Is the market going to crash?
Everyone jostled by the news the housing market could crash has every reason to be worried. And why is that so? Because when the last time the housing market sored like this — it sparked a great recession that left many in financial ruins.
The Real Estate Market Crash is Coming Sooner Than You Think
Always — fueled by a rapid increase in home prices, a rising housing demand, and home flippers — the market then crashes.
Real estate is experiencing record low-interest rates that make housing affordable. However, that has skyrocketed the house prices. It’s crystal clear demand is outpacing supply; what next? Could the mobile and modular homes be the fix?
Mobile homes for sale (tyrone woods) might just be the potential fix to the American housing shortage going by the fact they take a shorter time to build than site-built homes.
Recently, Google reported that the search “When is the housing market going to crash?” had spiked 2,450% in the past month. Many are anticipating history to repeat itself, just like the 2008 housing market crash.
Speculations are rampant about how when the real estate markets could crash — but first, what can we learn from the 2008 housing market crash? Here are some interesting facts about the events preceding the crash back then.
What Caused the Housing Market Crash 2008?
The housing market crash 15 years ago ignited a worldwide recession. The sole reason for the crash and financial crisis were down to predatory private mortgage lending and unregulated markets. Here’s what preceded the great recession in 2008.
Housing Prices and Foreclosures
A similar event like the one happening now ruled prior to the market crash in 2008. Housing prices shoot through the roof, with speculative buyers flooding the market, leading to a demand exceeding supply.
In the early-to-mid 2000s, mortgage lenders revised their lending standards of a desirable borrower which opened a window to borrowers with poor credit to get access to loans and secure home purchase. The easing of lending standards created an opening for many to access mortgages.
The rise of Mortgage-Backed Securities (MBS) was hugely misunderstood by many investors.
The high demand in the housing market propelled an increase in risky mortgage lending practices. On the other hand, the Federal Reserve Bank raised the interest rate to 5.6 percent by June 2006.
What About Adjustable Rate Mortgages?
While those with conventional type of loan weren’t affected, the majority with Adjustable-Rate Mortgage (ARM) were the casualties. Plunged into unforeseeable debt, many defaulted, leading to a huge rise in foreclosures in the housing market.
In 2008, the number of foreclosures spiked to a record high of 81%, according to a CNN report. A total of 861,664 families lost their homes to foreclosure that year. This led to more inventory availability, and subsequently, a crash followed suit.
Banks’ Risky Behavior
The rise of Mortgage-Backed Securities (MBS) led to financial institutions extending their mortgage lending. Many banks seized the opportunity for a lucrative long-term benefit. All was well until the bubble burst. Leaving huge collateral of subprime mortgages.
On the other hand, banks stopped lending to each other in fear of being trapped with subprime mortgages. Even after the Federal reserve cutting down the interest rates, it wasn’t enough to stop the bleeding economy (the panic).
The Stock Market Crash
The stock market crash led to many losing their wealth caused by the increasing number of closures and housing busts. In fact, the major financial markets lost more than 30% of their value by September 2008, when the Dow Jones Industrial Average fell 777.68 points, which surprised 684.81 loss on Sept. 17, 2001, the first trading after the September 11 attack.
According to a report by NCBI, between 2007-2011 one fourth of American families lost at least 75 percent of their wealth, and more than half of all families lost at least 25 percent of their wealth.
Are You Following Current Events?
Now, back to our topic discussion, can you see the similarity in the current events? Well, it’s crystal clear the housing markets are in a bubble. In this article, we’ll uncover why we think the real estate market crash coming soon.
Real Estate Market Crash Coming Soon
Analysts have made their point; the federal government has had its say, different perspectives have been put forward in a bid to break down the events of the current housing market.
Statistics and History
Statistics and history all have been gathered around and pinned to where it’s due. The only question remains, will the housing market crash this year?
Whether you love statistics or not, we’ll try to make it as lenient as possible, a step-by-step guide on how and why the market could crash sooner rather than later. Market crash doesn’t happen in a split of a second; it builds over time.
Watch Economic Factors
Economic factors at play, the forces of demand and supply, are often the case of a free market like real estate. Frank Nothaft, a chief economist at CoreLogic, says, “We’ve got an acute shortage of supply on the market for sale at the same time that record-low mortgage rates are driving the appetite to buy by millennials and Gen-Xers.”
New York City Prices Among Others
For instance, Bloomberg reported New York City home prices are rising fast. New Yorkers who may still be working from home a year into the pandemic are fanning out across the boroughs in search of another housing that is spacious and cheaper housing.
Whenever one side outplays the other, a disequilibrium is created, an imbalance in the market that needs a solution to revert to the initial position. For example, basic economics dictates that interest rates and housing prices have an inverse relationship. As such, when the interest is low, the house price goes up. Why is that?
It’s simple when the rate is low; housing becomes cheaper or affordable to acquire; this, in turn, creates a high demand for housing since it’s affordable at the time.
Investors or homeowners on the other hand will try to take advantage of the rising demand by increasing the prices. As prices rise it’ll cut off some people who will suddenly be unable to purchase the home at the asking price. Now, demand is being brought down by price growth, thus justifying the inverse relationship with the interest rates.
The Housing Bubbles Burst
Up until now, the most common term you’ve probably heard is a housing bubble? Do you know what it means? What causes it? And if it burst, then what could be the factors or forces that are the last straw that breaks the camel’s back?
Day by day, it’s harder to deny the fact the US housing market is overheating.
According to the Wall Street Journal, some regions are experiencing low inventory, which is a worrying sign as far as the housing market crash is concerned. Across the country, the housing market is 3.8 million single-family homes short of what is needed to meet the country’s demand, according to a new analysis by mortgage-finance company Freddie Mac.
Home price surge also suggests an asset bubble.
The COVID-19 hasn’t slowed home prices at all, Instead, they’ve skyrocketed. In September 2020, they were a record $226,800, according to the Case-Shiller Home Price Index.
According to the National Association of Realtors, the sales rate reached 5.86 million homes in July, and by October, it had blossomed to 6.86 million, beating the pre-pandemic peak. Many people are taking advantage of the low rates to buy either residential homes or income-based apartments, which seem affordable.
The COVID-19, on the other hand, has created a slow economic activity resulting in a high unemployment rate.
According to the Labor Department, the US lost 140,000 jobs in December 2020 alone. A rising number of job losses means few people will afford to buy houses, while those with mortgages will likely default and increase the number of foreclosures.
On the other hand, the job losses have forced many people to seek Plan B, going for mobile homes for rent that is exceptionally cheaper and affordable during this time.
What is a Housing Bubble?
A housing bubble happens when the market price of residential real estate sharply rises. Usually, this happens when the demand for houses exceeds the supply in the market. The sudden rise of house demand triggers speculators to enter the market to profit from future expectations.
The presence of speculators in the market further pushed the demand higher.
So, yes, speculators entered the market, and in response, the home prices shot up, creating a bubble stretch in the housing market to grow even further. Now it reaches a time when the home prices are high up and no longer affordable to buyers. The unsustainability caused by the rising prices leads to homes being overvalued. In other words, price inflation.
When the prices become unsustainable and buyers pull out, demand falls.
The prices become unsustainable — but interestingly, the supply increases. Simple economics at play here; now that the demand has fallen, what happens next? Prices come down crashing and the bubble bursts.
When questioned about the possibility of a bubble:
Ali Wolf, chief economist at housing research firm Zonda says, “Homebuyers today are purchasing for many healthy reasons: Low-interest rates, more flexibility to work from home and increased saving are all rational reasons for buying a house. The frenzy fueled by these factors, combined with fear of missing out, has the potential to create a bubble though.”
What Causes a Housing Bubble?
Real estate is a free market; the law of demand and supply applies unconditionally. When the demand for housing increases, subsequently, home prices go up. Usually, the supply of homes takes time to match the rising population of young Millenials who are seeking first-time home buying. It always plays a catch-up game.
Building a house takes time, causing a deficit in supply and thus demand exceeding it. Either way, prices will eventually increase the moment demand outpaces supply. To sum it up, the asset bubble is down to a combination of factors. One such factor — a healthy economy, where disposable income grows, and people feel secure in their jobs and confident about searching for a house, increasing the demand.
The mortgage rates also play a huge role in the asset bubble.
Low mortgage rates drive up demand; why so? The mortgage has become more affordable and buying a house is a lot easier attracting many borrowers to run for cheap loans.
The rising number of subprime borrowers also causes the demand to further rise in the real market. The market is currently experiencing a record low mortgage, driving housing demand up. Record low mortgage with 30 years fixed rate fell to 3.20 percent, according to Bankrate.
The other factor is the speculators who are always in waiting mode to take advantage of an opportunity whenever it presents itself. Further rise in demand leads to overvaluation of houses which asserts the asset bubble growth.
Forces that Burst the Bubble
When pushes come to shove, and the prices aren’t reflective of anything close to fundamentals, the bubble burst. At this point, the demand decreases while supply increases resulting in a sharp fall in home prices.
No one has to pay for high home prices anymore; on the other hand, investors are at a huge loss, mortgage lenders reeling on the risk of defaulters. How does that happen?
Firstly, the interest rises to put some homeownership out of reach, while at the same time, in the case of Adjustable-Rate Mortgage, makes the home a person owns unaffordable, leading to defaulting and foreclosure.
Secondly, a downturn or slow economic activity often leads to less disposable income, fewer jobs, and job loss. Such a situation causes a decline in demand for housing since a person can not afford to buy a home.
Lastly, when demand is exhausted, an equilibrium is restored, slowing down the rapid rise of the home price growth. When house price appreciation stagnates, those who depend on it to afford their home may lose their houses, bringing more supply to the market.
Higher Interest Rates
As stated earlier, interest rate and house prices tend to have an inverse relationship such that when the interest is low, price appreciation occurs, and the reverse is also true. Interest rates play a huge role in marketing crashing. And if it’s going to happen soon, it’ll surely be a contributing factor by far.
Rates rise will make mortgages very expensive.
It’ll discourage borrowers from taking loans. On the other hand, home buildings will be affected too, costs will rise, and an immediate effect will be the supply of housing in the market falling.
However, a steady rise in interest rates will not cause much damage in the housing market, unlike a rapid rise. In 2006 before the housing market crash, many people were tied to interest-only and adjustable-rate mortgages that are initially cheap within the first few years, and then a reset that increases the monthly mortgage payment.
Unlike conventional loans, adjustable-rate mortgage rises along with the feds fund rate.
Between 2004 and 2006, the Federal Reserve increased the rates rapidly. For instance, The top rate was 1.0% in June 2004 and doubled to 2.25% by December. It doubled again to 4.25% by December 2005. Six months later, the rate was 5.25%.
Rising Number of House Flippers
A flipped home is basically bought, renovated, and sold in less than a year. Usually, the rise of home flippers further increases the demand for housing in the real estate market, resulting in a further increase in house prices. Surging prices are reflective of an asset bubble that could potentially burst.
Home flipping played a huge role in factors contributing to the 2008 recession.
Speculators would buy homes, make moderate improvement, and sold it to fast-rising house prices. In 2006, flips comprised 11.4% of home sales.
According to Attom Data Solutions, in the third quarter of 2020, 5.1% of all home sales were bought for quick resale. That’s down from 6.7% of home sales in the second quarter of 2020. It’s also lower than the post-recession high of 7.2% in first-quarter 2019.
The decline in flipping is due to the reduced inventory of housing stock. However, Attom Data Solutions reports that the pandemic’s effect on flipping is contradictory and difficult to forecast.
The Alarming Increase in Unregulated Mortgage Brokers
In the events leading to the 2008 financial crisis, mortgage lenders fueled the asset bubble by issuing out loans to high-risk borrowers. Many of the lenders opted to borrow against lines of credit, a totally different strategy than what banks and mortgage lending normally work by tapping into deposits.
Non-Bank lenders are a warning sign of a crash.
The increase in non-bank lenders is alarming and a clear warning sign of what may come sooner rather than later in real estate. In 2019, they originated 54.5% of all loans. That’s up from 53.6% in 2018. Six of the 10 largest mortgage lenders are not banks. Three years ago, five of the top 10 were unregulated.
The most worrying part about unregulated mortgage brokers is that they don’t have the same government oversight as banks. Making them vulnerable to collapse in case of anything going south in real estate.
A section of the Washington Post read “Although observers say non-bank lenders are probably not engaged in the sort of risky lending that dragged down their predecessors, the business model still makes them vulnerable to a housing market downturn.”
Inverted Yield Curve
Prior to the recession of 2008,2000 and 1991, 1981, the yield curve inverted. According to a definition by Investopedia, an inverted yield curve represents a situation in which long-term debt instruments have lower yields than short-term debt instruments of the same credit quality. When the yield curve inverts, short-term interest rates become higher than long-term rates.
The inverted yield curve is the rarest and considered to be a predictor of economic recession.
Usually, they draw attention from all parts of the financial world. A normal yield curve slopes upwards reflecting the fact that short-term interest rate is usually lower than long-term rates.
Affordable Housing Crisis
The affordable housing crisis is caused by the imbalance in the market forces of supply and demand. A market boom in real estate will result in home prices skyrocketing. The scarcity of affordable housing across the country is always a sign that the market is in a bubble.
The Bottom Line
Is the market going to crash?
The market could crash if the combination of the above factors comes to pass. Already many are in play, and as the home prices sores, it’s evident that the US housing market is overheating.
The pandemic has had a mixed reaction on the real estate performance.
While many people expected COVID-19 to crash real estate, there was a sudden surge in homes for sale. More homes for sale listings were done last year, with people rushing to buy homes in the suburbs. The rising homes for sale listings sparked the speculators to enter the market, further pushing the demand up.
Move to Prevent Foreclosures
Elsewhere, millions went behind their mortgage payment plan; however, the Consumer Financial Protection Bureau (CFPB) mortgage servicing changes to prevent a wave of COVID-19 foreclosures.
Consumer Financial Protection Bureau (CFPB) Acting Director Dave Uejio says, “The nation has endured more than a year of a deadly pandemic and a punishing economic crisis. We must not lose sight of the dangers so many consumers still face.”
Image Credit: kindel media; pexels; thank you!
How Preql is Transforming Data Transformation
More than one million small businesses use ecommerce platform Shopify to reach a global audience of consumers. That includes direct-to-consumer (DTC) all-stars like Allbirds, Rothy’s and Beefcake Swimwear.
But online sellers like these are also ingesting data from platforms like Google Analytics, Klaviyo, Attentive and Facebook Ads, which quickly complicates weekly reporting.
That’s where data transformation comes in.
dbt and Preql
As the name implies, data transformation tools help convert data from its raw format to clean, usable data that enables analytics and reporting. Centralizing and storing data is easier than it’s ever been, but creating reporting-ready datasets requires aligning on business definitions, designing output tables, and encoding logic into a series of interdependent SQL scripts, or “transformations.” Businesses are making significant investments in data infrastructure tooling, such as ingestion tools, data storage, and visualization/BI without having the internal expertise to transform their data effectively. But they quickly learn if you can’t effectively structure your data for reporting, they won’t get value from the data they’re storing—or the investment they’ve made.
The space includes two major players: dbt and startups.
Founded in 2016, dbt “built the primary tool in the analytics engineering toolbox,” as the company says, and it is now used by more than 9,000 companies—and it is backed by more than $414 million.
But dbt is a tool for developers at companies with established analytics engineering teams.
Preql, on the other hand, is a startup building no-code data transformation tool that targets business users who might not have expertise in programming languages but who nevertheless need trusted, accessible data.
Preql’s goal is to automate the hardest, most time-intensive steps in the data transformation process so businesses can be up and running within days as opposed to the six- to 12-month window for other tools.
“We built Preql because the transformation layer is the most critical part of the data stack, but the resources and talent required to manage it make reliable reporting and analytics inaccessible for companies without large data functions,” said Gabi Steele, co-founder and co-CEO of Preql.
The startup is therefore positioning itself as an alternative to hiring full analytics engineering teams solely to model and manage business definitions—especially among early-stage companies that are first building out their data capabilities.
In other words, Preql is the buffer between the engineering team and the people who actually need to use the data.
“Data teams tend to be highly reactive. The business is constantly asking for data to guide decision making, but in the current transformation ecosystem, even small changes to data models require time and expertise. If business users can truly manage their own metrics, data talent will be able to step out of the constant back and forth of fulfilling reporting requests and focus on more sophisticated analyses,” said Leah Weiss, co-founder and co-CEO of Preql.
But that’s not to say dbt and Preql are bitter rivals. In fact, they are part of the same data transformation community—and there’s a forthcoming integration.
“One way to think about it is we want to help the organizations get up and running really quickly and get the time to value from the data they’re already collecting and storing without having to have the specialized talent that’s really well versed in dbt,” Steele added. “But as these companies become more sophisticated, we will be outputting dbt, so they can leverage it if that’s the tool that they’re most comfortable with.”
A Closer Look at Preql
The startup raised a $7 million seed round in May, led by Bessemer Venture Partners, with participation from Felicis.
Preql collects business context and metric definitions and then abstracts away the data transformation process. It helps organizations get up and running with a central source of truth for reporting without having a data team or writing SQL.
Preql reads in data from the warehouse and writes back clean, reporting-ready schemas. It partners with data ingestion tools that move data from source applications into the warehouse such as Airbyte and Fivetran and cloud data warehouses like Snowflake, Redshift and BigQuery. For businesses who consume data in BI tools, it also partners with Looker, Tableau and Sigma Computing.
Preql is initially focused on the DTC market in part because the metrics, such as cost of customer acquisition (CAC), conversion rate and life-time value (LTV), are standardized. They also tend to have lean operations.
“We’ve found that these companies are working really hard to download data from disparate sources—third-party platforms that they use, Shopify, their paid marketing platforms—in order to get a sense of even basic business health and performance,” Weiss said.
They also tend to use manual reporting processes, which means “it’s often an operations person who’s downloading data from a bunch of sources, consolidating that in spreadsheets, making a bunch of manual interventions and then outputting weekly reporting or quarterly reporting,” she added.
But much of what these companies want to measure about performance is consistent and a lot of the data sources are structured the same way.
“With Preql, we were able to make some assumptions about what we wanted to measure with the flexibility to customize a few of those definitions that are specific to our business,” added Cynthia Plotch, co-founder at Stix, a women’s health essentials ecommerce site. “Preql gave us clean, usable data for reporting. We were up and running with weekly reporting within days, saving us months of effort if we had to invest in data engineering teams.”
Data Transformation in 2027
Steele and Weiss believe the next five years will be about “delivering on the promise of the modern data stack.”
In other words, answering questions like: Now that we have scalable storage and ingestion, how can we make sure we can actually leverage data for decision making? And how can we build trust in reporting so we can build workflows around it and act on it?
This is because a lot of companies struggle to move on to predictive analytics and machine learning because they never solved the fundamental issue of creating trusted, accessible data.
What’s more, Preql believes the next phase of tools will go beyond building infrastructure to deliver more value as data talent sits closer and closer to the business.
“Data analytics will only get more complicated because the number of data sources is growing, along with their complexity, and the need is becoming more acute for real time results. And the more data you have, the more granular the questions become and even more is expected of it,” Amit Karp, partner at Bessemer Venture Partners added. “I think we’re in the very early innings of what’s going to be a very long wave—five, ten or even 20 years down the road. It’s a giant market.”
Can Traditional Companies Act Like Start-Ups?
Much has been made about the culture clash between older, slower, more traditional companies and younger, more dynamic, faster-moving tech start-ups. Each has advantages and disadvantages, but, generally speaking, it is very hard to reconcile the two approaches, as they are naturally in opposition to each other.
The general motto among start-ups of “move fast and break things” has led to very quick yet massive successes, with some companies, Google and Amazon being the most obvious examples, growing larger than traditional competitors who have been around for decades and decades. But it has also led to a lot of unconsidered damage to traditional industries like transportation and publishing, their ‘disruption’ doing as much harm as good. And, more often than not, start-ups can see millions or even billions in investment being wasted on bad ideas and unproven tech (Theranos, anyone?). “Fake it till you make it” means that, eventually, you actually do need to make it.
Meanwhile, traditional companies, while providing more useful and regular forms of employment, great institutional knowledge, and decades of business experience, have their own problems. Because they often resemble large, inefficient bureaucracies, they are slow to move and respond to change. Old companies can be blind to, and even fearful of, innovation and new technology. This can leave them dead in the water when the future finally arrives. Kodak, for example, went from venerated, dominant business to almost nothing in just a few years because it refused to accept the revolution of digital photography.
But is there a way to integrate the two approaches? To take the best from both cultures and business plans and use those aspects to move into the future? To get big, old businesses to work, at least in some ways, like small, agile, young start-ups? Yes, but it isn’t easy.
Innovation Without Disruption
As stated, one of the greatest fears of traditional companies is having their business, or their entire sector, undercut by a growing start-up. While independent start-ups are expected to disrupt, be change agents, or however you want to put it, more traditional companies are prone to be much more risk averse. Naturally, one of the smartest things that an old company can do to avoid being left behind is to lead the disruption themselves.
Many traditional businesses are currently investing in, and should continue to invest in, the digital transformation of their business model, from top to bottom. This, however, is a slow process, especially in sizable companies. The use of machine learning, predictive analysis, AI, and other cutting edge digital tools allows old business models to become more efficient, and respond to changes in supply and demand, and market tumult, in better and smarter ways. But it isn’t as easy as flipping a switch.
A New Business to Try New Things
Quite a few traditional businesses are spinning out new sectors, tech labs, and other separate silos to do the work of digital innovation for them. This isn’t uncommon. Businesses have, basically forever, had subsidiaries. The problem is that old businesses have trouble actually committing to the idea.
Often, the business that is spun-out is, essentially, a temporary one. The leaders of the core business get cold feet, limit the new project’s mandate, and pull it back in as soon as possible. Such hesitance is limiting in today’s digital world, where the next revolutionary innovation is always just around the corner.
Furthermore, spin-outs with good ideas and potential for growth are frequently allowed to die on the vine, just as often they go to seed. Or, to make things clearer, the core business doesn’t invest in the digital spin-out’s success. The great advance of digital companies is their ability to scale with almost lightning speed. But core business have to be ready with resources and support for the scale-up to even happen, let alone work. Otherwise, a grand opportunity will go to waste.
If a business spin-out does well enough, it should be allowed to grow and change as it needs to, provided that it remains successful and worthwhile. Whether the goal is for the new business to simply make money in an area the core business isn’t directly addressing, or developing digital innovations for the core business to take up, if it works it works. Don’t get in the way of success just because it is new, or comes in an unfamiliar form. At the same time, core businesses must be careful of how they measure success for these new experiments. Measuring the new company or spin-out with the same metrics as the core business can sometimes choke the momentum and not give an accurate picture. Afterall, newer, smaller businesses, or initiatives shouldn’t be expected to be profitable immediately.
Cultural Change, From the Executive Level On Down
All the innovation in the world won’t mean anything if the people running the business itself refuse to change. Older companies, and older executives, can become set in their ways, dismissive of new technologies and ways of doing business, and ignore the automation and efficiencies of advanced digital tools. We saw this at the beginning of the widespread use of the internet twenty years ago, and we’re seeing it now.
More important than this, is the need for people in positions of real power in companies to implement the changes needed for innovation and advancement, and do so thoroughly and effectively. There must be a willingness to let the start-up culture infiltrate and influence the way business is done at every level, or it won’t be effective enough to help.
It is painfully common for large, traditional companies to put money into research and development of new ideas and new technologies, only for executives and other decision makers to ignore what’s in front of them, either because of cost, or risk, or something as simple as a fear of the future.
But the future of business is changing in a digital world. Things move and change with an almost frightening speed. The Covid-19 pandemic is absolute proof of that; it wasn’t just companies with digital tools at the ready that were able to survive. While they had an advantage, it was the companies that were able to acknowledge the rapidly changing situation, and react to it quickly and efficiently, that kept things going and in some cases, even improved their bottom lines.
But It’s More Than Just a Cultural Change
One of the biggest advantages of tech start up culture is that it is forward-facing. It is an attitude towards business and technology that is not just looking towards the future (every business does that), but is actively trying to grapple with it, and even to shape it, if possible. Traditional, legacy businesses need to admit that the world is not static, and they have a responsibility in influencing how their industry develops.
Part of that responsibility is letting innovators be innovators. If a large company spins out a business unit to study and improve its digital technology, that company can’t then balk when those innovators recommend widespread change, or create a new idea that could shake the company, or its whole industry, to its core.
To put it as simply as possible, for an older, more traditional company to reap the benefits of adopting a start-up model, it has to actually adopt it. It can’t just make superficial changes, it needs to truly invest. But that kind of investment carries risk, which can make more traditional companies nervous. The work of transformation must actually be done.
That means supporting digital innovations and changes when they make things more efficient. It means letting spin-out businesses actually try new things, and grow to scale when they hit upon something new and successful. It means executives getting out of the way so the forces of change can actually, you know, change things. Otherwise, the ‘traditional’ company will just be the ‘old’ company, sitting around waiting for some new tech upstart to disrupt it into obsolescence.
Understanding Edge Computing and Why it Matters to Businesses Today
The edge computing market is expected to reach $274 billion by 2025, focusing on segments like the internet of things, public cloud services, and patents and standards.
Most of this contribution is backed by enterprises shifting their data centers to the cloud. This has enabled enterprises to move beyond cloud systems to edge computing systems and extract the maximum potential from their computing resources.
This blog will provide a closer understanding of edge computing and how it helps businesses in the technology sector.
Understanding edge computing
From a technical standpoint, edge computing is a distributed computing framework that bridges the gap between enterprise applications and data sources, including IoT devices or local edge servers.
For an easier understanding, edge computing helps businesses recreate experiences for people and profitability through improved response time and bandwidth availability.
Why does edge computing matter for businesses?
When we talk about the most significant industry zones worldwide, for instance, the GCC region, which is heavily focused on the focus areas like cloud services, the transition from cloud technology to edge computing is now more prominent than ever for enterprises to leverage the potential of the technology.
And with only 3% of businesses at an advanced stage in digital transformation initiatives, the potential of edge computing is up for grabs.
It doesn’t matter if you’re running a mobile app development company, a grocery store next door, or a next-gen enterprise. You need to understand how cloud edge helps businesses and invest in this open-source technology.
Edge computing is primarily sought in industries where value-added assets have a massive impact on the business in case of losses.
The technology has enabled reports delivery systems to send and receive documentation in seconds, usually taking days to weeks.
Consider the example of the oil and gas industry, where some enterprises utilize edge computing. The predictive maintenance allowed them to proactively manage their pipeline and locate the underlying issues to prevent any accumulated problems.
Support for remote operations
The pandemic has forced businesses to opt for remote operations, or a hybrid work model at the least, with the workforce, spread across different geographical boundaries.
This drastic shift has brought in the use of edge apps that would permit employees to secure access to their organization’s official servers and systems.
Edge computing helps remote operations and hybrid teams by reducing the amount of data volume commuting via networks, providing computing density and adaptability, limiting data redundancy, and helping users comply with compliance and regulatory guidelines.
Faster response time
Businesses can enjoy lower latency by deploying computational processes near edge devices. For instance, employees typically experience delays when corresponding with their colleagues on another floor due to a server connected in any part of the world.
While an edge computing application would route data transfer across the office premises, lower the delays, and considerably save bandwidth at the same time.
You can quickly scale this example of in-office communication to the fact that around 50% of data created by businesses worldwide gets created outside the cloud. Putting it simply, edge computing allows instant transmission of data.
Robust data security
According to Statista, by 2025, global data production is expected to exceed 180 zettabytes. However, the data security concerns will equally increase proportionately.
And with businesses producing and relying on data more than ever, edge computing is a solid prospect to process large amounts of data sets more efficiently and securely when done near the data source.
When businesses take the cloud as their sole savior for data storage in a single centralized location, it opens up risks for hacking and phishing activities.
On the other hand, an edge-computing architecture puts an extra layer of security as it doesn’t depend on a single point of storage or application. In fact, it is distributed to different devices.
In case of a hack or phishing attempt, a single compromised component of the network can be disconnected from the rest of the network, preventing a complete shutdown.
Convenient IoT adoption
Global IoT spending is expected to surpass $410 billion by 2025. For businesses, especially in the manufacturing sector, who rely on connected technology, the internet of things is at the thickest of things in the global industry today.
Such organizations are on the constant hunt to up their computational potential and probe into IoT through a more dedicated data center.
The adoption of edge computing makes the subsequent adoption of enterprise IoT quite cheap and puts little stress on the network’s bandwidth.
Businesses with computational prowess can leverage the IoT market without adding any major infrastructure expenses.
Lower IT costs
The global IT spending on devices, enterprise software, and communication services rose from $4.21 trillion to $4.43 trillion in 2022. While a considerable share of the global spending accounts for cloud solutions, obviously as the pandemic has only pushed the remote operations and hybrid working model further up.
When users keep the data physically closer to the network’s edge, the cost of sending the data to the cloud reduces. Consequently, it encourages businesses to save on IT expenses.
Besides cutting costs, edge computing also contributes to helping businesses increase their ROI through enhanced data transmission speed and improved networks needed to experiment with new models.
How is edge computing different from cloud computing?
Although edge computing and cloud computing are each other’s counterparts for data storage and distribution, there are some key differences regarding the user’s context.
Edge computing deploys resources at the point where data generates. In contrast, cloud computing deploys resources at global locations.
Edge computing operates in a decentralized fashion, while cloud computing is centralized.
Edge is made on a stable architecture, and cloud resources are made on loose-coupled components.
Edge-based resources respond instantaneously, and cloud resources have a higher response time.
Edge computing requires lower bandwidth, while the cloud counterpart consumes a higher bandwidth.
Although, the above difference makes edge computing a clear winner in all aspects for any business. But there’s a catch!
Suppose your business resides at multiple physical locations, and you need a lower latency network to promptly cater to your customers who are away from your on-prem location. In that case, edge computing is the right choice for you.
Top edge computing use cases
Although there are numerous examples of edge computing use cases, I’ll talk about a few that I find the most interesting.
Autonomous flocking of truck convoys is the easiest example we can come for autonomous vehicles. With the entire fleet traveling close while saving fuel expenses and limiting congestion, edge computing has the power to eliminate the needs of all the drivers except the one in the front vehicle.
The idea being the trucks will be able to communicate with the others via low latency.
Remote monitoring of oil and gas industry assets
Oil and gas accidents have proved catastrophic throughout the industry’s history. This requires extreme vigilance when monitoring the assets.
Although oil and gas assets are placed at remote locations, the edge computing technology facilitates real-time analytics with processing closer to the asset, indicating less dependency on high-quality connectivity to a centralized cloud.
Edge computing is on course to elevate the adoption of smart grids, enabling enterprises to handle their energy consumption better.
Modern factories, plants, and office buildings use edge platform-connected sensors and IoT devices to observe energy usage and examine their consumption in real-time.
The data from real-time analytics will aid energy management companies in creating suitable, efficient workarounds. For example, watching where high energy consumption machinery runs during off-peak hours for electricity demand.
Cloud gaming, seemingly the next-big-thing in the gaming business like Google Stadia, PlayStation Now, etc., dramatically leans on latency.
Moreover, cloud gaming companies are on the quest to build edge servers as close to gamers as possible to reduce latency and provide a fully immersive, glitch-less experience.
This concludes our discussion on understanding edge computing and how it matters for enterprises worldwide.
Now that you understand the benefits of edge computing and its applications in different industries and use cases, it is evident that it’s a great value proposition for businesses that want to acquire competitive advantages and lead their spaces from the front line.
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