single-seat RCV, also known as instant runoff voting, and a more complicated version called at-large RCV that is rarely used in the United States. You can read more about how they work at the RCV advocacy website, It is a well-organized site that provides lots of information. Here is what I discovered there:

Nationally, 23 jurisdictions presently or imminently use RCV to elect officials. Of those, 17 exclusively use single-seat RCV (instant runoff voting).

To give you some perspective, the nine-county Bay Area alone has about 100 jurisdictions, including cities and counties. There are thousands of public jurisdictions in the United States.

Instant runoff voting makes a lot of sense for cities that have been conducting primary elections followed by runoff elections. Holding two elections can strain the resources of both cities and candidates, and the instant runoff process saves them both time and money.

However, Albany doesn’t use primary and runoff elections for council members, so one of the main justifications for switching to RCV is missing in our city. If Albany were to switch to instant runoff voting from its current at-large system, it is not clear what we would gain. In practice, the vote-transfer process of RCV doesn’t appear to make much difference.

According to, “There have been 15 RCV races in the U.S. which were won by a candidate other than the first-round leader. That’s 4.2 percent of the 353 single-winner RCV races since 2004.”

In the Bay Area, four cities that vote by districts use instant runoff voting — Oakland, Berkeley, San Leandro and San Francisco. San Francisco has been using RCV since 2004. Oakland, San Leandro and Berkeley have been using it since 2010. According to online voter registrar records, there have been 60 RCV elections in San Francisco and 83 in the East Bay, a total of 143.

In 94.4 percent of these elections, the candidate who won the first round of counting either prevailed in the first round (just like in a conventional election) or won after additional rounds of counting rank-choice ballots. In only 5.6 percent of the elections (8 of 143) did a candidate who did not take the lead in the first round come from behind to win. Both nationally and in the Bay Area, the extended ballot counting of RCV only affects the outcome of about one election in twenty.

At-large ranked choice voting

The RCV initiative on the ballot this November in Albany also allows our city to implement the even more complicated at-large version of RCV. Referring again to the website:

Two jurisdictions exclusively use multi-winner RCV (single transferable vote) – Cambridge, MA and Eastpointe, MI.

Two use a combination of single- and multi-winner RCV – Minneapolis, MN and Palm Desert, CA.

Two use a form of multi-winner RCV called preferential block voting – Payson, UT and Vineyard, UT.

Albany’s RCV advocates are asking the voters to make Albany only the fifth city in the country to adopt at-large RCV. It would be an experiment, and one that, in my opinion, will not do much for Albany. Here is an example of how at-large RCV work from the website.

The example above involves six candidates for three seats in a partisan election. This is not how elections work in Albany. First of all, our elections are non-partisan. Secondly, it would be rare to have six candidates for an Albany election. Finally, we are left to assume that one Republican winning thanks to RCV is a better outcome than electing three Democrats. I’m not sure why, in general, that should be the case.

It can be difficult to picture how adding second- or third-choice candidates, or even lower-ranked candidates, from the ballots of eliminated candidates can shape the eventual outcome of the election. There are three conditions that typically apply in the unusual case that counting the subsequent ranked choices beyond the first round makes a difference:

1) If, after the first round, two candidates are nearly tied.

2) If there are enough candidates to create a depth of ranked ballots that are capable of making a difference.

3) If the voting patterns in the subsequent rounds are sufficiently different from the patterns in the first round.

A useful analogy is to the counting of vote-by-mail (VBM) ballots after the polls have closed. In the past, VMBs were mostly absentee ballots, but over time more and more voters have switched to becoming permanent VBM voters. Imaging the following scenario:

In an election, 100,000 voters go to the polls. After the polls close, it is announced that Candidate A leads Candidate B by 51,000 votes to 49,000. The candidates are nearly tied, separated only by 2,000 votes (as in #1 above). The voter registrar’s office announces that there are 3,000 VBMs that need to be counted. There are enough remaining votes to make a difference (as in #2 above). If Candidate B was the choice of all the VBM voters, she would win by 52,000 to 51,000 votes. However, in order squeak out a victory, she would need only 2,501 of the VBM votes to win.

 If Candidate B does get 2,501 VBM votes and Candidate A gets 499, then Candidate B wins by 51,501 to 51,499. In this example, Candidate B would have to take more than five-sixths of the VBM ballots (83.33 percent). During the in-person ballot box voting, she only earned 49 percent of the vote (as in #3 above). Therefore, it seems unlikely that Candidate B will be the eventual winner.

Simulated examples from Albany elections

Keeping these concepts in mind, let’s apply them to a hypothetical Albany example. In the 2018 city council election, there were two open seats and three candidates. Here are the three candidates and their vote totals:

Peggy McQuaid          4,716

Rochelle Nason           4,245

Preston Jordan            4,009

Total votes                  12,970

Note that this election was not held under at-large RCV rules, but let’s use this as an example of how an at-large RCV election would play out. In an at-large RCV election with three candidates, in addition to making a first-rank vote, voters also would make second- and third-ranked choices. In an at-large RCV election with two open seats, any candidate that gets more than one-third of the vote is automatically elected. In this case, one-third rounded up is 4,324 votes. Peggy McQuaid is elected with 392 over-votes, or votes over the minimum she would have needed. Neither Nason nor Jordan are over the threshold of 4,324 votes. Nason is 79 votes short, and Jordan is 315 votes short.

In the next step, McQuaid’s 392 over-votes must be redistributed to the remaining candidates in proportion to the second-rank choices of the voters who ranked her first. We have no information on those choices, but we can make some reasonable guesses. Nason would need 79 of McQuaid’s 392 over-votes, or 20.15 percent, to have the 4,324 ranked choice votes necessary for election. Jordan would need 315, or 80.36 percent.

Given that the two female candidates were both incumbents, it is reasonable to assume Nason would get more than 20.15 percent and would be declared the second winner. In this example, 1) the leaders were not close to being tied, 2) there were enough ranked ballots to make a difference, and 3) the voting patterns were unlikely to be sufficiently different from the first-round votes. Therefore, the conventional election and an at-large RCV election would have yielded the same results.

Here is another example based on Albany’s 2018 school board elections:

Hinkley                       4,922

Duron                          4,575

Doss                            3,092

Blanchard                   2,940

Stapleton-Gray            2,438

Total votes                  17,967

Again, this election was not held under at-large RCV rules, but let’s use this as an example of how an at-large RCV election would play out. In at-large RCV election with five candidates, in addition to making a first-rank vote, voters also would make second- through fifth-ranked choices. In an at-large RCV election with three open seats, any candidate that gets more than one-quarter of the votes is automatically elected. In this case, one-quarter rounded up is 4,492 votes. Hinkley and Duron are elected with 430 and 83 over-votes respectively. Doss is short 1,400 votes, Blanchard is short 1,552 votes, and Stapleton-Gray is short 2,054 votes.

In this election, there was one slate—the Albany Teacher’s Association (ATA) slate of Hinkley, Duron and Doss, and the two white male incumbents. For the sake of simplicity, let’s assume that the over-votes in the ATA slate all stayed within the slate. In other words, all of the ATA slate voters placed Hinkley, Duron and Doss in first- through third-ranked positions and the incumbents in fourth and fifth positions.

If so, when the over-votes are distributed at the end of the first round of voting, Doss gets all of Hinkley’s 430 and all of Duron’s 83 over-votes. That brings Doss’s vote tally to 3,605 votes, still 887 votes short of the threshold level of 4,492.

The candidate with the least number of votes, Stapleton-Gray, is now eliminated, and his votes are redistributed to the second-ranked candidates of the 2,438 voters who voted for him. Again, we have no information on those, but we can make some educated guesses. If Doss gets 887 of the 2,438 transferred votes, (36.38 percent), then he is over the threshold, and he is the third candidate elected.

However, if the voters tended to vote for the incumbents as a slate, and therefore Blanchard gets at least 1,552 of Stapleton-Gray’s transfer votes, (63.66 percent), then that puts Blanchard across the threshold and Blanchard is elected rather than Doss. This example points out two things contrary to misperceptions about RCV. First, slates form and they matter, and RCV doesn’t necessarily favor candidates of color.

Note in this example that 1) two of the candidates were nearly tied, 2) because there were two more candidates than open seats, there were enough ranked ballots after the first round to make a difference, and 3) if the voting in subsequent rounds were sufficiently different than the first round, RCV can matter. If Doss was elected in this scenario, then the conventional and at-large RCV results would have been the same.  

An example of when at-large rank choice voting works

Imagine a small agricultural town in the Central Valley. Although the Latino population is in the majority, not all the adults are documented, so they have only 40 percent of the 10,000 voters in town, while the Anglos have 60 percent, or 6,000. Voting is at-large, just like Albany’s current system. There are five seats on the city council to be filled, so each voter can vote for up to five candidates. Both the Anglos and the Latinos run five-candidate slates.

Let’s further assume that Anglos only vote for Anglo candidates, while all Latinos only vote for Latino candidates. All voters use all five of their votes. Within the two groups, all candidates are equally popular. Under these assumptions, it is easy to predict the outcome of the election. All five Anglos are elected with 6,000 votes each, while none of the Latinos are elected because they only got 4,000 votes each. This is known as block voting.

In towns like this, Anglo-only city councils can be sustained for decades. The State of California stepped in to remedy this type of voter suppression with the California Voter Rights Act of 2001 (CVRA), which typically requires such cities to move to district elections. In this case, our little Central Valley city could be split into five voting districts with roughly equal populations. Two might be Latino majority, two Anglo majority, and one mixed. With district-based elections, Latinos can easily win two or more seats on the city council.

But what if Anglo and Latino families all live in mixed neighborhoods? The town could consist of single-family homes occupied by Anglos, with one apartment building on each block occupied by Latino farm workers. In that case, moving to voting districts will not remedy the voter suppression. Since the town is homogeneous, all potential districts would have the same ethnic balance.

In this situation, at-large RCV make sense. Under this model, all voters in town are given ballots with 10 lines to rank-order their choice for the five Latino and five Anglo candidates. The 6,000 Anglo voters rank order the Anglo candidates first through fifth, and the Latino candidates sixth though tenth. The 4,000 Latino voters do the opposite. At the end of the first round, each Anglo candidate has 6,000/5 or 1,200 votes. Each Latino candidate has 4,000/5 or 800 votes. The election threshold under at-large RCV rules is 10,000/6, or 1,667 votes. No candidates win in the first round.

As the ranked votes from last-place candidates are transferred, Latinos win two seats, which require 3,334 of their 4,000 votes. Anglos win three seats with 5,001 transferred votes out of their total of 6,000 votes. Note that each group is represented proportionately. Latinos have 40 percent of the voters and 40 percent of the five seats, while Anglos have 60 percent of the voters and 60 percent of the five seats.

This proportional outcome is the story RCV supporters like to tell. However, in real life, there are not just two interest groups. They can be a mix of Anglo and Latino, male and female, low-income and high-income, gay and straight, meat-eating and vegetarian, and so forth. When voter identities are more complicated, it is not clear how well RCV does to proportionately represent all the possible groupings, especially if, as in Albany, there are typically only two or three seats open in each election.

The at-large ranked choice voting record of Cambridge, MA

Cambridge is the city across the Charles River from Boston. It is the home of Harvard University and the Massachusetts Institute of Technology. Cambridge is one of four cities in the United States that uses at-large RCV voting. The city has used at-large RCV continuously since 1941. The more recent election results are online. I found the City of Cambridge’s RCV voting records for their School Committee and their City Council for the five elections that occurred in the odd years from 2011-2019.

Let me discuss the School Committee first. There were six members elected in every odd year in the five elections from 2011-2019. The number of candidates ranged from a minimum of 9 and a maximum of 12. I listed the top six vote-getters in Round 1 of the voting and compared that list to the list of the six elected winners at the end of the at-large ranked-choice voting process. Here is what I found:

In four of the elections, those occurring in 2011, 2013, 2015 and 2019, all six of the Round 1 winners were eventually elected. The at-large RCV process didn’t change the outcome. In 2015, the sixth- and seventh-placed candidates at the end of Round 1 were separated by only 12 votes, and in the at-large RCV process, the seventh-placed candidate acquired a few more votes than the sixth-place candidate and was elected instead.

Now for the Cambridge City Council elections: Using at-large RCV, Cambridge elects nine members to its city council in odd years. I found records for the same time period as the school committee elections, 2011-2019. The total number of candidates in each election varied from 18 to 26. In three of the elections, 2011, 2013 and 2017, all nine of the top vote earners in Round 1 were elected to the council. The at-large RCV process made no difference.

In 2015, the 11th-placed Round 1 candidate replaced the ninth-placed candidate during the at large-RCV process. In the 2019 election, the 10th-placed Round 1 candidate prevailed over the seventh-placed Round 1 candidate to earn a seat on the city council. The at-large RCV process made a minor difference in two elections.   

Note that even though there was a considerable depth of candidates to rank order, it seldom mattered. Also note that with nine open city council seats, any candidate who accumulates more than 10 percent of the votes is elected. Unlike with single-seat instant runoff voting, where one candidate eventually achieves a majority, with nine open seats, no candidate is required to get anywhere near a majority to be elected.

Also notice how different the reality of at-large RCV in Cambridge is when compared to our hypothetical model of a Central Valley farm town. In our farm town example, the ranked-vote transfer process radically altered the eventual winners. In real life in Cambridge, the differences were minor to non-existent.

A brief note on Eastpointe, Michigan

Eastpointe, Michigan, is a city of 32,500 in the Detroit area. According the latest census data, it is now 49 percent African-American. According to the website, Eastpointe is the only jurisdiction other than Cambridge that exclusively uses at-large RCV. This is not quite accurate. As part of a settlement with the federal Dept. of Justice, Eastpointe agreed to to switch to at-large RCV for electing its four city council members. Unlike Albany, Eastpointe has a directly elected mayor, one who is not elected using RCV.

The first election under the new rules took place in November 2019. There were four candidates for two seats on the city council, elected by at-large RCV. The two winners were a white female city council incumbent and a white male. The two candidates who did not win were both African-American, one male, and one female. The conventionally elected mayor was also a city council member who became the first African-American female mayor of the city (more here and here).

We have to keep in mind that this is the first at-large RCV election in Eastpointe. However, it is worth noting that this result is far from what the advocates have advertised about what to expect from an at-large RCV election. It was the conventional election that elected an African-American mayor, and the at-large RCV election that chose two white candidates over two African-Americans.

Closing thoughts about RCV

The advocates of RCV make the assumption that ranked choices provide more information in an election, and that more information is good. But there is another possibility. Perhaps beyond their second- or third-ranked choice, the voters do not research their choices and are confused about the attributes of their lower-ranked candidates. If so, it’s likely they just guess, or fill in their lower-ranked choices randomly.

If so, the RCV process incorporates some information along with a lot of noise. That would explain why RCV elections, either the instant runoff or at-large versions, seldom overturn the first-round winners. For all its technical sophistication, RCV cannot overcome the simple human problem of voters who lack either the resources or the enthusiasm to carefully study all the candidates.

In addition, the notion that every vote counts in RCV is generally not true. In single-seat RCV (instant runoff voting), if one candidate gets more than 50 percent of the vote, the election is over, just like in a conventional election. In at-large RCV with two open seats, if two candidates get more than one-third of the votes each, the election is over, just like in a conventional election. In an at-large RCV election with three empty seats, if three candidates get more than one-quarter of the votes each, the election is over, just like a conventional election. If I didn’t vote for one of the winners in the examples above, none of my votes counted.

At-large RCV creates incentives for candidates to spend more money. In the 2016 council election I joked that my plan was to spend as little money as possible and still come in third. That is exactly what I achieved. If that election was held under at-large RCV rules, I would have had much more incentive to spend more to try to clear the safe-harbor hurdle of getting one-quarter of the votes, which would have protected me from any vote-redistribution surprises. The amount of money candidates need to raise to compete for a volunteer elected position in Albany is a major reason more residents don’t run. RCV won’t help this problem, and may make it worse.

As in the past, this November we are having barely competitive elections for both the city council and school board. For the city council, there are four candidates running for three seats. For the school board, there are three candidates running for two seats. In elections with only one more candidate than open seats, at-large RCV elections are unlikely to yield a different result than conventional elections. That’s because only over-votes of the leading candidates can make a difference, and this is unlikely to happen because there are usually relatively few over-votes, and they tend to be distributed like the first-round votes.   

What do we make of at-large RCV? At least for Albany, I think it is much ado about very little. It does little or nothing to encourage citizen participation in local government. And it does little or nothing to change the outcome of elections. It is a Rube Goldberg machine. Perhaps that explains with so few public jurisdictions in the United States use it.

The advocates for RCV in Albany are asking the citizens to become human guinea pigs in an experiment that isn’t that useful to start with. In addition, Albany residents will have to pay $26,000 per election for the privilege of participating in the experiment. I just don’t see any good reasons to do this, so my advice to Albany voters is to vote no.


California’s COVID-19 deaths keep rising as counties reopen

I’ve put together some a simple chart and two tables to follow up on my last post. With the possibility of reopening some California counties, it’s a good idea to see how they are doing. To start, the chart below shows the top 16 counties by the number of COVID-19 deaths in California.

Chart 1: COVID-19 deaths for 16 California counties with largest numbers of deaths. Blue denotes total deaths until April 20, 2020. Gold indicates deaths in the following week ending April 27.

By both its size and number of deaths, LA County is in a category all its own. The county added 325 deaths in one week, a jump from 619 to 944 COVID-19 deaths. Table 1 below lists the data for those 16 counties in Chart 1, which include more than 95 percent of all COVID-19 deaths in California.

Table 1: Data for the 16 counties included in Chart 1 above, including land areas in square miles and and deaths per million people.

It is helpful to put California counties into four groups. LA County alone is a region, with about one-quarter of the population of the state and more than half the cases. The other regions are described below (note the typo in the title–the data is actually from 4/27/2020).

Table 2: All 58 California counties sorted into four regions. LA County is its own region. Note typo in title, deaths are from the 4/27/2020 LA Times count.

LA county is similar in population to both Sweden (10.23 million) and Greece (10.72 million). In LA County there have been 944 deaths. Sweden has been hailed as a model by pundits who don’t seem to have examined the data closely. Sweden has had 2,568 COVID-19 deaths, almost three times the number in LA County. A better but lesser known model is Greece, with only 140 deaths.

Closer to home, a quick check of Table 1 above reveals that LA County has a death rate of 92 deaths per million, while its neighbor to the south, Orange County, has only 12 deaths per million. This is true even though Orange County has a higher population density. However, after a crowded weekend on Orange County beaches Governor Newsom ordered them to close temporarily. We’ll see if the crowds brought an increase of COVID-19 cases and deaths to what has been a relative safe haven in crowded coastal Southern California.

The LA region and South Bay/Sacramento region have very similar death rates at about one-third the rate of LA County. The remaining 42 counties together have had 76 deaths, only 4.3 percent of the total. The death rate is low, 9.3 deaths per million. Part of the reason for the low death rate is that people are spread very thinly across those 42 counties with a population density of 73.9 people per square mile. That’s about 7.9 million people, one-fifth of the state’s population, spread across more than 106,700 square miles. If that was a rectangle 100 miles wide, it would have to be 1,067 miles tall. Old-fashioned, labor-intensive contact tracing, what’s been called “shoe-leather epidemiology,” will require lots of trained workers willing to travel many, many miles. Newer technologies may not help that much (LA Times, requires registration). Even so, Modoc County, in the far northeastern corner of the state, has reopened.

Meanwhile, construction will begin again in the Bay Area, a move that the East Bay Times questioned in an editorial. The real issues concern testing, whether we have enough test kits, how fast we can process them, and how accurate they are. The rule of thumb for a disease of low prevalence (less than 10 percent of population infected) is that if the true (unknown) prevalence of the disease is roughly the same as the false positive rate of the test, then a positive test result is wrong almost 50 percent of the time.

Here’s an example: Assume one percent of Californians in a random sample have COVID-19. A test accurately reveals the one percent that are infected (i.e. no false negatives). However, 99 percent of the people in the sample don’t have COVID-19. If the test has a one percent false positive rate, then 0.99 percent of the sample will have a false positive result. The study’s results show that 1.99 percent of the sample tested positive, yet we know the true positive rate is only one percent, and almost half the people with positive results really don’t have the disease.

This implies that people will have to be tested more than once, and tested repeatedly, just like professional athletes are tested for performance-enhancing drugs. And even if we start testing people for antibodies to the disease, either from having COVID-19 or getting (someday) a vaccination, we’ll still have to test them to verify that their immunity remains, a least until we have enough experience with the new vaccines and have vaccinated a sufficient proportion of the population.

So far, humankind has only eliminated one virulent disease by vaccination and outbreak tracing–smallpox. We are close to eliminating a second disease, polio, but mostly due to warfare and poverty in some developing countries, finishing the job is proving to be tough. Due partly to anti-vaccination hysteria, we still suffer from occasional outbreaks of whooping cough and measles. COVID-19, even with plentiful and reliable testing and effective vaccines, may be with us for years.


California Counties and COVID-19

There have been several newspaper articles (examples here and here and here) that speculate that population density encourages the spread of COVID-19 by making social distancing more difficult. New York City is the prime example in the U.S. I think it’s fair to say that the YIMBYs and other pro-growth urbanists have taken the position that the problem is not density but “crowding,” which is a more amorphous concept. By crowding I think they mean not enough housing and too many people per housing unit. If that’s the case, then here in California we have the county-level data to examine this question. That’s what I’ve done in the charts below.

The LA Times reports COVID-19 deaths by county and updates the information at least daily. On the evening of April 20, 2020, the online edition reported 619 COVID-19 deaths in California. From the California Dept. of Finance demographics group I found data on county population and average number of people per household. And from Wikipedia I found data on the land area of all 58 California counties. The LA Times excludes five small counties that don’t report data. For those 53 remaining counties I have created a spreadsheet that lists COVID-19 deaths, population, area, and household size, and I calculate population density and deaths per million people.

In Chart 1, I winnowed the data down to the 18 counties that have reported five or more deaths. With respect to the 53 reporting counties, the subset of 18 includes 96 percent of the deaths, 84 percent of the population, but only 44 percent of the land area. In other words, I am excluding the large, lightly populated rural and wilderness counties. A quick look at Mono County explains why. This county has the highest death rate in California, at 73.4 per million people. However, Mono County has had only one death in a population of 13,616 people. Los Angeles is the county with the second highest death rate, at 60.4 per million people. But LA Country has over 10 million people and 619 deaths. I have excluded these rural counties to make the charts less cluttered. 

Chart 1: COVID-19 deaths vs. population for 17 California counties, Los Angeles excluded.

It’s always a good idea to look at the raw data, which I have done in Chart 1 (above). Here’s a handy description: California has about 40 million people and 1,200 COVID-19 deaths. That’s a death rate of 30 per million. Of these amounts, LA County has about one-quarter of the state’s population and almost half of the COVID-19 deaths. I’ve included in the chart a blue line which indicates the average death rate of 30.69 deaths per million in the 53-county data set. I’ve had to exclude LA County because due to its size it is way off the chart. Of the remaining 17 counties that dot the chart, note that two Silicon Valley counties, Santa Clara and San Mateo, lie above the line. Riverside County, just east of LA County, is also above the line. That means their death rates are higher than average.

Orange County is the obvious outlier below the line. This is curious because the county just to the north, LA County, is a huge outlier in the opposite direction. One possible explanation for this is that COVID-19 deaths are counted where they occur in hospitals, and not where the victims lived. If Orange County residents are traveling to LA County hospitals and dying there, that would explain, to some extent, why both counties are outliers. Due to privacy concerns and lack of time during the crisis, coroners may not be reporting deaths based on where people lived. A final thing to note is that San Francisco lies below the average line.

Chart 2: COVID-19 deaths per million vs. population per square mile. San Francisco excluded.

Chart 2 compares the death rate with population density. In this chart, San Francisco County is the excluded outlier, which a population density almost 19,000 people per square mile, which puts it way off the chart to the right. That’s because San Francisco is only 47 square miles and is the only county in California that is also a city. As we’ve discussed, San Francisco’s death rate is below average (22.6 deaths per million). Again, the obvious outlier is Orange County, which is second only to San Francisco in population density, while LA is third. If you hold your hand over the Orange County dot, it’s more obvious that there is a correlation between population density and the death rate. And as before, note that LA is an outlier the the upper part of the graph.

Chart 3: COVID-19 deaths per million people vs. persons per household.

Chart 3 compares the death rate to average persons per household. It contains all 18 counties. Most of the large counties lie near the vertical straight line at 3 persons per household (the state average 2.986). Almost all of the 18 counties in the sample have average household sizes between 2.8 and 3.2. Note that, as before, LA and Orange counties are outliers, LA with a high death rate and Orange County with a low one. The counties to the right tend to be more rural, located in Central Valley and Inland Empire counties, with lower incomes and perhaps more children.

Note that Tulare County is also an outlier. The county is the home of Sequoia National Park and the agricultural city of Visalia. Its relatively high death rate may be due to the high number of cases in nursing homes in the county. Although rural, Republican and anxious to reopen, the county’s Highway 198 is a major gateway to Sequoia and Kings Canyon National Parks and sees throngs of tourists during the summer months.

The three counties to the left in the graph are an interesting group. Placer County lies along the I-80 corridor from Roseville to Lake Tahoe and includes the NW shore of the lake and the ski resorts south of Truckee. Marin and San Francisco counties are located in the Bay Area and are two of the wealthiest counties in California. Although their population densities are very different (18,806 for San Francisco, 506 for Marin), their persons per household numbers are very similar (2.350 for San Francisco, 2.441 for Marin). It’s important to note that in Marin County, only the north-south Highway 101 corridor is densely populated. Most of the rest of the county consists of dairy farms, protected agricultural land and a vast network of regional, state and national parks.

Meanwhile, in San Francisco, the proportion of children has been shrinking for years (see this). The local wisdom is that twenty-somethings meet in San Francisco, get married and move to the East Bay to raise their families. This party explains the low number of persons per household. However, cities in general have lower numbers of persons per household. Berkeley has only 2.28 persons per household, while Albany has 2.57 (and less than 10 COVID-19 cases). The main message of Chart 3 is that if important aspects of crowding are captured by household size, then crowding (unlike population density) doesn’t have much effect on the COVID-19 death rate.

Now for the caveats: County data is far from ideal. First, California counties vary wildly in size, from San Francisco at 47 square miles, to San Bernardino at 20,062 square miles, the largest county in the United States. In addition, population density varies within counties. The populated one square mile of my little town of Albany has about 20,000 people, with a density greater than that of San Francisco. Yet the county in which we are located, Alameda, has an average population of 2,262 people per square mile. Any serious analysis of the spread of COVID-19 will someday require more disaggregated geographical data, perhaps at the city, census tract, or zipcode level.

The COVID-19 pandemic is far from over. Various counties have taken different approaches along different times to sheltering in place, and that will matter, too. The snapshot in time that I’ve been describing may look very different a month from now. As the virus moves inland from more densely populated coastal regions to the more remote inland counties like Tulare, we may see a dramatic late surge in cases and deaths. While it might seem better to track cases and not deaths, testing remains much too unavailable and inconsistent. Death is a lagging indicator of the progress of the pandemic, but it is a certain one. You are either dead or you’re not, and by now we know how to tell a COVID-19 death from those caused by other medical problems.

Finally, the distinction between density and crowding is mushy. A commuter may live in a quiet suburb, but commute to San Francisco on a crowded BART train. And for some urbanists, crowding is the point–crowded bars, crowded concerts and crowded sporting events are not considered negatives. But at the aggregated county level, the COVID-19 death rate appears to correlate more closely with population density than household size.

I’m happy to send the spreadsheet that I used to create these charts to anyone who would like a copy. I’ll update this information every week or so.


Here’s why I’m not voting for AUSD Measure B

This will be short note, I’m afraid. I’m up to my eyeballs in writing projects, so I have to make this quick. The Albany Unified School District (AUSD) has put a parcel tax on the March 2020 ballot as Measure B. After having given it some thought, and after doing some background research, I’ve decided to vote against Measure B. Here’s why:

Measure B replaces the existing Measure LL which will sunset (terminate) in July of 2021. If Measure B simply asked to continue at the same inflation-adjusted level as Measure LL, I would have no trouble endorsing it. However, according my current property tax bill, we now pay $318 annually for Measure LL, while Measure B will start at $448 annually, and increase of 41 percent.

If Measure B guaranteed that the new money would be held in a restricted fund to deal with pension costs, I would vote yes. But it doesn’t. There needs to be some justification for an increase of this amount, and I’m not seeing it. For comparison, note that in the 2018 elections, the City of Albany asked to voters to extend our half-percent sales tax (Measure L) and our parks and open space funding (Measure M), but at the existing rates.

I’ll be the first to admit that when I was on the school board from 2002-06 we used the same sort of heart-warming photos of cute Albany kids in our campaign literature. But times have changed. Local governments are facing a serious pension-funding crisis, one that will play for a decade or longer (here and here and here). AUSD’s literature would be more honest if it featured age 60+ folks like me holding up signs that say “SAVE OUR PENSION SYSTEM.”

In Albany we have some folk wisdom that needs a critical second look. Do we have great public schools? Depends on your benchmarks. Compared to our surrounding schools districts–Berkeley and West Contra Costa, we do have high-performing schools. But compared to schools nationally, Albany schools are solid mid-pack peformers, as this interesting graphic from the NY Times shows. Albany is sometimes compared to another small local city, Piedmont. Oddly enough, the wealthy Piedmont school district tends to underperform when measured against its peers.

And are Albany teachers underpaid? Again, it depends on the comparison. The Albany teacher’s step-and-column table is here. Similar pay schedules for nearby districts are here for Berkeley (scroll down to Appendix 12), Piedmont and West Contra Costa schools. The steps (rows) indicate years of service, while the columns indicate the amount of education.

Albany teachers are paid at least as well as teachers in nearby districts. However, compared to other public organizations, Albany teachers are paid fairly well. For example, I retired as a UC Berkeley science writer and editor after 20 years of working at UC. My pay was roughly the same as a Column 3 Albany teacher with 14 years of experience.

For more comparisons, the City of Albany’s salary schedule is here. The Sacramento Bee maintains a database of public sector employee compensation (here) but it is annoying to use. This database is easier to use, and it is looks accurate (including for my data), but it is put together by a conservative political organization in Nevada.

There are a few caveats that I should mention. First, as a UC employee, I continued to pay into the social security system, so when I retired, I got my UC pension and I am eligible for social security benefits. There is an employer match in social security, so my pay + benefits are higher because of that. When a teacher joins the STRS pension system, they stop paying into the Social Security system. This creates some unusual incentives.

For teachers who enrolled in STRS in 2012 or earlier, their pension payment is based on their highest one year of pay. This leads to the what called spiking, or pay scales with a big bump in the final year. Albany’s step-and-column is a good example. After remaining relatively flat for several years, in the final year, the annual pay jumps by almost $5,000. For teachers hired after 2012, the pension benefit is based on the highest three years of salary, which is the typical practice for public sector organizations like UC and city governments.

More generally, the STRS pension system creates incentives for a steep system of steps, meaning starting teachers get paid less, while senior teachers get paid more. If teachers also paid into the social security system and got full benefits from both (as I do), the incentives for spiking and steeper steps would be reduced. This would benefit starting teachers, who get paid considerably less than senior teachers.

Now, a note on our property tax bills. I just crunched the numbers for 2019-20 (we paid the 1st installment already, and have the 2nd installment due in April). On the left hand side of our bill, the ad valorum portion, the county gets one percent of our assessed value (less the $7,000 homeowner exemption). For me that’s 56.31 percent of my total tax bill. The county gets a bit of parcel tax revenue, but it’s pretty small. From both ad valorum and parcel taxes, the school district gets 20.98 percent of my property taxes. That amount would rise to 22.17 percent if Measure B passes. The city gets 16.56 percent.

Finally, it’s is important to note that California school districts get significant funding from the state in the form of average daily attendance (ADA) money. For AUSD, that amounts to a little more than $9,000 annually per student. If AUSD is turning to Albany residents for extra funding at the top of the business cycle when state budget has a healthy surplus, what is going to happen when the next recession inevitably occurs as our public pension obligations are increasing?

When I was on the school board, the district was run Dr. William Wong, whose management goals were shaped by running poor rural school districts in Southern California. Wong ran a tight ship financially. From my conversations with various Bay Area educators, AUSD since then has developed a reputation for (how to put this politely) getting looser with its financial management. But then if you have a group of soft-hearted, naïve citizens willing to bail you out with parcel taxes, why bother to run a tight ship?

If Albany residents are going to be the funders of last resort for our local government agencies, we need to start thinking seriously about how we will address our long-term funding needs. I don’t think Measure B does that. Albany residents will have plenty of opportunities to tax themselves in the coming decade. For now we might want to hold off.


The naive economics of SB 50


San Francisco State Senator Scott Wiener, along with our own State Senator Nancy Skinner (in the news recently), have resubmitted their zoning bill SB 50, which was converted to a two-year bill at end of the last legislative session. The final version of the bill is not yet available, but the flaws in previous incarnations of this bill no doubt will remain.

The rhetoric from the bill’s supporters has been sloppy enough that I think it’s time to frame the issues the bill raises in the rigorous analytic framework of neoclassical economics. SB 50’s emphasis on housing supply recalls the supply-side economics of the Reagan administration. But neither supply-side economics nor SB 50 are based on mainstream economics. In what follows, I’ll lay out the groundwork my analysis, which will be familiar to any undergraduate economics major. I know because in the early 1990s, I taught economics at UC Berkeley as an graduate student instructor and as an acting instructor.

Here’s my first question, which is one that could have been drawn from a quiz early in an intro econ course: In a market with a standard downward sloping demand curve and upward sloping supply curve, in order to lower equilibrium price and raise equilibrium quantity, is it sufficient to shift the supply curve outward? If not, what other conditions must be assumed?

Figure 1: SB-50’s implicit vision of the housing market.

Figure 1 describes, in a standard intro econ graph, the question posed. In a housing market, assume a fixed demand curve (D), and an outward shift in supply from S1 to S2. The price of housing falls from P1 to P2, while the quantity of housing supplied rises from Q1 to Q2.

This is a result that proponents of SB 50 like to assume. But the result rests upon a very strong, and very unrealistic assumption–that the demand curve for housing is fixed. In the Bay Area, the demand for housing has shifted outward at a dramatic rate, driven by the growth of large monopolistic tech firms like Apple, Google and Facebook, and by the billions of dollars of venture capital being funneled to tech startups here. This growth requires more tech workers, more office buildings and ultimately more housing.

The choice of the expression “housing crisis” was a deliberate, misleading attempt on the part of SB 50 supporters and other pro-growth advocates to shape the debate. The state’s Office of Housing and Community Development (HCD) instead uses the term “housing shortage.” Statewide, the shortage is the result of both a physical shortage of housing and an income mismatch that HCD estimates requires the construction of 1.5 million units of affordable housing for the poorest of California’s residents.

If simple poverty is the major problem for the whole state of California, in the Bay Area the problem is mostly due to relative poverty–the influx of a highly paid cohort of tech workers crowding out lower-income residents. This is not a housing crisis–there was no hurricane or outbreak of mutant termites that destroyed thousands of apartments. What we have is a venture-capital driven influx of tech workers. It would be more accurate to call this a “housing demand shock.”

Figure 2: In the real world, demand for housing is shifting outward.

Here’s how we could graph a housing demand shock. For simplicity, in Figure 2 above, the supply curve of housing is held constant, while the housing demand shock shifts the demand curve from D1 to D2. While the assumption that the supply curve is constant is too simple, it is very realistic to assume that the demand for housing in California, and especially in the Bay Area, has been shifting outward. In this example, like the example in Figure 1, the equilibrium quantity of housing supplied rises from Q1 to Q2. However, unlike in Figure 1, the equilibrium price rises from P1 to P2.

The outward shifts in supply and demand both cause quantities to rise, while these outward shifts have contradictory affects on prices. To explore this more fully, let’s combine shifts in demand and supply together in one graph.

Figure 3: Demand and supply both shift outward, but demand shifts out more.

In this example, demand and supply curves both shift outward. Equilibrium quantities rise as before, and prices rise somewhat. The price response is moderated by the relatively small outward shift of the supply curve.

When both supply and demand curves are shifting out, it is the relative size of the shifts that matter. By now the reader can probably see that if the S2 supply curve continued to shift out far enough, with its intersection point moving down the D2 demand curve, the new price would be lower, not higher. The reader is encouraged to draw diagrams of their own, not only shifting the positions of the curves, but also drawing them steeper (more price-inelastic) or flatter (more price-elastic).

Three general points should be made here: 1) It is unreasonable to assume that housing supply can shift rapidly enough to accommodate a housing demand shock driven by volatile capital flows. This is especially true because builders of new affordable housing shared in very little of this largess. 2) It is generally faster to build office buildings than new communities. With respect to communities, office buildings require far less services like police and fire departments, schools, parks and libraries and utilities. 3) When they ignore the demand issues, SB 50 proponents violate one of the fundamental concepts of economics–that in a market, prices and quantities are set by the interaction of supply and demand, and not by supply conditions alone.

At least in San Francisco, SB 50 isn’t the only game in town. An initiative that explicitly links housing demand and supply will be on the ballot in March 2020. Sponsored by the community development organization Todco, Measure E will cap office construction unless the city meets its affordable housing goals (Links here and here).

Figure 4: Converting office space to housing, especially affordable housing.

While capping office construction to allow new housing to catch up is an idea worth supporting, restoring the jobs/housing balance could still take years–and without something like the Todco proposal it may never happen at all, since SB 50 makes no attempt to control housing demand.

An intriguing possibility to speed up that process is suggested by Figure 4. What if we could increase housing supply and simultaneously decrease housing demand by converting offices to apartments? In such a scenario, small office buildings could be completely retrofitted and converted to apartments, while whole floors of taller office buildings could be converted.

The advantages to this plan are many. Since building any new housing is very expensive, money would be saved by utilizing existing buildings. Downtown office workers could walk to work (or perhaps just take an elevator), and their presence in the neighborhood in the evenings would create a lively after-work social scene with new bars, restaurants and shops. If some of the new apartment units were affordable, inclusionary and affordable housing programs could be tapped for revenue to subsidize the retrofitting.

Of course, this would mean that some tech businesses would leave the city, a trend that is already beginning. But is this so bad? By moving to cities with lower housing costs, tech workers could afford houses instead of apartments, and move into neighborhoods with good schools and other amenities. And they might not have to move far. Oakland, Concord and Walnut Creek, Sacramento, Las Vegas, Phoenix and Austin all are possibilities. It makes sense to move jobs to where housing is more available.

In the story told in Figure 4, as offices start to close and workers move to other cities, the demand curve shift inward from D1 to D2. Housing quantities and prices both fall temporarily. But as the former office spaces are converted to housing, and the supply curve shifts from S1 to S2, housing prices continue to fall while musicians, artists, people of color, students, new immigrants and commuters are drawn back to the city. This is a story of the degentrification of San Francisco.


If SB 50’s advocates fail in part because they do not grasp the interaction of supply and demand, there remains a deeper failure. Upzoning will not effectively increase housing supply, at least not for many decades, if at all. Zoning is a constraint, although a complicated one. But in the current housing market in the Bay Area, zoning is mostly a non-binding constraint. That is why changing zoning laws will not be effective in the short run. By definition a “crisis” is something happening here and now and requires effective solutions in the short run. Zoning changes are not that solution.

To discuss this, I want to introduce the topic of constrained optimization in a form that is familiar–the model of consumer behavior in standard neoclassical economics.

Figure 5: A simple model of consumer choice in the present of a budget constraint.

Figure 5 presents a typical graph that could be found in many intro econ textbooks. A consumer is faced with choosing how many mangoes and avocados to purchase. The goal is to maximize utility subject to a budget constraint. To keep things simple, let’s just assume the consumer budgets six dollars per week on fruit, and they only like to eat mangoes and avocados. Mangos cost $1 each and avocados cost $2 each. If they spend their entire fruit budget of $6 per week, the consumer could buy six mangoes or three avocados, or some combination of the two. Their purchase decision will lie along the blue budget constraint line.

The graph also features a series of indifference curves which display the consumer’s preferences between mangoes and avocados. Along each curve, the consumer is equally satisfied with the options available. As we move to indifference curves further up and to the right, the consumer’s satisfaction, or utility, goes up. That’s another way of stating that more is better, a basic the assumption in these models. The consumer maximizes their utility by reaching the highest indifference curve possible without violating their budget constraint. In this example, our consumer will choose to buy two avocados and two mangoes.

Now let’s assume that due to the popularity of avocado toast, the local grocery where our consumer buys fruit limits customers to one avocado per day, or seven per week. We represent this new constraint on the graph as a dashed vertical blue line at the number seven on the horizontal avocado axis. Now the figure contains two constraints, one binding, one non-binding.

In this example, the budget constraint is the binding constraint, and the store’s limit is non-binding. But if our consumer was shopping for a family, their budget for fruit might be far larger and their budget constraint could lie much farther to the right. If, for example, our family shopper had a fruit budget of $60 per week and wanted to purchase 20 mangoes and 20 avocados, their choices would be constrained by the grocery store’s limits. In that case, the budget constraint would be non-binding, and the store’s limit would be binding.

Here is another example in a different context: Assume you are a very fast center fielder playing for a major league baseball team. The center field wall is short and you a facing a team with lots of power hitters. For you the outfield wall will be a constraint if the opposing team’s hitters are whacking balls into the grandstands. On the other hand, if you are playing a team that focuses on line drives and high batting averages, the outfield wall may not be a constraint because balls aren’t being hit that far.

The important point to remember is that removing non-binding constraints does not change the equilibrium outcome. Whether or not a constraint is binding or non-binding depends on the situation. In large, complex, multi-dimensional models, it is often not obvious which constraints are binding, and computer algorithms are used to determine the optimum outcome. In the real world, building housing is subject to many constraints. In the current Bay Area context, zoning rules are typically not the binding constraints for two reasons. First, there are many other constraints that are binding. Second, zoning rules don’t work quite in way that many SB 50 supporters seem think they do.

In an excellent letter date June 14, 2019 (here), the City Council of Rohnert Park sent to various legislators a list of the many constraints on building new housing. Excerpts from the letter appear in italics below:

There is a flood of proposed legislation in California intended to address housing that are a result of a misdiagnosis of the root causes of the housing shortage. The bills seem to assume that a lack of approvals is unduly constraining housing construction. In reality, it is a complex problem with many contributing factors to the housing shortage including:

• An economic expansion including significant regional construction demand in Silicon Valley and San Francisco for office buildings and campuses

A lack of specialty trade construction subcontractors

• A lack of construction workers

• Immigration uncertainty and hostility from federal government

• Cost, long delays, and uncertainty associated with the California Environmental Quality Act lawsuits

• Tariffs and trade uncertainty driving up materials costs

• A building boom to replace homes lost due to wildfires

• Lack of available sites due to land use protections such as urban growth boundaries, community separators, etc.

• High costs associated with mitigating water, sewer, transportation, and environmental impacts including endangered species (e.g. California tiger salamander, various vernal pool wild flowers)

• State regulatory requirements such as low-impact-development storm water requirements

• Affordable housing inclusionary requirements added to market rate housing projects

• Loss of redevelopment which was the greatest affordable housing producer in the history of California

• Federal tax reform which lowered the value of affordable housing tax credits leading to a widened funding gap for affordable housing projects

Increased local government capital project spending from new gas taxes, regional tolls and other revenue improvements

• Whole-house-vacation-rentals taking housing stock off the market

Lender reticence to extend credit to construction projects post 2008 melt-down

Lack of affordable housing gap funding.

Rather than address those issues within its control, some state legislators are seeking to impose “by-right” development projects on local governments, elimination of fees, removing parking, overriding local plans, and limiting public input.

As the letter describes, there are many binding constraints that prevent housing from being built, constraints which SB 50 does little or nothing to address. But even if all these constraints could be removed, there is still another problem. Upzoning–allowing multifamily and other large housing developments in neighborhoods in which they were previously restricted–requires homeowners to do nothing.

Let me give an example from my own neighborhood. I live in an R-1 neighborhood where only single-family homes can be built. My 1,100 sq. ft. house was built in the 1920s. If my neighborhood was upzoned to R-2 zoning (which allows for multifamily housing), what would I be required to do? Nothing. Upzoning removes a constraint–if my neighborhood became R-2, I could sell my house to someone who plans to build a duplex. But I’m not really interested in doing that. I think I’d rather sell to a family who wants to live in the perfectly adequate house that is already here, and until then I might add some plumbing to my backyard studio to convert it a legal accessory dwelling unit (ADU), which I would probably continue to use as an occasional guest house.

For me, R-1 zoning is a non-binding constraint. As in the examples above, if you remove a non-binding constraint, it doesn’t change the outcome. Some people seem to think that zoning is like eminent domain, where the state can condemn your house, force you to sell, and then demolish it to make room for a freeway (or an apartment building). That’s not how zoning works. Upzoning allows someone to build something bigger on my property, but it can’t require me to let them do it, or to sell to them. I still maintain my property rights.

Even if I wanted a duplex where my little house exists now, there is another problem. I might not be able to find a developer who would want to build it. The project very likely wouldn’t be profitable. Let’s just say because I live in a town with good schools within walking distance, with a charming walkable commercial district nearby, I could sell my house to a young family for $1 million. As an alternative, I could sell it to a developer who wanted to build a duplex.

First the developer would have to pay $1 million for the property. Then they would have to demolish the old house and build two new units. That’s expensive. Then they would have to find a buyer for the project. The problem is that privacy, space and aesthetics are all what economists call normal goods–as your income rises, you demand more of them. If you tear down a charming 1920s bungalow and replace it with a boxy duplex, you are destroying the very characteristics that made the property valuable in the first place.

Given how valuable single-family homes are in the Bay Area, and how expensive it is to build for all the reasons listed above, upzoning R-1 neighborhoods like mine might lead to very little building in the short run. In the long run it might lead to more, but as John Maynard Keynes famously said, “In the long run we are all dead.” If we really want to solve our “housing crisis,” solutions that take several decades are inadequate to the task.

However, the combination of upzoning and gentrifying low-income neighborhoods, typically occupied by families of color, could be profitable under SB 50. That’s why low-income community organizations tend to be among the most vociferous opponents of SB 50. Various versions of the bill in the past have attempted to mollify these critics, but the neighborhood groups are right to be extremely skeptical. They have been hesitant to abandon their positions on the bill (and possibly their positions in their old neighborhoods).

For a good example how and where such problems could emerge, consider Minneapolis. Advocates for upzoning consider the city a model. Minneapolis recently banned single-family zoning in favor of allowing residential triplexes “by right,” which means the city has very limited ability to block the projects. In an article that is both fascinating and disturbing, a Minneapolis planning commissioner, architect and resident of low-income North Minneapolis, dissects this policy (here).

To summarize, the arguments for SB 50 fail for two reasons. First, expanding supply will not bring down prices unless demand is constrained. Second, although zoning is a type of constraint, in the current situation, it is not a binding constraint. However, several other constraints are binding. Upzoning R-1 residential neighborhoods does not require a homeowner to move or prevent them from selling their house to a new owner who might live in it for decades.

If SB 50 is ineffective in bringing about its stated goals, what then is the real purpose of the proposed legislation? The real purpose of SB 50 is to destroy local control and small-homeowner property rights. Real democracy exists at the local level. But for corporate real estate developers and their sycophants (see here and here), local democracy is a nuisance. If democracy, at least on paper, must exist, they would prefer its decision-makers to be housed a compact space, like a state capital building, where they become easier targets for lobbyists and campaign-funding checks.

On the other hand, under local democracy, there are too many decision makers, and too many homeowners, to be bought off easily. Influencing local government officials is like herding cats, and homeowners are a group of independent and opinionated Jeffersonian free holders (at least after the mortgage is paid off). Local governance is messy. Some corporate real estate interests would prefer to do away with local governance and small homeowners altogether, and, judging from the legislation they support, require the little people to live in large, drab apartment blocks like those in the old East Berlin, or to house tech workers in Shenzhen-style worker barracks–quick to build, no design review required. SB 50, along with related bills like Skinner’s SB 330, take a giant step in that direction.


Since legislation like SB 50 and SB 330 are not the solution, it’s a good idea to step back and ask how we got into this mess. If we don’t understand how we got here, if we don’t understand the nature of the malady, we will keep on prescribing for ourselves the wrong remedies.

First, a note about rural California. In many respects, the problem there is not that the rich are getting richer, but that the poor are getting poorer (here and here). Although it is true that lack of affordable housing can exacerbate rural poverty, the opposite is also true–lack of effective demand due to poverty can reduce the amount of new housing. Poverty is both a cause and effect of the rural housing shortage.

Economic relationships in which cause and effect flow in both direction are difficult to disentangle. But in the real world, they are common. In his classic essay, “Politics and the English Language,” George Orwell stated this succinctly:

“But an effect can become a cause, reinforcing the original cause and producing the same effect in an intensified form, and so on indefinitely. A man may take to drink because he feels himself to be a failure, and then fail all the more completely because he drinks.”

In California, the combination of rural poverty and lack of housing are nothing new. Does anyone believe that in 1962, when Dolores Huerta and Cezar Chavez began organizing the United Farm Workers, those farm workers were better housed than they are today?

Here in urban coastal California, things have changed. As I mentioned earlier, the rise of demand for housing has been driven by the growth of large monopolistic tech firms like Apple, Google and Facebook, and by the billions of dollars of venture capital being funneled to tech startups here. The clustering of firms based on emerging technologies has been happening at least since the industrial revolution, and analyzing this new round of tech clustering is keeping economic geographers busy.

One of the new aspects of tech clustering in the United States is that it’s happening during an era of weak antitrust enforcement. Especially given the privacy issues engulfing Google and Facebook, is there any economic efficiency argument for Google Maps and Gmail to be run by the same company? How about Facebook and WhatsApp? The gigantism of tech firms is now drawing the attention of Congress.

New York University finance professor Thomas Philippon is the best known current thinker exploring the failures of U.S. antitrust policy. As he notes in an Atlantic magazine article, “In 1999, the United States had free and competitive markets in many industries that, in Europe, were dominated by oligopolies.Today the opposite is true.” A New York Times article about Philippon’s work on corporate concentration states, “Philippon’s biggest contribution is to explain that it isn’t some natural result of globalization and technological innovation. If it were, the trends would be similar around the world. But they’re not. What explains the difference? Politics.”

Facebook, Google, Apple and other tech firm are not immutable forces of nature. Our rules, or rather the lack of enforcement of them, have led to their growth. Bay Area citizens have every right to use the rule of law to restrain the behavior of, and the problems created by, these behemoths of the Bay.

Many of the problem big tech creates are what economists call negative externalities.The negative externality most often in the news today results from the burning of fossil fuels. The price of burning coal, oil and gas does not include the social and environmental damage caused by increasing levels of carbon dioxide in the Earth’s atmosphere, or their contribution to climate change. One partial solution would be to charge a carbon tax to increase the cost of burning fossil fuels and internalize those costs in the higher price.

Writing in the Jan. 18, 2018, edition of the New York Times, columnist E. Tammy Kim tied the logic of taxing negative externalities to the tech housing demand shock:

“A half-century ago, it seemed inconceivable that factories, smelters or power plants should have to account for the toxins they released into the air. But we have since accepted the idea that businesses should pay the public for the negative externalities they cause. Today, corporations must answer for increased rents and evictions, and for worsening traffic jams. Like air and water pollution, these costs are shared by all of us.”

Approximately 11 months later, in Dec. 2019, a report from the Brookings Institution mirrored the concerns of Kim:

“At the economic end of the equation, the costs of excessive tech concentration are creating serious negative externalities. These range from spiraling home prices and traffic gridlock in the superstar hubs to a problematic “sorting” of workers, with college-educated workers clustering in the star cities, leaving other metro areas to make do with thinner talent reservoirs.”

The Bookings report stresses subsidies to develop new regional growth centers. Their goal is to:

Assemble a major package of federal innovation inputs and supports for innovation-sector scale-up in metropolitan areas distant from existing tech hubs. Central to this package will be a direct R&D funding surge worth up to $700 million a year in each metro area for 10 years. Beyond that will be significant inputs such as workforce development funding, tax and regulatory benefits, business financing, economic inclusion, urban placemaking, and federal land and infrastructure supports.

The New York Times’s Kim endorses not the “corporate takeover of housing policy” (as the advocates of SB 50 suggest), but taxation of negative externalities in existing tech centers:

What is needed in Seattle — as well as San Francisco; Austin, Tex.; New York City; Boulder, Colo.; and other urban areas where the rapid influx of high-paid tech workers has made housing unaffordable for nearly everyone else — isn’t a corporate takeover of housing policy but, rather, a per-employee “head tax” that would fund real investments in affordable housing, which should be a public good.

These two policies are complimentary. In addition to taxing tech’s negative externalities to subsidize affordable housing, the tax revenue could fund the development of new regional growth centers–although if state taxing and funding mechanisms were used, the new regional grow centers would have to be in California.


In this blog post I have attempted to explain how the advocates for SB 50 do not understand the basics of supply and demand. These advocates misunderstand or ignore the many constraints to building housing, and they do not have a clear understanding of zoning or the unintended consequences of upzoning. In their arguments they fail to recognize the broader economic context that includes antitrust and negative externalities.

SB 50 cannot fulfill its stated mission of reducing housing costs in the short run. However, if enacted, the bill could be effective in its intended, long run mission–removing local control of land use and encouraging the corporate takeover of housing policy.

NOTE: Jan. 14, 2019: I made changes to two paragraphs, at the suggestion of a reader. Typos continue to be corrected as I find them. Jan 15: I added link to Nancy Skinner news story in first paragraph.