Is California a Big Spender?

People say so.  But what people say is sometimes not true.

I did the following exercise.  I took Census of Government Data on State and Local Spending for 2016 (the most recent available year) and divided it by State GDP for 2016.  This is what I got:

California is right in the middle, ranking 23rd, and sitting next to that well-known hotbed of socialism, Utah.

Two Moral Dilemmas for Housing Policy

Of course there are far more than two, but two seem particularly all encompassing to me. They are

(1) Should everyone, regardless of income, have access to housing in every neighborhood?

(2) What is the minimum acceptable quality of a house?

These are questions whose answers come with tradeoffs.  With respect to (1): suppose we decide that everyone should, if they wish, be able to live in a house on a beach in Laguna (as it happens, the Southern California beach town I most enjoy visiting).  Such a policy would be costly, and has implications for the distribution of other goods.  But there are reasons to think it is socially desirable for people of mixed income to live together.  The correct answer will involve normative judgments we make as a society, but we need to make these judgments explicitly.  I don't know that we do that.  Personally, I think everyone should be able to live in a safe neighborhood, be in a place where they can send their kids to a decent, publicly funded, school, and have a reasonable commute to work (30 minutes or less?  45?) within their choice set.  If people choose to live further away, that is their business.

As for (2), in the US context, I would think the minimum acceptable house would have indoor plumbing, clean water (I wish I could say this was a given),  good sanitation, reliable electricity,  a minimum amount of floor space per person (although I am not sure what that is), and be very, very fire resistant.  I am perhaps leaving something out, but anything beyond an agreed upon minimum adds to the cost of providing housing.  Again, I don't think when we discuss housing we discuss the tradeoffs enough.   

Rent Control

New York state is about to pass a suite of the most restrictive rent control laws in many years.  Among other things, the laws would restrict vacancy decontrol, and limit the ability to pass the cost of improvements through to tenants.  As such, it is moving New York away from second generation rent control toward first generation rent control.  Richard Arnott gives some examples of second generation control:

Second-generation rent controls commonly permit automatic percentage rent increases related to the rate of inflation. They also often contain provisions for other rent increases: cost pass-through provisions which permit landlords to apply for rent increases above the automatic rent increase, if justified by cost increases; hardship provisions, which allow discretionary increases to assure that landlords do not have cash-flow problems; and rate-of-return provisions, which permit discretionary rent increases to ensure landlords a "fair" or "reasonable" rate of return. Second-generation controls commonly exempt rental housing constructed after the application of controls, although new housing may be brought under the controls at a later time.  

In some jurisdictions, second-generation rent control has permitted full vacancy decontrol, whereby the unit becomes completely decontrolled when it is vacated. Other jurisdictions' programs permit inter-tenancy decontrol, whereby controls apply during successive tenancies but no restrictions are placed on inter-tenancy rent increases. Others contain alternative decontrol mechanisms; probably the most common has been rent level decontrol, whereby a unit is decontrolled when its controlled rent rises above a certain level. Yet others have no decontrol provisions.  

Such rent regulation often contains provisions which accord tenants improved security of tenure—rent increase appeal procedures, eviction procedures more favorable to the tenant, and so on—and it often includes restrictions to prevent cutbacks in maintenance, and on the conversion of controlled rental housing to owner-occupied housing.

Richard is one of the finest urban economists alive, and has taken the view that second generation rent control might do more good than harm.  This is certainly not Econ 101 gospel, but one one needs only to move onto Econ 201 to see where he is coming from.    If property owners have pricing power, the equilibrium rental rate could well be above the social optimum, and the quantity of housing produced could fall below the social optimum.  The idea that property owners might have pricing power goes back at least as far as David Ricardo, who worried about the corrosive effects of "economic rent" on social welfare.  It is certainly plausible to think that in a select few markets, such as New York and San Francisco, landowners do indeed have pricing power.  And this may argue for second generation rent control--but not the move toward first generation rent control, which puts tight caps on rent increases, eliminates vacancy decontrol, and imposes controls on new buildings.

Nevertheless, it is not clear to me that even second best rent control helps much with allocative inefficiency in markets where supply is inelastic--if there is a ceiling on the number of units builders can build, a price intervention is not going to bring about additional units.  So the issue is about redistribution.

Rajasekaran, 

Treskon and 

Greene 

have a nice white paper summarizing the literature on the winners and losers of rent control in the few jurisdictions where it exists in the US.  That literature shows that (1) rent control does indeed benefit incumbents; (2) does harm to those outside the rent control system (except, perhaps in Cambridge, MA); (3) probably reduces the stock of rental housing and (4) probably reduces the quality of the housing stock.  Diamond, McQuade and Qian find that the costs and benefits of San Francisco's second generation rent control (which has vacancy decontrol and no control of new buildings) are about equal.  This does not mean that this would be true for first generation rent control.

But even if second generation rent control is neutral in terms of costs and benefits, it doesn't necessarily lead to desirable distributional outcomes.  It is almost certainly true that the average property holder is wealthier than the average renter, and therefore that on average rent control redistributes income from higher to lower wealth people.  But rent control does not target the incomes/wealth of either property owners or renters.

We don't know much about the distribution of wealth among property owners.  We can turn to the US Census Rental Housing Finance Survey to see that nearly half of all units (and about 3/4 of all properties) are owned by individual investors.  Similar numbers are managed by either the owner herself or an unpaid agent of the owner.  So a substantial number of units are held by Mom and Pops.  According to the 2016 Survey of Consumer Finances, the median value of "equity in non-residential property (which includes residential properties with 5 or more units)" among those who hold such properties is about $70,000.  It is safe to say that a substantial number of owners of properties for rent do not have oodles of wealth.

As for the distribution of benefits to renters, consider the following graph:

Rent control in Los Angeles applied to buildings constructed through 1978.  I took data from the 2016 American Community Survey (I know, I need to update it) to look at the income distribution of those living in buildings built in the 1970s (the best I could do to get at the newest rent stabilized buildings) and those living in buildings built in the 1980s (i.e., the oldest non-rent stabilized buildings).  Do you see a difference in these distributions?  Neither do I.  And to me, good social welfare policy is targeted to those who need help.

The thing that bothers me most about rent control is that it allows elected officials to say they are tackling housing issues in our most expensive MSAs, while they continue to punt on the issue of supply elasticity.  I am waiting to see more of our great cities take up the example of Minneapolis, whose government eliminated single family zoning in that city.  I would even be OK with cities combining temporary rent control with Minneapolis style zoning, knowing that in the presence of sufficient supply, people would ultimately no longer feel the need for rent control.  Rent control alone, however, just gives electeds cover not to fix the fundamental problem.





  

Is there in the US a necessity of life...

...other than housing, where governments impose supply ceilings?  Racking my brain on this, and I can't think of another product.  I could be wrong, though. 

Supply and Demand do explain why LA has a housing problem

The most recent homeless counts came in last week, and homelessness in Southern California is getting worse.  As my colleague Gary Painter has shown, this is not because LA "attracts homeless people," which is the view of the data-free opinionaters in the LA Times comments section.

The change in homelessness reflects how very expensive housing is here in LA, particularly as it attracts people with college degrees who outbid lower skilled workers for the housing stock that is here.  Many people without college degrees are leaving LA, but people with strong social or familial ties have reasons not to do so.

So why are things so bad here?  I would argue the problem arises from the fact that LA policymaker "solutions," which include bespoke zoning and some affordable units here and there, do not address the fundamental problem facing the LA housing market--that it has an inelastic supply curve.  As my friend and frequent co-author Steve Malpezzi taught me a long time ago, there is a big policy difference between nudging an inelastic supply curve to the right and making supply more elastic.

We may approximate the state of the LA housing market with the following supply and demand picture.  Albert Saiz showed in 2008 that Metro Los Angeles had the lowest supply elasticity of any American MSA, and given its small levels of construction in the face of large rent increases since then, it is hard to imagine how it has gotten any larger.

Now suppose municipalities relax the constraint on supply through a little up-zoning here and there.  The picture changes to this.

The relaxation of the constraint allows some housing price relief.  But there is a problem.

Housing demand for LA continues to increase, not (any longer) because of in-migration, but because of the age profile of the population.  Large numbers of kids are becoming adults, and, as Sarah Mawhorter shows, want to move out of the parental home (the parents want this too!), but don't want to leave the town where they grew up.

For LA to accommodate housing demand, it would need to have a supply curve that more closely resembles other cities--it would look like this:

The supply curve would still be upward sloping, but changes in demand would also lead to large increases in supply, and hence bring about milder price increases.  Policy can move this relatively elastic demand curve rightward, by reducing the requirements necessary to build (these include things like fees, and in LA's case, linkage fees and parking requirements).

I should note that LAs current vertical supply curve is not the result of topography alone.  In his excellent dissertation at UCLA, Greg Morrow showed that Los Angeles zoning contemplated a city of 10 million; various down-zonings since then have reduced the city's allowable density by 60 percent.  LAs current population of 4 million largely uses up its allowable zoning, and as affluent small households replace less affluent large households (something Hyojung Lee has documented), it will be able to hold even fewer people--unless land use policy is completely overturned.

For those who think LA is overcrowded, let me use Alain Bertaud's work to point out the LA's density is one-third of Krakow's, one-fourth of Paris' and one-fifth of Singapore.   I can testify that these are all very pleasant, livable cities.  If LA were to completely change its land-use policy, it could at once become considerably more affordable and have even more walkable neighborhoods than it currently has.  But incrementalism won't work.

California out-streaming

I was looking at Census estimates for 2018 this morning (I know, I know, but I also took a nice walk first), and saw that after seven years of positive in-migration of 50,000 per year, we in California had net outmigration of 38,000 between 2017 and 2018.  This reduces the pressure on the housing market by ~ 30,000 units, or 3-4 months of production at current levels.  Some thoughts:

(1) This doubtless explains why the housing market is slowing (and I am sticking with my call of a small price reduction over the next few years).

(2) Perhaps this reflects a tipping point--we have just become too expensive as a state, regardless of the economic productivity here.

(3) Perhaps also this reflects that policy hostility toward immigrants is really mattering.  California has long had domestic outmigration, but had more than enough foreign migration to make up for it.  This is no longer true.

It is particularly striking that this is happening at a time when there are lots of jobs in California.

Here is the site from which to download migration data. https://www.census.gov/data/tables/time-series/demo/popest/2010s-state-total.html …. Looks at annual components of changes and cumulative components of change.

Ten Year House Price Volatility

I'm doing some work on reverse mortgages.  One of the issues confronting the analysis of reverse mortgages is long-term house price volatility.  While house prices can be quite volatile from year to year, this doesn't necessarily mean they are volatile for long-term holding periods.

Below are computations of 10 year, annualized, house price growth rates, standard deviations, minima, maxima, and coefficients of variation for the 100 largest US MSAs.  The data are the Federal Housing Finance Administration Purchase Only Index Data, and the computations are based on data from the first quarter of 1991 through the second quarter of 2018 (which means we have ten year hold data for 17+ years).  All data are nominal prices.

Note that in all cities, the average ten year growth rate is nominally positive.  The average across cities is 3.4 percent, with a range from .8 percent (Detroit) to 6 percent (San Francisco).  Neither of these should be surprising. 

More interesting (to me, anyway)  are the 42 cities that never had a negative house price period over a ten year hold.  Texas has a number of them (San Antonio has the maximum, minimum house price growth rate over ten years), and Pittsburgh and Oklahoma City are very steady too.  But a surprise to me are San Francisco and San Jose--markets that have has large short term drops in house prices.  In these markets, if one waited ten years, one never saw a house price drop over the holding period (again, we're talking in nominal terms here).  There have, however, been ten year periods in LA where nominal house price dropped by a shade under one percent per year.

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metro_name	andelta10	sdandelta10	minandelta10	maxandelta10	coefficient of variation
Akron, OH	0.015	0.019	-0.011	0.044	1.292
Albany-Schenectady-Troy, NY	0.036	0.025	0.001	0.072	0.695
Albuquerque, NM	0.028	0.018	-0.008	0.056	0.630
Allentown-Bethlehem-Easton, PA-NJ	0.029	0.029	-0.016	0.076	0.985
Anaheim-Santa Ana-Irvine, CA  (MSAD)	0.055	0.042	-0.004	0.132	0.772
Atlanta-Sandy Springs-Roswell, GA	0.025	0.024	-0.017	0.053	0.977
Austin-Round Rock, TX	0.050	0.011	0.025	0.073	0.226
Bakersfield, CA	0.029	0.041	-0.035	0.110	1.422
Baltimore-Columbia-Towson, MD	0.045	0.036	-0.010	0.100	0.781
Baton Rouge, LA	0.036	0.011	0.015	0.053	0.303
Birmingham-Hoover, AL	0.028	0.016	0.006	0.049	0.564
Boise City, ID	0.033	0.023	0.000	0.078	0.685
Boston, MA  (MSAD)	0.048	0.039	-0.005	0.105	0.795
Bridgeport-Stamford-Norwalk, CT	0.036	0.039	-0.021	0.093	1.080
Buffalo-Cheektowaga-Niagara Falls, NY	0.027	0.008	0.011	0.037	0.285
Cambridge-Newton-Framingham, MA  (MSAD)	0.046	0.035	-0.001	0.097	0.753
Camden, NJ  (MSAD)	0.033	0.035	-0.024	0.086	1.040
Cape Coral-Fort Myers, FL	0.031	0.045	-0.033	0.116	1.435
Charleston-North Charleston, SC	0.048	0.027	0.012	0.096	0.573
Charlotte-Concord-Gastonia, NC-SC	0.028	0.012	0.008	0.044	0.407
Chicago-Naperville-Arlington Heights, IL  (MSAD)	0.026	0.032	-0.018	0.070	1.219
Cincinnati, OH-KY-IN	0.020	0.017	-0.001	0.041	0.823
Cleveland-Elyria, OH	0.012	0.020	-0.013	0.042	1.660
Colorado Springs, CO	0.035	0.021	0.005	0.071	0.607
Columbia, SC	0.026	0.015	0.005	0.047	0.570
Columbus, OH	0.024	0.015	0.002	0.043	0.612
Dallas-Plano-Irving, TX  (MSAD)	0.035	0.011	0.012	0.054	0.305
Dayton, OH	0.011	0.014	-0.007	0.031	1.271
Denver-Aurora-Lakewood, CO	0.048	0.025	0.007	0.090	0.518
Detroit-Dearborn-Livonia, MI  (MSAD)	0.008	0.042	-0.052	0.067	5.142
El Paso, TX	0.028	0.016	-0.003	0.056	0.576
Elgin, IL  (MSAD)	0.016	0.029	-0.022	0.056	1.807
Fort Lauderdale-Pompano Beach-Deerfield Beach, FL  (MSAD)	0.044	0.045	-0.023	0.122	1.016
Fort Worth-Arlington, TX  (MSAD)	0.031	0.009	0.014	0.048	0.302
Fresno, CA	0.034	0.043	-0.032	0.110	1.293
Gary, IN  (MSAD)	0.021	0.012	0.004	0.038	0.577
Grand Rapids-Wyoming, MI	0.020	0.024	-0.014	0.053	1.197
Greensboro-High Point, NC	0.019	0.013	0.001	0.036	0.708
Greenville-Anderson-Mauldin, SC	0.028	0.009	0.012	0.042	0.334
Hartford-West Hartford-East Hartford, CT	0.030	0.029	-0.013	0.074	0.967
Honolulu ('Urban Honolulu'), HI	0.049	0.031	-0.011	0.093	0.622
Houston-The Woodlands-Sugar Land, TX	0.044	0.007	0.028	0.057	0.161
Indianapolis-Carmel-Anderson, IN	0.020	0.011	0.003	0.035	0.522
Jacksonville, FL	0.039	0.037	-0.015	0.099	0.959
Kansas City, MO-KS	0.027	0.021	0.001	0.054	0.779
Knoxville, TN	0.031	0.012	0.010	0.052	0.397
Lake County-Kenosha County, IL-WI  (MSAD)	0.019	0.029	-0.021	0.058	1.546
Las Vegas-Henderson-Paradise, NV	0.018	0.045	-0.041	0.095	2.524
Little Rock-North Little Rock-Conway, AR	0.027	0.012	0.007	0.043	0.432
Los Angeles-Long Beach-Glendale, CA  (MSAD)	0.053	0.046	-0.009	0.140	0.864
Louisville/Jefferson County, KY-IN	0.029	0.014	0.010	0.050	0.490
Memphis, TN-MS-AR	0.019	0.016	0.000	0.039	0.839
Miami-Miami Beach-Kendall, FL  (MSAD)	0.049	0.044	-0.014	0.126	0.896
Milwaukee-Waukesha-West Allis, WI	0.030	0.024	-0.005	0.059	0.825
Minneapolis-St. Paul-Bloomington, MN-WI	0.036	0.036	-0.008	0.083	0.994
Montgomery County-Bucks County-Chester County, PA  (MSAD)	0.039	0.028	-0.004	0.078	0.715
Nashville-Davidson--Murfreesboro--Franklin, TN	0.037	0.009	0.020	0.049	0.253
Nassau County-Suffolk County, NY  (MSAD)	0.052	0.044	-0.014	0.116	0.843
New Haven-Milford, CT	0.032	0.036	-0.022	0.086	1.118
New Orleans-Metairie, LA	0.038	0.017	0.010	0.066	0.435
New York-Jersey City-White Plains, NY-NJ  (MSAD)	0.048	0.040	-0.011	0.105	0.825
Newark, NJ-PA  (MSAD)	0.042	0.037	-0.013	0.096	0.887
North Port-Sarasota-Bradenton, FL	0.037	0.043	-0.027	0.117	1.172
Oakland-Hayward-Berkeley, CA  (MSAD)	0.047	0.048	-0.011	0.132	1.012
Oklahoma City, OK	0.035	0.008	0.022	0.049	0.236
Omaha-Council Bluffs, NE-IA	0.026	0.016	0.007	0.051	0.615
Orlando-Kissimmee-Sanford, FL	0.034	0.039	-0.023	0.105	1.163
Oxnard-Thousand Oaks-Ventura, CA	0.048	0.045	-0.015	0.127	0.935
Philadelphia, PA  (MSAD)	0.049	0.030	0.006	0.094	0.598
Phoenix-Mesa-Scottsdale, AZ	0.038	0.038	-0.017	0.107	0.997
Pittsburgh, PA	0.032	0.005	0.023	0.042	0.171
Portland-Vancouver-Hillsboro, OR-WA	0.047	0.019	0.020	0.075	0.402
Providence-Warwick, RI-MA	0.041	0.041	-0.019	0.100	0.996
Raleigh, NC	0.030	0.009	0.014	0.044	0.306
Richmond, VA	0.038	0.024	0.000	0.078	0.643
Riverside-San Bernardino-Ontario, CA	0.039	0.050	-0.026	0.133	1.275
Rochester, NY	0.020	0.007	0.010	0.031	0.358
Sacramento--Roseville--Arden-Arcade, CA	0.037	0.046	-0.026	0.119	1.246
Salt Lake City, UT	0.042	0.016	0.016	0.075	0.384
San Antonio-New Braunfels, TX	0.039	0.007	0.032	0.057	0.167
San Diego-Carlsbad, CA	0.052	0.046	-0.012	0.131	0.878
San Francisco-Redwood City-South San Francisco, CA  (MSAD)	0.060	0.033	0.011	0.118	0.547
San Jose-Sunnyvale-Santa Clara, CA	0.053	0.037	0.004	0.116	0.700
Seattle-Bellevue-Everett, WA  (MSAD)	0.049	0.025	0.017	0.096	0.519
Silver Spring-Frederick-Rockville, MD  (MSAD)	0.048	0.038	-0.011	0.109	0.790
St. Louis, MO-IL	0.030	0.023	-0.001	0.060	0.748
Stockton-Lodi, CA	0.026	0.051	-0.041	0.120	1.945
Syracuse, NY	0.025	0.014	0.005	0.049	0.566
Tacoma-Lakewood, WA  (MSAD)	0.039	0.029	-0.001	0.093	0.735
Tampa-St. Petersburg-Clearwater, FL	0.040	0.039	-0.017	0.111	0.966
Tucson, AZ	0.033	0.035	-0.026	0.088	1.070
Tulsa, OK	0.029	0.011	0.014	0.045	0.365
Virginia Beach-Norfolk-Newport News, VA-NC	0.043	0.034	-0.013	0.094	0.787
Warren-Troy-Farmington Hills, MI  (MSAD)	0.013	0.037	-0.038	0.063	2.848
Washington-Arlington-Alexandria, DC-VA-MD-WV  (MSAD)	0.052	0.035	-0.005	0.112	0.675
West Palm Beach-Boca Raton-Delray Beach, FL  (MSAD)	0.042	0.043	-0.021	0.120	1.026
Wichita, KS	0.024	0.012	0.010	0.041	0.473
Wilmington, DE-MD-NJ  (MSAD)	0.034	0.031	-0.016	0.081	0.905
Winston-Salem, NC	0.020	0.013	-0.001	0.038	0.664
Worcester, MA-CT	0.036	0.038	-0.016	0.093	1.051

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.....