The Omnichannel Trap

I’ve been reading hundreds of retailer earnings transcripts over the last year, and I’m noticing some recurring feedback loops and circular logic in their current business strategies.

Here’s a simple and relatively benign example, not specific to retailers, that you may recognize:

  1. You promote your loyalty program (or app, or store card) with benefits that make it a no brainer for frequent or high volume customers to sign up
  2. Now you say “we need to focus even more resources on our Elite loyalty members because they drive 60% of our sales / spend 30% more on average than non-Elites / visit 40% more often…”

Of course they do, but which way does that causation run? Is it that your loyalty members are your most valuable customers, or that you forced your most valuable customers to become loyalty members? If the goal was just to get more data on them, how are you using that data to drive your bottom line? Are they spending more than they already did (not just more than other members) or are you retaining them at higher rates, or what?

I always assume there’s a lot more of that number-crunching going on behind the scenes, but after a while you do start to wonder. If they have meaningful stats, why are they giving out these meaningless ones instead? Loyalty programs have been around for centuries, but as they get more complex, data driven and expensive, does anyone really know how well they’re still working?

In any case, here’s another feedback loop that’s a little more complicated:

  1. You redirect capital away from your stores to build out your digital storefront, leading to
  2. rapid online sales growth on a low base, while the stores stagnate;
  3. the online channel is now your main growth driver, so you push it even more and start closing more underperforming stores…
  4. but then that online growth slows down sooner than expected, at a point where it’s still only 10-30% of your total sales and still a drag on margins…
  5. so you turn back to your leaner, omnichannel-enabled store fleet, where you’ve finally cleared out inventory and become “less promotional”… 
  6. but traffic is still dropping there too, and there’s nothing in your “omnichannel” toolkit to actually get more people in the door without siphoning them from your already-slowing online business.

You’ve launched this whole new channel and cleaned up the old one, you’ve done everything you’re supposed to, but your total sales are still falling. What happened?

The answer may not be so different from the loyalty program example above: you’ve been pushing your existing customers around without acquiring enough new ones. That early online growth was mostly from them, and even the first cohort of new customers that you “acquired” online were probably those who already knew something about your brand and were positively disposed to it. It only gets harder from there.

Meanwhile, your relationship with all these customers has gotten a lot more intrusive. Rather than just mailing catalogs and sale fliers, you’re flooding them with emails and nagging them at every opportunity to do more than just buy — you need them to download your app, follow you on social media, review what they bought, rate someone else’s review…

And for what? None of this is cheap for you either. What is it you actually want them to do? First it was to switch from the store to the website, then the app, then maybe you closed their store, now you’re raising their free shipping minimum to nudge them to pick up their online order in a store that’s further away… you’re “optimizing” their behavior to fit your shifting cost structure, but it’s probably driving some of them away, and in any case it’s not really growing your overall business.

Where did you go wrong? Your landlords might say: “well, you can’t make money online-only after shipping and returns, and customers also want a nice ‘experiential’ store where they can touch and feel the merchandise.” They’ve got a few of their own “omnichannel” stats that also make you wonder about the direction of causality: “Did you know that online sales drop 20% in every zip code where you close a store? That the average customer who returns an online purchase in store spends another 30%?”

And your digital marketing agency might answer “look, your stores are in dying malls anyway, your competitors are all online, they’re all doing this stuff. It may not be easy but you have to go where your customers are, there’s no turning back the clock. We just need to find the right Instagram influencer this time.”

They could both be right! For example, you may have conflated a format problem with a channel problem, and you’d have been better off moving those mall stores to more urban and open-air settings rather than closing them outright.

But let’s set that debate aside. You’ve heard it all before anyway, and what’s done is done. What do you do now?

This is where it gets interesting for landlords and creditors, and for landlords especially. Because a retailer in this position can start to look at their remaining leases and even their namesake brand as a wasting asset, where they can harvest customer data and dwindling traffic to build new brands — either online only, or with an entirely new store base.

In some cases it’s still better to give them a rent cut to stay open and avoid more vacancy even when you know they’re closing at the end of the lease — but where landlords have a choice, it’s increasingly important to distinguish between tenants in this “trap” and those who can really still turn around their core business.

The Myth of the Open Plan Office

Is there anyone who still believes that open plan office layouts increase productivity? Article after article, study after study has made it clear that the massive distraction they create outweighs any benefits from “collaboration” or “spontaneous interaction.” It’s almost a running joke at this point in the tech world:

And yet some of the smartest companies in the world keep building them, over the objections of their own employees. What’s going on?

The obvious answer is cost: office space is expensive, so companies are always trying to reduce the space per employee, and the collaboration/creativity/community stuff is just spin. But even that story only takes you so far, because at some point the lower productivity hurts the company as much as (or more than!) the employee. It’s crazy for a big tech company to save $10k/year on rent by seating a $300k/year programmer on a trading desk where they need to wear headphones all day.

So on some level, there must be people at the top who still believe the myth. In his recent book Deep Work, Cal Newport dissects two of the post-war stories of highly productive collaboration that have driven it: Building 20 at MIT, and Bell Labs in New Jersey.

Neither building offered anything resembling a modern open office plan. They were instead constructed using the standard layout of private offices connected to shared hallways. Their creative mojo had more to do with the fact that these offices shared a small number of long connecting spaces — forcing researchers to interact whenever they needed to travel from one location to another.

Originally, open plan offices were about surveillance as much as cost:

The first modern office was the Larkin Administration Building, which opened in 1906 in New York. Designed by Frank Lloyd Wright, it was based on an open-plan factory, with a giant atrium and very few walls.

“He was interested in creating big cathedral spaces for aesthetic reasons,” says Jeremy Myerson, professor of design at the Royal College of Art.

“But actually it was very useful for managers because no walls meant they could supervise workers in this open space and keep an eye on them. It was all about control.”

Every time I’m in a WeWork or similar coworking office, they seem to be getting more and more subdivided: they’re discovering that members will almost always pay a premium for a tiny glass-walled interior closet compared to a seat in an open bullpen. Which shows you that the closer the decision making is to the actual occupants, the higher the premium becomes on privacy. And if you visit even the newest and best-amenitized corporate open plan offices, you’ll find huge waiting lists for the conference rooms, people running in and out to take phone calls, and those giant headphones everywhere. (A friend at one of these told me their colleagues were starting to replace their Bose noise-cancelling headphones with the industrial ones worn by lumberjacks.)

So many of the changes in the typical office over the last few decades are about trading easy-to-measure cost reductions for diffuse, hard-to-measure costs in time and attention; think of the way that software has eliminated so much admin staffing by making everyone into their own assistant, spending hours logging expenses, contacts and meetings on a dozen different websites. Each of these trade-offs individually is so small and hard to measure (sometimes there are real efficiency gains involved) that you can see why they keep happening, even if they’re collectively a mistake. But this one is just too big to be explained that way.

This is especially true at those big tech companies with more cash than they know what to do with. In these cases an open plan office may be a kind of costly signaling device (like a peacock’s tail) or a way to keep employees distracted from the fine print on their stock options. But more likely it’s just another irrational management trend in an industry that prides itself on rationality. Hopefully we’re finally getting close to a tipping point.

The Problem with Prediction Markets

Prediction markets — websites where you can bet on politics — are rising in prominence. This election season, their “odds” are often quoted in news stories alongside poll averages.

Of course, these odds aren’t worth much if there aren’t many people betting, and there aren’t. This article by Philip Wallach is an excellent survey of the landscape, and as he points out, the US election has 50 times more betting volume at Betfair (a UK sports book) than at PredictIt, the largest current prediction market.

Social scientists have been infatuated with the idea of prediction markets for decades, but gambling is first and foremost a habit, and the problem with these markets is that there’s not enough to bet on to get people in the habit of coming back. The political world simply doesn’t offer frequent enough events that enough people care about.

These two variables — frequency and interest — can explain the popularity of sports betting and day trading, which are the two most widespread forms of gambling that prediction markets could emulate. You probably care a lot about how your favorite team or fantasy players perform, and they play often; meanwhile you may not care as much where the price of gold or Apple stock is in an hour, but the feedback is even more frequent (and traders are highly vulnerable to conspiracy theories about market manipulation, because they want to care).

Meanwhile, you may care who wins the election, but if it’s weeks or months away, what’s going to keep you coming back to the site? Suppose you came to PredictIt in June convinced that Clinton would win, saw that she was “trading” at 66% (1/2 or -200) odds, and bet $100 — so you’d win about $50 if she won the election. Now you come back during her post-convention poll bounce and she’s at 75%; what do you do? You could cash out with a $15 profit, but then what? You could have made $50. Or you could add to your bet at worse odds. Neither is particularly tempting. My guess is you’ll do nothing, and pretty soon you’ll stop visiting the site as frequently.

One way to describe that problem is that the site is trying to get you to think about sports-like betting in a financial market-like framework. But even compared to sports, the idea of waiting months for your bet to settle is frustrating. Most of the betting on sports happens much closer to the event. (According to this chart, 50% of the money bet on an average soccer match is wagered within the preceding two days.)

PredictIt has gotten a lot of things right in terms of basic market structure and fees that previous prediction markets like Intrade got wrong, but I’m not sure how they can solve this basic problem.

One solution would be to mix in political contracts with sports, like they do at Betfair, or with financial markets, as Intrade tried to do at one point. (“Will the Dow close above x?”) But regulators in the US are not about to allow either of those things. Even these political markets require an exemption from the CFTC to operate.

What they’re doing instead at PredictIt is creating lots of arbitrary questions to bet on, so there’s always something new and always something settling soon — like the exact poll average on some random date:

capture

There’s not much trading volume on these questions, because, not to put too fine a point on it, no one cares about them. All you’ve got are the would-be bookies trading with each other.

Let’s go back to our Clinton example for a moment. If you were a perfect rational actor in an economics textbook, you’d go in both times with your exact estimate of her odds of winning, having mentally updated it for events like the convention, and judge the expected value or rate of return of your bet vs. alternative uses of your capital given what the market’s offering, so you’d wind up transacting more frequently before the event itself. You’d also think about the arbitrage opportunities created by closely related markets (will Clinton win, will the Democratic candidate win, will a female candidate win). But very few people really think that way, and for many it’s stressful and unpleasant to be forced to do so.

I’m reminded of another site called Stickk, also started by social scientists, where you can make “commitment contracts” with yourself to quit a bad habit, and attach monetary penalties: for example, you might commit to quit smoking by year end or pay the site $500. Tyler Cowen was skeptical:

I’ve long predicted this won’t work; one group of potential customers doesn’t really want to change, the other group is unwilling to give up control.  It’s not exaggerating to say that human nature is on the line here, and that if I am wrong this is probably the most important idea you will ever encounter.

Prediction markets are running into an analagous problem, and it’s a very tough problem. Even if the novelty of the idea gets people to try it, how do you get them to keep coming back?

Remember those two key factors: interest and frequency. If you wanted to play to interest, you’d let people bet on more non-political subjects. For some of these there are too many people with advance information (Oscar winners, reality show outcomes, who will die on Game of Thrones) and the market could end up spoiling the surprise. For others, like the outcome of high profile trials, there may be considerations of taste or privacy. But from a moral perspective, it’s probably a lot better than focusing on frequency, like lotteries or casinos, and trying to get people hooked on betting on the weather or traffic or some other quasi-random pattern.

I fear that those of us who want to see prediction markets succeed (and I do) have not been focused enough on the core problem: we’re asking people to bet on a subject that 90% of us only care about (in the US) every four years, if even then. Without changing that underlying proposition, I suspect that the current interest in prediction markets, under the klieg lights of a particularly insane election season, is as high as it will ever be.

Graphing Calculators and Competition

This two-year old Washington Post article describes an odd situation that seems to persist today: Texas Instruments is still selling their TI-84 graphing calculator for about $100, though it likely costs only $15-20 to manufacture and they haven’t updated the hardware in over a decade. Phones and tablets can now do all the same things, of course, but they’re not allowed on tests, and I guess you don’t want everyone’s phone out in class either.

At a time when startups are targeting every non-tech business for “disruption,” why has a tech product like this managed to hold on to such a lucrative monopoly? The article describes a cycle in which teachers require the TI models they’re familiar with, parents have to buy them whatever the price, and TI puts a bit of those excess profits back into teacher training and support to make sure they’re still the standard.

No question that’s a big advantage, but it doesn’t sound insurmountable. The competing model mentioned in the article, from Casio, is $80 and running a distant second in sales; they claim they can’t crack that “TI ecosystem,” but my guess is the price difference just isn’t big enough. Shouldn’t someone be able to do this for $40 or $50? Or create a $10 app that locks your phone/tablet in a way that can be monitored or later audited, to eliminate distractions or cheating? (Remember, the TI calculators aren’t perfect in that regard either, since they have persistent memory that can be used for games or cheat sheets, and even the firmware can apparently be overwritten.)

It also reminded me of the RPN calculators made by HP that are still used somewhat in finance, science and engineering. If you’re going to rip someone off, a bond trader is obviously a less sympathetic target than a ninth-grader, but in any case they seem to have maintained the same inflated pricing outside the K-12 education ecosystem.

My first thought was “this is perfect for a big crowdfunding campaign.” A graphing calculator seems like exactly the kind of old-timey hardware device that a small team could design and produce without any proprietary technology, and there’s a social/education angle to the story that should help the campaign spread online. But a quick search on Kickstarter and Indiegogo shows no such projects, and after a little more thought, it’s easy to see why not. Crowdsourced products are infamous for shipping late, and a calculator that doesn’t show up at the beginning of the semester is useless. At the same time, you couldn’t give yourself too much of a time cushion, since your target audience may not even be thinking about this before back-to-school season. And these customers — adolescents and their parents — will be hard to reach anyway, since they straddle the current demographic for Kickstarter and the social networks where the campaigns are promoted.

Why not a venture-backed device startup? It’s probably not a big enough opportunity. According to the article, about 1.6 million calculators are sold per year, and that number has been slowly shrinking as more and more classrooms get school-issued iPads or other new alternatives. It’s still a great business for TI when they’ve got 90% of the market at $100 apiece, but if even if you took half their market share at, say, a $40 price point, your margins would be much lower, you’d need to spend a ton on marketing and support to get there, and when you do, no one’s going to put a tech multiple on a shrinking business.

What about a more developed secondary market? The article doesn’t say what, if anything, TI has done to suppress sales of used models. They’re all over eBay, with almost 10,000 sold in the last couple months (though this may be the seasonal back-to-school peak). That’s still not much compared to 1.6 million total units, though. Here’s one online storefront that sells them refurbished for $83, and another for $84. Again, not the level of discount that would indicate real competition.

Does this represent a gap in the funding market, an opportunity for a bootstrapped lifestyle business or an investor like IndieVC that isn’t built around big exits? What’s the minimum scale for a project like this to pay off? In any case, I’m sure there are a few other examples of obsolete hardware that’s still making 50%+ profit margins, but this seems like a particularly egregious one.

Restaurant Rankings vs. Ratings

This idea seems so obvious to me that I’m sure someone has tried it, but I can’t find any good examples. In a nutshell, it’s an app for user ratings of restaurants that’s based on ranking them in categories, rather than rating them individually on a star or point scale.

So for example, you might have your personal lists of the best burgers, the best Italian food, the best steak, the best vegetarian restaurants, etc. You’d only rank categories where you’ve tried at least two restaurants, of course, and when you try a new one you could add it to your list. So, for example, my list for burgers in NYC might start as follows:

  1. Paul’s
  2. J.G. Melon
  3. Donovan’s
  4. Molly’s
  5. DuMont

Actually, what I’d really have is a ranking of the best burgers I’ve had anywhere, and we could still filter them down to NYC if necessary. But you get the idea.

Now, once we’ve got enough users, we can start aggregating those rankings to present a single ranked list in each category. And this has some major advantages over existing ratings sites:

  • It’s closer to how people already think about restaurants. Everyone has running debates with their friends about their top five burgers, BBQ, or whatever else. Everyone asks “is it better than ___?” No one asks “is it three stars or three and a half?” But sites like Yelp and TripAdvisor ask their users to convert those relative opinions into absolute ones, only to aggregate them back into relative lists (“10 Best Mexican Restaurants in Los Angeles”) — and a lot of useful information is lost in that process.
  • Relative ratings are more useful than absolute ones in this context, because they remove a lot of ambiguity. Is a three-star rating for a Mexican restaurant in New York really the same as a three-star rating for one in California, where the standard for Mexican food is higher? It probably depends on the user. But this way anyone who’s had Mexican food in both places is ranking every restaurant on the same list. (In fact, if you wanted to convert the relative rankings back into star ratings, you’d be more accurate than if users just entered star ratings to begin with — but again, why would you?)
  • It’s faster, more fun and more social than ratings — which means you’d get a different and broader pool of reviewers. Rankings feel like much more of a “curation” experience, more of an expression of your individual identity or tastes. And they still allow for a certain “power user” dynamic without allowing them to be quite as dominant.
  • It doesn’t emphasize rants and raves. Think about how many reviews on Yelp are motivated more by anger or affection for the establishment than any pure desire to inform or help the reader. It means that even with the power users, you only get their input on the best and worst restaurants — these reviews are not a very sensitive instrument for distinguishing one three-star restaurant from another.

This would also be a relatively easy project to get off the ground; you could start with a hundred restaurants in 5-10 categories in a single big city, and a relatively small user base. Even a couple hundred frequent restaurant-goers would provide meaningful data. Then you publish those lists on your site, spend a little money on advertising/promotion and see if anyone cares about them.

I think a lot of people would. There’s a certain appeal to the crowdsourcing and mystery-shopping aspects — which the old Zagat guides really tapped into — but the information would also be more reliable and actionable than anything else available. If a new Szechuan restaurant (my current obsession) opened in New York, I’d honestly be more interested in where it landed in this app’s ranking after a month or two — even if that meant only 5-10 people had visited and ranked it — than in a review in the NY Times or on some food blog.

The algorithm would take a little work, but it also doesn’t have to be as complicated with a small user base in a single city as it would be later — I imagine that cross-city comparisons and aging of reviews would both add some complexity down the road. But that would be a nice problem to have.

Is there a business model? The (alleged) Yelp approach of extorting restaurants to buy overpriced advertising so you’ll remove/underweight their bad reviews doesn’t really work. It would be pretty unpleasant for a restaurant to be ranked last in their category, but unless you’re actively trying to exploit that, you’d probably be showing just the top ten anyway in the aggregate lists — who cares about last place vs. next-to-last?

Could you charge users instead, making this an exclusive/insider thing where you need an existing user’s invitation to join, and pay a small monthly fee to participate? Maybe you could still display your own lists to non-members, but you couldn’t show them the aggregate rankings. (You might waive the fee for any month where someone ranks at least x new restaurants, though you don’t want to give people an incentive to lie and rank a restaurant they haven’t actually visited.)

If you wanted a larger user base, another option is for your ratings to be a loss leader for reservations (looks like Opentable has an affiliate program) or some other commission product, as they are at TripAdvisor and most online travel portals. Even as a completely free service that just plugs into major ad networks, you’d probably at least pay the bills while building up a valuable database.

Now, what else would it work for besides restaurants? It’s hard to think of anything, and maybe that’s why no one has tried this. The startup machine is very good at applying cookie-cutter business models to different categories (so for example, I’m sure there have been a few “Rotten Tomatoes for restaurants” attempts) but ratings are an area where no two categories really work the same way, and most of the other obvious ones have less of a clear advantage for rankings over ratings. There are too many variables on which to rate a hotel, and it would be hard to categorize them. Same with music, books and movies. And with consumer products, most people don’t try enough different ones to rank them; could you tell me your top five printers, cars or vacuum cleaners with the same confidence as your top five pizza places? Better to rely on professional reviews there.

By contrast, there’s one clear variable on which to rate restaurants — quality of the food — and 90% of them fall neatly into a type of cuisine. You don’t need 100% coverage either; if you leave out some fusion or vague “New American” restaurants because they don’t fit into a clear category, so what? There are still a million other review sites that are all over them. And while it’s true that people rate restaurants on other things like service, atmosphere, delivery time, etc, that’s often just as much of a bug as a feature, because sensitivity to these things varies widely and it corrupts the ratings. You could still have people enter text comments on these rankings, and excerpt them like Zagat reviews for the master lists — but they would be more like the brief comments on Instagram posts or FourSquare check-ins, and less like the long, performative stories that seem to rise to the top among Yelp reviews.

The main point is that people care about rankings much more viscerally than ratings. It’s why clickbait sites publish so many arbitrary ranked lists; they know they’ll get lots of comments and shares just from people arguing with them. And I guarantee you some NYC readers have not even made it this far because they were so disgusted by my top five burger choices above. But unlike most exercises in list-making, with restaurants there’s a lot of useful, structured information embedded there. Why not take advantage of it?

The Airbnb Building

By Jeangagnon (Own work) [CC BY-SA 3.0 (http://creativecommons.org/licenses/by-sa/3.0)], via Wikimedia Commons

There are really two separate businesses on Airbnb and other hosting platforms. Let’s call them “amateur” and “professional”:

  • Amateur: People renting out homes that they live in themselves at least part of the time (either a spare room while they’re home or the whole place while they’re not home)
  • Professional: People renting out homes that they never occupy themselves, for short periods rather than the standard monthly/annual lease

They service much of the same demand, but on the supply side they’ll be governed by different rules and economics in the long run. Let’s look past the current city-by-city political fights and even Airbnb itself (because these businesses will persist whatever happens to one particular company) and think about how the underlying rules and consumer behavior are likely to shake out.

There are two systems of rules in play here, which we might call public and private. The public rules are municipal laws and regulations. The private rules are a matter of contracts – leases, condo/HOA bylaws, insurance policies – and will start to draw more focus once the public rules have been sorted out.

In large US cities, it’s often the case that both types of hosting are technically illegal, but these laws are rarely and inconsistently enforced. Over the next few years, most large cities will settle on a new set of rules with more enforcement behind them, with San Francisco’s recent Prop F debate as one of the first major rounds in this process. I think a reasonable bet is that it shakes out as follows, with one or two cities passing comprehensive legislation and others copying and tweaking it:

  • “Amateur” hosting will be explicitly allowed, with
    • a limit on rental days per year, probably 30-90
    • new transaction taxes (though less than hotel/tourism taxes)
    • new safety requirements (again not as strict as hotels)
    • some type of public registry for hosts, to enforce the above
  • “Professional” hosting will be more or less disallowed, or at least regulated much more harshly

Now, it’s obvious that Airbnb, or any platform that combines both types of host, has an interest in blurring this distinction. Most of their hosts are amateurs, but a professional is worth far more in commissions, because they run higher occupancies and often list multiple units. So Airbnb does everything they can to highlight the more sympathetic amateurs, while pushing for regulation that’s friendly to the less sympathetic professionals.

Ultimately, most voters and public officials will see the difference, and at that point there’s no real public interest or powerful constituency to stand behind the professionals. And the hotel industry, which lobbies against both businesses, is also smart enough to know that the pros are a far greater threat to them than the amateurs and eventually exploit the same wedge.

We won’t argue about the fate of professional hosts, which is really a more interesting question for Airbnb investors than the rest of us. If I’m wrong and that business keeps growing, it’s really nothing new, just an update of the European-style “holiday flats” businesses that have been around since long before the internet.

What’s new, and much more of a social change, is the widespread adoption of “amateur” hosting, and what I want to talk about is how those private, contractual problems – the landlord/neighbor problems – will shake out once the regulatory groundwork has been laid.

These problems are particularly annoying for urban apartment dwellers, and will get a lot worse once those public registries are in place. After all, even if your city allows you up to sixty days of short-term sublets, your landlord can still tear up your lease if they catch you – or if you’re a condo or co-op owner, your neighbors can complain (not unreasonably) that they didn’t buy into a hotel and don’t want strangers around, and get the building rules changed.

So here’s my idea: an “Airbnb building” which explicitly allows short-term hosting and manages it through the building itself.

It could work for a rental or condo building. Either way, when you know you’ll be out of town for a night or more, you’d simply tell the doormen or staff, or enter it into a web calendar, and they’d list it on Airbnb for those nights. If someone books it, they’d arrange for the keys, cleaning and so on. And then you split the extra rent – so for a three night booking at $200/night (after Airbnb’s commission), the building would take in $600 and give you $300.

This may sound familiar — it’s more or less how condo hotels and many other vacation properties are managed. Although in those cases the usage is reversed; the owner is typically there only a few weeks a year, and most of the time the unit’s in the rental pool. In our example, the primary resident is still there most of the time.

This approach neatly solves the landlord/neighbor problem, because the landlord’s getting their cut and the neighbors all know what they signed up for. Most people are still not comfortable renting out their homes to strangers, but you’d expect the people who are to self-select into buildings like this as soon as they start to become available. A typical rental building turns over as much as half of its tenant base every year, so it wouldn’t take long to convert all the leases to allow this; with a condo you might have to sell it that way from construction.

There are obvious time efficiencies and economies of scale in having the building management – who already have photos, floorplans, spare keys, and so on – manage the whole process, as opposed to individual hosts.

And as a traveler, I would hugely prefer to stay in a building like this. I wouldn’t have to worry about whether the neighbors and staff know I’m a paid guest, and the level of trust is far higher. Right now I’d probably never stay somewhere without previous positive ratings, but if I was booking with the building rather than an individual, then just a few positive ratings for any units in the building would make me a lot more willing.

Finally, what if your half of the Airbnb rent was passed on as a reduction or partial rebate of your base rent or condo maintenance fees, rather than additional taxable income? That’s how many amateur hosts think of it already, and the tax savings could be substantial.

It doesn’t solve the insurance problems (liability for theft, damages, injury, etc), and I don’t know enough about that business to speculate, but more professional and centralized management certainly can’t hurt in sorting those out.

Why hasn’t Airbnb set up a building like this themselves, as a kind of prototype to show how well their concept can work? Maybe they don’t want large property management companies to get any ideas. After all, they might get tired of paying those high commissions and just start their own website. In a way, Airbnb benefits from having its hosts operate in an uncomfortable gray area with their landlords and neighbors, because it prevents them from working together openly to gain negotiating power with the platform.

I get the feeling that some newer condo buildings with a high proportion of investment buyers are drifting towards my model already, on the professional side; maybe those buyers had initially planned to rent out their units in standard one-year terms, but they’re realizing they can make much more by listing them on a short-term rental platform and tipping the staff enough to look the other way.

But again, this isn’t meant to be an argument for or against Airbnb, HomeAway or any other particular platform. It’s more about starting from the bottom up: what do hosts and guests want, and what systems and structures can address that in the most efficient way? In the end, the companies that adapt themselves to those structures will succeed. The model I’m describing certainly doesn’t work everywhere, but for serviced buildings in large urban markets (which are already a substantial share of inventory on these sites) it seems like a much better approach. I wonder who’ll be the first to try it.