Camilo Galvis is the CEO and a founding member of AssetCast, a quantitative real estate pricing company. AssetCast has developed an innovative approach for pricing residential real estate assets that optimizes sales profitability and manages the risk of real estate portfolios. The method they have cultivated is built upon more than a decade of research and when used, has been scientifically proven to achieve 3% to 7% total revenue uplift.
New York Real Estate Journal recently had the opportunity to sit down with Galvis to learn more about his experiences in real estate and the creation of AssetCast.
Q: How did you get your start in real estate?
A: After working at IBM as an integrated technology specialist, I started in real estate in 2001. I joined a Florida-based sales and development firm specializing mostly in condos, named Fortune International. At the time, I started a system to better match the company's prospects and clients, real estate agents and the company's available inventory, and was responsible for the unit pricing of several developments either being built by the company or under the wing of the sales and marketing division.
Q: Where did the idea for AssetCast come from?
A: In 2004-2005, at the onset of the economic crisis that exploded in late 2007 and early 2008, I discovered the disconnect that existed - and still exists - between the pricing of the real estate assets in pre- and post-construction and the timely fulfillment of all financial obligations associated with the development of a building or a condo, such as the timely execution of the development plan with the use of pre-development sales, the repayment of a construction loan or other financial obligations, commissions, etc.
I realized that the intuitive pricing practices that are the norm in the real estate industry lag with respect to all the scientific and technological innovation that, for example, has projected the financial industry since the 1960s. The theory and efficiency behind a mortgage backed security was built on some of the best financial and economic minds participating in the development of such a product, which in turn provided tons more liquidity in the real estate market through securitized mortgage loans.
In 2007, everyone with a long position on a development at the onset of the crisis and with financial deadlines to meet with banks and other lenders, were constrained by their own strategies and self-imposed pricing restrictions to navigate the market. They stopped being able to hit the many milestones they required as demand for real estate assets decreased and prevailing, rigid pricing levels cut off a large segment of the demand
I decided to take the time to understand the financial and mathematical tools required and available to bring pricing of real estate assets to technology levels equivalent to those of the financial industry. Knowing who the experts in the industry were and where to find them, at Columbia University, I was privileged enough to have Professors Costis Maglaras and Soulaymane Kachani become, since its origin, part of the AssetCast team.
After many years of academic and industry work, we have identified the proper way to approach real estate asset pricing for large inventories or multi-unit property developments, while also gaining great understanding that a quantitative pricing strategy in the industry can have a revenue uplift for sellers and developers of 3% to 7% when benchmarked against the most common current pricing practices such as using comps and cap-rates.
Q: What differentiates AssetCast from its competitors?
A: AssetCast's technology is based on a decade of academic and industry development. It has been scientifically proven that our method approaches optimality to the largest extent possible: optimality as defined by revenue maximization for developers and sellers, as dictated by absorption speed and financial milestones, which simultaneously provides transparency and structure to the market and ultimately to the buyers.
Q: How has technology impacted the industry?
A: We are currently working to promote an industry-wide adoption of the technology. If we can get to a high percentage level of adoption, 20% or greater, we would make the industry extremely efficient in terms of the understanding of a property price and the associated expected time to see absorption, when a transaction occurs, which would thus result in an industry that understands it's demand and supply dynamics in real time. To illustrate, the direction of the real estate industry in 2005, prior to the crisis, would have become evident in a matter of hours instead of months, time that can prove fundamental in preventing such predicaments.
Q: What does the future of pricing residential real estate look like?
A: The future of residential real pricing looks like AssetCast. More specifically, having access in real time to price information not only gives you a number figure, but explains the expected time to see a sale materialize at any certain price level. Equivalently, you are able to understand what the maximum price of a property would be if you want to sell it within a week versus selling the same property within six months or a year.
The latter brings continuous and real time accuracy to the market in a way that sellers, buyers, lenders and financial institutions can rely on, not only minimizing the current risk levels that the latter currently manage when providing liquidity to the real estate market but, since information is complete and understood by everyone, we could envision a market where you can purchase a property with just a tap of a button on your phone, once a buyer has made a decision, and having all lending, financial, property assessment and taxes happen automatically under such a system.
Q: Why does real time pricing matter to developers? To sales and marketing teams? To the consumer?
A: To better understand the benefits, it might be best to first understand the problem. One of the studies we conducted observed the difference between the asking and closing price of all new real estate properties on the Upper West Side of Manhattan in N.Y.C. from 2007 to date. The miss-match on the expectation of the seller and the expectation of the buyer is, on average, a 6.5% as observed by the closing price and the initial listing or asking price.
Incorporating real time pricing under our framework reduces this volatility by taking the guessing game out of the equation, in a way that maximizes total revenues (under current pricing practices, this is the 3% to 7% total revenue update we expect from the implementation of our model). The latter is the fundamental component as it directly affects the bottom-line. You are not leaving money on the table as you rigorously manage your inventory sales and receive immediate feedback information of the market dynamics so that, equivalently, you can better adjust yourself under your best business interests in development and sales and marketing. Tags: