The US Rental Housing Market [feedly]

The US Rental Housing Market
http://conversableeconomist.blogspot.com/2020/02/the-us-rental-housing-market.html

The US rental housing market is in the middle of some major shifts, as outlines by the Joint Center for Housing Studies of Harvard University in its report "America's Rental Housing 2020" (January 2020). Here are some of the changes.

The "rentership rate"--the share of households renting--rose sharply from about 2004 to about 2016, before leveling out the last few years.

From 2000 to 2010, most of the growth in the housing rental market was coming from those with relatively lower incomes. But in the last decade, most of the growth in the rental housing market is coming from those with relatively higher incomes. "But at 22 percent in 2019, rentership rates among households earning $75,000 or more are at their highest levels on record. Even accounting for overall income growth, rentership rates for households in the top decile jumped from 8.0 percent in 2005 to 15.1 percent in 2018 as their numbers more than doubled."

Rent is a big burden for many. The report looks at renters who are "cost burdened," referring to those who pay more than 30% of their income in rent. "Thanks to strong growth in the number of high-income renters, the share of renters with cost burdens fell more noticeably from a peak of 50.7 percent in 2011 to 47.4 percent in 2017, followed by a modest 0.1 percentage point increase in 2018. ... Meanwhile, 10.9 million renters—or one in four—spent more than half their incomes on housing in 2018." Another big shift is that there is a rise in the "cost-burdened renters" in middle-income groups (say, $30,000-$75,000 per year in annual income), especially in  "larger, high-cost metropolitan areas."

Vacancy rates for rentals are down, and are especially low for lower-cost, lower-quality rentals.

Meanwhile, rents are consistently rising faster than inflation.

The value of apartment properties has risen quickly, too.

Some background factors are also shifting. In the market for rental properties, stock of rentals rising in two areas  over last 15-20 year: single-family homes, and multi-family buildings with 20 or more units. These changes represent a shift in the rental housing market away from individual landlords and toward corporate ownership of rentals. In the area of single-family homes, for example, a number of institutional investors bought houses as rental properties in the aftermath of the drop in housing prices around 2010. The report notes:

Ownership of rental housing shifted noticeably between 2001 and 2015, with institutional owners such as LLCs, LLPs, and REITs accounting for a growing share of the stock. Meanwhile, individual ownership fell across rental properties of all sizes, but especially among buildings with 5–24 units. Indeed, the share of mid-sized apartment properties owned by individuals dropped from nearly two-thirds in 2001 to about two-fifths in 2015. Given that units in these structures are generally older and have relatively low rents, institutional investors may consider them prime candidates for purchase and upgrading. These changes in ownership have thus helped to keep rents on the climb.

Another shift is that many renters seem happier being renters, and less likely to view a rental as a short-term stop on the path to homeownership. Renters are staying in place longer, too. The report notes:

Changes in attitudes toward homeownership may lead some households to continue to rent later in life. The latest Freddie Mac Survey of Homeowners and Renters reports that the share of genX renters (aged 39–54 in 2019) with no interest in ever owning homes rose from 10 percent in March 2017 to 17 percent in April 2019. ... Fully 75 percent of renters overall, and 72 percent of genX renters, stated that renting best fits their current lifestyle. ...

[M]any renters are staying in the same rental units for longer periods. Between 2008 and 2018, the share of renters that had lived in their units for at least two years increased from 36 percent to 41 percent among those under age 35, and from 62 percent to 68 percent among those aged 35–64. Similarly, the National Apartment Association reported a turnover rate of just 46.8 percent in 2018— the lowest rate of move-outs since the survey began in 2000.

The US rate of homeownership has often been in the range of 63-65%, going up above that range during the housing boom around 2006, back down after that, and then rebounding a bit in the last few years.  Looking at long-run trends of aging, marriage/parenthood, and income, the US Department of Housing and Urban Development organized a pro-and-con symposium a few years ago on the question of whether the US homeownership rate will have fallen to less than 50% by 2050. Homeownership rates for young adults and for blacks are especially low. The US rate of homeownership was about average by international standards 20-25 years ago, but now is below the average. For earlier posts on these themes, see:

• "Will the US Become a Nation of Renters?" (October 21, 2016)
• "U.S. Homeownership Patterns" (June 28, 2018)
• "Homeownership Rates: Some International Comparisons"(February 16, 2018) 

With regard to the broader social issue of rental prices being so high for so many people, the economic answer is straightforward. For those with very low incomes, help them afford the rent. But for the market as a whole, the way to get lower prices is to raise supply. For example, it's an interesting question as to why the individual landlord has been in such decline, and the extent to which this drop has been due to additional administrative, regulatory, and zoning costs being imposed at the state and local level. It seems to me possible that we are in the middle of a social shift in which many households at a variety of income levels put less emphasis on homeownership--which in turn means greater public attention to conditions of supply and demand in housing rental markets  

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Resources to Help Gig Workers Understand Taxes [feedly]

Resources to Help Gig Workers Understand Taxes
https://www.cbpp.org/blog/resources-to-help-gig-workers-understand-taxes

The gig economy has grown to about a quarter of all workers, but their participation in it varies widely: while 1 in 10 workers relies on gig work for their primary income, most gig workers are active just a few months in a year. Such employment can have complicated tax implications, which is why CBPP's Get It Back campaign offers an online tax tool to help these workers understand how they can file a correct tax return and reduce their tax liability.

Aimed at transportation workers, who dominate what's known as the online platform economy, this tool — the Roadmap to Rideshare Taxes — is a helpful guide with step-by-step information on how to pay taxes when the IRS treats you like a business. The site covers tricky self-employment tax topics, including:

• Understanding how self-employment taxes work;
• Counting income;
• Tracking tax deductions for business expenses;
• Tracking mileage; and
• Making quarterly estimated tax payments.

There's also a one-page cheat sheet that guides rideshare workers through carefully tracking their deductions, paying taxes quarterly, and filing their tax returns annually. Tax deductions for driving expenses (like the mileage deduction) are the best way to reduce the amount of income subject to both income and self-employment taxes.

In addition, self-employed workers need to pay taxes throughout the year because, unlike with an employer, platform companies don't withhold a portion of workers' taxes from their paychecks. Anyone expecting to owe more than $1,000 in taxes (which is anyone with roughly $5,000 in self-employment income) must pay estimated taxes quarterly. The amount owed can be hard to calculate without outside assistance, and our tool helps with that, too.

Gig work offers important benefits for many, including flexible hours, a low barrier to entry, and immediate work. But filing taxes from such work can be stressful and complex. The Roadmap to Rideshare Taxes is designed to help.

Have questions or suggestions? Email eitcoutreach@cbpp.org.

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Budget Exposes Trump Administration’s Empty Rhetoric on Homelessness, Fair Housing [feedly]

Budget Exposes Trump Administration's Empty Rhetoric on Homelessness, Fair Housing
https://www.cbpp.org/blog/budget-exposes-trump-administrations-empty-rhetoric-on-homelessness-fair-housing

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Budget Exposes Trump Administration's Empty Rhetoric on Homelessness, Fair Housing

42Center on Budget: Comprehensive News Fee...by ogarahan / 15h

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The Trump Administration has said that it wants to help communities address the nation's serious housing affordability challenges, but the President's 2021 budget would do the opposite, slashing housing assistance and community development aid next year by $8.6 billion, or 15.2 percent (not counting the impact of inflation).

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Methods of causal inquiry [feedly]

Dan Little is kind of a wonky Marxist. He is deep into sociological lingo in this this (also wonky) piece. Nonetheless the techniques of proving, or at least making strong arguments, about causation in social sciences is an important and provocative subject, including in economics.

Methods of causal inquiry
http://understandingsociety.blogspot.com/2020/02/methods-of-causal-inquiry.html

Methods of causal inquiry

47UnderstandingSocietyby Dan Little / 13h

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This diagram provides a map of an extensive set of methods of causal inquiry in the social sciences. The goal here is to show that the many approaches that social scientists have taken to discovering causal relationships have an underlying order, and they can be related to a small number of ontological ideas about social causation. (Here is a higher resolution version of the image; link.)

We begin with the idea that causation involves the production of an outcome by a prior set of conditions mediated by a mechanism. The task of causal inquiry is to discover the events, conditions, and processes that combine to bring about the outcome of interest. Given that causal relationships are often unobservable and complexly intertwined with multiple other causal processes, we need to have methods of inquiry to allow us to use observable evidence and hypothetical theories about causal mechanisms to discover valid causal relationships.

The upper left node of the diagram reviews the basic elements of the ontology of social causation. It gives priority to the idea of causal realism -- the view that social causes are real and inhere in a substrateof social action constituted by social actors and their relations and interactions. This substrate supports the existence of causal mechanisms (and powers) through which causal relations unfold. It is noted that causes are often manifest in a set of necessary and/or sufficient conditions: if X had not occurred, Y would not have occurred. Causes support (and are supported by) counterfactual statements -- our reasoning about what would have occurred in somewhat different circumstances. The important qualification to the simple idea of exceptionless causation is the fact that much causation is probabilisticrather than exceptionless: the cause increases (or decreases) the likelihood of occurrence of its effect. Both exceptionless causation and probabilistic causation supports the basic Humean idea that causal relations are often manifest in observable regularities.

These features of real causal relations give rise to a handful of different methods of inquiry.

First, there is a family of methods of causal inquiry that involve search for underlying causal mechanisms. These include process tracing, individual case studies, paired comparisons, comparative historical sociology, and the application of theories of the middle range.

Second, the ontology of generative causal mechanisms suggests the possibility of simulations as a way of probing the probable workings of a hypothetical mechanism. Agent-based models and computational simulations more generally are formal attempts to identify the dynamics of the mechanisms postulated to bring about specific social outcomes. 

Third, the fact that causes produce their effects supports the use of experimental methods. Both exceptionless causation and probabilistic causation supports experimentation; the researcher attempts to discern causation by creating a pair of experimental settings differing only in the presence or absence of the "treatment" (hypothetical causal agent), and observing the outcome.

Fourth, the fact that exceptionless causation produces a set of relationships among events that illustrate the logic of necessary and sufficient conditions permits a family of methods inspired by JS Mills' methods of similarity and difference. If we can identify all potentially relevant causal factors for the occurrence of an outcome and if we can discover a real case illustrating every combination of presence and absence of those factors and the outcome of interest, then we can use truth-functional logic to infer the necessary and/or sufficient conditions that produce the outcome. These results constitute JL Mackie's INUS conditions for the causal system under study (insufficient but non-redundant parts of a condition which is itself unnecessary but sufficient for the occurrence of the effect). Charles Ragin's Boolean methods and fuzzy-set theories of causal analysis and the method of quantitative comparative analysis conform to the same logical structure.

Probabilistic causation cannot be discovered using these Boolean methods, but it is possible to use statistical and probabilistic methods in application to large datasets to discover facilitating and inhibiting conditions and multifactoral and conjunctural causal relations. Statistical analysis can produce evidence of what Wesley Salmon refers to as "causal relevance" (conditional probabilities that are not equal to background population probabilities). This is expressed as: P(O|A&B&C) <> P(O).

Finally, the fact that causal factors can be relied upon to give rise to some kind of statistical associations between factors and outcomes supports the application of methods of inquiry involving regression, correlation analysis, and structural equation modeling.  

It is important to emphasize that none of these methods is privileged over all the others, and none permits a purely inductive or empirical study to arrive at valid claims about causation. Instead, we need to have hypotheses about the mechanisms and powers that underlie the causal relationships we identify, and the features of the causal substrate that give these mechanisms their force. In particular, it is sometimes believed that experimental methods, random controlled trials, or purely statistical analysis of large datasets can establish causation without reference to hypothesis and theory. However, none of these claims stands up to scrutiny. There is no "gold standard" of causal inquiry.

This means that causal inquiry requires a plurality of methods of investigation, and it requires that we arrive at theories and hypotheses about the real underlying causal mechanisms and substrate that give rise to ("generate") the outcomes that we observe.

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World’s Most Innovative Economies [feedly]

World's Most Innovative Economies
https://ritholtz.com/2020/02/worlds-most-innovative-economies/

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Source: World Economic Forum