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    How Real Estate Developers Successfully Scale Operations

    The Core Problem

    Successful real estate developers often begin their career running smaller projects that are less than $50 million. This is how they earn their stripes and gain credibility among their peers and potential financial partners. But, oftentimes, as those teams decide to scale and take on larger projects, they struggle to succeed. 

    That is because the greatest challenge developers face at this stage is their failure to adapt their tools and methods to handle greater complexity as they scale. When taking on larger, more complex projects, they typically assume that they’ll be able to easily replicate the successes they had on smaller projects without changing any of their techniques or strategies. With larger, more complex development projects, however, not only are the stakes much higher, but there are also more unforeseen execution risks that can drastically decrease predictability around outcomes. This means that any small misstep can undercut the trust that has been built with stakeholders, evaporate equity and destroy investor capital, ruin reputations and potentially put a firm out of business. 

    So, how can this be avoided? In shore, on more complex projects, developers and their financial partners must transform themselves into data-driven decision-makers that de-risk project delivery by taking a more proactive approach. This can be nearly impossible when project leads are mired in paperwork and tedious administrative tasks brought on by old processes and dated tools, like traditional spreadsheets. Also, as good stewards of capital, savvy real estate financial partners often evaluate - and prefer - developers based on their ability to operate or transform into a more modern real estate team.

    [Whitepaper] What Works (and Doesn’t) for CRE Software Procurement

    Where Problems Generally Arise

    For many developers that aspire to scale, it is not immediately apparent how they’ll need to transform their existing models and processes. What worked for smaller projects often does not work when executing on projects of greater complexity. Here is a short list based on our research:

    • More volatility in project forecasting.
      When steeped in backward-looking reporting like draw requests, teams are unable to account for any future challenges or opportunities on their projects before they turn into realized cost events. Also, when the number of vendors on a project increases from 50 to 200 or more, critical oversight of those vendors must increase as well to be able to protect the bottom line. Together, the additional variables do not increase risk in a linear fashion, but they play off one another to create far more uncertainty than the proportional risk associated with a smaller project. 
    • Substantial schedule delays due to bottlenecks in decision-making.
      Project DelaysThe larger the project, the more decisions. Unfortunately, hiring additional junior staff or external support to expand a lean development team does not alleviate the need for experienced and informed decision-makers that are all in sync. In fact, adding to the team often exacerbates the issue of internal information silos. This often forces development teams to lean on accounting to pull specific project information to decide critical next steps, instead of having real-time access to information to make informed, fast decisions. 
      Project leads have two choices when trying to manage the constant barrage of decisions thrown their way: lean on gut feelings and instinct instead of data; or experience mini-delays that compound into major schedule overruns over the project lifecycle.
    • Schedule slippage due to slow vendor pay cycles.
      Compiling a 100-page draw request for a smaller project is a different challenge than compiling a 500-page draw request for a large, complex development; it often takes teams 2+ weeks to pull the latter together, not to mention the time spent on back-and-forth with financial partners (due to increased queries about the content of the package and substantially more instances of manual error occurring as the funding request is compiled). If a team is unable to resolve monthly draw request errors or discrepancies with their lenders in a timely manner, vendor payments will ultimately be delayed. Unpaid vendors will deprioritize a project, reduce manpower, stop showing up to the site and cause significant - and costly - delays.
    • Slow, ineffective decision-making.
      Making one suboptimal decision may not negatively impact projects in a major way, but on large-scale projects that require thousands of decisions over its lifespan, it can create major issues. If teams do not have access to critical project information that enable them to make swift, smart decisions, they risk blundering at least 10-20% of those decisions, which will negatively impact a project’s direction, increase risk and could lead to negative financial consequences. 
    • Inability to navigate challenges or opportunities on a project.
      Most teams rely on their monthly draw requests to understand what is and could happen on a project. Not only is that a bad management tool for decision-making (due to its backward-looking nature), but it isn’t timely and doesn’t inform a project’s future. On complex projects, teams need more than 12 static financial snapshots per year that only capture what happened 30 days prior. This line of thinking requires teams to lean on luck and happenstance instead of data and informed decision-making. How can a team build credible, data-driven forecasts based off of cost events that occurred 30+ days in the past? 
    • Inability to benchmark or negotiate a proposal or change order.
      Most teams cannot easily access historical information because it’s stored in filing cabinets, disorganized servers or inconsistent spreadsheets on various team members’ desktops. They also experience troubling data silos between external or internal project accountants and the development team. This means it becomes a tedious chore to pull representative data or examples when reviewing a proposal or change order. Even if it’s a vendor that you’ve worked with before on smaller projects, digging up actionable data to make fast decisions can be impossible when you have hundreds of changes on larger projects.
    • Loss of institutional knowledge and key person risk.
      Large, complex projects typically extend for 2 to 4+ years, including final Institutional Knowledgestabilization of the building and any associated fit-out projects. It is highly likely that teams will experience staff turnover during this time - sometimes at very critical junctures. Without a centralized source of real-time information, these teams end up losing key project data that was stored in convoluted spreadsheets and inside the head of individual team members. This creates an unnecessary risk to the success of a project, with teams scrambling to fill knowledge gaps and keep everything running smoothly. 

    Any combination of the above could have a catastrophic effect on a project’s budget. It will also exhaust staff, tarnish a firm’s reputation and hurt a team’s ability to secure capital from future lenders.

    How Modern Developers Are Solving These Problems

    Other high-skilled industries - like finance and manufacturing, which also operate nuanced, capital-intensive processes at scale - recognized similar obstacles years ago. They addressed the specific pain points that led to bigger headaches and have been leveraging modern data-analytics, automation and artificial intelligence to streamline the way they do business and grow aggressively, while protecting investor capital from downside risk.

    In many cases, the root of these problems is continued reliance on tools and processes that don’t scale effectively to accommodate the nuances and intricacies of larger, complex projects. This includes: 1) spreadsheets; 2) manual forecasting and reporting processes; and 3) gut-driven and data-less decision-making.

    Why spreadsheets hold teams back:

    • They are static and often error-prone.
    • The most effective spreadsheets that achieve all the analysis and reporting needs for a team tend to be cumbersome and complex, which can lead to broken formulas and inaccurate calculations.
    • For custom-built models, only one or a handful of individuals may know how to decipher them, undercutting the usefulness as a team-wide decision-making tool.
    Why manual forecasting and reporting processes hold teams back:
    • They require a heavy administrative lift and ongoing upkeep. For example: compiling a 200+ page draw request every month, doing data-entry into spreadsheets and Manual Forecastingorganizing files on a server or in a filing cabinet.
    • They create unnecessary information silos between project decision-makers, accounting teams and their financial partners. Examples include: files scattered between different email inboxes, servers, accountants, etc.
    • These actions can lead to a cascading “house of cards” effect when trying to navigate numerous decisions on large, complex projects. One delayed or poor decision leads to additional missteps in a chain of subsequent events.
    • Valuable time from highly-skilled project talent gets redirected into tedious, low-value admin tasks that could be better spent focusing on the strategic direction of a project or portfolio.
    Why gut-driven and data-less decision-making holds teams back:
    • It forces teams to be reactive - instead of proactive - to the myriad twists and turns of a project.
    • It leads to guesswork and reliance on “shotgun” decision-making, instead of experience-driven intuition coupled with data-empowered decision-making.
    • It fails to leverage project teams’ most valuable asset: current and historical project data. Without this, teams are limiting their potential to be more effective in achieving easier, consistent and scalable predictable outcomes on projects.
    • It impacts stakeholder confidence. Stakeholders demand increased visibility and transparency and want project teams that are organized, strategic and able to make fast, informed decisions.
    • It slows down and derails projects. Without data, decisions are made slower and with less certainty, and therefore have a higher likelihood of impacting the project’s overall budget and timeline.

    While the tried and true spreadsheet or legacy construction application was able to support the delivery of smaller, less complex projects of the past, it won’t suffice for larger, $100million+ projects. Nor will it appeal to more sophisticated financial partners that have exponentially more financial exposure at risk.

    And, no, scaling a project team to accommodate the heightened expectations and risk is not the answer. That won’t help increase visibility into a project, access to real-time data to inform strategic decision-making or a team’s ability to be proactive. Not to mention, do teams really want to incur the additional overhead cost of hiring highly-skilled talent to work below their potential by managing data entry and other tedious administrative tasks?

    Instead, teams need to demonstrate to their financial partners that they are an organized, modern firm that can provide timely and coherent updates throughout the lifetime of a project. The only way to do that successfully? Technology built specifically for project delivery teams. There is far too much nuance and far too much at risk on a project to leave its success in the hands of a generic, error-prone spreadsheet or, even worse, an application designed for accountants, property managers, asset managers or construction teams.

    Learn more about how automation, data analytics and proactive intelligence can help you achieve consistent, predictable outcomes across your projects and portfolio. Upgrade with Northspyre.

    CRE software

    Tag(s): Best Practices


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