Real Estate Simulator

Simulate first,
then advise

Four simulations along the brokerage business. The initial values are examples: set the sliders to the scale of your office. The calculation paths sit beneath each simulation.

From analysis to a testable simulation

In brokerage the commission is the only revenue and the time of the agents is the decisive bottleneck: every day a property sits on the market ties up a mandate and pushes the next closing out. Three stages turn the marketing day into a calculation.

Stage 1
Analysis
Looks back and shows where the days go: in pricing, viewings and mandate care.
Stage 2
Forecast
A model per property and location estimates duration and closing probability before the mandate starts, with a stated error instead of gut feeling.
Stage 3
Simulation
You change a control variable and see what the intervention would lead to, before it touches a mandate.
Control

The fourth stage happens inside the office: no model sets a price, invites to a viewing or prioritises a mandate. After introduction, the simulated trajectory is held against CRM and portal data of the running mandates. What the measurement refutes gets replaced.

Marketing days are capacity

Whoever takes properties off the market faster achieves more closings with the same team. You set both durations yourself; only the consequences are calculated.

Every slider is an assumption, not a fact. All initial values are fictional examples: no customer data, no industry figures, not the values of the sample brokerage from the six modules.

Agents in sales
Parallel mandates per agent
Time on market today
Target time on market
Mandate success rate
Commission per closing
Refill rate of freed capacity
Closings per year over the time on market: markers at both settings
Closings per year
today → target
Additional closings
after refill rate
Additional revenue per year
Closings per agent
today → target

Calculation: closings per year = agents × parallel mandates × success rate × 365 / duration. Additional closings = (closings target − closings today) × refill rate. Additional revenue = additional closings × commission. The refill rate is your assumption of how much freed capacity is actually filled with new mandates, and at 0 % there is no additional revenue.

Control

How fast a property leaves the market is still decided by your office with price, listing and care. The simulation only calculates what the gained time is worth in closings.

Pricing strategy: close to market or with a premium

The pricing conversation with the owner as a per-property calculation. You set both scenarios yourself. No relationship between starting premium and time on market is claimed; it would emerge only from your data.

Market value of the property
Commission rate
Care cost per property and month
Scenario A: started close to market
Achieved final price (share of market value)
Time on market
Scenario B: started with a premium
Achieved final price (share of market value)
Time on market
Commission, care costs and net per scenario
Commission
A → B
Care costs
A → B
Net difference
per property, A minus B
Difference in days
what the time is worth is calculated by simulation 01

Calculation: price = market value × final price factor. Commission = price × commission rate. Care costs = duration / 30 × monthly cost. Net = commission − care costs. The month is fixed at 30 days. The difference in days is deliberately not converted into euros here, because the capacity value of time is calculated only by simulation 01.

Control

Both scenarios calculate exclusively with your own assumptions. The tool embeds no curve between premium and duration; which starting price is defensible is decided by you at the specific property.

What the viewing apparatus costs

The simplest calculation on this page: every result can be checked on a pocket calculator.

Leads per year
Buyer rate
Viewings per sale
Hours per viewing (incl. travel and follow-up)
Full cost per sales hour
Share of avoidable viewings through pre-qualification
From lead to hour: the apparatus behind it
Sales hours
per year
Cost per closing
Freed hours
per year
Savings per year

Calculation: sales = leads × buyer rate. Viewings = sales × viewings per sale. Hours = viewings × hours per viewing. Costs = hours × hourly rate. Cost per closing = costs / sales. Savings = costs × avoidable share. The calculation ends at hours and costs, because converting freed time into closings is done exclusively by simulation 01.

Control

The scoring sorts prospects; it turns nobody away. Which viewing takes place is decided by the agent.

Annual commission under uncertainty

With what probability does the office reach its annual target? 2,000 simulated runs per scenario, two planning assumptions you set yourself. The random generator starts from a fixed seed, every visit shows the same result.

Mandates per year
Closing probability per mandate, scenario A
Closing probability per mandate, scenario B
Spread of the commission
Annual commission target
Distribution of annual commission revenue across 2,000 runs per scenario, marker: annual target
Median scenario A
Median scenario B
Target reached (A)
out of 100 runs each
Target reached (B)
out of 100 runs each

Calculation: per run and mandate a coin flip decides with the set closing probability. Per closing the commission is normally distributed around the mean commission from simulation 01, with the set spread and a floor of 500 EUR. 2,000 runs per scenario, fixed random seed, independent random streams per scenario, and the metric "target reached" compares self-contained runs. Very expensive properties are represented only as far as the flank of the distribution allows.

Control

The closing probabilities are your planning assumptions, not a forecast by the tool. In operation the module delivers scores per mandate; the decision per mandate stays in the office.

What this page assumes, fully disclosed

Every number on this page follows arithmetically from the slider values. In addition:

  • Example values: all initial values are fictional. They use no customer data, no study benchmarks and deliberately not the values of the sample brokerage from the six modules.
  • Not additive: the results of simulations 01 to 03 must not be summed; simulation 04 is a range around a plan and is added to nothing. The page therefore shows no grand total.
  • Separation from the modules: the capacity value of faster marketing is translated into euros only in simulation 01. Final price and care cost effects per property are calculated only by simulation 02, and simulation 03 ends at hours and costs. Pitch rates and additional acquisition contacts appear nowhere on this page.
  • Refill rate: simulation 01 assumes mandate replenishment only via the open refill slider, and at 0 % there is no additional revenue.
  • Pricing strategy: no relationship between starting premium and time on market is embedded; both scenarios are entirely your assumptions. The month is fixed at 30 days.
  • Annual commission: commission normally distributed with a floor of 500 EUR, 2,000 runs, fixed random seed, independent random streams per scenario; very expensive properties are only represented as far as the flank of the distribution allows. Scenario B is a planning assumption, not a promised module effect.
  • In the browser: all calculations run entirely in your browser; no inputs are transmitted or stored.
Model scenario: fictional brokerage

This page is a basis for conversation, not an offer. All values are model calculations based on freely adjustable assumptions; no number is a commitment, a forecast for a specific office or an industry figure.

In a real project your data replaces the sliders: your CRM history provides durations and success rates, your portal statistics the demand, your closing history the commission distribution. The calculation paths stay the same.

All 6 modules: AI for real estate agents