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

��AV 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

Mason’s School of Business co-hosting online conference on the future of local retail business

Colleen Rich — Wed, 20 Jan 2021 16:29:11 +0000

Mason’s School of Business co-hosting online conference on the future of local retail business Colleen Rich Wed, 01/20/2021 - 11:29

��AV’s School of Business is co-hosting an online conference on Thursday, Feb. 11, on preserving and rebuilding a viable retail industry in the Washington, D.C., area.

During the webinar, speakers will discuss how the coronavirus pandemic and consumer trends have changed the retail landscape and how to respond by reimagining retail spaces. The Center for Retail Transformation and the Center for Real Estate Entrepreneurship are hosting the full-day event with the Washington, D.C., chapter of the American Institute of Architects.

“We’ll discuss the full picture of retail space, and we’ll project what’s next,” said Eric Maribojoc, executive director of Mason’s Center for Real Estate Entrepreneurship.

Kathleen Spencer, program director of the Washington, D.C., chapter of the American Institute of Architects, said that the organization “looks forward to another fruitful partnership with ��AV during the February symposium that will allow industry experts and participants to explore ways to design and build back better after the challenges of the pandemic.”

While the coronavirus pandemic has brought challenges to the real estate industry, Maribojoc said that’s not the whole story. Some portions of the real estate industry, such as housing sales, data centers, and industrial space, are doing well, while other areas, such as office space, are struggling.

But retail, Maribojoc said, is a “tale of two worlds.”

“There’s a section of retail that has recovered very nicely, such as strip malls that have groceries and pharmacies in them,” said Maribojoc. “But retail centers that are heavily dependent on restaurants, sports venues or movie theaters are not doing as well.”

Maribojoc emphasized that high-end malls that were doing well before the pandemic, such as Tyson’s Corner Center in McLean, have recovered since the shutdown in the spring. However, malls that were struggling before the pandemic are continuing to lose business, he said.

“In many ways, the pandemic accelerated consumer trends, such as a move towards online shopping,” Maribojoc said.

The business school’s Center for Retail Transformation, which opened in 2020, is focused on helping small to mid-sized businesses think creatively as consumer expectations change. Gautham Vadakkepatt, the center’s director and an assistant professor of marketing, said that retailers are closing down because of the pandemic, and many will not reopen. To survive, local retailers will have to be able to pivot to match changing consumer habits, such as an increased interest in delivery.

“There’s an effort to support local businesses, so it’s helpful to create increased awareness about local options available to consumers,” said Vadakkepatt. “But physical stores are also going to need to react to the new world, be agile and innovative. Those businesses that have survived have been nimble and creative, such as restaurants that have also stocked themselves to work as small groceries.”

Vadakkepatt and Maribojoc say that the Washington, D.C., area’s retail industry is suffering, and that they hope their efforts will benefit the local retail industry.

“We need to work on ways to get the sector back in to a thriving state,” said Vadakkepatt.

Topics

Campus News

Research

Costello College of Business

real estate entrepreneurship