Melbourne shows how Airbnb is reshaping our cities

The sharing economy at work. Image: Getty.

Infrastructure in our cities – let’s call it the hardware – remains much the same as ever. But the software – the way we use it – is transforming rapidly. One piece of that software, Airbnb, is dramatically reshaping the world’s cities.

The digital platform allows citizens to find and rent short-term accommodation from other citizens. Airbnb has the potential to rupture the traditional spatial relationship between tourist and local, making our cities more vibrant and diverse places to live in and to visit.

The question is: what opportunities and dangers does the platform present? What are the implications of repurposing existing residential infrastructure for short-term accommodation? What happens when the global “sharing economy” meets a city’s suburbs?

Lessons from an early adopter

Melbourne was an early adopter of Airbnb. It is also one of the top 10 cities for global travellers on Airbnb. What insights can be gathered from its experience?

According to Airbnb, three-quarters of listings worldwide are outside major hotel districts. Airbnb has three types of property listings: entire homes, private rooms and shared rooms.

Concentration of Airbnb entire-house rentals in Melbourne. Image: Jacqui Alexander & Tom Morgan/author provided.

Entire homes make up over half the total number of Melbourne’s metropolitan listings. Data collected in January 2016 reveals that their distribution is relatively consistent with that of hotels and licensed accommodation, which exist in large concentrations in the CBD and inner city.

Many hosts who list entire homes lease or sublet when they go away. In Australia, tenants require permission from their landlord to sublet, so there is little risk for the landlord if they follow due process. But analysis by website Inside Airbnb indicates that about 75 per cent of entire-house listings in Melbourne are available for over 90 days per year.

Hosts with multiple properties manage about a third of all the entire-house listings in Melbourne. These operators hold an average of three properties, but some have dozens. Through Airbnb, these brokers are turning existing housing infrastructure into informal, distributed hotels while saving on capital costs, overheads and wages.

Globally, the Airbnb phenomenon has been blamed for driving up rents, accelerating gentrification and displacing local residents by reducing available housing stock.

In Melbourne, the boom in high-density development in the CBD has resulted in an excess of homogeneous apartment dwellings. Bedrooms without natural light, as well as insufficient floor area, outdoor space and storage space, characterise many of these developments, rendering them effectively unlivable for long-term residents. But these properties are attractive to itinerant tenants seeking affordable inner city accommodation.

Concentration of Airbnb shared-room rentals in Melbourne. Image: Jacqui Alexander & Tom Morgan/author provided.

Shared rooms in Melbourne constitute only about 2 per cent of all listings, but they are almost exclusively confined to the CBD. Box Hill (14km east of Melbourne), and Maidstone/Braybrook (8km west of Melbourne) are secondary outlying hotspots. The majority of CBD listings are around new apartment towers near Southern Cross Station (at the western end of the CBD) and RMIT University.


A number of already small two-bedroom apartments in the Neo200, Upper West Side and QV1 towers are operating as gendered dormitories. These often sleep eight, with four to a room. Overloading these apartments creates potential fire-safety and hygiene-compliance issues.

Short-term letting via sites like Airbnb allows investors to earn up to three times the amount they’d receive in rent (the average cost to rent an entire home is $189 per night). Travellers benefit from competitive accommodation rates, cooking facilities, convenient locations and access to private pools and gymnasiums intended for residents.

Airbnb acknowledges that professional hosts with multiple listings are exploiting the so-called sharing economy, but has not yet taken steps to regulate this. Governments would do well to implement the long-awaited and much-needed minimum design standards for apartments to curb the construction of developments in the city that fail to cater for residents or which are purpose-built for the Airbnb market (a few local examples are already emerging).

Beyond the obvious need to protect the amenity of citizens, protection of the liveliness and heterogeneity of the city is essential to maintain the kind of “authentic” experience that appeals to Airbnb users in the first place. Melbourne is beginning to follow the trajectory of international cities like London where the investor market, fuelled by capital gains tax exemptions, has pushed residents further and further out. Dispersing the concentration of entire-house and private-room rental is vital.

Concentration of Airbnb private room rentals in Melbourne. Image: Jacqui Alexander & Tom Morgan/author provided.

More promising is the dispersed pattern of private rooms in Melbourne. These represent around 45 per cent of listings across the city. While private rooms are still concentrated in and around the CBD, diffuse listings across Melbourne’s middle-ring suburbs realise Airbnb’s ambition to enable access to the everyday spaces of cities.

This pattern makes sense given the mismatch between Australian house sizes, which remain the largest in the world, and changing household structures – most significantly, the decline of the nuclear family. An increase in housing diversity in the middle-ring suburbs is likely to facilitate more entire-house listings in these areas in the future.

We are also seeing evidence of Airbnb driving housing diversity. Annexed and granny-flat configurations are commonly listed in suburbs close to the Melbourne CBD like Brunswick and Caulfield. Loose-fit arrangements like these provide more flexibility to cater to both residents and visitors, and the by-product is slow but genuine “bottom-up” densification.

Government incentives for this kind of small-scale development would help to make this a viable (and, for many, welcome) alternative to densification through high-rise apartment development.

In 2015, Tourism Victoria entered into an agreement with Airbnb Melbourne to promote buzzing inner-city suburbs Fitzroy and St Kilda as “sharing economy” hotspots. But the cost of renting in these suburbs is already exorbitant. Fitzroy was named the second-most-expensive suburb in Melbourne for apartment rental in 2015.

Instead, policymakers could encourage disruption in the suburbs that would benefit both sides.

What can be done to capture local benefits?

Airbnb claims that tourists who use the platform “stay longer and spend more”. Through taxation and additional revenue from the sharing economy, governments could fund more extensive and efficient transport networks to service both locals and visitors. Extending transport infrastructure would support the intensification of distributed neighbourhoods and maximise intermingling between tourists and locals.

Airbnb rentals in Perth. Image: Jacqui Alexander/author provided.

Bottom-up densification could also be a way forward for Perth. The distribution of Airbnb accommodation towards Perth’s coastal suburbs highlights potential in this space: here, tourism-specific and local infrastructure can converge. This is an exciting prospect for a state that positions itself as a unique travel destination.

Airbnb emerges from the same cultural tendency as the pop-up shop and interim-use place activation. Built environment professionals must recognise it as an urban issue and lead with a framework for targeted, productive disruption.

Airbnb can increase the density of people within existing building stock, while dispersing the positive effects of the tourist economy. This requires more imagination from planners and designers, who first and foremost must consider the interests of individual citizens, whether they are renters or home owners.

Can Airbnb be a part of the solution of increasing urban infill without compromising a minimum standard of living?The Conversation

Jacqui Alexander is lecturer in architecture at Monash University.

This article was originally published on The Conversation. Read the original article.

 
 
 
 

Just like teenagers, self-driving cars need practice to really learn to drive

A self-driving car, of unknown level of education. Image: Grendelkhan/Flickr/creative commons.

What do self-driving cars and teenage drivers have in common?

Experience. Or, more accurately, a lack of experience.

Teenage drivers – novice drivers of any age, actually – begin with little knowledge of how to actually operate a car’s controls, and how to handle various quirks of the rules of the road. In North America, their first step in learning typically consists of fundamental instruction conveyed by a teacher. With classroom education, novice drivers are, in effect, programmed with knowledge of traffic laws and other basics. They then learn to operate a motor vehicle by applying that programming and progressively encountering a vast range of possibilities on actual roadways. Along the way, feedback they receive – from others in the vehicle as well as the actual experience of driving – helps them determine how best to react and function safely.

The same is true for autonomous vehicles. They are first programmed with basic knowledge. Red means stop; green means go, and so on. Then, through a form of artificial intelligence known as machine learning, self-driving autos draw from both accumulated experiences and continual feedback to detect patterns, adapt to circumstances, make decisions and improve performance.

For both humans and machines, more driving will ideally lead to better driving. And in each case, establishing mastery takes a long time. Especially as each learns to address the unique situations that are hard to anticipate without experience – a falling tree, a flash flood, a ball bouncing into the street, or some other sudden event. Testing, in both controlled and actual environments, is critical to building know-how. The more miles that driverless cars travel, the more quickly their safety improves. And improved safety performance will influence public acceptance of self-driving car deployment – an area in which I specialise.

Starting with basic skills

Experience, of course, must be built upon a foundation of rudimentary abilities – starting with vision. Meeting that essential requirement is straightforward for most humans, even those who may require the aid of glasses or contact lenses. For driverless cars, however, the ability to see is an immensely complex process involving multiple sensors and other technological elements:

  • radar, which uses radio waves to measure distances between the car and obstacles around it;
  • LIDAR, which uses laser sensors to build a 360-degree image of the car’s surroundings;
  • cameras, to detect people, lights, signs and other objects;
  • satellites, to enable GPS, global positioning systems that can pinpoint locations;
  • digital maps, which help to determine and modify routes the car will take;
  • a computer, which processes all the information, recognising objects, analysing the driving situation and determining actions based on what the car sees.

How a driverless car ‘sees’ the road.

All of these elements work together to help the car know where it is at all times, and where everything else is in relation to it. Despite the precision of these systems, however, they’re not perfect. The computer can know which pictures and sensory inputs deserve its attention, and how to correctly respond, but experience only comes from traveling a lot of miles.

The learning that is occurring by autonomous cars currently being tested on public roads feeds back into central systems that make all of a company’s cars better drivers. But even adding up all the on-road miles currently being driven by all autonomous vehicles in the U.S. doesn’t get close to the number of miles driven by humans every single day.

Dangerous after dark

Seeing at night is more challenging than during the daytime – for self-driving cars as well as for human drivers. Contrast is reduced in dark conditions, and objects – whether animate or inanimate – are more difficult to distinguish from their surroundings. In that regard, a human’s eyes and a driverless car’s cameras suffer the same impairment – unlike radar and LIDAR, which don’t need sunlight, streetlights or other lighting.

This was a factor in March in Arizona, when a pedestrian pushing her bicycle across the street at night was struck and killed by a self-driving Uber vehicle. Emergency braking, disabled at the time of the crash, was one issue. The car’s sensors were another issue, having identified the pedestrian as a vehicle first, and then as a bicycle. That’s an important distinction, because a self-driving car’s judgments and actions rely upon accurate identifications. For instance, it would expect another vehicle to move more quickly out of its path than a person walking.


Try and try again

To become better drivers, self-driving cars need not only more and better technological tools, but also something far more fundamental: practice. Just like human drivers, robot drivers won’t get better at dealing with darkness, fog and slippery road conditions without experience.

Testing on controlled roads is a first step to broad deployment of driverless vehicles on public streets. The Texas Automated Vehicle Proving Grounds Partnership, involving the Texas A&M Transportation Institute, University of Texas at Austin, and Southwest Research Institute in San Antonio, Texas, operates a group of closed-course test sites.

Self-driving cars also need to experience real-world conditions, so the Partnership includes seven urban regions in Texas where equipment can be tested on public roads. And, in a separate venture in July, self-driving startup Drive.ai began testing its own vehicles on limited routes in Frisco, north of Dallas.

These testing efforts are essential to ensuring that self-driving technologies are as foolproof as possible before their widespread introduction on public roadways. In other words, the technology needs time to learn. Think of it as driver education for driverless cars.

People learn by doing, and they learn best by doing repeatedly. Whether the pursuit involves a musical instrument, an athletic activity or operating a motor vehicle, individuals build proficiency through practice.

The ConversationSelf-driving cars, as researchers are finding, are no different from teens who need to build up experience before becoming reliably safe drivers. But at least the cars won’t have to learn every single thing for themselves – instead, they’ll talk to each other and share a pool of experience.

Johanna Zmud, Senior Research Scientist, Texas A&M Transportation Institute, Texas A&M University .

This article was originally published on The Conversation. Read the original article.