A.CRE

Monte Carlo Method

A technique used in Stochastic (i.e. Probabilistic) Analysis whereby the professional performs simulations that result in a range of outcomes due to the uncertain nature of the inputs.

This method involves repeatedly running simulations hundreds or thousands of times, recording the outcomes of each simulation, and then aggregating those outcomes to understand the mean (i.e. most probable outcome), standard deviation (i.e. range of outcomes), minimum value, and maximum value of all of the outcomes.

Putting ‘Monte Carlo Method’ in Context

Apex Capital Lending, a new real estate debt shop, is looking to establish itself as a leader in underwriting complex deals. To gain an edge in the competitive lending market, the firm is building a proprietary underwriting model that incorporates the Monte Carlo Method. Their focus is on analyzing the risk associated with funding The Union Point Project, a $120 million mixed-use development located in the urban core of Chicago, Illinois.

The Scenario

The Union Point Project is a 450,000-square-foot development featuring:

  • 300 luxury residential units making up 60% of the rentable square footage.
  • 50,000 square feet of street-level retail space intended for upscale shops and restaurants.

The sponsor has requested $75 million in debt financing to cover the construction and lease-up phases. While the project promises significant returns, the risks include:

  • Leasing uncertainty in the retail space.
  • Interest rate fluctuations during the construction period.
  • Cost overruns in a high-construction-cost market.

Applying the Monte Carlo Method

Apex Capital Lending’s underwriting team uses the Monte Carlo Method to model the risks and potential outcomes associated with the loan. They begin by identifying uncertain variables critical to the deal:

  1. Lease-up velocity: The pace at which the residential and retail spaces are expected to lease.
  2. Achieved rental rates: Variability in market rents due to economic conditions.
  3. Construction costs: Potential for overruns based on historical cost data.
  4. Exit cap rates: Uncertainty in market conditions when the project is stabilized.

The team runs 10,000 simulations using the following input ranges:

  • Lease-up velocity: 85%-100% occupancy within 18-36 months.
  • Achieved rental rates: $2.80 to $3.20 per square foot for residential; $40 to $60 per square foot annually for retail.
  • Construction costs: 5%-15% over budget.
  • Exit cap rates: 5.0% to 6.5%.

Each simulation produces a potential outcome, including net operating income (NOI), project completion value, and debt coverage ratio (DCR). The results reveal:

  • Mean NOI: $8.5 million annually.
  • Standard deviation of NOI: $1.2 million.
  • Range of potential values:
    • Minimum: NOI of $6.8 million, stabilizing at 70% occupancy.
    • Maximum: NOI of $10.2 million, stabilizing at 100% occupancy.

Decision-Making with Monte Carlo Results

The Monte Carlo simulation helps Apex Capital Lending to:

  • Assess the probability of achieving a DCR above 1.25x at stabilization.
  • Quantify downside risks, such as scenarios where construction costs exceed budget or market rents decline.
  • Price the loan appropriately by adding a risk premium to account for the high standard deviation in potential outcomes.

Ultimately, Apex determines that the project has a 70% probability of achieving stabilization within acceptable risk thresholds. They proceed to offer a $75 million construction loan with a 7.5% interest rate, reflecting the project’s opportunistic risk profile.

Takeaway

The Monte Carlo Method enables Apex Capital Lending to build a robust framework for evaluating the uncertainty inherent in real estate projects. By leveraging simulations, they quantify risks and improve decision-making, giving the new debt shop a competitive edge in a nationwide lending market.

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