It starts with a company/platform identifying investable Grade A properties like commercial buildings or warehouses.
Such premium properties usually cost crores.
So, the company invites multiple investors to pool money.
Imagine if you owned lavish office spaces.
You could earn lakhs as rent every month.
But it isn’t easy. Such properties cost crores.
You can, however, buy a part of them.
How? Through fractional real estate platforms (different from REITs).
A thread🧵
It starts with a company/platform identifying investable Grade A properties like commercial buildings or warehouses.
Such premium properties usually cost crores.
So, the company invites multiple investors to pool money.
And the investors become shareholders in this SPV.
Examples of these companies/platforms include Strata, hBits, Property Share, etc.
There are two ways.
• One, they earn rent from the tenants, who are usually locked for a long-term lease.
• Two, capital appreciation from the property when they sell it.
Platforms like hBits and Strata promise 8-10% yearly rental returns on their listed assets.
Post capital appreciation, the overall average annual return they promise is 12-20% in the long term (typically 5 years).
They could be of two types:
One, you need to pay an asset management fee of around 1%.
Two, the platforms may take in a share of the profits if your returns exceed a particular number (hurdle rate).
This appealing investment option isn’t everybody’s cup of tea.
For most platforms, the minimum investment amount starts from Rs 25 lakh.
In addition, selling your investments won’t be easy.
Let’s understand why. ⬇️
Now, these companies allow you to list your share of the property on their platform.
But there’s no guarantee that you will find one immediately.
So, liquidity can be a challenge.
Like in the case of any property, fractional real estate ownership is exposed to risks like lease renewals, tenant defaulting on rent, occupancy challenges, etc.
Besides, there is no regulatory oversight.
By now, you must have figured out several differences between the two.
You can check more details in the table below.
https://t.co/nvuo43oOeQ
In the past month, shares of Embassy Office Parks REIT have dropped over 5%\U0001f4c9
— ET Money (@ETMONEY) October 4, 2022
Will you benefit if you buy units of a #RealEstate Investment Trust(REIT) after such a fall?
The answer is not straightforward. #REIT units traded on the exchange don\u2019t work exactly like stocks
A \U0001f9f5
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#ArudraDarisanam
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#ArudraDarisanam
Unique Natarajar made of emerlad is abt 6 feet tall.
It is always covered with sandal paste.Only on Thriuvadhirai Star in month Margazhi-Nataraja can be worshipped without sandal paste.
After removing the sandal paste,day long rituals & various abhishekam will be https://t.co/e1Ye8DrNWb day Maragatha Nataraja sannandhi will be closed after anointing the murthi with fresh sandal paste.Maragatha Natarajar is covered with sandal paste throughout the year
as Emerald has scientific property of its molecules getting disturbed when exposed to light/water/sound.This is an ancient Shiva temple considered to be 3000 years old -believed to be where Bhagwan Shiva gave Veda gyaana to Parvati Devi.This temple has some stunning sculptures.
How can we use language supervision to learn better visual representations for robotics?
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
🧵👇(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)
Introducing Voltron: Language-Driven Representation Learning for Robotics!
Paper: https://t.co/gIsRPtSjKz
Models: https://t.co/NOB3cpATYG
Evaluation: https://t.co/aOzQu95J8z
🧵👇(1 / 12)
Videos of humans performing everyday tasks (Something-Something-v2, Ego4D) offer a rich and diverse resource for learning representations for robotic manipulation.
Yet, an underused part of these datasets are the rich, natural language annotations accompanying each video. (2/12)
The Voltron framework offers a simple way to use language supervision to shape representation learning, building off of prior work in representations for robotics like MVP (https://t.co/Pb0mk9hb4i) and R3M (https://t.co/o2Fkc3fP0e).
The secret is *balance* (3/12)
Starting with a masked autoencoder over frames from these video clips, make a choice:
1) Condition on language and improve our ability to reconstruct the scene.
2) Generate language given the visual representation and improve our ability to describe what's happening. (4/12)
By trading off *conditioning* and *generation* we show that we can learn 1) better representations than prior methods, and 2) explicitly shape the balance of low and high-level features captured.
Why is the ability to shape this balance important? (5/12)
