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Walmart Black Friday Deal: Brother Wireless All-in-One Laser Printer for $99 (Reg. $199.99)

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Walmart Black Friday Deal: Brother Wireless All-in-One Laser Printer for $99 (Reg. $199.99)


Brother Wireless All-in-One Laser Printer for $99

Walmart has the Brother MFC-L2760DW Wireless All-in-One Laser Printer for just $99, down from $199.99. 

This compact black-and-white laser printer is a good option for home offices, with printing, scanning, copying, and faxing in one device.

Key Features

  • Prints up to 30 pages per minute
  • Automatic two-sided printing
  • Wireless and mobile printing
  • 250-sheet paper tray
  • 50-page automatic document feeder
  • Print, scan, copy, and fax capabilities

 

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what persisted and what changed – Bank Underground

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what persisted and what changed – Bank Underground


Eugenio Sanchez Pelayo and Gary Harper

UK house prices rose quickly during the pandemic. Earlier Bank of England analysis linked part of that rise to buyers paying more for houses than they would for a flat with similar characteristics, but could not show whether this revaluation would persist. With four more years of data, we find that it did. We also find London’s relative weakness began before Covid continued, helping explain the particularly weak performance of London flats. Finally, we decompose annual house-price growth to see what drove the cycle. Changes in the prices associated with property type and location contributed most during the pandemic boom, transaction composition accounted for most of the 2023–24 fall, and both contributed to the recovery in 2025.

The House Price Index (HPI) can move for very different reasons, and those reasons may have different implications. House prices feed into mortgage borrowing, collateral values and affordability, so it is important to look beneath the headline movements. The index can move because the prices associated with particular property characteristics have changed, such as houses becoming more expensive relative to similar flats. But it can also reflect persistent changes in the types of homes being sold and therefore in the weights used to construct the index. Identifying which force is at work helps us assess whether a movement is broad-based, concentrated in particular parts of the market, or more likely to persist or unwind. Fazio and Harper used transaction-level data matched to property characteristics from Energy Performance Certificates to show that part of the pandemic boom reflected houses becoming more expensive relative to otherwise similar flats, consistent with remote work changing what households wanted from a home. But their data ended in 2021, leaving open whether that revaluation unwound as pandemic conditions faded. We extend the analysis by four more years to test whether it persisted and to examine the longer-running weakness of London.

Our framework follows the broad approach used to construct the Office for National Statistics (ONS) HPI. It starts with monthly ‘hedonic regressions‘, a standard way of relating a property’s observed characteristics to the price paid. In each month, we regress the logarithm of prices on property type, number of rooms, floor size, new-build status, region, ONS Local Authority District and output area groups. In plain English, this estimates how much more or less was paid for each characteristic, holding the others constant. Where a characteristic is compared with a reference group, we describe the estimated effect as a premium or discount: for example, houses relative to flats, or regions relative to London. We then track those estimates over time and weight them by each combination’s share of transactions in the previous period to build a model-based HPI. This index is the fitted value implied by the model after controlling for observed property characteristics, allowing us to compare its movements with the official HPI and then decompose the drivers of those movements.

Chart 1: Our framework tracks the official house price cycle closely

Model-based and ONS house price growth relative to 12 months earlier

The model-based index captures the broad shape of the ONS HPI. The two series separate most in the unwind and recovery, when our index falls further in December 2023 and rises more sharply in 2025. The discrepancy may partly reflect different inputs: the ONS draws property characteristics from Valuation Office Agency records rather than Energy Performance Certificates.

Houses stayed expensive relative to flats

During the pandemic, buyers paid more for houses relative to otherwise similar flats, and that increase has persisted. We estimate it by comparing prices paid for different property types holding size, rooms, new-build status and location constant.

Chart 2: The pandemic revaluation of houses has persisted

Estimated house-over-flat premia from the monthly model, relative to otherwise similar flats

Detached houses already sold for substantially more than comparable flats before the pandemic, and the estimated premium over flats subsequently rose across all three house types. These increases have not unwound by the end of our sample. The premium held through the end of the stamp duty holiday (July 2020 to September 2021, with full relief ending in June 2021), the 2022 rise in mortgage rates and a subsequent period of lower transaction volumes. That said, the latest increase from June 2025 should be read with caution, as late registrations, especially for new builds, can revise the data.

London flats were hit by both shifts

The continued relative underperformance of London is only partially explained by the house-over-flat premium. Prices outside London had also been gaining ground relative to London before the pandemic. Once other characteristics are considered, London’s underperformance appears to have started around 2016–17 and continued after Covid. The two differ in timing: the house-over-flat premium rose sharply during the pandemic, while London’s underperformance was more gradual and longer-running.

Chart 3: London’s relative weakness predates the pandemic

Estimated regional discount from the monthly model, relative to otherwise similar properties in London

Most regions became less discounted relative to London, so upward movements in Chart 3 mean comparable properties outside London were gaining ground. The narrowing was largest in Wales, the North West and the Midlands, with Yorkshire and The Humber moving similarly. The South East, East and South West stayed closer to London throughout, while the North East changed little and remained the cheapest relative to London by the end of the sample.

This helps explain why London flats were particularly weak. They sat on the wrong side of both shifts: flats underperformed houses, and London underperformed most other regions.

Decomposing the boom, unwind and recovery

To see what drove growth at different points in the cycle, we compare the model-based index with counterfactual versions where one input is held at its value from 12 months earlier. That input can be either the estimated prices associated with property characteristics or the composition of transactions, meaning how the index is weighted across the homes sold. The approach builds on Fazio and Harper, who fixed selected regression coefficients at their pre-pandemic values. Unlike Fazio and Harper, who compared outcomes with a fixed pre-pandemic baseline, we focus on the drivers of year-on-year growth. We therefore compare each period with the preceding 12 months, so that the decomposition is directly aligned with the annual growth rates being measured.

Chart 4 summarises the decomposition across three phases of the cycle: the pandemic boom, when average annual model-based growth was 8.1%; the 2023–24 unwind, when growth averaged -1.8%; and the recovery in 2025, when growth averaged 3.7%.

Changes in the estimated prices associated with property type and location accounted for just over half of growth during the pandemic boom and around four fifths of the recovery. Transaction composition also supported growth in both periods.

Chart 4: Decomposition of growth drivers

Contributions to average annual model-based house-price growth, percentage points.

Notes: ‘Property type and location characteristics’ combines changes in the estimated prices associated with property type, region and neighbourhood. ‘Other factors’ combines the remaining model components, including rooms, floor size, new-build status and the reference-property contribution.

This decomposition is best understood as an accounting exercise rather than a causal estimate of why prices changed. It depends on the characteristics recorded in the data and on the model we use to estimate the prices associated with them. It cannot capture changes in unobserved property quality, or shifts in demand that are not reflected in those recorded characteristics.

Conclusion and policy implications

Before Covid, London property prices were already weakening relative to the rest of the country, and flats were a weaker part of the market. The pandemic reinforced that split. The premium paid for houses over flats rose and remained high, while London’s relative decline, underway since 2016, continued. That left London flats exposed to both shifts.

The persistence of the house-over-flat premium helps answer a question left open by the earlier analysis. If the revaluation had been a temporary response to pandemic conditions, some of it might have unwound as those conditions faded. So far, we find little evidence that this has happened. This does not mean that the premium cannot reverse, or that wider house prices will not respond to interest rates, incomes and other macroeconomic developments. But it suggests that the fading of pandemic conditions alone has not restored the previous relative-price pattern.

The decomposition also helps us interpret movements in headline house-price growth. During 2023–24, the decline in our model-based index mainly reflected a shift in transactions towards homes with fewer higher-priced characteristics, rather than a broad fall in the estimated prices associated with those characteristics. These two developments do not necessarily carry the same implications for the wider housing market. Distinguishing between them provides a clearer picture of whether headline movements are broad-based or concentrated in particular property types and locations, which is relevant when monitoring housing affordability, mortgage collateral and financial-stability risks.

Eugenio Sanchez Pelayo and Gary Harper work in the Data and Statistics Division.

If you want to get in touch, please email us at bankunderground@bankofengland.co.uk or leave a comment below.

Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

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Silicon Valley Doesn’t Have a Monopoly on Startup Wisdom

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Opinions expressed by Entrepreneur contributors are their own.

Key Takeaways

  • Founders in small ecosystems design for international markets from day one, planning for localization, payments and regulation before they need to.
  • Scarcity forces discipline as founders who learn to operate lean and treat relationships as infrastructure are better prepared for market corrections and fundraising delays.
  • Local knowledge is a competitive advantage.

Silicon Valley teaches founders to think big. Smaller startup markets teach them how to build when almost nothing is guaranteed.

Over the past year, I have met founders and investors across the Baltics, Georgia and Guatemala through Baltic Fintech Days, a 500 Global Eurasia Demo Day in Tbilisi and Volcano Summit. These ecosystems differ in language, history and access to capital, but the strongest founders shared a common advantage: They could not rely on a large domestic market, abundant venture funding or proximity to the world’s most powerful networks.

That constraint shaped how they built. They thought internationally earlier, stretched resources further and cultivated relationships across borders. As capital becomes more selective and growth at all costs continues to fall out of favor, founders everywhere can learn from companies built in markets that have never had the luxury of excess.

Here are five lessons from some of the world’s smallest startup ecosystems.

1. Build for the world from day one

A founder in the United States can build a large company without ever leaving the domestic market. Founders in Latvia, Lithuania, Estonia, Georgia or Guatemala rarely have that option.

The entire population of Lithuania is smaller than that of Los Angeles. Latvia has fewer than 2 million residents, while Georgia has fewer than 4 million. Even Guatemala, the largest of the markets I visited, represents only a fraction of the purchasing power available in the United States.

As a result, international expansion is not a distant ambition for many of these founders. It is part of the original business model.

At Baltic Fintech Days, I met entrepreneurs who were thinking about cross-border payments, compliance and market entry well before reaching the scale at which an American startup might begin considering international growth. Their home markets gave them useful places to test, but not enough room to stop there.

This forces founders to ask important questions early: Can the product travel? Can it serve customers across languages, currencies and regulatory systems? Does the problem exist beyond the founder’s immediate network?

Building globally does not mean launching in 10 countries at once. It means avoiding assumptions that make international expansion unnecessarily difficult later. Founders should consider localization, payment infrastructure, regulation and cross-border distribution while the company is still young enough to adapt.

2. Treat constraints as a design advantage

Founders love to talk about resourcefulness, but the concept takes on a different meaning in ecosystems where capital, talent and customers are all more difficult to access.

At 500 Global’s Eurasia Demo Day in Tbilisi, I saw companies working to gain attention far beyond their immediate geography. They could not assume that investors would already understand their markets, recognize their universities or trust their local references. They had to communicate the problem, the opportunity and their credibility with unusual precision.

That constraint can produce better companies.

When capital is scarce, founders have less room for performative hiring, unnecessary tools or growth that cannot be tied to customer demand. They learn to test assumptions quickly, sell before everything is perfect and use partnerships to extend their reach.

Founders in major technology hubs should apply the same discipline even when more resources are available. Ask what the company would prioritize if it had half the budget, half the team and half the time. The answer often reveals which initiatives are truly essential.

Scarcity is painful, but it can also create focus. A company that learns to operate efficiently before raising significant capital may be better prepared to survive market corrections, fundraising delays and unexpected competition.

3. Make your ecosystem part of your competitive advantage

Smaller markets are often discussed only in terms of what they lack. That framing overlooks what they can offer.

The Baltics have developed deep expertise in fintech, digital infrastructure and cross-border business. Georgia sits at the intersection of Europe and Asia and is developing a generation of founders accustomed to navigating multiple markets. Guatemala offers access to Central America, proximity to the United States and entrepreneurs with firsthand knowledge of industries and consumers that remain poorly understood by many global investors.

At Volcano Summit in Guatemala, the value of the ecosystem was not simply that it brought international investors into the country. It created a place where local and regional founders could build relationships without first relocating to San Francisco, Miami or New York.

Founders should resist the urge to erase where they come from in an attempt to appear more investable. Geography can be a source of differentiated insight.

A founder who understands regional payment behavior, informal commerce, remittances or local regulation may see an opportunity long before an outsider does. The goal is not to build a business that only works locally. It is to turn local knowledge into an advantage that can travel.

The most compelling question is not, “Why would a major company come from this market?” It is, “What can a founder see from this market that others cannot?”

4. Relationships are infrastructure

In mature startup hubs, founders can mistake proximity for access. They may attend dozens of events, collect hundreds of contacts and still fail to build relationships that materially help the company.

In smaller ecosystems, relationships often function as infrastructure.

One trusted introduction can connect a founder to a customer, investor or market that would otherwise be difficult to reach. Government agencies, accelerators, diaspora networks and local operators frequently play an outsized role in helping companies cross borders.

I saw versions of this at Baltic Fintech Days, through 500 Global’s network in Georgia and at Volcano Summit. These gatherings mattered because they connected local credibility with international reach. They reduced the distance between founders and people who could help them enter new markets.

Founders should approach relationship-building with the same discipline they apply to product development. Instead of asking how many people they met, they should ask whether they are building trust with the right customers, partners and advocates.

That also means becoming useful before asking for something. Founders can share market intelligence, make introductions and offer thoughtful feedback. In smaller ecosystems, reputations travel quickly. Generosity and reliability compound.

5. Learn to tell the market why it should care

A founder in a well-known ecosystem benefits from familiar signals. Investors recognize the schools, employers, accelerators and other funds around the table. Founders from less visible markets must often establish credibility without those shortcuts.

That makes storytelling a core business capability.

The strongest founders I encountered did not simply explain what their products did. They connected a local problem to a much larger shift. A payments challenge became a story about global commerce. A compliance product became infrastructure for the international workforce. A regional marketplace became evidence of changing consumer behavior across emerging economies.

Founders should not expect investors or customers to do this translation themselves.

A strong pitch should answer four questions: Why does this problem matter? Why is the opportunity larger than it first appears? Why is this team uniquely equipped to solve it? And why is now the right moment?

This is especially important for founders building outside established technology centers. Investors may incorrectly interpret an unfamiliar market as a small opportunity. The founder’s job is to distinguish the company’s starting point from its potential destination.

Great companies are not limited to great startup hubs

Talent is distributed far more evenly than capital, attention or access. Some of the world’s most ambitious founders are building in places that receive only a fraction of global venture investment.

The Baltics, Georgia and Guatemala are not interchangeable, nor should they be reduced to a single category of “emerging markets.” Each has its own strengths, challenges and history. What connects them is the resourcefulness required to build beyond the limits of a small domestic ecosystem.

Founders everywhere can learn from that mindset.

Start globally. Use constraints to sharpen your decisions. Turn local knowledge into an advantage. Treat relationships as infrastructure. And make the scale of your ambition impossible to misunderstand.

The next category-defining company may not come from the place investors visit most often. It may come from the market whose founders learned to build without waiting for the world to notice.

Key Takeaways

  • Founders in small ecosystems design for international markets from day one, planning for localization, payments and regulation before they need to.
  • Scarcity forces discipline as founders who learn to operate lean and treat relationships as infrastructure are better prepared for market corrections and fundraising delays.
  • Local knowledge is a competitive advantage.

Silicon Valley teaches founders to think big. Smaller startup markets teach them how to build when almost nothing is guaranteed.

Over the past year, I have met founders and investors across the Baltics, Georgia and Guatemala through Baltic Fintech Days, a 500 Global Eurasia Demo Day in Tbilisi and Volcano Summit. These ecosystems differ in language, history and access to capital, but the strongest founders shared a common advantage: They could not rely on a large domestic market, abundant venture funding or proximity to the world’s most powerful networks.

That constraint shaped how they built. They thought internationally earlier, stretched resources further and cultivated relationships across borders. As capital becomes more selective and growth at all costs continues to fall out of favor, founders everywhere can learn from companies built in markets that have never had the luxury of excess.

Best Business Schools in the World

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Best Business Schools in the World



These are the highest ranked business programs by school, according to US News.

#collegemajor #student #business #finance

source

Marsh CFA Institute Global Pension Index 2026

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Why non-QM is keeping top mortgage brokers in business

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Kevin Oto: Nobody knew what a high rate looked like. I’m always optimistic. Even in markets like this, I feel like there’s always opportunities. 

Matt Sexton: Hello again and welcome to the latest edition of MPA TV. I’m Matt Sexton, mortgage journalist here with Mortgage Professional America. Today we continue our broker intel series featuring discussions with the top mortgage pros across the US on the most important mortgage topics of the day. I’m honored to be joined once again by two esteemed guests on this episode. They are Kevin Oto, broker owner of Pinnacle Mortgage Corporation, and Damon Germanides, co-founder of Insignia Mortgage. Gentlemen, thank you so much for joining us on today’s edition of MPA TV. Thanks for having us. Yeah, a lot has happened since the last time. 

Matt Sexton: we chatted. So, let’s dive right into where the market is since the last time we spoke. Obviously, mortgage rates have increased now floating back toward the mid7s, although we did get a little bit of a reprieve today from the Freddie Mac rates. We’ll start with Kevin on this one. What direct or indirect impact have you seen rates have on your business if any? 

Kevin Oto: I mean, obviously it has had an impact. You know, obviously the biggest impact which you would see right away is affordability, but really other than that is there’s like a psychological effect that I’m seeing with my borrowers when rates go up. it just affects their confidence and their urgency and you know they get to this analysis paralysis and they start becoming uncertain you know they think about maybe should I wait are rates too high are they going to come back down so you know really the rates go up they still may qualify but it just creates that uncertainty Damon your thoughts. 

Damon Germanides: I mean yeah Kevin’s spot on I feel like uh just having done this for so many years feel like anything under 6% is uh people are uh happy to go buy a home. Uh where we are today um a lot tougher to qualify folks. Uh some folks even if they do qualify uh the payments are higher than they imagined especially like you know we’re a uh I’m not sure where Kevin’s at but we’re out of California predominantly so obviously costs are high here on insurance on all other cost of living. So, putting that all into the, you know, kind of bundling that all together as part of their process after they know what the home financing is going to cost. You know, lot lots of lot lots of uh tension there on whether or not they want to move forward. Um, and you know, and I will say this though, uh, it’s not a direct answer to your question, but we’re we’re working very hard, as I imagine Kevin is too. I am sourcing so many banks, credit unions, non-QM lenders, trying to find an edge. Uh who needs volume? Who’s going to price sharper? Uh what local banks that we have access to are maybe uh not as uh needing volume. So maybe keeping rates uh you know under that seven, maybe in the low sixes. And so that’s that’s been the positive in a market like this. But the general trend is not our friend here at the moment. So, uh, definitely, uh, a difficult time to approve, uh, borrowers for financing. 

Matt Sexton: That kind of leads into the next question. So, Damon, we’ll just stay with you to start this one. Obviously, there’s opportunities in every market, no matter what rates are. What would be your advice to mortgage brokers out there as it relates to trying to find these opportunities in the current market? I mean, look, I think it’s like you got. 

Damon Germanides: to be a student of the game here. like this isn’t like just take the call and pass it on to your processor to figure out, you know, is it going to it sounds like it works great. I mean, these are I could just speak the way I’m attacking uh like yesterday is a great example. I had four uh you know, four folks call me for loans. Two actually, two and two refinances, two um uh purchases. I mean, I spent 45 minutes on each call trying to figure out if I could help them because we had to go that deep because there’s so much going on. There was a lot of questions. Um I wasn’t sure if we needed to bring a co-barorrower in. I was explaining to them on one self-employed borrower, we can do a bank statement loan and a combination of a pay stub for your wife. Like going through all this takes time. Um so that those in the in a few years ago, those four calls would have been done in 15 minutes, maybe an hour total with all four. So it’s 3x the upfront uh effort. Same thing applies on the back end. our team, you know, who, you know, day by day pricing is volatile. Who’s in the market? Who’s out of the market? Who’s got that niche for the non-QM product that the that’ll make this loan work? It’s really, you know, you have to be very uh diligent about uh your lenders and who does what well and putting that into the memory bank. And obviously some of this is helpful with AI now too where you can you can uh start to feed it a lot of information, guidelines and stuff and have it help you be uh guide you where the deals should go. But yeah, that’s that’s I mean that’s that’s what I’m I’m doing. 

Matt Sexton: Kevin, your thoughts? Yeah, I agree with a lot um with what Damon said, but you. 

Kevin Oto: know, from a borrower perspective, um you know, I’m spending less time trying to convince someone that rates aren’t that high and you know, it’s still okay time to buy, but it’s really from a borrower perspective, asking a lot of questions, spending more time like Damon said, but how can we create a transaction that makes more sense and just kind of reminding people that, you know, if it is more of a buyer market, there’s opt there’s actually more opportunity, right? There’s negotiation for the purchase price. Maybe we can get seller credits to do a 2-1 buydown or a permanent buy down or looking at ARMs or changing your down payment or just comparing different, you know, financing strategies just to try to make the deal make sense, right? And if the buyer can get a credit for a 2-1 buydown or get the house 20 grand under what they’re expecting, you know, in the long run, that could be still a good deal for them. Um, but really I understand it is harder to find new clients, but like Damon said, leaning on different lenders, seeing what products they have, trying to find solutions for these borrowers that, you know, are scared off by the higher rates. But a lot of times, you got to just look at the market you’re in and look at how to solve the market you’re in. It’s not waiting till rates drop. It’s not, you know, hoping things change. It’s like, look, this is the market we’re in, and this is how we’re going to solve the problems and get you in a home and get you a comfortable payment. So, um, it’s really just spending more time with borrowers, looking for more opportunity, and trying to take advantage of having less buyers in the market. Kevin, I’d. 

Damon Germanides: like to that you make a really good point and I was just at a real estate office and the there was a lot of questions from realtors uh at this meeting that normally they didn’t ask about the treasury market, what’s driving inflation, what’s what’s driving rates higher and then productwise these uh in we’re we’re more of a jumbo lender. So 2-1 buydowns the jumbo space is not that common. I mean, it’s there, but not at like some of the portfolio banks we work with, but there are a couple that offer that. And that was a great uh that was a great um conversation with the realtors because they didn’t really know how they those things even work. So, we walked them through the calculation and how you could go back to your seller and say, “Okay, you don’t want to drop your price by 50 grand because you’re worried you’re going to have to keep doing it. offer a lower mortgage uh rate by giving a credit to the to the to the uh to the for the buy downs and then if that doesn’t work then you can go back to your seller and say look we’re going to have to reduce the price if you want to sell but giving another tool in the toolkit and again I’ve been doing this for so many years most of the time at these lunch and learns and stuff you know four or five people show up I mean it was a full house if that’s any indication what’s happening in and you know of the realtors trying to figure out Okay, how can I get some, you know, get this get some activity going? You know, how what could I do to kind of educate my seller on what’s going on and then what could I offer them to maybe get them to uh help move this home? So, I think that’s a great point you made, Kevin, with these buy downs. Thank you. Yeah. Well, I’d like to add. 

Kevin Oto: one more thing, too. So, like I also notice when you know things are slow, there’s a lot of agents and there’s a lot of loan officers that actually take time off, too. So that gives you an opportunity to capitalize on maybe some new relationships. I actually I’m part of real producers in our area in Sacramento. I’m in California as well. I hosted an event yesterday and I talked to a few agents and they actually said, “You know what? My loan officer hasn’t been as responsive as lately. She has a family. She’s with her kids, which is great, but you know, when business is slow, sometimes they check out and focus on other things. And unfortunately, that gives opportunity for people who are in the business at their desk every day to capitalize on new opportunities as well. 

Damon Germanides: 100%. That’s such a great point. And by the way, those realtors and the downtime if you’re helping them and really trying, that’s a great way to build like sticky long-term relationships because you’re everybody knows it’s tough, right? And I again with Kevin, perfect, you know, point as a pro. Yeah, we’re not going to sell that rates are great or whatever. I mean, rates are high. They’re not like they’re they’re just back to historical norms, maybe a little bit above now, but this is we’re just coming off such a low level of interest rates for so many years. I mean, almost dating back to when I started the in my career like in ‘ 04. So maybe by ‘ 07 till 2023, nobody knew what a high rate looked like. I mean, so uh you know, it’s it’s definitely a different world and it the it happened so fast. Uh, I mean it didn’t go up gradually. It went up all with inside of six months. So, it’s a it’s been a big adjustment period for a lot a lot of folks. 

Matt Sexton: Absolutely. Great stuff, guys. One of the things I want to talk a little bit about this month at MPA, we’ve been focused on non-QM lending. It’s obviously been a growing sector. We’ll start with Kevin on this one. What are your general thoughts about what non-QM and non- agency lending has done with the mortgage industry, especially considering its continued growth here in 2026? 

Kevin Oto: You know, non-QM has never been a huge part of my business. But recently, when markets get tougher like this, you find yourself stumbling into more non-QM loans. I recently did a non-warrantable condo, more bank statements, a DSCR loans. So, I actually think non-QM has really helped the mortgage industry catch up with the way people actually earn and invest money, right? Not everyone’s a W-2, stable employee, predictable income. You know, things change. You know, you have business owners, consultants, like I had, you know, a music producer making, you know, royalties that was hard to prove. So, there’s really a lot of different ways people are making income. And I think this non-QM product has really just given a lot of opportunity to not just loan officers but you know home buyers and people out there to really qualify for good loan programs that they wouldn’t have qualified for before. 

Matt Sexton: Damon, your thoughts? 

Damon Germanides: I mean non-QM has kind of been what we’ve we’ve always done even before it was called non-QM. Um look, I think it’s it’s a major difference maker. It’s it’s probably the reason why we’re we still have the lights on. I mean, if we were just relying on traditional jumbo financing, uh, with the outlets that we have, uh, it would be very tough. Uh, the ability to go to a borrower and say, I can do a, uh, like a portfolio bank loan, and if that doesn’t work, I can go to a like a different product that’s a little bit wider with respect to guidelines. Interest rate not that all that much different. Um and then when you have these situations where you have a borrower who has a W-2 but a bunch of non-passive losses uh or passive losses on the tax return, other types of activities that are going on, you could just focus on the income from the W-2, not even deal with the tax returns. These are these are major difference makers and uh um without the product, I mean, we would candidly in our market be in trouble. I mean there it’s probably every other loan is going non-QM route right now. So one it used to be maybe every third or fourth loan but like Kevin was saying with affordability where it is I mean uh self-employed borrowers I think are more risk tolerant and they also uh understand their cash flows in even if they don’t know them perfectly intuitively given what their expenses are what they’re rolling into their business expenses that they can adjust or whatever. So, these products are allowing folks to buy homes. All we need is that we need we need home prices to come in a little bit to adjust for the higher rates and we’ll we’ll see, you know, I think that’s kind of what we need if rates stay up here to kind of reactivate the market. But, but the non-QM stuff is that it’s it’s big uh for us right now. Very big. Another thing I wanted to talk a. 

Matt Sexton: little bit about is especially in the technology front, it feels like things are changing so quickly. Damon, we’ll start with you. Is there any new technology or maybe it’s new products or just a new way of doing business that you found over the last year that could be something that brokers could use to uh help get them more business especially in this current market? I. 

Damon Germanides: mean, look, the AI and the automations and the ability to read uh unstructured data uh is huge. Uh we’re spending, you know, unfortunately, in a tough market, a lot of money on really uh re imagining our uh internal uh processes and where these fantastic tools can help us. Um, you do got to be careful because it’s not like you can take someone’s personal information and put it on a, you know, a chat GPT that’s not been or one of these, uh, uh, you know, kind of AI tools that hasn’t been, uh, ring fenced internally because you don’t, you do not want that stuff leaking out. Um, but I see a world where uh an a loan officer inside of a couple years, even a small shop, can have uh connect their CRM, to their LOS, to their email, to their Box accounts or where they’re storing the data uh to their LinkedIn, to their all of their social stuff and kind of get a run by day type of dashboard where you’re reminded that John, you know, ex borrower called you 6 months ago for a loan. This time, maybe you should check in. These three realtors have just posted new homes on LinkedIn, LinkedIn or on some social media or MLS. Hey, that you’ve done like bunch of stuff for them. Why don’t you go call them, see what’s going on? Like all of these things to just help manage your day. Like for me, I got so much going on sometimes I forget to email somebody back, which is fine if it’s something not important, but what if it’s a lead that came in at like five o’clock and I was distracted? like I should be getting back to that person right away just like Kevin said previously because in this market dude that realtor is relying on you to connect right away because he might he or she might only be selling a few houses this whole year. So if you’re like that slips till tomorrow like that real estate broker’s calling the next loan broker uh for the next deal because that guy’s going to get back to them right away. So all of these things just to make you more efficient and better um uh be more responsive and um you know more control of your of your pipeline and informed of what’s going on even internally or externally with the with your clients and with the market. 

Matt Sexton: Kevin, your thoughts? Yeah, I agree with them. I mean, we’ve. 

Kevin Oto: been investing some time and money in, you know, AI governance and making sure that we’re using AI responsibly um to really just eliminate a lot of repetitive tasks, become more efficient, limit the errors, and just help our loan officers just better convert their leads, and just be sharper. Um, I know it’s going to come to a point where most of these tasks are going to be automated and it’s really going to boost efficiency, but just to be one step ahead, putting tools in place and making sure that we’re giving our loan officers everything that they could possibly get to give them the edge, to be more competitive, to help convert, to be smarter, faster, stronger. I mean, that is what we’re doing right now. 

Matt Sexton: One thing I wanted to ask both of you, we’ll start with Kevin. This kind of a busy time of year for trade industry shows just came off of NAM personally. Uh but it was a stretch of shows that a lot of brokers are headed to. Just curious to get your general thoughts about these shows and these trade groups and if there’s, you know, one thing that might be really good about what they’re doing and maybe something that they could do better to either make the shows better or just make the or make the mortgage industry better. 

Kevin Oto: I mean, for me personally, I feel like I need to go to more shows. It’s been it’s been actually a couple years since I’ve I’ve been to any. But I think the best thing about it when I did go to any of these shows was just getting out of my own company’s bubble and just meeting these other people, meeting, you know, networking, seeing what they’re doing different differently than what I’m doing. I think that added a lot of value. Um, you know, as far as what they can do differently, I think some of the information you get from some of these people are just surface level. There may be some gatekeeping. It may not be all that helpful, but I found the thing that is most helpful is really just building the relationship and just seeing what other people are actually doing differently and trying to get some value from that and actually sharing what we do as well just to try and help the other people in our industry as well. Damon, your thoughts? 

Damon Germanides: Well, to my wife’s displeasure, I’ve been a big show guy for a long time. Not too many, but I’m on my I’m on my circuit starts actually uh Monday. I’ll be at uh I’ll be on our private uh direct lending business. I’ll be at a conference there. Then I’m headed to a uh another conference and then and then I got another conference and then two more uh beginning of next year. Um so uh but in terms of what I try to get out of them um you know they’ve been very valuable uh for um general information on you know what’s going on in the economy. Uh get a lot of value out of that. Meeting a lot of different vendors all at once. you know, kind of understanding what’s coming down the pike with respect to technology. Um, meeting folks in the industry. Uh, I mean, we found a uh at these conferences through the years. I found a bank to do business with. I found a whole loan buyer on our bridge lending business at them. So, they’ve been they’ve been valuable. Plus, you meet people in your industry uh that like Kevin was saying that you could connect with and you share information and just someone to talk to. Um so yeah, I’ve I’ve gotten conferences have treated me well, but I do pick the ones that make the most sense. I just don’t go to go. And I do think the one thing that I think is missing in a lot of these things is there a way to bring um like brokers, mortgage brokers or mortgage bankers together with any of the actually the real estate um uh agents that might be there or real estate, you know, other types of brokers that do different things. So, it’s a little bit not all conferences are created equally, but the some of the networking stuff can be a little bit uh like just it’s not really um that thought out and so that makes it a little bit hard to go find who you really want to talk to. Sure, you can look through the app, but that there might be 600 people there like how you doing that. So, I just, you know, I’m sure that would be my only gripe sometimes with these things. Totally makes sense. I’ll leave you with. 

Matt Sexton: this last question, Damon. I’ll start with you on this one. Well, since we’ve talked about some heavy topics, let’s try to find a silver lining to wrap up as we head toward 2027. What is one thing that you’re optimistic about? Uh whether it’s about your business individually or the market in general as we head into the new year. 

Damon Germanides: I mean, I think two things. Uh again having been at this for like 25 years uh just about or 24 years um you know instinctively it’s I feel I’ve got a good emotional memory of things and I’m sensing like in a different way you know not um not a great financial crisis kind of event but I am seeing stresses and uh you know with the rates and everything else. So maximum pain uh with our with our colleagues like you’re going to see people just not uh be in the business anymore as Kevin was saying they’re just not making any money. So like how long do you go without doing a deal before you say this isn’t for me. Um so I think if you can get through to the other side there’s still a need for these services. I think that’s good. And the other thing I believe will happen, again, we’re more of a jumbo focused uh originator there. There’s a lot of loans that are starting to uh adjust. So, a lot of these seven-year arms that were taken out during COVID or before when rates were low, five-year arms, people thinking they were going to move in 5 years, those are all coming uh to adjustment periods. and those rates, if you count the uh interestonly period burning off and the amortization getting squeezed, those payments are going up almost 3x. So people are going to be forced to make a decision to either refinance or sell. And so I think 27 if is going to be a better year for refinances. And then hopefully if again if rates come down or home buyers start to accept a little bit less, you get a little drop in price. I think the combination can put the business in a better place uh than it is today. 

Matt Sexton: Kevin, your thoughts? I’m always excited about the market. 

Kevin Oto: I’m always optimistic. Even in markets like this, I feel like there’s always opportunities. Even when, you know, people might be taking time off, I’m investing in my business, investing in relationships, thinking how I can better prepare myself. So if rates do drop next year, I can capitalize on all these loans and more and capitalize on more market share. So, you know, I’ve been in it for over 20 years as well. I started in 2004, so I understand that there’s ups and downs. And, you know, if there are down times when rates are higher, the longer that is, the bigger the refi boom down the line of rates drop. So, it’s all about being patient, being smart, and investing with your business in your business. And I believe that, you know, when the market shifts to our advantage, you know, we’ll be there to capitalize. 

Matt Sexton: Closing with a little optimism today on MPA TV. That’s going to wrap things up for this edition of NPA TV. Thanks again to our guest and thank you for watching this edition of MPA TV. For my guest, I’m Matt Sexton saying so long and we’ll see you again next time. 

What playing cricket for the U.S. taught me about the hardest decisions CEOs face

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What playing cricket for the U.S. taught me about the hardest decisions CEOs face



Long before I became a CEO, I played international cricket for the United States. The lesson I draw on most in business is this: Before deciding how aggressively to play, you have to read the pitch.

In cricket, the pitch is the strip of ground at the center of the field. Its condition can change the game. On one surface, 200 runs might be an excellent score. On another, 400 may be achievable. You have to judge the surface, the opposition, and your team.

Misread it, and you can execute exceptionally well and still lose. You might reach the target you set, only to discover you aimed far too low.

CEOs face a similar risk. We spend enormous energy asking whether teams are performing and plans are being executed. Those questions matter, but they come after a harder one: Have we correctly understood what is possible?

A CEO’s highest-leverage contribution is setting the right ambition. That requires three judgments.

1. Identify the few decisions that actually change the game

CEOs don’t create value by making the most decisions. If an organization works properly, people make thousands of decisions about products, customers and operations every day. The CEO’s disproportionate value often comes from a few choices that determine where all that execution is directed.

I faced one within my first few months at Bluebeam. There was pressure to move more aggressively from 2D into 3D. Newer technologies were attracting attention, and it was reasonable to ask whether our established approach would remain relevant. I had to decide without anything close to perfect information.

I had customer conversations, research, and pattern recognition from other industries, where technologies sometimes remain valuable long after people predict their replacement. I came to believe 2D was not a declining market we needed to escape. It was a growing market where Bluebeam already had a significant advantage.

We made two deliberate choices: stay focused on 2D rather than aggressively pursue 3D, and resist becoming a broader platform company. That simplified our strategy and freed resources to invest in what we did well. We chose where our innovation could matter most.

The results reinforced our conviction. When we made that decision in September 2023, Bluebeam had more than 3 million users worldwide. By the fourth quarter of 2025, that number had reached 4 million. Bluebeam also experienced approximately 45% new-user growth from 2024 to 2026.

No single decision explains a company’s growth, and user counts alone don’t show what caused it. But the results strengthened my belief that focusing on a proven strength could be more valuable than chasing an adjacent opportunity simply because it was attracting attention.

I knew the decision carried real risk. Had I misread the market, we might have directed the organization away from its future. But that is exactly where CEOs have to add value: choosing among plausible paths when information is incomplete and waiting for certainty has a cost of its own.

Organizations naturally gravitate toward visible problems: a struggling product, a weak geography, a missed target. The most visible problem, though, is not always the largest opportunity. Sometimes the best allocation of leadership attention is to contain a weakness and put disproportionate resources behind a strength.

2. Decide what the conditions actually support

When I joined Bluebeam, I had to determine not simply whether the company was performing well, but what it could become. Those are different questions.

I looked at the market, product, team, and broader environment and believed the company had more potential than we were asking of it. The cricket analogy I used internally was simple: We were playing as though we were on a 200-run pitch. I believed it was a 400-run pitch.

So we raised the ambition. Importantly, much of the same team began performing against a higher bar. That experience reinforced a mistake leaders can make: confusing limited ambition with limited capability.

Leaders’ instinct is often to change the people, rewrite the strategy, or reorganize. Sometimes that is necessary. But a company can hit its plan and still underperform its opportunity.

“Think bigger” is not a strategy. Ambition must be grounded in what the market, product, and organization can support. Sometimes the right diagnosis requires lowering expectations. Other times, the greater failure is asking too little of a capable team.

3. Know what would prove your diagnosis wrong

Experience makes those calls possible. Over time, leaders build a library of patterns from markets that turned, products that succeeded, and strategies that failed. But experience can also become a trap. You begin seeing the same pattern everywhere.

I try to treat pattern recognition as a hypothesis, not a verdict. The question is not only why I believe I am right, but what evidence would change my mind.

When we made the 2D decision, I knew which signals to watch. If customer conversations shifted decisively toward other needs, or sales and revenue failed to respond, we would need to reconsider our judgment.

But once a strategy has been funded, presented to a board and communicated to employees, reversing course becomes psychologically expensive. Leaders can end up defending the plan rather than testing its assumptions.

The answer is not to react to every new data point. Constantly changing direction makes sustained execution impossible. It is to distinguish noise from evidence, and conviction from stubbornness.

Execution starts with diagnosis

Cricket taught me that before deciding how aggressively to play, you must understand the game in front of you. Business leadership is much the same.

Before concluding a team is underperforming, ask whether the objective is right. Before fixing the most obvious weakness, ask whether a bigger opportunity deserves your attention. And before celebrating that the company hit its plan, ask whether the plan captured the opportunity in the first place.

Sometimes the most consequential thing a CEO can do is see the same company and the same market everyone else sees — and recognize that they are playing a 200-run game on a 400-run pitch.

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

a view through the lens of a structural model – Bank Underground

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a view through the lens of a structural model – Bank Underground


Daniel Albuquerque, Jamie Lenney, Matthew McKernan, David Murakami, Peter Paz and Philip Schnattinger

Wholesale energy prices surged in 2021 and again in 2022, but UK inflation remained high well beyond the direct pass-through of higher energy costs. With energy prices moving sharply again following the recent conflict in the Middle East, could a similar scenario happen again? We use a structural macroeconomic model to show that the answer depends on labour market conditions. When workers are scarce, they are better able to recoup real income losses through higher wages, raising firms’ costs and broadening inflationary pressure. The model implies that the same energy shock is roughly twice as inflationary when vacancies are plentiful relative to job-seekers as when the labour market is loose.

The tight 2022 labour market

In 2022, UK vacancies were near high while unemployment was low. A useful way to summarise this is the Beveridge curve: the empirical relationship between the vacancy rate and the unemployment rate, shown in Chart 1. When the labour market is ‘tight’ (top left), firms find it hard to hire. When vacancies are low and unemployment high, the labour market is ‘loose’ or ‘slack’, workers find it hard to get jobs. During the 2022 episode (red triangle), the UK labour market was off the historical Beveridge-curve relationship: for any given unemployment rate, vacancies were far higher than past experience would predict. This contrasts with the position in 2025 Q3 which is much closer to the historical curve, suggesting the labour market has markedly loosened since the 2022 episode.

Chart 1: The UK Beveridge curve

Notes: The UK vacancy rate against unemployment rate with past episodes of large unemployment increases (1975, 1980, 1991, 2008 and 2020) traced out very different cycles. Arrows trace the labour market’s path over time; the black line is the fitted Beveridge curve.

Standard linear model solutions impose that the economy responds to a shock in the same way regardless of where it starts: a given energy shock adds the same amount to inflation whether unemployment is 3.5% or 8%. However, when the labour market is tight, firms face greater hiring pressures, production is closer to capacity and workers have more bargaining power, so an energy shock is more likely to translate into higher wages, costs and inflation. Our analysis asks whether the state of the labour market matters quantitatively, and finds that it does.

Why labour market tightness matters in our model

We use an open-economy New Keynesian model that integrates the search-and-matching labour market of Broer et al (2025) with the treatment of energy in Chan et al (2024). The broader macroeconomic structure is related to COMPASS, the Bank’s workhorse forecasting model described in Burgess et al (2013).

We solve the model in a way that allows for non-linearity. Specifically, we solve the model using higher-order perturbation rather than linearising it around a long-run average. We then use the time series generated by simulating the non-linear model to compute generalised impulse response functions conditional on the state of the labour market. This preserves three sources of non-linearity already present in our model’s search-and-matching foundations, rather than assuming them away via a linearisation:

  • How full is employment? When firms are already running close to full capacity each new hire adds a bit less to output than the last, so a labour market tightening shock pushes costs and prices up more quickly. In the model this is captured by diminishing returns to labour in the production function.
  • Matching is harder when slack is scarce. If unfilled jobs outnumber job-seekers, firms end up competing for the same workers. The result is that any change in firms’ demand for labour produces larger moves in vacancies, wages and how quickly people find jobs, that’s the steep part of the Beveridge curve. In the model this is captured by the curvature of the matching function, which makes vacancy-filling rates fall sharply as tightness rises.
  • Workers’ bargaining position. When jobs are easy to find, workers are better placed to secure pay rises that keep up with the cost of living because employers competing for scarce staff are more likely to meet them. So an energy-driven jump in prices feeds through more strongly into wages, and from there into firms’ costs. In the model this is captured by the wage bargaining process, in which workers’ effective bargaining position strengthens as the labour market tightens.

All three sources of non-linearity pull in the same direction in our model: a tight labour market amplifies the inflationary effects of an energy shock. Their combined effect can be summarised in a stylised Phillips curve implied by the model:

Here πt is inflation, 𝔼tπt+1 expected future inflation, mctnonlab non-labour marginal costs such as energy, and xt labour market pressure on inflation. The key term is φ(Vt/Ut): the effect of labour market pressure on inflation depends on tightness, and φ′ > 0 means it strengthens as vacancies rise relative to unemployment. In other words, the Phillips curve is steeper when the labour market is tighter.

Previous studies find that the Phillips curve steepens in a tight labour market. These include Schmitt-Grohé and Uribe (2022) and Benigno and Eggertsson (2023) for the US. For the UK, this is consistent with machine-learning evidence of non-linear relationships between inflation and labour market tightness by Buckmann et al (2025) and the evidence in Gaffney et al (2026) using a threshold VAR. These papers establish the state dependence either empirically or in stylised settings. Our exercise is different in kind: because the state dependence arises inside a fully specified structural model solved non-linearly, we can decompose which mechanisms generate it, run controlled experiments that hold monetary policy fixed, and compute how the optimal interest rate response varies with the state of the labour market.

We also examine the effect of real wage resistance: an idea dating back to the 1970s energy crises that workers try to make up lost purchasing power after a burst of unexpected inflation, closely related to the mechanism outlined recently in Lorenzoni and Werning (2023). In response to a squeeze on living standards, people seek pay rises to keep their spending power in line with the cost of living. The economic question is not whether workers want to restore their real wages, but whether prevailing labour market conditions give them the bargaining power to do so. In our model, a worker’s bargaining power depends on labour market tightness, which links real wage resistance directly to the state of the labour market.

Energy shocks bite harder in tighter labour markets via a steeper Phillips curve

We trace the effects of a one-standard-deviation energy-price shock, equivalent to a 13.5% increase based on historical volatility, across tight and loose labour markets. This is smaller than the exceptional increases in 2021–22, and because our model is non-linear, our estimates are therefore conservative for shocks of that magnitude.

Our model simulations confirm that the shock is more inflationary when the labour market is tight: peak wage inflation is roughly twice as high as in the loose regime, and CPI inflation also rises more sharply. Chart 2 summarises this non-linearity through the slope of the price Phillips curve, holding the monetary policy response fixed across regimes. The differences are substantial. In tight states the simulated Phillips curve slope is roughly three times that in loose ones.

Chart 2: Price Philips curve non-linearity

Notes: The chart plots the slope of the reduced form price Phillips curve against the log vacancy to unemployment ratio. Each bin groups Monte Carlo draws by their initial tightness and computes the average price inflation response per unit of output response. The chart shows separate linear fits for the loose half of the distribution and the tight half of the distribution.

What does this mean for monetary policy?

The structural model allows us to go beyond describing the shock’s effects and ask what interest-rate response best stabilises the economy in different labour market states. We compute the model-implied optimal interest-rate path using a stylised loss function that penalises inflation, the output gap and changes in the policy rate. Chart 3 shows the results under alternative policy objectives.

Chart 3: Optimal Rate paths by labour market state in the baseline and wage resistance case

Notes: The chart shows the nominal Bank Rate paths that minimise the policymaker loss function following the energy price shock shown in Chart 2, for the tight, normal and loose labour market regimes. Panel (a) reports the baseline specification; Panel (b) reports the case with consumption wage resistance. In each panel, the solid lines are the usual OPP, which targets CPI inflation, and the dashed lines are the look-through variant, which instead targets domestically generated inflation. The counterfactual paths are constructed from local, model-based approximations to monetary transmission that are specific to each regime.

Three key results emerge:

  • The model-implied optimal interest-rate response to the energy shock is earlier and larger when labour markets are tight. In panel (a) of Chart 3, the peak rate response starting from a tight labour market is roughly twice that starting from a normal one. In a loose labour market, rates rise only later as activity recovers.
  • The case for ‘looking through’ an energy shock is stronger in a loose labour market than in a tight one. In loose states, much of the inflation increase proves temporary and returns to target largely on its own. In tight states, energy prices feed more strongly into wages and other goods inflation, requiring rates to remain high for longer.
  • Real wage resistance amplifies inflation persistence in tight labour markets. In panel (b), stronger wage resistance makes the model-implied rate response materially larger and more persistent in a tight market, while making little difference in a loose one. Attempts to recover lost purchasing power translate into wage growth more strongly when workers have greater bargaining power.

Conclusion

The labour market changes how an energy shock affects inflation. In our model, the same shock generates roughly twice the wage inflation and warrants roughly twice the peak interest-rate response when the labour market starts tight rather than normal. The UK labour market has loosened since its 2022 peak, so this amplification mechanism appears less pronounced today than during the 2021–22 energy crisis. A renewed tightening of the labour market would steepen the Phillips curve and raise the inflationary effects of a fresh energy shock. These state-dependent effects are invisible to linearised models, supporting a role for structural models with non-linear labour markets for understanding inflation dynamics.

\pi_t = \mathbb{E}_t \pi_{t+1} + \kappa {mc}_t^{\textrm{nonlab}} + \varphi\left\(\frac{V_t}{U_t}\right\) x_t, \quad \varphi ‘\left\(\frac{V_t}{U_t}\right\) > 0.

Daniel Albuquerque, Jamie Lenney and David Murakami work in the Bank’s Macro Model Development Division, Matthew McKernan and Philip Schnattinger work in the Bank’s Structural Economics Division, and Peter Paz works in the Bank’s Economic Outlook Division.

If you want to get in touch, please email us at bankunderground@bankofengland.co.uk or leave a comment below.

Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

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Chase Offers/BofA: Get 10% Back At Marriott, Up To $85 Cashback

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Chase Offers/BofA: Get 10% Back At Marriott, Up To $85 Cashback


Update 11/11/26: Deal is back until November 23, 2026

Update 2/6/24: Deal is back through 3/11/24; this time it’s for 10% up to $85. There are also various offers showing for specific Marriott brands.

Update 3/22/22: Deal is back and valid until 4/29/22.

The Offer

No direct link, check your Chase Offers

  • Save the offer to get 10% back at Marriott Bonvoy hotels purchases of $100+, up to $80 maximum cash back.

Offer is showing with Chase and various banks: Chase, Bank of America, Regions Bank, Suntrust Bank, BBVA, BB&T, PNC, Columbia Bank, Beneficial Bank, and Christian Community CU. You can read this post for more information.

The Fine Print

  • Offer valid one time only
  • Complete payment for your stay must be made by 9/1/20
  • Offer valid at all Marriott Bonvoy brands
  • Payment must be made directly with the merchant

Our Verdict

Nice discount for Marriott. You can often buy a gift card at the front desk of Marriott which is useful for locking in the discount for the future.

There’s also an AmEx Offer for Marriott.

Nvidia’s Board May Oust Jensen Huang. 5 Major Consequences Could Follow

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Nvidia’s Board May Oust Jensen Huang. 5 Major Consequences Could Follow



A forced exit could create a power vacuum, deepen Nvidia’s stock slide, invite competitors, and expose the risks of a company built around one leader.

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