Bitcoin flash crashes to $8,900 on BitMEX

Bitcoin (BTC) experienced a sudden drop to $8,900 on BitMEX, deviating significantly from its stable $60,000+ valuation on other exchanges.
What is a Bitcoin flash crash?
On a late Monday, Bitcoin experienced a swift downturn, dropping to a low of $8,900 on the cryptocurrency exchange BitMEX, a stark contrast to its stability above $60,000 on other platforms. This is known as a flash crash.
A sharp decline started at 22:40 UTC that within two minutes led Bitcoin’s price down to $8,900, marking its lowest point since the beginning of 2020. However, the recovery was just as rapid, with the price surging back to $67,000 by 22:50 UTC.
During this rapid rise and fall on BitMEX’s spot market, the global average price of BTC hovered around $67,400. Speculation arose on the social media platform X, with some users suggesting that massive sell-offs by large holders, or ‘whales’ triggered the price drop.
User @syq pointed out that an individual had sold more than 850 BTC (valued at approximately $55.49 million) on BitMEX, causing the XBT/USDT spot pair to dive to $8,900.
The XBT index on BitMEX monitors the price of Bitcoin, whereas the XBT/USDT pair indicates the price of Bitcoin in terms of Tether (USDT), the leading stablecoin pegged to the US dollar. Despite the turbulence in the spot market, BitMEX’s derivatives markets, worth billions, remained unaffected.
In response to the incident, BitMEX announced via social media that it had initiated an investigation into the large sell orders. Furthermore, BitMEX reassured its users, stating, “The trading platform is fully functional, and all user funds are secure.”

As mentioned earlier today already – we are investigating unusual activity on our BTC-USDT Spot Market.
This did not affect any of our derivative markets, nor the index price for our popular XBT derivatives contracts.
The trading platform is operating as normal and all funds…
— BitMEX (@BitMEX) March 19, 2024

The development follows Bitcoin reaching new all-time highs of almost $74,000 on Mar. 15, before then dropping to $66,885 on Mar. 15. The world’s first cryptocurrency’s price started skyrocketing following the approval of Bitcoin spot exchange-traded fund (ETFs) contracts back in January.
The newly-approved ETF contracts have attracted significant attention from market participants, reaching daily net inflows in excess of $1 billion for the first time on Mar. 12.
Featured image: ideogram
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6 Ways AI is Changing Real Estate

The real estate industry, known for its reliance on traditional methods, is undergoing a major shift thanks to the integration of AI. From property valuations to customer service, AI is not only streamlining operations but also enhancing the customer experience and reshaping business strategies.
Understanding AI’s Influence in Real Estate
AI currently has its fingers on every major industry – instigating big changes from the inside out of sectors that have been stuck in their ways for 20-plus years. But it could be argued that there’s no industry with more potential for major, sweeping change than real estate.
Let’s explore a few of the big ways AI is currently changing real estate, along with peeking at some of the ways it will continue to evolve moving forward.
1. AI-Driven Analytics for Property Valuations
Accurately valuing a property is both an art and a science. It has traditionally relied on a blend of physical inspections and market trend analysis. However, AI is proactively changing this process by introducing AI-driven analytics, offering a new level of precision and efficiency.
At the core of AI-driven property valuations are sophisticated algorithms capable of sifting through extensive datasets, including historical transaction prices, current market trends, property features, and even socio-economic indicators. 
Unlike traditional appraisal methods, which might depend heavily on the appraiser’s experience and available local data, AI systems can analyze national and global data trends to make more accurate predictions. 
Practical applications and use cases of AI in property valuations are already evident across the real estate industry. For example, Zillow’s “Zestimate” leverages AI to provide instant property valuations based on public and user-submitted data. Similarly, Redfin uses machine learning models to deliver accurate home value estimates, taking into account thousands of data points.
Another example is the use of AI by banks and financial institutions in their appraisal processes for mortgage lending. By integrating AI analytics, they quickly assess a property’s value, making the loan approval process faster and more efficient.
2. Enhancing Customer Service with AI
AI has been fast at work over the past few years, using advanced chatbots and virtual assistants that are capable of handling client inquiries quickly in real-time, regardless of the time of day.
Several different companies leverage AI chatbots and digital assistants, including HouseJet. Their platform has two different AI technologies: MAX AI and LISA AI. MAX is an AI-powered lead generation tool that optimizes real estate agent marketing strategies to help them improve the number (and quality) of leads they bring in on a daily basis. Then, LISA acts as a digital AI assistant that texts clients on the agent’s behalf to set appointments. This frees the agent up to actually go to appointments, take calls, and spend more time with family during “off” hours.
This is the new approach that agents will be using moving forward. AI won’t necessarily be the one showing the house or doing the negotiations – we still need real, breathing agents for that – but it will help offload some of the time-consuming administrative tasks that eat up a realtor’s schedule. 
3. AI in Property Management
When it comes to property management, AI has the ability to automate routine and time-consuming tasks. This includes everything from sorting and responding to tenant inquiries to managing lease renewals and payments. By automating these processes, AI frees up property managers to focus on higher-level strategic aspects of their role, such as tenant relations and property improvements.
For example, AI-powered systems can prioritize maintenance requests based on urgency and schedule them automatically with the service providers. Technology is also making strides in energy management within properties, using smart algorithms to optimize heating, cooling, and lighting systems. These systems can analyze usage patterns, weather forecasts, and even occupancy rates to adjust settings in real-time, reducing energy consumption and costs.
Beyond routine maintenance, AI is paving the way for predictive maintenance. By analyzing data from various sensors and systems within a building, AI can predict when equipment might fail or when structures may need repairs before they become critical issues. This proactive approach can save huge amounts of money in emergency repairs and downtime, ensuring that properties remain in top condition and enjoy optimum cash flow.
4. AI and Real Estate Marketing
One of the big areas where AI excels (in a marketing context) is in its ability to analyze large volumes of data, including browsing habits, interaction rates, and past purchasing behavior. Based on this, it can create highly targeted marketing campaigns. This means real estate agents can now send property listings that align closely with a client’s specific preferences, such as location, price range, and property features (without spending hours browsing listings or sifting through the MLS). This increases the likelihood of engagement and response, which makes both the client and the agent happy.
Redfin recently released a new AI-powered tool that improves listing photos by allowing home buyers to see what they could do with a space by just clicking a few buttons. This shows them different flooring types, paint colors, countertop finishes, etc. As a result, even outdated homes get a fair shake when listed.
On top of all of this, AI-driven marketing tools can segment audiences with surgical-like precision, ensuring that marketing messages are tailored to different groups based on their unique characteristics and interests. This level of personalization enhances the client experience and improves the efficiency of marketing efforts. The result is better ROI across the board. 
5. Virtual Property Tours and AI
Virtual property tours are not entirely new; however, AI has created a whole new world of opportunities and advantages. Traditionally, virtual tours have been simple panoramic images or video walkthroughs. Now, with AI, these tours are highly interactive and personalized experiences.
AI can tailor the virtual tour to highlight features specific to a client’s preferences, such as focusing on the kitchen. Or if another client is big on having outdoor living, the virtual tour can revolve around the outdoor space.
Not only that, but AI-driven virtual tours can provide real-time information overlay, such as material finishes, appliance details, or even renovation possibilities. This level of interaction and information has never been possible before now.
AI also enhances virtual tours by making them more engaging and informative. Chatbots can answer questions, provide additional property details, and guide potential buyers to areas of the home that match their interests. This gives home buyers more flexibility in how they decide to purchase a home.
6. AI in Real Estate Financing
The loan processing stage has historically been defined by lots of paperwork and waiting periods. Now, thanks to AI, it’s seeing significant improvements in efficiency. AI algorithms are capable of rapidly analyzing vast amounts of data like credit scores, income history, and debt ratios. This can expedite the pre-approval and application processes.
We’re also seeing some advances in the industry where AI is able to automate document verification and fraud detection. This speeds up the process and allows human loan officers to focus on more complex cases or customer service.
It’s also worth noting that AI is giving lenders more sophisticated risk assessment models to use in the underwriting and loan approval processes. Whereas traditional lending models often rely on a narrow set of criteria, AI can incorporate a broader range of factors. This includes non-traditional credit data to further assess a borrower’s creditworthiness. 
The result of all of this is more nuanced risk profiles. In some cases, this opens up financing opportunities for individuals who might be marginalized by traditional models. For example, this would include freelancers with irregular income streams or first-time buyers with limited credit history.
The Future is Bright for AI in Real Estate
If you were to fast forward 25 years and look at this current moment in time, it would become overwhelmingly evident that we’re only at the very origins of the AI journey in real estate. With as much innovation as has occurred over the past two to three years, it’s easy to feel like we’ve arrived. However, the proverbial train is only just now leaving the station. 
Now’s the time for real estate professionals to really lean in and understand what’s happening, so they can grow along with the technology that’s feeding this industry!
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