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Learned Helplessness: Of Roaches, Restaurant Vents, and Reality

Published by Anonymous (not verified) on Wed, 06/04/2022 - 8:54pm in

A worrisome case study of helpless and inaction in New York City tenants.

Housing Is a Human Right—Here’s How to Make It a Reality

Published by Anonymous (not verified) on Sat, 02/04/2022 - 9:48pm in

e federal government has enables the private market to profit from housing. Time for a public option for housing?

Houston, We Have a Credit Problem

by Neil Tracey

In 2021, China had around 30 million homes sitting vacant for extended periods. There’s enough unused housing in China to house around 80 million people, roughly the population of Germany. This isn’t “slack” in the market; there is little hope that these homes will someday find an occupant. These homes are bound to remain empty.

Pile of credit cards

Our growth-obsessed economy requires credit to “succeed,” leaving millions in debt in a bloated economy. (CC BY 2.0, Sean MacEntee)

Indeed, most of these homes are simply held as financial assets; people who already own one home buy another and hang on to it, expecting it to appreciate. Even more peculiarly, Chinese property developers continue to build more homes. The absurdity of this market made headlines late last year with the story of Evergrande, a Chinese property developer that had accumulated over $350 billion in debt, then defaulted on large sums. Now, months after Evergrande first threatened to default, it has slipped from the headlines. However, it’s worth revisiting the story of Evergrande to understand just how it came to be. Why was a developer building more homes in a country that already had available housing for another 80 million people?

The answer lies in credit. Credit is driven by, and in turn reinforces, expectations for the future. By driving expectations for the future, credit steals democratic control over the future from citizens and gives it to market forces. We’ll explore this issue by looking at Houston, Texas and how the credit market drove its growth. Then we will address what credit may look like in a steady state economy and how steady-state economics may return control of their futures to citizens.

Extreme Growth and Houston: The “Limitless City”

Visiting Houston, one thing stands out: it’s a BIG city. With a population of over 5 million and a metropolitan area of over 9,000 square miles, Houston is the only American city without formal zoning restrictions. In his book, Ages of American Capitalism, Jonathan Levy explains that Houston is culturally, economically, and geographically defined by its cycle of credit-driven growth.

In Houston, there was an expectation that the city would expand. This expectation was the result of fomenting hysteria over oil, housing expansion, and pop culture. In their 1981 song Houston is Hot Tonight,  Iggy Pop sings, “Bright lights, Houston is hot tonight / Arabian sheiks and money, up in the sky / Now I don’t mind, a bloodbath / When I’ve got oil on my breath.” Combining exotifying imagery, money, and oil, Iggy Pop captures the overwhelming expectation of growth that seized Houston. This expectation of growth led to the expansion of cheap credit that let Houston expand so rapidly that its edges became undefinable. Urban geographers, trying to understand the limits of Houston, had to come up with a whole new set of terminology. Houston was a “multi-node city,” an “edge city,” an “edgeless city,” and a “boundless city.” Indeed, the only thing that seemed certain was that Houston was growing, and wouldn’t stop.

NASA satellite image of Houston, Texas lit up at night

Houston is certainly “hot tonight,” and getting hotter. (CC BY-NC 2.0, NASA’s Marshall Space Flight Center)

Houston’s role as a “limitless city” was due to credit. Credit is money or goods extended by a creditor to a recipient based on the understanding that the recipient will pay it back in the future, plus interest. Therefore, credit is a bet on the future ability of the recipient to pay back the money they borrow, and then some.

For this article, we’ll limit our discussion of credit to credit extended by a private creditor; in practice, that may be an individual, bank, or company seeking to make a profit (as opposed to a government agency advancing a social objective). Given this definition of credit, we can see how it may create a cycle of growth. Creditors decide to whom they should give credit based on who is likeliest to repay in the future. Creditors look to companies and individuals with historically high growth rates as a determining factor. In turn, having access to credit enables recipients to grow. A cyclical relationship between credit and growth ensues, whereby credit leads to growth, which leads to more credit, which leads to more growth, and so on.

This model of credit-driven growth can be seen in Houston, where growth expectations attracted the market for credit, since the promise of growth suggested that future property prices would increase. Thus, the expectation of growth meant that Houston applicants were perceived as “good bets” for repayment. This enabled the credit-fueled expansion of Houston as a business. At the same time, Houston’s expansion fueled the expectation that it would continue to expand. This expectation, then, fueled its expansion. Thus, the self-reinforcing mechanism of credit and growth expectations persisted.

At some point, this cycle of growth confronts the physical constraints of the natural world. After Hurricane Harvey dumped 51 inches of rain on Houston in 2017, the New York Times published: “A Storm Forces Houston, the Limitless City, to Consider Its Limits.” Since the flood, Houston has made little progress in considering those limits. Houston appointed a “flood czar” who wants to increase Houston’s green spaces to help absorb flood waters, but there’s no movement to limit the city’s growth. Only two years after Hurricane Harvey, the Houston City Council recklessly approved the development of a 100-year floodplain into condominiums.

Possibilities for Credit in a Steady State Economy

Due to this self-reinforcing cycle, the credit market is incompatible with a steady state economy. Ideas for how to reform this cycle come from a rather surprising source: John Maynard Keynes. Far from a steady stater, Keynes was famous for pitching the “propensity to consume” as well as government policies designed for growth. However, towards the end of his General Theory of Employment, Interest, and Money, Keynes presents some rather unique thoughts on the role of credit.

One idea for reform comes from the price regulation of credit. The price of credit (that is, the real interest rate) could be regulated to avoid unsustainable growth. The Federal Reserve already uses its power to set interest rates, and has long prioritized low rates to stimulate growth. However, the Fed could use a more nuanced approach to setting particular interest rates for loans in specific markets. There are certain areas of the economy that need credit to launch, such as the renewable energy sector. The Fed, with a little urging from Congress and the president, could set low interest rates for these sectors, essentially subsidizing them via differential interest rate. Outside of these sectors, the Fed would set higher interest rates to lessen the rate of growth in other sectors, and of GDP at large.

As a macroeconomic actor, the Fed wouldn’t be keen on dabbling with sectoral distinctions. If necessary, Congress could pass a bill to establish differential interest rates, if not directly via the Fed, then indirectly via fiscal policy such as credit supplements or taxes. Presumably such a law would have a sunset clause or be revisited and readjusted annually, as with an appropriations bill.

A related option is for credit to be socialized and overseen by a government agency. (While the quasi-governmental Fed exerts control over interest rates, most actual credit is extended through private banks.) Credit would be fully controlled, in other words, by democratic institutions.

Socializing credit would enable the government to marry its fiscal and monetary policies, extending credit to essential industries and limiting credit for increasingly outdated or harmful ones. However, for this proposal to work, the federal government would have to concurrently abolish the private credit system and limit access to foreign credit.

Portrait of Neil Tracey, CASSE's economic policy intern Spring 2022Neil Tracey is a junior at Georgetown University and an economic policy intern at CASSE.

The post Houston, We Have a Credit Problem appeared first on Center for the Advancement of the Steady State Economy.

Is 4% the “Magic Number” for Mortgage Rates to Prick the Housing Market (and Stocks)?

Published by Anonymous (not verified) on Fri, 25/02/2022 - 2:05am in

Is the housing market about to hit a tipping point?

On-Time Rent Payments Sag Amid Massive Spike in Rents

Rents are up a lot in most places in the US of any consequence. Not all tenants are keeping pace.

The Great Amazon Land Grab – How Brazil’s Government Is Turning Public Land Private, Clearing the Way for Deforestation

Published by Anonymous (not verified) on Sun, 06/02/2022 - 9:55pm in

At the U.N. climate summit in 2021, 141 countries – including Brazil – signed a pledge to end deforestation by 2030. Yet land grabs continue to place the Amazon ecosystem at risk.

Housing Returns in Big and Small Cities

Published by Anonymous (not verified) on Wed, 02/02/2022 - 6:41am in

 Aerial view of Washington Square in NY

Houses are the largest asset for most households in the United States, as is the case in many other countries as well. Within countries, there is substantial regional variation in house prices—compare real estate values in Manhattan, New York City, with those in Manhattan, Kansas, for example. But what about returns on investment? Are long-run returns on real estate investment—the sum of price appreciation and rental income flows—higher in superstar cities like New York than in the rest of the country? In this blog post, we present new and potentially surprising insights from research comparing long-run returns on residential real estate in a nation’s largest cities to those experienced in the rest of the country (Amaral et al., 2021), covering the U.S. and fourteen other advanced economies over the past century.

A New Regional Housing Return Database

For the analysis, we compiled a new long-run city-level data set covering annual house prices and rents in twenty-seven large (“superstar”) cities in fifteen OECD countries over the past 150 years. We borrow the “superstar city” terminology from the well-known paper by Gyourko, Mayer, and Sinai (2013) for the U.S., but take it global in the sense that we study the main economic agglomerations in each of these fifteen countries. For each national superstar city, we calculate long-run total returns on residential real estate investments as the sum of price appreciation and rent returns—and compare them to returns in the rest of the country. For the construction of the data set, we partly drew on existing historical research for individual cities. In most cases, however, we hand-collected new house price and rental series from city yearbooks or primary sources such as newspapers, tax records, and notary archives.

The data show that, over the long-run, superstar cities have witnessed lower total returns on residential real estate than other parts of the same country. The table below shows average capital gains, rent returns and total returns for the superstar cities (Column 1) and for the national housing portfolios as defined in Jordà et al. (2019) (Column 2). Column (4) shows the implied population-weighted return for the rest of the country, excluding the superstars.

City-Level and National Yearly Housing Returns (Log Points), 1950-2018

27 National Superstars

Cities
National
Difference
RoC
Diff. to RoC

Capital gain
2.25
1.82
0.43* (0.23)
1.64
0.61** (0.26)

Rent return
3.55
4.94
-1.39*** (0.04)
5.21
-1.65*** (0.05)

Total return
5.72
6.68
-0.95*** (0.23)
6.76
-1.04*** (0.26)

N
1767


Notes: The table shows averages of city-level and national log capital gains, log rent returns and log housing returns as well as the differences. National return averages are weighted by the number of cities in the respective country in the sample. Standard errors of differences (in parenthesis) and significance stars are calculated using paired t-tests. Rest of country (RoC) returns are calculated as national housing portfolio returns share after taking out the returns of the 27 national superstars. We use previous year population shares as weights of the portfolio share of our cities, such that the estimate should be interpreted a lower bound. ∗ : p < 0.1; ∗∗ : p < 0.05; ∗∗∗ : p < 0.01.

While house prices have grown faster in the large cities, the rental returns are substantially higher in more remote locations, leading to overall higher returns in the rest of the country. Average total returns have been 5.75 log points per year in the national superstars, compared to the national average of 6.68 log points. In other words, an investment in the most important cities within a country comes with a negative return premium of approximately 90-100 basis points relative to national average returns. We call this the negative “superstar premium.” The return differences are a robust feature of the data across countries and time periods, and statistically highly significant. A negative return premium of around 1 percentage point accumulates to substantial return differences in the long run. For instance, an investment in the superstar portfolio earned only about half the cumulative return of the national average portfolio over the past 70 years.

Housing Returns over the City-Size Distribution in the U.S.

To better understand the negative superstar premium we take a closer look at the U.S. housing market, for which we have comprehensive return data across the entire city-distribution since 1950. We combine the data compiled by Gyourko, Mayer, and Sinai (2013) with data from the American Community Survey for the 2010-18 period. The chart below shows average log total returns by increasing MSA size for the U.S. The key result is that in the postwar U.S., total returns to housing decrease with city size. There is an almost monotonic negative relation between total returns and city size with the biggest differences between the largest and smallest MSAs. We calculate a return premium of small vs. large MSAs in the U.S. of about 80 basis points annually. This estimate is statistically highly significant.

Total Returns for 316 MSAs in the U.S. (Log Points) by Population Size, 1950-2018


Notes: All returns are log returns. Cities are divided into bins based on the size of MSA population in 1950. The middle 8 bins cover size deciles 2 to 9. The 4 extreme bins split the smallest and largest deciles in half.

Risk and Return

Why are housing returns lower in large cities than in the rest of the country? Our key finding can be rationalized in a standard asset pricing framework where excess returns are a compensation for higher risk. Observable long-run return differences between different assets must be attributable to differences in risk, or to violations of standard assumptions (such as behavioral biases in expectations).

Now suppose that everything that makes a national superstar city–its diversified economy, its large market, its amenities, the international demand (Black and Henderson (1999), Desmet and Henderson (2015))–also makes it a safer place as an investment. A consequence would be that the present value of future housing services will be subject to less risk so that buyers are willing to pay a higher price and accept a lower return for housing investments in large agglomerations. In turn, higher returns outside the superstars would be compensation for higher risk. For remote locations to attract capital, they have to offer higher returns.

There is empirical evidence for differences in housing risk across locations. On the one hand, the co-variance between housing returns and income growth is lower in large cities. The table below shows the differences in the co-variance between large and small MSAs for the U.S. The co-variance between MSA-level income growth and MSA-level housing returns has been significantly larger in smaller MSAs.

Differences in Co-Variances between Income and Housing Returns by City Size, U.S, 1950-2018

Sample
Capital Gain
Rental Yield
Total Return
N

Large vs rest
-0.68** (0.317)
-0.65*** (0.126)
-0.75** (0.298)
316

Large vs small
-2.00*** (0.606)
-1.23*** (0.250)
-2.25*** (0.584)
31


Notes: The table shows differences in the co-variance between income growth and log excess total returns, log excess capital gains and log excess rental yields between large MSAs and the rest of the sample or small MSAs. Differences are measured as coefficients in a cross-sectional regression of the dependent variable (co-variance) on a large MSA dummy. Robust standard errors in parenthesis. Large MSAs are defined as being at or above the 95th percentile of the MSA population distribution in 1950. The second row shows the same, but comparing large MSAs only to small MSAs, which are defined as being at or below the 5th percentile of the MSA population distribution in 1950. Overall, we use estimates for 316 MSAs between 1950 and 2018. ∗ : p < 0.1; ∗∗ : p < 0.05; ∗∗∗ : p < 0.01.

On the other hand, households are also exposed to other risks such as lower liquidity outside large cities. Using U.S. transaction-level data from Corelogic, one can show that this so-called idiosyncratic component of housing risk decreases with MSA size. In the chart below we plot average house-specific risk by MSA size for the period between 1990 and 2020. Similar to the patterns we found for housing returns, idiosyncratic risk seems to decrease almost monotonically with MSA size. As liquidity is low, homeowners in thinner markets face a greater risk of not realizing the local market return at the point of sale. Real estate search engine data confirm a significant increase of housing liquidity with city size. These findings mesh nicely with recent work by Giacoletti (2021), Sagi (2021), and Kotova and Zhang (2019), who show a strong relationship between house-specific risk and housing market liquidity.

Annual Idiosyncratic House Price Risk by MSA Size in the U.S., 1990-2020


Notes: The chart shows average annual idiosyncratic house price risk for different MSA size groups for the period between 1990 and 2020. MSAs are divided into bins based on the size of MSA population in 1990. The middle 8 bins cover size deciles 2 to 9. The 4 extreme bins split the smallest and largest deciles in half. All series are real and annualized.

Conclusion

Houses are the most important asset for families. They typically hold nondiversified real estate assets in specific locations. The distribution of housing returns across space therefore plays a central for household finance and consumption, the evolution of household wealth and its distribution, as well as financial stability risks. Our study takes the first steps toward a better understanding of spatial risk and return patterns in housing markets over the long run.

In particular, this blog post presented a novel data set covering housing return series for twenty-seven superstar cities. The key finding across international and U.S. data is that the big cities tend to underperform the rest of the country in terms of total returns. On average, investments in residential real estate in a national superstar city (such as New York, London, or Paris) yielded lower returns than investments in the rest of the country. Investors buying real estate outside of the large cities earned higher returns. The reason is that superstar real estate is comparatively safe. The returns are less strongly correlated with income growth, and market liquidity is higher, leading to lower sales price uncertainty. Higher returns outside the big agglomerations are a compensation for higher risks.

Francisco Amaral is a professor at the University of Bonn.

Martin Dohmen is a professor at the University of Bonn.

Sebastian Kohl is a senior researcher at the Max Planck Institute for the Study of Societies, Cologne.

At the time this post was written, Moritz Schularick was an assistant vice president in the Federal Reserve Bank of New York’s Research and Statistics Group. He is currently a professor of economics at the University of Bonn.

Related reading:
Mapping Home Price Changes 

References: 
Francisco Amaral, Martin Dohmen, Sebastian Kohl, and Moritz Schularick (2021), Superstar Returns, Federal Reserve Bank of New York Staff Reports, no. 999, December 2021

Disclaimer
The views expressed in this post are those of the authors and do not necessarily reflect the position of the Federal Reserve Bank of New York or the Federal Reserve System. Any errors or omissions are the responsibility of the authors.

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