Lower Variances to Improve Efficiencies
Making or Breaking Your Fix and Flip Deals By Joshua Jensen Over my 15+ years as a real estate investor, there is one single piece of advice that always proves true more than any other: “You make your money when you buy the home.” What this boils down to is the better you negotiate and the tighter you control costs, the better the final profit is. Continuing with my one liners, the core issue of cost variances comes down to one thing I learned in statistics class in graduate school, “Garbage in equals garbage out.” Translating this to renovation scope writing, if you build a renovation scope using bad data, almost uniformly, the scope is going to be incorrect and have large variances. So, what are sources of bad data when it comes to building scopes? To answer this, think about your current process and how you go about writing scopes. Are you building scopes on site? Or are you building them afterwards based upon a site visit? Are you walking the property yourself or with one of your employees? Are you hiring a contractor? Maybe an inspector? Think about how the data is being captured. Are notes being taken in an app? In a notebook? In the back of your mind? Each of these paths create variability and you need to focus on minimizing that variability and working towards collecting a uniform data set from which you can build scopes. Let’s take the example of you sending your own employees to build scopes on site and having them collect data in a mobile app. A zero-variance process would be one where each employee walks the property in the same way, builds a scope with the exact same requirements, and documents it in the mobile app in a standardized way. The latter can be solved with good software along with guiding the employees to walk the property in a similar fashion. The middle component, building scopes with the exact same requirements, will require each employee to understand not only what constitutes adding a scope item, but also the details of that scope item being added. In fact, it is this component that we have seen to be the largest driver of variances in project scopes. By solving this, we have seen variances reduce 10X over the past few years. When we started working with institutional investors in early 2021, our solution for building scopes for our clients was very similar to the industry norm; have our inspectors walk the property and build the scope on site vs. focusing on collecting a uniform dataset. Because of our nationwide scale and fast turnaround times, we grew quickly. But we also quickly found out that our scope variances were abysmal, ranging upwards to 20-30%. The main driver? Our thousands of inspectors did not uniformly understand what constituted adding a work item and the details of that work item. Out of necessity, we made a fundamental shift. Instead of having our inspectors build scopes on site, we focused the software toward having them collect a uniform data set on the property (asset level data, conditions, dimensions) and built software for our end customers, investors, to build scopes remotely using that uniform data set. This simple-yet-profound change, was based upon one principle: It is far easier to train a large workforce to document the current state of a property than to identify what the future state should look like. The latter is best reserved for a smaller, highly leveraged workforce building scopes remotely. To give an example of why this is true, think about the last time you walked a property you were acquiring. I would venture to say that the vast majority of the scope items you added were subjective in nature, for example, replacing the carpets with new LVP flooring. This is more based upon your opinion of an improvement to be completed vs actual facts. Compare this to if you were simply documenting the condition of the property, which is far more objective in nature. For example, is there carpet in the room? By having your large workforce focus on objective inputs and leaving the subjective outputs to a smaller team, you can significantly reduce variance in scopes. When we made this change internally, two things happened. The first, which was expected, was our scope variances decreased by simply minimizing the number of people making subjective outputs. The second, which was more nuanced, was that we started to identify patterns between the objective inputs (i.e., property data) and the subjective outputs (i.e., scope data). While there is still much work to be done, we have begun to automate the creation of scopes using software to further reduce variances and improve overall efficiency in the scoping of renovations. Real estate investing is an extremely diverse industry in terms of processes, and there will never be a one-size-fits-all solution to every business. We found a method that works perfectly for our model and our customers, but it is in no way a solution that will work for everyone. That being said, in principle, the more you can lower your variances, the tighter you can run your business to improve efficiencies and your bottom line.
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