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George Mason University statistics professor Abolfazl Safikhani recently applied his cutting-edge, interdisciplinary research to analyzing land use dynamics and property pricing shifts over time, work that underscores the transformative potential of data-driven insights, especially in urban planning and real estate.
Safikhani earned bachelor’s and master’s degrees in mathematics before earning a doctorate in statistics.
“I decided to do a PhD in statistics because throughout the master’s I had become more and more interested in connecting real world problems to data. And I'm very happy that I made that decision,” he said.
Along with a former colleague at the University of Florida in the urban planning department, Safikhani applied machine learning techniques to a dataset comprising millions of land parcels in Florida. The two endeavored to decipher the intricate dynamics of land use transformations over time and predict future developments with unprecedented accuracy. Their predictions surpassed 98% accuracy.
But the team didn't stop with successful predictions. They recognized the importance of understanding the underlying mechanisms driving these predictions. With the addition of a new collaborator, Tianshu Feng in George Mason’s Systems Engineering and Operations Research Department, the researchers aim to present their land use analysis software as explainable artificial intelligence (XAI). By elucidating the black box of machine learning algorithms, Safikhani hopes local government decision-makers and urban planners can confidently leverage the software to optimize resource allocation effectively.
Another of Safikhani’s projects considers land use and value specifically concerning the price of residential real estate. Safikhani’s own experience buying real estate in Fairfax County, Virginia, in 2022, inspired this project. When he asked his real estate agent to estimate a fair price of a certain house, the agent came back with an estimate based on the price of three comparable local properties that had recently sold. Ever a “quant guy,” Safikhani said, he thought there could be a better way: applying the idea of transfer learning.
“The big idea of transfer learning is, within your big data set, try to find areas that have similar dynamics to your area of interest. And then use that similarity to improve your prediction,” Safikhani explained. “So, imagine that there is a little neighborhood somewhere in DC or somewhere in Maryland or somewhere in California that has dynamics very similar to the specific neighborhood where you want to buy a house in Northern Virginia. Once you account for some changes, let's say, regulations and things that are different, then the remaining dynamics are their similarities.”
He continued, “If you only use your neighborhood, you can have three data points. If you use another, similar neighborhood, it's going to be 20. If you use neighborhoods from other places over the 50 states of the U.S., you may end up getting a thousand data points.”
Safikhani is working with a colleague from the University of California – Los Angeles to bring in funding to develop this pricing software. Their preliminary results show the benefit of their proposed model versus current pricing systems.
Safikhani's research is poised to revolutionize sectors like urban planning and real estate. In fact, his research has attracted the attention of startups keen to translate his findings into real estate–disrupting tools.
“It seems there's actually a growing interest in having such AI tools that would understand land use development and then really match it with pricing,” he said. “And sooner or later, this [technology] is going to come out. Platforms like Zillow are doing a good job, but there's much more that can be done.”