Richard Florida on Choi, Green and Noh

He writes about what we write about on education, migration and rent.

It’s abundantly clear that in today’s economy, the ability to attract and mobilize highly educated people—so-called human capital—is the key factor in the the wealth of nations as well of that of cities. But the driving force of talent in economic growth also contributes to our worsening divides. While metropolitan areas with more educated people have higher levels of income, they also have higher housing costs. And the burden of those costs falls hardest on the less educated.
A working paper by urban economist Richard Green, of the University of Southern California, and Jung Choi, of the Urban Institute takes, a deep dive into this conundrum....

Ten things data have taught me about the world.

(1) Tax cuts do not magically create growth; 

(2) Vaccines are among the best things we have ever invented; 

(3) raising the minimum wage to a point improves living standards for low wage workers (and that point may be somewhere between $11 and $15 per hour), beyond that point, it lowers living standards for low wage workers; 

(4) GMOs are fine; 

(5) the benefits of the Clean Air Act swamp the costs by an order of magnitude or more; 

(6) the mortgage interest deduction has a vanishingly small impact on the homeownership rate; 

(7) trade has raised living standards for hundreds of millions around the world; 

(8) trade has reduced living standards for low skilled workers in the US; 

(9) rent control reduces the stock of rental housing; 

(10) even though I like Lebron better than Jordan, MJ was the better player.

Rent Stabilization Fails to Target Those in Need

Rent stabilization is a transfer from those who own rent stabilized units to those who live in such units.  As such, it is not a specific redistribution from high income households to low income households, but rather a random distribution from owners of various income levels (who can range from middle-class owners of one unit to large holders of private equity or REITS) to renters of various income levels.

I know of no good way to recover the incomes of property owners, but we can get a flavor of the distribution of income among beneficiaries of rent stabilized properties in Los Angeles, by looking at the income distribution of those who live in properties built just before rent stabilization and just after.  We can't exactly nail it, because rent stabilization in LA went into effect into effect in October 1978, and the census tells us the decade in when properties were being built.  Still, comparing the incomes of renters living in buildings built in the 1970s with those of the 1980s can tell us something about how well targeted rent stabilization is.

I downloaded American Community Survey data from IPUMS USA.   (See Steven Ruggles, Katie Genadek, Ronald Goeken, Josiah Grover, and Matthew Sobek. Integrated Public Use Microdata Series: Version 7.0 [dataset]. Minneapolis, MN: University of Minnesota, 2017. 
https://doi.org/10.18128/D010.V7.0).  I looked at the city of Los Angeles, and stripped out single family detached houses, and, of course, owner houses.  I used the ACS Household Weights.  Here are the income distributions I found for properties built in the 1970s and 1980s.

Note that the median income of those in (largely) rent stabilized units is higher than those in units that are not stabilized.  Also note that the incomes at the 75th percentile are nearly the same.  At the 90th percentile, people in 1970s vintage properties have a lower income than those in 1980s properties, but their income is still rather high (i.e., it is a reasonable question to ask whether households who make $114,000 a year or more should be receiving a housing subsidy).

Taxing people of means (which we can identify) to provide housing subsidies to those without is good policy.  It is the correct way to help those whose income is insufficient to pay for adequate housing.

(p.s., whenever I post something like this, I welcome any and all attempts to reproduce it.  I makes mistakes!).

A short piece on the GOP Tax Plan

I write for Fox and Hounds Daily:

I am a Keynesian.  By that I mean that John Maynard Keynes’ predictions are generally confirmed by evidence—and that the key to economic vitality is aggregate demand.  While Keynes has been dead for more than 70 years, new evidence suggests that his educated suppositions developed during the great depression were generally correct.....

How Harvey and Irma might flood Ginnie Mae issuers

I got to spend some time this week at Toni Moss' Americatalyst event with Ted Tozer, President of Ginnie Mae during the Obama years. I always learn stuff when I spend time with Ted, and in this case, what I learned was a little scary--that for FHA to make an insurance payout to a lender, the property that is foreclosed upon must be conveyable.  Which is to say, if an FHA loan is foreclosed upon by a lender, before the lender receives compensation for its losses, it needs to make sure a house can be sold.  A house ruined by a hurricane is not conveyable.

Unlike Fannie Mae and Freddie Mac securities (which are issued by the two GSEs), Ginnie Maes are issued by hundreds of individual firms.  Quicken is an issuer; so is Wells Fargo.  The loans inside of Ginnie Mae are all explicitly guaranteed by the US Government--they are all FHA, VA, or rural housing loans.

Also unlike FF, Ginnie does not guarantee securities; it guarantees the issuers of securities.  If an issuer fails to meet its obligations to make principal and interest payments, Ginnie Mae takes them over, much like FDIC takes over a failed bank.  When a loan within a security goes into default, the issuer is obligated the pull the loan out of the Ginnie Mae pool and pay the investors the principal balance at the point--from the standpoint of the investors, the default becomes a prepayment event.

So now the issuer has basically fronted a loan to the government: the issuer pays the security holder, and then is reimbursed for that payment when FHA/VA/Rural Housing pay a claim.  Issuers should hold sufficient capital (or have sufficient lines of credit) to float the money to the government.  But an event like Harvey could produce a big problem--lots of houses that go into foreclosure might never become conveyable, and so never get a mortgage insurance claim fulfilled.  For issuers with concentrated business in Texas and Florida, this could create enormous stress.

There are measures government could take to prevent this problem, such as providing zero cost loans to homeowners for reconstruction, particularly outside of designated flood areas.  But the leadership necessary to solve this problem is now, well, nonexistent.  There is no FHA Commissioner and no Ginnie Mae President.  A Deputy Secretary (who is a very good candidate) has been nominated but not confirmed.  There is nobody home now, when we most need somebody.

I hope I am worrying over nothing, but I kind of doubt it.

Tony Yezer on Tax Avoidance and Incidence

He writes:

"I teach this in urban economics.  However, in this case there is a 4.5% CAP rate (note that is operating revenue net of operating cost including taxes, insurance, etc) and 4% appreciation per year for 8.5% before taxes.  Pretty sweet.  If this asset is so tax-preferred, then how is this possible?  Why don't capital markets arbitrage this away?  Why doesn't the tax expenditure to to the renters.  In urban economics class we learn that the tax expenditure goes to the renters to offset the owner tax expenditure.  So the 8.5% never materializes.  What does happen is that we all (owners and renters) consume more housing space because that is the primary determinant of greenhouse gas emissions by households and we want to maximize those emissions..... Note that the household emissions arise BOTH because the units are larger and contain more stuff AND because commuting distances are longer in less dense cities due to the policy.  I have a JUE paper about all this.    This is not new and it is obvious.  The problem is that no one cares about the incidence of taxes.  I bet that fewer than 2% of the American people know that the corporate income tax falls largely on workers in America.  A society ignorant of the difference between statutory and economic incidence of taxes is likely to make very poor and perverse decisions.  

The most important idea that I include in principles of economics is the difference between statutory and economic incidence.  In the case of GW students, it begins with the idea that taxes on liquor are not paid by the saloon owner or the bartender.  That gets their attention and then we make some progress."

What Tony writes is true, but it also underlines a problem--how do we judge tax fairness based on economic incidence?  That would involve knowing a lot of elasticities that we don't know.  If we think fairness is a critical consideration when making tax policy (and I, for one, do), I don't see how we avoid using statutory incidence, if only as a first approximation to economic incidence.  

I think I support a tax cut...

...for below median income households.

Mitt Romney infamously complained during his presidential campaign that 47 percent of Americans paid nothing for their government benefits.  What he really meant is that 47 percent did not pay federal income tax; they still paid lots of property, sales and FICA taxes.

A story by Jordan Weissman in Slate this morning underscored this fact; indeed, the story, in my view, buried its lede by focussing on the fact that the top one percent pay about 1/6 less in taxes as a share of income when compared with the 1950s.  To me, the most interesting thing was demonstrated in this graph by Piketty, Saez and Zucman:

Taxes as a share of income on the bottom 50 percent of the income distribution have risen about 60 percent (from 15 percent of income to 25 percent).  This falls into the category of facts I didn't know that I should have known.
  

How the very rich legally avoid paying taxes (h/t/ Ed McCaffery)

It is not that difficult--if you have access to capital.  Here are the steps:

(1) Buy an apartment complex for $10,000,000 at a 4.5 percent cap rate with a 35 percent downpayment; finance $6,500,000 with an interest only loan at 3.5 percent that comes due in five years.

(2) Let's say 35 percent of the value of the property is land and the remainder is improvements. Improvements on apartments are depreciated on a straight line basis over 27.5 years.  So taxable income is

450,000-227,500 (interest) - 236,363 = -13,863 or a taxable loss.  

Meanwhile, cash flow is 222,500 per year.  So one gets cash while taking a tax loss.

(3) It gets better.  Suppose when refinancing happens in five years, the property has gained 20 percent in value.  Now one gets a 65 percent LTV loan on a $12,000,000 property--and gets to pull $1,300,000 out of the property.  Suppose NOI has also gone up 20 percent.  Sow now taxable income is 

540,000-273,000-236,363 = 30,636.

Assume that the owner's all in marginal tax rate is 50 percent.  In exchange for a one time $1,300,000 in cash and cash flow of $267,000, the owner pays a little over $15,000 in taxes and 3.5 percent in interest on the extra money.  No matter how one looks at it, this is a tax rate on cash of less than 10 percent.

It keeps going for 27.5 years, at which point the owner can defer taxes via a like-kind exchange. All of this is perfectly legal.  And it explains why salaried workers pay more in taxes than owners of capital.

Missing Car Talk (because Tom and Ray spoke not of what they did not know)

Saturday morning errands were more attractive when, while driving, one could listen the Tappet Brothers give sound advice on auto repair and safety.  Something they did not do, however, is give advice on the financial implications of leasing/owning  (even though I suspect that their MIT educations would have allowed them to figure out how to make good financial choices).

This morning, I thought I found a substitute for the Magliozzi boys--a car-advice program on KNX, a local newsradio station.  But within ten minutes, I heard the host give terrible advice.  When his sidekick asked him if buyers should make an upfront payment on a lease in order to buy down their monthly payments, the host said no, that such an upfront payment was a waste of money, because of the absence of equity value at the end of the lease term.  But the buydown can, in fact, be a very sensible thing to do, depending on the nature of the deal.

To give one example, consider this lease calculator for a Honda Accord.  With a $3000 downpayment, the monthly payments for the car are $186 per month for 36 months.  At zero down, the payments are $266 per month.  So by investing $3000 more up front, you are reducing your payments by $80 per month.  Now lets consider the implicit rate at which you are borrowing the $3000, by using the excel function RATE.

RATE(36,266-186,-3000) =  .0022.

So the cost of borrowing here is 22 basis points per month, or, on an annualized, compounded, basis, 3 percent.  This is not a great return on investment, so the lower down payment may make sense.  But to give general advice without doing the math first is to give bad advice.



  

Fannie and Freddie don't really do 30-year mortgages

The reason is prepayment.  I just happened to notice recently that even in periods where there isn't an interest rate incentive for people to prepay their mortgages, lots of people do.  As this piece in Mortgage News Daily shows, conditional prepayment rates on GSE secured loans are essentially always above 10 percent, regardless of market interest rates.  When people have mortgages whose rates are lower than market rates, some still prepay, either to move, or to get cash, or to consolidate debt.

At a 10 percent conditional prepayment rate, 65 percent or mortgages are paid off in less than 10 years (and when one adds in amortization, 73 percent of mortgage balances are paid off, assuming a rate of 4 percent).  Of course, 10 percent is the minimum, so actual mortgage payoffs are much higher than 65 percent.

One of the justifications (and one I have used myself) for GSEs is that they allow borrowers access to 30-year, fixed rate mortgages.  Consumers generally pay more for the very long term--a payment that may be justified as an insurance premium.  But if very few people use the insurance, it is not clear whether the cost is worth it to consumers.  At the same time, because of slow amortization, the 30-year mortgage--particularly one that is being refinanced regularly, is not a great savings commitment device.

Perhaps a better product for consumers would be a 7-year adjustable rate mortgage, or even better, a 7-year ARM with a 20 year amortization term.  The 30 year mortgage arose as an affordability product when interest rates neared and exceeded double digits, and was a good product for those times.  But in a world of very low interest rates, it may no longer be the gold standard for consumers.  And so if we are to ever get to housing finance reform, perhaps the next model of housing finance should be very different from today's.      

Leaks

I got to spend a year (July 2015-June 2016) working as a Senior Advisor at the Department of Housing and Urban Development.  I wasn't particularly high up in the pecking order, but I got to work with a number of people from HUD, Treasury and the White House who were.

Here's the thing (sorry for using a Sorkinism): all of these people--every, single, one--liked President Obama; were proud to work for President Obama.

Did they think he was perfect, or always made the correct decision?  Of course not.  But I have to say, in all the meetings in which I got to participate, there was reasoned deliberation, and comportment really mattered.  The atmosphere was professional and respectful.  And I think because of this, no one wanted to embarrass the president--certainly no one wanted to go out of his or her way to damage the president.

Just saying...

Troika

Troika is the forecasting process for the federal government; it is called Troika because it is a joint project of Treasury, the Office of Management and Budget, and the Council of Economics Advisors.

Last year, while I was a Senior Advisor at HUD, I got a peek into Troika; I was invited to participate in a meeting to offer a perspective on the US housing market.  I am not going to say much about the details of the meeting, except that the Troika process is very much based on econometric modeling, that the modelers are really good at their jobs, and that the debates about the models are exactly the sorts of the debates one would wish government officials to have.  To give one example, at the meeting I attended, there was a debate about a parameter estimate.

The debate arose from the following conditions: suppose economic theory implies that a parameter b = b*. The estimated parameter b = b*+a.  The standard error of the parameter is 2a.  The debate was whether the forecast should be based on b*, or b*+a.  Needless to say, on could make reasonable arguments either way (showing that no matter how good a modeler is, she needs to rely on judgment at some point).

This is how government forecasting has been done--empirically, rigorously, and without an agenda. It saddens me to think that this is under attack.

Why I like what Quicken is doing

There is a piece in the New York Times from yesterday that sort of implies that Quicken Loan's rapid rate of growth (they are now the second largest FHA lender after Wells-Fargo) must mean the lender is up to no good.  But unlike Countrywide and WAMU, whose growth in the previous decade was the result of unsound lending practices, Quicken has developed a business model that, in my view, can result in lending that is sounder than traditional lending, expanded access to credit, and reduce loan applicant frustration.

I suppose I should say here that I have no financial interest in Quicken (it is closely held, so I couldn't even if I wanted to). I met its CEO, Bill Emerson, once, and spoke to him on the phone once, and we had nice conversations, but I would hardly say we now each other socially (for all I know he wouldn't even remember talking with me).  I have also had cordial conversations with other Quicken executives, which I think gave me a little insight into how the company operates.

Yesterday's piece notes that Quicken is viewed more as a technology company than a mortgage company, but it doesn't expand on what that means.  Here is what I think it means--it uses technology to improve quality control and compliance, and to do its own underwriting.  Specifically, when a potential borrower applies for a loan using the Rocket Mortgage app, she gives permission to Quicken to download financial information from the IRS, bank accounts, and other accounts. Because the information flows directly from the source, loan applications are complete and accurate, and hence comply with an important requirement for FHA loans.

The information is then run through the FHA TOTAL scorecard, where it receives an accept or refer (a refer means that for a loan to be approved, it can be manually underwritten, but is often rejected) and through Quicken's own underwriting algorithm.  The executives I spoke with at Quicken told me that the algorithm is updated frequently.  My guess--I don't know this for a fact--is that the algorithm's foundation is the sort of regression that I discussed in a previous post.

As noted in that post, statistically based algorithms can both improve access to credit and the performance of loans.  As the pool of potential borrowers becomes less and less like previous borrowers (in terms of source of income, credit behavior, family participation in loan repayment, etc.), using data to continuously improve and refine underwriting will be important for sustaining the mortgage market.  To the extent that Quicken is doing this, it makes the mortgage market better.

This is not to say it would be good for Quicken ultimately to dominate the market (such dominance is never healthy).  It would be nice to see fast followers of Quicken to enter the market.  But I suspect the reason the company has grown so rapidly is that it has built a better mousetrap.

Danny Ben-Shahar leads me to reflect on whether data should be treated as a public good

Danny Ben-Shahar gave a really nice paper (co-authored with Roni Golan) at the ASSA meetings yesterday about a natural experiment in the impact of information provision on price dispersion.  I want to talk about it, but first a little background.

Price dispersion is an ingredient in understanding whether markets are efficient.  When prices for the same good vary (for reasons other than, say, transport costs or convenience), it means that consumers lack the information necessary to make optimal decisions, and the economy suffers from deadweight loss as a result.

Houses have lots of measured price dispersion, even after controlling for physical characteristics. Think about a regression for a housing market, where

HP = XB + h+e

where HP is a vector of house prices, and X is a matrix of house characteristics.  The residual h+e has two components—unmeasured house characteristics, h, and an error term, e, which reflects “mistakes” consumers of houses make, perhaps because of an absence of information.  The h might reflect something like the quality of view, or absence of noise, etc.

When we run this regression, we can compute a variance of the regression residuals.  Because we can only observe h+e, we cannot know if this variance is the result of unobserved house characteristics, or of consumer errors.  But if h remains fixed, and there is an information shock that reduces consumer errors,  e will get smaller, and so will the regression variance.

Here is where Danny’s paper comes in.  In April 2010, authorities in Israel began publishing on-line information about house transactions, and in October 2010, they launched a “user-friendly web site.”  (Details may be found in the paper).  The paper measures the change in measured price dispersion before and after the information was publicly available, and, at minimum, found reductions in dispersion of about 17 percent. The paper takes pains to make sure their result isn’t a function of some shock that happened simultaneously to the release of the information.  For example, they show that price dispersion fell less in neighborhoods with well-educated people.  This could either reflect that (1) well educated people were better informed about housing markets to begin with, and so got less benefit from the new information or (2) that a greater share of the residuals in well-educated neighborhoods comes from non-measured house characteristics.[i]  In either event, the result is consistent with the idea that the information shock is what contributed to the decline in measured price dispersion.

So more information really does seem to produce a more efficient housing market.  The policy implication may be that data, in general, should be a public good.  Data meet half of Musgrave’s definition of a public good—they are non-rival (one person’s use of a data-set does not detract from another person’s use).  And while data are excludable (services such as CoreLogic show this to be true), their creation produces a classical fixed-cost marginal-cost problem.  The fixed cost of producing a good dataset is very large; once it is created, the marginal cost of providing the data to users is very low.  This suggests that the efficient price of data should be very low. 

Currently, data services have something like natural monopolies, with long downward sloping average cost curves.  Theory says that this means they are setting prices such that marginal revenue equals marginal costs, instead of setting price equal to marginal cost.  All this implies that data are underprovided.  Danny and Roni’s work shows that this under-provision has meaningful consequences for the broader economy.

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[i] BTW, this second interpretation is mine (I don’t want the authors on the hook for it if they disagree).

The Trouble with DTI as an Underwriting Variable--and as an Overlay

Access to mortgage credit continues to be a problem.  Laurie Goodman at the Urban Institute shows that, under normal circumstances (say those of the pre-2002 period), we would expect to see 1 million more mortgage originations per year in the market than we are seeing. I suspect an important reason for this is the primacy of Debt-to-Income (DTI) as an underwriting variable.

There are two issues here.  First, while DTI is a predictor of mortgage default, it is a fairly weak predictor.  The reason is that it tends to be measured badly, for a variety of reasons.  For instance, suppose someone applying for a loan has salary income and non-salary income.  If the salary income is sufficient to obtain a mortgage, both the borrower and the lender have incentives not to report the more difficult to document non-salary income.  The borrower's income will thus be understated, the DTI will be overstated, and the variable's measurement contaminated.  There are a number of other examples that also apply.

Let's get more specific.  Below are results from a linear default probability regression model based on the performance of all fixed rate mortgages purchased by Freddie Mac in the first quarter of 2004. This is a good year to pick, because it is rich in high DTI loans, and because its loans went through a (ahem) difficult period.  The coefficients are predicting probability of not defaulting.

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                              COEF          SE             T-STAT

FICO >= 620    .1324914   .0039244    33.76   

FICO >= 680    .1259424   .0021756    57.89   

FICO >= 740    .0600775   .0020249    29.67   

FICO >= 790   -.0030439   .0036585    -0.83   

CLTV >=  60    -.0336153   .0025297   -13.29  

CLTV >=  80    -.0375928   .0021508   -17.48   

CLTV >=  90     -.0155193   .0029713    -5.22   

CLTV >=  95     -.0261145   .0035061    -7.45   

DTI                    -.0013991    .000069   -20.26   

Broker              -.0439482   .0308106    -1.43   

Corresp.           -.0128272   .0277559    -0.46   

Other                -.0295511   .0277441    -1.07   

Cash-out           -.0520243   .0023775   -21.88   

Refi no cash      -.0364152   .0021331   -17.07  

The definition of default is ever-90 days late.  I tried adding a quadratic term for DTI, but it was not different from zero.  This is an estimation sample with 166,585 randomly chosen observations; I did not include 114,583 observations so I could do out-of-sample prediction (which will come later).  The default rate for the estimation sample is 14.34 percent; for the hold out sample is 14.31 percent, so Stata's random number generator did its job properly.  For those that care, the R^2 is .12.

Note that while DTI is significant, it is not particularly important as a predictor of default.  To place this in context, note that a cash-out refinance is 5.2 percentage points more likely to default than a purchase money loan, while a 10 percentage point change in DTI will produce a 1.3 percent increase the probability of default.  One can look at the other coefficients to see the point more broadly.

But while this is an issue, it is not a big issue.  It is certainly reasonable to include DTI within the confines of a scoring model based on its contribution to a regression.  The problem arises when we look at overlays.

The Consumer Financial Protection Board has deemed mortgages with DTIs above 43 percent to not be "qualified."  This means lenders making these loans do not have a safe-harbor for proving that the loans meet an ability to repay standard.  Fannie and Freddie are for now exempt from this rule, but they have generally not been willing to originate loans with DTIs in excess of 45 percent.  This basically means that no matter the loan-applicant's score arising from a regression model predicting default, if her DTI is above 45 percent, she will not get a loan.

This is not only analytically incoherent, it means that high quality borrowers are failing to get loans, and that the mix of loans being originated is worse in quality than it otherwise would be.  That's because a well-specified regression will do a better job sorting borrowers more likely to default than a heuristic such as a DTI limit.

To make the point, I run the following comparison using my holdout sample: the default rate observed if we use the DTI cut-off rule vs a rule that ranks borrowers based on default likelihood.  If we used the DTI rule, we would have made loans to 91185 borrowers within the holdout sample, and observed a default rate of 14.0 percent.  If we use the regression based rule, and make loans to slightly more borrowers (91194--I am having trouble nailing the 91185 number), we get an observed default rate of 10.0 percent.  One could obviously loosen up on the regression rule, give more borrowers access to credit, and still have better loan performance.  

Let's do one more exercise, and impose the DTI rule on top of the regression rule I used above.  The number of borrowers getting loans drops to 73133 (or about 20 percent), while the default rate drops by .7 percent relative to the model alone.  That means an awful lot of borrowers are rejected in exchange for a modest improvement in default.  If one used the model alone to reduce the number of approved loans by 20 percent, one would improve default performance by 1.4 percent relative to the 10 percent baseline.  In short, whether the goal is access to credit, or loan performance (or, ideally, both), regression based underwriting just works far better than DTI overlays.  

(I am happy to send code and results to anyone interested).

Update: if you want output files, please write directly to me at richarkg@usc.edu.  To obtain the dataset, which is freely available, you need to register with Freddie Mac at link referenced above.

  
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