How Much House Can You Really Afford? A Monte Carlo Approach to Home Purchase Risk

Goal

Determine the maximum affordable house price and the minimum savings cushion needed to sustain a mortgage and living expenses — even under adverse scenarios like income disruption — without running out of cash.

Challenge

Traditional mortgage affordability calculators use simple income-to-debt ratios, ignoring the real-world variability of living expenses, the timing randomness of supplemental income, and the probability of job loss. A family considering a $500K home purchase needed to understand not just whether they could qualify for a mortgage, but whether they could survive one — across hundreds of possible futures.

Model Summary

The model was built in Foresight using 50 Monte Carlo scenarios, starting from April 2026 with $20,000 in initial savings. It captured the household's full financial picture: Income streams: Primary salary of $6,500 received biweekly (fixed, reliable) Supplemental international transfers averaging $1,000/month with high timing variability (Normal distribution, σ = $500 on amount, 14-day std dev on occurrence) Fixed obligations: Mortgage payment: $2,600/month Two car loans: $450/month each Utility bills: $250/month Subscriptions: $50/month Variable expenses: Discretionary/unexpected spending modeled as Uniform($1,000–$6,000) per occurrence, with variable timing (~60 days between events ± 15 days) Risk layer (Causal Logic): A "Layoff" signal event was modeled as a quarterly risk trigger with a 10% probability, using Foresight's logic gate system. When triggered, it activates an "Anti Income" dependent event — a $3,000 biweekly expense that simulates the income gap during unemployment. This causal structure allowed the model to test how layoff timing, duration, and coincidence with large discretionary expenses compound financial stress.

Results

Across 50 simulated futures, the model revealed critical insights: Cash runway sensitivity: With $20,000 in savings and the current expense structure, the household faces meaningful probability of negative cash balance within the first 18 months — particularly in scenarios where a layoff coincides with a cluster of high discretionary expenses. Key risk driver: The Layoff event, despite being modeled at only ~10% quarterly probability, appeared as the dominant risk factor. Because it triggers a sustained income loss ($3,000 biweekly) rather than a one-time hit, its impact compounds rapidly. Savings as insurance: The model showed that the initial savings balance acts as the primary buffer. Varying it from $20K to $50K dramatically changed the probability of surviving a layoff scenario without running out of cash. House price sensitivity: By adjusting the mortgage amount as a proxy for house price, the model can identify the price threshold where the probability of financial distress exceeds the household's risk tolerance.

Decision

The analysis demonstrated that a $500K house purchase with only $20,000 in savings carries significant financial risk under realistic conditions. The model recommended either increasing the savings cushion to at least $40,000 before purchase, reducing the target home price by 15–20%, or securing supplemental income stability before committing. The family used Foresight's scenario comparison feature to run multiple house price points ($400K, $450K, $500K) against different savings levels, ultimately choosing a $430K home with a $35K savings target — the combination that kept their probability of negative cash balance below 5% even in the worst simulated scenarios.