About Miles Greve
Forty years across research, operations, and hotel investment.
Forty years in hotel real estate research, operations, and investment, based in Las Vegas. The career below is context for why the question interests me — it is not evidence for any conclusion on this site.
On myself · in my own voice
Operations first. Transactions second. Capital last. That is the order I learned it in, and it is the reason I read a hotel the way I do.
I am telling you the whole arc because judgment is not a credential. It is a scar count — and none of it makes an untested idea correct.
Who I am
The work started where the numbers meet the guest.
President & Founder — What Einstein Thinks Hotel Intelligence Research.
The work began at CBRE in 1986, building property-level commercial real estate databases, running telephone and on-site surveys, and producing quarterly market reports brokers used to evaluate properties and market conditions. A later engagement tracked major New York City commercial foreclosures — an early exposure to distressed-asset analysis that still shapes how this work reads a declined hotel file.
A decade at Hilton Hotels & Resorts followed — reservations, convention operations, rooms division, and restaurant management — then fourteen years inside the Las Vegas Strip at Bellagio, MGM Grand, and Wynn Las Vegas, including work on the $9.2 billion CityCenter development through escrow, closings, broker agreements, and sales contract administration.
Hotel investment sales work began with an internship at the Las Vegas Marcus & Millichap office, Hospitality Practice, and continued at InnVest Hotel Brokers LLC, where a $55M joint venture originated through an introduction to a national hotel REIT — helping fund three hotel construction loans and contributing to $88M in transaction volume that year. Since 2012, BBMG Group has been the independent vehicle for hotel market research, investment analysis, and strategic advisory.
This capstone is the formal research layer on top of that career: a Master's-level attempt to codify what forty years of watching hotels get financed, refinanced, and declined has taught about where automated bank scoring gets it wrong.
The record
Four numbers behind the lens.
40+ yrs
$9.2B
$55M
14 yrs
Figures reflect career participation and transactions, not offers of any kind.
The route
The work is a sequence of chapters.
Each role added a different way of seeing. Together they form the lens brought to hotel investment research today.
1986–1997 · career
The data bank
CBRE — Hotel & Commercial Real Estate Research
Property-by-property databases, quarterly market reports, custom economic analysis, broker conversations, and the discipline of seeing what the market was actually saying.
1989–1998 · career
The rooms
Hilton Hotels & Resorts — Rooms Division
Nine years inside reservations and convention sales taught how a hotel earns a dollar one guest, one night, and one rate at a time.
1998–2012 · career
The Strip
Bellagio · MGM Grand · Wynn Las Vegas
Fourteen years in the most competitive hotel market on earth, moving from group room coordination to convention sales and contract administration for the $9.2 billion, 67-acre CityCenter joint venture.
2004–2005 · career
The island
Hilton Hawaiian Village Waikiki Beach Resort
Front Services made the first and last impression tangible: valet, bell, and door operations — the standards guests never see but always feel.
2012–present · advisory
The beat
Miles Greve · BBMG Group — Hotel Real Estate Research
Independent research on the business of hotel real estate: following the signals beneath the headlines and bringing an operator's memory to the questions investors, brokers, and owners are already asking.
UNLV · education
B.S. Hotel Administration
University of Nevada, Las Vegas
The formal hospitality foundation — the degree behind fourteen years on the Strip.
eCornell · education
Financial Analysis of Hotel Investments
Cornell / eCornell
Hotel investment analysis, studied inside one of the discipline's flagship programs.
In progress · education
M.S. Entrepreneurial & Small Business Operations
American Public University
The degree this capstone belongs to — where the lending model and the Einstein Intel concept are being formally researched and tested.
What the background gives me
An angle on the question — not a claim that the answer is right.
Operator's read
I can tell whether a hotel's expense line is a management problem or a market problem. Most credit committees cannot.
Broker's read
I know what an asset actually clears for, who the real buyer pool is, and how quickly collateral moves in a workout.
Capital read
I have sourced and carried institutional-scale hotel debt to close, and understand what each lender type genuinely needs to say yes.
The coursework
A curriculum built around the question.
Asset & Valuation
- Hotel acquisition underwriting
- Hospitality valuation
- Feasibility and market studies
- Financial modeling — Excel / Argus
Credit & Capital Structure
- Commercial real estate finance
- Debt placement
- CMBS
- SBA lending
- Bridge lending
- Mezzanine debt
- Preferred equity
- Private credit
Special Situations
- Distressed hotel acquisitions
- Loan servicing and workouts
- Receivership and note purchases
- Restructuring economics
Markets & Technology
- Investment banking and capital markets
- Alternative lending and fintech
- AI applications in CRE
- Commercially relevant tokenization concepts
Why this coursework
The study was chosen to test the idea, not to add a credential.
A general advanced degree signals capacity. It does not tell a capital partner whether I can size a bridge loan on a select-service asset with a pending PIP, or model the exit when the franchise agreement expires eighteen months after maturity. The specialization path answers exactly those questions.
Every item above is examinable against live work: a model I can open, a memo I can hand over, a structure I can defend. Knowledge that cannot be demonstrated on a deal does not enter the curriculum.
The learning is also sequenced against the platform's needs rather than an academic calendar. When a transaction requires depth in a specific structure, that becomes the next unit of study — and the deal itself becomes the examination.
Three networks
Operators, brokers, and academics — three rooms the idea has to survive.
- I
UNLV Hospitality
Executives, owners, operators, developers, and applied research. This network supplies asset-level ground truth, sponsor introductions, and the operating perspective that keeps underwriting honest.
- II
UNLV Entrepreneurship
Venture development, mentors, and business model refinement. This network pressure-tests the platform as a company — structure, economics, and scalability — rather than as a series of transactions.
- III
The [WET] WhatEinsteinThinks Platform
My own originations, capital relationships, and advisory work. This is where the other two networks convert into evidence: closed transactions, live pipeline, and a track record that speaks without introduction.
No affiliation
Published in the open for practitioner critique. No firm is a partner, source, or endorser.
The work is shared in the open so the people who lend, broker, and advise for a living can say where it is wrong. No firm is a partner, data source, or endorser of this project.
The distinction matters. A job seeker asks for a seat. A platform operator brings something to the table: deal flow, an underwriting point of view, and a client base that needs valuation, feasibility, and market work. The second conversation is materially different from the first.
The sequence is therefore: build demonstrable platform experience, produce work product of institutional quality, then engage those firms around specific transactions and mandates where collaboration is mutually useful.
Whether this research ever becomes practice is a downstream question, answered from evidence or not at all. Today it is published academic work, open for critique.
The corrections this needs
Seven gaps in the research, each one a place I need to be told I am wrong.
Select a domain to see the gap and the kind of correction that would move the reading forward. Nothing here is a role, a seat, or an engagement — only the sort of expertise the research is missing.
Profile
Senior debt or equity professional with hospitality transaction volume.
Remit
Pressure-test credit structures, lender appetite assumptions, and pricing. Advise on which capital sources to build durable relationships with, and in what order.
Time asked
One quarterly review of live structures, plus reachability on specific term-sheet questions.
On my desk now
What I am reading and questioning at the moment.
- 01
Underwriting standard
Codifying the hotel credit model and diligence checklist into a repeatable standard that a capital partner can inspect and trust.
- 02
Capital relationships
Building a mapped universe of debt funds, private credit sources, family offices, and participating lenders — with each one's real credit box documented, not assumed.
- 03
Origination pipeline
Sourcing selective hotel financing opportunities where the operating insight produces a defensible edge over generalist lenders.
- 04
Applied AI layer
Building the screening and extraction tooling that lets a small platform run diligence at a much larger firm's throughput.
- 05
Advisory positioning
Establishing the brokerage and advisory function as both a revenue line and a proprietary source of deal intelligence.
- 06
Board formation
Filling the seven advisory seats deliberately, one qualified person at a time.
Open questions
Where an outside perspective would change my thinking.
These are unresolved, not rhetorical.
What is the right first credit product — bridge, mezzanine, or participations — for a platform establishing a track record from a standing start?
At what point does capital formation shift from deal-by-deal syndication to a committed vehicle, and what evidence do LPs actually require to make that jump?
How should servicing and workout capability be built or outsourced before the first loan needs it rather than after?
Which institutional relationships are worth cultivating early, and which are better approached only once the track record speaks for itself?
Where is the genuine defensible edge — proprietary origination, operating insight, speed of execution, or structuring flexibility — and how should the platform be built around that answer?
The useful range
A broad skill set, grounded in the asset class.
- Hotel market research
- Property & market data analysis
- Competitive market analysis
- Hotel investment analysis
- Financial analysis
- Market reporting
- Acquisition & disposition analysis
- Hospitality real estate
- Hotel operations
- Investment sales
- Strategic advisory
- Las Vegas hospitality market
The venture
Einstein Intel — the ambition this research is testing.
Einstein Intel is the AI-assisted decision-support concept at the heart of this capstone: a model built to challenge broker assumptions, normalize complex hotel P&Ls, and end every analysis in a defensible investment posture. The capstone exists to research and test whether those features genuinely work — before anyone is asked to believe in them.
- 01
Challenge assumptions
Offering memorandums are marketing documents. Every RevPAR ramp, wage line, and exit cap gets interrogated before capital is ever discussed.
- 02
Normalize financials
USALI statements can hide operational truth behind departmental allocations, one-time credits, and under-reserved FF&E. The model reads the asset, not the brochure.
- 03
Name the decision
Analysis without a verdict is a memo. Every read ends in a stated posture — pursue, renegotiate, watch, or pass — with zero fabricated data.
The next step is UNLV — the university's startup and entrepreneurship resources — for structured input on the concept. Only after that testing would specific plans go in front of VCs and industry experts. This remains a graduate research project, not an operating company.
The next chapter
If you are looking for a point of view, let us compare notes.
Open to conversations about hotel investment research, gaming and hospitality markets, operating realities, and the future of hotel intelligence. If your question is still taking shape, that is a good place to begin.
Coda
If any of this overlaps your own work, I would like to hear where I have it wrong.