UK Investment Yields | June 2018

UK Investment Yields | June 2018

David Tudor

David Tudor

Senior Director, UK Valuation & Advisory Services

+44 207 182 2689
david.tudor@cbre.com

Negative retail sector news affecting yields as multi let industrials continue to motor ahead

  • Demand remains strong for prime assets with strong and secure income streams. Alternative sectors provide good options.
  • Continued occupier weakness and CVAs in the retail sector is beginning to weaken yields across all sub sectors except long secure income streams. Offices remain stable with activity higher in the main regional centres. Central London is quieter.
  • Industrial estates are particularly strong with London and the South East again challenging
    current yields, and more evidence of regional and good secondary estates trading well.

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The Property Perspective | Alternatives |  H1 2018

The Property Perspective | Alternatives | H1 2018

 

We are pleased to launch The Property Perspective: Alternatives

KEY TAKEAWAYS

  • With the income stream offered by Retail, Offices and Industrial shortening and becoming more volatile over the last 20 years, investors seeking a “traditional, bond-like” property return can arguably now only find this in the Alternatives sector
  • But the Alternatives sectors offer so much more than this to investors eager to achieve higher returns by taking on direct or indirect operational exposure
  • Alternatives are poised to benefit from the myriad forces of disruption – political, social, technological – that are buffeting all markets, including real estate; to some extent all are protected by tight supply, strong covenants, and broad and deep pools of occupational demand
#6 How Can AI Impact? – “Future of Logistics” Series

#6 How Can AI Impact? – “Future of Logistics” Series

Ben Thomas

Ben Thomas

Director, National Valuation

t: +44 207 182 2663 Ben.Thomas@cbre.com

Judy Zhu

Judy Zhu

Associate Director, National Valuation

t: +44 207 182 2683 Judy.Zhu@cbre.com

In the last post ” How Can We Use AI?”, we explored possible areas where AI technology could be used. In this post, we are going to focus on the potential impact of AI.

 

Impact on Business

Efficiency

Without doubt, AI can help us improve work efficiency, especially in the areas that human workers are weak. For example, humans struggle with processing large amount of information and data, which is a particular strength of AI.

AI is already being used at the very highest level. According to SKY News, the Serious Fraud Office hired an “AI lawyer” to assist human lawyers in reviewing documents. The SFO said that technology was up to 80% cheaper than using outside counsel to review those documents and identify legally privileged material.

As we mentioned in the last post, AI can be used to manage warehouse space by predicting what will be needed at different times. Warehouse managers are then able to maximise the use of their space, racking and delivery times.

With the improvement of work efficiency, human workers can focus more on the creative and value-added jobs.

Quality and Variety of Products

With the increased work efficiency, more resources can be saved and reinvested to improve the quality of products and services.

AI can also directly impact on the quality of products. We mentioned a case in the previous post “Robotics in Logistics”: where a phone part manufacturer automated the manufacturing line, reducing the defect rate from 25% to 5%. AI can improve product quality in the same way – by reducing manual mistakes and optimising the operation.

AI can help to provide better service to clients too. For example, the online fashion retailer ASOS has introduced a new feature in their phone app, which searches products in ASOS inventory to match the items in uploaded photos by customers. This feature simplifies the searching process and improves the customer experience. Moreover, it increases sales – when given more options, customers tend to buy more.

As a result, the variety of products and services expands. One of the contributions to this expansion is the increase of personalised products, which usually generate higher margins than generic commoditised offerings. Personalised ad recommendations and discount vouchers already feature in our daily life, and more personalised products will be available to us in the future. For example, Nike has launched the product NIKEiD, which allows the customers to design their own personalised sneakers. Unsurprisingly, this product is powered by AI.

In the logistics sector, AI can help to provide customised warehousing service, as we explored in the post “Is On-Demand Warehousing the Airbnb of Logistics Market?”. Stowga, the on-demand warehousing platform which was involved in helping KFC find emergency space recently, manages their warehouse space by using AI.

As a result, we expect demand for products and services will increase and the suppliers can generate better profit.

 

Impact on Public Sector

Policy

Regulators need to take AI into consideration when penning down future policies. Regarding the regulation of AI programme development, they need to make sure any AI programme being developed does no harm to human beings. This issue has been warned by many high-profile figures and some of them jointly signed on an open letter on the matter.

In the domain of labour and social welfare, policy makers need to consider the impact of AI on the labour market, encourage the skill shift and, during the shifting period, protect those people whose jobs might be impacted.

At the international level, certain regulations need to be put in place and agreed on by different countries, to ensure the development of AI has an open environment and the use of AI is fair across the globe.

Education

AI technology is being used to improve teaching methods, but there is more than just using AI in education. School education needs to equip future generations with skill sets irreplaceable by AI and instil a mindset to work closely with AI. This means education will need to blend the traditional teaching of knowledge and facts with developing creativity and problem-solving skills.

At the same time, ongoing education is needed for the current work force too, to get them prepared for the shift to a new work environment. That said, it doesn’t mean all employees need to learn how to program, but they will need to adopt a mindset of working with data and adapting to constant changes.

 

Will AI replace us in workplace?

Finally, we come to the question and the answer is:

Yes

Throughout history, human beings have always adapted to change. Animals and tools replaced human labour in farming and then machines came to revolutionise manufacturing. Similarly, part of the human work force will be, and is already being, replaced by AI.

A simple example is the spell check function in some word editing software. When we mistype a word, the word is either automatically replaced by a correct one or highlighted with a recommendation. We can ‘train’ this AI as well, every time we add a new word to the existing dictionary.

…And No

AI will not replace human workers completely, at least not in the near future.

Developing and using AI requires significant resources, such as financial investment and high-skilled workers. Therefore, businesses will start to consider replacing human workers only when they see the cost of AI can be covered by the reduction of human labour. But the labour cost of the most replaceable jobs (ones that are repetitive and predictable) is generally low. This will delay the adoption of AI in business.

Even if the decision has been made to adopt AI, the development of AI programs will take a long time. This is in addition to one AI currently only solving one type of problem (read more details about ‘Narrow AI’ here ).

With these factors combined, AI is unlikely to replace human workers completely in the near future.

 

Summary

The proper use of AI will bring benefit to human society. We should embrace it with a new mindset look to tailor policy and education to ensure we get the best from it in the future. If delivered and developed properly and carefully, AI won’t replace us in the workplace. Instead it will help us work better and more efficiently.

 


If you are interested in more details of this report or our other logistics reports, please contact Ben Thomas, Director of CBRE National Valuation – Logistics & Distribution, or Judy Zhu, Associate Director of CBRE National Valuation.

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#5 How Can We Use AI? – “Future of Logistics” Series

#5 How Can We Use AI? – “Future of Logistics” Series

Ben Thomas

Ben Thomas

Director, National Valuation

t: +44 207 182 2663 Ben.Thomas@cbre.com

Judy Zhu

Judy Zhu

Associate Director, National Valuation

t: +44 207 182 2683 Judy.Zhu@cbre.com

AI is a complicated thing and in our last post we explained the basic concepts of Artificial Intelligence “What is AI?”. Now we are going to have a look at the potential applications of this technology in the world of logistics property.

What can AI do?

As we know, AI runs through the same process as any other computer programs:

Input –> Process –> Output.

There are multiple areas in each step where AI can be applied, as seen below:

 

Input

  • Data: AI can efficiently process data and data is the most common input. With the progression of the Internet-of-Things (IoT), data can be collected anytime and anywhere. From connecting to a free Wi-Fi service to running with activity trackers our data is being collected.
  • Natural Language: AI is able to understand human natural language whether written or spoken. We see this in search engines and in smart speakers which can answer our questions.
  • Image: AI can reliably recognise images, a big step forward that has become a technology used in daily life. Most commonly it is used in social media apps to tag friends and edit selfies; or to automatically categorise photos.
  • Sound: Similar to natural language, AI can also recognise sound. Sound recognition technology has been heavily researched and developed in recent years since it has a potential to change the way how people interact with machines. While it is most prominent in voice recognition it is also used in song identification.

Output

  • Knowledge: AI can help us store and manage our knowledge and gives us answers when questioned. This is being adopted in education, medical and legal industry.
  • Prediction: Based on the previously identified patterns as a result of analysing large amount of data, AI can help people to predict. For example, AI is used to predict traffic volumes on a road, footfall on the high street and energy consumption in factories.
  • Decision: AI can identify patterns within data, on which it can then make decisions. People can choose to make decisions by themselves, with the analysis results produced by AI, or they can simply leave it to the AI to decide. The best example is AI decides what ads to show us on a website.
  • Problem Solving: AI can try many different approaches to solve a given problem within a fraction of the time a human can. The solution created by AI sometimes can be brand new, unexpected and constantly evolving. This helps people to solve problems with increasing efficiency.
  • Planning: With patterns learned from data and optimised problem solving solutions, AI planning can consider more scenarios than human and keep evolving with constant iteration.

 

Use in logistics property

Knowledge management

How can this be applied to logistics units? AI can be used to manage knowledge on all aspects of buildings, markets and clients. Asset managers can work more smartly and efficiently with the assistance of AI, through knowledge and analysis of tenants, leases, building facilities, markets and bespoke solutions. Another example is client care, which is important to any business. AI can help the customer service team find answers to client’s questions faster and more accurately; it can help client care team make better client care strategy with a fuller collection of client knowledge.

Warehouse Management

In warehouses, AI can be used to manage warehouse space. AI can predict the space needed at different times, so can help warehouse managers to maximise the use of their space, racking and delivery times.

Route PlannerAI can also collaborate with the IoT to plan better routes for forklifts, picking robots and workers throughout a warehouse, which improves operational efficiency and reduces the resources required. IoT in warehouses can be in the form of sensors on storage shelves or wearable equipment worn by workers or robots.

Summary

AI is already being used in the logistics and property industry. It will play an increasingly important role in this sector to improve work efficiency and better client experience.

Now AI is advanced enough to process almost any information available and it has the potential to be used in all aspects of business and life. We will work closer with AI and benefit more when AI becomes smarter.

 


If you are interested in more details of this report or our other logistics reports, please contact Ben Thomas, Director of CBRE National Valuation – Logistics & Distribution, or Judy Zhu, Associate Director of CBRE National Valuation.

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Property Investment Yields | March 2018

Property Investment Yields | March 2018

David Tudor

David Tudor

Senior Director, UK Valuation & Advisory Services

+44 207 182 2689
david.tudor@cbre.com

Slow start to 2018 with limited stock coming to market

• Q1 has seen low market activity and subdued growth forecasts for the year ahead. Interest continues to concentrate on the industrial sector, particularly in the South East.
• Demand is strongest for longer income streams in other sectors, with continued growth in long income and specialist sector weightings.
• Central London offices and large portfolios continue to attract interest from overseas investors, with some now prepared to look outside London.

Find out more

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#4 What is AI? – “Future of Logistics” Series

#4 What is AI? – “Future of Logistics” Series

Ben Thomas

Ben Thomas

Director, National Valuation

t: +44 207 182 2663 Ben.Thomas@cbre.com

Judy Zhu

Judy Zhu

Associate Director, National Valuation

t: +44 207 182 2683 Judy.Zhu@cbre.com

In our last post ”Robotics in Logistics”, we looked at the use of robotics in the logistics sector. In this post, we are going to focus on the topic of Artificial Intelligence, also known as AI.

What is AI?

The concept of AI was coined in The Dartmouth Conference back in 1956, defined as: “Every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”

Over time, the definition has evolved. Now the Oxford Dictionary stating it is: “The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages.”

Following these definitions to the letter defines many of the programs we use today as AI, even if they appear very simple. What is clear is that with the rapid development of AI, how it is defined will continue to change.

Types of AI

There are various ways to categorise AI, and two of the most common are by intelligence level and by application type.

  • By intelligence level

As things stand, current AI is programmed for particular tasks in a specific field, namely narrow AI. It has an intelligence limited to the area in which it operates. AI with a level of general knowledge, able to operate across multiple fields and make decisions based on this wider understanding is the next level – general AI. While it doesn’t yet exist, it is expected to at some point in the future.

  • By application type

AI can be applied to almost every aspect of our life and business. Some examples of AI applications include: recognition (image, speech, handwriting), knowledge management (medical, legal, education), prediction (purchase, economy, sports) and natural language generation (customer service, reporting, business intelligence). We will discuss these applications in more detail in the next post.

What is Machine Learning?

One of the most common type of AI is Machine Learning (ML). ML is program designed to find solutions by itself, as opposed to a traditional program, which is programmed with defined solutions to specific problems. A traditional program works something like this:

The solutions to certain problems are programmed in. Then whenever a program is run, it processes the pre-built solutions and returns a result. Machine learning works differently:

Here, solutions are not built in. ML is designed to find the answer by itself through trial and error and verifying against large data sets. ML often need a human to “teach” it the basic principles and tell it right from wrong, like an infant.  Then as ML “grows up”, it is able to understand patterns and discover solutions by itself. Over time, ML can evolve with the accumulation of data and adapt to changes. In essence the majority of programs we consider as AI are Machine Learning.

Then what is Deep Learning?AI-ML-DL

One of the most common approaches of Machine Learning is known as Deep Learning (DL), which is designed to mimic the way a biological nervous system works. Human neural networks have multiple layers in order to extract information and learn patterns. Deep learning attempts to operate in an equivalent way, but it requires more raw data to establish its understanding.

The graph on the right explains the relations between AI, ML and DL.

Summary

AI by its nature is a challenge to define with various researchers, programmers and experts all having different versions. On top of that, we live in a time when technologies change rapidly, so as AI evolves, its definition will too. However, when all is said and done: AI is a technology that with the proper application can help people work more efficiently and live better.

In the next post, we are going to look at the current and potential applications of AI in the property and logistics industry.

 


If you are interested in more details of this report or our other logistics reports, please contact Ben Thomas, Director of CBRE National Valuation – Logistics & Distribution, or Judy Zhu, Senior Analyst of CBRE National Valuation.

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