Results for “housing” 707 found
New results on the housing boom and bust
We build a model of the US economy with multiple aggregate shocks that generate fluctuations in equilibrium house prices. Through counterfactual experiments, we study the housing boom-bust around the Great Recession, with three main results. First, the main driver of movements in house prices and rents was a shift in beliefs, not a change in credit conditions. Second, the boom-bust in house prices explains half of the corresponding swings in nondurable expenditures through a wealth effect. Third, a large-scale debt forgiveness program would have done little to temper the collapse of house prices and expenditures but would have dramatically reduced foreclosures and induced a small, but persistent, increase in consumption during the recovery.
That is from a recently published article by Greg Kaplan, Kurt Mitman, and Giovanni L. Violante in the Journal of Political Economy. Here are some less gated versions.
The Supply of Housing Has Become LESS Elastic
We are now well into another housing boom but as shown by Aasteveit, Albuquerque and Anundsen this boom is in some ways worse than the previous 1996-2006 boom because the supply response has been lower. The first figure, for example, shows that since the trough in 2012 house prices have risen a little bit faster in this boom than in the 1996-2006 boom and they have risen much faster relative to income (HPI is housing price index).
Over the same time, however, the number of new building permits and housing starts has been lower than in the previous boom (top two panels of figure 2 below). If prices have gone up as much as before but quantity has not, it follows that the elasticity of housing supply has fallen. Occasionally it’s suggested that there is an “overhang” of housing from the previous boom but that is not true. If anything, as shown in the bottom left panel, there is a decline in the housing stock relative to population.
The authors suggest that one reason why the elasticity of housing supply has fallen is that developers are fearful of being hit in another bust. I find that implausible. Developers don’t hold onto their stock for very long and often sell even before completion so they worry at most about a year or so forward. A better explanation is that housing supply remains especially constrained in the coastal cities by regulation and limited land capacity and those constraints are becoming more binding over time–in other words, the previous boom filled the infill. It may also be the case that fear of the bust is increasing regulation as people worry even more about downward fluctuations in the price of their primary asset.
Either way, housing continues to eat the world.
California’s regulatory code for housing is too strict
The sponsors of SB 50 seem to recognize that the state’s housing problems are at least partially man-made. Indeed, California is a leader in regulating just about everything — including insurance carriers, public utilities and housing construction. If California’s regulatory code underwent some serious spring cleaning, it could help the state at least make a dent in its housing affordability crisis.
The California Code of Regulations — the compilation of the state’s administrative rules — contains more than 21 million words. If reading it was a 40-hour-a-week job, it would take more than six months to get through it, and understanding all that legalese is another matter entirely.
Included in the code are more than 395,000 restrictive terms such as “shall,” “must” and “required,” a good gauge of how many actual requirements exist. This is by far the most regulation of any state in the country, according to a new database maintained by the Mercatus Center, a research institute at George Mason University. The average state has about 137,000 restrictive terms in its code, or roughly one-third as many as California. Alaska and Montana are among the states with as few as 60,000.
That is from James Broughel and Emily Hamilton at Mercatus, in The Los Angeles Times.
Housing zoning reform in Oregon
After a dramatic false start, the Oregon Senate on Sunday gave final legislative approval to a bill that would effectively eliminate single-family zoning in large Oregon cities.
House Bill 2001 passed in a 17-9 vote. It now heads to Gov. Kate Brown desk to be signed into law.
It will allow duplexes, triplexes, fourplexes and “cottage clusters” on land previously reserved for single family houses in cities with more than 25,000 residents, as well as smaller cities in the Portland metro area. Cities with at least 10,000 residents would be required to allow duplexes in single-family zones.
Here is more by Elliott Njus, via Jan Fure and several other MR readers. Next up perhaps is this…
Revisiting the Global Decline of the (Non-Housing) Labor Share
That is a new paper by Germán Gutiérrez and Sophie Pitony. I am on the road and have not had a chance to go through this, but the abstract is of interest:
We identify two undocumented measurement challenges affecting corporate sector labor shares outside the United States: the inclusion of dwellings and the inclusion of self-employed workers in the corresponding sectoral accounts. Both issues have become more important over time, biasing corporate labor shares downward. We propose two methods to correct for these challenges and obtain `true’ non-housing labor share series. Contrary to common wisdom, the corrected series exhibit stable labor shares across all major economies, except the US, where the corrected labor share declines by 6 percentage points since 1980.
For the pointer I thank Ilya Novak.
Does housing wealth boost start-ups?
I don’t mainly mean tech start-ups but rather the broader concept:
…house price effects work through wealth, liquidity and collateral effects on the propensity to start new firms and expand young ones. Aggregating local effects to the national level, housing market ups and downs play a major role – as transmission channel and driving force – in medium-run fluctuations in young-firm employment shares in recent decades. The great housing bust after 2006 largely drove the cyclical collapse of young-firm activity during the Great Recession, reinforced by a contraction in bank loan supply. As we also show, when the young-firm activity share falls (rises), local employment shifts strongly away from (towards) younger and less-educated workers.
That is from a new paper by Steven J. Davis and John C. Haltiwanger.
Facts that contradict the standard housing bubble story
Here I am doing a mix of quoting and paraphrasing the excellent Kevin Erdmann:
1. “Housing construction has been constricted in our most prosperous cities.”
2. “Home prices in many developed countries rose at least as sharply as inthe US.”
3. “…rent inflation has been persistently high for 20 years.”
4. “Growth in real rent expenditures generally had been declining throughout the supposed boom period.”
5. “During the boom, the relative income of the typical homebuyer did not decline.”
6. During the boom, homeowners were not “buying up.”
7. Homeownership rates, even at their peak levels in 2004, among age groups under 65 years old, were no higher than homeownership rates had been in the late 1970s and early 1980s.”
8. “…when taking into account all types of housing, the number of new housing units never even rose very far above the long-term average.”
Those are all from Kevin’s new and very important book Shut Out: How a Housing Shortage Caused the Great Recession and Crippled Our Economy. The simple “housing bubble” story is not in fact as true as it might seem, as Kevin shows, and furthermore just look at how many parts of America now have home prices at or above their “bubbly peaks.” I hope this work gets the attention it deserves.
How much did the housing shock drive political polarization?
From Henry van Straehlen, a job market candidate from Northwestern:
This paper studies the effect of economic conditions on political polarization using micro-data on house prices, mortgages, and individual political contributions. I argue that shocks to housing wealth — the largest asset for most households in the U.S. — lead to political polarization. Using the housing market bust of 2007-2011 as an empirical laboratory, I show that negative shocks to housing wealth increase political polarization. The richness of the data enables me to use individual heterogeneity in housing location and timing of home purchase to disentangle changes in personal wealth from other factors that might be at play in determining political polarization. The effect of housing shocks on polarization is stronger during the crisis, and cannot be attributed to reverse causality or changing neighborhood composition. Survey evidence comparing homeowners and renters shows that only homeowners polarize in response to house price shocks, while renters do not — suggesting that house price shocks are not merely a proxy for other economic shocks. Furthermore, extreme politicians benefit electorally from negative house price shocks to their contributor network, whereas moderate politicians are hurt by negative house price shocks. Financial crises destabilize politics, which then can feed back into the crisis. These results provide insight into the difficulty of adopting structural economic reforms following financial crises.
Work in progress by Henry argues: “I show that when the common ownership between two firms increases through mutual fund acquisition of their stock, the firms converge in political donation behavior and lobbying activity.”
The Economic Effects of Social Networks: Evidence from the Housing Market
That is by Michael Bailey, Ruiqing Cao, Theresa Kuchler, and Johannes Stroebel¶, there is a demonstration effect in consumption, namely you are more likely to buy a house if your friends did well buying homes. Here is from the working paper version:
We show how data from online social networking services can help researchers better understand the effects of social interactions on economic decision making. We use anonymized data from Facebook, the world’s largest online social network, to first explore heterogeneity in the structure of individuals’ social networks. We then exploit the rich variation in the data to analyze the effects of social interactions on housing market investments. To do this, we combine the social network information with housing transaction data. Variation in the geographic dispersion of social networks, combined with time-varying regional house price changes, induces heterogeneity in the house price experiences of different individuals’ friends. We show that individuals whose geographically distant friends experienced larger recent house price increases are more likely to transition from renting to owning. They also buy larger houses and pay more for a given house. Similarly, when homeowners’ friends experience less positive house price changes, these homeowners are more likely to become renters, and more likely to sell their property at a lower price. We find that these relationships are driven by the effects of social interactions on individuals’ housing market expectations. Survey data show that individuals whose geographically distant friends experienced larger recent house price increases consider local property a more attractive investment, with bigger effects for individuals who regularly discuss such investments with their friends.
PreFab Housing
A four-story building built in four days with apartments that include closets, a kitchenette, a sofa that converts to a queen-size bed, and a flat-screen TV? We are used to seeing that kind of thing in China but this development was in, of all places, Berkeley.
Berkeleyside: This new 22-unit project from local developer Patrick Kennedy (Panoramic Interests) is the first in the nation to be constructed of prefabricated all-steel modular units made in China. Each module, which looks a little like sleekly designed shipping containers with picture windows on one end, is stacked on another like giant Legos.
Cost savings on the housing itself were significant but local assembly was still expensive. Organized labor isn’t very happy:
Organized labor also dislikes that these MicroPADs are manufactured abroad.
“We’d rather they be constructed here instead of China so they don’t undercut wages and conditions,” said Michael Theriault, secretary-treasurer of the San Francisco Building and Construction Trades Council, in 2016 to the San Francisco Chronicle. “And we want them built under local building code and inspected by local inspectors.”
Cost savings won’t be passed on to consumers if the quantity of housing supplied isn’t increased so this isn’t a solution to high-prices in quantity-constrained cities. Nevertheless, construction costs rather than land supply are an important constraint elsewhere in the country. Moreover, it’s good to see experiments in improving construction productivity, one of our most important but productivity lagging sectors.
Housing Costs Reduce the Return to Education
In normal times and places house prices are kept fairly close to construction costs by the ordinary processes of supply and demand. Average house prices didn’t rise much over the entire 20th century, for example. Even today, house prices are kept close to construction costs in most of the United States. But extreme supply restrictions in a small number of important places (San Francisco, San Jose, LA, New York, Boston etc.), have driven average prices well above any seen in the entire 20th century.
Over the last several decades high productivity industries have become more geographically concentrated. As a result, a substantial share of the productivity gains from technology, bio-tech and finance have gone not to producers but to non-productive landowners. High returns to land have meant lower returns to other factors of production.
The return to education, for example, has increased in the United States but it’s less well appreciated that in order to earn high wages college educated workers must increasingly live in expensive cities. One consequence is that the net college wage premium is not as large as it appears and inequality has been over-estimated. Remarkably Enrico Moretti (2013) estimates that 25% of the increase in the college wage premium between 1980 and 2000 was absorbed by higher housing costs. Moreover, since the big increases in housing costs have come after 2000, it’s very likely that an even larger share of the college wage premium today is being eaten by housing. High housing costs don’t simply redistribute wealth from workers to landowners. High housing costs reduce the return to education reducing the incentive to invest in education. Thus higher housing costs have reduced human capital and the number of skilled workers with potentially significant effects on growth.
Housing is eating the world.
Does housing wealth make people more complacent?
It seems so:
I propose the status quo bias hypothesis, which predicts that housing wealth increases preference for status quo arrangements with respect to Social Security. I contrast the status quo bias hypothesis with the claim that housing wealth reduces support for social insurance, and test the hypothesis in two empirical studies. A survey experiment finds that homeowners informed about high historical home price appreciation (HPA) are about 8 percentage points more likely to prefer existing Social Security arrangements to privatized retirement accounts, compared to those informed about low historical HPA. Observational data from the 2000-2004 ANES panel show that homeowners who experience higher HPA are about 11 percentage points more likely to prefer status quo levels of spending on Social Security than those in the bottom HPA quartile. No significant HPA effects are observed among renters, and for other domains of social insurance among homeowners. The evidence suggests that housing wealth’s conservatizing effect should be interpreted as a status quo preference, rather than opposition to redistributive social policies.
That is from Weihuang Wong, via the excellent Kevin Lewis.
What will it take to reduce Bay Area housing costs?
A 2016 academic analysis by David Albouy, Gabriel Ehrlich and Yingyi Liu estimated that, in general, rents decrease by 3 percent for each 2 percent increase in the housing stock. (This estimate is close to the estimate of a lengthy blog post analysis at Experimental Geography, done two years ago, looking specifically at San Francisco’s history over the last six decades.)
If our goal is to reduce the average market-rate apartment rent to 27.5 percent of median household income (the midpoint between the 25-30 percent range that is normal), that means reducing the rent from $43,200 to $24,895, a 42.4 percent reduction. Using our ratio of a 2 percent housing stock increase leading to a 3 percent decrease in rents, that means, keeping all else equal, the Bay Area would theoretically need to increase the number of housing units overnight by 28.3 percent. (Let’s round up to 30 percent to make the subsequent calculations more intuitive).
…For example, if it takes 20 years to make up our housing deficit, and underlying trend growth for the U.S. population is 0.7 percent per year (15 percent over 20 years), and the average household size remains 2.3 persons, then the Bay Area will need to grow households 30 percent more than the amount of households needed to accommodate trend U.S. population growth (i.e. 30 percent more than the underlying 15 percent population growth), for a total growth of housing stock of approximately 50 percent over 20 years.
Let’s state it plainly: The Bay Area must increase its total housing stock by 50 percent over the next 20 years to bring affordability down to a reasonable level.
That is from the excellent Patrick Wolff.
Was there a supply overhang before the housing crash?
Kevin Erdmann has a revisionist take, namely no:
How bad was the supply overhang? Surprisingly, the answer may be that there never was one.
We can think about this in terms of stock (the number of homes in the United States) or flow (the rate at which new homes were being built).
In terms of stock, the Census Bureau maintains estimates of both US population and the number of housing units. As shown in figure 1, the ratio of homes to adults in the United States rose in the 1980s as a result of factors such as changing marriage norms. The ratio then declined in the 1990s. The relative number of housing units increased somewhat from 2000 to 2005 but remained below the previous peak level. After the crisis, the decline continued.
…The Census data provide surprisingly little support for the claim that there were too many homes in 2005…
Contrary to Chairman Bernanke’s assumption, at the national level there was no overhang of housing supply that needed to be worked off in 2011. Indeed, even in 2005 there was no national oversupply of housing. Rather, the American economy was burdened by a shortage of housing, especially in the Closed Access cities.
Not surprisingly, three of the worst six “closed” cities are in California (San Francisco, San Diego, and San Jose).
Here is the full study.
Zoning Increases the Price of Housing in Australia by a Lot
Researchers at the Reserve Bank of Australia estimate that house prices in major Australian cities are pushed well above the cost of production, including the land, by zoning regulations such as floor space index (video link) restrictions.
Zoning regulations provide benefits, but they also restrict housing supply and hence raise prices. This paper quantifies their importance by comparing prices to the marginal costs of supply at different points in time. For detached houses, marginal costs comprise the dwelling structure and the land that other home owners need to forego. Relative to our estimates of these costs, we find that, as of 2016, zoning raised detached house prices 73 per cent above marginal costs in Sydney, 69 per cent in Melbourne, 42 per cent in Brisbane and 54 per cent in Perth. Zoning has also raised the price of apartments well above the marginal cost of supply, especially in Sydney. We emphasise that this is not the amount that housing prices would fall in the absence of zoning. The effect of zoning has increased dramatically over the past two decades, likely due to existing restrictions binding more tightly as demand has risen.
Hat tip: Matt Yglesias.