REITs : Net Lease vs. Gross Lease

An excellent overview from
https://www.dividend.com/how-to-invest/reits-net-lease-vs-gross-lease/



Have you ever wished for the safety of bonds, but the return potential of common stocks? If so, preferred stocks are potentially a good choice to explore.

Real Estate Investment Trusts (REITs) are one of the most dividend-rich segments of the financial market. Understanding the lease structure of commercial REITs can help you identify optimal investment opportunities to include in your portfolio.

Commercial REITs give investors exposure to income-producing real estate in the form of offices, apartment buildings, warehouses, shopping centers and hotels, among others. Commercial real estate leases are generally broken down into three basic categories, which are based on two rent calculation methods: net and gross.

A gross lease requires the tenant to pay one lump sum for a rental property from which the landlord deducts expenses. A net lease has a smaller rental rate but requires the tenant to pay for other expenses.

Click here to learn more about the different types of REITs.

Gross Lease

Under a gross lease, the rent is all-inclusive, which means the landlord pays for all or most of the expenses associated with the property. This includes taxes, insurance, maintenance, utilities and janitorial services. A gross lease offers predictability for the tenant because they can forecast expenses without worrying about unexpected costs like maintenance. Under this arrangement, the landlord assumes all responsibility for maintaining the building.

Net Lease

In a net lease, the tenant is charged a lower base rent for the commercial space and is also on the hook for some or all of the associated costs. These costs often include real estate taxes, property insurance and common area maintenance items. Net leases are broken down into three sub-categories: single net lease, double net lease and triple net lease. Below is a breakdown of each.

• Single Net Lease: Tenant pays base rent plus a pro-rata share of the property tax, utilities and janitorial services. The landlord pays all other building expenses.

• Double Net Lease*: Tenant pays rent plus a pro-rata share of property tax and insurance, as well as janitorial and utility expenses. The landlord pays for repairs and common area maintenance.

• Triple Net Lease: Tenant pays all or part of the property taxes, insurance and common area maintenance on top of a base monthly rent. These tend to be more landlord friendly as they ensure predictability, which can help landlords better manage expenses down the road.

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Modified Gross Lease

To bridge the two calculation methods, there’s something called a modified gross lease. While similar to the gross lease in that the rent is requested up front in one lump sum, it can include any or all of the associated “nets,” such as property taxes, insurance and common area maintenance. For most buildings, utilities and janitorial services are excluded from the rent and covered by the tenant. The modified gross lease has proven to be more popular with tenants because it provides greater flexibility.

Use the Dividend Screener to find high-quality dividend stocks. You can even screen stocks with DARSratings above a certain threshold.

Implications for Investors

For investors, REITs with a triple net lease structure are easier to predict in terms of dividend payment. This makes them more attractive for yield-seeking investors, especially those nearing retirement or looking for steady income growth. STORE Capital (STOR ) is one of the most notable triple net REITproviders. The company focuses primarily on fragmented subsectors of the leasing industry, including middle-market and larger companies that don’t have credit ratings. As of 2017, STORE’s leadership team had invested more than $12 billion across 8,000 properties.

REITs structured around single or double net leases also make good investments. For example, W.P. Carey(WPC ) is a global provider of net lease REITs focused on long-term, sale-leaseback and build-to-suit financing solutions.

Realty Income (O ) is a net lease REIT that offers diversification across tenants, industry and geography. By the end of 2017, Realty Income had a portfolio of nearly 250 commercial tenants across 47 industries. It has also proactively managed rollover, including a 99.5% recapture (i.e., re-leasing prior rent).

Although REITs offer tremendous dividend-earning potential, they are highly sensitive to economic cycles and real estate dynamics. Historically, they have underperformed the market during periods of rising interest rates. As we’ve seen during the Federal Reserve’s latest rate-tightening cycle, higher borrowing costs have already impacted the market negatively. Investors should also pay attention to upfront fees, which tend to be exorbitant for non-traded REITs that might require an upfront fee of between 9 and 15%.

Don’t forget to read this article to learn more about how a REIT is valued.

The Final Word

Net lease REITs with long-term leases can provide your portfolio with a sense of stability and transparency. As the previous discussion illustrated, the triple net lease structure offers the most predictability for investors looking for stable earnings over long durations.

Morgan Stanley buying Solium Capital shrinking field of billion-dollar Canadian tech companies

U.S. financial services giant Morgan Stanley is buying Calgary-based Solium Capital Inc. for $1.1-billion in the latest deal that will wrest a sizable technology player from Canadian control.

https://www.theglobeandmail.com/business/article-morgan-stanley-is-buying-calgarys-solium-capital-for-11-billion/

It only takes 15 minutes to do 99% of the things you want to accomplish

To figure out whether or not you really want to meet a goal you’re not meeting, clear fifteen minutes a day in your calendar. Tell yourself one very small thing you can do in that fifteen minutes to move toward meeting that goal. And see if you do it.

Why this tactics works:

1. You can’t meet big goals without breaking them down. A to-do list works best if it’s full of specific, manageable things you can do to move one, small step toward the very big goal. After breaking down the goal into items on a to-do list, you notice that worthy goals require sustained focus over a very long time.

2. Self-discipline is what creates change. And self-discipline snowballs. For example, people who write lists end up using lists, and people who use lists get more done. But also, if you balance a book on your head for ten minutes a day, you are more likely to do pushups for ten minutes a day. Because self-discipline begets self-discipline — even if it’s something silly.

3. People don’t want to accomplish the goals they set and don’t meet. I set aside fifteen minutes every day for a week and did nothing. Each day I told myself to do something different with the fifteen minutes. And each day I did not do the something different. So I decided I’m revealing to myself my true goal: to be depressed.

So I laid on the sofa with the dog for 15 minutes a day. And remember the part I told you about snowballing? Well that snowballed into two hours. That’s about as long as I can be in the mode of sleeping on the sofa in the middle of the day before the kids start to worry I’ve lost my ability to function.

I wonder if other people’s kids would start to wonder much earlier. I wonder if maybe it’s a litmus test of one’s parenting to see how long you can sleep on the sofa in the middle of the day before the kids think something is wrong.

Forget it. There’s no measure to tell if you’re a good parent. Which is why I’m obsessed with meeting goals. I want to accomplish something. I meet goals with my kids but it’s not like then I’m a good parent. Because meeting goals is not even what parenting is about — loving kids is what parenting is about. Not that you don’t know that. But I need to keep writing it to remind myself.

Wait. An aside: if my kids look back on these posts and think I was a bad parent, they should know that I do understand that the purpose of parenting is love. To the future daughter-in-law, twenty years in the future, who is telling my son that his mother fucked him up and she is not coming to Passover anymore because of family dysfunction: this is a record to show I understood what my job was and I did it. And also, wait until you have kids and see how hard it is to express love in a way that is not overbearing.

One of the ways I learned how to see the goal I’m not meeting is by coaching so many people who want help with the goal they are not meeting. Which is, like, almost everyone.

Probably the most common goal not being met is career advancement.  Many people think their careers should be advancing no matter what. But in most cases the person doesn’t really care if their career advances, they just think they should care.

The second most common goal not being met is having a meaningful career. Many people think their career should have meaning. But in most cases the person doesn’t really believe that careers give meaning to life, they think jobs support what is meaningful in life.

The other way I learn how to see the goal I’m not meeting is to look at people who are not meeting the goal I want them to meet. Tonight that is Melissa.

It used to be that she took all the pictures for the blog. Then she moved and I emailed her pictures I take, and she edited them. Or deleted them if she didn’t like them. She was incredibly slow, but she was the best at it. We did that for a long time.

Then I moved to Swarthmore and she stopped doing it. She told me to use all the pictures she edited that I didn’t use. But I do not view this as a tongue-to-tail thing where we are eating the whole cow before we butcher a new one. I view this as a one-pancake-left thing where it doesn’t feel good to eat when you know you’re taking the only one that’s left. People like a choice of pancakes. That’s why restaurants serve a stack.

But the real problem is I don’t want to look at all the pictures of our life at the farm. I get sad every time, and then I never write. So I don’t care that there are a lot of photos I did’t use.

At first I was pissed that Melissa isn’t hearing how upset I’ve been. But the goals I set for Melissa should not be goals if she’s not meeting them. Just like the goals I set for myself should not be goals if I’m not meeting them.

So I am posting all the pictures of our move from Wisconsin to Pennsylvania. I had no idea we would never go back to the farm. I feel ill and anxious every time I look at these pictures. I want the whole day out of the photo queue. So I’m putting it on the blog. I’m taking steps to meet my goal. This is the way I can move forward.

Melissa will tell you these pictures are evidence that she is right and there are plenty of pictures for me to choose from. But I see it as evidence that O’Hare is a patchwork of memorable ceilings that all make me sad.

And what is this picture? Even if you can’t identify this as the floor in Terminal C, you can identify this as the face of a dog that portends ominous doom.

If only I had paid more attention to the dog.

But really what would I have done differently? Probably nothing. I’m not the type to second-guess my decisions. One of the only times that still happens is when I flip through photos to add to my post. Now there are no more photos that makes me sad waiting in the queue. I used them all right here.

It’s my small specific step to move forward. And I’m taking action, because not being sad about what we lost when we moved is a goal that’s important to me. All the other goals; I guess I don’t want them as much I want this.

http://blog.penelopetrunk.com/2018/05/26/it-only-takes-15-minutes-to-do-99-of-the-things-you-want-to-accomplish/

Open-plan offices have a surprising effect on workplace communication

A new before-and-after study led by a Harvard Business School professor might bolster the already strong case against the open office plan.

Unlike previous research, it uses empirical evidence rather self-reported data to show that airy, communal spaces do not a buzzing, collaborative environment make.

Ethan Bernstein, an associate professor of organizational behavior, built the research around a real-life renovation at the headquarters of an unnamed Fortune 500 company engaged in a “so-called war on walls.”  He had employees wear people analytics badges that track (but do not record) conversations through anonymized sensors, which gave the professor and his co-author data they could compare against changes in online communication. (To minimize the effects of outside factors, their research took snapshots of two three-week periods that fell at that same point in different business quarters, one before walls were banished, and one after.)

In two studies, the researchers found that conversations by email and instant messaging (IM) increased significantly after the office redesign, while productivity declined, and, for most people, face-to-face interaction decreased. Participants in the first study spent 72% less time interacting in person in the open space. Before the renovation, employees had met face to face for nearly 5.8 hours per person over three weeks. In the after picture, the same people held face-to-face conversations for only about 1.7 hours per person.

These employees were emailing and IM-ing much more often, however, sending 56% more email messages to other participants in the study. This is how employees sought the privacy that their cubicle walls once provided, the authors reason. IM messages soared, both in terms of messages sent and total word count, by 67%  and 75%, respectively.

The second study compared dyads, or conversational partners, among 100 employees on the same floor of the building. It found that people who sat near each other spoke more to those in their pod of six or eight desks when they were no longer in cubicles. Overall, however, face to face exchanges decreased.

Humans are not like insects

The authors call the social withdraw they captured in data a “natural human response” triggered by a change in environment, but they acknowledge their findings contradict an established theory about collective intelligence. When forced to share space, humans behave much like swarms of insects. This has appeared to be true in a range of contexts, the authors note, citing studies involving the US Congress, college dormitories, co-working spaces, and corporate buildings.

However, as far as we’re aware, hornets and wasps are not as psychologically and socially complex as people. For instance, they do not regularly switch between their front-stage self and back-stage self, managing the impression they’re making, per a longstanding theory about humans.

People are better at rote tasks, rather than creative ones, when we feel we’re on display, and part of our mind is therefore preoccupied by social pressures, Harvard’s Bernstein has suggested. Knowing that others are watching us limits the degree to which we might creatively solve a problem, and therefore be more productive, according to a study he conducted with factory workers. “Do I look busy?” becomes more important than “Am I doing my best work?”

Importantly, the new study also found that when spatial boundaries disappeared, employees didn’t simply take their usual in-person exchanges online. Rather, they began emailing more with some people and communicating less with others. In other words, an open office can reconfigure employee networks, which obviously can change the way teams work.

Social media versus social offices

Bernstein believes the new study reinforces an existing argument that says intermittent social interactions, rather than constant ones, optimize our ability to work out complex problems. Spatial boundaries, he writes, help people “make sense of their environment by modularizing it, clarifying who is watching and who is not, who has information and who does not, who belongs and who does not, who controls what and who does not, to whom one answers and to whom one does not.”

Keeping an eye on all of these things in a sprawling, open space can lead to overload, distraction, and poorer decisions.

It’s perhaps a bit strange we haven’t adapted better to this, in an age that has many of us openly sharing vast portions of our lives on social media. But as Bernstein once told workplace strategy consultant Leigh Stringer, in an interview on her website, “We want people to follow us online, but not necessarily motion-by-motion in the office.”

Join our Quartz at Work community on Facebook

https://work.qz.com/1322146/a-harvard-business-school-study-found-open-plan-offices-have-a-surprising-effect-on-our-collective-intelligence/

‘I want to learn Artificial Intelligence and Machine Learning. Where can I start?’

@mrdbourke on Instagram, Photo by Madison Kanna

I was working at the Apple Store and I wanted a change. To start building the tech I was servicing.

I began looking into Machine Learning (ML) and Artificial Intelligence (AI).

There’s so much going on in the field.

Every week it seems like Google or Facebook are releasing a new kind of AI to make things faster or improve our experience.

And don’t get me started on the number of self-driving car companies. This is a good thing though. I’m not a fan of driving and roads are dangerous.

Even with all this happening, there’s still yet to be an agreed definition of what exactly artificial intelligence is.

Some argue deep learning can be considered AI, others will say it’s not AI unless it passes the Turing Test.

This lack of definition really stunted my progress in the beginning. It was hard to learn something which had so many different definitions.

Enough with the definitions.

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How did I get started?

My friends and I were building a web startup. It failed. We gave up due to a lack of meaning. But along the way, I was starting to hearing more and more about ML and AI.

“The computer learns the things for you?” I couldn’t believe it.

I stumbled across Udacity’s Deep Learning Nanodegree. A fun character called Siraj Raval was in one of the promo videos. His energy was contagious. Despite not meeting the basic requirements (I had never written a line of Python before), I signed up.

Three weeks before the course start date I emailed Udacity support asking what the refund policy was. I was scared I wouldn’t be able to complete the course.

I didn’t get a refund. I completed the course within the designated timeline. It was hard. Really hard at times. My first two projects were handed in four days late. But the excitement of being involved in one of the most important technologies in the world drove me forward.

Finishing the Deep Learning Nanodegree, I had guaranteed acceptance into either Udacity’s AI Nanodegree, Self-Driving Car Nanodegree or Robotics Nanodegree. All great options.

I was a little lost. “Where do I go next?”

I needed a curriculum. I’d built a little foundation with the Deep Learning Nanodegree, now it was time to figure out where I’d head next.

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My Self-Created AI Masters Degree

I didn’t plan on going back to university anytime soon. I didn’t have $100,000 for a proper Masters Degree anyway.

So I did what I did in the beginning. Asked my mentor, Google, for help.

I’d jumped into deep learning without any prior knowledge of the field. Instead of climbing to the tip of the AI iceberg, a helicopter had dropped me off on the top.

After researching a bunch of courses, I put a list of which ones interested me the most in Trello.

Trello is my personal assistant/course coordinator.

I knew online courses had a high drop out rate. I wasn’t going to let myself be a part of this number. I had a mission.

To make myself accountable, I started sharing my learning journey online. I figured I could practice communicating what I learned plus find other people who were interested in the same things I was. My friends still think I’m an alien when I go on one of my AI escapades.

I made the Trello board public and wrote a blog post about my endeavours.

The curriculum has changed slightly since I first wrote it but it’s still relevant and I visit the Trello board multiple times per week to track my progress.

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Getting a job

I bought a plane ticket to the US with no return flight. I’d been studying for a year and I figured it was about time I started putting my skills into practice.

My plan was to rock up to the US and get hired.

Then Ashlee messaged me on LinkedIn, “Hey I’ve seen your posts and they’re really cool, I think you should meet Mike.”

I met Mike.

I told him my story of learning online, how I loved healthtech and my plans to go to the US.

“You may be better off staying here a year or so and seeing what you can find, I’ think you’d love to meet Cameron.”

I met Cameron.

We had a similar chat what Mike and I talked about. Health, tech, online learning, US.

“We’re working on some health problems, why don’t you come in on Thursday?”

Thursday came. I was nervous. But someone once told me being nervous is the same as being excited. I flipped to being excited.

I spent the day meeting the Max Kelsen team and the problems they were working on.

Two Thursday’s later, Nick, the CEO, Athon, lead machine learning engineer, and I went for coffee.

“How would you like to join the team?” Asked Nick.

“Sure.” I said.

So it turns out, my US flight got pushed back a couple months and now I’ve got a return ticket.

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Sharing your work

Learning online, I knew it was unconventional. All the roles I’d gone to apply for had Masters Degree requirements or at least some kind of technical degree.

I didn’t have either of these. But I did have the skills I’d gathered from a plethora of online courses.

Along the way, I was sharing my work online. My GitHub contained all the projects I’d done, my LinkedIn was stacked out and I’d practiced communicating what I learned through YouTube and articles on Medium.

I never handed in a resume for Max Kelsen. “We checked you out on LinkedIn.”

My body of work was my resume.

Regardless if you’re learning online or through a Masters Degree, having a portfolio of what you’ve worked on is a great way to build skin in the game.

ML and AI skills are in demand but that doesn’t mean you don’t have to showcase them. Even the best product won’t sell without any shelf space.

Whether it be GitHub, Kaggle, LinkedIn or a blog, have somewhere where people can find you. Plus, having your own corner of the internet is great fun.

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How do you start?

Where do you go to learn these skills? What courses are the best?

There’s no best answer. Everyone’s path will be different. Some people learn better with books, others learn better through videos.

What’s more important than how you start is why you start.

Start with why.

• Why do you want to learn these skills?
• Do you want to make money?
• Do you want to build things?
• Do you want to make a difference?

Again, no right reason. All are valid in their own way.

Start with why because having a why is more important than how. Having a why means when it gets hard and it will get hard, you’ve got something to turn to. Something to remind you why you started.

Got a why? Good. Time for some hard skills.

I can only recommend what I’ve tried.

I’ve completed courses from (in order):

• Treehouse — Introduction to Python
• Udacity — Deep Learning & AI Nanodegree
• Coursera — Deep Learning by Andrew Ng
• fast.ai — Part 1, soon to be Part 2

They’re all world class. I’m a visual learner. I learn better seeing things being done/explained to me on. So all of these courses reflect that.

If you’re an absolute beginner, start with some introductory Python courses and when you’re a bit more confident, move into data science, machine learning and AI.

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How much math?

The highest level of math education I’ve had was in high school. The rest I’ve learned through Khan Academy as I’ve needed it.

There are many different opinions on how much math you need to know to get into machine learning and AI. I’ll share mine.

If you want to apply machine learning and AI techniques to a problem, you don’t necessarily need an in-depth understanding of the math to get a good result. Libraries such as TensorFlow and PyTorch allow someone with a bit of Python experience to build state of the art models whilst the math is taken care of behind the scenes.

If you’re looking to get deep into machine learning and AI research, through means of a PhD program or something similar, having an in-depth knowledge of the math is paramount.

In my case, I’m not looking to dive deep into the math and improve an algorithm’s performance by 10%. I’ll leave that to people smarter than me.

Instead, I’m more than happy to use the libraries available to me and manipulate them to help solve problems as I see fit.

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What does a machine learning engineer actually do?

What a machine engineer does in practice might not be what you think.

Despite the cover photos of many online articles, it doesn’t always involve working with robots that have red eyes.

Here are a few questions an ML engineer has to ask themselves daily.

• Context — How can ML be used to help learn more about your problem?
• Data — Do you need more data? What form does it need to be in? What do you do when data is missing?
• Modeling — Which model should you use? Does it work too well on the data (overfitting)? Or why doesn’t it work very well (underfitting)?
• Production — How can you take your model to production? Should it be an online model or should it be updated at time intervals?
• Ongoing — What happens if your model breaks? How do you improve it with more data? Is there a better way of doing things?

I borrowed these from a great article by Rachel Thomas, one of the co-founders of fast.ai, she goes into more depth in the full text.

For more, I made a video of what we usually get up to on Monday’s at Max Kelsen.

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No set path

There’s no right or wrong way to get into ML or AI.

The beautiful thing about this field is we have access to some of the best technologies in the world, all we’ve got to do is learn how to use them.

You could begin by learning Python code.

You could begin by studying calculus and statistics.

You could begin by learning about the philosophy of decision making.

Machine learning and AI fascinates me because of this intersection of fields.

The more I learn about it, the more I realise there’s plenty more to learn. And this hypes me up.

Sometimes I get frustrated when my code doesn’t run. Or I don’t understand a concept. So I give up temporarily. I give up by letting myself walk away from the problem and take a nap. Or go for a walk. When I come back it feels like I’m looking at it with different eyes. The excitement comes back. I keep learning.

There’s so much happening in the field it can be daunting to get started. Too many options lead to no options. Ignore this.

Start wherever interests you most and follow it. If it leads to a dead end, great, you’ve figured out what you’re not interested in. Retrace your steps and take the other fork in the road instead.

Computers are smart but they still can’t learn on their own. They need your help.

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PS if you want have any questions, feel free to reach out to me anytime at mrdbourke.com.

*This article originally appeared as an answer by Daniel Bourke on Quora.

https://hackernoon.com/i-want-to-learn-artificial-intelligence-and-machine-learning-where-can-i-start-7a392a3086ec