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Decision-Making Tools From Real Estate Data | Touchstone Words

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Decision-Making Tools From Real Estate Data

By Katie Lee on 2018-09-17

Real estate data is always increasing as estates are bought and sold, but never did experts suggest that we would have the amount of data we have today. This data, moreover, provides an opportunity for researchers and computer scientists alike to analyze trends in order to develop models. These models could serve as predictive formulas to mathematically suggest the probability for a certain person to buy estate based on multiple factors such as income.


Recently, MIT has started the Real Estate Price Dynamic Research Platform (or the REPD Platform) in response to the cmpounding data. This platform has the potential to explore possible models and provide real-world solutions to discrepancies in those models.


David Geltner, the principal investigator, says "Real estate investment has always been a world with a lack of good empirical data," says Geltner, a pioneer in the development of transaction price based commercial property price and investment performance indices over a decade ago. "But with the digital revolution, there's an explosion of data aggregators, information companies, and other sources of empirical data relevant to commercial real estate investment."


If it is known that real estate data has basically been compounding continuously, then why hasn’t anyone wanted to make a mathematical model at any point in history? It is true that real estate data has been ever increasing as more and more people enter the market for homes, but it is also accurate to say that we are more capable today to analyze big data than any other time in the past. Essentially, our econometric capabilities of handling data have improved due to the invention of computers and algorithms, especially big data algorithms which allow for large amounts of data to be analyzed quickly.


The term econometric refers to the tools of statistic and mathematical methods utilized by economists for real-world economy data. Thus if econometric capabilities improve, then we will be able to handle more of this real-world data to provide accurate models, which, in this case, would be used to predict the probability of a buyer to actually buy a home or other estate.


The research and analysis performed by the platform will be available to the public in whole. But the goal of the platform is to expand the field of real estate investment analytics in areas such as price and rent index. A minor goal of the platform is also to improve big-data analytics which have improved vastly over the years, but are still capable of further advancement.


There are other analytic platforms that employ the same generalized methods as the REPD Platform, but the REPD Platform has an advantage over other methods of real estate analysis in that it also uses prior knowledge as well as economic theory which, in turn, makes the REPD Platform models far more accurate than the models from other platform.


A very important issue that the researchers have to take into account is location. Defining factors in the probability of buying estate may be different in different locales. As Van De Minne, the lead researcher of the platform, said "What is going on in San Francisco in terms of asset pricing may be totally different from what is going on in Dallas. And even what's going on in the Dallas central business district is different from what's going on in North Dallas." The REPD platform, if successful, may provide a basis for further research into the compounding real estate data of our world.


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