As the US restaurant sector starts to get back on its feet, we have been monitoring foot traffic numbers with interest to see which are turning around most quickly.
The 3 chains we analyzed saw significant falls in traffic between mid-March and the end of April. This was most dramatic for Darden, where foot traffic fell to almost zero given the sit-down nature of the chain's restaurants. Yum brands saw a 60% fall in foot traffic at the nadir and Chipotle a 70% drop.
Since the beginning of May, all have started to see a steady increase in foot traffic. Yum is up approximately 40% from its low, Chipotle is up almost 30% and foot traffic at Darden is up about 20% since the end of April.
Importantly, our analysis takes into account a manual review of opening and closing times of every location, to ensure the data is accurate - this is information that may not be available or accurate via other sources.
The steady increase in foot traffic to all three restaurant chains reflects two related trends: first, people are going out more as restrictions are lifted; and, secondly, some restaurants are now allowing people to enter and even dine-in, rather than picking up take-out at the door, which increases linger times at these locations.
To complement this data, we also reviewed our Hotspots map. This interactive map highlights city blocks where more than 50 people have gathered for longer than 15 minutes, excluding residences.
The screenshot of our map below shows the difference in social gathering between April 1 and May 24 (red = more gathering, green = less). Based on the increased overall mobility of the US population between these dates, it seems restaurants may be a lagging indicator for recovery.
For more detailed data please contact us directly contact us.
Geolocation data gives us an almost infinite number of ways to track and measure the extent and impact of relaxing social distancing measures, such as we are starting to witness in many states across the country.
For us -- based on our extensive analysis of this kind of data -- "miles driven" is a key leading indicator of behavior. Our proprietary miles driven index is an accurate way to track how many miles individuals in the US are driving on a daily basis.
During normal times in the US, the aggregate number of miles driven is typically higher on weekends, as Americans visit friends, go shopping and ferry their kids to sports and other activities.
On March 22, we saw this trend sharply reversed. With the majority of Americans sheltering in place the chart shows a dip, rather than a peak, in miles driven on weekends during March and April. Then, starting the first weekend in May, we see traffic has begun reverting to historic patterns.
The last three weekends have once again seen spikes on Saturdays and Sundays:
This is a clear indicator that people are leaving their homes more, and traveling further distances. But it doesn't tell us whether they are getting out of their cars, to shop or use other services. For a more complete picture, we can look at this data alongside foot traffic figures for the consumer discretionary sector.
Our Consumer Discretionary index shows average weekly traffic for this sector was down over 50% in mid-April. Over the last week, however, foot traffic at stores focused on discretionary items was only down 22%. A significant recovery from the low that supports the trend seen in the miles driven data, and indicating that more and more consumers are leaving home and heading back to stores.
Once again, it's important to remember that single data point may offer an incomplete picture. While overall in the US, people may be venturing out more frequently and further afield, the story is not necessarily the same in each state.
The chart below shows the stark difference in our Consumer Discretionary index for Texas (red) and New York (blue). In both states we can see traffic ticking up, but in New York the bottom of the U looks like it will be longer and lower than in Texas.
For more information on our Miles Driven, Consumer Discretionary or other foot traffic indices, please contact us.
Over the past several weeks, meat processing plants around the world have been receiving increased attention in the media. The high numbers of COVID-19 cases in these locations, where physical distancing can be difficult, has affected meat supply chains across the country.
While some plants closed, many have now reopened. Physical distancing measures are in place but the number of workers on production lines remains depressed with many having fallen sick and others cautious about returning to work due to the risk of infection.
We looked at foot traffic for meat processing facilities in the 5 states with the highest number of plants: Arkansas, Georgia, Iowa, Mississippi and Texas.
The chart shows the percentage change in traffic since the start of the year. In the first two months, foot traffic was fairly steady. In March, for most states other than Iowa, traffic started its downward trend. The current number of visitors seen at meat plants in these 5 keys states is down on average between 13% and 21% since January. The exception is Mississippi, where traffic has recovered to almost the same levels as the start of the year.
In the following weeks we will watch to see how these plants are able to balance the risks of COVID-19 to their workforces, with the market demand for meat products, with foot traffic as an accurate leading indicator of potential supply chain issues for meat availability in the US.
For more detailed data please contact us.
Cellphone geolocation data has proven to be an invaluable tool in helping us understand how COVID-19 is evolving, and to support our recovery.
We have been working closely with clients and other partners to help track the evolution of the pandemic and how it is impacting different sectors, among the most important of which is hospitals.
To help visualize the environment that hospitals are operating under, as well as to provide a measure of hospital bed utilization which is an important metric of the available capacity of the hospitals, we generated a detailed analysis of hospital traffic, filtering out doctors, staff, and visits that did not result in long-term admissions, in order to estimate the actual hospital bed utilization during the pandemic.
Our ongoing analysis includes over 13,000 hospitals and clinics mostly in the US and territories, plus some in the UK, with the data updated daily.
A chart of average daily hospital admissions in New York State, Georgia and Louisiana shows an interesting, though not wholly unexpected trend.
In mid-March, when it became clear that the pandemic was accelerating in the US, we saw hospital admission in all US states fall significantly, as the majority of elective procedures were cancelled.
Within a week, however, we saw this fall level-off and begin to rise again as COVID cases increased. With New York seeing almost twice as many admissions as Georgia:
and almost three times as many as Louisiana during the third week of April:
While New York was clearly the hardest hit state, there are important differences within the state itself, with New York City seeing a marked increase in daily admissions through April, while those in Albany gradually fell over the same period:
Tracking admissions is an immediate way to view how these vital resources are coping with a new influx of patients across the US states and around the world.
For more detailed data please contact us.
The biggest mall operator in the US, Simon Group, announced this week that it will open dozens of locations across the US. With Macy's and other retailers following suit in states where restrictions have been relaxed, investors, consumers and retailers will be watching with interest to see how soon visitor numbers begin to recover.
One indicator of customer appetite for returning to stores in the US is to look at how foot traffic to malls in China has fared since restrictions began to be lifted in late February and early March.
Our China Mall index includes a representative sample of the biggest malls in mainland China. Following a steep decline in traffic numbers in January, visitor numbers to malls has been increasing very gradually but with a clear upward trend. By mid-April, foot traffic was trending towards almost 30% of that seen in December 2019, before the pandemic had hit.
We will be watching with interest as an increasing number of retailers begin to reopen and customers become more comfortable with going back to shopping, albeit in a much changed environment.
When we launched Advan 5 years ago, we were really excited at the prospect of developing a new type of dataset. One that would give investors an alternative view into the companies and markets that they care about.
We are data geeks at heart and we spent years agonizing over how to make our data more precise; manually drawing geofences around millions of locations, for thousands of companies. Normalizing data became almost an obsession. We wanted to remove any possible bias - from the way data had been collected to how it was mapped. By eliminating noise from the data, and backtesting it against multiple sources we made sure traders and investors could rely on it for critical decisions.
With the markets steady and rising, investors looking for an edge found that this new technology could provide one. But while we and our clients continuously model potential risks to specific investments and broad portfolios, few of us anticipated the extent of the situation we now find ourselves in.
Now, markets are trying to make sense of the unpredictable. While governments and authorities are balancing decisions that literally mean life or death for many, balanced against a potentially catastrophic effect on our economies.
Over the past several weeks, as the crisis due to the COVID-19 pandemic has evolved, it has become clear that geolocation data can play a key role by also providing an alternative view into the world we live in. And that our focus on tracking foot traffic - with an accuracy of 10 meters - could serve a broader public good.
By pinpointing the number of people at any location across the country, within hours, it is possible to understand how and where people are moving at any given time. Retailers can track which location their customers are visiting, and where they have travelled from. They can easily compare data for different US States, and even in other countries. For investors, location data provides the most accurate source of information about likely company performance.
Perhaps most importantly, for governments and authorities, location data can help with decision-making that supports both businesses and individuals in their communities.
To help make it easier to understand the changing landscape, we developed an interactive map of global hotspots, that instantly shows where groups of more than 50 people are gathering, for more than 15 minutes at a time. This offers an informative and accurate picture of movement around the world, on an almost real-time basis. In addition, we've partnered with Knoema, which aggregated visualizations from multiple alternative data sources to its website to help policy-makers, media outlets and the public track the spread of COVID-19 and measure its impact.
The examples below show how foot traffic changed in central London, UK between mid-January and mid-April.
Each map shows the 200 busiest locations on the respective dates. The size of the red spots is proportionate to the total number of people seen at each location and excludes residents.
In January, you can see many large hotspots, with particular concentrations in the City and Canary Wharf as well as in West London - places where people work and shop.
London - January 15, 2020
In April, you can see the stark difference, once the majority of people are working from home. With hotspots primarily around hospitals and other essential businesses.
As businesses and workplaces gradually reopen, we will be able to easily see how behavior is shifting and the extent to which people are adhering to mandates that limit social interaction. We also hope that it will aid a swift recovery - with fast and accurate data at its core.
To view our Hotspot map, please visit: https://www.advan.us/hotspots/
This week in our Covid-19 report using foot traffic data from Advan we examine the impact of the virus on student housing. We looked at three student housing buildings, located in College Station (Texas A&M), Boulder (University of Colorado) and Syracuse. All three universities sent students home and switched to online learning in mid-March. Knowing how quickly tenants reacted to the shutdown and when they come back allows owners, operators and leasing agents to make focused decisions that could potentially enhance value and lead to outperformance.
Foot traffic for the property near Texas A&M fell between December and February, so the particular property was already facing challenges from other factors. The properties near Syracuse and Boulder saw significant increases in February and were both up the first week of March.
Unfortunately, due to differences in the timing of spring break this year and last and the time that each school transitioned to online learning, the first week of March is the only week that permits valid year over year (yoy) comparisons. All three schools had yoy declines of 30% or more.
By early April -- as schools continued online learning, states, cities and counties imposed more restrictions, and society worked to bend the curve -- foot traffic fell by more than 50% at all three properties. At the Syracuse property, foot traffic fell 96%! Some students remained at the properties near the other two schools, but clearly not many.
Syracuse announced on March 13 that students would begin online learning after Spring Break, the week of March 16. UC-Boulder had the same timeline with students gone the week of March 16. Texas A&M had students leave by March 23. We can see the immediate impact of those announcements on foot traffic at each building.
Given its real time nature, how could a real estate owner, operator or investor use this data? The decline in foot traffic shows a decrease in people in the building. Once tenants had left, a deep clean to help insulate the common areas of the building from the virus may be done . This would disrupt fewer tenants and help ease the anxiety of the remaining tenants. By decreasing the burden on the tenant base, the potential for renewals increases.
This data could be used to change how the building is operated and leased. Accessing real time data to adjust strategy and operations allows for better decision making and hopefully outperformance. Real time data may lead to cost-savings or just as important may prevent future spikes with operating problems averted.
Advan and Eigen10 Advisors are working to track and analyze the data in a broader and more long-term indexed fashion. For additional analysis of your property contact Eddy Hribar at or Jeff Havsy at .
1 NAA and NMHC have issued guidelines for apartment owners.
Over the past weeks we have been working on new ways to analyze foot traffic, in order to help our clients understand how patterns of movement are changing, and what this means for businesses.
As part of our COVID-19 package we have developed over 200 indices for multiple sectors and industries including restaurants, grocery stores, electronics, clothing/accessories. These sectors can be segmented by US state as a way to visualize the response to COVID19 by using foot traffic as a proxy for social distancing.
As an example, in the chart below, we've plotted our Commercial and Office REIT Index in two key states - New York and Florida. This Index is composed of more than a hundred REITS from malls and shopping centers to office buildings across the country.
It shows the daily percentage change in foot traffic for these type of locations in each state. In January and February, during the early days of COVID-19 - and before businesses and offices started to close - you can see how the typical weekly pattern differs between the two states. New York has more office buildings and therefore sees more commercial foot traffic during the week. There is also less variation between weekday traffic and weekend traffic (plenty of shopping in New York as well) as the chart shows. Florida sees greater variation, with bigger peaks on weekends to its larger proportion of malls and shopping centers, and fewer office buildings.
While shopping centers and work offices in New York and Florida closed on approximately the same day on both states, we can see that New York reacted more quickly and decisively, with a steeper drop in their daily footfall index and has maintained this downward trend.
Using this index as a barometer we can see how residents of the two states differed in their response. By this measure, it seems that New York took the order to implement social distancing more stringently as soon as it was put in place.
It is important to note that this measure of social distancing is not necessarily correlated with the number of infections in each state. But it does provide a window into how residents are behaving and will give us key insights into the pace of recovery across the various States as offices and stores begin to reopen.
Average daily traffic to Amazon warehouses spiked dramatically in March as the retailer ramped up its staff. Average daily traffic in March was up 43% compared to year earlier, far outstripping seasonal staffing increases.
In the first few days of April the number of Amazon employees seen at their warehouses has continued to grow, as demand for home delivery surges, with increasing numbers of people following strict orders from local authorities to stay home.
Historically, based on Advan's analysis, the number of employees seen at Amazon warehouses has a strong correlation - approximately 0.85 - with company revenues.
Advan also tracked truck traffic for Amazon across the US. This measure shows a slight increase in delivery traffic, but not as pronounced as the increase in employees. This may reflect a reduction in the number of available drivers, or the Amazon's ability to efficiently consolidate deliveries in order to keep up with demand.
The coming days will be critical for the retailers as it navigates the difficult line between fulfilling the essential duty of delivering products to customers staying home, and protecting its employees from the COVID-19 virus through distancing measures within the workplace.
As we continue our trip across the property types analyzing the change in foot traffic using Advan data and the impact of Covid-19 on commercial real estate, Eigen 10 advisors examines the industrial property sector this week. The data this week comes from the Port of LA and an industrial warehouse park outside of Chicago.
The industrial park is a mix of food distribution, light manufacturing, auto part distribution and other uses. We picked this park since it has a mix of critical and non-critical components within the distribution chain.
Foot traffic at both the port and industrial park was down 17% this February compared to February last year1. Foot traffic was already down in both locations due to the virus, having fallen 20% at the port and 14% at the industrial park in January. The impact at the port continued into March with foot traffic down 19% the first week and 29% the second week. In contrast, foot traffic at the park started to rebound with the decline in the first week of March down to 6% and 13% in the second week of March.
By the third week, restrictions had been put in place in both California and Illinois with regards to non-essential workers. Retail and restaurant sales had slowed dramatically. You can see the impact on foot traffic in both locations -- down more than 40% compared to the same week the previous year and that trend continued last week.
There is still foot traffic in both locations as ships arrive at the port to be unloaded and food distribution to supermarkets and other food retailers remains strong. However, other types of retail sales have plummeted, and a lot of manufacturing has stopped or slowed.
As China and other parts of Asia begin to rebound, there will likely be some increase in activity, though many retailers have limited or halted new shipments. Depending on what products are stored in the warehouses, activity will be impacted; traffic will remain strong for essential uses but greatly reduced for those deemed non-essential.
As the economy begins to recover and retailers prepare for the peak fall and Christmas season, port traffic should start to return towards previous levels. Depending on how the virus impacts both economic growth and any changes to supply chains, returning to previous levels may happen sooner or later than expectations. Warehouse activity will respond quickly to the pace of economic growth.
Advan and Eigen10 Advisors are working to track and analyze the data in a broader and more long-term indexed fashion. For additional analysis of your property contact Eddy Hribar at or Jeff Havsy at .
1 The analysis used the first 28 days of February 2020 to account for the additional day in the month due to Leap Year.
Last week using data from Advan we showed the impact of Covid-19 on retail real estate. Our analysis this week looks at the impact of the virus on office real estate. Using data from Advan that tracks anonymized cell phone movement over time, Eigen10 Advisors examined the change in foot traffic for a New York City office building.
The building’s vacancy rate did not change significantly between first quarter 2019 and 2020, from low single digits to mid-single digits. So, the change in occupancy didn’t significantly impact foot traffic.
Foot traffic in the building was up 9% this February compared to last year1. Foot traffic was relatively flat compared to a year ago for the first week in March. As the virus spread and New York felt the impact, foot traffic declined the second week of March, falling 17%. As the full impact of social distancing hit and non-essential workers were asked to stay home, foot traffic continued to fall. By the third week of March, foot traffic had fallen 75% compared to the same week a year ago. A staggering decline and one that shows the true impact of the virus on the economy. With all non-essential workers staying away the numbers for the last week of March are likely to be even greater.
New York was one of the first states to strongly encourage, if not mandate, non-essential workers work from home. As additional closures and limits grow around the country, other office buildings will see similar declines. There is non-essential retail at the base of the building which is included in the data. As other buildings in the city closed and fewer tourists visited New York, the retail establishments were impacted.
It is unknown how quickly the market will bounce back, but the building is almost fully leased. So as soon as the ‘shelter in place’ mandate is lifted, foot traffic should rebound quickly for both the office building and the retail at the base. The short-term impact will be painful for the landlord, but the property should be able to recover quickly. This office building is well located and almost full, other buildings without these advantages may not recover as quickly. For additional analysis of your property contact Eddy Hribar at or Jeff Havsy at .
1 The analysis used the first 28 days of February 2020 to account for the additional day in the month due to Leap Year.
The coronavirus pandemic that is reverberating across the country and the globe has impacted commercial real estate in ways unforeseen from previous “black swan” events. This impact has not been even across sectors or markets or even subsectors within a property type. Fortunately, with today’s technology and analytics we are able assess some of the impact as it is happening rather than waiting in arrears for the data. Using data from Advan that tracks anonymized cell phone movement over time, Eigen10 Advisors examined the change in foot traffic at a sample of different types of retail centers in different markets.
For the purpose of this analysis we compared a prime “non-essential1” retail location and a prime “essential” retail location in each of three different markets to see how the number of people visiting those locations changed in February and the first two weeks of March compared to a year ago. The first market we examined was Seattle since that is where the first Covid-19 case in the U.S. was identified and unfortunately, where the first fatalities occurred. The other two markets included in the analysis are New York, where the second large outbreak occurred a few weeks after Seattle, and Dallas which hasn’t been a significant hotspot yet.
All three non-essential retail locations and two of the three essential retail locations showed increases in visitor traffic for February 2020 compared to 2019 as shown in the graph below. The decline at the third essential retail location was slight (-2%). Given the natural noise within the data, the foot traffic was essentially flat. Compare that with March, where all the non-essential retail locations showed declines greater than 13%. In contrast, the essential retail location in Dallas showed a significant increase in visitors, 14%, and the New York essential retail location had a healthy 7% increase. The essential retail location in Seattle showed a significant decline, -7%, but that was less than half the decline of the nearby non-essential retail location.
Not surprising, the declines at both Seattle stores were greater than the declines in the other two locations.
If we split March even further, looking at the first week separate from the second week, a more pronounced pattern emerges. The declines accelerated dramatically after March 7 compared to a year ago as the crisis became better understood by the public and health officials discouraged large crowds and social distancing. All three non-essential retail locations registered significant declines and the declines in Seattle were more than double the drops in New York and Dallas.
Traffic at the Dallas and New York essential retail location’s increase for the second week of March compared to a year ago. However, it is likely that the increase was fueled by fear since the data for the following day shows declines.
Comparing the third Sunday in March 2020, which was March 15, against the similar Sunday in 2019 shows how quickly visitor traffic dropped. Using a single day adds noise to the analysis and the third Sunday in 2019 was March 17, St. Patrick’s Day. However, the decline in foot traffic in all three non-essential retail locations was so dramatic that noise does not account for the decline.
Foot traffic at all three essential retail locations are down significantly on Sunday compared to the previous week as state and local governments encouraged social distancing. Even Dallas, which hasn’t seen the same number of cases as greater New York and Seattle had a decrease in foot traffic.
As health, state and local officials encourage or enforce limits on social interactions, the retail sector will feel the pain. Many investors believe that necessity retail will ‘hold up’ in a recession. The impact to necessity retail appears to be less, but the decline in foot traffic in Seattle and to a lesser extent New York shows that it isn’t immune.
It is unknown how quickly the market will bounce back. However, in contrast to the 2008 recession, new technology allows us to have much more precise and timely views on economic activity. These trends can be viewed at both market and property levels which will give investors much improved market transparency and hopefully alleviate unnecessary risk adjustments over the long term.
While this analysis was a simple sampling and not necessarily a reflection of the larger U.S. market, Advan and Eigen10 Advisors are working to continue to track and analyze the data in a broader and more long-term indexed fashion. For further information in using these indices, contact Eddy Hribar at or Jeff Havsy at .
1 Non-essential defined as a location that is heavily aimed towards clothing, jewelry, entertainment and restaurants whereas essential is defined as retail that is more heavily aimed towards, grocery, pharmaceutical, cleaning and other daily household needs.
In New York City, the MTA has stated that it’s seen “ridership tick down a bit in the last week or so.” For anyone who lives there, it probably feels like more than a bit of a tick down, with subway platforms empty at rush hour and the usually bustling Grand Central terminal eerily quiet.
In order to visualize what is happening at one of the busiest train terminals in the world, we looked at year-over-year foot traffic numbers at Grand Central to see just how much visitors numbers have fallen in the past few weeks.
The blue line on the chart below shows average daily foot traffic since January 2019, with the orange bar showing the year over year change in average daily traffic by month.
As it clearly shows, traffic in February declined from January but was up year-over-year. Whereas we see in March a dramatic year-over-year drop.
This is no doubt due, in part, to many more people working from home over the past couple of weeks. And is likely compounded by a drastic fall in visitor numbers from overseas during the usually busy school holiday period.
Advan will continue to track foot traffic numbers at key locations as the full impact of the COVID-19 virus unfolds.
The New York auto show, due to take place in April at the Jacob Javits Center, has been postponed until late August. Is this just a temporary setback for the Javits center - one that is likely to be seen across all convention centers, as the appetite for large gatherings dwindles with the spread of COVID-19? Or is it a sign of a longer term hit to the convention industry as a whole?
We analyzed the average monthly foot traffic in the Jacon Javits Center over the last 3 years.
According to our data, the traffic in the center has generally been lower in most of the trailing 12 months, but the fall in traffic during February and March (to-date) has been among the worst in the location's history. It is clear that the reapidly spreading coronavirus is having significant impact on this sector. We will continue to track these trends as the situation evolves over the coming weeks.
Advan's REveal platform can be used to generate these insights on any custom location, such as the Vegas strip or other large convention centers worldwide, which are some of the locations that will feel the biggest initial impact of the virus.
Recent news reports have highlighted an increase in foot traffic for CostCo as the spread of the coronavirus spurs people to stock up on essentials. Some analysis has put the number of visitors to CotsCo stores in the US up 72% year-over-year.
At Advan, we ran a detailed analysis of true foot traffic at CostCo. The graph below shows average daily traffic for each month since October 2015, with year-over-year changes overlaid.
According to our data, CostCo saw an increase in traffic of 11.5% compared to last March. This number is in line with CostCo's published revenue growth. It also well within the range of growth that CostCo has shown in the past.
What drives these large differences in our reports versus past published reports? The reality is, it takes a lot of hard and detailed work to derive accurate measurements. Here is what Advan has done to arrive at the above numbers:
- Manually geofence every single CostCo, and its parking lot, separately. Have 2 QA teams verify that the correct locations are geofenced.
- Capture and verify the opening and closing dates (although the latter is not applicable in CostCo's case) of each of these locations.
- Parse the hours of operations of each location, so that any data points before or after store hours are not included in the analysis
- Procure multiple data sources, test their consistency and performance historically, choose which ones to combine and how.
- Normalize the data to remove collection, geographical and other biases.
- Verify that the data is correct versus published CostCo revenue: 0.89 year over year correlation over the last 3 years
Each of the above is a major undertaking, both in time and effort. But that is what is necessary and distinguishes a correct analysis from a claim that can be completely off the mark. It is common knowledge, but worth repeating: "half knowledge is a dangerous".
Has travel been affected by the coronavirus, and if so by how much and where? The overall volume of passengers traveling by air affects many of disparate industries - not just airlines but travel sites and aggregators that sell tickets, hotels and car rental companies that rely on airports for a large volume of their business, credit card companies, restaurants and retailers. The list can go on.
To try and gauge the impact that a reduction in travel due to the coronavirus might be having on these sectors and more, we analyzed foot traffic in key US and international airports.
First, within the US, we looked at the largest airports for international passengers: Boston, Chicago, Los Angeles, New York and San Franscisco, as well as Atlanta, which is a major airport hub.
Measuring the average daily visitors in each airport we can see that the West Coast airports, and to a lesser extent, Chicago O'Hare, show a significant drop in traffic between January and February, compared to the previous 3 years. Los Angeles, in particular, saw a drop of almost 7% in traffic from January to February of this year, whereas in previous years the trend was more or less flat.
This is a clear indicator that air travel to and from Asia is starting to show identifiable weakness.
While the month over month change is material, on a day-to-day basis we do not observe any pronounced changes. The chart below shows year-to-date traffic on a daily basis. The effects are mild compared, for example, to the weather delays in Boston and Chicago on Saturday, January 11.
The picture in international airports, however, is materially different. Hong Kong, Seoul and Vancouver have large and statistically significant drops from prior years, with Hong Kong and Seoul the most affected.
This drop in foot traffic for international airports is also visible in the daily traffic trends:
More in depth analysis traffic across all US airports as well as the major US airlines is already available in Advan's graphical interface, Excel API and data feeds. Daily historical airport traffic on any international airport, or any international location for that matter, can be generated on the fly using REveal, Advan's online platform.
For more insights into how this trend is affecting airport retailers, see this article from today's Wall Street Journal: Airport shops suffer crisis as Coronavirus upends travel, WSJ, March 3, 2020
Each company trades based on different metrics, but it is fair to say that most consumer discretionary companies, especially those with a growing footprint, are judged by their comparable store sales. This is defined differently by each company, as it is not a standardized GAAP metric, but it typically means "we only measure the performance of stores that have been open for a certain amount of time", usually anywhere from 3 to 13 months, depending on the company.
Shake Shack is a good example. Armed with geolocation data's ability to measure traffic down to individual locations for any company, and using the consistent 4 years of history available to us, we set to analyze the chain's performance in Q4 2019.
To compute the traffic correctly, we select only the specific SHAK stores that (a) are owned by the company, and therefore have comparable store traffic reported, and (b) have been open for at least 13 months before the prior-year fiscal quarter (or 25 months as of the fiscal quarter being reported), as this is the measure that SHAK uses to compute comparable store traffic.
This is Advan's traffic data versus SHAK's reported traffic:
If it does not look perfect it's because there are a lot of variables going into the computation. It is unreasonable to expect that even SHAK's traffic numbers are perfect. For example, some of the traffic that we compute may include delivery drivers, which SHAK may or may not be including. We also do not know if SHAK is measuring traffic that enters their stores or measures the outside seating areas as well; this depends on the people counting systems that they use. What is important is that the numbers are correlated, especially in the last 5 fiscal quarters, and can be used as a good indicator of the directionality of the traffic -- is it going to be higher this quarter or lower?
As you can see from the above, we forecasted a 7.5% drop in comp store traffic, which was below the concensus estimate.
After the market close on February 24th the company reported same store traffic drop of 5.2% and comp store sales of down 3.6% which were below the concensus estimate of -2.5%. Shake Shack's guidance was also below the lowest range of expectations. As a result the stock opened at $63.92, a drop of 13.3% versus the close of $73.57 on February 25th.
Usually the first considerations when a company acquires another is how to realize economies of scale, cross-marketing opportunities, and areas of common improvement. If one knew which shoppers visit each location and quantified the cross-visits between malls then they could make educated decisions about which malls to focus on for improvements, tenant acquisition, or cross marketing.
With traditional measuring methods this is hard to achieve, but cellphone location data is the perfect fit for such an exercise.
But not all visitors are created equal. To get the true picture one needs to go a step further and adjust visits by the purchasing power of the visitors. Hence at Advan we computed the income of each device using its shopping behavior, and then adjusted the visits and cross-visits by income. Here are our findings.
First, looking at traffic alone, here is the breakdown of traffic by state. Florida has the highest number of visitors for both Simon and Taubman, and the highest number of cross-visitors: 27% of Simon shoppers also visit a Taubman mall. California on the other hand (which is the next largest Simon state that has Taubman presence) only sees 3% cross-visits:
The picture is similar, but more informative, when taking into account the income of visitors. We see that Florida is still the largest state, with $590 billion of annual purchasing power across all visitors, of which 28% ($168bn) also visit Taubman, and California is now #2 in Simon presence at $430bn purchasing power with only $16.5bn of that cross-shopping at Taubman:
Of course the full details can be gleaned only when breaking down the above by individual mall, where one can identify which visitors, and which specific cohorts of visitors (high income/low income, millennials / baby boomers, etc) are visiting multiple locations, and make educated decisions on where to invest and how to expand the efficacy of each mall.
Department Stores, Restaurants, Malls, all continue growing their traffic on Black Friday on a year over year basis. The growth rate has decelerated a bit, but it's still in the healthy double-digit numbers, from an already high base.
Only Hotels showed negative growth, but less so than in prior years -- a weighted average of -1% vs -3% and -2% in 2018 and 2017 respectively.
Below are a couple of graphs that illustrate the traffic. Do not be fooled by the simplicity of the graphs, the data underlying them consists of 3 Trillion data observations across 2 Million geofenced locations pertaining to 1,400 companies, all normalized using Advan's custom algorithms for reliable year over year comparisons. Simple is hard to do!
Sector performance: This shows how many more people visited a sector on Black Friday than they did the year prior.
As we can see all Consumer sectors (which include Apparel, Clothing, Malls, Supermarkets, etc) grew in 2019 between 8% and 25% versus 2018. Their growth rate is down between 20% to 40% since 2018, but it's growth nevertheless. Certainly much better than the meagre 2017 situation where traffic was falling.
Drilling down to individual industries, the picture becomes clearer:
- Department Stores and Restaurants lead the growth, followed by Malls ("REITS") and Supermarkets.
- Growth is generally more muted than in the prior years.
- Clothing, Shoes and Accessories are still growing, just not as much as they used to anymore. 1% vs last year's 4%.
- Apparel is flat, vs a 1% growth in 2017 & 2018.
- Hotels are slightly down -- people stick around with family apparently.
- Airports show 0.5% growth (note, this is only on Black Friday, not the whole week of Thanksgiving), but note that
- Gas Stations ("Integrated Oil Companies") grew 1.7%. It takes some driving to go to all these stores!
It is important to keep in mind that the growth patterns above are not uniform across companies. In fact, on average the Consumer sectors are down vs 2018. This means the big department stores / retailers / malls are dominating the growth (so that overall growth is positive) whereas most of the small chains are hurting (so that on average, traffic is down on a company by company basis):
The same story unfolds on an industry basis, where the Department Stores and Malls are overall slightly down on average, meaning the little guy is hurting, whereas the high trafficked stores see their traffic growing each year.
One of the interesting things you can do with access to anonymized, large scale and accurate cellphone location data, is to measure traffic patterns. Consistent with the Big Data theme, we decided to compute this across the whole US, to measure Total Miles Driven.
Total Miles Driven is estimated by the Federal Government (aka "FRED") in a monthly report, which is generated with about a 35 day delay (e.g., the September total miles driven is published in early November). The information is useful because it is an indicator of:
- Gas Demand
- Insurance Claims (all else being equal, the more people drive, the more accidents they will have, and the worse the insurance companies will perform, as the typically charge a fixed fee for insurance, independent of mileage)
There are two versions of Miles Driven published by FRED: seasonally adjusted, and unadjusted. We worked with the unadjusted, because it is really hard to replicate the seasonal adjustments (the government does not provide detailed steps of how they perform the adjustments).
The results are as close as it gets! The correlation between Advan's Miles Driven to the Federal Government's is 0.92. But Advan's data is available T+1 (i.e., with 1 day delay), so more than a full month ahead of FRED's. Talk about having an unfair trading advantage!
But we did not stop there. The beautiful thing about the breadth of the data available, is that we can also figure out which cellphone devices belong to truck drivers. Or for that matter, which devices live in a given Cebsus Block Group or Zipcode. Or which ones have a certain income level; or which ones buy or service their cars in a Ford dealer, or a Mercedes dealer; and so on. The miles driven can then be computed only for these devices, to generate custom insights that not even the government, or anyone else for that matter, has access to today, but will be unable to live without in the future!
Here are Advan's Trucks miles driven sourced from (the devices belonging to the drivers of) about half the commercial (class 8) trucks in the US:
The possibilities are endless, so free up your imagination and let us know what's the next interesting insight you want us to compute!
It comes in many forms: Speech. Movement. Draw within the lines. Wait, scratch that last one.
We didn’t like that last one either. When it comes to measuring foot traffic, trade areas, migration patterns, demographics, cross traffic, and in general get behavioral metrics from mobile location data, yesterday’s state of the art was to get canned results on some buildings or venues someone else has decided you might be interested in.
What’s the reason for the limitation? In two words: Big Data. When you are dealing with billions of data points you can’t just write a query to extract any information you want because it will be very slow and very costly (sometimes in the hundreds of thousands of dollars).
It’s even worse in our case because we have 6 trillion cellphone observations over the last 5 years! And growing at a rate of over half a trillion every quarter. Gulp.
But the ability to draw any area, be it a building, a retail location, a factory, an oil field, a neighborhood, you name it, and get instant foot traffic measurements for it was making us loose too much sleep. A sure sign we had to do something about it.
So after several months of head scratching, experimentation, optimization, design, testing, design again, and all the other sleep-reducing activities favored by software engineers, we built a tool to be able to do just that. If you thought we would have shown it to the whole world the very next day, you would be right, but... once a nerd always a nerd. It took us 2 full years to open it up to our clients. Well, better late than ever...
So finally last month we announced REveal, a self service website where the user gets full control of the data.
Recite your favorite poem, and voila! True Home Trade area:
- Want to find out where those living in a building you are considering buying work?
- And how has that changed over time?
- What is the true trade area of this Mall?
- Where do the visitors live and work?
- Are more people moving into this allegedly hot neighborhood, or is it all hype?
And on and on. The possibilities are truly endless.
As recently as 10 years ago it would be unfathomable to think that you can crunch 2 Petabytes of data (that’s 2 Million Gigabytes!) in seconds and get detailed answers to any question you can think of. Progress is sometimes pretty close to science fiction!
- Spock: Over and out.
It is everyone's favorite game to perfectly forecast earnings by looking in the rear view mirror. We are trying really hard not to fall into the same trap ourselves.
Here is why: it is very easy to make random predictions, some of which come true, and then look back and cherry-pick the ones that you were right. Really, a monkey can do it. If you give them a banana for each correct prediction they would get pretty fat, fast!
What really distinguishes a good forecasting method from random chance is, the percentage of times you get it right versus the ones you get it wrong. If you are correct more often than not, even 51% correct vs 49% wrong, then you have something to say. Even if you are wrong more often but you have higher conviction (and therefore make more money) when you are correct, that also has value.
Advan's Machine Learning algorithms take the foot traffic data we compute and forecast top-line revenue. We get it right about 60% of the times. 2 out of 3. If that doesn't sound impressive, consider that many quantitative funds can build profitable algorithms from a mere 51% advantage. And in our own "paper trading" backtests, the performance of a long/short neutral portfolio constructed using Advan's foot traffic data has a Sharpe ratio over 2.
Considering we do not claim to be experts in either Portfolio Construction nor Machine Learning, these performance results are pretty good, if we may say so ourselves. The average Hedge Fund has Sharpe under 1 (not to ding Hedge Funds, actual trading is much harder than paper trading).
Having said that, we can't resist the urge to brag about individual hits. Just this once:
• Texas Roadhouse (TXRH):
The consensus estimated revenue in Q3 2019 was $649.2mm. Advan forecasted $651.32 on foot traffic growth of 6.1%. The actual revenue reported on October 28th after the market close was $650.42mm. The stock closed at $50.17 on the 28th and traded up 20% the next day!
Advan's Year over Year reported traffic and TXRH top-line revenue have correlation of 0.7 over the last 8 fiscal quarters; Quarter over Quarter traffic and top line revenue have correlation 0.96. This correct forecast wasn't an accident.
Is Gamestop's traffic up? Is Jimmy John's traffic growing more than Subway's?
Every day some new analysis of cellphone location data portrays to measure the exact foot traffic in one or all of these, and every day we emit a collective gasp at the incredulous claims.
Let's get this quickly out of the way: Gamestop traffic is trending down; Jimmy John is down too and that trend has not changed for 3 years straight; it's also worse than Subway's downward trend, except for some bright, but inconsistent, spots in 2019.
• Jimmy John's (blue) vs Subway (pink):
But that's only the beginning of the story.
First, it is a disservice to the reader to portray that any dataset, and in particular cellphone location data, can estimate within a fraction of a percentage point the actual traffic of a company. With extremely detailed geofencing work, taking into account the hours of operation of every single location, and after testing hundreds of normalizations versus the actual revenue data and versus our partner's (Consumer Edge) credit & debit card transaction data, our research team at Advan can come close. We strive to be approximately right, instead of precisely wrong, as Warren Buffet said.
Second, the actual claims we have been hearing are completely off the mark. Gamestop's traffic for example -- and we have nothing against the company, these are the fundamentals talking -- is down. About 6% down year over year in Q2 2019 in fact, and not looking better in Q3. There are no two ways about it (if you insist on exact numbers, then 6.04% down, but remember, this is approximate).
Jimmy John's is doing better than Subway in 2019 (comparatively speaking; Subway has 13x the traffic), but that trend has started running out of steam in the first 2 weeks of October. Here's hoping it's just a small aberration. More worryingly though, if you go back to 2018 and 2017 the 2 chains' traffic is changing at the same rate, and that rate is downward trending in both cases. Not a bullish sign.
So please, do not believe everything you read without confirming how the analysis was performed. Consider placing more weight on analytics performed by the experts in crunching and normalizing location data for financial performance. Data is good; but incorrect and misleading data is worse than no data.
Advan is the leader in the Big Data geolocation space, enabling participants in the financial industry to analyze foot traffic data across multiple sectors, including consumer services, energy, technology, healthcare, REITS, financials and others. Advan derives its datasets using multi parameter models that analyze cellphone location data crossed with curated geofenced areas.
Top tier institutional investors spanning from quantitative hedge funds to fundamental asset managers have been the main consumers of Advan’s products.