> ## Documentation Index > Fetch the complete documentation index at: https://terrascout.ai/docs/llms.txt > Use this file to discover all available pages before exploring further. # Arbitrage Analysis > Find undervalued properties using statistical analysis Finding properties that sold below market value is one of the most powerful ways to identify opportunities. Ploti uses statistical analysis to compare sales against submarket medians and flag potential arbitrage opportunities. ## The Opportunity Arbitrage analysis helps you: * Establish realistic offering prices * Support your valuation in negotiations * Understand market trends by submarket * **Identify potential arbitrage opportunities**—properties that sold below market ## Walkthrough: Finding Undervalued Land Sales Let's use statistical analysis to find vacant land that sold significantly below the median price per acre for its area. These could represent motivated seller situations, data errors worth investigating, or genuine arbitrage opportunities. ### Step 1: Select Your Search Area First, select the boundary you want to analyze. Selecting a county as the search boundary ### Step 2: Ask for Statistical Analysis Asking Ploti to find undervalued land sales We asked: > Find vacant land sales from the last 2 years over 2 acres. Calculate the price per acre, remove statistical outliers, and show me parcels that sold for significantly below the median price per acre for their city. This request triggers a multi-step statistical analysis: * Filter to recent vacant land sales * Calculate price per acre for each parcel * Use IQR method to remove outliers * Compare each sale to its city's median * Flag properties 25%+ below median ### Step 3: Review the Analysis Plan Ploti creates a detailed analysis plan showing the methodology: Statistical analysis plan with IQR methodology The plan shows: * **Base dataset**: 26 vacant land sales over 2 acres from the last 2 years * **Outlier detection**: IQR method (Tukey's fences) to remove extreme values * **City medians**: Oakland at $349,059/acre, Winter Garden at $520,532/acre * **Threshold**: Parcels 25%+ below their city's median ### Step 4: Review Base Dataset First, Ploti creates a checkpoint with all qualifying sales: Base dataset of 26 vacant land sales This shows 26 parcels across 2 cities with price per acre ranging from $4,000 to $1.6M. ### Step 5: Review Undervalued Results After applying the statistical filters, Ploti identifies 10 undervalued parcels: 10 undervalued parcels identified ### Step 6: Analyze the Findings Summary of undervalued parcels with statistics | City | Median \$/Acre | Parcels Found | | ------------- | -------------- | ------------- | | Oakland | \$349,059 | 1 | | Winter Garden | \$520,532 | 9 | **Most undervalued finds:** * **Winter Garden** — 8.00 acres at \$4,000/acre (99.2% below median) * **Winter Garden** — 2.39 acres at \$16,736/acre (96.8% below median) * **Oakland** — 25.37 acres at \$177,375/acre (49.2% below median) The extremely low-priced sales warrant investigation—they could be: * Distressed or motivated seller situations * Family/related party transfers * Sales with deed restrictions * Data entry errors worth verifying ## Why This Matters This isn't just filtering—it's real statistical analysis: 1. **IQR outlier removal** prevents skewed medians from data anomalies 2. **City-level grouping** compares apples to apples within submarkets 3. **Percentage thresholds** identify genuinely significant deviations 4. **Actionable output** gives you specific parcels to investigate Each flagged property is a lead worth researching: Why did it sell below market? Is there an opportunity here? ## The Time Savings Without Ploti, this analysis would require: 1. Exporting sales data from multiple county sources 2. Cleaning and normalizing the data in Excel or pandas 3. Calculating price per acre for each parcel 4. Writing Python code to compute IQR bounds 5. Implementing outlier removal logic 6. Grouping by submarket and calculating medians 7. Comparing each sale to its submarket median 8. Flagging properties below threshold **This isn't Excel work—it's data science.** You'd need to write actual Python code: ```python theme={null} # What you'd have to write yourself Q1 = df.groupby('city')['price_per_acre'].quantile(0.25) Q3 = df.groupby('city')['price_per_acre'].quantile(0.75) IQR = Q3 - Q1 lower_bound = Q1 - 1.5 * IQR upper_bound = Q3 + 1.5 * IQR # ... and so on ``` **This is the type of analysis only major institutional investors and REITs can afford to do.** They have data science teams writing custom Python scripts to find market inefficiencies. Individual investors and small shops? They're stuck with gut feel and manual spreadsheet work. Ploti levels the playing field. You described what you wanted in plain English, and the agent wrote the code, ran the analysis, and delivered actionable results in under 2 minutes. ## Tips * The best comps are recent, nearby, and similar in size and zoning * Extremely low sales (90%+ below median) often indicate non-arm's-length transactions * Verify outliers before acting—some are data errors, others are real opportunities * Consider why a property sold cheap before assuming it's undervalued ## Other Example Prompts ### Simple Comp Search > Show me vacant land sales between 5-20 acres from the last 18 months Find recent transactions without statistical analysis. ### Price Per Acre by Submarket > What's the average price per acre for vacant land sales by city in the last 2 years? Understand pricing differences across submarkets. ### High-Value Transactions > Find all land sales over \$1 million in the last year and show the price per acre Focus on institutional-scale transactions. ### Zoned Land Pricing > Compare the median sale price per acre for commercially-zoned vs residentially-zoned vacant land Understand the zoning premium in your market.