Editor’s note: This installment of Built for This is part of HousingWire’s ongoing examination of homebuilding leaders and companies improving their businesses in a housing market that offers little outside help. Brookfield Residential CIO Brandon Sharp will take the stage at the HousingWire Homebuilder Summit, Oct. 19–21 in Dallas.
When Brandon Sharp applied for a job to join the ranks of homebuilders in 2003, he had no homebuilding experience.
Sharp, now Chief Information Officer at Brookfield Residential, was interviewing for a controller job with Standard Pacific in Austin. The division president wanted somebody who knew the business. Sharp flexed into explaining why he should hire somebody who didn’t. He answered without missing a beat.
“Because I am going to own the data more than anyone has ever owned the data,” Sharp recalls telling him.
He got the job, and 23 years later, Sharp describes what followed as a rigorous education in how difficult keeping that promise actually is.
“This stuff is hard,” he says. “It is genuinely, notoriously hard.”
Despite enormous investments in enterprise software, reporting systems, and business intelligence, Sharp, now an advanced student in the field of interoperable systems, says a genuine single source of truth still eludes many large homebuilders.
Homebuilding companies and their business and operational partners are all driving toward artificial intelligence and its dividends. That elusive promise of a single source of [data] truth remains table stakes in the rules of the game.
AI promises faster analysis, better forecasting, more productive employees and the ability to find relationships among enormous amounts of information. Yet the quality of any of those outputs ultimately depends on something considerably less exciting: whether the information flowing through the business can be trusted in the first place.
Brookfield Residential has spent much of the past eight years working on solutions to that challenge.
The result offers an important lesson for builders facing a difficult market while also trying to prepare their organizations for a technology transition that is moving faster than most can comfortably absorb.
Before an organization can become much smarter with AI, it may first have to become considerably better at making sound decisions about the basics.
The hard work underarching the technology
Sharp frames successful data transformation around three non-negotiable enterprise commitments, each requiring time and day-to-day focus: conviction, clarity and capital.
Conviction means leadership genuinely believes the work matters enough to continue when it gets difficult. Clarity means people understand where the organization is going, why it is going there, and what it will require of them. Capital includes the obvious financial investment.
Sharp also emphasizes another scarce resource: the time and heads-down resolve of team members who still have homes to sell, trades to manage, and customers to serve while the transformation is underway.
Brookfield brought each of the three commitments together in a binding alignment, integration, adoption and implementation of operational initiatives.
Beginning in 2018 and continuing through the pandemic, the company set off on a company-wide transformation that included an ERP, a common unified data schema, an enterprise data warehouse and a series of reporting systems.
Sharp estimates that Brookfield invested more than 250,000 internal hours across 20 business groups and implemented seven major business systems, much of it while employees were working remotely during COVID.
Software can be purchased. Organizational capability cannot
Brookfield’s investment would have been for naught without determined buy-in from finance professionals, operators, division presidents, and regional leaders who participated in work that could easily have been dismissed or at least relegated to a lower priority as an IT initiative. Sharp says the financial commitment mattered, particularly when budgets came under pressure in 2020. The human, pan-organization commitment mattered even more.
“Your teams doing their day jobs, managing trades, selling homes, serving customers, and doing the important work of this transformation, leaning in, they have to become part of the transformation,” Sharp says. “Without their commitment and belief, data cannot create real value.”
Against a backdrop of severe, long-term headwinds, a downturn strategy cannot consist solely of waiting for mortgage rates to improve, buyers to return, or costs to decline.
Companies also have to get better at the things they control, without the “outside help” of external factors. Data quality, it has become increasingly evident, is one of them.
Accountability follows the information
The first payoff at Brookfield lacked any scintilla of glamor. The company started with dry reports on lot inventory, weekly sales and closings. Still, as people began trusting the information, demand for more of it spread across the organization.
As that practice took hold, information became a mechanism for accountability along the operational food chain. When Brookfield processes a home sale, its data warehouse can assemble pricing, gross margin, broker and delivery information and distribute it to leaders and local management teams in near real time. If something looks wrong, Sharp says, responsibility doesn’t revert to IT; rather, the owners are the people closest to the transaction.
“It’s not just that more people can see the data,” Sharp says. “It’s that the people closest to the data own its quality. The information flows to the edges of the organization, and so does the accountability.”
The objective isn’t to give senior management more get-me-one-of-those-style dashboards. It is to create a common version of the business that people across the organization recognize as their own and take responsibility for.
In a more forgiving, high-pace market, inaccuracies, delays and disconnected systems can hide behind rising prices and strong absorption. Of course, a harder market exposes them.
When margins compress and every sale is harder to win, operators need to know what changed, where it changed, and what that means for the economics of a community or home. A decision based on information 60 or 90 days out of date can be materially different from one based on what the business is doing today.
When internal trust touches the customer
Brookfield’s second payoff shows that data quality goes far beyond an internal efficiency exercise. Once the company trusted the bulwarks of its internal information, it became comfortable exposing much of that same information directly to customers.
Its public-facing systems can display available lots and applicable premiums, inventory homes and anticipated move-in timing, pricing, options, and incentives. Change a lot premium or the options attached to an inventory home in the operating system, and that change can flow through to what the customer sees.
The same principle extends to Brookfield’s visualization tools. Option pricing originates with the ERP rather than being manually maintained in a separate customer-facing system.
Sharp’s point is less about website functionality than consistency.
“What the customer sees, what they hear from sales counselors in the sales office, and what we look at internally, operationally, all say the same thing because it is coming from the same credible, trusted data source,” he says.
There is a customer-care lesson embedded there. Trust becomes harder to maintain when the website says one thing, the salesperson says another, and an internal spreadsheet provides a third answer. The quality of the customer experience therefore begins considerably farther upstream than the sales office.
It begins with whether the organization itself knows what is true.
A business plan that moves with the business
Sharp’s favorite example may have the greatest relevance for builders navigating the next 12 to 24 months.
For years, like many finance executives, he managed homebuilding business plans using increasingly elaborate Excel models. They could be powerful, but they relied heavily on manual updates and institutional knowledge. At Brookfield, the company eventually connected its operating data to a standardized asset-level cash-flow model that could be applied across geographies and housing types.
As base prices, lot premiums, options, absorption, cancellations and broker assumptions change, the underlying business plan can change with them.
“The plan is not necessarily waiting for someone to update it,” Sharp says.
The conventional planning cycle can take weeks or months. By the time a plan is reviewed, revised, and approved, the operating business may already have moved away from it. Management then waits for the next forecast while making decisions based on assumptions that may be 30, 60, or 90 days old. Brookfield’s objective is different: turn the business plan from a periodic management exercise into a continuously updated, “real-time” representation of the business itself.
For operators trying to protect margins, manage inventory, pace starts, and decide where capital should go next, this reporting improvement delivers operating leverage that delivers outsized business impact.
Earning the right to use AI
Only after all of that does Sharp want to talk about AI, which Brookfield is now pursuing across the organization. It has established a cross-functional AI Accelerator Council, invested in employee training and certification, and identified scores of potential applications across finance, sales, purchasing, HR, IT, and other functions.
One experiment is what Sharp calls an “operational narrator.” It uses AI, together with Brookfield’s warehouse and planning data, to produce written accounts of what is happening in the business at different time intervals and across geographic markets and submarkets. As to why that is even possible, Sharp does not equivocate, just as he never missed a beat during his first homebuilding job interview.
“That capability is entirely impossible without having done all of the very disruptive, very foundational work that we had done starting back in 2019,” he says. Without trustworthy data and the discipline required to maintain it, Sharp says, AI would produce “confident-sounding noise dressed up in complete sentences.”
That line gets closer to the issue facing homebuilding leaders than much of the current AI conversation.
AI can make an organization faster. It does not necessarily help the organization learn, improve, or become more durably profitable. If the underlying information is inconsistent, poorly governed, or detached from operating reality, greater processing power can simply allow a company to produce bad answers faster and more efficiently.
Brookfield’s approach is therefore to use AI where it earns its place. The company describes it as a thinking, drafting, and execution partner, while retaining human judgment and accountability. Its internal AI commitment explicitly states that the technology should strengthen thinking rather than replace judgment.
Built for this
That aligns Sharp’s story with the theme of our Built for This series. The companies best positioned for a difficult market aren’t necessarily those with the most technology, the biggest data warehouse, or the longest list of AI pilots. They are organizations that can and do improve.
That means people know what they’re accountable for. Information flows across functions. Leaders can see what is happening soon enough to act. Customers receive information the company itself trusts. Business plans remain connected to operating reality. And when new technology arrives, the organization has enough discipline to put that technology to useful work.
Brookfield Residential is a large enterprise, and the scale of its commitment and investment reflects that. Still, the embedded principle doesn’t require a firm to be classified as large, medium, or small.
“The single source of truth still evades a lot of builders,” Sharp told me while preparing for his HousingWire Homebuilder Summit session. His explanation for why transformations succeed, or stall, comes back to those same three requirements: conviction, clarity, and capital.
At the HousingWire Homebuilder Summit in Dallas, Oct. 19–21, Sharp will take homebuilding leaders inside that journey in a session we’ve called “Data Before Everything Else.”
The title is intentionally simple: Before the AI strategy comes the data strategy. That work is difficult, tediously repetitive and largely invisible. It may also be the lynchpin to being around when market momentum returns in 2028 or so.
