Frequently Asked Questions

The 4% rule (Bengen 1994) suggests withdrawing 4% of your initial portfolio annually, adjusted for inflation. It was derived from US historical data and targets a 30-year retirement. Our simulator lets you test this — and any other rate — against 150+ years of multi-country data. Under global pooled data, the safe rate for 95% success is typically closer to 3.2-3.5%.

Monte Carlo simulation generates thousands of possible retirement scenarios by randomly sampling from historical market returns. Unlike a single projection using average returns, it shows the full range of possible outcomes and the probability of your plan succeeding under different market conditions.

Block Bootstrap samples contiguous blocks of historical returns (e.g., 3-7 consecutive years at a time) rather than individual years. This preserves the year-to-year momentum, volatility clustering, and cross-asset correlations that exist in real markets. Pure random sampling would destroy these patterns and underestimate the risk of prolonged downturns.

A risk-based guardrail strategy sets upper and lower bounds on your portfolio's estimated survival probability. When markets perform well and the success rate rises above the upper guardrail, you increase spending. When markets decline and success falls below the lower guardrail, you cut spending. This dynamic approach typically allows 15-25% higher initial withdrawals compared to a fixed strategy at the same risk level. FIRE Lab provides this as a free, open tool — similar functionality to advisor-only platforms like IncomeLab, but accessible to individual investors.

Success rate is binary — did the portfolio last the full retirement, yes or no. Funded ratio is the average fraction of retirement years that were funded. A plan with 80% success rate could have failed paths depleting at year 5 or year 28 — funded ratio captures this difference. A higher funded ratio means failures, when they occur, happen later.

US-only data reflects a single country's exceptional historical performance and may overestimate future returns. Global pooled data (16 countries, each sampled with equal probability) provides a more conservative and arguably more robust baseline, especially for investors with internationally diversified portfolios. We recommend global pooled data for primary planning and US data as an optimistic scenario.

More simulations produce more stable results. 2,000-5,000 is sufficient for most analyses. The server automatically recommends a count based on available computing resources. For sensitivity analysis or asset allocation scans, fewer simulations per combination are used to keep total runtime manageable.

Research (Hurd & Rohwedder 2022) shows that real spending typically declines 1.7-2.4% annually after age 65 as retirees naturally reduce consumption. The spending decline strategy models this pattern, allowing a higher initial withdrawal rate since future spending needs decrease over time.

The calculator models the major financial factors: mortgage payments, property taxes, maintenance, home appreciation, rent growth, and investment returns for the renter. It uses historical data distributions when available. However, it does not model local tax benefits (mortgage interest deduction), rental market constraints, or non-financial factors like stability and flexibility.

No. FIRE Lab is an educational tool for exploring retirement scenarios. Historical returns do not guarantee future performance. The simulator cannot account for your complete financial situation, tax circumstances, or personal risk tolerance. Consult a qualified financial advisor for personalized advice.