Professor applies statistics and AI to land use modeling and real estate pricing

Teresa Donnellan — Wed, 29 May 2024 16:18:12 +0000

Professor applies statistics and AI to land use modeling and real estate pricing Teresa Donnellan Wed, 05/29/2024 - 12:18

In This Story

Abolfazl Safikhani

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.

Abolfazl Safikhani

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.”

Topics

Applied Statistics

Department of Statistics

Statistics Faculty

Computational statistics

Big Data

big data analytics

real estate entrepreneurship

Artificial Intelligence

Research

Will China surpass the US in AI technology? Mason experts weigh in.

Melanie Balog — Tue, 23 Apr 2019 21:33:01 +0000

Will China surpass the US in AI technology? Mason experts weigh in. Melanie Balog Tue, 04/23/2019 - 17:33

The United States and China recognize artificial intelligence (AI) as critical to the future of the global economy and military. But the United States needs to speed up its development efforts and invest significantly more in research to remain the world leader, said George Mason University computer science professors Amarda Shehu and Craig Yu.

China released a policy note in 2017 predicting it would be the world leader in AI by 2030. A new report shows China is about a decade ahead of schedule, based on its output of highly cited research papers, which will soon be greater than those coming out of the United States.

One reason China is outpacing its prediction is because of the strategic plan of China’s State Council, Shehu and Yu agreed.

“Washington [D.C.] needs to take a page out of Beijing’s plan and accompany their strategic plan with strategic investments,” Shehu said.

China’s plan mentions more specifics on how investments will be made in AI research and technologies than America’s Artificial Intelligence Initiative does, Shehu said.

China’s investments, including their latest $30 billion venture capital fund, impact state-owned firms, academic and research institutions, commercial companies and startups, Yu said.

“With abundant funding support, government policy support and a big market to serve, some of these entities are able to grow very fast in a very short amount of time,” said Shehu, who gave the example of Sensetime, a Beijing-based company focusing on computer vision technologies that started in 2014 and is now valued at more than $4.5 billion.

But the news that China is expected to exceed the United States in AI research publications doesn’t mean they’re automatically going to be the next world leader in AI, Yu said.

“One needs to consider the impact and innovative side of research publications,” he said. “A significant amount of the most innovative breakthroughs in AI algorithms are still originating in U.S. research institutes—these U.S. institutes also have the largest pool of leading AI researchers.”

There is another challenge for the United States, Shehu said.

“Fundamental AI research happens at U.S. institutions, but it is becoming harder for these institutions to retain their best talent,” Shehu said, adding that a significant amount of AI professionals and PhD graduates choose industry jobs with lucrative benefits instead of staying in academia.

The trend could have a long-term impact as “we may not have enough professors to nurture the next generation of computer scientists and AI visionaries,” Shehu said.

“Universities need to make competitive offers to retain research talent, and it’s imperative to motivate undergraduate students to pursue graduate studies in computer science related fields, including AI,” Yu said. “This is key to maintaining a sustainable talent and innovation pipeline that propels the whole industry and research community.”

Amarda Shehu can be reached at 703-993-4135 or ashehu@gmu.edu.

Craig Yu can be reached at 703-993-4813 or craigyu@gmu.edu.

For more information, contact Mariam Aburdeineh at 703-993-9518 or maburdei@gmu.edu.

About George Mason

George Mason University is Virginia’s largest public research university. Located near Washington, D.C., Mason enrolls 37,000 students from 130 countries and all 50 states. Mason has grown rapidly over the past half-century and is recognized for its innovation and entrepreneurship, remarkable diversity and commitment to accessibility. For more information visit gmu.edu.