Remote sensing is reshaping the way we understand real estate economics

When Professor Diana Mock was completing her PhD, she had a chance encounter with a roommate that has since inspired new housing research more than 20 years later.

Mok, a professor in DAN’s Department of Management & Organizational Studies who studies the economics of urban real estate, sought to answer how price volatility in a city’s housing market can affect how compact that city is. To answer this question, he had to find a way to accurately measure urban sprawl.

This led to the idea of ​​remote sensing, the ability to study and measure an area using technology such as satellite imagery.

“The first time I was introduced to the discipline of remote sensing was when I saw my roommate using it to detect changes in the migratory patterns of wild geese. I remember at the time I had a passing thought: what about habitats for people?” Mock said.

The conventional method for measuring urban sprawl uses existing data on populations and boundaries (from Statistics Canada) to determine population density on the fringes of a city, but according to Mok this method can be inaccurate.

If a fixed marginal area is large but the area where people actually live is clustered (due to large areas of undeveloped land), the population density could be calculated as low despite a high degree of physical clustering.

In this way, the calculated density is an inaccurate representation of physical reality.

In 2022, Mok collaborated with geography professor Jinfei Wang and then-student Xiaoxuan Sun, both of whom specialize in remote sensing. Together, they launched a truly interdisciplinary approach to real estate research with results published in January 2023.

“The use of satellite imagery and big data and advances in technology are like new treasures that have allowed us to find, improve and implement more creative solutions to answer research questions,” said Mok.

Over the course of two studies, Sun, under the supervision of Mok and Wang, worked to combine the use of satellite imagery with property analysis.

“This interdisciplinary research was a challenge for me, especially without a background in economics, but it was also an opportunity. I am grateful for this opportunity to apply remote sensing technology to the real estate industry and explore some practical situations and issues,” he said. Sun.

Focusing on 11 cities in Canada, the team used remote sensing to examine the ground truth against data estimates of exactly where urban land is being developed outside of cities.

The team found that greater price volatility means potentially more profit in the future for developers, meaning there is an incentive to wait and delay developments. Looking at satellite imagery, they found that cities with higher price volatility are more compact than cities with more stable housing markets.

The implication of their findings is that housing supply could become less responsive to market factors, and even a slight change in demand could create a larger price swing that then adds to the volatile housing market, leading to less construction.

Each pixel of interpreted satellite imagery showed the team the type of land use or cover on a 10m by 10m grid. One challenge was the sheer amount of data, Toronto alone had about seven billion pixels of satellite images in 2016. Despite the sheer volume of images, the team was able to draw their conclusions about the concentration of land use in the city.

Mok argues that real estate can only be better understood by putting it in a geographical context, and researchers need to consider real estate and cities more comprehensively in terms of their dynamics.

“Research is about improvements. How can we improve the way we understand or explain a problem or phenomenon? In our case, I can’t imagine how the project could have been completed without remote sensing,” said Mok.

Moving forward, Mok says they now face a new technological challenge. This first project looked at urban land outside cities, but what about land inside cities? He says the challenge is that satellite imagery doesn’t allow detecting changes in building height over time.

“We need to go beyond 2D and use aerial imagery to detect height changes in 3D. Unfortunately, these images can be expensive to acquire,” said Mok.

But Mok said she feels “rich, in a data sense” because the city of Toronto has donated aerial photos of the entire city to study changes in urban development. This allows interdisciplinary work to continue and reshape our understanding of real estate geography.

The findings are published in the journal Regional Science Papers.