A good strategic plan will allow for certain unanticipated future conditions and still result in a reasonable profit at an acceptable risk, whereas a poor strategic plan, even with perfect conditions, will still eventually result in financial loss and/or excessive risk.
Real Estate Development is an inherently high-risk, high-return endeavor. Any strategy that fails to take note of this historical lesson is doomed to repeat it.
All design decisions, including the overall strategic plan, should be based upon a data-driven approach that examines the specific market participants.
Who is the customer?
What is his age, income and household composition?
What are his wants, needs, and desires with respect to housing?
Who are the competitors, not only now, but during our exposure period?
What are its strengths, and weaknesses?
How can it be defeated?
To better understand this methodology, an analogy to the old “ceilings and floors” approach is relevant. Using this approach, one would postulate that the quality of the subject was above one comparable (the floor) and below another (the ceiling). This approach was useful, but limited. It resulted in an imprecise range of pricing for the subject.
Qualitative Market Analysis is superior in that it quantifies the degree to which the subject is superior or inferior to its comparables. The result is a more precise pricing model to predict the behavior of prospective customers.
Specifically, this methodology will use the ratio of qualitative measurements and multiply it by the empirical prices paid by customers to the comparables. It has been my experience that this will result in a highly accurate pricing model of what customers will be willing to pay for a certain product in a certain location.
The following are my assumptions in the endeavor: First, humans intuitively ascertain value via the division of quality by price. (Please note it is not my assertion that humans are consciously performing this mathematical exercise, but rather that it is a completely intuitive process) Therefore, if one can measure quality, and price is known, one can develop a highly accurate model of how humans assess value in multifamily real estate.
How much additional rent, if any, will our target customer be willing to pay for enhancements to the building, landscaping, amenities, or finishes such as flooring, cabinetry, countertops, appliances, plumbing fixtures, lighting fixtures, door hardware, etc?
The economic risks associated with failing in this endeavor are substantial.
Each of the decisions must be accretive to the project (and they will be because we will know how much additional rent will be paid by the customer and the cost of each of the product specification alternatives).
With respect to unit mix, we obtain the age, income, and household composition demographics for our submarket. These are aggregate data, and therefore pertain to all households in the submarket, regardless of whether they are renter households or ownership households. Therefore, it is necessary for us to disaggregate these data on the basis of marginal propensity to consume new for-rent housing.
Once our data are disaggregated, we can then look at the percentage of single-person households versus two or three-person households. This gives us an insight into the optimal unit mix.
With respect to unit size, we must optimize the relationship between net operating income per square foot and total project cost per square foot.
Regression analysis is a statistical analysis tool that allows us to understand causality within a complex data set. It is predictive in nature, and, if employed properly, can be highly useful to real estate developers.
Specifically, regression analysis utilizes the “least squares” method to fit a line through a set of observations. We can analyze how a single dependent variable is affected by the values of one or more independent variables – for example, how an athlete’s performance is affected by such factors as age, height, and weight. We can apportion shares in the performance measure to each of these three factors, based on a set of performance data, and then use the results to predict the performance of a new, untested athlete.
First, we use regression analysis to optimize the net operating income, keeping in mind that because many operational expenses are driven by the number of units and are not the number of square feet, operational expenses per square foot actually decrease with larger unit sizes.
Finally, the relationship between net operating income and total project cost will be optimized, keeping in mind that there are a number of factors to be considered, for example, parking costs and the density of kitchens and baths.
Regression analysis also screens out the “noise” associated with atypical transactions and helps us avoid product selection decisions that might otherwise be emotionally driven or have a limited appeal to our target market.
Real Estate Development is an inherently high-risk, high-return endeavor. Any strategy that fails to take note of this historical lesson is doomed to repeat it. Therefore, it is our strategy to prioritize Risk Management as a primary goal.
