Fabián Sánchez
Executive President of CREA
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View from the Top

Quantifying the Feasibility of Real Estate Projects

Thu, 11/01/2018 - 10:27

Q: What advice would you give to developers of a commercial real estate project and what is your added value in the process?
A: CREA has been working with institutional investors, banks and developers, analyzing their commercial real estate projects in Mexico and Latin America since 2006. Commercial real estate projects must provide an income in the same way that hotels, shopping malls, offices, hospitals, industrial parks and institutional rental housing do. This sort of project must take into account several quantifiable variables, such as the size of the supply, the size of the demand, the price and occupation rate of commercial real estate in an area. Measuring these elements allows the definition of whether a project represents a good or bad opportunity. Our core value proposition is quantifying the economic and financial feasibility of a given project.
The developer should also choose the best investment opportunity, always thinking of the cost opportunity. For example, in financial terms, the internal return rate (IRR) for institutional multifamily rental is not competitive. Investors do not necessarily find real estate attractive with a 12 percent IRR compared to a risk-free rate approaching 8 percent on the secondary market in Mexico, available through Treasury Bonds. Considering the processes and risks involved in a project, which include obtaining permits, the time for construction and hiring staff, it may not always be the best option. Also, we have seen a compression in cap rates in the commercial real estate space over the past few years.  These rates are approaching those of mature markets like the US and Canada. With the perceived higher risk profile of Mexico, these rates are becoming less attractive to investors
Q: How can a developer make a real estate project profitable?
A: First, a developer should analyze what type of commercial real estate is best for a specific property, whether it is a shopping mall, office, industrial or institutional multifamily. We provide a “best use of land” study to assess the greatest use for a property. For example, if a developer wants to build a hotel, it is our role to advise it on the market conditions and highlight other possible uses of land such as an apartment or office building it can lease or sell, which could yield a much higher return when considering the expenses associated with operating a hotel. We give our clients numbers so they can analyze what the most cost-effective option is. We provide not only the market study, but also information on the financials of the project. Seventy percent of our clients work in mixed-use developments.
In quantifying the feasibility of real estate projects, it is hard to speak qualitatively as interviewees can distort the research. But measuring the average expenditure in consumption, income and retail sales of a particular area can provide better insight for the client. INEGI is providing high-quality quantitative information. Qualitative data is hard to come by as it is more about feeling. Ultimately, it does not yield hard data and the representativeness of the samples is low.
Q: What has been CREA’s biggest challenge in venturing into Mexican real estate consultancy?
A: The biggest challenge for us is the lack of information in developing countries. Compared to the rest of Latin America, Mexico has good statistical data. But some sources are not very good or are not focused enough on retail. Also, USMCA and other commercial treaties gave way to many foreign brands that are used to working with data measurement. The challenge becomes one of obtaining the required information to also supply foreign customers, which translates into getting the qualified human capital that can accomplish this. The ultimate challenge is to elevate the use of all this information and turn it into data science. Many companies are using web algorithms to do data mining but this is useless if not combined with data science to analyze the mined information using regression and econometrics.