Big Data: The New "Location, Location, Location" for Commercial Real Estate

The massive amount of capital chasing commercial real estate deals today has forced investors to seek out tools to be distinguished from the competition. Professionals’ increasingly turning to technology for solutions leads to booming investments in this field. One of the most effective tools is non-traditional data such as cell phone pings data.

Compared to traditional financial data, cell phone data contains rich information about human mobility and behavior with unprecedented granularity: With this data, analysts can map the spatial distribution and temporal changes in population density, identify users' home and work locations, estimate commuters' origin and destination flows, infer the functionality of different urban areas, and make smarter investment decisions and operating strategies.

Minyi Bigdata2

Habidatum catchment areas with different travel times and modes of transport

Analyzing macro commuting patterns and implications on city functionAcademic mostly applies cell phone pings to study macro-level commuting or migration patterns. Such as the work of Couture et al. (2021), which used PlaceIQ smartphone movement data to quantify cross-regional movement and social contact; and the work by Miyauchi et al. (2021), who use cell phone data to study the rich patterns of spatial mobility and find that it will give rise to consumption externalities across locations -- having a good reason to visit one place makes it more attractive to visit other locations that are nearby or along the way.

In practice, using the daytime and nighttime cell phone location can also identify the urban cluster, the commuter flows between clusters, understand what function different spaces serve, and evaluate the sufficiency of services and infrastructure provided in other areas. Understanding this information from cell phone data has important implications for discovering investment opportunities and strategies when selecting the region options.

Optimizing property investment location choice by redefining catchment area
Retail property investors can optimize location choices by leveraging cell phone data to identify real-time customer activity and explore opportunities for growth and adaptation. The catchment area -- the servicing area of a certain location -- might not be a uniform circle as the broker expected. Instead, it could be an irregular shape extended in unexpected directions based on real visitor traffic. This could change due to geographic barriers, traffic, and congestion. In these cases, the travel time tells the story better than the distance. Mobile location data offers a window into customer trade areas given retail consumer travel time, allowing for the delineation of digital boundaries around cities, neighborhoods, or even specific stores. These services area’s geographic coverage cannot be observed from traditional data such as demographic statistics.

More precise measurement of “real occupancy” of property
Identify the performance of the property just based on the current base rent income or operating income might be misleading: rental income is stable as long as the signed lease still exists, but that wouldn’t show the whole picture of whether people are visiting the property, which is especially important for office property and retail property, and will decide how much spaces the tenant need currently, and will consequently affect their space demand when the leasing contract expired. For example, even though the average office vacancy rate is only 13% in the post-Covid era -- which seems not that bad, granular data tells us a different story: data from the Kastle Systems, which tracks the swiping card of the entrance of 10 biggest office market in united states shows that the average office attendance is about only half of pre-pandemic levels. Data from other footprint tracking companies like Placer. ai also shows a similar conclusion of a 60%-65% visitation. Thus the real traffic data can measure visits more accurately and help the property owner to have more reasonable expectations of the space demand, and take action in advance to improve performance.

Identify best property usage using comparative advantage
If the property’s walking distance is far away from amenities or the city center, does it mean it cannot be a good investment target? Or if the property is located in a busy area, does it necessarily mean that it does reach the optimal profitability potential? To answer these questions, we need to specify functions the properties are most likely to fulfill and whether the current usage is matched with it. Usually, we use the quality and amenities of the area surrounding the property to decide whether the location of the property is a good one. However, the comparative advantage of the location is determined by not only just business diversity but also the type of traffic flow and traffic accessibility. For example, a property that is 30 min walking from nearby amenities but 5 min driving from the busy area, might be an efficient place to build a warehouse, which only relies on automobile accessibility. But for investors who want to build a hotel, the accessibility will likely depend upon the travel time for walking, driving, and public transport. Using the cell phone visitation data could help with doing statistics of the catchment area with different transportation and travel times, and provide insights into discovering what are the best use options for property.

The future of commercial real estate lies in data-driven insight, investors are embracing non-traditional data sources. While the amenities surrounding location data present big opportunities, visitation mode is becoming to play an essential role, which relies on cell phone footprint data. Nowadays, no wonder big data is emerging as the new slogan of "Location, Location, Location."


Couture, V., Dingel, J. I., Green, A., Handbury, J., & Williams, K. R. (2022). JUE Insight: Measuring movement and social contact with smartphone data: a real-time application to COVID-19. Journal of Urban Economics, 127, 103328.

Miyauchi, Y., Nakajima, K., & Redding, S. J. (2021). The economics of spatial mobility: Theory and evidence using