For years, consumers have been promised that their homes will be connected and smart, integrating the latest technology to optimize and control lighting, heating, energy consumption, electronic devices, and security features.
However, the future has come a little slower than expected. By the end of 2017, it’s estimated that only 16.3% of Americans will live in a smart home, though this percentage will increase to 35.6% by 2021.
Examining the Smart Home Market
Today’s infographic comes from Insurance Quotes, and it helps to give an overview of the current market as well as the reasons for hesitation in the switch to smart homes.
The infographic also provides a future outlook, including the impending movement to “autonomous” smart homes.
In 2016, smart systems were installed in about 45% of all homes in the U.S. that got renovated.
However, they are far from ubiquitous yet – many consumers still have concerns that are holding the market back from reaching its full potential.
The largest hindrance to smart homes for now is cost, which is cited by 42% of consumers as an obstacle.
However, there is also evidence that a fear of devices being hacked is also a challenge for many wanting to adopt the technology – in fact, 17% prospective buyers cite privacy and security concerns as a top hindrance. Further, about 10% of consumers have already had smart home devices hacked, and 87% of them had to shell out money to solve the issue.
Paradoxically, even though technologically superior security systems are a top reason that homeowners want to have smarter homes in the first place, the vast majority of IT experts say that IoT apps such as those used at home are far harder to secure than regular mobile apps.
Autonomous Smart Homes
After smart homes, the next logical step is an autonomous smart home that can learn based on your habits and behaviors. Such a home would recognize you and other family members, adapting things like temperature, lighting, or recommendations to you automatically based on your lifestyle and activities.
For this to work – everything would need to be truly connected: your mattress would assess how you sleep, your alarm would connect to your coffee maker, and the morning lighting would be shifted to match your evolving preferences.
While there are many uncertainties about what an autonomous smart home would mean, the inevitability of their rise is clear.
Form and Function: Visualizing the Shape of Cities and Economies
Economies create distinct spatial patterns. This week’s chart visualizes the relationships businesses and industry imprint on the urban environment.
Visualizing the Shape of Cities and Economies
The Industrial Revolution changed the form and function of cities. New patterns of work resulted in massive wealth and distinct advantages for certain regions. Urbanization emerged as a defining characteristic of this age.
During the latter part of the Industrial Revolution, Cambridge School economist Alfred Marshall looked at a particular question: why did certain industries concentrate in specific places?
Marshall argued that the local concentration of industry created powerful economies promoting technical dynamism and innovation.
This Chart of the Week highlights the spatial patterns and business relationships created at the urban scale. Marshall’s insights from the past help us understand present-day tech and media economies and the massive growth of urban regions.
The Logic of Concentration
Marshall observed that industrial concentration led to long-term tendencies such as increasing returns on capital and compounding regional advantages.
The heart of this observation is that knowledge resides within the companies that make up a particular industry. Over time, these companies can accumulate even more information and direct the flow of new and innovative ideas. This creates local specialization and increasing profits, while also concentrating success, knowledge, and wealth into one key locale.
He defined this pattern as a Marshallian Industrial District.
An Evolving Landscape: Four Patterns
Marshall’s work would later influence the work of Ann Markusen, who created a typology of three additional industrial patterns. The patterns identify what makes a city attractive or repellent to income-generating activities.
|Marshallian Industrial District||This is a clustering of firms in a similar industry, operating within a certain geographic area.||Social media marketing companies in San Francisco|
|Satellite Platform District||A set of unconnected branches with links beyond regional boundaries, each part of its own globally oriented supply chain.||Suburban neighborhoods|
|Hub and Spoke District||An industrial sector with suppliers clustering around one, or several, dominant firms.||Airplane manufacturer Boeing and the region of Seattle.|
|State-anchored District||Industrial activities are anchored to a region by a public or non-profit entity, such as a military base, a university, or a concentration of public laboratories or government offices.||Madison, WI and Columbus, OH are examples of university towns, as are many cities with large defense installations such as Pearl Harbor in Hawaii.|
There are both benefits and problems—called “externalities”—associated with the spatial agglomeration of physical capital, companies, consumers, and workers:
Clusters for a Digital Age
In the past, the physical constraints of an area defined the structure of cities. Now that so many companies are free from the shackles of producing physical goods, does geography still matter?
Researcher Marlen Komorowski re-examined the concept of clustering with this question in mind. Here are five types of media clusters identified in her research.
|The Creative Region||A metropolitan region that provides advantages due to readily available infrastructures and institutions, and encourages the development of face-to-face interaction and collaboration networks.||Berlin, Singapore, Amsterdam|
|The Giant Anchor||A location defined by the activities of one or several large media institutions, which attract complementary firms to agglomerate. Similar to the hub-and-spoke cluster model.||Seattle, (Microsoft, Amazon), and Cambridge (Harvard, MIT)|
|The Specialized Area||A media cluster that is located either in a neighborhood within a big metropolitan area or in a small urbanized area. The Specialized Area is marked by a readily available, large pool of employees from a specialized field.||Soho (London), Silicon Valley|
|The Attracting Enabler||Determined by the location of certain facilities or resources that can be shared that enable media activities. Movie studios are a prime example.||Los Angeles, Vancouver|
|The Real Estate||This type of cluster is centered around office space, sometimes purpose-built for media and creative companies. This space can also include incubators / accelerators.||Dubai Media City, Dublin’s Digital Hub|
Four rationales drive these patterns: agglomeration, urbanization, localization economies. and artificial formation.
The Shadow of the Industrial Revolution
Alfred Marshall made the argument that local concentration of industry can offer powerful economies and technical dynamism and innovation.
We now see this pattern with the emergence of megacities that accrue the majority of the financial and knowledge returns. These megaregions set the perfect stage for dynamic economic exchanges between skilled labor, technology, and networks.
What does your city look like?
Mapped: The Countries With the Highest Housing Bubble Risks
Which real estate markets have the highest risk of seeing a correction? These maps highlight housing bubble risks using data from four key indicators.
Mapped: Countries With the Highest Housing Bubble Risks
With a decade-long bull market and an ultra low interest rate environment globally, it’s not surprising to see capital flock to housing assets.
For many investors, real estate is considered as good of a place as any to park money—but what happens when things get a little too frothy, and the fundamentals begin to slip away?
In recent years, experts have been closely watching several indicators that point to rising bubble risks in some housing markets. Further, they are also warning that countries like Canada and New Zealand may be overdue for a correction in housing prices.
Key Housing Market Indicators
Earlier this week, Bloomberg published results from a new study by economist Niraj Shah as he aimed to build a housing bubble dashboard.
It tracks four key metrics:
- House Price-Rent Ratio
The ratio of house prices to the annualized cost of rent
- House Price-Income Ratio
The ratio of house prices to household income
- Real House Prices
Housing prices adjusted for inflation
- Credit to Households (% of GDP)
Amount of debt held by households, compared to total economic output
Ranking high on just one of these metrics is a warning sign for a country’s housing market, while ranking high on multiple measures signals even greater fragility.
Housing Bubble Risks, by Indicator
Let’s look at each bubble risk indicator, and see how they apply to the 22 countries covered by the housing dashboard.
It should be noted that most of the measures here are shown in an index form, using the year 2015 as a base year. In other words, the data is not representative of the ratio itself—but instead, how much the ratio has risen or fallen since 2015.
1. House Price-Rent Ratio
When looking at housing prices in comparison to rents, there are four countries that stand out.
New Zealand (196.8) and Canada (195.9) have seen ratios of housing prices to rents nearly double since 2015. Meanwhile, Sweden (172.8) and Norway (168.2) are not far behind.
Elsewhere in the world, this ratio is much more in line with expectations. For example, in Portugal—where house prices have skyrocketed over recent years—rents have increased at nearly the same rate, giving the country a 99.2 score.
2. House Price-Income Ratio
There are three familiar names at the top of this bubble indicator: New Zealand (156.8), Canada (155.3), and Sweden (145.7).
In places where rents are lagging housing prices, so are the levels of household income. For how long will people afford to buy increasingly expensive houses, if their incomes continue to lag?
3. Real House Prices
Real house prices have increased in all of the 22 markets, with the exception of Italy (95.5).
For this indicator, there are five markets that stand out as having fast-rising prices: Portugal (131.8), Ireland (127.6), Netherlands (121.9), Canada (124.1), and New Zealand (121.9). The latter two (Canada/New Zealand) have appeared near the top of all three bubble indicators, so far.
4. Credit to Households (% of GDP)
Exceedingly high debt ratios point to a strain on consumer finances – and when finances are strained, the chance of a default increases.
Switzerland (128.7%), Australia (120.3%), and Denmark (115.4%) top the list here with consumer debt far exceeding country GDP levels. However, Canada still makes an appearance in the top five with a debt-to-GDP ratio of 100.7%.
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