Tag Archives: Finance

Light On Dark Matter

I posted recently on the question of what makes up the “dark matter” intangible assets that today are most of firm assets. Someone pointed me to a 2009 paper of answers:

IntangibleShares

[C.I. = ] Computerized information is largely composed of the NIPA series for business investment in computer software. …

[Scientific R&D] is designed to capture innovative activity built on a scientific base of knowledge. … Non-scientific R&D includes the revenues of the non-scientific commercial R&D industry … the costs of developing new motion picture films and other forms of entertainment, investments in new designs, and a crude estimate of the spending for new product development by financial services and insurance firms. …

[Brand equity] includes spending on strategic planning, spending on redesigning or reconfiguring existing products in existing markets, investments to retain or gain market share, and investments in brand names. Expenditures for advertising are a large part of the investments in brand equity, but … we estimated that only about 60 percent of total advertising expenditures were for ads that had long-lasting effects. …

Investment in firm-specific human and structural resources … includes the costs of employer-provided worker training and an estimate of management time devoted to enhancing the productivity of the firm. … business investments in firm-specific human and structural resources through strategic planning, adaptation, reorganization, and employee-skill building. (more)

According to this paper, more firm-specific resources is the biggest story, but more product development is also important. More software is third in importance.

Added 15Apr: On reflection, this seems to suggest that the main story is our vast increase in product variety. That explains the huge increase in investments in product development and firm-specific resources, relative to more generic development and resources.

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Firms Now 5/6 Dark Matter!

Scott Sumner:

We all know that the capital-intensive businesses of yesteryear like GM and US steel are an increasingly small share of the US economy. But until I saw this post by Justin Fox I had no idea how dramatic the transformation had been since 1975:

intangibles

Wow. I had no idea as well. As someone who teaches graduate industrial organization, I can tell you this is HUGE. And I’ve been pondering it for the week since Scott posted the above.

Let me restate the key fact. The S&P 500 are five hundred big public firms listed on US exchanges. Imagine that you wanted to create a new firm to compete with one of these big established firms. So you wanted to duplicate that firm’s products, employees, buildings, machines, land, trucks, etc. You’d hire away some key employees and copy their business process, at least as much as you could see and were legally allowed to copy.

Forty years ago the cost to copy such a firm was about 5/6 of the total stock price of that firm. So 1/6 of that stock price represented the value of things you couldn’t easily copy, like patents, customer goodwill, employee goodwill, regulator favoritism, and hard to see features of company methods and culture. Today it costs only 1/6 of the stock price to copy all a firm’s visible items and features that you can legally copy. So today the other 5/6 of the stock price represents the value of all those things you can’t copy.

So in forty years we’ve gone from a world where it was easy to see most of what made the biggest public firms valuable, to a world where most of that value is invisible. From 1/6 dark matter to 5/6 dark matter. What can possibly have changed so much in less than four decades? Some possibilities:

Error – Anytime you focus on the most surprising number you’ve seen in a long time, you gotta wonder if you’ve selected for an error. Maybe they’ve really screwed up this calculation.

Selection – Maybe big firms used to own factories, trucks etc., but now they hire smaller and foreign firms that own those things. So if we looked at all the firms we’d see a much smaller change in intangibles. One check: over half of Wilshire 5000 firm value is also intangible.

Methods – Maybe firms previously used simple generic methods that were easy for outsiders to copy, but today firms are full of specialized methods and culture that outsiders can’t copy because insiders don’t even see or understand them very well. Maybe, but forty years ago firm methods sure seemed plenty varied and complex.

Innovation – Maybe firms are today far more innovative, with products and services that embody more special local insights, and that change faster, preventing others from profiting by copying. But this should increase growth rates, which we don’t see. And product cycles don’t seem to be faster. Total US R&D spending hasn’t changed much as a GDP fraction, though private spending is up by less than a factor of two, and public spending is down.

Patents – Maybe innovation isn’t up, but patent law now favors patent holders more, helping incumbents to better keep out competitors. Patents granted per year in US have risen from 77K in 1975 to 326K in 2014. But Patent law isn’t obviously so much more favorable. Some even say it has weakened a lot in the last fifteen years.

Regulation – Maybe regulation favoring incumbents is far stronger today. But 1975 wasn’t exact a low regulation nirvana. Could regulation really have changed so much?

Employees – Maybe employees used to jump easily from firm to firm, but are now stuck at firms because of health benefits, etc. So firms gain from being able to pay stuck employees due to less competition for them. But in fact average and median employee tenure is down since 1975.

Advertising – Maybe more ads have created more customer loyalty. But ad spending hasn’t changed much as fraction of GDP. Could ads really be that much more effective? And if they were, wouldn’t firms be spending more on them?

Brands – Maybe when we are richer we care more about the identity that products project, and so are willing to pay more for brands with favorable images. And maybe it takes a long time to make a new favorable brand image. But does it really take that long? And brand loyalty seems to actually be down.

Monopoly – Maybe product variety has increased so much that firm products are worse substitutes, giving firms more market power. But I’m not aware that any standard measures of market concentration (such as HHI) have increased a lot over this period.

Alas, I don’t see a clear answer here. The effect that we are trying to explain is so big that we’ll need a huge cause to drive it. Yes it might have several causes, but each will then have to be big. So something really big is going on. And whatever it is, it is big enough to drive many other trends that people have been puzzling over.

Added 5p: This graph gives the figure for every year from ’73 to ’07.

Added 8p: This post shows debt/equity of S&P500 firms increasing from ~28% to ~42% from ’75 to ’15 . This can explain only a small part of the increase in intangible assets. Adding debt to tangibles in the numerator and denominator gives intangibles going from 13% in ’75 to 59% in ’15.

Added 8a 6Apr: Tyler Cowen emphasizes that accountants underestimate the market value of ordinary capital like equipment, but he neither gives (nor points to) an estimate of the typical size of that effect.

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Irreducible Detail

Our best theories vary in generality. Some theories are very general, but most are more context specific. Putting all of our best theories together usually doesn’t let us make exact predictions on most variables of interest. We often express this fact formally in our models via “noise,” which represents other factors that we can’t yet predict.

For each of our theories there was a point in time when we didn’t have it yet. Thus we expect to continue to learn more theories, which will let us make more precise predictions. And so it might seem like we can’t constrain our eventual power of prediction; maybe we will have powerful enough theories to predict everything exactly.

But that doesn’t seem right either. Our best theories in many areas tell us about fundamental limits on our prediction abilities, and thus limits on how powerful future simple general theories could be. For example:

  • Thermodynamics – We can predict some gross features of future physical states, but the entropy of a system sets a very high (negentropy) cost to learn precise info about the state of that system. If thermodynamics is right, there will never be a general theory to let one predict future states more cheaply than this.
  • Finance – Finance theory has identified many relevant parameters to predict the overall distribution of future assets returns. However, finance theory strongly suggests that it is usually very hard to predict details of the specific future returns of specific assets. The ability to do so would be worth such a huge amount that there just can’t be many who often have such an ability. The cost to gain such an ability must usually be more than the gains from trading it.
  • Cryptography – A well devised code looks random to an untrained eye. As there are a great many possible codes, and a great many ways to find weaknesses in them, it doesn’t seem like there could be any general way to break all codes. Instead code breaking is a matter of knowing lots of specific things about codes and ways they might be broken. People use codes when they expect the cost of breaking them to be prohibitive, and such expectations are usually right.
  • Innovation – Economic theory can predict many features of economies, and of how economies change and grow. And innovation contributes greatly to growth. But economists also strongly expect that the details of particular future innovations cannot be predicted except at a prohibitive cost. Since knowing of innovations ahead of time can often be used for great private profit, and would speed up the introduction of those innovations, it seems that no cheap-to-apply simple general theories can exist which predict the details of most innovations well ahead of time.
  • Ecosystems – We understand some ways in which parameters of ecosystems correlate with their environments. Most of these make sense in terms of general theories of natural selection and genetics. However, most ecologists strongly suspect that the vast majority of the details of particular ecosystems and the species that inhabit them are not easily predictable by simple general theories. Evolution says that many details will be well matched to other details, but to predict them you must know much about the other details to which they match.

In thermodynamics, finance, cryptography, innovations, and ecosystems, we have learned that while there are many useful generalities, the universe is also chock full of important irreducible incompressible detail. As this is true at many levels of abstraction, I would add this entry to the above list:

  • Intelligence – General theories tell us what intelligence means, and how it can generalize across tasks and contexts. But most everything we’ve learned about intelligence suggests that the key to smarts is having many not-fully-general tools. Human brains are smart mainly by containing many powerful not-fully-general modules, and using many modules to do each task. These modules would not work well in all possible universes, but they often do in ours. Ordinary software also gets smart by containing many powerful modules. While the architecture that organizes those modules can make some difference, that difference is mostly small compared to having more better modules. In a world of competing software firms, most ways to improve modules or find new ones cost more than the profits they’d induce.

If most value in intelligence comes from the accumulation of many expensive parts, there may well be no powerful general theories to be discovered to revolutionize future AI, and give an overwhelming advantage to the first project to discover them. Which is the main reason that I’m skeptical about AI foom, the scenario where an initially small project quickly grows to take over the world.

Added 7p: Peter McCluskey has thoughtful commentary here.

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Big Signals

Between $6 and $9 trillion dollars—about 8% of annual world-wide economic production—is currently being spent on projects that individually cost more than $1 billion. These mega-projects (including everything from buildings to transportation systems to digital infrastructure) represent the biggest investment boom in human history, and a lot of that money will be wasted. …

Over the course of the last fifteen years, [Flyvbjerg] has looked at hundreds of mega-projects, and he found that projects costing more than $1 billion almost always face massive cost overruns. Nine out of ten projects faces a cost overrun, with costs 50% higher than expected in real terms not unusual. …

In fact, the number of mega-projects completed successfully—on time, on budget, and with the promised benefits—is actually too small for Flyvbjerg to determine why they succeeded with any statistical validity. He estimates that only one in a thousand mega-projects fit that criteria. (more; paper)

You can probably throw most big firm mergers into this big inefficient project pot.

There’s a simple signaling explanation here. We like to do big things, as they make us seem big. We don’t want to be obvious about this motive, so we pretend to have financial calculations to justify them. But we are purposely sloppy about those calculations, so that we can justify the big projects we want.

It would be possible to make prediction markets that accurately told us on average that these financial calculations are systematically wrong. That could enable us to reject big projects that can’t be justified by reasonable calculations. But the people initiating these projects don’t want that, so it would have to be outsiders who set up these whistleblowing prediction markets. But alas as with most whistleblowers, the supply of these sort of whistleblowers is quite limited.

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Multiplier Isn’t Reason Not To Wait

On the issue of whether to help now vs. later, many reasonable arguments have been collected on both sides. For example, positive interest rates argue for helping later, while declining need due to rising wealth argues for helping now. But I keep hearing one kind of argument I think is unreasonable, that doing stuff has good side effects:

Donating to organizations (especially those that focus on influencing people) can help them reach more people and raise even more money. (more)

Giving can send a social signal, which is useful for encouraging more giving, building communities, demonstrating our generosity, and coordinating with charities. (more)

Influencing people to become effective altruists is a pretty high value strategy for improving the world. … You can do more good with time in the present than you can with time in the future. If you spend the next 2 years doing something at least as good as influencing people to become effective altruists, then these 2 years will plausibly be more valuable than all of the rest of your life. (more)

Yes doing things now can have good side effects, but unless something changes in the side-effect processes, doing things later should have exactly the same sort of side effects. And because of positive interest rates, you can do more later, and thus induce more of those good side effects. (Also, almost everyone can trade time for money, and so convert money or time now into more money or time later.)

For example, if you can earn 7% interest you can convert $1 now into $2 a decade from now. Yes, that $1 now might lend respectability now, induce others to copy your act soon, and induce learning by the charity and its observers. But that $2 in a decade should be able to induce twice as much of all those benefits, just delayed by a decade.

In math terms, good side effects are multipliers, which multiply the gains from your good act. But multipliers are just not good reasons to prefer $1 over $2, if both of them will get the same multiplier. If the multiplier is M, you’d just be preferring $1M to $2M.

Now it does seem that many people are arguing that these side-effect processes are in fact changing, and changing a lot. They suggest that that if you work with or donate to them or their friends, then these efforts today can produce huge gains in inducing others to copy you, or in learning better how to do things, gains that won’t be available in the future. Because they and you and now are special.

I think one should in general be rather suspicious of investing or donating to groups on the basis that they, or you, or now, is special. Better to just do what would be good even if you aren’t special. Because usually, you aren’t.

Now one very believable way in which you might be special now is that you might be at a particular age. But the objectively best age to help is probably when you have peak abilities and resources, around age 40 or 60. If you are near your peak age, then, yes, maybe you should help now. If you are younger though, you should probably wait.

Added 14Apr: Every generation has new groups with seemingly newly urgent or valuable causes. So you need some concrete evidence to believe that your new cause is especially good relative to the others. I am not at all persuaded that today is very special just because some people throw around the phrase “effective altruism.”

Added 19Apr: Since my point doesn’t seem to get through just using simple words, here is a more formal math explanation:

Without loss of generality, we can define help x so that it is time-independent, i.e., so that x gives the same amount of direct help no matter the time t it is given. Also, assume that the process by which direct help x at time t results in indirect help at later times is stationary. That is, for every small x spent at time t, a distribution of gains are produced at later delays s according to the same function f(s). Thus the total help resulting from direct help x at time t is x*(1+Integral_t^Infty f(u-t)*du) = x*(1+Integral_0^Infty f(s)*ds. So if this integral is finite, then direct help x induces a constant indirect help multiplier M = 1+Integral_0^Infty f(s)*ds.

One might define a rate of return r for this indirect help as the r that solves the equation 1 = Integral_0^+Infty exp(-r*s)*f(s)*ds. And this rate of return r might in fact be huge. But note that regardless of the return r one calculates from a formula like this, one always gives more total help by choosing a larger amount of direct help x. So if you can give more direct help by helping later, you should.

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Hidden Asset Taxes Must Be Huge

Paul Krugman:

Piketty’s big idea is that we are in the early stages of returning to a society dominated by great dynastic fortunes, by inherited wealth. … Imagine a wealthy family that has managed, somehow or other, to guarantee that a large fraction of its income is used to accumulate more wealth. Can this family thereby acquire a dominant position in society?

The answer depends on the relationship between r, the rate of return on assets, and g, the overall rate of economic growth. If r is less than g, dynasties are doomed to erode: even if all income from a very large fortune is devoted to accumulation, the family’s wealth will grow more slowly than the economy, and it will slowly slide into obscurity. But if r is greater than g, dynastic wealth can indeed grow to gigantic size. …

Piketty tells us something remarkable: historically, r has almost always exceeded g – but there was an exceptional period in the 20th century, a period of rapid labor force growth and technological progress, when r was less than g. And he asserts that the kind of society we consider normal, in which high incomes reflect personal achievement rather than inherited wealth, is in fact an aberration driven by this exceptional period. … A couple of questions:

1. How much of the decline in r relative to g in the 20th century reflected fast growth, and how much reflected policies that either taxed or in effect confiscated inherited wealth? In other words, how much was destiny, how much wars and political upheaval? Piketty stresses both factors, but never gives us a relative quantitative assessment. (more from Piketty here, here)

This rate of return on assets r that Krugman and Piketty discuss is something like the ratio of rental to purchase price of land. I don’t have access to Piketty’s book, but I’ve been pondering this question for a few months, and I’ve concluded that the usual estimates of asset returns r must fail to include many taxes that in practice reduce the actual rate of return r that growing dynasties can achieve. And I think that once we include all hidden taxes, the actual rate of return r that dynasties could achieve in practice must have usually be no more than the economic growth rate g. Let me explain.

Some taxes are explicit, like property taxes. Other taxes are implicit in the property destruction and transfer that result from wars, political upheavals, and legal corruption, and in the costs of reasonable efforts to prevent such losses. Finally, there are implicit taxes resulting from local legal limits on who one may use to manage a dynastic fund. For example, if a dynasty must give its eldest living male wide discretion over spending and investment choices, and if such males often turn out to be spent-thrift fools, this will greatly limit this dynasty’s ability to grow over the long run. An ideal might be to delegate dynasty management to a reputed professional trust that is legally obligated to follow explicit instructions to grow the fund as fast as possible over the long run. But, as I’ve discussed before, most societies have put substantial legal obstacles before solutions like this.

I argue that the net effect of all these hidden taxes on dynastic funds must have been to usually reduce asset returns to below growth rates. My argument is simple: If asset returns had typically been above growth rates, then if any dynastic funds had chosen to grow at the maximum possible rate, then even if those funds had started small they would have come to dominate investments worldwide. And they would have done so on a timescale short compared to the time period over which historical records suggest that asset returns have exceeded growth rates. By competing with each other, such dominating dynastic funds would then have increased the supply of investment so much as to drive down asset returns to or below the sustainable level, which is the economic growth rate.

I conclude that consistently across space and time, the net effects of all forms of taxes on dynastic investment funds, including taxes implicit in limiting who one may trust not to pilfer those funds, has been to reduce real assets returns to below growth rates. Perhaps well below.

Of course, if the main hidden tax in history has been pilfering by dynasty managers, that can result in a world where such pilferers spend a large fraction of world income, without much social value to show for it. One might easily dislike such a scenario, and want to prevent it. But instead of adding more explicit taxes to prevent the growth of dynastic funds, it seems to me better to cut the pilfering tax. Because this should encourage much more investment overall, which seems a good thing. This includes investment in helping and protecting the future, including protection from disasters, including existential risks. Which also seem like good things.

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Speculators Foresee No Catastrophe

In the latest American Economic Journal, Pindyck and Wang work out what financial prices and their fluctuations suggest about what speculators believe to be the chances of big economic catastrophes. Bottom line: [simple models that estimate the beliefs of] speculators see very low chances of really big disasters. (Quotes below.)

For example, they find that over fifty years speculators see a 57% chance of a sudden shock destroying at least 15% of capital. If I apply their estimated formula to questions they didn’t ask in the paper, I find that over two centuries, speculators see only a 1.6 in a hundred thousand chance of a shock that destroys over half of capital. And a shock destroying 80% or more of capital has only a one in a hundred trillion chance. Of course these would all be lamentable, and very newsworthy. But hardly existential risks.

The authors do note that others have estimated a thicker tail of bad events:

We obtain … a value for the [power] α of 23.17. … Barro and Jin (2009) … estimated α [emprically] for their sample of contractions. In our notation, their estimates of α were 6.27 for consumption contractions and 6.86 for GDP.

If I plug in the worst of these, I find that over two centuries there’s an 85% chance of a 50% shock, a 0.6% chance of an 80% shock, and one in a million chance of a shock that destroys 95% or more of capital. Much worse chances, but still nothing like an existential risk.

Of course speculative markets wouldn’t price in the risk of extinction, since all assets and investors are destroyed in those events. But how likely could extinction really be if there’s almost no chance of an event that destroys 95% of capital?

Added 11a: They use a power law to fit price changes, and so would miss ways in which very big disasters have a different distribution than small disasters. But to the extent that this does accurately model speculator beliefs, if you disagree you should expect to profit by buying options that pay off mainly in the case of huge disasters. So why aren’t you buying?

Those promised quotes: Continue reading "Speculators Foresee No Catastrophe" »

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Graeber’s Debt book

About a year ago I finished David Graeber’s 2011 book Debt: The First 5000 Years. Since he’s an Occupy Wall Street anthropologist, you might expect me to dislike the book. But I enjoyed it, and learned a lot, even though it does ramble, and his economics is weak.

Graeber’s overall mood is anti-debt:

For thousands of years, the struggle between rich and poor has largely taken the form of conflicts between creditors and debtors – of arguments about the rights and wrongs of interest payments, debt peonage, amnesty, repossession, restitution, the sequestering of sheep, the seizing of vineyards, and the selling of debtors’ children into slavery. By the same token, for the last five thousand years, with remarkable regularity, popular insurrections have begun the same way: with the ritual destructions of the debt records – tablets, papyri, ledgers, whatever form they might have taken in any particular time and place. (After that, rebels usually go after the records of landholding and tax assessments.) As the great classicist Moses Finley often liked to say, in the ancient world, all revolutionary movements had a single program: “Cancel the debts and redistribute the land.” (p.8)

That is sure a dramatic image, and makes one’s opinion on debt seem pretty fundamental. Oddly, Graeber never actually comes out directly against debt. He doesn’t seem to want to forbid it. Instead he seems to just want to set a low bar for forgiving the debts of the poor, mainly because helping the poor is a good thing. The closest thing to an argument I found:

The remarkable thing about the statement “one has to pay one’s debts” is that even according to standard economic theory, it isn’t true. A lender is supposed to accept a certain degree of risk. If all loans, no matter how idiotic, were still retrievable – if there were no bankruptcy laws, for instance – the results would be disastrous. What reason would lenders have not to make a stupid loan? (p.3)

Actually standard economic theory doesn’t say that the results without bankruptcy laws would be disastrous. Yes, the more stuff people can promise as collateral to support loans, or promise to suffer if they fail to pay, the more loans will be made, and the more people there will end up poorer or suffering because they can’t pay loans. But economists can’t say this is bad without adding assumptions about why such poverty is inefficient.

You might say that poverty is economically inefficient because it makes other people feel bad to know it exists, or because it keeps investments from being made in poor folks’ human capital. It could make sense to support general redistribution to deal with such problems. But debt forgiveness is not general redistribution. A policy of forgiving the debts of the especially poor mainly keeps the nearly poor from taking out loans from which they expect to gain overall, and raises the loan interest rates they pay.

Standard economic theory says that such debt forgiveness redistributes to the very poor, but not by taxing the rich. Anticipated future debt forgiveness instead taxes the nearly poor who take out loans and then do well, by raising the interest rates at which they repay their loans.

Yes debts are one of the ways by which people take chances with their wealth level, sometimes rising and sometimes falling. And yes if we stopped the nearly poor from taking such chances we might reduce the numbers of the very poor. But why pick only on loans? There are lots of other ways in which the nearly poor take chances with their wealth level, such as by trying new careers, jobs, neighborhoods, and social groups. Should we try to stop these risky behaviors as well?

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Beware Extended Family

In the last few weeks I’ve come across many sources emphasizing the same big theme that I hadn’t sufficiently appreciated: our industrial world was enabled and has become rich in large part because we’ve reduced the power and importance of extended families. This post ends with a long list of quotes, but I’ll summarize here.

In most farmer-era cultures extended families, or clans, were the main unit of social organization, for production, marriage, politics, war, law, and insurance. People trusted their clans, but not outsiders, and felt little obligation to treat outsiders fairly. Our industrial economy, in contrast, relies on our trusting and playing fair in new kinds of organizations: firms, cities, and nations, and on our changing our activities and locations to support them.

The first places where clans were weak, like northern Europe, had bigger stronger firms, cities, and nations, and are richer today. Today people with stronger family cultures are happier and healthier, all else equal, but are less willing to move or intermarry, and are nepotistical in firms and politics. Family firms do well worldwide, but by having a single family dominate, and by being smaller, younger, and less innovative.

Thus it seems that strong families tend to be good for people individually, but bad for the world as a whole. Family clans tend to bring personal benefits, but social harms, such as less sorting, specialization, agglomeration, innovation, trust, fairness, and rule of law.

All those promised quotes: Continue reading "Beware Extended Family" »

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Real Real Estate Agents

A real-estate agent keeps her own home on the market an average of ten days longer [than she would for a client] and sells it for an extra 3-plus percent, or $10,000 on a $300,000 house. When she sells her own house, an agent holds out for the best offer; when she sells yours, she encourages you to take the first decent offer that comes along. (more)

Lecturing on incentives on Wednesday, I used the classic example of the bad incentives of real estate agents. They usually get a fixed percentage (3%) of the sale price, which mostly makes them want to close a deal as fast as possible, regardless of the sale price. This is bad for seller’s agents and positively perverse for buyer agents – they worse the deal they get for you, the more they get paid. And the scope for individual agent reputations is pretty limited, because most people only ever buy or sell a few houses in their lifetime, usually in geographically separated places.

Alex just posted on the continuing puzzle of why this fixed percentage doesn’t seem to respond to changing market conditions, arguing that neither monopoly nor signaling explains it, and suggests:

Part of the problem in the realtor market is that other realtors can easily discriminate against discount brokers by pushing their clients one way or the other. (more)

That may be why we won’t see something better soon, but my lecture prompted me to think about the still interesting question: what exactly would be a better contract between you and your real estate agent, one that would better align their interests with yours?

Searching I found this paper from 2000, which proposes that selling homeowners sell their home to the selling agent, but also give that agent an option to sell the home back at the same price, to give that homeowner an incentive to help sell. They make no suggestion about how to contract with a buyers agent.

Here is what I came up with after my lecture. On the sell side, have the homeowner sell a 20% stake in their house to the selling agent, for an agreed-on cash price. The homeowner might hold an auction to find the local agent willing to pay the highest price to take on this role. The agent turns that back into cash when the house actually sells, or if it doesn’t sell the agent can sell their 20% stake back for the same price they paid if they want to give up on the process for now. If the homeowner wants to give up on the process, a similar reverse sale would happen, but perhaps the homeowner should suffer a penalty, such as 10% of that price paid for the 20% stake.

On the buy side, I’d have the buyer agent agree to pay (20-X)% of the house purchase price to gain a 20% stake in the house at the time of the home purchase. The X% number would be the agent’s fee, which might be chosen by an auction among the local agents. Unless they could find someone else who agreed to buy this stake after the purchase, they’d have to hold on to it until the house is next sold. Perhaps for many years. Because the buyer would get to live in the house or rent it, while the agent would not, the homeowner would owe the agent 20% of some assigned rental price each month until the house was again sold. This rental price could come from a simple regression of rental prices on local home features. People would know this price wasn’t exactly right, but they could take deviations into account in setting the price X.

The 20% number in the above is obviously arbitrary. It is probably a better place to start than the 100% number in the other proposal I mentioned, being a less radical change from the status quo. But my proposal is really to have some percentage number like that, not the exact 20% number.

This proposal clearly requires the agents to take on more financial risk than they do today, and so would encourage them to organize into agent firms that jointly take on the risk together. But that seems pretty reasonable.

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