# Debugging surprising behavior in SciPy numerical integration

I wrote a Python app to apply Bayes’ rule to continuous distributions. It looks like this:

Screenshot

I’m learning a lot about numerical analysis from this project. The basic idea is simple:

def unnormalized_posterior_pdf(x):
return prior.pdf(x)*likelihood.pdf(x)

# integrate unnormalized_posterior_pdf over the reals

def posterior_pdf(x):
return unnormalized_posterior_pdf(x)/normalization_constant


However, when testing my code on complicated distributions, I ran into some interesting puzzles.

A first set of problems was caused by the SciPy numerical integration routines that my program relies on. They were sometimes returning incorrect results or RuntimeErorrs. These problems appeared when the integration routines had to deal with ‘extreme’ values: small normalization constants or large inputs into the cdf function. I eventually learned to hold the integration algorithm’s hand a little bit and show it where to go.

A second set of challenges had to do with how long my program took to run: sometimes 30 seconds to return the percentiles of the posterior distribution. While 30 seconds might be acceptable for someone who desperately needed that bayesian update, I didn’t want my tool to feel like a punch card mainframe. I eventually managed to make the program more than 10 times faster. The tricks I used all followed the same strategy. In order to make it less expensive to repeatedly evaluate the posterior’s cdf by numerical integration, I tried to find ways to make the interval to integrate narrower.

You can follow along with all the tests described in this post using this file, whereas the code doing the calculations for the webapp is here.

# Small normalization constants When the prior and likelihood are far apart, the unnormalized posterior takes tiny values.

It turns out that SciPy’s integration routine, integrate.quad, (incidentally, written in actual Fortran!) has trouble integrating such a low-valued pdf.

prior = stats.lognorm(s=.5,scale=math.exp(.5)) # a lognormal(.5,.5) in SciPy notation
likelihood = stats.norm(20,1)

class Posterior_scipyrv(stats.rv_continuous):
def __init__(self,d1,d2):
super(Posterior_scipyrv, self).__init__()
self.d1= d1
self.d2= d2

def unnormalized_pdf(self,x):
return self.d1.pdf(x) * self.d2.pdf(x)

def _pdf(self,x):
return self.unnormalized_pdf(x)/self.normalization_constant

posterior = Posterior_scipyrv(prior,likelihood)

print('normalization constant:',posterior.normalization_constant)
print("CDF values:")
for i in range(30):
print(i,posterior.cdf(i))


The cdf converges to… 52,477. This is not so good.

Because the cdf does converge, but to an incorrect value, we can conclude that the normalization constant is to blame. Because the cdf converges to a number greater than 1, posterior.normalization_constant, about 3e-12, is an underestimate of the true value.

If we shift the likelihood distribution just a little bit to the left, to likelihood = stats.norm(18,1), the cdf converges correctly, and we get a normalization constant of about 6e-07. Obviously, the normalization constant should not jump five orders of magnitude from 6e-07 to 3e-12 as a result of this small shift.

The program is not integrating the unnormalized pdf correctly.

Difficulties with integration usually have to do with the shape of the function. If your integrand zig-zags up and down a lot, the algorithm may miss some of the peaks. But here, the shape of the posterior is almost the same whether we use stats.norm(18,1) or stats.norm(20,1)1. So the problem really seems to occur once we are far enough in the tails of the prior that the unnormalized posterior pdf takes values below a certain absolute (rather than relative) threshold. I don’t yet understand why. Perhaps some of the values are becoming too small to be represented with standard floating point numbers.

This seems rather bizarre, but here’s a piece of evidence that really demonstrates that low absolute values are what’s tripping up the integration routine that calculates the normalization constant. We just multiply the unnormalized pdf by 10000 (which will cancel out once we normalize).

def unnormalized_pdf(self,x):
return 10000*self.d1.pdf(x) * self.d2.pdf(x)


Now the cdf converges to 1 perfectly (??!).

# Large inputs into cdf

We take a prior and likelihood that are unproblematically close together:

prior = stats.lognorm(s=.5,scale=math.exp(.5))# a lognormal(.5,.5) in SciPy notation
likelihood = stats.norm(5,1)
posterior = Posterior_scipyrv(prior,likelihood)

for i in range(100):
print(i,posterior.cdf(i))


At first, the cdf goes to 1 as expected, but suddenly all hell breaks loose and the cdf decreases to some very tiny values:

22 1.0000000000031484
23 1.0000000000095246
24 1.0000000000031442
25 2.4520867144186445e-09
26 2.7186998869943613e-12
27 1.1495658559228458e-15


What’s going on? When asked to integrate the pdf from minus infinity up to some large value like 25, quad doesn’t know where to look for the probability mass. When the upper bound of the integral is in an area with still enough probability mass, like 23 or 24 in this example, quad finds its way to the mass. But if you ask it to find a peak very far away, it fails.

A piece of confirmatory evidence is that if we make the peak spikier and harder to find, by setting the likelihood’s standard deviation to 0.5 instead of 1, the cdf fails earlier:

22 1.000000000000232
23 2.9116983489798973e-12


We need to hold the integration algorithm’s hand and show it where on the real line the peak of the distribution is located. In SciPy’s quad, you can supply the points argument to point out places ‘where local difficulties of the integrand may occur’, but only when the integration interval is finite. The solution I came up with is to split the interval into two halves.

def split_integral(f,splitpoint,integrate_to):
a,b = -np.inf,np.inf
if integrate_to < splitpoint:
# just return the integral normally
else:
integral_left = integrate.quad(f, a, splitpoint)
integral_right = integrate.quad(f, splitpoint, integrate_to)
return integral_left + integral_right


This definitely won’t work for every difficult integral, but should help for many cases where most of the probability mass is not too far from the splitpoint.

For splitpoint, a simple choice is the average of the prior and likelihood’s expected values.

class Posterior_scipyrv(stats.rv_continuous):
def __init__(self,d1,d2):
self.splitpoint = (self.d1.expect()+self.d2.expect())/2


We can now override the built-in cdf method, and specify our own method that uses split_integral:

class Posterior_scipyrv(stats.rv_continuous):
def _cdf(self,x):
return split_integral(self.pdf,self.splitpoint,x)


Now things run correctly:

22 1.0000000000000198
23 1.0000000000000198
24 1.0000000000000198
25 1.00000000000002
26 1.0000000000000202
...
98 1.0000000000000198
99 1.0000000000000193


# Defining support of posterior

So far I’ve only talked about problems that cause the program to return the wrong answer. This section is about a problem that only causes inefficiency, at least when it isn’t combined with other problems.

If you don’t specify the support of a continuous random variable in SciPy, it defaults to the entire real line. This leads to inefficiency when querying quantiles of the distribution. If I want to know the 50th percentile of my distribution, I call ppf(0.5). As I described previously, ppf works by numerically solving the equation $$cdf(x)=0.5$$. The ppf method automatically passes the support of the distribution into the equation solver and tells it to only look for solutions inside the support. When a distribution’s support is a subset of the reals, searching over the entire reals is inefficient.

To remedy this, we can define the support of the posterior as the intersection of the prior and likelihood’s support. For this we need a small function that calculates the intersection of two intervals.

def intersect_intervals(two_tuples):
d1 , d2 = two_tuples

d1_left,d1_right = d1,d1
d2_left,d2_right = d2,d2

if d1_right < d2_left or d2_right < d2_left:
raise ValueError("the distributions have no overlap")

intersect_left,intersect_right = max(d1_left,d2_left),min(d1_right,d2_right)

return intersect_left,intersect_right


We can then call this function:

class Posterior_scipyrv(stats.rv_continuous):
def __init__(self,d1,d2):
super(Posterior_scipyrv, self).__init__()
a1, b1 = d1.support()
a2, b2 = d2.support()

# 'a' and 'b' are scipy's names for the bounds of the support
self.a , self.b = intersect_intervals([(a1,b1),(a2,b2)])


To test this, let’s use a beta distribution, which is defined on $$[0,1]$$:

prior = stats.beta(1,1)
likelihood = stats.norm(1,3)


We know that the posterior will also be defined on $$[0,1]$$. By defining the support of the posterior inside the the __init__ method of Posterior_scipyrv, we give SciPy access to this information.

We can time the resulting speedup in calculating posterior.ppf(0.99):

print("support:",posterior.support())
s = time.time()
print("result:",posterior.ppf(0.99))
e = time.time()
print(e-s,'seconds to evalute ppf')

support: (-inf, inf)
result: 0.9901821216897447
3.8804399967193604 seconds to evalute ppf

support: (0.0, 1.0)
result: 0.9901821216904315
0.40013647079467773 seconds to evalute ppf


We’re able to achieve an almost 10x speedup, with very meaningful impact on user experience. For less extreme quantiles, like posterior.ppf(0.5), I still get a 2x speedup.

The lack of properly defined support causes only inefficiency if we continue to use split_integral to calculate the cdf. But if we leave the cdf problem unaddressed, it can combine with the too-wide support to produce outright errors.

For example, suppose we use a beta distribution again for the prior, but we don’t use the split integral for the cdf, and nor do we define the support of the posterior as $$[0,1]$$ instead of $${\rm I\!R}$$.

prior = stats.beta(1,1)
likelihood = stats.norm(1,3)

class Posterior_scipyrv(stats.rv_continuous):
def __init__(self,d1,d2):
super(Posterior_scipyrv, self).__init__()
self.d1= d1
self.d2= d2

def unnormalized_pdf(self,x):
return self.d1.pdf(x) * self.d2.pdf(x)

def _pdf(self,x):
return self.unnormalized_pdf(x)/self.normalization_constant

posterior = Posterior_scipyrv(prior,likelihood)

print("cdf values:")
for i in range(20):
print(i/5,posterior.cdf(i/5))


The cdf fails quickly now:

3.2 0.9999999999850296
3.4 0.0
3.6 0.0


When the integration algorithm is looking over all of $$(-\infty,3.4]$$, it has no way of knowing that all the probability mass is in $$[0,1]$$. The posterior distribution has only one big bump in the middle, so it’s not surprising that the algorithm misses it.

If we now ask the equation solver in ppf to find quantiles, without telling it that all the solutions are in $$[0,1]$$, it will try to evaluate points like cdf(4), which return 0 – but ppf is assuming that the cdf is increasing. This leads to catastrophe. Running posterior.ppf(0.5) gives a RuntimeError: Failed to converge after 100 iterations. At first I wondered why beta distributions would always give me RuntimeErrors…

# Optimization: CDF memoization

When we call ppf, the equation solver calls cdf for the same distribution many times. This suggests we could optimize things further by storing known cdf values, and only doing the integration from the closest known value to the desired value. This will result in the same number of integration calls, but each will be over a smaller interval (except the first). This is a form of memoization.

We can also squeeze out some additional speedup by considering the cdf to be 1 forevermore once it reaches values close to 1.

class Posterior_scipyrv(stats.rv_continuous):
def _cdf(self,x):
# exploit considering the cdf to be 1
# forevermore once it reaches values close to 1
for x_lookup in self.cdf_lookup:
if x_lookup < x and np.around(self.cdf_lookup[x_lookup],5)==1.0:
return 1

# check lookup table for largest integral already computed below x
sortedkeys = sorted(self.cdf_lookup ,reverse=True)
for key in sortedkeys:
#find the greatest key less than x
if key<x:
self.cdf_lookup[float(x)] = ret
return ret

# Initial run
ret = split_integral(self.pdf,self.splitpoint,x)
self.cdf_lookup[float(x)] = ret
return ret


If we return to our earlier prior and likelihood

prior = stats.lognorm(s=.5,scale=math.exp(.5)) # a lognormal(.5,.5) in SciPy notation
likelihood = stats.norm(5,1)


and make calls to ppf([0.1, 0.9, 0.25, 0.75, 0.5]), the memoization gives us about a 5x speedup:

memoization False
[2.63571613 5.18538207 3.21825988 4.56703016 3.88645864]
length of lookup table: 0
2.1609253883361816 seconds to evalute ppf

memoization True
[2.63571613 5.18538207 3.21825988 4.56703016 3.88645864]
length of lookup table: 50
0.4501194953918457 seconds to evalute ppf


These speed gains again occur over a range that makes quite a difference to user experience: going from multiple seconds to a fraction of a second.

# Optimization: ppf with bounds

In my webapp, I give the user some standard percentiles: 0.1, 0.25, 0.5, 0.75, 0.9.

Given that ppf works by numerical equation solving on the cdf, if we give the solver a smaller domain in which to look for the solutions, it should find them more quickly. When we calculate multiple percentiles, each percentile we calculate helps us close in on the others. If the 0.1 percentile is 12, we have a lower bound of 12 for on any percentile $$p>0.1$$. If we have already calculated a percentile on each side, we have both a lower and upper bound.

We can’t directly pass the bounds to ppf, so we have to wrap the method, which is found here in the source code. (To help us focus, I give a simplified presentation below that cuts out some code designed to deal with unbounded supports. The code below will not run correctly).

class Posterior_scipyrv(stats.rv_continuous):
def ppf_with_bounds(self, q, leftbound, rightbound):
left, right = self._get_support()

# SciPy ppf code to deal with case where left or right are infinite.
# Omitted for simplicity.

if leftbound is not None:
left = leftbound
if rightbound is not None:
right = rightbound

# brentq is the equation solver (from Brent 1973)
# _ppf_to_solve is simply cdf(x)-q, since brentq
# finds points where a function equals 0
return optimize.brentq(self._ppf_to_solve,left, right, args=q)


To get some bounds, we run the extreme percentiles first, narrowing in on the middle percentiles from both sides. For example in 0.1, 0.25, 0.5, 0.75, 0.9, we want to evaluate them in this order: 0.1, 0.9, 0.25, 0.75, 0.5. We store each of the answers in result.

class Posterior_scipyrv(stats.rv_continuous):
def compute_percentiles(self, percentiles_list):
result = {}
percentiles_list.sort()

# put percentiles in the order they should be computed
percentiles_reordered = sum(zip(percentiles_list,reversed(percentiles_list)), ())[:len(percentiles_list)] # see https://stackoverflow.com/a/17436999/8010877

def get_bounds(dict, p):
# get bounds (if any) from already computed results
keys = list(dict.keys())
keys.append(p)
keys.sort()
i = keys.index(p)
if i != 0:
leftbound = dict[keys[i - 1]]
else:
leftbound = None
if i != len(keys) - 1:
rightbound = dict[keys[i + 1]]
else:
rightbound = None
return leftbound, rightbound

for p in percentiles_reordered:
leftbound , rightbound = get_bounds(result,p)
res = self.ppf_with_bounds(p,leftbound,rightbound)
result[p] = np.around(res,2)

sorted_result = {key:value for key,value in sorted(result.items())}
return sorted_result


The speedup is relatively minor when calculating just 5 percentiles.

Using ppf bounds? True
total time to compute percentiles: 3.1997928619384766 seconds

Using ppf bounds? False
total time to compute percentiles: 3.306936264038086 seconds


It grows a little bit with the number of percentiles, but calculating a large number of percentiles would just lead to information overload for the user.

This was surprising to me. Using the bounds dramatically cuts the width of the interval for equation solving, but leads to only a minor speedup. Using fulloutput=True in optimize.brentq, we can see the number of function evaluations that brentq uses. This lets us see that the number of evaluations needed by brentq is highly non-linear in the width of the interval. The solver gets quite close to the solution very quickly, so giving it a narrow interval hardly helps.

Using ppf bounds? True
brentq looked between 0.0 10.0 and took 11 iterations
brentq looked between 0.52 10.0 and took 13 iterations
brentq looked between 0.52 2.24 and took 8 iterations
brentq looked between 0.81 2.24 and took 9 iterations
brentq looked between 0.81 1.73 and took 7 iterations
total time to compute percentiles: 3.1997928619384766 seconds

Using ppf bounds? False
brentq looked between 0.0 10.0 and took 11 iterations
brentq looked between 0.0 10.0 and took 10 iterations
brentq looked between 0.0 10.0 and took 10 iterations
brentq looked between 0.0 10.0 and took 10 iterations
brentq looked between 0.0 10.0 and took 9 iterations
total time to compute percentiles: 3.306936264038086 seconds


Brent’s method is a very efficient equation solver.

1. It has a very similar shape to the likelihood (because the likelihood has much lower variance than the prior).

July 1, 2020

# How long does it take to sample from a distribution?

Suppose a study comes out about the effect of a new medication and you want to precisely compute how to update your beliefs given this new evidence. You might use Bayes’ theorem for continuous distributions.

$p(\theta | x) =\frac{p(x | \theta) p(\theta) }{p(x)}=\frac{p(x | \theta) p(\theta) }{\int_\Theta p(x | \theta) p(\theta) d \theta}$

The normalization constant (the denominator of the formula) is an integral that is not too difficult to compute, as long as the distributions are one-dimensional.

For example, with:

from scipy import stats
from scipy import integrate

prior = stats.lognorm(scale=math.exp(1),s=1)
likelihood = stats.norm(loc=5,scale=20)
def unnormalized_posterior_pdf(x):
return prior.pdf(x)*likelihood.pdf(x)
unnormalized_posterior_pdf,-np.inf,np.inf)


the integration runs in less than 100 milliseconds on my machine. So we can get a PDF for an arbitrary 1-dimensional posterior very easily.

But taking a single sample from the (normalized) distribution takes about a second:

# Normalize unnormalized_posterior_pdf
# using the method above and return the posterior as a
# scipy.stats.rv_continuous object.
# This takes about 100 ms
posterior = update(prior,likelihood)

# Take 1 random sample, this takes about 1 s
posterior.rvs(size=1)


And this difference can be even starker for higher-variance posteriors (with s=4 in the lognormal prior, I get 250 ms for the normalization constant and almost 10 seconds for 1 random sample).

For a generic continuous random variable, rvs uses inverse transform sampling. It first generates a random number from the uniform distribution between 0 and 1, then passes this number to ppf, the percent point function, or more commonly quantile function, of the distribution. This function is the inverse of the CDF. For a given percentile, it tells you what value corresponds to that percentile of the distribution. Randomly selecting a percentile $$x$$ and evaluating the $$x$$ th percentile of the distribution is equivalent to randomly sampling from the distribution.

How is ppf evaluated? The CDF, which in general (and in fact most of the time1) has no explicit expression at all, is inverted by numerical equation solving, also known as root finding. For example, evaluating ppf(0.7) is equivalent to solving cdf(x)-0.7=0, which can be done with numerical methods. The simplest such method is the bisection algorithm, but more efficient ones have been developed (ppf uses Brent’s method). The interesting thing for the purposes of runtime is that the root finding algorithm must repeatedly call cdf in order to narrow in on the solution. Each call to cdf means an expensive integration of the PDF. The bisection algorithm to solve cdf(x)-0.7=0

An interesting corollary is that getting one random number is just as expensive as computing a chosen percentile of the distribution using ppf (assuming that drawing a random number between 0 and 1 takes negligible time). For approximately the cost of 10 random numbers, you could characterize the distribution by its deciles.

On the other hand, sampling from a distribution whose family is known (like the lognormal) is extremely fast with rvs. I’m getting 10,000 samples in a millisecond (prior.rvs(size=10000)). This is not because there exists an analytical expression for its inverse CDF, but because there are very efficient algorithms2 for sampling from these specific distributions3.

So far I have only spoken about 1-dimensional distributions. The difficulty of computing the normalization constant in multiple dimensions is often given as a reason for using numerical approximation methods like Markov chain Monte Carlo (MCMC). For example, here:

Although in low dimension [the normalization constant] can be computed without too much difficulties, it can become intractable in higher dimensions. In this last case, the exact computation of the posterior distribution is practically infeasible and some approximation techniques have to be used […]. Among the approaches that are the most used to overcome these difficulties we find Markov Chain Monte Carlo and Variational Inference methods.

However, the difficulty of sampling from a posterior distribution that isn’t in a familiar family could be a reason to use such techniques even in the one-dimensional case. This is true despite the fact that we can easily get an analytic expression for the PDF of the posterior.

For example, with the MCMC package emcee, I’m able to get 10,000 samples from the posterior in 8 seconds, less than a millisecond per sample and a 1,000x improvement over rvs!

ndim, nwalkers, nruns = 1, 20, 500

start = time.time()
def log_prob(x):
if posterior.pdf(x)>0:
return math.log(posterior.pdf(x))
else:
return -np.inf
sampler = emcee.EnsembleSampler(nwalkers, 1, log_prob)
sampler.run_mcmc(p0, nruns) #p0 are the starting samples


These samples will only be drawn from a distribution approximating the posterior, whereas rvs is as precise as SciPy’s root finding and integration algorithms. However, I think there are MCMC algorithms out there that converge very well.

Here’s the code for running the timings on your machine.

1. “For a continuous distribution, however, we need to integrate the probability density function (PDF) of the distribution, which is impossible to do analytically for most distributions (including the normal distribution).” Wikipedia on Inverse transform sampling

2. “For the normal distribution, the lack of an analytical expression for the corresponding quantile function means that other methods (e.g. the Box–Muller transform) may be preferred computationally. It is often the case that, even for simple distributions, the inverse transform sampling method can be improved on: see, for example, the ziggurat algorithm and rejection sampling. On the other hand, it is possible to approximate the quantile function of the normal distribution extremely accurately using moderate-degree polynomials, and in fact the method of doing this is fast enough that inversion sampling is now the default method for sampling from a normal distribution in the statistical package R.” Wikipedia on Inverse transform sampling

3. The way it works in Python is that, in the definition of the class Lognormal (a subclass of the continuous random variable class), the generic inverse transform rvs method is overwritten with a more tailored sampling algorithm. SciPy will know to apply the more efficient method when rvs is called on an instance of class Lognormal.

May 31, 2020

# Hidden subsidies for cars

Personal vehicles are ubiquitous. They dominate cities. They are actually so entrenched that they can blend into the background, no longer rising to our attention. Having as many cars as we do can seem to be the ‘natural’ state of affairs.

Our level of car use could perhaps be called natural if it were the result of people’s preferences interacting in well-functioning markets. No reader of this blog, I take it, would believe such a claim. The negative externalities of cars are well-documented: pollution, congestion, noise, and so on.

The subsidies for cars are less obvious, but I think they’re also important.

In our relationship to cars in the urban environment, we’re almost like David Foster Wallace’s fish who asked ‘what the hell is water?’. I want to flip that perspective and point out some specific government policies that increase the number of cars in cities. “Manhattan, 1964” by Evelyn Hofer

# Free or cheap street parking

Privately provided parking in highly desirable city centres can cost hundreds of dollars a month. But the government provides car storage on the side of the street for a fraction of that, often for free.1

# The width of roads

Streets and sidewalks sit on large amounts of strategically placed land that is publicly owned. Most of that land is devoted to cars. On large thoroughfares, I’d guess cars take easily 70% of the space, leaving only thin slivers on each side for pedestrians.

This blogger estimates, apparently by eyeballing Google Maps, that streets take up 43% of the land in Washington DC, 25% in Paris, and 20% in Tokyo.

Space that is now used for parked cars or moving cars could be used, for example, by shops and restaurants, for bikeshare stations, to plant trees, for parklets, or even to add more housing. And if there was a market for this land I’m sure people would come up with many other clever uses.

# Highways

Even if highways aren’t actually inside the city, they have important indirect effects on urban life. Whether the government pays for highways or train lines to connect cities to each other is a policy choice with clear effects on day to day life in the city, even for those who do not travel.

In the United States, this implicit subsidy for cars is large. According to the department of transportation, in 2018 $49 billion out of the department’s budget of$87 billion was spent on highways2.

In this post I don’t want to get into the very complicated question of how much governments should optimally spend on highways. For all I know the U.S. policy may be optimal. My point is only that any government spending on highways indirectly subsidises the presence of cars in cities. This is non-obvious and worth pointing out. When the government pays for a Metro in your city, the subsidy to Metros plain to see. Meanwhile, the subsidy to cars via a huge network of roads across the country passes unnoticed by many.

To be fair, in the United States federal spending on highways is largely financed by taxes on vehicle fuel. So it’s not clear whether federal highways policy is a net subsidy to cars. However, the way highway spending is financed varies by country. For example, in Germany, “federal highways are funded by the federation through a combination of general revenue and receipts from tolls imposed on truck traffic”.

# Minimum parking requirements

Many zoning codes require new buildings to include some fixed number of off-street parking spaces. This isn’t as much of a problem in the European cities I’m familiar with, but in the US, parking minimums are far beyond what the market would provide, and are a significant cost to developers. One paper estimated that the cost of parking in Los Angeles increases the cost of office space by 27-67%3.

# Suburban sprawl

United States built sprawling suburbs in the postwar period. I still remember the famous aerial view of Levittown, the prototypical prefabricated suburb, from my middle school history book.

The growth of suburbia was aided by specific government policies that tipped the scales in favour of individual homes in the suburbs, and against apartments in cities. The growth of suburbia led to more cars in the city, because people who live in suburbs are much more likely to drive to work.

Devon Zuegel has an excellent exposition of how federal mortgage insurance subsidized suburbia4:

[The federal housing administration] provides insurance on mortgages that meet certain criteria, repaying the principal to lenders if borrowers default. […] Mortgages had to meet an opinionated set of criteria to qualify for the federal insurance. […] The ideal house had “sunshine, ventilation, scenic outlook, privacy, and safety”, and “effective landscaping and gardening” added to its worth. The guide recommended that houses should be set back at least 15 feet from the road, and well-tended lawns that matched the neighbors’ yards helped the rating. […] [The FHA manual] prescribed minimum street widths and other specific measurements.

The federal government was effectively prescribing how millions of Americans should live, down to their landscaping and gardening! I wonder if Khrushchev brought up this interesting fact about American life in his conversations with Eisenhower. ;)

• A study from the Canadian Victoria Tansport Policy Institute, Transportation Land Valuation
• Anything by Donald Shoup, an economist and urban planner
• Some cool colour-coded maps of U.S. cities, showing the surface area devoted to surface parking, above-ground parking garages, and park space.
• Barcelona’s superblocks
1. If you want more on this topic, economist and urban planner Donald Shoup has a 733-page tome called The High Cost of Free Parking

2. See the supporting summary table on page 82 of this document. The sum of spending for the Federal Highway Administration, the Federal Motor Carrier Safety Administration, and the National Traffic Safety Administration comes to \$49 billion. Thanks to Devin Jacob for the pointer.

3. Shoup 1999, The trouble with minimum parking requirements, in section 3.1, estimates that parking requirements in Los Angeles increase the cost of office space by 27% for aboveground parking, and 67% for underground parking.

4. Devon wrote a two-part series: Part 1, quoted above, deals with federal mortgage policy, and lays out a convincing case that it included large implicit subsidies. Part 2 is about “how suburban sprawl gets special treatment in our tax code”. It shows that owning and building homes is heavily subsidized, for example by the gargantuan mortgage interest deduction. I agree that this means people are encouraged to consume more housing, but I don’t see how it differentially encourages suburban housing. Devon quotes economist Edward Glaeser, who says that

More than 85 percent of people in detached homes are owner-occupiers, in part because renting leads to home depreciation. More than 85 percent of people in larger buildings rent. Since ownership and structure type are closely connected, subsidizing homeownership encourages people to leave urban high-rises and move into suburban homes.

So the key link in the argument is the connection between ownership and structure type. I’d like to see it spelled out and sourced better. Could the observed correlation just be due to a selection effect? If there’s a true causal effect, do large buildings have more renters because it’s genuinely more efficient that way, or is there some some market failure that prevents people from being apartment-owners in the city?

December 6, 2019