Pull similar comps within a milage( this needs to be able to modify each time base off the area, in philadelphia, we can give a very persize distance between the property because every property is different, but other city we will need to redefine it to make the comps accurate. Find size similar to the target ( the property we are analysing) sqft needs to be 400 sqft +/- range (this can be adjust later) Identify the condition of the comps ( the comparable property, what is the condition. New or not new 2 options Compare to the target property ( 6 category of rehab needed to get to the ARV comps ) Identify similar property to be comparable property and find at least 2, maximum 5 of comparable property and then take average pricing per sqft, and use that to times the sqft of the target property and see the ARV of our specific property Build a dynamic Comparable Sales Engine that: Pulls geographically relevant comps Filters by size similarity Adjusts for condition (new vs non-new) Scores rehab gap vs ARV comps Selects 2–5 most relevant comps Calculates ARV using averaged $/sqft This must be configurable by city and neighborhood. SYSTEM LOGIC 1 Geographic Radius Logic (Dynamic Radius System) Problem: Philadelphia rowhouses can require very tight comp radius (e.g., 0.1–0.3 miles), but suburban markets may need 0.5–1.0+ miles. Required Feature: Radius must be: Adjustable per city Adjustable per neighborhood Adjustable per property density Override-able manually Implementation Suggestion: if city == "Philadelphia": default_radius = 0.25 miles elif density == "urban": default_radius = 0.5 miles elif density == "suburban": default_radius = 1.0 miles else: default_radius = 1.5 miles Allow: Admin override input Per-property radius override 2 Size Similarity Filter Target Property Square Footage = T Comp must satisfy: Comp Sqft >= T - 400 Comp Sqft <= T + 400 Make ±400 adjustable in admin settings. Example: Target = 1,600 sqft Comp range = 1,200–2,000 sqft This prevents using 900 sqft homes to comp 2,000 sqft homes. 3 Condition Classification Each comp must be tagged as: NEW / FULLY RENOVATED NOT NEW / AVERAGE CONDITION Binary for now (expand later). Condition detection methods: Option A: MLS keywords: "fully renovated" "new construction" "brand new" "gut rehab" Option B: Manual override field Target property must also be tagged: Current Condition (as-is) After-Rehab Condition (planned level) 4. Rehab Gap Classification (6 Categories) You want to measure the difference between: Comp condition vs Target condition Define 6 rehab categories: Example categories: Demo -> new construction Major structural issues, Total gut renovation, including front and back yard Interior total renovation only Cosmetic renovation only, no electric, plumbing, hvac needed(or minor fix) Bathroom/Kitchen/floor upgrade only cosmetic pricing Total rehab score calculated. This does NOT change ARV. It helps estimate rehab budget and risk. 5. Comparable Selection Logic After filtering by: Radius Sqft range Sold within X months (recommend 6–12 months) Condition match (if ARV target is “fully renovated”, use renovated comps) Then: Sort by similarity score. Similarity score example formula: Similarity Score = (Distance Weight * Distance Score) + (Sqft Weight * Sqft Difference Score) + (Condition Match Weight) Select: Minimum: 2 comps Maximum: 5 comps If >5 eligible: Select top 5 most similar. If <2: Expand radius slightly (increment 0.1 miles until minimum 2 found). 6. ARV Calculation Logic For each selected comp: Price Per Sqft = Sold Price / Comp Sqft Then: Average PPSF = (Sum of PPSF of selected comps) / Number of comps Then: ARV = Average PPSF × Target Sqft Output: ARV value Comp list used Average PPSF Median PPSF (optional, safer metric) Full Flow Summary for Developer Input Target Property: Address Sqft Current condition Planned condition Determine Radius (dynamic) Pull sold comps within: Radius Sold within last X months Sqft ±400 range Filter by condition type Score similarity Select 2–5 best comps Calculate: Average PPSF ARV Output structured report Note to clarify logic We will have to identify 2 facts, 1st is the size of the bathroom, etc., second is sqft interior, but there are going to be different types of comparible i want to add in the future, but the feature I am looking for is, I identify the type of deal first , and when you looking for ARV, you have to have a selection of each type of deal, what they are looking for, and they are specifically looking for comps in those specific condition, so i want to make sure that this filter can be manually change easily Determine Type of deal selection Flips 3. Flips with total renovation, interior/exterior Existing condition Front is not maintained, obvious crack, structural issues, for example, wall falling apart, major grass, the door is boarded up If inside imagine is pulled, the house is full of trash, old kitchen, no recess lighting ( led lighting) old panels, bathroom is old vanity and dirty tile and there are signs that all mechanical needs to be replaced, and floors are not leveled. And the comps in the area is all new updated floor, kitchen, bathroom, everything inside and outside Flips with cosmetic renovation New construction Multi-family (2-4 units) Multi- family 4-9 units Multi - family 10 units