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Why you need a supercomputer to build a house

When the hell did building a house become so complicated?

Don’t let the folks on HGTV fool you. The process of building a home nowadays is incredibly painful. Just applying for the necessary permits can be a soul-crushing undertaking that’ll have you running around the city, filling out useless forms, and waiting in motionless lines under fluorescent lights at City Hall wondering whether you should have just moved back in with your parents.

Consider this an ongoing discussion about Urban Tech, its intersection with regulation, issues of public service, and other complexities that people have full PHDs on. I’m just a bitter, born-and-bred New Yorker trying to figure out why I’ve been stuck in between subway stops for the last 15 minutes, so please reach out with your take on any of these thoughts: @Arman.Tabatabai@techcrunch.com.

And to actually get approval for those permits, your future home will have to satisfy a set of conditions that is a factorial of complex and conflicting federal, state and city building codes, separate sets of fire and energy requirements, and quasi-legal construction standards set by various independent agencies.

It wasn’t always this hard – remember when you’d hear people say “my grandparents built this house with their bare hands?” These proliferating rules have been among the main causes of the rapidly rising cost of housing in America and other developed nations. The good news is that a new generation of startups is identifying and simplifying these thickets of rules, and the future of housing may be determined as much by machine learning as woodworking.

When directions become deterrents

Photo by Bill Oxford via Getty Images

Cities once solely created the building codes that dictate the requirements for almost every aspect of a building’s design, and they structured those guidelines based on local terrain, climates and risks. Over time, townships, states, federally-recognized organizations and independent groups that sprouted from the insurance industry further created their own “model” building codes.

The complexity starts here. The federal codes and independent agency standards are optional for states, who have their own codes which are optional for cities, who have their own codes that are often inconsistent with the state’s and are optional for individual townships. Thus, local building codes are these ever-changing and constantly-swelling mutant books made up of whichever aspects of these different codes local governments choose to mix together. For instance, New York City’s building code is made up of five sections, 76 chapters and 35 appendices, alongside a separate set of 67 updates (The 2014 edition is available as a book for $155, and it makes a great gift for someone you never want to talk to again).

In short: what a shit show.

Because of the hyper-localized and overlapping nature of building codes, a home in one location can be subject to a completely different set of requirements than one elsewhere. So it’s really freaking difficult to even understand what you’re allowed to build, the conditions you need to satisfy, and how to best meet those conditions.

There are certain levels of complexity in housing codes that are hard to avoid. The structural integrity of a home is dependent on everything from walls to erosion and wind-flow. There are countless types of material and technology used in buildings, all of which are constantly evolving.

Thus, each thousand-page codebook from the various federal, state, city, township and independent agencies – all dictating interconnecting, location and structure-dependent needs – lead to an incredibly expansive decision tree that requires an endless set of simulations to fully understand all the options you have to reach compliance, and their respective cost-effectiveness and efficiency.

So homebuilders are often forced to turn to costly consultants or settle on designs that satisfy code but aren’t cost-efficient. And if construction issues cause you to fall short of the outcomes you expected, you could face hefty fines, delays or gigantic cost overruns from redesigns and rebuilds. All these costs flow through the lifecycle of a building, ultimately impacting affordability and access for homeowners and renters.

Startups are helping people crack the code

Photo by Caiaimage/Rafal Rodzoch via Getty Images

Strap on your hard hat – there may be hope for your dream home after all.

The friction, inefficiencies, and pure agony caused by our increasingly convoluted building codes have given rise to a growing set of companies that are helping people make sense of the home-building process by incorporating regulations directly into their software.

Using machine learning, their platforms run advanced scenario-analysis around interweaving building codes and inter-dependent structural variables, allowing users to create compliant designs and regulatory-informed decisions without having to ever encounter the regulations themselves.

For example, the prefab housing startup Cover is helping people figure out what kind of backyard homes they can design and build on their properties based on local zoning and permitting regulations.

Some startups are trying to provide similar services to developers of larger scale buildings as well. Just this past week, I covered the seed round for a startup called Cove.Tool, which analyzes local building energy codes – based on location and project-level characteristics specified by the developer – and spits out the most cost-effective and energy-efficient resource mix that can be built to hit local energy requirements.

And startups aren’t just simplifying the regulatory pains of the housing process through building codes. Envelope is helping developers make sense of our equally tortuous zoning codes, while Cover and companies like Camino are helping steer home and business-owners through arduous and analog permitting processes.

Look, I’m not saying codes are bad. In fact, I think building codes are good and necessary – no one wants to live in a home that might cave in on itself the next time it snows. But I still can’t help but ask myself why the hell does it take AI to figure out how to build a house? Why do we have building codes that take a supercomputer to figure out?

Ultimately, it would probably help to have more standardized building codes that we actually clean-up from time-to-time. More regional standardization would greatly reduce the number of conditional branches that exist. And if there was one set of accepted overarching codes that could still set precise requirements for all components of a building, there would still only be one path of regulations to follow, greatly reducing the knowledge and analysis necessary to efficiently build a home.

But housing’s inherent ties to geography make standardization unlikely. Each region has different land conditions, climates, priorities and political motivations that cause governments to want their own set of rules.

Instead, governments seem to be fine with sidestepping the issues caused by hyper-regional building codes and leaving it up to startups to help people wade through the ridiculousness that paves the home-building process, in the same way Concur aids employee with infuriating corporate expensing policies.

For now, we can count on startups that are unlocking value and making housing more accessible, simpler and cheaper just by making the rules easier to understand. And maybe one day my grandkids can tell their friends how their grandpa built his house with his own supercomputer.

And lastly, some reading while in transit:

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By automating code compliance, UpCodes AI is ‘the spellcheck for buildings’

For many architects, the hardest part of their job starts after they finish designing a building, when the onerous process of code compliance begins. Written to ensure the safety and accessibility of buildings, codes dictate everything from the height and depth of stairs and where railings end, to the amount of floor space in front of toilets and the height of windows. Regulations are constantly updated, which means that even the most diligent team of architects often miss violations, resulting in costly delays. Y Combinator alum UpCodes wants to help them by using artificial intelligence, including natural language processing, to create what the San Francisco-based startup describes as “the spellcheck for buildings.”

Called UpCodes AI, the program is a plug-in that scans 3D models created with building information modeling (BIM) data and alerts architects about potential issues. It draws on the same backend as UpCodes’ first product, an app that compiles regulations into a constantly updated, searchable database with collaboration tools. UpCodes AI, which launched to the public last week, currently supports recent versions of Autodesk Revit and will add ARCHICAD, Sketchup and IFC in the future.

“This is like Grammarly for the construction industry. By highlighting code errors in real-time, the software acts as a code consultant working beside you at all times,” UpCodes co-founder and CEO Scott Reynolds tells TechCrunch.

UpCodes’ co-founders Garrett and Scott Reynolds and UpCodes AI technical lead Mark Vulfson

UpCodes was founded in 2016 after Reynolds became so frustrated by traditional code compliance while working as an architect that he switched career paths and launched the startup with his brother Garrett, a former software engineer at PlanGrid, to fix the process.

Building codes change so often that they are sometimes referred to as “living documents.” UpCodes’ database draws directly on regulations put online by municipalities and is updated almost in real-time. This eases a major pain point because many architects who thought they had followed regulations find out too late that they missed an amendment. In worst case scenarios, completed work needs to be torn out and rebuilt, potentially costing tens of thousands of dollars. This is a frequent occurrence and Scott Reynolds points to studies by McKinsey and the National Association of Home Builders that cite the complexity of code compliance as a major reason for reduced productivity in the construction industry and rising home prices.

Automating code compliance may also make it easier for architects to expand their practices, since regulations can vary dramatically between jurisdictions. UpCodes currently covers building codes in 26 states and the District of Columbia. Though UpCodes AI is still in its early stages, Reynolds tells TechCrunch that during its private beta it identified an average of about 27 violations per project.

One of its private beta users was Nicholas LoCicero, a designer with CallisonRTKL, an architecture firm known for retail design. LoCicero told TechCrunch in an email that the company used UpCodes AI on two retail locations that needed brand updates. Accessibility, which includes making sure that there are unobstructed ways of exiting a building from any point within it, is one of the most important parts of code compliance, and LoCicero said UpCodes AI was able to flag issues with door clearance, depth on stairs and tread width more quickly than the typical compliance process.

The program “definitely has the potential to save us hours of time with smart egress and accessibility tools and components that will help us develop projects faster during different phases of design” while ensuring that compliance is maintained, he added.

So far, UpCodes has raised $785,000 in funding from angel investors, as well as Y Combinator and Foundation Capital. It now has over 100,000 monthly active users and recently hired Mark Vulfson, former senior manager of engineering at PlanGrid, to serve as UpCodes AI’s technical lead.

Though the adoption of BIM data has made planning buildings more efficient, that’s “only a modest use of BIM’s full potential,” Reynolds says. He notes that it’s just within the past few years that more than 50% of American architecture firms have started using 3D information-rich modeling instead of 2D modeling. Programs like Revit and ARCHICAD, and new developments in APIs, finally made automated code compliance possible.

The use of AI in architecture is still new, but there are already several companies, including Autodesk and CoPlannery, exploring how to apply AI technologies to solve common problems in design, construction and engineering. Since AI is used in other major industries, including finance and healthcare, to automate compliance, it makes sense to assume that somewhere down the line, another company might try to build a competitor to UpCodes AI.

Reynolds believes that the UpCodes team’s combined industry and technical expertise will give it an edge over future rivals. He says his brother Garrett has a background in diffusion MRI analyzing large 4D data sets, while Vulfson brings “extensive experience deploying client side and web-based products” to the startup. UpCodes also works with a building code consultant who is based in New York City.

“The whole industry of code compliance has been neglected by software engineers for so long that it’s hard to imagine someone else doing what we’re doing,” Reynolds says.

“Building codes are a creativity killer.  These regulations are one of the most restrictive components of design,” he adds. “Imagine restricting every brush stroke an artist makes with ten thousand rules–that’s what building codes feel like to an architect. That’s why I quit my career to do this. I want to take away that frustration and make architecture more fun, like it is in school.”

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Oracle to buy construction collaboration software maker Aconex for $1.2B in cash

 Oracle will pay $1.2 billion in cash to buy construction software developer Aconex, the companies announced today. Based in Melbourne, Aconex’s cloud-based software allow teams working on building projects to collaborate and share documents. Oracle agreed to pay AUD $7.80 (about $5.97) per share in cash for a total of $1.2 billion. This price represents a 47% premium over… Read More

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