Here’s why counting houses is hard

We literally have no idea how many houses could be in there. Image: Getty.

We may be getting better at building more houses but unfortunately we’re not very good at counting them.

In August, the housing minister was citing the latest DCLG New Build statistics as proof that the country is building again. Completions across England had apparently reached 153,000 in the year to June 2017“the highest level since 2008”. On this basis, the minister may be pleasantly surprised and slightly confused when he reads the DCLG’s Net Supply release in November and finds out that housebuilding completions had already reached 155,000 in 2014-15, and are actually much higher.

The housing minister can perhaps be forgiven some excitement over the first release of housebuilding statistics during his tenure. Based on the average tenure of previous housing ministers, he’s probably only got another three or four to look forward to. However, despite some allowance for over-excitement, it is irresponsible for the housing minister to be quoting the New Build statistics as absolute measures of housebuilding as they under-count the number of new homes actually being built.

It is particularly irresponsible because DCLG are well aware that there are issues with the New Build statistics. In the introduction to their New Build statistical release they suggest the New Build figures should only be “regarded as a leading indicator of overall housing supply”, and instead the Net Supply release “is the primary and most comprehensive measure of housing supply”.

The scale of the under-count is apparent when comparing the New Build data to the more comprehensive Net Supply release. While the Net Supply release includes conversions, changes of use, and demolitions to calculate the net change in dwellings, it also includes a more comprehensive measure of housebuilding.

The latest available Net Supply data for 2015-16 recorded 164,000 housebuilding completions across England compared to only 140,000 completions recorded in the New Build data. That suggests the New Build release is currently missing around 15 per cent of the housebuilding market.

Beyond the widespread confusion created by the publication of different housebuilding numbers, this issue has important consequences for policy makers. Our failure to accurately measure housebuilding and our limited understanding of who is doing the building make it very difficult to accurately assess the success or failure of existing policies and identify new ones that could increase new supply.

The exact reasons for the under-count are not confirmed but it appears to be linked to the falling market share of the largest provider of warranties on new homes. The National Home Building Council (NHBC) provides a substantial share of the data used to create the New Build statistics, and it’s been widely assumed that they have a market share of around 80 per cent. Based on an assumed market share, the NHBC data is grossed up to provide a measure for the whole market alongside other sources of building control inspection data.

However, recent years have seen a broader range of groups delivering new homes. Volume housebuilders still deliver the majority of new homes but there has been an increase in activity by SME housebuilders, high-density luxury developers, build-to-rent investors, and housing associations. For some of the firms and organisations in these groups, an NHBC warranty may be too expensive or not attractive compared to the alternatives. NHBC’s market share has probably fallen over this period.

A fall in NHBC’s market share is apparently confirmed by the request for a review of its market undertakings from the Competition & Markets Authority (CMA). Although most of the market share data published by the NHBC and the CMA in the review is confidential, there is an interesting finding in the CMA’s provisional decision (paragraph 4.32). Using new home data from nine warranty providers including NHBC, the CMA estimated the NHBC market share at around 70 per cent.

If, instead of grossing up the NHBC data by 80 per cent market share, we use 70 per cent then we would expect the DCLG New Supply data to be around 14 per cent higher (0.8/0.7). That difference would account for nearly all of the shortfall in the New Build completions when compared to the Net Supply housebuilding data. While there may be other factors causing the under-count, it would appear that this market share issue is the most significant factor.


It would be great if we had an accurate and regularly updated measure of housebuilding, but it turns out that counting houses is actually quite difficult. The Net Supply data is far from perfect, and it’s only released once a year with a substantial delay but it’s the best we currently have.

Meanwhile, in Ireland they’ve had the opposite problem, with an over-count of new homes. Official completions data uses electricity connections – but it turned out that the actual number of new build completions between 2011 and 2015 was 42 per cent lower than the official figures due to a large number of re-connections.

Until we see a substantial re-working of the DCLG’s New Build statistics, it appears the best option is to assess the full range of available indicators that cover both housebuilding and total supply. However, perhaps the biggest frustration is that DCLG are aware of the issues with the New Build statistics yet we still see quarterly political point scoring based on these flawed data. Given the complexities of the housing market, it is only once we move past this short-term politicking that we have any hope of solving the crisis.

Neal Hudson is an independent housing analyst, who tweets as @resi_analyst. This article originally appeared on his blog.

 
 
 
 

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.