Castle Land AI
Castle Land AI
AI to Rezone Land Faster
We understand investors have concerns about risk. We're investors too.
From the inception of this company, we focused on applying risk mitigation
principles and AI to the real estate market. Here’s a brief summary:
Risk Mitigation
Our fundamental approach is based on the classic Black-Scholes model, the Nobel Prize-winner which aims to minimize investor risk. We’ve added innovative techniques with the goal to help limit volatility and hedge against market downtowns.
Some of these include our customized application of:
• Planck’s Constant
• Ricardo’s Theory of Comparative Advantage
• Meerkat / Metcalfe / Reed on Network
• Newton’s First Law
• Malthusian Trap
• Walpole’s Prophecy
Risk Mitigation
Our fundamental approach is based on the classic Black-Scholes model, the Nobel Prize-winner which aims to minimize investor risk. We’ve added innovative techniques with the goal to help limit volatility and hedge against market downtowns. Some of these include our customized application of: • Planck’s Constant • Ricardo’s Theory of Comparative Advantage • Meerkat / Metcalfe / Reed on Network • Newton’s First Law • Malthusian Trap • Walpole’s Prophecy
Our fundamental approach is based on the classic Black-Scholes model, the Nobel Prize-winner which aims to minimize investor risk. We’ve added innovative techniques with the goal to help limit volatility and hedge against market downtowns.
Some of these include our customized application of:
• Planck’s Constant
• Ricardo’s Theory of Comparative Advantage
• Meerkat / Metcalfe / Reed on Network
• Newton’s First Law
• Malthusian Trap
• Walpole’s Prophecy
Business Model
Rezoning land to a higher use is not a new, untested concept. In fact it’s been done for centuries … so this aspect of a typical startup is eliminated. The fundamental business is not an unproven SAAS model or new phone app. Then, we use AI to supercharge the process. We liken it to powering up from checkers to four-dimensional chess. Put another way, our goal is to use machine learning, predictive analytics, proprietary databases, and dynamic time warping to arbitrage the disaggregated land market. Simply put … AI to Rezone Land Faster. What’s more, we focus on acreage land in growing markets (with a nod to Vilfredo Pareto). Almost by definition with such parameters, these deals are $10 million-plus. The sale amounts can be much higher. $20M – $30M land sales are common. The founder’s most recent property deal is valued in excess of $40M. Pulte Homes recently spent $ 90M on 549 acres of residential land in one purchase (in one of our target markets). The large homebuilders spend over $ 1B a year on land. A $10M purchase is a small portion of that category (circa 1%). Overall then, our goal is to automate $10M deals with a 73% gross margin.
Business Model
Rezoning land to a higher use is not a new, untested concept. In fact it’s been done for centuries … so this aspect of a typical startup is eliminated. The fundamental business is not an unproven SAAS model or new phone app. Then, we use AI to supercharge the process. We liken it to powering up from checkers to four-dimensional chess. Put another way, our goal is to use machine learning, predictive analytics, proprietary databases, and dynamic time warping to arbitrage the disaggregated land market. Simply put … AI to Rezone Land Faster. What’s more, we focus on acreage land in growing markets (with a nod to Vilfredo Pareto). Almost by definition with such parameters, these deals are $10 million-plus. The sale amounts can be higher; $20M – $30M land sales are not uncommon. The founder’s most recent property exceeded $40M. The large homebuilders spend over $ 1 Billion a year on land. As such, a $10M purchase for them is a small portion of that category. Overall then, our goal is to automate $10M deals with a 73% gross margin.
Return on Investment
No ROI can be promised or guaranteed.
One popular method to value a company is the revenue multiple. It is often derived from
the exit sales price of a company (or valuation at IPO) divided by the annual revenue.
For example if a company sold for $ 500 million and the annual revenue was $ 50
million, its valuation multiple -- based on revenue -- is expressed as 10x.
Current valuation multiples for AI companies:
Its analysis covered 15 companies, with revenue valuation multiples running from 8.6x to 150x.
These are only the two most recent studies we located. You may locate additional studies with different results.
Note: different analysis, methodology, and data sets will yield different valuation multiples. There is no one single, generally accepted revenue multiple.
Note also that any multiple will change over time.
Return on Investment
No ROI can be promised or guaranteed.
With our Business Model parameters and current $10M minimum deal size, please contact us if you would like to review our revenue projections. Note that any projection assumes subsequent funding rounds are closed, in order to facilitate AI implementation and scaling, among other milestones being attained.
One popular method to value a company is the revenue multiple. It is often derived from
the exit sales price of a company (or valuation at IPO) divided by the annual revenue.
For example if a company sold for
$ 500 million and the annual revenue was $ 50
million, its valuation multiple -- based on revenue -- is expressed as 10x.
Current valuation multiples for AI companies:
Its analysis covered 15 companies, with revenue valuation multiples running from 8.6x to 150x.
These are only the two most recent studies we located. You may locate additional studies with different results.
Note: different analysis, methodology, and data sets will yield different valuation multiples. There is no one single, generally accepted revenue multiple.
Note also that any multiple will change over time.
Large Market
The U.S. Housing Market is huge – some $ 3 Trillion a year.
In turn, the residential land market is large. We estimate the Serviceable Addressable
Market is some
$ 215 Billion per year in the U.S. alone.
In addition to this market, our proprietary algorithms can be expanded to other land
types, and to other countries.
Breakeven
We project Breakeven in Year 4.
AI
We have already completed the first 3 components. Once the prototype is complete and our Product Market Fit revised, we’ll retain AI specialist companies to implement.
Team
We’ve worked together before and solved many problems under pressure.
Ed has over 30 years of experience rezoning farm and ranch land to residential. He’s sold land and lots to the major homebuilders and he’s worked for the major homebuilders. Ed has a B.S. in Civil Engineering.
Rob is a proven innovator and veteran of auto-generated solutions over a variety of platforms.
Pat has scaled up several companies and helped exit one to Getty Images.
Bill started investing in land over 20 years ago. He has sold to Fortune 500 companies including McDonalds, Coca-Cola, Pfizer, Viacom, Dollar General, Alberto-Culver, Pepsi, Frito-Lay, CBS, General Mills, Hallmark, and more
We use local experts to secure the rezoning entitlements (architects, civil engineers, etc.). They know the county zoning laws and land use regulations, as well as the local government processing staff and county development goals.
To view our one minute intro video, click here.
If you’d like to take your interest to the next level,
request our 5-minute investor pitch deck video here
Disclaimer
Except for statements of historical fact, the information contained herein constitutes forward-looking statements and includes, but is not limited to projected financial performance of the Company.
Forward-looking statements are provided for the opportunity to understand management’s beliefs and opinions in respect of the future so that potential lenders and investors may use such beliefs and opinions as one factor in evaluating an opportunity.
These statements are not guarantees of future performance and undue reliance should not be placed on them.
