基于市场数据计算房产在不同周期内的售出概率
Alright, let's walk through how to calculate these probabilities using a standard real estate absorption model—this is the go-to method when you have basic market inventory and historical sales data. Here's the breakdown:
Key Inputs We Have
- Historical sales (past 365 days): 21 comparable homes sold
- Current active comparable inventory: 12 homes
Step 1: Calculate Daily Absorption Rate
First, find how many comparable homes sell on average each day:
Daily absorption = Total annual sales / 365 days = 21 / 365 ≈ 0.0575 homes per day
This tells us the market is absorbing roughly 0.058 comparable homes every day.
Step 2: Estimate Daily Sale Probability per Active Home
Since there are 12 active homes competing for those daily sales, we can approximate the daily probability that any single home (including your target property) sells:
Daily sale probability per home = Daily absorption / Current inventory = 0.0575 / 12 ≈ 0.00479 (or ~0.48% per day)
Step 3: Calculate Sale Probabilities for Target Timeframes
We’ll use the exponential distribution (a standard model for time-to-event probabilities like this) to find the probability that the home sells within T days. The formula is:
Probability = 1 - e^(-Daily probability * T)
Plugging in the numbers for each timeframe:
- 30 days: 1 - e^(-0.00479 * 30) ≈ 13.4%
- 60 days: 1 - e^(-0.00479 * 60) ≈ 25.0%
- 90 days: 1 - e^(-0.00479 * 90) ≈ 35.0%
- 120 days: 1 - e^(-0.00479 * 120) ≈ 43.7%
Important Caveats
These are rough estimates based on steady-state assumptions:
- The market won’t see sudden shifts in supply (no flood of new comparable listings) or demand (no major economic changes affecting buyer interest)
- Your target property is truly comparable to the historical sales and active inventory (same size, location, condition, price point)
- No major adjustments are needed for price (e.g., if your home is priced above/below average, you’d need to tweak these probabilities up/down accordingly)
内容的提问来源于stack exchange，提问作者Tim Schoenberg
