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Technological Trends

Think global: which markets have the highest risk of retail disruption?

Angela Goodings
Director of Research, Real Estate, Europe
Haoran Wu
Research Analyst, Real Estate, Europe
Retail disruption: which countries have the highest risk

Retail sales are migrating online and away from physical stores, damaging the investment returns of retail property. The process is underway in all developed retail markets, but the pace of migration is uneven. Very little research exists to explain why sales migrate online faster in some markets than others and very few frameworks exist to forecast which markets are likely to migrate most towards sales online in the future. That is why Nuveen Real Estate’s research team has undertaken significant analysis to fill that gap by delivering a tool to assess where the risk of disruption is highest over the medium term.

Speedometer: Drivers of the retail disruption Gif

Drivers of the retail disruption process


The migration of retail sales to online channels is a complex process which requires a number of facilitator conditions, strong demand drivers and a lack of mitigating factors.
  1. Demand drivers: For consumers to demand e‑commerce transactions, they must value the benefits of e‑commerce (lower search and transaction costs) more than they value the risk of attempting unfulfilled transactions.
  2. Facilitator conditions: The most basic requirements are widespread access to the internet and electronic payments system. The primary payment system is card payment but other media exist, for example, the use of AliPay & WeChat Pay in China and iDEAL in the Netherlands.
  3. Mitigating factors: Even if facilitating conditions are in place and there is consumer demand for e‑commerce, migration of sales to online will be limited if mitigating factors, which support visits to physical retail stores, are prevalent.
One such mitigating factor is food retail. This has proven to be much less vulnerable to online sales erosion due to consumers’ concerns over freshness, their desire to be personally involved in produce selection and the low margins in food retail making delivery systems uneconomic for smaller basket sizes. In addition, where food retail is anchored within wider retail environments, we expect to see online migration progress slowly because the consumer’s visit to the physical food grocery store will support associated visits to nearby non-food physical stores.

The number of consumers who regularly eat out is also used as a mitigating factor as we have isolated a negative relationship between eating out and the pace of online migration. This relationship is particularly strong in Asian markets and works similarly to the food retail dynamic: when consumers go out to eat, they take advantage of their proximity to physical retail stores to make traditional purchases.

Markets will also exhibit lower risk if demand is supported by a significant influx of external consumers through tourism and inward migration. The growth of tourism has generally supported the physical retail real estate in the host market, and we expect this to continue because tourists make visits to physical stores. In addition, loss of physical sales to the online arena can be compensated by strong inward migration: although migrants are likely to be young and make online purchases, they will also make a significant amount of additional purchases in the physical estate that can offset increasing online purchases from the host population.

The final mitigating factor we consider is the level of retailer debt. Consumers are not directly concerned with the debt levels of retailers, but the experience in the United Kingdom and United States so far is that retailers with high levels of debt are least able to absorb the negative sales impact of online migration because a much higher proportion of their cost base is fixed. Therefore, disruption will be greater if retailer debt levels are high because more retailers will be unable to support prevailing rent levels.

Compiling the index

A wide range of sources have been used to compile the required data. Data on payment methods was supplied by central banks and data on online sales dynamics and migration was provided by national statistics agencies. To access cultural resistance to e-commerce, we used surveys from the EU and United Nations Conference on Trade and Development (UNCTAD). We derived the debt levels of retailers from annual reports. The knowledge of Nuveen Real Estate’s global real estate experts was used to assess the presence of food retailers within retail environments. Data related to the adoption of technology (i.e. internet access, subscription to fixed and mobile phone) and tourism (flow and expenditure) was sourced from the World Bank. The percentage of consumer spend on eating out was calculated from the Oxford Economics estimate.

All data has been checked by Nuveen Real Estate, controlled for outliers and transformed into a 0-1 range.

Weightings were assigned to individual factors based on their significance within the disruption process. The highest weighted factor has a significance 3.6 times greater than the lowest weighted factor.

Markets included in the research cover the three major investment regions of North America, Europe and Asia Pacific. Geographical coverage was designed to cover the investible universe of retail real estate.

For results for all markets, please see the appendix in the report which is available to download.

Results


Figure 1 

The index of retail disruption risk assesses both the risk of pronounced online migration of retail sales and the ability of the physical retail real estate to withstand that migration. It should be used to identify retail property investments that are most at risk of value loss due to online migration, rather than to identify where online migration itself could be greatest.

The index is designed to assess risk over the medium term i.e. 3-5 years by using stock variables whose distributions are relatively stable year-on-year.

To ensure the index is predictive rather than backward looking, the level of online penetration of retail sales has not been used as a contributory factor to the index. Therefore, a comparison of index scores and current online penetration is a good way of checking the reliability of the index. The two markets that have a particularly high level of online penetration are China and the United Kingdom, and form a clear leading group based on current penetration. Both markets appear in the group of three markets that have the highest index scores, confirming that the index is reliably predicting the risk of retail disruption. While China and the United Kingdom are ranked high within the index, their score makeup varies significantly across the facilitator, demand and mitigating factors.

One varying factor is the availability of the internet which is surprisingly low for China. There are currently over 850 million internet users in China, around one fifth of the world’s total users. That said, the market is characterised by a significant digital divide, with internet users largely concentrated in metropolitan areas. While a rapid urbanisation process is underway, the proportion of people living in rural areas in China remains sizeable (41% of the overall population vs. 17% in the United Kingdom, for example). Nonetheless internet accessibility of Chinese rural residents is a third of their urban counterparts, and as a result at a country level, China ranks at the lower end of the spectrum globally (in terms of internet penetration). Figure 2 demonstrates four key factors with high variations between these markets.

Chart: China 

Four markets with very similar online sales penetration form a second group: Denmark, South Korea, Germany and United States. However, they generate differing index scores which suggests that their future evolution will be divergent. South Korea is clearly identified as the highest risk of these markets and in fact carries a higher risk than the United Kingdom. Germany generates the lowest index score of this group of markets and carries significantly less risk than Denmark or the United States. South Korea’s high score is due to low inward migration, a relatively young population, high consumer trust in e‑commerce, high internet access and a high usage of card payments. Germany scores relatively low because of its older population, high inward migration, low trend growth of online sales and low use of card payments.

Chart: South Korea 

At the other end of the index spectrum, the lowest scoring of the markets is Portugal, behind France, Italy, Japan and Hong Kong. These markets are judged to be the safest of the markets covered and all feature similarities in cultural aversion to e‑commerce, low use of card payments and a low trend growth rate of online sales. Variant factors exist and are highlighted in Figure 4.

Chart: Japan 

Just behind this group are Finland and Sweden. Both are markets with a high propensity for early adoption of technology and their low risk scoring shows that the risk evaluation is more complicated than just separating early adopters from laggards. In Sweden’s case, there are a number of factors outside the technology space where it has low risk scores, and few where it has a high risk score. In Finland’s case, stable e‑commerce sales, a high presence of food retailers and low growth in card payments explain the low risk score.

Chart: Sweden 

There are an interesting group of markets that currently have low levels of online penetration but generate quite high index scores. This suggests they are likely to undergo the most rapid changes. The most prominent of this group are Australia, Hungary and Poland. They all tend to exhibit high levels of consumer trust in e‑commerce but there is no other common theme. Australia and Hungary have a fast trend growth rate of online sales and high availability of internet access, while Poland has a fast growth in card payments and a relatively young population.

Chart: Australia 

Conclusion


Our retail disruption index has identified that global migration of online retail sales will continue at different rates and it provides valuable insight into the drivers that accelerate or decelerate this process. The index provides an overall market ranking in terms of online vulnerability, but a deeper dive of retail real estate is important to understand individual market geographies and the disparity across cities as well as the difference in retail formats. In the future, retail disruption will also be limited by the ‘halo’ effect. The fact that more retailers are now realizing the importance of a store presence in driving online success as a result of brand awareness, showrooming and click and collect. This mitigating factor has not been considered within the index due to a lack of consistent global data. Markets which score high on the disruption index will continue to be challenged in terms of online migration, but investment criteria will seek assets with characteristics which we believe will remain sound. These characteristics include food anchors, assets with strong connectivity, convenience aspects and tourism hotspots. Likewise those markets with lower disruption scores does not mean a green light to invest in all retail, and understanding key drivers remains essential in making sound investment decisions.

 

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