The Office Theory of Everything

AI will struggle to make us more productive. Offices can help — but not the way you think.

Eli Dourado has a new article on why artificial intelligence will have a disappointing impact on overall productivity. Economists have long been perplexed that productivity stagnated despite incredible advances in computing, bandwidth, and logistics. Dourado points out that AI's impact could be equally muted.

Why?

Immovable Objects

Dourado considers AI's potential to revolutionize the largest sectors of the economy, starting with housing:

The biggest driver of housing costs in major urban areas is land use policy. Computers don’t really help us here. Local policy is firmly in the realm of flesh and blood, of trust and human relationships and beating the other side. Perhaps the Internet has been helpful in spreading information about land use liberalization and organizing the YIMBY movement. But it’s not clear to me, even granting that, how AI could revolutionize land use policy.

One area where I wrongly expected the Internet to play a bigger role is in breaking the real estate agent cartel. In the Year of Our Lord 2023, real estate agents still reap nearly six percent of the sale price of most homes. Prices have gone up, so in dollar terms, real estate commissions are at all time highs. If the Internet could not break the real estate cartel, I am not sure why machine learning models would.

What else could AI do to make our housing sector more productive? Maybe something in construction? Boston Dynamics’s Atlas robot can do impressive construction-like things, but I am skeptical of such capital-intensive approaches to construction. For now, I am not seeing it and am writing off housing as a big chunk of the economy that the AI revolution will not really affect. Go prove me wrong.

To summarize his argument.

• Land prices: Housing costs are driven by government restrictions on how much and how quickly we can build new housing.
• Transaction costs: Real estate agents, title insurers, and lawyers take a huge cut off housing transactions due to (1) laws that essentially force buyers to pay them; (2) distinctive houses and local regulations that make it hard to standardize and streamline transactions; (3) behavioral and cultural reasons that drive people to be over-cautious when they make the biggest financial decision of their lives.  
• Construction costs: There are already many ways to build houses cheaper and faster (prefab, etc.). And yet, these methods fail to impact prices because it's hard to deploy them at scale, regulations make them hard to implement, the market structure makes it hard to build en masse, and more.
• Technology is not a significant constraint on any of the above drivers of housing costs. It's unclear whether even better technology (AI) will achieve what existing technology could not.

Dourado makes similar arguments about other large sectors, including energy, healthcare, and transportation. But let's stay with housing.

The Housing Theory of Everything

Reading Dourado's argument made me think of an even bigger "problem": The housing sector's failure to innovate is not just a driver of more expensive housing; it also hinders the productivity growth of the economy as a whole. A couple of years ago, Sam Bowman, John Myers, and Ben Southwood elaborated on this idea in The Housing Theory of Everything. A few highlights:

• Affordability: Over the past 50 years, most durable goods became cheaper while housing became more expensive. Despite rising salaries and cheaper goods, "both parents in a family now typically have to work to afford a decent family house in a major city, and people have had to move farther and farther outside city centers to find somewhere they can afford to live, spending more time and money on commuting to and from work."
• Productivity: "When housing is scarce in high-productivity areas, some people are priced out of the area altogether, so they can’t move within range of better jobs." In other words, the housing market prevents people from living where their skills can be put to their best use — and forces them to spend more time and money commuting instead of doing productive work (including childcare).  
• Innovation: "Nearly all innovation happens, and has always happened, in cities. Just as cities have vast labor pools that make it easier for workers to find jobs that match their skills, they also allow innovators to collaborate to come up with new ways of doing things." And so, "by limiting the number of people who can go to live in places like the Bay Area" or New York, we may be "missing out on the new ideas that drive society forward."
• Inequality: "Constraints on supply have made houses into scarce assets, more like bonds, fine art or precious metals than durable goods like refrigerators or cars. This only feels normal because we’re used to it, and does not happen in places where developers can easily add more homes to an area, such as Tokyo, Seoul, or New York City before the 1920s."
  
  The limited supply of new housing " means improvements in people’s aggregate incomes often partially go to landowners, since people bid up the price of housing with some of their increased income." Our current tax system fails to redistribute such gains, which means a large share of the fruits of better productivity is "absorbed" into housing prices and only benefit homeowners and landlords. (read more about alternative land taxes here)

There are many benefits to building more housing, faster, and cheaper. But I agree with Eli Dourado that technology — even AI — is not likely to directly impact that sector anytime soon. The housing sector is held back by local politics, historical path dependencies, and human behaviors that are difficult to change and untangle.

And yet, there is hope.

Step Into My Office

The Housing Theory of Everything is about, well, housing. But it often mentions the words "work" or "workers," The main problems it describes are as much a function of where we work as they are of where we live. The housing sector hinders productivity and innovation because it limits people's ability to work where their skills can be put to their best use. Focusing only on housing, we miss an important corollary:  productivity and innovation are constrained by where people work, not just by where people live.

The authors assume that offices and homes are co-dependent complements: houses are only valuable if they're within commuting distance to offices and vice versa; and productivity can increase when the supply of both office and housing goes up.

These are fair assumptions. But we now know that they are no longer valid, or at least not always true. Many jobs can be done remotely. And hiring anywhere can often beat cities in their own game: it enables people to be matched with their higher and best use. It facilitates collaboration at an intensity and scale that even the largest cities cannot offer. In addition, many urban offices might soon be converted into housing — meaning that fewer offices can result in more housing.

It follows that changes to how we use (or don't use) offices can open up plenty of new housing supply by enabling people to access employment opportunities outside cities and freeing up more land for housing within cities.

The revolution in the office sector can ease many of the bottlenecks in the housing sector. And once these bottlenecks are reduced, we can enjoy more productivity, more innovation, and a better distribution of the fruits of both. Existing offices can increase productivity by remaining empty!

It also follows that if AI and the internet can ultimately enable more people to work from anywhere, they will end up affecting the housing sector. Indirectly, but dramatically.