SB 828 (part 2)

Let’s recap.  SB 828 would empower the California Department of Housing and Community Development (HCD) and COGs to make significant changes to the Regional Housing Needs Allocations (or Assessments) for which local governments must identify adequate sites for new housing development.  The RHNA is explained in this tweet thread.  In this context, SB 828 states that HCD must:

(b) Establish a methodology for the comprehensive assessment on unmet need that acknowledges the following:

      (1) Median rent or home prices that exceed median income will be alleviated by        rapidly increasing housing supply, particularly housing supply for moderate and above-moderate income households.
      (2) Communities with high rates of income growth must also have a high rate of new housing production for households of all income levels to ensure equity and stabilize home prices and communities.”

Part 1 of this analysis concerned (b)(1), and how how “high cost tests” comparing the use of median incomes against median rents and home values would subject nearly all California jurisdictions to large increases in allocations.  This posts considers (b)(2).  Let’s take a deep dive with some initial thoughts and questions:

  • What is a “high” rate of income growth?  
  • What will be defined as ‘income’?
  • Will median or mean income be used?  
    • Income inequality will, in some contexts, show up more obviously in a statistical mean versus a statistical median.  This could mean that using a mean based definition is a good thing… e.g. if you believe that an increase in high-income earners has a disproportionate impact on housing costs versus an equally sized increase in middle or low income earners.

There are many ways this could go. For this analysis I’ll focus on the SF Bay Area for now just because its easier.

Modelling the Question:

To rephrase the language of SB 828 into our research question: Who would be subject to “a high rate of new housing production at all income levels to ensure equity and stabilize home prices and communities” because they are “communities with high rates of income growth” ?

Some Definitions of High Income Growth to test:

  • A city has high rates of income growth if its median income grows faster than the regional median income on a dollar basis (as opposed to a percentage basis).
  • A city has high rates of income growth if its mean income grows faster than the regional mean income on a dollar basis (as opposed to a percentage basis).

In this case, the region is the Combined Statistical Area (SF-SJ-OAK CSA).  The base year is 2011 (from the 2007-11 ACS Waves) and second time point is 2016 (from the 2012-2016 ACS Waves).  So who passes the (b)(2) high cost test under these definitions, thus “requiring them to ensure equity and stabilize home prices and communities”  ala Senator Wiener’s bill?  Maps below:


So as expected, the mean based definition catches more localities as “high rate of income growth” than the median one.  Some details:

  • Cities that pass the test under both definitions include:
    • A Marin Cohort: Corte Madera,Mill Valley, Santa Venetia,
    • A Silicon Cohort: Burlingame, Portola Valley, Los Altos Hills, Los Altos, Coupertino, Campbell, Los Gatos (Yesss).
    • An Oakland Hills Cohort: Orinda, Alamo, Alhambra Valley & San Miguel.
  • Each Cohort adds some folks under just the mean-based definition:
    •  Marin Cohort pickups: Sausalito, Tiburon, Belvedere, Stinson Beach,
    • Silicon Cohort pickups: Sunnyvale, Mountain View, Saratoga, Ladera, Atherton, Half Moon Bay,
    • Oakland Hills pickups: Danville, Kingston, North Gate.

So here we see the role that income inequality and economic segregation play in impacting the meanings of our statistics, and why something like selecting the median or the mean would matter in implementation of language like this.

I thought it would be fun to test a definition of high wage growth against inflation (e.g. a scenario where cities would pass the high cost test if median and/or mean incomes grew faster than the rate of inflation).  I tried it out and, by those metrics, nearly all jurisdictions in the Bay Area are experiencing high rates of income growth.

Measuring this In Terms of Equity: Comparing Percentiles Within Cities

It might be better to have the requirement test if each city’s 75th percentile or 90th percentile income is rising faster than the median (or 40th percentile) income for that same jurisdiction.  After all, the bill states that this second part (b)(2) is about “requiring them [localities] to ensure equity and stabilize home prices and communities.” (emphasis added).  lets take out “home prices and” and the sentence reads: “requiring them [localities] to ensure equity and stabilize communities.

I like that. Under the percentiles comparison approach, cities will be required to zone for housing at all income levels if their higher income earners have incomes growing faster than their lower income earners.  Just a thought.

Code and data to make all this stuff is here.  If you check it out, let me know if you find an error.

SB 828 Review (Part 1)

I am trying to wrap my head around Scott Wiener’s bill SB 828.  There’s a lot in it I really like, and there’s things that confuse me… particularly how it relates to the existing RHNA priorities.  For example, the law would have the Department of Housing and Community Development (HCD):

(b) Establish a methodology for the comprehensive assessment on unmet need that acknowledges the following:
      (1) Median rent or home prices that exceed median income will be alleviated by        rapidly increasing housing supply, particularly housing supply for moderate and above-moderate income households.
      (2) Communities with high rates of income growth must also have a high rate of new housing production for households of all income levels to ensure equity and stabilize home prices and communities.”
So the language reads to me, as a non-lawyer, as ensuring that HCD’s methodology must reflect (or whatever “acknowledge” means in legalese) the belief that supply (particularly market rate supply) can alleviate imbalances between incomes and prices (Even though this is not the experience in many places–but let’s sidebar that for now).
1.  Let’s start with point (b)(1) in the language above…
Let’s take (b)(1) literally and examine which cities will have new needs assessments linked to “rapidly increasing housing supply” because median rents and/or median home values exceed median income.  (Lets call these the (b)(1) high cost tests).
Initial thoughts:
  • What datasets will we use to identify median rents and median incomes?  For smaller jurisdictions we will have to use the five year rolling average of the American Community Survey, meaning a hypothetical assessment done by HCD at present would require the department to use ACS data that is a combined sample of surveys taken between 2012 and 2016.  Maybe for long-term regional planning this is actually appropriate… I don’t know.  But congress is probably going to continue to weaken the ACS, making even the 5 year wave estimates problematic (what do you do when the margin of error on the rental estimate is so big there *could* be a housing a crisis and there *could* be a glut–there are answers to that question that involve some aggressive computational geography, but no precedent for using those tools in policy just yet).
  • How do we relate median incomes to median home values?  The classic ‘rule of thumb’ is that a unit is affordable (for potential owners) if the price is 3 times the potential owner’s annual income.  How will HCD operationalize what the median income can afford in each locality?

The latter question matters because when I tested this against ACS data, I found most cities would only pass the (b)(1) high cost tests under this owner rule of thumb (annual income 3 times price), not under the rental median test. This is because median incomes are actually high enough to afford median rents in many cities.  That may surprise you. If it does, the main issue here is the reliance on medians which do not really capture ‘affordability’ problems for most people below said median incomes.  Another issue is that homeowners have higher incomes than renters, so when you compare the median rent to median income (as opposed to the median renter income), rents dont look too bad.  I’m not sure if SB 828 as written would empower HCD to differentiate median incomes by tenure in determining who needs to be ‘rapidly increasing housing supply’ as the bill states.

I have mapped which cities pass the (b)(1) high cost tests (and thus are high cost).  Below is the Bay Area Map.SB828Part1BAY

There are a few caveats/things to note here:

  1. I guess the rental estimates were not robust enough in many Silicon Valley cities (the ones in the west, in blue on the map).  These cities still count as passing this (b)(1) high cost test with respect to home ownership prices (this is a given).  But there are apparently so few rentals in those cities captured in the ACS sample that the Census Bureau was not comfortable publishing them.  (Maybe we say any city with more than 20,000 people that has too few rentals for the ACS to estimate median rents is automatically ‘too expensive’ by virtue of its exclusionary nature–that would apply Los Altos, CA ala the map above).
  2. There are very few communities that pass both of these high cost tests, and there are two clusters of them:
    1. Expensive coastal towns (Santa Cruz, Seaside) and Watsonville (coastal hyper-gentrification???)
    2. Disadvantaged inner-suburbs and southern suburbs (Florin, Parkway, Fruitridge, Pocket, Lemon Hill–of Sacramento that area not part of the city proper).

Here’s the Map for Southern California:


Here data issues are less of a problem.  A few things to note:

  • A few pockets where both high cost tests are applicable, mostly places where incomes may have crashed and not recovered from the recession like Adelanto and Victorville, Sun Village.
  • There is one pocket of places where only the rental test applies: Winchester and Homeland in Riverside County.
  • And low and behold our first set of cities which are not high cost under either test! Pala (San Diego County), Green Acres & Warm Springs (Riverside), Mojave CDP, Rosamond and California City (Kern County),  and Barstow, Lentwood and Silver Lakes (San Bernardino County).

So to summarize, via cutting and pasting the language in SB 828: particularly housing supply for moderate and above-moderate income households” “will be” “rapidly increased where median rents or home prices exceed median incomes” which is almost everywhere under traditional definitions of affordability for home ownership.  

HCD  and the MPOs/COGs will be interesting places to work at if this passes and the old ‘common sense’ approaches to measuring affordable home ownership are applied.  I’ll get to (b)(2) of the law in my next post.

Ideal Alt-Ac Job #4: Right of Return Coordinator


4.  Right Of Return Coordinator, City of Los Angeles

The City of Los Angeles is looking for an energetic people person to lead the new Right of Return divisions of the Los Angeles Housing + Community Investment Department (HCIDLA).  California’s newly passed legislation (SB 827) requires each locality with transit rich up-zoned neighborhoods to establish a Right of Return (RoR) program to ensure residents displaced from redevelopment are allocated units in new buildings as close to their original homes as possible or provided vouchers.

30%:  Develop and implement an outreach and service plan to counsel displaced residents and match them with new accommodations.

10%: Develop forecasts for RoR services based on permitting information and census data and ensure outreach and service plan is aligned to meet anticipated need.

20%: Develop an RoR voucher program for submission to the city council for approval.

10%: Collaborate with other HCIDLA units to identify state and federal funds to support RoR oriented developments; coordinate with other units to ensure displaced residents receive priority access to current and future HCIDLA managed properties.

30%: Manage a team of counselors and coordinators to effectively meet benchmark goal.

We have a statutory goal set by the council to have this division achieve a 90% rate of return of displaced people at rents no more than 5% of the pre-law baseline and no more than an average of three miles from their original homes.  Are you up to the challenge?


#InflatableCities #BounceHausUrbanism

I feel like a lot of the “Urbanist” space out there on the interwebs is a scam.  We are so desperate to find to new ways of doing things that we jump on all kinds of ideas for their novelty, efficiency, or ability to repackage something old as something new, like when Toyota re-invented the bus.  In this fine tradition, I’d like to repackage something already done into my own conceptual scam: #InflatableCities, or #BounceHausUrbanism.

Look, we are faced with climate change, raging inequality and our society’s fundamental refusal to look at capital and the financialisation of everything, we need a new way to live…

So what we really need is: #InflatableCities#BounceHausUrbanism.

Not just another bourgeois fad! I swear! VCs please email meeee!

Thrilling Alt-Ac Fantasy Jobs

I’m processing the reality that my time doing research in academia will end and, as part of this, I am fantasizing about the kinds of jobs I’d love to do if they existed.  Here are some, with their imagined job postings (some of these are real jobs that exist, Yes I know, but they aren’t common yet or the actual postings would never be as honest as what I’m provding):

  1.  Pandemic Planner for PT Victoria


Public Transport Victoria is hiring a new manager for its Pandemic Planning and Prevention department (PPP).  The duties and time allocation for this position are as follow:

  • 40% Prevention Program management: designing and managing the implementation of a pandemic prevention program that includes identifying when, where and how to sanitize all trains, trams, escalators, elevators, railings and myki stations, as well as deploying advanced air filtration systems.
  • 40% Outbreak Response planning:  designing a pandemic response plan and overseeing and ensuring the preparedness of PT Vic in successful execution of response plans.  This includes organizing and executing drills and mock tram quarantines.
  • 10% Compliance: Ensuring PT Victoria pandemic prevention and response policies and plans are consistent with state and federal regulations [har har har].
  • 10% Coordination: Ensuring effective coordination of PT Victoria Pandemic response plans with allied agencies in emergency services, police, and government.
  1.  Urban Building RecyclorCityREcycle, Inc.


CityREcycle, Inc., is looking for qualified candidates to work in its Building Recycling team.  You will be joining a group of passionate individuals who seek to make an impact in this rapidly growing field. The duties and time allocation are as follows:

  • 60% Building Recycling: take apart abandoned buildings and categorize resulting materials for post-rummage processing.  Involves use of heavy machinery and lifting up to 50 lbs of materials.
  • 30% Material Reprocessing: sorting and sifting through materials pulled from abandoned buildings to ensure usability.
  • 10% Compliance and Training: maintain up to date knowledge of regulations regarding building recycling; maintain training certifications in safe building recycling practices.


3. Regional Housing Needs Allocator (RHNA), San Diego Association of Governments

SANDAG is looking to hire someone to develop and apply a formula for assigning regional housing needs allocations for all our member jurisdictions. The duties and time allocation are as follows:

  • 5% Utilizing Census, state and local data to develop formulas for assigning housing need allocations to each member jurisdiction.
  • 5% Coordinating with the California Department of Housing and Community Development (HCD) to ensure compliance with state law.
  • 10% Developing and executing a consultation program to ensure allocations are aligned with members’ existing transport, land use and housing plans, as well as stakeholder priorities.
  • 80% Politely smiling as angry residents scream at you in public forums and town halls.


What are your dream post-ac jobs? (non academics’ ideas welcome)

Selectivity of “Open Data” Will Make It Irrelevant

This piece will read as somewhat of a rant, but I feel I have to write it.

The City of Melbourne is really into open data.  They have an entire page devoted to it.  Quite a bit of the data is interesting and potentially valuable for better urban decision making, particularly for parking policy.  But the data needed to do an effective analysis of public policy around housing and land values is nowhere to be found.  Instead, the building and land use data includes a lot of rich and seemingly edgy datasets, like rooftop use, that are fun but irrelevant to tackling the major urban issues facing Melbourne and Australia.

Australian political leaders have started eagerly cheer-leading land value capture techniques as a way to finance urban infrastructure.  But few people are talking about how such a scheme would be governed to ensure just outcomes.  Data plays a key role in that, as hypothetical valuations or counter-factual studies of land values without infrastructure investment are necessary to estimate the ‘effect’ of infrastructure on land values and, thus, the resulting taxes to financing said infrastructure.  In California, where I grew up, property taxes are assessed for the year buildings are purchased and are then pegged to inflation–this de-politicizes the data to a large degree.  As such, I can get the property valuation data from California counties online for free or for a small fee.  This includes building attribute data such as the year built and the square footage. I’ve used this data in multiple unrelated studies on housing affordability, and such data would be easy to use to validate or challenge any land value uplift tax assessment.

Australia does not make this data easy to access.  I asked for the year built of every structure in Victoria for a study on what makes so-called ‘naturally occurring affordable housing’ (NOAHs) affordable.  The assumption my colleagues and I are testing is that these so-called “NOAHs” are affordable simply because they are old and likely in need of major repair.  The policy implication is that governments hoping to preserve their affordability might offer grants for upkeep and rehabilitation in exchange for the landlord keeping the rates low.  When I asked for this data I was told, “For privacy reasons, we are restricted in the access we can provide to this data at the individual property level” (correspondence with State of Victoria staff).  I found this really interesting.  The age of a building is apparently a closely guarded secret in this country.  I struggle with accepting this because, in most cases, one could walk by the building and probably guess its age (down to the decade, anyhow), based on architectural features. So why treat it like such a sacred secret?

Supposing I’m just wrong and a bit crazy (as a data oriented researcher I obviously have a vested interest in data access), the question remains: how will society determine things like land value capture taxes in a just way if the data to conduct the analysis is kept confidential?  Households and firms disagreeing with a government assessment will need to hire out their own valuer to do battle with the government’s, I imagine.  The lack of public access of relevant data will ensure most of the debate and its nuance remains the purview of a select few who have access to the data.  I imagine that monopolies will emerge as firms doing the most business will come to have the most data and require the least amount of capital to acquire ever more data.  If I’ve already done 1,000 valuations in a city in the last ten years, it will be easy for me to do ten more, data-acquisition wise.  But for groups just starting up, those first ten will cost a lot more.  And why would firms sell data to their competitors.  Is this how we want urban governance to function?

Am I missing something? Valuers can still thrive in open-data environments.  They manage to thrive in California where most data is public, for example.  So what is the big deal?


Engaging Topics In Transport + Land Use


I am prepping to teach a course on Transport and Land Use in the first semester of 2018 here in Melbourne.  I’ve been encouraged to push my students to see transport planning beyond the lenses of traditional four step modelling and mode-choice questions, and to inspire them to consider all the major changes underway in our society and their implication for transport planning.  To that end, my syllabus provides a list of hypothetical paper topics to get my students’ thinking that I’m sharing below.  Do any pique your interest? What paper topics would you want to write about in a class on transport and land use?

  • Telecommuting and 3D Printing: the end of origin-destination modelling as we know it?
  • Gender equity in cycling: not just for spandex dudebros anymore
  • Cost Benefit Analysis: what are we really measuring?
  • Why is my street Uber-jammed?
  • Aboriginal mobilities in Urban Australia
  • Sex appeal and mobility: is our love affair with cars over?
  • Best practices for safe routes to school
  • Designing inclusive public transport
  • The lessons from congestion pricing schemes around the world
  • Is high speed rail feasible for Australia?
  • Reducing bicycle crashes in rapidly changing cities: what we know
  • Social justice and air quality around highways
  • The role of transportation preferences in residential location decisions
  • Transport in the just city
  • Electric bikes in China: alternative to driving?
  • The high cost of free parking
  • Can we expand transport without gentrifying communities?
  • Amazon, drones and the end of freight as we know it
  • Reducing emissions from transport: what can be done?
  • Designing multimodal public spaces: lessons from Amsterdam
  • Pavement equity: whose roads are in the worst shape?
  • Transport for whom? The Google bus and the destruction of San Francisco
  • The politics of transportation: why some projects fail to win public support
  • Reducing drink driving through travel demand management
  • Parking at TOD hubs: just, why?



Aussie-U.S. Housing Policy Translator

 USflag.png  AUFlag.jpg
Technical Terms Technical Terms
Down Payment Deposit
Deposit Bond
Appraiser/Appraisal Valuer/Valuation
Real Estate Property
Mortgage Interest Deduction (owner-occupied)
Mortgage Interest Deduction (investment home) Negative Gearing
Single Room Occupancy (SROs) Rooming Houses
Homelessness/homeless Rough sleeping/rough sleepers
CEQA lawsuits (California only) 3rd party appeals
Granny flats/ADUs Laneway units/ADUs
Chapter 40B (Massachusetts only) Ministerial approval
 Apartments/ multifamily build to rent
 Condos Apartments
Policy Approaches/Programs Policy Approaches/Programs
Section 8 voucher/housing voucher Commonwealth Rental Assistance
Low Income Housing Tax Credit National Rental Affordability Scheme
Choice Neighborhoods Initiative, HOPE VI Public Housing Renewal
National Disability Insurance Scheme
Inclusionary Zoning
Density Bonusses

Responding to “Why Is ‘Affordable’ Housing So Expensive”

I’m writing this piece in response to Joe Cotright’s “The High Cost of Affordable Housing.”

I’m writing because Cotright’s piece leaves enough to the imagination to paint a picture that affordable housing in the U.S. is dramatically too expensive and over subsidized.  In some cases this may be true, and where true, it is likely the result of decades of regulators slowly introducing new incentives and requirements on affordable housing providers. One advocate described housing in California to me as being like a “Christmas Tree,” because it offered presents for every other interest group: transit pass requirements, solar energy requirements, gray water requirements, proximity to grocery store requirements, etc.  In this piece, without minimising the significance of what I now call the ‘Christmas Tree Problem,’ I am going to use data on over 500 affordable housing projects gathered for a recent peer reviewed paper to push back on Cotright’s presentation of the magnitude of the cost issue.  He’s right that we need serious innovation to reduce costs, but we shouldn’t base the conversation on cherry picked, extreme examples.

This San Francisco Project may cost $850,000 per apartment.  Thankfully, very few affordable housing projects in California are actually this expensive.  It turns out developing affordable housing post-gentrification is costlier than doing it before the hipsters arrive…

First, the $850,000/door number Cotright pulls from San Francisco is scary and real, but it is hardly representative of affordable housing across the state of California as a whole. The 2017 applicants for housing tax credits in California advanced projects that, in Cotright’s lingo, “clocked in” at an average of $378,612/door (1). High? Maybe. $850,000 high? Not so much.  That’s less than half the per-home cost of Cotright’s San Francisco example, actually.

Second, much of the ‘cost’ in these projects isn’t actually a direct cost to taxpayers, or if it is, it’s not ‘cost’ as most residents understand it. When a city leases land to an affordable developer for free, the developer will count the estimated value of the land as a ‘cost’ that is ‘paid for’ by the city.  This ‘cost’ of the free land is added to the Total Development Cost of the project–the final number the state of California and Joe Cotright both use.  But the city didn’t actually spend that amount of money, they just donated land for free–with their opportunity cost essentially added to the cost of the project (2).  Waived impact fees are also similarly counted as ‘costs’ that are ‘paid for’ by cities.  California includes these as ‘costs’ on projects’ balance sheets to properly measure local support for affordable housing, as state agencies strive to align their subsidies with local funding priorities. There is ongoing debate in California over this strategy, but the point is “cost” isn’t always a direct cost.

Third, some share of the cost of these projects is absorbed by the projects themselves  — particularly when projects mix higher and lower income households.  This absorption can range from 0% of a project’s budget, to almost 80%. There are sometimes hidden subsidies here–for example, if the hard debt is leveraged through tax exempt bonds, or if some of the units receive rental subsidies like project-based Section 8. I’ve measured the percentage of each project’s budget carried by the project itself using data from a recent paper.  This is sometimes called “hard debt,” or cost the project is paying back through rent and rental subsidies. I’ve plotted the distribution of this percentage across the 500 projects I have data on (2008-2016) below:


Most projects support between 0% and 30% of their costs (although these numbers above include rental subsidies like Section 8, so the true distribution will be even lower).  But in general: the lower the rents charged, the less the project can support itself–so the fact that some projects can cover 40% of their costs with rents could be a good or bad thing, depending on your goal.  Many affordable housing agencies across the country prefer to get the rents on these sites as low as possible, making the subsidy requirements high.  Why would they do this?  Because what is affordable to a “low income” household will still be un-affordable for someone who has been homeless for years.

But let’s get back to the questions that Cotright’s piece might lead a reader to ask: How do we get costs down? If you’re a fan of Joe Cotright you already know some of the answers.  Here are hard numbers from a suite of studies using the data of hundreds of actual projects and parcels to prove what works:

Let Developers Build Up, Especially Near Transit.   In a recent study, a colleague and I attempted to measure the impact of building near rail transit on affordable housing construction costs.  We found developers were absorbing the higher land costs near rail by building taller on their sites, a policy goal of the state’s TOD program.  We also found that for every 10% increase in the total number of homes in a project, costs went down by 1.8%, on average.  A forthcoming study in the Berkeley Planning Journal by researcher Scott Littlehale suggests the effect is higher: a 10% increase in homes reduces affordable project costs by 5.7%, on average.  This concept applies to all housing, too: Berkeley’s Terner Center for Housing Innovation found that increasing site densities by 20% increases the likelihood sites will be developed by 25%.  Density bonuses are an excellent way to take advantage of these effects.  It’s worth noting these economies of scale work in reverse at certain points, e.g. when another story requires switching to more expensive building materials.

Economies of Scale! From the State of California’s Affordable Housing Cost Study:

Reduce Parking.  Most planners know this by now, but the magnitude of parking’s impact on development costs is not fully appreciated.  The Berkeley Planning Journal study estimates that parking ratios can increase affordable housing development costs between 25% and 40%.  The Terner Center study of market rate and affordable units finds that reducing parking requirements by only 20% can greatly improve the likelihood a site gets developed by up to 87%.  These are astounding statistics that should upend how local governments zone for housing.

Waive Impact Fees.  Cities charge new developments for impacts to services like water, transport and storm-water.  California jurisdictions charge some of the highest impact fees in the country. In a preliminary study, I find these fees equal roughly 4.8% of affordable housing development costs in California, while Littlehale places the impact around 4%.  This may seem small, but when you add this cost on top of every other local requirement and charge, local government fees can add up to a big impact. The Terner Center suggests impact fees in some Bay Area jurisdictions are as high as $40,000 a door.

There is real hope modular housing will help, which Cotright mentions.  There’s also an entire study on the topic by the state of California’s housing agencies that mentions other options.

But if were going to talk about costs, though, we should really think about the historical context that’s led us to this point–why affordable housing financiers and providers are sometimes so eager to overload projects with bells and whistles. The answer is painfully obvious yet somehow easily forgotten by many “urbanists.”

Thanks to the OC Weekly for this one

So why the regulatory overload?

Whenever talk of affordable housing comes up in our leafy suburbs, neighbours begin to worry that it will ruin the neighbourhood. Even today, in  2017, people drag up the haunting specter of old public housing estates–like Cabrini Green and Pruitt Igoe, two old projects criticized by the mainstream for being aesthetically displeasing hotbeds of crime and drugs.  This historical narrative, accurate or not, feeds the perception that affordable housing is dangerous, unsightly, unhealthy and ultimately a threat to neighbourhood character. With this history in mind, the choice of an affordable housing provider to include a zen garden in a project appears less like excess and more like a thoughtful preempting of NIMBYISM and its associated bigotries: “look at what nice things we are bringing to the community! Please don’t kill our project!”

Why did Cotright not consider this? I’ve always believed that, to locate affordable housing in those exclusive and privileged suburbs, these developers generally have to pay some kind of penalty to get projects approved–by only serving ‘deserving’ populations (seniors), providing more parking, adopting higher setbacks, or, hey, putting in a zen garden.

Don’t get me wrong, I’m also a bit of a utilitarian who would rather we focus on making affordable housing more cost effective rather than aesthetically pleasing to rich neighbours.  I’d love to see policy move in this direction, and have governments ram these projects down the arterials of every suburb in the country. But I’m not god, and this isn’t Singapore.

So in reflecting on Cotright’s piece on “Why is Affordable Housing So Expensive To Build?” I agree we need to reign in costs where possible, but I feel his piece exaggerates the problem and avoids answering the hard questions.   In reviewing pieces like his, I wonder if we are on the cusp of a new cycle in housing policy–the ‘newness’ of housing tax credits has worn off, and thought leaders are now assaulting it the way they assaulted public housing.

(1) I used both the first and second round of 9% projects, New Construction applicants only.  Average development cost is weighted on a per unit, not a per project, basis. 

(2) I’m using opportunity cost loosely here.  The ‘value of the land’ is usually determined under the assumption the land would serve affordable housing.  The true ‘opportunity cost’ might be another use.

Access to Opportunity(?)

In 2016, the California Tax Credit Allocation Committee (CTCAC) proposed restricting the expenditure of LIHTC subsidy in any area deemed “low opportunity” according to the UC Davis Center for Regional Change (CRC)’s Regional Opportunity Index. Affordable developers, particularly non-profit organisations, pushed hard against the proposal, which CTCAC abandoned.

CTCAC is again moving forward with a similar proposal in 2017, but after months of consultation with a team of academics who devised the mapping.  (full disclosure as a former CalHFA staffer I sat in on and contributed to those conversations).

The idea of making it a policy goal to build more affordable housing in high opportunity areas is a noble one, but many people disagreed.  I want to highlight common argument made by affordable developers against proposal that challenge the notion of what access to opportunity is.

Here’s a comment that really struck out at me:

“The most troubling aspect of this proposal is that it would bar the
development of farmworker housing in agricultural communities. ” Pat Sabelhaus, California Council for Affordable Housing

Not everyone lives in “high opportunity areas” because the jobs they do aren’t always there because of the nature of the work.

Here’s another common argument:

” In many cases, low-opportunity areas are those most at risk of gentrification and displacement. There are other alternatives that should be considered.” (Rob Wiener, California Coalition for Rural Housing; Rachel Iskow, Mutual Housing California)

Opportunity itself is not static over time, it moves as we do–could rejecting affordable housing in low opportunity areas today mean we have none in the gentrifying areas of tomorrow?

And here’s the last very common argument:

“In the East Bay alone, there are thirteen BART stations that are in or within one-quarter mile of a “Lowest Opportunity Area.” We believe it is incredibly important to build housing in these transit-oriented areas to most effectively link people to job opportunities. ” (Andy Madeira, Eden Housing)

This brings up a lot of questions.  The ridership of BART is systematically wealthier and whiter than the general population.  Is this because the land value uplift effects push people out, or is it because the system was designed to connect people to San Francisco’s Financial District, which is disproportionately a middle and upper class jobs hub?

If it is the former, then Andy is right. We should build lots of affordable housing around BART.  But if it is the latter, then maybe we need to re-conceptualize what is important for job access and affordable housing.  There’s a wide literature suggesting that jobs access to affordable housing does not make it easier for those tenants to actually access employment… Are we still focusing on this because it’s easy to measure?

Here’s a look at the new opportunity map.  The darker the colour, the higher the opportunity:


We know people are, unfortunately, calling Bay View-Hunters’ Point the “last frontier” of gentrification in SF–which means now is the time to place a lot of affordable housing in there.  But this map does not prioritise it.

We also know Marin County is a bunch of exclusive, hypocritical jerks who need to build more affordable housing–and this map suggests we should build more there.

So maybe just carrots for affordable developers on LIHTC and access to opportunity, and no sticks?