The newsLINK Group - Setting the Right Price

Editorial Library Category: Multi-Family & Property Management Topics: Setting Prices Title: Setting the Right Price Author: newsLINK Staff Synopsis: Statistics has changed baseball and other sports as well. You probably already know how it happened; Michael Lewis wrote a book about a man named Billy Beane, and the impact of statistics on baseball, when he wrote Moneyball: The Art of Winning an Unfair Game . Editorial: Setting the Right Price 4064 South Highland Drive, Millcreek, Utah 84124 │ thenewslinkgroup.com │ (v) 801.676.9722 │ (tf) 855.747.4003 │ (f) 801.742.5803 Editorial Library | © The newsLINK Group LLC 1 Statistics has changed baseball and other sports as well. You probably already know how it happened; Michael Lewis wrote a book about a man named Billy Beane, and the impact of statistics on baseball, when he wrote Moneyball: The Art of Winning an Unfair Game . The book was published in 2003 and was made into a movie in 2011, starring Brad Pitt and Jonah Hill. The movie was nominated for six Oscars. The idea behind both the movie and the book changed baseball. Billy Beane, the general manager of the Oakland A’s baseball team, used statistics to find good players who were undervalued. His strategy gave him more wins than losses between 1999 and 2006. In 2002, the team’s peak year, the players won 64 percent of all the games they played. In 2004, a 30-year-old Yale graduate named Theo Epstein was the general manager of the Boston Red Sox, and he used Billy Beane’s methods to win the 2004 World Series. He did it again in 2007. The world of baseball took note, and today Billy Beane’s methods are used by all of the teams. November 13, 2011, for example, Slate magazine’s Simon Kuper reported that the New York Yankees had recently hired 21 statisticians. You can use a similar approach to revenue management. Revenue management systems can help you formulate effective rents by implementing predictive analysis. Factors such as occupancy rates, rent rates, information about each apartment, and information about resident behavior can be used to figure out the right price for an apartment at a specific time. The methods are not unique to baseball and the multifamily housing industry. Airlines have been using statistics to help them price tickets since the early 1980s. Other industries followed their example: hotels, car rental companies, train companies, theaters and cinemas, restaurants, and even merchants like Ikea have all gotten in on the idea. Historically, most apartment managers usually set their prices once a year. If no one was coming forward to rent units, that was the time to come up with concessions. The first change in that process occurred when apartment managers realized that some of their apartments were more desirable than others. An apartment with a great view, convenient access, or proximity to the swimming pool or community gym was worth more than an apartment without such a great location. The second change occurred when apartment managers started looking at rents more frequently than just once a year, and also started looking at market conditions so they could respond more quickly to changes that were taking place. The current approach involves real-time pricing strategies that are rooted in statistics. Rents can be evaluated on a season-by-season basis. It’s also possible to evaluate month-to-month leases and to determine whether the number of month-to-month leases in a community poses a threat to revenue. If you have a building with 300 units, for example, and only one or two units are on a month-to-month lease, that’s not a problem. Increase that number to 10 or 15 units and the picture changes. Those tenants could decide to move at any time. Revenue management should predict the following: Current supply and demand. Whether tenants are likely to renew or break their leases. Data science can give you a more accurate picture of the market than it would be possible for you to get otherwise. It can pinpoint traffic originations and comments from guests. It helps you set a more accurate price because it analyzes demand. Suppose you have a down market but you have a forecast for stronger demand. You will want to reduce the price a little bit, but you won’t reduce it as much as you probably would have if you didn’t know demand was going to increase. As a result, you can probably get

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